Predictive Mapping of Forest Attributes on the Fishlake National Forest

Treesearch

Tracey S. Frescino; Gretchen G. Moisen

2005-01-01

Forest land managers increasingly need maps of forest characteristics to aid in planning and management. A set of 30-m resolution maps was prepared for the Fishlake National Forest by modeling FIA plot variables as nonparametric functions of ancillary digital data. The set includes maps of volume, biomass, growth, stand age, size, crown cover, and various aspen...

Application of Modis Data to Assess the Latest Forest Cover Changes of Sri Lanka

NASA Astrophysics Data System (ADS)

Perera, K.; Herath, S.; Apan, A.; Tateishi, R.

2012-07-01

Assessing forest cover of Sri Lanka is becoming important to lower the pressure on forest lands as well as man-elephant conflicts. Furthermore, the land access to north-east Sri Lanka after the end of 30 years long civil war has increased the need of regularly updated land cover information for proper planning. This study produced an assessment of the forest cover of Sri Lanka using two satellite data based maps within 23 years of time span. For the old forest cover map, the study used one of the first island-wide digital land cover classification produced by the main author in 1988. The old land cover classification was produced at 80 m spatial resolution, using Landsat MSS data. A previously published another study by the author has investigated the application feasibility of MODIS and Landsat MSS imagery for a selected sub-section of Sri Lanka to identify the forest cover changes. Through the light of these two studies, the assessment was conducted to investigate the application possibility of MODIS 250 m over a small island like Sri Lanka. The relation between the definition of forest in the study and spatial resolution of the used satellite data sets were considered since the 2012 map was based on MODIS data. The forest cover map of 1988 was interpolated into 250 m spatial resolution to integrate with the GIS data base. The results demonstrated the advantages as well as disadvantages of MODIS data in a study at this scale. The successful monitoring of forest is largely depending on the possibility to update the field conditions at regular basis. Freely available MODIS data provides a very valuable set of information of relatively large green patches on the ground at relatively real-time basis. Based on the changes of forest cover from 1988 to 2012, the study recommends the use of MODIS data as a resalable method to forest assessment and to identify hotspots to be re-investigated. It's noteworthy to mention the possibility of uncounted small isolated pockets of forest, or sub-pixel size forest patches when MODIS 250 m x 250 m data used in small regions.

Mapping Land Cover and Land Use Changes in the Congo Basin Forests with Optical Satellite Remote Sensing: a Pilot Project Exploring Methodologies that Improve Spatial Resolution and Map Accuracy

NASA Astrophysics Data System (ADS)

Molinario, G.; Baraldi, A.; Altstatt, A. L.; Nackoney, J.

2011-12-01

The University of Maryland has been a USAID Central Africa Rregional Program for the Environment (CARPE) cross-cutting partner for many years, providing remote sensing derived information on forest cover and forest cover changes in support of CARPE's objectives of diminishing forest degradation, loss and biodiversity loss as a result of poor or inexistent land use planning strategies. Together with South Dakota State University, Congo Basin-wide maps have been provided that map forest cover loss at a maximum of 60m resolution, using Landsat imagery and higher resolution imagery for algorithm training and validation. However, to better meet the needs within the CARPE Landscapes, which call for higher resolution, more accurate land cover change maps, UMD has been exploring the use of the SIAM automatic spectral -rule classifier together with pan-sharpened Landsat data (15m resolution) and Very High Resolution imagery from various sources. The pilot project is being developed in collaboration with the African Wildlife Foundation in the Maringa Lopori Wamba CARPE Landscape. If successful in the future this methodology will make the creation of high resolution change maps faster and easier, making it accessible to other entities in the Congo Basin that need accurate land cover and land use change maps in order, for example, to create sustainable land use plans, conserve biodiversity and resources and prepare Reducing Emissions from forest Degradation and Deforestation (REDD) Measurement, Reporting and Verification (MRV) projects. The paper describes the need for higher resolution land cover change maps that focus on forest change dynamics such as the cycling between primary forests, secondary forest, agriculture and other expanding and intensifying land uses in the Maringa Lopori Wamba CARPE Landscape in the Equateur Province of the Democratic Republic of Congo. The Methodology uses the SIAM remote sensing imagery automatic spectral rule classifier, together with pan-sharpened Landsat imagery with 15m resolution and Very High Resolution imagery from different sensors, obtained from the Department of Defense database that was recently opened to NASA and its Earth Observation partners. Particular emphasis is placed on the detection of agricultural fields and their expansion in primary forests or intensification in secondary forests and fallow fields, as this is the primary driver of deforestation in this area. Fields in this area area also of very small size and irregular shapes, often partly obscured by neighboring forest canopy, hence the technical challenge of correctly detecting them and tracking them through time. Finally, the potential for use of this methodology in other regions where information on land cover changes is needed for land use sustainability planning, is also addressed.

Potential for Monitoring Snow Cover in Boreal Forests by Combining MODIS Snow Cover and AMSR-E SWE Maps

NASA Technical Reports Server (NTRS)

Riggs, George A.; Hall, Dorothy K.; Foster, James L.

2009-01-01

Monitoring of snow cover extent and snow water equivalent (SWE) in boreal forests is important for determining the amount of potential runoff and beginning date of snowmelt. The great expanse of the boreal forest necessitates the use of satellite measurements to monitor snow cover. Snow cover in the boreal forest can be mapped with either the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) microwave instrument. The extent of snow cover is estimated from the MODIS data and SWE is estimated from the AMSR-E. Environmental limitations affect both sensors in different ways to limit their ability to detect snow in some situations. Forest density, snow wetness, and snow depth are factors that limit the effectiveness of both sensors for snow detection. Cloud cover is a significant hindrance to monitoring snow cover extent Using MODIS but is not a hindrance to the use of the AMSR-E. These limitations could be mitigated by combining MODIS and AMSR-E data to allow for improved interpretation of snow cover extent and SWE on a daily basis and provide temporal continuity of snow mapping across the boreal forest regions in Canada. The purpose of this study is to investigate if temporal monitoring of snow cover using a combination of MODIS and AMSR-E data could yield a better interpretation of changing snow cover conditions. The MODIS snow mapping algorithm is based on snow detection using the Normalized Difference Snow Index (NDSI) and the Normalized Difference Vegetation Index (NDVI) to enhance snow detection in dense vegetation. (Other spectral threshold tests are also used to map snow using MODIS.) Snow cover under a forest canopy may have an effect on the NDVI thus we use the NDVI in snow detection. A MODIS snow fraction product is also generated but not used in this study. In this study the NDSI and NDVI components of the snow mapping algorithm were calculated and analyzed to determine how they changed through the seasons. A blended snow product, the Air Force Weather Agency and NASA (ANSA) snow algorithm and product has recently been developed. The ANSA algorithm blends the MODIS snow cover and AMSR-E SWE products into a single snow product that has been shown to improve the performance of snow cover mapping. In this study components of the ANSA snow algorithm are used along with additional MODIS data to monitor daily changes in snow cover over the period of 1 February to 30 June 2008.

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.

Comparison results of forest cover mapping of Peninsular Malaysia using geospatial technology

NASA Astrophysics Data System (ADS)

Hamid, Wan Abdul; Abd Rahman, Shukri B. Wan

2016-06-01

Climate change and global warming transpire due to several factors. Among them is deforestation which occur mostly in developing countries including Malaysia where forested areas are converted to other land use for tangible economic returns and to a smaller extent, as subsistence for local communities. As a cause for concern, efforts have been taken by the World Resource Institute (WRI) and World Wildlife Fund (WWF) to monitor forest loss using geospatial technology - interpreting time-based remote sensing imageries and producing statistics of forested areas lost since 2001. In Peninsular Malaysia, the Forestry Department of Peninsular Malaysia(FDPM) has conducted forest cover mapping for the region using the same technology since 2011, producing GIS maps for 2009-2010,2011-2012,2013-2014 and 2015. This paper focuses on the comparative study of the results generated from WRI,WWF and FDPM interpretations between 2010 and 2015, the methodologies used, the similarities and differences, challenges and recommendations for future enhancement of forest cover mapping technique.

Implementation of forest cover and carbon mapping in the Greater Mekong subregion and Malaysia project - A case study of Thailand

NASA Astrophysics Data System (ADS)

Pungkul, S.; Suraswasdi, C.; Phonekeo, V.

2014-02-01

The Great Mekong Subregion (GMS) contains one of the world's largest tropical forests and plays a vital role in sustainable development and provides a range of economic, social and environmental benefits, including essential ecosystem services such as climate change mitigation and adaptation. However, the forest in this Subregion is experiencing deforestation rates at high level due to human activities. The reduction of the forest area has negative influence to the environmental and natural resources issues, particularly, more severe disasters have occurred due to global warming and the release of the greenhouse gases. Therefore, in order to conduct forest management in the Subregion efficiently, the Forest Cover and Carbon Mapping in Greater Mekong Subregion and Malaysia project was initialized by the Asia-Pacific Network for Sustainable Forest Management and Rehabilitation (APFNet) with the collaboration of various research institutions including Institute of Forest Resource Information Technique (IFRIT), Chinese Academy of Forestry (CAF) and the countries in Sub region and Malaysia comprises of Cambodia, the People's Republic of China (Yunnan province and Guangxi province), Lao People's Democratic Republic, Malaysia, Myanmar, Thailand, and Viet Nam. The main target of the project is to apply the intensive use of recent satellite remote sensing technology, establishing regional forest cover maps, documenting forest change processes and estimating carbon storage in the GMS and Malaysia. In this paper, the authors present the implementation of the project in Thailand and demonstrate the result of forest cover mapping in the whole country in 2005 and 2010. The result of the project will contribute towards developing efficient tools to support decision makers to clearly understand the dynamic change of the forest cover which could benefit sustainable forest resource management in Thailand and the whole Subregion.

Spatiotemporal Change Detection in Forest Cover Dynamics Along Landslide Susceptible Region of Karakoram Highway, Pakistan

NASA Astrophysics Data System (ADS)

Rashid, Barira; Iqbal, Javed

2018-04-01

Forest Cover dynamics and its understanding is essential for a country's social, environmental, and political engagements. This research provides a methodical approach for the assessment of forest cover along Karakoram Highway. It has great ecological and economic significance because it's a part of China-Pakistan Economic Corridor. Landsat 4, 5 TM, Landsat 7 ETM and Landsat 8 OLI imagery for the years 1990, 2000, 2010 and 2016 respectively were subjected to supervised classification in ArcMap 10.5 to identify forest change. The study area was categorized into five major land use land cover classes i.e., Forest, vegetation, urban, open land and snow cover. Results from post classification forest cover change maps illustrated notable decrease of almost 26 % forest cover over the time period of 26 years. The accuracy assessment revealed the kappa coefficients 083, 0.78, 0.77 and 0.85, respectively. Major reason for this change is an observed replacement of native forest cover with urban areas (12.5 %) and vegetation (18.6 %) However, there is no significant change in the reserved forests along the study area that contributes only 2.97 % of the total forest cover. The extensive forest degradation and risk prone topography of the region has increased the environmental risk of landslides. Hence, effective policies and forest management is needed to protect not only the environmental and aesthetic benefits of the forest cover but also to manage the disaster risks. Apart from the forest assessment, this research gives an insight of land cover dynamics, along with causes and consequences, thereby showing the forest degradation hotspots.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Technical Reports Server (NTRS)

Kumar, Uttam; Nemani, Ramakrishna R.; Ganguly, Sangram; Kalia, Subodh; Michaelis, Andrew

2017-01-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS-national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91 percent was achieved, which is a 6 percent improvement in unmixing based classification relative to per-pixel-based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

Linear Subpixel Learning Algorithm for Land Cover Classification from WELD using High Performance Computing

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2017-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

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.

Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression

Treesearch

Jeffrey T. Walton

2008-01-01

Three machine learning subpixel estimation methods (Cubist, Random Forests, and support vector regression) were applied to estimate urban cover. Urban forest canopy cover and impervious surface cover were estimated from Landsat-7 ETM+ imagery using a higher resolution cover map resampled to 30 m as training and reference data. Three different band combinations (...

Forest land cover change (1975-2000) in the Greater Border Lakes region

Treesearch

Peter T. Wolter; Brian R. Sturtevant; Brian R. Miranda; Sue M. Lietz; Phillip A. Townsend; John Pastor

2012-01-01

This document and accompanying maps describe land cover classifications and change detection for a 13.8 million ha landscape straddling the border between Minnesota, and Ontario, Canada (greater Border Lakes Region). Land cover classifications focus on discerning Anderson Level II forest and nonforest cover to track spatiotemporal changes in forest cover. Multi-...

Determining coniferous forest cover and forest fragmentation with NOAA-9 advanced very high resolution radiometer data

NASA Technical Reports Server (NTRS)

Ripple, William J.

1995-01-01

NOAA-9 satellite data from the Advanced Very High Resolution Radiometer (AVHRR) were used in conjunction with Landsat Multispectral Scanner (MSS) data to determine the proportion of closed canopy conifer forest cover in the Cascade Range of Oregon. A closed canopy conifer map, as determined from the MSS, was registered with AVHRR pixels. Regression was used to relate closed canopy conifer forest cover to AVHRR spectral data. A two-variable (band) regression model accounted for more variance in conifer cover than the Normalized Difference Vegetation Index (NDVI). The spectral signatures of various conifer successional stages were also examined. A map of Oregon was produced showing the proportion of closed canopy conifer cover for each AVHRR pixel. The AVHRR was responsive to both the percentage of closed canopy conifer cover and the successional stage in these temperate coniferous forests in this experiment.

Calibration and Validation of Landsat Tree Cover in the Taiga-Tundra Ecotone

NASA Technical Reports Server (NTRS)

Montesano, Paul Mannix; Neigh, Christopher S. R.; Sexton, Joseph; Feng, Min; Channan, Saurabh; Ranson, Kenneth J.; Townshend, John R.

2016-01-01

Monitoring current forest characteristics in the taiga-tundra ecotone (TTE) at multiple scales is critical for understanding its vulnerability to structural changes. A 30 m spatial resolution Landsat-based tree canopy cover map has been calibrated and validated in the TTE with reference tree cover data from airborne LiDAR and high resolution spaceborne images across the full range of boreal forest tree cover. This domain-specific calibration model used estimates of forest height to determine reference forest cover that best matched Landsat estimates. The model removed the systematic under-estimation of tree canopy cover greater than 80% and indicated that Landsat estimates of tree canopy cover more closely matched canopies at least 2 m in height rather than 5 m. The validation improved estimates of uncertainty in tree canopy cover in discontinuous TTE forests for three temporal epochs (2000, 2005, and 2010) by reducing systematic errors, leading to increases in tree canopy cover uncertainty. Average pixel-level uncertainties in tree canopy cover were 29.0%, 27.1% and 31.1% for the 2000, 2005 and 2010 epochs, respectively. Maps from these calibrated data improve the uncertainty associated with Landsat tree canopy cover estimates in the discontinuous forests of the circumpolar TTE.

Forest fire risk zonation mapping using remote sensing technology

NASA Astrophysics Data System (ADS)

Chandra, Sunil; Arora, M. K.

2006-12-01

Forest fires cause major losses to forest cover and disturb the ecological balance in our region. Rise in temperature during summer season causing increased dryness, increased activity of human beings in the forest areas, and the type of forest cover in the Garhwal Himalayas are some of the reasons that lead to forest fires. Therefore, generation of forest fire risk maps becomes necessary so that preventive measures can be taken at appropriate time. These risk maps shall indicate the zonation of the areas which are in very high, high, medium and low risk zones with regard to forest fire in the region. In this paper, an attempt has been made to generate the forest fire risk maps based on remote sensing data and other geographical variables responsible for the occurrence of fire. These include altitude, temperature and soil variations. Key thematic data layers pertaining to these variables have been generated using various techniques. A rule-based approach has been used and implemented in GIS environment to estimate fuel load and fuel index leading to the derivation of fire risk zonation index and subsequently to fire risk zonation maps. The fire risk maps thus generated have been validated on the ground for forest types as well as for forest fire risk areas. These maps would help the state forest departments in prioritizing their strategy for combating forest fires particularly during the fire seasons.

Assessing double counting of carbon emissions between forest land cover change and forest wildfires: a case study in the United States, 1992-2006

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey; Brad Quayle

2013-01-01

The relative contributions of double counting of carbon emissions between forest-to-nonforest cover change (FNCC) and forest wildfires are an unknown in estimating net forest carbon exchanges at large scales. This study employed land-cover change maps and forest fire data in the four representative states (Arkansas, California, Minnesota, and Washington) of the US for...

Interpreting forest biome productivity and cover utilizing nested scales of image resolution and biogeographical analysis

NASA Technical Reports Server (NTRS)

Iverson, Louis R.; Cook, Elizabeth A.; Graham, Robin L.; Olson, Jerry S.; Frank, Thomas D.; Ying, KE

1988-01-01

The objective was to relate spectral imagery of varying resolution with ground-based data on forest productivity and cover, and to create models to predict regional estimates of forest productivity and cover with a quantifiable degree of accuracy. A three stage approach was outlined. In the first stage, a model was developed relating forest cover or productivity to TM surface reflectance values (TM/FOREST models). The TM/FOREST models were more accurate when biogeographic information regarding the landscape was either used to stratigy the landscape into more homogeneous units or incorporated directly into the TM/FOREST model. In the second stage, AVHRR/FOREST models that predicted forest cover and productivity on the basis of AVHRR band values were developed. The AVHRR/FOREST models had statistical properties similar to or better than those of the TM/FOREST models. In the third stage, the regional predictions were compared with the independent U.S. Forest Service (USFS) data. To do this regional forest cover and forest productivity maps were created using AVHRR scenes and the AVHRR/FOREST models. From the maps the county values of forest productivity and cover were calculated. It is apparent that the landscape has a strong influence on the success of the approach. An approach of using nested scales of imagery in conjunction with ground-based data can be successful in generating regional estimates of variables that are functionally related to some variable a sensor can detect.

Deriving Continuous Fields of Tree Cover at 1-m over the Continental United States From the National Agriculture Imagery Program (NAIP) Imagery to Reduce Uncertainties in Forest Carbon Stock Estimation

NASA Astrophysics Data System (ADS)

Ganguly, S.; Basu, S.; Mukhopadhyay, S.; Michaelis, A.; Milesi, C.; Votava, P.; Nemani, R. R.

2013-12-01

An unresolved issue with coarse-to-medium resolution satellite-based forest carbon mapping over regional to continental scales is the high level of uncertainty in above ground biomass (AGB) estimates caused by the absence of forest cover information at a high enough spatial resolution (current spatial resolution is limited to 30-m). To put confidence in existing satellite-derived AGB density estimates, it is imperative to create continuous fields of tree cover at a sufficiently high resolution (e.g. 1-m) such that large uncertainties in forested area are reduced. The proposed work will provide means to reduce uncertainty in present satellite-derived AGB maps and Forest Inventory and Analysis (FIA) based regional estimates. Our primary objective will be to create Very High Resolution (VHR) estimates of tree cover at a spatial resolution of 1-m for the Continental United States using all available National Agriculture Imaging Program (NAIP) color-infrared imagery from 2010 till 2012. We will leverage the existing capabilities of the NASA Earth Exchange (NEX) high performance computing and storage facilities. The proposed 1-m tree cover map can be further aggregated to provide percent tree cover at any medium-to-coarse resolution spatial grid, which will aid in reducing uncertainties in AGB density estimation at the respective grid and overcome current limitations imposed by medium-to-coarse resolution land cover maps. We have implemented a scalable and computationally-efficient parallelized framework for tree-cover delineation - the core components of the algorithm [that] include a feature extraction process, a Statistical Region Merging image segmentation algorithm and a classification algorithm based on Deep Belief Network and a Feedforward Backpropagation Neural Network algorithm. An initial pilot exercise has been performed over the state of California (~11,000 scenes) to create a wall-to-wall 1-m tree cover map and the classification accuracy has been assessed. Results show an improvement in accuracy of tree-cover delineation as compared to existing forest cover maps from NLCD, especially over fragmented, heterogeneous and urban landscapes. Estimates of VHR tree cover will complement and enhance the accuracy of present remote-sensing based AGB modeling approaches and forest inventory based estimates at both national and local scales. A requisite step will be to characterize the inherent uncertainties in tree cover estimates and propagate them to estimate AGB.

Spatial patterns of land cover in the United States: a technical document supporting the Forest Service 2010 RPA Assessment

Treesearch

Kurt H. Riitters

2011-01-01

Land cover patterns inventoried from a national land cover map provide information about the landscape context and fragmentation of the Nation’s forests, grasslands, and shrublands. This inventory is required to quantify, map, and evaluate the capacities of landscapes to provide ecological goods and services sustainably. This report documents the procedures to...

Stratifying FIA Ground Plots Using A 3-Year Old MRLC Forest Cover Map and Current TM Derived Variables Selected By "Decision Tree" Classification

Treesearch

Michael Hoppus; Stan Arner; Andrew Lister

2001-01-01

A reduction in variance for estimates of forest area and volume in the state of Connecticut was accomplished by stratifying FIA ground plots using raw, transformed and classified Landsat Thematic Mapper (TM) imagery. A US Geological Survey (USGS) Multi-Resolution Landscape Characterization (MRLC) vegetation cover map for Connecticut was used to produce a forest/non-...

Forest cover from Landsat Thematic Mapper data for use in the Catahoula anger District geographic information system.

Treesearch

David L. Evans

1994-01-01

A forest cover classification of the Kisatchie National Forest, Catahoula Ranger district, was performed with Landsat Thematic Mapper data. Data base retrievals and map products from this analysis demonstrated use of Landsat for forest management decisions.

Forest Resource Information System (FRIS)

NASA Technical Reports Server (NTRS)

1983-01-01

The technological and economical feasibility of using multispectral digital image data as acquired from the LANDSAT satellites in an ongoing operational forest information system was evaluated. Computer compatible multispectral scanner data secured from the LANDSAT satellites were demonstrated to be a significant contributor to ongoing information systems by providing the added dimensions of synoptic and repeat coverage of the Earth's surface. Major forest cover types of conifer, deciduous, mixed conifer-deciduous and non-forest, were classified well within the bounds of the statistical accuracy of the ground sample. Further, when overlayed with existing maps, the acreage of cover type retains a high level of positional integrity. Maps were digitized by a graphics design system, overlayed and registered onto LANDSAT imagery such that the map data with associated attributes were displayed on the image. Once classified, the analysis results were converted back to map form as a cover type of information. Existing tabular information as represented by inventory is registered geographically to the map base through a vendor provided data management system. The notion of a geographical reference base (map) providing the framework to which imagery and tabular data bases are registered and where each of the three functions of imagery, maps and inventory can be accessed singly or in combination is the very essence of the forest resource information system design.

Mapping national scale land cover disturbance for the continental United States, 2006 to 2010

NASA Astrophysics Data System (ADS)

Hansen, M. C.; Potapov, P. V.; Egorov, A.; Roy, D. P.; Loveland, T. R.

2011-12-01

Data from the Web-Enabled Landsat Data (WELD) project were used to quantify forest cover loss and bare ground gain dynamics for the continental United States at a 30 meter spatial resolution from 2006 to 2010. Results illustrate the land cover dynamics associated with forestry, urbanization and other medium to long-term cover conversion processes. Ephemeral changes, such as crop rotations and fallows or inundation, were not quantified. Forest disturbance is pervasive at the national-scale, while increasing bare ground is found in growing urban areas as well as in mining regions. The methods applied are an outgrowth of the Vegetation Continuous Field (VCF) method, initially employed with MODIS data and then WELD data to map percent cover variables. As in our previous work with MODIS in mapping forest change, we applied the VCF method to characterize forest cover loss and bare ground gain probability per pixel. Additional themes will be added to provide a more comprehensive picture of national-scale land dynamics based on these initial results using WELD.

Regional forest cover estimation via remote sensing: the calibration center concept

Treesearch

Louis R. Iverson; Elizabeth A. Cook; Robin L. Graham; Robin L. Graham

1994-01-01

A method for combining Landsat Thematic Mapper (TM), Advanced Very High Resolution Radiometer (AVHRR) imagery, and other biogeographic data to estimate forest cover over large regions is applied and evaluated at two locations. In this method, TM data are used to classify a small area (calibration center) into forest/nonforest; the resulting forest cover map is then...

A stem-map model for predicting tree canopy cover of Forest Inventory and Analysis (FIA) plots

Treesearch

Chris Toney; John D. Shaw; Mark D. Nelson

2009-01-01

Tree canopy cover is an important stand characteristic that affects understory light, fuel moisture, decomposition rates, wind speed, and wildlife habitat. Canopy cover also is a component of most definitions of forest land used by US and international agencies. The USDA Forest Service Forest Inventory and Analysis (FIA) Program currently does not provide a national...

Forest Aboveground Biomass Mapping and Canopy Cover Estimation from Simulated ICESat-2 Data

NASA Astrophysics Data System (ADS)

Narine, L.; Popescu, S. C.; Neuenschwander, A. L.

2017-12-01

The assessment of forest aboveground biomass (AGB) can contribute to reducing uncertainties associated with the amount and distribution of terrestrial carbon. With a planned launch date of July 2018, the Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) will provide data which will offer the possibility of mapping AGB at global scales. In this study, we develop approaches for utilizing vegetation data that will be delivered in ICESat-2's land-vegetation along track product (ATL08). The specific objectives are to: (1) simulate ICESat-2 photon-counting lidar (PCL) data using airborne lidar data, (2) utilize simulated PCL data to estimate forest canopy cover and AGB and, (3) upscale AGB predictions to create a wall-to-wall AGB map at 30-m spatial resolution. Using existing airborne lidar data for Sam Houston National Forest (SHNF) located in southeastern Texas and known ICESat-2 beam locations, PCL data are simulated from discrete return lidar points. We use multiple linear regression models to relate simulated PCL metrics for 100 m segments along the ICESat-2 ground tracks to AGB from a biomass map developed using airborne lidar data and canopy cover calculated from the same. Random Forest is then used to create an AGB map from predicted estimates and explanatory data consisting of spectral metrics derived from Landsat TM imagery and land cover data from the National Land Cover Database (NLCD). Findings from this study will demonstrate how data that will be acquired by ICESat-2 can be used to estimate forest structure and characterize the spatial distribution of AGB.

Automatic crown cover mapping to improve forest inventory

Treesearch

Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

2009-01-01

To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

Theoretical Accuracy of Global Snow-Cover Mapping Using Satellite Data in the Earth Observing System (EOS) Era

NASA Technical Reports Server (NTRS)

Hall, D. K.; Foster, J. L.; Salomonson, V. V.; Klein, A. G.; Chien, J. Y. L.

1998-01-01

Following the launch of the Earth Observing System first morning (EOS-AM1) satellite, daily, global snow-cover mapping will be performed automatically at a spatial resolution of 500 m, cloud-cover permitting, using Moderate Resolution Imaging Spectroradiometer (MODIS) data. A technique to calculate theoretical accuracy of the MODIS-derived snow maps is presented. Field studies demonstrate that under cloud-free conditions when snow cover is complete, snow-mapping errors are small (less than 1%) in all land covers studied except forests where errors are greater and more variable. The theoretical accuracy of MODIS snow-cover maps is largely determined by percent forest cover north of the snowline. Using the 17-class International Geosphere-Biosphere Program (IGBP) land-cover maps of North America and Eurasia, the Northern Hemisphere is classified into seven land-cover classes and water. Snow-mapping errors estimated for each of the seven land-cover classes are extrapolated to the entire Northern Hemisphere for areas north of the average continental snowline for each month. Average monthly errors for the Northern Hemisphere are expected to range from 5 - 10%, and the theoretical accuracy of the future global snow-cover maps is 92% or higher. Error estimates will be refined after the first full year that MODIS data are available.

Perspectives of Maine Forest Cover Change from Landsat Imagery and Forest Inventory Analysis (FIA)

Treesearch

Steven Sader; Michael Hoppus; Jacob Metzler; Suming Jin

2005-01-01

A forest change detection map was developed to document forest gains and losses during the decade of the 1990s. The effectiveness of the Landsat imagery and methods for detecting Maine forest cover change are indicated by the good accuracy assessment results: forest-no change, forest loss, and forest gain accuracy were 90, 88, and 92% respectively, and the good...

Difficulties with estimating city-wide urban forest cover change from national, remotely-sensed tree canopy maps

Treesearch

Jeffrey T. Walton

2008-01-01

Two datasets of percent urban tree canopy cover were compared. The first dataset was based on a 1991 AVHRR forest density map. The second was the US Geological Survey's National Land Cover Database (NLCD) 2001 sub-pixel tree canopy. A comparison of these two tree canopy layers was conducted in 36 census designated places of western New York State. Reference data...

Thematic accuracy assessment of the 2011 National Land Cover Database (NLCD)

USGS Publications Warehouse

Wickham, James; Stehman, Stephen V.; Gass, Leila; Dewitz, Jon; Sorenson, Daniel G.; Granneman, Brian J.; Poss, Richard V.; Baer, Lori Anne

2017-01-01

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest loss, forest gain, and urban gain had user's accuracies that exceeded 70%. Lower user's accuracies for the other change reporting themes may be attributable to the difficulty in determining the context of grass (e.g., open urban, grassland, agriculture) and between the components of the forest-shrubland-grassland gradient at either the mapping phase, reference label assignment phase, or both. NLCD 2011 user's accuracies for forest loss, forest gain, and urban gain compare favorably with results from other land cover change accuracy assessments.

An enhanced forest classification scheme for modeling vegetation-climate interactions based on national forest inventory data

NASA Astrophysics Data System (ADS)

Majasalmi, Titta; Eisner, Stephanie; Astrup, Rasmus; Fridman, Jonas; Bright, Ryan M.

2018-01-01

Forest management affects the distribution of tree species and the age class of a forest, shaping its overall structure and functioning and in turn the surface-atmosphere exchanges of mass, energy, and momentum. In order to attribute climate effects to anthropogenic activities like forest management, good accounts of forest structure are necessary. Here, using Fennoscandia as a case study, we make use of Fennoscandic National Forest Inventory (NFI) data to systematically classify forest cover into groups of similar aboveground forest structure. An enhanced forest classification scheme and related lookup table (LUT) of key forest structural attributes (i.e., maximum growing season leaf area index (LAImax), basal-area-weighted mean tree height, tree crown length, and total stem volume) was developed, and the classification was applied for multisource NFI (MS-NFI) maps from Norway, Sweden, and Finland. To provide a complete surface representation, our product was integrated with the European Space Agency Climate Change Initiative Land Cover (ESA CCI LC) map of present day land cover (v.2.0.7). Comparison of the ESA LC and our enhanced LC products (https://doi.org/10.21350/7zZEy5w3) showed that forest extent notably (κ = 0.55, accuracy 0.64) differed between the two products. To demonstrate the potential of our enhanced LC product to improve the description of the maximum growing season LAI (LAImax) of managed forests in Fennoscandia, we compared our LAImax map with reference LAImax maps created using the ESA LC product (and related cross-walking table) and PFT-dependent LAImax values used in three leading land models. Comparison of the LAImax maps showed that our product provides a spatially more realistic description of LAImax in managed Fennoscandian forests compared to reference maps. This study presents an approach to account for the transient nature of forest structural attributes due to human intervention in different land models.

Aerial photo guide to New England forest cover types

Treesearch

Rachel Riemann Hershey; William A. Befort

1995-01-01

NOTE large file size. Presents color infrared photos in stereo pairs for the identification of New England forest cover types. Depicts range maps, ecological relations, and range of composition for each forest cover type described. The guide is designed to assist the needs of interpreters of medium to large-scale color infrared aerial photography.

Classification of very high resolution satellite remote sensing data in a pilot phase of the forest cover classification of the Democratic Republic of Congo, Forêts d'Afrique Central Evaluées par Télédetection (FACET) product

NASA Astrophysics Data System (ADS)

Singa Monga Lowengo, C.

2012-12-01

The Observatoire Satellital des Forêts d'Afrique Centrale (OSFAC) based in Kinshasa, serves as the focal point of the GOFC-GOLD network for Central Africa. OSFAC's long term objective is building regional capacity to use remotely sensed data to map forest cover and forest cover change across Central Africa. OSFAC archives and disseminates satellite data, offers training in geospatial data applications in coordination with the University of Kinshasa, and provides technical support to CARPE partners. Forêts d'Afrique Centrale Évaluées par Télédétection (FACET) is an OSFAC initiative that implements the UMD/SDSU methodology at the national level and quantitatively evaluates the spatiotemporal dynamics of forest cover in Central Africa. The multi-temporal series of FACET data is a useful contribution to many projects, such as biodiversity monitoring, climate modeling, conservation, natural resource management, land use planning, agriculture and REDD+. I am working as Remote Sensing and GIS Officer in various projects of OSFAC. My activities include forest cover and lands dynamics monitoring in Congo Basin. I am familiar with the use of digital mapping software, GIS and RS (Arc GIS, ENVI and PCI Geomatica etc.), classification and spatial Analysis of satellite images, 3D modeling, etc. I started as an intern at OSFAC, Assistant Trainer (Professional Training) and Consultant than permanent employee since October 2009. To assist in the OSFAC activities regarding the monitoring of forest cover and the CARPE program in the context of natural resources management, I participated in the development of the FACET Atlas (Republic of Congo). I received data from Matt Hansen (map.img), WRI and Brazzaville (shapefiles). With all these data I draw maps of the ROC Atlas and statistics of forest cover and forest loss. We organize field work on land to collect data to validate the FACET product. Therefore, to assess forest cover in the region of Kwamouth and Kahuzi-Maiko Biega landscape with very high resolution data and field work for validating FACET product (Remotelly Sensing Product).;

Capability of Integrated MODIS Imagery and ALOS for Oil Palm, Rubber and Forest Areas Mapping in Tropical Forest Regions

PubMed Central

Razali, Sheriza Mohd; Marin, Arnaldo; Nuruddin, Ahmad Ainuddin; Shafri, Helmi Zulhaidi Mohd; Hamid, Hazandy Abdul

2014-01-01

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions. PMID:24811079

Capability of integrated MODIS imagery and ALOS for oil palm, rubber and forest areas mapping in tropical forest regions.

PubMed

Razali, Sheriza Mohd; Marin, Arnaldo; Nuruddin, Ahmad Ainuddin; Shafri, Helmi Zulhaidi Mohd; Hamid, Hazandy Abdul

2014-05-07

Various classification methods have been applied for low resolution of the entire Earth's surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer's Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.

Evaluating the Effectiveness of Flood Control Strategies in Contrasting Urban Watersheds and Implications for Houston's Future Flood Vulnerability

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kumar, U.; Nemani, R. R.; Kalia, S.; Michaelis, A.

2016-12-01

In this work, we use a Fully Constrained Least Squares Subpixel Learning Algorithm to unmix global WELD (Web Enabled Landsat Data) to obtain fractions or abundances of substrate (S), vegetation (V) and dark objects (D) classes. Because of the sheer nature of data and compute needs, we leveraged the NASA Earth Exchange (NEX) high performance computing architecture to optimize and scale our algorithm for large-scale processing. Subsequently, the S-V-D abundance maps were characterized into 4 classes namely, forest, farmland, water and urban areas (with NPP-VIIRS - national polar orbiting partnership visible infrared imaging radiometer suite nighttime lights data) over California, USA using Random Forest classifier. Validation of these land cover maps with NLCD (National Land Cover Database) 2011 products and NAFD (North American Forest Dynamics) static forest cover maps showed that an overall classification accuracy of over 91% was achieved, which is a 6% improvement in unmixing based classification relative to per-pixel based classification. As such, abundance maps continue to offer an useful alternative to high-spatial resolution data derived classification maps for forest inventory analysis, multi-class mapping for eco-climatic models and applications, fast multi-temporal trend analysis and for societal and policy-relevant applications needed at the watershed scale.

View Angle Effects on MODIS Snow Mapping in Forests

NASA Technical Reports Server (NTRS)

Xin, Qinchuan; Woodcock, Curtis E.; Liu, Jicheng; Tan, Bin; Melloh, Rae A.; Davis, Robert E.

2012-01-01

Binary snow maps and fractional snow cover data are provided routinely from MODIS (Moderate Resolution Imaging Spectroradiometer). This paper investigates how the wide observation angles of MODIS influence the current snow mapping algorithm in forested areas. Theoretical modeling results indicate that large view zenith angles (VZA) can lead to underestimation of fractional snow cover (FSC) by reducing the amount of the ground surface that is viewable through forest canopies, and by increasing uncertainties during the gridding of MODIS data. At the end of the MODIS scan line, the total modeled error can be as much as 50% for FSC. Empirical analysis of MODIS/Terra snow products in four forest sites shows high fluctuation in FSC estimates on consecutive days. In addition, the normalized difference snow index (NDSI) values, which are the primary input to the MODIS snow mapping algorithms, decrease as VZA increases at the site level. At the pixel level, NDSI values have higher variances, and are correlated with the normalized difference vegetation index (NDVI) in snow covered forests. These findings are consistent with our modeled results, and imply that consideration of view angle effects could improve MODIS snow monitoring in forested areas.

Mapping forest types in Worcester County, Maryland, using LANDSAT data

NASA Technical Reports Server (NTRS)

Burtis, J., Jr.; Witt, R. G.

1981-01-01

The feasibility of mapping Level 2 forest cover types for a county-sized area on Maryland's Eastern Shore was demonstrated. A Level 1 land use/land cover classification was carried out for all of Worcester County as well. A June 1978 LANDSAT scene was utilized in a classification which employed two software packages on different computers (IDIMS on an HP 3000 and ASTEP-II on a Univac 1108). A twelve category classification scheme was devised for the study area. Resulting products include black and white line printer maps, final color coded classification maps, digitally enhanced color imagery and tabulated acreage statistics for all land use and land cover types.

Mapping forest canopy disturbance in the Upper Great Lakes, USA

Treesearch

James D. Garner; Mark D. Nelson; Brian G. Tavernia; Charles H. (Hobie) Perry; Ian W. Housman

2015-01-01

A map of forest canopy disturbance was generated for Michigan, Wisconsin, and most of Minnesota using 42 Landsat time series stacks (LTSS) and a vegetation change tracker (VCTw) algorithm. Corresponding winter imagery was used to reduce commission errors of forest disturbance by identifying areas of persistent snow cover. The resulting disturbance age map was classed...

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.

Assessment and Mapping of Forest Parcel Sizes

Treesearch

Brett J. Butler; Susan L. King

2005-01-01

A method for analyzing and mapping forest parcel sizes in the Northeastern United States is presented. A decision tree model was created that predicts forest parcel size from spatially explicit predictor variables: population density, State, percentage forest land cover, and road density. The model correctly predicted parcel size for 60 percent of the observations in a...

Forest cover type analysis of New England forests using innovative WorldView-2 imagery

NASA Astrophysics Data System (ADS)

Kovacs, Jenna M.

For many years, remote sensing has been used to generate land cover type maps to create a visual representation of what is occurring on the ground. One significant use of remote sensing is the identification of forest cover types. New England forests are notorious for their especially complex forest structure and as a result have been, and continue to be, a challenge when classifying forest cover types. To most accurately depict forest cover types occurring on the ground, it is essential to utilize image data that have a suitable combination of both spectral and spatial resolution. The WorldView-2 (WV2) commercial satellite, launched in 2009, is the first of its kind, having both high spectral and spatial resolutions. WV2 records eight bands of multispectral imagery, four more than the usual high spatial resolution sensors, and has a pixel size of 1.85 meters at the nadir. These additional bands have the potential to improve classification detail and classification accuracy of forest cover type maps. For this reason, WV2 imagery was utilized on its own, and in combination with Landsat 5 TM (LS5) multispectral imagery, to evaluate whether these image data could more accurately classify forest cover types. In keeping with recent developments in image analysis, an Object-Based Image Analysis (OBIA) approach was used to segment images of Pawtuckaway State Park and nearby private lands, an area representative of the typical complex forest structure found in the New England region. A Classification and Regression Tree (CART) analysis was then used to classify image segments at two levels of classification detail. Accuracies for each forest cover type map produced were generated using traditional and area-based error matrices, and additional standard accuracy measures (i.e., KAPPA) were generated. The results from this study show that there is value in analyzing imagery with both high spectral and spatial resolutions, and that WV2's new and innovative bands can be useful for the classification of complex forest structures.

Predicting relative species composition within mixed conifer forest pixels using zero‐inflated models and Landsat imagery

Treesearch

Shannon L. Savage; Rick L. Lawrence; John R. Squires

2015-01-01

Ecological and land management applications would often benefit from maps of relative canopy cover of each species present within a pixel, instead of traditional remote-sensing based maps of either dominant species or percent canopy cover without regard to species composition. Widely used statistical models for remote sensing, such as randomForest (RF),...

Completing the Picture: Importance of Considering Participatory Mapping for REDD+ Measurement, Reporting and Verification (MRV)

PubMed Central

Rafanoharana, Serge; Boissière, Manuel; Wijaya, Arief; Wardhana, Wahyu

2016-01-01

Remote sensing has been widely used for mapping land cover and is considered key to monitoring changes in forest areas in the REDD+ Measurement, Reporting and Verification (MRV) system. But Remote Sensing as a desk study cannot capture the whole picture; it also requires ground checking. Therefore, complementing remote sensing analysis using participatory mapping can help provide information for an initial forest cover assessment, gain better understanding of how local land use might affect changes, and provide a way to engage local communities in REDD+. Our study looked at the potential of participatory mapping in providing complementary information for remotely sensed maps. The research sites were located in different ecological and socio-economic contexts in the provinces of Papua, West Kalimantan and Central Java, Indonesia. Twenty-one maps of land cover and land use were drawn with local community participation during focus group discussions in seven villages. These maps, covering a total of 270,000ha, were used to add information to maps developed using remote sensing, adding 39 land covers to the eight from our initial desk assessment. They also provided additional information on drivers of land use and land cover change, resource areas, territory claims and land status, which we were able to correlate to understand changes in forest cover. Incorporating participatory mapping in the REDD+ MRV protocol would help with initial remotely sensed land classifications, stratify an area for ground checks and measurement plots, and add other valuable social data not visible at the RS scale. Ultimately, it would provide a forum for local communities to discuss REDD+ activities and develop a better understanding of REDD+. PMID:27977685

Comparing Forest/Nonforest Classifications of Landsat TM Imagery for Stratifying FIA Estimates of Forest Land Area

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Greg C. Liknes; Geoffrey R. Holden

2005-01-01

Landsat Thematic Mapper (TM) satellite imagery and Forest Inventory and Analysis (FIA) plot data were used to construct forest/nonforest maps of Mapping Zone 41, National Land Cover Dataset 2000 (NLCD 2000). Stratification approaches resulting from Maximum Likelihood, Fuzzy Convolution, Logistic Regression, and k-Nearest Neighbors classification/prediction methods were...

 A global evaluation of forest interior area dynamics using tree cover data from 2000 to 2012

Treesearch

Kurt Riitters; James Wickham; Jennifer K. Costanza; Peter Vogt

2016-01-01

Context Published maps of global tree cover derived from Landsat data have indicated substantial changes in forest area from 2000 to 2012. The changes can be arranged in different patterns, with different consequences for forest fragmentation. Thus, the changes in forest area do not necessarily equate to changes in...

Evaluating ecoregions for sampling and mapping land-cover patterns

Treesearch

Kurt H. Riitters; James D. Wickham; Timothy G. Wade

2006-01-01

Ecoregional stratification has been proposed for sampling and mapping land-cover composition and pattern over time. Using a wall-to-wall land-cover map of the United States, we evaluated geographic scales of variance for nine landscapelevel and eight forest pattern indices, and compared stratification by ecoregions, administrative units, and watersheds. Ecoregions...

Spatial and Temporal Analysis of Industrial Forest Clearcuts in the Conterminous United States

NASA Astrophysics Data System (ADS)

Huo, L. Z.; Boschetti, L.

2015-12-01

Remote sensing has been widely used for mapping and characterizing changes in forest cover, but the available remote sensing forest change products are not discriminating between deforestation (permanent transition from forest to non forest) and industrial forest management (logging followed by regrowth, with no land cover/ land use class change) (Hansen et al, 2010). Current estimates of carbon-equivalent emissions report the contribution of deforestation as 12% of total anthropogenic carbon emissions (van der Werf et al., 2009), but accurate monitoring of forest carbon balance should discriminate between land use change related to forest natural disturbances, and forest management. The total change in forest cover (Gross Forest Cover Loss, GFLC) needs to be characterized based on the cause (natural/human) and on the outcome of the change (regeneration to forest/transition to non/forest)(Kurtz et al, 2010). This paper presents the methodology used to classify the forest loss detected by the University of Maryland Global Forest Change product (Hansen, 2013) into deforestation, disturbances (fires, insect outbreaks) and industrial forest clearcuts. The industrial forest clearcuts were subsequently analysed by converting the pixel based detections into objects, and applying patch level metrics (e.g. size, compactness, straightness of boundaries) and contextual measures. The analysis is stratified by region and by dominant forest specie, to highlight changes in the rate of forest resource utilization in the 2003-2013 period covered by the Maryland Forest Cover Change Product. References Hansen, M.C., Stehman, S.V., & Potapov, P.V. (2010). Reply to Wernick et al.: Global scale quantification of forest change. Proceedings of the National Academy of Sciences, 107, E148-E148 Hansen, M.C., Potapov, P.V., Moore, R et al., (2013), "High resolution Global Maps for the 21stCentury Forest Cover Change", Science 342: 850-853 Kurz, W.A. (2010). An ecosystem context for global gross forest cover loss estimates. Proceedings of the National Academy of Sciences, 107, 9025-9026 van der Werf, G.R., Morton, D.C., DeFries, R.S., Olivier, J.G., Kasibhatla, P.S., Jackson, R.B., Collatz, G.J., & Randerson, J. (2009). CO2 emissions from forest loss. Nature Geoscience, 2, 737-738

The effectiveness of texture analysis for mapping forest land using the panchromatic bands of Landsat 7, SPOT, and IRS imagery

Treesearch

Michael L. Hoppus; Rachel I. Riemann; Andrew J. Lister; Mark V. Finco

2002-01-01

The panchromatic bands of Landsat 7, SPOT, and IRS satellite imagery provide an opportunity to evaluate the effectiveness of texture analysis of satellite imagery for mapping of land use/cover, especially forest cover. A variety of texture algorithms, including standard deviation, Ryherd-Woodcock minimum variance adaptive window, low pass etc., were applied to moving...

EnviroAtlas - Austin, TX - Tree Cover Configuration and Connectivity, Water Background Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas). The EnviroAtlas Austin, TX tree cover configuration and connectivity map categorizes forest land cover into structural elements (e.g. core, edge, connector, etc.). In this community, Forest is defined as Trees & Forest (Trees & Forest - 40 = 1; All Else = 0). Water was considered background (value 129) during the analysis to create this dataset, however it has been converted into value 10 to distinguish it from land area background. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

A simple algorithm for large-scale mapping of evergreen forests in tropical America, Africa and Asia

Treesearch

Xiangming Xiao; Chandrashekhar M. Biradar; Christina Czarnecki; Tunrayo Alabi; Michael Keller

2009-01-01

The areal extent and spatial distribution of evergreen forests in the tropical zones are important for the study of climate, carbon cycle and biodiversity. However, frequent cloud cover in the tropical regions makes mapping evergreen forests a challenging task. In this study we developed a simple and novel mapping algorithm that is based on the temporal profile...

European Forest Cover During the Past 12,000 Years: A Palynological Reconstruction Based on Modern Analogs and Remote Sensing

PubMed Central

Zanon, Marco; Davis, Basil A. S.; Marquer, Laurent; Brewer, Simon; Kaplan, Jed O.

2018-01-01

Characterization of land cover change in the past is fundamental to understand the evolution and present state of the Earth system, the amount of carbon and nutrient stocks in terrestrial ecosystems, and the role played by land-atmosphere interactions in influencing climate. The estimation of land cover changes using palynology is a mature field, as thousands of sites in Europe have been investigated over the last century. Nonetheless, a quantitative land cover reconstruction at a continental scale has been largely missing. Here, we present a series of maps detailing the evolution of European forest cover during last 12,000 years. Our reconstructions are based on the Modern Analog Technique (MAT): a calibration dataset is built by coupling modern pollen samples with the corresponding satellite-based forest-cover data. Fossil reconstructions are then performed by assigning to every fossil sample the average forest cover of its closest modern analogs. The occurrence of fossil pollen assemblages with no counterparts in modern vegetation represents a known limit of analog-based methods. To lessen the influence of no-analog situations, pollen taxa were converted into plant functional types prior to running the MAT algorithm. We then interpolate site-specific reconstructions for each timeslice using a four-dimensional gridding procedure to create continuous gridded maps at a continental scale. The performance of the MAT is compared against methodologically independent forest-cover reconstructions produced using the REVEALS method. MAT and REVEALS estimates are most of the time in good agreement at a trend level, yet MAT regularly underestimates the occurrence of densely forested situations, requiring the application of a bias correction procedure. The calibrated MAT-based maps draw a coherent picture of the establishment of forests in Europe in the Early Holocene with the greatest forest-cover fractions reconstructed between ∼8,500 and 6,000 calibrated years BP. This forest maximum is followed by a general decline in all parts of the continent, likely as a result of anthropogenic deforestation. The continuous spatial and temporal nature of our reconstruction, its continental coverage, and gridded format make it suitable for climate, hydrological, and biogeochemical modeling, among other uses. PMID:29568303

European Forest Cover During the Past 12,000 Years: A Palynological Reconstruction Based on Modern Analogs and Remote Sensing.

PubMed

Zanon, Marco; Davis, Basil A S; Marquer, Laurent; Brewer, Simon; Kaplan, Jed O

2018-01-01

Characterization of land cover change in the past is fundamental to understand the evolution and present state of the Earth system, the amount of carbon and nutrient stocks in terrestrial ecosystems, and the role played by land-atmosphere interactions in influencing climate. The estimation of land cover changes using palynology is a mature field, as thousands of sites in Europe have been investigated over the last century. Nonetheless, a quantitative land cover reconstruction at a continental scale has been largely missing. Here, we present a series of maps detailing the evolution of European forest cover during last 12,000 years. Our reconstructions are based on the Modern Analog Technique (MAT): a calibration dataset is built by coupling modern pollen samples with the corresponding satellite-based forest-cover data. Fossil reconstructions are then performed by assigning to every fossil sample the average forest cover of its closest modern analogs. The occurrence of fossil pollen assemblages with no counterparts in modern vegetation represents a known limit of analog-based methods. To lessen the influence of no-analog situations, pollen taxa were converted into plant functional types prior to running the MAT algorithm. We then interpolate site-specific reconstructions for each timeslice using a four-dimensional gridding procedure to create continuous gridded maps at a continental scale. The performance of the MAT is compared against methodologically independent forest-cover reconstructions produced using the REVEALS method. MAT and REVEALS estimates are most of the time in good agreement at a trend level, yet MAT regularly underestimates the occurrence of densely forested situations, requiring the application of a bias correction procedure. The calibrated MAT-based maps draw a coherent picture of the establishment of forests in Europe in the Early Holocene with the greatest forest-cover fractions reconstructed between ∼8,500 and 6,000 calibrated years BP. This forest maximum is followed by a general decline in all parts of the continent, likely as a result of anthropogenic deforestation. The continuous spatial and temporal nature of our reconstruction, its continental coverage, and gridded format make it suitable for climate, hydrological, and biogeochemical modeling, among other uses.

Object-based Mapping of the Circumpolar Taiga-Tundra Ecotone with MODIS Tree Cover

NASA Technical Reports Server (NTRS)

Ransom, Kenneth J.; Montesano, Paul M.; Nelson, Ross F.

2011-01-01

The circumpolar taiga-tundra ecotone was delineated using an image segmentation based mapping approach with multi-annual MODIS Vegetation Continuous Fields (VCF) tree cover data. Circumpolar tree canopy cover (TCC) throughout the ecotone was derived by averaging MODIS VCF data from 2000 - 2005 and adjusting the averaged values using linear equations relating MODIS TCC to Quickbird-derived tree cover estimates. The adjustment helped mitigate VCF's overestimation of tree cover in lightly forested regions. An image segmentation grouped pixels representing similar tree cover into polygonal features (objects) that form the map of the transition zone. Eachfeature represents an area much larger than the 500m MODIS pixel to characterize thepatterns of sparse forest patches on a regional scale. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1deg longitudinal interval in North America and Eurasia and (2) Landsat-derived Canadianproportion of forest cover for Canada. The adjusted TCC from MODIS VCF shows, on average, greater than 12% TCC for all but one regional zone at the intersection with independently delineated tree lines. Adjusted values track closely with Canadian proportion of forest cover data in areas of low tree cover. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values between 5-20% , or (2) mean adjusted TCC values <5% but with a standard deviation > 5% were used to identify the ecotone.

Annual Dynamics of Forest Areas in South America during 2007-2010 at 50-m Spatial Resolution

NASA Astrophysics Data System (ADS)

Qin, Y.; Xiao, X.; Dong, J.; Zhou, Y.; Wang, J.; Doughty, R.; Chen, Y.; Zou, Z.; Moore, B., III

2017-12-01

The user community has an urgent need for high accuracy tropical forest distribution and spatio-temporal changes since tropical forests are facing defragmentation and persistent clouds. In this study, we selected South America as a hotspot and presented a robust approach to map annual forests during 2007-2010 based on the coupled greenness-relevant MOD13Q1 NDVI and structure/biomass-relevant ALOS PALSAR time series data. We analyzed the consistency and uncertainty among eight major forest maps at continental, country, and pixel scales. The 50-m PALSAR/MODIS forest area in South America was about 8.63×106 km2 in 2010. Large differences in total forest area (8.2×106 km2-12.7×106 km2) existed among these forest products. Forest products generated under a similar forest definition had similar or even larger variation than those generated under differing forest definitions. One needs to consider leaf area index as an adjusting factor and use much higher threshold values in the VCF datasets to estimate forest cover. Analyses of PALSAR/MODIS forest maps showed a relatively small and equivalent rate of loss (3.2×104 km2 year-1) in net forest cover to that of FAO FRA (3.3×104 km2 year-1). PALSAR/MODIS forest maps showed that more and more deforestation occurred in the intact forest areas. The rate of forest loss (1.95×105 km2 year-1) was higher than that of Global Forest Watch (0.81×105 km2 year-1). Caution should be used when using the different forest maps to analyze forest loss and make policies regarding forest ecosystem services and biodiversity conservation.

Fusion of optical and SAR remote sensing images for tropical forests monitoring

NASA Astrophysics Data System (ADS)

Wang, C.; Yu, M.; Gao, Q.; Wang, X.

2016-12-01

Although tropical deforestation prevails in South America and Southeast Asia, reforestation appeared in some tropical regions due to economic changes. After the economic shift from agriculture to industry, the tropical island of Puerto Rico has experienced rapid reforestation as well as urban expansion since the late 1940s. Continued urban growth without the guide of sustainable planning might prevent further forest regrowth. Accurate and timely mapping of LULC is of great importance for evaluating the consequences of reforestation and urban expansion on the coupled human and nature systems. However, owning to persistent cloud cover in tropics, it remains a challenge to produce reliable LULC maps in fine spatial resolution. Here, we retrieved cloud-free Landsat surface reflectance composite data by removing clouds and shades from the USGS Landsat Surface Reflectance (SR) product for each scene using the CFmask and Fmask algorithms in Google Earth Engine. We then produced high accuracy land cover classification maps using SR optical data for the year of 2000 and fused optical and ALOS SAR data for 2010 and 2015, with an overall accuracy of 92.0%, 92.5%, and 91.6%, respectively. The classification result indicated that a successive forest gain of 6.52% and 1.03% occurred between the first (2000-2010) and second (2010-2015) study periods, respectively. We also conducted a comparative spatial analysis of patterns of deforestation and reforestation based on a series of forest cover zones (50 × 50 pixels, 150 ha). The annual rates of deforestation and reforestation against forest cover presented the similar trends during two periods: decreasing with the forest cover increasing. However, the annual net forest change rate was different in the zones with forest cover less than 30%, presenting significant gain (2.2-8.4% yr-1) for the first period and significant loss (2.3-6.4% yr-1) for the second period. It indicated that both deforestation and reforestation mostly occurred near the forest edges and low density secondary forests.

Scan angle calculation and image compositing for the Mexico forest mapping project

Treesearch

Zhiliang Zhu

1994-01-01

Data from the Advanced Very High Resolution Radiometer (AVHRR) were used in a cooperative project, sponsored by the U.S. Department of Agriculture, Forest Service, Southern Forest Experiment Station, and the United Nations, Food and Agriculture Organization (FAO), to map Mexicos forest cover types.To provide satisfactory AVHRR data sets for the project, the sensor scan...

Spatio-temporal change in forest cover and carbon storage considering actual and potential forest cover in South Korea.

PubMed

Nam, Kijun; Lee, Woo-Kyun; Kim, Moonil; Kwak, Doo-Ahn; Byun, Woo-Hyuk; Yu, Hangnan; Kwak, Hanbin; Kwon, Taesung; Sung, Joohan; Chung, Dong-Jun; Lee, Seung-Ho

2015-07-01

This study analyzes change in carbon storage by applying forest growth models and final cutting age to actual and potential forest cover for six major tree species in South Korea. Using National Forest Inventory data, the growth models were developed to estimate mean diameter at breast height, tree height, and number of trees for Pinus densiflora, Pinus koraiensis, Pinus rigida, Larix kaempferi, Castanea crenata and Quercus spp. stands. We assumed that actual forest cover in a forest type map will change into potential forest covers according to the Hydrological and Thermal Analogy Groups model. When actual forest cover reaches the final cutting age, forest volume and carbon storage are estimated by changed forest cover and its growth model. Forest volume between 2010 and 2110 would increase from 126.73 to 157.33 m(3) hm(-2). Our results also show that forest cover, volume, and carbon storage could abruptly change by 2060. This is attributed to the fact that most forests are presumed to reach final cutting age. To avoid such dramatic change, a regeneration and yield control scheme should be prepared and implemented in a way that ensures balance in forest practice and yield.

