Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The primary objective is to identify and analyze vegetation types in as great of detail as possible on ERTS-1 imagery and to classify and delineate them through mapping. This is basic to the identification, definition, and mapping of ecosystems. Major conclusions are: (1) the ERTS-1 system is useful for regional scale studies of broadly defined Alaskan vegetation types; (2) the resolution and spectral capabilities of ERTS-1 MSS imagery in photographic formats is adequate for certain phytecenologic purposes; and (3) preparation of an improved State vegetation map will be feasible.

Vegetation mapping from ERTS imagery of the Okavango Delta. [Botswana

NASA Technical Reports Server (NTRS)

Willamson, D. T.

1974-01-01

The Okavango is Botswana's major water resource. The present study has been specifically directed at mapping vegetation types within the delta and generally concerned with finding what information of value to plant and animal ecologists could be extracted from the imagery. To date it has been found that. (1) It is possible to map broad vegetation types from the imagery. (2) Imagery of the delta records the state of the system in a manner which will facilitate long-term studies of plant succession. (3) Phenological events can be detected. (4) The imagery can be used to detect and map wild fires. This will be useful in determining the role of fire in the ecology of the region. Using the imagery it is thus possible to map existing vegetation and monitor both short and long-term changes.

Mapping vegetation in Yellowstone National Park using spectral feature analysis of AVIRIS data

USGS Publications Warehouse

Kokaly, Raymond F.; Despain, Don G.; Clark, Roger N.; Livo, K. Eric

2003-01-01

Knowledge of the distribution of vegetation on the landscape can be used to investigate ecosystem functioning. The sizes and movements of animal populations can be linked to resources provided by different plant species. This paper demonstrates the application of imaging spectroscopy to the study of vegetation in Yellowstone National Park (Yellowstone) using spectral feature analysis of data from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). AVIRIS data, acquired on August 7, 1996, were calibrated to surface reflectance using a radiative transfer model and field reflectance measurements of a ground calibration site. A spectral library of canopy reflectance signatures was created by averaging pixels of the calibrated AVIRIS data over areas of known forest and nonforest vegetation cover types in Yellowstone. Using continuum removal and least squares fitting algorithms in the US Geological Survey's Tetracorder expert system, the distributions of these vegetation types were determined by comparing the absorption features of vegetation in the spectral library with the spectra from the AVIRIS data. The 0.68 μm chlorophyll absorption feature and leaf water absorption features, centered near 0.98 and 1.20 μm, were analyzed. Nonforest cover types of sagebrush, grasslands, willows, sedges, and other wetland vegetation were mapped in the Lamar Valley of Yellowstone. Conifer cover types of lodgepole pine, whitebark pine, Douglas fir, and mixed Engelmann spruce/subalpine fir forests were spectrally discriminated and their distributions mapped in the AVIRIS images. In the Mount Washburn area of Yellowstone, a comparison of the AVIRIS map of forest cover types to a map derived from air photos resulted in an overall agreement of 74.1% (kappa statistic=0.62).

The response of vegetation dynamics of the different alpine grassland types to temperature and precipitation on the Tibetan Plateau.

PubMed

Sun, Jian; Qin, Xiaojing; Yang, Jun

2016-01-01

The spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) of three vegetation types (alpine steppe, alpine meadow, and alpine desert steppe) across the Tibetan Plateau was analyzed from 1982 to 2013. In addition, the annual mean temperature (MAT) and annual mean precipitation (MAP) trends were quantified to define the spatiotemporal climate patterns. Meanwhile, the relationships between climate factors and NDVI were analyzed in order to understand the impact of climate change on vegetation dynamics. The results indicate that the maximum of NDVI increased by 0.3 and 0.2 % per 10 years in the entire regions of alpine steppe and alpine meadow, respectively. However, no significant change in the NDVI of the alpine desert steppe has been observed since 1982. A negative relationship between NDVI and MAT was found in all these alpine grassland types, while MAP positively impacted the vegetation dynamics of all grasslands. Also, the effects of temperature and precipitation on different vegetation types differed, and the correlation coefficient for MAP and NDVI in alpine meadow is larger than that for other vegetation types. We also explored the percentages of precipitation and temperature influence on NDVI variation, using redundancy analysis at the observation point scale. The results show that precipitation is a primary limiting factor for alpine vegetation dynamic, rather than temperature. Most importantly, the results can serve as a tool for grassland ecosystem management.

Vegetation Water Content Mapping for Agricultural Regions in SMAPVEX16

NASA Astrophysics Data System (ADS)

White, W. A.; Cosh, M. H.; McKee, L.; Berg, A. A.; McNairn, H.; Hornbuckle, B. K.; Colliander, A.; Jackson, T. J.

2017-12-01

Vegetation water content impacts the ability of L-band radiometers to measure surface soil moisture. Therefore it is necessary to quantify the amount of water held in surface vegetation for an accurate soil moisture remote sensing retrieval. A methodology is presented for generating agricultural vegetation water content maps using Landsat 8 scenes for agricultural fields of Iowa and Manitoba for the Soil Moisture Active Passive Validation Experiments in 2016 (SMAPVEX16). Manitoba has a variety of row crops across the region, and the study period encompasses the time frame from emergence to reproduction, as well as a forested region. The Iowa study site is dominated by corn and soybeans, presenting an easier challenge. Ground collection of vegetation biomass and water content were also collected to provide a ground truth data source. Errors for the resulting vegetation water content maps ranged depending upon crop type, but generally were less than 15% of the total plant water content per crop type. Interpolation is done between Landsat overpasses to produce daily vegetation water content maps for the summer of 2016 at a 30 meter resolution.

Vegetation classification, mapping, and monitoring at Voyageurs National Park, Minnesota: An application of the U.S. National Vegetation Classification

USGS Publications Warehouse

Faber-Langendoen, D.; Aaseng, N.; Hop, K.; Lew-Smith, M.; Drake, J.

2007-01-01

Question: How can the U.S. National Vegetation Classification (USNVC) serve as an effective tool for classifying and mapping vegetation, and inform assessments and monitoring? Location: Voyageurs National Park, northern Minnesota, U.S.A and environs. The park contains 54 243 ha of terrestrial habitat in the sub-boreal region of North America. Methods: We classified and mapped the natural vegetation using the USNVC, with 'alliance' and 'association' as base units. We compiled 259 classification plots and 1251 accuracy assessment test plots. Both plot and type ordinations were used to analyse vegetation and environmental patterns. Color infrared aerial photography (1:15840 scale) was used for mapping. Polygons were manually drawn, then transferred into digital form. Classification and mapping products are stored in publicly available databases. Past fire and logging events were used to assess distribution of forest types. Results and Discussion: Ordination and cluster analyses confirmed 49 associations and 42 alliances, with three associations ranked as globally vulnerable to extirpation. Ordination provided a useful summary of vegetation and ecological gradients. Overall map accuracy was 82.4%. Pinus banksiana - Picea mariana forests were less frequent in areas unburned since the 1930s. Conclusion: The USNVC provides a consistent ecological tool for summarizing and mapping vegetation. The products provide a baseline for assessing forests and wetlands, including fire management. The standardized classification and map units provide local to continental perspectives on park resources through linkages to state, provincial, and national classifications in the U.S. and Canada, and to NatureServe's Ecological Systems classification. ?? IAVS; Opulus Press.

Land cover

USGS Publications Warehouse

Jorgenson, Janet C.; Joria, Peter C.; Douglas, David C.; Douglas, David C.; Reynolds, Patricia E.; Rhode, E.B.

2002-01-01

Documenting the distribution of land-cover types on the Arctic National Wildlife Refuge coastal plain is the foundation for impact assessment and mitigation of potential oil exploration and development. Vegetation maps facilitate wildlife studies by allowing biologists to quantify the availability of important wildlife habitats, investigate the relationships between animal locations and the distribution or juxtaposition of habitat types, and assess or extrapolate habitat characteristics across regional areas.To meet the needs of refuge managers and biologists, satellite imagery was chosen as the most cost-effective method for mapping the large, remote landscape of the 1002 Area.Objectives of our study were the following: 1) evaluate a vegetation classification scheme for use in mapping. 2) determine optimal methods for producing a satellite-based vegetation map that adequately met the needs of the wildlife research and management objectives; 3) produce a digital vegetation map for the Arctic Refuge coastal plain using Lands at-Thematic Mapper(TM) satellite imagery, existing geobotanical classifications, ground data, and aerial photographs, and 4) perform an accuracy assessment of the map.

Chapter 7 - Mapping potential vegetation type for the LANDFIRE Prototype Project

Treesearch

Tracey S. Frescino; Matthew G. Rollins

2006-01-01

Mapped potential vegetation functioned as a key component in the Landscape Fire and Resource Management Planning Tools Prototype Project (LANDFIRE Prototype Project). Disturbance regimes, vegetation response and succession, and wildland fuel dynamics across landscapes are controlled by patterns of the environmental factors (biophysical settings) that entrain the...

Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1972-01-01

The author has identified the following significant results. A reconstituted color infrared image covering the western Seward Peninsula was used for identifying vegetation types by simple visual examination. The image was taken by ERTS-1 approximately 1120 hours on August 1, 1972. Seven major colors were identified. Four of these were matched with four units on existing vegetation maps: bright red - shrub thicket; light gray-red - upland tundra; medium gray-red - coastal wet tundra; gray - alpine barrens. In the bright red color, two phases, violet and orange, were recognized and tentatively ascribed to differences in species composition in the shrub thicket type. The three colors which had no map unit equivalents were interpreted as follows: pink - grassland tundra; dark gray-red - burn scars; light orange-red - senescent vegetation. It was concluded that the image provides a considerable amount of information regarding the distribution of vegetation types, even at so simple a leval of analysis. It was also concluded that sequential imagery of this type could provide useful information on vegetation fires and phenologic events.

Using Vegetation Maps to Provide Information on Soil Distribution

NASA Astrophysics Data System (ADS)

José Ibáñez, Juan; Pérez-Gómez, Rufino; Brevik, Eric C.; Cerdà, Artemi

2016-04-01

Many different types of maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research has indicated that comparing the results of different but related maps (e.g., soil and geology maps) may aid in identifying deficiencies in those maps. Therefore, this study was undertaken in the Almería Province (Andalusia, Spain) to (i) compare the underlying map structures of soil and vegetation maps and (ii) to investigate if a vegetation map can provide useful soil information that was not shown on a soil map. To accomplish this soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis. Results of the spatial analysis were exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence (P/A): (i) climatophilous (climate is the only determinant of P/A) (ii); lithologic-climate (climate and parent material determine PNV P/A); and (iii) edaphophylous (soil features determine PNV P/A). The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophylous units (which demand more soil water than is supplied by other soil types in the surrounding landscape) were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity that are threatened by anthropogenic activity in the region. Therefore, this study revealed areas in Almería Province that need to be revisited and studied pedologically. The vegetation mapped in these areas and the soils that support it are key components of the earth's critical zone that must be studied, understood, and preserved.

Profiles of California vegetation

Treesearch

William B. Critchfield

1971-01-01

This publication brings together 57 elevational profiles illustrating the dominant vegetation of much of the Sierra Nevada, southern Coast Ranges, and montane southern California as it existed in the 1930's. The profiles were drawn by Michael N. Dobrotin for the U.S. Forest Service's Vegetation Type Map survey, which mapped nearly half of the State's...

The feasibility of using a universal Random Forest model to map tree height across different locations and vegetation types

NASA Astrophysics Data System (ADS)

Su, Y.; Guo, Q.; Jin, S.; Gao, S.; Hu, T.; Liu, J.; Xue, B. L.

2017-12-01

Tree height is an important forest structure parameter for understanding forest ecosystem and improving the accuracy of global carbon stock quantification. Light detection and ranging (LiDAR) can provide accurate tree height measurements, but its use in large-scale tree height mapping is limited by the spatial availability. Random Forest (RF) has been one of the most commonly used algorithms for mapping large-scale tree height through the fusion of LiDAR and other remotely sensed datasets. However, how the variances in vegetation types, geolocations and spatial scales of different study sites influence the RF results is still a question that needs to be addressed. In this study, we selected 16 study sites across four vegetation types in United States (U.S.) fully covered by airborne LiDAR data, and the area of each site was 100 km2. The LiDAR-derived canopy height models (CHMs) were used as the ground truth to train the RF algorithm to predict canopy height from other remotely sensed variables, such as Landsat TM imagery, terrain information and climate surfaces. To address the abovementioned question, 22 models were run under different combinations of vegetation types, geolocations and spatial scales. The results show that the RF model trained at one specific location or vegetation type cannot be used to predict tree height in other locations or vegetation types. However, by training the RF model using samples from all locations and vegetation types, a universal model can be achieved for predicting canopy height across different locations and vegetation types. Moreover, the number of training samples and the targeted spatial resolution of the canopy height product have noticeable influence on the RF prediction accuracy.

Calculation of upper confidence bounds on proportion of area containing not-sampled vegetation types: An application to map unit definition for existing vegetation maps

Treesearch

Paul L. Patterson; Mark Finco

2011-01-01

This paper explores the information forest inventory data can produce regarding forest types that were not sampled and develops the equations necessary to define the upper confidence bounds on not-sampled forest types. The problem is reduced to a Bernoulli variable. This simplification allows the upper confidence bounds to be calculated based on Cochran (1977)....

Use of satellite imagery for wildland resource evaluation in the Great Basin

NASA Technical Reports Server (NTRS)

Tueller, P. T. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Most major vegetation types of Nevada have been mapped with success. The completed set of mosaic overlays will be more accurate and detailed than previous maps compiled by various State and Federal agencies due to the excellent vantage point that ERTS-1 data affords. This new vegetation type map will greatly aid resource agencies in their daily work. Such information as suitable grazing areas, wildlife habitat, forage production, and approximate wildland production potentials can be inferred from such a map. There has been some success in detecting vegetational changes with the use of ERTS-1 MSS imagery, but exposure differences have somewhat confounded the results. Future plans include work to solve this problem.

High-resolution mapping of wetland vegetation biomass and distribution with L-band radar in southeastern coastal Louisiana

NASA Astrophysics Data System (ADS)

Thomas, N. M.; Simard, M.; Byrd, K. B.; Windham-Myers, L.; Castaneda, E.; Twilley, R.; Bevington, A. E.; Christensen, A.

2017-12-01

Louisiana coastal wetlands account for approximately one third (37%) of the estuarine wetland vegetation in the conterminous United States, yet the spatial distribution of their extent and aboveground biomass (AGB) is not well defined. This knowledge is critical for the accurate completion of national greenhouse gas (GHG) inventories. We generated high-resolution baselines maps of wetland vegetation extent and biomass at the Atchafalaya and Terrebonne basins in coastal Louisiana using a multi-sensor approach. Optical satellite data was used within an object-oriented machine learning approach to classify the structure of wetland vegetation types, offering increased detail over currently available land cover maps that do not distinguish between wetland vegetation types nor account for non-permanent seasonal changes in extent. We mapped 1871 km2 of wetlands during a period of peak biomass in September 2015 comprised of flooded forested wetlands and leaf, grass and emergent herbaceous marshes. The distribution of aboveground biomass (AGB) was mapped using JPL L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR). Relationships between time-series radar imagery and field data collected in May 2015 and September 2016 were derived to estimate AGB at the Wax Lake and Atchafalaya deltas. Differences in seasonal biomass estimates reflect the increased AGB in September over May, concurrent with periods of peak biomass and the onset of the vegetation growing season, respectively. This method provides a tractable means of mapping and monitoring biomass of wetland vegetation types with L-band radar, in a region threatened with wetland loss under projections of increasing sea-level rise and terrestrial subsidence. Through this, we demonstrate a method that is able to satisfy the IPCC 2013 Wetlands Supplement requirement for Tier 2/Tier 3 reporting of coastal wetland GHG inventories.

Analysis and Mapping of Vegetation and Habitat for the Sheldon National Wildlife Refuge

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tagestad, Jerry D.

The Lakeview, Oregon, office of the U.S. Fish and Wildlife Service (USFWS) contracted Pacific Northwest National Laboratory to classify vegetation communities on Sheldon National Wildlife Refuge in northeastern Nevada. The objective of the mapping project was to provide USFWS refuge biologists and planners with detailed vegetation and habitat information that can be referenced to make better decisions regarding wildlife resources, fuels and fire risk, and land management. This letter report describes the datasets and methods used to develop vegetation cover type and shrub canopy cover maps for the Sheldon National Wildlife Refuge. The two map products described in this reportmore » are (1) a vegetation cover classification that provides updated information on the vegetation associations occurring on the refuge and (2) a map of shrub canopy cover based on high-resolution images and field data.« less

Calculation of upper confidence bounds on not-sampled vegetation types using a systematic grid sample: An application to map unit definition for existing vegetation maps

Treesearch

Paul L. Patterson; Mark Finco

2009-01-01

This paper explores the information FIA data can produce regarding forest types that were not sampled and develops the equations necessary to define the upper confidence bounds on not-sampled forest types. The problem is reduced to a Bernoulli variable. This simplification allows the upper confidence bounds to be calculated based on Cochran (1977). Examples are...

On vegetation mapping in Alaska using LANDSAT imagery with primary concerns for method and purpose in satellite image-based vegetation and land-use mapping and the visual interpretation of imagery in photographic format

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1976-01-01

The author has identified the following significant results. A simulated color infrared LANDSAT image covering the western Seward Peninsula was used for identifying and mapping vegetation by direct visual examination. The 1:1,083,400 scale print used was prepared by a color additive process using positive transparencies from MSS bands 4, 5, and 7. Seven color classes were recognized. A vegetation map of 3200 sq km area just west of Fairbanks, Alaska was made. Five colors were recognized on the image and identified to vegetation types roughly equivalent to formations in the UNESCO classification: orange - broadleaf deciduous forest; gray - needleleaf evergreen forest; light violet - subarctic alpine tundra vegetation; violet - broadleaf deciduous shrub thicket; and dull violet - bog vegetation.

Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. A reconstituted, simulated color-infrared print, enlarged to a scale of 1:250,000, was used to make a vegetation map of a 3,110 sq km area just west of Fairbanks, Alaska. Information was traced from the print which comprised the southeastern part of ERTS-1 scene 1033-21011. A 1:1,000,000 scale color-infrared transparency of this scene, obtained from NASA, was used along side the print as an aid in recognizing colors, color intensities and blends, and mosaics of different colors. Color units on the transparency and print were identified according to vegetation types using NASA air photos, U.S. Forest Service air photos, and experience of the investigator. Five more or less pure colors were identified and associated with vegetation types. These colors were designated according to their appearances on the print: (1) orange for forest vegetation dominated by broad-leaved trees: (2) gray for forest vegetation dominated by needle-leaved trees; (3) violet for scrub vegetation; (4) light violet denoting herbaceous tundra vegetation; and (5) dull violet for muskeg vegetation. This study has shown, through close examinations of the NASA transparency, that much more detailed vegetation landscape, or ecosystem maps could be produced, if only spectral signatures could be consistently and reliably recognized and transferred to a map of suitable scale.

Remote sensing-based characterization, 2-m, Plant Functional Type Distributions, Barrow Environmental Observatory, 2010

DOE Data Explorer

Langford, Zachary; Kumar, Jitendra; Hoffman, Forrest

2014-01-01

Arctic ecosystems have been observed to be warming faster than the global average and are predicted to experience accelerated changes in climate due to global warming. Arctic vegetation is particularly sensitive to warming conditions and likely to exhibit shifts in species composition, phenology and productivity under changing climate. Mapping and monitoring of changes in vegetation is essential to understand the effect of climate change on the ecosystem functions. Vegetation exhibits unique spectral characteristics which can be harnessed to discriminate plant types and develop quantitative vegetation indices. We have combined high resolution multi-spectral remote sensing from the WorldView 2 satellite with LIDAR-derived digital elevation models to characterize the tundra landscape on the North Slope of Alaska. Classification of landscape using spectral and topographic characteristics yields spatial regions with expectedly similar vegetation characteristics. A field campaign was conducted during peak growing season to collect vegetation harvests from a number of 1m x 1m plots in the study region, which were then analyzed for distribution of vegetation types in the plots. Statistical relationships were developed between spectral and topographic characteristics and vegetation type distributions at the vegetation plots. These derived relationships were employed to statistically upscale the vegetation distributions for the landscape based on spectral characteristics. Vegetation distributions developed are being used to provide Plant Functional Type (PFT) maps for use in the Community Land Model (CLM).

Land cover mapping of the National Park Service northwest Alaska management area using Landsat multispectral and thematic mapper satellite data

USGS Publications Warehouse

Markon, C.J.; Wesser, Sara

1998-01-01

A land cover map of the National Park Service northwest Alaska management area was produced using digitally processed Landsat data. These and other environmental data were incorporated into a geographic information system to provide baseline information about the nature and extent of resources present in this northwest Alaskan environment.This report details the methodology, depicts vegetation profiles of the surrounding landscape, and describes the different vegetation types mapped. Portions of nine Landsat satellite (multispectral scanner and thematic mapper) scenes were used to produce a land cover map of the Cape Krusenstern National Monument and Noatak National Preserve and to update an existing land cover map of Kobuk Valley National Park Valley National Park. A Bayesian multivariate classifier was applied to the multispectral data sets, followed by the application of ancillary data (elevation, slope, aspect, soils, watersheds, and geology) to enhance the spectral separation of classes into more meaningful vegetation types. The resulting land cover map contains six major land cover categories (forest, shrub, herbaceous, sparse/barren, water, other) and 19 subclasses encompassing 7 million hectares. General narratives of the distribution of the subclasses throughout the project area are given along with vegetation profiles showing common relationships between topographic gradients and vegetation communities.

Inventory and analysis of natural vegetation and related resources from space and high altitude photography

NASA Technical Reports Server (NTRS)

Poulton, C. E.; Faulkner, D. P.; Johnson, J. R.; Mouat, D. A.; Schrumpf, B. J.

1971-01-01

A high altitude photomosaic resource map of Site 29 was produced which provided an opportunity to test photo interpretation accuracy of natural vegetation resource features when mapped at a small (1:133,400) scale. Helicopter reconnaissance over 144 previously selected test points revealed a highly adequate level of photo interpretation accuracy. In general, the reasons for errors could be accounted for. The same photomosaic resource map enabled construction of interpretive land use overlays. Based on features of the landscape, including natural vegetation types, judgements for land use suitability were made and have been presented for two types of potential land use. These two, agriculture and urbanization, represent potential land use conflicts.

A Comparison of Electromagnetic Induction Mapping to Measurements of Maximum Effluent Flow Depth for Assessing Flow Paths in Vegetative Treatment Areas

USDA-ARS?s Scientific Manuscript database

Vegetative treatment systems (VTSs) are one type of control structure that has shown potential to control runoff from open feedlots. To achieve maximum performance, sheet-flow over the width of the vegetative treatment area (VTA) is required. Tools, such as maps of flow paths through the VTA, are ne...

Approaches to vegetation mapping and ecophysiological hypothesis testing using combined information from TIMS, AVIRIS, and AIRSAR

NASA Technical Reports Server (NTRS)

Oren, R.; Vane, G.; Zimmermann, R.; Carrere, V.; Realmuto, V.; Zebker, Howard A.; Schoeneberger, P.; Schoeneberger, M.

1991-01-01

The Tropical Rainforest Ecology Experiment (TREE) had two primary objectives: (1) to design a method for mapping vegetation in tropical regions using remote sensing and determine whether the result improves on available vegetation maps; and (2) to test a specific hypothesis on plant/water relations. Both objectives were thought achievable with the combined information from the Thermal Infrared Multispectral Scanner (TIMS), Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), and Airborne Synthetic Aperture Radar (AIRSAR). Implicitly, two additional objectives were: (1) to ascertain that the range within each variable potentially measurable with the three instruments is large enough in the site, relative to the sensitivity of the instruments, so that differences between ecological groups may be detectable; and (2) to determine the ability of the three systems to quantify different variables and sensitivities. We found that the ranges in values of foliar nitrogen concentration, water availability, stand structure and species composition, and plant/water relations were large, even within the upland broadleaf vegetation type. The range was larger when other vegetation types were considered. Unfortunately, cloud cover and navigation errors compromised the utility of the TIMS and AVIRIS data. Nevertheless, the AIRSAR data alone appear to have improved on the available vegetation map for the study area. An example from an area converted to a farm is given to demonstrate how the combined information from AIRSAR, TIMS, and AVIRIS can uniquely identify distinct classes of land use. The example alludes to the potential utility of the three instruments for identifying vegetation at an ecological scale finer than vegetation types.

The Circumpolar Arctic vegetation map

USGS Publications Warehouse

Walker, Donald A.; Raynolds, Martha K.; Daniels, F.J.A.; Einarsson, E.; Elvebakk, A.; Gould, W.A.; Katenin, A.E.; Kholod, S.S.; Markon, C.J.; Melnikov, E.S.; Moskalenko, N.G.; Talbot, S. S.; Yurtsev, B.A.; Bliss, L.C.; Edlund, S.A.; Zoltai, S.C.; Wilhelm, M.; Bay, C.; Gudjonsson, G.; Ananjeva, G.V.; Drozdov, D.S.; Konchenko, L.A.; Korostelev, Y.V.; Ponomareva, O.E.; Matveyeva, N.V.; Safranova, I.N.; Shelkunova, R.; Polezhaev, A.N.; Johansen, B.E.; Maier, H.A.; Murray, D.F.; Fleming, Michael D.; Trahan, N.G.; Charron, T.M.; Lauritzen, S.M.; Vairin, B.A.

2005-01-01

Question: What are the major vegetation units in the Arctic, what is their composition, and how are they distributed among major bioclimate subzones and countries? Location: The Arctic tundra region, north of the tree line. Methods: A photo-interpretive approach was used to delineate the vegetation onto an Advanced Very High Resolution Radiometer (AVHRR) base image. Mapping experts within nine Arctic regions prepared draft maps using geographic information technology (ArcInfo) of their portion of the Arctic, and these were later synthesized to make the final map. Area analysis of the map was done according to bioclimate subzones, and country. The integrated mapping procedures resulted in other maps of vegetation, topography, soils, landscapes, lake cover, substrate pH, and above-ground biomass. Results: The final map was published at 1:7 500 000 scale map. Within the Arctic (total area = 7.11 x 106 km 2), about 5.05 ?? 106 km2 is vegetated. The remainder is ice covered. The map legend generally portrays the zonal vegetation within each map polygon. About 26% of the vegetated area is erect shrublands, 18% peaty graminoid tundras, 13% mountain complexes, 12% barrens, 11% mineral graminoid tundras, 11% prostrate-shrub tundras, and 7% wetlands. Canada has by far the most terrain in the High Arctic mostly associated with abundant barren types and prostrate dwarf-shrub tundra, whereas Russia has the largest area in the Low Arctic, predominantly low-shrub tundra. Conclusions: The CAVM is the first vegetation map of an entire global biome at a comparable resolution. The consistent treatment of the vegetation across the circumpolar Arctic, abundant ancillary material, and digital database should promote the application to numerous land-use, and climate-change applications and will make updating the map relatively easy. ?? IAVS; Opulus Press.

Description and validation of an automated methodology for mapping mineralogy, vegetation, and hydrothermal alteration type from ASTER satellite imagery with examples from the San Juan Mountains, Colorado

USGS Publications Warehouse

Rockwell, Barnaby W.

2012-01-01

The efficacy of airborne spectroscopic, or "hyperspectral," remote sensing for geoenvironmental watershed evaluations and deposit-scale mapping of exposed mineral deposits has been demonstrated. However, the acquisition, processing, and analysis of such airborne data at regional and national scales can be time and cost prohibitive. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor carried by the NASA Earth Observing System Terra satellite was designed for mineral mapping and the acquired data can be efficiently used to generate uniform mineral maps over very large areas. Multispectral remote sensing data acquired by the ASTER sensor were analyzed to identify and map minerals, mineral groups, hydrothermal alteration types, and vegetation groups in the western San Juan Mountains, Colorado, including the Silverton and Lake City calderas. This mapping was performed in support of multidisciplinary studies involving the predictive modeling of surface water geochemistry at watershed and regional scales. Detailed maps of minerals, vegetation groups, and water were produced from an ASTER scene using spectroscopic, expert system-based analysis techniques which have been previously described. New methodologies are presented for the modeling of hydrothermal alteration type based on the Boolean combination of the detailed mineral maps, and for the entirely automated mapping of alteration types, mineral groups, and green vegetation. Results of these methodologies are compared with the more detailed maps and with previously published mineral mapping results derived from analysis of high-resolution spectroscopic data acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor. Such comparisons are also presented for other mineralized and (or) altered areas including the Goldfield and Cuprite mining districts, Nevada and the central Marysvale volcanic field, Wah Wah Mountains, and San Francisco Mountains, Utah. The automated mineral group mapping products described in this study are ideal for application to mineral resource and mineral-environmental assessments at regional and national scales.

Testing methods to produce landscape-scale presettlement vegetation maps from the U.S. public land survey records

USGS Publications Warehouse

Manies, K.L.; Mladenoff, D.J.

2000-01-01

The U.S. Public Land Survey (PLS) notebooks are one of the best records of the pre-European settlement landscape and are widely used to recreate presettlement vegetation maps. The purpose of this study was to evaluate the relative ability of several interpolation techniques to map this vegetation, as sampled by the PLS surveyors, at the landscape level. Field data from Sylvania Wilderness Area, MI (U.S.A.), sampled at the same scale as the PLS data, were used for this test. Sylvania is comprised of a forested landscape similar to that present during presettlement times. Data were analyzed using two Arc/Info interpolation processes and indicator kriging. The resulting maps were compared to a 'correct' map of Sylvania, which was classified from aerial photographs. We found that while the interpolation methods used accurately estimated the relative forest composition of the landscape and the order of dominance of different vegetation types, they were unable to accurately estimate the actual area occupied by each vegetation type. Nor were any of the methods we tested able to recreate the landscape patterns found in the natural landscape. The most likely cause for these inabilities is the scale at which the field data (and hence the PLS data) were recorded. Therefore, these interpolation methods should not be used with the PLS data to recreate pre-European settlement vegetation at small scales (e.g., less than several townships or areas < 104 ha). Recommendations are given for ways to increase the accuracy of these vegetation maps.

Fuel loads and fuel type mapping

USGS Publications Warehouse

Chuvieco, Emilio; Riaño, David; Van Wagtendonk, Jan W.; Morsdof, Felix; Chuvieco, Emilio

2003-01-01

Correct description of fuel properties is critical to improve fire danger assessment and fire behaviour modeling, since they guide both fire ignition and fire propagation. This chapter deals with properties of fuel that can be considered static in short periods of time: biomass loads, plant geometry, compactness, etc. Mapping these properties require a detail knowledge of vegetation vertical and horizontal structure. Several systems to classify the great diversity of vegetation characteristics in few fuel types are described, as well as methods for mapping them with special emphasis on those based on remote sensing images.

The Circumpolar Arctic Vegetation Map: A tool for analysis of change in permafrost regions

NASA Astrophysics Data System (ADS)

Walker, D. A.; Raynolds, M. K.; Maier, H. A.

2003-12-01

Arctic vegetation occurs beyond the northern limit of trees, in areas that have an Arctic climate and Arctic flora. Here we present an overview of the recently published Circumpolar Arctic Vegetation Map (CAVM), an area analysis of the vegetation map, and a discussion of its potential for analysis of change in the Arctic. Six countries have Arctic tundra vegetation, Canada, Greenland, Iceland, Russia, Norway (Svalbard), and the US (Total Arctic area = 7.1 million km2). Some treeless areas, such as most of Iceland and the Aluetian Islands are excluded from the map because they lack an Arctic climate. The CAVM divides the Arctic into five bioclimate subzones, A thru E (Subzone A is the coldest and Subzone E is the warmest), based on a combination of summer temperature and vegetation. Fifteen vegetation types are mapped based on the dominant plant growth forms. More detailed, plant-community-level, information is contained in the database used to construct the map. The reverse side of the vegetation map has a false-color infrared image constructed from Advanced Very-High Resolution (AVHRR) satellite-derived raster data, and maps of bioclimate subzones, elevation, landscape types, lake cover, substrate chemistry, floristic provinces, the maximum normalized difference vegetation index (NDVI), and aboveground phytomass. The vegetation map was analyzed by vegetation type and biomass for each county, bioclimate subzone, and floristic province. Biomass distribution was analyzed by means of a correlation between aboveground phytomass and the normalized difference vegetation index (NDVI), a remote-sensing index of surface greenness. Biomass on zonal surfaces roughly doubles within each successively warmer subzone, from about 50 g m-2 in Subzone A to 800 g m-2- in Subzone E. But the pattern of vegetation increase is highly variable, and depends on a number of other factors. The most important appears to be the glacial history of the landscape. Areas that were glaciated during the late-Pleistocene, such as Canada, Svalbard, and Greenland, do not show such strong increases in NDVI with temperature as do areas that were not glaciated. Abundant lakes and rocky surfaces limit the greenness of these recently glaciated surfaces. The highest NDVI and phytomass are found in non-glaciated regions of Alaska and Russia. Soil acidity also affects NDVI patterns. In Subzone D, where the NDVI/ soil acidity relationship has been studied most closely, NDVI is lower on nonacidic surfaces. This has been attributed to fewer shrubs and higher proportion of graminoids (more standing dead sedge leaves) in nonacidic areas. This trend is probably caused by generally drier soils, with less production, on limestone-derived soils. The trend is less clear in Subzone E because of fewer nonacidic surfaces, and the abundance of glacial lakes with low NDVI on the acidic shield areas of Canada. Time series analysis of trends in NDVI in Subzones C, D, and E in Alaska have shown a 17% increase in the NDVI over the 21-year record. The increases have been greatest in moist nonacidic tundra. Future analyses of the circumpolar database will be directed at examining which geographic regions and vegetation types have shown the strongest increases, and how these are correlated with temperature changes.

Quantifying biological integrity of California sage scrub communities using plant life-form cover.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hamada, Y.; Stow, D. A.; Franklin, J.

2010-01-01

The California sage scrub (CSS) community type in California's Mediterranean-type ecosystems supports a large number of rare, threatened, and endangered species, and is critically degraded and endangered. Monitoring ecological variables that provide information about community integrity is vital to conserving these biologically diverse communities. Fractional cover of true shrub, subshrub, herbaceous vegetation, and bare ground should fill information gaps between generalized vegetation type maps and detailed field-based plot measurements of species composition and provide an effective means for quantifying CSS community integrity. Remote sensing is the only tool available for estimating spatially comprehensive fractional cover over large extent, and fractionalmore » cover of plant life-form types is one of the measures of vegetation state that is most amenable to remote sensing. The use of remote sensing does not eliminate the need for either field surveying or vegetation type mapping; rather it will likely require a combination of approaches to reliably estimate life-form cover and to provide comprehensive information for communities. According to our review and synthesis, life-form fractional cover has strong potential for providing ecologically meaningful intermediate-scale information, which is unattainable from vegetation type maps and species-level field measurements. Thus, we strongly recommend incorporating fractional cover of true shrub, subshrub, herb, and bare ground in CSS community monitoring methods. Estimating life-form cover at a 25 m x 25 m spatial scale using remote sensing would be an appropriate approach for initial implementation. Investigation of remote sensing techniques and an appropriate spatial scale; collaboration of resource managers, biologists, and remote sensing specialists, and refinement of protocols are essential for integrating life-form fractional cover mapping into strategies for sustainable long-term CSS community management.« less

An evaluation of thematic mapper simulator data for the geobotanical discrimination of rock types in Southwest Oregon

NASA Technical Reports Server (NTRS)

Weinstock, K. J.; Morrissey, L. A.

1984-01-01

Rock type identification may be assisted by the use of remote sensing of associated vegetation, particularly in areas of dense vegetative cover where surface materials are not imaged directly by the sensor. The geobotanical discrimination of ultramafic parent materials was investigated and analytical techniques for lithologic mapping and mineral exploration were developed. The utility of remotely sensed data to discriminate vegetation types associated with ultramafic parent materials in a study area in southwest Oregon were evaluated. A number of specific objectives were identified, which include: (1) establishment of the association between vegetation and rock types; (2) examination of the spectral separability of vegetation types associated with rock types; (3) determination of the contribution of each TMS band for discriminating vegetation associated with rock types and (4) comparison of analytical techniques for spectrally classifying vegetation.

Detailed maps of tropical forest types are within reach: forest tree communities for Trinidad and Tobago mapped with multiseason Landsat and multiseason fine-resolution imagery

Treesearch

Eileen H. Helmer; Thomas S. Ruzycki; Jay Benner; Shannon M. Voggesser; Barbara P. Scobie; Courtenay Park; David W. Fanning; Seepersad Ramnarine

2012-01-01

Tropical forest managers need detailed maps of forest types for REDD+, but spectral similarity among forest types; cloud and scan-line gaps; and scarce vegetation ground plots make producing such maps with satellite imagery difficult. How can managers map tropical forest tree communities with satellite imagery given these challenges? Here we describe a case study of...

Using remote sensing and GIS techniques to estimate discharge and recharge fluxes for the Death Valley regional groundwater flow system, USA

USGS Publications Warehouse

D'Agnese, F. A.; Faunt, C.C.; Turner, A.K.; ,

1996-01-01

The recharge and discharge components of the Death Valley regional groundwater flow system were defined by techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were used to calculate discharge volumes for these area. An empirical method of groundwater recharge estimation was modified to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.

Identification of phenological stages and vegetative types for land use classification

NASA Technical Reports Server (NTRS)

Mckendrick, J. D. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Classification of digital data for mapping Alaskan vegetation has been compared to ground truth data and found to have accuracies as high as 90%. These classifications are broad scale types as are currently being used on the Major Ecosystems of Alaska map prepared by the Joint Federal-State Land Use Planning Commission for Alaska. Cost estimates for several options using the ERTS-1 digital data to map the Alaskan land mass at the 1:250,000 scale ranged between $2.17 to $1.49 per square mile.

An expanded map of vegetation communities at Big Muddy National Fish and Wildlife Refuge

USGS Publications Warehouse

Struckhoff, Matthew A.

2013-01-01

In 2012, a map of vegetation communities on Big Muddy National Fish and Wildlife Refuge was expanded based on interpretation of aerial photographs and field data. National Agricultural Imagery Program aerial photographs were used to identify distinct communities on previously unmapped refuge units and newly acquired parcels. Newly mapped polygons were then visited to adjust map boundaries, classify communities according to the National Vegetation Classification System, and quantify the abundance of dominant species and non-native, invasive species of concern to the refuge and other resource management agencies along the Missouri River. The expanded map now covers 6,136 hectares representing 33 community types, including 6 previously unmapped types. The full map includes 1,113 polygons, of which 627 are new, 21 are updated from the 2009 mapping effort, and 465 are unchanged from 2009. Mortality of primarily cottonwood stems, because of growing-season floods between 2008 and 2011, has reduced foliar cover of woody stems and created more open wooded communities. In herbaceous communities, dominance by herbaceous old fields has increased due to the inclusion of refuge units dominated by lands in recent agricultural production in the expanded map. Wetland community abundance has increased slightly due to recent flooding.

Mapping the spectral variability in photosynthetic and non-photosynthetic vegetation, soils, and shade using AVIRIS

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Smith, Milton O.; Sabol, Donald E.; Adams, John B.; Ustin, Susan L.

1992-01-01

The primary objective of this research was to map as many spectrally distinct types of green vegetation (GV), non-photosynthetic vegetation (NPV), shade, and soil (endmembers) in an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene as is warranted by the spectral variability of the data. Once determined, a secondary objective was to interpret these endmembers and their abundances spatially and spectrally in an ecological context.

Airphoto assessment of changes in aquatic vegetation

NASA Technical Reports Server (NTRS)

Markham, B. L.; Philipson, W. R.; Russel, A. E.

1977-01-01

Large scale, multiyear, color and color infrared aerial photographs were used to evaluate changes in aquatic vegetation that have accompanied a reduction in phosphorus inputs to a phosphorus-limited, eutrophic lake in New York State. The study showed that the distribution of emergent, floating and submersed vegetation could be determined with little or no concurrent ground data; that various emergent and floating types could be separated and, with limited field checks, identified; and that different submersed types are generally not separable. Major vegetative types are characterized by spectral and nonspectral features, and a classification is developed for compiling time-sequential vegetation maps.

Accuracy assessment/validation methodology and results of 2010–11 land-cover/land-use data for Pools 13, 26, La Grange, and Open River South, Upper Mississippi River System

USGS Publications Warehouse

Jakusz, J.W.; Dieck, J.J.; Langrehr, H.A.; Ruhser, J.J.; Lubinski, S.J.

2016-01-11

Similar to an AA, validation involves generating random points based on the total area for each map class. However, instead of collecting field data, two or three individuals not involved with the photo-interpretative mapping separately review each of the points onscreen and record a best-fit vegetation type(s) for each site. Once the individual analyses are complete, results are joined together and a comparative analysis is performed. The objective of this initial analysis is to identify areas where the validation results were in agreement (matches) and areas where validation results were in disagreement (mismatches). The two or three individuals then perform an analysis, looking at each mismatched site, and agree upon a final validation class. (If two vegetation types at a specific site appear to be equally prevalent, the validation team is permitted to assign the site two best-fit vegetation types.) Following the validation team’s comparative analysis of vegetation assignments, the data are entered into a database and compared to the mappers’ vegetation assignments. Agreements and disagreements between the map and validation classes are identified, and a contingency table is produced. This document presents the AA processes/results for Pools 13 and La Grange, as well as the validation process/results for Pools 13 and 26 and Open River South.

The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

NASA Technical Reports Server (NTRS)

Hogan, Christine A.

1996-01-01

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation composition was noted in the change detection image.

An empirical study on the utility of BRDF model parameters and topographic parameters for mapping vegetation in a semi-arid region with MISR imagery

USDA-ARS?s Scientific Manuscript database

Multi-angle remote sensing has been proved useful for mapping vegetation community types in desert regions. Based on Multi-angle Imaging Spectro-Radiometer (MISR) multi-angular images, this study compares roles played by Bidirectional Reflectance Distribution Function (BRDF) model parameters with th...

A study to explore the use of orbital remote sensing to determine native arid plant distribution. [Arizona Regional Ecological Test Site

NASA Technical Reports Server (NTRS)

Mcginnies, W. G. (Principal Investigator); Lepley, L. K.; Haase, E. F.; Conn, J. S.; Musick, H. B.; Foster, K. E.

1974-01-01

The author has identified the following significant results. It is possible to determine, from ERTS imagery, native arid plant distribution. Using techniques of multispectral masking and extensive fieldwork, three native vegetation communities were defined and mapped in the Avra Valley study area. A map was made of the Yuma area with the aid of ground truth correlations between areas of desert pavement visible on ERTS images and unique vegetation types. With the exception of the Yuma soil-vegetation correlation phenomena, only very gross differentiations of desert vegetation communities can be made from ERTS data. Vegetation communities with obvious vegetation density differences such as saguaro-paloverde, creosote bush, and riparian vegetation can be separated on the Avra Valley imagery while more similar communities such as creosote bush and saltbush could not be differentiated. It is suggested that large differences in vegetation density are needed before the signatures of two different vegetation types can be differentiated on ERTS imagery. This is due to the relatively insignificant contribution of vegetation to the total radiometric signature of a given desert scene. Where more detailed information concerning the vegetation of arid regions is required, large scale imagery is appropriate.

Using Land Surface Phenology as the Basis for a National Early Warning System for Forest Disturbances

NASA Astrophysics Data System (ADS)

Hargrove, W. W.; Spruce, J.; Norman, S. P.; Hoffman, F. M.

2011-12-01

The National Early Warning System (EWS) provides an 8-day coast-to-coast snapshot of potentially disturbed forests across the U.S.. A prototype system has produced national maps of potential forest disturbances every eight days since January 2010, identifying locations that may require further investigation. Through phenology, the system shows both early and delayed vegetation development and detects all types of unexpected forest disturbances, including insects, disease, wildfires, frost and ice damage, tornadoes, hurricanes, blowdowns, harvest, urbanization, landslides, drought, flood, and climate change. The USDA Forest Service Eastern Forest Environmental Threat Assessment Center is collaborating with NASA Stennis Space Center and the Western Wildland Environmental Threat Assessment Center to develop the tool. The EWS uses differences in phenological responses between an expectation based on historical data and a current view to strategically identify potential forest disturbances and direct attention to locations where forest behavior seems unusual. Disturbance maps are available via the Forest Change Assessment Viewer (FCAV) (http://ews.forestthreats.org/gis), which allows resource managers and other users to see the most current national disturbance maps as soon as they are available. Phenology-based detections show not only vegetation disturbances in the classical sense, but all departures from normal seasonal vegetation behavior. In 2010, the EWS detected a repeated late-frost event at high elevations in North Carolina, USA, that resulted in delayed seasonal development, contrasting with an early spring development at lower elevations, all within close geographic proximity. Throughout 2011, there was a high degree of correspondence between the National Climatic Data Center's North American Drought Monitor maps and EWS maps of phenological drought disturbance in forests. Urban forests showed earlier and more severe phenological drought disturbance than surrounding non-urban forests. An EWS news page (http://www.geobabbble.org/~hnw/EWSNews) highlights disturbances the system has detected during the 2011 season. Unsupervised statistical multivariate clustering of smoothed phenology data every 8 days over an 11-year period produces a detailed map of national vegetation types, including major disturbances. Examining the constancy of these phenological classifications at a particular location from year to year produces a national map showing the persistence of vegetation, regardless of vegetation type. Using spectral unmixing methods, national maps of evergreen decline can be produced which are a composite of insect, disease, and anthropogenic factors causing chronic decline in these forests, including hemlock wooly adelgid, mountain pine beetle, wildfire, tree harvest, and urbanization. Because phenology shows vegetation responses, all disturbance and recovery events detected by the EWS are viewed through the lens of the vegetation.

A comparison of the IGBP DISCover and University of Maryland 1 km global land cover products

USGS Publications Warehouse

Hansen, M.C.; Reed, B.

2000-01-01

Two global 1 km land cover data sets derived from 1992-1993 Advanced Very High Resolution Radiometer (AVHRR) data are currently available, the International Geosphere-Biosphere Programme Data and Information System (IGBP-DIS) DISCover and the University of Maryland (UMd) 1 km land cover maps. This paper makes a preliminary comparison of the methodologies and results of the two products. The DISCover methodology employed an unsupervised clustering classification scheme on a per-continent basis using 12 monthly maximum NDVI composites as inputs. The UMd approach employed a supervised classification tree method in which temporal metrics derived from all AVHRR bands and the NDVI were used to predict class membership across the entire globe. The DISCover map uses the IGBP classification scheme, while the UMd map employs a modified IGBP scheme minus the classes of permanent wetlands, cropland/natural vegetation mosaic and ice and snow. Global area totals of aggregated vegetation types are very similar and have a per-pixel agreement of 74%. For tall versus short/no vegetation, the per-pixel agreement is 84%. For broad vegetation types, core areas map similarly, while transition zones around core areas differ significantly. This results in high regional variability between the maps. Individual class agreement between the two 1 km maps is 49%. Comparison of the maps at a nominal 0.5 resolution with two global ground-based maps shows an improvement of thematic concurrency of 46% when viewing average class agreement. The absence of the cropland mosaic class creates a difficulty in comparing the maps, due to its significant extent in the DISCover map. The DISCover map, in general, has more forest, while the UMd map has considerably more area in the intermediate tree cover classes of woody savanna/ woodland and savanna/wooded grassland.

Multi-discipline resource inventory of soils, vegetation and geology

NASA Technical Reports Server (NTRS)

Simonson, G. H. (Principal Investigator); Paine, D. P.; Lawrence, R. D.; Norgren, J. A.; Pyott, W. Y.; Herzog, J. H.; Murray, R. J.; Rogers, R.

1973-01-01

The author has identified the following significant results. Computer classification of natural vegetation, in the vicinity of Big Summit Prairie, Crook County, Oregon was carried out using MSS digital data. Impure training sets, representing eleven vegetation types plus water, were selected from within the area to be classified. Close correlations were visually observed between vegetation types mapped from the large scale photographs and the computer classification of the ERTS data (Frame 1021-18151, 13 August 1972).

Radar response to vegetation. [soil moisture mapping via microwave backscattering

NASA Technical Reports Server (NTRS)

Ulaby, F. T.

1975-01-01

Active microwave measurements of vegetation backscatter were conducted to determine the utility of radar in mapping soil moisture through vegetation and mapping crop types. Using a truck-mounted boom, spectral response data were obtained for four crop types (corn, milo, soybeans, and alfalfa) over the 4-8 GHz frequency band, at incidence angles of 0 to 70 degrees in 10-degree steps, and for all four linear polarization combinations. Based on a total of 125 data sets covering a wide range of soil moisture, content, system design criteria are proposed for each of the aforementioned objectives. Quantitative soil moisture determination was best achieved at the lower frequency end of the 4-8 GHz band using HH polarized waves in the 5- to 15-degree incidence angle range. A combination of low and high frequency measurements are suggested for classifying crop types. For crop discrimination, a dual-frequency dual-polarization (VV and cross) system operating at incidence angles above 40 degrees is suggested.

An analysis and comparison of LANDSAT-1, Skylab (S-192) and aircraft data for delineation of land-water cover types of the Green Swamp, Florida

NASA Technical Reports Server (NTRS)

Higer, A. L. (Principal Investigator); Coker, A. E.; Schmidt, N. F.; Reed, I. E.

1975-01-01

The author has identified the following significant results. LANDSAT 1 and Skylab (S192) data from the Green Swamp area of central Florida were categorized into five classes: water, cypress, other wetlands, pine, and pasture. These categories were compared with similar categories on a detailed vegetative map made using low altitude aerial photography. Agreement of LANDSAT and Skylab categorized data with the vegetation map was 87 percent and 83 percent respectively. The Green Swamp vegetative categories may be widespread but often consist of numerous small isolated areas, because LANDSAT has a greater resolution than Skylab, it is more favorable for mapping the small vegetative categories.

Remote sensing analysis of vegetation at the San Carlos Apache Reservation, Arizona and surrounding area

USGS Publications Warehouse

Norman, Laura M.; Middleton, Barry R.; Wilson, Natalie R.

2018-01-01

Mapping of vegetation types is of great importance to the San Carlos Apache Tribe and their management of forestry and fire fuels. Various remote sensing techniques were applied to classify multitemporal Landsat 8 satellite data, vegetation index, and digital elevation model data. A multitiered unsupervised classification generated over 900 classes that were then recoded to one of the 16 generalized vegetation/land cover classes using the Southwest Regional Gap Analysis Project (SWReGAP) map as a guide. A supervised classification was also run using field data collected in the SWReGAP project and our field campaign. Field data were gathered and accuracy assessments were generated to compare outputs. Our hypothesis was that a resulting map would update and potentially improve upon the vegetation/land cover class distributions of the older SWReGAP map over the 24,000  km2 study area. The estimated overall accuracies ranged between 43% and 75%, depending on which method and field dataset were used. The findings demonstrate the complexity of vegetation mapping, the importance of recent, high-quality-field data, and the potential for misleading results when insufficient field data are collected.

Unmanned Aerial Vehicles Produce High-Resolution Seasonally-Relevant Imagery for Classifying Wetland Vegetation

NASA Astrophysics Data System (ADS)

Marcaccio, J. V.; Markle, C. E.; Chow-Fraser, P.

2015-08-01

With recent advances in technology, personal aerial imagery acquired with unmanned aerial vehicles (UAVs) has transformed the way ecologists can map seasonal changes in wetland habitat. Here, we use a multi-rotor (consumer quad-copter, the DJI Phantom 2 Vision+) UAV to acquire a high-resolution (< 8 cm) composite photo of a coastal wetland in summer 2014. Using validation data collected in the field, we determine if a UAV image and SWOOP (Southwestern Ontario Orthoimagery Project) image (collected in spring 2010) differ in their classification of type of dominant vegetation type and percent cover of three plant classes: submerged aquatic vegetation, floating aquatic vegetation, and emergent vegetation. The UAV imagery was more accurate than available SWOOP imagery for mapping percent cover of submergent and floating vegetation categories, but both were able to accurately determine the dominant vegetation type and percent cover of emergent vegetation. Our results underscore the value and potential for affordable UAVs (complete quad-copter system < 3,000 CAD) to revolutionize the way ecologists obtain imagery and conduct field research. In Canada, new UAV regulations make this an easy and affordable way to obtain multiple high-resolution images of small (< 1.0 km2) wetlands, or portions of larger wetlands throughout a year.

Linking imaging spectroscopy and trait data to better understand spatial and temporal variability in functional traits

NASA Astrophysics Data System (ADS)

Townsend, Philip; Kruger, Eric; Wang, Zhihui; Singh, Aditya

2017-04-01

Imaging spectroscopy exhibits great potential for mapping foliar functional traits that are impractical or expensive to regularly measure on the ground, and are essentially impossible to characterize comprehensively across space. Specifically, the high information content in spectroscopic data enables us to identify narrow spectral feature that are associated with vegetation primary and secondary biochemistry (nutrients, pigments, defensive compounds), leaf structure (e.g., leaf mass per area), canopy structure, and physiological capacity. Ultimately, knowledge of the variability in such traits is critical to understanding vegetation productivity, as well as responses to climatic variability, disturbances, pests and pathogens. The great challenge to the use of imaging spectroscopy to supplement trait databases is the development of trait retrieval approaches that are broadly applicable within and between ecosystem types. Here, we outline how we are using the US National Ecological Observatory Network (NEON) to prototype the scaling and comparison of trait distributions derived from field measurements and imagery. We find that algorithms to map traits from imagery are robust across ecosystem types, when controlling for physiognomy and vegetation percent cover, and that among all vegetation types, the chemometric algorithms utilize similar features for mapping of traits.

Vegetation map of the greater Denver area, Front Range urban corridor, Colorado

USGS Publications Warehouse

Marr, J.W.; Boyd, W.S.

1979-01-01

Vegetation is one of our most valuable renewable resources; it is the primary producer of organic matter on which all nongreen organisms are dependent for energy, construction materials, aesthetic enjoyment, and other necessities of life. In order to secure the greatest possible returns from the utilization of the different types of vegetation, people need to know what species are present, the ecological processes in which they are involved, and the ways in which they are arranged in the landscape. This vegetation map is designed to help persons in a wide variety of activities to secure that information.

A new approach for predicting drought-related vegetation stress: Integrating satellite, climate, and biophysical data over the U.S. central plains

USGS Publications Warehouse

Tadesse, Tsegaye; Brown, Jesslyn F.; Hayes, M.J.

2005-01-01

Droughts are normal climate episodes, yet they are among the most expensive natural disasters in the world. Knowledge about the timing, severity, and pattern of droughts on the landscape can be incorporated into effective planning and decision-making. In this study, we present a data mining approach to modeling vegetation stress due to drought and mapping its spatial extent during the growing season. Rule-based regression tree models were generated that identify relationships between satellite-derived vegetation conditions, climatic drought indices, and biophysical data, including land-cover type, available soil water capacity, percent of irrigated farm land, and ecological type. The data mining method builds numerical rule-based models that find relationships among the input variables. Because the models can be applied iteratively with input data from previous time periods, the method enables to provide predictions of vegetation conditions farther into the growing season based on earlier conditions. Visualizing the model outputs as mapped information (called VegPredict) provides a means to evaluate the model. We present prototype maps for the 2002 drought year for Nebraska and South Dakota and discuss potential uses for these maps.

Mapping tropical dry forest habitats integrating landsat NDVI, Ikonos imagery, and topographic information in the Caribbean island of Mona.

PubMed

Martinuzzi, Sebastiáin; Gould, William A; Ramos Gonzalez, Olga M; Martinez Robles, Alma; Calle Maldonado, Paulina; Pérez-Buitrago, Néstor; Fumero Caban, José J

2008-06-01

Assessing the status of tropical dry forest habitats using remote sensing technologies is one of the research priorities for Neotropical forests. We developed a simple method for mapping vegetation and habitats in a tropical dry forest reserve, Mona Island, Puerto Rico, by integrating the Normalized Difference Vegetation Index (NDVI) from Landsat, topographic information, and high-resolution Ikonos imagery. The method was practical for identifying vegetation types in areas with a great variety of plant communities and complex relief, and can be adapted to other dry forest habitats of the Caribbean Islands. NDVI was useful for identifying the distribution of forests, woodlands, and shrubland, providing a natural representation of the vegetation patterns on the island. The use of Ikonos imagery allowed increasing the number of land cover classes. As a result, sixteen land-cover types were mapped over the 5500 ha area, with a kappa coefficient of accuracy equal to 79%. This map is a central piece for modeling vertebrate species distribution and biodiversity patterns by the Puerto Rico Gap Analysis Project, and it is of great value for assisting research and management actions in the island.

Localizing National Fragmentation Statistics with Forest Type Maps

Treesearch

Kurt H. Riitters; John W. Coulston; James D. Wickham

2003-01-01

Fragmentation of forest types is an indicator of biodiversity in the Montreal Process, but the available national data permit assessment of only overall forestland fragmentation, not forest type fragmentation. Here we illustrate how to localize national statistics from the 2003 National Report on Sustainable Forests by combining state vegetation maps with national...

Computer-aided classification of forest cover types from small scale aerial photography

NASA Astrophysics Data System (ADS)

Bliss, John C.; Bonnicksen, Thomas M.; Mace, Thomas H.

1980-11-01

The US National Park Service must map forest cover types over extensive areas in order to fulfill its goal of maintaining or reconstructing presettlement vegetation within national parks and monuments. Furthermore, such cover type maps must be updated on a regular basis to document vegetation changes. Computer-aided classification of small scale aerial photography is a promising technique for generating forest cover type maps efficiently and inexpensively. In this study, seven cover types were classified with an overall accuracy of 62 percent from a reproduction of a 1∶120,000 color infrared transparency of a conifer-hardwood forest. The results were encouraging, given the degraded quality of the photograph and the fact that features were not centered, as well as the lack of information on lens vignetting characteristics to make corrections. Suggestions are made for resolving these problems in future research and applications. In addition, it is hypothesized that the overall accuracy is artificially low because the computer-aided classification more accurately portrayed the intermixing of cover types than the hand-drawn maps to which it was compared.

The identification of selected vegetation types in Arizona through the photointerpretation of intermediate scale aerial photography. M.S. Thesis

NASA Technical Reports Server (NTRS)

Ross, G. F. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Nine photography interpretation tests were performed with a total of 19 different interpreters. Three tests were conducted with black and white intermediate scale photography and six tests with color infrared intermediate scale photography. The black and white test results show that the interpretation of vegetation mapped at the association level of classification is reliable for all the classes used at 61%. The color infrared tests indicate that the association level of mapping is unsatisfactory for vegetation interpretation of classes 1 and 6. Students' t-test indicated that intermediate scale black and white photography is significantly better than this particular color infrared photography for the interpretation of southeastern Arizona vegetation mapped at the association level.

Rapid Characterisation of Vegetation Structure to Predict Refugia and Climate Change Impacts across a Global Biodiversity Hotspot

PubMed Central

Schut, Antonius G. T.; Wardell-Johnson, Grant W.; Yates, Colin J.; Keppel, Gunnar; Baran, Ireneusz; Franklin, Steven E.; Hopper, Stephen D.; Van Niel, Kimberley P.; Mucina, Ladislav; Byrne, Margaret

2014-01-01

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region. Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R2 of 0.8–0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia. PMID:24416149

Rapid characterisation of vegetation structure to predict refugia and climate change impacts across a global biodiversity hotspot.

PubMed

Schut, Antonius G T; Wardell-Johnson, Grant W; Yates, Colin J; Keppel, Gunnar; Baran, Ireneusz; Franklin, Steven E; Hopper, Stephen D; Van Niel, Kimberley P; Mucina, Ladislav; Byrne, Margaret

2014-01-01

Identification of refugia is an increasingly important adaptation strategy in conservation planning under rapid anthropogenic climate change. Granite outcrops (GOs) provide extraordinary diversity, including a wide range of taxa, vegetation types and habitats in the Southwest Australian Floristic Region (SWAFR). However, poor characterization of GOs limits the capacity of conservation planning for refugia under climate change. A novel means for the rapid identification of potential refugia is presented, based on the assessment of local-scale environment and vegetation structure in a wider region. This approach was tested on GOs across the SWAFR. Airborne discrete return Light Detection And Ranging (LiDAR) data and Red Green and Blue (RGB) imagery were acquired. Vertical vegetation profiles were used to derive 54 structural classes. Structural vegetation types were described in three areas for supervised classification of a further 13 GOs across the region. Habitat descriptions based on 494 vegetation plots on and around these GOs were used to quantify relationships between environmental variables, ground cover and canopy height. The vegetation surrounding GOs is strongly related to structural vegetation types (Kappa = 0.8) and to its spatial context. Water gaining sites around GOs are characterized by taller and denser vegetation in all areas. The strong relationship between rainfall, soil-depth, and vegetation structure (R(2) of 0.8-0.9) allowed comparisons of vegetation structure between current and future climate. Significant shifts in vegetation structural types were predicted and mapped for future climates. Water gaining areas below granite outcrops were identified as important putative refugia. A reduction in rainfall may be offset by the occurrence of deeper soil elsewhere on the outcrop. However, climate change interactions with fire and water table declines may render our conclusions conservative. The LiDAR-based mapping approach presented enables the integration of site-based biotic assessment with structural vegetation types for the rapid delineation and prioritization of key refugia.

Habitat mapping using hyperspectral images in the vicinity of Hekla volcano in Iceland

NASA Astrophysics Data System (ADS)

Vilmundardóttir, Olga K.; Sigurmundsson, Friðþór S.; Pedersen, Gro B. M.; Falco, Nicola; Rustowicz, Rose; Gísladóttir, Guðrún; Benediktsson, Jón A.

2016-04-01

Hekla, one of the most active volcanoes in Iceland, has created a diverse volcanic landscape with lava flows, hyaloclastite and tephra fields. The variety of geological formations and different times of formation create diverse vegetation within Hekla's vicinity. The region is subjected to extensive loss of vegetation cover and soil erosion due to human utilization of woodlands and ongoing sheep grazing. The eolian activity and frequent tephra deposition has created vast areas of sparse vegetation cover. Over the 20th century, many activities have centered on preventing further loss of vegetated land and restoring ecosystems. The benefit of these activities is now noticeable in the increased vegetation and woodland cover although erosion is still active within the area. For mapping and monitoring this highly dynamic environment remote sensing techniques are extremely useful. One of the principal goals of the project 'Environmental Mapping and Monitoring of Iceland with Remote Sensing' (EMMIRS) is to use hyperspectral images and LiDAR data to classify and map the vegetation within the Hekla area. The data was collected in an aerial survey in summer 2015 by the Natural Environment Research Council (NERC), UK. The habitat type classification, currently being developed at the Icelandic Institute of Natural History and follows the structure of the EUNIS classification system, will be used for classifying the vegetation. The habitat map created by this new technique's outcome will be compared to the existent vegetation maps made by the conventional vegetation mapping method and the multispectral image classification techniques. In the field, vegetation cover, soil properties and spectral reflectance were measured within different habitat types. Special emphasis was on collecting data on vegetation and soil in the historical lavas from Hekla for assessing habitats forming over the millennia. A lava-chronosequence was established by measuring vegetation and soil in lavas formed in 2000, 1991, 1980-81, 1970, 1947, 1913, 1878, 1845, 1766-68, 1693, 1554, 1389-90, 1300, and 1206, representing surfaces of age 15-809 years. Results showed that vegetation cover established rather quickly on the lavas where mosses and lichens already created a full cover after 24 years. The cover remained stable and mosses were the dominant plant group for centuries, unless where tephra fall had occurred or where eolian deposition prevailed. The colonization of vascular plants on the lava was slow except at sites of eolian deposition and tephra fall. Dwarf shrubs and shrubs were rare or even absent on the lavas formed during the last century but their cover increased with increasing age of the lava fields. The older lava fields featured a variety of vegetation classes, indicating different rates and pathways of succession depending on altitude, proximity to eolian sources, land use and other factors. The many similarities yet big contrasts in the habitats featured within the Hekla region pose a challenge for creating a habitat map of the area, testing the potency of the hyperspectral data and classification techniques to the fullest.

Markers linked to vegetative incompatibility (vic) genes and a region of high heterogeneity and reduced recombination near the mating type locus (MAT) in Cryphonectria parasitica

Treesearch

Thomas L. Kubisiak; Michael g. Milgroom

2006-01-01

To find markers linked to vegetative incompatibility (vic) genes in the chestnut blight fungus, Cryphonectria parasitica, we constructed a preliminary linkage map. In general, this map is characterized by low levels of polymorphism, as evident from the more than 24 linkage groups observed, compared to seven expected from electrophoretic karyotyping....

Inventory and monitoring of natural vegetation and related resources in an arid environment: A comprehensive evaluation of ERTS-1 imagery. [Arizona

NASA Technical Reports Server (NTRS)

Schrumpf, B. J. (Principal Investigator); Johnson, J. R.; Mouat, D. A.; Pyott, W. T.

1974-01-01

The author has identified the following significant results. A vegetation classification, with 31 types and compatible with remote sensing applications, was developed for the test site. Terrain features can be used to discriminate vegetation types. Elevation and macrorelief interpretations were successful on ERTS photos, although for macrorelief, high sun angle stereoscopic interpretations were better than low sun angle monoscopic interpretations. Using spectral reflectivity, several vegetation types were characterized in terms of patterns of signature change. ERTS MSS digital data were used to discriminate vegetation classes at the association level and at the alliance level when image contrasts were high or low, respectively. An imagery comparison technique was developed to test image complexity and image groupability. In two stage sampling of vegetation types, ERTS plus high altitude photos were highly satisfactory for estimating kind and extent of types present, and for providing a mapping base.

Exploiting differential vegetation phenology for satellite-based mapping of semiarid grass vegetation in the southwestern United States and northern Mexico

USGS Publications Warehouse

Dye, Dennis G.; Middleton, Barry R.; Vogel, John M.; Wu, Zhuoting; Velasco, Miguel G.

2016-01-01

We developed and evaluated a methodology for subpixel discrimination and large-area mapping of the perennial warm-season (C4) grass component of vegetation cover in mixed-composition landscapes of the southwestern United States and northern Mexico. We describe the methodology within a general, conceptual framework that we identify as the differential vegetation phenology (DVP) paradigm. We introduce a DVP index, the Normalized Difference Phenometric Index (NDPI) that provides vegetation type-specific information at the subpixel scale by exploiting differential patterns of vegetation phenology detectable in time-series spectral vegetation index (VI) data from multispectral land imagers. We used modified soil-adjusted vegetation index (MSAVI2) data from Landsat to develop the NDPI, and MSAVI2 data from MODIS to compare its performance relative to one alternate DVP metric (difference of spring average MSAVI2 and summer maximum MSAVI2), and two simple, conventional VI metrics (summer average MSAVI2, summer maximum MSAVI2). The NDPI in a scaled form (NDPIs) performed best in predicting variation in perennial C4 grass cover as estimated from landscape photographs at 92 sites (R2 = 0.76, p < 0.001), indicating improvement over the alternate DVP metric (R2 = 0.73, p < 0.001) and substantial improvement over the two conventional VI metrics (R2 = 0.62 and 0.56, p < 0.001). The results suggest DVP-based methods, and the NDPI in particular, can be effective for subpixel discrimination and mapping of exposed perennial C4 grass cover within mixed-composition landscapes of the Southwest, and potentially for monitoring of its response to drought, climate change, grazing and other factors, including land management. With appropriate adjustments, the method could potentially be used for subpixel discrimination and mapping of grass or other vegetation types in other regions where the vegetation components of the landscape exhibit contrasting seasonal patterns of phenology.

Vegetation survey in Amazonia using LANDSAT data. [Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Shimabukuro, Y. E.; Dossantos, J. R.; Deaquino, L. C. S.

1982-01-01

Automatic Image-100 analysis of LANDSAT data was performed using the MAXVER classification algorithm. In the pilot area, four vegetation units were mapped automatically in addition to the areas occupied for agricultural activities. The Image-100 classified results together with a soil map and information from RADAR images, permitted the establishment of the final legend with six classes: semi-deciduous tropical forest; low land evergreen tropical forest; secondary vegetation; tropical forest of humid areas, predominant pastureland and flood plains. Two water types were identified based on their sediments indicating different geological and geomorphological aspects.

Increased wetness confounds Landsat-derived NDVI trends in the central Alaska North Slope region, 1985-2011

NASA Astrophysics Data System (ADS)

Raynolds, Martha K.; Walker, Donald A.

2016-08-01

Satellite data from the circumpolar Arctic have shown increases in vegetation indices correlated to warming air temperatures (e.g. Bhatt et al 2013 Remote Sensing 5 4229-54). However, more information is needed at finer scales to relate the satellite trends to vegetation changes on the ground. We examined changes using Landsat TM and ETM+ data between 1985 and 2011 in the central Alaska North Slope region, where the vegetation and landscapes are relatively well-known and mapped. We calculated trends in the normalized difference vegetation index (NDVI) and tasseled-cap transformation indices, and related them to high-resolution aerial photographs, ground studies, and vegetation maps. Significant, mostly negative, changes in NDVI occurred in 7.3% of the area, with greater change in aquatic and barren types. Large reflectance changes due to erosion, deposition and lake drainage were evident. Oil industry-related changes such as construction of artificial islands, roads, and gravel pads were also easily identified. Regional trends showed decreases in NDVI for most vegetation types, but increases in tasseled-cap greenness (56% of study area, greatest for vegetation types with high shrub cover) and tasseled-cap wetness (11% of area), consistent with documented degradation of polygon ice wedges, indicating that increasing cover of water may be masking increases in vegetation when summarized using the water-sensitive NDVI.

Application of thematic mapper-type data over a porphyry-molybdenum deposit in Colorado

NASA Technical Reports Server (NTRS)

Rickman, D. L.; Sadowski, R. M.

1983-01-01

The objective of the study was to evaluate the utility of thematic mapper data as a source of geologically useful information for mountainous areas of varying vegetation density. Much of the processing was done in an a priori manner without prior ground-based information. This approach resulted in a successfull mapping of the alteration associated with the Mt. Emmons molybdenum ore body as well as several other hydrothermal systems. Supervised classification produced a vegetation map at least as accurate as the mapping done for the environmental impact statement. Principal components were used to map zones of general, subtle alteration and to separate hematitically stained rock from staining associated with hydrothermal activity. Decorrelation color composites were found to be useful field mapping aids, easily delineating many lithologies and vegetation classes of interest. The factors restricting the interpretability and computer manipulation of the data are examined.

Identification, definition and mapping of terrestrial ecosystems in interior Alaska. [vegetation, land use, glaciology

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. The vegetation map in preparation at the time of the last report was refined and labeled. This map is presented as an indication of the spatial and classificatory detail possible from interpretations of enlarged ERTS-1 color photographs. Using this map, areas covered by the several vegetation types characterized by white spruce were determined by planimetry. A 1:63,360 scale land use map of the Juneau area was drawn. This map incorporates the land use classification system now under development by the U.S. Geological Survey. The ERTS-1 images used in making the Juneau map were used to determine changes in surface area of the terminal zones of advancing and receding glaciers, the Taku, Norris, and Mendenhall. A new 1:63,360 scale land use map of the Bonanza Creek Experimental Forest and vicinity was drawn. Several excellent new sciences of test areas were received from NASA in color-infrared transparency format. These are being used for making photographic prints for analysis and mapping according to procedures outlined in this report.

Characterizing sub-arctic peatland vegeation using height estimates from structure from motion and an unmanned aerial system (UAS)

NASA Astrophysics Data System (ADS)

Palace, M. W.; DelGreco, J.; Herrick, C.; Sullivan, F.; Varner, R. K.

2017-12-01

The collapse of permafrost, due to thawing, changes landscape topography, hydrology and vegetation. Changes in plant species composition influence methane production pathways and methane emission rates. The complex spatial heterogeneity of vegetation composition across peatlands proves important in quantifying methane emissions. Effort to characterize vegetation across these permafrost peatlands has been conducted with varied success, with difficulty seen in estimating some cover types that are at opposite ends of the permafrost collapse transition, ie palsa/tall shrub and tall graminoid. This is because some of the species are the same (horsetail) and some of the species have similar structure (horsetail/Carex spp.). High resolution digital elevation maps, developed with airborne LIght Detection And Ranging (lidar) have provided insight into some wetland attributes, but lidar collection is costly and requires extensive data processing effort. Lidar information also lacks the spectral information that optical sensors provide. We used an inexpensive Unmanned Aerial Vehicle (UAV) with an optical sensor to image a mire in northern Sweden (Stordalen Mire) in 2015. We collected 700 overlapping images that were stitched together using Structure from Motion (SfM). SfM analysis also provided, due to parallax, the ability to develop a height map of vegetation. This height map was used, along with textural analysis, to develop an artificial neural network to predict five vegetation cover types. Using 200 training points, we found improvements in our prediction of these cover types. We suggest that using the digital height model from SfM provides useful information in remotely sensing vegetation across a permafrost collapsing region that exhibit resulting changes in vegetation composition. The ability to rapidly and inexpensively deploy such a UAV system provides the opportunity to examine multiple sites with limited personnel effort in remote areas.

Analysis of change in marsh types of coastal Louisiana, 1978-2001

USGS Publications Warehouse

Linscombe, Robert G.; Hartley, Stephen B.

2011-01-01

Scientists and geographers have provided multiple datasets and maps to document temporal changes in vegetation types and land-water relationships in coastal Louisiana. Although these maps provide useful historical information, technological limitations prevented these and other mapping efforts from providing sufficiently detailed calculations of areal changes and shifts in habitat coverage. The current analysis of habitat change draws upon these past mapping efforts but is based on an advanced, geographic information system dataset that was created by using Landsat 5 Thematic Mapper imagery and digital orthophoto quarter quadrangles. The objective of building this dataset was to more specifically define land-water relationships over time in coastal Louisiana, and it provides the most detailed analysis of vegetation shifts to date. In the current study, we have attempted to explain these vegetation shifts by interpreting them in the context of rainfall records, data from the Palmer Drought Severity Index, and salinity data. During the 23 years we analyzed, total marsh acreage decreased, with conversion of marsh to open water. Furthermore, the general trend across coastal Louisiana was a shift to increasingly fresh marsh types. Although fresh marsh remained almost the same during the 1978-88 study period, there were greater increases during the 1988-2001 study periods. Intermediate marsh followed the same pattern, whereas brackish marsh showed a reverse (decreasing) pattern. Changes in saline (saltwater) marsh were minimal. Interpreting shifts in marsh vegetation types by using climate and salinity data provides better understanding of factors influencing these changes and, therefore, can improve our ability to make predictions about future marsh loss related to vegetation changes. Results of our study indicate that precipitation fluctuations prior to vegetation surveys impacted salinities differently across the coast. For example, a wet 6 months prior to the survey may or may not have made up for a dry period during the earlier 12 months. More research is needed to better understand rainfall periods and how they affect salinity changes. The ability to understand past dynamics and to anticipate future trends in vegetation change and related land loss in the coastal region of Louisiana is a vital part of ongoing and future efforts to conserve its critical wetland ecosystem. With the loss of marsh and resultant changes in hydrology, it is likely that changes in marsh type may show greater variation in the future, even if given only minor changes in precipitation levels.