Applications of national land cover maps in United States forestry

Treesearch

Kurt H. Riitters; Gregory A. Reams

2008-01-01

Land cover maps derived from satellite imagery have a long and varied history of uses in United States forestry science and management. This article reviews recent developments concerning the use of national- to continental-scale land cover maps for inventory, monitoring, and resource assessment in the U.S. Forest Service. The use of mid-scale digital resolution...

Mapping deforestation and urban expansion in Freetown, Sierra Leone, from pre- to post-war economic recovery.

PubMed

Mansaray, Lamin R; Huang, Jingfeng; Kamara, Alimamy A

2016-08-01

Freetown, the capital of Sierra Leone has experienced vast land-cover changes over the past three decades. In Sierra Leone, however, availability of updated land-cover data is still a problem even for environmental managers. This study was therefore, conducted to provide up-to-date land-cover data for Freetown. Multi-temporal Landsat data at 1986, 2001, and 2015 were obtained, and a maximum likelihood supervised classification was employed. Eight land-cover classes or categories were recognized as follows: water, wetland, built-up, dense forest, sparse forest, grassland, barren, and mangrove. Land-cover changes were mapped via post-classification change detection. The persistence, gain, and loss of each land-cover class, and selected land conversions were also quantified. An overall classification accuracy of 87.3 % and a Kappa statistic of 0.85 were obtained for the 2015 map. From 1986 to 2015, water, built-up, grassland, and barren had net gains, whereas forests, wetlands, and mangrove had net loses. Conversion analyses among forests, grassland, and built-up show that built-up had targeted grassland and avoided forests. This study also revealed that, the overall land-cover change at 2001-2015 was higher (28.5 %) than that recorded at 1986-2001 (20.9 %). This is attributable to the population increase in Freetown and the high economic growth and infrastructural development recorded countrywide after the civil war. In view of the rapid land-cover change and its associated environmental impacts, this study recommends the enactment of policies that would strike a balance between urbanization and environmental sustainability in Freetown.

Improving the MODIS Global Snow-Mapping Algorithm

NASA Technical Reports Server (NTRS)

Klein, Andrew G.; Hall, Dorothy K.; Riggs, George A.

1997-01-01

An algorithm (Snowmap) is under development to produce global snow maps at 500 meter resolution on a daily basis using data from the NASA MODIS instrument. MODIS, the Moderate Resolution Imaging Spectroradiometer, will be launched as part of the first Earth Observing System (EOS) platform in 1998. Snowmap is a fully automated, computationally frugal algorithm that will be ready to implement at launch. Forests represent a major limitation to the global mapping of snow cover as a forest canopy both obscures and shadows the snow underneath. Landsat Thematic Mapper (TM) and MODIS Airborne Simulator (MAS) data are used to investigate the changes in reflectance that occur as a forest stand becomes snow covered and to propose changes to the Snowmap algorithm that will improve snow classification accuracy forested areas.

Impacts of Myanmar's Democratic Transition on its Land Cover Dynamics.

NASA Astrophysics Data System (ADS)

Biswas, S.

2016-12-01

Recently Myanmar transitioned from a closed economy, military government to market based economy and democracy. The impacts of the political and economic transition on its land cover can be described by characterizing the land cover dynamics during the transition period. Preliminary stratified sampling of forest conversions revealed that most changes from forest to non-forest are due to establishment of rubber plantations. Agricultural concessions are granted by the government to develop the agriculture sector and rubber is the most common plantation crop in Southern Myanmar. This study establishes a method to map and quantify the extent and age of rubber plantations in Thaton district of Myanmar using satellite remote sensing, GIS and ground data. The resultant rubber maps can be used to inform policy on land use planning, agriculture, forest and sustainable development.

Rapid mapping of hurricane damage to forests

Treesearch

Erik M. Nielsen

2009-01-01

The prospects for producing rapid, accurate delineations of the spatial extent of forest wind damage were evaluated using Hurricane Katrina as a test case. A damage map covering the full spatial extent of Katrina?s impact was produced from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery using higher resolution training data. Forest damage...

An Effort to Map and Monitor Baldcypress Forest Areas in Coastal Louisiana, Using Landsat, MODIS, and ASTER Satellite Data

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Sader, Steve; Smoot, James

2012-01-01

This presentation discusses a collaborative project to develop, test, and demonstrate baldcypress forest mapping and monitoring products for aiding forest conservation and restoration in coastal Louisiana. Low lying coastal forests in the region are being negatively impacted by multiple factors, including subsidence, salt water intrusion, sea level rise, persistent flooding, hydrologic modification, annual insect-induced forest defoliation, timber harvesting, and conversion to urban land uses. Coastal baldcypress forests provide invaluable ecological services in terms of wildlife habitat, forest products, storm buffers, and water quality benefits. Before this project, current maps of baldcypress forest concentrations and change did not exist or were out of date. In response, this project was initiated to produce: 1) current maps showing the extent and location of baldcypress dominated forests; and 2) wetland forest change maps showing temporary and persistent disturbance and loss since the early 1970s. Project products are being developed collaboratively with multiple state and federal agencies. Products are being validated using available reference data from aerial, satellite, and field survey data. Results include Landsat TM- based classifications of baldcypress in terms of cover type and percent canopy cover. Landsat MSS data was employed to compute a circa 1972 classification of swamp and bottomland hardwood forest types. Landsat data for 1972-2010 was used to compute wetland forest change products. MODIS-based change products were applied to view and assess insect-induced swamp forest defoliation. MODIS, Landsat, and ASTER satellite data products were used to help assess hurricane and flood impacts to coastal wetland forests in the region.

Forest cover of Champaign County, Illinois in 1993

Treesearch

Jesus Danilo Chinea; Louis R. Iverson

1997-01-01

The forest cover of Champaign County, in east-central Illinois, was mapped from 1993 aerial photography and entered in a geographical information system database. One hundred and six forest patches cover 3,380 ha. These patches have a mean area of 32 ha, a mean perimeter of 4,851 m, a mean perimeter to area ratio of 237, a fractal dimension of 1.59, and a mean nearest...

The Impact of Charcoal Production on Forest Degradation: a Case Study in Tete, Mozambique

NASA Technical Reports Server (NTRS)

Sedano, F.; Silva. J. A.; Machoco, R.; Meque, C. H.; Sitoe, A.; Ribeiro, N.; Anderson, K.; Ombe, Z. A.; Baule, S. H.; Tucker, C. J.

2016-01-01

Charcoal production for urban energy consumption is a main driver of forest degradation in sub-Saharan Africa. Urban growth projections for the continent suggest that the relevance of this process will increase in the coming decades. Forest degradation associated to charcoal production is difficult to monitor and commonly overlooked and underrepresented in forest cover change and carbon emission estimates. We use a multi-temporal dataset of very high-resolution remote sensing images to map kiln locations in a representative study area of tropical woodlands in central Mozambique. The resulting maps provided a characterization of the spatial extent and temporal dynamics of charcoal production. Using an indirect approach we combine kiln maps and field information on charcoal making to describe the magnitude and intensity of forest degradation linked to charcoal production, including aboveground biomass and carbon emissions. Our findings reveal that forest degradation associated to charcoal production in the study area is largely independent from deforestation driven by agricultural expansion and that its impact on forest cover change is in the same order of magnitude as deforestation. Our work illustrates the feasibility of using estimates of urban charcoal consumption to establish a link between urban energy demands and forest degradation. This kind of approach has potential to reduce uncertainties in forest cover change and carbon emission assessments in sub-Saharan Africa.

An accuracy assessment of forest disturbance mapping in the western Great Lakes

Treesearch

P.L. Zimmerman; I.W. Housman; C.H. Perry; R.A. Chastain; J.B. Webb; M.V. Finco

2013-01-01

The increasing availability of satellite imagery has spurred the production of thematic land cover maps based on satellite data. These maps are more valuable to the scientific community and land managers when the accuracy of their classifications has been assessed. Here, we assessed the accuracy of a map of forest disturbance in the watersheds of Lake Superior and Lake...

Comparison of U.S. Forest Land AreaEstimates From Forest Inventory and Analysis, National Resources Inventory, and Four Satellite Image-Derived Land Cover Data Sets

Treesearch

Mark D. Nelson; Ronald E. McRoberts; Veronica C. Lessard

2005-01-01

Our objective was to test one application of remote sensing technology for complementing forest resource assessments by comparing a variety of existing satellite image-derived land cover maps with national inventory-derived estimates of United States forest land area. National Resources Inventory (NRI) 1997 estimates of non-Federal forest land area differed by 7.5...

Northern Forest Ecosystem Dynamics Using Coupled Models and Remote Sensing

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Knox, R. G.; Levine, E. R.; Weishampel, J. F.; Fifer, S. T.

1999-01-01

Forest ecosystem dynamics modeling, remote sensing data analysis, and a geographical information system (GIS) were used together to determine the possible growth and development of a northern forest in Maine, USA. Field measurements and airborne synthetic aperture radar (SAR) data were used to produce maps of forest cover type and above ground biomass. These forest attribute maps, along with a conventional soils map, were used to identify the initial conditions for forest ecosystem model simulations. Using this information along with ecosystem model results enabled the development of predictive maps of forest development. The results obtained were consistent with observed forest conditions and expected successional trajectories. The study demonstrated that ecosystem models might be used in a spatial context when parameterized and used with georeferenced data sets.

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)

Mapping Secondary Forest Succession on Abandoned Agricultural Land in the Polish Carpathians

NASA Astrophysics Data System (ADS)

Kolecka, N.; Kozak, J.; Kaim, D.; Dobosz, M.; Ginzler, Ch.; Psomas, A.

2016-06-01

Land abandonment and secondary forest succession have played a significant role in land cover changes and forest cover increase in mountain areas in Europe over the past several decades. Land abandonment can be easily observed in the field over small areas, but it is difficult to map over the large areas, e.g., with remote sensing, due to its subtle and spatially dispersed character. Our previous paper presented how the LiDAR (Light Detection and Ranging) and topographic data were used to detect secondary forest succession on abandoned land in one commune located in the Polish Carpathians by means of object-based image analysis (OBIA) and GIS (Kolecka et al., 2015). This paper proposes how the method can be applied to efficiently map secondary forest succession over the entire Polish Carpathians, incorporating spatial sampling strategy supported by various ancillary data. Here we discuss the methods of spatial sampling, its limitations and results in the context of future secondary forest succession modelling.

Snow-covered Landsat time series stacks improve automated disturbance mapping accuracy in forested landscapes

Treesearch

Kirk M. Stueve; Ian W. Housman; Patrick L. Zimmerman; Mark D. Nelson; Jeremy B. Webb; Charles H. Perry; Robert A. Chastain; Dale D. Gormanson; Chengquan Huang; Sean P. Healey; Warren B. Cohen

2011-01-01

Accurate landscape-scale maps of forests and associated disturbances are critical to augment studies on biodiversity, ecosystem services, and the carbon cycle, especially in terms of understanding how the spatial and temporal complexities of damage sustained from disturbances influence forest structure and function. Vegetation change tracker (VCT) is a highly automated...

Evaluation and prediction of shrub cover in coastal Oregon forests (USA)

Treesearch

Becky K. Kerns; Janet L. Ohmann

2004-01-01

We used data from regional forest inventories and research programs, coupled with mapped climatic and topographic information, to explore relationships and develop multiple linear regression (MLR) and regression tree models for total and deciduous shrub cover in the Oregon coastal province. Results from both types of models indicate that forest structure variables were...

Development and applications of the LANDFIRE forest structure layers

Treesearch

Chris Toney; Birgit Peterson; Don Long; Russ Parsons; Greg Cohn

2012-01-01

The LANDFIRE program is developing 2010 maps of vegetation and wildland fuel attributes for the United States at 30-meter resolution. Currently available vegetation layers include ca. 2001 and 2008 forest canopy cover and canopy height derived from Landsat and Forest Inventory and Analysis (FIA) plot measurements. The LANDFIRE canopy cover layer for the conterminous...

Sample project: establishing a global forest monitoring capability using multi-resolution and multi-temporal remotely sensed data sets

USGS Publications Warehouse

Hansen, Matt; Stehman, Steve; Loveland, Tom; Vogelmann, Jim; Cochrane, Mark

2009-01-01

Quantifying rates of forest-cover change is important for improved carbon accounting and climate change modeling, management of forestry and agricultural resources, and biodiversity monitoring. A practical solution to examining trends in forest cover change at global scale is to employ remotely sensed data. Satellite-based monitoring of forest cover can be implemented consistently across large regions at annual and inter-annual intervals. This research extends previous research on global forest-cover dynamics and land-cover change estimation to establish a robust, operational forest monitoring and assessment system. The approach integrates both MODIS and Landsat data to provide timely biome-scale forest change estimation. This is achieved by using annual MODIS change indicator maps to stratify biomes into low, medium and high change categories. Landsat image pairs can then be sampled within these strata and analyzed for estimating area of forest cleared.

Mapping Brazilian Cropland Expansion, 2000-2013

NASA Astrophysics Data System (ADS)

Zalles, V.; Hansen, M.; Potapov, P.

2016-12-01

Brazil is one of the world's leading producers and exporters of agricultural goods. Despite undergoing significant increases in its cropland area in the last decades, it remains one of the countries with the most potential for further agricultural expansion. Most notably, the expansion in production areas of commodity crops such as soybean, corn, and sugarcane has become the leading cause of land cover conversion in Brazil. Natural land covers, such as the Amazon and Cerrado forests, have been negatively affected by this agricultural expansion, causing carbon emissions, biodiversity loss, altered water cycles, and many other disturbances to ecosystem services. Monitoring of change in cropland area extent can provide relevant information to decision makers seeking to understand and manage land cover change drivers and their impacts. In this study, the freely-available Landsat archive was leveraged to produce a large-scale, methodologically consistent map of cropland cover at 30 m. resolution for the entire Brazilian territory in the year 2000. Additionally, we mapped cropland expansion from 2000 to 2013, and used statistical sampling techniques to accurately estimate cropland area per Brazilian state. Using the Global Forest Change product produced by Hansen et al. (2013), we can disaggregate forest cover loss due to cropland expansion by year, revealing spatiotemporal trends that could advance our understanding of the drivers of forest loss.

Using satellite image-based maps and ground inventory data to estimate the area of the remaining Atlantic forest in the Brazilian state of Santa Catarina

Treesearch

Alexander C. Vibrans; Ronald E. McRoberts; Paolo Moser; Adilson L. Nicoletti

2013-01-01

Estimation of large area forest attributes, such as area of forest cover, from remote sensing-based maps is challenging because of image processing, logistical, and data acquisition constraints. In addition, techniques for estimating and compensating for misclassification and estimating uncertainty are often unfamiliar. Forest area for the state of Santa Catarina in...

Quantifying scaling effects on satellite-derived forest area estimates for the conterminous USA

Treesearch

Daolan Zheng; L.S. Heath; M.J. Ducey; J.E. Smith

2009-01-01

We quantified the scaling effects on forest area estimates for the conterminous USA using regression analysis and the National Land Cover Dataset 30m satellite-derived maps in 2001 and 1992. The original data were aggregated to: (1) broad cover types (forest vs. non-forest); and (2) coarser resolutions (1km and 10 km). Standard errors of the model estimates were 2.3%...

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.

Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data

NASA Astrophysics Data System (ADS)

Steyaert, L. T.; Hall, F. G.; Loveland, T. R.

1997-12-01

A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. The land cover classification was developed by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly normalized difference vegetation index (NDVI) image composites (April-September 1992). Quantitative areal proportions of the major boreal forest components were determined for a 821 km × 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, l km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.

Land cover mapping, fire regeneration, and scaling studies in the Canadian boreal forest with 1 km AVHRR and Landsat TM data

USGS Publications Warehouse

Steyaert, L.T.; Hall, F.G.; Loveland, Thomas R.

1997-01-01

A multitemporal 1 km advanced very high resolution radiometer (AVHRR) land cover analysis approach was used as the basis for regional land cover mapping, fire disturbance-regeneration, and multiresolution land cover scaling studies in the boreal forest ecosystem of central Canada. The land cover classification was developed by using regional field observations from ground and low-level aircraft transits to analyze spectral-temporal clusters that were derived from an unsupervised cluster analysis of monthly normalized difference vegetation index (NDVI) image composites (April-September 1992). Quantitative areal proportions of the major boreal forest components were determined for a 821 km ?? 619 km region, ranging from the southern grasslands-boreal forest ecotone to the northern boreal transitional forest. The boreal wetlands (mostly lowland black spruce, tamarack, mosses, fens, and bogs) occupied approximately 33% of the region, while lakes accounted for another 13%. Upland mixed coniferous-deciduous forests represented 23% of the ecosystem. A SW-NE productivity gradient across the region is manifested by three levels of tree stand density for both the boreal wetland conifer and the mixed forest classes, which are generally aligned with isopleths of regional growing degree days. Approximately 30% of the region was directly affected by fire disturbance within the preceding 30-35 years, especially in the Canadian Shield Zone where large fire-regeneration patterns contribute to the heterogeneous boreal landscape. Intercomparisons with land cover classifications derived from 30-m Landsat Thematic Mapper (TM) data provided important insights into the relative accuracy of the 1 km AVHRR land cover classification. Primarily due to the multitemporal NDVI image compositing process, the 1 km AVHRR land cover classes have an effective spatial resolution in the 3-4 km range; therefore fens, bogs, small water bodies, and small patches of dry jack pine cannot be resolved within the wet conifer mosaic. Major differences in the 1-km AVHRR and 30-m Landsat TM-derived land cover classes are most likely due to differences in the spatial resolution of the data sets. In general, the 1 km AVHRR land cover classes are vegetation mosaics consisting of mixed combinations of the Landsat classes. Detailed mapping of the global boreal forest with this approach will benefit from algorithms for cloud screening and to atmospherically correct reflectance data for both aerosol and water vapor effects. We believe that this 1 km AVHRR land cover analysis provides new and useful information for regional water, energy, carbon, and trace gases studies in BOREAS, especially given the significant spatial variability in land cover type and associated biophysical land cover parameters (e.g., albedo, leaf area index, FPAR, and surface roughness). Multiresolution land cover comparisons (30 m, 1 km, and 100 km grid cells) also illustrated how heterogeneous landscape patterns are represented in land cover maps with differing spatial scales and provided insights on the requirements and challenges for parameterizing landscape heterogeneity as part of land surface process research.

Random forests and stochastic gradient boosting for predicting tree canopy cover: Comparing tuning processes and model performance

Treesearch

Elizabeth A. Freeman; Gretchen G. Moisen; John W. Coulston; Barry T. (Ty) Wilson

2015-01-01

As part of the development of the 2011 National Land Cover Database (NLCD) tree canopy cover layer, a pilot project was launched to test the use of high-resolution photography coupled with extensive ancillary data to map the distribution of tree canopy cover over four study regions in the conterminous US. Two stochastic modeling techniques, random forests (RF...

Changes of forest cover and disturbance regimes in the mountain forests of the Alps☆

PubMed Central

Bebi, P.; Seidl, R.; Motta, R.; Fuhr, M.; Firm, D.; Krumm, F.; Conedera, M.; Ginzler, C.; Wohlgemuth, T.; Kulakowski, D.

2017-01-01

Natural disturbances, such as avalanches, snow breakage, insect outbreaks, windthrow or fires shape mountain forests globally. However, in many regions over the past centuries human activities have strongly influenced forest dynamics, especially following natural disturbances, thus limiting our understanding of natural ecological processes, particularly in densely-settled regions. In this contribution we briefly review the current understanding of changes in forest cover, forest structure, and disturbance regimes in the mountain forests across the European Alps over the past millennia. We also quantify changes in forest cover across the entire Alps based on inventory data over the past century. Finally, using the Swiss Alps as an example, we analyze in-depth changes in forest cover and forest structure and their effect on patterns of fire and wind disturbances, based on digital historic maps from 1880, modern forest cover maps, inventory data on current forest structure, topographical data, and spatially explicit data on disturbances. This multifaceted approach presents a long-term and detailed picture of the dynamics of mountain forest ecosystems in the Alps. During pre-industrial times, natural disturbances were reduced by fire suppression and land-use, which included extraction of large amounts of biomass that decreased total forest cover. More recently, forest cover has increased again across the entire Alps (on average +4% per decade over the past 25–115 years). Live tree volume (+10% per decade) and dead tree volume (mean +59% per decade) have increased over the last 15–40 years in all regions for which data were available. In the Swiss Alps secondary forests that established after 1880 constitute approximately 43% of the forest cover. Compared to forests established previously, post-1880 forests are situated primarily on steep slopes (>30°), have lower biomass, a more aggregated forest structure (primarily stem-exclusion stage), and have been more strongly affected by fires, but less affected by wind disturbance in the 20th century. More broadly, an increase in growing stock and expanding forest areas since the mid-19th century have - along with climatic changes - contributed to an increasing frequency and size of disturbances in the Alps. Although many areas remain intensively managed, the extent, structure, and dynamics of the forests of the Alps reflect natural drivers more strongly today than at any time in the past millennium. PMID:28860675

Changes of forest cover and disturbance regimes in the mountain forests of the Alps.

PubMed

Bebi, P; Seidl, R; Motta, R; Fuhr, M; Firm, D; Krumm, F; Conedera, M; Ginzler, C; Wohlgemuth, T; Kulakowski, D

2017-03-15

Natural disturbances, such as avalanches, snow breakage, insect outbreaks, windthrow or fires shape mountain forests globally. However, in many regions over the past centuries human activities have strongly influenced forest dynamics, especially following natural disturbances, thus limiting our understanding of natural ecological processes, particularly in densely-settled regions. In this contribution we briefly review the current understanding of changes in forest cover, forest structure, and disturbance regimes in the mountain forests across the European Alps over the past millennia. We also quantify changes in forest cover across the entire Alps based on inventory data over the past century. Finally, using the Swiss Alps as an example, we analyze in-depth changes in forest cover and forest structure and their effect on patterns of fire and wind disturbances, based on digital historic maps from 1880, modern forest cover maps, inventory data on current forest structure, topographical data, and spatially explicit data on disturbances. This multifaceted approach presents a long-term and detailed picture of the dynamics of mountain forest ecosystems in the Alps. During pre-industrial times, natural disturbances were reduced by fire suppression and land-use, which included extraction of large amounts of biomass that decreased total forest cover. More recently, forest cover has increased again across the entire Alps (on average +4% per decade over the past 25-115 years). Live tree volume (+10% per decade) and dead tree volume (mean +59% per decade) have increased over the last 15-40 years in all regions for which data were available. In the Swiss Alps secondary forests that established after 1880 constitute approximately 43% of the forest cover. Compared to forests established previously, post-1880 forests are situated primarily on steep slopes (>30°), have lower biomass, a more aggregated forest structure (primarily stem-exclusion stage), and have been more strongly affected by fires, but less affected by wind disturbance in the 20th century. More broadly, an increase in growing stock and expanding forest areas since the mid-19th century have - along with climatic changes - contributed to an increasing frequency and size of disturbances in the Alps. Although many areas remain intensively managed, the extent, structure, and dynamics of the forests of the Alps reflect natural drivers more strongly today than at any time in the past millennium.

Integrated use of SRS Data &GIS Technique for Monitoring Changes in Riverine Forest of Sindh, Pakistan

NASA Astrophysics Data System (ADS)

Siddiqui, M.; Ali, Z.

Deforestation / depletion in forest area threaten the sustainability of agricultural production systems and en-danger the economy of the country. Every year extensive areas of arable agricultural and forestlands are degraded and turned into wastelands, due to natural causes or human interventions. There are several causes of deforestation, such as expansion in agricultural area, urban development, forest fires, commercial logging, illicit cutting, grazing, constructions of dams / reservoirs and barrages, com munication links, etc. Depletion in forest cover, therefore, has an important impact on socio - economic development and ecological balance. High population growth rate in Pakistan is one of the main causes for the rapid deterioration of physical environment and natural resource base. In view of this, it is felt necessary to carryout land -u s e studies focusing on strategies for mapping the past and present conditions and extent of forests and rangelands using Satellite Remote Sensing (SRS) data and GIS t echnology. The SRS and GIS technology provides a possible means of monitoring and mapping changes occurring in natural resources and the environment on a continuing basis. The riverine forests of Sindh mostly grow along the River Indus in the flood plains, spread over an area of 241,000 ha are disappearing very rapidly. Construction of dams / barrages on the upper reaches of the River Indus for hydroelectric power and irrigation works have significantly reduced the discharge of fresh water into the lower Indus basin and as a result, 100,000 acres of forests have disappeared. Furthermore, the heavy floods that occurred in 1978, 1988, 1992 and 1997, altered the course of the River Indus in many places, especially in the lower reaches, this has also damaged the riverine forests of Sindh. An integrated approach involving analysis of SRS data from 1977 to 1998 and GIS technique have been used to evaluate the geographic ex-tent and distribution of the riverine forests of Sindh and to monitor temporal changes in the forest cover between 1977 &1990 and 1990 &1998. The integrated landuse forest cover maps of riverine forest, shows temporal changes in the forest cover between 1977 &1990 and 1990 &1998, as well as in the River Indus course. The digital thematic maps based on SRS data and GIS technology can supplement existing conventional ground based sources of information for monitoring changes in forest cover on a regular basis, which can be helpful for forest resource management and planning and monitoring environmental changes.

National forest cover monitoring in mainland South and Southeast Asia: method development and capacity building

NASA Astrophysics Data System (ADS)

Tyukavina, A.; Potapov, P.; Hansen, M.; Talero, Y.; Turubanova, S.; Pickering, J.; Pickens, A. H.; Quyen, N. H.; Spirovska Kono, M.

2017-12-01

Timely forest monitoring data produced following good practice guidance are required for national reporting on greenhouse gas emissions, national forest resource assessments, and monitoring for REDD+ projects. Remote sensing provides a cost-efficient supplement to national forest inventories, and is often the single viable source of data on forest extent for countries still in the process of establishing field-based inventories. Operational forest monitoring using remotely sensed data requires technical capacity to store, process, and analyze high volumes of satellite imagery. The University of Maryland Global Land Analysis and Discovery (UMD GLAD) lab possesses such technical capacity and is seeking to transfer it to national agencies responsible for forest reporting, national academic institutions, and NGOs. Our projects in South and Southeast Asia include regional forest monitoring in the lower Mekong region in support of the Regional Land Cover Monitoring System (funded by the NASA SERVIR program) and building capacity for forest monitoring in Nepal, Bangladesh, Vietnam, Cambodia, Laos, and Thailand (funded by the SilvaCarbon program). Our forest monitoring approach is a regional scale adaptation of methods developed for the global analysis (Hansen et al. 2013). The methodology to track large-scale clearing of natural forests (e.g. in Brazil and Indonesia) is well established; however, the methods for small-scale disturbance mapping and tree cover rotation assessment are still in development. In Bangladesh our mapping of tree cover change between 2000-2014 revealed that 54% of the tree canopy cover was outside forests, and the majority of canopy changes were smaller than 0.1 ha. Landsat's 30-m resolution was therefore insufficient to monitor changes in tree cover. By using a probability sample of high resolution (circa 1 m) imagery we were able to quantify change in tree canopy cover outside forests (including village woodlots, tree plantations and agroforestry) and in different forest types. Our result shows that while the net tree cover change in Bangladesh is rather small, the gross dynamics are significant and can vary by forest type.

Object-Based Mapping of the Circumpolar Taiga-Tundra Ecotone with MODIS Tree Cover

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Montesano, P. M.; Nelson, R.

2011-01-01

The circumpolar taiga tundra ecotone was delineated using an image-segmentation-based mapping approach with multi-annual MODIS Vegetation Continuous Fields (VCF) tree cover data. Circumpolar tree canopy cover (TCC) throughout the ecotone was derived by averaging MODIS VCF data from 2000 to 2005 and adjusting the averaged values using linear equations relating MODIS TCC to Quickbird-derived tree cover estimates. The adjustment helped mitigate VCF's overestimation of tree cover in lightly forested regions. An image segmentation procedure was used to group pixels representing similar tree cover into polygonal features (segmentation objects) that form the map of the transition zone. Each polygon represents an area much larger than the 500 m MODIS pixel and characterizes the patterns of sparse forest patches on a regional scale. Those polygons near the boreal/tundra interface with either (1) mean adjusted TCC values from5 to 20%, or (2) mean adjusted TCC values greater than 5% but with a standard deviation less than 5% were used to identify the ecotone. Comparisons of the adjusted average tree cover data were made with (1) two existing tree line definitions aggregated for each 1 degree longitudinal interval in North America and Eurasia, (2) Landsat-derived Canadian proportion of forest cover for Canada, and (3) with canopy cover estimates extracted from airborne profiling lidar data that transected 1238 of the TCC polygons. The adjusted TCC from MODIS VCF shows, on average, less than 12% TCC for all but one regional zone at the intersection with independently delineated tree lines. Adjusted values track closely with Canadian proportion of forest cover data in areas of low tree cover. A comparison of the 1238 TCC polygons with profiling lidar measurements yielded an overall accuracy of 67.7%.

High-resolution maps of forest-urban watersheds present an opportunity for ecologists and managers

EPA Science Inventory

Dense populations of people and abundant impervious surfaces contribute to poor water quality and increased flooding in forest-urban watersheds. Green infrastructure mitigates these effects, but precisely quantifying benefits is difficult because most land cover maps rely on coar...

Mapping Mexico's Forest Lands with Advanced Very High Resolution Radiometer

Treesearch

David J. Evans; Zhiliang Zhu; Susan Eggen-McIntosh; Pedro García Mayoral; Jose Luis Ornelas de Anda

1992-01-01

Data from the Advanced Very High Resolution Radiometer (AVHRR) were used in a program sponsored by the U.S. Department of Agriculture, Forest Service, and the United Nations Food and Agriculture Organization to help scientists from Mexico generate forest-cover maps of that country. Two near-cloud-free composite images were generated for December and March 1990 from...

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.

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...

Simulating landscape change in the Olympic Peninsula using spatial ecological and socioeconomic data

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

Flamm, R.O.; Gottfried, R.; Lee, R.G.

1994-06-01

Ecological and socioeconomic data were integrated to study landscape change for the Dungeness River basin in the Olympic Peninsula, Washington State. A multinomial logit procedure was used to evaluate twenty-two maps representing various data themes to derive transition probabilities of land cover change. Probabilities of forest disturbance were greater on private land than public. Between 1975 and 1988, forest cover increased, grassy/brushy covers decreased, and the number of forest patches increased about 30%. Simulations were run to estimate future land cover. These results were represented as frequency distributions for proportion cover and patch characteristics.

Michigan resource inventories: Characteristics and costs of selected projects using high altitude color infrared imagery. Remote Sensing Project

NASA Technical Reports Server (NTRS)

Enslin, W. R.; Hill-Rowley, R.

1976-01-01

The procedures and costs associated with mapping land cover/use and forest resources from high altitude color infrared (CIR) imagery are documented through an evaluation of several inventory efforts. CIR photos (1:36,000) were used to classify the forests of Mason County, Michigan into six species groups, three stocking levels, and three maturity classes at a cost of $4.58/sq. km. The forest data allow the pinpointing of marketable concentrations of selected timber types, and facilitate the establishment of new forest management cooperatives. Land cover/use maps and area tabulations were prepared from small scale CIR photography at a cost of $4.28/sq. km. and $3.03/sq. km. to support regional planning programs of two Michigan agencies. procedures were also developed to facilitate analysis of this data with other natural resource information. Eleven thematic maps were generated from Windsor Township, Michigan at a cost of $1,500 by integrating grid-geocoded land cover/use, soils, topographic, and well log data using an analytical computer program.

Land cover mapping of North and Central America—Global Land Cover 2000

USGS Publications Warehouse

Latifovic, Rasim; Zhu, Zhi-Liang

2004-01-01

The Land Cover Map of North and Central America for the year 2000 (GLC 2000-NCA), prepared by NRCan/CCRS and USGS/EROS Data Centre (EDC) as a regional component of the Global Land Cover 2000 project, is the subject of this paper. A new mapping approach for transforming satellite observations acquired by the SPOT4/VGTETATION (VGT) sensor into land cover information is outlined. The procedure includes: (1) conversion of daily data into 10-day composite; (2) post-seasonal correction and refinement of apparent surface reflectance in 10-day composite images; and (3) extraction of land cover information from the composite images. The pre-processing and mosaicking techniques developed and used in this study proved to be very effective in removing cloud contamination, BRDF effects, and noise in Short Wave Infra-Red (SWIR). The GLC 2000-NCA land cover map is provided as a regional product with 28 land cover classes based on modified Federal Geographic Data Committee/Vegetation Classification Standard (FGDC NVCS) classification system, and as part of a global product with 22 land cover classes based on Land Cover Classification System (LCCS) of the Food and Agriculture Organisation. The map was compared on both areal and per-pixel bases over North and Central America to the International Geosphere–Biosphere Programme (IGBP) global land cover classification, the University of Maryland global land cover classification (UMd) and the Moderate Resolution Imaging Spectroradiometer (MODIS) Global land cover classification produced by Boston University (BU). There was good agreement (79%) on the spatial distribution and areal extent of forest between GLC 2000-NCA and the other maps, however, GLC 2000-NCA provides additional information on the spatial distribution of forest types. The GLC 2000-NCA map was produced at the continental level incorporating specific needs of the region.

EnviroAtlas - Austin, TX - Riparian Buffer Land Cover by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of forested, vegetated, and impervious land within 15- and 50-meters of hydrologically connected streams, rivers, and other water bodies within the EnviroAtlas community area. Forest is defined as Trees & Forest. Vegetated cover is defined as Trees & Forest and Grass & Herbaceous. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Austin, TX - Land Cover by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of each block group that is classified as impervious, forest, green space, and agriculture. Forest is defined as Trees & Forest. Green space is defined as Trees & Forest, Grass & Herbaceous, and Agriculture. This dataset also includes the area per capita for each block group for some land cover types. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

SAR For REDD+ in the Mai Ndombe District (DRC)

NASA Astrophysics Data System (ADS)

Haarpaintner, Jorg

2016-08-01

The overall goal of the project "SAR for REDD" is to provide cloud-penetrating satellite synthetic aperture radar (SAR) pre-processing and analysing capabilities and tools to support operational tropical forest monitoring in REDD countries and primarily in Africa. The project's end-user is the Observatoir Satellitale des Forêts d'Afrique Centrale (OSFAC).This paper presents an overall summary of the project and shows first results of the satellite products, that will be delivered to the user in addition to software tools to enhance the user's own technical capacity.The products shown here are SAR mosaics and derived forest-land cover maps based on C-band Sentinel-1A data for 2015, ALOS-PALSAR data for the period 2007-2010 and ALOS-2 PALSAR-2 for 2015. In addition, a forest cover change map from 2007 to 2010 based on ALOS PALSAR has been produced and is compared to results from the Global Forest Cover project [1].

GLCF: Research

Science.gov Websites

Global Land Cover Facility About GLCF Research Publications Data & Products Gallery Library Services Contact Site Map Go Research The Global Land Cover Facility is a research center focusing on the GLCF in developing forest change products. Past research efforts were directed at boreal forests in

Relief influence on the spatial distribution of the Atlantic Forest cover on the Ibiúna Plateau, SP.

PubMed

Silva, W G; Metzger, J P; Simões, S; Simonetti, C

2007-08-01

Several studies suggest that, on a large scale, relief conditions influence the Atlantic Forest cover. The aim of this work was to explore these relationships on a local scale, in Caucaia do Alto, on the Ibiúna Plateau. Within an area of about 78 km(2), the distribution of forest cover, divided into two successional stages, was associated with relief attribute data (slope, slope orientation and altitude). The mapping of the vegetation was based on the interpretation of stereoscopic pairs of aerial photographs, from April 2000, on a scale of 1:10,000, while the relief attributes were obtained by geoprocessing from digitalized topographic maps on a scale of 1:10,000. Statistical analyses, based on qui-square tests, revealed that there was a more extensive forest cover, irrespective of the successional stage, in steeper areas (>10 degrees) located at higher altitudes (>923 m), but no influence of the slope orientation. There was no sign of direct influence of relief on the forest cover through environmental gradients that might have contributed to the forest regeneration. Likewise, there was no evidence that these results could have been influenced by the distance from roads or urban areas or with respect to permanent preservation areas. Relief seems to influence the forest cover indirectly, since agricultural land use is preferably made in flatter and lower areas. These results suggest a general distribution pattern of the forest remnants, independent of the scale of study, on which relief indirectly has a strong influence, since it determines human occupation.

Satellite-based primary forest degradation assessment in the Democratic Republic of the Congo, 2000-2010

NASA Astrophysics Data System (ADS)

Zhuravleva, I.; Turubanova, S.; Potapov, P.; Hansen, M.; Tyukavina, A.; Minnemeyer, S.; Laporte, N.; Goetz, S.; Verbelen, F.; Thies, C.

2013-06-01

Primary forest extent, loss and degradation within the Democratic Republic of the Congo (DRC) were quantified from 2000 to 2010 by combining directly mapped forest cover extent and loss data (CARPE) with indirectly mapped forest degradation data (intact forest landscapes, IFL). Landsat data were used to derive both map inputs, and data from the GLAS (Geoscience Laser Altimetry System) sensor were employed to validate the discrimination of primary intact and primary degraded forests. In the year 2000, primary humid tropical forests occupied 104 455 kha of the country, with 61% of these forests classified as intact. From 2000 to 2010, 1.02% of primary forest cover was lost due to clearing, and almost 2% of intact primary forests were degraded due to alteration and fragmentation. While primary forest clearing increased by a factor of two between 2000-2005 and 2005-2010, the degradation of intact forests slightly decreased. Fragmentation and selective logging were the leading causes of intact forest degradation, accounting for 91% of IFL area change. The 10 year forest degradation rate within designated logging permit areas was 3.8 times higher compared to other primary forest areas. Within protected areas the forest degradation rate was 3.7 times lower than in other primary forest areas. Forest degradation rates were high in the vicinity of major urban areas. Given the observed forest degradation rates, we infer that the degradation of intact forests could increase up to two-fold over the next decade.

Land cover map for map zones 8 and 9 developed from SAGEMAP, GNN, and SWReGAP: a pilot for NWGAP

Treesearch

James S. Kagan; Janet L. Ohmann; Matthew Gregory; Claudine Tobalske

2008-01-01

As part of the Northwest Gap Analysis Project, land cover maps were generated for most of eastern Washington and eastern Oregon. The maps were derived from regional SAGEMAP and SWReGAP data sets using decision tree classifiers for nonforest areas, and Gradient Nearest Neighbor imputation modeling for forests and woodlands. The maps integrate data from regional...

The influence of forest cover on landslide occurrence explored with spatio-temporal information

NASA Astrophysics Data System (ADS)

Schmaltz, Elmar M.; Steger, Stefan; Glade, Thomas

2017-08-01

Multi-temporal landslide inventories in widely forested landscapes are scarce and further studies are required to face the challenges of producing reliable inventories in woodland areas. An elaboration of valuable empirical relationships between shallow landslides and forest cover based on recent remote sensing data alone is often hampered due to constant land cover changes, differing ages of landslides within a landslide inventory and the fact that usage of different data sets for mapping might lead to various systematic mapping biases. Within this study, we attempted to overcome these difficulties in order to explore the effect of forest cover on shallow landslide occurrences. Thus, forest dynamics were examined on the basis of 9 orthophoto series from 1950s to 2015, distinguishing 3 forest classes, based on the wood type. These classes were furthermore distinguished in 12 subclasses, considering stand density and age. A multi-temporal landslide inventory was compiled for the same period based on the aerial photography, 2 airborne LiDAR imageries, 8 field surveys and archive data. We derived topographical parameters (slope, topographical positioning index and convergency index) from the digital elevation model for areal correction and accounting for topographical confounders within a logistic regression model. Empirical relationships were assessed by means of (a) areal changes of forests and logged areas, (b) spatio-temporal distribution of shallow translational landslides, (c) frequency ratios and (d) logistic regression analysis. The findings revealed that forests increased by 16.2% from 1950s to 2015. 311 landslides of 351 in total that where mapped in total could be assigned to the observed time series and were considered for our analyses. Frequency ratios and odds ratios indicated a stabilising effect of all forest classes on landslide occurrences. Odds ratios observed for the models based on aggregated data sets (3 forest classes) indicated provided evidence that forest was constantly estimated to be less prone to slope failure than their non-forested counterparts. The chances for forest classes to be affected by shallow landslides were estimated to be considerably lower whenever topographic predictors were as well included in the model. A detailed inspection of the statistical results suggests that the obtained empirical relationships should be interpreted with care. Challenges in the mapping procedures of forests and landslides, implications of the applied methods and potential pitfalls are discussed.

Use of Road Maps in National Assessments of Forest Fragmentation in the United States

Treesearch

Kurt H. Riitters; James Wickham; John Coulston

2004-01-01

The question of incorporating road maps into U.S. national assessments of forest fragmentation has been a contentious issue, but there has not been a comparative national analysis to inform the debate. Using data and indices from previous national assessments, we compared fragmentation as calculated from high-resolution land-cover maps alone (Method 1) and after...

Mapping stand-age distribution of Russian forests from satellite data

NASA Astrophysics Data System (ADS)

Chen, D.; Loboda, T. V.; Hall, A.; Channan, S.; Weber, C. Y.

2013-12-01

Russian boreal forest is a critical component of the global boreal biome as approximately two thirds of the boreal forest is located in Russia. Numerous studies have shown that wildfire and logging have led to extensive modifications of forest cover in the region since 2000. Forest disturbance and subsequent regrowth influences carbon and energy budgets and, in turn, affect climate. Several global and regional satellite-based data products have been developed from coarse (>100m) and moderate (10-100m) resolution imagery to monitor forest cover change over the past decade, record of forest cover change pre-dating year 2000 is very fragmented. Although by using stacks of Landsat images, some information regarding the past disturbances can be obtained, the quantity and locations of such stacks with sufficient number of images are extremely limited, especially in Eastern Siberia. This paper describes a modified method which is built upon previous work to hindcast the disturbance history and map stand-age distribution in the Russian boreal forest. Utilizing data from both Landsat and the Moderate Resolution Imaging Spectroradiometer (MODIS), a wall-to-wall map indicating the estimated age of forest in the Russian boreal forest is created. Our previous work has shown that disturbances can be mapped successfully up to 30 years in the past as the spectral signature of regrowing forests is statistically significantly different from that of mature forests. The presented algorithm ingests 55 multi-temporal stacks of Landsat imagery available over Russian forest before 2001 and processes through a standardized and semi-automated approach to extract training and validation data samples. Landsat data, dating back to 1984, are used to generate maps of forest disturbance using temporal shifts in Disturbance Index through the multi-temporal stack of imagery in selected locations. These maps are then used as reference data to train a decision tree classifier on 50 MODIS-based indices. The resultant map provides an estimate of forest age based on the regrowth curves observed from Landsat imagery. The accuracy of the resultant map is assessed against three datasets: 1) subset of the disturbance maps developed within the algorithm, 2) independent disturbance maps created by the Northern Eurasia Land Dynamics Analysis (NELDA) project, and 3) field-based stand-age distribution from forestry inventory units. The current version of the product presents a considerable improvement on the previous version which used Landsat data samples at a set of randomly selected locations, resulting a strong bias of the training samples towards the Landsat-rich regions (e.g. European Russia) whereas regions such as Siberia were under-sampled. Aiming at improving accuracy, the current method significantly increases the number of training Landsat samples compared to the previous work. Aside from the previously used data, the current method uses all available Landsat data for the under-sampled regions in order to increase the representativeness of the total samples. The finial accuracy assessment is still ongoing, however, the initial results suggested an overall accuracy expressed in Kappa > 0.8. We plan to release both the training data and the final disturbance map of the Russian boreal forest to the public after the validation is completed.

The use of historical topographic maps in the study of forest-cover changes in Southern Romania

NASA Astrophysics Data System (ADS)

Imecs, Zoltán; Bartos-Elekes, Zsombor; Timár, Gábor; Magyari-Sáska, Zsolt

2014-05-01

In the post-communist period the term "deforestation" becomes well known in Romania. By the middle of 19th-century more than 27% of the country was covered by forests, but since then certain changes took place in this respect. The study of the phenomena can be done by the help of maps. In this regard it is very important to have old maps which can emphasize the situation from the past. As the map of Southern Romania, made about Walachia in 1864, called Charta României Meridionale is now georeferenced and accessible on the web, it can be used as a basis for such studies. Researchers are now able to make quantitative studies. In our poster we made a study of two different regions from Southern Romania: one from a mountain region and one from a plain region. Both are in the basin of Argeş river, tributary of Danube. The mountain region lies in the upper basin of Argeş river which is now occupied Vidraru artificial lake. The plain region lies on wetland and today is a natural reserve. The study regions have almost the same size (about 400 km2). In order to follow the evolution in time of the forest cover we used four data sources which covers a period of more than 150 years: Charta României Meridionale (the survey was made between 1855 and 1859); Lambert-Cholesky maps (the survey was made at the end of the 19th century); Gauss-Krüger maps (from the 1960s) and orthophotographs made in 2005. All these materials are georeferenced. With the help of GIS software we digitized the areas covered by forests in both regions. The areas were determined and compared. Using GIS techniques we can overlap the areas covered by forests, the illustrations were made this way. As a conclusion we can say that the plain region suffered important changes as the natural landscape turns into an agricultural-human landscape in the first part of the 20th century. We can say that the actual forest is preserved only because now it is a protected area. In the mountain region the territory was partially transformed into artificial lake, the forests are preserved to reduce the flow of wash materials into the lake. But in the mountain regions more and more clearings appear. The study demonstrates that with the help of historic maps landscape changes can be studied with good results. This work was supported by a grant of the Romanian National Authority for Scientific Research, CNCS - UEFISCDI, project number PN-II-RU-TE-2011-3-0125.

Monitoring conterminous United States (CONUS) land cover change with Web-Enabled Landsat Data (WELD)

USGS Publications Warehouse

Hansen, M.C.; Egorov, Alexey; Potapov, P.V.; Stehman, S.V.; Tyukavina, A.; Turubanova, S.A.; Roy, David P.; Goetz, S.J.; Loveland, Thomas R.; Ju, J.; Kommareddy, A.; Kovalskyy, Valeriy; Forsyth, C.; Bents, T.

2014-01-01

Forest cover loss and bare ground gain from 2006 to 2010 for the conterminous United States (CONUS) were quantified at a 30 m spatial resolution using Web-Enabled Landsat Data available from the USGS Center for Earth Resources Observation and Science (EROS) (http://landsat.usgs.gov/WELD.php). The approach related multi-temporal WELD metrics and expert-derived training data for forest cover loss and bare ground gain through a decision tree classification algorithm. Forest cover loss was reported at state and ecoregional scales, and the identification of core forests' absent of change was made and verified using LiDAR data from the GLAS (Geoscience Laser Altimetry System) instrument. Bare ground gain correlated with population change for large metropolitan statistical areas (MSAs) outside of desert or semi-desert environments. GoogleEarth™ time-series images were used to validate the products. Mapped forest cover loss totaled 53,084 km2 and was found to be depicted conservatively, with a user's accuracy of 78% and a producer's accuracy of 68%. Excluding errors of adjacency, user's and producer's accuracies rose to 93% and 89%, respectively. Mapped bare ground gain equaled 5974 km2 and nearly matched the estimated area from the reference (GoogleEarth™) classification; however, user's (42%) and producer's (49%) accuracies were much less than those of the forest cover loss product. Excluding errors of adjacency, user's and producer's accuracies rose to 62% and 75%, respectively. Compared to recent 2001–2006 USGS National Land Cover Database validation data for forest loss (82% and 30% for respective user's and producer's accuracies) and urban gain (72% and 18% for respective user's and producer's accuracies), results using a single CONUS-scale model with WELD data are promising and point to the potential for national-scale operational mapping of key land cover transitions. However, validation results highlighted limitations, some of which can be addressed by improving training data, creating a more robust image feature space, adding contemporaneous Landsat 5 data to the inputs, and modifying definition sets to account for differences in temporal and spatial observational scales. The presented land cover extent and change data are available via the official WELD website (ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/ftp://weldftp.cr.usgs.gov/CONUS_5Y_LandCover/).

Mapping forested wetlands in the Great Zhan River Basin through integrating optical, radar, and topographical data classification techniques.

PubMed

Na, X D; Zang, S Y; Wu, C S; Li, W L

2015-11-01

Knowledge of the spatial extent of forested wetlands is essential to many studies including wetland functioning assessment, greenhouse gas flux estimation, and wildlife suitable habitat identification. For discriminating forested wetlands from their adjacent land cover types, researchers have resorted to image analysis techniques applied to numerous remotely sensed data. While with some success, there is still no consensus on the optimal approaches for mapping forested wetlands. To address this problem, we examined two machine learning approaches, random forest (RF) and K-nearest neighbor (KNN) algorithms, and applied these two approaches to the framework of pixel-based and object-based classifications. The RF and KNN algorithms were constructed using predictors derived from Landsat 8 imagery, Radarsat-2 advanced synthetic aperture radar (SAR), and topographical indices. The results show that the objected-based classifications performed better than per-pixel classifications using the same algorithm (RF) in terms of overall accuracy and the difference of their kappa coefficients are statistically significant (p<0.01). There were noticeably omissions for forested and herbaceous wetlands based on the per-pixel classifications using the RF algorithm. As for the object-based image analysis, there were also statistically significant differences (p<0.01) of Kappa coefficient between results performed based on RF and KNN algorithms. The object-based classification using RF provided a more visually adequate distribution of interested land cover types, while the object classifications based on the KNN algorithm showed noticeably commissions for forested wetlands and omissions for agriculture land. This research proves that the object-based classification with RF using optical, radar, and topographical data improved the mapping accuracy of land covers and provided a feasible approach to discriminate the forested wetlands from the other land cover types in forestry area.

Geospatial assessment and monitoring of historical forest cover changes (1920-2012) in Nilgiri Biosphere Reserve, Western Ghats, India.

PubMed

Satish, K V; Saranya, K R L; Reddy, C Sudhakar; Krishna, P Hari; Jha, C S; Rao, P V V Prasada

2014-12-01

Deforestation in the biosphere reserves, which are key Protected Areas has negative impacts on biodiversity, climate, carbon fluxes and livelihoods. Comprehensive study of deforestation in biosphere reserves is required to assess the impact of the management effectiveness. This article assesses the changes in forest cover in various zones and protected areas of Nilgiri Biosphere Reserve, the first declared biosphere reserve in India which forms part of Western Ghats-a global biodiversity hotspot. In this study, we have mapped the forests from earliest available topographical maps and multi-temporal satellite data spanning from 1920's to 2012 period. Mapping of spatial extent of forest cover, vegetation types and land cover was carried out using visual interpretation technique. A grid cell of 1 km × 1 km was generated for time series change analysis to understand the patterns in spatial distribution of forest cover (1920-1973-1989-1999-2006-2012). The total forest area of biosphere reserve was found to be 5,806.5 km(2) (93.8 % of total geographical area) in 1920. Overall loss of forest cover was estimated as 1,423.6 km(2) (24.5 % of the total forest) with reference to 1920. Among the six Protected Areas, annual deforestation rate of >0.5 was found in Wayanad wildlife sanctuary during 1920-1973. The deforestation in Nilgiri Biosphere Reserve is mainly attributed to conversion of forests to plantations and agriculture along with submergence due to construction of dams during 1920 to 1989. Grid wise analysis indicates that 851 grids have undergone large-scale negative changes of >75 ha of forest loss during 1920-1973 while, only 15 grids have shown >75 ha loss during 1973-1989. Annual net rate of deforestation for the period of 1920 to 1973 was calculated as 0.5 followed by 0.1 for 1973 to 1989. Our analysis shows that there was large-scale deforestation before the declaration of area as biosphere reserve in 1986; however, the deforestation has drastically reduced after the declaration due to high degree of protection, thus indicating the secure future of reserve in the long term under the current forest management practices. The present work will stand as the most up-to-date assessment on the forest cover of the Nilgiri Biosphere Reserve with immediate applications in monitoring and management of forest biodiversity.

The Forest Types and Ages Cleared for Land Development in Puerto Rico.

Treesearch

Todd Kennaway; E. H. Helmer

2007-01-01

On the Caribbean island of Puerto Rico, forest, urban/built-up, and pasture lands have replaced most formerly cultivated lands. The extent and age distribution of each forest type that undergoes land development, however, is unknown. This study assembles a time series of four land cover maps for Puerto Rico. The time series includes two digitized paper maps of land...

Evaluation of Thematic Mapper data for mapping forest, agricultural and soil resources

NASA Technical Reports Server (NTRS)

Degloria, S.; Benson, A.; Dummer, K.; Fakhoury, E.

1985-01-01

Color composite TM film products which include TM5, TM4, and a visible band (TM1, TM2, or TM3) are superior to composites which exclude TM4 for discriminating most forest and agricultural cover types and estimating area proportions for inventory and sampling purposes. Clustering a subset of TM data results in a spectral class map which groups diverse forest cover types into spectrally and ecologically similar areas suitable for use as a stratification base in traditional forest inventory practices. Analysis of simulated Thematic Mapper data indicate that the location and number of TM spectral bands are suitable for detecting differences in major soil properties and characterizing soil spectral curve form and magnitude.

Vegetation Analysis and Land Use Land Cover Classification of Forest in Uttara Kannada District India Using Remote Sensign and GIS Techniques

NASA Astrophysics Data System (ADS)

Koppad, A. G.; Janagoudar, B. S.

2017-10-01

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.

An integrated approach to mapping forest conditions in the Southern Appalachians (North Carolina)

Treesearch

Weimin Xi; Lei Wang; Andrew G Birt; Maria D. Tchakerian; Robert N. Coulson; Kier D. Klepzig

2009-01-01

Accurate and continuous forest cover information is essential for forest management and restoration (SAMAB 1996, Xi et al. 2007). Ground-truthed, spatially explicit forest data, however, are often limited to federally managed land or large-scale commercial forestry operations where forest inventories are regularly collected. Moreover,...

Mapping of land cover in northern California with simulated hyperspectral satellite imagery

NASA Astrophysics Data System (ADS)

Clark, Matthew L.; Kilham, Nina E.

2016-09-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Analysis of hyperspectral, or imaging spectrometer, imagery has shown an impressive capacity to map a wide range of natural and anthropogenic land cover. Applications have been mostly with single-date imagery from relatively small spatial extents. Future hyperspectral satellites will provide imagery at greater spatial and temporal scales, and there is a need to assess techniques for mapping land cover with these data. Here we used simulated multi-temporal HyspIRI satellite imagery over a 30,000 km2 area in the San Francisco Bay Area, California to assess its capabilities for mapping classes defined by the international Land Cover Classification System (LCCS). We employed a mapping methodology and analysis framework that is applicable to regional and global scales. We used the Random Forests classifier with three sets of predictor variables (reflectance, MNF, hyperspectral metrics), two temporal resolutions (summer, spring-summer-fall), two sample scales (pixel, polygon) and two levels of classification complexity (12, 20 classes). Hyperspectral metrics provided a 16.4-21.8% and 3.1-6.7% increase in overall accuracy relative to MNF and reflectance bands, respectively, depending on pixel or polygon scales of analysis. Multi-temporal metrics improved overall accuracy by 0.9-3.1% over summer metrics, yet increases were only significant at the pixel scale of analysis. Overall accuracy at pixel scales was 72.2% (Kappa 0.70) with three seasons of metrics. Anthropogenic and homogenous natural vegetation classes had relatively high confidence and producer and user accuracies were over 70%; in comparison, woodland and forest classes had considerable confusion. We next focused on plant functional types with relatively pure spectra by removing open-canopy shrublands, woodlands and mixed forests from the classification. This 12-class map had significantly improved accuracy of 85.1% (Kappa 0.83) and most classes had over 70% producer and user accuracies. Finally, we summarized important metrics from the multi-temporal Random Forests to infer the underlying chemical and structural properties that best discriminated our land-cover classes across seasons.

Recent forest cover changes (2002-2015) in the Southern Carpathians: A case study of the Iezer Mountains, Romania.

PubMed

Mihai, Bogdan; Săvulescu, Ionuț; Rujoiu-Mare, Marina; Nistor, Constantin

2017-12-01

The paper explores the dynamics of the forest cover change in the Iezer Mountains, part of Southern Carpathians, in the context of the forest ownership recovery and deforestation processes, combined with the effects of biotic and abiotic disturbances. The aim of the study is to map and evaluate the typology and the spatial extension of changes in the montane forest cover between 700 and 2462m a.s.l., sampling all the representative Carpathian ecosystems, from the European beech zone up to the spruce-fir zone and the subalpine-alpine pastures. The methodology uses a change detection analysis of satellite imagery with Landsat ETM+/OLI and Sentinel-2 MSI data. The workflow started with a complete calibration of multispectral data from 2002, before the massive forest restitution to private owners, after the Law 247/2005 empowerment, and 2015, the intensification of deforestation process. For the data classification, a Maximum Likelihood supervised classification algorithm was utilized. The forest change map was developed after combining the classifications in a unitary formula using image difference. The principal outcome of the research identifies the type of forest cover change using a quantitative formula. This information can be integrated in the future decision-making strategies for forest stand management and sustainable development. Copyright © 2017 Elsevier B.V. All rights reserved.

Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011

USGS Publications Warehouse

Soulard, Christopher E.; Acevedo, William; Cohen, Warren B.; Yang, Zhiqiang; Stehman, Stephen V.; Taylor, Janis L.

2017-01-01

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986–1992, 1992–2001, 2001–2006, and 2006–2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Harmonization of forest disturbance datasets of the conterminous USA from 1986 to 2011.

PubMed

Soulard, Christopher E; Acevedo, William; Cohen, Warren B; Yang, Zhiqiang; Stehman, Stephen V; Taylor, Janis L

2017-04-01

Several spatial forest disturbance datasets exist for the conterminous USA. The major problem with forest disturbance mapping is that variability between map products leads to uncertainty regarding the actual rate of disturbance. In this article, harmonized maps were produced from multiple data sources (i.e., Global Forest Change, LANDFIRE Vegetation Disturbance, National Land Cover Database, Vegetation Change Tracker, and Web-Enabled Landsat Data). The harmonization process involved fitting common class ontologies and determining spatial congruency to produce forest disturbance maps for four time intervals (1986-1992, 1992-2001, 2001-2006, and 2006-2011). Pixels mapped as disturbed for two or more datasets were labeled as disturbed in the harmonized maps. The primary advantage gained by harmonization was improvement in commission error rates relative to the individual disturbance products. Disturbance omission errors were high for both harmonized and individual forest disturbance maps due to underlying limitations in mapping subtle disturbances with Landsat classification algorithms. To enhance the value of the harmonized disturbance products, we used fire perimeter maps to add information on the cause of disturbance.

Carbon emissions risk map from deforestation in the tropical Amazon

NASA Astrophysics Data System (ADS)

Ometto, J.; Soler, L. S.; Assis, T. D.; Oliveira, P. V.; Aguiar, A. P.

2011-12-01

Assis, Pedro Valle This work aims to estimate the carbon emissions from tropical deforestation in the Brazilian Amazon associated to the risk assessment of future land use change. The emissions are estimated by incorporating temporal deforestation dynamics, accounting for the biophysical and socioeconomic heterogeneity in the region, as well secondary forest growth dynamic in abandoned areas. The land cover change model that supported the risk assessment of deforestation, was run based on linear regressions. This method takes into account spatial heterogeneity of deforestation as the spatial variables adopted to fit the final regression model comprise: environmental aspects, economic attractiveness, accessibility and land tenure structure. After fitting a suitable regression models for each land cover category, the potential of each cell to be deforested (25x25km and 5x5 km of resolution) in the near future was used to calculate the risk assessment of land cover change. The carbon emissions model combines high-resolution new forest clear-cut mapping and four alternative sources of spatial information on biomass distribution for different vegetation types. The risk assessment map of CO2 emissions, was obtained by crossing the simulation results of the historical land cover changes to a map of aboveground biomass contained in the remaining forest. This final map represents the risk of CO2 emissions at 25x25km and 5x5 km until 2020, under a scenario of carbon emission reduction target.

Analysis of Multipsectral Time Series for supporting Forest Management Plans

NASA Astrophysics Data System (ADS)

Simoniello, T.; Carone, M. T.; Costantini, G.; Frattegiani, M.; Lanfredi, M.; Macchiato, M.

2010-05-01

Adequate forest management requires specific plans based on updated and detailed mapping. Multispectral satellite time series have been largely applied to forest monitoring and studies at different scales tanks to their capability of providing synoptic information on some basic parameters descriptive of vegetation distribution and status. As a low expensive tool for supporting forest management plans in operative context, we tested the use of Landsat-TM/ETM time series (1987-2006) in the high Agri Valley (Southern Italy) for planning field surveys as well as for the integration of existing cartography. As preliminary activity to make all scenes radiometrically consistent the no-change regression normalization was applied to the time series; then all the data concerning available forest maps, municipal boundaries, water basins, rivers, and roads were overlapped in a GIS environment. From the 2006 image we elaborated the NDVI map and analyzed the distribution for each land cover class. To separate the physiological variability and identify the anomalous areas, a threshold on the distributions was applied. To label the non homogenous areas, a multitemporal analysis was performed by separating heterogeneity due to cover changes from that linked to basilar unit mapping and classification labelling aggregations. Then a map of priority areas was produced to support the field survey plan. To analyze the territorial evolution, the historical land cover maps were elaborated by adopting a hybrid classification approach based on a preliminary segmentation, the identification of training areas, and a subsequent maximum likelihood categorization. Such an analysis was fundamental for the general assessment of the territorial dynamics and in particular for the evaluation of the efficacy of past intervention activities.

High resolution satellite remote sensing used in a stratified random sampling scheme to quantify the constituent land cover components of the shifting cultivation mosaic of the Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Molinario, G.; Hansen, M.; Potapov, P.

2016-12-01

High resolution satellite imagery obtained from the National Geospatial Intelligence Agency through NASA was used to photo-interpret sample areas within the DRC. The area sampled is a stratifcation of the forest cover loss from circa 2014 that either occurred completely within the previosly mapped homogenous area of the Rural Complex, at it's interface with primary forest, or in isolated forest perforations. Previous research resulted in a map of these areas that contextualizes forest loss depending on where it occurs and with what spatial density, leading to a better understading of the real impacts on forest degradation of livelihood shifting cultivation. The stratified random sampling approach of these areas allows the characterization of the constituent land cover types within these areas, and their variability throughout the DRC. Shifting cultivation has a variable forest degradation footprint in the DRC depending on many factors that drive it, but it's role in forest degradation and deforestation had been disputed, leading us to investigate and quantify the clearing and reuse rates within the strata throughout the country.

Mapping forest tree species over large areas with partially cloudy Landsat imagery

NASA Astrophysics Data System (ADS)

Turlej, K.; Radeloff, V.

2017-12-01

Forests provide numerous services to natural systems and humankind, but which services forest provide depends greatly on their tree species composition. That makes it important to track not only changes in forest extent, something that remote sensing excels in, but also to map tree species. The main goal of our work was to map tree species with Landsat imagery, and to identify how to maximize mapping accuracy by including partially cloudy imagery. Our study area covered one Landsat footprint (26/28) in Northern Wisconsin, USA, with temperate and boreal forests. We selected this area because it contains numerous tree species and variable forest composition providing an ideal study area to test the limits of Landsat data. We quantified how species-level classification accuracy was affected by a) the number of acquisitions, b) the seasonal distribution of observations, and c) the amount of cloud contamination. We classified a single year stack of Landsat-7, and -8 images data with a decision tree algorithm to generate a map of dominant tree species at the pixel- and stand-level. We obtained three important results. First, we achieved producer's accuracies in the range 70-80% and user's accuracies in range 80-90% for the most abundant tree species in our study area. Second, classification accuracy improved with more acquisitions, when observations were available from all seasons, and is the best when images with up to 40% cloud cover are included. Finally, classifications for pure stands were 10 to 30 percentage points better than those for mixed stands. We conclude that including partially cloudy Landsat imagery allows to map forest tree species with accuracies that were previously only possible for rare years with many cloud-free observations. Our approach thus provides important information for both forest management and science.

NLCD tree canopy cover (TCC) maps of the contiguous United States and coastal Alaska

Treesearch

Robert Benton; Bonnie Ruefenacht; Vicky Johnson; Tanushree Biswas; Craig Baker; Mark Finco; Kevin Megown; John Coulston; Ken Winterberger; Mark Riley

2015-01-01

A tree canopy cover (TCC) map is one of three elements in the National Land Cover Database (NLCD) 2011 suite of nationwide geospatial data layers. In 2010, the USDA Forest Service (USFS) committed to creating the TCC layer as a member of the Multi-Resolution Land Cover (MRLC) consortium. A general methodology for creating the TCC layer was reported at the 2012 FIA...

Global Survey of Anthropogenic Neighborhood Threats to Conservation of Grass-Shrub and Forest Vegetation

EPA Science Inventory

We report a survey of land cover patterns focusing on forest, grassland, and shrubland for the United States. To provide information for a national resource assessment, an integrated survey of patterns was conducted using a circa 2001 land cover map. The survey was designed to ac...

Carbon changes in conterminous US forests associated with growth and major disturbances: 1992-2001

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey; James E. Smith

2011-01-01

We estimated forest area and carbon changes in the conterminous United States using a remote sensing based land cover change map, forest fire data from the Monitoring Trends in Burn Severity program, and forest growth and harvest data from the USDA Forest Service, Forest Inventory and Analysis Program. Natural and human-associated disturbances reduced the forest...

Pattern-based, multi-scale segmentation and regionalization of EOSD land cover

NASA Astrophysics Data System (ADS)

Niesterowicz, Jacek; Stepinski, Tomasz F.

2017-10-01

The Earth Observation for Sustainable Development of Forests (EOSD) map is a 25 m resolution thematic map of Canadian forests. Because of its large spatial extent and relatively high resolution the EOSD is difficult to analyze using standard GIS methods. In this paper we propose multi-scale segmentation and regionalization of EOSD as new methods for analyzing EOSD on large spatial scales. Segments, which we refer to as forest land units (FLUs), are delineated as tracts of forest characterized by cohesive patterns of EOSD categories; we delineated from 727 to 91,885 FLUs within the spatial extent of EOSD depending on the selected scale of a pattern. Pattern of EOSD's categories within each FLU is described by 1037 landscape metrics. A shapefile containing boundaries of all FLUs together with an attribute table listing landscape metrics make up an SQL-searchable spatial database providing detailed information on composition and pattern of land cover types in Canadian forest. Shapefile format and extensive attribute table pertaining to the entire legend of EOSD are designed to facilitate broad range of investigations in which assessment of composition and pattern of forest over large areas is needed. We calculated four such databases using different spatial scales of pattern. We illustrate the use of FLU database for producing forest regionalization maps of two Canadian provinces, Quebec and Ontario. Such maps capture the broad scale variability of forest at the spatial scale of the entire province. We also demonstrate how FLU database can be used to map variability of landscape metrics, and thus the character of landscape, over the entire Canada.

Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program

NASA Astrophysics Data System (ADS)

Hojas-Gascon, L.; Belward, A.; Eva, H.; Ceccherini, G.; Hagolle, O.; Garcia, J.; Cerutti, P.

2015-04-01

The forthcoming European Space Agency's Sentinel-2 mission promises to provide high (10 m) resolution optical data at higher temporal frequencies (5 day revisit with two operational satellites) than previously available. CNES, the French national space agency, launched a program in 2013, 'SPOT4 take 5', to simulate such a dataflow using the SPOT HRV sensor, which has similar spectral characteristics to the Sentinel sensor, but lower (20m) spatial resolution. Such data flow enables the analysis of the satellite images using temporal analysis, an approach previously restricted to lower spatial resolution sensors. We acquired 23 such images over Tanzania for the period from February to June 2013. The data were analysed with aim of discriminating between different forest cover percentages for landscape units of 0.5 ha over a site characterised by deciduous intact and degraded forests. The SPOT data were processed by one extracting temporal vegetation indices. We assessed the impact of the high acquisition rate with respect to the current rate of one image every 16 days. Validation data, giving the percentage of forest canopy cover in each land unit were provided by very high resolution satellite data. Results show that using the full temporal series it is possible to discriminate between forest units with differences of more than 40% tree cover or more. Classification errors fell exclusively into the adjacent forest canopy cover class of 20% or less. The analyses show that forestation mapping and degradation monitoring will be substantially improved with the Sentinel-2 program.

Spatially quantifying and attributing 17 years of land cover change to examine post-agricultural forest transition in Hawai`i

NASA Astrophysics Data System (ADS)

Lucas, M.; Trauernicht, C.; Carlson, K. M.; Miura, T.; Giambelluca, T. W.; Chen, Q.

2017-12-01

The past decades in Hawaii have seen large scale land use change and land cover shifts. However, much these dynamics are only described anecdotally or studied at a single locale, with little information on the extent, rate, or direction of change. This lack of data hinders any effort to assess, plan, and prioritize land management. To improve assessments of statewide vegetation and land cover change, this project developed high resolution, sub-pixel, percent cover maps of forest, grassland and bare earth at annual time steps from 1999 to 2016. Vegetation cover was quantified using archived LANDSAT imagery and a custom remote-sensing algorithm developed in the Google Earth Engine platform. A statistical trend analysis of annual maps of the these three proportional land covers were then used to detect land cover transitions across the archipelago. The aim of this work focused on quantifying the total area of change, annual rates of change and final vegetation cover outcomes statewide. Additionally these findings were attributed to past and current land uses and management history by compiling spatial datasets of development, agriculture, forest restoration sites and burned areas statewide. Results indicated that nearly 10% of the state's land surfaces are suspected to have transitioned between the three cover classes during the study period. Total statewide net change resulted in a gain in forest cover with largest areas of change occurring in unmanaged areas, current and past pastoral land, commercial forestry and abandoned cultivated land. The fastest annual rates of change were forest increases that occurred in restoration areas and commercial forestry. These findings indicate that Hawaii is going through a forest transition, primarily driven by agricultural abandonment with likely feedbacks from invasive species, but also influenced by the establishment of forestry production on former agricultural lands that show potential for native forest restoration. These results directly link land management history to land cover outcomes using an innovative approach to quantify change. It is also the first study to quantify forest transition dynamics in Hawaii and points to the need for similar assessments in post-agricultural landscapes on other oceanic islands.

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

Gross and net land cover changes in the main plant functional types derived from the annual ESA CCI land cover maps (1992-2015)

NASA Astrophysics Data System (ADS)

Li, Wei; MacBean, Natasha; Ciais, Philippe; Defourny, Pierre; Lamarche, Céline; Bontemps, Sophie; Houghton, Richard A.; Peng, Shushi

2018-01-01

Land-use and land-cover change (LULCC) impacts local energy and water balance and contributes on global scale to a net carbon emission to the atmosphere. The newly released annual ESA CCI (climate change initiative) land cover maps provide continuous land cover changes at 300 m resolution from 1992 to 2015, and can be used in land surface models (LSMs) to simulate LULCC effects on carbon stocks and on surface energy budgets. Here we investigate the absolute areas and gross and net changes in different plant functional types (PFTs) derived from ESA CCI products. The results are compared with other datasets. Global areas of forest, cropland and grassland PFTs from ESA are 30.4, 19.3 and 35.7 million km2 in the year 2000. The global forest area is lower than that from LUH2v2h (Hurtt et al., 2011), Hansen et al. (2013) or Houghton and Nassikas (2017) while cropland area is higher than LUH2v2h (Hurtt et al., 2011), in which cropland area is from HYDE 3.2 (Klein Goldewijk et al., 2016). Gross forest loss and gain during 1992-2015 are 1.5 and 0.9 million km2 respectively, resulting in a net forest loss of 0.6 million km2, mainly occurring in South and Central America. The magnitudes of gross changes in forest, cropland and grassland PFTs in the ESA CCI are smaller than those in other datasets. The magnitude of global net cropland gain for the whole period is consistent with HYDE 3.2 (Klein Goldewijk et al., 2016), but most of the increases happened before 2004 in ESA and after 2007 in HYDE 3.2. Brazil, Bolivia and Indonesia are the countries with the largest net forest loss from 1992 to 2015, and the decreased areas are generally consistent with those from Hansen et al. (2013) based on Landsat 30 m resolution images. Despite discrepancies compared to other datasets, and uncertainties in converting into PFTs, the new ESA CCI products provide the first detailed long-term time series of land-cover change and can be implemented in LSMs to characterize recent carbon dynamics, and in climate models to simulate land-cover change feedbacks on climate. The annual ESA CCI land cover products can be downloaded from http://maps.elie.ucl.ac.be/CCI/viewer/download.php (Land Cover Maps - v2.0.7; see details in Sect. 5). The PFT map translation protocol and an example in 2000 can be downloaded from https://doi.org/10.5281/zenodo.834229. The annual ESA CCI PFT maps from 1992 to 2015 at 0.5° × 0.5° resolution can also be downloaded from https://doi.org/10.5281/zenodo.1048163.

EnviroAtlas - Portland, ME - Land Cover by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of each block group that is classified as impervious, forest, green space, wetland, and agriculture. Impervious is a combination of dark and light impervious. Forest is combination of trees and forest and woody wetlands. Green space is a combination of trees and forest, grass and herbaceous, agriculture, woody wetlands, and emergent wetlands. Wetlands includes both Woody and Emergent Wetlands. This dataset also includes the area per capita for each block group for impervious, forest, and green space land cover. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Development of deforestation and land cover database for Bhutan (1930-2014).

PubMed

Reddy, C Sudhakar; Satish, K V; Jha, C S; Diwakar, P G; Murthy, Y V N Krishna; Dadhwal, V K

2016-12-01

Bhutan is a mountainous country located in the Himalayan biodiversity hotspot. This study has quantified the total area under land cover types, estimated the rate of forest cover change, analyzed the changes across forest types, and modeled forest cover change hotpots in Bhutan. The topographical maps and satellite remote sensing images were analyzed to get the spatial patterns of forest and associated land cover changes over the past eight decades (1930-1977-1987-1995-2005-2014). Forest is the largest land cover in Bhutan and constitutes 68.3% of the total geographical area in 2014. Subtropical broad leaved hill forest is predominant type occupies 34.1% of forest area in Bhutan, followed by montane dry temperate (20.9%), montane wet temperate (18.9%), Himalayan moist temperate (10%), and tropical moist sal (8.1%) in 2014. The major forest cover loss is observed in subtropical broad leaved hill forest (64.5 km 2 ) and moist sal forest (9.9 km 2 ) from 1977 to 2014. The deforested areas have mainly been converted into agriculture and contributed for 60.9% of forest loss from 1930 to 2014. In spite of major decline of forest cover in time interval of 1930-1977, there is no net rate of deforestation is recorded in Bhutan since 1995. Forest cover change analysis has been carried out to evaluate the conservation effectiveness in "Protected Areas" of Bhutan. Hotspots that have undergone high transformation in forest cover for afforestation and deforestation were highlighted in the study for conservation prioritisation. Forest conservation policies in Bhutan are highly effective in controlling deforestation as compared to neighboring Asian countries and such service would help in mitigating climate change.

An assessment of forest cover trends in South and North Korea, from 1980 to 2010.

PubMed

Engler, Robin; Teplyakov, Victor; Adams, Jonathan M

2014-01-01

It is generally believed that forest cover in North Korea has undergone a substantial decrease since 1980, while in South Korea, forest cover has remained relatively static during that same period of time. The United Nations Food and Agriculture Organization (FAO) Forest Resources Assessments--based on the reported forest inventories from North and South Korea--suggest a major forest cover decrease in North Korea, but only a slight decrease in South Korea during the last 30 years. In this study, we seek to check and validate those assessments by comparing them to independently derived forest cover maps compiled for three time intervals between 1990 and 2010, as well as to provide a spatially explicit view of forest cover change in the Korean Peninsula since the 1990s. We extracted tree cover data for the Korean Peninsula from existing global datasets derived from satellite imagery. Our estimates, while qualitatively supporting the FAO results, show that North Korea has lost a large number of densely forested areas, and thus in this sense has suffered heavier forest loss than the FAO assessment suggests. Given the limited time interval studied in our assessment, the overall forest loss from North Korea during the whole span of time since 1980 may have been even heavier than in our estimate. For South Korea, our results indicate that the forest cover has remained relatively stable at the national level, but that important variability in forest cover evolution exists at the regional level: While the northern and western provinces show an overall decrease in forested areas, large areas in the southeastern part of the country have increased their forest cover.

EnviroAtlas - Austin, TX - Tree Cover Configuration and Connectivity, Water Background

EPA Pesticide Factsheets

This EnviroAtlas dataset categorizes forest land cover into structural elements (e.g. core, edge, connector, etc.). In this community, Forest is defined as Trees & Forest (Trees & Forest - 40 = 1; All Else = 0). Water was considered background (value 129) during the analysis to create this dataset, however it has been converted into value 10 to distinguish it from land area background. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Thematic Accuracy Assessment of the 2011 National Land ...

EPA Pesticide Factsheets

Accuracy assessment is a standard protocol of National Land Cover Database (NLCD) mapping. Here we report agreement statistics between map and reference labels for NLCD 2011, which includes land cover for ca. 2001, ca. 2006, and ca. 2011. The two main objectives were assessment of agreement between map and reference labels for the three, single-date NLCD land cover products at Level II and Level I of the classification hierarchy, and agreement for 17 land cover change reporting themes based on Level I classes (e.g., forest loss; forest gain; forest, no change) for three change periods (2001–2006, 2006–2011, and 2001–2011). The single-date overall accuracies were 82%, 83%, and 83% at Level II and 88%, 89%, and 89% at Level I for 2011, 2006, and 2001, respectively. Many class-specific user's accuracies met or exceeded a previously established nominal accuracy benchmark of 85%. Overall accuracies for 2006 and 2001 land cover components of NLCD 2011 were approximately 4% higher (at Level II and Level I) than the overall accuracies for the same components of NLCD 2006. The high Level I overall, user's, and producer's accuracies for the single-date eras in NLCD 2011 did not translate into high class-specific user's and producer's accuracies for many of the 17 change reporting themes. User's accuracies were high for the no change reporting themes, commonly exceeding 85%, but were typically much lower for the reporting themes that represented change. Only forest l

Detailed forest formation mapping in the land cover map series for the Caribbean islands

NASA Astrophysics Data System (ADS)

Helmer, E. H.; Schill, S.; Pedreros, D. H.; Tieszen, L. L.; Kennaway, T.; Cushing, M.; Ruzycki, T.

2006-12-01

Forest formation and land cover maps for several Caribbean islands were developed from Landsat ETM+ imagery as part of a multi-organizational project. The spatially explicit data on forest formation types will permit more refined estimates of some forest attributes. The woody vegetation classification scheme relates closely to that of Areces-Malea et al. (1), who classify Caribbean vegetation according to standards of the US Federal Geographic Data Committee (FGDC, 1997), with modifications similar to those in Helmer et al. (2). For several of the islands, we developed image mosaics that filled cloudy parts of scenes with data from other scene dates after using regression tree normalization (3). The regression tree procedure permitted us to develop mosaics for wet and drought seasons for a few of the islands. The resulting multiseason imagery facilitated separation between classes such as seasonal evergreen forest, semi-deciduous forest (including semi-evergreen forest), and drought deciduous forest or woodland formations. We used decision tree classification methods to classify the Landsat image mosaics to detailed forest formations and land cover for Puerto Rico (4), St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines and Grenada. The decision trees classified a stack of raster layers for each mapping area that included the Landsat image bands and various ancillary raster data layers. For Puerto Rico, for example, the ancillary data included climate parameters (5). For some islands, the ancillary data included topographic derivatives such as aspect, slope and slope position, SRTM (6) or other topographic data. Mapping forest formations with decision tree classifiers, ancillary geospatial data, and cloud-free image mosaics, accurately distinguished spectrally similar forest formations, without the aid of ecological zone maps, on the islands where the approach was used. The approach resulted in maps of forest formations with comparable or better detail than when IKONOS or Landsat imagery was hand-digitized, as it was for the Dominican Republic (7) and Barbados. 1. T. Kennaway, E. H. Helmer. (Intl Inst of Tropical Forestry, USDA Forest Service, Río Piedras, Puerto Rico, 2006). 2. A. Areces-Mallea et al. (The Nature Conservancy, 1999). 3. E. H. Helmer, O. Ramos, T. Lopez, M. Quiñones, W. Diaz, Carib J Sci 38, 165-183 (2002). 4. C. Daly, E. H. Helmer, M. Quiñones, Int J Climatology 23, 1359-1381 (2003). 5. T. G. Farr, M. Kobrick, Eos Transactions 81, 583-585 (2000). 6. E. H. Helmer, B. Ruefenacht, Photogrammetric Eng Rem Sens 71, 1079-1089 (2005). 7. S. Hernández, M. Pérez. (Secretaría de Estado de Medio Ambiente y Recursos Naturales de la República Dominicana, Santo Domingo, Dominican Republic, 2005).

FRAGMENTATION OF CONTINENTAL UNITES STATES FORESTS

EPA Science Inventory

We report a multiple-scale analysis of forest fragmentation based on 30-m land-cover maps for the conterminous United States. Each 0.09-ha unit of forest was classified according to fragmentation indices measured within the surrounding landscape, for five landscape sizes from 2....

Estimating number and size of forest patches from FIA plot data

Treesearch

Mark D. Nelson; Andrew J. Lister; Mark H. Hansen

2009-01-01

Forest inventory and analysis (FIA) annual plot data provide for estimates of forest area, type, volume, growth, and other attributes. Estimates of forest landscape metrics, such as those describing abundance, size, and shape of forest patches, however, typically are not derived from FIA plot data but from satellite image-based land cover maps. Associating image-based...

First forest soil survey gives significant results.

Treesearch

Robert F. Tarrant

1947-01-01

The first forest soil survey on national forest lands in the Pacific Northwest was completed last year on the Pringle Falls Experimental Forest when a detailed soil map covering four square miles was made by W.J. Leighty, Assistant Inspector, Bureau of Plant Industry, Soils and Agricultural Engineering. Arrangements for the survey were made by Region 6 of the Forest...

Predicting the temporal and spatial probability of orographic cloud cover in the Luquillo Experimental Forest in Puerto Rico using generalized linear (mixed) models.

Treesearch

Wei Wu; Charlesb Hall; Lianjun Zhang

2006-01-01

We predicted the spatial pattern of hourly probability of cloud cover in the Luquillo Experimental Forest (LEF) in North-Eastern Puerto Rico using four different models. The probability of cloud cover (defined as “the percentage of the area covered by clouds in each pixel on the map” in this paper) at any hour and any place is a function of three topographic variables...

High-Resolution Regional Biomass Map of Siberia from Glas, Palsar L-Band Radar and Landsat Vcf Data

NASA Astrophysics Data System (ADS)

Sun, G.; Ranson, K.; Montesano, P.; Zhang, Z.; Kharuk, V.

2015-12-01

The Arctic-Boreal zone is known be warming at an accelerated rate relative to other biomes. The taiga or boreal forest covers over 16 x106 km2 of Arctic North America, Scandinavia, and Eurasia. A large part of the northern Boreal forests are in Russia's Siberia, as area with recent accelerated climate warming. During the last two decades we have been working on characterization of boreal forests in north-central Siberia using field and satellite measurements. We have published results of circumpolar biomass using field plots, airborne (PALS, ACTM) and spaceborne (GLAS) lidar data with ASTER DEM, LANDSAT and MODIS land cover classification, MODIS burned area and WWF's ecoregion map. Researchers from ESA and Russia have also been working on biomass (or growing stock) mapping in Siberia. For example, they developed a pan-boreal growing stock volume map at 1-kilometer scale using hyper-temporal ENVISAT ASAR ScanSAR backscatter data. Using the annual PALSAR mosaics from 2007 to 2010 growing stock volume maps were retrieved based on a supervised random forest regression approach. This method is being used in the ESA/Russia ZAPAS project for Central Siberia Biomass mapping. Spatially specific biomass maps of this region at higher resolution are desired for carbon cycle and climate change studies. In this study, our work focused on improving resolution ( 50 m) of a biomass map based on PALSAR L-band data and Landsat Vegetation Canopy Fraction products. GLAS data were carefully processed and screened using land cover classification, local slope, and acquisition dates. The biomass at remaining footprints was estimated using a model developed from field measurements at GLAS footprints. The GLAS biomass samples were then aggregated into 1 Mg/ha bins of biomass and mean VCF and PALSAR backscatter and textures were calculated for each of these biomass bins. The resulted biomass/signature data was used to train a random forest model for biomass mapping of entire region from 50oN to 75oN, and 80oE to 145oE. The spatial patterns of the new biomass map is much better than the previous maps due to spatially specific mapping in high resolution. The uncertainties of field/GLAS and GLAS/imagery models were investigated using bootstrap procedure, and the final biomass map was compared with previous maps.

Overview of South‐east Asia land cover using a NOAA AVHRR one kilometer composite

USGS Publications Warehouse

Defourny, Pierre; Pradhan, Udai C.; Vinay, Sritharan; Johnson, Gary E.

1994-01-01

A cloud free AVHRR composite of South‐East Asia at one kilometer resolution has been produced from 38 selected daily NOAA‐11 AVHRR images. Geometric accuracy of about 1 pixel is achieved using a two‐step rectification algorithm (orbital model and transformation by ground control points). A spatial and spectral enhancement has been performed, the sea masked out and political boundaries included in the final product. This AVHRR composite is particularly useful for a comprehensive overview of land cover at a regional scale. Qualitative comparison between a monthly composite and the existing forest maps highlights the forest cover change and points out the hot spots where the maps have to be updated.

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.

Application of geoinformatics for landscape assessment and conserving forest biodiversity in northeast India

Treesearch

Ashish Kumar; Bruce G. Marcot; Gautam Talukdar; P.S. Roy

2012-01-01

Herein, we summarize our work, within forest ecosystems of Garo Hills in northeast India, on mapping vegetation and land cover conditions, delineating wildlife habitat corridors among protected areas, evaluating forest conservation values of influence zones bordering protected areas, analyzing dispersion patterns of native forests, and determining potential effects of...

Comprehensive monitoring of Bangladesh tree cover inside and outside of forests, 2000-2014

NASA Astrophysics Data System (ADS)

Potapov, P.; Siddiqui, B. N.; Iqbal, Z.; Aziz, T.; Zzaman, B.; Islam, A.; Pickens, A.; Talero, Y.; Tyukavina, A.; Turubanova, S.; Hansen, M. C.

2017-10-01

A novel approach for satellite-based comprehensive national tree cover change assessment was developed and applied in Bangladesh, a country where trees outside of forests play an important role in the national economy and carbon sequestration. Tree cover change area was quantified using the integration of wall-to-wall Landsat-based mapping with a higher spatial resolution sample-based assessment. The total national tree canopy cover area was estimated as 3165 500 ± 186 600 ha in the year 2000, with trees outside forests making up 54% of total canopy cover. Total tree canopy cover increased by 135 700 (± 116 600) ha (4.3%) during the 2000-2014 time interval. Bangladesh exhibits a national tree cover dynamic where net change is rather small, but gross dynamics significant and variable by forest type. Despite the overall gain in tree cover, results revealed the ongoing clearing of natural forests, especially within the Chittagong hill tracts. While forests decreased their tree cover area by 83 600 ha, the trees outside forests (including tree plantations, village woodlots, and agroforestry) increased their canopy area by 219 300 ha. Our results demonstrated method capability to quantify tree canopy cover dynamics within a fine-scale agricultural landscape. Our approach for comprehensive monitoring of tree canopy cover may be recommended for operational implementation in Bangladesh and other countries with significant tree cover outside of forests.

EnviroAtlas - Memphis, TN - Tree Cover Configuration and Connectivity, Water Background

EPA Pesticide Factsheets

This EnviroAtlas dataset categorizes forest land cover into structural elements (e.g. core, edge, connector, etc.). Forest is defined as Trees & Forest and Woody Wetlands. Water was considered background (value 129) during the analysis to create this dataset, however it has been converted into value 10 to distinguish it from land area background. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Integration of ALOS/PALSAR backscatter with a LiDAR-derived canopy height map to quantify forest fragmentation

NASA Astrophysics Data System (ADS)

Pinto, N.; Dubayah, R.; Simard, M.; Fatoyinbo, T. E.

2011-12-01

Habitat loss is the main predictor of species extinctions and must be characterized in high-biodiversity ecosystems where land cover change is pervasive. Forests' ability to support viable animal populations is typically modeled as a function of the presence of linkages or corridors, and quantified with fragmentation metrics. In this scenario, small forest patches and linear (e.g. riparian) zones can act as keystone structures. Fine-resolution, all-weather Synthetic Aperture Radar (SAR) data from ALOS/PALSAR is well-suited to resolve forest fragments in tropical sites. This study summarizes a technique for integrating fragmentation metrics from ALOS/PALSAR with vertical structure data from ICESat/GLAS to produce fine-resolution (30 m) forest habitat metrics that capture both local quality (canopy height) as well as spatial context and multi-scale connectivity. We illustrate our approach with backscatter images acquired over the Brazilian Atlantic Forest, a biodiversity hotspot. ALOS/PALSAR 1.1 images acquired over the dry season were calibrated to calculate gamma naught and map forest cover via tresholding. We employ network algorithms to locate dispersal bottlenecks between conservation units. The location of keystone structures is compared against a model that uses coarse (500m) percent tree cover as an input.

A methodology for mapping forest latent heat flux densities using remote sensing

NASA Technical Reports Server (NTRS)

Pierce, Lars L.; Congalton, Russell G.

1988-01-01

Surface temperatures and reflectances of an upper elevation Sierran mixed conifer forest were monitored using the Thematic Mapper Simulator sensor during the summer of 1985 in order to explore the possibility of using remote sensing to determine the distribution of solar energy on forested watersheds. The results show that the method is capable of quantifying the relative energy allocation relationships between the two cover types defined in the study. It is noted that the method also has the potential to map forest latent heat flux densities.

Geographical Distribution of Woody Biomass Carbon in Tropical Africa: An Updated Database for 2000 (NDP-055.2007, NDP-055b))

DOE Data Explorer

Gibbs, Holly K. [Center for Sustainability and the Global Environment (SAGE), University of Wisconsin, Madison, WI (USA); Brown, Sandra [Winrock International, Arlington, VA (USA); Olsen, L. M. [Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, Oak Ridge, TN (USA); Boden, Thomas A. [Carbon Dioxide Information Analysis Center (CDIAC), Oak Ridge National Laboratory, Oak Ridge, TN (USA)

2007-09-01

Maps of biomass density are critical inputs for estimating carbon emissions from deforestation and degradation of tropical forests. Brown and Gatson (1996) pioneered methods to use GIS analysis to map forest biomass based on forest inventory data (ndp055). This database is an update of ndp055 (which represent conditions in circa 1980) and accounts for land cover changes occurring up to the year 2000.

Four Decades of Forest Persistence, Clearance and Logging on Borneo

PubMed Central

Gaveau, David L. A.; Sloan, Sean; Molidena, Elis; Yaen, Husna; Sheil, Doug; Abram, Nicola K.; Ancrenaz, Marc; Nasi, Robert; Quinones, Marcela; Wielaard, Niels; Meijaard, Erik

2014-01-01

The native forests of Borneo have been impacted by selective logging, fire, and conversion to plantations at unprecedented scales since industrial-scale extractive industries began in the early 1970s. There is no island-wide documentation of forest clearance or logging since the 1970s. This creates an information gap for conservation planning, especially with regard to selectively logged forests that maintain high conservation potential. Analysing LANDSAT images, we estimate that 75.7% (558,060 km2) of Borneo's area (737,188 km2) was forested around 1973. Based upon a forest cover map for 2010 derived using ALOS-PALSAR and visually reviewing LANDSAT images, we estimate that the 1973 forest area had declined by 168,493 km2 (30.2%) in 2010. The highest losses were recorded in Sabah and Kalimantan with 39.5% and 30.7% of their total forest area in 1973 becoming non-forest in 2010, and the lowest in Brunei and Sarawak (8.4%, and 23.1%). We estimate that the combined area planted in industrial oil palm and timber plantations in 2010 was 75,480 km2, representing 10% of Borneo. We mapped 271,819 km of primary logging roads that were created between 1973 and 2010. The greatest density of logging roads was found in Sarawak, at 0.89 km km−2, and the lowest density in Brunei, at 0.18 km km−2. Analyzing MODIS-based tree cover maps, we estimate that logging operated within 700 m of primary logging roads. Using this distance, we estimate that 266,257 km2 of 1973 forest cover has been logged. With 389,566 km2 (52.8%) of the island remaining forested, of which 209,649 km2 remains intact. There is still hope for biodiversity conservation in Borneo. Protecting logged forests from fire and conversion to plantations is an urgent priority for reducing rates of deforestation in Borneo. PMID:25029192

Four decades of forest persistence, clearance and logging on Borneo.

PubMed

Gaveau, David L A; Sloan, Sean; Molidena, Elis; Yaen, Husna; Sheil, Doug; Abram, Nicola K; Ancrenaz, Marc; Nasi, Robert; Quinones, Marcela; Wielaard, Niels; Meijaard, Erik

2014-01-01

The native forests of Borneo have been impacted by selective logging, fire, and conversion to plantations at unprecedented scales since industrial-scale extractive industries began in the early 1970s. There is no island-wide documentation of forest clearance or logging since the 1970s. This creates an information gap for conservation planning, especially with regard to selectively logged forests that maintain high conservation potential. Analysing LANDSAT images, we estimate that 75.7% (558,060 km2) of Borneo's area (737,188 km2) was forested around 1973. Based upon a forest cover map for 2010 derived using ALOS-PALSAR and visually reviewing LANDSAT images, we estimate that the 1973 forest area had declined by 168,493 km2 (30.2%) in 2010. The highest losses were recorded in Sabah and Kalimantan with 39.5% and 30.7% of their total forest area in 1973 becoming non-forest in 2010, and the lowest in Brunei and Sarawak (8.4%, and 23.1%). We estimate that the combined area planted in industrial oil palm and timber plantations in 2010 was 75,480 km2, representing 10% of Borneo. We mapped 271,819 km of primary logging roads that were created between 1973 and 2010. The greatest density of logging roads was found in Sarawak, at 0.89 km km-2, and the lowest density in Brunei, at 0.18 km km-2. Analyzing MODIS-based tree cover maps, we estimate that logging operated within 700 m of primary logging roads. Using this distance, we estimate that 266,257 km2 of 1973 forest cover has been logged. With 389,566 km2 (52.8%) of the island remaining forested, of which 209,649 km2 remains intact. There is still hope for biodiversity conservation in Borneo. Protecting logged forests from fire and conversion to plantations is an urgent priority for reducing rates of deforestation in Borneo.

A Practical and Automated Approach to Large Area Forest Disturbance Mapping with Remote Sensing

PubMed Central

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions. PMID:24717283

A practical and automated approach to large area forest disturbance mapping with remote sensing.

PubMed

Ozdogan, Mutlu

2014-01-01

In this paper, I describe a set of procedures that automate forest disturbance mapping using a pair of Landsat images. The approach is built on the traditional pair-wise change detection method, but is designed to extract training data without user interaction and uses a robust classification algorithm capable of handling incorrectly labeled training data. The steps in this procedure include: i) creating masks for water, non-forested areas, clouds, and cloud shadows; ii) identifying training pixels whose value is above or below a threshold defined by the number of standard deviations from the mean value of the histograms generated from local windows in the short-wave infrared (SWIR) difference image; iii) filtering the original training data through a number of classification algorithms using an n-fold cross validation to eliminate mislabeled training samples; and finally, iv) mapping forest disturbance using a supervised classification algorithm. When applied to 17 Landsat footprints across the U.S. at five-year intervals between 1985 and 2010, the proposed approach produced forest disturbance maps with 80 to 95% overall accuracy, comparable to those obtained from traditional approaches to forest change detection. The primary sources of mis-classification errors included inaccurate identification of forests (errors of commission), issues related to the land/water mask, and clouds and cloud shadows missed during image screening. The approach requires images from the peak growing season, at least for the deciduous forest sites, and cannot readily distinguish forest harvest from natural disturbances or other types of land cover change. The accuracy of detecting forest disturbance diminishes with the number of years between the images that make up the image pair. Nevertheless, the relatively high accuracies, little or no user input needed for processing, speed of map production, and simplicity of the approach make the new method especially practical for forest cover change analysis over very large regions.

Afro-alpine forest cover change on Mt. Guna (Ethiopia)

NASA Astrophysics Data System (ADS)

Birhanu, Adugnaw; Frankl, Amaury; Jacob, Miro; Lanckriet, Sil; Hendrickx, Hanne; Nyssen, Jan

2016-04-01

High mountain forests, such as the afro-alpine Erica arborea L. forests in Ethiopia, are very important for the livelihood of local communities, in relation to their impacts on the water balance of mountain ecosystems and surrounding agricultural areas. On volcanoes, the dominance of volcanic tuffs on the slopes, as well as that of gelifracts near the top further enhances infiltration, making it recharge areas. Earlier forest cover change studies in the Ethiopian highlands mainly deal with the lower vegetation belts. In this study, 3.37 km² on the western slopes of Mount Guna (one of the dozens of Miocene shield volcanoes that exist on top of the Ethiopian plateau) was mapped. The slope has an elevation between 3200 at its base and 4113 m a.s.l. at the peak. The present forest cover was recorded from high-resolution georeferenced satellite imagery from Google Maps and field data (2015), while historical forest cover was studied from georeferenced aerial photographs of 1982. In addition, key informant interviews were conducted to identify the trend of forest cover change and management practices. Whereas burning of the Erica forest for sake of land clearance (a typical practice on all Ethiopian mountains until the 1980s) most strikingly took place for three consecutive days in 1975, large-scale deforestation resulting from agricultural expansion and livestock pressure continued thereafter. However, between 2000 and 2014, due to active involvement of local and governmental institutions there was a slight regeneration of the vegetation and the Erica forest. Protection and regeneration of the forest was particularly efficient after it was given into custody of an orthodox church established in 1999 at the lower side of the forest. Overall, the study revealed that human and livestock pressures are the strongest drivers of deforestation. Furthermore, the study indicated that integrating the actions of local and governmental institutions is key for the protection of the afro-alpine vegetation on the other parts of Mt. Guna.

Earth observation data for assessment of nationwide land cover and long-term deforestation in Afghanistan

NASA Astrophysics Data System (ADS)

Sudhakar Reddy, C.; Saranya, K. R. L.

2017-08-01

This study has generated a national level spatial database of land cover and changes in forest cover of Afghanistan for the 1975-1990, 1990-2005 and 2005-2014 periods. Using these results we have analysed the annual deforestation rates, spatial changes in forests, forest types and fragmentation classes over a period of 1975 to 2014 in Afghanistan. The land cover map of 2014 provides distribution of forest (dry evergreen, moist temperate, dry temperate, pine, sub alpine) and non-forest (grassland, scrub, agriculture, wetlands, barren land, snow and settlements) in Afghanistan. The largest land cover, barren land, contributes to 56% of geographical area of country. Forest is distributed mostly in eastern Afghanistan and constitutes an area of 1.02% of geographical area in 2014. The annual deforestation rate in Afghanistan's forests for the period from 1975 to 1990 estimated as 0.06% which was declined significantly from 2005 to 2014. The predominant forest type in Afghanistan is moist temperate which shows loss of 80 km2 of area during the last four decades of the study period. At national level, the percentage of large core forest area was calculated as 52.20% in 2014.

Large-area Mapping of Forest Cover and Biomass using ALOS PALSAR

NASA Astrophysics Data System (ADS)

Cartus, O.; Kellndorfer, J. M.; Walker, W. S.; Goetz, S. J.; Laporte, N.; Bishop, J.; Cormier, T.; Baccini, A.

2011-12-01

In the frame of a Pantropical mapping project, we aim at producing high-resolution forest cover maps from ALOS PALSAR. The ALOS data was obtained through the Americas ALOS Data Node (AADN) at ASF. For the forest cover classification, a pan-tropical network of calibrated reference data was generated from ancillary satellite data (ICESAT GLAS). These data are used to classify PALSAR swath data to be combined to continental forest probability maps. The maps are validated with withheld training data for testing, as well as through independent operator verification with very high-resolution image. In addition, we aim at developing robust algorithms for the mapping of forest biophysical parameters like stem volume or biomass using synergy of PALSAR, optical and Lidar data. Currently we are testing different approaches for the mapping of forest biophysical parameters. 1) For the showcase scenario of Mexico, where we have access to ~1400 PALSAR FBD images as well as the 30 m Landsat Vegetation Continuous Field product, VCF, we test a traditional ground-data based approach. The PALSAR HH/HV intensity data and VCF are used as predictor layers in RandomForest for predicting aboveground forest biomass. A network of 40000 in situ biomass plots is used for model development (for each PALSAR swath) as well as for validation. With this approach a first 30 m biomass map for entire Mexico was produced. An initial validation of the map resulted in an RMSE of 41 t/ha and an R2 of 0.42. Pronounced differences between different ecozones were observed. In some areas the retrieval reached an R2 of 0.6 (e.g. pine-oak forests) whereas, for instance, in dry woodlands, the retrieval accuracy was much lower (R2 of 0.1). A major limitation of the approach was also represented by the fact that for the development of models for each ALOS swath, in some cases too few sample plots were available. 2) Chile: At a forest site in Central Chile, dominated by plantations of pinus radiata, synergy of ALOS PALSAR, Landsat and small-footprint Lidar is investigated for the mapping of forest growing stock volume and canopy height. Canopy Height Models with 1 m pixel size that were generated from the first/last return Lidar data were used to produce surrogate sampling plots to upscale stand-level inventory measurements to wall-to-wall maps with the aid of multi-temporal ALOS and Landsat data. The Lidar data allowed the estimation of volume and canopy height with high accuracy: 23 % error in case of volume and 7 % error in case of height. Using the Lidar estimates as surrogate training data for the development of models relating the ALOS backscatter to volume and height we obtained retrieval errors of ~60 % in case of volume and 31 % in case of height when using only one ALOS FBD image. Significant improvements could be achieved when 1) using three ALOS images for retrieval (50 % error for volume and 26 % for height) and 2) when including also Landsat data (42 % error for volume and 20 % for height).

Application of Landsat 5-TM and GIS data to elk habitat studies in northern Idaho

NASA Astrophysics Data System (ADS)

Hayes, Stephen Gordon

1999-12-01

An extensive geographic information system (GIS) database and a large radiotelemetry sample of elk (n = 153) were used to study habitat use and selection differences between cow and bull elk (Cervus elaphus) in the Coeur d'Alene Mountains of Idaho. Significant sex differences in 40 ha area use, and interactive effects of sex and season on selection of 40 ha areas from home ranges were found. In all seasons, bulls used habitats with more closed canopy forest, more hiding cover, and less shrub and graminoid cover, than cows. Cows selected areas with shrub and graminoid cover in winter and avoided areas with closed canopy forest and hiding cover in winter and summer seasons. Both sexes selected 40 ha areas of unfragmented hiding cover and closed canopy forest during the hunting season. Bulls also avoided areas with high open road densities during the rut and hunting season. These results support present elk management recommendations, but our observations of sexual segregation provide biologists with an opportunity to refine habitat management plans to target bulls and cows specifically. Furthermore, the results demonstrate that hiding cover and canopy closure can be accurately estimated from Landsat 5-TM imagery and GIS soil data at a scale and resolution to which elk respond. As a result, our habitat mapping methods can be applied to large areas of private and public land with consistent, cost-efficient results. Non-Lambertian correction models of Landsat 5-TM imagery were compared to an uncorrected image to determine if topographic normalization increased the accuracy of elk habitat maps of forest structure in northern Idaho. The non-Lambertian models produced elk habitat maps with overall and kappa statistic accuracies as much as 21.3% higher (p < 0.0192) than the uncorrected image. Log-linear models and power analysis were used to study the dependence of commission and omission error rates on topographic normalization, vegetation type, and solar incidence angle. Examination of Type I and Type II likelihood ratio test error rates indicated that topographic normalization increased accuracy in sapling/pole closed forest, clearcuts, open forest, and shrubfields. Non-Lambertian models that allowed the Minnaert constant (k) to vary as a function of solar incidence and vegetation type offered no improvement in accuracy over the non-Lambertian model with k estimated for each TM band. The bias of habitat use proportion estimates, derived from the most accurate map, was quantified and the applicability of the non-Lambertian model to elk habitat mapping is discussed.

Accuracy assessments and areal estimates using two-phase stratified random sampling, cluster plots, and the multivariate composite estimator

Treesearch

Raymond L. Czaplewski

2000-01-01

Consider the following example of an accuracy assessment. Landsat data are used to build a thematic map of land cover for a multicounty region. The map classifier (e.g., a supervised classification algorithm) assigns each pixel into one category of land cover. The classification system includes 12 different types of forest and land cover: black spruce, balsam fir,...

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%.

Low-Altitude AVIRIS Data for Mapping Land Cover in Yellowstone National Park: Use of Isodata Clustering Techniques

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.

2001-01-01

Northeast Yellowstone National Park (YNP) has a diversity of forest, range, and wetland cover types. Several remote sensing studies have recently been done in this area, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project conducted by Yellowstone Ecosystems Studies (YES) on the use of hyperspectral imaging for assessing riparian and in-stream habitats. In 1999, YES and NASA's Commercial Remote Sensing Program Office began collaborative study of this area, assessing the potential of synergistic use of hyperspectral, synthetic aperture radar (SAR), and multiband thermal data for mapping forest, range, and wetland land cover. Since the beginning, a quality 'reference' land cover map has been desired as a tool for developing and validating other land cover maps produced during the project. This paper recounts an effort to produce such a reference land cover map using low-altitude Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data and unsupervised classification techniques. The main objective of this study is to assess ISODATA classification for mapping land cover in Northeast YNP using select bands of low-altitude AVIRIS data. A secondary, more long-term objective is to assess the potential for improving ISODATA-based classification of land cover through use of principal components analysis and minimum noise fraction (MNF) techniques. This paper will primarily report on work regarding the primary research objective. This study focuses on an AVIRIS cube acquired on July 23, 1999, by the confluence of Soda Butte Creek with the Lamar River. Range and wetland habitats dominate the image with forested habitats being a comparatively minor component of the scene. The scene generally tracks from southwest to northeast. Most of the scene is valley bottom with some lower side slopes occurring on the western portion. Elevations within the AVIRIS scene range from approximately 1998 to 2165 m above sea level, based on US Geological Survey (USGS) 30-m digital elevation model (DEM) data. Despain and the National Park Service (NPS) provide additional description of the study area.

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.

[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.

A statistically valid method for using FIA plots to guide spectral class rejection in producing stratification maps

Treesearch

Michael L. Hoppus; Andrew J. Lister

2002-01-01

A Landsat TM classification method (iterative guided spectral class rejection) produced a forest cover map of southern West Virginia that provided the stratification layer for producing estimates of timberland area from Forest Service FIA ground plots using a stratified sampling technique. These same high quality and expensive FIA ground plots provided ground reference...

Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010

PubMed Central

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhang, Geli; Roy, Partha Sarathi; Joshi, Pawan Kumar; Gilani, Hammad; Murthy, Manchiraju Sri Ramachandra; Jin, Cui; Wang, Jie; Zhang, Yao; Chen, Bangqian; Menarguez, Michael Angelo; Biradar, Chandrashekhar M.; Bajgain, Rajen; Li, Xiangping; Dai, Shengqi; Hou, Ying; Xin, Fengfei; Moore III, Berrien

2016-01-01

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 106 km2. The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests. PMID:26864143

Mapping forests in monsoon Asia with ALOS PALSAR 50-m mosaic images and MODIS imagery in 2010.

PubMed

Qin, Yuanwei; Xiao, Xiangming; Dong, Jinwei; Zhang, Geli; Roy, Partha Sarathi; Joshi, Pawan Kumar; Gilani, Hammad; Murthy, Manchiraju Sri Ramachandra; Jin, Cui; Wang, Jie; Zhang, Yao; Chen, Bangqian; Menarguez, Michael Angelo; Biradar, Chandrashekhar M; Bajgain, Rajen; Li, Xiangping; Dai, Shengqi; Hou, Ying; Xin, Fengfei; Moore, Berrien

2016-02-11

Extensive forest changes have occurred in monsoon Asia, substantially affecting climate, carbon cycle and biodiversity. Accurate forest cover maps at fine spatial resolutions are required to qualify and quantify these effects. In this study, an algorithm was developed to map forests in 2010, with the use of structure and biomass information from the Advanced Land Observation System (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) mosaic dataset and the phenological information from MODerate Resolution Imaging Spectroradiometer (MOD13Q1 and MOD09A1) products. Our forest map (PALSARMOD50 m F/NF) was assessed through randomly selected ground truth samples from high spatial resolution images and had an overall accuracy of 95%. Total area of forests in monsoon Asia in 2010 was estimated to be ~6.3 × 10(6 )km(2). The distribution of evergreen and deciduous forests agreed reasonably well with the median Normalized Difference Vegetation Index (NDVI) in winter. PALSARMOD50 m F/NF map showed good spatial and areal agreements with selected forest maps generated by the Japan Aerospace Exploration Agency (JAXA F/NF), European Space Agency (ESA F/NF), Boston University (MCD12Q1 F/NF), Food and Agricultural Organization (FAO FRA), and University of Maryland (Landsat forests), but relatively large differences and uncertainties in tropical forests and evergreen and deciduous forests.

Forest Stand Segmentation Using Airborne LIDAR Data and Very High Resolution Multispectral Imagery

NASA Astrophysics Data System (ADS)

Dechesne, Clément; Mallet, Clément; Le Bris, Arnaud; Gouet, Valérie; Hervieu, Alexandre

2016-06-01

Forest stands are the basic units for forest inventory and mapping. Stands are large forested areas (e.g., ≥ 2 ha) of homogeneous tree species composition. The accurate delineation of forest stands is usually performed by visual analysis of human operators on very high resolution (VHR) optical images. This work is highly time consuming and should be automated for scalability purposes. In this paper, a method based on the fusion of airborne laser scanning data (or lidar) and very high resolution multispectral imagery for automatic forest stand delineation and forest land-cover database update is proposed. The multispectral images give access to the tree species whereas 3D lidar point clouds provide geometric information on the trees. Therefore, multi-modal features are computed, both at pixel and object levels. The objects are individual trees extracted from lidar data. A supervised classification is performed at the object level on the computed features in order to coarsely discriminate the existing tree species in the area of interest. The analysis at tree level is particularly relevant since it significantly improves the tree species classification. A probability map is generated through the tree species classification and inserted with the pixel-based features map in an energetical framework. The proposed energy is then minimized using a standard graph-cut method (namely QPBO with α-expansion) in order to produce a segmentation map with a controlled level of details. Comparison with an existing forest land cover database shows that our method provides satisfactory results both in terms of stand labelling and delineation (matching ranges between 94% and 99%).

Roles of Fog and Topography in Redwood Forest Hydrology

NASA Astrophysics Data System (ADS)

Francis, E. J.; Asner, G. P.

2017-12-01

Spatial variability of water in forests is a function of both climatic gradients that control water inputs and topo-edaphic variation that determines the flows of water belowground, as well as interactions of climate with topography. Coastal redwood forests are hydrologically unique because they are influenced by coastal low clouds, or fog, that is advected onto land by a strong coastal-to-inland temperature difference. Where fog intersects the land surface, annual water inputs from summer fog drip can be greater than that of winter rainfall. In this study, we take advantage of mapped spatial gradients in forest canopy water storage, topography, and fog cover in California to better understand the roles and interactions of fog and topography in the hydrology of redwood forests. We test a conceptual model of redwood forest hydrology with measurements of canopy water content derived from high-resolution airborne imaging spectroscopy, topographic variables derived from high-resolution LiDAR data, and fog cover maps derived from NASA MODIS data. Landscape-level results provide insight into hydrological processes within redwood forests, and cross-site analyses shed light on their generality.

EnviroAtlas - Percent Stream Buffer Zone As Natural Land Cover for the Conterminous United States

EPA Pesticide Factsheets

This EnviroAtlas dataset shows the percentage of land area within a 30 meter buffer zone along the National Hydrography Dataset (NHD) high resolution stream network, and along water bodies such as lakes and ponds that are connected via flow to the streams, that is classified as forest land cover, modified forest land cover, and natural land cover using the 2006 National Land Cover Dataset (NLCD) for each Watershed Boundary Dataset (WBD) 12-digit hydrological unit (HUC) in the conterminous United States. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Spatial configuration and distribution of forest patches in Champaign County, Illinois: 1940 to 1993

Treesearch

J. Danilo Chinea

1997-01-01

Spatial configuration and distribution of landscape elements have implications for the dynamics of forest ecosystems, and, therefore, for the management of these resources. The forest cover of Champaign County, in east-central Illinois, was mapped from 1940 and 1993 aerial photography and entered in a geographical information system database. In 1940, 208 forest...

Use of remote sensing for monitoring deforestation in tropical and subtropical latitudes

USGS Publications Warehouse

Talbot, J. J.; Pettinger, Lawrence R.

1981-01-01

Factors limiting the application of Landsat data—including relatively low spatial resolution, persistent cloud cover in tropical regions, inadequate coverage of certain areas due to data-acquisition restraints and lack of local Landsat data receiving stations for real-time data recording—must be considered in any proposed study. Future improvements in Landsat capabilities might extend present applications beyond distinction of forest vs. non-forest cover, determination of gross vegetation or forest type, and generalized land use mapping.

A primer on stand and forest inventory designs

Treesearch

H. Gyde Lund; Charles E. Thomas

1989-01-01

Covers designs for the inventory of stands and forests in detail and with worked-out examples. For stands, random sampling, line transects, ricochet plot, systematic sampling, single plot, cluster, subjective sampling and complete enumeration are discussed. For forests inventory, the main categories are subjective sampling, inventories without prior stand mapping,...

American Samoa's forest resources, 2001.

Treesearch

Joseph A. Donnegan; Sheri S. Mann; Sarah L. Butler; Bruce A. Hiserote

2004-01-01

The Forest Inventory and Analysis Program of the Pacific Northwest Research Station collected, analyzed, and summarized data from field plots, and mapped land cover on four islands in American Samoa. This statistical sample provides estimates of forest area, stem volume, biomass, numbers of trees, damages to trees, and tree size distribution. The summary provides...

Mapping land cover through time with the Rapid Land Cover Mapper—Documentation and user manual

USGS Publications Warehouse

Cotillon, Suzanne E.; Mathis, Melissa L.

2017-02-15

The Rapid Land Cover Mapper is an Esri ArcGIS® Desktop add-in, which was created as an alternative to automated or semiautomated mapping methods. Based on a manual photo interpretation technique, the tool facilitates mapping over large areas and through time, and produces time-series raster maps and associated statistics that characterize the changing landscapes. The Rapid Land Cover Mapper add-in can be used with any imagery source to map various themes (for instance, land cover, soils, or forest) at any chosen mapping resolution. The user manual contains all essential information for the user to make full use of the Rapid Land Cover Mapper add-in. This manual includes a description of the add-in functions and capabilities, and step-by-step procedures for using the add-in. The Rapid Land Cover Mapper add-in was successfully used by the U.S. Geological Survey West Africa Land Use Dynamics team to accurately map land use and land cover in 17 West African countries through time (1975, 2000, and 2013).

Assessment of Large Scale Land Cover Change Classifications and Drivers of Deforestation in Indonesia

NASA Astrophysics Data System (ADS)

Wijaya, A.; Sugardiman Budiharto, R. A.; Tosiani, A.; Murdiyarso, D.; Verchot, L. V.

2015-04-01

Indonesia possesses the third largest tropical forests coverage following Brazilian Amazon and Congo Basin regions. This country, however, suffered from the highest deforestation rate surpassing deforestation in the Brazilian Amazon in 2012. National capacity for forest change assessment and monitoring has been well-established in Indonesia and the availability of national forest inventory data could largely assist the country to report their forest carbon stocks and change over more than two decades. This work focuses for refining forest cover change mapping and deforestation estimate at national scale applying over 10,000 scenes of Landsat scenes, acquired in 1990, 1996, 2000, 2003, 2006, 2009, 2011 and 2012. Pre-processing of the data includes, geometric corrections and image mosaicking. The classification of mosaic Landsat data used multi-stage visual observation approaches, verified using ground observations and comparison with other published materials. There are 23 land cover classes identified from land cover data, presenting spatial information of forests, agriculture, plantations, non-vegetated lands and other land use categories. We estimated the magnitude of forest cover change and assessed drivers of forest cover change over time. Forest change trajectories analysis was also conducted to observe dynamics of forest cover across time. This study found that careful interpretations of satellite data can provide reliable information on forest cover and change. Deforestation trend in Indonesia was lower in 2000-2012 compared to 1990-2000 periods. We also found that over 50% of forests loss in 1990 remains unproductive in 2012. Major drivers of forest conversion in Indonesia range from shrubs/open land, subsistence agriculture, oil palm expansion, plantation forest and mining. The results were compared with other available datasets and we obtained that the MOF data yields reliable estimate of deforestation.

Using the Landsat data archive to assess long-term regional forest dynamics assessment in Eastern Europe, 1985-2012

NASA Astrophysics Data System (ADS)

Turubanova, S.; Potapov, P.; Krylov, A.; Tyukavina, A.; McCarty, J. L.; Radeloff, V. C.; Hansen, M. C.

2015-04-01

Dramatic political and economic changes in Eastern European countries following the dissolution of the "Eastern Bloc" and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national scale. Our results allow national and sub-national level analysis of forest cover extent, change, and logging intensity and are available on-line as a baseline for further analyses of forest dynamics and its drivers.

Development of Ground Reference GIS for Assessing Land Cover Maps of Northeast Yellowstone National Park

NASA Technical Reports Server (NTRS)

Spruce, Joe; Warner, Amanda; Terrie, Greg; Davis, Bruce

2001-01-01

GIS technology and ground reference data often play vital roles in assessing land cover maps derived from remotely sensed data. This poster illustrates these roles, using results from a study done in Northeast Yellowstone National Park. This area holds many forest, range, and wetland cover types of interest to park managers. Several recent studies have focused on this locale, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project performed by Yellowstone Ecosystems Studies (YES) on riparian and in-stream habitat mapping. This poster regards a spin-off to the EOCAP project in which YES and NASA's Earth Science Applications Directorate explored the potential for synergistic use of hyperspecral, synthetic aperture radar, and multiband thermal imagery in mapping land cover types. The project included development of a ground reference GIS for site-specific data needed to evaluate maps from remotely sensed imagery. Field survey data included reflectance of plant communities, native and exotic plant species, and forest health conditions. Researchers also collected GPS points, annotated aerial photographs, and took hand held photographs of reference sites. The use of ESRI, ERDAS, and ENVI software enabled reference data entry into a GIS for comparision to georeferenced imagery and thematic maps. The GIS-based ground reference data layers supported development and assessment of multiple maps from remotely sensed data sets acquired over the study area.

Land Cover Change in Colombia: Surprising Forest Recovery Trends between 2001 and 2010

PubMed Central

Sánchez-Cuervo, Ana María; Aide, T. Mitchell; Clark, Matthew L.; Etter, Andrés

2012-01-01

Background Monitoring land change at multiple spatial scales is essential for identifying hotspots of change, and for developing and implementing policies for conserving biodiversity and habitats. In the high diversity country of Colombia, these types of analyses are difficult because there is no consistent wall-to-wall, multi-temporal dataset for land-use and land-cover change. Methodology/Principal Findings To address this problem, we mapped annual land-use and land-cover from 2001 to 2010 in Colombia using MODIS (250 m) products coupled with reference data from high spatial resolution imagery (QuickBird) in Google Earth. We used QuickBird imagery to visually interpret percent cover of eight land cover classes used for classifier training and accuracy assessment. Based on these maps we evaluated land cover change at four spatial scales country, biome, ecoregion, and municipality. Of the 1,117 municipalities, 820 had a net gain in woody vegetation (28,092 km2) while 264 had a net loss (11,129 km2), which resulted in a net gain of 16,963 km2 in woody vegetation at the national scale. Woody regrowth mainly occurred in areas previously classified as mixed woody/plantation rather than agriculture/herbaceous. The majority of this gain occurred in the Moist Forest biome, within the montane forest ecoregions, while the greatest loss of woody vegetation occurred in the Llanos and Apure-Villavicencio ecoregions. Conclusions The unexpected forest recovery trend, particularly in the Andes, provides an opportunity to expand current protected areas and to promote habitat connectivity. Furthermore, ecoregions with intense land conversion (e.g. Northern Andean Páramo) and ecoregions under-represented in the protected area network (e.g. Llanos, Apure-Villavicencio Dry forest, and Magdalena-Urabá Moist forest ecoregions) should be considered for new protected areas. PMID:22952816

Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA

Treesearch

Sarah A. Lewis; Andrew T. Hudak; Roger D. Ottmar; Peter R. Robichaud; Leigh B. Lentile; Sharon M. Hood; James B. Cronan; Penny Morgan

2011-01-01

Wildfire is a major forest disturbance in interior Alaska that can both directly and indirectly alter ecological processes. We used a combination of pre- and post-fire forest floor depths and post-fire ground cover assessments measured in the field, and high-resolution airborne hyperspectral imagery, to map forest floor conditions after the 2004 Taylor Complex in...

Monitoring forest dynamics with multi-scale and time series imagery.

PubMed

Huang, Chunbo; Zhou, Zhixiang; Wang, Di; Dian, Yuanyong

2016-05-01

To learn the forest dynamics and evaluate the ecosystem services of forest effectively, a timely acquisition of spatial and quantitative information of forestland is very necessary. Here, a new method was proposed for mapping forest cover changes by combining multi-scale satellite remote-sensing imagery with time series data. Using time series Normalized Difference Vegetation Index products derived from the Moderate Resolution Imaging Spectroradiometer images (MODIS-NDVI) and Landsat Thematic Mapper/Enhanced Thematic Mapper Plus (TM/ETM+) images as data source, a hierarchy stepwise analysis from coarse scale to fine scale was developed for detecting the forest change area. At the coarse scale, MODIS-NDVI data with 1-km resolution were used to detect the changes in land cover types and a land cover change map was constructed using NDVI values at vegetation growing seasons. At the fine scale, based on the results at the coarse scale, Landsat TM/ETM+ data with 30-m resolution were used to precisely detect the forest change location and forest change trend by analyzing time series forest vegetation indices (IFZ). The method was tested using the data for Hubei Province, China. The MODIS-NDVI data from 2001 to 2012 were used to detect the land cover changes, and the overall accuracy was 94.02 % at the coarse scale. At the fine scale, the available TM/ETM+ images at vegetation growing seasons between 2001 and 2012 were used to locate and verify forest changes in the Three Gorges Reservoir Area, and the overall accuracy was 94.53 %. The accuracy of the two layer hierarchical monitoring results indicated that the multi-scale monitoring method is feasible and reliable.

Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

USGS Publications Warehouse

Liu, J.; Liu, S.; Loveland, Thomas R.; Tieszen, L.L.

2008-01-01

Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975-2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2 Tg C (1 teragram = 1012 gram) per year, including 1.9 Tg C removed from the ecosystem as the consequences of land cover change. ?? 2008 Elsevier B.V.

Development of national database on long-term deforestation (1930-2014) in Bangladesh

NASA Astrophysics Data System (ADS)

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

2016-04-01

The aim of the present study is to prepare a nation-wide spatial database on forest cover to assess and monitor the land use changes associated with deforestation in Bangladesh. The multi-source data were interpreted to get the forest cover map of 1930, 1975, 1985, 1995, 2006 and 2014. The spatial information generated on total area under forest cover, rate of deforestation and afforestation, changes across forest types, forest canopy density, replacement land use in deforested area and deforestation hotspots. This spatial analysis has indicated that forest cover is undergoing significant negative change in area and quality. We report that forests in Bangladesh covered an area of 23,140 km2 in 1930 which has decreased to 14,086 km2 in 2014, a net loss of 9054 km2 (39.1%) in eight decades. Analysis of annual rate of gross deforestation for the recent period indicates 0.77% during 2006-2014. During the past eight decades, semi-evergreen forests show loss of 56.4% of forest cover followed by moist deciduous forests (51.5%), dry deciduous forests (43.1%) and mangroves (6.5%). The loss of 23.5% of dense forest cover was found from 1975 to 2014. Dense semi-evergreen forests shows more negative change (36.9%) followed by dense moist deciduous forest (32.7%) from 1975 to 2014. Annual rate of deforestation is higher in dense forests compared to open forests from 2006 to 2014 and indicates increased threat due to anthropogenic pressures. The spatial analysis of forest cover change in mangroves has shown a lower rate of deforestation. Most of the forest conversions have led to the degradation of forests to scrub and transition to agriculture and plantation. The study has identified the 'deforestation hotspots' can help in strategic planning for conservation and management of forest resources.

Large area mapping of southwestern forest crown cover, canopy height, and biomass using the NASA Multiangle Imaging Spectro-Radiometer

Treesearch

Mark Chopping; Gretchen G. Moisen; Lihong Su; Andrea Laliberte; Albert Rango; John V. Martonchik; Debra P. C. Peters

2008-01-01

A rapid canopy reflectance model inversion experiment was performed using multi-angle reflectance data from the NASA Multi-angle Imaging Spectro-Radiometer (MISR) on the Earth Observing System Terra satellite, with the goal of obtaining measures of forest fractional crown cover, mean canopy height, and aboveground woody biomass for large parts of south-eastern Arizona...

Climate Change for Agriculture, Forest Cover and 3d Urban Models

NASA Astrophysics Data System (ADS)

Kapoor, M.; Bassir, D.

2014-11-01

This research demonstrates the important role of the remote sensing in finding out the different parameters behind the agricultural crop change, forest cover and urban 3D models. Standalone software is developed to view and analysis the different factors effecting the change in crop productions. Open-source libraries from the Open Source Geospatial Foundation have been used for the development of the shape-file viewer. Software can be used to get the attribute information, scale, zoom in/out and pan the shapefiles. Environmental changes due to pollution and population that are increasing the urbanisation and decreasing the forest cover on the earth. Satellite imagery such as Landsat 5(1984) to Landsat TRIS/8 (2014), Landsat Data Continuity Mission (LDCM) and NDVI are used to analyse the different parameters that are effecting the agricultural crop production change and forest change. It is advisable for the development of good quality of NDVI and forest cover maps to use data collected from the same processing methods for the complete region. Management practices have been developed from the analysed data for the betterment of the crop and saving the forest cover

A Proposal for Phase 4 of the Forest Inventory and Analysis Program

Treesearch

Ronald E. McRoberts

2005-01-01

Maps of forest cover were constructed using observations from forest inventory plots, Landsat Thematic Mapper satellite imagery, and a logistic regression model. Estimates of mean proportion forest area and the variance of the mean were calculated for circular study areas with radii ranging from 1 km to 15 km. The spatial correlation among pixel predictions was...

US LAND-COVER MONITORING AND DETECTION OF CHANGES IN SCALE AND CONTEXT OF FOREST

EPA Science Inventory

Disparate land-cover mapping programs, previously focused solely on mission-oriented goals, have organized themselves as the Multi-Resolution Land Characteristics (MRLC) Consortium with a unified goal of producing land-cover nationwide at routine intervals. Under MRLC, United Sta...

Using indigenous knowledge to link hyper-temporal land cover mapping with land use in the Venezuelan Amazon: "The Forest Pulse".

PubMed

Olivero, Jesús; Ferri, Francisco; Acevedo, Pelayo; Lobo, Jorge M; Fa, John E; Farfán, Miguel Á; Romero, David; Real, Raimundo

2016-12-01

Remote sensing and traditional ecological knowledge (TEK) can be combined to advance conservation of remote tropical regions, e.g. Amazonia, where intensive in situ surveys are often not possible. Integrating TEK into monitoring and management of these areas allows for community participation, as well as for offering novel insights into sustainable resource use. In this study, we developed a 250 m resolution land-cover map of the Western Guyana Shield (Venezuela) based on remote sensing, and used TEK to validate its relevance for indigenous livelihoods and land uses. We first employed a hyper-temporal remotely sensed vegetation index to derive a land classification system. During a 1 300 km, eight day fluvial expedition in roadless areas in the Amazonas State (Venezuela), we visited six indigenous communities who provided geo-referenced data on hunting, fishing and farming activities. We overlaid these TEK data onto the land classification map, to link land classes with indigenous use. We characterized land classes using patterns of greenness temporal change and topo-hydrological information, and proposed 12 land-cover types, grouped into five main landscapes: 1) water bodies; 2) open lands/forest edges; 3) evergreen forests; 4) submontane semideciduous forests, and 5) cloud forests. Each land cover class was identified with a pulsating profile describing temporal changes in greenness, hence we labelled our map as "The Forest Pulse". These greenness profiles showed a slightly increasing trend, for the period 2000 to 2009, in the land classes representing grassland and scrubland, and a slightly decreasing trend in the classes representing forests. This finding is consistent with a gain in carbon in grassland as a consequence of climate warming, and also with some loss of vegetation in the forests. Thus, our classification shows potential to assess future effects of climate change on landscape. Several classes were significantly connected with agriculture, fishing, overall hunting, and more specifically the hunting of primates, Mazama americana, Dasyprocta fuliginosa, and Tayassu pecari. Our results showed that TEK-based approaches can serve as a basis for validating the livelihood relevance of landscapes in high-value conservation areas, which can form the basis for furthering the management of natural resources in these regions.

Evaluation of SLAR and simulated thematic mapper MSS data for forest cover mapping using computer-aided analysis techniques

NASA Technical Reports Server (NTRS)

Hoffer, R. M.; Dean, M. E.; Knowlton, D. J.; Latty, R. S.

1982-01-01

Kershaw County, South Carolina was selected as the study site for analyzing simulated thematic mapper MSS data and dual-polarized X-band synthetic aperture radar (SAR) data. The impact of the improved spatial and spectral characteristics of the LANDSAT D thematic mapper data on computer aided analysis for forest cover type mapping was examined as well as the value of synthetic aperture radar data for differentiating forest and other cover types. The utility of pattern recognition techniques for analyzing SAR data was assessed. Topics covered include: (1) collection and of TMS and reference data; (2) reformatting, geometric and radiometric rectification, and spatial resolution degradation of TMS data; (3) development of training statistics and test data sets; (4) evaluation of different numbers and combinations of wavelength bands on classification performance; (5) comparison among three classification algorithms; and (6) the effectiveness of the principal component transformation in data analysis. The collection, digitization, reformatting, and geometric adjustment of SAR data are also discussed. Image interpretation results and classification results are presented.

A multi-sensor lidar, multi-spectral and multi-angular approach for mapping canopy height in boreal forest regions

USGS Publications Warehouse

Selkowitz, David J.; Green, Gordon; Peterson, Birgit E.; Wylie, Bruce

2012-01-01

Spatially explicit representations of vegetation canopy height over large regions are necessary for a wide variety of inventory, monitoring, and modeling activities. Although airborne lidar data has been successfully used to develop vegetation canopy height maps in many regions, for vast, sparsely populated regions such as the boreal forest biome, airborne lidar is not widely available. An alternative approach to canopy height mapping in areas where airborne lidar data is limited is to use spaceborne lidar measurements in combination with multi-angular and multi-spectral remote sensing data to produce comprehensive canopy height maps for the entire region. This study uses spaceborne lidar data from the Geosciences Laser Altimeter System (GLAS) as training data for regression tree models that incorporate multi-angular and multi-spectral data from the Multi-Angle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging SpectroRadiometer (MODIS) to map vegetation canopy height across a 1,300,000 km2 swath of boreal forest in Interior Alaska. Results are compared to in situ height measurements as well as airborne lidar data. Although many of the GLAS-derived canopy height estimates are inaccurate, applying a series of filters incorporating both data associated with the GLAS shots as well as ancillary data such as land cover can identify the majority of height estimates with significant errors, resulting in a filtered dataset with much higher accuracy. Results from the regression tree models indicate that late winter MISR imagery acquired under snow-covered conditions is effective for mapping canopy heights ranging from 5 to 15 m, which includes the vast majority of forests in the region. It appears that neither MISR nor MODIS imagery acquired during the growing season is effective for canopy height mapping, although including summer multi-spectral MODIS data along with winter MISR imagery does appear to provide a slight increase in the accuracy of resulting height maps. The finding that winter, snow-covered MISR imagery can be used to map canopy height is important because clear sky days are nearly three times as common during the late winter period as during the growing season. The increased odds of acquiring cloud-free imagery during the target acquisition period make regularly updated forest height inventories for Interior Alaska much more feasible. A major advantage of the GLAS–MISR–MODIS canopy height mapping methodology described here is that this approach uses only data that is freely available worldwide, making the approach potentially applicable across the entire circumpolar boreal forest region.

Aggregation of Sentinel-2 time series classifications as a solution for multitemporal analysis

NASA Astrophysics Data System (ADS)

Lewiński, Stanislaw; Nowakowski, Artur; Malinowski, Radek; Rybicki, Marcin; Kukawska, Ewa; Krupiński, Michał

2017-10-01

The general aim of this work was to elaborate efficient and reliable aggregation method that could be used for creating a land cover map at a global scale from multitemporal satellite imagery. The study described in this paper presents methods for combining results of land cover/land use classifications performed on single-date Sentinel-2 images acquired at different time periods. For that purpose different aggregation methods were proposed and tested on study sites spread on different continents. The initial classifications were performed with Random Forest classifier on individual Sentinel-2 images from a time series. In the following step the resulting land cover maps were aggregated pixel by pixel using three different combinations of information on the number of occurrences of a certain land cover class within a time series and the posterior probability of particular classes resulting from the Random Forest classification. From the proposed methods two are shown superior and in most cases were able to reach or outperform the accuracy of the best individual classifications of single-date images. Moreover, the aggregations results are very stable when used on data with varying cloudiness. They also enable to reduce considerably the number of cloudy pixels in the resulting land cover map what is significant advantage for mapping areas with frequent cloud coverage.

SRTM-DEM and Landsat ETM+ data for mapping tropical dry forest cover and biodiversity assessment in Nicaragua

Treesearch

S.E. Sesnie; S.E. Hagell; S.M. Otterstrom; C.L. Chambers; B.G. Dickson

2008-01-01

Tropical dry and deciduous forest comprises as much as 42% of the world’s tropical forests, but has received far less attention than forest in wet tropical areas. Land use change threatens to greatly reduce the extent of dry forest that is known to contain high levels of plant and animal diversity. Forest fragmentation may further endanger arboreal mammals that play...

Using Landsat to Diagnose Trends in Disturbance Magnitude Across the National Forest System

NASA Astrophysics Data System (ADS)

Hernandez, A. J.; Healey, S. P.; Stehman, S. V.; Ramsey, R. D.

2014-12-01

The Landsat archive is increasingly being used to detect trends in the occurrence of forest disturbance. Beyond information about the amount of area affected, forest managers need to know if and how disturbance severity is changing. For example, the United States National Forest System (NFS) has developed a comprehensive plan for carbon monitoring, which requires a detailed temporal mapping of forest disturbance magnitudes across 75 million hectares. To meet this need, we have prepared multitemporal models of percent canopy cover that were calibrated with extensive field data from the USFS Forest Inventory and Analysis Program (FIA). By applying these models to pre- and post-event Landsat images at the site of known disturbances, we develop maps showing first-order estimates of disturbance magnitude on the basis of cover removal. However, validation activities consistently show that these initial estimates under-estimate disturbance magnitude. We have developed an approach, which quantifies this under-prediction at the landscape level and uses empirical validation data to adjust change magnitude estimates derived from initial disturbance maps. In an assessment of adjusted magnitude trends of NFS' Northern Region from 1990 to the present, we observed significant declines since 1990 (p < .01) in harvest magnitude, likely related to known reduction of clearcutting practices in the region. Fire, conversely, did not show strongly significant trends in magnitude, despite an increase in the overall area affected. As Landsat is used to provide increasingly precise maps of the timing and location of historical forest disturbance, a logical next step is to use the archive to generate widely interpretable and objective estimates of disturbance magnitude.

Multi-scale data to assess and monitor sudden oak death

Treesearch

Lisa M. Levien; Chris S. Fischer; Lianne C. Mahon; Jeff A. Mai

2002-01-01

The USDA Forest Service (FS) and California Department of Forestry and Fire Protection (CDF) are monitoring Sudden Oak Death (SOD) under the umbrella of the larger California Land Cover Mapping and Monitoring Program (LCMMP). The LCMMP is a statewide cooperative effort among the FS and CDF focused on mapping and monitoring California’s vegetation and land cover.

A PRELIMINARY ASSESSMENT OF THE MONTREAL PROCESS INDICATORS OF FOREST FRAGMENTATION FOR THE UNITED STATES

EPA Science Inventory

As part of the 2003 U.S. Report on Sustainable Forests, four metrics of forest fragmentation patch size, edge amount, inter-patch contrast - were measured within 142,602 non overlapping 56.25 km2 analysis units on land-cover maps derived from satellite imagery for the 48 contermi...

An efficient estimator to monitor rapidly changing forest conditions

Treesearch

Raymond L. Czaplewski; Michael T. Thompson; Gretchen G. Moisen

2012-01-01

Extensive expanses of forest often change at a slow pace. In this common situation, FIA produces informative estimates of current status with the Moving Average (MA) method and post-stratification with a remotely sensed map of forest-nonforest cover. However, MA "smoothes out" estimates over time, which confounds analyses of temporal trends; and post-...

Forest/non-forest stratification in Georgia with Landsat Thematic Mapper data

Treesearch

William H. Cooke

2000-01-01

Geographically accurate Forest Inventory and Analysis (FIA) data may be useful for training, classification, and accuracy assessment of Landsat Thematic Mapper (TM) data. Minimum expectation for maps derived from Landsat data is accurate discrimination of several land cover classes. Landsat TM costs have decreased dramatically, but acquiring cloud-free scenes at...

Tracking Trends in Fractional Forest Cover Change using Long Term Data from AVHRR and MODIS

NASA Astrophysics Data System (ADS)

Kim, D. H.; DiMiceli, C.; Sohlberg, R. A.; Hansen, M.; Carroll, M.; Kelly, M.; Townshend, J. R.

2014-12-01

Tree cover affects terrestrial energy and water exchanges, photosynthesis and transpiration, net primary production, and carbon and nutrient fluxes. Accurate and long-term continuous observation of tree cover change is critical for the study of the gradual ecosystem change. Tree cover is most commonly inferred from categorical maps which may inadequately represent within-class heterogeneity for many analyses. Alternatively, Vegetation Continuous Fields data measures fractions or proportions of pixel area. Recent development in remote sensing data processing and cross sensor calibration techniques enabled the continuous, long-term observations such as Land Long-Term Data Records. Such data products and their surface reflectance data have enhanced the possibilities for long term Vegetation Continuous Fields data, thus enabling the estimation of long term trend of fractional forest cover change. In this presentation, we will summarize the progress in algorithm development including automation of training selection for deciduous and evergreen forest, the preliminary results, and its future applications to relate trends in fractional forest cover change and environmental change.

Airborne Laser Swath Mapping: Improved Penetration of Dense Vegetation Opens New Applications

NASA Astrophysics Data System (ADS)

Carter, W. E.; Shrestha, R. L.; Slatton, K. C.

2009-12-01

Historically, mapping structures and terrain obscured by dense forests has been problematical, because shadows limit or prevent the use of airborne photogrammetric techniques, and ground surveying techniques are slow, labor intensive, and too costly for many applications. Airborne laser swath mapping (ALSM) units with pulse rates of a few thousand to a few tens of thousands of pulses per second typically resulted in 1 or 2 points per square meter of terrain, which worked reasonably well in sparse to moderately forested areas. For example, data collected with a 30 kHz laser, provided sufficient returns from the ground in areas covered with redwood, mixed hardwoods, and conifer forests, to create 1 to 2 meter resolution bare earth digital elevation models (DEM). These DEMs were useful in studies of forest covered landslides, terraces, and fault lines. However, in dense semi-tropical areas of Florida, with primary and secondary canopies that include dense brush such as palmetto, the DEMs were significantly degraded, and in many areas it was not possible to derive bare earth DEMs that were reliable in height to better than 0.5 to 1.0 meter. In 2007 the UF purchased a second generation Optech ALSM unit that has decimeter accuracy ranging with pulse rates of 100 to 125 kHz. Flying at 600 meters AGL, 60 meters per second, and using a scan angle of ± 20 degrees and scan rate of 40 Hz, results in about 5 laser pulses per square meter within a single swath. In April 2009 a UF team collected ALSM observations covering approximately 2000 acres at Caracol, Belize, to support archaeological studies of the ancient (650 to 900AD) Mayan city, which is largely covered with dense jungle. By overlapping adjacent swaths by 50%, and flying the project area twice with orthogonal flight lines, an accumulated data set containing approximately 20 pulses per square meter, with a distribution of incident angles was realized. The Caracol area has been under study for 25 years and traditional mapping techniques involved cutting pathways through the jungle, typically at 50 meter intervals, and using transits, electronic distance measuring instruments and total stations to map visible features. Without completely clearing the vegetation, it was difficult for ground surveyors to identify and map all of the pertinent features, and preliminary analysis suggest that the ALSM data display areas of previously unmapped mounded settlement, as well as subtle features in the terrain, including shallow agricultural terraces. The ability to map structures and terrain in areas covered with semi-tropical and tropical forests and jungles opens new opportunities for archaeological studies, and promises to impact geological and geophysical studies in these difficult to map regions as well.

Single-edition quadrangle maps

USGS Publications Warehouse

,

1998-01-01

In August 1993, the U.S. Geological Survey's (USGS) National Mapping Division and the U.S. Department of Agriculture's Forest Service signed an Interagency Agreement to begin a single-edition joint mapping program. This agreement established the coordination for producing and maintaining single-edition primary series topographic maps for quadrangles containing National Forest System lands. The joint mapping program saves money by eliminating duplication of effort by the agencies and results in a more frequent revision cycle for quadrangles containing national forests. Maps are revised on the basis of jointly developed standards and contain normal features mapped by the USGS, as well as additional features required for efficient management of National Forest System lands. Single-edition maps look slightly different but meet the content, accuracy, and quality criteria of other USGS products. The Forest Service is responsible for the land management of more than 191 million acres of land throughout the continental United States, Alaska, and Puerto Rico, including 155 national forests and 20 national grasslands. These areas make up the National Forest System lands and comprise more than 10,600 of the 56,000 primary series 7.5-minute quadrangle maps (15-minute in Alaska) covering the United States. The Forest Service has assumed responsibility for maintaining these maps, and the USGS remains responsible for printing and distributing them. Before the agreement, both agencies published similar maps of the same areas. The maps were used for different purposes, but had comparable types of features that were revised at different times. Now, the two products have been combined into one so that the revision cycle is stabilized and only one agency revises the maps, thus increasing the number of current maps available for National Forest System lands. This agreement has improved service to the public by requiring that the agencies share the same maps and that the maps meet a common standard, as well as by significantly reducing duplication of effort.

Continental estimates of forest cover and forest cover changes in the dry ecosystems of Africa between 1990 and 2000

PubMed Central

Bodart, Catherine; Brink, Andreas B; Donnay, François; Lupi, Andrea; Mayaux, Philippe; Achard, Frédéric

2013-01-01

Aim This study provides regional estimates of forest cover in dry African ecoregions and the changes in forest cover that occurred there between 1990 and 2000, using a systematic sample of medium-resolution satellite imagery which was processed consistently across the continent. Location The study area corresponds to the dry forests and woodlands of Africa between the humid forests and the semi-arid regions. This area covers the Sudanian and Zambezian ecoregions. Methods A systematic sample of 1600 Landsat satellite imagery subsets, each 20 km × 20 km in size, were analysed for two reference years: 1990 and 2000. At each sample site and for both years, dense tree cover, open tree cover, other wooded land and other vegetation cover were identified from the analysis of satellite imagery, which comprised multidate segmentation and automatic classification steps followed by visual control by national forestry experts. Results Land cover and land-cover changes were estimated at continental and ecoregion scales and compared with existing pan-continental, regional and local studies. The overall accuracy of our land-cover maps was estimated at 87%. Between 1990 and 2000, 3.3 million hectares (Mha) of dense tree cover, 5.8 Mha of open tree cover and 8.9 Mha of other wooded land were lost, with a further 3.9 Mha degraded from dense to open tree cover. These results are substantially lower than the 34 Mha of forest loss reported in the FAO's 2010 Global Forest Resources Assessment for the same period and area. Main conclusions Our method generates the first consistent and robust estimates of forest cover and change in dry Africa with known statistical precision at continental and ecoregion scales. These results reduce the uncertainty regarding vegetation cover and its dynamics in these previously poorly studied ecosystems and provide crucial information for both science and environmental policies. PMID:23935237

Mapping trees outside forests using high-resolution aerial imagery: a comparison of pixel- and object-based classification approaches.

PubMed

Meneguzzo, Dacia M; Liknes, Greg C; Nelson, Mark D

2013-08-01

Discrete trees and small groups of trees in nonforest settings are considered an essential resource around the world and are collectively referred to as trees outside forests (ToF). ToF provide important functions across the landscape, such as protecting soil and water resources, providing wildlife habitat, and improving farmstead energy efficiency and aesthetics. Despite the significance of ToF, forest and other natural resource inventory programs and geospatial land cover datasets that are available at a national scale do not include comprehensive information regarding ToF in the United States. Additional ground-based data collection and acquisition of specialized imagery to inventory these resources are expensive alternatives. As a potential solution, we identified two remote sensing-based approaches that use free high-resolution aerial imagery from the National Agriculture Imagery Program (NAIP) to map all tree cover in an agriculturally dominant landscape. We compared the results obtained using an unsupervised per-pixel classifier (independent component analysis-[ICA]) and an object-based image analysis (OBIA) procedure in Steele County, Minnesota, USA. Three types of accuracy assessments were used to evaluate how each method performed in terms of: (1) producing a county-level estimate of total tree-covered area, (2) correctly locating tree cover on the ground, and (3) how tree cover patch metrics computed from the classified outputs compared to those delineated by a human photo interpreter. Both approaches were found to be viable for mapping tree cover over a broad spatial extent and could serve to supplement ground-based inventory data. The ICA approach produced an estimate of total tree cover more similar to the photo-interpreted result, but the output from the OBIA method was more realistic in terms of describing the actual observed spatial pattern of tree cover.

Using MODIS and GLAS Data to Develop Timber Volume Estimates in Central Siberia

NASA Technical Reports Server (NTRS)

Ranson, K. Jon; Kimes, Daniel; Sun, Guoqing; Kharuk, Viatcheslav; Hyde, Peter; Nelson, Ross

2007-01-01

The boreal forest is the Earth's largest terrestrial biome, covering some 12 million km2 and accounting for about one third of this planet's total forest area. Mapping of boreal forest's type, structure parameters and biomass are critical for understanding the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. Ground based forest inventories, have much uncertainty in the inventory data, particularly in remote areas of Siberia where sampling is sparse and/or lacking. In addition, many of the forest inventories that do exist for Siberia are now a decade or more old. Thus, available forest inventories fail to capture the current conditions. Changes in forest structure in a particular forest-type and region can change significantly due to changing environment conditions, and natural and anthropogenic disturbance. Remote sensing methods can potentially overcome these problems. Multispectral sensors can be used to provide vegetation cover maps that show a timely and accurate geographic distribution of vegetation types rather than decade old ground based maps. Lidar sensors can be used to directly obtain measurements that can be used to derive critical forest structure information (e.g., height, density, and volume). These in turn can used to estimate biomass components using allometric equations without having to use out dated forest inventory. Finally, remote sensing data is ideally suited to provide a sampling basis for a rigorous statistical estimate of the variance and error bound on forest structure measures. In this study, new remote sensing methods were applied to develop estimates timber volume using NASA's MODerate resolution Imaging Spectroradiometer (MODIS) and unique waveform data of the geoscience laser altimeter system (GLAS) for a 10 deg x 10 deg area in central Siberia. Using MODIS and GLAS data, maps were produced for cover type and timber volume for 2003, and a realistic variance (error bound) for timber volume was calculated for the study area. In this 'study we used only GLAS footprints that had a slope value of less than 10 deg. This was done to avoid large errors due to the effect of slope on the GLAS models. The method requires the integration of new remote sensing methods with available ground studies of forest timber volume conducted in Russian forests. The results were compared to traditional ground forest inventory methods reported in the literature and to ground truth collected in the study area.

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.

Utilizing ERTS-A imagery for tectonic analysis through study of the Bighorn Mountains Region

NASA Technical Reports Server (NTRS)

Hoppin, R. A. (Principal Investigator)

1973-01-01

The author has identified the following significant results. Preliminary vegetation analysis has been undertaken on MSS scene 1085-17294, Oct. 16, 1973 in the Bighorn region. Forest Service maps showing detailed distribution of dominant forest types have been compared with MSS bands 5 and 7 positive transparencies, enlarged positive prints, and color imagery produced on an Addcol viewer. Patterns on the ERTS imagery match those on the Forest Service maps quite well. A tectonic map ovearlay of MSS band 7 of the Bighorn region reveals a strong concentration of linears in the uplift as compared to basins. Folds in the Bighorn Basin are visible where not covered by post-Paleocene deposits. In regions where far less is known of the geology than in this area, it might be possible to predict the subsurface occurrence of folds and lineaments on the basis of imagery analysis and more confidently explore covered areas for concealed oil structures and mineral deposits.

EnviroAtlas - Durham, NC - Land Cover Summaries by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of each block group that is classified as impervious, forest, green space, wetland, and agriculture. Impervious is a combination of dark and light impervious. Green space is a combination of trees and forest and grass and herbaceous. This dataset also includes the area per capita for each block group for impervious, forest, and green space land cover. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

Monitoring the Mesoamerican Biological Corridor: A NASA/CCAD Cooperative Research Project

NASA Technical Reports Server (NTRS)

Sever, Thomas; Irwin, Daniel; Sader, Steven A.; Saatchi, Sassan

2004-01-01

To foster scientific cooperation under a Memorandum of Understanding between NASA and the Central American countries, the research project developed regional databases to monitor forest condition and environmental change throughout the region. Of particular interest is the Mesoamerican Biological Corridor (MBC), a chain of protected areas and proposed conservation areas that will link segments of natural habitats in Central America from the borders of northern Columbia to southern Mexico. The first and second year of the project focused on the development of regional satellite databases (JERS-IC, MODIS, and Landsat-TM), training of Central American cooperators and forest cover and change analysis. The three regional satellite mosaics were developed and distributed on CD-ROM to cooperators and regional outlets. Four regional remote sensing training courses were conducted in 3 countries including participants from all 7 Central American countries and Mexico. In year 3, regional forest change assessment in reference to Mesoamerican Biological Corridor was completed and land cover maps (from Landsat TM) were developed for 7 Landsat scenes and accuracy assessed. These maps are being used to support validation of MODIS forest/non forest maps and to examine forest fragmentation and forest cover change in selected study sites. A no-cost time extension (2003-2004) allowed the completion of an M.S. thesis by a Costa Rican student and preparation of manuscripts for future submission to peer-reviewed outlets. Proposals initiated at the end of the project have generated external funding from the U.S. Forest Service (to U. Maine), NASA-ESSF (Oregon State U.) and from USAID and EPA (to NASA-MSFC-GHCC) to test MODIS capabilities to detect forest change; conduct literature review on biomass estimation and carbon stocks and develop a regional remote sensing monitoring center in Central America. The success of the project has led to continued cooperation between NASA, other federal agencies, and scientists from all seven Central American Countries (see SERVIR web site for this ongoing work - servir.nsstc.nasa.gov).

Land cover and land use mapping of the iSimangaliso Wetland Park, South Africa: comparison of oblique and orthogonal random forest algorithms

NASA Astrophysics Data System (ADS)

Bassa, Zaakirah; Bob, Urmilla; Szantoi, Zoltan; Ismail, Riyad

2016-01-01

In recent years, the popularity of tree-based ensemble methods for land cover classification has increased significantly. Using WorldView-2 image data, we evaluate the potential of the oblique random forest algorithm (oRF) to classify a highly heterogeneous protected area. In contrast to the random forest (RF) algorithm, the oRF algorithm builds multivariate trees by learning the optimal split using a supervised model. The oRF binary algorithm is adapted to a multiclass land cover and land use application using both the "one-against-one" and "one-against-all" combination approaches. Results show that the oRF algorithms are capable of achieving high classification accuracies (>80%). However, there was no statistical difference in classification accuracies obtained by the oRF algorithms and the more popular RF algorithm. For all the algorithms, user accuracies (UAs) and producer accuracies (PAs) >80% were recorded for most of the classes. Both the RF and oRF algorithms poorly classified the indigenous forest class as indicated by the low UAs and PAs. Finally, the results from this study advocate and support the utility of the oRF algorithm for land cover and land use mapping of protected areas using WorldView-2 image data.

High-resolution mapping and modelling of surface albedo in Norwegian boreal forests: from remotely sensed data to predictions

NASA Astrophysics Data System (ADS)

Cherubini, Francesco; Hu, Xiangping; Vezhapparambu, Sajith; Stromman, Anders

2017-04-01

Surface albedo, a key parameter of the Earth's climate system, has high variability in space, time, and land cover and its parameterization is among the most important variables in climate models. The lack of extensive estimates for model improvement is one of the main limitations for accurately quantifying the influence of surface albedo changes on the planetary radiation balance. We use multi-year satellite retrievals of MODIS surface albedo (MCD43A3), high resolution land cover maps, and meteorological records to characterize albedo variations in Norway across latitude, seasons, land cover type, and topography. We then use this dataset to elaborate semi-empirical models to predict albedo values as a function of tree species, age, volume and climate variables like temperature and snow water equivalents (SWE). Given the complexity of the dataset and model formulation, we apply an innovative non-linear programming approach simultaneously coupled with linear un-mixing. The MODIS albedo products are at a resolution of about 500 m and 8 days. The land cover maps provide vegetation structure information on relative abundance of tree species, age, and biomass volumes at 16 m resolution (for both deciduous and coniferous species). Daily observations of meteorological information on air temperature and SWE are produced at 1 km resolution from interpolation of meteorological weather stations in Norway. These datasets have different resolution and projection, and are harmonized by identifying, for each MODIS pixel, the intersecting land cover polygons and the percentage area of the MODIS pixel represented by each land cover type. We then filter the subplots according to the following criteria: i) at least 96% of the total pixel area is covered by a single land cover class (either forest or cropland); ii) if forest area, at least 98% of the forest area is covered by spruce, deciduous or pine. Forested pixels are then categorized as spruce, deciduous, or pine dominant if the fraction of the respective tree species is greater than 75%. Results show averages of albedo estimates for forests and cropland depicting spatial (along a latitudinal gradient) and temporal (daily, monthly, and seasonal) variations across Norway. As the case study region is a country with heterogeneous topography, we also study the sensitivity of the albedo estimates to the slope and aspect of the terrain. The mathematical programming approach uses a variety of functional forms, constraints and variables, leading to many different model outputs. There are several models with relatively high performances, allowing for a flexibility in the model selection, with different model variants suitable for different situations. This approach produces albedo predictions at the same resolution of the land cover dataset (16 m, notably higher than the MODIS estimates), can incorporate changes in climate conditions, and is robust to cross-validation between different locations. By integrating satellite measurements and high-resolution vegetation maps, we can thus produce semi-empirical models that can predict albedo values for boreal forests using a variety of input variables representing climate and/or vegetation structure. Further research can explore the possible advantages of its implementation in land surface schemes over existing approaches.

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.

Basic forest cover mapping using digitized remote sensor data and automated data processing techniques

NASA Technical Reports Server (NTRS)

Coggeshall, M. E.; Hoffer, R. M.

1973-01-01

Remote sensing equipment and automatic data processing techniques were employed as aids in the institution of improved forest resource management methods. On the basis of automatically calculated statistics derived from manually selected training samples, the feature selection processor of LARSYS selected, upon consideration of various groups of the four available spectral regions, a series of channel combinations whose automatic classification performances (for six cover types, including both deciduous and coniferous forest) were tested, analyzed, and further compared with automatic classification results obtained from digitized color infrared photography.

Analysis of land use changes over the last 200 years in the catchment of Lake Czechowskie (Pomerania, northern Poland)

NASA Astrophysics Data System (ADS)

Tyszkowski, Sebastian; Kaczmarek, Halina

2014-05-01

Changes in land cover in the catchment area are, beside climate change, some of the major factors affecting sedimentation processes in lakes. With increasing human impact, changes in land cover no longer depend primarily on climate. In relation to research on sediments of Lake Czechowskie in Pomeranian Province in North Poland, land use changes over the last 200 years were analysed, with particular reference to deforestation or afforestation. The study area was the lake catchment, which covers nearly 20 km2. The analysis was based on archival and contemporary cartographic and photogrammetric materials, georeferenced and rectified using ArcGIS software. The following materials were used: Schrötter-Engelhart, Karte von Ost-Preussen nebst Preussisch Litthauen und West-Preussen nebst dem Netzdistrict, 1:50 000, section 92, 93, 1796-1802; Map Messtishchblatt, 1:25000, sheet Czarnen, (mapping conducted in 1874), 1932; Map WIG (Military Geographical Institute - Wojskowy Instytut Geograficzny), 1:25000, sheet Osowo, (mapping conducted in 1929-31), 1933; aerial photos 1:13000, 1964, 1969; 1:25000, 1987; 1:26000, 1997; aerial ortophotomap , 1:5000, 2010. Today, over 60% of the catchment of Lake Czechowskie is covered with forests, dominated by planted Scots pine (Pinus sylvestris), while the remaining areas are used for agricultural purposes or are built up. The first cartographic materials indicate that in the late 18th c., forest covered almost 50% of the catchment surface. By the year 1870, there was a significant reduction in the forested area, as its contribution fell to 40%. Deforestation took place mainly between the main villages. In the 1920s the forest cover increased to 44%. Today, almost the entire lake is surrounded by forest and a wetland belt (at least 0.5 km wide). Deforestation in the catchment should not be attributed solely to logging because the area of Tuchola Forests (Bory Tucholskie) was repeatedly affected by natural disasters. In the 19th c. these predominantly included fires, while in the 20th c., mostly pest outbreaks were observed. Human activity in the catchment of Lake Czechowskie, shown in the cartographic materials from the late 18th and early 19th c., is also manifested by the creation of dams on the lake, which might have increased water level in the lake. The early 20th c., imaged on the map from 1933, was a period of intense change, leading to agricultural use of wetlands. They were drained by ditches, also in the Trzechowskie peatland. This study was supported by the Virtual Institute of Integrated Climate and Landscape Evolution (ICLEA) of the Helmholtz Association and the research project no. 2011/01/B/ST10/07367 Polish Ministry of Science and Higher Education

Forest cover dynamics of shifting cultivation in the Democratic Republic of Congo: a remote sensing-based assessment for 2000-2010

NASA Astrophysics Data System (ADS)

Molinario, G.; Hansen, M. C.; Potapov, P. V.

2015-09-01

Shifting cultivation has traditionally been practiced in the Democratic Republic of Congo by carving agricultural fields out of primary and secondary forest, resulting in the rural complex: a characteristic land cover mosaic of roads, villages, active and fallow fields and secondary forest. Forest clearing has varying impacts depending on where it occurs relative to this area: whether inside it, along its primary forest interface, or in more isolated primary forest areas. The spatial contextualization of forest cover loss is therefore necessary to understand its impacts and plan its management. We characterized forest clearing using spatial models in a Geographical Information System, applying morphological image processing to the Forets d’Afrique Central Evaluee par Teledetection product. This process allowed us to create forest fragmentation maps for 2000, 2005 and 2010, classifying previously homogenous primary forest into separate patch, edge, perforated, fragmented and core forest subtypes. Subsequently we used spatial rules to map the established rural complex separately from isolated forest perforations, tracking the growth of these areas in time. Results confirm that the expansion of the rural complex and forest perforations has high variance throughout the country, with consequent differences in local impacts on forest ecology and habitat fragmentation. Between 2000 and 2010 the rural complex grew by 10.2% (46 182 ha), increasing from 11.9% to 13.1% of the total land area (1.2% change) while perforated forest grew by 74.4% (23 856 ha), from 0.8% to 1.5%. Core forest decreased by 3.8% (54 852 ha), from 38% to 36.6% of the 2010 land area. Of particular concern is the nearly doubling of perforated forest, a land dynamic that represents greater spatial intrusion of forest clearing within core forest areas and a move away from the established rural complex.

The Habitat Susceptibility of Bali Starling (Leucopsar rothschildi Stresemann> 1912) Based on Forest Fire Vulnerability Mappin in West Bali National Park

NASA Astrophysics Data System (ADS)

Pramatana, F.; Prasetyo, L. B.; Rushayati, S. B.

2017-10-01

Bali starling is an endemic and endangered species which tend to decrease of its population in the wild. West Bali National Park (WBNP) is the only habitat of bali starling, however it is threatened nowadays by forest fire. Understanding the sensitivity of habitat to forest & land fire is urgently needed. Geographic Information System (GIS) can be used for mapping the vulnerability of forest fire. This study aims to analyze the contributed factor of forest fire, to develop vulnerability level map of forest fire in WBNP, to estimate habitat vulnerability of bali starling. The variable for mapping forest fire in WBNP were road distance, village distance, land cover, NDVI, NDMI, surface temperature, and slope. Forest fire map in WBNP was created by scoring from each variable, and classified into four classes of forest fire vulnerability which are very low (9 821 ha), low (5 015.718 ha), middle (6 778.656 ha), and high (2 126.006 ha). Bali starling existence in the middle and high vulnerability forest fire class in WBNP, consequently the population and habitat of bali starling is a very vulnerable. Management of population and habitat of bali starling in WBNP must be implemented focus on forest fire impact.

Syracuse urban forest master plan: guiding the city's forest resource into the 21st century

Treesearch

David J. Nowak; Paul R. O' Connor; [Compilers

2001-01-01

The Syracuse Urban Forest Master Plan is one of the most comprehensive urban forest assessments ever developed for a city. This report combines a high-resolution digital urban cover map with field vegetation sampling data from all land uses, a 100-percent street-tree inventory, a survey of city residents regarding desirable and undesirable tree characteristics and...

Meter-scale Urban Land Cover Mapping for EPA EnviroAtlas Using Machine Learning and OBIA Remote Sensing Techniques

NASA Astrophysics Data System (ADS)

Pilant, A. N.; Baynes, J.; Dannenberg, M.; Riegel, J.; Rudder, C.; Endres, K.

2013-12-01

US EPA EnviroAtlas is an online collection of tools and resources that provides geospatial data, maps, research, and analysis on the relationships between nature, people, health, and the economy (http://www.epa.gov/research/enviroatlas/index.htm). Using EnviroAtlas, you can see and explore information related to the benefits (e.g., ecosystem services) that humans receive from nature, including clean air, clean and plentiful water, natural hazard mitigation, biodiversity conservation, food, fuel, and materials, recreational opportunities, and cultural and aesthetic value. EPA developed several urban land cover maps at very high spatial resolution (one-meter pixel size) for a portion of EnviroAtlas devoted to urban studies. This urban mapping effort supported analysis of relations among land cover, human health and demographics at the US Census Block Group level. Supervised classification of 2010 USDA NAIP (National Agricultural Imagery Program) digital aerial photos produced eight-class land cover maps for several cities, including Durham, NC, Portland, ME, Tampa, FL, New Bedford, MA, Pittsburgh, PA, Portland, OR, and Milwaukee, WI. Semi-automated feature extraction methods were used to classify the NAIP imagery: genetic algorithms/machine learning, random forest, and object-based image analysis (OBIA). In this presentation we describe the image processing and fuzzy accuracy assessment methods used, and report on some sustainability and ecosystem service metrics computed using this land cover as input (e.g., carbon sequestration from USFS iTREE model; health and demographics in relation to road buffer forest width). We also discuss the land cover classification schema (a modified Anderson Level 1 after the National Land Cover Data (NLCD)), and offer some observations on lessons learned. Meter-scale urban land cover in Portland, OR overlaid on NAIP aerial photo. Streets, buildings and individual trees are identifiable.

Monitoring of reforestation on burnt areas in Western Russia using Landsat time series

NASA Astrophysics Data System (ADS)

Vorobev, Oleg; Kurbanov, Eldar

2017-04-01

Forest fires are main disturbance factor for the natural ecosystems, especially in boreal forests. Monitoring for the dynamic of forest cover regeneration in the post-fire period of ecosystem recovery is crucial to both estimation of forest stands and forest management. In this study, on the example of burnt areas of 2010 wildfires in Republic Mari El of Russian Federation we estimated post-fire dynamic of different classes of vegetation cover between 2011-2016 years with the use of time series Landsat satellite images. To validate the newly obtained thematic maps we used 80 test sites with independent field data, as well Canopus-B images of high spatial resolution. For the analysis of the satellite images we referred to Normalized Differenced Vegetation Index (NDVI) and Tasseled Cap transformation. The research revealed that at the post-fire period the area of thematic classes "Reforestation of the middle and low density" has maximum cover (44%) on the investigated burnt area. On the burnt areas of 2010 there is ongoing active process of grass overgrowing (up to 20%), also there are thematic classes of deadwood (15%) and open spaces (10%). The results indicate that there is mostly natural regeneration of tree species pattern corresponding to the pre-fire condition. Forest plantations cover only 2% of the overall burnt area. By the 2016 year the NDVI parameters of young vegetation cover were recovered to the pre-fire level as well. The overall unsupervised classification accuracy of more than 70% shows high degree of agreement between the thematic map and the ground truth data. The research results can be applied for the long term succession monitoring and management plan development for the reforestation activities on the lands disturbed by fire.

Potential of Pest and Host Phenological Data in the Attribution of Regional Forest Disturbance Detection Maps According to Causal Agent

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Hargrove, William; Norman Steve; Christie, William

2014-01-01

Near real time forest disturbance detection maps from MODIS NDVI phenology data have been produced since 2010 for the conterminous U.S., as part of the on-line ForWarn national forest threat early warning system. The latter has been used by the forest health community to identify and track many regional forest disturbances caused by multiple biotic and abiotic damage agents. Attribution of causal agents for detected disturbances has been a goal since project initiation in 2006. Combined with detailed cover type maps, geospatial pest phenology data offer a potential means for narrowing the candidate causal agents responsible for a given biotic disturbance. U.S. Aerial Detection Surveys (ADS) employ such phenology data. Historic ADS products provide general locational data on recent insect-induced forest type specific disturbances that may help in determining candidate causal agents for MODIS-based disturbance maps, especially when combined with other historic geospatial disturbance data (e.g., wildfire burn scars and drought maps). Historic ADS disturbance detection polygons can show severe and extensive regional forest disturbances, though they also can show polygons with sparsely scattered or infrequent disturbances. Examples will be discussed that use various historic disturbance data to help determine potential causes of MODIS-detected regional forest disturbance anomalies.

Forest Canopy Cover and Height from MISR in Topographically Complex Southwestern US Landscape Assessed with High Quality Reference Data

NASA Technical Reports Server (NTRS)

Chopping, Mark; North, Malcolm; Chen, Jiquan; Schaaf, Crystal B.; Blair, J. Bryan; Martonchik, John V.; Bull, Michael A.

2012-01-01

This study addresses the retrieval of spatially contiguous canopy cover and height estimates in southwestern USforests via inversion of a geometric-optical (GO) model against surface bidirectional reflectance factor (BRF) estimates from the Multi-angle Imaging SpectroRadiometer (MISR). Model inversion can provide such maps if good estimates of the background bidirectional reflectance distribution function (BRDF) are available. The study area is in the Sierra National Forest in the Sierra Nevada of California. Tree number density, mean crown radius, and fractional cover reference estimates were obtained via analysis of QuickBird 0.6 m spatial resolution panchromatic imagery usingthe CANopy Analysis with Panchromatic Imagery (CANAPI) algorithm, while RH50, RH75 and RH100 (50, 75, and 100 energy return) height data were obtained from the NASA Laser Vegetation Imaging Sensor (LVIS), a full waveform light detection and ranging (lidar) instrument. These canopy parameters were used to drive a modified version of the simple GO model (SGM), accurately reproducing patterns ofMISR 672 nm band surface reflectance (mean RMSE 0.011, mean R2 0.82, N 1048). Cover and height maps were obtained through model inversion against MISR 672 nm reflectance estimates on a 250 m grid.The free parameters were tree number density and mean crown radius. RMSE values with respect to reference data for the cover and height retrievals were 0.05 and 6.65 m, respectively, with of 0.54 and 0.49. MISR can thus provide maps of forest cover and height in areas of topographic variation although refinements are required to improve retrieval precision.

Automated Burned Area Delineation Using IRS AWiFS satellite data

NASA Astrophysics Data System (ADS)

Singhal, J.; Kiranchand, T. R.; Rajashekar, G.; Jha, C. S.

2014-12-01

India is endowed with a rich forest cover. Over 21% of country's area is covered by forest of varied composition and structure. Out of 67.5 million ha of Indian forests, about 55% of the forest cover is being subjected to fires each year, causing an economic loss of over 440 crores of rupees apart from other ecological effects. Studies carried out by Forest Survey of India reveals that on an average 53% forest cover of the country is prone to fires and 6.17% of the forests are prone to severe fire damage. Forest Survey of India in a countrywide study in 1995 estimated that about 1.45 million hectares of forest are affected by fire annually. According to Forest Protection Division of the Ministry of Environment and Forest (GOI), 3.73 million ha of forests are affected by fire annually in India. Karnataka is one of the southern states of India extending in between latitude 110 30' and 180 25' and longitudes 740 10' and 780 35'. As per Forest Survey of India's State of Forest Report (SFR) 2009, of the total geographic area of 191791sq.km, the state harbors 38284 sq.km of recorded forest area. Major forest types occurring in the study area are tropical evergreen and semi-evergreen, tropical moist and dry deciduous forests along with tropical scrub and dry grasslands. Typical forest fire season in the study area is from February-May with a peak during March-April every year, though sporadic fire episodes occur in other parts of the year sq.km, the state harbors 38284 sq.km of recorded forest area. Major forest types occurring in the study area are tropical evergreen and semi-evergreen, tropical moist and dry deciduous forests along with tropical scrub and dry grasslands. Significant area of the deciduous forests, scrub and grasslands is prone to recurrent forest fires every year. In this study we evaluate the feasibility of burned area mapping over a large area (Karnataka state, India) using a semi-automated detection algorithm applied to medium resolution multi spectral data from the IRS AWiFS sensor. The method is intended to be used by non-specialist users for diagnostic rapid burnt area mapping.

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).

Characterizing forest carbon stocks at tropical biome and landscape level in Mount Apo National Park, Philippines

NASA Astrophysics Data System (ADS)

Rubas, L. C.