Initial Development of Riparian and Marsh Vegetation on Dredged-material Islands in the Sacramento-San Joaquin River Delta, California

Treesearch

A. Sidney England; Mark K. Sogge; Roy A. Woodward

1989-01-01

Natural vegetation establishment and development were monitored for 3 1/2 years on a new, dredged-material island located within the breached levees at Donlon Island in the Sacramento-San Joaquin River Delta. Vegetation measurements and maps prepared annually indicate that marsh and riparian vegetation types have developed rapidly. Topographic data for the island has...

Hyperspectral remote sensing of vegetation

USGS Publications Warehouse

Thenkabail, Prasad S.; Lyon, John G.; Huete, Alfredo

2011-01-01

Hyperspectral narrow-band (or imaging spectroscopy) spectral data are fast emerging as practical solutions in modeling and mapping vegetation. Recent research has demonstrated the advances in and merit of hyperspectral data in a range of applications including quantifying agricultural crops, modeling forest canopy biochemical properties, detecting crop stress and disease, mapping leaf chlorophyll content as it influences crop production, identifying plants affected by contaminants such as arsenic, demonstrating sensitivity to plant nitrogen content, classifying vegetation species and type, characterizing wetlands, and mapping invasive species. The need for significant improvements in quantifying, modeling, and mapping plant chemical, physical, and water properties is more critical than ever before to reduce uncertainties in our understanding of the Earth and to better sustain it. There is also a need for a synthesis of the vast knowledge spread throughout the literature from more than 40 years of research.

Application of remote sensing in the study of vegetation and soils in Idaho

NASA Technical Reports Server (NTRS)

Tisdale, E. W. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Comparison of ERTS-1 imagery and USGS 1:250,000 scale maps of study areas with known ground points revealed significant map errors. These errors were sufficient to render impractical the projection of ERTS-1 imagery directly onto maps of the area. Marked differences were found in the delineation of ground features by different MSS bands. Generally, Band 4 was least useful, while Band 5 proved valuable for indicating patterns of native vegetation, cultivated areas - both dry and irrigated, lava fields, drainage basins, and deep bodies of water. Band 6 was better for landforms and drainages and for shallow bodies of water than Band 5 but inferior for indicating patterns in native vegetation and most types of cultivated land. Band 7 was best of all for indicating lava flows, water bodies, and landform features. Use of a additive color viewer-projector aided greatly in separation of images. A combination of Bands 5 and 7 with appropriate color filters proved best for separating most types of native vegetation and cultivated crops. Landform features and water bodies also showed well with this combination. The addition of Band 4 imagery to these further enhanced the identification of semi-dormant vegetation.

A study of the usefulness of Skylab EREP data for earth resources studies in Australia

NASA Technical Reports Server (NTRS)

Lambert, B. P.; Benson, M. L.; Borough, C. J.; Myers, B. J.; Maffi, C. E.; Simpson, C. J.; Perry, W. J.; Burns, K. L.; Shepherd, J.; Beattie, R. (Principal Investigator)

1975-01-01

The author has identified the following significant results. In subhumid, vegetated areas, S190B photography: (1) has a potentially operational role in detecting lineaments in 1:100,000 scale geological mapping and in major civil engineering surveys; (2) is of limited value for regional lithological mapping at 1:500,000 scale; and (3) provided much useful synoptic information and some detailed information of direct value to the mapping of nonmineral natural resources such as vegetation, land soil, and water. In arid, well exposed areas, S190B photography could be used: (1) with a limited amount of field traverses, to produce reliable 1:500,000 scale geological maps of sedimentary sequences; (2) to update superficial geology on 1:250,000 scale maps; and (3) together with the necessary field studies, to prepare landform, soil, and vegetation maps at 1:1,000,000 scale. Skylab photography was found to be more useful than LANDSAT images for small scale mapping of geology and land types, and for the revision of topographic maps at 1:100,000 scale, because of superior spatial resolution and stereoscopic coverage.

Mapping Vegetation Community Types in a Highly-Disturbed Landscape: Integrating Hiearchical Object-Based Image Analysis with Digital Surface Models

NASA Astrophysics Data System (ADS)

Snavely, Rachel A.

Focusing on the semi-arid and highly disturbed landscape of San Clemente Island, California, this research tests the effectiveness of incorporating a hierarchal object-based image analysis (OBIA) approach with high-spatial resolution imagery and light detection and range (LiDAR) derived canopy height surfaces for mapping vegetation communities. The study is part of a large-scale research effort conducted by researchers at San Diego State University's (SDSU) Center for Earth Systems Analysis Research (CESAR) and Soil Ecology and Restoration Group (SERG), to develop an updated vegetation community map which will support both conservation and management decisions on Naval Auxiliary Landing Field (NALF) San Clemente Island. Trimble's eCognition Developer software was used to develop and generate vegetation community maps for two study sites, with and without vegetation height data as input. Overall and class-specific accuracies were calculated and compared across the two classifications. The highest overall accuracy (approximately 80%) was observed with the classification integrating airborne visible and near infrared imagery having very high spatial resolution with a LiDAR derived canopy height model. Accuracies for individual vegetation classes differed between both classification methods, but were highest when incorporating the LiDAR digital surface data. The addition of a canopy height model, however, yielded little difference in classification accuracies for areas of very dense shrub cover. Overall, the results show the utility of the OBIA approach for mapping vegetation with high spatial resolution imagery, and emphasizes the advantage of both multi-scale analysis and digital surface data for accuracy characterizing highly disturbed landscapes. The integrated imagery and digital canopy height model approach presented both advantages and limitations, which have to be considered prior to its operational use in mapping vegetation communities.

Mapping the Climate of Puerto Rico, Vieques and Culebra.

Treesearch

CHRISTOPHER DALY; E. H. HELMER; MAYA QUINONES

2003-01-01

Spatially explicit climate data contribute to watershed resource management, mapping vegetation type with satellite imagery, mapping present and hypothetical future ecological zones, and predicting species distributions. The regression based Parameter-elevation Regressions on Independent Slopes Model (PRISM) uses spatial data sets, a knowledge base and expert...

DOE Office of Scientific and Technical Information (OSTI.GOV)

FOGWELL, T.W.

During the biological survey and inventory of the Hanford Site conducted in the mid-1990s (1995 and 1996), preliminary surveys of the riparian vegetation were conducted along the Hanford Reach. These preliminary data were reported to The Nature Conservancy (TNC), but were not included in any TNC reports to DOE or stakeholders. During the latter part of FY2001, PNNL contracted with SEE Botanical, the parties that performed the original surveys in the mid 1990s, to complete the data summaries and mapping associated with the earlier survey data. Those data sets were delivered to PNNL and the riparian mapping by vegetation typemore » for the Hanford Reach is being digitized during the first quarter of FY2002. These mapping efforts provide the information necessary to create subsequent spatial data layers to describe the riparian zone according to plant functional types (trees, shrubs, grasses, sedges, forbs). Quantification of the riparian zone by vegetation types is important to a number of DOE'S priority issues including modeling contaminant transport and uptake in the near-riverine environment and the determination of ecological risk. This work included the identification of vegetative zones along the Reach by changes in dominant plant species covering the shoreline from just to the north of the 300 Area to China Bar near Vernita. Dominant and indicator species included Agropyron dasytachyudA. smithii, Apocynum cannabinum, Aristida longiseta, Artemisia campestris ssp. borealis var scouleriana, Artemisa dracunculus, Artemisia lindleyana, Artemisia tridentata, Bromus tectorum, Chrysothamnus nauseosus, Coreopsis atkinsoniana. Eleocharis palustris, Elymus cinereus, Equisetum hyemale, Eriogonum compositum, Juniperus trichocarpa, Phalaris arundinacea, Poa compressa. Salk exigua, Scirpus acutus, Solidago occidentalis, Sporobolus asper,and Sporobolus cryptandrus. This letter report documents the data received, the processing by PNNL staff, and additional data gathered in FY2002 to support development of a complete data layer describing riparian vegetation cover types on the Columbia River adjacent to the Hanford Site boundaries. Included with this report are the preliminary riparian vegetation maps and the associated metadata for that GIS layer.« less

Modeling of vegetation canopy reflectance: Status, issues and recommended future strategy

NASA Technical Reports Server (NTRS)

Goel, N. S. (Editor)

1982-01-01

Various technical issues related to mapping of vegetative type, condition and stage of maturity, utilizing remotely sensed spectral data are reviewed. The existing knowledge base of models, especially of radiative properties of the vegetation canopy and atmosphere, is reviewed to establish the state of the art for addressing the problem of vegetation mapping. Activities to advance the state of the art are recommended. They include working on canopy reflectance and atmospheric scattering models, and field measurements of canopy reflectance as well as of canopy components. Leaf area index (LAI) and solar radiation interception (SRI) are identified as the two most important vegetation variables requiring further investigation. It is recommended that activities related to sensing them or understanding their relationships with measurable variables, should be encouraged and supported.

Global hierarchical classification of deepwater and wetland environments from remote sensing products

NASA Astrophysics Data System (ADS)

Fluet-Chouinard, E.; Lehner, B.; Aires, F.; Prigent, C.; McIntyre, P. B.

2017-12-01

Global surface water maps have improved in spatial and temporal resolutions through various remote sensing methods: open water extents with compiled Landsat archives and inundation with topographically downscaled multi-sensor retrievals. These time-series capture variations through time of open water and inundation without discriminating between hydrographic features (e.g. lakes, reservoirs, river channels and wetland types) as other databases have done as static representation. Available data sources present the opportunity to generate a comprehensive map and typology of aquatic environments (deepwater and wetlands) that improves on earlier digitized inventories and maps. The challenge of classifying surface waters globally is to distinguishing wetland types with meaningful characteristics or proxies (hydrology, water chemistry, soils, vegetation) while accommodating limitations of remote sensing data. We present a new wetland classification scheme designed for global application and produce a map of aquatic ecosystem types globally using state-of-the-art remote sensing products. Our classification scheme combines open water extent and expands it with downscaled multi-sensor inundation data to capture the maximal vegetated wetland extent. The hierarchical structure of the classification is modified from the Cowardin Systems (1979) developed for the USA. The first level classification is based on a combination of landscape positions and water source (e.g. lacustrine, riverine, palustrine, coastal and artificial) while the second level represents the hydrologic regime (e.g. perennial, seasonal, intermittent and waterlogged). Class-specific descriptors can further detail the wetland types with soils and vegetation cover. Our globally consistent nomenclature and top-down mapping allows for direct comparison across biogeographic regions, to upscale biogeochemical fluxes as well as other landscape level functions.

Monitoring land cover changes by remote sensing in north west Egypt

NASA Astrophysics Data System (ADS)

Richards, Timothy Steven

The Mediterratiean coastal strip of Egypt is a semi-arid environment which supports a variety of agricultural practices ranging from irrigated sedentary agriculture to semi-nomadic pastoralism. The sedentarisation of the nomadic Bedouin coupled with an increase in population of both people and livestock and a decrease in the extent of the rangelands, has resulted in severe pressure being exerted upon the environment. Satellite remote sensing of vegetation offers the potential to aid regional management by complementing conventional techniques of vegetation mapping and monitoring. This thesis examines the different techniques available for vegetation mapping using visible and near infrared spectral wave bands. The different techniques available for vegetation mapping using remotely sensed data are reviewed and discussed with reference to semi-arid environments. The underlying similarity of many of the techniques is emphasised and their individual merits discussed. The spectral feature-space of Landsat data of two representative study areas in northern Egypt is explored and examined using graphical techniques and principal components analysis. Hand held radiometric field data are also presented for individual soil types within the region. It is proposed that by using reference data for individual soil types, improved estimates of vegetation cover can be ascertained. A number of radiometric corrections are applied to the digital Landsat data in order to convert the arbitrary digital values of the different spectral bands into physical values of reflectance. The effect of this standardization on the principal components is examined. The stratified approach to vegetation mapping which was explored using the field data is applied in turn to the digital Landsat images. Whilst the stratified approach was not found to offer significant advantages over the non-stratified approach in this case, the analysis does serve to provide an accurate datum against which to measure vegetation. In conclusion a satellite based system for operational vegetation monitoring is proposed.

Using remote sensing and GIS techniques to estimate discharge and recharge. fluxes for the Death Valley regional groundwater flow system, USA

USGS Publications Warehouse

D'Agnese, F. A.; Faunt, C.C.; Keith, Turner A.

1996-01-01

The recharge and discharge components of the Death Valley regional groundwater flow system were defined by remote sensing and GIS techniques that integrated disparate data types to develop a spatially complex representation of near-surface hydrological processes. Image classification methods were applied to multispectral satellite data to produce a vegetation map. This map provided a basis for subsequent evapotranspiration and infiltration estimations. The vegetation map was combined with ancillary data in a GIS to delineate different types of wetlands, phreatophytes and wet playa areas. Existing evapotranspiration-rate estimates were then used to calculate discharge volumes for these areas. A previously used empirical method of groundwater recharge estimation was modified by GIS methods to incorporate data describing soil-moisture conditions, and a recharge potential map was produced. These discharge and recharge maps were readily converted to data arrays for numerical modelling codes. Inverse parameter estimation techniques also used these data to evaluate the reliability and sensitivity of estimated values.

Improving the Projections of Vegetation Biogeography by Integrating Climate Envelope Models and Dynamic Global Vegetation Models

NASA Astrophysics Data System (ADS)

Case, M. J.; Kim, J. B.

2015-12-01

Assessing changes in vegetation is increasingly important for conservation planning in the face of climate change. Dynamic global vegetation models (DGVMs) are important tools for assessing such changes. DGVMs have been applied at regional scales to create projections of range expansions and contractions of plant functional types. Many DGVMs use a number of algorithms to determine the biogeography of plant functional types. One such DGVM, MC2, uses a series of decision trees based on bioclimatic thresholds while others, such as LPJ, use constraining emergent properties with a limited set of bioclimatic threshold-based rules. Although both approaches have been used widely, we demonstrate that these biogeography outputs perform poorly at continental scales when compared to existing potential vegetation maps. Specifically, we found that with MC2, the algorithm for determining leaf physiognomy is too simplistic to capture arid and semi-arid vegetation in much of the western U.S., as well as is the algorithm for determining the broadleaf and needleleaf mix in the Southeast. With LPJ, we found that the bioclimatic thresholds used to allow seedling establishment are too broad and fail to capture regional-scale biogeography of the plant functional types. In response, we demonstrate a new approach to determining the biogeography of plant functional types by integrating the climatic thresholds produced for individual tree species by a series of climate envelope models with the biogeography algorithms of MC2 and LPJ. Using this approach, we find that MC2 and LPJ perform considerably better when compared to potential vegetation maps.

High spatial resolution mapping of land cover types in a priority area for conservation in the Brazilian savanna

NASA Astrophysics Data System (ADS)

Ribeiro, F.; Roberts, D. A.; Hess, L. L.; Davis, F. W.; Caylor, K. K.; Nackoney, J.; Antunes Daldegan, G.

2017-12-01

Savannas are heterogeneous landscapes consisting of highly mixed land cover types that lack clear distinct boundaries. The Brazilian Cerrado is a Neotropical savanna considered a biodiversity hotspot for conservation due to its biodiversity richness and rapid transformation of its landscape by crop and pasture activities. The Cerrado is one of the most threatened Brazilian biomes and only 2.2% of its original extent is strictly protected. Accurate mapping and monitoring of its ecosystems and adjacent land use are important to select areas for conservation and to improve our understanding of the dynamics in this biome. Land cover mapping of savannas is difficult due to spectral similarity between land cover types resulting from similar vegetation structure, floristically similar components, generalization of land cover classes, and heterogeneity usually expressed as small patch sizes within the natural landscape. These factors are the major contributor to misclassification and low map accuracies among remote sensing studies in savannas. Specific challenges to map the Cerrado's land cover types are related to the spectral similarity between classes of land use and natural vegetation, such as natural grassland vs. cultivated pasture, and forest ecosystem vs. crops. This study seeks to classify and evaluate the land cover patterns across an area ranked as having extremely high priority for future conservation in the Cerrado. The main objective of this study is to identify the representativeness of each vegetation type across the landscape using high to moderate spatial resolution imagery using an automated scheme. A combination of pixel-based and object-based approaches were tested using RapidEye 3A imagery (5m spatial resolution) to classify the Cerrado's major land cover types. The random forest classifier was used to map the major ecosystems present across the area, and demonstrated to have an effective result with 68% of overall accuracy. Post-classification modification was performed to refine information to the major physiognomic groups of each ecosystem type. In this step, we used segmentation in eCognition, considering the random forest classification as input as well as other environmental layers (e.g. slope, soil types), which improved the overall classification to 75%.

Texas Disasters II: Utilizing NASA Earth Observations to Assist the Texas Forest Service in Mapping and Analyzing Fuel Loads and Phenology in Texas Grasslands

NASA Technical Reports Server (NTRS)

Brooke, Michael; Williams, Meredith; Fenn, Teresa

2016-01-01

The risk of severe wildfires in Texas has been related to weather phenomena such as climate change and recent urban expansion into wild land areas. During recent years, Texas wild land areas have experienced sequences of wet and dry years that have contributed to increased wildfire risk and frequency. To prevent and contain wildfires, the Texas Forest Service (TFS) is tasked with evaluating and reducing potential fire risk to better manage and distribute resources. This task is made more difficult due to the vast and varied landscape of Texas. The TFS assesses fire risk by understanding vegetative fuel types and fuel loads. To better assist the TFS, NASA Earth observations, including Landsat and Moderate Resolution Imaging Specrtoradiometer (MODIS) data, were analyzed to produce maps of vegetation type and specific vegetation phenology as it related to potential wildfire fuel loads. Fuel maps from 2010-2011 and 2014-2015 fire seasons, created by the Texas Disasters I project, were used and provided alternating, complementary map indicators of wildfire risk in Texas. The TFS will utilize the end products and capabilities to evaluate and better understand wildfire risk across Texas.

Nitrogen critical loads and management alternatives for N-impacted ecosystems in California

Treesearch

M.E. Fenn; E.B. Allen; S.B. Weiss; S. Jovan; L. Geiser; G.S. Tonnesen; R.F. Johnson; L.E. Rao; B.S. Gimeno; F. Yuan; T. Meixner; A. Bytnerowicz

2010-01-01

Empirical critical loads for N deposition effects and maps showing areas projected to be in exceedance of the critical load (CL) are given for seven major vegetation types in California. Thirty-five percent of the land area for these vegetation types (99,639 km2) is estimated to be in excess of the N CL. Low CL values (3–8...

Mapping of Vegetation with the Geoinformation System and Determining of Carrying Capacity of the Pre-Urals Steppe area for a Newly Establishing Population of the Przewalski Horse Equus ferus przewalskii at the Orenburg State Nature Reserve

NASA Astrophysics Data System (ADS)

Fedorov, N. I.; Mikhailenko, O. I.; Zharkikh, T. L.; Bakirova, R. T.

2018-01-01

Mapping of the vegetation (1:25000) of the Pre-Urals Steppe area at the Orenburg State Nature Reserve was completed in 2016. A map created with the geoinformation system contains 1931 simple and complex polygons for 25 types of vegetation. In a drought year, the average stock of palatable vegetation of the whole area is estimated at 8380 tons dry weight. The estimation is based on the size of areas covered by different types of vegetation, their grass production, the correction coefficients for decreasing of pasture forage stocks in winter and decreasing of production of grass communities in dry years. Based on pasture forage stocks the area could tolerate the maximum population size of 1769 individuals of the Przewalski horse, their average density could be 0.11 horse per ha. Yet, as watering places for animals are limited in Pre-Urals Steppe, grazing pressures on the vegetation next to the water sources may increase in dry years. That is why the above-mentioned calculated maximum population size and density must be reduced at least by half until some additional watering places are established and monitoring of the grazing effect on the vegetation next to the places is carried out regularly. Thus, the maximum size of the population is estimated at 800 to 900 individuals, which is almost 1.5 times more than necessary to establish a self-sustained population of the Przewalski horse.

Mapping and monitoring changes in vegetation communities of Jasper Ridge, CA, using spectral fractions derived from AVIRIS images

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Roberts, Dar A.; Adams, John B.; Smith, Milton O.

1993-01-01

An important application of remote sensing is to map and monitor changes over large areas of the land surface. This is particularly significant with the current interest in monitoring vegetation communities. Most of traditional methods for mapping different types of plant communities are based upon statistical classification techniques (i.e., parallel piped, nearest-neighbor, etc.) applied to uncalibrated multispectral data. Classes from these techniques are typically difficult to interpret (particularly to a field ecologist/botanist). Also, classes derived for one image can be very different from those derived from another image of the same area, making interpretation of observed temporal changes nearly impossible. More recently, neural networks have been applied to classification. Neural network classification, based upon spectral matching, is weak in dealing with spectral mixtures (a condition prevalent in images of natural surfaces). Another approach to mapping vegetation communities is based on spectral mixture analysis, which can provide a consistent framework for image interpretation. Roberts et al. (1990) mapped vegetation using the band residuals from a simple mixing model (the same spectral endmembers applied to all image pixels). Sabol et al. (1992b) and Roberts et al. (1992) used different methods to apply the most appropriate spectral endmembers to each image pixel, thereby allowing mapping of vegetation based upon the the different endmember spectra. In this paper, we describe a new approach to classification of vegetation communities based upon the spectra fractions derived from spectral mixture analysis. This approach was applied to three 1992 AVIRIS images of Jasper Ridge, California to observe seasonal changes in surface composition.

Estimating Urban Gross Primary Productivity at High Spatial Resolution

NASA Astrophysics Data System (ADS)

Miller, David Lauchlin

Gross primary productivity (GPP) is an important metric of ecosystem function and is the primary way carbon is transferred from the atmosphere to the land surface. Remote sensing techniques are commonly used to estimate regional and global GPP for carbon budgets. However, urban areas are typically excluded from such estimates due to a lack of parameters specific to urban vegetation and the modeling challenges that arise in mapping GPP across heterogeneous urban land cover. In this study, we estimated typical midsummer GPP within and among vegetation and land use types in the Minneapolis-Saint Paul, Minnesota metropolitan region by deriving light use efficiency parameters specific to urban vegetation types using in situ flux observations and WorldView-2 high spatial resolution satellite imagery. We produced a land cover classification using the satellite imagery, canopy height data from airborne lidar, and leaf-off color-infrared aerial orthophotos, and used regional GIS layers to mask certain land cover/land use types. The classification for built-up and vegetated urban land cover classes distinguished deciduous trees, evergreen trees, turf grass, and golf grass from impervious and soil surfaces, with an overall classification accuracy of 80% (kappa = 0.73). The full study area had 52.1% vegetation cover. The light use efficiency for each vegetation class, with the exception of golf grass, tended to be low compared to natural vegetation light use efficiencies in the literature. The mapped GPP estimates were within 11% of estimates from independent tall tower eddy covariance measurements. The order of the mapped vegetation classes for the full study area in terms of mean GPP from lowest to highest was: deciduous trees (2.52 gC m -2 d-1), evergreen trees (5.81 gC m-2 d-1), turf grass (6.05 gC m-2 d-1), and golf grass (11.77 gC m-2 d-1). Turf grass GPP had a larger coefficient of variation (0.18) than the other vegetation classes (˜0.10). Mean land use GPP for the full study area varied as a function of percent vegetation cover. Urban GPP in general, both including and excluding non-vegetated areas, tended to be low relative to natural forests and grasslands. Our results demonstrate that, at the scale of neighborhoods and city blocks within heterogeneous urban landscapes, high spatial resolution GPP estimates are valuable to develop comparisons such as within and among vegetation cover classes and land use types.

Mapping Cropland and Major Crop Types Across the Great Lakes Basin Using MODIS-NDVI Data

EPA Science Inventory

This research evaluated the potential for using the MODIS Normalized Difference Vegetation Index (NDVI) 16-day composite (MOD13Q) 250-m time-series data to develop a cropland mapping capability throughout the 480 000 km2 Great Lakes Basin (GLB). Cropland mapping was conducted usi...

Reconnaissance geologic mapping in the Dry Valleys of Antarctica using the Earth Resources Technology Satellite

NASA Technical Reports Server (NTRS)

Houston, R. S. (Principal Investigator); Zochol, F. W.; Smithson, S. B.

1973-01-01

The author has identified the following significant results. Reconnaissance geologic mapping can be done with 60-70% accuracy in the Dry Valleys of Antarctica using ERTS-1 imagery. Bedrock geology can be mapped much better than unconsolidated deposits of Quaternary age. Mapping of bedrock geology is facilitated by lack of vegetation, whereas mapping of Quaternary deposits is hindered by lack of vegetation. Antarctic images show remarkable clarity and under certain conditions (moderate relief, selection of the optimum band for specific rock types, stereo-viewing) irregular contacts can be mapped in local areas that are amazing like those mapped at a scale of 1:25,000, but, of course, lack details due to resolution limitations. ERTS-1 images should be a valuable aid to Antarctic geologists who have some limited ground truth and wish to extend boundaries of geologic mapping from known areas.

Multistage, multiseasonal and multiband imagery to identify and qualify non-forest vegetation resources

NASA Technical Reports Server (NTRS)

Driscoll, R. S.; Francis, R. E.

1970-01-01

A description of space and supporting aircraft photography for the interpretation and analyses of non-forest (shrubby and herbaceous) native vegetation is presented. The research includes the development of a multiple sampling technique to assign quantitative area values of specific plant community types included within an assigned space photograph map unit. Also, investigations of aerial film type, scale, and season of photography for identification and quantity measures of shrubby and herbaceous vegetation were conducted. Some work was done to develop automated interpretation techniques with film image density measurement devices.

Multi-scale functional mapping of tidal marsh vegetation for restoration monitoring

NASA Astrophysics Data System (ADS)

Tuxen Bettman, Karin

2007-12-01

Nearly half of the world's natural wetlands have been destroyed or degraded, and in recent years, there have been significant endeavors to restore wetland habitat throughout the world. Detailed mapping of restoring wetlands can offer valuable information about changes in vegetation and geomorphology, which can inform the restoration process and ultimately help to improve chances of restoration success. I studied six tidal marshes in the San Francisco Estuary, CA, US, between 2003 and 2004 in order to develop techniques for mapping tidal marshes at multiple scales by incorporating specific restoration objectives for improved longer term monitoring. I explored a "pixel-based" remote sensing image analysis method for mapping vegetation in restored and natural tidal marshes, describing the benefits and limitations of this type of approach (Chapter 2). I also performed a multi-scale analysis of vegetation pattern metrics for a recently restored tidal marsh in order to target the metrics that are consistent across scales and will be robust measures of marsh vegetation change (Chapter 3). Finally, I performed an "object-based" image analysis using the same remotely sensed imagery, which maps vegetation type and specific wetland functions at multiple scales (Chapter 4). The combined results of my work highlight important trends and management implications for monitoring wetland restoration using remote sensing, and will better enable restoration ecologists to use remote sensing for tidal marsh monitoring. Several findings important for tidal marsh restoration monitoring were made. Overall results showed that pixel-based methods are effective at quantifying landscape changes in composition and diversity in recently restored marshes, but are limited in their use for quantifying smaller, more fine-scale changes. While pattern metrics can highlight small but important changes in vegetation composition and configuration across years, scientists should exercise caution when using metrics in their studies or to validate restoration management decisions, and multi-scale analyses should be performed before metrics are used in restoration science for important management decisions. Lastly, restoration objectives, ecosystem function, and scale can each be integrated into monitoring techniques using remote sensing for improved restoration monitoring.

Mapping the Spectral and Biochemical Characteristics of Riparian Vegetation and Soils

NASA Astrophysics Data System (ADS)

Balaji Bhaskar, M. S.

2016-12-01

Salt cedar (Tamarix ramosissima), an invasive plant species, has successfully invaded large extents of several riparian zones along the western United States and northern Mexico. Mapping the distribution and abundance of Tamarix over these large areas through a, multi-seasonal, cost-effective monitoring approach using satellite remote sensing is very essential. Hence, the objectives of this study are: 1) to identify the spectral characteristics of the major riparian, agricultural vegetation types and soils in the Lower Colorado River (LCR) region; and 2) to determine the biochemical characteristics of the vegetation and soils. Ground truth surveys were conducted at 79 locations where the spectral reflectance measurements of vegetation, type of plant species, plant heights, soil samples and GPS co-ordinates were recorded. All the sampling was designed to coincide with the satellite overpass period. From the LANDSAT TM image, dark-object-subtracted (DOS) digital number (DN) values of six LANDSAT single bands (1-5 and 7) were extracted and all the spectral ratios and vegetative indices were calculated. The NDVI, R1,5 and R1,7 were identified as the best ratios to distinguish the major vegetation types. The LANDSAT TM color-composite spectral ratio image (NDVI, R1,5 and R1,7 as GBR) can clearly identify and map the areas infested with Tamarix. The salt cedar infested riparian soils showed high concentrations of Ca, Mg and Na concentrations compared to other soils and the spectral reflectance of soils with high Na concentrations were significantly higher in the 350-2500 nm spectral range compared to other soils. The Leaf Area Index (LAI) data shows that the salt cedar has higher LAI compared to other riparian vegetation. The spectral and satellite image analysis shows that the selected spectral ratios can be applied to multiple satellite overpasses for monitoring the seasonal progression of the riparian growth over time. Extending the image analysis over wider areas of western United States can improve the understanding of the riparian dynamics in this region.

Vegetation types in coastal Louisiana in 2013

USGS Publications Warehouse

Sasser, Charles E.; Visser, Jenneke M.; Mouton, Edmond; Linscombe, Jeb; Hartley, Steve B.

2014-01-01

During the summer of 2013, the U.S. Geological Survey, Louisiana State University, University of Louisiana at Lafayette, and the Louisiana Department of Wildlife and Fisheries Coastal and Nongame Resources Division jointly completed an aerial survey to collect data on 2013 vegetation types in coastal Louisiana. Plant species were listed and their abundance classified. On the basis of species composition and abundance, each marsh sampling station was assigned a marsh type: fresh, intermediate, brackish, or saline (saltwater) marsh. The current map presents the data collected in this effort.

The ERTS-1 investigation (ER-600). Volume 4: ERTS-1 range analysis

NASA Technical Reports Server (NTRS)

Erb, R. B.

1974-01-01

The Range Analysis Team conducted an investigation to determine the utility of using LANDSAT 1 data for mapping vegetation-type information on range and related grazing lands. Two study areas within the Houston Area Test Site (HATS) were mapped to the highest classification level possible using manual image interpretation and computer aided classification techniques. Rangeland was distinguished from nonrangeland (water, urban area, and cropland) and was further classified as woodland versus nonwoodland. Finer classification of coastal features was attempted with some success in differentiating the lowland zone from the drier upland zone. Computer aided temporal analysis techniques enhanced discrimination among nearly all the vegetation types found in this investigation.

Microcopying wildland maps for distribution and scanner digitizing

Treesearch

Elliot L Amidon; Joyce E. Dye

1976-01-01

Maps for wildland resource inventory and managament purposes typically show vegetation types, soils, and other areal information. For field work, maps must be large-scale. For safekeeping and compact storage, however, they can be reduced onto film, ready to be enlarged on demand by office viewers. By meeting certain simple requirements, film images are potential input...

The use of color infrared photography for wetlands mapping with special reference to shoreline and waterfowl habitat assessment

NASA Technical Reports Server (NTRS)

1973-01-01

Evaluation of low altitude oblique photography obtained by hand-held cameras was useful in determining specifications of operational mission requirements for conventional smaller-scaled vertical photography. Remote sensing techniques were used to assess the rapid destruction of marsh areas at Pointe Mouillee. In an estuarian environment where shoreline features change yearly, there is a need for revision in existing area maps. A land cover inventory, mapped from aerial photography, provided essential data necessary for determining adjacent lands suitable for marshland development. To quantitatively assess the wetlands environment, a detailed inventory of vegetative communities (19 categories) was made using color infrared photography and intensive ground truth. A carefully selected and well laid-out transect was found to be a key asset to photointerpretation and to the analysis of vegetative conditions. Transect data provided the interpreter with locally representative areas of various vegetative types. This facilitated development of a photointerpretation key. Additional information on vegetative conditions in the area was also obtained by evaluating the transect data.

An integrated Landsat/ancillary data classification of desert rangeland

NASA Technical Reports Server (NTRS)

Price, K. P.; Ridd, M. K.; Merola, J. A.

1985-01-01

Range inventorying methods using Landsat MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, UT. The vegetation is predominately desert shrub and annual grasses, with same annual forbs. Three Landsat scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

An integrated LANDSAT/ancillary data classification of desert rangeland

NASA Technical Reports Server (NTRS)

Price, K. P.; Ridd, M. K.; Merola, J. A.

1984-01-01

Range inventorying methods using LANDSAT MSS data, coupled with ancillary data were examined. The study area encompassed nearly 20,000 acres in Rush Valley, Utah. The vegetation is predominately desert shrub and annual grasses, with some annual forbs. Three LANDSAT scenes were evaluated using a Kauth-Thomas brightness/greenness data transformation (May, June, and August dates). The data was classified using a four-band maximum-likelihood classifier. A print map was taken into the field to determine the relationship between print symbols and vegetation. It was determined that classification confusion could be greatly reduced by incorporating geomorphic units and soil texture (coarse vs fine) into the classification. Spectral data, geomorphic units, and soil texture were combined in a GIS format to produce a final vegetation map identifying 12 vegetation types.

Enhanced canopy fuel mapping by integrating lidar data

USGS Publications Warehouse

Peterson, Birgit E.; Nelson, Kurtis J.

2016-10-03

BackgroundThe Wildfire Sciences Team at the U.S. Geological Survey’s Earth Resources Observation and Science Center produces vegetation type, vegetation structure, and fuel products for the United States, primarily through the Landscape Fire and Resource Management Planning Tools (LANDFIRE) program. LANDFIRE products are used across disciplines for a variety of applications. The LANDFIRE data retain their currency and relevancy through periodic updating or remapping. These updating and remapping efforts provide opportunities to improve the LANDFIRE product suite by incorporating data from other sources. Light detection and ranging (lidar) is uniquely suitable for gathering information on vegetation structure and spatial arrangement because it can collect data in three dimensions. The Wildfire Sciences Team has several completed and ongoing studies focused on integrating lidar into vegetation and fuels mapping.

A multi-characteristic based algorithm for classifying vegetation in a plateau area: Qinghai Lake watershed, northwestern China

NASA Astrophysics Data System (ADS)

Ma, Weiwei; Gong, Cailan; Hu, Yong; Li, Long; Meng, Peng

2015-10-01

Remote sensing technology has been broadly recognized for its convenience and efficiency in mapping vegetation, particularly in high-altitude and inaccessible areas where there are lack of in-situ observations. In this study, Landsat Thematic Mapper (TM) images and Chinese environmental mitigation satellite CCD sensor (HJ-1 CCD) images, both of which are at 30m spatial resolution were employed for identifying and monitoring of vegetation types in a area of Western China——Qinghai Lake Watershed(QHLW). A decision classification tree (DCT) algorithm using multi-characteristic including seasonal TM/HJ-1 CCD time series data combined with digital elevation models (DEMs) dataset, and a supervised maximum likelihood classification (MLC) algorithm with single-data TM image were applied vegetation classification. Accuracy of the two algorithms was assessed using field observation data. Based on produced vegetation classification maps, it was found that the DCT using multi-season data and geomorphologic parameters was superior to the MLC algorithm using single-data image, improving the overall accuracy by 11.86% at second class level and significantly reducing the "salt and pepper" noise. The DCT algorithm applied to TM /HJ-1 CCD time series data geomorphologic parameters appeared as a valuable and reliable tool for monitoring vegetation at first class level (5 vegetation classes) and second class level(8 vegetation subclasses). The DCT algorithm using multi-characteristic might provide a theoretical basis and general approach to automatic extraction of vegetation types from remote sensing imagery over plateau areas.

Remote sensing and landslide hazard assessment

NASA Technical Reports Server (NTRS)

Mckean, J.; Buechel, S.; Gaydos, L.

1991-01-01

Remotely acquired multispectral data are used to improve landslide hazard assessments at all scales of investigation. A vegetation map produced from automated interpretation of TM data is used in a GIS context to explore the effect of vegetation type on debris flow occurrence in preparation for inclusion in debris flow hazard modeling. Spectral vegetation indices map spatial patterns of grass senescence which are found to be correlated with soil thickness variations on hillslopes. Grassland senescence is delayed over deeper, wetter soils that are likely debris flow source areas. Prediction of actual soil depths using vegetation indices may be possible up to some limiting depth greater than the grass rooting zone. On forested earthflows, the slow slide movement disrupts the overhead timber canopy, exposes understory vegetation and soils, and alters site spectral characteristics. Both spectral and textural measures from broad band multispectral data are successful at detecting an earthflow within an undisturbed old-growth forest.

Analysis of regional-scale vegetation dynamics of Mexico using stratified AVHRR NDVI data. [Normalized Difference Vegetaion Index

NASA Technical Reports Server (NTRS)

Turcotte, Kevin M.; Kramber, William J.; Venugopal, Gopalan; Lulla, Kamlesh

1989-01-01

Previous studies have shown that a good relationship exists between AVHRR Normalized Difference Vegetation Index (NDVI) measurements, and both regional-scale patterns of vegetation seasonality and productivity. Most of these studies used known samples of vegetation types. An alternative approach, and the objective was to examine the above relationships by analyzing one year of AVHRR NDVI data that was stratified using a small-scale vegetation map of Mexico. The results show that there is a good relationship between AVHRR NDVI measurements and regional-scale vegetation dynamics of Mexico.

Riparian habitat on the Humboldt River, Deeth to Elko, Nevada

NASA Technical Reports Server (NTRS)

Price, K. P.; Ridd, M. K.

1983-01-01

A map inventory of the major habitat types existing along the Humbolt River riparian zone in Nevada is described. Through aerialphotography, 16 riparian habitats are mapped that describe the ecological relationships between soil and vegetation types, flooding and soil erosion, and the various management practices employed to date. The specific land and water management techniques and their impact on the environment are considered.

Vegetation Change in Interior Alaska Over the Last Four Decades

NASA Astrophysics Data System (ADS)

Huhman, H.; Dewitz, J.; Cristobal, J.; Prakash, A.

2017-12-01

The Arctic has become a generally warmer place over the past decades leading to earlier snowmelt, permafrost degradation and changing plant communities. One area in particular, vegetation change, is responding relatively rapidly to climate change, impacting the surrounding environment with changes to forest fire regime, forest type, forest resiliency, habitat availability for subsistence flora and fauna, hydrology, among others. To quantify changes in vegetation in the interior Alaska boreal forest over the last four decades, this study uses the National Land Cover Database (NLCD) decision-tree based classification methods, using both C5 and ERDAS Imagine software, to classify Landsat Surface Reflectance Images into the following NLCD-consistent vegetation classes: planted, herbaceous, shrubland, and forest (deciduous, evergreen and mixed). The results of this process are a total of four vegetation cover maps, that are freely accessible to the public, one for each decade in the 1980's, 1990's, 2000's, and a current map for 2017. These maps focus on Fairbanks, Alaska and the surrounding area covering approximately 36,140 square miles. The maps are validated with over 4,000 ground truth points collected through organizations such as the Landfire Project and the Long Term Ecological Research Network, as well as vegetation and soil spectra collected from the study area concurrent with the Landsat satellite over-passes with a Spectral Evolution PSR+ 3500 spectro-radiometer (0.35 - 2.5 μm). We anticipate these maps to be viewed by a wide user-community and may aid in preparing the residents of Alaska for changes in their subsistence food sources and will contribute to the scientific community in understanding the variety of changes that can occur in response to changing vegetation.

Evaluation of a color-coded Landsat 5/6 ratio image for mapping lithologic differences in western South Dakota

USGS Publications Warehouse

Raines, Gary L.; Bretz, R.F.; Shurr, George W.

1979-01-01

From analysis of a color-coded Landsat 5/6 ratio, image, a map of the vegetation density distribution has been produced by Raines of 25,000 sq km of western South Dakota. This 5/6 ratio image is produced digitally calculating the ratios of the bands 5 and 6 of the Landsat data and then color coding these ratios in an image. Bretz and Shurr compared this vegetation density map with published and unpublished data primarily of the U.S. Geological Survey and the South Dakota Geological Survey; good correspondence is seen between this map and existing geologic maps, especially with the soils map. We believe that this Landsat ratio image can be used as a tool to refine existing maps of surficial geology and bedrock, where bedrock is exposed, and to improve mapping accuracy in areas of poor exposure common in South Dakota. In addition, this type of image could be a useful, additional tool in mapping areas that are unmapped.

Lidar and Hyperspectral Remote Sensing for the Analysis of Coniferous Biomass Stocks and Fluxes

NASA Astrophysics Data System (ADS)

Halligan, K. Q.; Roberts, D. A.

2006-12-01

Airborne lidar and hyperspectral data can improve estimates of aboveground carbon stocks and fluxes through their complimentary responses to vegetation structure and biochemistry. While strong relationships have been demonstrated between lidar-estimated vegetation structural parameters and field data, research is needed to explore the portability of these methods across a range of topographic conditions, disturbance histories, vegetation type and climate. Additionally, research is needed to evaluate contributions of hyperspectral data in refining biomass estimates and determination of fluxes. To address these questions we are a conducting study of lidar and hyperspectral remote sensing data across sites including coniferous forests, broadleaf deciduous forests and a tropical rainforest. Here we focus on a single study site, Yellowstone National Park, where tree heights, stem locations, above ground biomass and basal area were mapped using first-return small-footprint lidar data. A new method using lidar intensity data was developed for separating the terrain and vegetation components in lidar data using a two-scale iterative local minima filter. Resulting Digital Terrain Models (DTM) and Digital Canopy Models (DCM) were then processed to retrieve a diversity of vertical and horizontal structure metrics. Univariate linear models were used to estimate individual tree heights while stepwise linear regression was used to estimate aboveground biomass and basal area. Three small-area field datasets were compared for their utility in model building and validation of vegetation structure parameters. All structural parameters were linearly correlated with lidar-derived metrics, with higher accuracies obtained where field and imagery data were precisely collocated . Initial analysis of hyperspectral data suggests that vegetation health metrics including measures of live and dead vegetation and stress indices may provide good indicators of carbon flux by mapping vegetation vigor or senescence. Additionally, the strength of hyperspectral data for vegetation classification suggests these data have additional utility for modeling carbon flux dynamics by allowing more accurate plant functional type mapping.

Impact of Plant Functional Types on Coherence Between Precipitation and Soil Moisture: A Wavelet Analysis

NASA Astrophysics Data System (ADS)

Liu, Qi; Hao, Yonghong; Stebler, Elaine; Tanaka, Nobuaki; Zou, Chris B.

2017-12-01

Mapping the spatiotemporal patterns of soil moisture within heterogeneous landscapes is important for resource management and for the understanding of hydrological processes. A critical challenge in this mapping is comparing remotely sensed or in situ observations from areas with different vegetation cover but subject to the same precipitation regime. We address this challenge by wavelet analysis of multiyear observations of soil moisture profiles from adjacent areas with contrasting plant functional types (grassland, woodland, and encroached) and precipitation. The analysis reveals the differing soil moisture patterns and dynamics between plant functional types. The coherence at high-frequency periodicities between precipitation and soil moisture generally decreases with depth but this is much more pronounced under woodland compared to grassland. Wavelet analysis provides new insights on soil moisture dynamics across plant functional types and is useful for assessing differences and similarities in landscapes with heterogeneous vegetation cover.

Assessing vulnerable and expanding vegetation stands and species in the San Francisco Bay Area for conservation management under climate change

NASA Astrophysics Data System (ADS)

Morueta-Holme, N.; Heller, N. E.; McLaughlin, B.; Weiss, S. B.; Ackerly, D.

2015-12-01

The distribution of suitable climatic areas for species and vegetation types is expected to shift due to ongoing climate change. While the pace at which current distributions will shift is hard to quantify, predictions of where climatically suitable areas will be in the future can allow us to map 1) areas currently occupied by a species or vegetation type unlikely to persist through the end of this century (vulnerable stands), 2) areas likely to do better in the future and serve as nuclei for population expansion (expanding stands), and 3) areas likely to act as climate refugia (persisting stands). We quantified the vulnerability of 27 individual plant species and 27 vegetation types in the San Francisco Bay Area as well as the conservation importance, vulnerability, and resilience of selected management sites for climate change resilient conservation. To this end, we developed California-wide models of species and vegetation distributions using climate data from the 2014 California Basin Characterization Model at a 270 m resolution, projected to 18 different end-of century climate change scenarios. Combining these distribution models with high resolution maps of current vegetation, we were able to map projected vulnerable, expanding, and persisting stands within the Bay Area. We show that vegetation and species are expected to shift considerably within the study region over the next decades; although we also identify refugia potentially able to offset some of the negative impacts of climate change. We discuss the implications for managers that wish to incorporate climate change in conservation decisions, in particular related to choosing species for restoration, identifying areas to collect seeds for restoration, and preparing for expected major vegetation changes. Our evaluation of individual management sites highlights the need for stronger coordination of efforts across sites to prioritize monitoring and protection of species whose ranges are contracting elsewhere. Finally, we present and discuss novel ways in visualizing and communicating condensed predictions and their uncertainty to land managers and challenges inherent. This work is part of the Terrestrial Biodiversity and Climate Change Collaborative, committed to developing a scientific basis for climate adaptation conservation strategies.

Assessing Hurricane Katrina Vegetation Damage at Stennis Space Center using IKONOS Image Classification Techniques

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Ross, Kenton W.; Graham, William D.

2006-01-01

Hurricane Katrina inflicted widespread damage to vegetation in southwestern coastal Mississippi upon landfall on August 29, 2005. Storm damage to surface vegetation types at the NASA John C. Stennis Space Center (SSC) was mapped and quantified using IKONOS data originally acquired on September 2, 2005, and later obtained via a Department of Defense ClearView contract. NASA SSC management required an assessment of the hurricane s impact to the 125,000-acre buffer zone used to mitigate rocket engine testing noise and vibration impacts and to manage forestry and fire risk. This study employed ERDAS IMAGINE software to apply traditional classification techniques to the IKONOS data. Spectral signatures were collected from multiple ISODATA classifications of subset areas across the entire region and then appended to a master file representative of major targeted cover type conditions. The master file was subsequently used with the IKONOS data and with a maximum likelihood algorithm to produce a supervised classification later refined using GIS-based editing. The final results enabled mapped, quantitative areal estimates of hurricane-induced damage according to general surface cover type. The IKONOS classification accuracy was assessed using higher resolution aerial imagery and field survey data. In-situ data and GIS analysis indicate that the results compare well to FEMA maps of flooding extent. The IKONOS classification also mapped open areas with woody storm debris. The detection of such storm damage categories is potentially useful for government officials responsible for hurricane disaster mitigation.

Influences of watershed geomorphology on extent and composition of riparian vegetation

Treesearch

Blake M. Engelhardt; Peter J. Weisberg; Jeanne C. Chambers

2011-01-01

Watershed (drainage basin) morphometry and geology were derived from digital data sets (DEMs and geologic maps). Riparian corridors were classified into five vegetation types (riparian forest, riparian shrub, wet/mesic meadow, dry meadow and shrub dry meadow) using high-resolution aerial photography. Regression and multivariate analyses were used to relate geomorphic...

Development of genomic SSR markers for fingerprinting lettuce (Lactuca sativa L.) cultivars and mapping genes

USDA-ARS?s Scientific Manuscript database

Background: Lettuce (Lactuca sativa L.) is the major vegetable from the group of leafy vegetables. Several types of molecular markers were developed that are effictively used in lettuce breeding and genetic studies. However only a very limited number of microsattelite-based markers are publicly avai...

Identification of irrigated crop types from ERTS-1 density contour maps and color infrared aerial photography. [Wyoming

NASA Technical Reports Server (NTRS)

Marrs, R. W.; Evans, M. A.

1974-01-01

The author has identified the following significant results. The crop types of a Great Plains study area were mapped from color infrared aerial photography. Each field was positively identified from field checks in the area. Enlarged (50x) density contour maps were constructed from three ERTS-1 images taken in the summer of 1973. The map interpreted from the aerial photography was compared to the density contour maps and the accuracy of the ERTS-1 density contour map interpretations were determined. Changes in the vegetation during the growing season and harvest periods were detectable on the ERTS-1 imagery. Density contouring aids in the detection of such charges.

Regional modeling of wind erosion in the North West and South West of Iran

NASA Astrophysics Data System (ADS)

Mirmousavi, S. H.

2016-08-01

About two-thirds of the Iran's area is located in the arid and semiarid region. Lack of soil moisture and vegetation is poor in most areas can lead to soil erosion caused by wind. So that the annual suffered severe damage to large areas of rich soils. Modeling studies of wind erosion in Iran is very low and incomplete. Therefore, this study aimed to wind erosion modeling, taking into three factors: wind speed, vegetation and soil types have been done. Wind erosion sensitivity was modeled using the key factors of soil sensitivity, vegetation cover and wind erodibility as proxies. These factors were first estimated separately by factor sensitivity maps and later combined by fuzzy logic into a regional-scale wind erosion sensitivity map. Large areas were evaluated by using publicly available datasets of remotely sensed vegetation information, soil maps and meteorological data on wind speed. The resulting estimates were verified by field studies and examining the economic losses from wind erosion as compensated by the state insurance company. The spatial resolution of the resulting sensitivity map is suitable for regional applications, as identifying sensitive areas is the foundation for diverse land development control measures and implementing management activities.

Mapping ecological systems in southeastern Arizona

Treesearch

Jim Malusa; Donald Falk; Larry Laing; Brooke Gebow

2013-01-01

Beginning in 2007 in and around the Huachuca Mountains, the Coronado National Forest and other partners have been mapping ecosystems at multiple scales. The approach has focused on identifying land type associations (LTA), which represent the sum of bedrock and superficial geology, topography, elevation, potential and existing vegetation, soil properties, and local...

Classification of simple vegetation types using POLSAR image data

NASA Technical Reports Server (NTRS)

Freeman, A.

1993-01-01

Mapping basic vegetation or land cover types is a fairly common problem in remote sensing. Knowledge of the land cover type is a key input to algorithms which estimate geophysical parameters, such as soil moisture, surface roughness, leaf area index or biomass from remotely sensed data. In an earlier paper, an algorithm for fitting a simple three-component scattering model to POLSAR data was presented. The algorithm yielded estimates for surface scatter, double-bounce scatter and volume scatter for each pixel in a POLSAR image data set. In this paper, we show how the relative levels of each of the three components can be used as inputs to simple classifier for vegetation type. Vegetation classes include no vegetation cover (e.g. bare soil or desert), low vegetation cover (e.g. grassland), moderate vegetation cover (e.g. fully developed crops), forest and urban areas. Implementation of the approach requires estimates for the three components from all three frequencies available using the NASA/JPL AIRSAR, i.e. C-, L- and P-bands. The research described in this paper was carried out by the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration.

Comparison modeling for alpine vegetation distribution in an arid area.

PubMed

Zhou, Jihua; Lai, Liming; Guan, Tianyu; Cai, Wetao; Gao, Nannan; Zhang, Xiaolong; Yang, Dawen; Cong, Zhentao; Zheng, Yuanrun

2016-07-01

Mapping and modeling vegetation distribution are fundamental topics in vegetation ecology. With the rise of powerful new statistical techniques and GIS tools, the development of predictive vegetation distribution models has increased rapidly. However, modeling alpine vegetation with high accuracy in arid areas is still a challenge because of the complexity and heterogeneity of the environment. Here, we used a set of 70 variables from ASTER GDEM, WorldClim, and Landsat-8 OLI (land surface albedo and spectral vegetation indices) data with decision tree (DT), maximum likelihood classification (MLC), and random forest (RF) models to discriminate the eight vegetation groups and 19 vegetation formations in the upper reaches of the Heihe River Basin in the Qilian Mountains, northwest China. The combination of variables clearly discriminated vegetation groups but failed to discriminate vegetation formations. Different variable combinations performed differently in each type of model, but the most consistently important parameter in alpine vegetation modeling was elevation. The best RF model was more accurate for vegetation modeling compared with the DT and MLC models for this alpine region, with an overall accuracy of 75 % and a kappa coefficient of 0.64 verified against field point data and an overall accuracy of 65 % and a kappa of 0.52 verified against vegetation map data. The accuracy of regional vegetation modeling differed depending on the variable combinations and models, resulting in different classifications for specific vegetation groups.

Development of a high-resolution binational vegetation map of the Santa Cruz River riparian corridor and surrounding watershed, southern Arizona and northern Sonora, Mexico

USGS Publications Warehouse

Wallace, Cynthia S.A.; Villarreal, Miguel L.; Norman, Laura M.

2011-01-01

This report summarizes the development of a binational vegetation map developed for the Santa Cruz Watershed, which straddles the southern border of Arizona and the northern border of Sonora, Mexico. The map was created as an environmental input to the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) that is being created by the U.S. Geological Survey for the watershed. The SCWEPM is a map-based multicriteria evaluation tool that allows stakeholders to explore tradeoffs between valued ecosystem services at multiple scales within a participatory decision-making process. Maps related to vegetation type and are needed for use in modeling wildlife habitat and other ecosystem services. Although detailed vegetation maps existed for the U.S. side of the border, there was a lack of consistent data for the Santa Cruz Watershed in Mexico. We produced a binational vegetation classification of the Santa Cruz River riparian habitat and watershed vegetation based on NatureServe Terrestrial Ecological Systems (TES) units using Classification And Regression Tree (CART) modeling. Environmental layers used as predictor data were derived from a seasonal set of Landsat Thematic Mapper (TM) images (spring, summer, and fall) and from a 30-meter digital-elevation-model (DEM) grid. Because both sources of environmental data are seamless across the international border, they are particularly suited to this binational modeling effort. Training data were compiled from existing field data for the riparian corridor and data collected by the NM-GAP (New Mexico Gap Analysis Project) team for the original Southwest Regional Gap Analysis Project (SWReGAP) modeling effort. Additional training data were collected from core areas of the SWReGAP classification itself, allowing the extrapolation of the SWReGAP mapping into the Mexican portion of the watershed without collecting additional training data.

Airborne Laser Scanning - based vegetation classification in grasslands: a feasibility study

NASA Astrophysics Data System (ADS)

Zlinszky, András; Vári, Ágnes; Deák, Balázs; Mücke, Werner; Székely, Balázs

2013-04-01

Airborne Laser Scanning is traditionally used for topography mapping, exploiting its ability to map terrain elevation under vegetation cover. Parallel to this, the application of ALS for vegetation classification and mapping of ecological variables is rapidly emerging. Point clouds surveyed by ALS provide accurate representations of vegetation structure and are therefore considered suitable for mapping vegetation classes as long as their vertical structure is characteristic. For this reason, most ALS-based vegetation mapping studies have been carried out in forests, with some rare applications for shrublands or tall grass vegetation such as reeds. The use of remote-sensing derived vegetation maps is widespread in ecological research and is also gaining importance in practical conservation. There is an increasing demand for reliable, high-resolution datasets covering large protected areas. ALS can provide both the coverage and the high resolution, and can prove to be an economical solution due to the potential for automatic processing and the wide range of uses that allows spreading costs. Grasslands have a high importance in nature conservation as due to the drastical land use changes (arable lands, afforestation, fragmentation by linear structures) in the last centuries the extent of these habitats have been considerably reduced. Among the habitat types protected by the Habitat Directive of the Natura 2000 system, several grassland habitat types (e.g. hay meadows, dry grasslands harbouring rare Orchid species) have special priority for conservation. For preserving these habitat types application of a proper management - including mowing or grazing - has a crucial role. Therefore not only the mapping of the locations of habitats but the way of management is needed for representing the natural processes. The objective of this study was to test the applicability of airborne laser scanning for ecological vegetation mapping in and around grasslands. The study site is situated in the Sopron mountains (Western Hungary), in the Soproni-hegység Natura 2000 site which has an area of 52 km2 protected under Natura 2000. While the Natura 2000 site is dominated by forests, it also holds several grassland habitats: lowland and mountain hay meadows, dry grasslands, fringe communities and disturbed secondary grasslands in the forest clearings. In the framework of the Changehabitats2 project, ALS surveys of the area were carried out, under leaf-on conditions in July 2012 and March 2011, with a full-waveform sensor (Riegl LMS-Q680i) operating at 1550 nm. The resulting point density was 12.8 echoes/m2. 10 grasslands were selected from the study site varying in size between 0.1 and 3 km2. The ALS datasets of these sites were processed in OPALS software to a set of rasters representing different variables of the leaf-on and leaf-off point cloud, each with a raster size of 0.5 m * 0.5 m. Echo amplitude for single echoes was calibrated to reflectance using estimated reflectivity of an asphalt surface. A Digital Terrain Model was created using hierarchical robust filtering in SCOP++ software, and normalized digital surface models were calculated by subtracting this from the local surface models. Echo width and surface roughness were also calculated in OPALS. Surface openness was measured in order to distinguish circular and linear features, such as sedge clumps and ploughing marks. Finally, all these rasters were stacked in ENVI software, resulting in a pseudo-image, where each pseudo-channel corresponded to a different ALS-derived variable. Reference datasets were collected in the field using differential GNSS. Habitat type, dominant vegetation species and other features of interest were noted in the point attributes, and a set of 90 circular plots was recorded. These were overlain on the pseudo-image to create regions of interest (ROI), resulting in 100-4000 ROI pixels for each vegetation category. Multivariate statistics were then used in order to analyse the trends in the data: interdependence of the ALS-derived variables was tested by calculating covariance matrices and the separability of the vegetation categories was tested by the Jeffries-Matusita index. The results of the multivariate analysis were used for merging and excluding classes from the initial 24 to find those where the accuracy was sufficient and the results were relevant for conservation. Results show that a large number of variables can be derived from leaf-on and leaf-off ALS surveys that are statistically independent from each other and thus provide a good basis for classification. With appropriate calculation methods, a number of pseudo-bands comparable to multispectral imagery can be reached. These categories are of course not equally relevant for vegetation mapping. Not surprisingly, the categories with the strongest separability are those closely linked to vegetation height or texture. Mown grasslands can be well separated from abandoned grasslands; tussock-forming grasses from more uniform textured tall herbs. While quality control is still in progress and the trade-off between the number of categories and the classification accuracy is evident, it is expected that for a limited range of vegetation classes, the reliability of the method will be comparable to passive optical image processing. Using a classification method slightly different from the conventional ALS-based vegetation mapping approach, encouraging results have been obtained for an area where the vertical structure of the vegetation is limited. The present state of the art of ALS sensors including full waveform processing and calibration of surface reflectance allows vegetation mapping even in grasslands. It is expected that the further development of waveform digitization and the ever-increasing scanning frequencies will further support such studies in the future.

Sensory determinants of stated liking for vegetable names and actual liking for canned vegetables: A cross-country study among European adolescents.

PubMed

Dinnella, Caterina; Morizet, David; Masi, Camilla; Cliceri, Danny; Depezay, Laurence; Appleton, Katherine M; Giboreau, Agnés; Perez-Cueto, Federico J A; Hartwell, Heather; Monteleone, Erminio

2016-12-01

Sensory properties are reported as one of the main factors hindering an appropriate vegetable intake by the young. In the present work the sensory determinants of likings for vegetables were explored in adolescents of four European countries (Denmark, n = 88; France, n = 206; Italy, n = 110 and United Kingdom, n = 93). A questionnaire was designed to study cross country differences in stated liking for and familiarity with a list of vegetables popular among European markets (between-vegetable approach). A within-vegetable comparison approach with actual tasting was used to analyze differences and similarities in liking for canned pea and sweet corn samples across the countries. A close positive relationship between stated liking and familiarity was found. Irrespective of the country, one group of highly liked vegetables (carrots, tomatoes, green salad) was identified, characterized by innately liked tastes (sweet, umami), delicate flavour and bright appealing colour. A second group of highly disliked vegetables consists of cauliflowers and broccoli, characterized by disliked sensations such as bitter taste and objectionable flavour. Internal Preference Maps from actual liking scores indicate that the generally disliked tastes (bitter, sour), are clearly correlated with a negative hedonic response for both peas and sweet corn. The hedonic valence of a generally well accepted taste such as salty and texture descriptors depends on the type of vegetable. Internal preference maps from actual liking data indicate that flavour and appearance descriptors of the distinct sensory properties of each type of vegetable positively affect liking, while the intensity of unusual flavours is related to sample disliking. Copyright © 2016 Elsevier Ltd. All rights reserved.

Classification and spatial mapping of riparian habitat with applications toward management of streams impacted by nonpoint source pollution

NASA Astrophysics Data System (ADS)

Delong, Michael D.; Brusven, Merlyn A.

1991-07-01

Management of riparian habitats has been recognized for its importance in reducing instream effects of agricultural nonpoint source pollution. By serving as a buffer, well structured riparian habitats can reduce nonpoint source impacts by filtering surface runoff from field to stream. A system has been developed where key characteristics of riparian habitat, vegetation type, height, width, riparian and shoreline bank slope, and land use are classified as discrete categorical units. This classification system recognizes seven riparian vegetation types, which are determined by dominant plant type. Riparian and shoreline bank slope, in addition to riparian width and height, each consist of five categories. Classification by discrete units allows for ready digitizing of information for production of spatial maps using a geographic information system (GIS). The classification system was tested for field efficiency on Tom Beall Creek watershed, an agriculturally impacted third-order stream in the Clearwater River drainage, Nez Perce County, Idaho, USA. The classification system was simple to use during field applications and provided a good inventory of riparian habitat. After successful field tests, spatial maps were produced for each component using the Professional Map Analysis Package (pMAP), a GIS program. With pMAP, a map describing general riparian habitat condition was produced by combining the maps of components of riparian habitat, and the condition map was integrated with a map of soil erosion potential in order to determine areas along the stream that are susceptible to nonpoint source pollution inputs. Integration of spatial maps of riparian classification and watershed characteristics has great potential as a tool for aiding in making management decisions for mitigating off-site impacts of agricultural nonpoint source pollution.

Unsupervised classification of lidar-based vegetation structure metrics at Jean Lafitte National Historical Park and Preserve

USGS Publications Warehouse

Kranenburg, Christine J.; Palaseanu-Lovejoy, Monica; Nayegandhi, Amar; Brock, John; Woodman, Robert

2012-01-01

Traditional vegetation maps capture the horizontal distribution of various vegetation properties, for example, type, species and age/senescence, across a landscape. Ecologists have long known, however, that many important forest properties, for example, interior microclimate, carbon capacity, biomass and habitat suitability, are also dependent on the vertical arrangement of branches and leaves within tree canopies. The objective of this study was to use a digital elevation model (DEM) along with tree canopy-structure metrics derived from a lidar survey conducted using the Experimental Advanced Airborne Research Lidar (EAARL) to capture a three-dimensional view of vegetation communities in the Barataria Preserve unit of Jean Lafitte National Historical Park and Preserve, Louisiana. The EAARL instrument is a raster-scanning, full waveform-resolving, small-footprint, green-wavelength (532-nanometer) lidar system designed to map coastal bathymetry, topography and vegetation structure simultaneously. An unsupervised clustering procedure was then applied to the 3-dimensional-based metrics and DEM to produce a vegetation map based on the vertical structure of the park's vegetation, which includes a flotant marsh, scrub-shrub wetland, bottomland hardwood forest, and baldcypress-tupelo swamp forest. This study was completed in collaboration with the National Park Service Inventory and Monitoring Program's Gulf Coast Network. The methods presented herein are intended to be used as part of a cost-effective monitoring tool to capture change in park resources.

Measuring phenological variability from satellite imagery

USGS Publications Warehouse

Reed, Bradley C.; Brown, Jesslyn F.; Vanderzee, D.; Loveland, Thomas R.; Merchant, James W.; Ohlen, Donald O.

1994-01-01

Vegetation phenological phenomena are closely related to seasonal dynamics of the lower atmosphere and are therefore important elements in global models and vegetation monitoring. Normalized difference vegetation index (NDVI) data derived from the National Oceanic and Atmospheric Administration's Advanced Very High Resolution Radiometer (AVHRR) satellite sensor offer a means of efficiently and objectively evaluating phenological characteristics over large areas. Twelve metrics linked to key phenological events were computed based on time-series NDVI data collected from 1989 to 1992 over the conterminous United States. These measures include the onset of greenness, time of peak NDVI, maximum NDVI, rate of greenup, rate of senescence, and integrated NDVI. Measures of central tendency and variability of the measures were computed and analyzed for various land cover types. Results from the analysis showed strong coincidence between the satellite-derived metrics and predicted phenological characteristics. In particular, the metrics identified interannual variability of spring wheat in North Dakota, characterized the phenology of four types of grasslands, and established the phenological consistency of deciduous and coniferous forests. These results have implications for large- area land cover mapping and monitoring. The utility of re- motely sensed data as input to vegetation mapping is demonstrated by showing the distinct phenology of several land cover types. More stable information contained in ancillary data should be incorporated into the mapping process, particularly in areas with high phenological variability. In a regional or global monitoring system, an increase in variability in a region may serve as a signal to perform more detailed land cover analysis with higher resolution imagery.

Automatically Generated Vegetation Density Maps with LiDAR Survey for Orienteering Purpose

NASA Astrophysics Data System (ADS)

Petrovič, Dušan

2018-05-01

The focus of our research was to automatically generate the most adequate vegetation density maps for orienteering purpose. Application Karttapullatuin was used for automated generation of vegetation density maps, which requires LiDAR data to process an automatically generated map. A part of the orienteering map in the area of Kazlje-Tomaj was used to compare the graphical display of vegetation density. With different settings of parameters in the Karttapullautin application we changed the way how vegetation density of automatically generated map was presented, and tried to match it as much as possible with the orienteering map of Kazlje-Tomaj. Comparing more created maps of vegetation density the most suitable parameter settings to automatically generate maps on other areas were proposed, too.

HOTEX: An Approach for Global Mapping of Human Built-Up and Settlement Extent

NASA Technical Reports Server (NTRS)

Wang, Panshi; Huang, Chengquan; Tilton, James C.; Tan, Bin; Brown De Colstoun, Eric C.

2017-01-01

Understanding the impacts of urbanization requires accurate and updatable urban extent maps. Here we present an algorithm for mapping urban extent at global scale using Landsat data. An innovative hierarchical object-based texture (HOTex) classification approach was designed to overcome spectral confusion between urban and nonurban land cover types. VIIRS nightlights data and MODIS vegetation index datasets are integrated as high-level features under an object-based framework. We applied the HOTex method to the GLS-2010 Landsat images to produce a global map of human built-up and settlement extent. As shown by visual assessments, our method could effectively map urban extent and generate consistent results using images with inconsistent acquisition time and vegetation phenology. Using scene-level cross validation for results in Europe, we assessed the performance of HOTex and achieved a kappa coefficient of 0.91, compared to 0.74 from a baseline method using per-pixel classification using spectral information.

The Change in the area of various land covers on the Tibetan Plateau during 1957-2015

NASA Astrophysics Data System (ADS)

Cuo, Lan; Zhang, Yongxin

2017-04-01

With average elevation of 4000 m and area of 2.5×106 km2, Tibetan Plateau hosts various fragile ecosystems such as perennial alpine meadow, perennial alpine steppe, temperate evergreen needleleaf trees, temperate deciduous trees, temperate shrub grassland, and barely vegetated desert. Perennial alpine meadow and steppe are the two dominant vegetation types on the heartland of the plateau. MODIS Leaf Area Index (LAI) ranges from 0 to 2 in most part of the plateau. With climate change, these ecosystems are expected to undergo alteration. This study uses a dynamic vegetation model - Lund-Potsdam-Jena (LPJ) to investigate the change of the barely vegetated area and other vegetation types caused by climate change during 1957-2015 on the Tibetan Plateau. Model simulated foliage projective coverage (FPC) and plant functional types (PFTs) are selected for the investigation. The model is evaluated first using both field surveyed land cover map and MODIS LAI images. Long term trends of vegetation FPC is examined. Decadal variations of vegetated and barely vegetated land are compared. The impacts of extreme precipitation, air temperature and CO2 on the expansion and contraction of barely vegetated and vegetated areas are shown. The study will identify the dominant climate factors in affecting the desert area in the region.

Large-scale mapping and predictive modeling of submerged aquatic vegetation in a shallow eutrophic lake.

PubMed

Havens, Karl E; Harwell, Matthew C; Brady, Mark A; Sharfstein, Bruce; East, Therese L; Rodusky, Andrew J; Anson, Daniel; Maki, Ryan P

2002-04-09

A spatially intensive sampling program was developed for mapping the submerged aquatic vegetation (SAV) over an area of approximately 20,000 ha in a large, shallow lake in Florida, U.S. The sampling program integrates Geographic Information System (GIS) technology with traditional field sampling of SAV and has the capability of producing robust vegetation maps under a wide range of conditions, including high turbidity, variable depth (0 to 2 m), and variable sediment types. Based on sampling carried out in August-September 2000, we measured 1,050 to 4,300 ha of vascular SAV species and approximately 14,000 ha of the macroalga Chara spp. The results were similar to those reported in the early 1990s, when the last large-scale SAV sampling occurred. Occurrence of Chara was strongly associated with peat sediments, and maximal depths of occurrence varied between sediment types (mud, sand, rock, and peat). A simple model of Chara occurrence, based only on water depth, had an accuracy of 55%. It predicted occurrence of Chara over large areas where the plant actually was not found. A model based on sediment type and depth had an accuracy of 75% and produced a spatial map very similar to that based on observations. While this approach needs to be validated with independent data in order to test its general utility, we believe it may have application elsewhere. The simple modeling approach could serve as a coarse-scale tool for evaluating effects of water level management on Chara populations.

Classification of a wetland area along the upper Mississippi River with aerial videography

USGS Publications Warehouse

Jennings, C.A.; Vohs, P.A.; Dewey, M.R.

1992-01-01

We evaluated the use of aerial videography for classifying wetland habitats along the upper Mississippi River and found the prompt availability of habitat feature maps to be the major advantage of the video imagery technique. We successfully produced feature maps from digitized video images that generally agreed with the known distribution and areal coverages of the major habitat types independently identified and quantified with photointerpretation techniques. However, video images were not sufficiently detailed to allow us to consistently discriminate among the classes of aquatic macrophytes present or to quantify their areal coverage. Our inability to consistently distinguish among emergent, floating, and submergent macrophytes from the feature maps may have been related to the structural complexity of the site, to our limited vegetation sampling, and to limitations in video imagery. We expect that careful site selection (i.e., the desired level of resolution is available from video imagery) and additional vegetation samples (e.g., along a transect) will allow improved assignment of spectral values to specific plant types and enhance plant classification from feature maps produced from video imagery.

Dynamics of active layer in wooded palsas of northern Quebec

NASA Astrophysics Data System (ADS)

Jean, Mélanie; Payette, Serge

2014-02-01

Palsas are organic or mineral soil mounds having a permafrost core. Palsas are widespread in the circumpolar discontinuous permafrost zone. The annual dynamics and evolution of the active layer, which is the uppermost layer over the permafrost table and subjected to the annual freeze-thaw cycle, are influenced by organic layer thickness, snow depth, vegetation type, topography and exposure. This study examines the influence of vegetation types, with an emphasis on forest cover, on active layer dynamics of palsas in the Boniface River watershed (57°45‧ N, 76°00‧ W). In this area, palsas are often colonized by black spruce trees (Picea mariana (Mill.) B.S.P.). Thaw depth and active layer thickness were monitored on 11 wooded or non-wooded mineral and organic palsas in 2009, 2010 and 2011. Snow depth, organic layer thickness, and vegetation types were assessed. The mapping of a palsa covered by various vegetation types and a large range of organic layer thickness were used to identify the factors influencing the spatial patterns of thaw depth and active layer. The active layer was thinner and the thaw rate slower in wooded palsas, whereas it was the opposite in more exposed sites such as forest openings, shrubs and bare ground. Thicker organic layers were associated with thinner active layers and slower thaw rates. Snow depth was not an important factor influencing active layer dynamics. The topography of the mapped palsa was uneven, and the environmental factors such as organic layer, snow depth, and vegetation types were heterogeneously distributed. These factors explain a part of the spatial variation of the active layer. Over the 3-year long study, the area of one studied palsa decreased by 70%. In a context of widespread permafrost decay, increasing our understanding of factors that influence the dynamics of wooded and non-wooded palsas and understanding of the role of vegetation cover will help to define the response of discontinuous permafrost landforms to changing climatic conditions.

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia.

PubMed

Hüttich, Christian; Herold, Martin; Strohbach, Ben J; Dech, Stefan

2011-05-01

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.

Presettlement fire regime and vegetation mapping in Southeastern Coastal Plain forest ecosystems

Treesearch

Andrew D. Bailey; Robert Mickler; Cecil Frost

2007-01-01

Fire-adapted forest ecosystems make up 95 percent of the historic Coastal Plain vegetation types in the Southeastern United States. Fire suppression over the last century has altered the species composition of these ecosystems, increased fuel loads, and increased wildfire risk. Prescribed fire is one management tool used to reduce fuel loading and restore fire-adapted...

Use of LANDSAT imagery for wildlife habitat mapping in northeast and eastcentral Alaska

NASA Technical Reports Server (NTRS)

Laperriere, A. J. (Principal Investigator)

1976-01-01

The author has identified the following significant results. Indications are that Alaskan scenes dated later than about September 5th are unsuitable for vegetational analyses. Such fall data exhibit a limited dynamic range relative to summer scenes and the informational content of the data is reduced such that discrimination between many vegetation types is no longer possible.

Classifying plant series-level forest potential types: methods for subbasins sampled in the midscale assessment of the interior Columbia basin.

Treesearch

Paul F. Hessburg; Bradley G. Smith; Scott D. Kreiter; Craig A. Miller; Cecilia H. McNicoll; Michele. Wasienko-Holland

2000-01-01

In the interior Columbia River basin midscale ecological assessment, we mapped and characterized historical and current vegetation composition and structure of 337 randomly sampled subwatersheds (9500 ha average size) in 43 subbasins (404 000 ha average size). We compared landscape patterns, vegetation structure and composition, and landscape vulnerability to wildfires...

Evaluating rapid ground sampling and scaling estimated plant cover using UAV imagery up to Landsat for mapping arctic vegetation

NASA Astrophysics Data System (ADS)

Nelson, P.; Paradis, D. P.

2017-12-01

The small stature and spectral diversity of arctic plant taxa presents challenges in mapping arctic vegetation. Mapping vegetation at the appropriate scale is needed to visualize effects of disturbance, directional vegetation change or mapping of specific plant groups for other applications (eg. habitat mapping). Fine spatial grain of remotely sensed data (ca. 10 cm pixels) is often necessary to resolve patches of many arctic plant groups, such as bryophytes and lichens. These groups are also spectrally different from mineral, litter and vascular plants. We sought to explore method to generate high-resolution spatial and spectral data to explore better mapping methods for arctic vegetation. We sampled ground vegetation at seven sites north or west of tree-line in Alaska, four north of Fairbanks and three northwest of Bethel, respectively. At each site, we estimated cover of plant functional types in 1m2 quadrats spaced approximately every 10 m along a 100 m long transect. Each quadrat was also scanned using a field spectroradiometer (PSR+ Spectral Evolution, 400-2500 nm range) and photographed from multiple perspectives. We then flew our small UAV with a RGB camera over the transect and at least 50 m on either side collecting on imagery of the plot, which were used to generate a image mosaic and digital surface model of the plot. We compare plant functional group cover ocular estimated in situ to post-hoc estimation, either automated or using a human observer, using the quadrat photos. We also compare interpolated lichen cover from UAV scenes to estimated lichen cover using a statistical models using Landsat data, with focus on lichens. Light and yellow lichens are discernable in the UAV imagery but certain lichens, especially dark colored lichens or those with spectral signatures similar to graminoid litter, present challenges. Future efforts will focus on integrating UAV-upscaled ground cover estimates to hyperspectral sensors (eg. AVIRIS ng) for better combined spectral and spatial resolution.

Bird-habitat relationships in interior Columbia Basin shrubsteppe

USGS Publications Warehouse

Earnst, S.L.; Holmes, A.L.

2012-01-01

Vegetation structure is considered an important habitat feature structuring avian communities. In the sagebrush biome, both remotely-sensed and field-acquired measures of big sagebrush (Artemisia tridentata) cover have proven valuable in understanding avian abundance. Differences in structure between the exotic annual cheatgrass (Bromus tectorum) and native bunchgrasses are also expected to be important. We used avian abundance data from 318 point count stations, coupled with field vegetation measurements and a detailed vegetation map, to model abundance for four shrub- and four grassland-associated avian species in southeastern Washington shrubsteppe. Specifically, we ask whether species distinguish between bunchgrass and cheatgrass, and whether mapped, categorical cover types adequately explain species' abundance or whether fine-grained, field-measured differences in vegetation cover are also important. Results indicate that mapped cover types alone can be useful for predicting patterns of distribution and abundance within the sagebrush biome for several avian species (five of eight studied here). However, field-measured sagebrush cover was a strong positive predictor for Sage Sparrow (Amphispiza belli), the only sagebrush obligate in this study, and a strong negative predictor for two grassland associates, Horned Lark (Eremophila alpestris) and Grasshopper Sparrow (Ammodramus savannarum). Likewise, shrub associates did not differ in abundance in sagebrush with a cheatgrass vs. bunchgrass understory, but grassland associates were more common in either bunchgrass (Horned Lark and Grasshopper Sparrow) or cheatgrass grasslands (Long-billed Curlew, Numenius americanus), or tended to use sagebrush-cheatgrass less than sagebrush-bunchgrass (Horned Lark, Grasshopper Sparrow, and Savannah Sparrow, Passerculus sandwichensis).

The genus Anthia Weber in the Republic of South Africa, Identification, distribution, biogeography, and behavior (Coleoptera, Carabidae)

PubMed Central

Mawdsley, Jonathan R.; Erwin, Terry L.; Sithole, Hendrik; Mawdsley, James L.; Mawdsley, Alice S.

2011-01-01

Abstract A key is presented for the identification of the four species of Anthia Weber (Coleoptera: Carabidae) recorded from the Republic of South Africa: Anthia cinctipennis Lequien, Anthia circumscripta Klug, Anthia maxillosa (Fabricius), and Anthia thoracica (Thunberg). For each of these species, illustrations are provided of adult beetles of both sexes as well as illustrations of male reproductive structures, morphological redescriptions, discussions of morphological variation, annual activity histograms, and maps of occurrence localities in the Republic of South Africa. Maps of occurrence localities for these species are compared against ecoregional and vegetation maps of southern Africa; each species of Anthia shows a different pattern of occupancy across the suite of ecoregions and vegetation types in the Republic of South Africa. Information about predatory and foraging behaviors, Müllerian mimicry, and small-scale vegetation community associations is presented for Anthia thoracica based on field and laboratory studies in Kruger National Park, South Africa. PMID:22144866

The natural resources inventory system ASVT project

NASA Technical Reports Server (NTRS)

Joyce, A. T.

1979-01-01

The hardware/software and the associated procedures for a natural resource inventory and information system based on the use of LANDSAT-acquired multispectral scanner digital data is described. The system is designed to derive land cover/vegetation information from LANDSAT data and geographically reference this information for the production of various types of maps and for the compilation of acreage by land cover/vegetation category. The system also provides for data base building so that the LANDSAT-derived information can be related to information digitized from other sources (e.g., soils maps) in a geographic context in order to address specific applications. These applications include agricultural crop production estimation, erosion hazard-reforestation need assessment, whitetail deer habitat assessment, and site selection. The system is tested in demonstration areas located in the state of Mississippi, and the results of these application demonstrations are presented. A cost-efficiency comparison of producing land cover/vegetation maps and statistics with this system versus the use of small-scale aerial photography is made.

Ground-based photo monitoring

Treesearch

Frederick C. Hall

2000-01-01

Ground-based photo monitoring is repeat photography using ground-based cameras to document change in vegetation or soil. Assume those installing the photo location will not be the ones re-photographing it. This requires a protocol that includes: (1) a map to locate the monitoring area, (2) another map diagramming the photographic layout, (3) type and make of film such...

Nationwide classification of forest types of India using remote sensing and GIS.

PubMed

Reddy, C Sudhakar; Jha, C S; Diwakar, P G; Dadhwal, V K

2015-12-01

India, a mega-diverse country, possesses a wide range of climate and vegetation types along with a varied topography. The present study has classified forest types of India based on multi-season IRS Resourcesat-2 Advanced Wide Field Sensor (AWiFS) data. The study has characterized 29 land use/land cover classes including 14 forest types and seven scrub types. Hybrid classification approach has been used for the classification of forest types. The classification of vegetation has been carried out based on the ecological rule bases followed by Champion and Seth's (1968) scheme of forest types in India. The present classification scheme has been compared with the available global and national level land cover products. The natural vegetation cover was estimated to be 29.36% of total geographical area of India. The predominant forest types of India are tropical dry deciduous and tropical moist deciduous. Of the total forest cover, tropical dry deciduous forests occupy an area of 2,17,713 km(2) (34.80%) followed by 2,07,649 km(2) (33.19%) under tropical moist deciduous forests, 48,295 km(2) (7.72%) under tropical semi-evergreen forests and 47,192 km(2) (7.54%) under tropical wet evergreen forests. The study has brought out a comprehensive vegetation cover and forest type maps based on inputs critical in defining the various categories of vegetation and forest types. This spatially explicit database will be highly useful for the studies related to changes in various forest types, carbon stocks, climate-vegetation modeling and biogeochemical cycles.

Fine scale vegetation classification and fuel load mapping for prescribed burning

Treesearch

Andrew D. Bailey; Robert Mickler

2007-01-01

Fire managers in the Coastal Plain of the Southeastern United States use prescribed burning as a tool to reduce fuel loads in a variety of vegetation types, many of which have elevated fuel loads due to a history of fire suppression. While standardized fuel models are useful in prescribed burn planning, those models do not quantify site-specific fuel loads that reflect...

Remote Sensing of Vegetation Species Diversity: The Utility of Integrated Airborne Hyperspectral and Lidar Data

NASA Astrophysics Data System (ADS)

Krause, Keith Stuart

The change, reduction, or extinction of species is a major issue currently facing the Earth. Efforts are underway to measure, monitor, and protect habitats that contain high species diversity. Remote sensing technology shows extreme value for monitoring species diversity by mapping ecosystems and using those land cover maps or other derived data as proxies to species number and distribution. The National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP) consists of remote sensing instruments such as an imaging spectrometer, a full-waveform lidar, and a high-resolution color camera. AOP collected data over the Ordway-Swisher Biological Station (OSBS) in May 2014. A majority of the OSBS site is covered by the Sandhill ecosystem, which contains a very high diversity of vegetation species and is a native habitat for several threatened fauna species. The research presented here investigates ways to analyze the AOP data to map ecosystems at the OSBS site. The research attempts to leverage the high spatial resolution data and study the variability of the data within a ground plot scale along with integrating data from the different sensors. Mathematical features are derived from the data and brought into a decision tree classification algorithm (rpart), in order to create an ecosystem map for the site. The hyperspectral and lidar features serve as proxies for chemical, functional, and structural differences in the vegetation types for each of the ecosystems. K-folds cross validation shows a training accuracy of 91%, a validation accuracy of 78%, and a 66% accuracy using independent ground validation. The results presented here represent an important contribution to utilizing integrated hyperspectral and lidar remote sensing data for ecosystem mapping, by relating the spatial variability of the data within a ground plot scale to a collection of vegetation types that make up a given ecosystem.

Assessment of sand encroachment in Kuwait using GIS

NASA Astrophysics Data System (ADS)

Al-Helal, Anwar B.; Al-Awadhi, Jasem M.

2006-04-01

Assessment of sand encroachment in Kuwait using Geographical Information System (GIS) technology has been formulated as a Multi-Criteria Decision Making problem. The Delphi method and Analytical Hierarchy Process were adopted as evaluating techniques, in which experts’ judgments were analyzed for objectively estimating and weighting control factors. Seven triggering factors, depicted in the form of maps, were identified and ordered according to their priority. These factors are (1) wind energy; (2) surface sediment; (3) vegetation density; (4) land use; (5) drainage density; (6) topographic change and (7) vegetation type. The factor maps were digitized, converted to raster data and overlaid to determine their possible spatial relationships. Applying a susceptibility model, a map of sand encroachment susceptibility in Kuwait was developed. The map showed that the areas of very high and high sand encroachment susceptibility are located within the main corridor of sand pathway that coincides with the northwesterly dominant wind direction.

Ecoregions as a level of ecological analysis

USGS Publications Warehouse

Wright, R.G.; Murray, M.P.; Merrill, T.

1998-01-01

There have been many attempts to classify geographic areas into zones of similar characteristics. Recent focus has been on ecoregions. We examined how well the boundaries of the most commonly used ecoregion classifications for the US matched the boundaries of existing vegetation cover mapped at three levels of classification, fine, mid- and coarse scale. We analyzed ecoregions in Idaho, Oregon and Washington. The results were similar among the two ecoregion classifications. For both ecoregion delineations and all three vegetation classifications, the patterns of existing vegetation did not correspond well with the patterns of ecoregions. Most vegetation types had a small proportion of their total area in a given ecoregion. There was also no dominance by one or more vegetation types in any ecoregion and contrary to our hypothesis, the level of congruence of vegetation patterns with ecoregion boundaries decreased as the level of classification became more general. The implications of these findings on the use of ecoregions as a planning tool and in the development of land conservation efforts are discussed.

Mapping Landslides Susceptibility in a Traditional Northern Nigerian City

NASA Astrophysics Data System (ADS)

Oluwafemi, Olawale A.; Yakubu, Tahir A.; Muhammad, Mahmud U.; Shitta, Nyofo; Akinwumiju, Akinola S.

2018-05-01

As a result of dearth of relevant information about Landslides Susceptibility in Nigeria, the monitoring and assessment appears intractable. Hence, the study developed a Remote Sensing approach to mapping landslides susceptibility, landuse and landcover analysis in Jos South LGA, Plateau State, Nigeria. Field Observation, SPOT 5 2009 and 2012, ASTER DEM 2009, Geological Map 2006, Topographical Map 1966 were used to map Landslide Susceptibility and Landuse /Lancover Analysis in the study area. Geospatial Analytical Operations employed using ArcGIS 10.3 and Erdas Imagine 2014 include Spatial Modeling, Vectorization, Pre-lineament Extraction, Image Processing among others. Result showed that 72.38 % of the study area is underlain by granitic rocks. The landuse/cover types delineated for the study area include floodplain (29.27 %), farmland (23.96 %), sparsely vegetated land (15.43 %), built up area (13.65 %), vegetated outcrop (8.48 %), light vegetation (5.37 %), thick vegetation (2.39 %), water body (0.58 %), plantation (0.50 %) and mining pond (0.37 %). Landslide Susceptibility Analysis also revealed that 87 % of the study area is relatively at low to very low risk of landslide event. While only 13 % of the study area is at high to very high risk of landslide event. The study revealed that the susceptibility of landslide event is very low in the study area. However, possible landslide event in the hot spots could be pronounced and could destabilize the natural and man-made environmental systems of the study area.

Vegetation burn severity mapping using Landsat-8 and WorldView-2

USGS Publications Warehouse

Wu, Zhuoting; Middleton, Barry R.; Hetzler, Robert; Vogel, John M.; Dye, Dennis G.

2015-01-01

We used remotely sensed data from the Landsat-8 and WorldView-2 satellites to estimate vegetation burn severity of the Creek Fire on the San Carlos Apache Reservation, where wildfire occurrences affect the Tribe's crucial livestock and logging industries. Accurate pre- and post-fire canopy maps at high (0.5-meter) resolution were created from World- View-2 data to generate canopy loss maps, and multiple indices from pre- and post-fire Landsat-8 images were used to evaluate vegetation burn severity. Normalized difference vegetation index based vegetation burn severity map had the highest correlation coefficients with canopy loss map from WorldView-2. Two distinct approaches - canopy loss mapping from WorldView-2 and spectral index differencing from Landsat-8 - agreed well with the field-based burn severity estimates and are both effective for vegetation burn severity mapping. Canopy loss maps created with WorldView-2 imagery add to a short list of accurate vegetation burn severity mapping techniques that can help guide effective management of forest resources on the San Carlos Apache Reservation, and the broader fire-prone regions of the Southwest.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ellis, J.E.; Swift, D.M.; Hart, T.C.

Landsat multi-spectral scanner (MSS) imagery was used to develop a vegetation type-biomass map of the 84,000 Km/sup 2/ Turkana District, Kenya. NOAA satellite advanced very high resolution radiometry (AVHRR) imagery was overlaid on the MSS map to trace the seasonal and annual dynamics of vegetation communities used by Turkana pastoral nomads, 1981-1984. Four regions (sub-sectional territories) were compared with respect to peak herbaceous biomass, woody canopy cover, and seasonal fluxes in total green biomass. Results demonstrated major variations among regions and between wet and dry season ranges within regions. Pastoral land use patterns appear to minimize effects of seasonal vegetationmore » fluxes on livestock herds.« less

Fourth international circumpolar arctic vegetation mapping workshop

USGS Publications Warehouse

Raynolds, Martha K.; Markon, C.J.

2002-01-01

During the week of April 10, 2001, the Fourth International Circumpolar Arctic Vegetation Mapping Workshop was held in Moscow, Russia. The purpose of this meeting was to bring together the vegetation scientists working on the Circumpolar Arctic Vegetation Map (CAVM) to (1) review the progress of current mapping activities, (2) discuss and agree upon a standard set of arctic tundra subzones, (3) plan for the production and dissemination of a draft map, and (4) begin work on a legend for the final map.

Mapping Vegetation Structure in Kakadu National Park: An AIRSAR and GIS Application in Conservation

NASA Technical Reports Server (NTRS)

Imhoff, Marc L.; Sisk, Thomas D.; Hampton, Haydee; Milne, Anthony K.

1999-01-01

Airborne Synthetic Aperture Radar (AIRSAR) data were used to map vegetation structure in Kakadu National Park Australia as part of the PACRIM project. SAR data were co-registered with Landsat TM, aerial photos, and map data in a geographic information system for a small test area consisting of mangrove, floodplain grasslands, lowland tropical evergreen forest and upland mixed deciduous and evergreen tropical forest near the South Alligator River. Landsat (Thematic Mapper) TM very clearly showed the floristic composition and burn scars from the previous years fires and the AIRSAR data provided a profile of vegetation structure. Extensive field data on vegetation species composition and structure were collected across a series of transects in cooperation with a survey of avifauna in an effort to link the habitat edge structure with bird species responses. A test site was found that contained two types of habitat edges: 1) A structure specific edge - characterized by the appearance of a very strong structural change in the forest canopy occurring in the absence of a substantial turnover in floristics. 2) Floristic edge - a sharp transition in vegetation genetic composition with a mixed set of structural changes. Specific polarization combinations were selected that were highly correlated to a set of desired structural parameters found in the field data. Classification routines were employed to group radar pixels into 3 structural classes based on: the Surface Area to Volume ratio (SA/V) of the stems, the SA/V of the branches, and the leaf area index of the canopy. Separate canopy structure maps were then entered into the GIS and bird responses were observed relative to the classes and their boundaries. Follow-on work will consist of extending this approach to neighboring areas, generating structure maps, predicting bird responses across the edges, and make accuracy assessments.

Vegetation Changes in the Permafrost Regions of the Qinghai-Tibetan Plateau from 1982-2012: Different Responses Related to Geographical Locations and Vegetation Types in High-Altitude Areas.

PubMed

Wang, Zhiwei; Wang, Qian; Wu, Xiaodong; Zhao, Lin; Yue, Guangyang; Nan, Zhuotong; Wang, Puchang; Yi, Shuhua; Zou, Defu; Qin, Yu; Wu, Tonghua; Shi, Jianzong

2017-01-01

The Qinghai-Tibetan Plateau (QTP) contains the largest permafrost area in a high-altitude region in the world, and the unique hydrothermal environments of the active layers in this region have an important impact on vegetation growth. Geographical locations present different climatic conditions, and in combination with the permafrost environments, these conditions comprehensively affect the local vegetation activity. Therefore, the responses of vegetation to climate change in the permafrost region of the QTP may be varied differently by geographical location and vegetation condition. In this study, using the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) product based on turning points (TPs), which were calculated using a piecewise linear model, 9 areas within the permafrost region of the QTP were selected to investigate the effect of geographical location and vegetation type on vegetation growth from 1982 to 2012. The following 4 vegetation types were observed in the 9 selected study areas: alpine swamp meadow, alpine meadow, alpine steppe and alpine desert. The research results show that, in these study areas, TPs mainly appeared in 2000 and 2001, and almost 55.1% and 35.0% of the TPs were located in 2000 and 2001. The global standardized precipitation evapotranspiration index (SPEI) and 7 meteorological variables were selected to analyze their correlations with NDVI. We found that the main correlative variables to vegetation productivity in study areas from 1982 to 2012 were precipitation, surface downward long-wave radiation and temperature. Furthermore, NDVI changes exhibited by different vegetation types within the same study area followed similar trends. The results show that regional effects rather than vegetation type had a larger impact on changes in vegetation growth in the permafrost regions of the QTP, indicating that climatic factors had a larger impact in the permafrost regions than the environmental factors (including permafrost) related to the underlying surface conditions.

Vegetation Changes in the Permafrost Regions of the Qinghai-Tibetan Plateau from 1982-2012: Different Responses Related to Geographical Locations and Vegetation Types in High-Altitude Areas

PubMed Central

Wu, Xiaodong; Zhao, Lin; Yue, Guangyang; Nan, Zhuotong; Wang, Puchang; Yi, Shuhua; Zou, Defu; Qin, Yu; Wu, Tonghua; Shi, Jianzong

2017-01-01

The Qinghai-Tibetan Plateau (QTP) contains the largest permafrost area in a high-altitude region in the world, and the unique hydrothermal environments of the active layers in this region have an important impact on vegetation growth. Geographical locations present different climatic conditions, and in combination with the permafrost environments, these conditions comprehensively affect the local vegetation activity. Therefore, the responses of vegetation to climate change in the permafrost region of the QTP may be varied differently by geographical location and vegetation condition. In this study, using the latest Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) product based on turning points (TPs), which were calculated using a piecewise linear model, 9 areas within the permafrost region of the QTP were selected to investigate the effect of geographical location and vegetation type on vegetation growth from 1982 to 2012. The following 4 vegetation types were observed in the 9 selected study areas: alpine swamp meadow, alpine meadow, alpine steppe and alpine desert. The research results show that, in these study areas, TPs mainly appeared in 2000 and 2001, and almost 55.1% and 35.0% of the TPs were located in 2000 and 2001. The global standardized precipitation evapotranspiration index (SPEI) and 7 meteorological variables were selected to analyze their correlations with NDVI. We found that the main correlative variables to vegetation productivity in study areas from 1982 to 2012 were precipitation, surface downward long-wave radiation and temperature. Furthermore, NDVI changes exhibited by different vegetation types within the same study area followed similar trends. The results show that regional effects rather than vegetation type had a larger impact on changes in vegetation growth in the permafrost regions of the QTP, indicating that climatic factors had a larger impact in the permafrost regions than the environmental factors (including permafrost) related to the underlying surface conditions. PMID:28068392

A Sensitivity Analysis of a Map of Habitat Quality for the California Spotted Owl (Strix occidentalis occidentalis) in southern California

Treesearch

Ellen M. Hines; Janet Franklin

1997-01-01

Using a Geographic Information System (GIS), a sensitivity analysis was performed on estimated mapping errors in vegetation type, forest canopy cover percentage, and tree crown size to determine the possible effects error in these data might have on delineating suitable habitat for the California Spotted Owl (Strix occidentalis occidentalis) in...

Satellite geological and geophysical remote sensing of Iceland

NASA Technical Reports Server (NTRS)

Williams, R. S., Jr. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Under a binational, multidisciplinary experiment ERTS-1 imagery is being used to measure and map dynamic natural phenomena in Iceland. A few of the initial results from the project are: (1) a large variety of geological and volcanic features can be studied, particularly on imagery acquired at low sun angle ( 10 deg), which have not been previously recognized; (2) under optimum snow cover conditions, geothermal areas can be discerned by their snowmelt pattern or by warm spring discharge into frozen lakes; (3) a variety of map types at scale of 1:1,000,000 and 1:500,000, can be compiled, made more accurate, or updated (changes in coastline, glaciers, lakes, etc.); (4) the persistence of snow in the highland areas, during the summer months, has important ramifications to rangeland management; (5) false color composites (MSS) permitted the mapping of four types of vegetation; forested, reclaimed, cultivated areas and grasslands, and the mapping of the seasonal change of the vegetation, all of high value to rangeland management when complete, repetitive coverage of Iceland becomes a reality with an operational satellite; and (6) the volcanic eruption on Heimaey was recorded.

Islands of biogeodiversity in arid lands on a polygons map study: Detecting scale invariance patterns from natural resources maps.

PubMed

Ibáñez, J J; Pérez-Gómez, R; Brevik, Eric C; Cerdà, A

2016-12-15

Many maps (geology, hydrology, soil, vegetation, etc.) are created to inventory natural resources. Each of these resources is mapped using a unique set of criteria, including scales and taxonomies. Past research indicates that comparing results of related maps (e.g., soil and geology maps) may aid in identifying mapping deficiencies. Therefore, this study was undertaken in Almeria Province, Spain to (i) compare the underlying map structures of soil and vegetation maps and (ii) investigate if a vegetation map can provide useful soil information that was not shown on a soil map. Soil and vegetation maps were imported into ArcGIS 10.1 for spatial analysis, and results then exported to Microsoft Excel worksheets for statistical analyses to evaluate fits to linear and power law regression models. Vegetative units were grouped according to the driving forces that determined their presence or absence: (i) climatophilous (ii) lithologic-climate; and (iii) edaphophylous. The rank abundance plots for both the soil and vegetation maps conformed to Willis or Hollow Curves, meaning the underlying structures of both maps were the same. Edaphophylous map units, which represent 58.5% of the vegetation units in the study area, did not show a good correlation with the soil map. Further investigation revealed that 87% of the edaphohygrophilous units were found in ramblas, ephemeral riverbeds that are not typically classified and mapped as soils in modern systems, even though they meet the definition of soil given by the most commonly used and most modern soil taxonomic systems. Furthermore, these edaphophylous map units tend to be islands of biodiversity that are threatened by anthropogenic activity in the region. Therefore, this study revealed areas that need to be revisited and studied pedologically. The vegetation mapped in these areas and the soils that support it are key components of the earth's critical zone that must be studied, understood, and preserved. Copyright © 2016 Elsevier B.V. All rights reserved.

LANDFIRE 2015 Remap – Utilization of Remotely Sensed Data to Classify Existing Vegetation Type and Structure to Support Strategic Planning and Tactical Response

USGS Publications Warehouse

Picotte, Joshua J.; Long, Jordan; Peterson, Birgit; Nelson, Kurtis

2017-01-01

The LANDFIRE Program produces national scale vegetation, fuels, fire regimes, and landscape disturbance data for the entire U.S. These data products have been used to model the potential impacts of fire on the landscape [1], the wildfire risks associated with land and resource management [2, 3], and those near population centers and accompanying Wildland Urban Interface zones [4], as well as many other applications. The initial LANDFIRE National Existing Vegetation Type (EVT) and vegetation structure layers, including vegetation percent cover and height, were mapped circa 2001 and released in 2009 [5]. Each EVT is representative of the dominant plant community within a given area. The EVT layer has since been updated by identifying areas of landscape change and modifying the vegetation types utilizing a series of rules that consider the disturbance type, severity of disturbance, and time since disturbance [6, 7]. Non-disturbed areas were adjusted for vegetation growth and succession. LANDFIRE vegetation structure layers also have been updated by using data modeling techniques [see 6 for a full description]. The subsequent updated versions of LANDFIRE include LANDFIRE 2008, 2010, 2012, and LANDFIRE 2014 is being incrementally released, with all data being released in early 2017. Additionally, a comprehensive remap of the baseline data, LANDFIRE 2015 Remap, is being prototyped, and production is tentatively planned to begin in early 2017 to provide a more current baseline for future updates.

A vegetation mapping strategy for conifer forests by combining airborne LiDAR data and aerial imagery

Treesearch

Yanjun Su; Qinghua Guo; Danny L. Fry; Brandon M. Collins; Maggi Kelly; Jacob P. Flanagan; John J. Battles

2016-01-01

Abstract. Accurate vegetation mapping is critical for natural resources management, ecological analysis, and hydrological modeling, among other tasks. Remotely sensed multispectral and hyperspectral imageries have proved to be valuable inputs to the vegetation mapping process, but they can provide only limited vegetation structure...

Methodology to assess and map the potential development of forest ecosystems exposed to climate change and atmospheric nitrogen deposition: A pilot study in Germany.

PubMed

Schröder, Winfried; Nickel, Stefan; Jenssen, Martin; Riediger, Jan

2015-07-15

A methodology for mapping ecosystems and their potential development under climate change and atmospheric nitrogen deposition was developed using examples from Germany. The methodology integrated data on vegetation, soil, climate change and atmospheric nitrogen deposition. These data were used to classify ecosystem types regarding six ecological functions and interrelated structures. Respective data covering 1961-1990 were used for reference. The assessment of functional and structural integrity relies on comparing a current or future state with an ecosystem type-specific reference. While current functions and structures of ecosystems were quantified by measurements, potential future developments were projected by geochemical soil modelling and data from a regional climate change model. The ecosystem types referenced the potential natural vegetation and were mapped using data on current tree species coverage and land use. In this manner, current ecosystem types were derived, which were related to data on elevation, soil texture, and climate for the years 1961-1990. These relations were quantified by Classification and Regression Trees, which were used to map the spatial patterns of ecosystem type clusters for 1961-1990. The climate data for these years were subsequently replaced by the results of a regional climate model for 1991-2010, 2011-2040, and 2041-2070. For each of these periods, one map of ecosystem type clusters was produced and evaluated with regard to the development of areal coverage of ecosystem type clusters over time. This evaluation of the structural aspects of ecological integrity at the national level was added by projecting potential future values of indicators for ecological functions at the site level by using the Very Simple Dynamic soil modelling technique based on climate data and two scenarios of nitrogen deposition as input. The results were compared to the reference and enabled an evaluation of site-specific ecosystem changes over time which proved to be both, positive and negative. Copyright © 2015 Elsevier B.V. All rights reserved.

Four years of UAS Imagery Reveals Vegetation Change Due to Permafrost Thaw

NASA Astrophysics Data System (ADS)

DelGreco, J. L.; Herrick, C.; Varner, R. K.; McArthur, K. J.; McCalley, C. K.; Garnello, A.; Finnell, D.; Anderson, S. M.; Crill, P. M.; Palace, M. W.

2017-12-01

Warming trends in sub-arctic regions have resulted in thawing of permafrost which in turn induces change in vegetation across peatlands. Collapse of palsas (i.e. permafrost plateaus) has also been correlated to increases in methane (CH4) emissions to the atmosphere. Vegetation change provides new microenvironments that promote CH4 production and emission, specifically through plant interactions and structure. By quantifying the changes in vegetation at the landscape scale, we will be able to understand the impact of thaw on CH4 emissions in these complex and climate sensitive northern ecosystems. We combine field-based measurements of vegetation composition and high resolution Unmanned Aerial Systems (UAS) imagery to characterize vegetation change in a sub-arctic mire. At Stordalen Mire (1 km x 0.5 km), Abisko, Sweden, we flew a fixed-wing UAS in July of each year between 2014 and 2017. High precision GPS ground control points were used to georeference the imagery. Seventy-five randomized square-meter plots were measured for vegetation composition and individually classified into one of five cover types, each representing a different stage of permafrost degradation. With this training data, each year of imagery was classified by cover type. The developed cover type maps were also used to estimate CH4 emissions across the mire based on average flux CH4 rates from each cover type obtained from flux chamber measurements collected at the mire. This four year comparison of vegetation cover and methane emissions has indicated a rapid response to permafrost thaw and changes in emissions. Estimation of vegetation cover types is vital in our understanding of the evolution of northern peatlands and its future role in the global carbon cycle.

Impact of Land Use Land Cover Change on East Asian monsoon

NASA Astrophysics Data System (ADS)

Chilukoti, N.; Xue, Y.; Liu, Y.; Lee, J.

2017-12-01

Humans modify the Earth's terrestrial surface on a continental scale by removing natural vegetation for crops/grazing. The current rates, extents and intensities of Land Use and Land Cover Change (LULCC) are greater than ever in history. The earlier studies of Land-atmosphere interactions used specified land surface conditions without interannual variations. In this study using NCEP CFSv2 coupled with Simplified Simple Biosphere (SSiB) model, biogeophysical impacts of LULCC on climate variability, anomaly, and changes are investigated by using the LULCC map from the Hurtt et al. (2006, 2011), which covered 66 years from 1950-2015 with annual variability. We combined the changes in crop and pasture fractions and consider as LULCC. A methodology had been developed to convert the Hurtt LULCC change map with 1° resolution to the GCM grid points. Since the GCM has only one dominant type, when the crop and pasture frction value at one point was larger than the critical value, that grid was assigned as degraded. Comprehensive evaluation was conducted to ensure the consistence of the trend of land degradation in the Hurtt's map and in the GCM LULCC map. In the degraded point, trees were changed to low vegetation or grasses, and low vegetation to bare soil. A set of surface parameters such as leaf area index, vegetation height, roughness length, and soil parameters, associated with vegetation are changed to show the degradation effects. We integrated the model with the potential vegetation map and the map with LULCC from 1950 to 2015, and the results indicate the LULCC causes precipitation reduction globally, with the strongest signals over monsoon regions. For instance, the degradation in Mexico, West Africa, south and East Asia and South America produced significant precipitation anomalies, some of which are consistent with observed regional precipitation anomalies. Meanwhile, it has also found that the LULCC enhances the surface warming during the summer in monsoon regions. The LULCC caused reduction in water released into the atmosphere from the surface through a reduction in transpiration and canopy evaporation, and changes in magnitude and pattern of moisture flux convergence, resulting in precipitation changes, and reduced evaporation lead to warm surface temperature during the summer season.

The Circumpolar Arctic Vegetation Map: AVHRR-derived base maps, environmental controls, and integrated mapping procedures

Treesearch

D. A. WALKER; W. A. GOULD; MAIERH. A.; M. K. RAYNOLDS

2002-01-01

A new false-colour-infrared image derived from biweekly 1993 and 1995 Advanced Very High Resolution Radiometer (AVHRR) data provides a snow-free and cloud-free base image for the interpretation of vegetation as part of a 1:7.5M-scale Circumpolar Arctic Vegetation Map (CAVM). A maximum-NDVI (Normalized DiVerence Vegetation Index) image prepared from the same data...

Relation of MODIS EVI and LAI across time, vegetation types and hydrological regimes

NASA Astrophysics Data System (ADS)

Alexandridis, Thomas; Ovakoglou, George

2015-04-01

Estimation of the Leaf Area Index (LAI) of a landscape is considered important to describe the ecosystems activity and is used as an important input parameter in hydrological and biogeochemical models related to water and carbon cycle, desertification risk, etc. The measurement of LAI in the field is a laborious and costly process and is mainly done by indirect methods, such as hemispherical photographs that are processed by specialized software. For this reason there have been several attempts to estimate LAI with multispectral satellite images, using theoretical biomass development models, or empirical equations using vegetation indices and land cover maps. The aim of this work is to study the relation of MODIS EVI and LAI across time, vegetation type, and hydrological regime. This was achieved by studying 120 maps of EVI and LAI which cover a hydrological year and five hydrologically diverse areas: river Nestos in Greece, Queimados catchment in Brazil, Rijnland catchment in The Netherlands, river Tamega in Portugal, and river Umbeluzi in Mozambique. The following Terra MODIS composite datasets were downloaded for the hydrological year 2012-2013: MOD13A2 "Vegetation Indices" and MCD15A2 "LAI and FPAR", as well as the equivalent quality information layers (QA). All the pixels that fall in a vegetation land cover (according to the MERIS GLOBCOVER map) were sampled for the analysis, with the exception of those that fell at the border between two vegetation or other land cover categories, to avoid the influence of mixed pixels. Using linear regression analysis, the relationship between EVI and LAI was identified per date, vegetation type and study area. Results show that vegetation type has the highest influence in the variation of the relationship between EVI and LAI in each study area. The coefficient of determination (R2) is high and statistically significant (ranging from 0.41 to 0.83 in 90% of the cases). When plotting the EVI factor from the regression equation across time, there is an evident temporal change in all test sites. The sensitivity of EVI to LAI is smaller in periods of high biomass production. The range of fluctuation is different across sites, and is related to biomass quantity and type. Higher fluctuation is noted in the winter season in Tamega, possibly due to cloud infected pixels that the QA and compositing algorithms did not successfully detect. Finally, there was no significant difference in the R2 and EVI factor when including in the analyses pixels indicated as "low and marginal quality" by the QA layers, thus suggesting that the use of low quality pixels can be justified when good quality pixels are not enough. Future work will study the transferability of these relations across scales and sensors. This study is supported by the Research Committee of Aristotle University of Thessaloniki project "Improvement of the estimation of Leaf Area Index (LAI) at basin scale using satellite images". MODIS data are provided by USGS.

Disaggregating and mapping crop statistics using hypertemporal remote sensing

NASA Astrophysics Data System (ADS)

Khan, M. R.; de Bie, C. A. J. M.; van Keulen, H.; Smaling, E. M. A.; Real, R.

2010-02-01

Governments compile their agricultural statistics in tabular form by administrative area, which gives no clue to the exact locations where specific crops are actually grown. Such data are poorly suited for early warning and assessment of crop production. 10-Daily satellite image time series of Andalucia, Spain, acquired since 1998 by the SPOT Vegetation Instrument in combination with reported crop area statistics were used to produce the required crop maps. Firstly, the 10-daily (1998-2006) 1-km resolution SPOT-Vegetation NDVI-images were used to stratify the study area in 45 map units through an iterative unsupervised classification process. Each unit represents an NDVI-profile showing changes in vegetation greenness over time which is assumed to relate to the types of land cover and land use present. Secondly, the areas of NDVI-units and the reported cropped areas by municipality were used to disaggregate the crop statistics. Adjusted R-squares were 98.8% for rainfed wheat, 97.5% for rainfed sunflower, and 76.5% for barley. Relating statistical data on areas cropped by municipality with the NDVI-based unit map showed that the selected crops were significantly related to specific NDVI-based map units. Other NDVI-profiles did not relate to the studied crops and represented other types of land use or land cover. The results were validated by using primary field data. These data were collected by the Spanish government from 2001 to 2005 through grid sampling within agricultural areas; each grid (block) contains three 700 m × 700 m segments. The validation showed 68%, 31% and 23% variability explained (adjusted R-squares) between the three produced maps and the thousands of segment data. Mainly variability within the delineated NDVI-units caused relatively low values; the units are internally heterogeneous. Variability between units is properly captured. The maps must accordingly be considered "small scale maps". These maps can be used to monitor crop performance of specific cropped areas because of using hypertemporal images. Early warning thus becomes more location and crop specific because of using hypertemporal remote sensing.

Wetland mapping from digitized aerial photography. [Sheboygen Marsh, Sheboygen County, Wisconsin

NASA Technical Reports Server (NTRS)

Scarpace, F. L.; Quirk, B. K.; Kiefer, R. W.; Wynn, S. L.

1981-01-01

Computer assisted interpretation of small scale aerial imagery was found to be a cost effective and accurate method of mapping complex vegetation patterns if high resolution information is desired. This type of technique is suited for problems such as monitoring changes in species composition due to environmental factors and is a feasible method of monitoring and mapping large areas of wetlands. The technique has the added advantage of being in a computer compatible form which can be transformed into any georeference system of interest.

Evaluation of linear spectral unmixing and deltaNBR for predicting post-fire recovery in a North American ponderosa pine forest

Treesearch

A. M. S. Smith; L. B. Lenilte; A. T. Hudak; P. Morgan

2007-01-01

The Differenced Normalized Burn Ratio (deltaNBR) is widely used to map post-fire effects in North America from multispectral satellite imagery, but has not been rigorously validated across the great diversity in vegetation types. The importance of these maps to fire rehabilitation crews highlights the need for continued assessment of alternative remote sensing...

On the potential of long wavelength imaging radars for mapping vegetation types and woody biomass in tropical rain forests

NASA Technical Reports Server (NTRS)

Rignot, Eric J.; Zimmermann, Reiner; Oren, Ram

1995-01-01

In the tropical rain forests of Manu, in Peru, where forest biomass ranges from 4 kg/sq m in young forest succession up to 100 kg/sq m in old, undisturbed floodplain stands, the P-band polarimetric radar data gathered in June of 1993 by the AIRSAR (Airborne Synthetic Aperture Radar) instrument separate most major vegetation formations and also perform better than expected in estimating woody biomass. The worldwide need for large scale, updated biomass estimates, achieved with a uniformly applied method, as well as reliable maps of land cover, justifies a more in-depth exploration of long wavelength imaging radar applications for tropical forests inventories.

Characterizing Exterior and Interior Tropical Forest Structure Variability with Full-Waveform Airborne LIDAR Data in Lopé, Gabon

NASA Astrophysics Data System (ADS)

Marselis, S.; Tang, H.; Blair, J. B.; Hofton, M. A.; Armston, J.; Dubayah, R.

2017-12-01

Terrestrial ecotones, transition zones between ecological systems, have been identified as important regions to monitor the effects of environmental and human pressures on ecosystems. To observe such changes, the variability in vegetation horizontal and vertical structure must be characterized. The objective of this study is to quantify changes in vegetation structure in a tropical forest-savanna mosaic using airborne waveform lidar data. The study area is located in the northern part of the Lopé National Park in Gabon and is comprised of the vegetation types: savanna, colonizing forest, monodominant Okoumé forest, young Marantaceae forest and mixed Marantaceae forest. The lidar data were collected by the Land Vegetation and Ice Sensor (LVIS) in early March 2016, during the AfriSAR campaign. Metrics derived from the LVIS waveforms were then used to classify the five main vegetation types and characterize observed structural variability within types and across ecotones. Several supervised and unsupervised classification alogrithms, in combination with statistical analysis, were applied. The investigated methods are promising in their use to directly describe the structural variability within and between different vegetation types, map these vegetation types and the extent and location of their transition zones, and to characterize, among other attributes, the sharpness and width of such ecotones. These results provide important information in ecosystem studies as these methods can be used to study changes in vegetation structure, species-specific habitat, or the effects of deforestation and other human and natural pressures on the exterior and interior forest structure. These methods thus provide ample opportunity to assess the vegetation structure in degraded and second growth tropical forests to explore effects of e.g. grazing, logging or fragmentation. From this study we can conclude that lidar waveform remote sensing is highly useful in distinguishing vegetation types and their transition zones which will be increasingly important when assessing the impact of natural and human pressures on the world's tropical forests.

Assessments of SENTINEL-2 Vegetation Red-Edge Spectral Bands for Improving Land Cover Classification

NASA Astrophysics Data System (ADS)

Qiu, S.; He, B.; Yin, C.; Liao, Z.

2017-09-01

The Multi Spectral Instrument (MSI) onboard Sentinel-2 can record the information in Vegetation Red-Edge (VRE) spectral domains. In this study, the performance of the VRE bands on improving land cover classification was evaluated based on a Sentinel-2A MSI image in East Texas, USA. Two classification scenarios were designed by excluding and including the VRE bands. A Random Forest (RF) classifier was used to generate land cover maps and evaluate the contributions of different spectral bands. The combination of VRE bands increased the overall classification accuracy by 1.40 %, which was statistically significant. Both confusion matrices and land cover maps indicated that the most beneficial increase was from vegetation-related land cover types, especially agriculture. Comparison of the relative importance of each band showed that the most beneficial VRE bands were Band 5 and Band 6. These results demonstrated the value of VRE bands for land cover classification.

Vegetation map of the watersheds between Kawela and Kamalō Gulches, Island of Molokaʻi, Hawaiʻi

USGS Publications Warehouse

Jacobi, James D.; Ambagis, Stephen

2013-01-01

In this document we describe the methods and results of a project to produce a large-scale map of the dominant plant communities for an area of 5,118.5 hectares encompassing the Kawela and Kamalō watersheds on the island of Molokaʻi, Hawaiʻi, using digital image analysis of multi-spectral satellite imagery. Besides providing a base map of the area for land managers to use, this vegetation map serves as spatial background for the U.S. Geological Survey’s (USGS) Molokaʻi Ridge-to-Reef project, which is an interdisciplinary study of erosion and sediment transport within these watersheds. A total of 14 mapping units were identified for the Kawela-Kamalō project area. The most widespread units were the ʻŌhiʻa montane wet or mesic forest and No vegetation or very sparse grasses/shrubs communities, each present in more than 800 hectares, or 16 percent of the mapping area. Next largest were the Kiawe woodland with alien grass understory and ʻAʻaliʻi dry shrubland units, each of which covered more than 500 hectares, or more than 12 percent of the area; followed by the Mixed native mesic shrubland, ʻIlima and mixed grass dry shrubland, Mixed alien grass with ʻilima shrubs, and the Mixed alien forest with alien shrub/grass understory communities, which ranged in size from approximately 391 to 491 hectares, or 7.6 to 9.6 percent of the project site. The other six mapped units covered less than 170 hectares of the landscape. Six of the map units were dominated by native vegetation, covering a total of 2,535.2 hectares combined, or approximately 50 percent of the project area. The remaining map units were dominated by nonnative species and represent vegetation types that have resulted from invasion and establishment of plant species that had been either purposely or accidently introduced into Hawaiʻi since humans arrived in these islands more than 1,500 years ago. The preponderance of mapping units that are dominated by alien species of plants is a strong indication of how much anthropogenic disturbance has occurred in this area. The native-dominated ʻŌhiʻa forest and uluhe fern communities are probably most similar to the vegetation that was originally found in the upper part of the project area this area. Portions of the mixed mesic native shrub community still persist in the lowland mesic zone, but below that area, the vegetation is either dominated by alien species, or artificially opened by animal grazing and erosion, even in the few units that are still dominated by native species. The map produced for the Kawela to Kamalō watersheds can be used as a baseline for assessing the distribution and abundance of the various plant communities found across this landscape at the time of the imagery (2004). It can also be used to help understand the dynamics of the vegetation and other attributes of this watershed—such as erosion and surface transport of sediment, relative to current and future habitat conditions.

The applicability of ERTS-1 data covering the major landforms of Kenya. [landforms, vegetation, soils, forests

NASA Technical Reports Server (NTRS)

Omino, J. H. O. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Five investigators report on the applicability of ERTS-1 data covering the major landforms of Kenya. Deficiencies due to lack of equipment, repetitive coverage and interpretation know-how are also reported on. Revision of lake shorelines is an immediate benefit. Basement system metasediments are rapidly differentiated, but dune areas are not readily distinguishable from sandy soils. Forest, moorland, high altitude grass, tea, and conifer plantations are readily distinguished, with podocarpus forest especially distinguishable from podocarpus/juniperus forest. In the arid areas physiographic features, indicating the major soil types, are readily identified and mapped. Preliminary vegetation type analysis in the Mara Game Reserve indicates that in a typical savannah area about 36% of the vegetation types are distinguishable at a scale of 1:1 million as well as drainage patterns and terrain features.

High spatial resolution mapping of the Cerrado's land cover and land use types in the priority area for conservation Chapada da Contagem, Brazil.

NASA Astrophysics Data System (ADS)

Ribeiro, F.; Roberts, D. A.; Davis, F. W.; Antunes Daldegan, G.; Nackoney, J.; Hess, L. L.

2016-12-01

The Brazilian savanna, Cerrado, is the second largest biome over South America and the most floristically diverse savanna in the world. This biome is considered a conservation hotspot in respect to its biodiversity importance and rapid transformation of its landscape. The Cerrado's natural vegetation has been severely transformed by agriculture and pasture activities. Currently it is the main agricultural frontier in Brazil and one of the most threatened Brazilian biomes. This scenario results in environmental impacts such as ecosystems fragmentation as well as losses in connectivity, biodiversity and gene flow, changes in the microclimate and energy, carbon and nutrients cycles, among others. The Priority Areas for Conservation is a governmental program from Brazil that identifies areas with high conservation priority. One of this program's recommendation is a natural vegetation map including their major ecosystem classes. This study aims to generate more precise information for the Cerrado's vegetation. The main objective of this study is to identify which ecosystems are being prioritized and/or threatened by land use, refining information for further protection. In order to test methods, the priority area for conservation Chapada da Contagem was selected as the study site. This area is ranked as "extremely high priority" by the government and is located in the Federal District and Goias State, Brazil. Satellites with finer spatial resolution may improve the classification of the Cerrado's vegetation. Remote sensing methods and two criteria were tested using RapidEye 3A imagery (5m spatial resolution) collected in 2014 in order to classify the Cerrado's major land cover types of this area, as well as its land use. One criterion considers the Cerrado's major terrestrial ecosystems, which are divided into forest, savanna and grassland. The other involves scaling it down to the major physiognomic groups of each ecosystem. Other sources of environmental dataset such as soil type and slope were incorporated into this test as they are correlated with the ecosystems and physiognomies presence. A Decision Tree was used to map the land cover and land use types present in the region and demonstrated to have an effective result due to the map's high accuracy and incorporation of environmental dataset.

Change in land use in the Phoenix (1:250,000) Quadrangle, Arizona between 1970 and 1973: ERTS as an aid in a nationwide program for mapping general land use. [Phoenix Quadrangle, Arizona

NASA Technical Reports Server (NTRS)

Place, J. L.

1974-01-01

Changes in land use between 1970 and 1973 in the Phoenix (1:250,000 scale) Quadrangle in Arizona have been mapped using only the images from ERTS-1, tending to verify the utility of a standard land use classification system proposed for use with ERTS images. Types of changes detected have been: (1) new residential development of former cropland and rangeland; (2) new cropland from the desert; and (3) new reservoir fill-up. The seasonal changing of vegetation patterns in ERTS has complemented air photos in delimiting the boundaries of some land use types. ERTS images, in combination with other sources of information, can assist in mapping the generalized land use of the fifty states by the standard 1:250,000 quadrangles. Several states are already working cooperatively in this type of mapping.

Estimating vegetation vulnerability to detect areas prone to land degradation in the Mediterranean basin

NASA Astrophysics Data System (ADS)

Imbrenda, Vito; Coluzzi, Rosa; D'Emilio, Mariagrazia; Lanfredi, Maria; Simoniello, Tiziana

2013-04-01

Vegetation is one of the key components to study land degradation vulnerability because of the complex interactions and feedbacks that link it to soil. In the Mediterranean region, degradation phenomena are due to a mix of predisposing factors (thin soil horizons, low soil organic matter, increasing aridity, etc.) and bad management practices (overgrazing, deforestation, intensification of agriculture, tourism development). In particular, in areas threatened by degradation processes but still covered by vegetation, large scale soil condition evaluation is a hard task and the detection of stressed vegetation can be useful to identify on-going soil degradation phenomena and to reduce their impacts through interventions for recovery/rehabilitation. In this context the use of satellite time series can increase the efficacy and completeness of the land degradation assessment, providing precious information to understand vegetation dynamics. In order to estimate vulnerability levels in Basilicata (a Mediterranean region of Southern Italy) in the framework of PRO-LAND project (PO-FESR Basilicata 2007-2013), we crossed information on potential vegetation vulnerability with information on photosynthetic activity dynamics. Potential vegetation vulnerability represents the vulnerability related to the type of present cover in terms of fire risk, erosion protection, drought resistance and plant cover distribution. It was derived from an updated land cover map by separately analyzing each factor, and then by combining them to obtain concise information on the possible degradation exposure. The analysis of photosynthetic activity dynamics provides information on the status of vegetation, that is fundamental to discriminate the different vulnerability levels within the same land cover, i.e. the same potential vulnerability. For such a purpose, we analyzed a time series (2000-2010) of a satellite vegetation index (MODIS NDVI) with 250m resolution, available as 16-day composite from the NASA LP DAAC dataset. Vegetation activity trends were estimated and then normalized to the starting conditions to obtain the percentage variation (NDVI-PV) for the considered period. Information on the potential vulnerability and vegetation activity dynamics were classified into indexes and combined to obtain the final map of the actual vegetation vulnerability and to identify on-going degradation phenomena and priority sites within areas already compromised. As for the investigated area, this map shows a composite picture in which only a few values of high vulnerability are scattered along areas where medium-high vulnerability values generally prevail. Here, we singled out two kind of areas: one largely devoted to intensive agriculture, and other one mostly characterized by bare soils and sparse vegetation. On the contrary, a large part of natural and seminatural vegetation located along the Apennine chain does not show critical vulnerability values. By comparing the vegetation vulnerability map with the vulnerability map due to anthropic factors (pressure induced by agricultural and grazing activities, estimated by indicators derived from census data), we found correlation, confirming the anthropogenic cause of vulnerability and therefore the major role held by soil management in areas mainly devoted to intensive farming.

Integrating Vegetation Classification, Mapping, and Strategic Inventory for Forest Management

Treesearch

C. K. Brewer; R. Bush; D. Berglund; J. A. Barber; S. R. Brown

2006-01-01

Many of the analyses needed to address multiple resource issues are focused on vegetation pattern and process relationships and most rely on the data models produced from vegetation classification, mapping, and/or inventory. The Northern Region Vegetation Mapping Project (R1-VMP) data models are based on these three integrally related, yet separate processes. This...

Mapping wetlands on beaver flowages with 35-mm photography

USGS Publications Warehouse

Kirby, R.E.

1976-01-01

Beaver flowages and associated wetlands on the Chippewa National Forest, north-central Minnesota, were photographed from the ground and from the open side window of a small high-wing monoplane. The 35-mm High Speed Ektachrome transparencies obtained were used to map the cover-type associations visible on the aerial photographs. Nearly vertical aerial photos were rectified by projecting the slides onto a base map consisting ofcontrol points located by plane-table survey. Maps were prepared by tracing the recognizable stands of vegetation in the rectified projection at the desired map scale. Final map scales ranging from 1:260 to 1:571 permitted identification and mapping of 26 cover-type associations on 10 study flowages in 1971. This cover-mapping technique was economical and substituted for detailed ground surveys. Comparative data from 10 flowages were collected serially throughout the entire open-water season. Although developed for analysis of waterfowl habitat, the technique has application to other areas of wildlife management and ecological investigation.

Extrapolation of forest community types with a geographic information system

Treesearch

W.K. Clatterbuck; J. Gregory

1991-01-01

A geographic information system (GIS) was used to project eight forest community types from a 1,190-acre (482-ha) intensively sampled area to an unsampled 19,887-acre (8,054-ha) adjacent area with similar environments on the Western Highland Rim of Tennessee. Both physiographic and vegetative parameters were used to distinguish, extrapolate, and map communities.

Evaluation of open source data mining software packages

Treesearch

Bonnie Ruefenacht; Greg Liknes; Andrew J. Lister; Haans Fisk; Dan Wendt

2009-01-01

Since 2001, the USDA Forest Service (USFS) has used classification and regression-tree technology to map USFS Forest Inventory and Analysis (FIA) biomass, forest type, forest type groups, and National Forest vegetation. This prior work used Cubist/See5 software for the analyses. The objective of this project, sponsored by the Remote Sensing Steering Committee (RSSC),...

Developing a Method to Mask Trees in Commercial Multispectral Imagery

NASA Astrophysics Data System (ADS)

Becker, S. J.; Daughtry, C. S. T.; Jain, D.; Karlekar, S. S.

2015-12-01

The US Army has an increasing focus on using automated remote sensing techniques with commercial multispectral imagery (MSI) to map urban and peri-urban agricultural and vegetative features; however, similar spectral profiles between trees (i.e., forest canopy) and other vegetation result in confusion between these cover classes. Established vegetation indices, like the Normalized Difference Vegetation Index (NDVI), are typically not effective in reliably differentiating between trees and other vegetation. Previous research in tree mapping has included integration of hyperspectral imagery (HSI) and LiDAR for tree detection and species identification, as well as the use of MSI to distinguish tree crowns from non-vegetated features. This project developed a straightforward method to model and also mask out trees from eight-band WorldView-2 (1.85 meter x 1.85 meter resolution at nadir) satellite imagery at the Beltsville Agricultural Research Center in Beltsville, MD spanning 2012 - 2015. The study site included tree cover, a range of agricultural and vegetative cover types, and urban features. The modeling method exploits the product of the red and red edge bands and defines accurate thresholds between trees and other land covers. Results show this method outperforms established vegetation indices including the NDVI, Soil Adjusted Vegetation Index, Normalized Difference Water Index, Simple Ratio, and Normalized Difference Red Edge Index in correctly masking trees while preserving the other information in the imagery. This method is useful when HSI and LiDAR collection are not possible or when using archived MSI.

Plant diversity of the Pantanal wetland.

PubMed

Pott, A; Oliveira, A K M; Damasceno-Junior, G A; Silva, J S V

2011-04-01

This is a review of current studies in diversity of the flora and main vegetation types in the Brazilian Pantanal. The flora of this wetland, nearly 2,000 species, constitutes a pool of elements of wide distribution and from more or less adjacent phytogeographic provinces, such as Cerrado, dry seasonal forests, Chaco, Amazonia and Atlantic Forest. The most numerous group includes wide-distribution species, mainly herbs, while the second contingent comes from the Cerrado. Endemic plants are rare, numbering only seven. The vegetation of the sedimentary floodplain is a mosaic of aquatics, floodable grasslands, riparian forests, savannas (cerrados), cerrado woodlands, dry forests, and a large area of mono-dominant savannas, and pioneer woodlands. The main vegetation types are briefly described with their characteristic species, and their estimated areas are given according to the latest mapping.

Discerning spatial and temporal LAI and clear-sky FAPAR variability during summer at the Toolik Lake vegetation monitoring grid (North Slope, Alaska)

NASA Astrophysics Data System (ADS)

Heim, B.; Beamish, A. L.; Walker, D. A.; Epstein, H. E.; Sachs, T.; Chabrillat, S.; Buchhorn, M.; Prakash, A.

2016-12-01

Ground data for the validation of satellite-derived terrestrial Essential Climate Variables (ECVs) at high latitudes are sparse. Also for regional model evaluation (e.g. climate models, land surface models, permafrost models), we lack accurate ranges of terrestrial ground data and face the problem of a large mismatch in scale. Within the German research programs `Regional Climate Change' (REKLIM) and the Environmental Mapping and Analysis Program (EnMAP), we conducted a study on ground data representativeness for vegetation-related variables within a monitoring grid at the Toolik Lake Long-Term Ecological Research station; the Toolik Lake station lies in the Kuparuk River watershed on the North Slope of the Brooks Mountain Range in Alaska. The Toolik Lake grid covers an area of 1 km2 containing Eight five grid points spaced 100 meters apart. Moist acidic tussock tundra is the most dominant vegetation type within the grid. Eight five permanent 1 m2 plots were also established to be representative of the individual gridpoints. Researchers from the University of Alaska Fairbanks have undertaken assessments at these plots, including Leaf Area Index (LAI) and field spectrometry to derive the Normalized Difference Vegetation Index (NDVI). During summer 2016, we conducted field spectrometry and LAI measurements at selected plots during early, peak and late summer. We experimentally measured LAI on more spatially extensive Elementary Sampling Units (ESUs) to investigate the spatial representativeness of the permanent 1 m2 plots and to map ESUs for various tundra types. LAI measurements are potentially influenced by landscape-inherent microtopography, sparse vascular plant cover, and dead woody matter. From field spectrometer measurements, we derived a clear-sky mid-day Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). We will present the first data analyses comparing FAPAR and LAI, and maps of biophysically-focused ESUs for evaluation of the use of remote sensing data to estimate these ecosystem properties.

GPS-aided inertial technology and navigation-based photogrammetry for aerial mapping the San Andreas fault system

USGS Publications Warehouse

Sanchez, Richard D.; Hudnut, Kenneth W.

2004-01-01

Aerial mapping of the San Andreas Fault System can be realized more efficiently and rapidly without ground control and conventional aerotriangulation. This is achieved by the direct geopositioning of the exterior orientation of a digital imaging sensor by use of an integrated Global Positioning System (GPS) receiver and an Inertial Navigation System (INS). A crucial issue to this particular type of aerial mapping is the accuracy, scale, consistency, and speed achievable by such a system. To address these questions, an Applanix Digital Sensor System (DSS) was used to examine its potential for near real-time mapping. Large segments of vegetation along the San Andreas and Cucamonga faults near the foothills of the San Bernardino and San Gabriel Mountains were burned to the ground in the California wildfires of October-November 2003. A 175 km corridor through what once was a thickly vegetated and hidden fault surface was chosen for this study. Both faults pose a major hazard to the greater Los Angeles metropolitan area and a near real-time mapping system could provide information vital to a post-disaster response.

Importance of MAP Kinases during Protoperithecial Morphogenesis in Neurospora crassa

PubMed Central

Jeffree, Chris E.; Oborny, Radek; Boonyarungsrit, Patid; Read, Nick D.

2012-01-01

In order to produce multicellular structures filamentous fungi combine various morphogenetic programs that are fundamentally different from those used by plants and animals. The perithecium, the female sexual fruitbody of Neurospora crassa, differentiates from the vegetative mycelium in distinct morphological stages, and represents one of the more complex multicellular structures produced by fungi. In this study we defined the stages of protoperithecial morphogenesis in the N. crassa wild type in greater detail than has previously been described; compared protoperithecial morphogenesis in gene-deletion mutants of all nine mitogen-activated protein (MAP) kinases conserved in N. crassa; confirmed that all three MAP kinase cascades are required for sexual development; and showed that the three different cascades each have distinctly different functions during this process. However, only MAP kinases equivalent to the budding yeast pheromone response and cell wall integrity pathways, but not the osmoregulatory pathway, were essential for vegetative cell fusion. Evidence was obtained for MAP kinase signaling cascades performing roles in extracellular matrix deposition, hyphal adhesion, and envelopment during the construction of fertilizable protoperithecia. PMID:22900028

Vegetation analysis in the Laramie Basin, Wyoming from ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Evans, M. A.; Redfern, F. R.

1973-01-01

The author has identified the following significant results. The application of ERTS-1 imagery to vegetation mapping and identification was tested and confirmed by field checking. ERTS-1 imagery interpretation and density contour mapping allows definition of minute vegetation features and estimation of vegetative biomass and species composition. Large- and small-scale vegetation maps were constructed for test areas in the Laramie Basin and Laramie mountains of Wyoming. Vegetative features reflecting grazing intensity, moisture availability, changes within the growing season, cutting of hay crops, and plant community constituents in forest and grassland are discussed and illustrated. Theoretical considerations of scattering, sun angle, slope, and instrument aperture upon image and map resolution were investigated. Future suggestions for applications of ERTS-1 data to vegetative analysis are included.

Modeling and Prediction of Wildfire Hazard in Southern California, Integration of Models with Imaging Spectrometry

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Church, Richard; Ustin, Susan L.; Brass, James A. (Technical Monitor)

2001-01-01

Large urban wildfires throughout southern California have caused billions of dollars of damage and significant loss of life over the last few decades. Rapid urban growth along the wildland interface, high fuel loads and a potential increase in the frequency of large fires due to climatic change suggest that the problem will worsen in the future. Improved fire spread prediction and reduced uncertainty in assessing fire hazard would be significant, both economically and socially. Current problems in the modeling of fire spread include the role of plant community differences, spatial heterogeneity in fuels and spatio-temporal changes in fuels. In this research, we evaluated the potential of Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data for providing improved maps of wildfire fuel properties. Analysis concentrated in two areas of Southern California, the Santa Monica Mountains and Santa Barbara Front Range. Wildfire fuel information can be divided into four basic categories: fuel type, fuel load (live green and woody biomass), fuel moisture and fuel condition (live vs senesced fuels). To map fuel type, AVIRIS data were used to map vegetation species using Multiple Endmember Spectral Mixture Analysis (MESMA) and Binary Decision Trees. Green live biomass and canopy moisture were mapped using AVIRIS through analysis of the 980 nm liquid water absorption feature and compared to alternate measures of moisture and field measurements. Woody biomass was mapped using L and P band cross polarimetric data acquired in 1998 and 1999. Fuel condition was mapped using spectral mixture analysis to map green vegetation (green leaves), nonphotosynthetic vegetation (NPV; stems, wood and litter), shade and soil. Summaries describing the potential of hyperspectral and SAR data for fuel mapping are provided by Roberts et al. and Dennison et al. To utilize remotely sensed data to assess fire hazard, fuel-type maps were translated into standard fuel models accessible to the FARSITE fire spread simulator. The FARSITE model and BEHAVE are considered industry standards for fire behavior analysis. Anderson level fuels map, generated using a binary decision tree classifier are available for multiple dates in the Santa Monica Mountains and at least one date for Santa Barbara. Fuel maps that will fill in the areas between Santa Barbara and the Santa Monica Mountains study sites are in progress, as part of a NASA Regional Earth Science Application Center, the Southern California Wildfire Hazard Center. Species-level maps, were supplied to fire managing agencies (Los Angeles County Fire, California Department of Forestry). Research results were published extensively in the refereed and non-refereed literature. Educational outreach included funding of several graduate students, undergraduate intern training and an article featured in the California Alliance for Minorities Program (CAMP) Quarterly Journal.

Sensitivity of volatile organic compounds (VOCs) and ozone to land surface processes and vegetation distributions in California

NASA Astrophysics Data System (ADS)

Zhao, C.; Huang, M.; Fast, J. D.; Berg, L. K.; Qian, Y.; Guenther, A. B.; Gu, D.; Shrivastava, M. B.; Liu, Y.; Walters, S.; Jin, J.

2014-12-01

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect secondary organic aerosol (SOA) formation and ultimately aerosol radiative forcing. These uncertainties result from many factors, including coupling strategy between biogenic emissions and land-surface schemes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (VOCs). In this study, sensitivity experiments are conducted using the Weather Research and Forecasting model with chemistry (WRF-Chem) to examine the sensitivity of simulated VOCs and ozone to land surface processes and vegetation distributions in California. The measurements collected during the California Nexus of Air Quality and Climate Experiment (CalNex) and the Carbonaceous Aerosol and Radiative Effects Study (CARES) conducted during May and June of 2010 provide a good opportunity to evaluate the simulations. First, the biogenic VOC emissions in the WRF-Chem simulations with the two land surface schemes, Noah and CLM4, are estimated by the Model of Emissions of Gases and Aerosols from Nature version one (MEGANv1), which has been publicly released and widely used with WRF-Chem. The impacts of land surface processes on estimating biogenic VOC emissions and simulating VOCs and ozone are investigated. Second, in this study, a newer version of MEGAN (MEGANv2.1) is coupled with CLM4 as part of WRF-Chem to examine the sensitivity of biogenic VOC emissions to the MEGAN schemes used and determine the importance of using a consistent vegetation map between a land surface scheme and the biogenic VOC emission scheme. Specifically, MEGANv2.1 is embedded into the CLM4 scheme and shares a consistent vegetation map for estimating biogenic VOC emissions. This is unlike MEGANv1 in WRF-Chem that uses a standalone vegetation map that differs from what is used in land surface schemes. Furthermore, we examine the impact of vegetation distribution on simulating VOCs and ozone by comparing coupled WRF-Chem-CLM-MEGANv2.1 simulations using multiple vegetation maps.

Mapping Landscape Phenology Preference of Yellow-billed Cuckoo with AVHRR data

NASA Astrophysics Data System (ADS)

Wallace, C.; Villarreal, M. L.; Van Riper, C., III

2011-12-01

The yellow-billed cuckoo (Coccycus americanus occidentalis) is a neo-tropical migrant bird that travels north from South America into the southwestern United States during the summer to nest. In Arizona, favored riparian forest and woodland nesting habitat has declined in recent decades, due primarily to human activities and the prolonged drought conditions. As a result, western yellow-billed cuckoos have been petitioned for possible listing under the Endangered Species Act. In this study, we map yellow-billed cuckoo habitat in the state of Arizona using the temporal greenness dynamics of the landscape, or the landscape phenology. Landscape phenometrics were derived from Advanced Very High Resolution Radiometer (AVHRR) satellite Normalized Difference Vegetation Index (NDVI) composite data using Fourier harmonic analysis. Applying Fourier analysis to the waveform composed of the 26 annual composite NDVI values produces phenometrics related to the overall vegetation amount, variability and timing. Field data on Cuckoo presence were obtained from 1998 surveys conducted by Northern Arizona University (NAU), the Arizona Game and Fish Department (AGFD) and the U.S. Geological Survey (USGS). To focus the research within probable landscapes, an AGFD vegetation map (derived from the Arizona GAP program) was used to extract polygons of riparian vegetation and cottonwood-willow riparian vegetation. To create the models, we coupled the satellite phenometrics with field data of cuckoo presence or absence and with points sampling the entirety of mapped riparian and cottonwood-willow vegetation types. Statistical tests reveal that locations with cuckoos present are landscapes with greenness that is significantly more variable and that peaks significantly later than locations in average riparian vegetation, average cottonwood-willow vegetation, or with cuckoos absent. Interestingly, the mean peak greenness date of July 3 for survey locations with cuckoos present coincides with the first day of the 1998 monsoon season recorded for Tucson in southern Arizona. Models developed from the 1998 parameters and applied to 1999 data were effective at predicting cuckoo presence for survey locations visited in 1999, with up to 64 percent of cuckoos located in the highest preference class.

Legume Diversity Patterns in West Central Africa: Influence of Species Biology on Distribution Models

PubMed Central

de la Estrella, Manuel; Mateo, Rubén G.; Wieringa, Jan J.; Mackinder, Barbara; Muñoz, Jesús

2012-01-01

Objectives Species Distribution Models (SDMs) are used to produce predictions of potential Leguminosae diversity in West Central Africa. Those predictions are evaluated subsequently using expert opinion. The established methodology of combining all SDMs is refined to assess species diversity within five defined vegetation types. Potential species diversity is thus predicted for each vegetation type respectively. The primary aim of the new methodology is to define, in more detail, areas of species richness for conservation planning. Methodology Using Maxent, SDMs based on a suite of 14 environmental predictors were generated for 185 West Central African Leguminosae species, each categorised according to one of five vegetation types: Afromontane, coastal, non-flooded forest, open formations, or riverine forest. The relative contribution of each environmental variable was compared between different vegetation types using a nonparametric Kruskal-Wallis analysis followed by a post-hoc Kruskal-Wallis Paired Comparison contrast. Legume species diversity patterns were explored initially using the typical method of stacking all SDMs. Subsequently, five different ensemble models were generated by partitioning SDMs according to vegetation category. Ecological modelers worked with legume specialists to improve data integrity and integrate expert opinion in the interpretation of individual species models and potential species richness predictions for different vegetation types. Results/Conclusions Of the 14 environmental predictors used, five showed no difference in their relative contribution to the different vegetation models. Of the nine discriminating variables, the majority were related to temperature variation. The set of variables that played a major role in the Afromontane species diversity model differed significantly from the sets of variables of greatest relative important in other vegetation categories. The traditional approach of stacking all SDMs indicated overall centers of diversity in the region but the maps indicating potential species richness by vegetation type offered more detailed information on which conservation efforts can be focused. PMID:22911808

Wildlife management by habitat units: A preliminary plan of action

NASA Technical Reports Server (NTRS)

Frentress, C. D.; Frye, R. G.

1975-01-01

Procedures for yielding vegetation type maps were developed using LANDSAT data and a computer assisted classification analysis (LARSYS) to assist in managing populations of wildlife species by defined area units. Ground cover in Travis County, Texas was classified on two occasions using a modified version of the unsupervised approach to classification. The first classification produced a total of 17 classes. Examination revealed that further grouping was justified. A second analysis produced 10 classes which were displayed on printouts which were later color-coded. The final classification was 82 percent accurate. While the classification map appeared to satisfactorily depict the existing vegetation, two classes were determined to contain significant error. The major sources of error could have been eliminated by stratifying cluster sites more closely among previously mapped soil associations that are identified with particular plant associations and by precisely defining class nomenclature using established criteria early in the analysis.

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

USGS Publications Warehouse

Miller, J.D.; Knapp, E.E.; Key, C.H.; Skinner, C.N.; Isbell, C.J.; Creasy, R.M.; Sherlock, J.W.

2009-01-01

Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land management agencies to map fire severity due to wildland fire. However, absolute differenced vegetation indices are correlated to the pre-fire chlorophyll content of the vegetation occurring within the fire perimeter. Normalizing dNBR to produce a relativized dNBR (RdNBR) removes the biasing effect of the pre-fire condition. Employing RdNBR hypothetically allows creating categorical classifications using the same thresholds for fires occurring in similar vegetation types without acquiring additional calibration field data on each fire. In this paper we tested this hypothesis by developing thresholds on random training datasets, and then comparing accuracies for (1) fires that occurred within the same geographic region as the training dataset and in similar vegetation, and (2) fires from a different geographic region that is climatically and floristically similar to the training dataset region but supports more complex vegetation structure. We additionally compared map accuracies for three measures of fire severity: the composite burn index (CBI), percent change in tree canopy cover, and percent change in tree basal area. User's and producer's accuracies were highest for the most severe categories, ranging from 70.7% to 89.1%. Accuracies of the moderate fire severity category for measures describing effects only to trees (percent change in canopy cover and basal area) indicated that the classifications were generally not much better than random. Accuracies of the moderate category for the CBI classifications were somewhat better, averaging in the 50%-60% range. These results underscore the difficulty in isolating fire effects to individual vegetation strata when fire effects are mixed. We conclude that the models presented here and in Miller and Thode ([Miller, J.D. & Thode, A.E., (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109, 66-80.]) can produce fire severity classifications (using either CBI, or percent change in canopy cover or basal area) that are of similar accuracy in fires not used in the original calibration process, at least in conifer dominated vegetation types in Mediterranean-climate California.

Vulnerability of forest vegetation to anthropogenic climate change in China.

PubMed

Wan, Ji-Zhong; Wang, Chun-Jing; Qu, Hong; Liu, Ran; Zhang, Zhi-Xiang

2018-04-15

China has large areas of forest vegetation that are critical to biodiversity and carbon storage. It is important to assess vulnerability of forest vegetation to anthropogenic climate change in China because it may change the distributions and species compositions of forest vegetation. Based on the equilibrium assumption of forest communities across different spatial and temporal scales, we used species distribution modelling coupled with endemics-area relationship to assess the vulnerability of 204 forest communities across 16 vegetation types under different climate change scenarios in China. By mapping the vulnerability of forest vegetation to climate change, we determined that 78.9% and 61.8% of forest vegetation should be relatively stable in the low and high concentration scenarios, respectively. There were large vulnerable areas of forest vegetation under anthropogenic climate change in northeastern and southwestern China. The vegetation of subtropical mixed broadleaf evergreen and deciduous forest, cold-temperate and temperate mountains needleleaf forest, and temperate mixed needleleaf and broadleaf deciduous forest types were the most vulnerable under climate change. Furthermore, the vulnerability of forest vegetation may increase due to high greenhouse gas concentrations. Given our estimates of forest vegetation vulnerability to anthropogenic climate change, it is critical that we ensure long-term monitoring of forest vegetation responses to future climate change to assess our projections against observations. We need to better integrate projected changes of temperature and precipitation into climate-adaptive conservation strategies for forest vegetation in China. Copyright © 2017 Elsevier B.V. All rights reserved.

Comparing Pixel and Object-Based Approaches to Map an Understorey Invasive Shrub in Tropical Mixed Forests

PubMed Central

Niphadkar, Madhura; Nagendra, Harini; Tarantino, Cristina; Adamo, Maria; Blonda, Palma

2017-01-01

The establishment of invasive alien species in varied habitats across the world is now recognized as a genuine threat to the preservation of biodiversity. Specifically, plant invasions in understory tropical forests are detrimental to the persistence of healthy ecosystems. Monitoring such invasions using Very High Resolution (VHR) satellite remote sensing has been shown to be valuable in designing management interventions for conservation of native habitats. Object-based classification methods are very helpful in identifying invasive plants in various habitats, by their inherent nature of imitating the ability of the human brain in pattern recognition. However, these methods have not been tested adequately in dense tropical mixed forests where invasion occurs in the understorey. This study compares a pixel-based and object-based classification method for mapping the understorey invasive shrub Lantana camara (Lantana) in a tropical mixed forest habitat in the Western Ghats biodiversity hotspot in India. Overall, a hierarchical approach of mapping top canopy at first, and then further processing for the understorey shrub, using measures such as texture and vegetation indices proved effective in separating out Lantana from other cover types. In the first method, we implement a simple parametric supervised classification for mapping cover types, and then process within these types for Lantana delineation. In the second method, we use an object-based segmentation algorithm to map cover types, and then perform further processing for separating Lantana. The improved ability of the object-based approach to delineate structurally distinct objects with characteristic spectral and spatial characteristics of their own, as well as with reference to their surroundings, allows for much flexibility in identifying invasive understorey shrubs among the complex vegetation of the tropical forest than that provided by the parametric classifier. Conservation practices in tropical mixed forests can benefit greatly by adopting methods which use high resolution remotely sensed data and advanced techniques to monitor the patterns and effective functioning of native ecosystems by periodically mapping disturbances such as invasion. PMID:28620400

Effects of experimental protocol on global vegetation model accuracy: a comparison of simulated and observed vegetation patterns for Asia

USGS Publications Warehouse

Tang, Guoping; Shafer, Sarah L.; Barlein, Patrick J.; Holman, Justin O.