2012-12-01

Forest resources sequester and store carbon, and serve as a natural brake on climate change. In the tropics, the largest source of greenhouse emission is from deforestation and forest degradation (Gibbs et al 2007). This paper attempts to compile sixty (60) existing studies on using remote sensing to measure key environmental forest indicators at two levels of scales: biome and landscape level. At the tropical forest biome level, there is not as much remote sensing studies that have been done as compared to other forest biomes. Also, existing studies on tropical Asia is still sparse compared to other tropical regions in Latin America and Africa. Biomass map is also produced for the tropical biome using keyhole macro language (KML) which is projected on Google Earth. The compiled studies showed there are four indicators being measured using remote sensors in tropical forest. These are biomass, landcover classification, deforestation and cloud cover. The landscape level will focus on Mount Apo National Park in the Philippines which is encompassing a total area of 54,974.87 hectares. It is one of the ten priority sites targeted in the World Bank-assisted Biodiversity Conservation Program. This park serves as the major watershed for the three provinces with 19 major rivers emanating from the montane formations. Only a small fraction of the natural forest that once covered the country remains. In spite of different policies that aim to reduce logging recent commercial deforestation, illegal logging and agricultural expansion pose an important threat to the remaining forest areas. In some locations in the country, these hotspots of deforestation overlap with the protected areas (Verburg et al 2006). The study site was clipped using ArcGIS from the forest biomass carbon density map produced by Gibbs and Brown (2007). Characterization on this national park using vegetation density, elevation, slope, land cover and precipitation will be conducted to determine factors that would affect the magnitude of stored carbon. Vegetation density will be derived from 5m SPOT imagery. Digital elevation model at a resolution of 90m will be obtained as part of NASA's Shuttle Radar Topography Mission (SRTM). Land cover data will be sourced from Landsat imagery. Mean annual precipitation data (MAP) will be collected from Worldclim dataset. Change detection analysis will be made using 2-time period of Landsat imagery. Accuracy assessment will be conducted following image classification. Changes in land cover will further be related to recommending necessary land use policies for reducing deforestation and the preservation of this protected area.

Modeling grain-size dependent bias in estimating forest area: a regional application

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey

2008-01-01

A better understanding of scaling-up effects on estimating important landscape characteristics (e.g. forest percentage) is critical for improving ecological applications over large areas. This study illustrated effects of changing grain sizes on regional forest estimates in Minnesota, Wisconsin, and Michigan of the USA using 30-m land-cover maps (1992 and 2001)...

Fragmentation of Continental United States Forests

Treesearch

Kurt H. Riitters; James D. Wickham; Robert V. O' Neill; K. Bruce Jones; Elizabeth R. Smith; John W. Coulston; Timothy G. Wade; Jonathan H. Smith

2002-01-01

We report a multiple-scale analysis of forest fragmentation based on 30-m (0.09 ha pixel-1) land- cover maps for the conterminous United States. Each 0.09-ha unit of forest was classified according to fragmentation indexes measured within the surrounding landscape, for five landscape sizes including 2.25, 7.29, 65.61, 590.49, and 5314.41 ha....

Global-scale patterns of forest fragmentation

Treesearch

Kurt H. Riitters; James D. Wickham; R. O' Neill; B. Jones; E. Smith

2000-01-01

We report an analysis of forest fragmentation based on 1-km resolution land-cover maps for the globe. Measurements in analysis windows from 81 km 2 (9 x 9 pixels, "small" scale) to 59,049 km 2 (243 x 243 pixels, "large" scale) were used to characterize the fragmentation around each forested pixel. We identified six categories of fragmentation (...

Using a remote sensing-based, percent tree cover map to enhance forest inventory estimation

Treesearch

Ronald E. McRoberts; Greg C. Liknes; Grant M. Domke

2014-01-01

For most national forest inventories, the variables of primary interest to users are forest area and growing stock volume. The precision of estimates of parameters related to these variables can be increased using remotely sensed auxiliary variables, often in combination with stratified estimators. However, acquisition and processing of large amounts of remotely sensed...

High-resolution global maps of 21st-century forest cover change

USGS Publications Warehouse

Hansen, M.C.; Potapov, P.V.; Moore, R.; Hancher, M.; Turubanova, S.A.; Tyukavina, A.; Thau, D.; Stehman, S.V.; Goetz, S.J.; Loveland, Thomas R.; Kommareddy, A.; Egorov, Alexey; Chini, L.; Justice, C.O.; Townshend, J.R.G.

2013-01-01

Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil’s well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

High-resolution global maps of 21st-century forest cover change.

PubMed

Hansen, M C; Potapov, P V; Moore, R; Hancher, M; Turubanova, S A; Tyukavina, A; Thau, D; Stehman, S V; Goetz, S J; Loveland, T R; Kommareddy, A; Egorov, A; Chini, L; Justice, C O; Townshend, J R G

2013-11-15

Quantification of global forest change has been lacking despite the recognized importance of forest ecosystem services. In this study, Earth observation satellite data were used to map global forest loss (2.3 million square kilometers) and gain (0.8 million square kilometers) from 2000 to 2012 at a spatial resolution of 30 meters. The tropics were the only climate domain to exhibit a trend, with forest loss increasing by 2101 square kilometers per year. Brazil's well-documented reduction in deforestation was offset by increasing forest loss in Indonesia, Malaysia, Paraguay, Bolivia, Zambia, Angola, and elsewhere. Intensive forestry practiced within subtropical forests resulted in the highest rates of forest change globally. Boreal forest loss due largely to fire and forestry was second to that in the tropics in absolute and proportional terms. These results depict a globally consistent and locally relevant record of forest change.

Forest cover loss and urban area expansion in the Conterminous Unites States in the first decade of the third millennium

NASA Astrophysics Data System (ADS)

Huo, L. Z.; Boschetti, L.

2016-12-01

Remote sensing has been successfully used for global mapping of changes in forest cover, but further analysis is needed to characterize those changes - and in particular to classify the total loss of forest loss (Gross Forest Cover Loss, GFCL) based on the cause (natural/human) and on the outcome of the change (regeneration to forest/transition to non-forest) (Kurtz et al., 2010). While natural forest disturbances (fires, insect outbreaks) and timber harvest generally involve a temporary change of land cover (vegetated to non-vegetated), they generally do not involve a change in land use, and it is expected that the forest cover loss is followed by recovery. Change of land use, such as the conversion of forest to agricultural or urban areas, is instead generally irreversible. The proper classification of forest cover loss is therefore necessary to properly model the long term effects of the disturbances on the carbon budget. The present study presents a spatial and temporal analysis of the forest cover loss due to urban expansion in the Conterminous United States. The Landsat-derived University of Maryland Global Forest Change product (Hansen et al, 2013) is used to identify all the areas of gross forest cover loss, which are subsequently classified into disturbance type (deforestation, stand-replacing natural disturbances, industrial forest clearcuts) using an object-oriented time series analysis (Huo and Boschetti, 2015). A further refinement of the classification is conducted to identify the areas of transition from forest land use to urban land use based on ancillary datasets such as the National Land Cover Database (Homer et al., 2015) and contextual image analysis techniques (analysis of object proximity, and detection of shapes). Results showed that over 4000 km2of forest were lost to urban area expansion in CONUS over the 2001 to 2010 period (1.8% of the gross forest cover loss). Most of the urban growth was concentrated in large urban areas: Atlanta, GA ranked first, followed by Houston, TX; Charlotte, NC; Jacksonville, FL; and Raleigh, NC. At the state level, the top 10 states with urban growth due to forest loss were GA, FL, TX, NC, SC, AL, LA, MS, VA and WA, which cumulatively accounted for 76 % of the total forest cover loss due to urban growth.

Land cover characterization and mapping of South America for the year 2010 using Landsat 30 m satellite data

USGS Publications Warehouse

Giri, Chandra; Long, Jordan

2014-01-01

Detailed and accurate land cover and land cover change information is needed for South America because the continent is in constant flux, experiencing some of the highest rates of land cover change and forest loss in the world. The land cover data available for the entire continent are too coarse (250 m to 1 km) for resource managers, government and non-government organizations, and Earth scientists to develop conservation strategies, formulate resource management options, and monitor land cover dynamics. We used Landsat 30 m satellite data of 2010 and prepared the land cover database of South America using state-of-the-science remote sensing techniques. We produced regionally consistent and locally relevant land cover information by processing a large volume of data covering the entire continent. Our analysis revealed that in 2010, 50% of South America was covered by forests, 2.5% was covered by water, and 0.02% was covered by snow and ice. The percent forest area of South America varies from 9.5% in Uruguay to 96.5% in French Guiana. We used very high resolution (<5 m) satellite data to validate the land cover product. The overall accuracy of the 2010 South American 30-m land cover map is 89% with a Kappa coefficient of 79%. Accuracy of barren areas needs to improve possibly using multi-temporal Landsat data. An update of land cover and change database of South America with additional land cover classes is needed. The results from this study are useful for developing resource management strategies, formulating biodiversity conservation strategies, and regular land cover monitoring and forecasting.

Going beyond the green: senesced vegetation material predicts basal area and biomass in remote sensing of tree cover conditions in an African tropical dry forest (miombo woodland) landscape

NASA Astrophysics Data System (ADS)

Mayes, Marc; Mustard, John; Melillo, Jerry; Neill, Christopher; Nyadzi, Gerson

2017-08-01

In sub-Saharan Africa (SSA), tropical dry forests and savannas cover over 2.5 million km2 and support livelihoods for millions in fast-growing nations. Intensifying land use pressures have driven rapid changes in tree cover structure (basal area, biomass) that remain poorly characterized at regional scales. Here, we posed the hypothesis that tree cover structure related strongly to senesced and non-photosynthetic (NPV) vegetation features in a SSA tropical dry forest landscape, offering improved means for satellite remote sensing of tree cover structure compared to vegetation greenness-based methods. Across regrowth miombo woodland sites in Tanzania, we analyzed relationships among field data on tree structure, land cover, and satellite indices of green and NPV features based on spectral mixture analyses and normalized difference vegetation index calculated from Landsat 8 data. From satellite-field data relationships, we mapped regional basal area and biomass using NPV and greenness-based metrics, and compared map performances at landscape scales. Total canopy cover related significantly to stem basal area (r 2 = 0.815, p < 0.01) and biomass (r 2 = 0.635, p < 0.01), and NPV dominated ground cover (> 60%) at all sites. From these two conditions emerged a key inverse relationship: skyward exposure of NPV ground cover was high at sites with low tree basal area and biomass, and decreased with increasing stem basal area and biomass. This pattern scaled to Landsat NPV metrics, which showed strong inverse correlations to basal area (Pearson r = -0.85, p < 0.01) and biomass (r = -0.86, p < 0.01). Biomass estimates from Landsat NPV-based maps matched field data, and significantly differentiated landscape gradients in woody biomass that greenness metrics failed to track. The results suggest senesced vegetation metrics at Landsat scales are a promising means for improved monitoring of tree structure across disturbance and ecological gradients in African and other tropical dry forests.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

NASA Astrophysics Data System (ADS)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

Natural vegetation cover in the landscape and edge effects: differential responses of insect orders in a fragmented forest.

PubMed

González, Ezequiel; Salvo, Adriana; Valladares, Graciela

2017-10-01

Human activities have led to global simplification of ecosystems, among which Neotropical dry forests are some of the most threatened. Habitat loss as well as edge effects may affect insect communities. Here, we analyzed insects sampled with pan traps in 9 landscapes (at 5 scales, in 100-500 m diameter circles) comprising cultivated fields and Chaco Serrano forests, at overall community and taxonomic order level. In total 7043 specimens and 456 species of hexapods were captured, with abundance and richness being directly related to forest cover at 500 m and higher at edges in comparison with forest interior. Community composition also varied with forest cover and edge/interior location. Different responses were detected among the 8 dominant orders. Collembola, Hemiptera, and Orthoptera richness and/or abundance were positively related to forest cover at the larger scale, while Thysanoptera abundance increased with forest cover only at the edge. Hymenoptera abundance and richness were negatively related to forest cover at 100 m. Coleoptera, Diptera, and Hymenoptera were more diverse and abundant at the forest edge. The generally negative influence of forest loss on insect communities could have functional consequences for both natural and cultivated systems, and highlights the relevance of forest conservation. Higher diversity at the edges could result from the simultaneous presence of forest and matrix species, although "resource mapping" might be involved for orders that were richer and more abundant at edges. Adjacent crops could benefit from forest proximity since natural enemies and pollinators are well represented in the orders showing positive edge effects. © 2016 Institute of Zoology, Chinese Academy of Sciences.

Meta-Analysis of Land Use / Land Cover Change Factors in the Conterminous US and Prediction of Potential Working Timberlands in the US South from FIA Inventory Plots and NLCD Cover Maps

NASA Astrophysics Data System (ADS)

Jeuck, James A.

This dissertation consists of research projects related to forest land use / land cover (LULC): (1) factors predicting LULC change and (2) methodology to predict particular forest use, or "potential working timberland" (PWT), from current forms of land data. The first project resulted in a published paper, a meta-analysis of 64 econometric models from 47 studies predicting forest land use changes. The response variables, representing some form of forest land change, were organized into four groups: forest conversion to agriculture (F2A), forestland to development (F2D), forestland to non-forested (F2NF) and undeveloped (including forestland) to developed (U2D) land. Over 250 independent econometric variables were identified, from 21 F2A models, 21 F2D models, 12 F2NF models, and 10 U2D models. These variables were organized into a hierarchy of 119 independent variable groups, 15 categories, and 4 econometric drivers suitable for conducting simple vote count statistics. Vote counts were summarized at the independent variable group level and formed into ratios estimating the predictive success of each variable group. Two ratio estimates were developed based on (1) proportion of times independent variables successfully achieved statistical significance (p ≤0.10), and (2) proportion of times independent variables successfully met the original researchers'expectations. In F2D models, popular independent variables such as population, income, and urban proximity often achieved statistical significance. In F2A models, popular independent variables such as forest and agricultural rents and costs, governmental programs, and site quality often achieved statistical significance. In U2D models, successful independent variables included urban rents and costs, zoning issues concerning forestland loss, site quality, urban proximity, population, and income. F2NF models high success variables were found to be agricultural rents, site quality, population, and income. This meta-analysis provides insight into the general success of econometric independent variables for future forest use or cover change research. The second part of this dissertation developed a method for predicting area estimates and spatial distribution of PWT in the US South. This technique determined land use from USFS Forest Inventory and Analysis (FIA) and land cover from the National Land Cover Database (NLCD). Three dependent variable forms (DV Forms) were derived from the FIA data: DV Form 1, timberland, other; DV Form 2, short timberland, tall timberland, agriculture, other; and DV Form 3, short hardwood (HW) timberland, tall HW timberland, short softwood (SW) timberland, tall SW timberland, agriculture, other. The prediction accuracy of each DV Form was investigated using both random forest model and logistic regression model specifications and data optimization techniques. Model verification employing a "leave-group-out" Monte Carlo simulation determined the selection of a stratified version of the random forest model using one-year NLCD observations with an overall accuracy of 0.53-0.94. The lower accuracy side of the range was when predictions were made from an aggregated NLCD land cover class "grass_shrub". The selected model specification was run using 2011 NLCD and the other predictor variables to produce three levels of timberland prediction and probability maps for the US South. Spatial masks removed areas unlikely to be working forests (protected and urbanized lands) resulting in PWT maps. The area of the resulting maps compared well with USFS area estimates and masked PWT maps and had an 8-11% reduction of the USFS timberland estimate for the US South compared to the DV Form. Change analysis of the 2011 NLCD to PWT showed (1) the majority of the short timberland came from NLCD grass_shrub; (2) the majority of NLCD grass_shrub predicted into tall timberland, and (3) NLCD grass_shrub was more strongly associated with timberland in the Coastal Plain. Resulting map products provide practical analytical tools for those interested in studying the area and distribution of PWT in the US South.

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

NASA Technical Reports Server (NTRS)

Ganguly, Sangram; Kalia, Subodh; Li, Shuang; Michaelis, Andrew; Nemani, Ramakrishna R.; Saatchi, Sassan A

2017-01-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Ganguly, S.; Kalia, S.; Li, S.; Michaelis, A.; Nemani, R. R.; Saatchi, S.

2017-12-01

Uncertainties in input land cover estimates contribute to a significant bias in modeled above gound biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition/ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree/non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial/satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products.

Vietnam's forest transition in retrospect: demonstrating weaknesses in business-as-usual scenarios for REDD.

PubMed

Ankersen, Jeppe; Grogan, Kenneth; Mertz, Ole; Fensholt, Rasmus; Castella, Jean-Christophe; Lestrelin, Guillaume; Nguyen, Dinh Tien; Danielsen, Finn; Brofeldt, Søren; Rasmussen, Kjeld

2015-05-01

One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government's efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios.

Vietnam's Forest Transition in Retrospect: Demonstrating Weaknesses in Business-as-Usual Scenarios for REDD+

NASA Astrophysics Data System (ADS)

Ankersen, Jeppe; Grogan, Kenneth; Mertz, Ole; Fensholt, Rasmus; Castella, Jean-Christophe; Lestrelin, Guillaume; Nguyen, Dinh Tien; Danielsen, Finn; Brofeldt, Søren; Rasmussen, Kjeld

2015-05-01

One of the prerequisites of the REDD+ mechanism is to effectively predict business-as-usual (BAU) scenarios for change in forest cover. This would enable estimation of how much carbon emission a project could potentially prevent and thus how much carbon credit should be rewarded. However, different factors like forest degradation and the lack of linearity in forest cover transitions challenge the accuracy of such scenarios. Here we predict and validate such BAU scenarios retrospectively based on forest cover changes at village and district level in North Central Vietnam. With the government's efforts to increase the forest cover, land use policies led to gradual abandonment of shifting cultivation since the 1990s. We analyzed Landsat images from 1973, 1989, 1998, 2000, and 2011 and found that the policies in the areas studied did lead to increased forest cover after a long period of decline, but that this increase could mainly be attributed to an increase in open forest and shrub areas. We compared Landsat classifications with participatory maps of land cover/use in 1998 and 2012 that indicated more forest degradation than was captured by the Landsat analysis. The BAU scenarios were heavily dependent on which years were chosen for the reference period. This suggests that hypothetical REDD+ activities in the past, when based on the remote sensing data available at that time, would have been unable to correctly estimate changes in carbon stocks and thus produce relevant BAU scenarios.

Vegetation and terrain mapping in Alaska using Landsat MSS and digital terrain data

USGS Publications Warehouse

Shasby, Mark; Carneggie, David M.

1986-01-01

During the past 5 years, the U.S. Geological Survey's (USGS) Earth Resources Observation Systems (EROS) Data Center Field Office in Anchorage, Alaska has worked cooperatively with Federal and State resource management agencies to produce land-cover and terrain maps for 245 million acres of Alaska. The need for current land-cover information in Alaska comes principally from the mandates of the Alaska National Interest Lands Conservation Act (ANILCA), December 1980, which requires major land management agencies to prepare comprehensive management plans. The land-cover mapping projects integrate digital Landsat data, terrain data, aerial photographs, and field data. The resultant land-cover and terrain maps and associated data bases are used for resource assessment, management, and planning by many Alaskan agencies including the U.S. Fish and Wildlife Service, U.S. Forest Service, Bureau of Land Management, and Alaska Department of Natural Resources. Applications addressed through use of the digital land-cover and terrain data bases range from comprehensive refuge planning to multiphased sampling procedures designed to inventory vegetation statewide. The land-cover mapping programs in Alaska demonstrate the operational utility of digital Landsat data and have resulted in a new land-cover mapping program by the USGS National Mapping Division to compile 1:250,000-scale land-cover maps in Alaska using a common statewide land-cover map legend.

Evaluating Anthropogenic Risk of Grassland and Forest Habitat Degradation Using Land-Cover Data

EPA Science Inventory

The effects of landscape context on habitat quality are receiving increased attention in conservation biology. The objective of this research is to demonstrate an approach to mapping and evaluating the anthropogenic risks of grassland and forest habitat degradation by examining ...

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.

Land cover mapping of Greater Mesoamerica using MODIS data

USGS Publications Warehouse

Giri, Chandra; Jenkins, Clinton N.

2005-01-01

A new land cover database of Greater Mesoamerica has been prepared using moderate resolution imaging spectroradiometer (MODIS, 500 m resolution) satellite data. Daily surface reflectance MODIS data and a suite of ancillary data were used in preparing the database by employing a decision tree classification approach. The new land cover data are an improvement over traditional advanced very high resolution radiometer (AVHRR) based land cover data in terms of both spatial and thematic details. The dominant land cover type in Greater Mesoamerica is forest (39%), followed by shrubland (30%) and cropland (22%). Country analysis shows forest as the dominant land cover type in Belize (62%), Cost Rica (52%), Guatemala (53%), Honduras (56%), Nicaragua (53%), and Panama (48%), cropland as the dominant land cover type in El Salvador (60.5%), and shrubland as the dominant land cover type in Mexico (37%). A three-step approach was used to assess the quality of the classified land cover data: (i) qualitative assessment provided good insight in identifying and correcting gross errors; (ii) correlation analysis of MODIS- and Landsat-derived land cover data revealed strong positive association for forest (r2 = 0.88), shrubland (r2 = 0.75), and cropland (r2 = 0.97) but weak positive association for grassland (r2 = 0.26); and (iii) an error matrix generated using unseen training data provided an overall accuracy of 77.3% with a Kappa coefficient of 0.73608. Overall, MODIS 500 m data and the methodology used were found to be quite useful for broad-scale land cover mapping of Greater Mesoamerica.

Application of SAR Remote Sensing in Land Surface Processes Over Tropical region

NASA Technical Reports Server (NTRS)

Saatchi, Sasan S.

1996-01-01

This paper outlines the potential applications of polarimetric SAR systems over tropical regions such as mapping land use and deforestation, forest regeneration, wetland and inundation studies, and mapping land cover types for biodiversity and habitat conservation studies.

Integrating Landsat Data and High-Resolution Imagery for Applied Conservation Assessment of Forest Cover in Latin American Heterogenous Landscapes

NASA Astrophysics Data System (ADS)

Thomas, N.; Rueda, X.; Lambin, E.; Mendenhall, C. D.

2012-12-01

Large intact forested regions of the world are known to be critical to maintaining Earth's climate, ecosystem health, and human livelihoods. Remote sensing has been successfully implemented as a tool to monitor forest cover and landscape dynamics over broad regions. Much of this work has been done using coarse resolution sensors such as AVHRR and MODIS in combination with moderate resolution sensors, particularly Landsat. Finer scale analysis of heterogeneous and fragmented landscapes is commonly performed with medium resolution data and has had varying success depending on many factors including the level of fragmentation, variability of land cover types, patch size, and image availability. Fine scale tree cover in mixed agricultural areas can have a major impact on biodiversity and ecosystem sustainability but may often be inadequately captured with the global to regional (coarse resolution and moderate resolution) satellite sensors and processing techniques widely used to detect land use and land cover changes. This study investigates whether advanced remote sensing methods are able to assess and monitor percent tree canopy cover in spatially complex human-dominated agricultural landscapes that prove challenging for traditional mapping techniques. Our study areas are in high altitude, mixed agricultural coffee-growing regions in Costa Rica and the Colombian Andes. We applied Random Forests regression tree analysis to Landsat data along with additional spectral, environmental, and spatial variables to predict percent tree canopy cover at 30m resolution. Image object-based texture, shape, and neighborhood metrics were generated at the Landsat scale using eCognition and included in the variable suite. Training and validation data was generated using high resolution imagery from digital aerial photography at 1m to 2.5 m resolution. Our results are promising with Pearson's correlation coefficients between observed and predicted percent tree canopy cover of .86 (Costa Rica) and .83 (Colombia). The tree cover mapping developed here supports two distinct projects on sustaining biodiversity and natural and human capital: in Costa Rica the tree canopy cover map is utilized to predict bird community composition; and in Colombia the mapping is performed for two time periods and used to assess the impact of coffee eco-certification programs on the landscape. This research identifies ways to leverage readily available, high quality, and cost-free Landsat data or other medium resolution satellite data sources in combination with high resolution data, such as that frequently available through Google Earth, to monitor and support sustainability efforts in fragmented and heterogeneous landscapes.

Use of NASA Satellite Data to Improve Coastal Cypress Forest Management

NASA Technical Reports Server (NTRS)

Spurce, Joseph; Graham, William; Barras, John

2010-01-01

Problem: Information gaps exist regarding health status and location of cypress forests in coastal Louisiana (LA). Such information is needed to aid coastal forest conservation and restoration programs. Approach to Issue Mitigation: Use NASA data to revise cypress forest cover type maps. Landsat and ASTER data. Use NASA data to identify and track cypress forest change. Landsat, ASTER, and MODIS data. Work with partners and end-users to transfer useful products and technology.

Assessment and monitoring of long-term forest cover changes in Odisha, India using remote sensing and GIS.

PubMed

Reddy, C Sudhakar; Jha, C S; Dadhwal, V K

2013-05-01

Deforestation and fragmentation are important concerns in managing and conserving tropical forests and have global significance. In the Indian context, in the last one century, the forests have undergone significant changes due to several policies undertaken by government as well as increased population pressure. The present study has brought out spatiotemporal changes in forest cover and variation in forest type in the state of Odisha (Orissa), India, during the last 75 years period. The mapping for the period of 1924-1935, 1975, 1985, 1995 and 2010 indicates that the forest cover accounts for 81,785.6 km(2) (52.5 %), 56,661.1 km(2) (36.4 %), 51,642.3 km(2) (33.2 %), 49,773 km(2) (32 %) and 48,669.4 km(2) (31.3 %) of the study area, respectively. The study found the net forest cover decline as 40.5 % of the total forest and mean annual rate of deforestation as 0.69 % year(-1) during 1935 to 2010. There is a decline in annual rate of deforestation during 1995 to 2010 which was estimated as 0.15 %. Forest type-wise quantitative loss of forest cover reveals large scale deforestation of dry deciduous forests. The landscape analysis shows that the number of forest patches (per 1,000) are 2.463 in 1935, 10.390 in 1975, 11.899 in 1985, 12.193 in 1995 and 15.102 in 2010, which indicates high anthropogenic pressure on the forests. The mean patch size (km(2)) of forest decreased from 33.2 in 1935 to 5.5 in 1975 and reached to 3.2 by 2010. The study demonstrated that monitoring of long term forest changes, quantitative loss of forest types and landscape metrics provides critical inputs for management of forest resources.

Non-supervised method for early forest fire detection and rapid mapping

NASA Astrophysics Data System (ADS)

Artés, Tomás; Boca, Roberto; Liberta, Giorgio; San-Miguel, Jesús

2017-09-01

Natural hazards are a challenge for the society. Scientific community efforts have been severely increased assessing tasks about prevention and damage mitigation. The most important points to minimize natural hazard damages are monitoring and prevention. This work focuses particularly on forest fires. This phenomenon depends on small-scale factors and fire behavior is strongly related to the local weather. Forest fire spread forecast is a complex task because of the scale of the phenomena, the input data uncertainty and time constraints in forest fire monitoring. Forest fire simulators have been improved, including some calibration techniques avoiding data uncertainty and taking into account complex factors as the atmosphere. Such techniques increase dramatically the computational cost in a context where the available time to provide a forecast is a hard constraint. Furthermore, an early mapping of the fire becomes crucial to assess it. In this work, a non-supervised method for forest fire early detection and mapping is proposed. As main sources, the method uses daily thermal anomalies from MODIS and VIIRS combined with land cover map to identify and monitor forest fires with very few resources. This method relies on a clustering technique (DBSCAN algorithm) and on filtering thermal anomalies to detect the forest fires. In addition, a concave hull (alpha shape algorithm) is applied to obtain rapid mapping of the fire area (very coarse accuracy mapping). Therefore, the method leads to a potential use for high-resolution forest fire rapid mapping based on satellite imagery using the extent of each early fire detection. It shows the way to an automatic rapid mapping of the fire at high resolution processing as few data as possible.

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

2001-01-01

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

Derivation of a northern-hemispheric biomass map for use in global carbon cycle models

NASA Astrophysics Data System (ADS)

Thurner, Martin; Beer, Christian; Santoro, Maurizio; Carvalhais, Nuno; Wutzler, Thomas; Schepaschenko, Dmitry; Shvidenko, Anatoly; Kompter, Elisabeth; Levick, Shaun; Schmullius, Christiane

2013-04-01

Quantifying the state and the change of the World's forests is crucial because of their ecological, social and economic value. Concerning their ecological importance, forests provide important feedbacks on the global carbon, energy and water cycles. In addition to their influence on albedo and evapotranspiration, they have the potential to sequester atmospheric carbon dioxide and thus to mitigate global warming. The current state and inter-annual variability of forest carbon stocks remain relatively unexplored, but remote sensing can serve to overcome this shortcoming. While for the tropics wall-to-wall estimates of above-ground biomass have been recently published, up to now there was a lack of similar products covering boreal and temperate forests. Recently, estimates of forest growing stock volume (GSV) were derived from ENVISAT ASAR C-band data for latitudes above 30° N. Utilizing a wood density and a biomass compartment database, a forest carbon density map covering North-America, Europe and Asia with 0.01° resolution could be derived out of this dataset. Allometric functions between stem, branches, root and foliage biomass were fitted and applied for different leaf types (broadleaf, needleleaf deciduous, needleleaf evergreen forest). Additionally, this method enabled uncertainty estimation of the resulting carbon density map. Intercomparisons with inventory-based biomass products in Russia, Europe and the USA proved the high accuracy of this approach at a regional scale (r2 = 0.70 - 0.90). Based on the final biomass map, the forest carbon stocks and densities (excluding understorey vegetation) for three biomes were estimated across three continents. While 40.7 ± 15.7 Gt of carbon were found to be stored in boreal forests, temperate broadleaf/mixed forests and temperate conifer forests contain 24.5 ± 9.4 Gt(C) and 14.5 ± 4.8 Gt(C), respectively. In terms of carbon density, most of the carbon per area is stored in temperate conifer (62.1 ± 20.7 Mg(C)/ha(Forest)) and broadleaf/mixed forests (58.0 ± 22.1 Mg(C)/ha(Forest)), whereas boreal forests have a carbon density of only 40.0 ± 15.4 Mg(C)/ha(Forest). While European forest carbon stocks are relatively small, the carbon density is higher compared to the other continents. The derived biomass map substantially improves the knowledge on the current carbon stocks of the northern-hemispheric boreal and temperate forests, serving as a new benchmark for spatially explicit and consistent biomass mapping with moderate spatial resolution. This product can be of great value for global carbon cycle models as well as national carbon monitoring systems. Further investigations concentrate on improving biomass parameterizations and representations in such kind of models. The presented map will help to improve the simulation of biomass spatial patterns and variability and enables identifying the dominant influential factors like climatic conditions and disturbances.

A comparison of FIA plot data derived from image pixels and image objects

Treesearch

Charles E. Werstak

2012-01-01

The use of Forest Inventory and Analysis (FIA) plot data for producing continuous and thematic maps of forest attributes (e.g., forest type, canopy cover, volume, and biomass) at the regional level from satellite imagery can be challenging due to differences in scale. Specifically, classification errors that may result from assumptions made between what the field data...

K-nearest neighbor imputation of forest inventory variables in New Hampshire

Treesearch

Andrew Lister; Michael Hoppus; Raymond L. Czaplewski

2005-01-01

The k-nearest neighbor (kNN) method was used to map stand volume for a mosaic of 4 Landsat scenes covering the state of New Hampshire. Data for gross cubic foot volume and trees per acre were summarized from USDA Forest Service Forest Inventory and Analysis (FIA) plots and used as training for kNN. Six bands of...

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.

Forest Cover Change and Soil Erosion in Toledo's Rio Grande Watershed

NASA Astrophysics Data System (ADS)

Chicas, S.; Omine, K.

2015-04-01

Toledo, the southernmost district, is the hub of Belize's Mayan population, descendants of the ancient Mayan civilization. The Toledo District is primarily inhibited by Kekchi and Mopan Mayans whose subsistence needs are met by the Milpa slash-and-burn agricultural system and the extraction of forest resources. The poverty assessment in the country indicates that Toledo is the district with the highest percentage of household an individual indigence of 37.5 % and 49.7 % respectively. Forest cover change in the area can be attributed to rapid population growth among the Maya, together with increase in immigration from neighboring countries, logging, oil exploration and improvement and construction of roads. The forest cover change analysis show that from 2001 to 2011 there was a decrease of Lowland broad-leaved wet forest of 7.53 km sq, Shrubland of 4.66 km sq, and Wetland of 0.08 km sq. Forest cover change has resulted in soil erosion which is causing the deterioration of soils. The land cover types that are contributing the most to total erosion in the Rio Grande watershed are no-forest, lowland broad-leaved wet forest and submontane broad-leaved wet forest. In this study the Revised Universal Soil Loss Equation (RUSLE) was employed in a GIS platform to quantify and assess forest cover change and soil erosion. Soil erosion vulnerability maps in Toledo's Rio Grande watershed were also created. This study provides scientifically sound information in order to understand and respond effectively to the impacts of soil erosion in the study site.

Global Survey of Anthropogenic Neighborhood Threats to Conservation of Grass-Shrub and Forest Vegetation

EPA Science Inventory

The ecological functions of natural vegetation are threatened when it is subsumed in anthropogenic landscapes. We report the first comparative global survey of anthropogenic landscape threats to forest and grass-shrub vegetation. Using a global land-cover map derived from remote...

Georeferenced historical forest maps of Bukovina (Northern Romania) - important tool for paleoenvironmental analyses

NASA Astrophysics Data System (ADS)

Popa, Ionel; Crǎciunescu, Vasile; Candrea, Bogdan; Timár, Gábor

2010-05-01

The historical region of Bukovina is one of the most forested areas of Romania. The name itself, beech land, suggest the high wood resources located here. The systematic wood exploitation started in Bukovina during the Austrian rule (1775 - 1918). To fully asses the region's wood potential and to make the exploitation and replantation processes more efficient, the Austrian engineers developed a dedicated mapping system. The result was a series of maps, surveyed for each forest district. In the first editions, we can find maps crafted at different scales (e.g. 1:50 000, 1: 20 000, 1: 25 000). Later on (after 1900), the map sheets scale was standardized to 1: 25 000. Each sheet was accompanied by a register with information regarding the forest parcels. The system was kept after 1918, when Bukovina become a part of Romania. For another 20 years, the forest districts were periodically surveyed and the maps updated. The basemap content also changed during time. For most of the maps, the background was compiled from the Austrian Third Military Survey maps. After the Second World War, the Romanian military maps ("planurile directoare de tragere") were also used. The forest surveys were positioned using the Austrian triangulation network with the closest baseline at Rădăuţi. Considered lost after WWII, an important part of this maps were recently recovered by a fortunate and accidental finding. Such informations are highly valuable for the today forest planners. By careful studying this kind of documents, a modern forest manager can better understand the way forests were managed in the past and the implications of that management in today's forest reality. In order to do that, the maps should be first georeferenced into a known coordinate system of the Third Survey and integrated with recent geospatial datasets using a GIS environment. The paper presents the challenges of finding and applying the right informations regarding the datum and projection used by the Austrian and Romanian forest surveyors, to correctly georeference the maps. A case study, demonstrating the usefulness of such old cartographic informations in understanding the forest landscape evolution is also included. The georeferenced map sheets provide an excellent basis of the paleo-environmental researches. Assessing the changes of the forest cover ratio is important for the analysis of the recent flash flood events at the eastern slopes of the Carpathian Mts.

Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) Global Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Riggs, George A.; Salomonson, Vincent V.; Scharfen, Greg R.

2000-01-01

Following the 1999 launch of the Earth Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS), the capability exists to produce global snow-cover maps on a daily basis at 500-m resolution. Eight-day composite snow-cover maps will also be available. MODIS snow-cover products are produced at Goddard Space Flight Center and archived and distributed by the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado. The products are available in both orbital and gridded formats. An online search and order tool and user-services staff will be available at NSIDC to assist users with the snow products. The snow maps are available at a spatial resolution of 500 m, and 1/4 degree x 1/4 degree spatial resolution, and provide information on sub-pixel (fractional) snow cover. Pre-launch validation work has shown that the MODIS snow-mapping algorithms perform best under conditions of continuous snow cover in low vegetation areas, but can also map snow cover in dense forests. Post-launch validation activities will be performed using field and aircraft measurements from a February 2000 validation mission, as well as from existing satellite-derived snow-cover maps from NOAA and Landsat-7 Enhanced Thematic Mapper Plus (ETM+).

Bringing Together Users and Developers of Forest Biomass Maps

NASA Technical Reports Server (NTRS)

Brown, Molly E.; Macauley, Molly

2011-01-01

Forests store carbon and thus represent important sinks for atmospheric carbon dioxide. Reducing uncertainty in current estimates of the amount of carbon in standing forests will improve precision of estimates of anthropogenic contributions to carbon dioxide in the atmosphere due to deforestation. Although satellite remote sensing has long been an important tool for mapping land cover, until recently aboveground forest biomass estimates have relied mostly on systematic ground sampling of forests. In alignment with fiscal year 2010 congressional direction, NASA has initiated work toward a carbon monitoring system (CMS) that includes both maps of forest biomass and total carbon flux estimates. A goal of the project is to ensure that the products are useful to a wide community of scientists, managers, and policy makers, as well as to carbon cycle scientists. Understanding the needs and requirements of these data users is helpful not just to the NASA CMS program but also to the entire community working on carbon-related activities. To that end, this meeting brought together a small group of natural resource managers and policy makers who use information on forests in their work with NASA scientists who are working to create aboveground forest biomass maps. These maps, derived from combining remote sensing and ground plots, aim to be more accurate than current inventory approaches when applied at local and regional scales.

Building capacity for national level carbon Measurement, Reporting, and Verification (MRV) systems for a ``Reduction of Emissions from Deforestation and Degradation'' (REDD)

NASA Astrophysics Data System (ADS)

Laporte, N.; Goetz, S. J.; Baccini, A.; Walker, W. S.; Ndunda, P.; Mekui, P.; Kellndorfer, J. M.; Knight, D.

2010-12-01

An international policy mechanism is under negotiation for compensating tropical nations that succeed in lowering their greenhouse gas emissions from tropical deforestation and forest degradation, responsible for approximately one-fifth of worldwide carbon emissions. One of the barriers to its success is the adoption of a unique MRV system and the participation of developing countries in carbon monitoring. A successful REDD policy must rely on a robust, scalable, cost effective method that will allow the Measurement Reporting and Verification from local to national scales, while also developing well-trained technical personnel to implement national REDD carbon monitoring systems. Participation of governments and forest stakeholders in forest and carbon monitoring methods at WHRC is achieved through ongoing technical workshops which include training of participants to collect field data to calibrate biomass models, and an annual Scholar’s Program where forest officers from the tropical regions of Latin America, Africa and Southeast Asia work with Woods Hole Research Center scientsts to improve skills in forest measurement and remote sensing monitoring techniques . Capacity building activities focus on technical aspects and approaches to forest-cover and carbon mapping and the use of satellite imagery together with ground-based measurement techniques in the development of forest cover and carbon-stock maps. After two years, the three-year project has involved more than 200 forest specialists from governments and NGOs in Bolivia, Cambodia, Colombia, the Democratic Republic of Congo, Gabon, Indonesia, Lao PDR, Kenya, Uganda, Vietnam and Zambia, among others with participation of ten scholars actively participating in the developement of National REDD plans for forest mapping and monitoring. Field Training Mbandaka- DR Congo 2010

Forest Management in Earth System Modelling: a Vertically Discretised Canopy Description for ORCHIDEE and Effects on European Climate Since 1750

NASA Astrophysics Data System (ADS)

McGrath, M.; Luyssaert, S.; Naudts, K.; Chen, Y.; Ryder, J.; Otto, J.; Valade, A.

2015-12-01

Forest management has the potential to impact surface physical characteristics to the same degree that changes in land cover do. The impacts of land cover changes on the global climate are well-known. Despite an increasingly detailed understanding of the potential for forest management to affect climate, none of the current generation of Earth system models account for forest management through their land surface modules. We addressed this gap by developing and reparameterizing the ORCHIDEE land surface model to simulate the biogeochemical and biophysical effects of forest management. Through vertical discretization of the forest canopy and corresponding modifications to the energy budget, radiation transfer, and carbon allocation, forest management can now be simulated much more realistically on the global scale. This model was used to explore the effect of forest management on European climate since 1750. Reparameterization was carried out to replace generic forest plant functional types with real tree species, covering the most dominant species across the continent. Historical forest management and land cover maps were created to run the simulations from 1600 until the present day. The model was coupled to the atmospheric model LMDz to explore differences in climate between 1750 and 2010 and attribute those differences to changes in atmospheric carbon dioxide concentrations and concurrent warming, land cover, species composition, and wood extraction. Although Europe's forest are considered a carbon sink in this century, our simulations show the modern forests are still experiencing carbon debt compared to their historical values.

Cropland management dynamics as a driver of forest cover change in European Russia (Invited)

NASA Astrophysics Data System (ADS)

Tyukavina, A.; Krylov, A.; Potapov, P.; Turubanova, S.; Hansen, M.; McCarty, J. L.

2013-12-01

The European part of Russia spans over 40% of the European subcontinent and comprises most of Europe's temperate and boreal forests. The region has undergone a socio-economic transition during the last two decades that has resulted in radical changes in land management. Large-scale agriculture land abandonment caused massive afforestation in the Central and Northern parts of the region (Alcantara et al. 2012). Afforestation of former croplands is currently not included in the official forestry statistical reports (Potapov et al. 2012), but is likely to have major impacts on regional carbon budgets (Kuemmerle et al. 2009). We employed a complete archive of Landsat TM and ETM+ imagery and automatic data processing algorithm to create regional time-sequential image composites and multi-temporal metrics for 1985-2012. Spectral metrics were used as independent variables to map forest cover and change with help of supervised machine learning algorithms and trend analysis. Forest cover loss was attributed to fires, harvesting, and wind/disease dynamics, while forest cover gain was disaggregated into reforestation and afforestation using pre-1990 TM imagery as baseline data. Special attention was paid to agricultural abandonment. Fire events of the last decade have been further characterized by ignition place, time, and burning intensity using MODIS fire detection data. Change detection products have been validated using field data collected during summer 2012 and 2013 and high resolution imagery. Massive arable land abandonment caused forest area increase within Central agricultural regions. While total logging area decreased after the USSR breakdown, logging and other forms of clearing increased within the Central and Western parts of the region. Gross forest gain and loss were nearly balanced within region; however, the most populated regions of European Russia featured the highest rate of net forest cover loss during the last decade. The annual burned forest area as well as area of windstorms damage significantly increased, especially in the Central regions. Fires predominantly affected pine forests and drained peatlands prone to summer droughts. Fire date and ignition analysis showed that forest fires are not related to extensive spring-time agricultural burning. References: Alcantara, C., T. Kuemmerle, A. V. Prishchepov & V. C. Radeloff. 2012. Mapping abandoned agriculture with multi-temporal MODIS satellite data. 334-347. Remote Sensing of Environment. Kuemmerle, T., O. Chaskovskyy, J. Knorn, V. C. Radeloff, I. Kruhlov, W. S. Keeton & P. Hostert. 2009. Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sensing of Environment, 113, 1194-1207. Potapov, P., S. Turubanova, I. Zhuravleva, M. Hansen, A. Yaroshenko & A. Manisha. 2012. Forest Cover Change within the Russian European North after the Breakdown of Soviet Union (1990-2005) 1-11. International Journal of Forestry Research.

National Level Assessment of Mangrove Forest Cover in Pakistan

NASA Astrophysics Data System (ADS)

Abbas, S.; Qamer, F. M.; Hussain, N.; Saleem, R.; Nitin, K. T.

2011-09-01

Mangroves ecosystems consist of inter tidal flora and fauna found in the tropical and subtropical regions of the world. Mangroves forest is a collection of halophytic trees, shrubs, and other plants receiving inputs from regular tidal flushing and from freshwater streams and rivers. A global reduction of 25 % mangroves' area has been observed since 1980 and it is categorized as one of to the most threatened and vulnerable ecosystems of the world. Forest resources in Pakistan are being deteriorating both quantitatively and qualitatively due to anthropogenic activities, climatic v and loose institutional management. According to the FAO (2007), extent of forest cover of Pakistan in 2005 is 1,902,000 ha, which is 2.5% of its total land area. Annual change rate during 2000-2005 was -2.1% which is highest among all the countries in Asia. The Indus delta region contains the world's fifth-largest mangrove forest which provides a range of important ecosystem services, including coastal stabilisation, primary production and provision of nursery habitat for marine fish. Given their ecological importance in coastal settings, mangroves receive special attention in the assessment of conservation efforts and sustainable coastal developments. Coastline of Pakistan is 1050km long shared by the provinces, Sind (350km) and Baluchistan (700 km). The coastline, with typical arid subtropical climate, possesses five significant sites that are blessed with mangroves. In the Sindh province, mangroves are found in the Indus Delta and Sandspit. The Indus Delta is host to the most extensive mangroves areas and extends from Korangi Creek in the West to Sir Creek in the East, whereas Sandspit is a small locality in the West of Karachi city. In the Balochistan province, mangroves are located at three sites, Miani Hor, Kalmat Khor and Jiwani. Contemporary methods of Earth observation sciences are being incorporated as an integral part of environmental assessment related studies in coastal areas. GIS and Remote Sensing based technologies and methods are in use to map forest cover since the last two decades in Pakistan. The national level forest cover studies based upon satellite images include, Forestry Sector Master Plan (FSMP) and National Forest & Range Resources Assessment Study (NFRRAS). In FSMP, the mangrove forest extent was visually determined from Landsat images of 1988 - 1991, and was estimated to be 155,369 ha; whereas, in NFRRAS, Landsat images of 1997 - 2001 were automated processed and the mangroves areas was estimated to be 158,000 ha. To our knowledge, a comprehensive assessment of current mangroves cover of Pakistan has not been made over the last decade, although the mangroves ecosystems have become the focus of intention in context of recent climate change scenarios. This study was conducted to support the informed decision making for sustainable development in coastal areas of Pakistan by providing up-todate mangroves forest cover assessment of Pakistan. Various types of Earth Observation satellite images and processing methods have been tested in relation to mangroves mapping. Most of the studies have applied classical pixel - based approached, there are a few studies which used object - based methods of image analysis to map the mangroves ecosystems. Object - based methods have the advantage of incorporating spatial neighbourhood properties and hierarchical structures into the classification process to produce more accurate surface patterns recognition compared with classical pixel - based approaches. In this research, we applied multi-scale hierarchical approach of object-based methods of image analysis to ALOS - AVNIR-2 images of the year 2008-09 to map the land cover in the mangroves ecosystems of Pakistan. Considering the tide height and phonological effects of vegetation, particularly the algal mats, these data sets were meticulously chosen. Incorporation of multi-scale hierarchical structures made it easy to effectively discriminate among the land cover classes, particularly the mudflats from sparse mangroves, at their respective scales. Results of current image analysis deciphered that the overall mangroves cover of Pakistan is ~ 98,128 ha. Mangroves cover along the Indus Delta is estimated to be 92, 412 ha that is ~94.17 % of the total mangroves area of the country. 1,056 ha of the forest thrive in Sandspit, whilst the remainin 4,660 ha mangroves occurs along the Makran coast in 3 isolated pockets at Miani Hor (4,018 ha), Kalmat Khor (407 ha) and Jiwani (235 ha). Overall accuracy of land cover maps, from 250 ground reference points, was estimated to be 83.2% (kappa value .7301; kappa variance .0029) which was considered acceptable for optical data in a semi-aquatic environment.

Remote Sensing of Forest Loss and Human Land Use to Predict Biodiversity Impacts in Myanmar

NASA Astrophysics Data System (ADS)

Connette, G.; Huang, Q.; Leimgruber, P.; Songer, M.

2017-12-01

Myanmar's ongoing transition from military rule towards a democratic government has largely ended decades of economic isolation. The resulting expansion of foreign investment, infrastructure development, and natural resource extraction has led to high rates of deforestation and the concurrent loss of critical wildlife habitat. To identify and mitigate the impacts of rapid land use change on Myanmar's globally-unique biodiversity, researchers at Smithsonian's Conservation Biology Institute have used moderate-resolution satellite imagery to map forest cover change at the national scale, while performing regional- or local-scale analyses to identify ecologically-distinct forest types. At the national scale, forest was lost at a rate of 0.55% annually from 2002-2014. Deforestation was more pronounced in Myanmar's closed-canopy forests (>80% cover), which experienced an annual rate of forest loss of 0.95%. Studies at regional and local scales show that ecologically-distinct forest types vary considerably in both geographic extent and risk of conversion to human land use. For instance, local deforestation rates around a proposed national park in Myanmar's Tanintharyi Region were 7.83% annually and have been accelerating. Recent integration of such results into wildlife habitat mapping and national conservation planning can play an important role in ensuring that future development in Myanmar is both informed and sustainable.

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.

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.

Harmonization of Multiple Forest Disturbance Data to Create a 1986-2011 Database for the Conterminous United States

NASA Astrophysics Data System (ADS)

Soulard, C. E.; Acevedo, W.; Yang, Z.; Cohen, W. B.; Stehman, S. V.; Taylor, J. L.

2015-12-01

A wide range of spatial forest disturbance data exist for the conterminous United States, yet inconsistencies between map products arise because of differing programmatic objectives and methodologies. Researchers on the Land Change Research Project (LCRP) are working to assess spatial agreement, characterize uncertainties, and resolve discrepancies between these national level datasets, in regard to forest disturbance. Disturbance maps from the Global Forest Change (GFC), Landfire Vegetation Disturbance (LVD), National Land Cover Dataset (NLCD), Vegetation Change Tracker (VCT), Web-enabled Landsat Data (WELD), and Monitoring Trends in Burn Severity (MTBS) were harmonized using a pixel-based data fusion process. The harmonization process reconciled forest harvesting, forest fire, and remaining forest disturbance across four intervals (1986-1992, 1992-2001, 2001-2006, and 2006-2011) by relying on convergence of evidence across all datasets available for each interval. Pixels with high agreement across datasets were retained, while moderate-to-low agreement pixels were visually assessed and either manually edited using reference imagery or discarded from the final disturbance map(s). National results show that annual rates of forest harvest and overall fire have increased over the past 25 years. Overall, this study shows that leveraging the best elements of readily-available data improves forest loss monitoring relative to using a single dataset to monitor forest change, particularly by reducing commission errors.

Remote sensing program

NASA Technical Reports Server (NTRS)

Whitmore, R. A., Jr. (Principal Investigator)

1980-01-01

A syllabus and training materials prepared and used in a series of one-day workshops to introduce modern remote sensing technology to selected groups of professional personnel in Vermont are described. Success in using computer compatible tapes, LANDSAT imagery and aerial photographs is reported for the following applications: (1) mapping defoliation of hardwood forests by tent caterpillar and gypsy moth; (2) differentiating conifer species; (3) mapping ground cover of major lake and pond watersheds; (4) inventorying and locating artificially regenerated conifer forest stands; (5) mapping water quality; (6) ascertaining the boat population to quantify recreational activity on lakes and waterways; and (7) identifying potential aquaculture sites.

High-Resolution Forest Canopy Height Estimation in an African Blue Carbon Ecosystem

NASA Technical Reports Server (NTRS)

Lagomasino, David; Fatoyinbo, Temilola; Lee, Seung-Kuk; Simard, Marc

2015-01-01

Mangrove forests are one of the most productive and carbon dense ecosystems that are only found at tidally inundated coastal areas. Forest canopy height is an important measure for modeling carbon and biomass dynamics, as well as land cover change. By taking advantage of the flat terrain and dense canopy cover, the present study derived digital surface models (DSMs) using stereophotogrammetric techniques on high-resolution spaceborne imagery (HRSI) for southern Mozambique. A mean-weighted ground surface elevation factor was subtracted from the HRSI DSM to accurately estimate the canopy height in mangrove forests in southern Mozambique. The mean and H100 tree height measured in both the field and with the digital canopy model provided the most accurate results with a vertical error of 1.18-1.84 m, respectively. Distinct patterns were identified in the HRSI canopy height map that could not be discerned from coarse shuttle radar topography mission canopy maps even though the mode and distribution of canopy heights were similar over the same area. Through further investigation, HRSI DSMs have the potential of providing a new type of three-dimensional dataset that could serve as calibration/validation data for other DSMs generated from spaceborne datasets with much larger global coverage. HSRI DSMs could be used in lieu of Lidar acquisitions for canopy height and forest biomass estimation, and be combined with passive optical data to improve land cover classifications.

Unmixing AVHRR Imagery to Assess Clearcuts and Forest Regrowth in Oregon

NASA Technical Reports Server (NTRS)

Hlavka, Christine A.; Spanner, Michael A.

1995-01-01

Advanced Very High Resolution Radiometer imagery provides frequent and low-cost coverage of the earth, but its coarse spatial resolution (approx. 1.1 km by 1.1 km) does not lend itself to standard techniques of automated categorization of land cover classes because the pixels are generally mixed; that is, the extent of the pixel includes several land use/cover classes. Unmixing procedures were developed to extract land use/cover class signatures from mixed pixels, using Landsat Thematic Mapper data as a source for the training set, and to estimate fractions of class coverage within pixels. Application of these unmixing procedures to mapping forest clearcuts and regrowth in Oregon indicated that unmixing is a promising approach for mapping major trends in land cover with AVHRR bands 1 and 2. Including thermal bands by unmixing AVHRR bands 1-4 did not lead to significant improvements in accuracy, but experiments with unmixing these four bands did indicate that use of weighted least squares techniques might lead to improvements in other applications of unmixing.

Land cover change map comparisons using open source web mapping technologies

Treesearch

Erik Lindblom; Ian Housman; Tony Guay; Mark Finco; Kevin Megown

2015-01-01

The USDA Forest Service is evaluating the status of current landscape change maps and assessing gaps in their information content. These activities have been occurring under the auspices of the Landscape Change Monitoring System (LCMS) project, which is a joint effort between USFS Research, USFS Remote Sensing Applications Center (RSAC), USGS Earth Resources...

Assessment of fire effects based on Forest Inventory and Analysis data and a long-term fire mapping data set

Treesearch

John D. Shaw; Sara A. Goeking; James Menlove; Charles E. Werstak

2017-01-01

Integration of Forest Inventory and Analysis (FIA) plot data with Monitoring Trends in Burn Severity (MTBS) data can provide new information about fire effects on forests. This integration allowed broad-scale assessment of the cover types burned in large fires, the relationship between prefire stand conditions and fire severity, and postfire stand conditions. Of the 42...

Future Forest Cover Change Scenarios with Implications for Landslide Risk: An Example from Buzau Subcarpathians, Romania

NASA Astrophysics Data System (ADS)

Malek, Žiga; Boerboom, Luc; Glade, Thomas

2015-11-01

This study focuses on future forest cover change in Buzau Subcarpathians, a landslide prone region in Romania. Past and current trends suggest that the area might expect a future increase in deforestation. We developed spatially explicit scenarios until 2040 to analyze the spatial pattern of future forest cover change and potential changes to landslide risk. First, we generated transition probability maps using the weights of evidence method, followed by a cellular automata allocation model. We performed expert interviews, to develop two future forest management scenarios. The Alternative scenario (ALT) was defined by 67 % more deforestation than the Business as Usual scenario (BAU). We integrated the simulated scenarios with a landslide susceptibility map. In both scenarios, most of deforestation was projected in areas where landslides are less likely to occur. Still, 483 (ALT) and 276 (BAU) ha of deforestation were projected on areas with a high-landslide occurrence likelihood. Thus, deforestation could lead to a local-scale increase in landslide risk, in particular near or adjacent to forestry roads. The parallel process of near 10 % forest expansion until 2040 was projected to occur mostly on areas with high-landslide susceptibility. On a regional scale, forest expansion could so result in improved slope stability. We modeled two additional scenarios with an implemented landslide risk policy, excluding high-risk zones. The reduction of deforestation on high-risk areas was achieved without a drastic decrease in the accessibility of the areas. Together with forest expansion, it could therefore be used as a risk reduction strategy.

Future Forest Cover Change Scenarios with Implications for Landslide Risk: An Example from Buzau Subcarpathians, Romania.

PubMed

Malek, Žiga; Boerboom, Luc; Glade, Thomas

2015-11-01

This study focuses on future forest cover change in Buzau Subcarpathians, a landslide prone region in Romania. Past and current trends suggest that the area might expect a future increase in deforestation. We developed spatially explicit scenarios until 2040 to analyze the spatial pattern of future forest cover change and potential changes to landslide risk. First, we generated transition probability maps using the weights of evidence method, followed by a cellular automata allocation model. We performed expert interviews, to develop two future forest management scenarios. The Alternative scenario (ALT) was defined by 67% more deforestation than the Business as Usual scenario (BAU). We integrated the simulated scenarios with a landslide susceptibility map. In both scenarios, most of deforestation was projected in areas where landslides are less likely to occur. Still, 483 (ALT) and 276 (BAU) ha of deforestation were projected on areas with a high-landslide occurrence likelihood. Thus, deforestation could lead to a local-scale increase in landslide risk, in particular near or adjacent to forestry roads. The parallel process of near 10% forest expansion until 2040 was projected to occur mostly on areas with high-landslide susceptibility. On a regional scale, forest expansion could so result in improved slope stability. We modeled two additional scenarios with an implemented landslide risk policy, excluding high-risk zones. The reduction of deforestation on high-risk areas was achieved without a drastic decrease in the accessibility of the areas. Together with forest expansion, it could therefore be used as a risk reduction strategy.