2009-01-01

Prognostic vegetation models have been widely used to study the interactions between environmental change and biological systems. This study examines the sensitivity of vegetation model simulations to: (i) the selection of input climatologies representing different time periods and their associated atmospheric CO2 concentrations, (ii) the choice of observed vegetation data for evaluating the model results, and (iii) the methods used to compare simulated and observed vegetation. We use vegetation simulated for Asia by the equilibrium vegetation model BIOME4 as a typical example of vegetation model output. BIOME4 was run using 19 different climatologies and their associated atmospheric CO2 concentrations. The Kappa statistic, Fuzzy Kappa statistic and a newly developed map-comparison method, the Nomad index, were used to quantify the agreement between the biomes simulated under each scenario and the observed vegetation from three different global land- and tree-cover data sets: the global Potential Natural Vegetation data set (PNV), the Global Land Cover Characteristics data set (GLCC), and the Global Land Cover Facility data set (GLCF). The results indicate that the 30-year mean climatology (and its associated atmospheric CO2 concentration) for the time period immediately preceding the collection date of the observed vegetation data produce the most accurate vegetation simulations when compared with all three observed vegetation data sets. The study also indicates that the BIOME4-simulated vegetation for Asia more closely matches the PNV data than the other two observed vegetation data sets. Given the same observed data, the accuracy assessments of the BIOME4 simulations made using the Kappa, Fuzzy Kappa and Nomad index map-comparison methods agree well when the compared vegetation types consist of a large number of spatially continuous grid cells. The results of this analysis can assist model users in designing experimental protocols for simulating vegetation.

Spatial and spectral resolution necessary for remotely sensed vegetation studies

NASA Technical Reports Server (NTRS)

Rock, B. N.

1982-01-01

An outline is presented of the required spatial and spectral resolution needed for accurate vegetation discrimination and mapping studies as well as for determination of state of health (i.e., detection of stress symptoms) of actively growing vegetation. Good success was achieved in vegetation discrimination and mapping of a heterogeneous forest cover in the ridge and valley portion of the Appalachians using multispectral data acquired with a spatial resolution of 15 m (IFOV). A sensor system delivering 10 to 15 m spatial resolution is needed for both vegetation mapping and detection of stress symptoms. Based on the vegetation discrimination and mapping exercises conducted at the Lost River site, accurate products (vegetation maps) are produced using broad-band spectral data ranging from the .500 to 2.500 micron portion of the spectrum. In order of decreasing utility for vegetation discrimination, the four most valuable TM simulator VNIR bands are: 6 (1.55 to 1.75 microns), 3 (0.63 to 0.69 microns), 5 (1.00 to 1.30 microns) and 4 (0.76 to 0.90 microns).

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data

PubMed Central

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2016-01-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting. PMID:27667901

Mapping paddy rice planting areas through time series analysis of MODIS land surface temperature and vegetation index data.

PubMed

Zhang, Geli; Xiao, Xiangming; Dong, Jinwei; Kou, Weili; Jin, Cui; Qin, Yuanwei; Zhou, Yuting; Wang, Jie; Menarguez, Michael Angelo; Biradar, Chandrashekhar

2015-08-01

Knowledge of the area and spatial distribution of paddy rice is important for assessment of food security, management of water resources, and estimation of greenhouse gas (methane) emissions. Paddy rice agriculture has expanded rapidly in northeastern China in the last decade, but there are no updated maps of paddy rice fields in the region. Existing algorithms for identifying paddy rice fields are based on the unique physical features of paddy rice during the flooding and transplanting phases and use vegetation indices that are sensitive to the dynamics of the canopy and surface water content. However, the flooding phenomena in high latitude area could also be from spring snowmelt flooding. We used land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor to determine the temporal window of flooding and rice transplantation over a year to improve the existing phenology-based approach. Other land cover types (e.g., evergreen vegetation, permanent water bodies, and sparse vegetation) with potential influences on paddy rice identification were removed (masked out) due to their different temporal profiles. The accuracy assessment using high-resolution images showed that the resultant MODIS-derived paddy rice map of northeastern China in 2010 had a high accuracy (producer and user accuracies of 92% and 96%, respectively). The MODIS-based map also had a comparable accuracy to the 2010 Landsat-based National Land Cover Dataset (NLCD) of China in terms of both area and spatial pattern. This study demonstrated that our improved algorithm by using both thermal and optical MODIS data, provides a robust, simple and automated approach to identify and map paddy rice fields in temperate and cold temperate zones, the northern frontier of rice planting.

Biomass Allocation Patterns across China’s Terrestrial Biomes

PubMed Central

Wang, Limei; Li, Longhui; Chen, Xi; Tian, Xin; Wang, Xiaoke; Luo, Geping

2014-01-01

Root to shoot ratio (RS) is commonly used to describe the biomass allocation between below- and aboveground parts of plants. Determining the key factors influencing RS and interpreting the relationship between RS and environmental factors is important for biological and ecological research. In this study, we compiled 2088 pairs of root and shoot biomass data across China’s terrestrial biomes to examine variations in the RS and its responses to biotic and abiotic factors including vegetation type, soil texture, climatic variables, and stand age. The median value of RS (RSm) for grasslands, shrublands, and forests was 6.0, 0.73, and 0.23, respectively. The range of RS was considerably wide for each vegetation type. RS values for all three major vegetation types were found to be significantly correlated to mean annual precipitation (MAP) and potential water deficit index (PWDI). Mean annual temperature (MAT) also significantly affect the RS for forests and grasslands. Soil texture and forest origin altered the response of RS to climatic factors as well. An allometric formula could be used to well quantify the relationship between aboveground and belowground biomass, although each vegetation type had its own inherent allometric relationship. PMID:24710503

Mapping coastal vegetation, land use and environmental impact from ERTS-1. [Delaware Bay area

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Vegetation map overlays at a scale of 1:24,000 compiled by multispectral analysis from NASA aircraft imagery for all of Delaware's wetlands are being used as ground truth for ERTS-1 mapping and by state agencies for wetlands management. Six major vegetation species were discriminated and mapped, including percentages of minor species. Analogue enhancements of wetlands vegetation and dredge-fill operations have been produced using General Electric's GEMS data processing and ERTS-1 false color composites. Digital, thematic land use, and vegetation mapping of entire Delaware Bay area is in progress using Bendix Corporation's Earth Resources Data System and ERTS-1 digital tapes. Statistical evaluation of target-group selection reliability has been completed. Three papers have been published on ERTS-1 coastal vegetation and land use. Local and state officials are participating in the ERTS-1 program as co-investigators.

Mediterranean maquis fuel model development and mapping to support fire modeling

NASA Astrophysics Data System (ADS)

Bacciu, V.; Arca, B.; Pellizzaro, G.; Salis, M.; Ventura, A.; Spano, D.; Duce, P.

2009-04-01

Fuel load data and fuel model maps represent a critical issue for fire spread and behaviour modeling. The availability of accurate input data at different spatial and temporal scales can allow detailed analysis and predictions of fire hazard and fire effects across a landscape. Fuel model data are used in spatially explicit fire growth models to attain fire behaviour information for fuel management in prescribed fires, fire management applications, firefighters training, smoke emissions, etc. However, fuel type characteristics are difficult to be parameterized due to their complexity and variability: live and dead materials with different size contribute in different ways to the fire spread and behaviour. In the last decades, a strong help was provided by the use of remote sensing imagery at high spatial and spectral resolution. Such techniques are able to capture fine scale fuel distributions for accurate fire growth projections. Several attempts carried out in Europe were devoted to fuel classification and map characterization. In Italy, fuel load estimation and fuel model definition are still critical issues to be addressed due to the lack of detailed information. In this perspective, the aim of the present work was to propose an integrated approach based on field data collection, fuel model development and fuel model mapping to provide fuel models for the Mediterranean maquis associations. Field data needed for the development of fuel models were collected using destructive and non destructive measurements in experimental plots located in Northern Sardinia (Italy). Statistical tests were used to identify the main fuel types that were classified into four custom fuel models. Subsequently, a supervised classification by the Maximum Likelihood algorithm was applied on IKONOS images to identify and map the different types of maquis vegetation. The correspondent fuel model was then associated to each vegetation type to obtain the fuel model map. The results show the potential of this approach in achieving a reasonable accuracy in fuel model development and mapping; fine scale fuel model maps can be potentially helpful to obtain realistic predictions of fire behaviour and fire effects.

Vegetation classification and distribution mapping report Mesa Verde National Park

USGS Publications Warehouse

Thomas, Kathryn A.; McTeague, Monica L.; Ogden, Lindsay; Floyd, M. Lisa; Schulz, Keith; Friesen, Beverly A.; Fancher, Tammy; Waltermire, Robert G.; Cully, Anne

2009-01-01

The classification and distribution mapping of the vegetation of Mesa Verde National Park (MEVE) and surrounding environment was achieved through a multi-agency effort between 2004 and 2007. The National Park Service’s Southern Colorado Plateau Network facilitated the team that conducted the work, which comprised the U.S. Geological Survey’s Southwest Biological Science Center, Fort Collins Research Center, and Rocky Mountain Geographic Science Center; Northern Arizona University; Prescott College; and NatureServe. The project team described 47 plant communities for MEVE, 34 of which were described from quantitative classification based on f eld-relevé data collected in 1993 and 2004. The team derived 13 additional plant communities from field observations during the photointerpretation phase of the project. The National Vegetation Classification Standard served as a framework for classifying these plant communities to the alliance and association level. Eleven of the 47 plant communities were classified as “park specials;” that is, plant communities with insufficient data to describe them as new alliances or associations. The project team also developed a spatial vegetation map database representing MEVE, with three different map-class schemas: base, group, and management map classes. The base map classes represent the fi nest level of spatial detail. Initial polygons were developed using Definiens Professional (at the time of our use, this software was called eCognition), assisted by interpretation of 1:12,000 true-color digital orthophoto quarter quadrangles (DOQQs). These polygons (base map classes) were labeled using manual photo interpretation of the DOQQs and 1:12,000 true-color aerial photography. Field visits verified interpretation concepts. The vegetation map database includes 46 base map classes, which consist of associations, alliances, and park specials classified with quantitative analysis, additional associations and park specials noted during photointerpretation, and non-vegetated land cover, such as infrastructure, land use, and geological land cover. The base map classes consist of 5,007 polygons in the project area. A field-based accuracy assessment of the base map classes showed overall accuracy to be 43.5%. Seven map classes comprise 89.1% of the park vegetated land cover. The group map classes represent aggregations of the base map classes, approximating the group level of the National Vegetation Classification Standard, version 2 (Federal Geographic Data Committee 2007), and reflecting physiognomy and floristics. Terrestrial ecological systems, as described by NatureServe (Comer et al. 2003), were used as the fi rst approximation of the group level. The project team identified 14 group map classes for this project. The overall accuracy of the group map classes was determined using the same accuracy assessment data as for the base map classes. The overall accuracy of the group representation of vegetation was 80.3%. In consultation with park staff , the team developed management map classes, consisting of park-defined groupings of base map classes intended to represent a balance between maintaining required accuracy and providing a focus on vegetation of particular interest or import to park managers. The 23 management map classes had an overall accuracy of 73.3%. While the main products of this project are the vegetation classification and the vegetation map database, a number of ancillary digital geographic information system and database products were also produced that can be used independently or to augment the main products. These products include shapefiles of the locations of field-collected data and relational databases of field-collected data.

Comparison of ASTER- and AVIRIS-Derived Mineraland Vegetation Maps of the White Horse Replacement Alunite Deposit and Surrounding Area, Marysvale Volcanic Field, Utah

USGS Publications Warehouse

Rockwell, Barnaby W.

2009-01-01

This report presents and compares mineral and vegetation maps of parts of the Marysvale volcanic field in west-central Utah that were published in a recent paper describing the White Horse replacement alunite deposit. Detailed, field-verified maps of the deposit were produced from Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data acquired from a low-altitude Twin Otter turboprop airborne platform. Reconnaissance-level maps of surrounding areas including the central and northern Tushar Mountains, Pahvant Range, and portions of the Sevier Plateau to the east were produced from visible, near-infrared, and shortwave-infrared data acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor carried aboard the Terra satellite platform. These maps are also compared to a previously published mineral map of the same area generated from AVIRIS data acquired from the high-altitude NASA ER-2 jet platform. All of the maps were generated by similar analysis methods, enabling the direct comparison of the spatial scale and mineral composition of surface geologic features that can be identified using the three types of remote sensing data. The high spatial (2-17 meter) and spectral (224 bands) resolution AVIRIS data can be used to generate detailed mineral and vegetation maps suitable for geologic and geoenvironmental studies of individual deposits, mines, and smelters. The lower spatial (15-30 meter) and spectral (9 bands) resolution ASTER data are better suited to less detailed mineralogical studies of lithology and alteration across entire hydrothermal systems and mining districts, including regional mineral resource and geoenvironmental assessments. The results presented here demonstrate that minerals and mineral mixtures can be directly identified using AVIRIS and ASTER data to elucidate spatial patterns of mineralogic zonation; AVIRIS data can enable the generation of maps with significantly greater detail and accuracy. The vegetation mapping results suggest that ASTER data may provide an efficient alternative to spectroscopic data for studies of burn severity after wildland fires. A new, semiautomated methodology for the analysis of ASTER data is presented that is currently being applied to ASTER data coverage of large areas for regional assessments of mineral-resource potential and mineral-environmental effects. All maps are presented in a variety of digital formats, including jpeg, pdf, and ERDAS Imagine (.img). The Imagine format files are georeferenced and suitable for viewing with other geospatial data in Imagine, ArcGIS, and ENVI. The mineral and vegetation maps are attributed so that the material identified for a pixel can be determined easily in ArcMap by using the Identify tool and in Imagine by using the Inquire Cursor tool.

Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant.

PubMed

Rascher, U; Alonso, L; Burkart, A; Cilia, C; Cogliati, S; Colombo, R; Damm, A; Drusch, M; Guanter, L; Hanus, J; Hyvärinen, T; Julitta, T; Jussila, J; Kataja, K; Kokkalis, P; Kraft, S; Kraska, T; Matveeva, M; Moreno, J; Muller, O; Panigada, C; Pikl, M; Pinto, F; Prey, L; Pude, R; Rossini, M; Schickling, A; Schurr, U; Schüttemeyer, D; Verrelst, J; Zemek, F

2015-12-01

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first validated maps of sun-induced fluorescence in that critical, intermediate spatial resolution, employing the novel airborne imaging spectrometer HyPlant. HyPlant has an unprecedented spectral resolution, which allows for the first time quantifying sun-induced fluorescence fluxes in physical units according to the Fraunhofer Line Depth Principle that exploits solar and atmospheric absorption bands. Maps of sun-induced fluorescence show a large spatial variability between different vegetation types, which complement classical remote sensing approaches. Different crop types largely differ in emitting fluorescence that additionally changes within the seasonal cycle and thus may be related to the seasonal activation and deactivation of the photosynthetic machinery. We argue that sun-induced fluorescence emission is related to two processes: (i) the total absorbed radiation by photosynthetically active chlorophyll; and (ii) the functional status of actual photosynthesis and vegetation stress. © 2015 John Wiley & Sons Ltd.

Fire and climate suitability for woody vegetation communities in the south central United States

USGS Publications Warehouse

Stroh, Esther; Struckhoff, Matthew; Stambaugh, Michael C.; Guyette, Richard P.

2018-01-01

using a physical chemistry fire frequency model. We then used the fire probability data with additional climate parameters to construct maximum entropy environmental suitability models for three south central US vegetation communities. The modeled communities included an oak type (dominated by post oak, Quercus stellata Wangenh., and blackjack oak, Q. marilandica Münchh.), a mesquite type (dominated by honey mesquite, Prosopis glandulosa Torr., and velvet mesquite, P. velutina Wooton), and a pinyon−juniper type (dominated by pinyon pine, Pinus edulis Engelm., and Utah juniper, Juniperus osteosperma [Torr.] Little). We mapped baseline and future mean fire-climate suitability using data from three global climate models for 2040 to 2069 and 2070 to 2099; we also mapped future locations of threshold conditions for which all three models agreed on suitability for each community. Future projections included northward, southward, and eastward shifts in suitable conditions for the oaks along a broad path of fire-climate stability; an overall reduction in suitable area for historic mesquite communities coupled with potential expansion to new areas; and constriction and isolation of suitable conditions for pinyon−juniper communities. The inclusion of fire probability adds an important driver of vegetation distribution to climate envelope modeling. The simple models showed good fit, but future projections failed to account for future management activities or land use changes. Results provided information on potential future de-coupling and spatial re-arrangement of environmental conditions under which these communities have historically persisted and been managed. In particular, consensus threshold maps can inform long-term planning for maintenance or restoration of these communities, and they can be used as a potential tool for other communities in fire-prone environments within the study area and beyond its borders.

Precipitation gradient determines the tradeoff between soil moisture and soil organic carbon, total nitrogen, and species richness in the Loess Plateau, China.

PubMed

Wang, Cong; Wang, Shuai; Fu, Bojie; Li, Zongshan; Wu, Xing; Tang, Qiang

2017-01-01

A tight coupling exists between biogeochemical cycles and water availability in drylands. However, studies regarding the coupling among soil moisture (SM), soil carbon/nitrogen, and plants are rare in the literature, and clarifying these relationships changing with climate gradient is challenging. Thus, soil organic carbon (SOC), total nitrogen (TN), and species richness (SR) were selected as soil-plant system variables, and the tradeoff relationships between SM and these variables and their variations along the precipitation gradient were quantified in the Loess Plateau, China. Results showed these variables increased linearly along the precipitation gradient in the woodland, shrubland, and grassland, respectively, except for the SR in the woodland and grassland, and SOC in the grassland (p>0.05). Correlation analysis showed that the SM-SOC and SM-TN tradeoffs were significantly correlated with mean annual precipitation (MAP) across the three vegetation types, and SM-SR tradeoff was significantly correlated with MAP in grassland and woodland. The linear piece-wise quantile regression was applied to determine the inflection points of these tradeoffs responses to the precipitation gradient. The inflection point for the SM-SOC tradeoff was detected at MAP=570mm; no inflection point was detected for SM-TN tradeoff; SM-SR tradeoff variation trends were different in the woodland and grassland, and the inflection points were detected at MAP=380mm and MAP=570mm, respectively. Before the turning point, constraint exerted by soil moisture on SOC and SR existed in the relatively arid regions, while the constraint disappears or is lessened in the relatively humid regions in this study. The results demonstrate the tradeoff revealed obvious trends along the precipitation gradient and were affected by vegetation type. Consequently, tradeoffs could be an ecological indicator and tool for restoration management in the Loess Plateau. In further study, the mechanism of how the tradeoff is affected by the precipitation gradient and vegetation type should be clarified. Copyright © 2016 Elsevier B.V. All rights reserved.

Management of natural resources through automatic cartographic inventory

NASA Technical Reports Server (NTRS)

Rey, P. (Principal Investigator); Gourinard, Y.; Cambou, F.

1972-01-01

The author has identified the following significant results. Return beam vidicon and multispectral band scanner imagery will be correlated with existing vegetation and geologic maps of southern France and northern Spain to develop correspondence codes between map units and space data. Microclimate data from six stations, spectral measurements from a few meters to 2 km using ERTS-type filter and spectrometers, and leaf reflectance measurements will be obtained to assist in correlation studies.

Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas.

PubMed

Mathieu, Renaud; Aryal, Jagannath; Chong, Albert K

2007-11-20

Effective assessment of biodiversity in cities requires detailed vegetation maps.To date, most remote sensing of urban vegetation has focused on thematically coarse landcover products. Detailed habitat maps are created by manual interpretation of aerialphotographs, but this is time consuming and costly at large scale. To address this issue, wetested the effectiveness of object-based classifications that use automated imagesegmentation to extract meaningful ground features from imagery. We applied thesetechniques to very high resolution multispectral Ikonos images to produce vegetationcommunity maps in Dunedin City, New Zealand. An Ikonos image was orthorectified and amulti-scale segmentation algorithm used to produce a hierarchical network of image objects.The upper level included four coarse strata: industrial/commercial (commercial buildings),residential (houses and backyard private gardens), vegetation (vegetation patches larger than0.8/1ha), and water. We focused on the vegetation stratum that was segmented at moredetailed level to extract and classify fifteen classes of vegetation communities. The firstclassification yielded a moderate overall classification accuracy (64%, κ = 0.52), which ledus to consider a simplified classification with ten vegetation classes. The overallclassification accuracy from the simplified classification was 77% with a κ value close tothe excellent range (κ = 0.74). These results compared favourably with similar studies inother environments. We conclude that this approach does not provide maps as detailed as those produced by manually interpreting aerial photographs, but it can still extract ecologically significant classes. It is an efficient way to generate accurate and detailed maps in significantly shorter time. The final map accuracy could be improved by integrating segmentation, automated and manual classification in the mapping process, especially when considering important vegetation classes with limited spectral contrast.

A Vegetation Database for the Colorado River Ecosystem from Glen Canyon Dam to the Western Boundary of Grand Canyon National Park, Arizona

USGS Publications Warehouse

Ralston, Barbara E.; Davis, Philip A.; Weber, Robert M.; Rundall, Jill M.

2008-01-01

A vegetation database of the riparian vegetation located within the Colorado River ecosystem (CRE), a subsection of the Colorado River between Glen Canyon Dam and the western boundary of Grand Canyon National Park, was constructed using four-band image mosaics acquired in May 2002. A digital line scanner was flown over the Colorado River corridor in Arizona by ISTAR Americas, using a Leica ADS-40 digital camera to acquire a digital surface model and four-band image mosaics (blue, green, red, and near-infrared) for vegetation mapping. The primary objective of this mapping project was to develop a digital inventory map of vegetation to enable patch- and landscape-scale change detection, and to establish randomized sampling points for ground surveys of terrestrial fauna (principally, but not exclusively, birds). The vegetation base map was constructed through a combination of ground surveys to identify vegetation classes, image processing, and automated supervised classification procedures. Analysis of the imagery and subsequent supervised classification involved multiple steps to evaluate band quality, band ratios, and vegetation texture and density. Identification of vegetation classes involved collection of cover data throughout the river corridor and subsequent analysis using two-way indicator species analysis (TWINSPAN). Vegetation was classified into six vegetation classes, following the National Vegetation Classification Standard, based on cover dominance. This analysis indicated that total area covered by all vegetation within the CRE was 3,346 ha. Considering the six vegetation classes, the sparse shrub (SS) class accounted for the greatest amount of vegetation (627 ha) followed by Pluchea (PLSE) and Tamarix (TARA) at 494 and 366 ha, respectively. The wetland (WTLD) and Prosopis-Acacia (PRGL) classes both had similar areal cover values (227 and 213 ha, respectively). Baccharis-Salix (BAXX) was the least represented at 94 ha. Accuracy assessment of the supervised classification determined that accuracies varied among vegetation classes from 90% to 49%. Causes for low accuracies were similar spectral signatures among vegetation classes. Fuzzy accuracy assessment improved classification accuracies such that Federal mapping standards of 80% accuracies for all classes were met. The scale used to quantify vegetation adequately meets the needs of the stakeholder group. Increasing the scale to meet the U.S. Geological Survey (USGS)-National Park Service (NPS)National Mapping Program's minimum mapping unit of 0.5 ha is unwarranted because this scale would reduce the resolution of some classes (e.g., seep willow/coyote willow would likely be combined with tamarisk). While this would undoubtedly improve classification accuracies, it would not provide the community-level information about vegetation change that would benefit stakeholders. The identification of vegetation classes should follow NPS mapping approaches to complement the national effort and should incorporate the alternative analysis for community identification that is being incorporated into newer NPS mapping efforts. National Vegetation Classification is followed in this report for association- to formation-level categories. Accuracies could be improved by including more environmental variables such as stage elevation in the classification process and incorporating object-based classification methods. Another approach that may address the heterogeneous species issue and classification is to use spectral mixing analysis to estimate the fractional cover of species within each pixel and better quantify the cover of individual species that compose a cover class. Varying flights to capture vegetation at different times of the year might also help separate some vegetation classes, though the cost may be prohibitive. Lastly, photointerpretation instead of automated mapping could be tried. Photointerpretation would likely not improve accuracies in this case, howev

Ecosystem services: Urban parks under a magnifying glass.

PubMed

Mexia, Teresa; Vieira, Joana; Príncipe, Adriana; Anjos, Andreia; Silva, Patrícia; Lopes, Nuno; Freitas, Catarina; Santos-Reis, Margarida; Correia, Otília; Branquinho, Cristina; Pinho, Pedro

2018-01-01

Urban areas' population has grown during the last century and it is expected that over 60% of the world population will live in cities by 2050. Urban parks provide several ecosystem services that are valuable to the well-being of city-dwellers and they are also considered a nature-based solution to tackle multiple environmental problems in cities. However, the type and amount of ecosystem services provided will vary with each park vegetation type, even within same the park. Our main goal was to quantify the trade-offs in ecosystem services associated to different vegetation types, using a spatially detailed approach. Rather than relying solely on general vegetation typologies, we took a more ecologically oriented approach, by explicitly considering different units of vegetation structure and composition. This was demonstrated in a large park (44ha) located in the city of Almada (Lisbon metropolitan area, Portugal), where six vegetation units were mapped in detail and six ecosystem services were evaluated: carbon sequestration, seed dispersal, erosion prevention, water purification, air purification and habitat quality. The results showed that, when looking at the park in detail, some ecosystem services varied greatly with vegetation type. Carbon sequestration was positively influenced by tree density, independently of species composition. Seed dispersal potential was higher in lawns, and mixed forest provided the highest amount of habitat quality. Air purification service was slightly higher in mixed forest, but was high in all vegetation types, probably due to low background pollution, and both water purification and erosion prevention were high in all vegetation types. Knowing the type, location, and amount of ecosystem services provided by each vegetation type can help to improve management options based on ecosystem services trade-offs and looking for win-win situations. The trade-offs are, for example, very clear for carbon: tree planting will boost carbon sequestration regardless of species, but may not be enough to increase habitat quality. Moreover, it may also negatively influence seed dispersal service. Informed practitioners can use this ecological knowledge to promote the role of urban parks as a nature-based solution to provide multiple ecosystem services, and ultimately improve the design and management of the green infrastructure. This will also improve the science of Ecosystem Services, acknowledging that the type of vegetation matters for the provision of ecosystem services and trade-offs analysis. Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.

Lossless Compression of Classification-Map Data

NASA Technical Reports Server (NTRS)

Hua, Xie; Klimesh, Matthew

2009-01-01

A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.

Developing a Carbon Monitoring System For Pinyon-juniper Forests and Woodlands

NASA Astrophysics Data System (ADS)

Falkowski, M. J.; Hudak, A. T.; Fekety, P.; Filippelli, S.

2017-12-01

Pinyon-juniper (PJ) forests and woodlands are the third largest vegetation type in the United States. They cover over 40 million hectares across the western US, representing 40% of the total forest and woodland area in the Intermountain West. Although the density of carbon stored in these ecosystems is relatively low compared to other forest types, the vast area of short stature forests and woodlands (both nationally and globally) make them critical components of regional, national, and global carbon budgets. The overarching goal of this research is to prototype a carbon monitoring, reporting, and verification (MRV) system for characterizing total aboveground biomass stocks and flux across the PJ vegetation gradient in the western United States. We achieve this by combining in situ forest measurements and novel allometric equations with tree measurements derived from high resolution airborne imagery to map aboveground biomass across 500,000 km2 in the Western US. These high-resolution maps of aboveground biomass are then leveraged as training data to predict biomass flux through time from Landsat time-series data. The results from this research highlight the potential in mapping biomass stocks and flux in open forests and woodlands, and could be easily adopted into an MRV framework.

A Multitemporal, Multisensor Approach to Mapping the Canadian Boreal Forest

NASA Astrophysics Data System (ADS)

Reith, Ernest

The main anthropogenic source of CO2 emissions is the combustion of fossil fuels, while the clearing and burning of forests contribute significant amounts as well. Vegetation represents a major reservoir for terrestrial carbon stocks, and improving our ability to inventory vegetation will enhance our understanding of the impacts of land cover and climate change on carbon stocks and fluxes. These relationships may be an indication of a series of troubling biosphere-atmospheric feedback mechanisms that need to be better understood and modeled. Valuable land cover information can be provided to the global climate change modeling community using advanced remote sensing capabilities such as Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR). Individually and synergistically, data were successfully used to characterize the complex nature of the Canadian boreal forest land cover types. The multiple endmember spectral mixture analysis process was applied against seasonal AVIRIS data to produce species-level vegetated land cover maps of two study sites in the Canadian boreal forest: Old Black Spruce (OBS) and Old Jack Pine (OJP). The highest overall accuracy was assessed to be at least 66% accurate to the available reference map, providing evidence that high-quality, species-level land cover mapping of the Canadian boreal forest is achievable at accuracy levels greater than other previous research efforts in the region. Backscatter information from multichannel, polarimetric SAR utilizing a binary decision tree-based classification technique methodology was moderately successfully applied to AIRSAR to produce maps of the boreal land cover types at both sites, with overall accuracies at least 59%. A process, centered around noise whitening and principal component analysis features of the minimum noise fraction transform, was implemented to leverage synergies contained within spatially coregistered multitemporal and multisensor AVIRIS and AIRSAR data sets to successfully produce high-accuracy boreal forest land cover maps. Overall land cover map accuracies of 78% and 72% were assessed for OJP and OBS sites, respectively, for either seasonal or multitemporal data sets. High individual land cover accuracies appeared to be independent of site, season, or multisensor combination in the minimum-noise fraction-based approach.

The 1977 tundra fire at Kokolik River, Alaska

NASA Technical Reports Server (NTRS)

Hall, D.; Brown, J.; Johnson, L.

1981-01-01

During the summer of 1977, fire totaled 44 sq km of tundra vegetation according to measurements using LANDSAT imagery. Based on the experience gained from analysis of this fire using ground observations, satellite imagery, and topographic maps, it appears that natural drainages form effective fire breaks on the subdued relief of the Arctic coastal plain and northern foothills. It is confirmed that the intensity of the fire is related to vegetation type and to the moisture content of the organic rich soils.

Hyperspectral Remote Sensing of Vegetation:Knowledge Gain and Knowledge Gap after 40 years of research

NASA Astrophysics Data System (ADS)

Thenkabail, P. S.; Huete, A. R.

2012-12-01

This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher: Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advancements in processing large volumes of hyperspectral data have generated tremendous interest in expanding the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband spectral data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) the ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reduced uncertainty in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) the ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing wavebands and indices with greater sensitivity for analyzing vegetation characteristics. Current state of knowledge on hyperspectral narrowbands (HNBs) for agricultural and vegetation studies inferred from the Book entitled hyperspectral remote sensing of vegetation by Thenkabail et al., 2011. Six study areas of the World for which we have extensive data from field spectroradiometers for 8 major world crops (wheat, corn, rice, barley, soybeans, pulses, and cotton). Approx. 10,500 such data points will be used in crop modeling and in building spectral libraries.

Evaluation of airborne image data for mapping riparian vegetation within the Grand Canyon

USGS Publications Warehouse

Davis, Philip A.; Staid, Matthew I.; Plescia, Jeffrey B.; Johnson, Jeffrey R.

2002-01-01

This study examined various types of remote-sensing data that have been acquired during a 12-month period over a portion of the Colorado River corridor to determine the type of data and conditions for data acquisition that provide the optimum classification results for mapping riparian vegetation. Issues related to vegetation mapping included time of year, number and positions of wavelength bands, and spatial resolution for data acquisition to produce accurate vegetation maps versus cost of data. Image data considered in the study consisted of scanned color-infrared (CIR) film, digital CIR, and digital multispectral data, whose resolutions from 11 cm (photographic film) to 100 cm (multispectral), that were acquired during the Spring, Summer, and Fall seasons in 2000 for five long-term monitoring sites containing riparian vegetation. Results show that digitally acquired data produce higher and more consistent classification accuracies for mapping vegetation units than do film products. The highest accuracies were obtained from nine-band multispectral data; however, a four-band subset of these data, that did not include short-wave infrared bands, produced comparable mapping results. The four-band subset consisted of the wavelength bands 0.52-0.59 µm, 0.59-0.62 µm, 0.67-0.72 µm, and 0.73-0.85 µm. Use of only three of these bands that simulate digital CIR sensors produced accuracies for several vegetation units that were 10% lower than those obtained using the full multispectral data set. Classification tests using band ratios produced lower accuracies than those using band reflectance for scanned film data; a result attributed to the relatively poor radiometric fidelity maintained by the film scanning process, whereas calibrated multispectral data produced similar classification accuracies using band reflectance and band ratios. This suggests that the intrinsic band reflectance of the vegetation is more important than inter-band reflectance differences in attaining high mapping accuracies. These results also indicate that radiometrically calibrated sensors that record a wide range of radiance produce superior results and that such sensors should be used for monitoring purposes. When texture (spatial variance) at near-infrared wavelength is combined with spectral data in classification, accuracy increased most markedly (20-30%) for the highest resolution (11-cm) CIR film data, but decreased in its effect on accuracy in lower-resolution multi-spectral image data; a result observed in previous studies (Franklin and McDermid 1993, Franklin et al. 2000, 2001). While many classification unit accuracies obtained from the 11-cm film CIR band with texture data were in fact higher than those produced using the 100-cm, nine-band multispectral data with texture, the 11-cm film CIR data produced much lower accuracies than the 100-cm multispectral data for the more sparsely populated vegetation units due to saturation of picture elements during the film scanning process in vegetation units with a high proportion of alluvium. Overall classification accuracies obtained from spectral band and texture data range from 36% to 78% for all databases considered, from 57% to 71% for the 11-cm film CIR data, and from 54% to 78% for the 100-cm multispectral data. Classification results obtained from 20-cm film CIR band and texture data, which were produced by applying a Gaussian filter to the 11-cm film CIR data, showed increases in accuracy due to texture that were similar to those observed using the original 11-cm film CIR data. This suggests that data can be collected at the lower resolution and still retain the added power of vegetation texture. Classification accuracies for the riparian vegetation units examined in this study do not appear to be influenced by season of data acquisition, although data acquired under direct sunlight produced higher overall accuracies than data acquired under overcast conditions. The latter observation, in addition to the importance of band reflectance for classification, implies that data should be acquired near summer solstice when sun elevation and reflectance is highest and when shadows cast by steep canyon walls are minimized.

Study of the relation between soil use, vegetation coverage, and the discharge of sediments from artificial reservoirs using MSS/LANDSAT images. Example: The Tres Marias reservoir and its supply basin

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Sausen, T. M.

1981-01-01

The land use and types of vegetation in the region of the upper Sao Francisco River, Brazil, are identified. This region comprises the supply basin of the Tres Marias reservoir. Imagery from channels 5 and 7 of the LANDSAT multispectral band scanner during wet and rainy seasons and ground truth data were employed to characterize and map the vegetation, land use, and sedimentary discharges from the reservoir. Agricultural and reforested lands, meadows, and forests are identified. Changes in land use due to human activity are demonstrated.

Present, Future, and Novel Bioclimates of the San Francisco, California Region

PubMed Central

Torregrosa, Alicia; Taylor, Maxwell D.; Flint, Lorraine E.; Flint, Alan L.

2013-01-01

Bioclimates are syntheses of climatic variables into biologically relevant categories that facilitate comparative studies of biotic responses to climate conditions. Isobioclimates, unique combinations of bioclimatic indices (continentality, ombrotype, and thermotype), were constructed for northern California coastal ranges based on the Rivas-Martinez worldwide bioclimatic classification system for the end of the 20th century climatology (1971–2000) and end of the 21st century climatology (2070–2099) using two models, Geophysical Fluid Dynamics Laboratory (GFDL) model and the Parallel Climate Model (PCM), under the medium-high A2 emission scenario. The digitally mapped results were used to 1) assess the relative redistribution of isobioclimates and their magnitude of change, 2) quantify the loss of isobioclimates into the future, 3) identify and locate novel isobioclimates projected to appear, and 4) explore compositional change in vegetation types among analog isobioclimate patches. This study used downscaled climate variables to map the isobioclimates at a fine spatial resolution −270 m grid cells. Common to both models of future climate was a large change in thermotype. Changes in ombrotype differed among the two models. The end of 20th century climatology has 83 isobioclimates covering the 63,000 km2 study area. In both future projections 51 of those isobioclimates disappear over 40,000 km2. The ordination of vegetation-bioclimate relationships shows very strong correlation of Rivas-Martinez indices with vegetation distribution and composition. Comparisons of vegetation composition among analog patches suggest that vegetation change will be a local rearrangement of species already in place rather than one requiring long distance dispersal. The digitally mapped results facilitate comparison with other Mediterranean regions. Major remaining challenges include predicting vegetation composition of novel isobioclimates and developing metrics to compare differences in climate space. PMID:23526985

Present, future, and novel bioclimates of the San Francisco, California region

USGS Publications Warehouse

Torregrosa, Alicia; Taylor, Maxwell D.; Flint, Lorraine E.; Flint, Alan L.

2013-01-01

Bioclimates are syntheses of climatic variables into biologically relevant categories that facilitate comparative studies of biotic responses to climate conditions. Isobioclimates, unique combinations of bioclimatic indices (continentality, ombrotype, and thermotype), were constructed for northern California coastal ranges based on the Rivas-Martinez worldwide bioclimatic classification system for the end of the 20th century climatology (1971–2000) and end of the 21st century climatology (2070–2099) using two models, Geophysical Fluid Dynamics Laboratory (GFDL) model and the Parallel Climate Model (PCM), under the medium-high A2 emission scenario. The digitally mapped results were used to 1) assess the relative redistribution of isobioclimates and their magnitude of change, 2) quantify the loss of isobioclimates into the future, 3) identify and locate novel isobioclimates projected to appear, and 4) explore compositional change in vegetation types among analog isobioclimate patches. This study used downscaled climate variables to map the isobioclimates at a fine spatial resolution −270 m grid cells. Common to both models of future climate was a large change in thermotype. Changes in ombrotype differed among the two models. The end of 20th century climatology has 83 isobioclimates covering the 63,000 km2 study area. In both future projections 51 of those isobioclimates disappear over 40,000 km2. The ordination of vegetation-bioclimate relationships shows very strong correlation of Rivas-Martinez indices with vegetation distribution and composition. Comparisons of vegetation composition among analog patches suggest that vegetation change will be a local rearrangement of species already in place rather than one requiring long distance dispersal. The digitally mapped results facilitate comparison with other Mediterranean regions. Major remaining challenges include predicting vegetation composition of novel isobioclimates and developing metrics to compare differences in climate space.

Forest Cover Mapping in Iskandar Malaysia Using Satellite Data

NASA Astrophysics Data System (ADS)

Kanniah, K. D.; Mohd Najib, N. E.; Vu, T. T.

2016-09-01

Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

Mapping vegetation communities using statistical data fusion in the Ozark National Scenic Riverways, Missouri, USA

USGS Publications Warehouse

Chastain, R.A.; Struckhoff, M.A.; He, H.S.; Larsen, D.R.

2008-01-01

A vegetation community map was produced for the Ozark National Scenic Riverways consistent with the association level of the National Vegetation Classification System. Vegetation communities were differentiated using a large array of variables derived from remote sensing and topographic data, which were fused into independent mathematical functions using a discriminant analysis classification approach. Remote sensing data provided variables that discriminated vegetation communities based on differences in color, spectral reflectance, greenness, brightness, and texture. Topographic data facilitated differentiation of vegetation communities based on indirect gradients (e.g., landform position, slope, aspect), which relate to variations in resource and disturbance gradients. Variables derived from these data sources represent both actual and potential vegetation community patterns on the landscape. A hybrid combination of statistical and photointerpretation methods was used to obtain an overall accuracy of 63 percent for a map with 49 vegetation community and land-cover classes, and 78 percent for a 33-class map of the study area.

Chapter 6 - Developing the LANDFIRE Vegetation and Biophysical Settings Map Unit Classifications for the LANDFIRE Prototype Project

Treesearch

Jennifer L. Long; Melanie Miller; James P. Menakis; Robert E. Keane

2006-01-01

The Landscape Fire and Resource Management Planning Tools Prototype Project, or LANDFIRE Prototype Project, required a system for classifying vegetation composition, biophysical settings, and vegetation structure to facilitate the mapping of vegetation and wildland fuel characteristics and the simulation of vegetation dynamics using landscape modeling. We developed...

Evaluating CMIP5 Simulations of Historical Continental Climate with Koeppen Bioclimatic Metrics

NASA Astrophysics Data System (ADS)

Phillips, T. J.; Bonfils, C.

2013-12-01

The classic Koeppen bioclimatic classification scheme associates generic vegetation types (e.g. grassland, tundra, broadleaf or evergreen forests, etc.) with regional climate zones defined by their annual cycles of continental temperature (T) and precipitation (P), considered together. The locations or areas of Koeppen vegetation types derived from observational data thus can provide concise metrical standards for simultaneously evaluating climate simulations of T and P in naturally defined regions. The CMIP5 models' collective ability to correctly represent two variables that are critically important for living organisms at regional scales is therefore central to this evaluation. For this study, 14 Koeppen vegetation types are derived from annual-cycle climatologies of T and P in some 3 dozen CMIP5 simulations of the 1980-1999 period. Metrics for evaluating the ability of the CMIP5 models to simulate the correct locations and areas of each vegetation type, as well as measures of overall model performance, also are developed. It is found that the CMIP5 models are generally most deficient in simulating: 1) climates of drier Koeppen zones (e.g. desert, savanna, grassland, steppe vegetation types) located in the southwestern U.S. and Mexico, eastern Europe, southern Africa, and central Australia; 2) climates of regions such as central Asia and western South America where topography plays a key role. Details of regional T or P biases in selected simulations that exemplify general model performance problems also will be presented. Acknowledgments: This work was funded by the U.S. Department of Energy Office of Science and was performed at the Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Map of Koeppen vegetation types derived from observed T and P.

Mapping urban forest tree species using IKONOS imagery: preliminary results.

PubMed

Pu, Ruiliang

2011-01-01

A stepwise masking system with high-resolution IKONOS imagery was developed to identify and map urban forest tree species/groups in the City of Tampa, Florida, USA. The eight species/groups consist of sand live oak (Quercus geminata), laurel oak (Quercus laurifolia), live oak (Quercus virginiana), magnolia (Magnolia grandiflora), pine (species group), palm (species group), camphor (Cinnamomum camphora), and red maple (Acer rubrum). The system was implemented with soil-adjusted vegetation index (SAVI) threshold, textural information after running a low-pass filter, and brightness threshold of NIR band to separate tree canopies from non-vegetated areas from other vegetation types (e.g., grass/lawn) and to separate the tree canopies into sunlit and shadow areas. A maximum likelihood classifier was used to identify and map forest type and species. After IKONOS imagery was preprocessed, a total of nine spectral features were generated, including four spectral bands, three hue-intensity-saturation indices, one SAVI, and one texture image. The identified and mapped results were examined with independent ground survey data. The experimental results indicate that when classifying all the eight tree species/ groups with the high-resolution IKONOS image data, the identifying accuracy was very low and could not satisfy a practical application level, and when merging the eight species/groups into four major species/groups, the average accuracy is still low (average accuracy = 73%, overall accuracy = 86%, and κ = 0.76 with sunlit test samples). Such a low accuracy of identifying and mapping the urban tree species/groups is attributable to low spatial resolution IKONOS image data relative to tree crown size, to complex and variable background spectrum impact on crown spectra, and to shadow/shaded impact. The preliminary results imply that to improve the tree species identification accuracy and achieve a practical application level in urban area, multi-temporal (multi-seasonal) or hyperspectral data image data should be considered for use in the future.

Multiseasonal and geobotanical approach in remote detection of greisenization areas in the Serra da Pedra Branca Granite, Goias State, Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Almeidafilho, R.

1983-01-01

Multiseasonal analysis of LANDSAT multispectral images in CCT format permitted the mapping of lithologic facies in the Pedra Branca Granite, using geobotanical associations, which occur in the form of variations in the density of cerrado vegetation, as well as the predominance of certain distinctive vegetation species. Dry season images did not show very good results in lithological differentiation due to anomalous illumination conditions related to the low solar elevation and the homogeneity in the vegetation cover, specially the grasses that become dry during this season. Rainy season image, on the other hand, allowed the separation of the lithological types, a fact that can be attributed to a greater differentiation among the geobotanical associations. As a result of this study, the muscovite-granite facies with greisenization zones, which are lithological indicators of important tin mineralization within the Serra da Pedra Branca Granite, were mapped. This methodology can be sucessfully applied to similar known granite bodies elsewhere in the Tin Province of Goias.

Multitemporal and geobotanical approach in the remote detection of Greisenization areas in the Serra da Pedra Branca Granite, Goias State, Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Filho, R. A.

1982-01-01

A multiseasonal analysis of LANDSAT multispectral images in CCT format permitted the mapping of lithologic facies in the Pedra Branca Granite, using geobotanical associations, which occur in the form of variations in the density of the cerrado vegetation, as well as the predominance of certain distinct vegetation species. Dry season images did not show very good results in lithological differentiation due to anomalous illumination conditions related to the low solar elevation and the homogeneity in the vegetation cover, specially the grass that becomes dry during this season. Rainy season images, on the other hand, allowed the separation of the lithological types, a fact that can be attributed to a greater differentiation among the geobotanical associations. The muscovite-granite facies with greisenization zones within the Serra da Pedra Branca were mapped. This methodology can be successfully applied to similar known granite bodies elsewhere in the Tin Province of Goias.

Assessment of vegetation change in a fire-altered forest landscape

NASA Technical Reports Server (NTRS)

Jakubauskas, Mark E.; Lulla, Kamlesh P.; Mausel, Paul W.

1990-01-01

This research focused on determining the degree to which differences in burn severity relate to postfire vegetative cover within a Michigan pine forest. Landsat MSS data from June 1973 and TM data from October 1982 were classified using an unsupervised approach to create prefire and postfire cover maps of the study area. Using a raster-based geographic information system (GIS), the maps were compared, and a map of vegetation change was created. An IR/red band ratio from a June 1980 Landsat scene was classified to create a map of three degres of burn severity, which was then compared with the vegetation change map using a GIS. Classification comparisons of pine and deciduous forest classes (1973 to 1982) revealed that the most change in vegetation occurred in areas subjected to the most intense burn. Two classes of regenerating forest comprised the majority of the change, while the remaining change was associated with shrub vegetation or another forest class.

Processing of airborne laser scanning data to generate accurate DTM for floodplain wetland

NASA Astrophysics Data System (ADS)

Szporak-Wasilewska, Sylwia; Mirosław-Świątek, Dorota; Grygoruk, Mateusz; Michałowski, Robert; Kardel, Ignacy

2015-10-01

Structure of the floodplain, especially its topography and vegetation, influences the overland flow and dynamics of floods which are key factors shaping ecosystems in surface water-fed wetlands. Therefore elaboration of the digital terrain model (DTM) of a high spatial accuracy is crucial in hydrodynamic flow modelling in river valleys. In this study the research was conducted in the unique Central European complex of fens and marshes - the Lower Biebrza river valley. The area is represented mainly by peat ecosystems which according to EU Water Framework Directive (WFD) are called "water-dependent ecosystems". Development of accurate DTM in these areas which are overgrown by dense wetland vegetation consisting of alder forest, willow shrubs, reed, sedges and grass is very difficult, therefore to represent terrain in high accuracy the airborne laser scanning data (ALS) with scanning density of 4 points/m2 was used and the correction of the "vegetation effect" on DTM was executed. This correction was performed utilizing remotely sensed images, topographical survey using the Real Time Kinematic positioning and vegetation height measurements. In order to classify different types of vegetation within research area the object based image analysis (OBIA) was used. OBIA allowed partitioning remotely sensed imagery into meaningful image-objects, and assessing their characteristics through spatial and spectral scale. The final maps of vegetation patches that include attributes of vegetation height and vegetation spectral properties, utilized both the laser scanning data and the vegetation indices developed on the basis of airborne and satellite imagery. This data was used in process of segmentation, attribution and classification. Several different vegetation indices were tested to distinguish different types of vegetation in wetland area. The OBIA classification allowed correction of the "vegetation effect" on DTM. The final digital terrain model was compared and examined within distinguished land cover classes (formed mainly by natural vegetation of the river valley) with archival height models developed through interpolation of ground points measured with GPS RTK and also with elevation models from the ASTER-GDEM and SRTM programs. The research presented in this paper allowed improving quality of hydrodynamic modelling in the surface water-fed wetlands protected within Biebrza National Park. Additionally, the comparison with other digital terrain models allowed to demonstrate the importance of accurate topography products in such modelling. The ALS data also significantly improved the accuracy and actuality of the river Biebrza course, its tributaries and location of numerous oxbows typical in this part of the river valley in comparison to previously available data. This type of data also helped to refine the river valley cross-sections, designate river banks and to develop the slope map of the research area.

Mapping agroecological zones and time lag in vegetation growth by means of Fourier analysis of time series of NDVI images

NASA Technical Reports Server (NTRS)

Menenti, M.; Azzali, S.; Verhoef, W.; Van Swol, R.

1993-01-01

Examples are presented of applications of a fast Fourier transform algorithm to analyze time series of images of Normalized Difference Vegetation Index values. The results obtained for a case study on Zambia indicated that differences in vegetation development among map units of an existing agroclimatic map were not significant, while reliable differences were observed among the map units obtained using the Fourier analysis.

Comparative mineral mapping in the Colorado Mineral Belt using AVIRIS and ASTER remote sensing data

USGS Publications Warehouse

Rockwell, Barnaby W.

2013-01-01

This report presents results of interpretation of spectral remote sensing data covering the eastern Colorado Mineral Belt in central Colorado, USA, acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors. This study was part of a multidisciplinary mapping and data integration project at the U.S. Geological Survey that focused on long-term resource planning by land-managing entities in Colorado. The map products were designed primarily for the regional mapping and characterization of exposed surface mineralogy, including that related to hydrothermal alteration and supergene weathering of pyritic rocks. Alteration type was modeled from identified minerals based on standard definitions of alteration mineral assemblages. Vegetation was identified using the ASTER data and subdivided based on per-pixel chlorophyll content (depth of 0.68 micrometer absorption band) and dryness (fit and depth of leaf biochemical absorptions in the shortwave infrared spectral region). The vegetation results can be used to estimate the abundance of fire fuels at the time of data acquisition (2002 and 2003). The AVIRIS- and ASTER-derived mineral mapping results can be readily compared using the toggleable layers in the GeoPDF file, and by using the provided GIS-ready raster datasets. The results relating to mineral occurrence and distribution were an important source of data for studies documenting the effects of mining and un-mined, altered rocks on aquatic ecosystems at the watershed level. These studies demonstrated a high correlation between metal concentrations in streams and the presence of hydrothermal alteration and (or) pyritic mine waste as determined by analysis of the map products presented herein. The mineral mapping results were also used to delineate permissive areas for various mineral deposit types.

Habitat types of the Tenderfoot Creek Experimental Forest

Treesearch

David M. Ondov

1975-01-01

In May 1974, a review draft of the Forest Habitat Types of Montana (Pfister et al. 1974) was released for use by Forest Service personnel and others requiring a method of ecosystem classification as a means to stratify forest environments in Montana. With the use of this review draft in mind, an objective was outlined to develop a vegetation map of the Tenderfoot Creek...

The LANDFIRE Total Fuel Change Tool (ToFuΔ) user’s guide

USGS Publications Warehouse

Smail, Tobin; Martin, Charley; Napoli, Jim

2011-01-01

LANDFIRE fuel data were originally developed from coarse-scale existing vegetation type, existing vegetation cover, existing vegetation height, and biophysical setting layers. Fire and fuel specialists from across the country provided input to the original LANDFIRE National (LF_1.0.0) fuel layers to help calibrate fuel characteristics on a more localized scale. The LANDFIRE Total Fuel Change Tool (ToFu∆) was developed from this calibration process. Vegetation is subject to constant change – and fuels are therefore also dynamic, necessitating a systematic method for reflecting changes spatially so that fire behavior can be accurately accessed. ToFuΔ allows local experts to quickly produce maps that spatially display any proposed fuel characteristics changes. ToFu∆ works through a Microsoft Access database to produce spatial results in ArcMap based on rule sets devised by the user that take into account the existing vegetation type (EVT), existing vegetation cover (EVC), existing vegetation height (EVH), and biophysical setting (BpS) from the LANDFIRE grid data. There are also options within ToFu∆ to add discrete variables in grid format through use of the wildcard option and for subdividing specific areas for different fuel characteristic assignments through the BpS grid. The ToFu∆ user determines the size of the area for assessment by defining a Management Unit, or “MU.” User-defined rule sets made up of EVT, EVC, EVH, and BpS layers, as well as any wildcard selections, are used to change or refine fuel characteristics within the MU. Once these changes have been made to the fuel characteristics, new grids are created for fire behavior analysis or planning. These grids represent the most common ToFu∆ output. ToFuΔ is currently under development and will continue to be updated in the future. The current beta version (0.12), released in March 2011, is compatible with Windows 7 and will be the last release until the fall of 2011.

Recovery and archiving key Arctic Alaska vegetation map and plot data for the Arctic-Boreal Vulnerability Field Experiment (ABoVE)

NASA Astrophysics Data System (ADS)

Walker, D. A.; Breen, A. L.; Broderson, D.; Epstein, H. E.; Fisher, W.; Grunblatt, J.; Heinrichs, T.; Raynolds, M. K.; Walker, M. D.; Wirth, L.

2013-12-01

Abundant ground-based information will be needed to inform remote-sensing and modeling studies of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE). A large body of plot and map data collected by the Alaska Geobotany Center (AGC) and collaborators from the Arctic regions of Alaska and the circumpolar Arctic over the past several decades is being archived and made accessible to scientists and the public via the Geographic Information Network of Alaska's (GINA's) 'Catalog' display and portal system. We are building two main types of data archives: Vegetation Plot Archive: For the plot information we use a Turboveg database to construct the Alaska portion of the international Arctic Vegetation Archive (AVA) http://www.geobotany.uaf.edu/ava/. High quality plot data and non-digital legacy datasets in danger of being lost have highest priority for entry into the archive. A key aspect of the database is the PanArctic Species List (PASL-1), developed specifically for the AVA to provide a standard of species nomenclature for the entire Arctic biome. A wide variety of reports, documents, and ancillary data are linked to each plot's geographic location. Geoecological Map Archive: This database includes maps and remote sensing products and links to other relevant data associated with the maps, mainly those produced by the Alaska Geobotany Center. Map data include GIS shape files of vegetation, land-cover, soils, landforms and other categorical variables and digital raster data of elevation, multispectral satellite-derived data, and data products and metadata associated with these. The map archive will contain all the information that is currently in the hierarchical Toolik-Arctic Geobotanical Atlas (T-AGA) in Alaska http://www.arcticatlas.org, plus several additions that are in the process of development and will be combined with GINA's already substantial holdings of spatial data from northern Alaska. The Geoecological Atlas Portal uses GINA's Catalog tool to develop a web interface to view and access the plot and map data. The mapping portal allows visualization of GIS data, sample-point locations and imagery and access to the map data. Catalog facilitates the discovery and dissemination of science-based information products in support of analysis and decision-making concerned with development and climate change and is currently used by GINA in several similar archive/distribution portals.

Understanding the spatial distribution of eroded areas in the former rural homelands of South Africa: Comparative evidence from two new non-commercial multispectral sensors

NASA Astrophysics Data System (ADS)

Sepuru, Terrence Koena; Dube, Timothy

2018-07-01

In this study, we determine the most suitable multispectral sensor that can accurately detect and map eroded areas from other land cover types in Sekhukhune rural district, Limpopo Province, South Africa. Specifically, the study tested the ability of multi-date (wet and dry season) Landsat 8 OLI and Sentinel-2 MSI images in detecting and mapping eroded areas. The implementation was done, using a robust non-parametric classification ensemble: Discriminant Analysis (DA). Three sets of analysis were applied (Analysis 1: Spectral bands as independent dataset; Analysis 2: Spectral vegetation indices as independent and Analysis 3: Combined spectral bands and spectral vegetation indices). Overall classification accuracies ranging between 80% to 81.90% for MSI and 75.71%-80.95% for OLI were derived for the wet and dry season, respectively. The integration of spectral bands and spectral vegetation indices showed that Sentinel-2 (OA = 83, 81%), slightly performed better than Landsat 8, with 82, 86%. The use of bands and vegetation indices as independent dataset resulted in slightly weaker results for both sensors. Sentinel-2 MSI bands located in the NIR (0.785-0.900 μm), red edge (0.698-0.785 μm) and SWIR (1.565-2.280 μm) regions were selected as the most optimal for discriminating degraded soils from other land cover types. However, for Landsat 8OLI, only the SWIR (1.560-2.300 μm), NIR (0.845-0.885 μm) region were selected as the best regions. Of the eighteen spectral vegetation indices computed, NDVI and SAVI and SAVI and Global Environmental Monitoring Index (GEMI) were ranked selected as the most suitable for detecting and mapping soil erosion. Additionally, SRTM DEM derived information illustrates that for both sensors eroded areas occur on sites that are 600 m and 900 m of altitude with similar trends observed in both dry and wet season maps. Findings of this work emphasize the importance of free and readily available new generation sensors in continuous landscape-scale soil erosion monitoring. Besides, such information can help to identify hotspots and potentially vulnerable areas, as well as aid in developing possible control and mitigation measures.

Global sampling of the seasonal changes in vegetation biophysical properties and associated carbon flux dynamics: using the synergy of information captured by spectral time series

NASA Astrophysics Data System (ADS)

Campbell, P. K. E.; Huemmrich, K. F.; Middleton, E.; Voorhis, S.; Landis, D.

2016-12-01

Spatial heterogeneity and seasonal dynamics in vegetation function contribute significantly to the uncertainties in regional and global CO2 budgets. High spectral resolution imaging spectroscopy ( 10 nm, 400-2500 nm) provides an efficient tool for synoptic evaluation of the factors significantly affecting the ability of the vegetation to sequester carbon and to reflect radiation, due to changes in vegetation chemical and structural composition. EO-1 Hyperion has collected more than 15 years of repeated observations for vegetation studies, and currently Hyperion time series are available for study of vegetation carbon dynamics at a number of FLUX sites. This study presents results from the analysis of EO-1 Hyperion and FLUX seasonal composites for a range of ecosystems across the globe. Spectral differences and seasonal trends were evaluated for each vegetation type and specific phenology. Evaluating the relationships between CO2 flux parameters (e.g., Net ecosystem production - NEP; Gross Ecosystem Exchange - GEE, CO2 flux, μmol m-2 s-1) and spectral parameters for these very different ecosystems, high correlations were established to parameters associated with canopy water and chlorophyll content for deciduous, and photosynthetic function for conifers. Imaging spectrometry provided high spatial resolution maps of CO2 fluxes absorbed by vegetation, and was efficient in tracing seasonal flux dynamics. This study will present examples for key ecosystem tipes to demonstrate the ability of imaging spectrometry and EO-1 Hyperion to map and compare CO2 flux dynamics across the globe.

Vegetation database for land-cover mapping, Clark and Lincoln Counties, Nevada

USGS Publications Warehouse

Charlet, David A.; Damar, Nancy A.; Leary, Patrick J.

2014-01-01

Floristic and other vegetation data were collected at 3,175 sample sites to support land-cover mapping projects in Clark and Lincoln Counties, Nevada, from 2007 to 2013. Data were collected at sample sites that were selected to fulfill mapping priorities by one of two different plot sampling approaches. Samples were described at the stand level and classified into the National Vegetation Classification hierarchy at the alliance level and above. The vegetation database is presented in geospatial and tabular formats.

Bolivian satellite technology program on ERTS natural resources

NASA Technical Reports Server (NTRS)

Brockmann, H. C. (Principal Investigator); Bartoluccic C., L.; Hoffer, R. M.; Levandowski, D. W.; Ugarte, I.; Valenzuela, R. R.; Urena E., M.; Oros, R.

1977-01-01

The author has identified the following significant results. Application of digital classification for mapping land use permitted the separation of units at more specific levels in less time. A correct classification of data in the computer has a positive effect on the accuracy of the final products. Land use unit comparison with types of soils as represented by the colors of the coded map showed a class relation. Soil types in relation to land cover and land use demonstrated that vegetation was a positive factor in soils classification. Groupings of image resolution elements (pixels) permit studies of land use at different levels, thereby forming parameters for the classification of soils.

EVALUATION OF LOW-SUN ILLUMINATED LANDSAT-4 THEMATIC MAPPER DATA FOR MAPPING HYDROTHERMALLY ALTERED ROCKS IN SOUTHERN NEVADA.

USGS Publications Warehouse

Podwysocki, Melvin H.; Power, Marty S.; Salisbury, Jack; Jones, O.D.

1984-01-01

Landsat-4 Thematic Mapper (TM) data of southern Nevada collected under conditions of low-angle solar illumination were digitally processed to identify hydroxyl-bearing minerals commonly associated with hydrothermal alteration in volcanic terrains. Digital masking procedures were used to exclude shadow areas and vegetation and thus to produce a CRC image suitable for testing the new TM bands as a means to map hydrothermally altered rocks. Field examination of a masked CRC image revealed that several different types of altered rocks displayed hues associated with spectral characteristics common to hydroxyl-bearing minerals. Several types of unaltered rocks also displayed similar hues.

Use of slope, aspect, and elevation maps derived from digital elevation model data in making soil surveys

USGS Publications Warehouse

Klingebiel, A.A.; Horvath, E.H.; Moore, D.G.; Reybold, W.U.

1987-01-01

Maps showing different classes of slope, aspect, and elevation were developed from U.S. Geological Survey digital elevation model data. The classes were displayed on clear Mylar at 1:24 000-scale and registered with topographic maps and orthophotos. The maps were used with aerial photographs, topographic maps, and other resource data to determine their value in making order-three soil surveys. They were tested on over 600 000 ha in Wyoming, Idaho, and Nevada under various climatic and topographic conditions. Field evaluations showed that the maps developed from digital elevation model data were accurate, except for slope class maps where slopes were <4%. The maps were useful to soil scientists, especially where (i) class boundaries coincided with soil changes, landform delineations, land use and management separations, and vegetation changes, and (ii) rough terrain and dense vegetation made it difficult to traverse the area. In hot, arid areas of sparse vegetation, the relationship of slope classes to kinds of soil and vegetation was less significant.

A classification of forest environments in the south Umpqua Basin.

Treesearch

Don Minore

1972-01-01

Forest environments are classified by elevation, temperature, moisture, potential solar radiation, and soil type. Broad elevation classes are derived from topographic maps or altimeter measurements, measured temperature and moisture conditions are related to vegetation by using plant indicator species (illustrated), and tabular values are employed in estimating...

Mapping wetland species and the impact of oil from the Deep Horizon using the Airborne/Visible Imaging Spectrometer and Multiple Endmember Spectral Mixture Analysis

NASA Astrophysics Data System (ADS)

Roberts, D. A.; Beland, M.; Kokaly, R. F.; Couvillion, B.; Ustin, S.; Peterson, S.

2011-12-01

Between April 20, 2010 and July 15, 2010 an estimated 4.4 million barrels of oil leaked from the Maconda well, making the Deep Horizon oil spill the largest in US history. In response to a need to determine the distribution of wetland plant species and quantify their condition prior to, during and after oil reached the shore, the Airborne/Visible Infrared Imaging Spectrometer (AVIRIS) was deployed multiple times in the gulf on high altitude and low altitude airborne platforms. Significant research questions included 1) What is the distribution of key wetland species in the impacted area?; 2) which areas were impacted by oil, when and to what extent?; 3) how much oil must be present to be detected in various cover types? and 4) which wetland species are more sensitive to oil? In an effort to answer some of these questions, we applied Multiple Endmember Spectral Mixture Analysis (MESMA) to AVIRIS data acquired prior to significant impacts in May, 2010 and after oil had reached wetlands in late summer and fall, 2010. Reference polygons for species dominants were located on the images and used to build a spectral library for all dominant wetland species and surface types. This spectral library was augmented by field spectra, acquired using a contact probe for senesced plants materials and beach sands. Spectra of heavily oiled surfaces were identified using the Hydrocarbon Index to identify potential oil endmembers and the Cellulose Absorption Index to discriminate oil from Non-photosynthetic Vegetation (NPV). Wetland species and cover fractions for Green Vegetation (GV), NPV, soils/beaches, oil and water were mapped using MESMA applied to images acquired in the Birds Foot Delta, Chandeleur Islands and Barataria Bay. Species maps, showing dominant species such as Phragmites australis, Spartina alternifolia and S. patens proved to be accurate. OIl was mapped along coastal areas of Barataria Bay, expressed as high oil fractions. However, significant confusion was also observed between oiled vegetation and senesced vegetation, either resulting from oil-induced mortality or natural senescence.

Evaluating the potential of vegetation indices for winter wheat LAI estimation under different fertilization and water conditions

NASA Astrophysics Data System (ADS)

Xie, Qiaoyun; Huang, Wenjiang; Dash, Jadunandan; Song, Xiaoyu; Huang, Linsheng; Zhao, Jinling; Wang, Renhong

2015-12-01

Leaf area index (LAI) is an important indicator for monitoring crop growth conditions and forecasting grain yield. Many algorithms have been developed for remote estimation of the leaf area index of vegetation, such as using spectral vegetation indices, inversion of radiative transfer models, and supervised learning techniques. Spectral vegetation indices, mathematical combination of reflectance bands, are widely used for LAI estimation due to their computational simplicity and their applications ranged from the leaf scale to the entire globe. However, in many cases, their applicability is limited to specific vegetation types or local conditions due to species specific nature of the relationship used to transfer the vegetation indices to LAI. The overall objective of this study is to investigate the most suitable vegetation index for estimating winter wheat LAI under eight different types of fertilizer and irrigation conditions. Regression models were used to estimate LAI using hyperspectral reflectance data from the Pushbroom Hyperspectral Imager (PHI) and in-situ measurements. Our results showed that, among six vegetation indices investigated, the modified soil-adjusted vegetation index (MSAVI) and the normalized difference vegetation index (NDVI) exhibited strong and significant relationships with LAI, and thus were sensitive across different nitrogen and water treatments. The modified triangular vegetation index (MTVI2) confirmed its potential on crop LAI estimation, although second to MSAVI and NDVI in our study. The enhanced vegetation index (EVI) showed moderate performance. However, the ratio vegetation index (RVI) and the modified simple ratio index (MSR) predicted the least accurate estimations of LAI, exposing the simple band ratio index's weakness under different treatment conditions. The results support the use of vegetation indices for a quick and effective LAI mapping procedure that is suitable for winter wheat under different management practices.

A study to explore the use of orbital remote sensing to determine native arid plant distribution. [Arizona

NASA Technical Reports Server (NTRS)

Mcginnies, W. G.; Haase, E. F. (Principal Investigator); Musick, H. B. (Compiler)

1973-01-01

The author has identified the following significant results. Ground truth spectral signature data for various types of scenes, including ground with and without annuals, and various shrubs, were collected. When these signature data are plotted with infrared (MSS band 6 or 7) reflectivity on one axis and red (MSS band 5) reflectivity on the other axis, clusters of data from the various types of scenes are distinct. This method of expressing spectral signature data appears to be more useful for distinguishing types of scenes than a simple infrared to red reflectivity ration. Large areas of varnished desert pavement are visible and mappable on ERTS-1 and high altitude aircraft imagery. A large scale vegetation pattern was found to be correlated with the presence of the desert pavement. The large scale correlation was used in mapping the vegetation of the area. It was found that a distinctive soil type was associated with the presence of the varnished desert pavement. The high salinity and exchangeable sodium percentage of this soil type provide a basis for the explanation of both the large scale and small scale vegetation pattern.

Amazonas project: Application of remote sensing techniques for the integrated survey of natural resources in Amazonas. [Brazil

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator)

1981-01-01

The use of LANDSAT multispectral scanner and return beam vidicon imagery for surveying the natural resources of the Brazilian Amazonas is described. Purposes of the Amazonas development project are summarized. The application of LANDSAT imagery to identification of vegetation coverage and soil use, identification of soil types, geomorphology, and geology and highway planning is discussed. An evaluation of the worth of LANDSAT imagery in mapping the region is presented. Maps generated by the project are included.

Conflation and aggregation of spatial data improve predictive models for species with limited habitats: a case of the threatened yellow-billed cuckoo in Arizona, USA

USGS Publications Warehouse

Villarreal, Miguel L.; van Riper, Charles; Petrakis, Roy E.

2013-01-01

Riparian vegetation provides important wildlife habitat in the Southwestern United States, but limited distributions and spatial complexity often leads to inaccurate representation in maps used to guide conservation. We test the use of data conflation and aggregation on multiple vegetation/land-cover maps to improve the accuracy of habitat models for the threatened western yellow-billed cuckoo (Coccyzus americanus occidentalis). We used species observations (n = 479) from a state-wide survey to develop habitat models from 1) three vegetation/land-cover maps produced at different geographic scales ranging from state to national, and 2) new aggregate maps defined by the spatial agreement of cover types, which were defined as high (agreement = all data sets), moderate (agreement ≥ 2), and low (no agreement required). Model accuracies, predicted habitat locations, and total area of predicted habitat varied considerably, illustrating the effects of input data quality on habitat predictions and resulting potential impacts on conservation planning. Habitat models based on aggregated and conflated data were more accurate and had higher model sensitivity than original vegetation/land-cover, but this accuracy came at the cost of reduced geographic extent of predicted habitat. Using the highest performing models, we assessed cuckoo habitat preference and distribution in Arizona and found that major watersheds containing high-probably habitat are fragmented by a wide swath of low-probability habitat. Focus on riparian restoration in these areas could provide more breeding habitat for the threatened cuckoo, offset potential future habitat losses in adjacent watershed, and increase regional connectivity for other threatened vertebrates that also use riparian corridors.

Accuracy assessment of vegetation community maps generated by aerial photography interpretation: perspective from the tropical savanna, Australia

NASA Astrophysics Data System (ADS)

Lewis, Donna L.; Phinn, Stuart

2011-01-01

Aerial photography interpretation is the most common mapping technique in the world. However, unlike an algorithm-based classification of satellite imagery, accuracy of aerial photography interpretation generated maps is rarely assessed. Vegetation communities covering an area of 530 km2 on Bullo River Station, Northern Territory, Australia, were mapped using an interpretation of 1:50,000 color aerial photography. Manual stereoscopic line-work was delineated at 1:10,000 and thematic maps generated at 1:25,000 and 1:100,000. Multivariate and intuitive analysis techniques were employed to identify 22 vegetation communities within the study area. The accuracy assessment was based on 50% of a field dataset collected over a 4 year period (2006 to 2009) and the remaining 50% of sites were used for map attribution. The overall accuracy and Kappa coefficient for both thematic maps was 66.67% and 0.63, respectively, calculated from standard error matrices. Our findings highlight the need for appropriate scales of mapping and accuracy assessment of aerial photography interpretation generated vegetation community maps.

SACRIFICING THE ECOLOGICAL RESOLUTION OF VEGETATION MAPS AT THE ALTAR OF THEMATIC ACCURACY: ASSESSED MAP ACCURACIES FOR HIERARCHICAL VEGETATION CLASSIFICATIONS IN THE EASTERN GREAT BASIN OF THE SOUTHWEST REGIONAL GAP ANALYSIS PROJECT (SW REGAP)

EPA Science Inventory

The Southwest Regional Gap Analysis Project (SW ReGAP) improves upon previous GAP projects conducted in Arizona, Colorado, Nevada, New Mexico, and Utah to provide a
consistent, seamless vegetation map for this large and ecologically diverse geographic region. Nevada's compone...

Mapping Collective Identity: Territories and Boundaries of Human Terrain

DTIC Science & Technology

2011-06-10

Line MAP-HT Mapping the Human Terrain NDVI Normalized Difference Vegetation Index NGA National Geospatial-Intelligence Agency xi OBIA Object-Based...The Normalized Difference Vegetation Index ( NDVI ) uses the red band to represent the low reflectance from vegetation and the expanded near infrared...spectrum to provide greater delineation of agricultural areas. This layer highlights different fields, crops, and their boundaries. NDVI layers are

Topography and vegetation as predictors of snow water equivalent across the alpine treeline ecotone at Lee Ridge, Glacier National Park, Montana, U.S.A.

USGS Publications Warehouse

Geddes, C.A.; Brown, D.G.; Fagre, D.B.

2005-01-01

We derived and implemented two spatial models of May snow water equivalent (SWE) at Lee Ridge in Glacier National Park, Montana. We used the models to test the hypothesis that vegetation structure is a control on snow redistribution at the alpine treeline ecotone (ATE). The statistical models were derived using stepwise and "best" subsets regression techniques. The first model was derived from field measurements of SWE, topography, and vegetation taken at 27 sample points. The second model was derived using GIS-based measures of topography and vegetation. Both the field- (R² = 0.93) and GIS-based models (R² = 0.69) of May SWE included the following variables: site type (based on vegetation), elevation, maximum slope, and general slope aspect. Site type was identified as the most important predictor of SWE in both models, accounting for 74.0% and 29.5% of the variation, respectively. The GIS-based model was applied to create a predictive map of SWE across Lee Ridge, predicting little snow accumulation on the top of the ridge where vegetation is scarce. The GIS model failed in large depressions, including ephemeral stream channels. The models supported the hypothesis that upright vegetation has a positive effect on accumulation of SWE above and beyond the effects of topography. Vegetation, therefore, creates a positive feedback in which it modifies its, environment and could affect the ability of additional vegetation to become established.

High-fidelity national carbon mapping for resource management and REDD+

PubMed Central

2013-01-01

Background High fidelity carbon mapping has the potential to greatly advance national resource management and to encourage international action toward climate change mitigation. However, carbon inventories based on field plots alone cannot capture the heterogeneity of carbon stocks, and thus remote sensing-assisted approaches are critically important to carbon mapping at regional to global scales. We advanced a high-resolution, national-scale carbon mapping approach applied to the Republic of Panama – one of the first UN REDD + partner countries. Results Integrating measurements of vegetation structure collected by airborne Light Detection and Ranging (LiDAR) with field inventory plots, we report LiDAR-estimated aboveground carbon stock errors of ~10% on any 1-ha land parcel across a wide range of ecological conditions. Critically, this shows that LiDAR provides a highly reliable replacement for inventory plots in areas lacking field data, both in humid tropical forests and among drier tropical vegetation types. We then scale up a systematically aligned LiDAR sampling of Panama using satellite data on topography, rainfall, and vegetation cover to model carbon stocks at 1-ha resolution with estimated average pixel-level uncertainty of 20.5 Mg C ha-1 nationwide. Conclusions The national carbon map revealed strong abiotic and human controls over Panamanian carbon stocks, and the new level of detail with estimated uncertainties for every individual hectare in the country sets Panama at the forefront in high-resolution ecosystem management. With this repeatable approach, carbon resource decision-making can be made on a geospatially explicit basis, enhancing human welfare and environmental protection. PMID:23866822

A Machine Learning and Cross-Validation Approach for the Discrimination of Vegetation Physiognomic Types Using Satellite Based Multispectral and Multitemporal Data.

PubMed

Sharma, Ram C; Hara, Keitarou; Hirayama, Hidetake

2017-01-01

This paper presents the performance and evaluation of a number of machine learning classifiers for the discrimination between the vegetation physiognomic classes using the satellite based time-series of the surface reflectance data. Discrimination of six vegetation physiognomic classes, Evergreen Coniferous Forest, Evergreen Broadleaf Forest, Deciduous Coniferous Forest, Deciduous Broadleaf Forest, Shrubs, and Herbs, was dealt with in the research. Rich-feature data were prepared from time-series of the satellite data for the discrimination and cross-validation of the vegetation physiognomic types using machine learning approach. A set of machine learning experiments comprised of a number of supervised classifiers with different model parameters was conducted to assess how the discrimination of vegetation physiognomic classes varies with classifiers, input features, and ground truth data size. The performance of each experiment was evaluated by using the 10-fold cross-validation method. Experiment using the Random Forests classifier provided highest overall accuracy (0.81) and kappa coefficient (0.78). However, accuracy metrics did not vary much with experiments. Accuracy metrics were found to be very sensitive to input features and size of ground truth data. The results obtained in the research are expected to be useful for improving the vegetation physiognomic mapping in Japan.

Digital classification of Landsat data for vegetation and land-cover mapping in the Blackfoot River watershed, southeastern Idaho

USGS Publications Warehouse

Pettinger, L.R.

1982-01-01

This paper documents the procedures, results, and final products of a digital analysis of Landsat data used to produce a vegetation and landcover map of the Blackfoot River watershed in southeastern Idaho. Resource classes were identified at two levels of detail: generalized Level I classes (for example, forest land and wetland) and detailed Levels II and III classes (for example, conifer forest, aspen, wet meadow, and riparian hardwoods). Training set statistics were derived using a modified clustering approach. Environmental stratification that separated uplands from lowlands improved discrimination between resource classes having similar spectral signatures. Digital classification was performed using a maximum likelihood algorithm. Classification accuracy was determined on a single-pixel basis from a random sample of 25-pixel blocks. These blocks were transferred to small-scale color-infrared aerial photographs, and the image area corresponding to each pixel was interpreted. Classification accuracy, expressed as percent agreement of digital classification and photo-interpretation results, was 83.0:t 2.1 percent (0.95 probability level) for generalized (Level I) classes and 52.2:t 2.8 percent (0.95 probability level) for detailed (Levels II and III) classes. After the classified images were geometrically corrected, two types of maps were produced of Level I and Levels II and III resource classes: color-coded maps at a 1:250,000 scale, and flatbed-plotter overlays at a 1:24,000 scale. The overlays are more useful because of their larger scale, familiar format to users, and compatibility with other types of topographic and thematic maps of the same scale.

Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image.

PubMed

Rapinel, Sébastien; Clément, Bernard; Magnanon, Sylvie; Sellin, Vanessa; Hubert-Moy, Laurence

2014-11-01

Identification and mapping of natural vegetation are major issues for biodiversity management and conservation. Remotely sensed data with very high spatial resolution are currently used to study vegetation, but most satellite sensors are limited to four spectral bands, which is insufficient to identify some natural vegetation formations. The study objectives are to discriminate natural vegetation and identify natural vegetation formations using a Worldview-2 satellite image. The classification of the Worldview-2 image and ancillary thematic data was performed using a hybrid pixel-based and object-oriented approach. A hierarchical scheme using three levels was implemented, from land cover at a field scale to vegetation formation. This method was applied on a 48 km² site located on the French Atlantic coast which includes a classified NATURA 2000 dune and marsh system. The classification accuracy was very high, the Kappa index varying between 0.90 and 0.74 at land cover and vegetation formation levels respectively. These results show that Wordlview-2 images are suitable to identify natural vegetation. Vegetation maps derived from Worldview-2 images are more detailed than existing ones. They provide a useful medium for environmental management of vulnerable areas. The approach used to map natural vegetation is reproducible for a wider application by environmental managers. Copyright © 2014 Elsevier Ltd. All rights reserved.

The long-term legacy of geomorphic and riparian vegetation feedbacks on the dammed Bill Williams River, Arizona, USA

USGS Publications Warehouse

Kui, Li; Stella, John C.; Shafroth, Patrick B.; House, P. Kyle; Wilcox, Andrew C.

2017-01-01

On alluvial rivers, fluvial landforms and riparian vegetation communities codevelop as a result of feedbacks between plants and abiotic processes. The influence of vegetation on river channel and floodplain geomorphology can be particularly strong on dammed rivers with altered hydrology and reduced flood disturbance. We used a 56-year series of aerial photos on the dammed Bill Williams River (Arizona, USA) to investigate how (a) different woody riparian vegetation types influence river channel planform and (b) how different fluvial landforms drive the composition of riparian plant communities over time. We mapped vegetation types and geomorphic surfaces and quantified how relations between fluvial and biotic processes covaried over time using linear mixed models. In the decades after the dam was built, woody plant cover within the river's bottomland nearly doubled, narrowing the active channel by 60% and transforming its planform from wide and braided to a single thread and more sinuous channel. Compared with native cottonwood–willow vegetation, nonnative tamarisk locally induced a twofold greater reduction in channel braiding. Vegetation expanded at different rates depending on the type of landform, with tamarisk cover on former high-flow channels increasing 17% faster than cottonwood–willow. Former low-flow channels with frequent inundation supported a greater increase in cottonwood–willow relative to tamarisk. These findings give insight into how feedbacks between abiotic and biotic processes in river channels accelerate and fortify changes triggered by dam construction, creating river systems increasingly distinct from predam ecological communities and landforms, and progressively more resistant to restoration of predam forms and processes.

Carbon stock loss from deforestation through 2013 in Brazilian Amazonia.

PubMed

Nogueira, Euler Melo; Yanai, Aurora M; Fonseca, Frederico O R; Fearnside, Philip Martin

2015-03-01

The largest carbon stock in tropical vegetation is in Brazilian Amazonia. In this ~5 million km(2) area, over 750,000 km(2) of forest and ~240,000 km(2) of nonforest vegetation types had been cleared through 2013. We estimate current carbon stocks and cumulative gross carbon loss from clearing of premodern vegetation in Brazil's 'Legal Amazonia' and 'Amazonia biome' regions. Biomass of 'premodern' vegetation (prior to major increases in disturbance beginning in the 1970s) was estimated by matching vegetation classes mapped at a scale of 1 : 250,000 and 29 biomass means from 41 published studies for vegetation types classified as forest (2317 1-ha plots) and as either nonforest or contact zones (1830 plots and subplots of varied size). Total biomass (above and below-ground, dry weight) underwent a gross reduction of 18.3% in Legal Amazonia (13.1 Pg C) and 16.7% in the Amazonia biome (11.2 Pg C) through 2013, excluding carbon loss from the effects of fragmentation, selective logging, fires, mortality induced by recent droughts and clearing of forest regrowth. In spite of the loss of carbon from clearing, large amounts of carbon were stored in stands of remaining vegetation in 2013, equivalent to 149 Mg C ha(-1) when weighted by the total area covered by each vegetation type in Legal Amazonia. Native vegetation in Legal Amazonia in 2013 originally contained 58.6 Pg C, while that in the Amazonia biome contained 56 Pg C. Emissions per unit area from clearing could potentially be larger in the future because previously cleared areas were mainly covered by vegetation with lower mean biomass than the remaining vegetation. Estimates of original biomass are essential for estimating losses to forest degradation. This study offers estimates of cumulative biomass loss, as well as estimates of premodern carbon stocks that have not been represented in recent estimates of deforestation impacts. © 2014 John Wiley & Sons Ltd.

Effectiveness of Vegetation Index Transformation for Land Use Identifying and Mapping in the Area of Oil palm Plantation based on SPOT-6 Imagery (Case Study: PT.Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu)

NASA Astrophysics Data System (ADS)

Setyowati, H. A.; S, S. H. Murti B.; Sukentyas, E. S.

2016-11-01

The reflection of land surface, atmosphere and vegetation conditions affect the reflectance value of the object is recorded on remote sensing image so that it can affect the outcome of information extraction from remote sensing imagery one multispectral classification. This study aims to assess the ability of the transformation of generic vegetation index (Wide Dynamic Range Vegetation Index), the vegetation index transformation that is capable reducing the influence of the atmosphere (Atmospherically Resistant Vegetation Index), and the transformation of vegetation index that is capable of reducing the influence of the background soil (Second Modified Soil Adjusted Vegetation Index) for the identification and mapping of land use in the oil palm plantation area based on SPOT-6 archived on June 13, 2013 from LAPAN. The study area selected oil palm plantations PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu, Riau Province. The method is using the transformation of the vegetation index ARVI, MSAVI2, and WDRVI. Sample selection method used was stratified random sampling. The test method used mapping accuracy of the confusion matrix. The results showed that the best transformation of the vegetation index for the identification and mapping of land use in the plantation area is ARVI transformation with a total of accuracy is 96%. Accuracy of mapping land use settlements 100%, replanting 82.35%, 81.25% young oil palm, old oil palm 99.46%, 100% bush, body of water 100%, and 100% bare-soil.

Hyperspectral remote sensing of vegetation: knowledge gain and knowledge gap after 50 years of research (Conference Presentation)

NASA Astrophysics Data System (ADS)

Thenkabail, Prasad S.

2017-04-01

This presentation summarizes the advances made over 40+ years in understanding, modeling, and mapping terrestrial vegetation as reported in the new book on "Hyperspectral Remote Sensing of Vegetation" (Publisher:Taylor and Francis inc.). The advent of spaceborne hyperspectral sensors or imaging spectroscopy (e.g., NASA's Hyperion, ESA's PROBA, and upcoming Italy's ASI's Prisma, Germany's DLR's EnMAP, Japanese HIUSI, NASA's HyspIRI) as well as the advances made in processing when handling large volumes of hyperspectral data have generated tremendous interest in advancing the hyperspectral applications' knowledge base to large areas. Advances made in using hyperspectral data, relative to broadband data, include: (a) significantly improved characterization and modeling of a wide array of biophysical and biochemical properties of vegetation, (b) ability to discriminate plant species and vegetation types with high degree of accuracy, (c) reducing uncertainties in determining net primary productivity or carbon assessments from terrestrial vegetation, (d) improved crop productivity and water productivity models, (e) ability to assess stress resulting from causes such as management practices, pests and disease, water deficit or water excess, and (f) establishing more sensitive wavebands and indices to study vegetation characteristics. The presentation will discuss topics such as: (1) hyperspectral sensors and their characteristics, (2) methods of overcoming the Hughes phenomenon, (3) characterizing biophysical and biochemical properties, (4) advances made in using hyperspectral data in modeling evapotranspiration or actual water use by plants, (5) study of phenology, light use efficiency, and gross primary productivity, (5) improved accuracies in species identification and land cover classifications, and (6) applications in precision farming.

New forest vegetation maps facilitate assessment of biodiversity indicators over large, multi-ownership regions.

Treesearch

Janet L. Ohmann

2003-01-01

Natural resource policy analysis and conservation planning are best served by broad-scale information about vegetation that is detailed, spatially complete, and consistent across land ownerships and allocations. In this paper I describe how a new generation of forest vegetation maps can be used to assess the distribution of vegetation biodiversity among land ownerships...

Enhanced Deforestation Mapping in North Korea using Spatial-temporal Image Fusion Method and Phenology-based Index

NASA Astrophysics Data System (ADS)

Jin, Y.; Lee, D.

2017-12-01

North Korea (the Democratic People's Republic of Korea, DPRK) is known to have some of the most degraded forest in the world. The characteristics of forest landscape in North Korea is complex and heterogeneous, the major vegetation cover types in the forest are hillside farm, unstocked forest, natural forest, and plateau vegetation. Better classification of types in high spatial resolution of deforested areas could provide essential information for decisions about forest management priorities and restoration of deforested areas. For mapping heterogeneous vegetation covers, the phenology-based indices are helpful to overcome the reflectance value confusion that occurs when using one season images. Coarse spatial resolution images may be acquired with a high repetition rate and it is useful for analyzing phenology characteristics, but may not capture the spatial detail of the land cover mosaic of the region of interest. Previous spatial-temporal fusion methods were only capture the temporal change, or focused on both temporal change and spatial change but with low accuracy in heterogeneous landscapes and small patches. In this study, a new concept for spatial-temporal image fusion method focus on heterogeneous landscape was proposed to produce fine resolution images at both fine spatial and temporal resolution. We classified the three types of pixels between the base image and target image, the first type is only reflectance changed caused by phenology, this type of pixels supply the reflectance, shape and texture information; the second type is both reflectance and spectrum changed in some bands caused by phenology like rice paddy or farmland, this type of pixels only supply shape and texture information; the third type is reflectance and spectrum changed caused by land cover type change, this type of pixels don't provide any information because we can't know how land cover changed in target image; and each type of pixels were applied different prediction methods. Results show that both STARFM and FSDAF predicted in low accuracy in second type pixels and small patches. Classification results used spatial-temporal image fusion method proposed in this study showed overall classification accuracy of 89.38%, with corresponding kappa coefficients of 0.87.

Diversity of mire massif types in the boreal zone of European Russia

NASA Astrophysics Data System (ADS)

Kuznetsov, O. L.

2018-03-01

In Russia, mire massif type is the principal structural unit for descriptions of the diversity of regional mire ecosystems of various ranks, vegetation mapping, and decision-making on the use of mires. The classification of mire massifs is based on various criteria and indicators. The botanical-geographical classification of mire massifs of the boreal zone of European Russia is four-tiered, and includes 22 types gathered in groups, subgroups and three classes. For most of the types their characteristic associations and diagnostic species are stated.

Vegetation mapping from high-resolution satellite images in the heterogeneous arid environments of Socotra Island (Yemen)

NASA Astrophysics Data System (ADS)

Malatesta, Luca; Attorre, Fabio; Altobelli, Alfredo; Adeeb, Ahmed; De Sanctis, Michele; Taleb, Nadim M.; Scholte, Paul T.; Vitale, Marcello

2013-01-01

Socotra Island (Yemen), a global biodiversity hotspot, is characterized by high geomorphological and biological diversity. In this study, we present a high-resolution vegetation map of the island based on combining vegetation analysis and classification with remote sensing. Two different image classification approaches were tested to assess the most accurate one in mapping the vegetation mosaic of Socotra. Spectral signatures of the vegetation classes were obtained through a Gaussian mixture distribution model, and a sequential maximum a posteriori (SMAP) classification was applied to account for the heterogeneity and the complex spatial pattern of the arid vegetation. This approach was compared to the traditional maximum likelihood (ML) classification. Satellite data were represented by a RapidEye image with 5 m pixel resolution and five spectral bands. Classified vegetation relevés were used to obtain the training and evaluation sets for the main plant communities. Postclassification sorting was performed to adjust the classification through various rule-based operations. Twenty-eight classes were mapped, and SMAP, with an accuracy of 87%, proved to be more effective than ML (accuracy: 66%). The resulting map will represent an important instrument for the elaboration of conservation strategies and the sustainable use of natural resources in the island.

Southern Pine Beetle Outbreak in Belize

Treesearch

Robert A. Haack; Claus M. Eckelmann; Earl Green

2000-01-01

Belize is a Central American country that borders Mexico, Guatemala, and the Caribbean Sea (see Map). Belize, formerly called British Honduras from 1862 until 1973, is about 23,000 square kilometers in size, which is about the area of Massachusetts. Elevation varies from sea level to 1120 meters. The major vegetation types include mangrove swamp, broadleafjungle,...

Global Forest Canopy Height Maps Validation and Calibration for The Potential of Forest Biomass Estimation in The Southern United States

NASA Astrophysics Data System (ADS)

Ku, N. W.; Popescu, S. C.

2015-12-01

In the past few years, three global forest canopy height maps have been released. Lefsky (2010) first utilized the Geoscience Laser Altimeter System (GLAS) on the Ice, Cloud and land Elevation Satellite (ICESat) and Moderate Resolution Imaging Spectroradiometer (MODIS) data to generate a global forest canopy height map in 2010. Simard et al. (2011) integrated GLAS data and other ancillary variables, such as MODIS, Shuttle Radar Topography Mission (STRM), and climatic data, to generate another global forest canopy height map in 2011. Los et al. (2012) also used GLAS data to create a vegetation height map in 2012.Several studies attempted to compare these global height maps to other sources of data., Bolton et al. (2013) concluded that Simard's forest canopy height map has strong agreement with airborne lidar derived heights. Los map is a coarse spatial resolution vegetation height map with a 0.5 decimal degrees horizontal resolution, around 50 km in the US, which is not feasible for the purpose of our research. Thus, Simard's global forest canopy height map is the primary map for this research study. The main objectives of this research were to validate and calibrate Simard's map with airborne lidar data and other ancillary variables in the southern United States. The airborne lidar data was collected between 2010 and 2012 from: (1) NASA LiDAR, Hyperspectral & Thermal Image (G-LiHT) program; (2) National Ecological Observatory Network's (NEON) prototype data sharing program; (3) NSF Open Topography Facility; and (4) the Department of Ecosystem Science and Management at Texas A&M University. The airborne lidar study areas also cover a wide variety of vegetation types across the southern US. The airborne lidar data is post-processed to generate lidar-derived metrics and assigned to four different classes of point cloud data. The four classes of point cloud data are the data with ground points, above 1 m, above 3 m, and above 5 m. The root mean square error (RMSE) and coefficient of determination (R2) are used for examining the discrepancies of the canopy heights between the airborne lidar-derived metrics and global forest canopy height map, and the regression and random forest approaches are used to calibrate the global forest canopy height map. In summary, the research shows a calibrated forest canopy height map of the southern US.

Mapping SOC (Soil Organic Carbon) using LiDAR-derived vegetation indices in a random forest regression model

NASA Astrophysics Data System (ADS)

Will, R. M.; Glenn, N. F.; Benner, S. G.; Pierce, J. L.; Spaete, L.; Li, A.

2015-12-01

Quantifying SOC (Soil Organic Carbon) storage in complex terrain is challenging due to high spatial variability. Generally, the challenge is met by transforming point data to the entire landscape using surrogate, spatially-distributed, variables like elevation or precipitation. In many ecosystems, remotely sensed information on above-ground vegetation (e.g. NDVI) is a good predictor of below-ground carbon stocks. In this project, we are attempting to improve this predictive method by incorporating LiDAR-derived vegetation indices. LiDAR provides a mechanism for improved characterization of aboveground vegetation by providing structural parameters such as vegetation height and biomass. In this study, a random forest model is used to predict SOC using a suite of LiDAR-derived vegetation indices as predictor variables. The Reynolds Creek Experimental Watershed (RCEW) is an ideal location for a study of this type since it encompasses a strong elevation/precipitation gradient that supports lower biomass sagebrush ecosystems at low elevations and forests with more biomass at higher elevations. Sagebrush ecosystems composed of Wyoming, Low and Mountain Sagebrush have SOC values ranging from .4 to 1% (top 30 cm), while higher biomass ecosystems composed of aspen, juniper and fir have SOC values approaching 4% (top 30 cm). Large differences in SOC have been observed between canopy and interspace locations and high resolution vegetation information is likely to explain plot scale variability in SOC. Mapping of the SOC reservoir will help identify underlying controls on SOC distribution and provide insight into which processes are most important in determining SOC in semi-arid mountainous regions. In addition, airborne LiDAR has the potential to characterize vegetation communities at a high resolution and could be a tool for improving estimates of SOC at larger scales.

Multidecadal Land Cover Change in the Los Angeles Basin and its Water Consumption Implications

NASA Astrophysics Data System (ADS)

Colombi, N. K.; Lettenmaier, D. P.; Marlier, M. E.

2017-12-01

Urban irrigation is an important component of the hydrologic cycle in areas with arid and semi-arid climates. In Los Angeles, outdoor irrigation has the largest potential for water conservation. However, there are significant uncertainties in predicting and quantifying irrigated water use due to unavailability of crucial landcover data. Irrigated vegetation must first be identified and mapped before irrigated water use can be modeled, and steps can be taken towards conservation. We utilized Landsat data at 30m spatial resolution from 1985 to present to quantify temporal dynamics of vegetation cover on a seasonal basis in the Los Angeles Basin based on the Normalized Difference Vegetation Index (NDVI). Previous vegetation surveys have estimated tree cover and other vegetation types as isolated "snapshots", but are of limited use in monitoring fine-scale temporal variations, and their implications for municipal water consumption in particular. When the temporal resolution of images is low, it becomes more difficult to distinguish between natural, as contrasted with irrigated, vegetation. Our work therefore should provide a better basis for identifying irrigated vegetation. In addition, we quantified NDVI changes within specific land cover classifications including, but not limited to, grassland, shrub, and developed land classes. These results will be useful in comparing natural and irrigated vegetation within urban and partially urban areas. They will also help us to understand relationships between NDVI and irrigated water use at fine temporal resolutions. Finally, we have created land cover change maps that allow us to examine the impact of historical urban ecosystem changes on the water balance of the Los Angeles Basin (LAB) over the last 30 years. Understanding historical changes is a first step in determining the most practical ways of improving water use sustainability in the Los Angeles urban area.

Regulation leads to increases in riparian vegetation, but not direct allochthonous inputs, along the Colorado River in Grand Canyon, Arizona

USGS Publications Warehouse

Kennedy, T.A.; Ralston, B.E.

2012-01-01

Dams and associated river regulation have led to the expansion of riparian vegetation, especially nonnative species, along downstream ecosystems. Nonnative saltcedar is one of the dominant riparian plants along virtually every major river system in the arid western United States, but allochthonous inputs have never been quantified along a segment of a large river that is dominated by saltcedar. We developed a novel method for estimating direct allochthonous inputs along the 387km-long reach of the Colorado River downstream of Glen Canyon Dam that utilized a GIS vegetation map developed from aerial photographs, empirical and literature-derived litter production data for the dominant vegetation types, and virtual shorelines of annual peak discharge (566m 3s -1 stage elevation). Using this method, we estimate that direct allochthonous inputs from riparian vegetation for the entire reach studied total 186metric tonsyear -1, which represents mean inputs of 470gAFDMm -1year -1 of shoreline or 5.17gAFDMm -2year -1 of river surface. These values are comparable to allochthonous inputs for other large rivers and systems that also have sparse riparian vegetation. Nonnative saltcedar represents a significant component of annual allochthonous inputs (36% of total direct inputs) in the Colorado River. We also estimated direct allochthonous inputs for 46.8km of the Colorado River prior to closure of Glen Canyon Dam using a vegetation map that was developed from historical photographs. Regulation has led to significant increases in riparian vegetation (270-319% increase in cover, depending on stage elevation), but annual allochthonous inputs appear unaffected by regulation because of the lower flood peaks on the post-dam river. Published in 2010 by John Wiley & Sons, Ltd.

Modeling mechanisms of vegetation change due to fire in a semi-arid ecosystem

USGS Publications Warehouse

White, J.D.; Gutzwiller, K.J.; Barrow, W.C.; Randall, L.J.; Swint, P.

2008-01-01

Vegetation growth and community composition in semi-arid environments is determined by water availability and carbon assimilation mechanisms specific to different plant types. Disturbance also impacts vegetation productivity and composition dependent on area affected, intensity, and frequency factors. In this study, a new spatially explicit ecosystem model is presented for the purpose of simulating vegetation cover type changes associated with fire disturbance in the northern Chihuahuan Desert region. The model is called the Landscape and Fire Simulator (LAFS) and represents physiological activity of six functional plant types incorporating site climate, fire, and seed dispersal routines for individual grid cells. We applied this model for Big Bend National Park, Texas, by assessing the impact of wildfire on the trajectory of vegetation communities over time. The model was initialized and calibrated based on landcover maps derived from Landsat-5 Thematic Mapper data acquired in 1986 and 1999 coupled with plant biomass measurements collected in the field during 2000. Initial vegetation cover change analysis from satellite data showed shrub encroachment during this time period that was captured in the simulated results. A synthetic 50-year climate record was derived from historical meteorological data to assess system response based on initial landcover conditions. This simulation showed that shrublands increased to the detriment of grass and yucca-ocotillo vegetation cover types indicating an ecosystem-level trajectory for shrub encroachment. Our analysis of simulated fires also showed that fires significantly reduced site biomass components including leaf area, stem, and seed biomass in this semi-arid ecosystem. In contrast to other landscape simulation models, this new model incorporates detailed physiological responses of functional plant types that will allow us to simulated the impact of increased atmospheric CO2 occurring with climate change coupled with fire disturbance. Simulations generated from this model are expected to be the subject of subsequent studies on landscape dynamics with specific regard to prediction of wildlife distributions associated with fire management and climate change.

Classification of bottom composition and bathymetry of shallow waters by passive remote sensing

NASA Astrophysics Data System (ADS)

Spitzer, D.; Dirks, R. W. J.

The use of remote sensing data in the development of algorithms to remove the influence of the watercolumn on upwelling optical signals when mapping the bottom depth and composition in shallow waters. Calculations relating the reflectance spectra to the parameters of the watercolumn and the diverse bottom types are performed and measurements of the underwater reflection coefficient of sandy, mud, and vegetation-type seabottoms are taken. The two-flow radiative transfer model is used. Reflectances within the spectral bands of the Landsat MSS, the Landsat TM, SPOT HVR, and the TIROS-N series AVHRR were computed in order to develop appropriate algorithms suitable for the bottom depth and type mapping. Bottom depth and features appear to be observable down to 3-20 m depending on the water composition and bottom type.

Groundwater dependant vegetation identified by remote sensing in the Iberian Peninsula

NASA Astrophysics Data System (ADS)

Gouveia, Célia; Pascoa, Patrícia; Kurz-Besson, Cathy

2017-04-01

Groundwater Dependant Ecosystems (GDEs) are defined as ecosystems whose composition, structure, and function depend on the water supplies from groundwater aquifers. Within GDEs, phreatophytes are terrestrial plants relying on groundwater through deep rooting. They can be found worldwide but are mostly adapted to environments facing scarce water availability or recurrent drought periods mainly in semi-arid to arid climate geographical areas, such as the Mediterranean basin. We present a map of the potential distribution of GDEs over the Iberian Peninsula (IP) obtained by remote sensing and identifying hotspots corresponding to the most vulnerable areas for rainfed vegetation facing the risk of desertification. The characterization of GDEs was assessed by remote sensing (RS), using CORINE land-cover information and the Normalized Difference Vegetation Index (NDVI) from VEGETATION recorded between 1998 and 2014 with a resolution of 1km. The methodology based on Gou et al (2015) relied on three approaches to map GDEs over the IP by: i) Detecting vegetation remaining green during the dry periods, since GDEs are more likely to show high NDVI values during summer of dry years; ii) Spotting vegetation with low seasonal changes since GDEs are more prone to have the lowest NDVI standard deviation along an entire year, and iii) Discriminating vegetation with low inter-annual variability since GDEs areas should provide the lowest NDVI changes between extreme wet and dry years. A geospatial analysis was performed to gather the potential area of GDEs (obtained with NDVI), vegetation land cover types (CORINE land cover) and climatic variables (temperature, precipitation and the Standardized Precipitation-Evapotranspiration Index SPEI). This analysis allowed the identification of hotspots of the most vulnerable areas for rainfed vegetation regarding water scarcity over the Iberian Peninsula, where protection measures should be urgently applied to sustain rainfed ecosystem and agro-systems and biodiversity in the near future. Keywords: NDVI, CORINE, SPEI, Groundwater, Mediterranean vegetation, Phreatophyte species. Reference: Gou S., Susana Gonzales S., and Gretchen R. Miller G. R. (2015). Mapping Potential Groundwater-Dependent Ecosystems for Sustainable Management. Groundwater 53, 99-110. Acknowledgements: This work was supported by the project PIEZAGRO (PTDC/AAG-REC/7046/2014) funded by the Fundação para a Ciência e a Tecnologia, Portugal.

Investigation of environmental change pattern in Japan

NASA Technical Reports Server (NTRS)

Maruyasu, T. (Principal Investigator)

1977-01-01

The author has identified the following significant results. In the Plains of Tokachi, where the scale of agricultural field was comparatively large in Japan, LANDSAT data with its accuracy have proved to be useful enough to observe the actual condition of agricultural land use and changes more accurately than present methods. Species and ages of grasses in pasture were identified and soils were classified into several types. The actual land cover and ecological environment were remarkably changeable at the rapidly industrialized area by the urbanization in the flat plane and also by the forest works and road construction in the mountainous area. The practical use of the recognition results was proved as the base map of the field survey or the retouching work of the vegetation and land use. There was a 10% cut in cost, labor, and time. Vegetation cover in Tokyo districts was estimated by both the multiregression model and the parametric model. Multicorrelation coefficient between observed value and estimated value was 0.87 and standard deviation was + or - 15%. Vegetation cover in Tokyo was mapped into five levels with equal intervals of 20%.

Forest and range mapping in the Houston area with ERTS-1

NASA Technical Reports Server (NTRS)

Heath, G. R.; Parker, H. D.

1973-01-01

ERTS-1 data acquired over the Houston area has been analyzed for applications to forest and range mapping. In the field of forestry the Sam Houston National Forest (Texas) was chosen as a test site, (Scene ID 1037-16244). Conventional imagery interpretation as well as computer processing methods were used to make classification maps of timber species, condition and land-use. The results were compared with timber stand maps which were obtained from aircraft imagery and checked in the field. The preliminary investigations show that conventional interpretation techniques indicated an accuracy in classification of 63 percent. The computer-aided interpretations made by a clustering technique gave 70 percent accuracy. Computer-aided and conventional multispectral analysis techniques were applied to range vegetation type mapping in the gulf coast marsh. Two species of salt marsh grasses were mapped.

Monitoring and mapping selected riparian habitat along the lower Snake River

DOE Office of Scientific and Technical Information (OSTI.GOV)

Downs, J. L; Tiller, B. L; Witter, M.

Studies in this document were initiated to establish baseline information on riparian and wetland habitat conditions at the areas studied under the current reservoir operations on the lower Snake River. Two approaches were used to assess habitat at 28 study sites selected on the four pools on the lower Snake River. These areas all contribute significant riparian habitat along the river, and several of these areas are designated habitat management units. At 14 of the 28 sites, we monitored riparian habitat on three dates during the growing season to quantify vegetation abundance and composition along three transects: soil nutrients, moisture,more » and pH and water level and pH. A second approach involved identifying any differences in the extent and amount of riparian/wetland habitat currently found at the study areas from that previously documented. We used both ground and boat surveys to map and classify the changes in vegetative cover along the shoreline at the 14 monitoring sites and at 14 additional sites along the lower Snake selected to represent various riparian/wetland habitat conditions. Results of these mapping efforts are compared with maps of cover types previously generated using aerial photography taken in 1987.« less

Mapping Critical Loads of Atmospheric Nitrogen Deposition in the Rocky Mountains, USA

NASA Astrophysics Data System (ADS)

Nanus, L.; Clow, D. W.; Stephens, V. C.; Saros, J. E.

2010-12-01

Atmospheric nitrogen (N) deposition can adversely affect sensitive aquatic ecosystems at high-elevations in the western United States. Critical loads are the amount of deposition of a given pollutant that an ecosystem can receive below which ecological effects are thought not to occur. GIS-based landscape models were used to create maps for high-elevation areas across the Rocky Mountain region showing current atmospheric deposition rates of nitrogen (N), critical loads of N, and exceedances of critical loads of N. Atmospheric N deposition maps for the region were developed at 400 meter resolution using gridded precipitation data and spatially interpolated chemical concentrations in rain and snow. Critical loads maps were developed based on chemical thresholds corresponding to observed ecological effects, and estimated ecosystem sensitivities calculated from basin characteristics. Diatom species assemblages were used as an indicator of ecosystem health to establish critical loads of N. Chemical thresholds (concentrations) were identified for surface waters by using a combination of in-situ growth experiments and observed spatial patterns in surface-water chemistry and diatom species assemblages across an N deposition gradient. Ecosystem sensitivity was estimated using a multiple-linear regression approach in which observed surface water nitrate concentrations at 530 sites were regressed against estimates of inorganic N deposition and basin characteristics (topography, soil type and amount, bedrock geology, vegetation type) to develop predictive models of surface water chemistry. Modeling results indicated that the significant explanatory variables included percent slope, soil permeability, and vegetation type (including barren land, shrub, and grassland) and were used to predict high-elevation surface water nitrate concentrations across the Rocky Mountains. Chemical threshold concentrations were substituted into an inverted form of the model equations and applied to estimate critical loads for each stream reach within a basin, from which critical loads maps were created. Atmospheric N deposition maps were overlaid on the critical loads maps to identify areas in the Rocky Mountain region where critical loads are being exceeded, or where they may do so in the future. This approach may be transferable to other high-elevation areas of the United States and the world.

Relationship of Productivity to Species Richness in the Xinjiang Temperate Grassland

PubMed Central

2016-01-01

The relationship between species richness (SR) and aboveground net primary productivity (ANPP) is still a central and debated issue in community ecology. Previous studies have often emphasized the relationship of alpha diversity (number of species identity) to the mean ANPP with respect to the SR-ANPP relationship while neglecting the contribution of beta diversity (dissimilarity in species composition) to the mean ANPP and to the stability of ANPP (coefficient of ANPP: CV of ANPP). In this study, we used alpha and beta diversity, mean ANPP and the CV of ANPP collected from 159 sites and belonging to three vegetation types in the Xinjiang temperate grassland to first examine their trends along climatic factors and among different vegetation types and then test the relationship among alpha (beta) diversity and mean ANPP and the CV of ANPP. Our results showed that in the Xinjiang temperate grasslands, alpha diversity was positively and linearly correlated with MAP but unimodally correlated with MAT. Meanwhile, beta diversity was unimodally correlated with MAP but linearly correlated with MAT. Relative to desert steppe, meadow steppe and typical steppe had the highest alpha and beta diversity, respectively. Except for ANPP exhibiting a quadratic relationship with MAP, no significant relationship was found among ANPP, the CV of ANPP and climatic factors. ANPP and the CV of ANPP also exhibited no apparent patterns in variation among different vegetation types. Our results further showed that mean ANPP was closely associated with alpha diversity. Both linear and unimodal relationships were detected between alpha diversity and mean ANPP, but their particular form was texture-dependent. Meanwhile, the CV of ANPP was positively correlated with beta diversity. Our results indicated that in addition to incorporating alpha diversity and mean ANPP, incorporating beta diversity and the CV of ANPP could expand our understanding of the SR-ANPP relationship. PMID:27100676

Regolith-geology mapping with support vector machine: A case study over weathered Ni-bearing peridotites, New Caledonia

NASA Astrophysics Data System (ADS)

De Boissieu, Florian; Sevin, Brice; Cudahy, Thomas; Mangeas, Morgan; Chevrel, Stéphane; Ong, Cindy; Rodger, Andrew; Maurizot, Pierre; Laukamp, Carsten; Lau, Ian; Touraivane, Touraivane; Cluzel, Dominique; Despinoy, Marc

2018-02-01

Accurate maps of Earth's geology, especially its regolith, are required for managing the sustainable exploration and development of mineral resources. This paper shows how airborne imaging hyperspectral data collected over weathered peridotite rocks in vegetated, mountainous terrane in New Caledonia were processed using a combination of methods to generate a regolith-geology map that could be used for more efficiently targeting Ni exploration. The image processing combined two usual methods, which are spectral feature extraction and support vector machine (SVM). This rationale being the spectral features extraction can rapidly reduce data complexity by both targeting only the diagnostic mineral absorptions and masking those pixels complicated by vegetation, cloud and deep shade. SVM is a supervised classification method able to generate an optimal non-linear classifier with these features that generalises well even with limited training data. Key minerals targeted are serpentine, which is considered as an indicator for hydrolysed peridotitic rock, and iron oxy-hydroxides (hematite and goethite), which are considered as diagnostic of laterite development. The final classified regolith map was assessed against interpreted regolith field sites, which yielded approximately 70% similarity for all unit types, as well as against a regolith-geology map interpreted using traditional datasets (not hyperspectral imagery). Importantly, the hyperspectral derived mineral map provided much greater detail enabling a more precise understanding of the regolith-geological architecture where there are exposed soils and rocks.

Developing Remote Sensing Methodology to Characterize Savanna Vegetation Structure and Composition for Rangeland Monitoring and Conservation Applications

NASA Astrophysics Data System (ADS)

Tsalyuk, M.; Kelly, M.; Getz, W.

2012-12-01

Rangeland ecosystems cover more than fifty percent of earth's land surface, host considerable biodiversity and provide vital ecosystem services. However, rangelands around the world face degradation due to climate change, land use change and overgrazing. Human-driven changes to fire and grazing regimes enhance degradation processes. The purpose of this research is to develop a remote sensing methodology to characterize the structure and composition of savanna vegetation, in order to improve the ability of conservation managers to monitor and address such degradation processes. Our study site, Etosha National Park, is a 22,270 km^2 semi-arid savanna located in north-central Namibia. Fencing and provision of artificial water sources for wildlife have changed the natural grazing patterns, which has caused bush encroachment and vegetation degradation across the park. We used MODIS and Landsat ETM+ 7 satellite imagery to map the vegetation type, dominant species, density, cover and biomass of herbaceous and woody vegetation in Etosha. We used imagery for 2007-2012 together with extensive field sampling, both in the wet and the dry seasons. At each sampling point, we identified the dominant species and measured the density, canopy size, height and diameter of the trees and shrubs. At only 31% of the sampling points, the identified vegetation type matched the class assigned at the 1996 classification. This may indicate significant habitat modifications in Etosha. We used two parallel analytical approaches to correlate between radiometric and field data. First, we show that traditional supervised classification identifies well five classes: bare soil, grassland, steppe, shrub savanna and tree savanna. We then refined this classification to enable us to identify the species composition in an area utilizing the phenological differences in timing and duration of greenness of the dominant tree and shrub species in Etosha. Specifically, using multi-date images we were able to identify additional six sub-classes based on the dominant species in each class: Colophospermum mopane woodland, Colophospermum mopane shrubland, Cataphractes alexandri woodland, Acacia nebrownii shrubland, mixed Combretum species woodland and Terminalia prunioides woodland. Second, we used quantitative methods to relate satellite-based vegetation indices to the biometric properties measured on the ground. We found a correlation among measured height, diameter and canopy cover of woody vegetation and used this to improve the correlation between cover and Normalized Difference Vegetation Index (NDVI). We showed that the Soil Adjusted Total Vegetation Index (SATVI) and Normalized Difference Water Index (NDWI) were related to both greenness and density at a site. In order to measure grass biomass in the field, we calibrated Disc Pasture Mater by clipping, weighing and drying grass in 1m^2 plots, in the dry and wet seasons, with resulting R^2 of 0.87 and 0.83, respectively. MODIS-derived leaf area index (LAI) data was best correlated with dry grass biomass. We used these correlations to produce detailed maps of each vegetation parameter for the whole park. These maps will provide a baseline to employ historical imagery to better understand the effects of the park's management and changing grazing pressure on vegetation structure.

A conceptual method for monitoring locust habitat

USGS Publications Warehouse

Howard, Stephen M.; Loveland, Thomas R.; Ohlen, Donald O.; Moore, Donald G.; Gallo, Kevin P.; Olsson, Jonathon

1987-01-01

A procedure to map and monitor vegetation conditions in near-real time was developed at the United States Geological Survey;s Earth Resources Observation Systems Data Center for use in locust control efforts. Meteorological satellite dat were acquired daily for 3 weeks in October and November 1986 over a 1.4-million-square-kilometer study area centered on Botswana in southern Africa. Advanced Very High Resolution Radiometer data were screened to remove cloud-contaminated data and registered to a 1-kilometer geographic base. Each day the normalized difference vegetation index (NDVI) was calculated to determine the presence and relative amounts of green vegetation in the area. Over a 10-day cycle, subsequent dates of NDVI data were composited to fill in data removed by the cloud-screening process. At any pixel location, the maximum NDVI value was retained. At the end of the 10-day cycle, a composite vegetation-greenness map was produced and another cycle started. Greenness-change maps were produced by comparing two 10-day composite greenness images. Automated map production procedures were used to merge the NDVI image data with cartographic data (boundaries, roads, tick marks) digitized from 1:1,000,000-scale operational navigation charts. The vegetation-greenness map shoes the current distribution of vegetation in the region and can be used to locate potential locust breeding area. The change map shows areas where increases and decreases in greenness have occurred between processing cycles. Significant areas of locust damage in remote regions are characterized by an unexpected decrease in greenness. These maps can be used by locust control teams to efficiently target areas for reconnaissance. In general, the procedures and products have utility for resource managers who are required to monitor vegetation resources over large geographic regions.

The global distribution of ecosystems in a world without fire.

PubMed

Bond, W J; Woodward, F I; Midgley, G F

2005-02-01

This paper is the first global study of the extent to which fire determines global vegetation patterns by preventing ecosystems from achieving the potential height, biomass and dominant functional types expected under the ambient climate (climate potential). To determine climate potential, we simulated vegetation without fire using a dynamic global-vegetation model. Model results were tested against fire exclusion studies from different parts of the world. Simulated dominant growth forms and tree cover were compared with satellite-derived land- and tree-cover maps. Simulations were generally consistent with results of fire exclusion studies in southern Africa and elsewhere. Comparison of global 'fire off' simulations with landcover and treecover maps show that vast areas of humid C(4) grasslands and savannas, especially in South America and Africa, have the climate potential to form forests. These are the most frequently burnt ecosystems in the world. Without fire, closed forests would double from 27% to 56% of vegetated grid cells, mostly at the expense of C(4) plants but also of C(3) shrubs and grasses in cooler climates. C(4) grasses began spreading 6-8 Ma, long before human influence on fire regimes. Our results suggest that fire was a major factor in their spread into forested regions, splitting biotas into fire tolerant and intolerant taxa.

Analysis of historical forest fire regime in Madrid region (1984-2010) and its relation with land-use/land-cover changes

NASA Astrophysics Data System (ADS)

Gómez-Nieto, Israel; Martín, María del Pilar; Salas, Francisco Javier; Gallardo, Marta

2013-04-01

Understanding the interaction between natural and socio-economic factors that determine fire regime is essential to make accurate projections and impact assessments. However, this requires having accurate historical, systematic, homogeneous and spatially explicit information on fire occurrence. Fire databases usually have serious limitations in this regard; therefore other sources of information, such as remote sensing, have emerged as alternatives to generate optimal fire maps on various spatial and temporal scales. Several national and international projects work in order to generate information to study the factors that determine the current fire regime and its future evolution. This work is included in the framework of the project "Forest fires under climate, social and economic Changes in Europe, the Mediterranean and other fire-affected areas of the World" (FUME http://www.fumeproject.eu), which aims to study the changes and factors related to fire regimes through time to determine the potential impacts on vegetation in Mediterranean regions and concrete steps to address future risk scenarios. We analyzed the changes in the fire regime in Madrid region (Spain) in the past three decades (1984-2010) and its relation to land use changes. We identified and mapped fires that have occurred in the region during those years using Landsat satellite images by combining digital techniques and visual analysis. The results show a clear cyclical behaviour of the fire, with years of high incidence (as 1985, 2000 and 2003, highlighted by the number of fires and the area concerned, over 2000 ha) followed by another with a clear occurrence decrease. At the same time, we analyzed the land use changes that have occurred in Madrid region between the early 80s and mid-2000s using as reference the CORINE Land-cover maps (1990, 2000 and 2006) and the Vegetation and Land Use map of the Community of Madrid, 1982. We studied the relationship between fire regimes and observed land-use and land-cover changes in the periods analyzed, it was determined that between years 1984 and 2006 most of the burned area remained pre-fire cover type (above 80% of the area). However, in areas that experienced change, the most important transitions were recorded in wooded areas, especially conifers, which became shrubs or sparsely vegetated areas, followed by non-irrigated crops, which were replaced by grasslands or industrial areas, and sparse vegetation which changed to shrubs. Finally, the analysis of land-use changes over burned areas situated shrubland as the most favored type of cover, either as a result of a vegetative degradation process after intense burning of wooded areas, especially conifers, or as stage of natural increase in areas previously covered by sparsely vegetation.

Demonstration of wetland vegetation mapping in Florida from computer-processed satellite and aircraft multispectral scanner data

NASA Technical Reports Server (NTRS)

Butera, M. K.

1979-01-01

The success of remotely mapping wetland vegetation of the southwestern coast of Florida is examined. A computerized technique to process aircraft and LANDSAT multispectral scanner data into vegetation classification maps was used. The cost effectiveness of this mapping technique was evaluated in terms of user requirements, accuracy, and cost. Results indicate that mangrove communities are classified most cost effectively by the LANDSAT technique, with an accuracy of approximately 87 percent and with a cost of approximately 3 cent per hectare compared to $46.50 per hectare for conventional ground survey methods.

Rethinking Chlorophyll Responses To Stress: Fluorescence and Flectance Remote Sensing in a Coastal Environment

DTIC Science & Technology

2010-11-15

fluorescence emission of vegetation for mapping vegetation stress as chlorophyll content and/or carotenoid content changes. 1. REPORT DATE (DD-MM-YYYY...that estimate fluorescence emission of vegetation for mapping vegetation stress as chlorophyll content and/or carotenoid content changes...not related to changes in chlorophyll content or the carotenoids /chlorophyll ratio. PRI is an indicator of chronic salinity stress and may be used as

Applications of ERTS data to coastal wetland ecology with special reference to plant community mapping and typing and impact of man. [Delaware, Maryland, Virginia, South Carolina, and Georgia

NASA Technical Reports Server (NTRS)

Anderson, R. R.; Carter, V. P.; Mcginness, J.

1974-01-01

Complete seasonal ERTS-1 coverage of Atlantic coastal wetlands from Delaware Bay to Georgia provides a basis for assessment of temporal data for wetland mapping, evaluation, and monitoring. Both MSS imagery and digital data have proved useful for gross wetland species delineation and determination of the upper wetland boundary. Tidal effects and (band to band or seasonal) spectral reflectance differences make it possible to type vegetatively coastal wetlands in salinity related categories. Management areas, spoil disposal sites, drainage ditches, lagoon-type developments and highway construction can be detected indicating a monitoring potential for the future. A northern test site (Maryland-Virginia) and a southern test site (Georgia-South Carolina), representing a range of coastal marshes from saline to fresh, were chosen for intensive study. Wetland maps were produced at various scales using both ERTS imagery (bands 5 and 7) and digital data (bands 4, 5 and 7).

Relating Vegetation Aerodynamic Roughness Length to Interferometric SAR Measurements

NASA Technical Reports Server (NTRS)

Saatchi, Sassan; Rodriquez, Ernesto

1998-01-01

In this paper, we investigate the feasibility of estimating aerodynamic roughness parameter from interferometric SAR (INSAR) measurements. The relation between the interferometric correlation and the rms height of the surface is presented analytically. Model simulations performed over realistic canopy parameters obtained from field measurements in boreal forest environment demonstrate the capability of the INSAR measurements for estimating and mapping surface roughness lengths over forests and/or other vegetation types. The procedure for estimating this parameter over boreal forests using the INSAR data is discussed and the possibility of extending the methodology over tropical forests is examined.

Extending a prototype knowledge- and object-based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

Extending a prototype knowledge and object based image analysis model to coarser spatial resolution imagery: an example from the Missouri River

USGS Publications Warehouse

Strong, Laurence L.

2012-01-01

A prototype knowledge- and object-based image analysis model was developed to inventory and map least tern and piping plover habitat on the Missouri River, USA. The model has been used to inventory the state of sandbars annually for 4 segments of the Missouri River since 2006 using QuickBird imagery. Interpretation of the state of sandbars is difficult when images for the segment are acquired at different river stages and different states of vegetation phenology and canopy cover. Concurrent QuickBird and RapidEye images were classified using the model and the spatial correspondence of classes in the land cover and sandbar maps were analysed for the spatial extent of the images and at nest locations for both bird species. Omission and commission errors were low for unvegetated land cover classes used for nesting by both bird species and for land cover types with continuous vegetation cover and water. Errors were larger for land cover classes characterized by a mixture of sand and vegetation. Sandbar classification decisions are made using information on land cover class proportions and disagreement between sandbar classes was resolved using fuzzy membership possibilities. Regression analysis of area for a paired sample of 47 sandbars indicated an average positive bias, 1.15 ha, for RapidEye that did not vary with sandbar size. RapidEye has potential to reduce temporal uncertainty about least tern and piping plover habitat but would not be suitable for mapping sandbar erosion, and characterization of sandbar shapes or vegetation patches at fine spatial resolution.

Cloud-based computation for accelerating vegetation mapping and change detection at regional to national scales

Treesearch

Matthew J. Gregory; Zhiqiang Yang; David M. Bell; Warren B. Cohen; Sean Healey; Janet L. Ohmann; Heather M. Roberts

2015-01-01

Mapping vegetation and landscape change at fine spatial scales is needed to inform natural resource and conservation planning, but such maps are expensive and time-consuming to produce. For Landsat-based methodologies, mapping efforts are hampered by the daunting task of manipulating multivariate data for millions to billions of pixels. The advent of cloud-based...

Vegetation Patterns and Degradation Thresholds in the Mulga Landscapes of Australia

NASA Astrophysics Data System (ADS)

Azadi, Samira; Saco, Patricia; Moreno-de las Heras, Mariano; Willgoose, Garry

2017-04-01

Drylands are often characterised by a spatially heterogeneous vegetation cover forming mosaics of patches dense vegetation within bare soil. This 'patterned' or 'patchy' vegetation cover is sensitive to human pressures. Previous work suggests that within these landscapes there is a critical vegetation cover threshold below which the landscape functionality is lost. This threshold behaviour is tightly linked to the overland flow redistribution and an increase in hydrologic connectivity that induces loss of resources (i.e., leakiness). In fact, disturbances (such as wildfire, overgrazing or harvesting activities) can disrupt the spatial structure of vegetation, increase landscape hydrologic connectivity, trigger erosion and produce a substantial loss of water. All these effects affect ecosystem functionality. Here we present the results of exploring the impact of degradation processes induced by vegetation disturbances (mainly grazing) on ecosystem functionality and connectivity in semiarid landscapes with various types of vegetation patterns. The sites are carefully selected in Mulga landscapes bioregion (New South Wales, Queensland) and in sites of Northern Territory in Australia, which display similar vegetation characteristics but with different vegetation patterns and good quality rainfall information. The analysis of vegetation patterns is derived from high resolution remote sensing images (IKONOS, QuickBird, Pleiades). Using MODIS NDVI and local precipitation data, we compute rainfall use efficiency and precipitation marginal response in order to assess the ecosystem functionality. We use vegetation binary maps and digital elevation models to estimate mean Flowlength as an indicator of structural hydrologic connectivity. We compare the trends for several sites with varying vegetation patterns (i.e., banded versus spotted patterns). Our results show that disturbances increase hydrologic connectivity and suggest threshold behaviour that affects landscape functionality. Though this threshold behaviour is found in all sites, the plots in higher rainfall landscapes with banded vegetation patterns show evidence of higher resilience. We will also present some preliminary modelling results that complement this analysis and capture the coevolution of vegetation and landforms (erosion), leading to this type of threshold behaviour.

Inexpensive Tools To Quantify And Map Vegetative Cover For Large-Scale Research Or Management Decisions.

USDA-ARS?s Scientific Manuscript database

Vegetative cover can be quantified quickly and consistently and often at lower cost with image analysis of color digital images than with visual assessments. Image-based mapping of vegetative cover for large-scale research and management decisions can now be considered with the accuracy of these met...

Biodiversity, ecology, and microelement composition of Kyzylkum Desert shrubs (Uzbekistan)

Treesearch

Lyuba A. Kapustina

2001-01-01

Geobotanic research and large-scale mapping with the help of Geographical Information System (GIS) permit us to find out the present state of Kyzylkum Desert shrublands, regularities of plant communities distribution, and chemical composition of the main dominant shrubs. Zonal vegetation types were formed on the basis of Old Xerophilous and Old Mediterranean floras in...

Use of LANDSAT imagery for wildlife habitat mapping in northeast and east central Alaska

NASA Technical Reports Server (NTRS)

Lent, P. C. (Principal Investigator)

1975-01-01

The author has identified the following significant results. Two scenes were analyzed by applying an iterative cluster analysis to a 2% random data sample and then using the resulting clusters as a training set basis for maximum likelihood classification. Twenty-six and twenty-seven categorical classes, respectively resulted from this process. The majority of classes in each case were quite specific vegetation types; each of these types has specific value as moose habitat.

Multi-temporal RADARSAT-1 and ERS backscattering signatures of coastal wetlands in southeastern Louisiana

USGS Publications Warehouse

Kwoun, Oh-Ig; Lu, Z.

2009-01-01

Using multi-temporal European Remote-sensing Satellites (ERS-1/-2) and Canadian Radar Satellite (RADARSAT-1) synthetic aperture radar (SAR) data over the Louisiana coastal zone, we characterize seasonal variations of radar backscat-tering according to vegetation type. Our main findings are as follows. First, ERS-1/-2 and RADARSAT-1 require careful radiometric calibration to perform multi-temporal backscattering analysis for wetland mapping. We use SAR backscattering signals from cities for the relative calibration. Second, using seasonally averaged backscattering coefficients from ERS-1/-2 and RADARSAT-1, we can differentiate most forests (bottomland and swamp forests) and marshes (freshwater, intermediate, brackish, and saline marshes) in coastal wetlands. The student t-test results support the usefulness of season-averaged backscatter data for classification. Third, combining SAR backscattering coefficients and an optical-sensor-based normalized difference vegetation index can provide further insight into vegetation type and enhance the separation between forests and marshes. Our study demonstrates that SAR can provide necessary information to characterize coastal wetlands and monitor their changes.

Simulation of boreal Summer Monsoon Rainfall using CFSV2_SSiB model: sensitivity to Land Use Land Cover (LULC)

NASA Astrophysics Data System (ADS)

Chilukoti, N.; Xue, Y.

2016-12-01

The land surface play a vital role in determining the surface energy budget, accurate representation of land use and land cover (LULC) is necessary to improve forecast. In this study, we have investigated the influence of surface vegetation maps with different LULC on simulating the boreal summer monsoon rainfall. Using a National Centres for Environmental Prediction (NCEP) Coupled Forecast System version 2(CFSv2) model coupled with Simplified Simple Biosphere (SSiB) model, two experiments were conducted: one with old vegetation map and one with new vegetation map. The significant differences between new and old vegetation map were in semi-arid and arid areas. For example, in old map Tibetan plateau classified as desert, which is not appropriate, while in new map it was classified as grasslands or shrubs with bare soil. Old map classified the Sahara desert as a bare soil and shrubs with bare soil, whereas in new map it was classified as bare ground. In addition to central Asia and the Sahara desert, in new vegetation map, Europe had more cropped area and India's vegetation cover was changed from crops and forests to wooded grassland and small areas of grassland and shrubs. The simulated surface air temperature with new map shows a significant improvement over Asia, South Africa, and northern America by some 1 to 2ºC and 2 to 3ºC over north east China and these are consistent with the reduced rainfall biases over Africa, near Somali coast, north east India, Bangladesh, east China sea, eastern Pacific and northern USA. Over Indian continent and bay of Bengal dry rainfall anomalies that is the only area showing large dry rainfall bias, however, they were unchanged with new map simulation. Overall the CFSv2(coupled with SSiB) model with new vegetation map show a promising result in improving the monsoon forecast by improving the Land -Atmosphere interactions. To compare with the LULC forcing, experiment was conducted using the Global Forecast System (GFS) simulations forced with different observed Sea Surface Temperatures (SST) for the same period: one is from NCEP reanalysis and one from Hadley Center. They have substantial difference in Indian Ocean. Preliminary analysis shows that, the impact of these two SST data sets on Indian summer monsoon rainfall has no significant impact.

Using Landsat MSS data with soils information to identify wetland habitats

NASA Technical Reports Server (NTRS)

Ernst, C. L.; Hoffer, R. M.

1981-01-01

A previous study showed that certain fresh water wetland vegetation types can be spectrally separated when a maximum likelihood classification procedure is applied to Landsat spectral data. However, wetland and upland types which have similar vegetative life forms (e.g., upland hardwoods and hardwood swamps) are often confused because of spectral similarity. Therefore, the current investigation attempts to differentiate similar wetland and upland types by combining Landsat multispectral scanner (MSS) data with soils information. The Pigeon River area in northern Indiana used in the earlier study was also employed in this investigation. A layered classification algorithm which combined soils and spectral data was used to generate a wetland classification. The results of the spectral/soils wetland classification are compared to the previous classification that had been based on spectral data alone. The results indicate wetland habitat mapping can be improved by combining soils and other ancillary data with Landsat spectral data.

A case study for evaluating potential soil sensitivity in aridland systems.

PubMed

Peterman, Wendy L; Ferschweiler, Ken

2016-04-01

Globally, ecosystems are subjected to prolonged droughts and extreme heat events, leading to forest die-offs and dominance shifts in vegetation. Some scientists and managers view soil as the main resource to be considered in monitoring ecosystem responses to aridification. As the medium through which precipitation is received, stored, and redistributed for plant use, soil is an important factor in the sensitivity of ecosystems to a drying climate. This study presents a novel approach to evaluating where on a landscape soils may be most sensitive to drying, making them less resilient to disturbance, and where potential future vegetation changes could lead to such disturbance. The drying and devegetation of arid lands can increase wind erosion, contributing to aerosol and dust emissions. This has implications for air quality, human health, and water resources. This approach combines soil data with vegetation simulations, projecting future vegetation change, to create maps of potential areas of concern for soil sensitivity and dust production in a drying climate. Consistent with recent observations, the projections show shifts from grasslands and woodlands to shrublands in much of the southwestern region. An increase in forested area occurs, but shifts in the dominant types and spatial distribution of the forests also are seen. A net increase in desert ecosystems in the region and some changes in alpine and tundra ecosystems are seen. Approximately 124,000 km(2) of soils flagged as "sensitive" are projected to have vegetation change between 2041 and 2050, and 82,927 km(2) of soils may become sensitive because of future vegetation changes. These maps give managers a way to visualize and identify where soils and vegetation should be investigated and monitored for degradation in a drying climate, so restoration and mitigation strategies can be focused in these areas. © 2015 SETAC.

Estimates of phytomass and net primary productivity in terrestrial ecosystems of the former Soviet Union identified by classified Global Vegetation Index

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gaston, G.G.; Kolchugina, T.P.

1995-12-01

Forty-two regions with similar vegetation and landcover were identified in the former Soviet Union (FSU) by classifying Global Vegetation Index (GVI) images. Image classes were described in terms of vegetation and landcover. Image classes appear to provide more accurate and precise descriptions for most ecosystems when compared to general thematic maps. The area of forest lands were estimated at 1,330 Mha and the actual area of forest ecosystems at 875 Mha. Arable lands were estimated to be 211 Mha. The area of the tundra biome was estimated at 261 Mha. The areas of the forest-tundra/dwarf forest, taiga, mixed-deciduous forest andmore » forest-steppe biomes were estimated t 153, 882, 196, and 144 Mha, respectively. The areas of desert-semidesert biome and arable land with irrigated land and meadows, were estimated at 126 and 237 Mha, respectively. Vegetation and landcover types were associated with the Bazilevich database of phytomass and NPP for vegetation in the FSU. The phytomass in the FSU was estimated at 97.1 Gt C, with 86.8 in forest vegetation, 9.7 in natural non-forest and 0.6 Gt C in arable lands. The NPP was estimated at 8.6 Gt C/yr, with 3.2, 4.8, and 0.6 Gt C/yr of forest, natural non-forest, and arable ecosystems, respectively. The phytomass estimates for forests were greater than previous assessments which considered the age-class distribution of forest stands in the FSU. The NPP of natural ecosystems estimated in this study was 23% greater than previous estimates which used thematic maps to identify ecosystems. 47 refs., 4 figs., 2 tabs.« less

Developing Land Surface Type Map with Biome Classification Scheme Using Suomi NPP/JPSS VIIRS Data

NASA Astrophysics Data System (ADS)

Zhang, Rui; Huang, Chengquan; Zhan, Xiwu; Jin, Huiran

2016-08-01

Accurate representation of actual terrestrial surface types at regional to global scales is an important element for a wide range of applications, such as land surface parameterization, modeling of biogeochemical cycles, and carbon cycle studies. In this study, in order to meet the requirement of the retrieval of global leaf area index (LAI) and fraction of photosynthetically active radiation absorbed by the vegetation (fPAR) and other studies, a global map generated from Suomi National Polar- orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) surface reflectance data in six major biome classes based on their canopy structures, which include: Grass/Cereal Crops, Shrubs, Broadleaf Crops, Savannas, Broadleaf Forests, and Needleleaf Forests, was created. The primary biome classes were converted from an International Geosphere-Biosphere Program (IGBP) legend global surface type data that was created in previous study, and the separation of two crop types are based on a secondary classification.

Linking in situ LAI and fine resolution remote sensing data to map reference LAI over cropland and grassland using geostatistical regression method

NASA Astrophysics Data System (ADS)

He, Yaqian; Bo, Yanchen; Chai, Leilei; Liu, Xiaolong; Li, Aihua

2016-08-01

Leaf Area Index (LAI) is an important parameter of vegetation structure. A number of moderate resolution LAI products have been produced in urgent need of large scale vegetation monitoring. High resolution LAI reference maps are necessary to validate these LAI products. This study used a geostatistical regression (GR) method to estimate LAI reference maps by linking in situ LAI and Landsat TM/ETM+ and SPOT-HRV data over two cropland and two grassland sites. To explore the discrepancies of employing different vegetation indices (VIs) on estimating LAI reference maps, this study established the GR models for different VIs, including difference vegetation index (DVI), normalized difference vegetation index (NDVI), and ratio vegetation index (RVI). To further assess the performance of the GR model, the results from the GR and Reduced Major Axis (RMA) models were compared. The results show that the performance of the GR model varies between the cropland and grassland sites. At the cropland sites, the GR model based on DVI provides the best estimation, while at the grassland sites, the GR model based on DVI performs poorly. Compared to the RMA model, the GR model improves the accuracy of reference LAI maps in terms of root mean square errors (RMSE) and bias.

Estimating Vegetation Structure in African Savannas using High Spatial Resolution Imagery

NASA Astrophysics Data System (ADS)

Axelsson, C.; Hanan, N. P.

2016-12-01

High spatial resolution satellite imagery allows for detailed mapping of trees in savanna landscapes, including estimates of woody cover, tree densities, crown sizes, and the spatial pattern of trees. By linking these vegetation parameters to rainfall and soil properties we gain knowledge of how the local environment influences vegetation. A thorough understanding of the underlying ecosystem processes is key to assessing the future productivity and stability of these ecosystems. In this study, we have processed and analyzed hundreds of sites sampled from African savannas across a wide range of rainfall and soil conditions. The vegetation at each site is classified using unsupervised classification with manual assignment into woody, herbaceous and bare cover classes. A crown delineation method further divides the woody areas into individual tree crowns. The results show that rainfall, soil, and topography interactively influence vegetation structure. We see that both total rainfall and rainfall seasonality play important roles and that soil type influences woody cover and the sizes of tree crowns.

An object-based approach for tree species extraction from digital orthophoto maps

NASA Astrophysics Data System (ADS)

Jamil, Akhtar; Bayram, Bulent

2018-05-01

Tree segmentation is an active and ongoing research area in the field of photogrammetry and remote sensing. It is more challenging due to both intra-class and inter-class similarities among various tree species. In this study, we exploited various statistical features for extraction of hazelnut trees from 1 : 5000 scaled digital orthophoto maps. Initially, the non-vegetation areas were eliminated using traditional normalized difference vegetation index (NDVI) followed by application of mean shift segmentation for transforming the pixels into meaningful homogeneous objects. In order to eliminate false positives, morphological opening and closing was employed on candidate objects. A number of heuristics were also derived to eliminate unwanted effects such as shadow and bounding box aspect ratios, before passing them into the classification stage. Finally, a knowledge based decision tree was constructed to distinguish the hazelnut trees from rest of objects which include manmade objects and other type of vegetation. We evaluated the proposed methodology on 10 sample orthophoto maps obtained from Giresun province in Turkey. The manually digitized hazelnut tree boundaries were taken as reference data for accuracy assessment. Both manually digitized and segmented tree borders were converted into binary images and the differences were calculated. According to the obtained results, the proposed methodology obtained an overall accuracy of more than 85 % for all sample images.

Monitoring grasshopper and locust habitats in Sahelian Africa using GIS and remote sensing technology

USGS Publications Warehouse

Tappan, G. Gray; Moore, Donald G.; Knauseberger, Walter I.

1991-01-01

Development programmes in Sahelian Africa are beginning to use geographic information system (GIS) technology. One of the GIS and remote sensing programmes introduced to the region in the late 1980s was the use of seasonal vegetation maps made from satellite data to support grasshopper and locust control. Following serious outbreaks of these pests in 1987, the programme addressed a critical need, by national and international crop protection organizations, to monitor site-specific dynamic vegetation conditions associated with grasshopper and locust breeding. The primary products used in assessing vegetation conditions were vegetation index (greenness) image maps derived from National Oceanic and Atmospheric Administration satellite imagery. Vegetation index data were integrated in a GIS with digital cartographic data of individual Sahelian countries. These near-real-time image maps were used regularly in 10 countries for locating potential grasshopper and locust habitats. The programme to monitor vegetation conditions is currently being institutionalized in the Sahel.

Mapping wetland and forest landscapes in Siberia with Landsat data

NASA Astrophysics Data System (ADS)

Maksyutov, Shamil; Kleptsova, Irina; Glagolev, Mikhail; Sedykh, Vladimir; Kuzmenko, Ekaterina; Silaev, Anton; Frolov, Alexander; Nikolaeva, Svetlana; Fedorov, Alexander

2014-05-01

Landsat data availability provides opportunity for improving the knowledge of the Siberian ecosystems necessary for quantifying the response of the regional carbon cycle to the climate change. We developed a new wetland map based on Landsat data for whole West Siberia aiming at scaling up the methane emission observations. Mid-summer Landsat scenes were used in supervised classification method, based on ground truth data obtained during multiple field surveys. The method allows distinguishing following wetland types: pine-dwarf shrubs-sphagnum bogs or ryams, ridge-hollows complexes, shallow-water complexes, sedge-sphagnum poor fens, herbaceous-sphagnum poor fens, sedge-(moss) poor fens and fens, wooded swamps or sogra, palsa complexes. In our estimates wetlands cover 36% of the taiga area. Total methane emission from WS taiga mires is estimated as 3.6 TgC/yr,which is 77% larger as compared to the earlier estimate based on partial Landsat mapping combined with low resolution map due to higher fraction of fen area. We make an attempt to develop a forest typology system useful for a dynamic vegetation modeling and apply it to the analysis of the forest type distribution for several test areas in West and East Siberia, aiming at capability of mapping whole Siberian forests based on Landsat data. Test region locations are: two in West Siberian middle taiga (Laryegan and Nyagan), and one in East Siberia near Yakutsk. The ground truth data are based on analysis of the field survey, forest inventory data from the point of view of the successional forest type classification. Supervised classification was applied to the areas where ample ground truth and inventory data are available, using several limited area maps and vegetation survey. In Laryegan basin the upland forest areas are dominated (as climax forest species) by Scots pine on sandy soils and Siberian pine with presence of fir and spruce on the others. Those types are separable using Landsat spectral data alone. In the permafrost area around Yakutsk the most widespread succession type is birch to larch succession. Three stages of the birch to larch succession are detectable from Landsat image. When Landsat data is used in both West and East Siberia, distinction between deciduous broad-leaved species (birch, aspen, and willow) is difficult due to similarity in spectral signatures. Same problem exists for distinguishing between dark coniferous species (Siberian pine, fir and spruce). Forest classification can be improved by applying landscape type analysis, such as separation into floodplain, terrace, sloping hills.

Rule-based mapping of fire-adapted vegetation and fire regimes for the Monongahela National Forest

Treesearch

Melissa A. Thomas-Van Gundy; Gregory J. Nowacki; Thomas M. Schuler

2007-01-01

A rule-based approach was employed in GIS to map fire-adapted vegetation and fire regimes within the proclamation boundary of the Monongahela National Forest. Spatial analyses and maps were generated using ArcMap 9.1. The resulting fireadaptation scores were then categorized into standard fire regime groups. Fire regime group V (200+ yrs) was the most common, assigned...

Identification of marsh vegetation and coastal land use in ERTS-1 imagery

NASA Technical Reports Server (NTRS)

Klemas, V.; Daiber, F. C.; Bartlett, D. S.

1973-01-01

Coastal vegetation species appearing in the ERTS-1 images taken of Delaware Bay on August 16, and October 10, 1972 have been correlated with ground truth vegetation maps and imagery obtained from high altitude RB-57 and U-2 overflights. The vegetation maps of the entire Delaware Coast were prepared during the summer of 1972 and checked out with ground truth data collected on foot, in small boats, and from low-altitude aircraft. Multispectral analysis of high altitude RB-57 and U-2 photographs indicated that five vegetation communities could be clearly discriminated from 60,000 feet altitude including: (1) salt marsh cord grass, (2) salt marsh hay and spike grass, (3) reed grass, (4) high tide bush and sea myrtle, and (5) a group of fresh water species found in impoundments built to attract water fowl. All of these species are shown in fifteen overlay maps, covering all of Delaware's wetlands prepared to match the USGS topographic map size of 1:24,000.

Use of remote sensing techniques for geological hazard surveys in vegetated urban regions. [multispectral imagery for lithological mapping

NASA Technical Reports Server (NTRS)

Stow, S. H.; Price, R. C.; Hoehner, F.; Wielchowsky, C.

1976-01-01

The feasibility of using aerial photography for lithologic differentiation in a heavily vegetated region is investigated using multispectral imagery obtained from LANDSAT satellite and aircraft-borne photography. Delineating and mapping of localized vegetal zones can be accomplished by the use of remote sensing because a difference in morphology and physiology results in different natural reflectances or signatures. An investigation was made to show that these local plant zones are affected by altitude, topography, weathering, and gullying; but are controlled by lithology. Therefore, maps outlining local plant zones were used as a basis for lithologic map construction.

Circumpolar Arctic vegetation mapping workshop

USGS Publications Warehouse

Walker, D. A.; Markon, C.J.

1996-01-01

The first Circumpolar Arctic Vegetation Mapping Workshop was held in the historic village of Lakta on the outskirts of St. Petersburg, Russia, March 21-25, 1994. The primary goals of the workshop were to: (1) review the status of arctic vegetation mapping in the circumpolar countries and (2) develop a strategy for synthesizing and updating the existing information into a new series of maps that portray the current state of knowledge. Such products are important for a number of purposes, such as the international effort to understand the consequences of global change in Arctic regions, to predict the direction of future changes, and for informed planning of resource development in the Arctic.

Association between mapped vegetation and Quaternary geology on Santa Rosa Island, California

NASA Astrophysics Data System (ADS)

Cronkite-Ratcliff, C.; Corbett, S.; Schmidt, K. M.

2017-12-01

Vegetation and surficial geology are closely connected through the interface generally referred to as the critical zone. Not only do they influence each other, but they also provide clues into the effects of climate, topography, and hydrology on the earth's surface. This presentation describes quantitative analyses of the association between the recently compiled, independently generated vegetation and geologic map units on Santa Rosa Island, part of the Channel Islands National Park in Southern California. Santa Rosa Island was heavily grazed by sheep and cattle ranching for over one hundred years prior to its acquisition by the National Park Service. During this period, the island experienced significant erosion and spatial reduction and diversity of native plant species. Understanding the relationship between geology and vegetation is necessary for monitoring the recovery of native plant species, enhancing the viability of restoration sites, and understanding hydrologic conditions favorable for plant growth. Differences in grain size distribution and soil depth between geologic units support different plant communities through their influence on soil moisture, while differences in unit age reflect different degrees of pedogenic maturity. We find that unsupervised machine learning methods provide more informative insight into vegetation-geology associations than traditional measures such as Cramer's V and Goodman and Kruskal's lambda. Correspondence analysis shows that unique vegetation-geology patterns associated with beach/dune, grassland, hillslope/colluvial, and fluvial/wetland environments can be discerned from the data. By combining geology and vegetation with topographic variables, mixture models can be used to partition the landscape into multiple representative types, which then be compared with conceptual models of plant growth and succession over different landforms. Using this collection of methods, we show various ways that that Quaternary geology provides valuable information on the distribution of vegetation species in recovering ecosystems. Going forward, these analyses provide insights on favorable areas for natural and managed recovery of native vegetation species as well as criteria for future field sampling and monitoring.

Seeing the trees for the forest: mapping vegetation biodiversity in coastal Oregon forests.

Treesearch

Sally. Duncan

2003-01-01

In order to address policy issues relating to biodiversity, productivity, and sustainability, we need detailed understanding of forest vegetation at broad geographic and time scales. Most existing maps developed from satellite imagery describe only general characteristics of the upper canopy. Detailed vegetation data are available from regional grids of field plots,...

Solar radiation as a global driver of hillslope asymmetry: Insights from an ecogeomorphic landscape evolution model

NASA Astrophysics Data System (ADS)

Yetemen, Omer; Istanbulluoglu, Erkan; Duvall, Alison R.

2015-12-01

Observations at the field, catchment, and continental scales across a range of arid and semiarid climates and latitudes reveal aspect-controlled patterns in soil properties, vegetation types, ecohydrologic fluxes, and hillslope morphology. Although the global distribution of solar radiation on earth's surface and its implications on vegetation dynamics are well documented, we know little about how variation of solar radiation across latitudes influence landscape evolution and resulting geomorphic difference. Here, we used a landscape evolution model that couples the continuity equations for water, sediment, and aboveground vegetation biomass at each model element in order to explore the controls of latitude and mean annual precipitation (MAP) on the development of hillslope asymmetry (HA). In our model, asymmetric hillslopes emerged from the competition between soil creep and vegetation-modulated fluvial transport, driven by spatial distribution of solar radiation. Latitude was a primary driver of HA because of its effects on the global distribution of solar radiation. In the Northern Hemisphere, north-facing slopes (NFS), which support more vegetation cover and have lower transport efficiency, get steeper toward the North Pole while south-facing slopes (SFS) get gentler. In the Southern Hemisphere, the patterns are reversed and SFS get steeper toward the South Pole. For any given latitude, MAP is found to have minor control on HA. Our results underscore the potential influence of solar radiation as a global control on the development of asymmetric hillslopes in fluvial landscapes.