Development of remote sensing technology in New Zealand, part 1. Seismotectonic, structural, volcanologic and geomorphic study of New Zealand, part 2. Indigenous forest assessment, part 3. Mapping land use and environmental studies in New Zealand, part 4. New Zealand forest service LANDSAT projects, part 5. Vegetation map and landform map of Aupouri Peninsula, Northland, part 6. Geographical applications of LANDSAT mapping, part 7

NASA Technical Reports Server (NTRS)

Probine, M. C.; Suggate, R. P.; Mcgreevy, M. G.; Stirling, I. F. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Inspection of pixels obtained from LANDSAT of New Zealand revealed that not only can ships and their wakes be detected, but that information on the size, state of motion, and direction of movement was inferred by calculating the total number of pixels occupied by the vessel and wake, the orientation of these pixels, and the sum of their radiance values above the background level. Computer enhanced images showing the Waimihia State Forest and much of Kaingaroa State Forest on 22 December 1975 were examined. Most major forest categories were distinguished on LANDSAT imagery. However, the LANDSAT imagery seemed to be most useful for updating and checking existing forest maps, rather than making new maps with many forest categories. Snow studies were performed using two basins: Six Mile Creek and Mt. Robert. The differences in radiance levels indicated that a greater areal snow cover in Six Mile Creek Basin with the effect of lower radiance values from vegetation/snow regions. A comparison of the two visible bands (MSS 4 and 5) demonstrate this difference for the two basins.

Response to comment on "High-resolution global maps of 21st-century forest cover change".

PubMed

Hansen, M; Potapov, P; Margono, B; Stehman, S; Turubanova, S; Tyukavina, A

2014-05-30

Tropek et al. critique the Hansen et al. global forest loss paper in terms of its utility and accuracy. Both criticisms suffer from a miscomprehension of the definition of forest employed as well as the requirements of product validation. Utility of the product is enhanced through its integration with forest type, carbon stock, protected area status, and other ancillary data. Copyright © 2014, American Association for the Advancement of Science.

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

NASA Astrophysics Data System (ADS)

Roy, P. S.; Behera, M. D.; Murthy, M. S. R.; Roy, Arijit; Singh, Sarnam; Kushwaha, S. P. S.; Jha, C. S.; Sudhakar, S.; Joshi, P. K.; Reddy, Ch. Sudhakar; Gupta, Stutee; Pujar, Girish; Dutt, C. B. S.; Srivastava, V. K.; Porwal, M. C.; Tripathi, Poonam; Singh, J. S.; Chitale, Vishwas; Skidmore, A. K.; Rajshekhar, G.; Kushwaha, Deepak; Karnatak, Harish; Saran, Sameer; Giriraj, A.; Padalia, Hitendra; Kale, Manish; Nandy, Subrato; Jeganathan, C.; Singh, C. P.; Biradar, C. M.; Pattanaik, Chiranjibi; Singh, D. K.; Devagiri, G. M.; Talukdar, Gautam; Panigrahy, Rabindra K.; Singh, Harnam; Sharma, J. R.; Haridasan, K.; Trivedi, Shivam; Singh, K. P.; Kannan, L.; Daniel, M.; Misra, M. K.; Niphadkar, Madhura; Nagabhatla, Nidhi; Prasad, Nupoor; Tripathi, O. P.; Prasad, P. Rama Chandra; Dash, Pushpa; Qureshi, Qamer; Tripathi, S. K.; Ramesh, B. R.; Gowda, Balakrishnan; Tomar, Sanjay; Romshoo, Shakil; Giriraj, Shilpa; Ravan, Shirish A.; Behera, Soumit Kumar; Paul, Subrato; Das, Ashesh Kumar; Ranganath, B. K.; Singh, T. P.; Sahu, T. R.; Shankar, Uma; Menon, A. R. R.; Srivastava, Gaurav; Neeti; Sharma, Subrat; Mohapatra, U. B.; Peddi, Ashok; Rashid, Humayun; Salroo, Irfan; Krishna, P. Hari; Hajra, P. K.; Vergheese, A. O.; Matin, Shafique; Chaudhary, Swapnil A.; Ghosh, Sonali; Lakshmi, Udaya; Rawat, Deepshikha; Ambastha, Kalpana; Malik, Akhtar H.; Devi, B. S. S.; Gowda, Balakrishna; Sharma, K. C.; Mukharjee, Prashant; Sharma, Ajay; Davidar, Priya; Raju, R. R. Venkata; Katewa, S. S.; Kant, Shashi; Raju, Vatsavaya S.; Uniyal, B. P.; Debnath, Bijan; Rout, D. K.; Thapa, Rajesh; Joseph, Shijo; Chhetri, Pradeep; Ramachandran, Reshma M.

2015-07-01

A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-resolution IRS LISS-III images is presented. The map was created using an on-screen visual interpretation technique and has an accuracy of 90%, as assessed using 15,565 ground control points. India has hitherto been using potential vegetation/forest type map prepared by Champion and Seth in 1968. We characterized and mapped further the vegetation type distribution in the country in terms of occurrence and distribution, area occupancy, percentage of protected area (PA) covered by each vegetation type, range of elevation, mean annual temperature and precipitation over the past 100 years. A remote sensing-amenable hierarchical classification scheme that accommodates natural and semi-natural systems was conceptualized, and the natural vegetation was classified into forests, scrub/shrub lands and grasslands on the basis of extent of vegetation cover. We discuss the distribution and potential utility of the vegetation type map in a broad range of ecological, climatic and conservation applications from global, national and local perspectives. We used 15,565 ground control points to assess the accuracy of products available globally (i.e., GlobCover, Holdridge's life zone map and potential natural vegetation (PNV) maps). Hence we recommend that the map prepared herein be used widely. This vegetation type map is the most comprehensive one developed for India so far. It was prepared using 23.5 m seasonal satellite remote sensing data, field samples and information relating to the biogeography, climate and soil. The digital map is now available through a web portal (http://bis.iirs.gov.in).

Land-Cover Trends of the Sierra Nevada Ecoregion, 1973-2000

USGS Publications Warehouse

Raumann, Christian G.; Soulard, Christopher E.

2007-01-01

The U.S. Geological Survey has developed and is implementing the Land Cover Trends project to estimate and describe the temporal and spatial distribution and variability of contemporary land-use and land-cover change in the United States. As part of the Land Cover Trends project, the purpose of this study was to assess land-use/land-cover change in the Sierra Nevada ecoregion for the period 1973 to 2000 using a probability sampling technique and satellite imagery. We randomly selected 36 100-km2 sample blocks to derive thematic images of land-use/land-cover for five dates of Landsat imagery (1973, 1980, 1986, 1992, 2000). We visually interpreted as many as 11 land-use/land-cover classes using a 60-meter minimum mapping unit from the five dates of imagery yielding four periods for analysis. Change-detection results from post-classification comparison of our mapped data showed that landscape disturbance from fire was the dominant change from 1973-2000. The second most-common change was forest disturbance resulting from harvest of timber resources by way of clear-cutting. The rates of forest regeneration from temporary fire and harvest disturbances coincided with the rates of disturbance from the previous period. Relatively minor landscape changes were caused by new development and reservoir drawdown. Multiple linear regression analysis suggests that land ownership and the proportion of forest and developed cover types were significant determinants of the likelihood of direct human-induced change occurring in sampling units. Driving forces of change include land ownership, land management such as fire suppression policy, and demand for natural resources.

Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification

PubMed Central

Ayanu, Yohannes; Conrad, Christopher; Jentsch, Anke; Koellner, Thomas

2015-01-01

The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential. PMID:26098107

Unveiling Undercover Cropland Inside Forests Using Landscape Variables: A Supplement to Remote Sensing Image Classification.

PubMed

Ayanu, Yohannes; Conrad, Christopher; Jentsch, Anke; Koellner, Thomas

2015-01-01

The worldwide demand for food has been increasing due to the rapidly growing global population, and agricultural lands have increased in extent to produce more food crops. The pattern of cropland varies among different regions depending on the traditional knowledge of farmers and availability of uncultivated land. Satellite images can be used to map cropland in open areas but have limitations for detecting undergrowth inside forests. Classification results are often biased and need to be supplemented with field observations. Undercover cropland inside forests in the Bale Mountains of Ethiopia was assessed using field observed percentage cover of land use/land cover classes, and topographic and location parameters. The most influential factors were identified using Boosted Regression Trees and used to map undercover cropland area. Elevation, slope, easterly aspect, distance to settlements, and distance to national park were found to be the most influential factors determining undercover cropland area. When there is very high demand for growing food crops, constrained under restricted rights for clearing forest, cultivation could take place within forests as an undercover. Further research on the impact of undercover cropland on ecosystem services and challenges in sustainable management is thus essential.

CASA Forest Cover Change Data Sets

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2012-01-01

Deforestation and forest fires are global land cover changes that can be caused by both natural and human factors. Although monitoring forest fires in near-real time is critical for operational wildfire management, mapping historical wildfires in a spatially explicit fashion is also important for a number of reasons, including climate change studies (e.g., examining the relationship between rising temperatures and frequency of fires), fuel load management (e.g., deciding when and where to conduct controlled burns), and carbon cycle studies (e.g., quantifying how much CO2 is emitted by fires and for emissions reduction efforts under the United Nations programs for Reducing Emissions from Deforestation and Degradation -- REDD).

Assessment of spruce (Picea obovata) abundance by spectral unmixing algorithm for sustainable forest management in highland Natural Reserve (case study of Zigalga Range, South-Ural State Natural Reserve, Russia).

NASA Astrophysics Data System (ADS)

Mikheeva, Anna; Moiseev, Pavel

2017-04-01

In mountain territories climate change affects forest productivity and growth, which results in the tree line advancing and increasing of the forest density. These changes pose new challenges for forest managers whose responsibilities include forest resources inventory, monitoring and protection of ecosystems, and assessment of forest vulnerability. These activities require a range of sources of information, including exact squares of forested areas, forest densities and species abundances. Picea obovata, dominant tree species in South-Ural State Natural Reserve, Russia has regenerated, propagated and increased its relative cover during the recent 70 years. A remarkable shift of the upper limit of Picea obovata up to 60-80 m upslope was registered by repeating photography, especially on gentle slopes. The stands of Picea obovata are monitored by Reserve inspectors on the test plots to ensure that forests maintain or improve their productivity, these studies also include projective cover measurements. However, it is impossible to cover the entire territory of the Reserve by detailed field observations. Remote sensing data from Terra ASTER imagery provides valuable information for large territories (scene covers an area of 60 x 60 km) and can be used for quantitative mapping of forest and non-forest vegetation at regional scale (spatial resolution is 15-30 m for visible and infrared bands). A case study of estimating Picea obovata abundance was conducted for forest and forest-tundra sites of Zigalga Range, using 9-band ASTER multispectral imagery of 23.08.2007, field data and spectral unmixing algorithm. This type of algorithms intends to derive object and its abundance from a mixed pixel of multispectral imagery which can be further converted to object's projective cover. Atmospheric correction was applied to the imagery prior to spectral unmixing, and then pure spectra of Picea obovata were extracted from the image in 10 points and averaged. These points located in Zigalga Range and were visited in summer 2016. We used Mixture-tuned Match Filtering (MTMF) algorithm, a non-linear subpixel classification technique which allows to separate the spectral mixture containing unknown objects, and to derive only known ones. The results of spectral unmixing classification were abundance maps of Picea obovata. The values were statistically determined (there was only selected abundances with high probabilities of presence and low probabilities of absence) and then constrained to the interval [0; 1]. Verification of maps was made at the sites of Iremel Mountains on the same ASTER image, where projective cover of Picea obovata was measured in the field in 147 points. The correlation coefficient between the spectral unmixing abundances and field-measured abundances was 0.7; not a very high value is due to the low sensitivity of the algorithm to detect abundances less than 0.25. The proposed method provides a tool for defining the Picea obovata boundaries more accurately than per-pixel automatic classification and locating new spruce islands in the mixing tree line environment. The abundances can be obtained for large areas with minimum field work which makes this approach cost-effective in providing timely information to nature reserve managers for adapting forest management actions to climate change.

SAFIS Area Estimation Techniques

Treesearch

Gregory A. Reams

2000-01-01

The Southern Annual Forest inventory System (SAFIS) is in various stages of implementation in 8 of the 13 southern states served by the Southern Research Station of the USDA Forest Service. Compared to periodic inventories, SAFIS requires more rapid generation of land use and land cover maps. The current photo system for phase one area estimation has changed little...

SAFIS area estimation techniques

Treesearch

Gregory A. Reams

2000-01-01

The Southern Annual Forest Inventory System (SAFIS) is in various stages of implementation in 8 of the 13 southern states served by the Southern Research Station of the USDA Forest Service. Compared to periodic inventories, SAFIS requires more rapid generation of land use and land cover maps. The current photo system for phase one area estimation has changed little...

Downscaling Indicators of Forest Habitat Structure from National Assessments

Treesearch

Kurt H. Riitters

2005-01-01

Downscaling is an important problem because consistent large-area assessments of forest habitat structure, while feasible, are only feasible when using relatively coarse data and indicators. Techniques are needed to enable more detailed and local interpretations of the national statistics. Using the results of national assessments from land-cover maps, this paper...

Atlantic tropical forest mapping in the northern coastal zone of Sao Paulo State, Brazil

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

Simi, R. Jr.; Almeida, S.A.S.; Manso, A.P.

1997-06-01

The northern coastal zone of Sao Paulo State includes the cities of Ubatuba, Caraguatatuba, Sao Sebastiao and Ilha Bela. Large development projects, such as road and highway constructions and joint real estate exploration of susceptible coastal ecosystems have threatened the harmony and ecological stability of these ecosystems. Recently, the Atlantic tropical rain forest has been the most destructed ecosystem in the coastal zone in response to real estate investments in urban areas along the main roads. In the northern coastal zone of Sao Paulo State, 80% of the counties are included in the State Park of Serra do Mar. Asmore » tourism is a strong growing economical activity, as well as coastal production, it should be of interest to create a plan for sustainable development. The objective of this study is to map and characterize land use cover changes with emphasis on the Atlantic tropical rain forest degradation using Landsat TM images. Preliminary results for land use cover changes indicate that the Atlantic tropical rain forest was reduced by 6.1 % during the period of July 1992 and October 1995.« less

Mapping of forest disturbance magnitudes across the US National Forest System

NASA Astrophysics Data System (ADS)

Hernandez, A. J.; Healey, S. P.; Ramsey, R. D.; McGinty, C.; Garrard, C.; Lu, N.; Huang, C.

2013-12-01

A precise record in conjunction with ongoing monitoring of carbon pools constitutes essentials inputs for the continuous modernization of an ever- dynamic science such as climate change. This is particularly important in forested ecosystems for which accurate field archives are available and can be used in combination with historic satellite imagery to obtain spatially explicit estimates of several indicators that can be used in the assessment of said carbon pools. Many forest disturbance processes limit storage of carbon in forested ecosystems and thereby reduce those systems' capacity to mitigate changes in the global climate system. A component of the US National Forest System's (NFS) comprehensive plan for carbon monitoring includes accounting for mapped disturbances, such as fires, harvests, and insect activity. A long-term time series of maps that show the timing, extent, type, and magnitude of disturbances going back to 1990 has been prepared for the United States Forest Service (USFS) Northern Region, and is currently under preparation for the rest of the NFS regions covering more than 75 million hectares. Our mapping approach starts with an automated initial detection of annual disturbances using imagery captured within the growing season from the Landsat archive. Through a meticulous process, the initial detections are then visually inspected, manually corrected and labeled using various USFS ancillary datasets and Google Earth high-resolution historic imagery. We prepared multitemporal models of percent canopy cover and live tree carbon (T/ha) that were calibrated with extensive (in excess of 2000 locations) field data from the US Forest Service Forest Inventory and Analysis program (FIA). The models were then applied to all the years of the radiometrically corrected and normalized Landsat time series in order to provide annual spatially explicit estimates of the magnitude of change in terms of these two attributes. Our results provide objective, widely interpretable estimates of per-year disturbance effects across large areas. Different stakeholders (scientists, managers, policymakers) should benefit from this broad survey of disturbance processes affecting US federal forests over the last 20 years.

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.

Spatial geologic data model for the Gunnison, Grand Mesa, Uncompahgre National Forests mineral assessment area, southwestern Colorado and digital data for the Leadville, Montrose, Durango, and Colorado parts of the Grand Junction, Moab, and Cortez 1 degree x 2 degrees geologic maps

USGS Publications Warehouse

Day, W.C.; Green, G.N.; Knepper, D.H.; Phillips, R.C.

1999-01-01

The digital geologic and geographic information system (GIS) data presented here were prepared to aid in Grand Mesa, Uncompahgre, Gunnison National Forest (GMUG) mineral resource assessment Project studies by the U.S. Geological Survey Mineral Resource Program. The goals of the GMUG Project is to provide mineral resource data and an assessment for undiscovered mineral resources in U.S. Forest Service (USFS) and Bureau of Land Management (BLM) lands in southwestern Colorado. The Project area covers a large region in southwestern Colorado that is bounded by latitudes 37o 45’ to 39o 30’ north and longitudes 106o to 109o west. The study area is covered by all or parts of six 1o x2o topographic and quadrangle geologic maps, which include geologic maps for the Leadville (Tweto and others, 1978), Montrose (Tweto and others, 1976), Durango (Steven and others, 1974), Grand Junction (Cashion, 1973), Moab (Williams, 1976), and Cortez (Haynes and others, 1972) quadrangles. These geologic maps were used inasmuch as a complete remapping and compilation effort for this study area was beyond the scope of the Project.

Using the Landsat Archive to Estimate and Map Changes in Agriculture, Forests, and other Land Cover Types in East Africa

NASA Astrophysics Data System (ADS)

Healey, S. P.; Oduor, P.; Cohen, W. B.; Yang, Z.; Ouko, E.; Gorelick, N.; Wilson, S.

2017-12-01

Every country's land is distributed among different cover types, such as: agriculture; forests; rangeland; urban areas; and barren lands. Changes in the distribution of these classes can inform us about many things, including: population pressure; effectiveness of preservation efforts; desertification; and stability of the food supply. Good assessment of these changes can also support wise planning, use, and preservation of natural resources. We are using the Landsat archive in two ways to provide needed information about land cover change since the year 2000 in seven East African countries (Ethiopia, Kenya, Malawi, Rwanda, Tanzania, Uganda, and Zambia). First, we are working with local experts to interpret historical land cover change from historical imagery at a probabilistic sample of 2000 locations in each country. This will provide a statistical estimate of land cover change since 2000. Second, we will use the same data to calibrate and validate annual land cover maps for each country. Because spatial context can be critical to development planning through the identification of hot spots, these maps will be a useful complement to the statistical, country-level estimates of change. The Landsat platform is an ideal tool for mapping land cover change because it combines a mix of appropriate spatial and spectral resolution with unparalleled length of service (Landsat 1 launched in 1972). Pilot tests have shown that time series analysis accessing the entire Landsat archive (i.e., many images per year) improves classification accuracy and stability. It is anticipated that this project will meet the civil needs of both governmental and non-governmental users across a range of disciplines.

Multiple baseline radar interferometry applied to coastal land cover classification and change analyses

USGS Publications Warehouse

Ramsey, Elijah W.; Lu, Z.; Rangoonwala, A.; Rykhus, Russ

2006-01-01

ERS-1 and ERS-2 SAR data were collected in tandem over a four-month period and used to generate interferometric coherence, phase, and intensity products that we compared to a classified land cover coastal map of Big Bend, Florida. Forests displayed the highest intensity, and marshes the lowest. The intensity for fresh marsh and forests progressively shifted while saline marsh intensity variance distribution changed with the season. Intensity variability suggested instability between temporal comparisons. Forests, especially hardwoods, displayed lower coherences and marshes higher. Only marshes retained coherence after 70 days. Coherence was more responsive to land cover class than intensity and provided discrimination in winter. Phase distributions helped reveal variation in vegetation structure, identify broad land cover classes and unique within-class variations, and estimate water-level changes. Copyright ?? 2006 by V. H. Winston & Son, Inc. All rights reserved.

A preliminary assessment of Montreal process indicators of forest fragmentation for the United States

Treesearch

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

2004-01-01

Abstract. As part of the U.S. 2003 National Report on Sustainable Forests, four metrics of forest fragmentation – patch size, edge amount, inter-patch distance, and patch contrast – were measured within 137 744 non-overlapping 5625 ha analysis units on land-cover maps derived from satellite imagery for the 48 conterminous States. The perimeter of a...

Synergistic use of FIA plot data and Landsat 7 ETM+ images for large area forest mapping

Treesearch

Chengquan Huang; Limin Yang; Collin Homer; Michael Coan; Russell Rykhus; Zheng Zhang; Bruce Wylie; Kent Hegge; Andrew Lister; Michael Hoppus; Ronald Tymcio; Larry DeBlander; William Cooke; Ronald McRoberts; Daniel Wendt; Dale Weyermann

2002-01-01

FIA plot data were used to assist in classifying forest land cover from Landsat imagery and relevant ancillary data in two regions of the U.S.: one around the Chesapeake Bay area and the other around Utah. The overall accuracies for the forest/nonforest classification were over 90 percent and about 80 percent, respectively, in the two regions. The accuracies for...

Global spatial assessment of WUI and related land cover in Portugal

NASA Astrophysics Data System (ADS)

Tonini, Marj; Parente, Joana; Pereira, Mário G.

2017-04-01

Forest fires as hazardous events are assuming an increasing importance all around the world, especially in relation to climate changes and to urban sprawl, which makes it difficult to outline a border between human infrastructures and wildland areas. This zone, known as the Wildland Urban Interface (WUI), is defined as the area where structures and other human development meet or intermingle with undeveloped wildland (USDA 2001). Its extension is influenced by anthropogenic features, since, as it was proved, the distance to roads and houses negatively influence the probability of forest fires ignitions, while the population density positively affects it. Land use is also a crucial feature to be considered in the analyses of the impact of forest fires, and each natural, semi-natural and artificial land cover can be affected in a different proportion. The aim of the present study is to investigate and mapping the wildland urban interface and its temporal dynamic in Portugal at global scale. Secondly, it aims at providing a quantitative characterization of forest fires occurred in the last few decades (1990 - 2012) in relation to the burned area and the land covers evolution. The National mapping burnt area dataset (by the Institute for the Conservation of Nature and Forests) provided the information allowing to precisely localize forest fires. The land cover classes were derived from the Corinne Land Cover, available for four periods (1990-2000-2006-2012). The following two classes were retained to outline the WUI: 1) artificial surfaces, as representative of the human development; 2) forest and semi-natural area, as representative of undeveloped wildland. First, we investigated the distribution of the burned areas among the different detailed land covers classes. Then, to map the WUI, we considered a buffer distance around artificial surfaces located in proximity of forests and semi-natural areas. The descriptive statistic carried out individually within each district revealed that in the southern part of the country forest fires are highly dispersed, while in the northern regions they tend to be aggregated around the anthropogenic infrastructures. This WUI-model can be replicated to assess the WUI at different periods, namely 1990, 2000, 2006, and to analyses the evolution of the WUI up to 2012. More accurate analyses at large scale for characterizing and mapping WUI using precise data (e.g. the true houses footprints) will be necessary to give practical indications in term of land and fire management. Nevertheless our study is necessary to give precious suggestions as for what is the global distribution on WUI in Portugal and which regions need to be prioritized in term of WUI extension and fires protection. References: Conedera M., Tonini M., Oleggini L., Vega Orozco C., Leuenberger M., Pezzati G.B. (2015) - Geospatial approach for defining the Wildland-Urban Interface in the Alpine environment. Computers, Environment and Urban Systems, Vol. 52: 10-20 Bouillon C., Fernandez R., Sirca C., Fierro G., Casula F., Vila B., Long Fournel M., Pellizzaro G., Arca B., Tedim F., Trebini F., Derudas A., Cane S. (2014) - A tool for mapping rural-urban interfaces on different scales. Advanced in Forest Fire Research, Imprensa da Universidade de Coimbra ED, pp. 611-625 Acknowledgements: This work was supported by: (i) the FIREXTR project, PTDC/ATP¬GEO/0462/2014; (ii) the project Interact - Integrative Research in Environment,Agro-Chain and Technology, NORTE-01-0145-FEDER-000017, research line BEST, cofinanced by FEDER/NORTE 2020; and, (iii) European Investment Funds by FEDER/COMPETE/POCI-Operacional Competitiveness and Internacionalization Programme, under Project POCI-01-0145-FEDER-006958 and National Funds by FCT - Portuguese Foundation for Science and Technology, under the project UID/AGR/04033. We are especially grateful to ICNF for providing the fire.

Forest biomass mapping from fusion of GEDI Lidar data and TanDEM-X InSAR data

NASA Astrophysics Data System (ADS)

Qi, W.; Hancock, S.; Armston, J.; Marselis, S.; Dubayah, R.

2017-12-01

Mapping forest above-ground biomass (hereafter biomass) can significantly improve our ability to assess the role of forest in terrestrial carbon budget and to analyze the ecosystem productivity. Global Ecosystem Dynamic Investigation (GEDI) mission will provide the most complete lidar observations of forest vertical structure and has the potential to provide global-scale forest biomass data at 1-km resolution. However, GEDI is intrinsically a sampling mission and will have a between-track spacing of 600 m. An increase in adjacent-swath distance and the presence of cloud cover may also lead to larger gaps between GEDI tracks. In order to provide wall-to-wall forest biomass maps, fusion algorithms of GEDI lidar data and TanDEM-X InSAR data were explored in this study. Relationship between biomass and lidar RH metrics was firstly developed and used to derive biomass values over GEDI tracks which were simulated using airborne lidar data. These GEDI biomass values were then averaged in each 1-km cell to represent the biomass density within that cell. Whereas for cells without any GEDI observations, regression models developed between GEDI-derived biomass and TDX InSAR variables were applied to predict biomass over those places. Based on these procedures, contiguous biomass maps were finally generated at 1-km resolution over three representative forest types. Uncertainties for these biomass maps were also estimated at 1 km following methods developed in Saarela et al. (2016). Our results indicated great potential of GEDI/TDX fusion for large-scale biomass mapping. Saarela, S., Holm, S., Grafstrom, A., Schnell, S., Naesset, E., Gregoire, T.G., Nelson, R.F., & Stahl, G. (2016). Hierarchical model-based inference for forest inventory utilizing three sources of information. Annals of Forest Science, 73, 895-910

Comparison of Hyperspectral and Multispectral Satellites for Discriminating Land Cover in Northern California

NASA Astrophysics Data System (ADS)

Clark, M. L.; Kilham, N. E.

2015-12-01

Land-cover maps are important science products needed for natural resource and ecosystem service management, biodiversity conservation planning, and assessing human-induced and natural drivers of land change. Most land-cover maps at regional to global scales are produced with remote sensing techniques applied to multispectral satellite imagery with 30-500 m pixel sizes (e.g., Landsat, MODIS). Hyperspectral, or imaging spectrometer, imagery measuring the visible to shortwave infrared regions (VSWIR) of the spectrum have shown impressive capacity to map plant species and coarser land-cover associations, yet techniques have not been widely tested at regional and greater spatial scales. The Hyperspectral Infrared Imager (HyspIRI) mission is a VSWIR hyperspectral and thermal satellite being considered for development by NASA. The goal of this study was to assess multi-temporal, HyspIRI-like satellite imagery for improved land cover mapping relative to multispectral satellites. We mapped FAO Land Cover Classification System (LCCS) classes over 22,500 km2 in the San Francisco Bay Area, California using 30-m HyspIRI, Landsat 8 and Sentinel-2 imagery simulated from data acquired by NASA's AVIRIS airborne sensor. Random Forests (RF) and Multiple-Endmember Spectral Mixture Analysis (MESMA) classifiers were applied to the simulated images and accuracies were compared to those from real Landsat 8 images. The RF classifier was superior to MESMA, and multi-temporal data yielded higher accuracy than summer-only data. With RF, hyperspectral data had overall accuracy of 72.2% and 85.1% with full 20-class and reduced 12-class schemes, respectively. Multispectral imagery had lower accuracy. For example, simulated and real Landsat data had 7.5% and 4.6% lower accuracy than HyspIRI data with 12 classes, respectively. In summary, our results indicate increased mapping accuracy using HyspIRI multi-temporal imagery, particularly in discriminating different natural vegetation types, such as spectrally-mixed woodlands and forests.

Forest Dragon-3: Decadal Trends of Northeastern Forests in China from Earth Observation Synergy

NASA Astrophysics Data System (ADS)

Schmullius, C.; Balling, J.; Schratz, P.; Thiel, C.; Santoro, M.; Wegmuller, U.; Li, Z.; Yong, P.

2016-08-01

In Forest DRAGON 3, synergy of Earth Observation products to derive information of decadal trends of forest in northeast China was investigated. Following up the results of Forest-DRAGON 1 and 2, Growing Stock Volume (GSV) products from different years were investigated to derive information on vegetational in north- east China. The BIOMASAR maps of 2005 and 2010, produced within the previous DRAGON projects, set the base for all analyses. We took a closer look at scale problems regarding GSV derivation, which are introduced by differing landcover within one pixel, to investigate differences throughout pixel classes with varying landcover class percentages. We developed an approach to select pixels containing forest only with the aim of undertaking a detailed analysis on retrieved GSV values for such pixels for the years 2005 and 2010. Using existing land cover products at different scales, the plausibility of changes in the BIOMASAR maps were checked.

Computer-aided analysis of Skylab scanner data for land use mapping, forestry and water resource applications

NASA Technical Reports Server (NTRS)

Hoffer, R. M.

1975-01-01

Skylab data were obtained over a mountainous test site containing a complex association of cover types and rugged topography. The application of computer-aided analysis techniques to the multispectral scanner data produced a number of significant results. Techniques were developed to digitally overlay topographic data (elevation, slope, and aspect) onto the S-192 MSS data to provide a method for increasing the effectiveness and accuracy of computer-aided analysis techniques for cover type mapping. The S-192 MSS data were analyzed using computer techniques developed at Laboratory for Applications of Remote Sensing (LARS), Purdue University. Land use maps, forest cover type maps, snow cover maps, and area tabulations were obtained and evaluated. These results compared very well with information obtained by conventional techniques. Analysis of the spectral characteristics of Skylab data has conclusively proven the value of the middle infrared portion of the spectrum (about 1.3-3.0 micrometers), a wavelength region not previously available in multispectral satellite data.

Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data.

PubMed

Fallati, Luca; Savini, Alessandra; Sterlacchini, Simone; Galli, Paolo

2017-08-01

The Maldives islands in recent decades have experienced dramatic land-use change. Uninhabited islands were turned into new resort islands; evergreen tropical forests were cut, to be replaced by fields and new built-up areas. All these changes happened without a proper monitoring and urban planning strategy from the Maldivian government due to the lack of national land-use and land-cover (LULC) data. This study aimed to realize the first land-use map of the entire Maldives archipelago and to detect land-use and land-cover change (LULCC) using high-resolution satellite images and socioeconomic data. Due to the peculiar geographic and environmental features of the archipelago, the land-use map was obtained by visual interpretation and manual digitization of land-use patches. The images used, dated 2011, were obtained from Digital Globe's WorldView 1 and WorldView 2 satellites. Nine land-use classes and 18 subclasses were identified and mapped. During a field survey, ground control points were collected to test the geographic and thematic accuracy of the land-use map. The final product's overall accuracy was 85%. Once the accuracy of the map had been checked, LULCC maps were created using images from the early 2000s derived from Google Earth historical imagery. Post-classification comparison of the classified maps showed that growth of built-up and agricultural areas resulted in decreases in forest land and shrubland. The LULCC maps also revealed an increase in land reclamation inside lagoons near inhabited islands, resulting in environmental impacts on fragile reef habitat. The LULC map of the Republic of the Maldives produced in this study can be used by government authorities to make sustainable land-use planning decisions and to provide better management of land use and land cover.

Multidecadal Rates of Disturbance- and Climate Change-Induced Land Cover Change in Arctic and Boreal Ecosystems over Western Canada and Alaska Inferred from Dense Landsat Time Series

NASA Astrophysics Data System (ADS)

Wang, J.; Sulla-menashe, D. J.; Woodcock, C. E.; Sonnentag, O.; Friedl, M. A.

2017-12-01

Rapid climate change in arctic and boreal ecosystems is driving changes to land cover composition, including woody expansion in the arctic tundra, successional shifts following boreal fires, and thaw-induced wetland expansion and forest collapse along the southern limit of permafrost. The impacts of these land cover transformations on the physical climate and the carbon cycle are increasingly well-documented from field and model studies, but there have been few attempts to empirically estimate rates of land cover change at decadal time scale and continental spatial scale. Previous studies have used too coarse spatial resolution or have been too limited in temporal range to enable broad multi-decadal assessment of land cover change. As part of NASA's Arctic Boreal Vulnerability Experiment (ABoVE), we are using dense time series of Landsat remote sensing data to map disturbances and classify land cover types across the ABoVE extended domain (spanning western Canada and Alaska) over the last three decades (1982-2014) at 30 m resolution. We utilize regionally-complete and repeated acquisition high-resolution (<2 m) DigitalGlobe imagery to generate training data from across the region that follows a nested, hierarchical classification scheme encompassing plant functional type and cover density, understory type, wetland status, and land use. Additionally, we crosswalk plot-level field data into our scheme for additional high quality training sites. We use the Continuous Change Detection and Classification algorithm to estimate land cover change dates and temporal-spectral features in the Landsat data. These features are used to train random forest classification models and map land cover and analyze land cover change processes, focusing primarily on tundra "shrubification", post-fire succession, and boreal wetland expansion. We will analyze the high resolution data based on stratified random sampling of our change maps to validate and assess the accuracy of our model predictions. In this paper, we present initial results from this effort, including sub-regional analyses focused on several key areas, such as the Taiga Plains and the Southern Arctic ecozones, to calibrate our random forest models and assess results.

Developing management guidelines that balance cattle and timber production with ecological interests in the Black Hills of South Dakota

NASA Astrophysics Data System (ADS)

Chowanski, Kurt M.

Forested lands contribute to the United States (US) economy by providing livestock and timber production. Livestock grazing of forested lands has been widespread throughout the western US since the settlement era, and currently occurs on 51.4 million hectares (ha) representing 16% of all US grazing land and 22% of all US forested land (Nickerson et al. 2011). While livestock grazing and timber harvest are occurring on a substantial amount of forested land, relationships between management practices, tree stocking, timber production, forage production, livestock grazing, wildlife, aesthetics, and ecological integrity are not well documented. Whether considering timber or cattle, finding a balance between production and resource conservation is a fundamental challenge to agricultural producers, and is often a tradeoff between short term gains and long term sustainability. This dissertation aims to identify livestock and timber management practices that optimize production and are ecologically conservative. Specifically, I focused on three objectives. First, I reviewed the published literature and summarized what is known about best-practices for concurrent management of livestock and timber production in pine forests in the US. I found most studies came from the southeastern and western US where timber and livestock production on the same land unit are common. The relationship between pine cover and forage seemed fairly consistent across the US, and production was optimized when cattle grazed open canopy forests with basal areas between 5 and 14 m2 ha-1 (15-35% tree canopy cover). Second, I developed forest cover maps to estimate forage production in the Black Hills, South Dakota (SD) for the period from 1999 to 2015. I developed a regression model based on Landsat and Ikonos satellite imagery and was able to detect large changes in forest cover over time. I then used these maps in combination with maps of soil type and Palmer Drought Severity Index (PDSI) to update forage production estimates for the region. These changes in forest cover have large implications for forage production in the Black Hills. Over the 15 year period, mean tree cover decreased in 181 pastures in the Mystic Ranger District by 17.6 +/- 0.6%, and there was a corresponding 15.5 +/- 0.6% increase in mean forage production. Third, I conducted a 2 -year field experiment in the Black Hills, SD to study the relationships between management practices such as livestock stocking rates, grazing pressure, and timber harvest history, and aspects of resource condition such as tree regeneration, forage production, and plant community composition. From 2014-2015, I visited 44 pastures across a spectrum of management practices and measured seedling regeneration (590 plots), plant species richness (393 plots), primary production (246 plots), and visual obstruction (120 transects). I found that cattle grazing did not affect ponderosa pine regeneration. Grazing did affect plant diversity, and I found the highest plant diversity in areas of moderate grazing pressure. This work suggests that moderate stocking rates should have no effect on the timber industry but could positively affect native plant diversity. In the conclusion, I summarize what I learned from the literature review, mapping exercise, and field study and provide some management recommendations based on this work. Overall, I found that updated forage production estimates based on satellite imagery, and using grazing pressure index (GPI) to identify optimal stocking rates are tools that can facilitate management of livestock and timber production in the Black Hills, SD.

Bridging scale gaps between regional maps of forest aboveground biomass and field sampling plots using TanDEM-X data

NASA Astrophysics Data System (ADS)

Ni, W.; Zhang, Z.; Sun, G.

2017-12-01

Several large-scale maps of forest AGB have been released [1] [2] [3]. However, these existing global or regional datasets were only approximations based on combining land cover type and representative values instead of measurements of actual forest aboveground biomass or forest heights [4]. Rodríguez-Veiga et al[5] reported obvious discrepancies of existing forest biomass stock maps with in-situ observations in Mexico. One of the biggest challenges to the credibility of these maps comes from the scale gaps between the size of field sampling plots used to develop(or validate) estimation models and the pixel size of these maps and the availability of field sampling plots with sufficient size for the verification of these products [6]. It is time-consuming and labor-intensive to collect sufficient number of field sampling data over the plot size of the same as resolutions of regional maps. The smaller field sampling plots cannot fully represent the spatial heterogeneity of forest stands as shown in Figure 1. Forest AGB is directly determined by forest heights, diameter at breast height (DBH) of each tree, forest density and tree species. What measured in the field sampling are the geometrical characteristics of forest stands including the DBH, tree heights and forest densities. The LiDAR data is considered as the best dataset for the estimation of forest AGB. The main reason is that LiDAR can directly capture geometrical features of forest stands by its range detection capabilities.The remotely sensed dataset, which is capable of direct measurements of forest spatial structures, may serve as a ladder to bridge the scale gaps between the pixel size of regional maps of forest AGB and field sampling plots. Several researches report that TanDEM-X data can be used to characterize the forest spatial structures [7, 8]. In this study, the forest AGB map of northeast China were produced using ALOS/PALSAR data taking TanDEM-X data as a bridges. The TanDEM-X InSAR data used in this study and forest AGB map was shown in Figure 2. The technique details and further analysis will be given in the final report. AcknowledgmentThis work was supported in part by the National Basic Research Program of China (Grant No. 2013CB733401, 2013CB733404), and in part by the National Natural Science Foundation of China (Grant Nos. 41471311, 41371357, 41301395).

High-resolution mapping of forest carbon stocks in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Asner, G. P.; Clark, J. K.; Mascaro, J.; Galindo García, G. A.; Chadwick, K. D.; Navarrete Encinales, D. A.; Paez-Acosta, G.; Cabrera Montenegro, E.; Kennedy-Bowdoin, T.; Duque, Á.; Balaji, A.; von Hildebrand, P.; Maatoug, L.; Bernal, J. F. Phillips; Yepes Quintero, A. P.; Knapp, D. E.; García Dávila, M. C.; Jacobson, J.; Ordóñez, M. F.

2012-07-01

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or light detection and ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high-resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (> 40%) of the Colombian Amazon - a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon maps have 14% uncertainty at 1 ha resolution, and the regional map based on stratification has 28% uncertainty in any given hectare. High-resolution approaches with quantifiable pixel-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

High-resolution Mapping of Forest Carbon Stocks in the Colombian Amazon

NASA Astrophysics Data System (ADS)

Asner, G. P.; Clark, J. K.; Mascaro, J.; Galindo García, G. A.; Chadwick, K. D.; Navarrete Encinales, D. A.; Paez-Acosta, G.; Cabrera Montenegro, E.; Kennedy-Bowdoin, T.; Duque, Á.; Balaji, A.; von Hildebrand, P.; Maatoug, L.; Bernal, J. F. Phillips; Knapp, D. E.; García Dávila, M. C.; Jacobson, J.; Ordóñez, M. F.

2012-03-01

High-resolution mapping of tropical forest carbon stocks can assist forest management and improve implementation of large-scale carbon retention and enhancement programs. Previous high-resolution approaches have relied on field plot and/or Light Detection and Ranging (LiDAR) samples of aboveground carbon density, which are typically upscaled to larger geographic areas using stratification maps. Such efforts often rely on detailed vegetation maps to stratify the region for sampling, but existing tropical forest maps are often too coarse and field plots too sparse for high resolution carbon assessments. We developed a top-down approach for high-resolution carbon mapping in a 16.5 million ha region (>40 %) of the Colombian Amazon - a remote landscape seldom documented. We report on three advances for large-scale carbon mapping: (i) employing a universal approach to airborne LiDAR-calibration with limited field data; (ii) quantifying environmental controls over carbon densities; and (iii) developing stratification- and regression-based approaches for scaling up to regions outside of LiDAR coverage. We found that carbon stocks are predicted by a combination of satellite-derived elevation, fractional canopy cover and terrain ruggedness, allowing upscaling of the LiDAR samples to the full 16.5 million ha region. LiDAR-derived carbon mapping samples had 14.6 % uncertainty at 1 ha resolution, and regional maps based on stratification and regression approaches had 25.6 % and 29.6 % uncertainty, respectively, in any given hectare. High-resolution approaches with reported local-scale uncertainties will provide the most confidence for monitoring changes in tropical forest carbon stocks. Improved confidence will allow resource managers and decision-makers to more rapidly and effectively implement actions that better conserve and utilize forests in tropical regions.

A large-area, spatially continuous assessment of land cover map error and its impact on downstream analyses.

PubMed

Estes, Lyndon; Chen, Peng; Debats, Stephanie; Evans, Tom; Ferreira, Stefanus; Kuemmerle, Tobias; Ragazzo, Gabrielle; Sheffield, Justin; Wolf, Adam; Wood, Eric; Caylor, Kelly

2018-01-01

Land cover maps increasingly underlie research into socioeconomic and environmental patterns and processes, including global change. It is known that map errors impact our understanding of these phenomena, but quantifying these impacts is difficult because many areas lack adequate reference data. We used a highly accurate, high-resolution map of South African cropland to assess (1) the magnitude of error in several current generation land cover maps, and (2) how these errors propagate in downstream studies. We first quantified pixel-wise errors in the cropland classes of four widely used land cover maps at resolutions ranging from 1 to 100 km, and then calculated errors in several representative "downstream" (map-based) analyses, including assessments of vegetative carbon stocks, evapotranspiration, crop production, and household food security. We also evaluated maps' spatial accuracy based on how precisely they could be used to locate specific landscape features. We found that cropland maps can have substantial biases and poor accuracy at all resolutions (e.g., at 1 km resolution, up to ∼45% underestimates of cropland (bias) and nearly 50% mean absolute error (MAE, describing accuracy); at 100 km, up to 15% underestimates and nearly 20% MAE). National-scale maps derived from higher-resolution imagery were most accurate, followed by multi-map fusion products. Constraining mapped values to match survey statistics may be effective at minimizing bias (provided the statistics are accurate). Errors in downstream analyses could be substantially amplified or muted, depending on the values ascribed to cropland-adjacent covers (e.g., with forest as adjacent cover, carbon map error was 200%-500% greater than in input cropland maps, but ∼40% less for sparse cover types). The average locational error was 6 km (600%). These findings provide deeper insight into the causes and potential consequences of land cover map error, and suggest several recommendations for land cover map users. © 2017 John Wiley & Sons Ltd.

Anaysis of the quality of image data required by the LANDSAT-4 Thematic Mapper and Multispectral Scanner. [agricultural and forest cover types in California

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1984-01-01

The spatial, geometric, and radiometric qualities of LANDSAT 4 thematic mapper (TM) and multispectral scanner (MSS) data were evaluated by interpreting, through visual and computer means, film and digital products for selected agricultural and forest cover types in California. Multispectral analyses employing Bayesian maximum likelihood, discrete relaxation, and unsupervised clustering algorithms were used to compare the usefulness of TM and MSS data for discriminating individual cover types. Some of the significant results are as follows: (1) for maximizing the interpretability of agricultural and forest resources, TM color composites should contain spectral bands in the visible, near-reflectance infrared, and middle-reflectance infrared regions, namely TM 4 and TM % and must contain TM 4 in all cases even at the expense of excluding TM 5; (2) using enlarged TM film products, planimetric accuracy of mapped poins was within 91 meters (RMSE east) and 117 meters (RMSE north); (3) using TM digital products, planimetric accuracy of mapped points was within 12.0 meters (RMSE east) and 13.7 meters (RMSE north); and (4) applying a contextual classification algorithm to TM data provided classification accuracies competitive with Bayesian maximum likelihood.

How Scientists Differentiate Between Land Cover Types

NASA Technical Reports Server (NTRS)

2002-01-01

Before scientists can transform raw satellite image data into land cover maps, they must decide on what categories of land cover they would like to use. Categories are simply the types of landscape that the scientists are trying to map and can vary greatly from map to map. For flood maps, there may be only two categories-dry land and wet land-while a standard global land cover map may have seventeen categories including closed shrub lands, savannas, evergreen needle leaf forest, urban areas, and ice/snow. The only requirement for any land cover category is that it have a distinct spectral signature that a satellite can record. As can be seen through a prism, many different colors (wavelengths) make up the spectra of sunlight. When sunlight strikes objects, certain wavelengths are absorbed and others are reflected or emitted. The unique way in which a given type of land cover reflects and absorbs light is known as its spectral signature. Anyone who has flown over the midwestern United States has seen evidence of this phenomenon. From an airplane window, the ground appears as a patchwork of different colors formed by the fields of crops planted there. The varying pigments of the leaves, the amount of foliage per square foot, the age of the plants, and many other factors create this tapestry. Most imaging satellites are sensitive to specific wavelengths of light, including infrared wavelengths that cannot be seen with the naked eye. Passive satellite remote sensors-such as those flown on Landsat 5, Landsat 7, and Terra-have a number of light detectors (photoreceptors) on board that measure the energy reflected or emitted by the Earth. One light detector records only the blue part of the spectrum coming off the Earth. Another observes all the yellow-green light and still another picks up on all the near-infrared light. The detectors scan the Earth's surface as the satellite travels in a circular orbit very nearly from pole-to-pole. To differentiate between types of land cover and their attributes, researchers manipulate the colors recorded by the satellite to get the combination of wavelengths that best distinguishes the spectral signature of the land cover they wish to identify. After an area of forest or water or grass is identified, they can outline the category on an easy-to-analyze, color-coded map. To verify their results, the scientists will often travel to the regions of interest and compare the results of the map with test sites on the ground. next: The Basic Vegetation Map back: Mapping Earth's Diverse Landscapes

Enhanced systems for measuring and monitoring REDD+: Opportunities to improve the accuracy of emission factor and activity data in Indonesia

NASA Astrophysics Data System (ADS)

Solichin

The importance of accurate measurement of forest biomass in Indonesia has been growing ever since climate change mitigation schemes, particularly the reduction of emissions from deforestation and forest degradation scheme (known as REDD+), were constitutionally accepted by the government of Indonesia. The need for an accurate system of historical and actual forest monitoring has also become more pronounced, as such a system would afford a better understanding of the role of forests in climate change and allow for the quantification of the impact of activities implemented to reduce greenhouse gas emissions. The aim of this study was to enhance the accuracy of estimations of carbon stocks and to monitor emissions in tropical forests. The research encompassed various scales (from trees and stands to landscape-sized scales) and a wide range of aspects, from evaluation and development of allometric equations to exploration of the potential of existing forest inventory databases and evaluation of cutting-edge technology for non-destructive sampling and accurate forest biomass mapping over large areas. In this study, I explored whether accuracy--especially regarding the identification and reduction of bias--of forest aboveground biomass (AGB) estimates in Indonesia could be improved through (1) development and refinement of allometric equations for major forest types, (2) integration of existing large forest inventory datasets, (3) assessing nondestructive sampling techniques for tree AGB measurement, and (4) landscape-scale mapping of AGB and forest cover using lidar. This thesis provides essential foundations to improve the estimation of forest AGB at tree scale through development of new AGB equations for several major forest types in Indonesia. I successfully developed new allometric equations using large datasets from various forest types that enable us to estimate tree aboveground biomass for both forest type specific and generic equations. My models outperformed the existing local equations, with lower bias and higher precision of the AGB estimates. This study also highlights the potential advantages and challenges of using terrestrial lidar and the acoustic velocity tool for non-destructive sampling of tree biomass to enable more sample collection without the felling of trees. Further, I explored whether existing forest inventories and permanent sample plot datasets can be integrated into Indonesia's existing carbon accounting system. My investigation of these existing datasets found that through quality assurance tests these datasets are essential to be integrated into national and provincial forest monitoring and carbon accounting systems. Integration of this information would eventually improve the accuracy of the estimates of forest carbon stocks, biomass growth, mortality and emission factors from deforestation and forest degradation. At landscape scale, this study demonstrates the capability of airborne lidar for forest monitoring and forest cover classification in tropical peat swamp ecosystems. The mapping application using airborne lidar showed a more accurate and precise classification of land and forest cover when compared with mapping using optical and active sensors. To reduce the cost of lidar acquisition, this study assessed the optimum lidar return density for forest monitoring. I found that the density of lidar return could be reduced to at least 1 return per 4 m2. Overall, this study provides essential scientific background to improve the accuracy of forest AGB estimates. Therefore, the described results and techniques should be integrated into the existing monitoring systems to assess emission reduction targets and the impact of REDD+ implementation.

Rendering Future Vegetation Change across Large Regions of the US

NASA Astrophysics Data System (ADS)

Sant'Anna Dias, Felipe; Gu, Yuting; Agarwalla, Yashika; Cheng, Yiwei; Patil, Sopan; Stieglitz, Marc; Turk, Greg

2015-04-01

We use two Machine Learning techniques, Decision Trees (DT) and Neural Networks (NN), to provide classified images and photorealistic renderings of future vegetation cover at three large regions in the US. The training data used to generate current vegetation cover include Landsat surface reflectance images, USGS Land Cover maps, 50 years of mean annual temperature and precipitation for the period 1950 - 2000, elevation, aspect and slope data. Present vegetation cover was generated on a 100m grid. Future vegetation cover for the period 2061- 2080 was predicted using the 1 km resolution bias corrected data from the NASA Goddard Institute for Space Studies Global Climate Model E simulation. The three test regions encompass a wide range of climatic gradients, topographic variation, and vegetation cover. The central Oregon site covers 19,182 square km and includes the Ochoco and Malheur National Forest. Vegetation cover is 50% evergreen forest and 50% shrubs and scrubland. The northwest Washington site covers 14,182 square km. Vegetation cover is 60% evergreen forest, 14% scrubs, 7% grassland, and 7% barren land. The remainder of the area includes deciduous forest, perennial snow cover, and wetlands. The third site, the Jemez mountain region of north central New Mexico, covers 5,500 square km. Vegetation cover is 47% evergreen forest, 31% shrubs, 13% grasses, and 3% deciduous forest. The remainder of the area includes developed and cultivated areas and wetlands. Using the above mentioned data sets we first trained our DT and NN models to reproduce current vegetation. The land cover classified images were compared directly to the USGS land cover data. The photorealistic generated vegetation images were compared directly to the remotely sensed surface reflectance maps. For all three sites, similarity between generated and observed vegetation cover was quite remarkable. The three trained models were then used to explore what the equilibrium vegetation would look like for the period 2061 - 2080. The predicted mean annual air temperature change for the three sites ranged from + 1.8°C to + 2.3°C. Precipitation for the three sites changed little. In Oregon, this resulted in a 37% shift of forested areas to shrub vegetation. In New Mexico, shrubs and evergreen vegetation increased by 18% and 5%, respectively. Deciduous and grassland vegetation decreased by 90% and 52%, respectively. In Washington, evergreen vegetation cover decreased by 4.5%. Deciduous vegetation increase by 25%. Shrubs and grasslands increased by 15% and 7%, respectively. Perennial snow cover on mountain tops fell by 46%. Beyond rendering a view of future vegetation cover, we also extracted information regarding the relative controls that climate and topography exert over local vegetation. The three most dominant controls are elevation (most dominant), temperature, and precipitation. In summary, we demonstrate a framework for rendering potential future vegetation in a visually realistic way. Moreover, these machine learning techniques provide a computationally fast framework for exploring the effects of climate change over large-areas and at high-spatial resolution that cannot be accomplished through simulation alone.

Forest height Mapping using the fusion of Lidar and MULTI-ANGLE spectral data

NASA Astrophysics Data System (ADS)

Pang, Y.; Li, Z.

2016-12-01

Characterizing the complexity of forest ecosystem over large area is highly complex. Light detection and Ranging (LIDAR) approaches have demonstrated a high capacity to accurately estimate forest structural parameters. A number of satellite mission concepts have been proposed to fuse LiDAR with other optical imagery allowing Multi-angle spectral observations to be captured using the Bidirectional Reflectance Distribution Function (BRDF) characteristics of forests. China is developing the concept of Chinese Terrestrial Carbon Mapping Satellite. A multi-beam waveform Lidar is the main sensor. A multi-angle imagery system is considered as the spatial mapping sensor. In this study, we explore the fusion potential of Lidar and multi-angle spectral data to estimate forest height across different scales. We flew intensive airborne Lidar and Multi-angle hyperspectral data in Genhe Forest Ecological Research Station, Northeast China. Then extended the spatial scale with some long transect flights to cover more forest structures. Forest height data derived from airborne lidar data was used as reference data and the multi-angle hyperspectral data was used as model inputs. Our results demonstrate that the multi-angle spectral data can be used to estimate forest height with the RMSE of 1.1 m with an R2 approximately 0.8.

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.

Extreme Differences in Forest Degradation in Borneo: Comparing Practices in Sarawak, Sabah, and Brunei

PubMed Central

Bryan, Jane E.; Shearman, Philip L.; Asner, Gregory P.; Knapp, David E.; Aoro, Geraldine; Lokes, Barbara

2013-01-01

The Malaysian states of Sabah and Sarawak are global hotspots of forest loss and degradation due to timber and oil palm industries; however, the rates and patterns of change have remained poorly measured by conventional field or satellite approaches. Using 30 m resolution optical imagery acquired since 1990, forest cover and logging roads were mapped throughout Malaysian Borneo and Brunei using the Carnegie Landsat Analysis System. We uncovered ∼364,000 km of roads constructed through the forests of this region. We estimated that in 2009 there were at most 45,400 km2 of intact forest ecosystems in Malaysian Borneo and Brunei. Critically, we found that nearly 80% of the land surface of Sabah and Sarawak was impacted by previously undocumented, high-impact logging or clearing operations from 1990 to 2009. This contrasted strongly with neighbouring Brunei, where 54% of the land area remained covered by unlogged forest. Overall, only 8% and 3% of land area in Sabah and Sarawak, respectively, was covered by intact forests under designated protected areas. Our assessment shows that very few forest ecosystems remain intact in Sabah or Sarawak, but that Brunei, by largely excluding industrial logging from its borders, has been comparatively successful in protecting its forests. PMID:23874983

Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps.

PubMed

Mitchard, Edward Ta; Saatchi, Sassan S; Baccini, Alessandro; Asner, Gregory P; Goetz, Scott J; Harris, Nancy L; Brown, Sandra

2013-10-26

Mapping the aboveground biomass of tropical forests is essential both for implementing conservation policy and reducing uncertainties in the global carbon cycle. Two medium resolution (500 m - 1000 m) pantropical maps of vegetation biomass have been recently published, and have been widely used by sub-national and national-level activities in relation to Reducing Emissions from Deforestation and forest Degradation (REDD+). Both maps use similar input data layers, and are driven by the same spaceborne LiDAR dataset providing systematic forest height and canopy structure estimates, but use different ground datasets for calibration and different spatial modelling methodologies. Here, we compare these two maps to each other, to the FAO's Forest Resource Assessment (FRA) 2010 country-level data, and to a high resolution (100 m) biomass map generated for a portion of the Colombian Amazon. We find substantial differences between the two maps, in particular in central Amazonia, the Congo basin, the south of Papua New Guinea, the Miombo woodlands of Africa, and the dry forests and savannas of South America. There is little consistency in the direction of the difference. However, when the maps are aggregated to the country or biome scale there is greater agreement, with differences cancelling out to a certain extent. When comparing country level biomass stocks, the two maps agree with each other to a much greater extent than to the FRA 2010 estimates. In the Colombian Amazon, both pantropical maps estimate higher biomass than the independent high resolution map, but show a similar spatial distribution of this biomass. Biomass mapping has progressed enormously over the past decade, to the stage where we can produce globally consistent maps of aboveground biomass. We show that there are still large uncertainties in these maps, in particular in areas with little field data. However, when used at a regional scale, different maps appear to converge, suggesting we can provide reasonable stock estimates when aggregated over large regions. Therefore we believe the largest uncertainties for REDD+ activities relate to the spatial distribution of biomass and to the spatial pattern of forest cover change, rather than to total globally or nationally summed carbon density.

Evaluating anthropogenic risk of grassland and forest habitat degradation using land-cover data

Treesearch

Kurt Riitters; James Wickham; Timothy Wade

2009-01-01

The effects of landscape context on habitat quality are receiving increased attention in conservation biology. The objective of this research is to demonstrate a landscape-level approach to mapping and evaluating the anthropogenic risks of grassland and forest habitat degradation by examining habitat context as defined by intensive anthropogenic land uses at multiple...

Techniques and computations for mapping plot clusters that straddle stand boundaries

Treesearch

Charles T. Scott; William A. Bechtold

1995-01-01

Many regional (extensive) forest surveys use clusters of subplots or prism points to reduce survey costs. Two common methods of handling clusters that straddle stand boundaries entail: (1) moving all subplots into a single forest cover type, or (2)"averaging" data across multiple conditions without regard to the boundaries. these methods result in biased...