Predictive mapping of soil organic carbon in wet cultivated lands using classification-tree based models: the case study of Denmark.

PubMed

Bou Kheir, Rania; Greve, Mogens H; Bøcher, Peder K; Greve, Mette B; Larsen, René; McCloy, Keith

2010-05-01

Soil organic carbon (SOC) is one of the most important carbon stocks globally and has large potential to affect global climate. Distribution patterns of SOC in Denmark constitute a nation-wide baseline for studies on soil carbon changes (with respect to Kyoto protocol). This paper predicts and maps the geographic distribution of SOC across Denmark using remote sensing (RS), geographic information systems (GISs) and decision-tree modeling (un-pruned and pruned classification trees). Seventeen parameters, i.e. parent material, soil type, landscape type, elevation, slope gradient, slope aspect, mean curvature, plan curvature, profile curvature, flow accumulation, specific catchment area, tangent slope, tangent curvature, steady-state wetness index, Normalized Difference Vegetation Index (NDVI), Normalized Difference Wetness Index (NDWI) and Soil Color Index (SCI) were generated to statistically explain SOC field measurements in the area of interest (Denmark). A large number of tree-based classification models (588) were developed using (i) all of the parameters, (ii) all Digital Elevation Model (DEM) parameters only, (iii) the primary DEM parameters only, (iv), the remote sensing (RS) indices only, (v) selected pairs of parameters, (vi) soil type, parent material and landscape type only, and (vii) the parameters having a high impact on SOC distribution in built pruned trees. The best constructed classification tree models (in the number of three) with the lowest misclassification error (ME) and the lowest number of nodes (N) as well are: (i) the tree (T1) combining all of the parameters (ME=29.5%; N=54); (ii) the tree (T2) based on the parent material, soil type and landscape type (ME=31.5%; N=14); and (iii) the tree (T3) constructed using parent material, soil type, landscape type, elevation, tangent slope and SCI (ME=30%; N=39). The produced SOC maps at 1:50,000 cartographic scale using these trees are highly matching with coincidence values equal to 90.5% (Map T1/Map T2), 95% (Map T1/Map T3) and 91% (Map T2/Map T3). The overall accuracies of these maps once compared with field observations were estimated to be 69.54% (Map T1), 68.87% (Map T2) and 69.41% (Map T3). The proposed tree models are relatively simple, and may be also applied to other areas. Copyright 2010 Elsevier Ltd. All rights reserved.

Distribution of submerged aquatic vegetation in the St. Louis River estuary: Maps and models

EPA Science Inventory

In late summer of 2011 and 2012 we used echo-sounding gear to map the distribution of submerged aquatic vegetation (SAV) in the St. Louis River Estuary (SLRE). From these data we produced maps of SAV distribution and we created logistic models to predict the probability of occurr...

Shaping the Herders' "Mental Maps": Participatory Mapping with Pastoralists' to Understand Their Grazing Area Differentiation and Characterization

NASA Astrophysics Data System (ADS)

Wario, Hussein T.; Roba, Hassan G.; Kaufmann, Brigitte

2015-09-01

Understanding the perception of environmental resources by the users is an important element in planning its sustainable use and management. Pastoralist communities manage their vast grazing territories and exploit resource variability through strategic mobility. However, the knowledge on which pastoralists' resource management is based and their perception of the grazing areas has received limited attention. To improve this understanding and to document this knowledge in a way that can be communicated with `outsiders', we adopted a participatory mapping approach using satellite imagery to explore how Borana pastoralists of southern Ethiopia differentiated and characterized their grazing areas. The Borana herders conceptualized their grazing areas as set of distinctive grazing units each having specific names and characteristics. The precise location and the borders of each grazing unit were identified on the satellite image. In naming of the grazing units, the main differentiating criteria were landforms, vegetation types, prevalence of wildlife species, and manmade features. Based on the dominant soil type, the grazing units were aggregated into seasonal grazing areas that were described using factors such as soil drainage properties, extent of woody cover, main grass species, and prevalence of ecto-parasites. Pastoralists ranking of the seasonal grazing areas according to their suitability for cattle grazing matched with vegetation assessment results on the abundance of desirable fodder varieties. Approaching grazing area differentiation from the pastoralists' perspectives improves the understanding of rangeland characteristics that pastoralists considered important in their grazing management and visualization of their mental representation in digital maps eases communication of this knowledge.

A Biome map for Modelling Global Mid-Pliocene Climate Change

NASA Astrophysics Data System (ADS)

Salzmann, U.; Haywood, A. M.

2006-12-01

The importance of vegetation-climate feedbacks was highlighted by several paleo-climate modelling exercises but their role as a boundary condition in Tertiary modelling has not been fully recognised or explored. Several paleo-vegetation datasets and maps have been produced for specific time slabs or regions for the Tertiary, but the vegetation classifications that have been used differ, thus making meaningful comparisons difficult. In order to facilitate further investigations into Tertiary climate and environmental change we are presently implementing the comprehensive GIS database TEVIS (Tertiary Environment and Vegetation Information System). TEVIS integrates marine and terrestrial vegetation data, taken from fossil pollen, leaf or wood, into an internally consistent classification scheme to produce for different time slabs global Tertiary Biome and Mega- Biome maps (Harrison & Prentice, 2003). In the frame of our ongoing 5-year programme we present a first global vegetation map for the mid-Pliocene time slab, a period of sustained global warmth. Data were synthesised from the PRISM data set (Thompson and Fleming 1996) after translating them to the Biome classification scheme and from new literature. The outcomes of the Biome map are compared with modelling results using an advanced numerical general circulation model (HadAM3) and the BIOME 4 vegetation model. Our combined proxy data and modelling approach will provide new palaeoclimate datasets to test models that are used to predict future climate change, and provide a more rigorous picture of climate and environmental changes during the Neogene.

Monitoring Corals and Submerged Aquatic Vegetation in Western Pacific Using Satellite Remote Sensing Integrated with Field Data

NASA Astrophysics Data System (ADS)

Roelfsema, C. M.; Phinn, S. R.; Lyons, M. B.; Kovacs, E.; Saunders, M. I.; Leon, J. X.

2013-12-01

Corals and Submerged Aquatic Vegetation (SAV) are typically found in highly dynamic environments where the magnitude and types of physical and biological processes controlling their distribution, diversity and function changes dramatically. Recent advances in the types of satellite image data and the length of their archives that are available globally, coupled with new techniques for extracting environmental information from these data sets has enabled significant advances to be made in our ability to map and monitor coral and SAV environments. Object Based Image Analysis techniques are one of the most significant advances in information extraction techniques for processing images to deliver environmental information at multiple spatial scales. This poster demonstrates OBIA applied to high spatial resolution satellite image data to map and monitor coral and SAV communities across a variety of environments in the Western Pacific that vary in their extent, biological composition, forcing physical factors and location. High spatial resolution satellite imagery (Quickbird, Ikonos and Worldview2) were acquired coincident with field surveys on each reef to collect georeferenced benthic photo transects, over various areas in the Western Pacific. Base line maps were created, from Roviana Lagoon Solomon island (600 km2), Bikini Atoll Marshall Island (800 Km2), Lizard Island, Australia (30 km2) and time series maps for geomorphic and benthic communities were collected for Heron Reef, Australia (24 km2) and Eastern Banks area of Moreton Bay, Australia (200 km2). The satellite image data were corrected for radiometric and atmospheric distortions to at-surface reflectance. Georeferenced benthic photos were acquired by divers or Autonomous Underwater Vehicles, analysed for benthic cover composition, and used for calibration and validation purposes. Hierarchical mapping from: reef/non-reef (1000's - 10000's m); reef type (100's - 1000's m); 'geomorphic zone' (10's - 100's m); to dominant components of benthic cover compositions (1 - 10's m); and individual benthic cover type scale (0.5-5.0's m), was completed using object based segmentation and semi-automated labelling through membership rules. Accuracy assessment of the satellite image based maps and field data sets scales maps produced with 90% maximum accuracy larger scales and less complex maps, versus 40 % at smaller scale and complex maps. The study showed that current data sets and object based analysis are able to reliable map at various scales and level of complexity covering a variety of extent and environments at various times; as a result science and management can use these tools to assess and understand the ecological processes taking place in coral and SAV environments.

Detecting emergence, growth, and senescence of wetland vegetation with polarimetric synthetic aperture radar (SAR) data

USGS Publications Warehouse

Gallant, Alisa L.; Kaya, Shannon G.; White, Lori; Brisco, Brian; Roth, Mark F.; Sadinski, Walter J.; Rover, Jennifer

2014-01-01

Wetlands provide ecosystem goods and services vitally important to humans. Land managers and policymakers working to conserve wetlands require regularly updated information on the statuses of wetlands across the landscape. However, wetlands are challenging to map remotely with high accuracy and consistency. We investigated the use of multitemporal polarimetric synthetic aperture radar (SAR) data acquired with Canada’s Radarsat-2 system to track within-season changes in wetland vegetation and surface water. We speculated, a priori, how temporal and morphological traits of different types of wetland vegetation should respond over a growing season with respect to four energy-scattering mechanisms. We used ground-based monitoring data and other ancillary information to assess the limits and consistency of the SAR data for tracking seasonal changes in wetlands. We found the traits of different types of vertical emergent wetland vegetation were detected well with the SAR data and corresponded with our anticipated backscatter responses. We also found using data from Landsat’s optical/infrared sensors in conjunction with SAR data helped remove confusion of wetland features with upland grasslands. These results suggest SAR data can provide useful monitoring information on the statuses of wetlands over time.

Post-fire vegetation recovery in Portugal based on spot/vegetation data

NASA Astrophysics Data System (ADS)

Gouveia, C.; Dacamara, C. C.; Trigo, R. M.

2010-04-01

A procedure is presented that allows identifying large burned scars and the monitoring of vegetation recovery in the years following major fire episodes. The procedure relies on 10-day fields of Maximum Value Composites of Normalized Difference Vegetation Index (MVC-NDVI), with a 1 km×1 km spatial resolution obtained from the VEGETATION instrument. The identification of fire scars during the extremely severe 2003 fire season is performed based on cluster analysis of NDVI anomalies that persist during the vegetative cycle of the year following the fire event. Two regions containing very large burned scars were selected, located in Central and Southwestern Portugal, respectively, and time series of MVC-NDVI analysed before the fire events took place and throughout the post-fire period. It is shown that post-fire vegetation dynamics in the two selected regions may be characterised based on maps of recovery rates as estimated by fitting a monoparametric model of vegetation recovery to MVC-NDVI data over each burned scar. Results indicated that the recovery process in the region located in Central Portugal is mostly related to fire damage rather than to vegetation density before 2003, whereas the latter seems to have a more prominent role than vegetation conditions after the fire episode, e.g. in the case of the region in Southwestern Portugal. These differences are consistent with the respective predominant types of vegetation. The burned area located in Central Portugal is dominated by Pinus Pinaster whose natural regeneration crucially depends on the destruction of seeds present on the soil surface during the fire, whereas the burned scar in Southwestern Portugal was populated by Eucalyptus that may quickly re-sprout from buds after fire. Besides its simplicity, the monoparametric model of vegetation recovery has the advantage of being easily adapted to other low-resolution satellite data, as well as to other types of vegetation indices.

The mapping of marsh vegetation using aircraft multispectral scanner data. [in Louisiana

NASA Technical Reports Server (NTRS)

Butera, M. K.

1975-01-01

A test was conducted to determine if salinity regimes in coastal marshland could be mapped and monitored by the identification and classification of marsh vegetative species from aircraft multispectral scanner data. The data was acquired at 6.1 km (20,000 ft.) on October 2, 1974, over a test area in the coastal marshland of southern Louisiana including fresh, intermediate, brackish, and saline zones. The data was classified by vegetational species using a supervised, spectral pattern recognition procedure. Accuracies of training sites ranged from 67% to 96%. Marsh zones based on free soil water salinity were determined from the species classification to demonstrate a practical use for mapping marsh vegetation.

Improved regional-scale Brazilian cropping systems' mapping based on a semi-automatic object-based clustering approach

NASA Astrophysics Data System (ADS)

Bellón, Beatriz; Bégué, Agnès; Lo Seen, Danny; Lebourgeois, Valentine; Evangelista, Balbino Antônio; Simões, Margareth; Demonte Ferraz, Rodrigo Peçanha

2018-06-01

Cropping systems' maps at fine scale over large areas provide key information for further agricultural production and environmental impact assessments, and thus represent a valuable tool for effective land-use planning. There is, therefore, a growing interest in mapping cropping systems in an operational manner over large areas, and remote sensing approaches based on vegetation index time series analysis have proven to be an efficient tool. However, supervised pixel-based approaches are commonly adopted, requiring resource consuming field campaigns to gather training data. In this paper, we present a new object-based unsupervised classification approach tested on an annual MODIS 16-day composite Normalized Difference Vegetation Index time series and a Landsat 8 mosaic of the State of Tocantins, Brazil, for the 2014-2015 growing season. Two variants of the approach are compared: an hyperclustering approach, and a landscape-clustering approach involving a previous stratification of the study area into landscape units on which the clustering is then performed. The main cropping systems of Tocantins, characterized by the crop types and cropping patterns, were efficiently mapped with the landscape-clustering approach. Results show that stratification prior to clustering significantly improves the classification accuracies for underrepresented and sparsely distributed cropping systems. This study illustrates the potential of unsupervised classification for large area cropping systems' mapping and contributes to the development of generic tools for supporting large-scale agricultural monitoring across regions.

Incorporating Sentinel-2-like remote sensing products in the hydrometeorological modelling over an agricultural area in south west France

NASA Astrophysics Data System (ADS)

Rivalland, Vincent; Gascoin, Simon; Etchanchu, Jordi; Coustau, Mathieu; Cros, Jérôme; Tallec, Tiphaine

2016-04-01

The Sentinel-2 mission will enable to monitor the land cover and the vegetation phenology at high-resolution (HR) every 5 days. However, current Land Surface Models (LSM) typically use land cover and vegetation parameters derived from previous low to mid resolution satellite missions. Here we studied the effect of introducing Sentinel-2-like data in the simulation of the land surface energy and water fluxes in a region dominated by cropland. Simulations were performed with the ISBA-SURFEX LSM, which is used in the operational hydrometeorological chain of Meteo-France for hydrological forecasts and drought monitoring. By default, SURFEX vegetation land surface parameters and temporal evolution are from the ECOCLIMAP II European database mostly derived from MODIS products at 1 km resolution. The model was applied to an experimental area of 30 km by 30 km in south west France. In this area the resolution of ECOCLIMAP is coarser than the typical size of a crop field. This means that several crop types can be mixed in a pixel. In addition ECOCLIMAP provides a climatology of the vegetation phenology and thus does not account for the interannual effects of the climate and land management on the crop growth. In this work, we used a series of 26 Formosat-2 images at 8-m resolution acquired in 2006. From this dataset, we derived a land cover map and a leaf area index map (LAI) at each date, which were substituted to the ECOCLIMAP land cover map and the LAI maps. The model output water and energy fluxes were compared to a standard simulation using ECOCLIMAP only and to in situ measurements of soil moisture, latent and sensible heat fluxes. The results show that the introduction of the HR products improved the timing of the evapotranspiration. The impact was the most visible on the crops having a growing season in summer (maize, sunflower), because the growth period is more sensitive to the climate.

Shifting Environmental Ranges and Biome Potential According to the Whittaker Relationship

NASA Astrophysics Data System (ADS)

de Jong, R.; Garonna, I.; Schaepman, M. E.

2015-12-01

Robert H. Whittaker classified biome types mainly as a function of Mean Annual Temperature (MAT) and Mean Annual Precipitation (MAP), resulting in the well-known Whittaker plot1. This relationship is still being used to map biomes globally2. The same inputs (MAT and MAP), augmented with a radiation proxy, are used in the resource-balance perspective for modeling large-scale vegetation productivity as a function of abiotic factors3. These two approaches, used in a temporally dynamic manner, provided us indicators of shifts in growth-limiting factors4 and associated environmental ranges of vegetation, which, in turn, are key indicators for the study of global change and biodiversity5. We present a study in which we used the Whittaker relationship and CRU TS 3.22 climatic data to map regions that showed variable biome potential. These regions are likely to indicate ecotones - i.e. interactions zones between biomes - that have been subject to abiotic change and where a change in the vegetation system can be anticipated. At the same time, we used remotely sensed data (GIMMS v3g 1982-2012) to study gradients in vegetation dynamics in these zones. Preliminary results show strongest environmental shifts in northern ecotones, e.g. on the tundra - boreal boundary, and associated changes in climatic growth-limiting factors4. [1] Whittaker RH (1975) Communities and Ecosystems, Macmillan, 385p.[2] Ricklefs RE (2008) The Economy of Nature, W. H. Freeman, 620p.[3] Field CB, Randerson JT, Malmström CM (1995) Global net primary production: Combining ecology and remote sensing. Remote Sensing of Environment, 51, 74-88.[4] Schenkel D, Garonna I, De Jong R, Schaepman ME (this conference) Linking Land Surface Phenology and Growth Limiting Factor Shifts over the Past 30 Years.[5] University of Zurich Research Priority Program on Global Change and Biodiversity, http://www.gcb.uzh.ch

Historical and contemporary geographic data reveal complex spatial and temporal responses of vegetation to climate and land stewardship

USGS Publications Warehouse

Villarreal, Miguel L.; Norman, Laura M.; Webb, Robert H.; Turner, Raymond M.

2013-01-01

Vegetation and land-cover changes are not always directional but follow complex trajectories over space and time, driven by changing anthropogenic and abiotic conditions. We present a multi-observational approach to land-change analysis that addresses the complex geographic and temporal variability of vegetation changes related to climate and land use. Using land-ownership data as a proxy for land-use practices, multitemporal land-cover maps, and repeat photography dating to the late 19th century, we examine changing spatial and temporal distributions of two vegetation types with high conservation value in the southwestern United States: grasslands and riparian vegetation. In contrast to many reported vegetation changes, notably shrub encroachment in desert grasslands, we found an overall increase in grassland area and decline of xeroriparian and riparian vegetation. These observed change patterns were neither temporally directional nor spatially uniform over the landscape. Historical data suggest that long-term vegetation changes coincide with broad climate fluctuations while fine-scale patterns are determined by land-management practices. In some cases, restoration and active management appear to weaken the effects of climate on vegetation; therefore, if land managers in this region act in accord with on-going directional changes, the current drought and associated ecological reorganization may provide an opportunity to achieve desired restoration endpoints.

Monitoring vegetation recovery in fire-affected areas using temporal profiles of spectral signal from time series MODIS and LANDSAT satellite images

NASA Astrophysics Data System (ADS)

Georgopoulou, Danai; Koutsias, Nikos

2015-04-01

Vegetation phenology is an important element of vegetation characteristics that can be useful in vegetation monitoring especially when satellite remote sensing observations are used. In that sense temporal profiles extracted from spectral signal of time series MODIS and LANDSAT satellite images can be used to characterize vegetation phenology and thus to be helpful for monitoring vegetation recovery in fire-affected areas. The aim of this study is to explore the vegetation recovery pattern of the catastrophic wildfires that occurred in Peloponnisos, southern Greece, in 2007. These fires caused the loss of 67 lives and were recognized as the most extreme natural disaster in the country's recent history. Satellite remote sensing data from MODIS and LANDSAT satellites in the period from 2000 to 2014 were acquired and processed to extract the temporal profiles of the spectral signal for selected areas within the fire-affected areas. This dataset and time period analyzed together with the time that these fires occurred gave the opportunity to create temporal profiles seven years before and seven years after the fire. The different scale of the data used gave us the chance to understand how vegetation phenology and therefore the recovery patterns are influenced by the spatial resolution of the satellite data used. Different metrics linked to key phenological events have been created and used to assess vegetation recovery in the fire-affected areas. Our analysis was focused in the main land cover types that were mostly affected by the 2007 wildland fires. Based on CORINE land-cover maps these were agricultural lands highly interspersed with large areas of natural vegetation followed by sclerophyllous vegetation, transitional woodland shrubs, complex cultivation patterns and olive groves. Apart of the use of the original spectral data we estimated and used vegetation indices commonly found in vegetation studies as well as in burned area mapping studies. In this study we explore the strength and the use of these time series satellite data to characterize vegetation phenology as an a aid to monitor vegetation recovery in fire affected-areas. In a recent study we found that the original spectral channels, based on which these indices are estimated, are sensitive to external vegetation parameters such as the spectral reflectance of the background soil. In such cases, the influence of the soil in the reflectance values is different in the various spectral regions depending on its type. The use of such indices is also justified according to a recent study on the sensitivity of spectral reflectance values to different burn and vegetation ratios, who concluded that the Near Infrared (NIR) and Short-Wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas. Additionally, it has been found that semi-burned classes are spectrally more consistent to their different fractions of scorched and non-scorched vegetation, than the original spectral channels based on which these indices are estimated.

VEGETATION COVER ANALYSIS OF HAZARDOUS WASTE SITES IN UTAH AND ARIZONA USING HYPERSPECTRAL REMOTE SENSING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Serrato, M.; Jungho, I.; Jensen, J.

2012-01-17

Remote sensing technology can provide a cost-effective tool for monitoring hazardous waste sites. This study investigated the usability of HyMap airborne hyperspectral remote sensing data (126 bands at 2.3 x 2.3 m spatial resolution) to characterize the vegetation at U.S. Department of Energy uranium processing sites near Monticello, Utah and Monument Valley, Arizona. Grass and shrub species were mixed on an engineered disposal cell cover at the Monticello site while shrub species were dominant in the phytoremediation plantings at the Monument Valley site. The specific objectives of this study were to: (1) estimate leaf-area-index (LAI) of the vegetation using threemore » different methods (i.e., vegetation indices, red-edge positioning (REP), and machine learning regression trees), and (2) map the vegetation cover using machine learning decision trees based on either the scaled reflectance data or mixture tuned matched filtering (MTMF)-derived metrics and vegetation indices. Regression trees resulted in the best calibration performance of LAI estimation (R{sup 2} > 0.80). The use of REPs failed to accurately predict LAI (R{sup 2} < 0.2). The use of the MTMF-derived metrics (matched filter scores and infeasibility) and a range of vegetation indices in decision trees improved the vegetation mapping when compared to the decision tree classification using just the scaled reflectance. Results suggest that hyperspectral imagery are useful for characterizing biophysical characteristics (LAI) and vegetation cover on capped hazardous waste sites. However, it is believed that the vegetation mapping would benefit from the use of 1 higher spatial resolution hyperspectral data due to the small size of many of the vegetation patches (< 1m) found on the sites.« less

Radar Images of the Earth and the World Wide Web

NASA Technical Reports Server (NTRS)

Chapman, B.; Freeman, A.

1995-01-01

A perspective of NASA's Jet Propulsion Laboratory as a center of planetary exploration, and its involvement in studying the earth from space is given. Remote sensing, radar maps, land topography, snow cover properties, vegetation type, biomass content, moisture levels, and ocean data are items discussed related to earth orbiting satellite imaging radar. World Wide Web viewing of this content is discussed.

Federal research natural areas in Oregon and Washington: a guidebook for scientists and educators.

Treesearch

Jerry F. Franklin; Fredrick C. Hall; C. T. Dyrness; Chris Maser

1972-01-01

A guide to the use of natural scientific preserves, Research Natural Areas, on Federal lands in Oregon and Washington. Detailed descriptions of physical and biological features, maps and photographs are provided for each of the 45 tracts presently reserved. Indices to Research Natural Areas by vegetation type and plant and mammalian species are included.

High-severity fire: Evaluating its key drivers and mapping its probability across western US forests

Treesearch

Sean A. Parks; Lisa M. Holsinger; Matthew H. Panunto; W. Matt Jolly; Solomon Z. Dobrowski; Gregory K. Dillon

2018-01-01

Wildland fire is a critical process in forests of the western United States (US). Variation in fire behavior, which is heavily influenced by fuel loading, terrain, weather, and vegetation type, leads to heterogeneity in fire severity across landscapes. The relative influence of these factors in driving fire severity, however, is poorly understood. Here, we explore the...

Humboldt Bay Wetlands Review and Baylands Analysis. Volume III. Habitat Classification and Mapping and Appendices.

DTIC Science & Technology

1980-08-01

also a mobile substrate habitat type, but not the massive dunes described previously; some vegetation is established. Most foredunes along the coastal...wvith the Fish and Wildlife Co~ordiinatioin ccnii h’ should be cdirected toe ard tin’, still Sit i~l~( ie . apliC ii n lilt Act IS 320.3ft Obovei

The use of multi-temporal Landsat Normalized Difference Vegetation Index (NDVI) data for mapping fuels in Yosemite National Park, USA

USGS Publications Warehouse

Van Wagtendonk, Jan W.; Root, Ralph R.

2003-01-01

The objective of this study was to test the applicability of using Normalized Difference Vegetation Index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground.

Quantifying spatial patterns in the Yakama Nation Tribal Forest and Okanogan-Wenatchee National Forest to assess forest health

NASA Astrophysics Data System (ADS)

Wilder, T. F.

2013-05-01

Over the past century western United States have experienced drastic anthropogenic land use change from practices such as agriculture, fire exclusion, and timber harvesting. These changes have complex social, cultural, economic, and ecological interactions and consequences. This research studied landscapes patterns of watersheds with similar LANDFIRE potential vegetation in the Southern Washington Cascades physiographic province, within the Yakama Nation Tribal Forest (YTF) and Okanogan-Wenatchee National Forest, Naches Ranger District (NRD). In the selected watersheds, vegetation-mapping units were delineated and populated based on physiognomy of homogeneous areas of vegetative composition and structure using high-resolution aerial photos. Cover types and structural classes were derived from the raw, photo-interpreted vegetation attributes for individual vegetation mapping units and served as individual and composite response variables to quantify and assess spatial patterns and forest health conditions between the two ownerships. Structural classes in both the NRD and YTF were spatially clustered (Z-score 3.1, p-value 0.01; Z-score 2.3, p-value 0.02, respectively), however, ownership and logging type both explained a significant amount of variance in structural class composition. Based on FRAGSTATS landscape metrics, structural classes in the NRD displayed greater clustering and fragmentation with lower interspersion relative to the YTF. The NRD landscape was comprised of 47.4% understory reinitiation structural class type and associated high FRAGASTAT class metrics demonstrated high aggregation with moderate interspersion. Stem exclusion open canopy displayed the greatest dispersal of structural class types throughout the NRD, but adjacencies were correlated to other class types. In the YTF, stem exclusion open canopy comprised 37.7% of the landscape and displayed a high degree of aggregation and interspersion about clusters throughout the YTF. Composite cover type-structural class spatial autocorrelation was clustered in the NRD (Z-score 5.1, p-value 0.01), while the YTF exhibited a random spatial pattern. After accounting for location effects, logging type was the most significant factor explaining variation in composite cover-structure composition. FRAGSTATS landscape metrics identified composite cover-structure classes in the NRD displayed greater aggregation and fragmentation with lower interspersion relative to the YTF. The NRD landscape was comprised of 30.5% Pinus ponderosa-understory reinitiation and associated class metrics demonstrated a high degree of aggregation and fragmentation with low interspersion. Pinus ponderosa-stem exclusion open canopy comprised 24.6% of the YTF landscape and associated class metrics displayed moderate aggregation and fragmentation with high interspersion. A discussion integrating the results and existing relevant literature was indited to assess management regime influences on landscape patterns and, in turn, forest health attributes. This dialog is in provision of enhancing collaboration to optimize forest-health restoration activities across ownerships throughout the study area.

Computer mapping of LANDSAT data for environmental applications

NASA Technical Reports Server (NTRS)

Rogers, R. H. (Principal Investigator); Mckeon, J. B.; Reed, L. E.; Schmidt, N. F.; Schecter, R. N.

1975-01-01

The author has identified the following significant results. Land cover overlays and maps produced from LANDSAT are providing information on existing land use and resources throughout the 208 study area. The overlays are being used to delineate drainage areas of a predominant land cover type. Information on cover type is also being combined with other pertinent data to develop estimates of sediment and nutrients flows from the drainage area. The LANDSAT inventory of present land cover together with population projects is providing a basis for developing maps of anticipated land use patterns required to evaluate impact on water quality which may result from these patterns. Overlays of forest types were useful for defining wildlife habitat and vegetational resources in the region. LANDSAT data and computer assisted interpretation was found to be a rapid cost effective procedure for inventorying land cover on a regional basis. The entire 208 inventory which include acquisition of ground truth, LANDSAT tapes, computer processing, and production of overlays and coded tapes was completed within a period of 2 months at a cost of about 0.6 cents per acre, a significant improvement in time and cost over conventional photointerpretation and mapping techniques.

A national framework for monitoring and reporting on environmental sustainability in Canada.

PubMed

Marshall, I B; Scott Smith, C A; Selby, C J

1996-01-01

In 1991, a collaborative project to revise the terrestrial component of a national ecological framework was undertaken with a wide range of stakeholders. This spatial framework consists of multiple, nested levels of ecological generalization with linkages to existing federal and provincial scientific databases. The broadest level of generalization is the ecozone. Macroclimate, major vegetation types and subcontinental scale physiographic formations constitute the definitive components of these major ecosystems. Ecozones are subdivided into approximately 200 ecoregions which are based on properties like regional physiography, surficial geology, climate, vegetation, soil, water and fauna. The ecozone and ecoregion levels of the framework have been depicted on a national map coverage at 1:7 500 000 scale. Ecoregions have been subdivided into ecodistricts based primarily on landform, parent material, topography, soils, waterbodies and vegetation at a scale (1:2 000 000) useful for environmental resource management, monitoring and modelling activities. Nested within the ecodistricts are the polygons that make up the Soil Landscapes of Canada series of 1:1 000 000 scale soil maps. The framework is supported by an ARC-INFO GIS at Agriculture Canada. The data model allows linkage to associated databases on climate, land use and socio-economic attributes.

The vegetation outlook (VegOut): a new method for predicting vegetation seasonal greenness

USGS Publications Warehouse

Tadesse, T.; Wardlow, B.; Hayes, M.; Svoboda, M.; Brown, J.

2010-01-01

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of "historical patterns" (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate-vegetation interactions and ocean-climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation's response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regionallevel vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing-based approaches that monitor "current" vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions.

Northern Everglades, Florida, satellite image map

USGS Publications Warehouse

Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Vegetation Types in Coastal Louisiana in 2007

USGS Publications Warehouse

Sasser, Charles E.; Visser, Jenneke M.; Mouton, Edmond; Linscombe, Jeb; Hartley, Steve B.

2008-01-01

During the summer and fall of 2007, the U.S. Geological Survey, the Louisiana State University Agricultural Center, and the Louisiana Department of Wildlife and Fisheries Fur and Refuge Division jointly completed an aerial survey to collect data on 2007 vegetation types in coastal Louisiana. The current map presents the data collected in this effort. The 2007 aerial survey was conducted by using techniques developed over the last thirty years while conducting similar vegetation surveys. Transects flown were oriented in a north-south direction and spaced 1.87 mi (3 km) apart and covered coastal marshes from the Texas State line to the Mississippi State line and from the northern extent of fresh marshes to the southern end of saline (saltwater) marshes on the beaches of the Gulf of Mexico or of coastal bays. Navigation along these transects and to each sampling site was accomplished by using Global Positioning System (GPS) technology and geographic information system (GIS) software. As the surveyors reached each sampling station, observed areas of marsh were assigned as fresh, intermediate, brackish, or saline (saltwater) types, and dominant plant species were listed and ranked according to abundance. Delineations of marsh boundaries usually followed natural levees, bayous, or other features that impede or restrict water flow.

Monitoring rangeland dynamics in Senegal with advanced very high resolution radiometer data

USGS Publications Warehouse

Tappan, G. Gray; Tyler, Dean J.; Wehde, M. E.; Moore, Donald G.

1992-01-01

Time‐series Normalized Difference Vegetation Index (NDVI) data, computed from Advanced Very High Resolution Radiometer data, are being used by regional and national programs in the African Sahel to monitor seasonal rangeland conditions. The data are often used as indicators of grazing conditions and drought. However, distinguishing rangelands from other vegetation cover types on NDVI images is difficult. A second complication is that rangeland types and their associated productivity vary geographically by soil type. To effectively assess rangeland conditions, seasonal fluctuations (due to climatic cycles) must be isolated from long‐term production characteristics associated with vegetation type and soil differences. Rangeland NDVI dynamics, including qualitative assessments of rangeland production, and the timing and length of the growing season in Senegal were examined by using 7.4‐km global area coverage satellite data. Analyses were based on 10‐day NDVI composite image data from 1982 through 1989. The NDVI image data were stratified by rangeland and soil polygons derived from locally available resource maps. Time‐series NDVI statistics were calculated from the resource polygons that had been interpreted into high, medium, and low production rangelands. Analysts monitoring rangeland conditions can better identify seasonal anomalies such as drought by comparing production potential within homogeneous; resource polygons with the current NDVI data.

The influence of badland surfaces and erosion processes on vegetation cover

NASA Astrophysics Data System (ADS)

Hardenbicker, Ulrike; Matheis, Sarah

2014-05-01

To assess the links between badland geomorphology and vegetation cover, we used detailed mapping in the Avonlea badlands, 60 km southwest of Regina, Saskatchewan Canada. Three badlands surfaces are typical in the study area: a basal pediment surface, a mid-slope of bentonitic mudstone with typical popcorn surface, and an upper slope with mud-cemented sandstone. Badland development was triggered by rapid post Pleistocene incision of a meltwater channel in Upper Cretaceous marine and lagoonal sediments. After surveying and mapping of a test area, sediment samples were taken to analyze geophysical parameters. A detailed geomorphic map and vegetation map (1:1000) were compared and analyzed in order to determine the geomorphic environment for plant colonization. The shrink-swell capacity of the bentonitic bedrock, slaking potential and dispersivity are controlled by soil texture, clay mineralogy and chemistry, strongly influencing the timing and location of runoff and the relative significance of surface and subsurface erosional processes. The absence of shrink-swell cracking of the alluvial surfaces of the pediments indicates a low infiltration capacity and sheetflow. The compact lithology of the sandstone is responsible for its low permeability and high runoff coefficient. Slope drainage of steep sandstone slopes is routed through a deep corrasional pipe network. Silver sagebrush (Artemisia cana) is the only species growing on the popcorn surface of the mudrock, which is in large parts vegetation free. The basal pediment shows a distinct 2 m band surrounding the mudrock outcrop without vegetation as a result of high sedimentation rate due to slope wash. Otherwise the typical pioneer vegetation of this basal pediment are grasses. In the transition zone below the steep sandstone cliffs and above the gentle bentonitic mudrock surfaces patches of short-grass vegetation are found, marking slumped blocks with intact vegetation and soil cover. These patches are surrounded by less dense pioneer vegetation consisting of grasses and sage bushes indicating minimal surface erosion or sedimentation. Geomorphic mapping documented a high density of active pipes in this area, transporting silt and fine sand from the sandstone cliffs to lower and basal pediments. Vegetation cover alone is a poor indicator of badland surfaces and erosion processes because of the three-dimensional nature of badland erosion processes, and the shrink-swell capacity of the bentonitic bedrock. A combination of geomorphic and vegetation mapping is needed to identify badland surfaces and processes in the study area.

Identifying western yellow-billed cuckoo breeding habitat with a dual modelling approach

USGS Publications Warehouse

Johnson, Matthew J.; Hatten, James R.; Holmes, Jennifer A.; Shafroth, Patrick B.

2017-01-01

The western population of the yellow-billed cuckoo (Coccyzus americanus) was recently listed as threatened under the federal Endangered Species Act. Yellow-billed cuckoo conservation efforts require the identification of features and area requirements associated with high quality, riparian forest habitat at spatial scales that range from nest microhabitat to landscape, as well as lower-suitability areas that can be enhanced or restored. Spatially explicit models inform conservation efforts by increasing ecological understanding of a target species, especially at landscape scales. Previous yellow-billed cuckoo modelling efforts derived plant-community maps from aerial photography, an expensive and oftentimes inconsistent approach. Satellite models can remotely map vegetation features (e.g., vegetation density, heterogeneity in vegetation density or structure) across large areas with near perfect repeatability, but they usually cannot identify plant communities. We used aerial photos and satellite imagery, and a hierarchical spatial scale approach, to identify yellow-billed cuckoo breeding habitat along the Lower Colorado River and its tributaries. Aerial-photo and satellite models identified several key features associated with yellow-billed cuckoo breeding locations: (1) a 4.5 ha core area of dense cottonwood-willow vegetation, (2) a large native, heterogeneously dense forest (72 ha) around the core area, and (3) moderately rough topography. The odds of yellow-billed cuckoo occurrence decreased rapidly as the amount of tamarisk cover increased or when cottonwood-willow vegetation was limited. We achieved model accuracies of 75–80% in the project area the following year after updating the imagery and location data. The two model types had very similar probability maps, largely predicting the same areas as high quality habitat. While each model provided unique information, a dual-modelling approach provided a more complete picture of yellow-billed cuckoo habitat requirements and will be useful for management and conservation activities.

Surface Emissivity Maps for Satellite Retrieval of the Longwave Radiation Budget

NASA Technical Reports Server (NTRS)

Gupta, Shashi K.; Wilber, Anne C.; Kratz, David P.

1999-01-01

This paper presents a brief description of the procedure used to produce global surface emissivity maps for the broadband LW, the 8-12 micrometer window, and 12 narrow LW bands. For a detailed description of the methodology and the input data, the reader is referred to Wilber et al. (1999). These maps are based on a time-independent surface type map published by the IGBP, and laboratory measurements of spectral reflectances of surface materials. These maps represent a first attempt to characterize emissivity based on surface types, and many improvements to the methodology presented here are already underway. Effects of viewing zenith angle and sea state on the emissivity of ocean surface (Smith et al. 1996, Wu and Smith 1997, Masuda et al. 1988) will be taken into account. Measurements form ASTER and MODIS will be incorporated as they become available. Seasonal variation of emissivity based on changes in the characteristics of vegetation will be considered, and the variability of emissivity of barren land areas will be accounted for with the use of Zobler World Soil Maps (Zobler 1986). The current maps have been made available to the scientific community from the web site: http://tanalo.larc.nasa.gov:8080/surf_htmls/ SARB_surf.html

Time series evaluation of landscape dynamics using annual Landsat imagery and spatial statistical modeling: Evidence from the Phoenix metropolitan region

NASA Astrophysics Data System (ADS)

Fan, Chao; Myint, Soe W.; Rey, Sergio J.; Li, Wenwen

2017-06-01

Urbanization is a natural and social process involving simultaneous changes to the Earth's land systems, energy flow, demographics, and the economy. Understanding the spatiotemporal pattern of urbanization is increasingly important for policy formulation, decision making, and natural resource management. A combination of satellite remote sensing and patch-based models has been widely adopted to characterize landscape changes at various spatial and temporal scales. Nevertheless, the validity of this type of framework in identifying long-term changes, especially subtle or gradual land modifications is seriously challenged. In this paper, we integrate annual image time series, continuous spatial indices, and non-parametric trend analysis into a spatiotemporal study of landscape dynamics over the Phoenix metropolitan area from 1991 to 2010. We harness local indicators of spatial dependence and modified Mann-Kendall test to describe the monotonic trends in the quantity and spatial arrangement of two important land use land cover types: vegetation and built-up areas. Results suggest that declines in vegetation and increases in built-up areas are the two prevalent types of changes across the region. Vegetation increases mostly occur at the outskirts where new residential areas are developed from natural desert. A sizable proportion of vegetation declines and built-up increases are seen in the central and southeast part. Extensive land conversion from agricultural fields into urban land use is one important driver of vegetation declines. The xeriscaping practice also contributes to part of vegetation loss and an increasingly heterogeneous landscape. The quantitative framework proposed in this study provides a pathway to effective landscape mapping and change monitoring from a spatial statistical perspective.

[Application of biotope mapping model integrated with vegetation cover continuity attributes in urban biodiversity conservation].

PubMed

Gao, Tian; Qiu, Ling; Chen, Cun-gen

2010-09-01

Based on the biotope classification system with vegetation structure as the framework, a modified biotope mapping model integrated with vegetation cover continuity attributes was developed, and applied to the study of the greenbelts in Helsingborg in southern Sweden. An evaluation of the vegetation cover continuity in the greenbelts was carried out by the comparisons of the vascular plant species richness in long- and short-continuity forests, based on the identification of woodland continuity by using ancient woodland indicator species (AWIS). In the test greenbelts, long-continuity woodlands had more AWIS. Among the forests where the dominant trees were more than 30-year-old, the long-continuity ones had a higher biodiversity of vascular plants, compared with the short-continuity ones with the similar vegetation structure. The modified biotope mapping model integrated with the continuity features of vegetation cover could be an important tool in investigating urban biodiversity, and provide corresponding strategies for future urban biodiversity conservation.

DEVELOPMENT OF LAND COVER AND TERRAIN DATA BASES FOR THE INNOKO NATIONAL WILDLIFE REFUGE, ALASKA, USING LANDSAT AND DIGITAL TERRAIN DATA.

USGS Publications Warehouse

Markon, Carl J.; Talbot, Stephen

1986-01-01

Landsat-derived land cover maps and associated elevation, slope, and aspect class maps were produced for the Innoko National Wildlife Refuge (3,850,000 acres; 1,555,095 hectares) in northwestern Alaska. These maps and associated digital data products are being used by the U. S. Fish and Wildlife Service for wildlife management, research, and comprehensive conservation planning. Portions of two Landsat Multispectral Scanner (MSS) scenes and digital terrain data were used to produce 1:250,000 scale land cover and terrain maps. Prints of summer and winter Landsat MSS scenes were used to manually interpret broad physiographic strata. These strata were transferred to U. S. Geological Survey 1:250,000-scale topographic maps and digitized. Seven major land cover classes and 23 subclasses were identified. The major land cover classes include: forest, scrub, dwarf scrub and related types, herbaceous, scarcely vegetated areas, water, and shadow.

Predictive mapping of forest composition and structure with direct gradient analysis and nearest neighbor imputation in coastal Oregon, U.S.A.

Treesearch

Janet L. Ohmann; Matthew J. Gregory

2002-01-01

Spatially explicit information on the species composition and structure of forest vegetation is needed at broad spatial scales for natural resource policy analysis and ecological research. We present a method for predictive vegetation mapping that applies direct gradient analysis and nearest-neighbor imputation to ascribe detailed ground attributes of vegetation to...

Sensitivity of biogenic volatile organic compounds to land surface parameterizations and vegetation distributions in California

NASA Astrophysics Data System (ADS)

Zhao, Chun; Huang, Maoyi; Fast, Jerome D.; Berg, Larry K.; Qian, Yun; Guenther, Alex; Gu, Dasa; Shrivastava, Manish; Liu, Ying; Walters, Stacy; Pfister, Gabriele; Jin, Jiming; Shilling, John E.; Warneke, Carsten

2016-05-01

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model with chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.

High-spatial resolution multispectral and panchromatic satellite imagery for mapping perennial desert plants

NASA Astrophysics Data System (ADS)

Alsharrah, Saad A.; Bruce, David A.; Bouabid, Rachid; Somenahalli, Sekhar; Corcoran, Paul A.

2015-10-01

The use of remote sensing techniques to extract vegetation cover information for the assessment and monitoring of land degradation in arid environments has gained increased interest in recent years. However, such a task can be challenging, especially for medium-spatial resolution satellite sensors, due to soil background effects and the distribution and structure of perennial desert vegetation. In this study, we utilised Pleiades high-spatial resolution, multispectral (2m) and panchromatic (0.5m) imagery and focused on mapping small shrubs and low-lying trees using three classification techniques: 1) vegetation indices (VI) threshold analysis, 2) pre-built object-oriented image analysis (OBIA), and 3) a developed vegetation shadow model (VSM). We evaluated the success of each approach using a root of the sum of the squares (RSS) metric, which incorporated field data as control and three error metrics relating to commission, omission, and percent cover. Results showed that optimum VI performers returned good vegetation cover estimates at certain thresholds, but failed to accurately map the distribution of the desert plants. Using the pre-built IMAGINE Objective OBIA approach, we improved the vegetation distribution mapping accuracy, but this came at the cost of over classification, similar to results of lowering VI thresholds. We further introduced the VSM which takes into account shadow for further refining vegetation cover classification derived from VI. The results showed significant improvements in vegetation cover and distribution accuracy compared to the other techniques. We argue that the VSM approach using high-spatial resolution imagery provides a more accurate representation of desert landscape vegetation and should be considered in assessments of desertification.

A LANDSAT study of ephemeral and perennial rangeland vegetation and soils

NASA Technical Reports Server (NTRS)

Bentley, R. G., Jr. (Principal Investigator); Salmon-Drexler, B. C.; Bonner, W. J.; Vincent, R. K.

1976-01-01

The author has identified the following significant results. Several methods of computer processing were applied to LANDSAT data for mapping vegetation characteristics of perennial rangeland in Montana and ephemeral rangeland in Arizona. The choice of optimal processing technique was dependent on prescribed mapping and site condition. Single channel level slicing and ratioing of channels were used for simple enhancement. Predictive models for mapping percent vegetation cover based on data from field spectra and LANDSAT data were generated by multiple linear regression of six unique LANDSAT spectral ratios. Ratio gating logic and maximum likelihood classification were applied successfully to recognize plant communities in Montana. Maximum likelihood classification did little to improve recognition of terrain features when compared to a single channel density slice in sparsely vegetated Arizona. LANDSAT was found to be more sensitive to differences between plant communities based on percentages of vigorous vegetation than to actual physical or spectral differences among plant species.

Evapotranspiration Within the Groundwater Model Domain of the Tuba City, Arizona, Disposal Site Interim Report

DOE Office of Scientific and Technical Information (OSTI.GOV)

None, None

The revised groundwater model includes estimates of evapotranspiration (ET). The types of vegetation and the influences of ET on groundwater hydrology vary within the model domain. Some plant species within the model domain, classified as phreatophytes, survive by extracting groundwater. ET within these plant communities can result in a net discharge of groundwater if ET exceeds precipitation. Other upland desert plants within the model domain survive on meteoric water, potentially limiting groundwater recharge if ET is equivalent to precipitation. For all plant communities within the model domain, excessive livestock grazing or other disturbances can tip the balance to a netmore » groundwater recharge. This task characterized and mapped vegetation within the groundwater model domain at the Tuba City, Arizona, Site, and then applied a remote sensing algorithm to estimate ET for each vegetation type. The task was designed to address five objectives: 1. Characterize and delineate different vegetation or ET zones within the groundwater model domain, focusing on the separation of plant communities with phreatophytes that survive by tapping groundwater and upland plant communities that are dependent on precipitation. 2. Refine a remote sensing method, developed to estimate ET at the Monument Valley site, for application at the Tuba City site. 3. Estimate recent seasonal and annual ET for all vegetation zones, separating phreatophytic and upland plant communities within the Tuba City groundwater model domain. 4. For selected vegetation zones, estimate ET that might be achieved given a scenario of limited livestock grazing. 5. Analyze uncertainty of ET estimates for each vegetation zone and for the entire groundwater model domain.« less

Multi-centennial ecosystem modelling in northeastern America at the species level

NASA Astrophysics Data System (ADS)

Steinkamp, J.; Biskupovic, A.; Rollinson, C.; Dawson, A.; Goring, S. J.; McLachlan, J. S.; Mladenoff, D. J.; Williams, J.; Hickler, T.

2016-12-01

Most dynamic global vegetation models (DGVM) are based on a small set of plant functional types (PFTs) to simulate biome distribution, vegetation dynamics, and carbon and nutrient cycles, which is of limited use for more regional studies and stakeholders. We tested a tree-species-based parameterization approach of the LPJ-GUESS DGVM in the northeastern USA, which previously has been successful in simulating the main potential natural vegetation zones in Europe. A transient model run was carried out from 850 A.D. to today, and the model results have been evaluated against pre-settlement vegetation maps and reconstructed vegetation from pollen within the PalEON project and hypothesized potential natural vegetation zones. We will analyze the simulation with respect to long term carbon cycling and the driving forces. Main reconstructed vegetation features were reproduced by the model, which implies that the general processes shaping the forested vegetation in parts of Europe and the northeastern USA are similar. However, so far the decrease in biomass towards the prairie in the west could not fully be captured by the model, which is currently analyzed with additional simulations. Moisture and fire are the important driver at the prairie forest transition zone, which we need to better constrain for this model domain.

Vegetation Water Content Mapping in a Diverse Agricultural Landscape: National Airborne Field Experiment 2006

NASA Technical Reports Server (NTRS)

Cosh, Michael H.; Jing Tao; Jackson, Thomas J.; McKee, Lynn; O'Neill, Peggy

2011-01-01

Mapping land cover and vegetation characteristics on a regional scale is critical to soil moisture retrieval using microwave remote sensing. In aircraft-based experiments such as the National Airborne Field Experiment 2006 (NAFE 06), it is challenging to provide accurate high resolution vegetation information, especially on a daily basis. A technique proposed in previous studies was adapted here to the heterogenous conditions encountered in NAFE 06, which included a hydrologically complex landscape consisting of both irrigated and dryland agriculture. Using field vegetation sampling and ground-based reflectance measurements, the knowledge base for relating the Normalized Difference Water Index (NDWI) and the vegetation water content was extended to a greater diversity of agricultural crops, which included dryland and irrigated wheat, alfalfa, and canola. Critical to the generation of vegetation water content maps, the land cover for this region was determined from satellite visible/infrared imagery and ground surveys with an accuracy of 95.5% and a kappa coefficient of 0.95. The vegetation water content was estimated with a root mean square error of 0.33 kg/sq m. The results of this investigation contribute to a more robust database of global vegetation water content observations and demonstrate that the approach can be applied with high accuracy. Keywords: Vegetation, field experimentation, thematic mapper, NDWI, agriculture.

Prior-knowledge-based spectral mixture analysis for impervious surface mapping

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Jinshui; He, Chunyang; Zhou, Yuyu

2014-01-03

In this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the Vegetation-Impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the Vegetation-Impervious-Soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, lowmore » albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas.« less

South Florida Everglades: satellite image map

USGS Publications Warehouse

Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

2001-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Hydrologic responses to restored wildfire regimes revealed by soil moisture-vegetation relationships

NASA Astrophysics Data System (ADS)

Boisramé, Gabrielle; Thompson, Sally; Stephens, Scott

2018-02-01

Many forested mountain watersheds worldwide evolved with frequent fire, which Twentieth Century fire suppression activities eliminated, resulting in unnaturally dense forests with high water demand. Restoration of pre-suppression forest composition and structure through a variety of management activities could improve forest resilience and water yields. This study explores the potential for "managed wildfire", whereby naturally ignited fires are allowed to burn, to alter the water balance. Interest in this type of managed wildfire is increasing, yet its long-term effects on water balance are uncertain. We use soil moisture as a spatially-distributed hydrologic indicator to assess the influence of vegetation, fire history and landscape position on water availability in the Illilouette Creek Basin in Yosemite National Park. Over 6000 manual surface soil moisture measurements were made over a period of three years, and supplemented with continuous soil moisture measurements over the top 1m of soil in three sites. Random forest and linear mixed effects models showed a dominant effect of vegetation type and history of vegetation change on measured soil moisture. Contemporary and historical vegetation maps were used to upscale the soil moisture observations to the basin and infer soil moisture under fire-suppressed conditions. Little change in basin-averaged soil moisture was inferred due to managed wildfire, but the results indicated that large localized increases in soil moisture had occurred, which could have important impacts on local ecology or downstream flows.

An integrated remote sensing approach for identifying ecological range sites. [parker mountain

NASA Technical Reports Server (NTRS)

Jaynes, R. A.

1983-01-01

A model approach for identifying ecological range sites was applied to high elevation sagebrush-dominated rangelands on Parker Mountain, in south-central Utah. The approach utilizes map information derived from both high altitude color infrared photography and LANDSAT digital data, integrated with soils, geological, and precipitation maps. Identification of the ecological range site for a given area requires an evaluation of all relevant environmental factors which combine to give that site the potential to produce characteristic types and amounts of vegetation. A table is presented which allows the user to determine ecological range site based upon an integrated use of the maps which were prepared. The advantages of identifying ecological range sites through an integrated photo interpretation/LANDSAT analysis are discussed.

Integrated terrain mapping with digital Landsat images in Queensland, Australia

USGS Publications Warehouse

Robinove, Charles Joseph

1979-01-01

Mapping with Landsat images usually is done by selecting single types of features, such as soils, vegetation, or rocks, and creating visually interpreted or digitally classified maps of each feature. Individual maps can then be overlaid on or combined with other maps to characterize the terrain. Integrated terrain mapping combines several terrain features into each map unit which, in many cases, is more directly related to uses of the land and to methods of land management than the single features alone. Terrain brightness, as measured by the multispectral scanners in Landsat 1 and 2, represents an integration of reflectance from the terrain features within the scanner's instantaneous field of view and is therefore more correlatable with integrated terrain units than with differentiated ones, such as rocks, soils, and vegetation. A test of the feasibilty of the technique of mapping integrated terrain units was conducted in a part of southwestern Queensland, Australia, in cooperation with scientists of the Queensland Department of Primary Industries. The primary purpose was to test the use of digital classification techniques to create a 'land systems map' usable for grazing land management. A recently published map of 'land systems' in the area (made by aerial photograph interpretation and ground surveys), which are integrated terrain units composed of vegetation, soil, topography, and geomorphic features, was used as a basis for comparison with digitally classified Landsat multispectral images. The land systems, in turn, each have a specific grazing capacity for cattle (expressed in beasts per km 2 ) which is estimated following analysis of both research results and property carrying capacities. Landsat images, in computer-compatible tape form, were first contrast-stretched to increase their visual interpretability, and digitally classified by the parallelepiped method into distinct spectral classes to determine their correspondence to the land systems classes and to areally smaller, but readily recognizable, 'land units.' Many land systems appeared as distinct spectral classes or as acceptably homogeneous combinations of several spectral classes. The digitally classified map corresponded to the general geographic patterns of many of the land systems. Statistical correlation of the digitally classified map and the published map was not possible because the published map showed only land systems whereas the digitally classified map showed some land units as well as systems. The general correspondence of spectral classes to the integrated terrain units means that the digital mapping of the units may precede fieldwork and act as a guide to field sampling and detailed terrain unit description as well as measuring of the location, area, and extent of each unit. Extension of the Landsat mapping and classification technique to other arid and semi-arid regions of the world may be feasible.

Estimating fractional vegetation cover and the vegetation index of bare soil and highly dense vegetation with a physically based method

NASA Astrophysics Data System (ADS)

Song, Wanjuan; Mu, Xihan; Ruan, Gaiyan; Gao, Zhan; Li, Linyuan; Yan, Guangjian

2017-06-01

Normalized difference vegetation index (NDVI) of highly dense vegetation (NDVIv) and bare soil (NDVIs), identified as the key parameters for Fractional Vegetation Cover (FVC) estimation, are usually obtained with empirical statistical methods However, it is often difficult to obtain reasonable values of NDVIv and NDVIs at a coarse resolution (e.g., 1 km), or in arid, semiarid, and evergreen areas. The uncertainty of estimated NDVIs and NDVIv can cause substantial errors in FVC estimations when a simple linear mixture model is used. To address this problem, this paper proposes a physically based method. The leaf area index (LAI) and directional NDVI are introduced in a gap fraction model and a linear mixture model for FVC estimation to calculate NDVIv and NDVIs. The model incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) Bidirectional Reflectance Distribution Function (BRDF) model parameters product (MCD43B1) and LAI product, which are convenient to acquire. Two types of evaluation experiments are designed 1) with data simulated by a canopy radiative transfer model and 2) with satellite observations. The root-mean-square deviation (RMSD) for simulated data is less than 0.117, depending on the type of noise added on the data. In the real data experiment, the RMSD for cropland is 0.127, for grassland is 0.075, and for forest is 0.107. The experimental areas respectively lack fully vegetated and non-vegetated pixels at 1 km resolution. Consequently, a relatively large uncertainty is found while using the statistical methods and the RMSD ranges from 0.110 to 0.363 based on the real data. The proposed method is convenient to produce NDVIv and NDVIs maps for FVC estimation on regional and global scales.

FIREX mission requirements document for renewable resources

NASA Technical Reports Server (NTRS)

Carsey, F.; Dixon, T.

1982-01-01

The initial experimental program and mission requirements for a satellite synthetic aperture radar (SAR) system FIREX (Free-Flying Imaging Radar Experiment) for renewable resources is described. The spacecraft SAR is a C-band and L-band VV polarized system operating at two angles of incidence which is designated as a research instrument for crop identification, crop canopy condition assessments, soil moisture condition estimation, forestry type and condition assessments, snow water equivalent and snow wetness assessments, wetland and coastal land type identification and mapping, flood extent mapping, and assessment of drainage characteristics of watersheds for water resources applications. Specific mission design issues such as the preferred incidence angles for vegetation canopy measurements and the utility of a dual frequency (L and C-band) or dual polarization system as compared to the baseline system are addressed.

Characteristics of vegetation phenology over the Alaskan landscape using AVHRR time-series data

USGS Publications Warehouse

Markon, Carl J.; Fleming, Michael D.; Binnian, Emily F.

1995-01-01

Advanced Very High Resolution Radiometer (AVHRR) satellite data were acquired and composited into twice-a-month periods from 1 May 1991 to 15 October 1991 in order to map vegetation characteristics of the Alaskan landscape. Unique spatial and temporal qualities of the AVHRR data provide information that leads to a better understanding of regional biophysical characteristics of vegetation communities and patterns. These data provided synoptic views of the landscape and depicted phenological diversity, temporal vegetation phenology (green-up, peak of green, and senescence), photosynthetic activity, and regional landscape patterns. Products generated from the data included a phenological class map, phenological composite maps (onset, peak, and duration), and photosynthetic activity maps (mean and maximum greenness). The time-series data provide opportunities to study phenological processes at small landscape scales over time periods of weeks, months, and years. Regional patterns identified on some of the maps are unique to specific areas; others correspond to biophysical or ecoregional boundaries. The data provide new insights to landscape processes, ecology, and landscape physiognomy that allow scientists to look at landscapes in ways that were previously difficult to achieve.

Winter wheat mapping combining variations before and after estimated heading dates

NASA Astrophysics Data System (ADS)

Qiu, Bingwen; Luo, Yuhan; Tang, Zhenghong; Chen, Chongcheng; Lu, Difei; Huang, Hongyu; Chen, Yunzhi; Chen, Nan; Xu, Weiming

2017-01-01

Accurate and updated information on winter wheat distribution is vital for food security. The intra-class variability of the temporal profiles of vegetation indices presents substantial challenges to current time series-based approaches. This study developed a new method to identify winter wheat over large regions through a transformation and metric-based approach. First, the trend surfaces were established to identify key phenological parameters of winter wheat based on altitude and latitude with references to crop calendar data from the agro-meteorological stations. Second, two phenology-based indicators were developed based on the EVI2 differences between estimated heading and seedling/harvesting dates and the change amplitudes. These two phenology-based indicators revealed variations during the estimated early and late growth stages. Finally, winter wheat data were extracted based on these two metrics. The winter wheat mapping method was applied to China based on the 250 m 8-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) 2-band Enhanced Vegetation Index (EVI2) time series datasets. Accuracy was validated with field survey data, agricultural census data, and Landsat-interpreted results in test regions. When evaluated with 653 field survey sites and Landsat image interpreted data, the overall accuracy of MODIS-derived images in 2012-2013 was 92.19% and 88.86%, respectively. The MODIS-derived winter wheat areas accounted for over 82% of the variability at the municipal level when compared with agricultural census data. The winter wheat mapping method developed in this study demonstrates great adaptability to intra-class variability of the vegetation temporal profiles and has great potential for further applications to broader regions and other types of agricultural crop mapping.

First Results of the Performance of the Global Forest/Non-Forest Map derived from TanDEM-X Interferometric Data

NASA Astrophysics Data System (ADS)

Gonzalez, Carolina; Rizzoli, Paola; Martone, Michele; Wecklich, Christopher; Bueso Bello, Jose Luis; Krieger, Gerhard; Zink, Manfred

2017-04-01

The globally acquired interferometric synthetic aperture radar (SAR) data set, used for the recently completed primary goal of the TanDEM-X mission, enables a big opportunity for scientific geo-applications. Of great importance for land characterization, classification, and monitoring is that the data set is globally acquired without gaps and includes multiple acquisitions of every region, with comparable parameters. One of the most valuable maps that can be derived from interferometric SAR data for land classification describes the presence/absence of vegetation. In particular, here we report about the deployment of the Global Forest/Non-Forest Map, derived from TanDEM-X interferometric SAR quick-look data, at a ground resolution of 50 m by 50 m. Presence of structures and in particular vegetation produces multiple scattering known as volume decorrelation. Its contribution can be directly estimated from the assessment of coherence loss in the interferometric bistatic pair, by compensating for all other decorrelation sources, such as poor signal-to-noise ratio or quantization noise. Three different forest types have been characterized based on the estimated volume decorrelation: tropical, temperate, and boreal forest. This characterization was then used in a fuzzy clustering approach for the discrimination of vegetated areas on a global scale. Water and cities are filtered out from the generated maps in order to distinguish volume decorrelation from other decorrelation sources. The validation and performance comparison of the delivered product is also presented, and represents a fundamental tool for optimizing the whole algorithm at all different stages. Furtheremore, as the time interval of the acquisitions is almost 4 years, change detection can be performed as well and examples of deforestation are also going to be included in the final paper.

Oil Spill Detection along the Gulf of Mexico Coastline based on Airborne Imaging Spectrometer Data

NASA Astrophysics Data System (ADS)

Arslan, M. D.; Filippi, A. M.; Guneralp, I.

2013-12-01

The Deepwater Horizon oil spill in the Gulf of Mexico between April and July 2010 demonstrated the importance of synoptic oil-spill monitoring in coastal environments via remote-sensing methods. This study focuses on terrestrial oil-spill detection and thickness estimation based on hyperspectral images acquired along the coastline of the Gulf of Mexico. We use AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) imaging spectrometer data collected over Bay Jimmy and Wilkinson Bay within Barataria Bay, Louisiana, USA during September 2010. We also employ field-based observations of the degree of oil accumulation along the coastline, as well as in situ measurements from the literature. As part of our proposed spectroscopic approach, we operate on atmospherically- and geometrically-corrected hyperspectral AVIRIS data to extract image-derived endmembers via Minimum Noise Fraction transform, Pixel Purity Index-generation, and n-dimensional visualization. Extracted endmembers are then used as input to endmember-mapping algorithms to yield fractional-abundance images and crisp classification images. We also employ Multiple Endmember Spectral Mixture Analysis (MESMA) for oil detection and mapping in order to enable the number and types of endmembers to vary on a per-pixel basis, in contast to simple Spectral Mixture Analysis (SMA). MESMA thus better allows accounting for spectral variabiltiy of oil (e.g., due to varying oil thicknesses, states of degradation, and the presence of different oil types, etc.) and other materials, including soils and salt marsh vegetation of varying types, which may or may not be affected by the oil spill. A decision-tree approach is also utilized for comparison. Classification results do indicate that MESMA provides advantageous capabilities for mapping several oil-thickness classes for affected vegetation and soils along the Gulf of Mexico coastline, relative to the conventional approaches tested. Oil thickness-mapping results from MESMA and the decision tree demonstrate that such products can be accurately generated in complex coastal enviroments.

Automated Land Cover Change Detection and Mapping from Hidden Parameter Estimates of Normalized Difference Vegetation Index (NDVI) Time-Series

NASA Astrophysics Data System (ADS)

Chakraborty, S.; Banerjee, A.; Gupta, S. K. S.; Christensen, P. R.; Papandreou-Suppappola, A.

2017-12-01

Multitemporal observations acquired frequently by satellites with short revisit periods such as the Moderate Resolution Imaging Spectroradiometer (MODIS), is an important source for modeling land cover. Due to the inherent seasonality of the land cover, harmonic modeling reveals hidden state parameters characteristic to it, which is used in classifying different land cover types and in detecting changes due to natural or anthropogenic factors. In this work, we use an eight day MODIS composite to create a Normalized Difference Vegetation Index (NDVI) time-series of ten years. Improved hidden parameter estimates of the nonlinear harmonic NDVI model are obtained using the Particle Filter (PF), a sequential Monte Carlo estimator. The nonlinear estimation based on PF is shown to improve parameter estimation for different land cover types compared to existing techniques that use the Extended Kalman Filter (EKF), due to linearization of the harmonic model. As these parameters are representative of a given land cover, its applicability in near real-time detection of land cover change is also studied by formulating a metric that captures parameter deviation due to change. The detection methodology is evaluated by considering change as a rare class problem. This approach is shown to detect change with minimum delay. Additionally, the degree of change within the change perimeter is non-uniform. By clustering the deviation in parameters due to change, this spatial variation in change severity is effectively mapped and validated with high spatial resolution change maps of the given regions.

Biweekly disturbance capture and attribution: case study in western Alberta grizzly bear habitat

NASA Astrophysics Data System (ADS)

Hilker, Thomas; Coops, Nicholas C.; Gaulton, Rachel; Wulder, Michael A.; Cranston, Jerome; Stenhouse, Gordon

2011-01-01

An increasing number of studies have demonstrated the impact of landscape disturbance on ecosystems. Satellite remote sensing can be used for mapping disturbances, and fusion techniques of sensors with complimentary characteristics can help to improve the spatial and temporal resolution of satellite-based mapping techniques. Classification of different disturbance types from satellite observations is difficult, yet important, especially in an ecological context as different disturbance types might have different impacts on vegetation recovery, wildlife habitats, and food resources. We demonstrate a possible approach for classifying common disturbance types by means of their spatial characteristics. First, landscape level change is characterized on a near biweekly basis through application of a data fusion model (spatial temporal adaptive algorithm for mapping reflectance change) and a number of spatial and temporal characteristics of the predicted disturbance patches are inferred. A regression tree approach is then used to classify disturbance events. Our results show that spatial and temporal disturbance characteristics can be used to classify disturbance events with an overall accuracy of 86% of the disturbed area observed. The date of disturbance was identified as the most powerful predictor of the disturbance type, together with the patch core area, patch size, and contiguity.

Vegetation Mapping in a Dryland Ecosystem Using Multi-temporal Sentinel-2 Imagery and Ensemble Learning

NASA Astrophysics Data System (ADS)

Enterkine, J.; Spaete, L.; Glenn, N. F.; Gallagher, M.

2017-12-01

Remote sensing and mapping of dryland ecosystem vegetation is notably problematic due to the low canopy cover and fugacious growing seasons. Recent improvements in available satellite imagery and machine learning techniques have enabled enhanced approaches to mapping and monitoring vegetation across dryland ecosystems. The Sentinel-2 satellites (launched June 2015 and March 2017) of ESA's Copernicus Programme offer promising developments from existing multispectral satellite systems such as Landsat. Freely-available, Sentinel-2 imagery offers a five-day revisit frequency, thirteen spectral bands (in the visible, near infrared, and shortwave infrared), and high spatial resolution (from 10m to 60m). Three narrow spectral bands located between the visible and the near infrared are designed to observe changes in photosynthesis. The high temporal, spatial, and spectral resolution of this imagery makes it ideal for monitoring vegetation in dryland ecosystems. In this study, we calculated a large number of vegetation and spectral indices from Sentinel-2 imagery spanning a growing season. This data was leveraged with robust field data of canopy cover at precise geolocations. We then used a Random Forests ensemble learning model to identify the most predictive variables for each landcover class, which were then used to impute landcover over the study area. The resulting vegetation map product will be used by land managers, and the mapping approaches will serve as a basis for future remote sensing projects using Sentinel-2 imagery and machine learning.

Quantifying forest vertical structure to determine bird habitat quality in the Greenbelt Corridor, Denton, TX

NASA Astrophysics Data System (ADS)

Matsubayashi, Shiho

This study presents the integration of light detection and range (LiDAR) and hyperspectral remote sensing to create a three-dimensional bird habitat map in the Greenbelt Corridor of the Elm Fork of the Trinity River. This map permits to examine the relationship between forest stand structure, landscape heterogeneity, and bird community composition. A biannual bird census was conducted at this site during the breeding seasons of 2009 and 2010. Census data combined with the three-dimensional map suggest that local breeding bird abundance, community structure, and spatial distribution patterns are highly influenced by vertical heterogeneity of vegetation surface. For local breeding birds, vertical heterogeneity of canopy surface within stands, connectivity to adjacent forest patches, largest forest patch index, and habitat (vegetation) types proved to be the most influential factors to determine bird community assemblages. Results also highlight the critical role of secondary forests to increase functional connectivity of forest patches. Overall, three-dimensional habitat descriptions derived from integrated LiDAR and hyperspectral data serve as a powerful bird conservation tool that shows how the distribution of bird species relates to forest composition and structure at various scales.

A system of regional agricultural land use mapping tested against small scale Apollo 9 color infrared photography of the Imperial Valley (California)

USGS Publications Warehouse

Johnson, Claude W.; Browden, Leonard W.; Pease, Robert W.

1969-01-01

Interpretation results of the small scale ClR photography of the Imperial Valley (California) taken on March 12, 1969 by the Apollo 9 earth orbiting satellite have shown that world wide agricultural land use mapping can be accomplished from satellite ClR imagery if sufficient a priori information is available for the region being mapped. Correlation of results with actual data is encouraging although the accuracy of identification of specific crops from the single image is poor. The poor results can be partly attributed to only one image taken during mid-season when the three major crops were reflecting approximately the same and their ClR image appears to indicate the same crop type. However, some incapacity can be attributed to lack of understanding of the subtle variations of visual and infrared color reflectance of vegetation and surrounding environment. Analysis of integrated color variations of the vegetation and background environment recorded on ClR imagery is discussed. Problems associated with the color variations may be overcome by development of a semi-automatic processing system which considers individual field units or cells. Design criteria for semi-automatic processing system are outlined.

Classification of wetlands vegetation using small scale color infrared imagery

NASA Technical Reports Server (NTRS)

Williamson, F. S. L.

1975-01-01

A classification system for Chesapeake Bay wetlands was derived from the correlation of film density classes and actual vegetation classes. The data processing programs used were developed by the Laboratory for the Applications of Remote Sensing. These programs were tested for their value in classifying natural vegetation, using digitized data from small scale aerial photography. Existing imagery and the vegetation map of Farm Creek Marsh were used to determine the optimal number of classes, and to aid in determining if the computer maps were a believable product.

[Ecological affinity and current distribution of primates (Cebidae) in Campeche, Mexico].

PubMed

Navarro Fernández, Eloísa; Pozo de la Tijera, Carmen; Escobedo Cabrera, Enrique

2003-06-01

We carried out surveys realized field work from March to September 2000 to get the current distribution of Cebids in the state of Campeche, Mexico. Based on interviews and direct observations. We defined the distribution of Ateles geoffroyi yucatanensis and Alouatta pigra and we documented the first time localities where Allouata palliata is found in the state. We made distributional maps of each species using vegetation overlays from Inventario Nacional Forestal (Inv For) and each point documented during fieldwork. We presented the distribution of species according to confiability of the verified or expected data. Using the attributes table of Inv For, we calculated the areas of distribution which were 22,735 km2 for Alouatta sp. and 18,501 km2 for A. g. yucatanensis. We also presented the area occupied by each species according to vegetation types and the relative proportion of these vegetation types in the state. We confirmed the ability of Alouatta sp. to survive in disturbed environments produced by habitat fragmentation, and the affinity of A. g. yucatanesis to well preserved habitats.

Distinguishing Bark Beetle-infested Vegetation by Tree Species Types and Stress Levels using Landsat Data

NASA Astrophysics Data System (ADS)

Sivanpillai, R.; Ewers, B. E.; Speckman, H. N.; Miller, S. N.

2015-12-01

In the Western United States, more than 3 million hectares of lodgepole pine forests have been impacted by the Mountain pine beetle outbreak, while another 166,000 hectares of spruce-fir forests have been attacked by Spruce beetle. Following the beetle attack, the trees lose their hydraulic conductivity thus altering their carbon and water fluxes. These trees go through various stages of stress until mortality, described by color changes in their needles prior to losing them. Modeling the impact of these vegetation types require thematically precise land cover data that distinguishes lodgepole pine and spruce-fir forests along with the stage of impact since the ecosystem fluxes are different for these two systems. However, the national and regional-scale land cover datasets derived from remotely sensed data do not have this required thematic precision. We evaluated the feasibility of multispectral data collected by Landsat 8 to distinguish lodgepole pine and spruce fir, and subsequently model the different stages of attack using field data collected in Medicine Bow National Forest (Wyoming, USA). Operational Land Imager, onboard Landsat 8 has more spectral bands and higher radiometric resolution (12 bit) in comparison to sensors onboard earlier Landsat missions which could improve the ability to distinguish these vegetation types and their stress conditions. In addition to these characteristics, its repeat coverage, rigorous radiometric calibration, wide swath width, and no-cost data provide unique advantages to Landsat data for mapping large geographic areas. Initial results from this study highlight the importance of SWIR bands for distinguishing different levels of stress, and the need for ancillary data for distinguishing species types. Insights gained from this study could lead to the generation of land cover maps with higher thematic precision, and improve the ability to model various ecosystem processes as a result of these infestations.

PRELIMINARY INVESTIGATION OF SUBMERGED AQUATIC VEGETATION MAPPING USING HYPERSPECTRAL REMOTE SENSING

EPA Science Inventory

The use of airborne hyperspectral remote sensing imagery for automated mapping of submersed aquatic vegetation in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery, together with in-situ spectral refl...

Using Small UAS for Mission Simulation, Science Validation, and Definition

NASA Astrophysics Data System (ADS)

Abakians, H.; Donnellan, A.; Chapman, B. D.; Williford, K. H.; Francis, R.; Ehlmann, B. L.; Smith, A. T.

2017-12-01

Small Unmanned Aerial Systems (sUAS) are increasingly being used across JPL and NASA for science data collection, mission simulation, and mission validation. They can also be used as proof of concept for development of autonomous capabilities for Earth and planetary exploration. sUAS are useful for reconstruction of topography and imagery for a variety of applications ranging from fault zone morphology, Mars analog studies, geologic mapping, photometry, and estimation of vegetation structure. Imagery, particularly multispectral imagery can be used for identifying materials such as fault lithology or vegetation type. Reflectance maps can be produced for wetland or other studies. Topography and imagery observations are useful in radar studies such as from UAVSAR or the future NISAR mission to validate 3D motions and to provide imagery in areas of disruption where the radar measurements decorrelate. Small UAS are inexpensive to operate, reconfigurable, and agile, making them a powerful platform for validating mission science measurements, and also for providing surrogate data for existing or future missions.