GOFC-GOLD :: Global Observation of Forest and Land Cover Dynamics

Science.gov Websites

Validation: Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps, A. Strahler et GOFC-GOLD-38: Report of the GOFC-GOLD/CEOS Workshop on Land Cover Change Accuracy Assessment as part of al., March 2006 860 kb GOFC-GOLD-24: A Revised Strategy for GOFC-GOLD, J.R. Townshend and M.A. Brady

Spatially explicit modeling of 1992-2100 land cover and forest stand age for the conterminous United States

USGS Publications Warehouse

Sohl, Terry L.; Sayler, Kristi L.; Bouchard, Michelle; Reker, Ryan R.; Friesz, Aaron M.; Bennett, Stacie L.; Sleeter, Benjamin M.; Sleeter, Rachel R.; Wilson, Tamara; Soulard, Christopher E.; Knuppe, Michelle; Van Hofwegen, Travis

2014-01-01

Information on future land-use and land-cover (LULC) change is needed to analyze the impact of LULC change on ecological processes. The U.S. Geological Survey has produced spatially explicit, thematically detailed LULC projections for the conterminous United States. Four qualitative and quantitative scenarios of LULC change were developed, with characteristics consistent with the Intergovernmental Panel on Climate Change (IPCC) Special Report on 5 Emission Scenarios (SRES). The four quantified scenarios (A1B, A2, B1, and B2) served as input to the Forecasting Scenarios of Land-use Change (FORE-SCE) model. Four spatially explicit datasets consistent with scenario storylines were produced for the conterminous United States, with annual LULC maps from 1992 through 2100. The future projections are characterized by a loss of natural land covers in most scenarios, with corresponding expansion of 10 anthropogenic land uses. Along with the loss of natural land covers, remaining natural land covers experience increased fragmentation under most scenarios, with only the B2 scenario remaining relatively stable in both proportion of remaining natural land covers and basic fragmentation measures. Forest stand age was also modeled. By 2100, scenarios and ecoregions with heavy forest cutting have relatively lower mean stand ages compared to those with less 15 forest cutting. Stand ages differ substantially between unprotected and protected forest lands, as well as between different forest classes. The modeled data were compared to the National Land Cover Database (NLCD) and other data sources to assess model characteristics. The consistent, spatially explicit, and thematically detailed LULC projections and the associated forest stand age data layers have been used to analyze LULC impacts on carbon and greenhouse gas fluxes, 20 biodiversity, climate and weather variability, hydrologic change, and other ecological processes.

Forest Connectivity Regions of Canada Using Circuit Theory and Image Analysis

PubMed Central

Pelletier, David; Lapointe, Marc-Élie; Wulder, Michael A.; White, Joanne C.; Cardille, Jeffrey A.

2017-01-01

Ecological processes are increasingly well understood over smaller areas, yet information regarding interconnections and the hierarchical nature of ecosystems remains less studied and understood. Information on connectivity over large areas with high resolution source information provides for both local detail and regional context. The emerging capacity to apply circuit theory to create maps of omnidirectional connectivity provides an opportunity for improved and quantitative depictions of forest connectivity, supporting the formation and testing of hypotheses about the density of animal movement, ecosystem structure, and related links to natural and anthropogenic forces. In this research, our goal was to delineate regions where connectivity regimes are similar across the boreal region of Canada using new quantitative analyses for characterizing connectivity over large areas (e.g., millions of hectares). Utilizing the Earth Observation for Sustainable Development of forests (EOSD) circa 2000 Landsat-derived land-cover map, we created and analyzed a national-scale map of omnidirectional forest connectivity at 25m resolution over 10000 tiles of 625 km2 each, spanning the forested regions of Canada. Using image recognition software to detect corridors, pinch points, and barriers to movements at multiple spatial scales in each tile, we developed a simple measure of the structural complexity of connectivity patterns in omnidirectional connectivity maps. We then mapped the Circuitscape resistance distance measure and used it in conjunction with the complexity data to study connectivity characteristics in each forested ecozone. Ecozone boundaries masked substantial systematic patterns in connectivity characteristics that are uncovered using a new classification of connectivity patterns that revealed six clear groups of forest connectivity patterns found in Canada. The resulting maps allow exploration of omnidirectional forest connectivity patterns at full resolution while permitting quantitative analyses of connectivity over broad areas, informing modeling, planning and monitoring efforts. PMID:28146573

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.

Classification and area estimation of land covers in Kansas using ground-gathered and LANDSAT digital data

NASA Technical Reports Server (NTRS)

May, G. A.; Holko, M. L.; Anderson, J. E.

1983-01-01

Ground-gathered data and LANDSAT multispectral scanner (MSS) digital data from 1981 were analyzed to produce a classification of Kansas land areas into specific types called land covers. The land covers included rangeland, forest, residential, commercial/industrial, and various types of water. The analysis produced two outputs: acreage estimates with measures of precision, and map-type or photo products of the classification which can be overlaid on maps at specific scales. State-level acreage estimates were obtained and substate-level land cover classification overlays and estimates were generated for selected geographical areas. These products were found to be of potential use in managing land and water resources.

Mapping Chinese tallow with color-infrared photography

USGS Publications Warehouse

Ramsey, Elijah W.; Nelson, G.A.; Sapkota, S.K.; Seeger, E.B.; Martella, K.D.

2002-01-01

Airborne color-infrared photography (CIR) (1:12,000 scale) was used to map localized occurrences of the widespread and aggressive Chinese tallow (Sapium sebiferum), an invasive species. Photography was collected during senescence when Chinese tallow's bright red leaves presented a high spectral contrast within the native bottomland hardwood and upland forests and marsh land-cover types. Mapped occurrences were conservative because not all senescing tallow leaves are bright red simultaneously. To simulate low spectral but high spatial resolution satellite/airborne image and digital video data, the CIR photography was transformed into raster images at spatial resolutions approximating 0.5 in and 1.0 m. The image data were then spectrally classified for the occurrence of bright red leaves associated with senescing Chinese tallow. Classification accuracies were greater than 95 percent at both spatial resolutions. There was no significant difference in either forest in the detection of tallow or inclusion of non-tallow trees associated with the two spatial resolutions. In marshes, slightly more tallow occurrences were mapped with the lower spatial resolution, but there were also more misclassifications of native land covers as tallow. Combining all land covers, there was no difference at detecting tallow occurrences (equal omission errors) between the two resolutions, but the higher spatial resolution was associated with less inclusion of non-tallow land covers as tallow (lower commission error). Overall, these results confirm that high spatial (???1 m) but low spectral resolution remote sensing data can be used for mapping Chinese tallow trees in dominant environments found in coastal and adjacent upland landscapes.

Analysis of thematic mapper simulator data acquired during winter season over Pearl River, Mississippi, test site

NASA Technical Reports Server (NTRS)

Anderson, J. E.; Kalcic, M. T. (Principal Investigator)

1982-01-01

Digital processed aircraft-acquired thematic mapping simulator (TMS) data collected during the winter season over a forested site in southern Mississippi are presented to investigate the utility of TMS data for use in forest inventories and monitoring. Analyses indicated that TMS data are capable of delineating the mixed forest land cover type to an accuracy of 92.5 % correct. The accuracies associated with river bottom forest and pine forest were 95.5 and 91.5 % correct. The accuracies associated with river bottom forest and pine forest were 95.5 and 91.5 % correct, respectively. The figures reflect the performance for products produced using the best subset of channels for each forest cover type. It was found that the choice of channels (subsets) has a significant effect on the accuracy of classification produced, and that the same channels are not the most desirable for all three forest types studied. Both supervised and unsupervised spectral signature development techniques are evaluated; the unsupervised methods proved unacceptable for the three forest types considered.

Advances in remote sensing of forest background reflectance with MODIS BRDF data across Europe

NASA Astrophysics Data System (ADS)

Pisek, Jan; Alikas, Krista; Lukeš, Petr; Lundin, Lars; Kobler, Johannes; Santos-Reis, Margarida; Chen, Jing

2017-04-01

Spatial and temporal patterns of forest background (understory) reflectance are crucial for retrieving biophysical parameters of forest canopies (overstory) and subsequently for ecosystem modeling. However, systematic reflectance data covering different site types are almost missing. This presentation will focus on the validation of background reflectance retrievals using MODIS bidirectional reflectance distribution function (BRDF) data against in-situ understory reflectance measurements covering a diverse set of long-term ecological research (LTER) sites distributed along a wide latitudinal and elevational gradient across Europe: protected coniferous blueberry forest in Sweden, karst forest system in Austria, floodplain broadleaf forest and coniferous forest in the Czech Republic, and Mediterranean agro-sylvo-pastoral woodlands in Portugal. The multi-angle remote sensing data-based methodology was originally developed for the forest background signal retrieval in a boreal region. Here its performance will be tested across diverse forest conditions and moments during the growing season, which is a necessary step before conducting extensive mapping over forested areas. The results can be also used as an input for improved modeling of local carbon and energy fluxes.

Evaluation of space SAR as a land-cover classification

NASA Technical Reports Server (NTRS)

Brisco, B.; Ulaby, F. T.; Williams, T. H. L.

1985-01-01

The multidimensional approach to the mapping of land cover, crops, and forests is reported. Dimensionality is achieved by using data from sensors such as LANDSAT to augment Seasat and Shuttle Image Radar (SIR) data, using different image features such as tone and texture, and acquiring multidate data. Seasat, Shuttle Imaging Radar (SIR-A), and LANDSAT data are used both individually and in combination to map land cover in Oklahoma. The results indicates that radar is the best single sensor (72% accuracy) and produces the best sensor combination (97.5% accuracy) for discriminating among five land cover categories. Multidate Seasat data and a single data of LANDSAT coverage are then used in a crop classification study of western Kansas. The highest accuracy for a single channel is achieved using a Seasat scene, which produces a classification accuracy of 67%. Classification accuracy increases to approximately 75% when either a multidate Seasat combination or LANDSAT data in a multisensor combination is used. The tonal and textural elements of SIR-A data are then used both alone and in combination to classify forests into five categories.

Modeling Precipitation Dependent Forest Resilience in India

NASA Astrophysics Data System (ADS)

Das, P.; Behera, M. D.; Roy, P. S.

2018-04-01

The impact of long term climate change that imparts stress on forest could be perceived by studying the regime shift of forest ecosystem. With the change of significant precipitation, forest may go through density change around globe at different spatial and temporal scale. The 100 class high resolution (60 meter spatial resolution) Indian vegetation type map was used in this study recoded into four broad categories depending on phrenology as (i) forest, (ii) scrubland, (iii) grassland and (iv) treeless area. The percentage occupancy of forest, scrub, grass and treeless were observed as 19.9 %, 5.05 %, 1.89 % and 7.79 % respectively. Rest of the 65.37 % land area was occupied by the cropland, built-up, water body and snow covers. The majority forest cover were appended into a 5 km × 5 km grid, along with the mean annual precipitation taken from Bioclim data. The binary presence and absence of different vegetation categories in relates to the annual precipitation was analyzed to calculate their resilience expressed in probability values ranging from 0 to 1. Forest cover observed having resilience probability (Pr) < 0.3 in only 0.3 % (200 km2) of total forest cover in India, which was 4.3 % < 0.5 Pr. Majority of the scrubs and grass (64.92 % Pr < 0.5) from North East India which were the shifting cultivation lands showing low resilience, having their high tendency to be transform to forest. These results have spatial explicitness to highlight the resilient and non-resilient distribution of forest, scrub and grass, and treeless areas in India.

Land cover mapping for development planning in Eastern and Southern Africa

NASA Astrophysics Data System (ADS)

Oduor, P.; Flores Cordova, A. I.; Wakhayanga, J. A.; Kiema, J.; Farah, H.; Mugo, R. M.; Wahome, A.; Limaye, A. S.; Irwin, D.

2016-12-01

Africa continues to experience intensification of land use, driven by competition for resources and a growing population. Land cover maps are some of the fundamental datasets required by numerous stakeholders to inform a number of development decisions. For instance, they can be integrated with other datasets to create value added products such as vulnerability impact assessment maps, and natural capital accounting products. In addition, land cover maps are used as inputs into Greenhouse Gas (GHG) inventories to inform the Agriculture, Forestry and other Land Use (AFOLU) sector. However, the processes and methodologies of creating land cover maps consistent with international and national land cover classification schemes can be challenging, especially in developing countries where skills, hardware and software resources can be limiting. To meet this need, SERVIR Eastern and Southern Africa developed methodologies and stakeholder engagement processes that led to a successful initiative in which land cover maps for 9 countries (Malawi, Rwanda, Namibia, Botswana, Lesotho, Ethiopia, Uganda, Zambia and Tanzania) were developed, using 2 major classification schemes. The first sets of maps were developed based on an internationally acceptable classification system, while the second sets of maps were based on a nationally defined classification system. The mapping process benefited from reviews from national experts and also from technical advisory groups. The maps have found diverse uses, among them the definition of the Forest Reference Levels in Zambia. In Ethiopia, the maps have been endorsed by the national mapping agency as part of national data. The data for Rwanda is being used to inform the Natural Capital Accounting process, through the WAVES program, a World Bank Initiative. This work illustrates the methodologies and stakeholder engagement processes that brought success to this land cover mapping initiative.

Estimating carbon sequestration in the piedmont ecoregion of the United States from 1971 to 2010

USGS Publications Warehouse

Liu, Jinxun; Sleeter, Benjamin M.; Zhu, Zhiliang; Heath, Linda S.; Tan, Zhengxi; Wilson, Tamara; Sherba, Jason T.; Zhou, Decheng

2016-01-01

Background: Human activities have diverse and profound impacts on ecosystem carbon cycles. The Piedmont ecoregion in the eastern United States has undergone significant land use and land cover change in the past few decades. The purpose of this study was to use newly available land use and land cover change data to quantify carbon changes within the ecoregion. Land use and land cover change data (60-m spatial resolution) derived from sequential remotely sensed Landsat imagery were used to generate 960-m resolution land cover change maps for the Piedmont ecoregion. These maps were used in the Integrated Biosphere Simulator (IBIS) to simulate ecosystem carbon stock and flux changes from 1971 to 2010. Results: Results show that land use change, especially urbanization and forest harvest had significant impacts on carbon sources and sinks. From 1971 to 2010, forest ecosystems sequestered 0.25 Mg C ha−1 yr−1, while agricultural ecosystems sequestered 0.03 Mg C ha−1 yr−1. The total ecosystem C stock increased from 2271 Tg C in 1971 to 2402 Tg C in 2010, with an annual average increase of 3.3 Tg C yr−1. Conclusions: Terrestrial lands in the Piedmont ecoregion were estimated to be weak net carbon sink during the study period. The major factors contributing to the carbon sink were forest growth and afforestation; the major factors contributing to terrestrial emissions were human induced land cover change, especially urbanization and forest harvest. An additional amount of carbon continues to be stored in harvested wood products. If this pool were included the carbon sink would be stronger. Keywords: Land-use change, Carbon change, Piedmont ecoregion, IBIS model

Moss and lichen cover mapping at local and regional scales in the boreal forest ecosystem of central Canada

USGS Publications Warehouse

Rapalee, G.; Steyaert, L.T.; Hall, F.G.

2001-01-01

Mosses and lichens are important components of boreal landscapes [Vitt et al., 1994; Bubier et al., 1997]. They affect plant productivity and belowground carbon sequestration and alter the surface runoff and energy balance. We report the use of multiresolution satellite data to map moss and lichens over the BOREAS region at a 10 m, 30 m, and 1 km scales. Our moss and lichen classification at the 10 m scale is based on ground observations of associations among soil drainage classes, overstory composition, and cover type among four broad classes of ground cover (feather, sphagnum, and brown mosses and lichens). For our 30 m map, we used field observations of ground cover-overstory associations to map mosses and lichens in the BOREAS southern study area (SSA). To scale up to a 1 km (AVHRR) moss map of the BOREAS region, we used the TM SSA mosaics plus regional field data to identify AVHRR overstory-ground cover associations. We found that: 1) ground cover, overstory composition and density are highly correlated, permitting inference of moss and lichen cover from satellite-based land cover classifications; 2) our 1 km moss map reveals that mosses dominate the boreal landscape of central Canada, thereby a significant factor for water, energy, and carbon modeling; 3) TM and AVHRR moss cover maps are comparable; 4) satellite data resolution is important; particularly in detecting the smaller wetland features, lakes, and upland jack pine sites; and 5) distinct regional patterns of moss and lichen cover correspond to latitudinal and elevational gradients. Copyright 2001 by the American Geophysical Union.

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.

Satellite Snow-Cover Mapping: A Brief Review

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.

1995-01-01

Satellite snow mapping has been accomplished since 1966, initially using data from the reflective part of the electromagnetic spectrum, and now also employing data from the microwave part of the spectrum. Visible and near-infrared sensors can provide excellent spatial resolution from space enabling detailed snow mapping. When digital elevation models are also used, snow mapping can provide realistic measurements of snow extent even in mountainous areas. Passive-microwave satellite data permit global snow cover to be mapped on a near-daily basis and estimates of snow depth to be made, but with relatively poor spatial resolution (approximately 25 km). Dense forest cover limits both techniques and optical remote sensing is limited further by cloudcover conditions. Satellite remote sensing of snow cover with imaging radars is still in the early stages of research, but shows promise at least for mapping wet or melting snow using C-band (5.3 GHz) synthetic aperture radar (SAR) data. Observing System (EOS) Moderate Resolution Imaging Spectroradiometer (MODIS) data beginning with the launch of the first EOS platform in 1998. Digital maps will be produced that will provide daily, and maximum weekly global snow, sea ice and lake ice cover at 1-km spatial resolution. Statistics will be generated on the extent and persistence of snow or ice cover in each pixel for each weekly map, cloudcover permitting. It will also be possible to generate snow- and ice-cover maps using MODIS data at 250- and 500-m resolution, and to study and map snow and ice characteristics such as albedo. been under development. Passive-microwave data offer the potential for determining not only snow cover, but snow water equivalent, depth and wetness under all sky conditions. A number of algorithms have been developed to utilize passive-microwave brightness temperatures to provide information on snow cover and water equivalent. The variability of vegetative Algorithms are being developed to map global snow and ice cover using Earth Algorithms to map global snow cover using passive-microwave data have also cover and of snow grain size, globally, limits the utility of a single algorithm to map global snow cover.

Impact Assessment of Mikania Micrantha on Land Cover and Maxent Modeling to Predict its Potential Invasion Sites

NASA Astrophysics Data System (ADS)

Baidar, T.; Shrestha, A. B.; Ranjit, R.; Adhikari, R.; Ghimire, S.; Shrestha, N.

2017-05-01

Mikania micrantha is one of the major invasive alien plant species in tropical moist forest regions of Asia including Nepal. Recently, this weed is spreading at an alarming rate in Chitwan National Park (CNP) and threatening biodiversity. This paper aims to assess the impacts of Mikania micrantha on different land cover and to predict potential invasion sites in CNP using Maxent model. Primary data for this were presence point coordinates and perceived Mikania micrantha cover collected through systematic random sampling technique. Rapideye image, Shuttle Radar Topographic Mission data and bioclimatic variables were acquired as secondary data. Mikania micrantha distribution maps were prepared by overlaying the presence points on image classified by object based image analysis. The overall accuracy of classification was 90 % with Kappa coefficient 0.848. A table depicting the number of sample points in each land cover with respective Mikania micrantha coverage was extracted from the distribution maps to show the impact. The riverine forest was found to be the most affected land cover with 85.98 % presence points and sal forest was found to be very less affected with only 17.02 % presence points. Maxent modeling predicted the areas near the river valley as the potential invasion sites with statistically significant Area Under the Receiver Operating Curve (AUC) value of 0.969. Maximum temperature of warmest month and annual precipitation were identified as the predictor variables that contribute the most to Mikania micrantha's potential distribution.

Mapping tsunami impacts on land cover and related ecosystem service supply in Phang Nga, Thailand

NASA Astrophysics Data System (ADS)

Kaiser, G.; Burkhard, B.; Römer, H.; Sangkaew, S.; Graterol, R.; Haitook, T.; Sterr, H.; Sakuna-Schwartz, D.

2013-12-01

The 2004 Indian Ocean tsunami caused damages to coastal ecosystems and thus affected the livelihoods of the coastal communities who depend on services provided by these ecosystems. The paper presents a case study on evaluating and mapping the spatial and temporal impacts of the tsunami on land use and land cover (LULC) and related ecosystem service supply in the Phang Nga province, Thailand. The method includes local stakeholder interviews, field investigations, remote-sensing techniques, and GIS. Results provide an ecosystem services matrix with capacity scores for 18 LULC classes and 17 ecosystem functions and services as well as pre-/post-tsunami and recovery maps indicating changes in the ecosystem service supply capacities in the study area. Local stakeholder interviews revealed that mangroves, casuarina forest, mixed beach forest, coral reefs, tidal inlets, as well as wetlands (peat swamp forest) have the highest capacity to supply ecosystem services, while e.g. plantations have a lower capacity. The remote-sensing based damage and recovery analysis showed a loss of the ecosystem service supply capacities in almost all LULC classes for most of the services due to the tsunami. A fast recovery of LULC and related ecosystem service supply capacities within one year could be observed for e.g. beaches, while mangroves or casuarina forest needed several years to recover. Applying multi-temporal mapping the spatial variations of recovery could be visualised. While some patches of coastal forest were fully recovered after 3 yr, other patches were still affected and thus had a reduced capacity to supply ecosystem services. The ecosystem services maps can be used to quantify ecological values and their spatial distribution in the framework of a tsunami risk assessment. Beyond that they are considered to be a useful tool for spatial analysis in coastal risk management in Phang Nga.

Types and rates of forest disturbance in Brazilian Legal Amazon, 2000–2013

PubMed Central

Tyukavina, Alexandra; Hansen, Matthew C.; Potapov, Peter V.; Stehman, Stephen V.; Smith-Rodriguez, Kevin; Okpa, Chima; Aguilar, Ricardo

2017-01-01

Deforestation rates in primary humid tropical forests of the Brazilian Legal Amazon (BLA) have declined significantly since the early 2000s. Brazil’s national forest monitoring system provides extensive information for the BLA but lacks independent validation and systematic coverage outside of primary forests. We use a sample-based approach to consistently quantify 2000–2013 tree cover loss in all forest types of the region and characterize the types of forest disturbance. Our results provide unbiased forest loss area estimates, which confirm the reduction of primary forest clearing (deforestation) documented by official maps. By the end of the study period, nonprimary forest clearing, together with primary forest degradation within the BLA, became comparable in area to deforestation, accounting for an estimated 53% of gross tree cover loss area and 26 to 35% of gross aboveground carbon loss. The main type of tree cover loss in all forest types was agroindustrial clearing for pasture (63% of total loss area), followed by small-scale forest clearing (12%) and agroindustrial clearing for cropland (9%), with natural woodlands being directly converted into croplands more often than primary forests. Fire accounted for 9% of the 2000–2013 primary forest disturbance area, with peak disturbances corresponding to droughts in 2005, 2007, and 2010. The rate of selective logging exploitation remained constant throughout the study period, contributing to forest fire vulnerability and degradation pressures. As the forest land use transition advances within the BLA, comprehensive tracking of forest transitions beyond primary forest loss is required to achieve accurate carbon accounting and other monitoring objectives. PMID:28439536

The potential for LiDAR technology to map fire fuel hazard over large areas of Australian forest.

PubMed

Price, Owen F; Gordon, Christopher E

2016-10-01

Fuel load is a primary determinant of fire spread in Australian forests. In east Australian forests, litter and canopy fuel loads and hence fire hazard are thought to be highest at and beyond steady-state fuel loads 15-20 years post-fire. Current methods used to predict fuel loads often rely on course-scale vegetation maps and simple time-since-fire relationships which mask fine-scale processes influencing fuel loads. Here we use Light Detecting and Remote Sensing technology (LiDAR) and field surveys to quantify post-fire mid-story and crown canopy fuel accumulation and fire hazard in Dry Sclerophyll Forests of the Sydney Basin (Australia) at fine spatial-scales (20 × 20 m cell resolution). Fuel cover was quantified in three strata important for crown fire propagation (0.5-4 m, 4-15 m, >15 m) over a 144 km(2) area subject to varying fire fuel ages. Our results show that 1) LiDAR provided a precise measurement of fuel cover in each strata and a less precise but still useful predictor of surface fuels, 2) cover varied greatly within a mapped vegetation class of the same fuel age, particularly for elevated fuel, 3) time-since-fire was a poor predictor of fuel cover and crown fire hazard because fuel loads important for crown fire propagation were variable over a range of fire fuel ages between 2 and 38 years post-fire, and 4) fuel loads and fire hazard can be high in the years immediately following fire. Our results show the benefits of spatially and temporally specific in situ fuel sampling methods such as LiDAR, and are widely applicable for fire management actions which aim to decrease human and environmental losses due to wildfire. Copyright © 2016 Elsevier Ltd. All rights reserved.

Accelerated deforestation in the humid tropics from the 1990s to the 2000s

NASA Astrophysics Data System (ADS)

Kim, Do-Hyung; Sexton, Joseph O.; Townshend, John R.

2015-05-01

Using a consistent, 20 year series of high- (30 m) resolution, satellite-based maps of forest cover, we estimate forest area and its changes from 1990 to 2010 in 34 tropical countries that account for the majority of the global area of humid tropical forests. Our estimates indicate a 62% acceleration in net deforestation in the humid tropics from the 1990s to the 2000s, contradicting a 25% reduction reported by the United Nations Food and Agriculture Organization Forest Resource Assessment. Net loss of forest cover peaked from 2000 to 2005. Gross gains accelerated slowly and uniformly between 1990-2000, 2000-2005, and 2005-2010. However, the gains were overwhelmed by gross losses, which peaked from 2000 to 2005 and decelerated afterward. The acceleration of humid tropical deforestation we report contradicts the assertion that losses decelerated from the 1990s to the 2000s.

GIS based Cadastral level Forest Information System using World View-II data in Bir Hisar (Haryana)

NASA Astrophysics Data System (ADS)

Mothi Kumar, K. E.; Singh, S.; Attri, P.; Kumar, R.; Kumar, A.; Sarika; Hooda, R. S.; Sapra, R. K.; Garg, V.; Kumar, V.; Nivedita

2014-11-01

Identification and demarcation of Forest lands on the ground remains a major challenge in Forest administration and management. Cadastral forest mapping deals with forestlands boundary delineation and their associated characterization (forest/non forest). The present study is an application of high resolution World View-II data for digitization of Protected Forest boundary at cadastral level with integration of Records of Right (ROR) data. Cadastral vector data was generated by digitization of spatial data using scanned mussavies in ArcGIS environment. Ortho-images were created from World View-II digital stereo data with Universal Transverse Mercator coordinate system with WGS 84 datum. Cadastral vector data of Bir Hisar (Hisar district, Haryana) and adjacent villages was spatially adjusted over ortho-image using ArcGIS software. Edge matching of village boundaries was done with respect to khasra boundaries of individual village. The notified forest grids were identified on ortho-image and grid vector data was extracted from georeferenced cadastral data. Cadastral forest boundary vectors were digitized from ortho-images. Accuracy of cadastral data was checked by comparison of randomly selected geo-coordinates points, tie lines and boundary measurements of randomly selected parcels generated from image data set with that of actual field measurements. Area comparison was done between cadastral map area, the image map area and RoR area. The area covered under Protected Forest was compared with ROR data and within an accuracy of less than 1 % from ROR area was accepted. The methodology presented in this paper is useful to update the cadastral forest maps. The produced GIS databases and large-scale Forest Maps may serve as a data foundation towards a land register of forests. The study introduces the use of very high resolution satellite data to develop a method for cadastral surveying through on - screen digitization in a less time as compared to the old fashioned cadastral parcel boundaries surveying method.

Mapping the Philippines' mangrove forests using Landsat imagery

USGS Publications Warehouse

Long, Jordan; Giri, Chandra

2011-01-01

Current, accurate, and reliable information on the areal extent and spatial distribution of mangrove forests in the Philippines is limited. Previous estimates of mangrove extent do not illustrate the spatial distribution for the entire country. This study, part of a global assessment of mangrove dynamics, mapped the spatial distribution and areal extent of the Philippines’ mangroves circa 2000. We used publicly available Landsat data acquired primarily from the Global Land Survey to map the total extent and spatial distribution. ISODATA clustering, an unsupervised classification technique, was applied to 61 Landsat images. Statistical analysis indicates the total area of mangrove forest cover was approximately 256,185 hectares circa 2000 with overall classification accuracy of 96.6% and a kappa coefficient of 0.926. These results differ substantially from most recent estimates of mangrove area in the Philippines. The results of this study may assist the decision making processes for rehabilitation and conservation efforts that are currently needed to protect and restore the Philippines’ degraded mangrove forests.

EnviroAtlas - Tampa, FL - Land Cover by Block Group

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of each block group that is classified as impervious, forest, green space, wetland, and agriculture. Impervious is a combination of dark and light impervious. Forest is a combination of trees and forest and woody wetlands. Green space is a combination of trees and forest, grass and herbaceous, agriculture, woody wetlands, and emergent wetlands. Wetlands includes both Woody and Emergent Wetlands.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Austin, TX - 15m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 15-m riparian buffer that is forested. Forest is defined as Trees & Forest. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Minneapolis/St. Paul, MN - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. In this community, forest is defined as Trees and Forest and Woody Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Cleveland, OH - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. In this community, forest is defined as Trees & Forest and Woody Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

EnviroAtlas - New York, NY - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. In this community, forest is defined as Trees & Forest. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

EnviroAtlas - Austin, TX - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. Forest is defined as Trees & Forest. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Memphis, TN - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. Forest is defined as Trees & Forest and Woody Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Des Moines, IA - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. Forest is defined as Trees & Forest. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://enviroatlas.epa.gov/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

Impacts of Landscape Context on Patterns of Wind Downfall Damage in a Fragmented Amazonian Landscape

NASA Astrophysics Data System (ADS)

Schwartz, N.; Uriarte, M.; DeFries, R. S.; Gutierrez-Velez, V. H.; Fernandes, K.; Pinedo-Vasquez, M.

2015-12-01

Wind is a major disturbance in the Amazon and has both short-term impacts and lasting legacies in tropical forests. Observed patterns of damage across landscapes result from differences in wind exposure and stand characteristics, such as tree stature, species traits, successional age, and fragmentation. Wind disturbance has important consequences for biomass dynamics in Amazonian forests, and understanding the spatial distribution and size of impacts is necessary to quantify the effects on carbon dynamics. In November 2013, a mesoscale convective system was observed over the study area in Ucayali, Peru, a highly human modified and fragmented forest landscape. We mapped downfall damage associated with the storm in order to ask: how does the severity of damage vary within forest patches, and across forest patches of different sizes and successional ages? We applied spectral mixture analysis to Landsat images from 2013 and 2014 to calculate the change in non-photosynthetic vegetation fraction after the storm, and combined it with C-band SAR data from the Sentinel-1 satellite to predict downfall damage measured in 30 field plots using random forest regression. We then applied this model to map damage in forests across the study area. Using a land cover classification developed in a previous study, we mapped secondary and mature forest, and compared the severity of damage in the two. We found that damage was on average higher in secondary forests, but patterns varied spatially. This study demonstrates the utility of using multiple sources of satellite data for mapping wind disturbance, and adds to our understanding of the sources of variation in wind-related damage. Ultimately, an improved ability to map wind impacts and a better understanding of their spatial patterns can contribute to better quantification of carbon dynamics in Amazonian landscapes.

Identification of wood energy resources in central Michigan

NASA Technical Reports Server (NTRS)

Hudson, W. D.; Kittleson, K.

1978-01-01

Existing biomass studies were compiled for determining their applicability in measuring forest biomass in an entirely new way. Over sixty tree-weight tables were prepared from existing tables or formulas. An estimate of forest biomass was made on a defined area by using Landsat Satellite data analysis, existing forest cover type maps and actual weighting of the entire biomass. Control plots were cruised for normal volume data and weight data, harvested and weighed to determine actual tonnage yields.

Estimated carbon emission from recent rapid forest loss in Southeast Asia

NASA Astrophysics Data System (ADS)

Chen, A.; Zeng, Z.; Peng, L.; Fei, S.

2017-12-01

Driven by agricultural expansion, industrial logging, oil palm and rubber plantations, and urbanization, Southeast Asia (SEA) is one of the hotspots for tropical deforestation over recent decades. The extent of the tropical SEA deforestation rate, as well as its impacts on carbon cycle and biodiversity, however, is still highly uncertain. In relevant work using high resolution global maps of the 21st-century forest cover, we find tropical SEA lost 22 million hectares, or 9%, of forest area during 2000-2014, a much higher deforestation rate than previously reported. Here we further conduct research investigating carbon emissions from tropical deforestation in SEA with satellite data of forest cover, a global tropical forest biomass map, and Earth system models. Preliminary results suggest that deforestation in SEA causes about 2.8 Tg C emissions to the atmosphere during the same period, also higher than that of previous studies. Meanwhile, carbon emission from deforestation shows high variations across different countries, topography and between the insular and maritime SEA. Indonesia and Malaysia tops in both total carbon loss and loss from per unit land area. Our results indicates that previous studies have underestimated the carbon loss due to deforestation in SEA. And until further effective forest conservation measures can be adopted, tropical SEA will continue playing a role of atmospheric carbon source in the coming decades.

Merging IceSAT GLAS and Terra MODIS Data in Order to Derive Forest Type Specific Tree Heights in the Central Siberian Boreal Forest

NASA Technical Reports Server (NTRS)

Ranson, K. Jon; Sun, Guoqing; Kimes, Daniel; Kovacs, Katalin; Kharuk, Viatscheslav

2006-01-01

Mapping of boreal forest's type, biomass, and other structural parameters are critical for understanding of the boreal forest's significance in the carbon cycle, its response to and impact on global climate change. We believe the nature of the forest structure information available from MISR and GLAS can be used to help identify forest type, age class, and estimate above ground biomass levels beyond that now possible with MODIS alone. The ground measurements will be used to develop relationships between remote sensing observables and forest characteristics and provide new information for understanding forest changes with respect to environmental change. Lidar is a laser altimeter that determines the distance from the instrument to the physical surface by measuring the time elapsed between the pulse emission and the reflected return. Other studies have shown that the returned signal may identify multiple returns originating from trees, building and other objects and permits the calculation of their height. Studies using field data have shown that lidar data can provide estimates of structural parameters such as biomass, stand volume and leaf area index and allows remarkable differentiation between primary and secondary forest. NASA's IceSAT Geoscience Laser Altimeter System (GLAS) was launched in January 2003 and collected data during February and September of that year. This study used data acquired over our study sites in central Siberia to examine the GLAS signal as a source of forest height and other structural characteristics. The purpose of our Siberia project is to improve forest cover maps and produce above-ground biomass maps of the boreal forest in Northern Eurasia from MODIS by incorporating structural information inherent in the Terra MISR and ICESAT Geoscience Laser Altimeter System (GLAS) instruments. A number of forest cover classifications exist for the boreal forest. We believe the limiting factor in these products is the lack of structural information, particularly in the vertical dimension. The emphasis of this project is to improve upon satellite maps of boreal forest structure parameters (i.e. height and biomass) using temporal, multi-angle, and vertical profile information of GLAS data. The existing and near future lidar data is useful for demonstrating these techniques and pursuing current estimates. Future lidar missions may be several years in the future, so we will work other new data sets that may aide in biomass estimates such as ALOS PALSAR We will continue this work to produce an accurate map of current above ground forest phytomass/carbon storage possible for the study area. We plan to develop, test, and integrate remote sensing methods for extracting forest canopy structure measures. We are compiling our field measurements and will compare them with the remote sensing methods where possible. We also be able to produce a realistic error bound on the remotely sensed carbon estimates.

Spatio-temporal analysis on land transformation in a forested tropical landscape in Jambi Province, Sumatra

NASA Astrophysics Data System (ADS)

Melati, Dian N.; Nengah Surati Jaya, I.; Pérez-Cruzado, César; Zuhdi, Muhammad; Fehrmann, Lutz; Magdon, Paul; Kleinn, Christoph

2015-04-01

Land use/land cover (LULC) in forested tropical landscapes is very dynamically developing. In particular, the pace of forest conversion in the tropics is a global concern as it directly impacts the global carbon cycle and biodiversity conservation. Expansion of agriculture is known to be among the major drivers of forest loss especially in the tropics. This is also the case in Jambi Province, Sumatra, Indonesia where it is the mainly expansion of tree crops that triggers deforestation: oil palm and rubber trees. Another transformation system in Jambi is the one from natural forest into jungle rubber, which is an agroforestry system where a certain density of forest trees accompanies the rubber tree crop, also for production of wood and non-wood forest products. The spatial distribution and the dynamics of these transformation systems and of the remaining forests are essential information for example for further research on ecosystem services and on the drivers of land transformation. In order to study land transformation, maps from the years 1990, 2000, 2011, and 2013 were utilized, derived from visual interpretation of Landsat images. From these maps, we analyze the land use/land cover change (LULCC) in the study region. It is found that secondary dryland forest (on mineral soils) and secondary swamp forest have been transformed largely into (temporary) shrub land, plantation forests, mixed dryland agriculture, bare lands and estate crops where the latter include the oil palm and rubber plantations. In addition, we present some analyses of the spatial pattern of land transformation to better understand the process of LULC fragmentation within the studied periods. Furthermore, the driving forces are analyzed.

A 30-meter spatial database for the nation's forests

Treesearch

Raymond L. Czaplewski

2002-01-01

The FIA vision for remote sensing originated in 1992 with the Blue Ribbon Panel on FIA, and it has since evolved into an ambitious performance target for 2003. FIA is joining a consortium of Federal agencies to map the Nation's land cover. FIA field data will help produce a seamless, standardized, national geospatial database for forests at the scale of 30-m...

Selective Cutting Impact on Carbon Storage in Fremont-Winema National Forest, Oregon

NASA Astrophysics Data System (ADS)

Huybrechts, C.; Cleve, C. T.

2004-12-01

Management personnel of the Fremont-Winema National Forest in southern Oregon were interested in investigating how selective cutting or fuel load reduction treatments affect forest carbon sinks and as an ancillary product, fire risk. This study was constructed with the objective of providing this information to the forest administrators, as well as to satisfy a directive to study carbon management, a component of the 2004 NASA's Application Division Program Plan. During the summer of 2004, a request for decision support tools by the forest management was addressed by a NASA sponsored student-led, student-run internship group called DEVELOP. This full-time10-week program was designed to be an introduction to work done by earth scientists, professional business / client relationships and the facilities available at NASA Ames. Four college and graduate students from varying educational backgrounds designed the study and implementation plan. The team collected data for five consecutive days in Oregon throughout the Fremont-Winema forest and the surrounding terrain, consisting of soil sampling for underground carbon dynamics, fire model and vegetation map validation. The goal of the carbon management component of the project was to model current carbon levels, then to gauge the effect of fuel load reduction treatments. To study carbon dynamics, MODIS derived fraction photosynthetically active radiation (FPAR) maps, regional climate data, and Landsat 5 generated dominant vegetation species and land cover maps were used in conjunction with the NASA - Carnegie-Ames-Stanford-Approach (CASA) model. To address fire risk the dominant vegetation species map was used to estimate fuel load based on species biomass in conjunction with a mosaic of digital elevation models (DEMs) as components to the creation of an Anderson-inspired fuel map, a rate of spread in meters/minute map and a flame length map using ArcMap 9 and FlamMap. Fire risk results are to be viewed qualitatively as maps output spatial distribution of data rather then quantitative assessment of risk. For the first time ever, the resource managers at the Fremont-Winema forest will be taking into consideration the value of carbon as a resource in their decision making process for the 2005 Fremont-Winema forest management plan.

Strata-based forest fuel classification for wild fire hazard assessment using terrestrial LiDAR

NASA Astrophysics Data System (ADS)

Chen, Yang; Zhu, Xuan; Yebra, Marta; Harris, Sarah; Tapper, Nigel

2016-10-01

Fuel structural characteristics affect fire behavior including fire intensity, spread rate, flame structure, and duration, therefore, quantifying forest fuel structure has significance in understanding fire behavior as well as providing information for fire management activities (e.g., planned burns, suppression, fuel hazard assessment, and fuel treatment). This paper presents a method of forest fuel strata classification with an integration between terrestrial light detection and ranging (LiDAR) data and geographic information system for automatically assessing forest fuel structural characteristics (e.g., fuel horizontal continuity and vertical arrangement). The accuracy of fuel description derived from terrestrial LiDAR scanning (TLS) data was assessed by field measured surface fuel depth and fuel percentage covers at distinct vertical layers. The comparison of TLS-derived depth and percentage cover at surface fuel layer with the field measurements produced root mean square error values of 1.1 cm and 5.4%, respectively. TLS-derived percentage cover explained 92% of the variation in percentage cover at all fuel layers of the entire dataset. The outcome indicated TLS-derived fuel characteristics are strongly consistent with field measured values. TLS can be used to efficiently and consistently classify forest vertical layers to provide more precise information for forest fuel hazard assessment and surface fuel load estimation in order to assist forest fuels management and fire-related operational activities. It can also be beneficial for mapping forest habitat, wildlife conservation, and ecosystem management.

A landscape indicator approach to the identification and articulation of the consequences of land-cover change in the Mid-Atlantic Region, 1973-2001

USGS Publications Warehouse

Slonecker, E. Terrence; Milheim, Lesley E.; Claggett, Peter

2009-01-01

Landscape indicators, derived from land-use and land-cover data, hydrology, nitrate deposition, and elevation data, were used by Jones and others (2001a) to calculate the ecological consequences of land-cover change. Nitrate loading and physical bird habitat were modeled from 1973 and 1992 land-cover and other spatial data for the Mid-Atlantic region. Utilizing the same methods, this study extends the analysis another decade with the use of the 2001 National Land Cover Dataset. Land-cover statistics and trends are calculated for three time periods: 1973-1992, 1992-2001 and 1973-2001. In addition, high-resolution aerial photographs (1 meter or better ground-sample distance) were acquired and analyzed for thirteen pairs of adjacent USGS 7.5 minute quadrangle maps in areas where distinct positive or negative changes to nitrogen loading and bird habitat were previously calculated. During the entire 30 year period, the data show that there was extensive loss of agriculture and forest area and a major increase in urban land-cover classes. However, the majority of the conversion of other classes to urban occurred during the 1992-2001 period. During the 1973-1992 period, there was only moderate increase in urban area, while there was an inverse relationship between agricultural change and forest change. In general, forest gain and agricultural loss was found in areas of improving landscape indicators, and forest loss and agricultural gain was found to occur in areas of declining indicators related to habitat and nitrogen loadings, which was generally confirmed by the aerial photographic analysis. In terms of the specific model results, bird habitat, which is mainly related to the extent of forest cover, declined overall with forest extent, but was also affected more in the decline of habitat quality. Nitrate loading, which is mainly related to agricultural land cover actually improved from 1992-2001, and in the overall study, mainly due to the conversion of agriculture to forests and urban. The high-resolution imagery analysis was significant in that it confirmed, at a very local level, the specific land-cover changes that were driving the landscape metrics and model results that were calculated from moderate resolution land-cover data and models. These were generally subtle changes in patch size of agriculture, forest, and urban areas, but had substantial effects on bird habitat and nitrogen loadings. This analysis of high-resolution imagery demonstrates and confirms the important ability of moderate-resolution land-cover data to capture significant landscape-level activity that is directly related to specific metrics of ecological significance. It also demonstrates consistent landscape-scale relationships between data derived from high-resolution, moderate-resolution and landscape-model sources. Finally, many of the areas of improvement and decline in bird habitat and nitrogen loadings appear to be potentially regional in nature and likely reflect some local trend in landscape activity. Although the use of ecoregions as sampling units has been criticized in recent years, these results show that basic changes in Level 1 land-cover categories, such as forest and agriculture, may still reflect ecoregional patterns and considerations at some scale of mapping and analysis. This is a potentially important area for future landscape-indicator research. This and other follow-on research opportunities are discussed.

Proposal for a study of computer mapping of terrain using multispectral data from ERTS-A for the Yellowstone National Park test site

NASA Technical Reports Server (NTRS)

Smedes, H. W. (Principal Investigator); Root, R. R.; Roller, N. E. G.; Despain, D.

1978-01-01

The author has identified the following significant results. A terrain map of Yellowstone National Park showed plant community types and other classes of ground cover in what is basically a wild land. The map comprised 12 classes, six of which were mapped with accuracies of 70 to 95%. The remaining six classes had spectral reflectances that overlapped appreciably, and hence, those were mapped less accurately. Techniques were devised for quantitatively comparing the recognition map of the park with control data acquired from ground inspection and from analysis of sidelooking radar images, a thermal IR mosaic, and IR aerial photos of several scales. Quantitative analyses were made in ten 40 sq km test areas. Comparison mechanics were performed by computer with the final results displayed on line printer output. Forested areas were mapped by computer using ERTS data for less than 1/4 the cost of the conventional forest mapping technique for topographic base maps.

Land cover change interacts with drought severity to change fire regimes in Western Amazonia.

PubMed

Gutiérrez-Vélez, Víctor H; Uriarte, María; DeFries, Ruth; Pinedo-Vásquez, Miguel; Fernandes, Katia; Ceccato, Pietro; Baethgen, Walter; Padoch, Christine

Fire is becoming a pervasive driver of environmental change in Amazonia and is expected to intensify, given projected reductions in precipitation and forest cover. Understanding of the influence of post-deforestation land cover change on fires in Amazonia is limited, even though fires in cleared lands constitute a threat for ecosystems, agriculture, and human health. We used MODIS satellite data to map burned areas annually between 2001 and 2010. We then combined these maps with land cover and climate information to understand the influence of land cover change in cleared lands and dry-season severity on fire occurrence and spread in a focus area in the Peruvian Amazon. Fire occurrence, quantified as the probability of burning of individual 232-m spatial resolution MODIS pixels, was modeled as a function of the area of land cover types within each pixel, drought severity, and distance to roads. Fire spread, quantified as the number of pixels burned in 3 × 3 pixel windows around each focal burned pixel, was modeled as a function of land cover configuration and area, dry-season severity, and distance to roads. We found that vegetation regrowth and oil palm expansion are significantly correlated with fire occurrence, but that the magnitude and sign of the correlation depend on drought severity, successional stage of regrowing vegetation, and oil palm age. Burning probability increased with the area of nondegraded pastures, fallow, and young oil palm and decreased with larger extents of degraded pastures, secondary forests, and adult oil palm plantations. Drought severity had the strongest influence on fire occurrence, overriding the effectiveness of secondary forests, but not of adult plantations, to reduce fire occurrence in severely dry years. Overall, irregular and scattered land cover patches reduced fire spread but irregular and dispersed fallows and secondary forests increased fire spread during dry years. Results underscore the importance of land cover management for reducing fire proliferation in this landscape. Incentives for promoting natural regeneration and perennial crops in cleared lands might help to reduce fire risk if those areas are protected against burning in early stages of development and during severely dry years.

Mapping and Monitoring Delmarva Fox Squirrel Habitat Using an Airborne LiDAR Profiler

NASA Technical Reports Server (NTRS)

Nelson, Ross; Ratnaswamy, Mary; Keller, Cherry

2004-01-01

Twenty five hundred thirty nine kilometers of airborne laser profiling and videography data were acquired over the state of Delaware during the summer of 2000. The laser ranging measurements and video from approximately one-half of that data set (1304 km) were analyzed to identify and locate forested sites that might potentially support populations of Delmarva fox squirrel (DFS, Sciurus niger cinereus). The DFS is an endangered species previously endemic to tall, dense, mature forests with open understories on the Eastern Shore of the Chesapeake Bay. The airborne LiDAR employed in this study can measure forest canopy height and canopy closure, but cannot measure or infer understory canopy conditions. Hence the LiDAR must be viewed as a tool to map potential, not actual, habitat. Fifty-three potentially suitable DFS sites were identified in the 1304 km of flight transect data. Each of the 53 sites met the following criteria according to the LiDAR and video record: (1 ) at least 120m of contiguous forest; (2) an average canopy height greater than 20m; (3) an average canopy closure of >80%; and (4) no roofs, impervious surface (e.g., asphalt, concrete), and/or open water anywhere along the 120m length of the laser segment. Thirty-two of the 53 sites were visited on the ground and measurements taken for a DFS habitat suitability model. Seventy eight percent of the sites (25 of 32) were judged by the model to be suited to supporting a DFS population. Twenty-eight of the 32 sites visited in the field were in forest cover types (hardwood, mixed wood, conifer, wetlands) according to a land cover GIS map. Of these, 23 (82%) were suited to support DFS. The remaining 4 sites were located in nonforest cover types - agricultural or residential areas. Two of the four, or 50% were suited to the DFS. All of the LiDAR flight data, 2539 km, were analyzed to

Mapping the invasive species, Chinese tallow, with EO1 satellite Hyperion hyperspectral image data and relating tallow occurrences to a classified Landsat Thematic Mapper land cover map

USGS Publications Warehouse

Ramsey, Elijah W.; Rangoonwala, A.; Nelson, G.; Ehrlich, R.

2005-01-01

Our objective was to provide a realistic and accurate representation of the spatial distribution of Chinese tallow (Triadica sebifera) in the Earth Observing 1 (EO1) Hyperion hyperspectral image coverage by using methods designed and tested in previous studies. We transformed, corrected, and normalized Hyperion reflectance image data into composition images with a subpixel extraction model. Composition images were related to green vegetation, senescent foliage and senescing cypress-tupelo forest, senescing Chinese tallow with red leaves ('red tallow'), and a composition image that only corresponded slightly to yellowing vegetation. These statistical and visual comparisons confirmed a successful portrayal of landscape features at the time of the Hyperion image collection. These landscape features were amalgamated in the Landsat Thematic Mapper (TM) pixel, thereby preventing the detection of Chinese tallow occurrences in the Landsat TM classification. With the occurrence in percentage of red tallow (as a surrogate for Chinese tallow) per pixel mapped, we were able to link dominant land covers generated with Landsat TM image data to Chinese tallow occurrences as a first step toward determining the sensitivity and susceptibility of various land covers to tallow establishment. Results suggested that the highest occurrences and widest distribution of red tallow were (1) apparent in disturbed or more open canopy woody wetland deciduous forests (including cypress-tupelo forests), upland woody land evergreen forests (dominantly pines and seedling plantations), and upland woody land deciduous and mixed forests; (2) scattered throughout the fallow fields or located along fence rows separating active and non-active cultivated and grazing fields, (3) found along levees lining the ubiquitous canals within the marsh and on the cheniers near the coastline; and (4) present within the coastal marsh located on the numerous topographic highs. ?? 2005 US Government.

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.

Choosing appropriate subpopulations for modeling tree canopy cover nationwide

Treesearch

Gretchen G. Moisen; John W. Coulston; Barry T. Wilson; Warren B. Cohen; Mark V. Finco

2012-01-01

In prior national mapping efforts, the country has been divided into numerous ecologically similar mapping zones, and individual models have been constructed for each zone. Additionally, a hierarchical approach has been taken within zones to first mask out areas of nonforest, then target models of tree attributes within forested areas only. This results in many models...

Spot-mapping underestimates song-territory size and use of mature forest by breeding golden-winged warblers in Minnesota, USA

USGS Publications Warehouse

Streby, Henry M.; Loegering, John P.; Andersen, David E.

2012-01-01

Studies of songbird breeding habitat often compare habitat characteristics of used and unused areas. Although there is usually meticulous effort to precisely and consistently measure habitat characteristics, accuracy of methods for estimating which areas are used versus which are unused by birds remains generally untested. To examine accuracy of spot-mapping to identify singing territories of golden-winged warblers (Vermivora chrysoptera), which are considered an early successional forest specialists, we used spot-mapping and radiotelemetry to record song perches and delineate song territories for breeding male golden-winged warblers in northwestern Minnesota, USA. We also used radiotelemetry to record locations (song and nonsong perches) of a subsample (n = 12) of males throughout the day to delineate home ranges. We found that telemetry-based estimates of song territories were 3 times larger and included more mature forest than those estimated from spot-mapping. In addition, home ranges estimated using radiotelemetry included more mature forest than spot-mapping- and telemetry-based song territories, with 75% of afternoon perches located in mature forest. Our results suggest that mature forest comprises a larger component of golden-winged warbler song territories and home ranges than is indicated based on spot-mapping in Minnesota. Because it appears that standard observational methods can underestimate territory size and misidentify cover-type associations for golden-winged warblers, we caution that management and conservation plans may be misinformed, and that similar studies are needed for golden-winged warblers across their range and for other songbird species.

Mapping Land Cover Types in Amazon Basin Using 1km JERS-1 Mosaic

NASA Technical Reports Server (NTRS)

Saatchi, Sassan S.; Nelson, Bruce; Podest, Erika; Holt, John

2000-01-01

In this paper, the 100 meter JERS-1 Amazon mosaic image was used in a new classifier to generate a I km resolution land cover map. The inputs to the classifier were 1 km resolution mean backscatter and seven first order texture measures derived from the 100 m data by using a 10 x 10 independent sampling window. The classification approach included two interdependent stages: 1) a supervised maximum a posteriori Bayesian approach to classify the mean backscatter image into 5 general land cover categories of forest, savannah, inundated, white sand, and anthropogenic vegetation classes, and 2) a texture measure decision rule approach to further discriminate subcategory classes based on taxonomic information and biomass levels. Fourteen classes were successfully separated at 1 km scale. The results were verified by examining the accuracy of the approach by comparison with the IBGE and the AVHRR 1 km resolution land cover maps.

Multi-Sensor Characterization of the Boreal Forest: Initial Findings

NASA Technical Reports Server (NTRS)

Reith, Ernest; Roberts, Dar A.; Prentiss, Dylan

2001-01-01

Results are presented in an initial apriori knowledge approach toward using complementary multi-sensor multi-temporal imagery in characterizing vegetated landscapes over a site in the Boreal Ecosystem-Atmosphere Study (BOREAS). Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Airborne Synthetic Aperture Radar (AIRSAR) data were segmented using multiple endmember spectral mixture analysis and binary decision tree approaches. Individual date/sensor land cover maps had overall accuracies between 55.0% - 69.8%. The best eight land cover layers from all dates and sensors correctly characterized 79.3% of the cover types. An overlay approach was used to create a final land cover map. An overall accuracy of 71.3% was achieved in this multi-sensor approach, a 1.5% improvement over our most accurate single scene technique, but 8% less than the original input. Black spruce was evaluated to be particularly undermapped in the final map possibly because it was also contained within jack pine and muskeg land coverages.

Representative landscapes in the forested area of Canada.

PubMed

Cardille, Jeffrey A; White, Joanne C; Wulder, Mike A; Holland, Tara

2012-01-01

Canada is a large nation with forested ecosystems that occupy over 60% of the national land base, and knowledge of the patterns of Canada's land cover is important to proper environmental management of this vast resource. To this end, a circa 2000 Landsat-derived land cover map of the forested ecosystems of Canada has created a new window into understanding the composition and configuration of land cover patterns in forested Canada. Strategies for summarizing such large expanses of land cover are increasingly important, as land managers work to study and preserve distinctive areas, as well as to identify representative examples of current land-cover and land-use assemblages. Meanwhile, the development of extremely efficient clustering algorithms has become increasingly important in the world of computer science, in which billions of pieces of information on the internet are continually sifted for meaning for a vast variety of applications. One recently developed clustering algorithm quickly groups large numbers of items of any type in a given data set while simultaneously selecting a representative-or "exemplar"-from each cluster. In this context, the availability of both advanced data processing methods and a nationally available set of landscape metrics presents an opportunity to identify sets of representative landscapes to better understand landscape pattern, variation, and distribution across the forested area of Canada. In this research, we first identify and provide context for a small, interpretable set of exemplar landscapes that objectively represent land cover in each of Canada's ten forested ecozones. Then, we demonstrate how this approach can be used to identify flagship and satellite long-term study areas inside and outside protected areas in the province of Ontario. These applications aid our understanding of Canada's forest while augmenting its management toolbox, and may signal a broad range of applications for this versatile approach.

Representative Landscapes in the Forested Area of Canada

NASA Astrophysics Data System (ADS)

Cardille, Jeffrey A.; White, Joanne C.; Wulder, Mike A.; Holland, Tara

2012-01-01

Canada is a large nation with forested ecosystems that occupy over 60% of the national land base, and knowledge of the patterns of Canada's land cover is important to proper environmental management of this vast resource. To this end, a circa 2000 Landsat-derived land cover map of the forested ecosystems of Canada has created a new window into understanding the composition and configuration of land cover patterns in forested Canada. Strategies for summarizing such large expanses of land cover are increasingly important, as land managers work to study and preserve distinctive areas, as well as to identify representative examples of current land-cover and land-use assemblages. Meanwhile, the development of extremely efficient clustering algorithms has become increasingly important in the world of computer science, in which billions of pieces of information on the internet are continually sifted for meaning for a vast variety of applications. One recently developed clustering algorithm quickly groups large numbers of items of any type in a given data set while simultaneously selecting a representative—or "exemplar"—from each cluster. In this context, the availability of both advanced data processing methods and a nationally available set of landscape metrics presents an opportunity to identify sets of representative landscapes to better understand landscape pattern, variation, and distribution across the forested area of Canada. In this research, we first identify and provide context for a small, interpretable set of exemplar landscapes that objectively represent land cover in each of Canada's ten forested ecozones. Then, we demonstrate how this approach can be used to identify flagship and satellite long-term study areas inside and outside protected areas in the province of Ontario. These applications aid our understanding of Canada's forest while augmenting its management toolbox, and may signal a broad range of applications for this versatile approach.