Interdisciplinary applications and interpretations of ERTS data within the Susquehanna River basin

NASA Technical Reports Server (NTRS)

Mcmurtry, G. J.; Petersen, G. W. (Principal Investigator)

1975-01-01

The author has identified the following significant results. The full potential of high quality data is achieved only with the application of efficient and effective interpretation techniques. An excellent operating system for handling, processing, and interpreting ERTS-1 and other MSS data was achieved. Programs for processing digital data are implemented on a large nondedicated general purpose computer. Significant results were attained in mapping land use, agricultural croplands, forest resources, and vegetative cover. Categories of land use classified and mapped depend upon the geographic location, the detail required, and the types of lands use of interest. Physiographic and structural provinces are spectacularly displayed on ERTS-1 MSS image mosaics. Geologic bedrock structures show up well and formation contacts can sometimes be traced for hundreds of kilometers. Large circular structures and regional features, previously obscured by the detail of higher resolution data, can be seen. Environmental monitoring was performed in three areas: coal strip mining, coal refuse problems, and damage to vegetation caused by insects and pollution.

A comparative interregional analysis of selected data from LANDSAT-1 and EREP for the inventory and monitoring of natural ecosystems

NASA Technical Reports Server (NTRS)

Poulton, C. E.

1975-01-01

Comparative statistics were presented on the capability of LANDSAT-1 and three of the Skylab remote sensing systems (S-190A, S-190B, S-192) for the recognition and inventory of analogous natural vegetations and landscape features important in resource allocation and management. Two analogous regions presenting vegetational zonation from salt desert to alpine conditions above the timberline were observed, emphasizing the visual interpretation mode in the investigation. An hierarchical legend system was used as the basic classification of all land surface features. Comparative tests were run on image identifiability with the different sensor systems, and mapping and interpretation tests were made both in monocular and stereo interpretation with all systems except the S-192. Significant advantage was found in the use of stereo from space when image analysis is by visual or visual-machine-aided interactive systems. Some cost factors in mapping from space are identified. The various image types are compared and an operational system is postulated.

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Treesearch

Harold S.J. Zald; Janet L. Ohmann; Heather M. Roberts; Matthew J. Gregory; Emilie B. Henderson; Robert J. McGaughey; Justin Braaten

2014-01-01

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000 ha in the...

Identification, definition and mapping of terrestrial ecosystems in interior Alaska

NASA Technical Reports Server (NTRS)

Anderson, J. H. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Two new, as yet unfinished vegetation maps are presented. These tend further to substantiate the belief that ERTS-1 imagery is a valuable mapping tool. Newly selected scenes show that vegetation interpretations can be refined through use of non-growing season imagery, particularly through the different spectral characteristics of vegetation lacking foliage and through the effect of vegetation structure on apparent snow cover. Scenes now are available for all test area north of the Alaska Range except Mt. McKinley National Park. No support was obtained for the hypothesis that similar interband ratios, from two areas apparently different spectrally because of different sun angles, would indicate similar surface features. However, attempts to test this hypothesis have so far been casual.

A dataset mapping the potential biophysical effects of vegetation cover change

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-02-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

A dataset mapping the potential biophysical effects of vegetation cover change

PubMed Central

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-01-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes. PMID:29461538

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar

NASA Astrophysics Data System (ADS)

Fedrigo, Melissa; Newnham, Glenn J.; Coops, Nicholas C.; Culvenor, Darius S.; Bolton, Douglas K.; Nitschke, Craig R.

2018-02-01

Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen's kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the 'ecotone') between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation.

Mapping Deforestation area in North Korea Using Phenology-based Multi-Index and Random Forest

NASA Astrophysics Data System (ADS)

Jin, Y.; Sung, S.; Lee, D. K.; Jeong, S.

2016-12-01

Forest ecosystem provides ecological benefits to both humans and wildlife. Growing global demand for food and fiber is accelerating the pressure on the forest ecosystem in whole world from agriculture and logging. In recently, North Korea lost almost 40 % of its forests to crop fields for food production and cut-down of forest for fuel woods between 1990 and 2015. It led to the increased damage caused by natural disasters and is known to be one of the most forest degraded areas in the world. The characteristic of forest landscape in North Korea is complex and heterogeneous, the major landscape types in the forest are hillside farm, unstocked forest, natural forest and plateau vegetation. Remote sensing can be used for the forest degradation mapping of a dynamic landscape at a broad scale of detail and spatial distribution. Confusion mostly occurred between hillside farmland and unstocked forest, but also between unstocked forest and forest. Most previous forest degradation that used focused on the classification of broad types such as deforests area and sand from the perspective of land cover classification. The objective of this study is using random forest for mapping degraded forest in North Korea by phenological based vegetation index derived from MODIS products, which has various environmental factors such as vegetation, soil and water at a regional scale for improving accuracy. The model created by random forest resulted in an overall accuracy was 91.44%. Class user's accuracy of hillside farmland and unstocked forest were 97.2% and 84%%, which indicate the degraded forest. Unstocked forest had relative low user accuracy due to misclassified hillside farmland and forest samples. Producer's accuracy of hillside farmland and unstocked forest were 85.2% and 93.3%, repectly. In this case hillside farmland had lower produce accuracy mainly due to confusion with field, unstocked forest and forest. Such a classification of degraded forest could supply essential information to decide the priority of forest management and restoration in degraded forest area.

Mapping evapotranspiration based on remote sensing: An application to Canada's landmass

NASA Astrophysics Data System (ADS)

Liu, J.; Chen, J. M.; Cihlar, J.

2003-07-01

The evapotranspiration (ET) from all Canadian landmass in 1996 is estimated at daily steps and 1 km resolution using a process model named boreal ecosystem productivity simulator (BEPS). The model is driven by remotely sensed leaf area index and land cover maps as well as soil water holding capacity and daily meteorological data. All the major ET components are considered: transpiration from vegetation, evaporation of canopy-intercepted rainfall, evaporation from soil, sublimation of snow in winter and in permafrost and glacier areas, and sublimation of canopy-intercepted snow. In forested areas the transpiration from both the overstory and understory vegetation is modeled separately. The Penman-Monteith method was applied to sunlit and shaded leaf groups individually in modeling the canopy-level transpiration, a methodological improvement necessary for forest canopies with considerable foliage clumping. The modeled ET map displays pronounced east-west and north-south gradients as well as detailed variations with cover types and vegetation density. It is estimated that for a relative wet year of 1996, the total ET from all Canada's landmass (excluding inland waters) was 2037 km3. If compared with the total precipitation of 5351 km3 based on the data from a medium range meteorological forecast model, the ratio of ET to precipitation was 38%. The ET averaged over Canadian land surface was 228 mm/yr in 1996, partitioned into transpiration of 102 mm yr-1 and evaporation and sublimation of 126 mm yr-1. Forested areas contributed the largest fraction of the total national ET at 59%. Averaged for all cover types, transpiration accounted for 45% of the total ET, while in forested areas, transpiration contributed 51% of ET. Modeled results of daily ET are compared with eddy covariance measurements at three forested sites with a r2 value of 0.61 and a root mean square error of 0.7 mm/day.

Production of high-resolution forest-ecosite maps based on model predictions of soil moisture and nutrient regimes over a large forested area.

PubMed

Yang, Qi; Meng, Fan-Rui; Bourque, Charles P-A; Zhao, Zhengyong

2017-09-08

Forest ecosite reflects the local site conditions that are meaningful to forest productivity as well as basic ecological functions. Field assessments of vegetation and soil types are often used to identify forest ecosites. However, the production of high-resolution ecosite maps for large areas from interpolating field data is difficult because of high spatial variation and associated costs and time requirements. Indices of soil moisture and nutrient regimes (i.e., SMR and SNR) introduced in this study reflect the combined effects of biogeochemical and topographic factors on forest growth. The objective of this research is to present a method for creating high-resolution forest ecosite maps based on computer-generated predictions of SMR and SNR for an area in Atlantic Canada covering about 4.3 × 10 6 hectares (ha) of forestland. Field data from 1,507 forest ecosystem classification plots were used to assess the accuracy of the ecosite maps produced. Using model predictions of SMR and SNR alone, ecosite maps were 61 and 59% correct in identifying 10 Acadian- and Maritime-Boreal-region ecosite types, respectively. This method provides an operational framework for the production of high-resolution maps of forest ecosites over large areas without the need for data from expensive, supplementary field surveys.

Mapping tree density in forests of the southwestern USA using Landsat 8 data

USGS Publications Warehouse

Humagain, Kamal; Portillo-Quintero, Carlos; Cox, Robert D.; Cain, James W.

2017-01-01

The increase of tree density in forests of the American Southwest promotes extreme fire events, understory biodiversity losses, and degraded habitat conditions for many wildlife species. To ameliorate these changes, managers and scientists have begun planning treatments aimed at reducing fuels and increasing understory biodiversity. However, spatial variability in tree density across the landscape is not well-characterized, and if better known, could greatly influence planning efforts. We used reflectance values from individual Landsat 8 bands (bands 2, 3, 4, 5, 6, and 7) and calculated vegetation indices (difference vegetation index, simple ratios, and normalized vegetation indices) to estimate tree density in an area planned for treatment in the Jemez Mountains, New Mexico, characterized by multiple vegetation types and a complex topography. Because different vegetation types have different spectral signatures, we derived models with multiple predictor variables for each vegetation type, rather than using a single model for the entire project area, and compared the model-derived values to values collected from on-the-ground transects. Among conifer-dominated areas (73% of the project area), the best models (as determined by corrected Akaike Information Criteria (AICc)) included Landsat bands 2, 3, 4, and 7 along with simple ratios, normalized vegetation indices, and the difference vegetation index (R2 values for ponderosa: 0.47, piñon-juniper: 0.52, and spruce-fir: 0.66). On the other hand, in aspen-dominated areas (9% of the project area), the best model included individual bands 4 and 2, simple ratio, and normalized vegetation index (R2 value: 0.97). Most areas dominated by ponderosa, pinyon-juniper, or spruce-fir had more than 100 trees per hectare. About 54% of the study area has medium to high density of trees (100–1000 trees/hectare), and a small fraction (4.5%) of the area has very high density (>1000 trees/hectare). Our results provide a better understanding of tree density for identifying areas in need of treatment and planning for more effective treatment. Our analysis also provides an integrated method of estimating tree density across complex landscapes that could be useful for further restoration planning.

Using vegetation cover type to predict and scale peatland methane dynamics.

NASA Astrophysics Data System (ADS)

McArthur, K. J.; McCalley, C. K.; Palace, M. W.; Varner, R. K.; Herrick, C.; DelGreco, J. L.

2015-12-01

Permafrost ecosystems contain about 50% of the global soil carbon. As these northern ecosystems experience warmer temperature, permafrost thaws and may result in an increase in atmospheric methane. We examined a thawing and discontinuous permafrost boundary at Stordalen Mire, in Northern Sweden, in an effort to better understand methane emissions. Stable isotope analysis of methane in peatland porewater can give insights into the pathway of methane production. By measuring δ13CH4 we can predict whether a system is dominated by either hydrogenotrophic or acetaclastic methane production. Currently, it is a challenge to scale these isotopic patterns, thus, atmospheric inversion models simply assume that acetoclastic production dominates. We analyzed porewater samples collected across a range of vegetation cover types for δ13CH4 using a QCL (Quantum Cascade Laser Spectrometer) in conjunction with highly accurate GPS (3-10cm) measurements and high-resolution UAV imaging. We found δ13CH4 values ranging from -88‰ to -41‰, with averages based on cover type and other vegetation features showing differences of up to -15‰. We then used a computer neural network to predict cover types across Stordalen Mire from UAV imagery based on field-based plot measurements and training samples.. This prediction map was used to scale methane flux and isotope measurements. Our results suggest that the current values used in atmospheric inversion studies may oversimplify the relationship between plant and microbial communities in complex permafrost landscapes. As we gain a deeper understanding of how vegetation relates to methanogenic communities, understanding the spatial component of ecosystem methane metabolism and distribution will be increasingly valuable.

Mapping local and global variability in plant trait distributions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Butler, Ethan E.; Datta, Abhirup; Flores-Moreno, Habacuc

2017-12-01

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusingmore » on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (N m) and phosphorus (P m), we characterize how traits vary within and among over 50,000 ~50×50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps further reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.« less

Mapping local and global variability in plant trait distributions.

PubMed

Butler, Ethan E; Datta, Abhirup; Flores-Moreno, Habacuc; Chen, Ming; Wythers, Kirk R; Fazayeli, Farideh; Banerjee, Arindam; Atkin, Owen K; Kattge, Jens; Amiaud, Bernard; Blonder, Benjamin; Boenisch, Gerhard; Bond-Lamberty, Ben; Brown, Kerry A; Byun, Chaeho; Campetella, Giandiego; Cerabolini, Bruno E L; Cornelissen, Johannes H C; Craine, Joseph M; Craven, Dylan; de Vries, Franciska T; Díaz, Sandra; Domingues, Tomas F; Forey, Estelle; González-Melo, Andrés; Gross, Nicolas; Han, Wenxuan; Hattingh, Wesley N; Hickler, Thomas; Jansen, Steven; Kramer, Koen; Kraft, Nathan J B; Kurokawa, Hiroko; Laughlin, Daniel C; Meir, Patrick; Minden, Vanessa; Niinemets, Ülo; Onoda, Yusuke; Peñuelas, Josep; Read, Quentin; Sack, Lawren; Schamp, Brandon; Soudzilovskaia, Nadejda A; Spasojevic, Marko J; Sosinski, Enio; Thornton, Peter E; Valladares, Fernando; van Bodegom, Peter M; Williams, Mathew; Wirth, Christian; Reich, Peter B

2017-12-19

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration-specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen ([Formula: see text]) and phosphorus ([Formula: see text]), we characterize how traits vary within and among over 50,000 [Formula: see text]-km cells across the entire vegetated land surface. We do this in several ways-without defining the PFT of each grid cell and using 4 or 14 PFTs; each model's predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

Vegetation cover and relationships of habitat-type with elevation on the Mississippi-Alabama Barrier Islands in the initial six years after Hurricane Katrina

NASA Astrophysics Data System (ADS)

Funderburk, W.; Carter, G. A.; Anderson, C. P.; Jeter, G. W., Jr.; Otvos, E. G.; Lucas, K. L.; Hopper, N. L.

2015-12-01

Quantifying change in vegetation and geomorphic features which occur during and after storm impact is necessary toward understanding barrier island habitat resiliency under continued climate warming and sea level rise. In August, 2005, the Mississippi-Alabama barrier islands, including, from west-to-east, Cat, West Ship, East Ship, Horn, Petit Bois and Dauphin islands, were completely inundated by the tidal surge of Hurricane Katrina. Overwash, scouring, burial under sand, and mechanical damage combined with saltwater flooding and post-storm drought resulted in immediate and long-term vegetation loss. Remotely-sensed data acquired before (2004-2005) and after (2005-2011) Katrina were compared via image classification to determine immediate storm impacts and assess natural re-growth of land area and vegetation. By 2008, merely three years after the storm, total land area of Cat, West Ship, East Ship, Horn, Petit Bois and West Dauphin had recovered to 92, 90, 33, 99, 93 and 91 percent, and total vegetated land area to 85, 101, 85, 94, 83 and 102 percent of pre-Katrina values, respectively. Habitat-type maps developed from field survey, SPOT-5 and radar data were compared with LIDAR-derived elevation models to assess 2010 habitat-type distribution with respect to ground elevation. Although median MSL elevations associated with habitat classes ranged only from 0.5 m to 1.4 m, habitat-type changed distinctively with decimeter-scale changes in elevation. Low marsh, high marsh, estuarine shrubland, slash pine woodland, beach dune, bare sand and beach dune herbland were associated with median elevations of 0.5, 0.9, 1.0, 1.1, 1.2, 1.3 and 1.4 m ± 0.1 m, respectively. The anticipated increases in sea level and tropical storm energy under a continually warming climate will likely inhibit the reformation of higher-elevation habitat-types, such as shrublands and woodlands, in the 21st century.

Mapping the Potential for Eolian Surface Activity in Grasslands of the High Plains using Landsat Images

NASA Technical Reports Server (NTRS)

Gutmann, Ethan Dain

2002-01-01

There are over 100,000 square kilometers of eolian sand dunes and sand sheets in the High Plains of the central United States. These land-forms may be unstable and may reactivate again as a result of land-use, climate change, or natural climatic variability. The main goal of this thesis was to develop a model that could be used to map an estimate of future dune activity. Multi-temporal calibrated Landsats 5 Thematic Mapper (TM) and 7 Enhanced Thematic Map per Plus (ETM+) NDVI imagery were used in conjunction with the CENTURY vegetation model to correlate vegetation cover to climatic variability. This allows the creation of a predicted vegetation map which, combined with current wind and soil data, was used to create a potential sand transport map for range land in the High Plains under drought conditions.

Retrieval of canopy water content of different crop types with two new hyperspectral indices: Water Absorption Area Index and Depth Water Index

NASA Astrophysics Data System (ADS)

Pasqualotto, Nieves; Delegido, Jesús; Van Wittenberghe, Shari; Verrelst, Jochem; Rivera, Juan Pablo; Moreno, José

2018-05-01

Crop canopy water content (CWC) is an essential indicator of the crop's physiological state. While a diverse range of vegetation indices have earlier been developed for the remote estimation of CWC, most of them are defined for specific crop types and areas, making them less universally applicable. We propose two new water content indices applicable to a wide variety of crop types, allowing to derive CWC maps at a large spatial scale. These indices were developed based on PROSAIL simulations and then optimized with an experimental dataset (SPARC03; Barrax, Spain). This dataset consists of water content and other biophysical variables for five common crop types (lucerne, corn, potato, sugar beet and onion) and corresponding top-of-canopy (TOC) reflectance spectra acquired by the hyperspectral HyMap airborne sensor. First, commonly used water content index formulations were analysed and validated for the variety of crops, overall resulting in a R2 lower than 0.6. In an attempt to move towards more generically applicable indices, the two new CWC indices exploit the principal water absorption features in the near-infrared by using multiple bands sensitive to water content. We propose the Water Absorption Area Index (WAAI) as the difference between the area under the null water content of TOC reflectance (reference line) simulated with PROSAIL and the area under measured TOC reflectance between 911 and 1271 nm. We also propose the Depth Water Index (DWI), a simplified four-band index based on the spectral depths produced by the water absorption at 970 and 1200 nm and two reference bands. Both the WAAI and DWI outperform established indices in predicting CWC when applied to heterogeneous croplands, with a R2 of 0.8 and 0.7, respectively, using an exponential fit. However, these indices did not perform well for species with a low fractional vegetation cover (<30%). HyMap CWC maps calculated with both indices are shown for the Barrax region. The results confirmed the potential of using generically applicable indices for calculating CWC over a great variety of crops.

Response of alpine vegetation growth dynamics to snow cover phenology on the Tibetan Plateau

NASA Astrophysics Data System (ADS)

Wang, X.; Wu, C.

2017-12-01

Alpine vegetation plays a crucial role in global energy cycles with snow cover, an essential component of alpine land cover showing high sensitivity to climate change. The Tibetan Plateau (TP) has a typical alpine vegetation ecosystem and is rich of snow resources. With global warming, the snow of the TP has undergone significant changes that will inevitably affect the growth of alpine vegetation, but observed evidence of such interaction is limited. In particular, a comprehensive understanding of the responses of alpine vegetation growth to snow cover variability is still not well characterized on TP region. To investigate this, we calculated three indicators, the start (SOS) and length (LOS) of growing season, and the maximum of normalized difference vegetation index (NDVImax) as proxies of vegetation growth dynamics from the Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000-2015. Snow cover duration (SCD) and melt (SCM) dates were also extracted during the same time frame from the combination of MODIS and the Interactive Multi-sensor Snow and Ice Mapping System (IMS) data. We found that the snow cover phenology had a strong control on alpine vegetation growth dynamics. Furthermore, the responses of SOS, LOS and NDVImax to snow cover phenology varied among plant functional types, eco-geographical zones, and temperature and precipitation gradients. The alpine steppes showed a much stronger negative correlation between SOS and SCD, and also a more evidently positive relationship between LOS and SCD than other types, indicating a longer SCD would lead to an earlier SOS and longer LOS. Most areas showed positive correlation between SOS and SCM, while a contrary response was also found in the warm but drier areas. Both SCD and SCM showed positive correlations with NDVImax, but the relationship became weaker with the increase of precipitation. Our findings provided strong evidences between vegetation growth and snow cover phenology, and changes in snow cover should be also considered when analyzing alpine vegetation growth dynamics in future.

Agriculture/forestry hydrology

NASA Technical Reports Server (NTRS)

Vanderoord, W. J. (Principal Investigator)

1977-01-01

The author has identified the following significant results. The main vegetation units of the lower Mekong basin and the land development conditions were mapped by interpretation of LANDSAT 1 data. By interpretation of various shades of gray on satellite images, it was possible to map the density of the vegetation cover. Study of seasonal variations makes it possible to distinguish between mainly deciduous forests. In the Mekong basin area, these are generally related to the vegetation cover density.

Mapping the response of riparian vegetation to possible flow reductions in the Snake River, Idaho

USGS Publications Warehouse

Johnson, W. Carter; Dixon, Mark D.; Simons, Robert W.; Jenson, Susan; Larson, Kevin

1995-01-01

This study was initiated to determine the general effects of potential flow reductions in the middle Snake River (Swan Falls Dam downstream to the Idaho-Oregon border) on its riparian vegetation. Considerable water from the river is currently used to irrigate the adjacent Snake River Plain, and increased demand for water in the future is likely. The problem was subdivided into several research components including: field investigation of the existing riparian vegetation and river environment, hydrological modeling to calculate the effects of one flow scenario on hydrological regime, and integration of vegetation and hydrological modeling results with a Geographic Information System (GIs) to map the riverbed, island, and bank conditions under the scenario flow. Field work was conducted in summer 1990. Riparian vegetation along 40 U.S. Geological Survey cross-sections was sampled at approximately 1.25 mile intervals within the 50 mile long study area. Cross-section and flow data were provided by the U.S. Geological. Survey. GIs mapping of land/water cover using ARC/INFO was based on 1987 aerial photographs. Riverbed contour maps were produced by linking cross-section data, topographic contouring software (anudem), and GIs. The maps were used to spatially display shallow areas in the channel likely to become vegetated under reduced flow conditions. The scenario would reduce flow by approximately 20% (160 MAF) and lower the river an average of 0.5 ft. The scenario flow could cause a drop in the elevation of the riparian zone comparable to the drop in mean river level and expansion of the lower riparian zone into shallow areas of the channel. The GIs maps showed that the shallow areas of the channel more likely to become vegetated under the scenario flow are located in wide reaches near islands. Some possible ecological consequences of the scenario flow include a greater area of riparian habitat, reduced flow velocity and sedimentation in shallow channels leading to channel deactivation, increased island visitation and nest predation by predatory mammals due to loss of a water barrier between some islands and banks, and larger populations of alien plant species in the new riparian vegetation.

FORUM: A Suggestion for an Improved Vegetation Scheme for Local and Global Mapping and Monitoring.

PubMed

ADAMS

1999-01-01

/ Understanding of global ecological problems is at least partly dependent on clear assessments of vegetation change, and such assessment is always dependent on the use of a vegetation classification scheme. Use of satellite remotely sensed data is the only practical means of carrying out any global-scale vegetation mapping exercise, but if the resulting maps are to be useful to most ecologists and conservationists, they must be closely tied to clearly defined features of vegetation on the ground. Furthermore, much of the mapping that does take place involves more local-scale description of field sites; for purposes of cost and practicality, such studies usually do not involve remote sensing using satellites. There is a need for a single scheme that integrates the smallest to the largest scale in a way that is meaningful to most environmental scientists. Existing schemes are unsatisfactory for this task; they are ambiguous, unnecessarily complex, and their categories do not correspond to common-sense definitions. In response to these problems, a simple structural-physiognomically based scheme with 23 fundamental categories is proposed here for mapping and monitoring on any scale, from local to global. The fundamental categories each subdivide into more specific structural categories for more detailed mapping, but all the categories can be used throughout the world and at any scale, allowing intercomparison between regions. The next stage in the process will be to obtain the views of as many people working in as many different fields as possible, to see whether the proposed scheme suits their needs and how it should be modified. With a few modifications, such a scheme could easily be appended to an existing land cover classification scheme, such as the FAO system, greatly increasing the usefulness and accessability of the results of the landcover classification. KEY WORDS: Vegetation scheme; Mapping; Monitoring; Land cover

Vegetation mapping of Nowitna National Wildlife Reguge, Alaska using Landsat MSS digital data

USGS Publications Warehouse

Talbot, S. S.; Markon, Carl J.

1986-01-01

A Landsat-derived vegetation map was prepared for Nowitna National Wildlife Refuge. The refuge lies within the middle boreal subzone of north central Alaska. Seven major vegetation classes and sixteen subclasses were recognized: forest (closed needleleaf, open needleleaf, needleleaf woodland, mixed, and broadleaf); broadleaf scrub (lowland, alluvial, subalpine); dwarf scrub (prostrate dwarf shrub tundra, dwarf shrub-graminoid tussock peatland); herbaceous (graminoid bog, marsh and meadow); scarcely vegetated areas (scarcely vegetated scree and floodplain); water (clear, turbid); and other areas (mountain shadow). The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photointerpretation, and digital Landsat data. Major steps in the Landsat analysis involved preprocessing (geometric correction), derivation of statistical parameters for spectral classes, spectral class labeling of sample areas, preliminary classification of the entire study area using a maximum-likelihood algorithm, and final classification utilizing ancillary information such as digital elevation data. The final product is a 1:250,000-scale vegetation map representative of distinctive regional patterns and suitable for use in comprehensive conservation planning.

Satellite detection of vegetative damage and alteration caused by pollutants emitted by a zinc smelter

NASA Technical Reports Server (NTRS)

Mcmurtry, G. J.; Petersen, G. W. (Principal Investigator); Fritz, E. L.; Pennypacker, S. P.

1974-01-01

The author has identified the following significant results. Field observations and data collected by low flying aircraft were used to verify the accuracy of maps produced from the satellite data. Although areas of vegetation as small as six acres can accurately be detected, a white pine stand that was severely damaged by sulfur dioxide could not be differentiated from a healthy white pine stand because spectral differences were not large enough. When winter data were used to eliminate interference from herbaceous and deciduous vegetation, the damage was still undetectable. The analysis was able to produce a character map that accurately delineated areas of vegetative alteration due to high zinc levels accumulating in the soil. The map depicted a distinct gradient of less damage and alteration as the distance from the smelter increased. Although the satellite data will probably not be useful for detecting small acreages of damaged vegetation, it is concluded that the data may be very useful as an inventory tool to detect and delineate large vegetative areas possessing differing spectral signatures.

Use of high resolution Airborne Laser Scanning data for landslide interpretation under mixed forest and tropical rainforest: case study in Barcelonnette, France and Cameron Highlands, Malaysia

NASA Astrophysics Data System (ADS)

Azahari Razak, Khamarrul; Straatsma, Menno; van Westen, Cees; Malet, Jean-Philippe; de Jong, Steven M.

2010-05-01

Airborne Laser Scanning (ALS) is the state of the art technology for topographic mapping over a wide variety of spatial and temporal scales. It is also a promising technique for identification and mapping of landslides in a forested mountainous landscape. This technology demonstrates the ability to pass through the gaps between forest foliage and record the terrain height under vegetation cover. To date, most of the images either derived from satellite imagery, aerial-photograph or synthetic aperture radar are not appropriate for visual interpretation of landslide features that are covered by dense vegetation. However, it is a necessity to carefully map the landslides in order to understand its processes. This is essential for landslide hazard and risk assessment. This research demonstrates the capabilities of high resolution ALS data to recognize and identify different types of landslides in mixed forest in Barcelonnette, France and tropical rainforest in Cameron Highlands, Malaysia. ALS measurements over the 100-years old forest in Bois Noir catchment were carried out in 2007 and 2009. Both ALS dataset were captured using a Riegl laser scanner. First and last pulse with density of one point per meter square was derived from 2007 ALS dataset, whereas multiple return (of up to five returns) pulse was derived from July 2009 ALS dataset, which consists of 60 points per meter square over forested terrain. Generally, this catchment is highly affected by shallow landslides which mostly occur beneath dense vegetation. It is located in the dry intra-Alpine zone and represented by the climatic of the South French Alps. In the Cameron Highlands, first and last pulse data was captured in 2004 which covers an area of up to 300 kilometres square. Here, the Optech laser scanner was used under the Malaysian national pilot study which has slightly low point density. With precipitation intensity of up to 3000 mm per year over rugged topography and elevations up to 2800 m a.s.l., mapping the landslides under tropical rainforest which are highly vegetated and rapidly re-vegetated still remains a challenge. With the advancement of point clouds processing algorithm, high resolution Digital Terrain Models (DTMs) are becoming a very valuable data source for the production of landslide related maps. In this study, two filtering algorithms, which are based on least square interpolation and progressive TIN densification, are used to extract the bare earth surface. Quantitative and qualitative assessment that was carried out under ISPRS Working Group III/3 shown that those algorithms performed well in terms of discontinuity preservation, vegetation on the slope and high outlier influence in the point clouds. Hence, they are capable to extract ground points under difficult scenarios, especially for application under rugged forested terrain. The optimal terrain information has been exploited from ALS point clouds, particularly to preserve important landslide characteristics and to filter out unnecessary features. Morphological characteristics and geometric signatures of landslides are taken into consideration for the derivation of high-quality digital terrain model. Furthermore, ALS-derived DTMs are investigated at different spatial scales for suitable hillslopes morphology representation. Hence, appropriate 2D and 3D visualization methods are presented in such a way to help the image interpreters to detect landslides and classify them according to type, movement mechanism and activity status in forested mountainous terrain.

A technique for the determination of Louisiana marsh salinity zone from vegetation mapped by multispectral scanner data: A comparison of satellite and aircraft data

NASA Technical Reports Server (NTRS)

Butera, M. K.

1977-01-01

Vegetation in selected study areas on the Louisiana coast was mapped using low altitude aircraft and satellite (LANDSAT) multispectral scanner data. Fresh, brackish, and saline marshes were then determined from the remotely sensed presence of dominant indicator plant associations. Such vegetational classifications were achieved from data processed through a standard pattern recognition computer program. The marsh salinity zone maps from the aircraft and satellite data compared favorably within the broad salinity regimes. The salinity zone boundaries determined by remote sensing compared favorably with those interpolated from line-transect field observations from an earlier year.

Classifying and mapping wetlands and peat resources using digital cartography

USGS Publications Warehouse

Cameron, Cornelia C.; Emery, David A.

1992-01-01

Digital cartography allows the portrayal of spatial associations among diverse data types and is ideally suited for land use and resource analysis. We have developed methodology that uses digital cartography for the classification of wetlands and their associated peat resources and applied it to a 1:24 000 scale map area in New Hampshire. Classifying and mapping wetlands involves integrating the spatial distribution of wetlands types with depth variations in associated peat quality and character. A hierarchically structured classification that integrates the spatial distribution of variations in (1) vegetation, (2) soil type, (3) hydrology, (4) geologic aspects, and (5) peat characteristics has been developed and can be used to build digital cartographic files for resource and land use analysis. The first three parameters are the bases used by the National Wetlands Inventory to classify wetlands and deepwater habitats of the United States. The fourth parameter, geological aspects, includes slope, relief, depth of wetland (from surface to underlying rock or substrate), wetland stratigraphy, and the type and structure of solid and unconsolidated rock surrounding and underlying the wetland. The fifth parameter, peat characteristics, includes the subsurface variation in ash, acidity, moisture, heating value (Btu), sulfur content, and other chemical properties as shown in specimens obtained from core holes. These parameters can be shown as a series of map data overlays with tables that can be integrated for resource or land use analysis.

Remote Sensing techniques used to characterize soil erosion in southwestern Sao Paulo state. M.S. Thesis - 29 Sep. 1982; [Brazil

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Pinto, S. D. A. F.

1983-01-01

Within randomly sampled squares of a 1 km x 1 km grid, rill/gullies frequency, land cover/land use type and shape of the slopes were extracted from aerial photographs of the Ribeirao Anhumas drainage basin. Mean slope gradient, stream frequency and slope length were calculated on topographic maps. Ground truth data on fine sand/coarse sand ratio and vegetation cover densities were obtained. The MSS-LANDSAT-2 data (CCTs) were analyzed using single-cell, cluster synthesis and slicer algorithms. Graphical and statistical analyses of the data indicate that different slope gradients and land cover/land use types are the most significant factors related to the soil erosion process. The digital analysis of MSS data allowed the association among gray level classes and vegetation cover classes, which defined seven classes. These gray level classes and slope gradient classes were used to rank erosion risk.

Revisions to the 1995 map of ecological subregions that affect users of the southern variant of the Forest Vegetation Simulator

Treesearch

W. Henry McNab; Chad E. Keyser

2011-01-01

The Southern Variant of the Forest Vegetation Simulator utilizes ecological units mapped in 1995 by the Forest Service, U.S. Department of Agriculture, to refine tree growth models for the Southern United States. The 2007 revision of the 1995 map resulted in changes of identification and boundary delineation for some ecoregion units. In this report, we summarize the...

Plan for the uniform mapping of earth resources and environmental complexes from Skylab imagery. [vegetation of Colorado Plateau and rice crops in California

NASA Technical Reports Server (NTRS)

Poulton, C. E. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Below approximately 25% cover visual photointerpretation of vegetation analogs of Skylab 2 Sl9OA color infrared imagery is poor. Correct identifications of vegetation analogs in this category range from 28 to 57%. Good photointerpretation results (64 to 96%) were obtained on vegetation analogs with higher cover values. The four semidesert vegetation analogs (greasewood, saltbush, big sagebrush, and pinyon-juniper) are consistently distinguishable as a group. Photointerpretation accuracy equals 90.1%. When these same types are broken into two sub-groups (salt desert vegetation and shrub steppe/sparse pinyon-juniper vegetation) interpretation success drops to 76% and 85%, respectively. Band ratioing and transmittance differences between two forested analogs as imaged on Skylab 2 S19OA film shows significant differences. In the infrared wavelength both analogs have very similar transmittance characteristics while the visible wavelength shows separation between the two. Relative transmittance values for stands of ponderosa pine forestland and pinyon-juniper woodland are 719.3 + or - 65.9 and 223.6 + or - 48.1, respectively on negative transparencies. In image interpretation along the low-elevation fringe of forested regions these are the two forest analogs most frequently requiring separation.

Comparison of Landsat MSS and merged MSS/RBV data for analysis of natural vegetation

NASA Technical Reports Server (NTRS)

Roller, N. E. G.; Cox, S.

1980-01-01

Improved resolution could make satellite remote sensing data more useful for surveys of natural vegetation. Although improved satellite/sensor systems appear to be several years away, one potential interim solution to the problem of achieving greater resolution without sacrificing spectral sensitivity is through the merging of Landsat RBV and MSS data. This paper describes the results of a study performed to obtain a preliminary evaluation of the usefulness of two types of products that can be made by merging Landsat RBV and MSS data. The products generated were a false color composite image and a computer recognition map. Of these two products, the false color composite image appears to be the most useful.

Mapping impervious surfaces using object-oriented classification in a semiarid urban region

USDA-ARS?s Scientific Manuscript database

Mapping the expansion of impervious surfaces in urbanizing areas is important for monitoring and understanding the hydrologic impacts of land development. The most common approach using spectral vegetation indices, however, is difficult in arid and semiarid environments where vegetation is sparse an...

ISSUES IN DIGITAL IMAGE PROCESSING OF AERIAL PHOTOGRAPHY FOR MAPPING SUBMERSED AQUATIC VEGETATION

EPA Science Inventory

The paper discusses the numerous issues that needed to be addressed when developing a methodology for mapping Submersed Aquatic Vegetation (SAV) from digital aerial photography. Specifically, we discuss 1) choice of film; 2) consideration of tide and weather constraints; 3) in-s...

Southern Arizona riparian habitat: Spatial distribution and analysis

NASA Technical Reports Server (NTRS)

Lacey, J. R.; Ogden, P. R.; Foster, K. E.

1975-01-01

The objectives of this study were centered around the demonstration of remote sensing as an inventory tool and researching the multiple uses of riparian vegetation. Specific study objectives were to: (1) map riparian vegetation along the Gila River, San Simon Creek, San Pedro River, Pantano Wash, (2) determine the feasibility of automated mapping using LANDSAT-1 computer compatible tapes, (3) locate and summarize existing mpas delineating riparian vegetation, (4) summarize data relevant to Southern Arizona's riparian products and uses, (5) document recent riparian vegetation changes along a selected portion of the San Pedro River, (6) summarize historical changes in composition and distribution of riparian vegetation, and (7) summarize sources of available photography pertinent to Southern Arizona.

Object-Based Classification and Change Detection of Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Park, J. G.; Harada, I.; Kwak, Y.

2016-06-01

Topography and geology are factors to characterize the distribution of natural vegetation. Topographic contour is particularly influential on the living conditions of plants such as soil moisture, sunlight, and windiness. Vegetation associations having similar characteristics are present in locations having similar topographic conditions unless natural disturbances such as landslides and forest fires or artificial disturbances such as deforestation and man-made plantation bring about changes in such conditions. We developed a vegetation map of Japan using an object-based segmentation approach with topographic information (elevation, slope, slope direction) that is closely related to the distribution of vegetation. The results found that the object-based classification is more effective to produce a vegetation map than the pixel-based classification.

Drought impacts on vegetation activity in the Mediterranean region: An assessment using remote sensing data and multi-scale drought indicators

NASA Astrophysics Data System (ADS)

Gouveia, C. M.; Trigo, R. M.; Beguería, S.; Vicente-Serrano, S. M.

2017-04-01

The present work analyzes the drought impacts on vegetation over the entire Mediterranean basin, with the purpose of determining the vegetation communities, regions and seasons at which vegetation is driven by drought. Our approach is based on the use of remote sensing data and a multi-scalar drought index. Correlation maps between fields of monthly Normalized Difference Vegetation Index (NDVI) and the Standardized Precipitation-Evapotranspiration Index (SPEI) at different time scales (1-24 months) were computed for representative months of winter (Feb), spring (May), summer (Aug) and fall (Nov). Results for the period from 1982 to 2006 show large areas highly controlled by drought, although presenting high spatial and seasonal differences, with a maximum influence in August and a minimum in February. The highest correlation values are observed in February for 3 months' time scale and in May for 6 and 12 months. The higher control of drought on vegetation in February and May is obtained mainly over the drier vegetation communities (Mediterranean Dry and Desertic) at shorter time scales (3 to 9 months). Additionally, in February the impact of drought on vegetation is lower for Temperate Oceanic and Continental vegetation types and takes place at longer time scales (18-24). The dependence of drought time-scale response with water balance, as obtained through a simple difference between precipitation and reference evapotranspiration, varies with vegetation communities. During February and November low water balance values correspond to shorter time scales over dry vegetation communities, whereas high water balance values implies longer time scales over Temperate Oceanic and Continental areas. The strong control of drought on vegetation observed for Mediterranean Dry and Desertic vegetation types located over areas with high negative values of water balance emphasizes the need for an early warning drought system covering the entire Mediterranean basin. We are confident that these results will provide a useful tool for drought management plans and play a relevant role in mitigating the impact of drought episodes.

A LAI inversion algorithm based on the unified model of canopy bidirectional reflectance distribution function for the Heihe River Basin

NASA Astrophysics Data System (ADS)

Ma, B.; Li, J.; Fan, W.; Ren, H.; Xu, X.

2017-12-01

Leaf area index (LAI) is one of the important parameters of vegetation canopy structure, which can represent the growth condition of vegetation effectively. The accuracy, availability and timeliness of LAI data can be improved greatly, which is of great importance to vegetation-related research, such as the study of atmospheric, land surface and hydrological processes to obtain LAI by remote sensing method. Heihe River Basin is the inland river basin in northwest China. There are various types of vegetation and all kinds of terrain conditions in the basin, so it is helpful for testing the accuracy of the model under the complex surface and evaluating the correctness of the model to study LAI in this area. On the other hand, located in west arid area of China, the ecological environment of Heihe Basin is fragile, LAI is an important parameter to represent the vegetation growth condition, and can help us understand the status of vegetation in the Heihe River Basin. Different from the previous LAI inversion models, the BRDF (bidirectional reflectance distribution function) unified model can be applied for both continuous vegetation and discrete vegetation, it is appropriate to the complex vegetation distribution. LAI is the key input parameter of the model. We establish the inversion algorithm that can exactly retrieve LAI using remote sensing image based on the unified model. First, we determine the vegetation type through the vegetation classification map to obtain the corresponding G function, leaf and surface reflectivity. Then, we need to determine the leaf area index (LAI), the aggregation index (ζ) and the sky scattered light ratio (β) range and the value of the interval, entering all the parameters into the model to calculate the corresponding reflectivity ρ and establish the lookup table of different vegetation. Finally, we can invert LAI on the basis of the established lookup table. The principle of inversion is least squares method. We have produced 1 km LAI products from 2000 to 2014, once every 8 days. The results show that the algorithm owns good stability and can effectively invert LAI in areas with very complex vegetation and terrain conditions.

A decision support system for map projections of small scale data

USGS Publications Warehouse

Finn, Michael P.; Usery, E. Lynn; Posch, Stephan T.; Seong, Jeong Chang

2004-01-01

The use of commercial geographic information system software to process large raster datasets of terrain elevation, population, land cover, vegetation, soils, temperature, and rainfall requires both projection from spherical coordinates to plane coordinate systems and transformation from one plane system to another. Decision support systems deliver information resulting in knowledge that assists in policies, priorities, or processes. This paper presents an approach to handling the problems of raster dataset projection and transformation through the development of a Web-enabled decision support system to aid users of transformation processes with the selection of appropriate map projections based on data type, areal extent, location, and preservation properties.

Use of remote sensing for land use policy formulation

NASA Technical Reports Server (NTRS)

1982-01-01

Research projects described include: (1) identifying coniferous forest types in Michigan using LANDSAT imagery; (2) investigating synoptic temperature patterns in Michigan as determined via GOES and HCMM thermal imagery; (3) land surface change detection using satellite data and a geographic data base; (4) determining soil map unit composition by electronic scanning densitometry; and (5) delimiting areas of virus infection in vineyards and blueberry fields in southwestern and western Michigan. Contractual activities involve important farmlands inventory, changes in aquatic vegetation in Saginaw Bay, digitized soil association map of Michigan, and aerial photography for hybrid-poplar research. On-going projects are also being conducted in Jamaica, Honduras, the Dominican Republic and Kenya.

Mapping of the Land Cover Spatiotemporal Characteristics in Northern Russia Caused by Climate Change

NASA Astrophysics Data System (ADS)

Panidi, E.; Tsepelev, V.; Torlopova, N.; Bobkov, A.

2016-06-01

The study is devoted to the investigation of regional climate change in Northern Russia. Due to sparseness of the meteorological observation network in northern regions, we investigate the application capabilities of remotely sensed vegetation cover as indicator of climate change at the regional scale. In previous studies, we identified statistically significant relationship between the increase of surface air temperature and increase of the shrub vegetation productivity. We verified this relationship using ground observation data collected at the meteorological stations and Normalised Difference Vegetation Index (NDVI) data produced from Terra/MODIS satellite imagery. Additionally, we designed the technique of growing seasons separation for detailed investigation of the land cover (shrub cover) dynamics. Growing seasons are the periods when the temperature exceeds +5°C and +10°C. These periods determine the vegetation productivity conditions (i.e., conditions that allow growth of the phytomass). We have discovered that the trend signs for the surface air temperature and NDVI coincide on planes and river floodplains. On the current stage of the study, we are working on the automated mapping technique, which allows to estimate the direction and magnitude of the climate change in Northern Russia. This technique will make it possible to extrapolate identified relationship between land cover and climate onto territories with sparse network of meteorological stations. We have produced the gridded maps of NDVI and NDWI for the test area in European part of Northern Russia covered with the shrub vegetation. Basing on these maps, we may determine the frames of growing seasons for each grid cell. It will help us to obtain gridded maps of the NDVI linear trend for growing seasons on cell-by-cell basis. The trend maps can be used as indicative maps for estimation of the climate change on the studied areas.

Phreatophytic land-cover map of the northern and central Great Basin Ecoregion: California, Idaho, Nevada, Utah, Oregon, and Wyoming

USGS Publications Warehouse

Mathie, Amy M.; Welborn, Toby L.; Susong, David D.; Tumbusch, Mary L.

2011-01-01

Increasing water use and changing climate in the Great Basin of the western United States are likely affecting the distribution of phreatophytic vegetation in the region. Phreatophytic plant communities that depend on groundwater are susceptible to natural and anthropogenic changes to hydrologic flow systems. The purpose of this report is to document the methods used to create the accompanying map that delineates areas of the Great Basin that have the greatest potential to support phreatophytic vegetation. Several data sets were used to develop the data displayed on the map, including Shrub Map (a land-cover data set derived from the Regional Gap Analysis Program) and Gap Analysis Program (GAP) data sets for California and Wyoming. In addition, the analysis used the surface landforms from the U.S. Geological Survey (USGS) Global Ecosystems Mapping Project data to delineate regions of the study area based on topographic relief that are most favorable to support phreatophytic vegetation. Using spatial analysis techniques in a GIS, phreatophytic vegetation classes identified within Shrub Map and GAP were selected and compared to the spatial distribution of selected landforms in the study area to delineate areas of phreatophyte vegetation. Results were compared to more detailed studies conducted in selected areas. A general qualitative description of the data and the limitations of the base data determined that these results provide a regional overview but are not intended for localized studies or as a substitute for detailed field analysis. The map is intended as a decision-support aide for land managers to better understand, anticipate, and respond to ecosystem changes in the Great Basin.

Use of radar remote sensing (RADARSAT) to map winter wetland habitat for shorebirds in an agricultural landscape.

PubMed

Taft, Oriane W; Haig, Susan M; Kiilsgaard, Chris

2004-05-01

Many of today's agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38 degrees incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with < or = 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with < or = 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while coverage of class 3 decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (about 30% coverage) in December and January, decreasing in March to approximately 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent.

Trend analysis of vegetation in Louisiana's Atchafalaya river basin

USGS Publications Warehouse

O'Neil, Calvin P.; deSteiguer, J. Edward; North, Gary W.

1978-01-01

The purpose of the study was to determine vegetation succession trends; produce a current vegetation map of the basin; and to develop a mathematical model capable of predicting vegetation changes based on hydrologic factors. A statistical relationship of forests and hydrological variables with forest succession constraints predicted forest acreage totals for 16 forest categories within 70% or better of actual values in two-thirds of the cases. Using time-lapsed photography covering 42 years, 23 categories were described. The succession trend of vegetation since 1930, by sedimentation, had been toward mixed hardwoods, except for isolated areas. Satellite MSS Band 7 imagery was used to map the current vegetation into three main categories and for assessment of acreage. Additionally, a geological anomaly was recognized on satellite imagery indication an effect on drainage and sedimentation.

Estimating and mapping forest biomass using regression models and Spot-6 images (case study: Hyrcanian forests of north of Iran).

PubMed

Motlagh, Mohadeseh Ghanbari; Kafaky, Sasan Babaie; Mataji, Asadollah; Akhavan, Reza

2018-05-21

Hyrcanian forests of North of Iran are of great importance in terms of various economic and environmental aspects. In this study, Spot-6 satellite images and regression models were applied to estimate above-ground biomass in these forests. This research was carried out in six compartments in three climatic (semi-arid to humid) types and two altitude classes. In the first step, ground sampling methods at the compartment level were used to estimate aboveground biomass (Mg/ha). Then, by reviewing the results of other studies, the most appropriate vegetation indices were selected. In this study, three indices of NDVI, RVI, and TVI were calculated. We investigated the relationship between the vegetation indices and aboveground biomass measured at sample-plot level. Based on the results, the relationship between aboveground biomass values and vegetation indices was a linear regression with the highest level of significance for NDVI in all compartments. Since at the compartment level the correlation coefficient between NDVI and aboveground biomass was the highest, NDVI was used for mapping aboveground biomass. According to the results of this study, biomass values were highly different in various climatic and altitudinal classes with the highest biomass value observed in humid climate and high-altitude class.

SRS 2010 Vegetation Inventory GeoStatistical Mapping Results for Custom Reaction Intensity and Total Dead Fuels.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Edwards, Lloyd A.; Paresol, Bernard

This report of the geostatistical analysis results of the fire fuels response variables, custom reaction intensity and total dead fuels is but a part of an SRS 2010 vegetation inventory project. For detailed description of project, theory and background including sample design, methods, and results please refer to USDA Forest Service Savannah River Site internal report “SRS 2010 Vegetation Inventory GeoStatistical Mapping Report”, (Edwards & Parresol 2013).

Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland mapping and assessment

USDA-ARS?s Scientific Manuscript database

Ultra high resolution digital aerial photography has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. We investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern U.S. arid rangelands...

Regional landscape ecosystems of Michigan, Minnesota and Wisconsin: a working map and classification.

Treesearch

Dennis A. Albert

1995-01-01

Describes the landscape ecosystems (ecoregions) of Michigan, Minnesota, and Wisconsin and includes maps of all three states. Regional descriptions include climate, bedrock geology, landforms, lakes and streams, soils, presettlement vegetation, natural disturbance, present vegetation and land use, rare biota, natural areas, public land managers, and conservation...

An evaluation of time-series MODIS 250-meter vegetation index data for crop mapping in the United States Central Great Plains

NASA Astrophysics Data System (ADS)

Wardlow, Brian Douglas

The objectives of this research were to: (1) investigate time-series MODIS (Moderate Resolution Imaging Spectroradiometer) 250-meter EVI (Enhanced Vegetation Index) and NDVI (Normalized Difference Vegetation Index) data for regional-scale crop-related land use/land cover characterization in the U.S. Central Great Plains and (2) develop and test a MODIS-based crop mapping protocol. A pixel-level analysis of the time-series MODIS 250-m VIs for 2,000+ field sites across Kansas found that unique spectral-temporal signatures were detected for the region's major crop types, consistent with the crops' phenology. Intra-class variations were detected in VI data associated with different land use practices, climatic conditions, and planting dates for the crops. The VIs depicted similar seasonal variations and were highly correlated. A pilot study in southwest Kansas found that accurate and detailed cropping patterns could be mapped using the MODIS 250-m VI data. Overall and class-specific accuracies were generally greater than 90% for mapping crop/non-crop, general crops (alfalfa, summer crops, winter wheat, and fallow), summer crops (corn, sorghum, and soybeans), and irrigated/non-irrigated crops using either VI dataset. The classified crop areas also had a high level of agreement (<5% difference) with the USDA reported crop areas. Both VIs produced comparable crop maps with only a 1-2% difference between their classification accuracies and a high level of pixel-level agreement (>90%) between their classified crop patterns. This hierarchical crop mapping protocol was tested for Kansas and produced similar classification results over a larger and more diverse area. Overall and class-specific accuracies were typically between 85% and 95% for the crop maps. At the state level, the maps had a high level of areal agreement (<5% difference) with the USDA crop area figures and their classified patterns were consistent with the state's cropping practices. In general, the protocol's performance was relatively consistent across the state's range of environmental conditions, landscape patterns, and cropping practices. The largest areal differences occurred in eastern Kansas due to the omission of many small cropland areas that were not resolvable at MODIS' 250-m resolution. Notable regional deviations in classified areas also occurred for selected classes due to localized precipitation patterns and specific cropping practices.

Long term land cover and seagrass mapping using Landsat and object-based image analysis from 1972 to 2010 in the coastal environment of South East Queensland, Australia

NASA Astrophysics Data System (ADS)

Lyons, Mitchell B.; Phinn, Stuart R.; Roelfsema, Chris M.

2012-07-01

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based remote sensing approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive (1972-2010). Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in situ field data input (but still using field knowledge) to produce land and seagrass cover maps every year data were available, resulting in over 60 map products over the 38 year archive. Land cover was mapped annually using vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projected foliage cover classes, sand and deep water. Land cover products were validated using aerial photography and seagrass maps were validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 80% was reported for seagrass and land cover products respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, created without the use of in situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density and extent, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several sudden and significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

Monitoring restoration impacts to endemic plant communities in soil inclusions of arid environments

USGS Publications Warehouse

Louhaichi, Mounir; Pyke, David A.; Shaff, Scott E.; Johnson, Douglas E.

2013-01-01

Soil inclusions are small patches of soil with different properties than the surrounding, dominant soil. In arid areas of western North America, soil inclusions called slickspot soils are saltier than adjacent soil and support different types of native vegetation. Traditional sagebrush restoration efforts, such as using drills to plant seeds or herbicides to control invasive vegetation, may damage sensitive slickspot soil and supporting vegetation. USGS scientists David Pyke and Scott Shaff and collaborators monitored slickspot size and cover of endangered slickspot peppergrass for two years to see if they were affected by the application of the herbicide glyphosate or by a minimum-till drill in the Snake River Plain, ID. The researchers examined the use of aerial photographs versus on-the-ground measurements and concluded that slickspot sizes were not affected by these treatments. Remote sensing using aerial photographs proved a useful method for mapping slickspot soils.

Ecological baseline studies in Los Alamos and Guaje Canyons County of Los Alamos, New Mexico. A two-year study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Foxx, T.S.

1995-11-01

During the summers of 1993 and 1994, the Biological Resource Evaluations Team (BRET) of the Environmental Protection Group (ESH-8) conducted baseline studies within two canyon systems, Los Alamos and Guaje Canyons. Biological data was collected within each canyon to provide background and baseline information for Ecological Risk models. Baseline studies included establishment of permanent vegetation plots within each canyon along the elevational gradient. Then, in association with the various vegetation types, surveys were conducted for ground dwelling insects, birds, and small mammals. The stream channels associated with the permanent vegetation plots were characterized and aquatic macroinvertebrates collected within the streammore » monthly throughout a six-month period. The Geographic Position System (GPS) in combination with ARC INFO was used to map the study areas. Considerable data was collected during these surveys and are summarized in individual chapters.« less

Land use survey and mapping and water resources investigation in Korea

NASA Technical Reports Server (NTRS)

Choi, J. H.; Kim, W. I.; Son, D. S. (Principal Investigator)

1978-01-01

The author has identified the following significant results. Land use imagery is applicable to land use classification for small scale land use mapping less than 1:250,000. Land use mapping by satellite is more efficient and more cost-effective than land use mapping from conventional medium altitude aerial photographs. Six categories of level 1 land use classification are recognizable from MSS imagery. A hydrogeomorphological study of the Han River basin indicates that band 7 is useful for recognizing the soil and the weathering part of bed rock. The morphological change of the main river is accurately recognized and the drainage system in the area observed is easily classified because of the more or less simple rock type. Although the direct hydrological characteristics are not obtained from the MSS imagery, the indirect information such as the permeability of the soil and the vegetation cover, is helpful in interpreting the hydrological aspects.

Analysis of recreational land using Skylab data. [Gratiot-Saginaw State Game Area, Michigan

NASA Technical Reports Server (NTRS)

Sattinger, I. J. (Principal Investigator); Sadowski, F. G.; Roller, N. E. G.

1976-01-01

The author has identified the following significant results. S192 data collected on 5 August 1973 were processed by computer to produce a classification map of a part of the Gratiot-Saginaw State Game Area in south central Michigan. A 10-category map was prepared of an area consisting of diverse terrain types, including forests, wetlands, brush, and herbaceous vegetation. An accuracy check indicated that 54% of the pixels were correctly recognized. When these ten scene classes were consolidated to a 5-category map, the accuracy increased to 72%. S190 A, S190 B, and S192 data can be used for regional surveys of existing and potential recreation sites, for delineation of open space, and for preliminary evaluation of geographically extensive sites.

Fuel type characterization and potential fire behavior estimation in Sardinia and Corsica islands

NASA Astrophysics Data System (ADS)

Bacciu, V.; Pellizzaro, G.; Santoni, P.; Arca, B.; Ventura, A.; Salis, M.; Barboni, T.; Leroy, V.; Cancellieri, D.; Leoni, E.; Ferrat, L.; Perez, Y.; Duce, P.; Spano, D.

2012-04-01

Wildland fires represent a serious threat to forests and wooded areas of the Mediterranean Basin. As recorded by the European Commission (2009), during the last decade Southern Countries have experienced an annual average of about 50,000 forest fires and about 470,000 burned hectares. The factor that can be directly manipulated in order to minimize fire intensity and reduce other fire impacts, such as three mortality, smoke emission, and soil erosion, is wildland fuel. Fuel characteristics, such as vegetation cover, type, humidity status, and biomass and necromass loading are critical variables in affecting wildland fire occurrence, contributing to the spread, intensity, and severity of fires. Therefore, the availability of accurate fuel data at different spatial and temporal scales is needed for fire management applications, including fire behavior and danger prediction, fire fighting, fire effects simulation, and ecosystem simulation modeling. In this context, the main aims of our work are to describe the vegetation parameters involved in combustion processes and develop fire behavior fuel maps. The overall work plan is based firstly on the identification and description of the different fuel types mainly affected by fire occurrence in Sardinia (Italy) and Corsica (France) Islands, and secondly on the clusterization of the selected fuel types in relation to their potential fire behavior. In the first part of the work, the available time series of fire event perimeters and the land use map data were analyzed with the purpose of identifying the main land use types affected by fires. Thus, field sampling sites were randomly identified on the selected vegetation types and several fuel variables were collected (live and dead fuel load partitioned following Deeming et al., (1977), depth of fuel layer, plant cover, surface area-to-volume ratio, heat content). In the second part of the work, the potential fire behavior for every experimental site was simulated using BEHAVE fire behavior prediction system (Andrews, 1989) and experimental fuel data. Fire behavior was simulated by setting different weather scenarios representing the most frequent summer meteorological conditions. The simulation outputs (fireline intensity, rate of spread, flame length) were then analyzed for clustering the different fuel types in relation to their potential fire behavior. The results of this analysis can be used to produce fire behavior fuel maps that are important tools in evaluating fire hazard and risk for land management planning, locating and rating fuel treatments, and aiding in environmental assessments and fire danger programs modeling. This work is supported by FUME Project FP7-ENV-2009-1, Grant Agreement Number 243888 and Proterina-C Project, EU Italia-Francia Marittimo 2007-2013 Programme.

Analysis of MASTER Thermal Data in the Greeley Area of the Front Range Urban Corridor, Colorado--Delineation of Sites for Infrastructure Resource Characterization

USGS Publications Warehouse

Livo, K. Eric; Watson, Ken

2002-01-01

Sand and soils southwest of Greeley, Colorado, were characterized for mineral composition and industrial quality. Radi-ance data from the thermal channels of the MASTER simulator were calibrated using estimated atmospheric parameters. Chan-nel emissivities were approximated using an estimated ground temperature. Subsequently, a decorrelation algorithm was used to calculate inverse wave emissivity images. Six soil classes, one vegetation class, water, and several small classes were defined using an unsupervised classification algorithm. Ground covered by each of the derived emissivity spectral classes was studied using color-infrared air photos, color-infrared composite MAS-TER data, geologic maps, NASA/JPL Airborne Visible and Infra-red Imaging Spectrometer (AVIRIS) data, and field examination. Spectral classes were characterized by their responses and related to their mineral content through field examination. Classes with a minimum at channel 44, and having a similar spectral shape to quartz, field checked as containing abundant quartz. Classes with a minimum at channel 45, and having a spectral shape similar to the sheet minerals, were found in the field to contain abundant mica and clay. Sandy soil was found to have a positive slope at the longer wavelengths; the more clay rich soils had a negative slope. Spectra with a strong downturn at channel 50 generally indicated low vegetation cover, whereas an upturn indicated more vegetation cover. Mapping revealed a range of classified soils with varying amounts of quartz, silt, clay, and plant humus. Sand and gravel operations along the St. Vrain River, gravel lots, and some fields spectrally classified as quartz-rich sands were confirmed through field examination. Other fields mapped as sandy soils, ranging from quartz-rich sandy soil to quartz-rich silt-sand soil with clay. Flood plains mapped as sandy-silty-organic-rich clay. The city of Greeley contained all classes of materials, with the sand classes mapping as various types of asphalt. Abundant quartz gravel was apparent within the asphalt during field check-ing. The clay classes mapped silt-clay soils in areas of irrigated grass landscaping, some fields, and roofing materials.

Classification of vegetation in an open landscape using full-waveform airborne laser scanner data

NASA Astrophysics Data System (ADS)

Alexander, Cici; Deák, Balázs; Kania, Adam; Mücke, Werner; Heilmeier, Hermann

2015-09-01

Airborne laser scanning (ALS) is increasingly being used for the mapping of vegetation, although the focus so far has been on woody vegetation, and ALS data have only rarely been used for the classification of grassland vegetation. In this study, we classified the vegetation of an open alkali landscape, characterized by two Natura 2000 habitat types: Pannonic salt steppes and salt marshes and Pannonic loess steppic grasslands. We generated 18 variables from an ALS dataset collected in the growing (leaf-on) season. Elevation is a key factor determining the patterns of vegetation types in the landscape, and hence 3 additional variables were based on a digital terrain model (DTM) generated from an ALS dataset collected in the dormant (leaf-off) season. We classified the vegetation into 24 classes based on these 21 variables, at a pixel size of 1 m. Two groups of variables with and without the DTM-based variables were used in a Random Forest classifier, to estimate the influence of elevation, on the accuracy of the classification. The resulting classes at Level 4, based on associations, were aggregated at three levels - Level 3 (11 classes), Level 2 (8 classes) and Level 1 (5 classes) - based on species pool, site conditions and structure, and the accuracies were assessed. The classes were also aggregated based on Natura 2000 habitat types to assess the accuracy of the classification, and its usefulness for the monitoring of habitat quality. The vegetation could be classified into dry grasslands, wetlands, weeds, woody species and man-made features, at Level 1, with an accuracy of 0.79 (Cohen's kappa coefficient, κ). The accuracies at Levels 2-4 and the classification based on the Natura 2000 habitat types were κ: 0.76, 0.61, 0.51 and 0.69, respectively. Levels 1 and 2 provide suitable information for nature conservationists and land managers, while Levels 3 and 4 are especially useful for ecologists, geologists and soil scientists as they provide high resolution data on species distribution, vegetation patterns, soil properties and on their correlations. Including the DTM-based variables increased the accuracy (κ) from 0.73 to 0.79 for Level 1. These findings show that the structural and spectral attributes of ALS echoes can be used for the classification of open landscapes, especially those where vegetation is influenced by elevation, such as coastal salt marshes, sand dunes, karst or alluvial areas; in these cases, ALS has a distinct advantage over other remotely sensed data.

Improving the Accuracy of Mapping Urban Vegetation Carbon Density by Combining Shadow Remove, Spectral Unmixing Analysis and Spatial Modeling

NASA Astrophysics Data System (ADS)

Qie, G.; Wang, G.; Wang, M.

2016-12-01

Mixed pixels and shadows due to buildings in urban areas impede accurate estimation and mapping of city vegetation carbon density. In most of previous studies, these factors are often ignored, which thus result in underestimation of city vegetation carbon density. In this study we presented an integrated methodology to improve the accuracy of mapping city vegetation carbon density. Firstly, we applied a linear shadow remove analysis (LSRA) on remotely sensed Landsat 8 images to reduce the shadow effects on carbon estimation. Secondly, we integrated a linear spectral unmixing analysis (LSUA) with a linear stepwise regression (LSR), a logistic model-based stepwise regression (LMSR) and k-Nearest Neighbors (kNN), and utilized and compared the integrated models on shadow-removed images to map vegetation carbon density. This methodology was examined in Shenzhen City of Southeast China. A data set from a total of 175 sample plots measured in 2013 and 2014 was used to train the models. The independent variables statistically significantly contributing to improving the fit of the models to the data and reducing the sum of squared errors were selected from a total of 608 variables derived from different image band combinations and transformations. The vegetation fraction from LSUA was then added into the models as an important independent variable. The estimates obtained were evaluated using a cross-validation method. Our results showed that higher accuracies were obtained from the integrated models compared with the ones using traditional methods which ignore the effects of mixed pixels and shadows. This study indicates that the integrated method has great potential on improving the accuracy of urban vegetation carbon density estimation. Key words: Urban vegetation carbon, shadow, spectral unmixing, spatial modeling, Landsat 8 images

Gross Primary Productivity and Vegetation Light Use Efficiency of a Large Metropolitan Region based on CO2 Flux Measurements and WorldView-2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Miller, D. L.; Roberts, D. A.; Clarke, K. C.; Peters, E. B.; Menzer, O.; Lin, Y.; McFadden, J. P.

2017-12-01

Gross primary productivity (GPP) is commonly estimated with remote sensing techniques over large regions of Earth; however, urban areas are typically excluded due to a lack of light use efficiency (LUE) parameters specific to urban vegetation and challenges stemming from the spatial heterogeneity of urban land cover. In this study, we estimated GPP during the middle of the growing season, both within and among vegetation and land use types, in the Minneapolis-Saint Paul, Minnesota metropolitan region (52.1% vegetation cover). We derived LUE parameters for specific urban vegetation types using estimates of GPP from eddy covariance and tree sap flow-based CO2 flux observations and fraction of absorbed photosynthetically active radiation derived from 2-m resolution WorldView-2 satellite imagery. We produced a pixel-based hierarchical land cover classification of built-up and vegetated urban land cover classes distinguishing deciduous broadleaf trees, evergreen needleleaf trees, turf grass, and golf course grass from impervious and soil surfaces. The overall classification accuracy was 80% (kappa = 0.73). The mapped GPP estimates were within 12% of estimates from independent tall tower eddy covariance measurements. Mean GPP estimates ( ± standard deviation; g C m-2 day-1) for the entire study area from highest to lowest were: golf course grass (11.77 ± 1.20), turf grass (6.05 ± 1.07), evergreen needleleaf trees (5.81 ± 0.52), and deciduous broadleaf trees (2.52 ± 0.25). Turf grass GPP had a larger coefficient of variation (0.18) than the other vegetation classes ( 0.10). Mean land use GPP for the full study area varied as a function of percent vegetation cover. Urban GPP in general, both including and excluding non-vegetated areas, was less than half that of literature estimates for nearby natural forests and grasslands.

Mapping croplands, cropping patterns, and crop types using MODIS time-series data

NASA Astrophysics Data System (ADS)

Chen, Yaoliang; Lu, Dengsheng; Moran, Emilio; Batistella, Mateus; Dutra, Luciano Vieira; Sanches, Ieda Del'Arco; da Silva, Ramon Felipe Bicudo; Huang, Jingfeng; Luiz, Alfredo José Barreto; de Oliveira, Maria Antonia Falcão

2018-07-01

The importance of mapping regional and global cropland distribution in timely ways has been recognized, but separation of crop types and multiple cropping patterns is challenging due to their spectral similarity. This study developed a new approach to identify crop types (including soy, cotton and maize) and cropping patterns (Soy-Maize, Soy-Cotton, Soy-Pasture, Soy-Fallow, Fallow-Cotton and Single crop) in the state of Mato Grosso, Brazil. The Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) time series data for 2015 and 2016 and field survey data were used in this research. The major steps of this proposed approach include: (1) reconstructing NDVI time series data by removing the cloud-contaminated pixels using the temporal interpolation algorithm, (2) identifying the best periods and developing temporal indices and phenological parameters to distinguish croplands from other land cover types, and (3) developing crop temporal indices to extract cropping patterns using NDVI time-series data and group cropping patterns into crop types. Decision tree classifier was used to map cropping patterns based on these temporal indices. Croplands from Landsat imagery in 2016, cropping pattern samples from field survey in 2016, and the planted area of crop types in 2015 were used for accuracy assessment. Overall accuracies of approximately 90%, 73% and 86%, respectively were obtained for croplands, cropping patterns, and crop types. The adjusted coefficients of determination of total crop, soy, maize, and cotton areas with corresponding statistical areas were 0.94, 0.94, 0.88 and 0.88, respectively. This research indicates that the proposed approach is promising for mapping large-scale croplands, their cropping patterns and crop types.

Using principal component analysis and annual seasonal trend analysis to assess karst rocky desertification in southwestern China.

PubMed

Zhang, Zhiming; Ouyang, Zhiyun; Xiao, Yi; Xiao, Yang; Xu, Weihua

2017-06-01

Increasing exploitation of karst resources is causing severe environmental degradation because of the fragility and vulnerability of karst areas. By integrating principal component analysis (PCA) with annual seasonal trend analysis (ASTA), this study assessed karst rocky desertification (KRD) within a spatial context. We first produced fractional vegetation cover (FVC) data from a moderate-resolution imaging spectroradiometer normalized difference vegetation index using a dimidiate pixel model. Then, we generated three main components of the annual FVC data using PCA. Subsequently, we generated the slope image of the annual seasonal trends of FVC using median trend analysis. Finally, we combined the three PCA components and annual seasonal trends of FVC with the incidence of KRD for each type of carbonate rock to classify KRD into one of four categories based on K-means cluster analysis: high, moderate, low, and none. The results of accuracy assessments indicated that this combination approach produced greater accuracy and more reasonable KRD mapping than the average FVC based on the vegetation coverage standard. The KRD map for 2010 indicated that the total area of KRD was 78.76 × 10 3  km 2 , which constitutes about 4.06% of the eight southwest provinces of China. The largest KRD areas were found in Yunnan province. The combined PCA and ASTA approach was demonstrated to be an easily implemented, robust, and flexible method for the mapping and assessment of KRD, which can be used to enhance regional KRD management schemes or to address assessment of other environmental issues.

Wetland Classification for Black Duck Habitat Management Using Combined Polarimetric RADARSAT 2 and SPOT Imagery

NASA Astrophysics Data System (ADS)

Zhang, W.; Hu, B.; Brown, G.

2018-04-01

The black duck population has decreased significantly due to loss of its breeding habitat. Wetlands are an important feature that relates to habitat management and requires monitoring. Synthetic Aperture Radar (SAR) systems are helpful to map the wetland as the microwave signals are sensitive to water content and can be used to map surface water extent, saturated soils, and flooded vegetation. In this study, RadarSat 2 Polarimetric data is employed to map surface water and track changes in extent over the years through image thresholding and reviewed different approaches of Polarimetric decompositions for detecting flooded vegetation. Also, object-based analysis associated with beaver activity is conducted with combined multispectral SPOT satellite imagery. Results show SAR data has proven ability to improve mapping open water areas and locate flooded vegetation areas.

Influence of vegetation structure on lidar-derived canopy height and fractional cover in forested riparian buffers during leaf-off and leaf-on conditions.

PubMed

Wasser, Leah; Day, Rick; Chasmer, Laura; Taylor, Alan

2013-01-01

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available.

Influence of Vegetation Structure on Lidar-derived Canopy Height and Fractional Cover in Forested Riparian Buffers During Leaf-Off and Leaf-On Conditions

PubMed Central

Wasser, Leah; Day, Rick; Chasmer, Laura; Taylor, Alan

2013-01-01

Estimates of canopy height (H) and fractional canopy cover (FC) derived from lidar data collected during leaf-on and leaf-off conditions are compared with field measurements from 80 forested riparian buffer plots. The purpose is to determine if existing lidar data flown in leaf-off conditions for applications such as terrain mapping can effectively estimate forested riparian buffer H and FC within a range of riparian vegetation types. Results illustrate that: 1) leaf-off and leaf-on lidar percentile estimates are similar to measured heights in all plots except those dominated by deciduous compound-leaved trees where lidar underestimates H during leaf off periods; 2) canopy height models (CHMs) underestimate H by a larger margin compared to percentile methods and are influenced by vegetation type (conifer needle, deciduous simple leaf or deciduous compound leaf) and canopy height variability, 3) lidar estimates of FC are within 10% of plot measurements during leaf-on periods, but are underestimated during leaf-off periods except in mixed and conifer plots; and 4) depth of laser pulse penetration lower in the canopy is more variable compared to top of the canopy penetration which may influence within canopy vegetation structure estimates. This study demonstrates that leaf-off lidar data can be used to estimate forested riparian buffer canopy height within diverse vegetation conditions and fractional canopy cover within mixed and conifer forests when leaf-on lidar data are not available. PMID:23382966

The Plant Foliage Projective Coverage Change over the Northern Tibetan Plateau during 1957-2009

NASA Astrophysics Data System (ADS)

Cuo, L.

2015-12-01

Northern Tibetan Plateau is the headwater of the Yellow River, the Yangtze River and the Mekong River that support billions of the population. Vegetation change will affect the regional ecosystem and water balances through the changes in biomass and evapotranspiration. Dynamic vegetation growth is determined by physiological, morphological, bioclimatic and phenological properties. These properties are affected by climate variables such as air temperature, precipitation, soil temperature and concentration of CO2, etc. Due to climate change, some parts of the northern Tibetan Plateau are under the threat of desertification. Identifying the places of vegetation degradation and the dominant driven climatic factors will help mitigate the climate change impacts on ecosystem and water resources in this region. In this study, the changes of foliage projective coverages (FPCs) of various plant functional types (PFTs) existed in the northern Tibetan Plateau and the responses of FPCs to the four climate variables over 1957-2009 are examined. The dominant factors among the four climate variables are also identified. The Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ-DGVM) is modified and used for the investigation. The modified LPJ-DGVM can better account for soil temperature in the top 0.4-m depth where vegetation root concentrates over the northern Tibetan Plateau. The modified model is evaluated by using monthly and annual soil temperature observed at stations across the region, and the eco-geographic maps that describe plant types and spatial distributions developed from field surveys and satellite images for this region.

[Comparison of GIMMS and MODIS normalized vegetation index composite data for Qing-Hai-Tibet Plateau].

PubMed

Du, Jia-Qiang; Shu, Jian-Min; Wang, Yue-Hui; Li, Ying-Chang; Zhang, Lin-Bo; Guo, Yang

2014-02-01

Consistent NDVI time series are basic and prerequisite in long-term monitoring of land surface properties. Advanced very high resolution radiometer (AVHRR) measurements provide the longest records of continuous global satellite measurements sensitive to live green vegetation, and moderate resolution imaging spectroradiometer (MODIS) is more recent typical with high spatial and temporal resolution. Understanding the relationship between the AVHRR-derived NDVI and MODIS NDVI is critical to continued long-term monitoring of ecological resources. NDVI time series acquired by the global inventory modeling and mapping studies (GIMMS) and Terra MODIS were compared over the same time periods from 2000 to 2006 at four scales of Qinghai-Tibet Plateau (whole region, sub-region, biome and pixel) to assess the level of agreement in terms of absolute values and dynamic change by independently assessing the performance of GIMMS and MODIS NDVI and using 495 Landsat samples of 20 km x20 km covering major land cover type. High correlations existed between the two datasets at the four scales, indicating their mostly equal capability of capturing seasonal and monthly phenological variations (mostly at 0. 001 significance level). Simi- larities of the two datasets differed significantly among different vegetation types. The relative low correlation coefficients and large difference of NDVI value between the two datasets were found among dense vegetation types including broadleaf forest and needleleaf forest, yet the correlations were strong and the deviations were small in more homogeneous vegetation types, such as meadow, steppe and crop. 82% of study area was characterized by strong consistency between GIMMS and MODIS NDVI at pixel scale. In the Landsat NDVI vs. GIMMS and MODIS NDVI comparison of absolute values, the MODIS NDVI performed slightly better than GIMMS NDVI, whereas in the comparison of temporal change values, the GIMMS data set performed best. Similar with comparison results of GIMMS and MODIS NDVI, the consistency across the three datasets was clearly different among various vegetation types. In dynamic changes, differences between Landsat and MODIS NDVI were smaller than Landsat NDVI vs. GIMMS NDVI for forest, but Landsat and GIMMS NDVI agreed better for grass and crop. The results suggested that spatial patterns and dynamic trends of GIMMS NDVI were found to be in overall acceptable agreement with MODIS NDVI. It might be feasible to successfully integrate historical GIMMS and more recent MODIS NDVI to provide continuity of NDVI products. The accuracy of merging AVHRR historical data recorded with more modern MODIS NDVI data strongly depends on vegetation type, season and phenological period, and spatial scale. The integration of the two datasets for needleleaf forest, broadleaf forest, and for all vegetation types in the phenological transition periods in spring and autumn should be treated with caution.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Chun; Huang, Maoyi; Fast, Jerome D.

Current climate models still have large uncertainties in estimating biogenic trace gases, which can significantly affect atmospheric chemistry and secondary aerosol formation that ultimately influences air quality and aerosol radiative forcing. These uncertainties result from many factors, including uncertainties in land surface processes and specification of vegetation types, both of which can affect the simulated near-surface fluxes of biogenic volatile organic compounds (BVOCs). In this study, the latest version of Model of Emissions of Gases and Aerosols from Nature (MEGAN v2.1) is coupled within the land surface scheme CLM4 (Community Land Model version 4.0) in the Weather Research and Forecasting model withmore » chemistry (WRF-Chem). In this implementation, MEGAN v2.1 shares a consistent vegetation map with CLM4 for estimating BVOC emissions. This is unlike MEGAN v2.0 in the public version of WRF-Chem that uses a stand-alone vegetation map that differs from what is used by land surface schemes. This improved modeling framework is used to investigate the impact of two land surface schemes, CLM4 and Noah, on BVOCs and examine the sensitivity of BVOCs to vegetation distributions in California. The measurements collected during the Carbonaceous Aerosol and Radiative Effects Study (CARES) and the California Nexus of Air Quality and Climate Experiment (CalNex) conducted in June of 2010 provided an opportunity to evaluate the simulated BVOCs. Sensitivity experiments show that land surface schemes do influence the simulated BVOCs, but the impact is much smaller than that of vegetation distributions. This study indicates that more effort is needed to obtain the most appropriate and accurate land cover data sets for climate and air quality models in terms of simulating BVOCs, oxidant chemistry and, consequently, secondary organic aerosol formation.« less

Assessing land ownership as a driver of change in the distribution, structure, and composition of California's forests.

NASA Astrophysics Data System (ADS)

Easterday, K.; Kelly, M.; McIntyre, P. J.

2015-12-01

Climate change is forecasted to have considerable influence on the distribution, structure, and function of California's forests. However, human interactions with forested landscapes (e.g. fire suppression, resource extraction and etc.) have complicated scientific understanding of the relative contributions of climate change and anthropogenic land management practices as drivers of change. Observed changes in forest structure towards smaller, denser forests across California have been attributed to both climate change (e.g. increased temperatures and declining water availability) and management practices (e.g. fire suppression and logging). Disentangling how these drivers of change act both together and apart is important to developing sustainable policy and land management practices as well as enhancing knowledge of human and natural system interactions. To that end, a comprehensive historical dataset - the Vegetation Type Mapping project (VTM) - and a modern forest inventory dataset (FIA) are used to analyze how spatial variations in vegetation composition and structure over a ~100 year period can be explained by land ownership.Climate change is forecasted to have considerable influence on the distribution, structure, and function of California's forests. However, human interactions with forested landscapes (e.g. fire suppression, resource extraction and etc.) have complicated scientific understanding of the relative contributions of climate change and anthropogenic land management practices as drivers of change. Observed changes in forest structure towards smaller, denser forests across California have been attributed to both climate change (e.g. increased temperatures and declining water availability) and management practices (e.g. fire suppression and logging). Disentangling how these drivers of change act both together and apart is important to developing sustainable policy and land management practices as well as enhancing knowledge of human and natural system interactions. To that end, a comprehensive historical dataset - the Vegetation Type Mapping project (VTM) - and a modern forest inventory dataset (FIA) are used to analyze how spatial variations in vegetation composition and structure over a ~100 year period can be explained by land ownership.

Selection of vegetation indices for mapping the sugarcane condition around the oil and gas field of North West Java Basin, Indonesia

NASA Astrophysics Data System (ADS)

Muji Susantoro, Tri; Wikantika, Ketut; Saepuloh, Asep; Handoyo Harsolumakso, Agus

2018-05-01

Selection of vegetation indices in plant mapping is needed to provide the best information of plant conditions. The methods used in this research are the standard deviation and the linear regression. This research tried to determine the vegetation indices used for mapping the sugarcane conditions around oil and gas fields. The data used in this study is Landsat 8 OLI/TIRS. The standard deviation analysis on the 23 vegetation indices with 27 samples has resulted in the six highest standard deviations of vegetation indices, termed as GRVI, SR, NLI, SIPI, GEMI and LAI. The standard deviation values are 0.47; 0.43; 0.30; 0.17; 0.16 and 0.13. Regression correlation analysis on the 23 vegetation indices with 280 samples has resulted in the six vegetation indices, termed as NDVI, ENDVI, GDVI, VARI, LAI and SIPI. This was performed based on regression correlation with the lowest value R2 than 0,8. The combined analysis of the standard deviation and the regression correlation has obtained the five vegetation indices, termed as NDVI, ENDVI, GDVI, LAI and SIPI. The results of the analysis of both methods show that a combination of two methods needs to be done to produce a good analysis of sugarcane conditions. It has been clarified through field surveys and showed good results for the prediction of microseepages.

Advanced Very High Resolution Radiometer Normalized Difference Vegetation Index Composites

USGS Publications Warehouse

,

2005-01-01

The Advanced Very High Resolution Radiometer (AVHRR) is a broad-band scanner with four to six bands, depending on the model. The AVHRR senses in the visible, near-, middle-, and thermal- infrared portions of the electromagnetic spectrum. This sensor is carried on a series of National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites (POES), beginning with the Television InfraRed Observation Satellite (TIROS-N) in 1978. Since 1989, the United States Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) has been mapping the vegetation condition of the United States and Alaska using satellite information from the AVHRR sensor. The vegetation condition composites, more commonly called greenness maps, are produced every week using the latest information on the growth and condition of the vegetation. One of the most important aspects of USGS greenness mapping is the historical archive of information dating back to 1989. This historical stretch of information has allowed the USGS to determine a 'normal' vegetation condition. As a result, it is possible to compare the current week's vegetation condition with normal vegetation conditions. An above normal condition could indicate wetter or warmer than normal conditions, while a below normal condition could indicate colder or dryer than normal conditions. The interpretation of departure from normal will depend on the season and geography of a region.

Development of a Dynamic Web Mapping Service for Vegetation Productivity Using Earth Observation and in situ Sensors in a Sensor Web Based Approach

PubMed Central

Kooistra, Lammert; Bergsma, Aldo; Chuma, Beatus; de Bruin, Sytze

2009-01-01

This paper describes the development of a sensor web based approach which combines earth observation and in situ sensor data to derive typical information offered by a dynamic web mapping service (WMS). A prototype has been developed which provides daily maps of vegetation productivity for the Netherlands with a spatial resolution of 250 m. Daily available MODIS surface reflectance products and meteorological parameters obtained through a Sensor Observation Service (SOS) were used as input for a vegetation productivity model. This paper presents the vegetation productivity model, the sensor data sources and the implementation of the automated processing facility. Finally, an evaluation is made of the opportunities and limitations of sensor web based approaches for the development of web services which combine both satellite and in situ sensor sources. PMID:22574019

Utilization of LANDSAT imagery for mapping vegetation on the millionth scale

NASA Technical Reports Server (NTRS)

Williams, D. L.; Coiner, J. C.

1975-01-01

A series of test sites were examined to determine if the information content of the LANDSAT imagery that may be obtained of these sites is sufficient to permit their mapping according to the vegetation classification system recently published by Unesco. These sites include examples from the humid tropics, arid and semi-arid subtropics and temperature zones: Western Highlands of Papua New Guinea, Mindoro Island in the Philippines, Great Smoky Mountains of the southeastern United States, East Tennessee Valley, interior of Western Australia, northeastern Uganda, and south-central Kansas. The results of the experiment were presented in the form of vegetation maps and annotated images which serve to illustrate the detectability of various formations. It was concluded that, for the test sites examined, the formations of the Unesco vegetation classification can be satisfactorily distinguished on LANDSAT MSS images, especially when used as color composites and judiciously chosen as to season.

Advances in Remote Sensing for Vegetation Dynamics and Agricultural Management

NASA Technical Reports Server (NTRS)

Tucker, Compton; Puma, Michael

2015-01-01

Spaceborne remote sensing has led to great advances in the global monitoring of vegetation. For example, the NASA Global Inventory Modeling and Mapping Studies (GIMMS) group has developed widely used datasets from the Advanced Very High Resolution Radiometer (AVHRR) sensors as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) map imagery and normalized difference vegetation index datasets. These data are valuable for analyzing vegetation trends and variability at the regional and global levels. Numerous studies have investigated such trends and variability for both natural vegetation (e.g., re-greening of the Sahel, shifts in the Eurasian boreal forest, Amazonian drought sensitivity) and crops (e.g., impacts of extremes on agricultural production). Here, a critical overview is presented on recent developments and opportunities in the use of remote sensing for monitoring vegetation and crop dynamics.

Estimating wetland vegetation abundance from Landsat-8 operational land imager imagery: a comparison between linear spectral mixture analysis and multinomial logit modeling methods

NASA Astrophysics Data System (ADS)

Zhang, Min; Gong, Zhaoning; Zhao, Wenji; Pu, Ruiliang; Liu, Ke

2016-01-01

Mapping vegetation abundance by using remote sensing data is an efficient means for detecting changes of an eco-environment. With Landsat-8 operational land imager (OLI) imagery acquired on July 31, 2013, both linear spectral mixture analysis (LSMA) and multinomial logit model (MNLM) methods were applied to estimate and assess the vegetation abundance in the Wild Duck Lake Wetland in Beijing, China. To improve mapping vegetation abundance and increase the number of endmembers in spectral mixture analysis, normalized difference vegetation index was extracted from OLI imagery along with the seven reflective bands of OLI data for estimating the vegetation abundance. Five endmembers were selected, which include terrestrial plants, aquatic plants, bare soil, high albedo, and low albedo. The vegetation abundance mapping results from Landsat OLI data were finally evaluated by utilizing a WorldView-2 multispectral imagery. Similar spatial patterns of vegetation abundance produced by both fully constrained LSMA algorithm and MNLM methods were observed: higher vegetation abundance levels were distributed in agricultural and riparian areas while lower levels in urban/built-up areas. The experimental results also indicate that the MNLM model outperformed the LSMA algorithm with smaller root mean square error (0.0152 versus 0.0252) and higher coefficient of determination (0.7856 versus 0.7214) as the MNLM model could handle the nonlinear reflection phenomenon better than the LSMA with mixed pixels.

Use of FIA plot data in the LANDFIRE project

Treesearch

Chris Toney; Matthew Rollins; Karen Short; Tracey Frescino; Ronald Tymcio; Birgit Peterson

2007-01-01

LANDFIRE is an interagency project that will generate consistent maps and data describing vegetation, fire, and fuel characteristics across the United States within a 5-year timeframe. Modeling and mapping in LANDFIRE depend extensively on a large database of georeferenced field measurements describing vegetation, site characteristics, and fuel. The LANDFIRE Reference...

Mapping of submerged vegetation using remote sensing technology

NASA Technical Reports Server (NTRS)

Savastano, K. J.; Faller, K. H.; Mcfadin, L. W.; Holley, H.

1981-01-01

Techniques for mapping submerged sea grasses using aircraft supported remote sensors are described. The 21 channel solid state array spectroradiometer was successfully used as a remote sensor in the experiment in that the system operated without problem and obtained data. The environmental conditions of clear water, bright sandy bottom and monospecific vegetation (Thalassia) were ideal.

Improving automated disturbance maps using snow-covered landsat time series stacks

Treesearch

Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen

2012-01-01

Snow-covered winter Landsat time series stacks are used to develop a nonforest mask to enhance automated disturbance maps produced by the Vegetation Change Tracker (VCT). This method exploits the enhanced spectral separability between forested and nonforested areas that occurs with sufficient snow cover. This method resulted in significant improvements in Vegetation...

Application of automated multispectral analysis to Delaware's coastal vegetation mapping

NASA Technical Reports Server (NTRS)

Klemas, V. (Principal Investigator); Daiber, D.; Bartlett, D. S.; Crichton, O. W.; Fornes, A. O.

1973-01-01

There are no author-identified significant results in this report. Overlay maps of Delaware's wetlands have been prepared, showing the dominant species or group of species of vegetation present. Five such categories of vegetation were used indicating marshes dominated by: (1) salt marsh cord grass; (2) salt marsh hay and spike grass; (3) reed grass; (4) high tide bush and sea myrtle; and (5) a group of fresh water species found in impounded areas built to attract water fowl. Fifteen such maps cover Delaware's wetlands from the Pennsylvania to the Maryland borders. The mapping technique employed utilizes the General Electric multispectral data processing system. This system is a hybrid analog-digital system designed as an analysis tool to be used by an operator whose own judgment and knowledge of ground truth can be incorporated at any time into the analyzing process. The result is a high speed, cost effective method for producing enhanced photomaps showing a number of spectral classes, each enhanced spectral class being representative of a vegetative species or group of species.

Spectroscopic Methods of Remote Sensing for Vegetation Characterization

NASA Astrophysics Data System (ADS)

Kokaly, R. F.

2013-12-01

Imaging spectroscopy (IS), often referred to as hyperspectral remote sensing, is one of the latest innovations in a very long history of spectroscopy. Spectroscopic methods have been used for understanding the composition of the world around us, as well as, the solar system and distant parts of the universe. Continuous sampling of the electromagnetic spectrum in narrow bands is what separates IS from previous forms of remote sensing. Terrestrial imaging spectrometers often have hundreds of channels that cover the wavelength range of reflected solar radiation, including the visible, near-infrared (NIR), and shortwave infrared (SWIR) regions. In part due to the large number of channels, a wide variety of methods have been applied to extract information from IS data sets. These can be grouped into several broad classes, including: multi-channel indices, statistical procedures, full spectrum mixing models, and spectroscopic methods. Spectroscopic methods carry on the more than 150 year history of laboratory-based spectroscopy applied to material identification and characterization. Spectroscopic methods of IS relate the positions and shapes of spectral features resolved by airborne and spaceborne sensors to the biochemical and physical composition of vegetation in a pixel. The chlorophyll 680nm, water 980nm, water 1200nm, SWIR 1700nm, SWIR 2100nm, and SWIR 2300nm features have been the subject of study. Spectral feature analysis (SFA) involves isolating such an absorption feature using continuum removal (CR) and calculating descriptors of the feature, such as center position, depth, width, area, and asymmetry. SFA has been applied to quantify pigment and non-pigment biochemical concentrations in leaves, plants, and canopies. Spectral feature comparison (SFC) utilizes CR of features in each pixel's spectrum and linear regression with continuum-removed features in reference spectra in a library of known vegetation types to map vegetation species and communities. SFC has been applied to map the distributions of minerals in soils and rocks; however, its application to characterize vegetation cover has been less widespread than SFA. Using IS data and the USGS Processing Routines in IDL for Spectroscopic Measurements (PRISM; http://pubs.usgs.gov/of/2011/1155/), this talk will examine requirements for and limitations in applying SFA and SFC to characterize vegetation. A time series of Airborne Visible/InfraRed Imaging Spectrometer (AVIRIS) data collected in the marshes of Louisiana following the Deepwater Horizon oil spill will be used to examine the impact of varying leaf water content on the shapes of the SWIR 1700, 2100, and 2300 nm features and the implications of these changes on vegetation identification and biochemical estimation. The USGS collection of HyMap data over Afghanistan, the largest terrestrial coverage of IS data to date, will be used to demonstrate the characterization of vegetation in arid and semi-arid regions, in which chlorophyll absorption is often weak and soil and rock mineral absorption features overlap vegetation features. Hyperion data, overlapping the HyMap data, will be presented to illustrate the complications that arise when signal-to-noise is low. The benefits of and challenges to applying a spectroscopic remote sensing approach to imaging spectrometer data will be discussed.

A forward-looking, national-scale remote sensing-based model of tidal marsh aboveground carbon stocks

NASA Astrophysics Data System (ADS)

Holmquist, J. R.; Byrd, K. B.; Ballanti, L.; Nguyen, D.; Simard, M.; Windham-Myers, L.; Thomas, N.

2017-12-01

Remote sensing based maps of tidal marshes, both of their extents and carbon stocks, have the potential to play a key role in conducting greenhouse gas inventories and implementing climate mitigation policies. Our goal was to generate a single remote sensing model of tidal marsh aboveground biomass and carbon that represents nationally diverse tidal marshes within the conterminous United States (CONUS). To meet this objective we developed the first national-scale dataset of aboveground tidal marsh biomass, species composition, and aboveground plant carbon content (%C) from six CONUS regions: Cape Cod, MA, Chesapeake Bay, MD, Everglades, FL, Mississippi Delta, LA, San Francisco Bay, CA, and Puget Sound, WA. Using the random forest algorithm we tested Sentinel-1 radar backscatter metrics and Landsat vegetation indices as predictors of biomass. The final model, driven by six Landsat vegetation indices and with the soil adjusted vegetation index as the most important (n=409, RMSE=310 g/m2, 10.3% normalized RMSE), successfully predicted biomass and carbon for a range of marsh plant functional types defined by height, leaf angle and growth form. Model error was reduced by scaling field measured biomass by Landsat fraction green vegetation derived from object-based classification of National Agriculture Imagery Program imagery. We generated 30m resolution biomass maps for estuarine and palustrine emergent tidal marshes as indicated by a modified NOAA Coastal Change Analysis Program map for each region. With a mean plant %C of 44.1% (n=1384, 95% C.I.=43.99% - 44.37%) we estimated mean aboveground carbon densities (Mg/ha) and total carbon stocks for each wetland type for each region. Louisiana palustrine emergent marshes had the highest C density (2.67 ±0.08 Mg/ha) of all regions, while San Francisco Bay brackish/saline marshes had the highest C density of all estuarine emergent marshes (2.03 ±0.06 Mg/ha). This modeling and data synthesis effort will allow for aboveground C stocks in tidal marshes to be included for the first time in the 2018 U.S. EPA Greenhouse Gas Inventory for coastal wetlands. As technical barriers have been reduced through the availability of free post-processed satellite data, cloud computing platforms and open source software, this approach can potentially be applied globally as well.

Intermediate-scale vegetation mapping of Innoko National Wildlife Refuge, Alaska using Landsat MSS digital data

USGS Publications Warehouse

Talbot, Stephen S.; Markon, Carl J.

1988-01-01

A Landsat-derived vegetation map was prepared for lnnoko National Wildlife Refuge. The refuge lies within the northern boreal subzone of northwestern central Alaska. Six major vegetation classes and 21 subclasses were recognized: forest (closed needleleaf, open needleleaf, needleleaf woodland, mixed, and broadleaf); broadleaf scrub (lowland, upland burn regeneration, subalpine); dwarf scrub (prostrate dwarf shrub tundra, erect dwarf shrub heath, dwarf shrub-graminoid peatland, dwarf shrub-graminoid tussock peatland, dwarf shrub raised bog with scattered trees, dwarf shrub-graminoid marsh); herbaceous (graminoid bog, graminoid marsh, graminoid tussock-dwarf shrub peatland); scarcely vegetated areas (scarcely vegetated scree and floodplain); and water (clear, sedimented). The methodology employed a cluster-block technique. Sample areas were described based on a combination of helicopter-ground survey, aerial photo-interpretation, and digital Landsat data. Major steps in the Landsat analysis involved preprocessing (geometric correction), derivation of statistical parameters for spectral classes, spectral class labeling of sample areas, preliminary classification of the entire study area using a maximum-likelihood algorithm, and final classification utilizing ancillary information such as digital elevation data. The final product is 1:250,000-scale vegetation map representative of distinctive regional patterns and suitable for use in comprehensive conservation planning.

Developing a 30-m grassland productivity estimation map for central Nebraska using 250-m MODIS and 30-m Landsat-8 observations

USGS Publications Warehouse

Gu, Yingxin; Wylie, Bruce K.

2015-01-01

Accurately estimating aboveground vegetation biomass productivity is essential for local ecosystem assessment and best land management practice. Satellite-derived growing season time-integrated Normalized Difference Vegetation Index (GSN) has been used as a proxy for vegetation biomass productivity. A 250-m grassland biomass productivity map for the Greater Platte River Basin had been developed based on the relationship between Moderate Resolution Imaging Spectroradiometer (MODIS) GSN and Soil Survey Geographic (SSURGO) annual grassland productivity. However, the 250-m MODIS grassland biomass productivity map does not capture detailed ecological features (or patterns) and may result in only generalized estimation of the regional total productivity. Developing a high or moderate spatial resolution (e.g., 30-m) productivity map to better understand the regional detailed vegetation condition and ecosystem services is preferred. The 30-m Landsat data provide spatial detail for characterizing human-scale processes and have been successfully used for land cover and land change studies. The main goal of this study is to develop a 30-m grassland biomass productivity estimation map for central Nebraska, leveraging 250-m MODIS GSN and 30-m Landsat data. A rule-based piecewise regression GSN model based on MODIS and Landsat (r = 0.91) was developed, and a 30-m MODIS equivalent GSN map was generated. Finally, a 30-m grassland biomass productivity estimation map, which provides spatially detailed ecological features and conditions for central Nebraska, was produced. The resulting 30-m grassland productivity map was generally supported by the SSURGO biomass production map and will be useful for regional ecosystem study and local land management practices.

Regional mapping of forest canopy water content and biomass using AIRSAR images over BOREAS study area

NASA Technical Reports Server (NTRS)

Saatchi, Sasan; Rignot, Eric; Vanzyl, Jakob

1995-01-01

In recent years, monitoring vegetation biomass over various climate zones has become the primary focus of several studies interested in assessing the role of the ecosystem responses to climate change and human activities. Airborne and spaceborne Synthetic Aperture Radar (SAR) systems provide a useful tool to directly estimate biomass due to its sensitivity to structural and moisture characteristics of vegetation canopies. Even though the sensitivity of SAR data to total aboveground biomass has been successfully demonstrated in many controlled experiments over boreal forests and forest plantations, so far, no biomass estimation algorithm has been developed. This is mainly due to the fact that the SAR data, even at lowest frequency (P-band) saturates at biomass levels of about 200 tons/ha, and the structure and moisture information in the SAR signal forces the estimation algorithm to be forest type dependent. In this paper, we discuss the development of a hybrid forest biomass algorithm which uses a SAR derived land cover map in conjunction with a forest backscatter model and an inversion algorithm to estimate forest canopy water content. It is shown that unlike the direct biomass estimation from SAR data, the estimation of water content does not depend on the seasonal and/or environmental conditions. The total aboveground biomass can then be derived from canopy water content for each type of forest by incorporating other ecological information. Preliminary results from this technique over several boreal forest stands indicate that (1) the forest biomass can be estimated with reasonable accuracy, and (2) the saturation level of the SAR signal can be enhanced by separating the crown and trunk biomass in the inversion algorithm. We have used the JPL AIRSAR data over BOREAS southern study area to test the algorithm and to generate regional scale water content and biomass maps. The results are compared with ground data and the sources of errors are discussed. Several SAR images in synoptic modes are used to generate the parameter maps. The maps are then combined to generate mosaic maps over the BOREAS modeling grid.

Age discrimination among eruptives of Menengai Caldera, Kenya, using vegetation parameters from satellite imagery

NASA Technical Reports Server (NTRS)

Blodget, Herbert W.; Heirtzler, James R.

1993-01-01

Results are presented of an investigation to determine the degree to which digitally processed Landsat TM imagery can be used to discriminate among vegetated lava flows of different ages in the Menengai Caldera, Kenya. A selective series of five images, consisting of a color-coded Landsat 5 classification and four color composites, are compared with geologic maps. The most recent of more than 70 postcaldera flows within the caldera are trachytes, which are variably covered by shrubs and subsidiary grasses. Soil development evolves as a function of time, and as such supports a changing plant community. Progressively older flows exhibit the increasing dominance of grasses over bushes. The Landsat images correlated well with geologic maps, but the two mapped age classes could be further subdivided on the basis of different vegetation communities. It is concluded that field maps can be modified, and in some cases corrected by use of such imagery, and that digitally enhanced Landsat imagery can be a useful aid to field mapping in similar terrains.

Preliminary investigation of submerged aquatic vegetation mapping using hyperspectral remote sensing.

PubMed

William, David J; Rybicki, Nancy B; Lombana, Alfonso V; O'Brien, Tim M; Gomez, Richard B

2003-01-01

The use of airborne hyperspectral remote sensing imagery for automated mapping of submerged aquatic vegetation (SAV) in the tidal Potomac River was investigated for near to real-time resource assessment and monitoring. Airborne hyperspectral imagery and field spectrometer measurements were obtained in October of 2000. A spectral library database containing selected ground-based and airborne sensor spectra was developed for use in image processing. The spectral library is used to automate the processing of hyperspectral imagery for potential real-time material identification and mapping. Field based spectra were compared to the airborne imagery using the database to identify and map two species of SAV (Myriophyllum spicatum and Vallisneria americana). Overall accuracy of the vegetation maps derived from hyperspectral imagery was determined by comparison to a product that combined aerial photography and field based sampling at the end of the SAV growing season. The algorithms and databases developed in this study will be useful with the current and forthcoming space-based hyperspectral remote sensing systems.

Climate change and Arctic ecosystems: 1. Vegetation changes north of 55°N between the last glacial maximum, mid-Holocene, and present

USGS Publications Warehouse

Bigelow, N.H.; Brubaker, L.B.; Edwards, M.E.; Harrison, S.P.; Prentice, I.C.; Anderson, P.M.; Andreev, A.A.; Bartlein, P.J.; Christensen, T.R.; Cramer, W.; Kaplan, J.O.; Lozhkin, A.V.; Matveyeva, N.V.; Murray, D.F.; McGuire, A.D.; Razzhivin, V.Y.; Ritchie, J.C.; Smith, B.; Walker, D.A.; Gajewski, K.; Wolf, V.; Holmqvist, B.H.; Igarashi, Y.; Kremenetskii, K.; Paus, A.; Pisaric, M.F.J.; Volkova, V.S.

2003-01-01

A unified scheme to assign pollen samples to vegetation types was used to reconstruct vegetation patterns north of 55??N at the last glacial maximum (LGM) and mid-Holocene (6000 years B.P.). The pollen data set assembled for this purpose represents a comprehensive compilation based on the work of many projects and research groups. Five tundra types (cushion forb tundra, graminoid and forb tundra, prostrate dwarf-shrub tundra, erect dwarf-shrub tundra, and low- and high-shrub tundra) were distinguished and mapped on the basis of modern pollen surface samples. The tundra-forest boundary and the distributions of boreal and temperate forest types today were realistically reconstructed. During the mid-Holocene the tundra-forest boundary was north of its present position in some regions, but the pattern of this shift was strongly asymmetrical around the pole, with the largest northward shift in central Siberia (???200 km), little change in Beringia, and a southward shift in Keewatin and Labrador (???200 km). Low- and high-shrub tundra extended farther north than today. At the LGM, forests were absent from high latitudes. Graminoid and forb tundra abutted on temperate steppe in northwestern Eurasia while prostrate dwarf-shrub, erect dwarf-shrub, and graminoid and forb tundra formed a mosaic in Beringia. Graminoid and forb tundra is restricted today and does not form a large continuous biome, but the pollen data show that it was far more extensive at the LGM, while low- and high-shrub tundra were greatly reduced, illustrating the potential for climate change to dramatically alter the relative areas occupied by different vegetation types.

Evaluation of DOD Priority Species at Risk (SAR) and Applications for Remote Sensing

DTIC Science & Technology

2009-02-01

Species Type Common Name Scientific Name Fort McClellan AL fish coldwater darter Etheostoma ditrema Camp Shelby MS crustacean Camp Shelby burrowing...plant in the Aster family that exhibits mat-forming growth characteristics (Figure 15). B. tetraneuris in- habits barren, light colored shale and...vegetated areas within Fort Lewis, WA have the poten- tial to be mapped using high-resolution multispectral imagery. Other open (i.e., disturbed) habited

An analysis of stream channel cross section technique as a means to determine anthropogenic change in second order streams at the Tenderfoot Creek Experimental Forest, Meagher County, Montana

Treesearch

Jeff Boice

1999-01-01

Five second order tributaries to Tenderfoot Creek were investigated: Upper Tenderfoot Creek, Sun Creek, Spring Park Creek, Bubbling Creek, and Stringer Creek. Second order reaches were initially located on 7.5 minute topographic maps using techniques first applied by Strahler (1952). Reach breaks were determined in the field through visual inspection. Vegetation type (...

MODIS GPP/NPP for complex land use area: a case study of comparison between MODIS GPP/NPP and ground-based measurements over Korea

NASA Astrophysics Data System (ADS)

Shim, C.

2013-12-01

The Moderate Resolution Imaging Radiometer (MODIS) Gross Primary Productivity (GPP)/Net Primary Productivity (NPP) has been widely used for the study on global terrestrial ecosystem and carbon cycle. The current MODIS product with ~ 1 km spatial resolution, however, has limitation on the information on local scale environment (< 1km), particularly on the regions with complex land-use types. Here we try to test the performance of MODIS annual GPP/NPP for a case of Korea, where the vegetation types are mostly heterogeneous within a size of MODIS products (~1km). We selected the sites where the ground/tower flux measurements and MODIS retrievals were simultaneously available and the land classification of sites agreed the forest type map (~71m) (1 site over Gwangneung flux tower (GDK) for 2006-2008 and 2 sites of ground measurements over Cheongju (CJ1 and CJ2) for 2011). The MODIS GPP are comparable to that of GDK (largely deciduous forest) within -6.3 ~ +2.3% of bias (-104.5 - 37.9 gCm-2yr-1). While the MODIS NPP of CJ1 at Cheongju (largely Larix leptolepis) underestimated NPP by 34% (-224.5 gCm-2yr-1), the MODIS NPP of CJ2 (largely Pinus densiflora) agreed well with -0.2% of bias (1.6 gCm-2yr-1). The fairly comparable values of the MODIS here however, cannot assure the quality of the MOD17 over the complex vegetation area of Korea since the ground measurements except the eddy covariance tower flux measurements are highly inconsistent. Therefore, the comprehensive experiments to represents GPP/NPP over diverse vegetation types for a comparable scale of MODIS with a consistent measurement technique are necessary in order to evaluate the MODIS vegetation productivity data over Korea, which contains a large portion of highly heterogeneous vegetation area.

Exploiting Synthetic Aperture Radar data to map and observe landslides

NASA Astrophysics Data System (ADS)

Bekaert, D. P.; Agram, P. S.; Fattahi, H.; Kirschbaum, D.; Amatya, P. M.; Stanley, T.

2017-12-01

Synthetic Aperture Radar instruments onboard satellites or airborne platforms are a powerful means to study landslides. How to best exploit the data and which techniques to apply strongly depend on the region of study and the landslide type which occurs. The amount of vegetation, snowfall, and steepness of the terrain, as well the shadowing effects of the mountain will determine if SAR is suitable to map a given landslide. Fast moving landslides occurring over a large area (e.g. >100 m) could benefit from pixel or feature tracking, while for slower moving landslides Interferometric SAR could be a more favorable approach. However, neither of those methods would work for critical landslide failures which do not preserve surface features. This type of slides would benefit from a change detection approach. Here we look at these three different cases and utilize Sentinel-1 space-borne SAR data and state-of-the-art processing techniques to map multiple landslides along the California State Route 1 and the Trishuli highway in the Langtang valley of Nepal. Our findings correlate with existing landslide catalogues and also identify landslides in regions earlier mapped to be dormant.

Evaluating a small footprint, waveform-resolving lidar over coastal vegetation communities

USGS Publications Warehouse

Nayegandhl, A.; Brock, J.C.; Wright, C.W.; O'Connell, M. J.

2006-01-01

NASA's Experimental Advanced Airborne Research Lidar (EAARL) is a raster-scanning, waveform-resolving, green-wavelength (532 nm) lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor records the time history of the return waveform within a small footprint (20 cm diameter) for each laser pulse, enabling characterization of vegetation canopy structure and "bare earth" topography under a variety of vegetation types. A collection of individual waveforms combined within a synthesized large footprint was used to define three metrics: canopy height (CH), canopy reflection ratio (CRR), and height of median energy (HOME). Bare Earth Elevation (BEE) metric was derived using the individual small-footprint waveforms. All four metrics were tested for reproducibility, which resulted in an average of 95 percent correspondence within two standard deviations of the mean. CH and BEE values were also tested for accuracy using ground-truth data. The results presented in this paper show that combining several individual small-footprint laser pulses to define a composite "large-footprint" waveform is a possible method to depict the vertical structure of a vegetation canopy. ?? 2006 American Society for Photogrammetry and Remote Sensing.

The Importance of Temporal and Spatial Vegetation Structure Information in Biotope Mapping Schemes: A Case Study in Helsingborg, Sweden

NASA Astrophysics Data System (ADS)

Gao, Tian; Qiu, Ling; Hammer, Mårten; Gunnarsson, Allan

2012-02-01

Temporal and spatial vegetation structure has impact on biodiversity qualities. Yet, current schemes of biotope mapping do only to a limited extend incorporate these factors in the mapping. The purpose of this study is to evaluate the application of a modified biotope mapping scheme that includes temporal and spatial vegetation structure. A refined scheme was developed based on a biotope classification, and applied to a green structure system in Helsingborg city in southern Sweden. It includes four parameters of vegetation structure: continuity of forest cover, age of dominant trees, horizontal structure, and vertical structure. The major green structure sites were determined by interpretation of panchromatic aerial photographs assisted with a field survey. A set of biotope maps was constructed on the basis of each level of modified classification. An evaluation of the scheme included two aspects in particular: comparison of species richness between long-continuity and short-continuity forests based on identification of woodland continuity using ancient woodland indicators (AWI) species and related historical documents, and spatial distribution of animals in the green space in relation to vegetation structure. The results indicate that (1) the relationship between forest continuity: according to verification of historical documents, the richness of AWI species was higher in long-continuity forests; Simpson's diversity was significantly different between long- and short-continuity forests; the total species richness and Shannon's diversity were much higher in long-continuity forests shown a very significant difference. (2) The spatial vegetation structure and age of stands influence the richness and abundance of the avian fauna and rabbits, and distance to the nearest tree and shrub was a strong determinant of presence for these animal groups. It is concluded that continuity of forest cover, age of dominant trees, horizontal and vertical structures of vegetation should now be included in urban biotope classifications.

Changes in future fire regimes under climate change

NASA Astrophysics Data System (ADS)

Thonicke, Kirsten; von Bloh, Werner; Lutz, Julia; Knorr, Wolfgang; Wu, Minchao; Arneth, Almut

2013-04-01

Fires are expected to change under future climate change, climatic fire is is increasing due to increase in droughts and heat waves affecting vegetation productivity and ecosystem function. Vegetation productivity influences fuel production, but can also limit fire spread. Vegetation-fire models allow investigating the interaction between wildfires and vegetation dynamics, thus non-linear effects between changes in fuel composition and production on fire as well as changes in fire regimes on fire-related plant mortality and fuel combustion. Here we present results from simulation experiments, where the vegetation-fire models LPJmL-SPITFIRE and LPJ-GUESS are applied to future climate change scenarios from regional climate models in Europe and Northern Africa. Climate change impacts on fire regimes, vegetation dynamics and carbon fluxes are quantified and presented. New fire-prone regions are mapped and changes in fire regimes of ecosystems with a long-fire history are analyzed. Fuel limitation is likely to increase in Mediterranean-type ecosystems, indicating non-linear connection between increasing fire risk and fuel production. Increased warming in temperate ecosystems in Eastern Europe and continued fuel production leads to increases not only in climatic fire risk, but also area burnt and biomass burnt. This has implications for fire management, where adaptive capacity to this new vulnerability might be limited.

[Extracting black soil border in Heilongjiang province based on spectral angle match method].

PubMed

Zhang, Xin-Le; Zhang, Shu-Wen; Li, Ying; Liu, Huan-Jun

2009-04-01

As soils are generally covered by vegetation most time of a year, the spectral reflectance collected by remote sensing technique is from the mixture of soil and vegetation, so the classification precision based on remote sensing (RS) technique is unsatisfied. Under RS and geographic information systems (GIS) environment and with the help of buffer and overlay analysis methods, land use and soil maps were used to derive regions of interest (ROI) for RS supervised classification, which plus MODIS reflectance products were chosen to extract black soil border, with methods including spectral single match. The results showed that the black soil border in Heilongjiang province can be extracted with soil remote sensing method based on MODIS reflectance products, especially in the north part of black soil zone; the classification precision of spectral angel mapping method is the highest, but the classifying accuracy of other soils can not meet the need, because of vegetation covering and similar spectral characteristics; even for the same soil, black soil, the classifying accuracy has obvious spatial heterogeneity, in the north part of black soil zone in Heilongjiang province it is higher than in the south, which is because of spectral differences; as soil uncovering period in Northeastern China is relatively longer, high temporal resolution make MODIS images get the advantage over soil remote sensing classification; with the help of GIS, extracting ROIs by making the best of auxiliary data can improve the precision of soil classification; with the help of auxiliary information, such as topography and climate, the classification accuracy was enhanced significantly. As there are five main factors determining soil classes, much data of different types, such as DEM, terrain factors, climate (temperature, precipitation, etc.), parent material, vegetation map, and remote sensing images, were introduced to classify soils, so how to choose some of the data and quantify the weights of different data layers needs further study.

Common raven occurrence in relation to energy transmission line corridors transiting human-altered sagebrush steppe

USGS Publications Warehouse

Coates, Peter S.; Howe, Kristy B.; Casazza, Michael L.; Delehanty, David J.

2014-01-01

Energy-related infrastructure and other human enterprises within sagebrush steppe of the American West often results in changes that promote common raven (Corvus corax; hereafter, raven) populations. Ravens, a generalist predator capable of behavioral innovation, present a threat to many species of conservation concern. We evaluate the effects of detailed features of an altered landscape on the probability of raven occurrence using extensive raven survey (n= 1045) and mapping data from southern Idaho, USA. We found nonlinear relationships between raven occurrence and distances to transmission lines, roads, and facilities. Most importantly, raven occurrence was greater with presence of transmission lines up to 2.2 km from the corridor.We further explain variation in raven occurrence along anthropogenic features based on the amount of non-native vegetation and cover type edge, such that ravens select fragmented sagebrush stands with patchy, exotic vegetative introgression. Raven occurrence also increased with greater length of edge formed by the contact of big sagebrush (Artemisia tridentate spp.) with non-native vegetation cover types. In consideration of increasing alteration of sagebrush steppe, these findings will be useful for planning energy transmission corridor placement and other management activities where conservation of sagebrush obligate species is a priority.

Quantification of soil mapping by digital analysis of LANDSAT data. [Clinton County, Indiana

NASA Technical Reports Server (NTRS)

Kirschner, F. R.; Kaminsky, S. A.; Hinzel, E. J.; Sinclair, H. R.; Weismiller, R. A.

1977-01-01

Soil survey mapping units are designed such that the dominant soil represents the major proportion of the unit. At times, soil mapping delineations do not adequately represent conditions as stated in the mapping unit descriptions. Digital analysis of LANDSAT multispectral scanner (MSS) data provides a means of accurately describing and quantifying soil mapping unit composition. Digital analysis of LANDSAT MSS data collected on 9 June 1973 was used to prepare a spectral soil map for a 430-hectare area in Clinton County, Indiana. Fifteen spectral classes were defined, representing 12 soil and 3 vegetation classes. The 12 soil classes were grouped into 4 moisture regimes based upon their spectral responses; the 3 vegetation classes were grouped into one all-inclusive class.

Monitoring the effects of land use/landcover changes on urban heat island

NASA Astrophysics Data System (ADS)

Gee, Ong K.; Sarker, Md Latifur Rahman

2013-10-01

Urban heat island effects are well known nowadays and observed in cities throughout the World. The main reason behind the effects of urban heat island (UHI) is the transformation of land use/ land cover, and this transformation is associated with UHI through different actions: i) removal of vegetated areas, ii) land reclamation from sea/river, iii) construction of new building as well as other concrete structures, and iv) industrial and domestic activity. In rapidly developing cities, urban heat island effects increases very hastily with the transformation of vegetated/ other types of areas into urban surface because of the increasing population as well as for economical activities. In this research the effect of land use/ land cover on urban heat island was investigated in two growing cities in Asia i.e. Singapore and Johor Bahru, (Malaysia) using 10 years data (from 1997 to 2010) from Landsat TM/ETM+. Multispectral visible band along with indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Build Index (NDBI), and Normalized Difference Bareness Index (NDBaI) were used for the classification of major land use/land cover types using Maximum Likelihood Classifiers. On the other hand, land surface temperature (LST) was estimated from thermal image using Land Surface Temperature algorithm. Emissivity correction was applied to the LST map using the emissivity values from the major land use/ land cover types, and validation of the UHI map was carried out using in situ data. Results of this research indicate that there is a strong relationship between the land use/land cover changes and UHI. Over this 10 years period, significant percentage of non-urban surface was decreased but urban heat surface was increased because of the rapid urbanization. With the increase of UHI effect it is expected that local urban climate has been modified and some heat related health problem has been exposed, so appropriate measure should be taken in order to reduce UHI effects as soon as possible.

Mapping Forest Fuels through Vegetation Phenology: The Role of Coarse-Resolution Satellite Time-Series

PubMed Central

Bajocco, Sofia; Dragoz, Eleni; Gitas, Ioannis; Smiraglia, Daniela; Salvati, Luca; Ricotta, Carlo

2015-01-01

Traditionally fuel maps are built in terms of ‘fuel types’, thus considering the structural characteristics of vegetation only. The aim of this work is to derive a phenological fuel map based on the functional attributes of coarse-scale vegetation phenology, such as seasonality and productivity. MODIS NDVI 250m images of Sardinia (Italy), a large Mediterranean island with high frequency of fire incidence, were acquired for the period 2000–2012 to construct a mean annual NDVI profile of the vegetation at the pixel-level. Next, the following procedure was used to develop the phenological fuel map: (i) image segmentation on the Fourier components of the NDVI profiles to identify phenologically homogeneous landscape units, (ii) cluster analysis of the phenological units and post-hoc analysis of the fire-proneness of the phenological fuel classes (PFCs) obtained, (iii) environmental characterization (in terms of land cover and climate) of the PFCs. Our results showed the ability of coarse-resolution satellite time-series to characterize the fire-proneness of Sardinia with an adequate level of accuracy. The remotely sensed phenological framework presented may represent a suitable basis for the development of fire distribution prediction models, coarse-scale fuel maps and for various biogeographic studies. PMID:25822505

A comparison of change detection methods using multispectral scanner data

USGS Publications Warehouse

Seevers, Paul M.; Jones, Brenda K.; Qiu, Zhicheng; Liu, Yutong

1994-01-01

Change detection methods were investigated as a cooperative activity between the U.S. Geological Survey and the National Bureau of Surveying and Mapping, People's Republic of China. Subtraction of band 2, band 3, normalized difference vegetation index, and tasseled cap bands 1 and 2 data from two multispectral scanner images were tested using two sites in the United States and one in the People's Republic of China. A new statistical method also was tested. Band 2 subtraction gives the best results for detecting change from vegetative cover to urban development. The statistical method identifies areas that have changed and uses a fast classification algorithm to classify the original data of the changed areas by land cover type present for each image date.

Long Term Land Cover and Seagrass Mapping using Landsat and Object-based Image Analysis from 1972 - 2010 in the Coastal Environment of South East Queensland, Australia

NASA Astrophysics Data System (ADS)

Lyons, M. B.; Phinn, S. R.; Roelfsema, C. M.

2011-12-01

Long term global archives of high-moderate spatial resolution, multi-spectral satellite imagery are now readily accessible, but are not being fully utilised by management agencies due to the lack of appropriate methods to consistently produce accurate and timely management ready information. This work developed an object-based approach to map land cover and seagrass distribution in an Australian coastal environment for a 38 year Landsat image time-series archive. Landsat Multi-Spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery were used without in-situ field data input to produce land and seagrass cover maps every year data was available, resulting in over 60 individual map products over the 38 year archive. Land cover was mapped annually and included several vegetation, bare ground, urban and agricultural classes. Seagrass distribution was also mapped annually, and in some years monthly, via horizontal projective foliage cover classes, sand and deepwater. Land cover products were validated using aerial photography and seagrass was validated with field survey data, producing several measures of accuracy. An average overall accuracy of 65% and 81% was reported for seagrass and land cover respectively, which is consistent with other studies in the area. This study is the first to show moderate spatial resolution, long term annual changes in land cover and seagrass in an Australian environment, without the use of in-situ data; and only one of a few similar studies globally. The land cover products identify several long term trends; such as significant increases in South East Queensland's urban density, vegetation clearing in rural and rural-residential areas, and inter-annual variation in dry vegetation types in western South East Queensland. The seagrass cover products show that there has been a minimal overall change in seagrass extent, but that seagrass cover level distribution is extremely dynamic; evidenced by large scale migrations of higher seagrass cover levels and several events of sudden, significant changes in cover level. These mapping products will allow management agencies to build a baseline assessment of their resources, understand past changes and help inform implementation and planning of management policy to address potential future changes.

Assessment of Atmospheric Algorithms to Retrieve Vegetation in Natural Protected Areas Using Multispectral High Resolution Imagery

PubMed Central

Marcello, Javier; Eugenio, Francisco; Perdomo, Ulises; Medina, Anabella

2016-01-01

The precise mapping of vegetation covers in semi-arid areas is a complex task as this type of environment consists of sparse vegetation mainly composed of small shrubs. The launch of high resolution satellites, with additional spectral bands and the ability to alter the viewing angle, offers a useful technology to focus on this objective. In this context, atmospheric correction is a fundamental step in the pre-processing of such remote sensing imagery and, consequently, different algorithms have been developed for this purpose over the years. They are commonly categorized as imaged-based methods as well as in more advanced physical models based on the radiative transfer theory. Despite the relevance of this topic, a few comparative studies covering several methods have been carried out using high resolution data or which are specifically applied to vegetation covers. In this work, the performance of five representative atmospheric correction algorithms (DOS, QUAC, FLAASH, ATCOR and 6S) has been assessed, using high resolution Worldview-2 imagery and field spectroradiometer data collected simultaneously, with the goal of identifying the most appropriate techniques. The study also included a detailed analysis of the parameterization influence on the final results of the correction, the aerosol model and its optical thickness being important parameters to be properly adjusted. The effects of corrections were studied in vegetation and soil sites belonging to different protected semi-arid ecosystems (high mountain and coastal areas). In summary, the superior performance of model-based algorithms, 6S in particular, has been demonstrated, achieving reflectance estimations very close to the in-situ measurements (RMSE of between 2% and 3%). Finally, an example of the importance of the atmospheric correction in the vegetation estimation in these natural areas is presented, allowing the robust mapping of species and the analysis of multitemporal variations related to the human activity and climate change. PMID:27706064

Assessment of Atmospheric Algorithms to Retrieve Vegetation in Natural Protected Areas Using Multispectral High Resolution Imagery.

PubMed

Marcello, Javier; Eugenio, Francisco; Perdomo, Ulises; Medina, Anabella

2016-09-30

The precise mapping of vegetation covers in semi-arid areas is a complex task as this type of environment consists of sparse vegetation mainly composed of small shrubs. The launch of high resolution satellites, with additional spectral bands and the ability to alter the viewing angle, offers a useful technology to focus on this objective. In this context, atmospheric correction is a fundamental step in the pre-processing of such remote sensing imagery and, consequently, different algorithms have been developed for this purpose over the years. They are commonly categorized as imaged-based methods as well as in more advanced physical models based on the radiative transfer theory. Despite the relevance of this topic, a few comparative studies covering several methods have been carried out using high resolution data or which are specifically applied to vegetation covers. In this work, the performance of five representative atmospheric correction algorithms (DOS, QUAC, FLAASH, ATCOR and 6S) has been assessed, using high resolution Worldview-2 imagery and field spectroradiometer data collected simultaneously, with the goal of identifying the most appropriate techniques. The study also included a detailed analysis of the parameterization influence on the final results of the correction, the aerosol model and its optical thickness being important parameters to be properly adjusted. The effects of corrections were studied in vegetation and soil sites belonging to different protected semi-arid ecosystems (high mountain and coastal areas). In summary, the superior performance of model-based algorithms, 6S in particular, has been demonstrated, achieving reflectance estimations very close to the in-situ measurements (RMSE of between 2% and 3%). Finally, an example of the importance of the atmospheric correction in the vegetation estimation in these natural areas is presented, allowing the robust mapping of species and the analysis of multitemporal variations related to the human activity and climate change.

Novel Developmental Genes, fruCD, of Myxococcus xanthus: Involvement of a Cell Division Protein in Multicellular Development

PubMed Central

Akiyama, Takuya; Inouye, Sumiko; Komano, Teruya

2003-01-01

Myxococcus xanthus is a gram-negative soil bacterium that undergoes multicellular development upon nutrient starvation. In the present study, two novel developmental genes, fruC and fruD, of M. xanthus were identified and characterized. The FruD protein has significant amino acid sequence similarity to the DivIVA proteins of many bacteria including Bacillus subtilis. Vegetative cells of the fruD mutant exhibited a filamentous phenotype. The fruC and fruD mutants displayed similar delayed-development phenotypes. The formation of tightly aggregated mounds by fruC and fruD mutants was slower than that by the wild-type strain. Spore formation by the fruC and fruD mutants initiated after 30 h poststarvation, whereas wild-type M. xanthus initiated spore formation after 18 h. The fruCD genes were constitutively expressed as an operon during vegetative growth and development. S1 mapping revealed that transcription initiation sites of the fruCD operon were located 114 (P1) and 55 bp (P2) upstream of the fruC initiation codon. Only the P1 promoter was active during vegetative growth, while both the P1 and P2 promoters were active during development. The FruD protein was produced as a cytoplasmic protein and formed an oligomer during vegetative growth and development. PMID:12754229

Timing constraints on remote sensing of wildland fire burned area in the southeastern US

USGS Publications Warehouse

Picotte, J.J.; Robertson, K.

2011-01-01

Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78-90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy. ?? 2011 by the authors.

Timing constraints on remote sensing of wildland fire burned area in the southeastern US

USGS Publications Warehouse

Picotte, Joshua J.; Robertson, Kevin

2011-01-01

Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78–90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy.

Neural Networks as a Tool for Constructing Continuous NDVI Time Series from AVHRR and MODIS

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Lary, David J.; Vrieling, Anton; Stathakis, Demetris; Mussa, Hamse

2008-01-01

The long term Advanced Very High Resolution Radiometer-Normalized Difference Vegetation Index (AVHRR-NDVI) record provides a critical historical perspective on vegetation dynamics necessary for global change research. Despite the proliferation of new sources of global, moderate resolution vegetation datasets, the remote sensing community is still struggling to create datasets derived from multiple sensors that allow the simultaneous use of spectral vegetation for time series analysis. To overcome the non-stationary aspect of NDVI, we use an artificial neural network (ANN) to map the NDVI indices from AVHRR to those from MODIS using atmospheric, surface type and sensor-specific inputs to account for the differences between the sensors. The NDVI dynamics and range of MODIS NDVI data at one degree is matched and extended through the AVHRR record. Four years of overlap between the two sensors is used to train a neural network to remove atmospheric and sensor specific effects on the AVHRR NDVI. In this paper, we present the resulting continuous dataset, its relationship to MODIS data, and a validation of the product.

Genome Sequencing and Mapping Reveal Loss of Heterozygosity as a Mechanism for Rapid Adaptation in the Vegetable Pathogen Phytophthora capsici

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lamour, Kurt H.; Mudge, Joann; Gobena, Daniel

2012-02-07

The oomycete vegetable pathogen Phytophthora capsici has shown remarkable adaptation to fungicides and new hosts. Like other members of this destructive genus, P. capsici has an explosive epidemiology, rapidly producing massive numbers of asexual spores on infected hosts. In addition, P. capsici can remain dormant for years as sexually recombined oospores, making it difficult to produce crops at infested sites, and allowing outcrossing populations to maintain significant genetic variation. Genome sequencing, development of a high-density genetic map, and integrative genomic or genetic characterization of P. capsici field isolates and intercross progeny revealed significant mitotic loss of heterozygosity (LOH) in diversemore » isolates. LOH was detected in clonally propagated field isolates and sexual progeny, cumulatively affecting >30percent of the genome. LOH altered genotypes for more than 11,000 single-nucleotide variant sites and showed a strong association with changes in mating type and pathogenicity. Overall, it appears that LOH may provide a rapid mechanism for fixing alleles and may be an important component of adaptability for P. capsici.« less

Mapping landscape phenology preference of yellow-billed cuckoo with AVHRR data

USGS Publications Warehouse

Wallace, Cynthia S.A.; Villarreal, Miguel; van Riper, Charles

2013-01-01

We mapped habitat for threatened Yellow-billed Cuckoo (Coccycus americanus occidentalis) in the State of Arizona using the temporal greenness dynamics of the landscape, or the landscape phenology. Landscape phenometrics were derived from Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data for 1998 and 1999 by using Fourier harmonic analysis to analyze the waveform of the annual NDVI profile at each pixel. We modeled the spatial distribution of Yellow-billed Cuckoo habitat by coupling the field data of Cuckoo presence or absence and point-based samples of riparian and cottonwood-willow vegetation types with satellite phenometrics for 1998. Models were validated using field and satellite data collected in 1999. The results indicate that Yellow-billed Cuckoo occupy locations within their preferred habitat that exhibit peak greenness after the start of the summer monsoon and are greener and more dynamic than “average” habitat. Identification of preferred phenotypes within recognized habitat areas can be used to refine habitat models, inform predictions of habitat response to climate change, and suggest adaptation strategies.

Radar and Lidar Radar DEM

NASA Technical Reports Server (NTRS)

Liskovich, Diana; Simard, Marc

2011-01-01

Using radar and lidar data, the aim is to improve 3D rendering of terrain, including digital elevation models (DEM) and estimates of vegetation height and biomass in a variety of forest types and terrains. The 3D mapping of vegetation structure and the analysis are useful to determine the role of forest in climate change (carbon cycle), in providing habitat and as a provider of socio-economic services. This in turn will lead to potential for development of more effective land-use management. The first part of the project was to characterize the Shuttle Radar Topography Mission DEM error with respect to ICESat/GLAS point estimates of elevation. We investigated potential trends with latitude, canopy height, signal to noise ratio (SNR), number of LiDAR waveform peaks, and maximum peak width. Scatter plots were produced for each variable and were fitted with 1st and 2nd degree polynomials. Higher order trends were visually inspected through filtering with a mean and median filter. We also assessed trends in the DEM error variance. Finally, a map showing how DEM error was geographically distributed globally was created.

Forest Types in the Lower Suwannee River Floodplain, Florida?-A Report and Interactive Map

USGS Publications Warehouse

Darst, M.R.; Light, H.M.; Lewis, L.J.; Sepulveda, A.A.

2003-01-01

A map of forest types in the lower Suwannee River floodplain, Florida, was created during a study conducted from 1996 to 2000 by the U.S. Geological Survey in cooperation with the Suwannee River Water Management District. The map is presented with this report on a compact disc with interactive viewing software. The forest map can be used by scientists for ecological studies in the floodplain based on land cover types and by landowners and management personnel making land use decisions. The study area is the 10-year floodplain of the lower Suwannee River from its confluence with the Santa Fe River to the lower limit of forests near the Gulf of Mexico. The floodplain is divided into three reaches: riverine (non-tidal), upper tidal, and lower tidal, due to changes in hydrology, vegetation, and soils with proximity to the coast. The 10-year floodplain covers about 21,170 hectares; nearly 88 percent of this area (18,580 hectares) is mapped as 14 major forest types. Approximately 29 percent (5,319 hectares) of these forests have been altered by agriculture or development. About 75 percent of the area of major forest types (13,994 hectares) is wetland forests and about 25 percent (4,586 hectares) is upland forests. Tidal wetland forests (8,955 hectares) cover a much greater area than riverine wetland forests (5,039 hectares). Oak/pine upland forests are present in the riverine and upper tidal reaches of the floodplain on elevations that are inundated only briefly during the highest floods. High bottomland hardwoods are present on the higher levees, ridges, and flats of the riverine reach where soils are usually sandy. Low bottomland hardwood forests are present in the riverine reach on swamp margins and low levees and flats that are flooded continuously for several weeks or longer every 1 to 3 years. Riverine swamps are present in the lowest and wettest areas of the non-tidal floodplain that are either inundated or saturated most of the time. Upper tidal bottomland hardwood forests are present on sandy soils on high flats and in transitional areas between upland forests and swamps. Upper tidal mixed forests are found on low levees or between swamps and higher forest types. Upper tidal swamps are present at elevations below median monthly high stage and usually have surface soils that are permanently saturated mucks. Lower tidal hammocks are found on higher elevations that do not receive regular tidal inundation but have a high water table and are briefly inundated by storm surges several times a decade. Lower tidal mixed forests include swamps with numerous small hummocks or less common larger hummocks. Lower tidal swamps are found on deep muck soils that are below the elevation of the median daily or monthly high stage. Seven additional land cover types (2,590 hectares) are mapped. Water in the main channel of the lower Suwannee River (1,767 hectares) was mapped separately from open water in the floodplain (239 hectares). Other land cover types are: seepage slopes (70 hectares), isolated forested wetlands (19 hectares), marshes upstream of the tree line (505 hectares), beds of emergent aquatic vegetation (21 hectares), and floodplain glades (46 hectares)

Efficiency of the spectral-spatial classification of hyperspectral imaging data

NASA Astrophysics Data System (ADS)

Borzov, S. M.; Potaturkin, O. I.

2017-01-01

The efficiency of methods of the spectral-spatial classification of similarly looking types of vegetation on the basis of hyperspectral data of remote sensing of the Earth, which take into account local neighborhoods of analyzed image pixels, is experimentally studied. Algorithms that involve spatial pre-processing of the raw data and post-processing of pixel-based spectral classification maps are considered. Results obtained both for a large-size hyperspectral image and for its test fragment with different methods of training set construction are reported. The classification accuracy in all cases is estimated through comparisons of ground-truth data and classification maps formed by using the compared methods. The reasons for the differences in these estimates are discussed.

Riparian vegetation as an indicator of riparian condition: Detecting departures from historic condition across the North American West.

PubMed

Macfarlane, William W; Gilbert, Jordan T; Jensen, Martha L; Gilbert, Joshua D; Hough-Snee, Nate; McHugh, Peter A; Wheaton, Joseph M; Bennett, Stephen N

2017-11-01

Floodplain riparian ecosystems support unique vegetation communities and high biodiversity relative to terrestrial landscapes. Accordingly, estimating riparian ecosystem health across landscapes is critical for sustainable river management. However, methods that identify local riparian vegetation condition, an effective proxy for riparian health, have not been applied across broad, regional extents. Here we present an index to assess reach-scale (500 m segment) riparian vegetation condition across entire drainage networks within large, physiographically-diverse regions. We estimated riparian vegetation condition for 53,250 km of perennial streams and rivers, 25,685 km in Utah, and 27,565 km in twelve watersheds of the interior Columbia River Basin (CRB), USA. We used nationally available, existing land cover classification derived from 30 m Landsat imagery (LANDFIRE EVT) and a modeled estimate of pre-European settlement land cover (LANDFIRE BpS). The index characterizes riparian vegetation condition as the ratio of existing native riparian vegetation cover to pre-European settlement riparian vegetation cover at a given reach. Roughly 62% of Utah and 48% of CRB watersheds showed significant (>33%) to large (>66%) departure from historic condition. Riparian vegetation change was predominantly caused by human land-use impacts (development and agriculture), or vegetation change (native riparian to invasive or upland vegetation types) that likely resulted from flow and disturbance regime alteration. Through comparisons to ground-based classification results, we estimate the existing vegetation component of the index to be 85% accurate. Our assessments yielded riparian condition maps that will help resource managers better prioritize sites and treatments for reach-scale conservation and restoration activities. Copyright © 2016 Elsevier Ltd. All rights reserved.

Impacts of Climate Change on Native Landcover: Seeking Future Climatic Refuges.

PubMed

Zanin, Marina; Mangabeira Albernaz, Ana Luisa

2016-01-01

Climate change is a driver for diverse impacts on global biodiversity. We investigated its impacts on native landcover distribution in South America, seeking to predict its effect as a new force driving habitat loss and population isolation. Moreover, we mapped potential future climatic refuges, which are likely to be key areas for biodiversity conservation under climate change scenarios. Climatically similar native landcovers were aggregated using a decision tree, generating a reclassified landcover map, from which 25% of the map's coverage was randomly selected to fuel distribution models. We selected the best geographical distribution models among twelve techniques, validating the predicted distribution for current climate with the landcover map and used the best technique to predict the future distribution. All landcover categories showed changes in area and displacement of the latitudinal/longitudinal centroid. Closed vegetation was the only landcover type predicted to expand its distributional range. The range contractions predicted for other categories were intense, even suggesting extirpation of the sparse vegetation category. The landcover refuges under future climate change represent a small proportion of the South American area and they are disproportionately represented and unevenly distributed, predominantly occupying five of 26 South American countries. The predicted changes, regardless of their direction and intensity, can put biodiversity at risk because they are expected to occur in the near future in terms of the temporal scales of ecological and evolutionary processes. Recognition of the threat of climate change allows more efficient conservation actions.

Mapping and monitoring carbon stocks with satellite observations: a comparison of methods.

PubMed

Goetz, Scott J; Baccini, Alessandro; Laporte, Nadine T; Johns, Tracy; Walker, Wayne; Kellndorfer, Josef; Houghton, Richard A; Sun, Mindy

2009-03-25

Mapping and monitoring carbon stocks in forested regions of the world, particularly the tropics, has attracted a great deal of attention in recent years as deforestation and forest degradation account for up to 30% of anthropogenic carbon emissions, and are now included in climate change negotiations. We review the potential for satellites to measure carbon stocks, specifically aboveground biomass (AGB), and provide an overview of a range of approaches that have been developed and used to map AGB across a diverse set of conditions and geographic areas. We provide a summary of types of remote sensing measurements relevant to mapping AGB, and assess the relative merits and limitations of each. We then provide an overview of traditional techniques of mapping AGB based on ascribing field measurements to vegetation or land cover type classes, and describe the merits and limitations of those relative to recent data mining algorithms used in the context of an approach based on direct utilization of remote sensing measurements, whether optical or lidar reflectance, or radar backscatter. We conclude that while satellite remote sensing has often been discounted as inadequate for the task, attempts to map AGB without satellite imagery are insufficient. Moreover, the direct remote sensing approach provided more coherent maps of AGB relative to traditional approaches. We demonstrate this with a case study focused on continental Africa and discuss the work in the context of reducing uncertainty for carbon monitoring and markets.

The influence of slope and peatland vegetation type on riverine dissolved organic carbon and water colour at different scales.

PubMed

Parry, L E; Chapman, P J; Palmer, S M; Wallage, Z E; Wynne, H; Holden, J

2015-09-15

Peatlands are important sources of fluvial carbon. Previous research has shown that riverine dissolved organic carbon (DOC) concentrations are largely controlled by soil type. However, there has been little work to establish the controls of riverine DOC within blanket peatlands that have not undergone major disturbance from drainage or burning. A total of 119 peatland catchments were sampled for riverine DOC and water colour across three drainage basins during six repeated sampling campaigns. The topographic characteristics of each catchment were determined from digital elevation models. The dominant vegetation cover was mapped using 0.5m resolution colour infrared aerial images, with ground-truthed validation revealing 82% accuracy. Forward and backward stepwise regression modelling showed that mean slope was a strong (and negative) determinant of DOC and water colour in blanket peatland river waters. There was a weak role for plant functional type in determining DOC and water colour. At the basin scale, there were major differences between the models depending on the basin. The dominance of topographic predictors of DOC found in our study, combined with a weaker role of vegetation type, paves the way for developing improved planning tools for water companies operating in peatland catchments. Using topographic data and aerial imagery it will be possible to predict which tributaries will typically yield lower DOC concentrations and which are therefore more suitable and cost-effective as raw water intakes. Copyright © 2015 Elsevier B.V. All rights reserved.

Monitoring flooding and vegetation on seasonally inundated floodplains with multifrequency polarimetric synthetic aperture radar

NASA Astrophysics Data System (ADS)

Hess, Laura Lorraine

The ability of synthetic aperture radar to detect flooding and vegetation structure was evaluated for three seasonally inundated floodplain sites supporting a broad variety of wetland and upland vegetation types: two reaches of the Solimoes floodplain in the central Amazon, and the Magela Creek floodplain in Northern Territory, Australia. For each site, C- and L-band polarimetric Shuttle Imaging Radar-C (SIR-C) data was obtained at both high- and low-water stages. Inundation status and vegetation structure were documented simultaneous with the SIR-C acquisitions using low-altitude videography and ground measurements. SIR-C images were classified into cover states defined by vegetation physiognomy and presence of standing water, using a decision-tree model with backscattering coefficients at HH, VV, and HV polarizations as input variables. Classification accuracy was assessed using user's accuracy, producer's accuracy, and kappa coefficient for a test population of pixels. At all sites, both C- and L-band were necessary to accurately classify cover types with two dates. HH polarization was most. useful for distinguishing flooded from non-flooded vegetation (C-HH for macrophyte versus pasture, L-HH for flooded versus non-flooded forest), and cross-polarized L-band data provided the best separation between woody and non-woody vegetation. Increases in L-HH backscattering due to flooding were on the order of 3--4 dB for closed-canopy varzea and igapo forest, and 4--7 dB, for open Melaleuca woodland. The broad range of physiognomies and stand structures found in both herbaceous and woody wetland communities, combined with the variation in the amount of emergent canopy caused by water level fluctuations and phenologic changes, resulted in a large range in backscattering characteristics of wetland communities both within and between sites. High accuracies cannot be achieved for these communities using single-date, single-band, single-polarization data, particularly in the case of distinguishing flooded macrophyte from non-flooded forest vegetation. However, the large changes in backscattering caused by flooding make it possible to achieve good accuracies (>85%) using multi-temporal data. Where river stage records are available, SAR-based maps of inundation status on a series of dates can be linked to long-term stage data to define wetland habitat types based on flooding regime and low-water vegetation cover.

Vegetation Analysis and Land Use Land Cover Classification of Forest in Uttara Kannada District India Through Geo-Informatics Approach

NASA Astrophysics Data System (ADS)

Koppad, A. G.; Janagoudar, B. S.

2017-05-01

The study was conducted in Uttara Kannada districts during the year 2012-2014. The study area lies between 13.92° N to 15.52° N latitude and 74.08° E to 75.09° E longitude with an area of 10,215 km2. The Indian satellite IRS P6 LISS-III imageries were used to classify the land use land cover classes with ground truth data collected with GPS through supervised classification in ERDAS software. The land use and land cover classes identified were dense forest, horticulture plantation, sparse forest, forest plantation, open land and agriculture land. The dense forest covered an area of 63.32 % (6468.70 sq km) followed by agriculture 12.88 % (1315.31 sq. km), sparse forest 10.59 % (1081.37 sq. km), open land 6.09 % (622.37 sq. km), horticulture plantation and least was forest plantation (1.07 %). Settlement, stony land and water body together cover about 4.26 percent of the area. The study indicated that the aspect and altitude influenced the forest types and vegetation pattern. The NDVI map was prepared which indicated that healthy vegetation is represented by high NDVI values between 0.1 and 1. The non-vegetated features such as water bodies, settlement, and stony land indicated less than 0.1 values. The decrease in forest area in some places was due to anthropogenic activities. The thematic map of land use land cover classes was prepared using Arc GIS Software.

Investigation of environmental change pattern in Japan. Investigation of the ecological environment index from observation of the regional vegetation cover and their growing condition

NASA Technical Reports Server (NTRS)

Maruyasu, T. (Principal Investigator); Nakajima, I.

1977-01-01

The author has identified the following significant results. Practical use of recognition results of LANDSAT data as the base map of the field survey or the retouching work of vegetation and land use has the effective benefit to cut down the cost, labor, and time lower than 10% of a conventional method. Correct and detailed vegetation maps were prepared using combined interpretation of repetition of data of different seasons at warm and temperate forested areas.

Vegetation map for the Hakalau Forest Unit of the Big Island National Wildlife Refuge Complex on the island of Hawai‘i

USGS Publications Warehouse

Jacobi, James D.

2017-01-01

This vegetation map was produced to serve as an updated habitat base for management of natural resources of the Hakalau Forest Unit (HFU) of the Big Island National Wildlife Refuge Complex (Refuge) on the island of Hawai‘i. The map is based on a vegetation map originally produced as part of the U.S. Fish and Wildlife Service’s Hawai‘i Forest Bird Survey to depict the distribution, structure, and composition of plant communities on the island of Hawai‘i as they existed in 1977. The current map has been updated to represent current conditions of plant communities in the HFU, based on WorldView 2 imagery taken in 2012 and very-high-resolution imagery collected by Pictometry International from 2010 to 2014. Thirty-one detailed plant communities are identified on this map, and fourteen of these units are found within the boundaries of HFU. Additionally, the mapped units can be displayed as five tree canopy cover units, three moisture zones units, eight dominant tree species units, and four habitat status units by choosing the various fields to group the units from the map attribute table. This updated map will provide a foundation for the refinement and tracking of management actions on the Refuge for the near future, particularly as the habitats in this area are subject to projected climate change.

Detection and mapping of hydrothermally altered rocks in the vicinity of the comstock lode, Virginia Range, Nevada, using enhanced LANDSAT images

NASA Technical Reports Server (NTRS)

Ashley, R. P. (Principal Investigator); Goetz, A. F. H.; Rowan, L. C.; Abrams, M. J.

1979-01-01

The author has identified the following significant results. LANDSAT images enhanced by the band-ratioing method can be used for reconnaissance alteration mapping in moderately heavily vegetated semiarid terrain as well as in sparsely vegetated to semiarid terrain where the technique was originally developed. Significant vegetation cover in a scene, however, requires the use of MSS ratios 4/5, 4/6, and 6/7 rather than 4/5, 5/6, and 6/7, and requires careful interpretation of the results. Supplemental information suitable to vegetation identification and cover estimates, such as standard LANDSAT false-color composites and low altitude aerial photographs of selected areas is desirable.

The Laser Vegetation Imaging Sensor: a medium-altitude, digitisation-only, airborne laser altimeter for mapping vegetation and topography

NASA Astrophysics Data System (ADS)

Blair, J. Bryan; Rabine, David L.; Hofton, Michelle A.

The Laser Vegetation Imaging Sensor (LVIS) is an airborne, scanning laser altimeter, designed and developed at NASA's Goddard Space Flight Center (GSFC). LVIS operates at altitudes up to 10 km above ground, and is capable of producing a data swath up to 1000 m wide nominally with 25-m wide footprints. The entire time history of the outgoing and return pulses is digitised, allowing unambiguous determination of range and return pulse structure. Combined with aircraft position and attitude knowledge, this instrument produces topographic maps with dm accuracy and vertical height and structure measurements of vegetation. The laser transmitter is a diode-pumped Nd:YAG oscillator producing 1064 nm, 10 ns, 5 mJ pulses at repetition rates up to 500 Hz. LVIS has recently demonstrated its ability to determine topography (including sub-canopy) and vegetation height and structure on flight missions to various forested regions in the US and Central America. The LVIS system is the airborne simulator for the Vegetation Canopy Lidar (VCL) mission (a NASA Earth remote sensing satellite due for launch in year 2000), providing simulated data sets and a platform for instrument proof-of-concept studies. The topography maps and return waveforms produced by LVIS provide Earth scientists with a unique data set allowing studies of topography, hydrology, and vegetation with unmatched accuracy and coverage.

Monitoring height and greenness of non-woody floodplain vegetation with UAV time series

NASA Astrophysics Data System (ADS)

van Iersel, Wimala; Straatsma, Menno; Addink, Elisabeth; Middelkoop, Hans

2018-07-01

Vegetation in river floodplains has important functions for biodiversity, but can also have a negative influence on flood safety. Floodplain vegetation is becoming increasingly heterogeneous in space and time as a result of river restoration projects. To document the spatio-temporal patterns of the floodplain vegetation, the need arises for efficient monitoring techniques. Monitoring is commonly performed by mapping floodplains based on single-epoch remote sensing data, thereby not considering seasonal dynamics of vegetation. The rising availability of unmanned airborne vehicles (UAV) increases monitoring frequency potential. Therefore, we aimed to evaluate the performance of multi-temporal high-spatial-resolution imagery, collected with a UAV, to record the dynamics in floodplain vegetation height and greenness over a growing season. Since the classification accuracy of current airborne surveys remains insufficient for low vegetation types, we focussed on seasonal variation of herbaceous and grassy vegetation with a height up to 3 m. Field reference data on vegetation height were collected six times during one year in 28 field plots within a single floodplain along the Waal River, the main distributary of the Rhine River in the Netherlands. Simultaneously with each field survey, we recorded UAV true-colour and false-colour imagery from which normalized digital surface models (nDSMs) and a consumer-grade camera vegetation index (CGCVI) were calculated. We observed that: (1) the accuracy of a UAV-derived digital terrain model (DTM) varies over the growing season and is most accurate during winter when the vegetation is dormant, (2) vegetation height can be determined from the nDSMs in leaf-on conditions via linear regression (RSME = 0.17-0.33 m), (3) the multitemporal nDSMs yielded meaningful temporal profiles of greenness and vegetation height and (4) herbaceous vegetation shows hysteresis for greenness and vegetation height, but no clear hysteresis was observed for grassland vegetation. These results show the high potential of using UAV-borne sensors for increasing the classification accuracy of low floodplain vegetation within the framework of floodplain monitoring.