Cloud and fog interactions with coastal forests in the California Channel Islands

NASA Astrophysics Data System (ADS)

Still, C. J.; Baguskas, S. A.; Williams, P.; Fischer, D. T.; Carbone, M. S.; Rastogi, B.

2015-12-01

Coastal forests in California are frequently covered by clouds or immersed in fog in the rain-free summer. Scientists have long surmised that fog might provide critical water inputs to these forests. However, until recently, there has been little ecophysiological research to support how or why plants should prefer foggy regions; similarly, there is very little work quantifying water delivered to ecosystems by fog drip except for a few notable sites along the California coast. However, without spatial datasets of summer cloudcover and fog inundation, combined with detailed process studies, questions regarding the roles of cloud shading and fog drip in dictating plant distributions and ecosystem physiology cannot be addressed effectively. The overall objective of this project is to better understand how cloudcover and fog influence forest metabolism, growth, and distribution. Across a range of sites in California's Channel Islands National Park we measured a wide variety of ecosystem processes and properties. We then related these to cloudcover and fog immersion maps created using satellite datasets and airport and radiosonde observations. We compiled a spatially continuous dataset of summertime cloudcover frequency of the Southern California bight using satellite imagery from the NOAA geostationary GOES-11 Imager. We also created map of summertime cloudcover frequency of this area using MODIS imagery. To assess the ability of our mapping approach to predict spatial and temporal fog inundation patterns, we compared our monthly average daytime fog maps for GOES pixels corresponding to stations where fog inputs were measured with fog collectors in a Bishop pine forest. We also compared our cloudcover maps to measurements of irradiance measurements. Our results demonstrate that cloudcover and fog strongly modulate radiation, water, and carbon budgets, as well as forest distributions, in this semi-arid environment. Measurements of summertime fog drip, pine sapflow and growth, and soil respiration are strongly related to variations in cloudcover and fog drip. Importantly, spatial variations in cloud cover and fog immersion drive large changes in modeled water budgets and correspond closely to patterns of tree growth and mortality.

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.

Intercomparison of Satellite-Derived Snow-Cover Maps

NASA Technical Reports Server (NTRS)

Hall, Dorothy K.; Tait, Andrew B.; Foster, James L.; Chang, Alfred T. C.; Allen, Milan

1999-01-01

In anticipation of the launch of the Earth Observing System (EOS) Terra, and the PM-1 spacecraft in 1999 and 2000, respectively, efforts are ongoing to determine errors of satellite-derived snow-cover maps. EOS Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-E (AMSR-E) snow-cover products will be produced. For this study we compare snow maps covering the same study area acquired from different sensors using different snow- mapping algorithms. Four locations are studied: 1) southern Saskatchewan; 2) a part of New England (New Hampshire, Vermont and Massachusetts) and eastern New York; 3) central Idaho and western Montana; and 4) parts of North and South Dakota. Snow maps were produced using a prototype MODIS snow-mapping algorithm used on Landsat Thematic Mapper (TM) scenes of each study area at 30-m and when the TM data were degraded to 1 -km resolution. National Operational Hydrologic Remote Sensing Center (NOHRSC) 1 -km resolution snow maps were also used, as were snow maps derived from 1/2 deg. x 1/2 deg. resolution Special Sensor Microwave Imager (SSM/1) data. A land-cover map derived from the International Geosphere-Biosphere Program (IGBP) land-cover map of North America was also registered to the scenes. The TM, NOHRSC and SSM/I snow maps, and land-cover maps were compared digitally. In most cases, TM-derived maps show less snow cover than the NOHRSC and SSM/I maps because areas of incomplete snow cover in forests (e.g., tree canopies, branches and trunks) are seen in the TM data, but not in the coarser-resolution maps. The snow maps generally agree with respect to the spatial variability of the snow cover. The 30-m resolution TM data provide the most accurate snow maps, and are thus used as the baseline for comparison with the other maps. Comparisons show that the percent change in amount of snow cover relative to the 3 0-m resolution TM maps is lowest using the TM I -km resolution maps, ranging from 0 to 40%. The highest percent change (less than 100%) is found in the New England study area, probably due to the presence of patchy snow cover. A scene with patchy snow cover is more difficult to map accurately than is a scene with a well-defined snowline such as is found on the North and South Dakota scene where the percent change ranged from 0 to 40%. There are also some important differences in the amount of snow mapped using the two different SSM/I algorithms because they utilize different channels.

Comparing forest fragmentation and its drivers in China and the USA with Globcover v2.2

USGS Publications Warehouse

Chen, Mingshi; Mao, Lijun; Zhou, Chunguo; Vogelmann, James E.; Zhu, Zhiliang

2010-01-01

Forest loss and fragmentation are of major concern to the international community, in large part because they impact so many important environmental processes. The main objective of this study was to assess the differences in forest fragmentation patterns and drivers between China and the conterminous United States (USA). Using the latest 300-m resolution global land cover product, Globcover v2.2, a comparative analysis of forest fragmentation patterns and drivers was made. The fragmentation patterns were characterized by using a forest fragmentation model built on the sliding window analysis technique in association with landscape indices. Results showed that China’s forests were substantially more fragmented than those of the USA. This was evidenced by a large difference in the amount of interior forest area share, with China having 48% interior forest versus the 66% for the USA. China’s forest fragmentation was primarily attributed to anthropogenic disturbances, driven particularly by agricultural expansion from an increasing and large population, as well as poor forest management practices. In contrast, USA forests were principally fragmented by natural land cover types. However, USA urban sprawl contributed more to forest fragmentation than in China. This is closely tied to the USA’s economy, lifestyle and institutional processes. Fragmentation maps were generated from this study, which provide valuable insights and implications regarding habitat planning for rare and endangered species. Such maps enable development of strategic plans for sustainable forest management by identifying areas with high amounts of human-induced fragmentation, which improve risk assessments and enable better targeting for protection and remediation efforts. Because forest fragmentation is a long-term, complex process that is highly related to political, institutional, economic and philosophical arenas, both nations need to take effective and comprehensive measures to mitigate the negative effects of forest loss and fragmentation on the existing forest ecosystems.

Comparing forest fragmentation and its drivers in China and the USA with Globcover v2.2.

PubMed

Li, Mingshi; Mao, Lijun; Zhou, Chunguo; Vogelmann, James E; Zhu, Zhiliang

2010-12-01

Forest loss and fragmentation are of major concern to the international community, in large part because they impact so many important environmental processes. The main objective of this study was to assess the differences in forest fragmentation patterns and drivers between China and the conterminous United States (USA). Using the latest 300-m resolution global land cover product, Globcover v2.2, a comparative analysis of forest fragmentation patterns and drivers was made. The fragmentation patterns were characterized by using a forest fragmentation model built on the sliding window analysis technique in association with landscape indices. Results showed that China's forests were substantially more fragmented than those of the USA. This was evidenced by a large difference in the amount of interior forest area share, with China having 48% interior forest versus the 66% for the USA. China's forest fragmentation was primarily attributed to anthropogenic disturbances, driven particularly by agricultural expansion from an increasing and large population, as well as poor forest management practices. In contrast, USA forests were principally fragmented by natural land cover types. However, USA urban sprawl contributed more to forest fragmentation than in China. This is closely tied to the USA's economy, lifestyle and institutional processes. Fragmentation maps were generated from this study, which provide valuable insights and implications regarding habitat planning for rare and endangered species. Such maps enable development of strategic plans for sustainable forest management by identifying areas with high amounts of human-induced fragmentation, which improve risk assessments and enable better targeting for protection and remediation efforts. Because forest fragmentation is a long-term, complex process that is highly related to political, institutional, economic and philosophical arenas, both nations need to take effective and comprehensive measures to mitigate the negative effects of forest loss and fragmentation on the existing forest ecosystems. Copyright © 2010 Elsevier Ltd. All rights reserved.

Satellite assessment of increasing tree cover 1982-2016

NASA Astrophysics Data System (ADS)

Song, X. P.; Hansen, M.

2017-12-01

The Earth's vegetation has undergone dramatic changes as we enter the Anthropocene. Recent studies have quantified global forest cover dynamics and resulting biogeochemical and biophysical impacts to the climate for the post-2000 time period. However, long-term gradual changes in undisturbed forests are less well quantified. We mapped annual tree cover using satellite data and quantified tree cover change during 1982-2016. The dataset was produced by combining optical observations from multiple satellite sensors, including the Advanced Very High Resolution Radiometer, the Moderate Resolution Imaging Spectroradiometer, the Landsat Enhanced Thematic Mapper Plus and various very high spatial resolution sensors. Contrary to current understanding of forest area change, global tree cover increased by 7%. The overall net gain in tree cover is a result of net loss in the tropics overweighed by net gain in the subtropical, temperate and boreal zones. All mountain systems, regardless of climate domain, experienced increases in tree cover. Regional patterns of tree cover gain including eastern United States, eastern Europe and southern China, indicate profound influences of socioeconomic, political or land management changes in shaping long-term environmental change. Results provide the first comprehensive record of global tree cover dynamics over the past four decades and may be used to reduce uncertainties in the quantification of the global carbon cycle.

A New Synthetic Global Biomass Carbon Map for the year 2010

NASA Astrophysics Data System (ADS)

Spawn, S.; Lark, T.; Gibbs, H.

2017-12-01

Satellite technologies have facilitated a recent boom in high resolution, large-scale biomass estimation and mapping. These data are the input into a wide range of global models and are becoming the gold standard for required national carbon (C) emissions reporting. Yet their geographical and/or thematic scope may exclude some or all parts of a given country or region. Most datasets tend to focus exclusively on forest biomass. Grasslands and shrublands generally store less C than forests but cover nearly twice as much global land area and may represent a significant portion of a given country's biomass C stock. To address these shortcomings, we set out to create synthetic, global above- and below-ground biomass maps that combine recently-released satellite based data of standing forest biomass with novel estimates for non-forest biomass stocks that are typically neglected. For forests we integrated existing publicly available regional, global and biome-specific biomass maps and modeled below ground biomass using empirical relationships described in the literature. For grasslands, we developed models for both above- and below-ground biomass based on NPP, mean annual temperature and precipitation to extrapolate field measurements across the globe. Shrubland biomass was extrapolated from existing regional biomass maps using environmental factors to generate the first global estimate of shrub biomass. Our new synthetic map of global biomass carbon circa 2010 represents an update to the IPCC Tier-1 Global Biomass Carbon Map for the Year 2000 (Ruesch and Gibbs, 2008) using the best data currently available. In the absence of a single seamless remotely sensed map of global biomass, our synthetic map provides the only globally-consistent source of comprehensive biomass C data and is valuable for land change analyses, carbon accounting, and emissions modeling.

Wildland inventory and resource modeling for Douglas and Carson City Counties, Nevada, using LANDSAT and digital terrain data

NASA Technical Reports Server (NTRS)

Brass, J. A.; Likens, W. C.; Thornhill, R. R.

1983-01-01

The potential of using LANDSAT satellite imagery to map and inventory pinyon-juniper desert forest types in Douglas and Carson City Counties, Nevada was demonstrated. Specific map and statistical products produced include land cover, mechanical operations capability, big game winter range habitat, fire hazard, and forest harvestability. The Nevada Division of Forestry determined that LANDSAT can produce a reliable and low-cost resource data. Added benefits become apparent when the data are linked to a geographical information system (GIS) containing existing ownership, planning, elevation, slope, and aspect information.

Developing New Coastal Forest Restoration Products Based on Landsat, ASTER, and MODIS Data

NASA Technical Reports Server (NTRS)

Spruce, Joseph P.; Graham, William; Smoot, James

2009-01-01

This paper discusses an ongoing effort to develop new geospatial information products for aiding coastal forest restoration and conservation efforts in coastal Louisiana and Mississippi. This project employs Landsat, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data in conjunction with airborne elevation data to compute coastal forest cover type maps and change detection products. Improved forest mapping products are needed to aid coastal forest restoration and management efforts of State and Federal agencies in the Northern Gulf of Mexico (NGOM) region. In particular, such products may aid coastal forest land acquisition and conservation easement procurements. This region's forests are often disturbed and subjected to multiple biotic and abiotic threats, including subsidence, salt water intrusion, hurricanes, sea-level rise, insect-induced defoliation and mortality, altered hydrology, wildfire, and conversion to non-forest land use. In some cases, such forest disturbance has led to forest loss or loss of regeneration capacity. In response, a case study was conducted to assess and demonstrate the potential of satellite remote sensing products for improving forest type maps and for assessing forest change over the last 25 years. Change detection products are needed for assessing risks for specific priority coastal forest types, such as live oak and baldcypress-dominated forest. Preliminary results indicate Landsat time series data are capable of generating the needed forest type and change detection products. Useful classifications were obtained using 2 strategies: 1) general forest classification based on use of 3 seasons of Landsat data from the same year; and 2) classification of specific forest types of concern using a single date of Landsat data in which a given targeted type is spectrally distinct compared to adjacent forested cover. When available, ASTER data was useful as a complement to Landsat data. Elevation data helped to define areas in which targeted forest types occur, such as live oak forests on natural levees. MODIS Normalized Difference Vegetation Index time series data aided visual assessments of coastal forest damage and recovery from hurricanes. Landsat change detection products enabled change to be identified at the stand level and at 10- year intervals with the earliest date preceding available change detection products from the National Oceanic and Atmospheric Administration and from the U.S. Geological Survey. Additional work is being done in collaboration with State and Federal agency partners in a follow-on NASA ROSES project to refine and validate these new, promising products. The products from the ROSES project will be available for aiding NGOM coastal forest restoration and conservation.

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

NASA Technical Reports Server (NTRS)

Rudasill-Neigh, Christopher S.; Bolton, Douglas K.; Diabate, Mouhamad; Williams, Jennifer J.; Carvalhais, Nuno

2014-01-01

Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km2). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

Ecosystem services of boreal forests - Carbon budget mapping at high resolution.

PubMed

Akujärvi, Anu; Lehtonen, Aleksi; Liski, Jari

2016-10-01

The carbon (C) cycle of forests produces ecosystem services (ES) such as climate regulation and timber production. Mapping these ES using simple land cover -based proxies might add remarkable inaccuracy to the estimates. A framework to map the current status of the C budget of boreal forested landscapes was developed. The C stocks of biomass and soil and the annual change in these stocks were quantified in a 20 × 20 m resolution at the regional level on mineral soils in southern Finland. The fine-scale variation of the estimates was analyzed geo-statistically. The reliability of the estimates was evaluated by comparing them to measurements from the national multi-source forest inventory. The C stocks of forests increased slightly from the south coast to inland whereas the changes in these stocks were more uniform. The spatial patches of C stocks were larger than those of C stock changes. The patch size of the C stocks reflected the spatial variation in the environmental conditions, and that of the C stock changes the typical area of forest management compartments. The simulated estimates agreed well with the measurements indicating a good mapping framework performance. The mapping framework is the basis for evaluating the effects of forest management alternatives on C budget at high resolution across large spatial scales. It will be coupled with the assessment of other ES and biodiversity to study their relationships. The framework integrated a wide suite of simulation models and extensive inventory data. It provided reliable estimates of the human influence on C cycle in forested landscapes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Characterizing fragmentation of the collective forests in southern China from multitemporal Landsat imagery: A case study from Kecheng district of Zhejiang province

USGS Publications Warehouse

Li, M.; Zhu, Z.; Vogelmann, James E.; Xu, D.; Wen, W.; Liu, A.

2011-01-01

Tropical and subtropical forests provide important ecosystem goods and services including carbon sequestration and biodiversity conservation. These forests are facing increasing socioeconomic pressures and are rapidly being degraded and fragmented. This analysis focuses on the rate of change and patterns of fragmentation in a collective forest area in Zhejiang province, China, during the time period 1988–2005. The research consisted of two parts. The first was the development of general land cover maps and the identification of land cover changes by interpreting Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) time series imagery. The second part involved the computation and analysis of forest fragmentation metrics. For this portion of the study, fragmentation statistics were analyzed, and images were developed to depict forest fragmentation patterns and trends. Results revealed that there was a net loss of 7.8% in forest coverage, dropping from 66.8% in 1988 to 59.0% in 2005, primarily caused by agricultural expansion and poor forest management practices. An acceleration of forest fragmentation was also witnessed during the time intervals, which was evidenced by a decreasing trend in interior forest (57.2% in 1988, 55.0% in 1996 and 54.8% in 2005 respectively) coupled with the scales of the selected geospatial metrics. Continued forest loss and fragmentation are closely correlated with the existing political, educational, institutional and economic processes of contemporary China. To unlock the developmental potentials of the collective forests and to effectively mitigate the rate of forest loss and fragmentation, reforms of forest tenure and ecological immigration practices are recognized as a prospective alternative. The produced fragmentation maps further illustrates the importance of assessing landscape change history, especially the spatiotemporal patterns of forest fragments, when developing landscape level plans for biodiversity conservation, land use management and ecologically sustainable forestry.

25m-resolution Global Mosaic and Forest/Non-Forest map using PALSAR-2 data set

NASA Astrophysics Data System (ADS)

Itoh, T.; Shimada, M.; Motooka, T.; Hayashi, M.; Tadono, T.; DAN, R.; Isoguchi, O.; Yamanokuchi, T.

2017-12-01

A continuous observation of forests is important as information necessary for monitoring deforestation, climate change and environmental changes i.e. Reducing Emissions from Deforestation and Forest Degradation in Developing Countries (REDD+). Japan Aerospace Exploration Agency (JAXA) is conducting research on forest monitoring using satellite-based L-Band Synthetic Aperture Radars (SARs) continuously. Using the FBD (Fine Beam Dual polarizations) data of the Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS), JAXA created the global 25 m-resolution mosaic images and the Forest/Non-Forest (FNF) maps dataset for forest monitoring. SAR can monitor forest areas under all weather conditions, and L-band is highly sensitive to forests and their changes, therefore it is suitable for forest observation. JAXA also created the global 25 m mosaics and FNF maps using ALOS-2/PALSAR-2 launched on 2014 as a successor to ALOS. FNF dataset by PALSAR and PALSAR-2 covers from 2007 to 2010, and from 2015 to 2016, respectively. Therefore, it is possible to monitor forest changes during approx. 10 years. The classification method is combination of the object-based classification and the thresholding of HH and HV polarized images, and the result of FNF was compared with Forest Resource Assessment (FRA, developed by FAO) and their inconsistency is less than 10 %. Also, by comparing with the optical image of Google Earth, rate of coincidence was 80 % or more. We will create PALSAR-2 global mosaics and FNF dataset continuously to contribute global forest monitoring.

Assessing the Tundra-taiga Boundary with Multi-Sensor Satellite Data

NASA Technical Reports Server (NTRS)

Ranson, K. J.; Sun, G.; Kharuk, V. I.; Kovacs, K.

2004-01-01

Monitoring the dynamics of the circumpolar boreal forest (taiga) and Arctic tundra boundary is important for understanding the causes and consequences of changes observed in these areas. This ecotone, the world's largest, stretches for over 13,400 km and marks the transition between the northern limits of forests and the southern margin of the tundra. Because of the inaccessibility and large extent of this zone, remote sensing data can play an important role for mapping the characteristics and monitoring the dynamics. Basic understanding of the capabilities of existing space borne instruments for these purposes is required. In this study we examined the use of several remote sensing techniques for identifying the existing tundra- taiga ecotone. These include Landsat-7, MISR, MODIS and RADARSAT data. Historical cover maps, recent forest stand measurements and high-resolution IKONOS images were used for local ground truth. It was found that a tundra-taiga transitional area can be characterized using multi- spectral Landsat ETM+ summer images, multi-angle MISR red band reflectance images, RADARSAT images with larger incidence angle, or multi-temporal and multi-spectral MODIS data. Because of different resolutions and spectral regions covered, the transition zone maps derived from different data types were not identical, but the general patterns were consistent.

The impact of gold mining and agricultural concessions on the tree cover and local communities in northern Myanmar.

PubMed

Papworth, Sarah; Rao, Madhu; Oo, Myint Myint; Latt, Kyaw Thinn; Tizard, Robert; Pienkowski, Thomas; Carrasco, L Roman

2017-04-24

Myanmar offers unique opportunities for both biodiversity conservation and foreign direct investment due to projected economic growth linked to natural resource exploitation. Industrial-scale development introduces new land uses into the landscape, with unknown repercussions for local communities and biodiversity conservation. We use participatory mapping of 31 communities, focus groups in 28 communities, and analyses of forest cover change during 2000-2010 using MODIS vegetation continuous fields images, to understand the social and environmental impacts of gold mining and agricultural concessions in Myanmar's Hukaung Valley (~21,800 km 2 ). Local communities, particularly the poorest households, benefit from work and trade opportunities offered by gold mining and agricultural companies but continue to depend on forests for house construction materials, food, and income from the sale of forest resources. However, gold mining and agricultural concessions reduce tree cover, potentially reducing access to forest resources and further marginalizing these households. Our analyses do not provide evidence that long-term resident communities contributed to forest cover loss between 2000 and 2010. We argue that landscape management, which recognizes local community rights to customary community use areas, and appropriate zoning for commercial land uses and protected areas could contribute to both local livelihoods and protect biodiversity throughout Myanmar during economic growth.

The impact of gold mining and agricultural concessions on the tree cover and local communities in northern Myanmar

PubMed Central

Papworth, Sarah; Rao, Madhu; Oo, Myint Myint; Latt, Kyaw Thinn; Tizard, Robert; Pienkowski, Thomas; Carrasco, L. Roman

2017-01-01

Myanmar offers unique opportunities for both biodiversity conservation and foreign direct investment due to projected economic growth linked to natural resource exploitation. Industrial-scale development introduces new land uses into the landscape, with unknown repercussions for local communities and biodiversity conservation. We use participatory mapping of 31 communities, focus groups in 28 communities, and analyses of forest cover change during 2000–2010 using MODIS vegetation continuous fields images, to understand the social and environmental impacts of gold mining and agricultural concessions in Myanmar’s Hukaung Valley (~21,800 km2). Local communities, particularly the poorest households, benefit from work and trade opportunities offered by gold mining and agricultural companies but continue to depend on forests for house construction materials, food, and income from the sale of forest resources. However, gold mining and agricultural concessions reduce tree cover, potentially reducing access to forest resources and further marginalizing these households. Our analyses do not provide evidence that long-term resident communities contributed to forest cover loss between 2000 and 2010. We argue that landscape management, which recognizes local community rights to customary community use areas, and appropriate zoning for commercial land uses and protected areas could contribute to both local livelihoods and protect biodiversity throughout Myanmar during economic growth. PMID:28436455

Determination of tropical deforestation rates and related carbon losses from 1990 to 2010

PubMed Central

Achard, Frédéric; Beuchle, René; Mayaux, Philippe; Stibig, Hans-Jürgen; Bodart, Catherine; Brink, Andreas; Carboni, Silvia; Desclée, Baudouin; Donnay, François; Eva, Hugh D; Lupi, Andrea; Raši, Rastislav; Seliger, Roman; Simonetti, Dario

2014-01-01

We estimate changes in forest cover (deforestation and forest regrowth) in the tropics for the two last decades (1990–2000 and 2000–2010) based on a sample of 4000 units of 10 ×10 km size. Forest cover is interpreted from satellite imagery at 30 × 30 m resolution. Forest cover changes are then combined with pan-tropical biomass maps to estimate carbon losses. We show that there was a gross loss of tropical forests of 8.0 million ha yr−1 in the 1990s and 7.6 million ha yr−1 in the 2000s (0.49% annual rate), with no statistically significant difference. Humid forests account for 64% of the total forest cover in 2010 and 54% of the net forest loss during second study decade. Losses of forest cover and Other Wooded Land (OWL) cover result in estimates of carbon losses which are similar for 1990s and 2000s at 887 MtC yr−1 (range: 646–1238) and 880 MtC yr−1 (range: 602–1237) respectively, with humid regions contributing two-thirds. The estimates of forest area changes have small statistical standard errors due to large sample size. We also reduce uncertainties of previous estimates of carbon losses and removals. Our estimates of forest area change are significantly lower as compared to national survey data. We reconcile recent low estimates of carbon emissions from tropical deforestation for early 2000s and show that carbon loss rates did not change between the two last decades. Carbon losses from deforestation represent circa 10% of Carbon emissions from fossil fuel combustion and cement production during the last decade (2000–2010). Our estimates of annual removals of carbon from forest regrowth at 115 MtC yr−1 (range: 61–168) and 97 MtC yr−1 (53–141) for the 1990s and 2000s respectively are five to fifteen times lower than earlier published estimates. PMID:24753029

Determination of tropical deforestation rates and related carbon losses from 1990 to 2010.

PubMed

Achard, Frédéric; Beuchle, René; Mayaux, Philippe; Stibig, Hans-Jürgen; Bodart, Catherine; Brink, Andreas; Carboni, Silvia; Desclée, Baudouin; Donnay, François; Eva, Hugh D; Lupi, Andrea; Raši, Rastislav; Seliger, Roman; Simonetti, Dario

2014-08-01

We estimate changes in forest cover (deforestation and forest regrowth) in the tropics for the two last decades (1990-2000 and 2000-2010) based on a sample of 4000 units of 10 ×10 km size. Forest cover is interpreted from satellite imagery at 30 × 30 m resolution. Forest cover changes are then combined with pan-tropical biomass maps to estimate carbon losses. We show that there was a gross loss of tropical forests of 8.0 million ha yr(-1) in the 1990s and 7.6 million ha yr(-1) in the 2000s (0.49% annual rate), with no statistically significant difference. Humid forests account for 64% of the total forest cover in 2010 and 54% of the net forest loss during second study decade. Losses of forest cover and Other Wooded Land (OWL) cover result in estimates of carbon losses which are similar for 1990s and 2000s at 887 MtC yr(-1) (range: 646-1238) and 880 MtC yr(-1) (range: 602-1237) respectively, with humid regions contributing two-thirds. The estimates of forest area changes have small statistical standard errors due to large sample size. We also reduce uncertainties of previous estimates of carbon losses and removals. Our estimates of forest area change are significantly lower as compared to national survey data. We reconcile recent low estimates of carbon emissions from tropical deforestation for early 2000s and show that carbon loss rates did not change between the two last decades. Carbon losses from deforestation represent circa 10% of Carbon emissions from fossil fuel combustion and cement production during the last decade (2000-2010). Our estimates of annual removals of carbon from forest regrowth at 115 MtC yr(-1) (range: 61-168) and 97 MtC yr(-1) (53-141) for the 1990s and 2000s respectively are five to fifteen times lower than earlier published estimates. © The Authors Global Change Biology Published by John Wiley & Sons Ltd.

Research Activities in Support of High-Resolution Land Cover Mapping in the North Central United States

Treesearch

Dacia M. Meneguzzo; Greg C. Liknes

2015-01-01

The USDA Agroforestry Strategic Framework and the 2014 Farm Bill call for inventory and monitoring of agroforestry practices; however, collecting such data over very large non-forested areas is costly. The Forest Inventory and Analysis (FIA) program at the Northern Research Station has addressed this challenge by forming a targeted task team whose primary purpose is to...

Application of two regression-based methods to estimate the effects harvest on forest structure using Landsat data

Treesearch

Sean P. Healey; Zhiqiang Yang; Warren B. Cohen; D. John Pierce

2006-01-01

Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percent cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos...

Comparison of LiDAR-derived data and high resolution true color imagery for extracting urban forest cover

Treesearch

Aaron E. Maxwell; Adam C. Riley; Paul Kinder

2013-01-01

Remote sensing has many applications in forestry. Light detection and ranging (LiDAR) and high resolution aerial photography have been investigated as means to extract forest data, such as biomass, timber volume, stand dynamics, and gap characteristics. LiDAR return intensity data are often overlooked as a source of input raster data for thematic map creation. We...

EnviroAtlas -Phoenix, AZ- One Meter Resolution Urban Land Cover Data (2010) Web Service

EPA Pesticide Factsheets

This EnviroAtlas web service supports research and online mapping activities related to EnviroAtlas (https://www.epa.gov/enviroatlas). The EnviroAtlas Phoenix, AZ land cover data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near-infrared) aerial photography taken from June through September, 2010 at 1 m spatial resolution. Seven land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, shrubland, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 598 land cover reference points yielded an overall accuracy of 69.2%. The area mapped includes the entirety of the Central Arizona-Phoenix Long-Term Ecological Research (CAP-LTER) area, which was classified by the Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University. The land cover dataset also includes an area of approximately 625 square kilometers which is located north of Phoenix. This section was classified by the EPA land cover classification team. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data

EnviroAtlas - Phoenix, AZ - One Meter Resolution Urban Land Cover Data (2010)

EPA Pesticide Factsheets

The EnviroAtlas Phoenix, AZ land cover (LC) data and map were generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near-infrared) aerial photography taken from June through September, 2010 at 1 m spatial resolution. Seven land cover classes were mapped: water, impervious surfaces, soil and barren land, trees and forest, shrubs, grass and herbaceous non-woody vegetation, and agriculture. An accuracy assessment using a completely random sampling of 598 land cover reference points yielded an overall accuracy of 69.2%. The area mapped includes the entirety of the Central Arizona-Phoenix Long-Term Ecological Research (CAP-LTER) area, which was classified by the Environmental Remote Sensing and Geoinformatics Lab (ERSG) at Arizona State University. The land cover dataset also includes an area of approximately 625 square kilometers which is located north of Phoenix. This section was classified by the EPA land cover classification team. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each at

Forest fragmentation and landscape transformation in a reindeer husbandry area in Sweden.

PubMed

Kivinen, Sonja; Berg, Anna; Moen, Jon; Ostlund, Lars; Olofsson, Johan

2012-02-01

Reindeer husbandry and forestry are two main land users in boreal forests in northern Sweden. Modern forestry has numerous negative effects on the ground-growing and arboreal lichens that are crucial winter resources for reindeer husbandry. Using digitized historical maps, we examined changes in the forest landscape structure during the past 100 years, and estimated corresponding changes in suitability of forest landscape mosaics for the reindeer winter grazing. Cover of old coniferous forests, a key habitat type of reindeer herding system, showed a strong decrease during the study period, whereas clear-cutting and young forests increased rapidly in the latter half of the 20th century. The dominance of young forests and fragmentation of old-growth forests (decreased patch sizes and increased isolation) reflect decreased amount of arboreal lichens as well as a lowered ability of the landscape to sustain long-term persistence of lichens. The results further showed that variation in ground lichen cover among sites was mainly related to soil moisture conditions, recent disturbances, such as soil scarification and prescribed burning, and possibly also to forest history. In general, the results suggest that the composition and configuration of the forest landscape mosaic has become less suitable for sustainable reindeer husbandry.

Detection of variations in aspen forest habitat from LANDSAT digital data: Bear River Range, Utah

NASA Technical Reports Server (NTRS)

Merola, J. A.; Jaynes, R. A. (Principal Investigator)

1982-01-01

The aspen forests of the Bear River Range were analyzed and mapped using data recorded on July 2, 1979 by the LANDSAT III satellite; study efforts yielded sixty-seven light signatures for the study area, of which three groups were identified as aspen and mapped at a scale of 1:24,000. Analysis and verification of the three groups were accomplished by random location of twenty-six field study plots within the LANDSAT-defined aspen areas. All study plots are included within the Cache portion of the Wasatch-Cache National Forest. The following selected site characteristics were recorded for each study plot: a list of understory species present; average percent cover density for understory species; aspen canopy cover estimates and stem measurements; and general site topographic characteristics. The study plot data were then analyzed with respect to corresponding Landsat spectral signatures. Field studies show that all twenty-six study plots are associated with one of the three aspen groups. Further study efforts concentration on characterizing the differences between the site characteristics of plots falling into each of the three aspen groups.

Spatial distribution of young forests and carbon fluxes within recent disturbances in Russia.

PubMed

Loboda, Tatiana V; Chen, Dong

2017-01-01

Forest stand age plays a major role in regulating carbon fluxes in boreal and temperate ecosystems. Young boreal forests represent a relatively small but persistent source of carbon to the atmosphere over 30 years after disturbance, while temperate forests switch from a substantial source over the first 10 years to a notable sink until they reach maturity. Russian forests are the largest contiguous forest belt in the world that accounts for 17% of the global forest cover; however, despite its critical role in controlling global carbon cycle, little is known about spatial patterns of young forest distribution across Russia as a whole, particularly before the year 2000. Here, we present a map of young (0-27 years of age) forests, where 12- to 27-year-old forests were modeled from the single-date 500 m satellite record and augmented with the 0- to 11-year-old forest map aggregated from the 30 m resolution contemporary record between 2001 and 2012. The map captures the distribution of forests with the overall accuracy exceeding 85% within three largest bioclimatic vegetation zones (northern, middle, and southern taiga), although mapping accuracy for disturbed classes was generally low (the highest of 31% for user's and producer's accuracy for the 12-27 age class and the maximum of 74% for user's and 32% for producer's accuracy for the 0-11 age class). The results show that 75.5 ± 17.6 Mha (roughly 9%) of Russian forests were younger than 30 years of age at the end of 2012. The majority of these 47 ± 4.7 Mha (62%) were distributed across the middle taiga bioclimatic zone. Based on the published estimates of net ecosystem production (NEP) and the produced map of young forests, this study estimates that young Russian forests represent a total sink of carbon at the rate of 1.26 Tg C yr -1 . © 2016 John Wiley & Sons Ltd.

Application of remote sensing and GIS techniques for forest cover monitoring in the southern part of Laos

NASA Astrophysics Data System (ADS)

Keonuchan, Ammala; Liu, Yaolin

2008-12-01

Forest resource is the important material foundation of national sustainable development. And it need to master the status and change of forest resource timely for reasonable exploitation of forest and its renewal. Laos is located in the heart of the Indochinese peninsular, in southeast Asia, latitude 14° to 23 °north and longitude 100°to 108°east, covered a total 236, 800 square kilometers, and country of nearly 6 million people. The forest of Laos dropped from close to two-third in the 1970's to less than half by the 1990's. This deforestation has been attributed to two human activities : a traditional of shifting cultivation or slash and burn farming, and logging without reforestation. Remote sensing and GIS are the most modern technologies which have been widely used in the field of natural resource management and monitoring. These technologies provide very powerful tools to observe and collect information on natural resources and dynamic phenomenon on the earth surface, and ability to integrate different data and present data in different formats. In this study, using forest cover map and Landsat 7 ETM data, we analyze and compare forest cover change from 1997 to 2002. And the maximum likelihood method of supervised classification was used to classify the remote sensing data, we processed Spectral Enhancement, including Normalized Difference Vegetation Index (NDVI) ,and re-classify data again base on Principle Components Analysis (PCA) and NDVI.

Evaluation of forest cover estimates for Haiti using supervised classification of Landsat data

NASA Astrophysics Data System (ADS)

Churches, Christopher E.; Wampler, Peter J.; Sun, Wanxiao; Smith, Andrew J.

2014-08-01

This study uses 2010-2011 Landsat Thematic Mapper (TM) imagery to estimate total forested area in Haiti. The thematic map was generated using radiometric normalization of digital numbers by a modified normalization method utilizing pseudo-invariant polygons (PIPs), followed by supervised classification of the mosaicked image using the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System. Classification results were compared to other sources of land-cover data produced for similar years, with an emphasis on the statistics presented by the FAO. Three global land cover datasets (GLC2000, Globcover, 2009, and MODIS MCD12Q1), and a national-scale dataset (a land cover analysis by Haitian National Centre for Geospatial Information (CNIGS)) were reclassified and compared. According to our classification, approximately 32.3% of Haiti's total land area was tree covered in 2010-2011. This result was confirmed using an error-adjusted area estimator, which predicted a tree covered area of 32.4%. Standardization to the FAO's forest cover class definition reduces the amount of tree cover of our supervised classification to 29.4%. This result was greater than the reported FAO value of 4% and the value for the recoded GLC2000 dataset of 7.0%, but is comparable to values for three other recoded datasets: MCD12Q1 (21.1%), Globcover (2009) (26.9%), and CNIGS (19.5%). We propose that at coarse resolutions, the segmented and patchy nature of Haiti's forests resulted in a systematic underestimation of the extent of forest cover. It appears the best explanation for the significant difference between our results, FAO statistics, and compared datasets is the accuracy of the data sources and the resolution of the imagery used for land cover analyses. Analysis of recoded global datasets and results from this study suggest a strong linear relationship (R2 = 0.996 for tree cover) between spatial resolution and land cover estimates.

A Backpack-Mounted Omnidirectional Camera with Off-the-Shelf Navigation Sensors for Mobile Terrestrial Mapping: Development and Forest Application

PubMed Central

Prol, Fabricio dos Santos; El Issaoui, Aimad; Hakala, Teemu

2018-01-01

The use of Personal Mobile Terrestrial System (PMTS) has increased considerably for mobile mapping applications because these systems offer dynamic data acquisition with ground perspective in places where the use of wheeled platforms is unfeasible, such as forests and indoor buildings. PMTS has become more popular with emerging technologies, such as miniaturized navigation sensors and off-the-shelf omnidirectional cameras, which enable low-cost mobile mapping approaches. However, most of these sensors have not been developed for high-accuracy metric purposes and therefore require rigorous methods of data acquisition and data processing to obtain satisfactory results for some mapping applications. To contribute to the development of light, low-cost PMTS and potential applications of these off-the-shelf sensors for forest mapping, this paper presents a low-cost PMTS approach comprising an omnidirectional camera with off-the-shelf navigation systems and its evaluation in a forest environment. Experimental assessments showed that the integrated sensor orientation approach using navigation data as the initial information can increase the trajectory accuracy, especially in covered areas. The point cloud generated with the PMTS data had accuracy consistent with the Ground Sample Distance (GSD) range of omnidirectional images (3.5–7 cm). These results are consistent with those obtained for other PMTS approaches. PMID:29522467

Global demand for gold is another threat for tropical forests

NASA Astrophysics Data System (ADS)

Alvarez-Berríos, Nora L.; Aide, T. Mitchell

2015-01-01

The current global gold rush, driven by increasing consumption in developing countries and uncertainty in financial markets, is an increasing threat for tropical ecosystems. Gold mining causes significant alteration to the environment, yet mining is often overlooked in deforestation analyses because it occupies relatively small areas. As a result, we lack a comprehensive assessment of the spatial extent of gold mining impacts on tropical forests. In this study, we provide a regional assessment of gold mining deforestation in the tropical moist forest biome of South America. Specifically, we analyzed the patterns of forest change in gold mining sites between 2001 and 2013, and evaluated the proximity of gold mining deforestation to protected areas (PAs). The forest cover maps were produced using the Land Mapper web application and images from the MODIS satellite MOD13Q1 vegetation indices 250 m product. Annual maps of forest cover were used to model the incremental change in forest in ˜1600 potential gold mining sites between 2001-2006 and 2007-2013. Approximately 1680 km2 of tropical moist forest was lost in these mining sites between 2001 and 2013. Deforestation was significantly higher during the 2007-2013 period, and this was associated with the increase in global demand for gold after the international financial crisis. More than 90% of the deforestation occurred in four major hotspots: Guianan moist forest ecoregion (41%), Southwest Amazon moist forest ecoregion (28%), Tapajós-Xingú moist forest ecoregion (11%), and Magdalena Valley montane forest and Magdalena-Urabá moist forest ecoregions (9%). In addition, some of the more active zones of gold mining deforestation occurred inside or within 10 km of ˜32 PAs. There is an urgent need to understand the ecological and social impacts of gold mining because it is an important cause of deforestation in the most remote forests in South America, and the impacts, particularly in aquatic systems, spread well beyond the actual mining sites.

Forgotten forests--issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study.

PubMed

Särkinen, Tiina; Iganci, João R V; Linares-Palomino, Reynaldo; Simon, Marcelo F; Prado, Darién E

2011-11-24

South America is one of the most species diverse continents in the world. Within South America diversity is not distributed evenly at both local and continental scales and this has led to the recognition of various areas with unique species assemblages. Several schemes currently exist which divide the continental-level diversity into large species assemblages referred to as biomes. Here we review five currently available biome maps for South America, including the WWF Ecoregions, the Americas basemap, the Land Cover Map of South America, Morrone's Biogeographic regions of Latin America, and the Ecological Systems Map. The comparison is performed through a case study on the Seasonally Dry Tropical Forest (SDTF) biome using herbarium data of habitat specialist species. Current biome maps of South America perform poorly in depicting SDTF distribution. The poor performance of the maps can be attributed to two main factors: (1) poor spatial resolution, and (2) poor biome delimitation. Poor spatial resolution strongly limits the use of some of the maps in GIS applications, especially for areas with heterogeneous landscape such as the Andes. Whilst the Land Cover Map did not suffer from poor spatial resolution, it showed poor delimitation of biomes. The results highlight that delimiting structurally heterogeneous vegetation is difficult based on remote sensed data alone. A new refined working map of South American SDTF biome is proposed, derived using the Biome Distribution Modelling (BDM) approach where georeferenced herbarium data is used in conjunction with bioclimatic data. Georeferenced specimen data play potentially an important role in biome mapping. Our study shows that herbarium data could be used as a way of ground-truthing biome maps in silico. The results also illustrate that herbarium data can be used to model vegetation maps through predictive modelling. The BDM approach is a promising new method in biome mapping, and could be particularly useful for mapping poorly known, fragmented, or degraded vegetation. We wish to highlight that biome delimitation is not an exact science, and that transparency is needed on how biomes are used as study units in macroevolutionary and ecological research.

Forgotten forests - issues and prospects in biome mapping using Seasonally Dry Tropical Forests as a case study

PubMed Central

2011-01-01

Background South America is one of the most species diverse continents in the world. Within South America diversity is not distributed evenly at both local and continental scales and this has led to the recognition of various areas with unique species assemblages. Several schemes currently exist which divide the continental-level diversity into large species assemblages referred to as biomes. Here we review five currently available biome maps for South America, including the WWF Ecoregions, the Americas basemap, the Land Cover Map of South America, Morrone's Biogeographic regions of Latin America, and the Ecological Systems Map. The comparison is performed through a case study on the Seasonally Dry Tropical Forest (SDTF) biome using herbarium data of habitat specialist species. Results Current biome maps of South America perform poorly in depicting SDTF distribution. The poor performance of the maps can be attributed to two main factors: (1) poor spatial resolution, and (2) poor biome delimitation. Poor spatial resolution strongly limits the use of some of the maps in GIS applications, especially for areas with heterogeneous landscape such as the Andes. Whilst the Land Cover Map did not suffer from poor spatial resolution, it showed poor delimitation of biomes. The results highlight that delimiting structurally heterogeneous vegetation is difficult based on remote sensed data alone. A new refined working map of South American SDTF biome is proposed, derived using the Biome Distribution Modelling (BDM) approach where georeferenced herbarium data is used in conjunction with bioclimatic data. Conclusions Georeferenced specimen data play potentially an important role in biome mapping. Our study shows that herbarium data could be used as a way of ground-truthing biome maps in silico. The results also illustrate that herbarium data can be used to model vegetation maps through predictive modelling. The BDM approach is a promising new method in biome mapping, and could be particularly useful for mapping poorly known, fragmented, or degraded vegetation. We wish to highlight that biome delimitation is not an exact science, and that transparency is needed on how biomes are used as study units in macroevolutionary and ecological research. PMID:22115315

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...

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

NASA Astrophysics Data System (ADS)

Hagensieker, Ron; Roscher, Ribana; Rosentreter, Johannes; Jakimow, Benjamin; Waske, Björn

2017-12-01

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial-temporal variability is a challenging task. We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain. The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.

The uses of ERTS-1 imagery in the analysis of landscape change. [agriculture, strip mining forests, urban-suburban growth, and flooding in Tennessee, Kentucky, Mississippi, and Alabama

NASA Technical Reports Server (NTRS)

Rehder, J. B. (Principal Investigator)

1974-01-01

The author has identified the following significant results. The analysis of strip mining from ERTS-1 data has resulted in the mapping of landscape changes for the Cumberland Plateau Test Site. Several mapping experiments utilizing ERTS-1 data have been established for the mapping of state-wide land use regions. The first incorporates 12 frames of ERTS-1 imagery for the generalized thematic mapping of forest cover for the state of Tennessee. In another mapping effort, 14 ERTS-1 images have been analyzed for plowed ground signatures to produce a map of agricultural regions for Tennessee, Kentucky, and the northern portions of Mississippi and Alabama. Generalized urban land use categories and transportation networks have been determined from ERTS-1 imagery for the Knoxville Test Site. Finally, through the analysis of ERTS-1 imagery, short-lived phenomena such as the 1973 spring floods on the Mississippi River in western Tennessee, have been detected, monitored, and mapped.

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

Mapping Forest Edge Using Aerial Lidar

NASA Astrophysics Data System (ADS)

MacLean, M. G.

2014-12-01

Slightly more than 60% of Massachusetts is covered with forest and this land cover type is invaluable for the protection and maintenance of our natural resources and is a carbon sink for the state. However, Massachusetts is currently experiencing a decline in forested lands, primarily due to the expansion of human development (Thompson et al., 2011). Of particular concern is the loss of "core areas" or the areas within forests that are not influenced by other land cover types. These areas are of significant importance to native flora and fauna, since they generally are not subject to invasion by exotic species and are more resilient to the effects of climate change (Campbell et al., 2009). However, the expansion of development has reduced the amount of this core area, but the exact amount is still unknown. Current methods of estimating core area are not particularly precise, since edge, or the area of the forest that is most influenced by other land cover types, is quite variable and situation dependent. Therefore, the purpose of this study is to devise a new method for identifying areas that could qualify as "edge" within the Harvard Forest, in Petersham MA, using new remote sensing techniques. We sampled along eight transects perpendicular to the edge of an abandoned golf course within the Harvard Forest property. Vegetation inventories as well as Photosynthetically Active Radiation (PAR) at different heights within the canopy were used to determine edge depth. These measurements were then compared with small-footprint waveform aerial LiDAR datasets and imagery to model edge depths within Harvard Forest.

Comparison of MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

NASA Astrophysics Data System (ADS)

Klein, A. G.; Thapa, S.

2017-12-01

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS. While it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage quantitative comparisons between their snow products are currently limited. Therefore this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI Snow Cover product at a snow cover fraction of 30% generated binary snow maps most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. Agreement was highest during the winter but lower during late fall and spring. Comparability was lowest over forest. MODIS and VIIRS often mapped snow/no-snow transition zones as cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicates that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67%, and cloud 65.02% and 59.91%, respectively. Visual comparisons depict good qualitative agreement between snow cover area visible in MODIS and VIIRS false color imagery and mapped in their respective snow cover products. While VIIRS and MODIS have similar capacity to map snow cover, VIIRS has the potential to more accurately map snow cover area for the successive development of climate data records.

Combining accuracy assessment of land-cover maps with environmental monitoring programs

USGS Publications Warehouse

Stehman, S.V.; Czaplewski, R.L.; Nusser, S.M.; Yang, L.; Zhu, Z.

2000-01-01

A scientifically valid accuracy assessment of a large-area, land-cover map is expensive. Environmental monitoring programs offer a potential source of data to partially defray the cost of accuracy assessment while still maintaining the statistical validity. In this article, three general strategies for combining accuracy assessment and environmental monitoring protocols are described. These strategies range from a fully integrated accuracy assessment and environmental monitoring protocol, to one in which the protocols operate nearly independently. For all three strategies, features critical to using monitoring data for accuracy assessment include compatibility of the land-cover classification schemes, precisely co-registered sample data, and spatial and temporal compatibility of the map and reference data. Two monitoring programs, the National Resources Inventory (NRI) and the Forest Inventory and Monitoring (FIM), are used to illustrate important features for implementing a combined protocol.

Monitoring rubber plantation expansion using Landsat data time series and a Shapelet-based approach

NASA Astrophysics Data System (ADS)

Ye, Su; Rogan, John; Sangermano, Florencia

2018-02-01

The expansion of tree plantations in tropical forests for commercial rubber cultivation threatens biodiversity which may affect ecosystem services, and hinders ecosystem productivity, causing net carbon emission. Numerous studies refer to the challenge of reliably distinguishing rubber plantations from natural forest, using satellite data, due to their similar spectral signatures, even when phenology is incorporated into an analysis. This study presents a novel approach for monitoring the establishment and expansion of rubber plantations in Seima Protection Forest (SPF), Cambodia (1995-2015), by detecting and analyzing the 'shapelet' structure in a Landsat-NDVI time series. This paper introduces a new classification procedure consisting of two steps: (1) an exhaustive-searching algorithm to detect shapelets that represent a period for relatively low NDVI values within an image time series; and (2) a t-test used to determine if NDVI values of detected shapelets are significantly different than their non-shapelet trend, thereby indicating the presence of rubber plantations. Using this approach, historical rubber plantation events were mapped over the twenty-year timespan. The shapelet algorithm produced two types of information: (1) year of rubber plantation establishment; and (2) pre-conversion land-cover type (i.e., agriculture, or natural forest). The overall accuracy of the rubber plantation map for the year of 2015 was 89%. The multi-temporal map products reveal that more than half of the rubber planting activity (57%) took place in 2010 and 2011, following the granting of numerous rubber concessions two years prior. Seventy-three percent of the rubber plantations were converted from natural forest and twenty-three percent were established on non-forest land-cover. The shapelet approach developed here can be used reliably to improve our understanding of the expansion of rubber production beyond Seima Protection Forest of Cambodia, and likely elsewhere in the tropics.

BOREAS TE-23 Map Plot Data

NASA Technical Reports Server (NTRS)

Rich, Paul M.; Fournier, Robert; Hall, Forrest G. (Editor); Papagno, Andrea (Editor)

2000-01-01

The Boreal Ecosystem-Atmospheric Study (BOREAS) TE-23 (Terrestrial Ecology) team collected map plot data in support of its efforts to characterize and interpret information on canopy architecture and understory cover at the BOREAS tower flux sites and selected auxiliary sites from May to August 1994. Mapped plots (typical dimensions 50 m x 60 m) were set up and characterized at all BOREAS forested tower flux and selected auxiliary sites. Detailed measurement of the mapped plots included: (1) stand characteristics (location, density, basal area); (2) map locations diameter at breast height (DBH) of all trees; (3) detailed geometric measures of a subset of trees (height, crown dimensions); and (4) understory cover maps. The data are stored in tabular ASCII files. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

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.

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.

High-resolution forest mapping for behavioural studies in the Nature Reserve ‘Les Nouragues’, French Guiana

PubMed Central

Ringler, Max; Mangione, Rosanna; Pašukonis, Andrius; Rainer, Gerhard; Gyimesi, Kristin; Felling, Julia; Kronaus, Hannes; Réjou-Méchain, Maxime; Chave, Jérôme; Reiter, Karl; Ringler, Eva

2015-01-01

For animals with spatially complex behaviours at relatively small scales, the resolution of a global positioning system (GPS) receiver location is often below the resolution needed to correctly map animals’ spatial behaviour. Natural conditions such as canopy cover, canyons or clouds can further degrade GPS receiver reception. Here we present a detailed, high-resolution map of a 4.6 ha Neotropical river island and a 8.3 ha mainland plot with the location of every tree >5 cm DBH and all structures on the forest floor, which are relevant to our study species, the territorial frog Allobates femoralis (Dendrobatidae). The map was derived using distance- and compass-based survey techniques, rooted on dGPS reference points, and incorporates altitudinal information based on a LiDAR survey of the area. PMID:27053943

Fragmentation increases wind disturbance impacts on forest structure and carbon stocks in a western Amazonian landscape.

PubMed

Schwartz, Naomi B; Uriarte, María; DeFries, Ruth; Bedka, Kristopher M; Fernandes, Katia; Gutiérrez-Vélez, Victor; Pinedo-Vasquez, Miguel A

2017-09-01

Tropical second-growth forests could help mitigate climate change, but the degree to which their carbon potential is achieved will depend on exposure to disturbance. Wind disturbance is common in tropical forests, shaping structure, composition, and function, and influencing successional trajectories. However, little is known about the impacts of extreme winds on second-growth forests in fragmented landscapes, though these ecosystems are often located in mosaics of forest, pasture, cropland, and other land cover types. Indirect evidence suggests that fragmentation increases risk of wind damage in tropical forests, but no studies have found such impacts following severe storms. In this study, we ask whether fragmentation and forest type (old vs. second growth) were associated with variation in wind damage after a severe convective storm in a fragmented production landscape in western Amazonia. We applied linear spectral unmixing to Landsat 8 imagery from before and after the storm, and combined it with field observations of damage to map wind effects on forest structure and biomass. We also used Landsat 8 imagery to map land cover with the goals of identifying old- and second-growth forest and characterizing fragmentation. We used these data to assess variation in wind disturbance across 95,596 ha of forest, distributed over 6,110 patches. We find that fragmentation is significantly associated with wind damage, with damage severity higher at forest edges and in edgier, more isolated patches. Damage was also more severe in old-growth than in second-growth forests, but this effect was weaker than that of fragmentation. These results illustrate the importance of considering landscape context in planning tropical forest restoration and natural regeneration projects. Assessments of long-term carbon sequestration potential need to consider spatial variation in disturbance exposure. Where risk of extreme winds is high, minimizing fragmentation and isolation could increase carbon sequestration potential. © 2017 by the Ecological Society of America.

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.

EnviroAtlas - Cleveland, OH - Estimated Percent Tree Cover Along Walkable Roads

EPA Pesticide Factsheets

This EnviroAtlas dataset estimates tree cover along walkable roads. The road width is estimated for each road and percent tree cover is calculated in a 8.5 meter strip beginning at the estimated road edge. Percent tree cover is calculated for each block between road intersections. In this community, tree cover is defined as Trees & Forest and Woody Wetlands. Tree cover provides valuable benefits to neighborhood residents and walkers by providing shade, improved aesthetics, and outdoor gathering spaces. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

EnviroAtlas - Minneapolis/St. Paul, MN - Estimated Percent Tree Cover Along Walkable Roads

EPA Pesticide Factsheets

This EnviroAtlas dataset estimates tree cover along walkable roads. The road width is estimated for each road and percent tree cover is calculated in a 8.5 meter strip beginning at the estimated road edge. Percent tree cover is calculated for each block between road intersections. In this community, tree cover is defined as Trees and Forest and Woody Wetlands. Tree cover provides valuable benefits to neighborhood residents and walkers by providing shade, improved aesthetics, and outdoor gathering spaces. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas/EnviroAtlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets)

Normalizing Landsat and ASTER Data Using MODIS Data Products for Forest Change Detection

NASA Technical Reports Server (NTRS)

Gao, Feng; Masek, Jeffrey G.; Wolfe, Robert E.; Tan, Bin

2010-01-01

Monitoring forest cover and its changes are a major application for optical remote sensing. In this paper, we present an approach to integrate Landsat, ASTER and MODIS data for forest change detection. Moderate resolution (10-100m) images (e.g. Landsat and ASTER) acquired from different seasons and times are normalized to one "standard" date using MODIS data products as reference. The normalized data are then used to compute forest disturbance index for forest change detection. Comparing to the results from original data, forest disturbance index from the normalized images is more consistent spatially and temporally. This work demonstrates an effective approach for mapping forest change over a large area from multiple moderate resolution sensors on various acquisition dates.

Forest-dweller demographics in West Kalimantan, Indonesia.

PubMed

Fox, J; Atok, K

1997-03-01

This study sought to ascertain, from census and other data, the number of people living on state-claimed forest land (SCFL) in West Kalimantan in the outer islands of Indonesia. One aim was to determine why data collection is problematic. In 1990 the outer islands accounted for 38% of total population, 93% of its land mass, and 98% of its forests. 72% of the land mass of the outer islands was designated SCFL. Kalimantan has 38.5 million hectares of SCFL, while West Kalimantan has 9.2 million hectares, or 63% of the land area of the province. In 1990, 3.2 million people lived in West Kalimantan. Two sets of forest cover maps and census statistics at the village level were integrated into the geographic information system (GIS) technology by district and regency boundaries and the location of villages. The fieldwork was conducted in Sengah Temila District in Pontianak Regency and Simpang Hulu District in Ketapang Regency. Four methods were used to estimate forest populations: 1) estimating gross population density, 2) mapping forest villages, 3) adjusting density to account for uneven population distribution, and 4) estimating population densities for specific villages and generalizing to the province level. Methods 3 and 4 gave the most reasonable estimates. Population varied from 650,000 to 1 million. Government census statistics proved to be accurate representations of human population. The 1:50,000 scale of topological maps of West Kalimantan correctly identified the location of villages listed in the census. The Indonesian Ministry of Forestry's forest-planning maps and the RePPProT maps both reported similar SCFL. The GIS technology was useful in integrating data from several sources. The lack of knowledge was not due to political or institutional interests.

A land-cover map for South and Southeast Asia derived from SPOT-VEGETATION data

USGS Publications Warehouse

Stibig, H.-J.; Belward, A.S.; Roy, P.S.; Rosalina-Wasrin, U.; Agrawal, S.; Joshi, P.K.; ,; Beuchle, R.; Fritz, S.; Mubareka, S.; Giri, C.

2007-01-01

Aim  Our aim was to produce a uniform ‘regional’ land-cover map of South and Southeast Asia based on ‘sub-regional’ mapping results generated in the context of the Global Land Cover 2000 project.Location  The ‘region’ of tropical and sub-tropical South and Southeast Asia stretches from the Himalayas and the southern border of China in the north, to Sri Lanka and Indonesia in the south, and from Pakistan in the west to the islands of New Guinea in the far east.Methods  The regional land-cover map is based on sub-regional digital mapping results derived from SPOT-VEGETATION satellite data for the years 1998–2000. Image processing, digital classification and thematic mapping were performed separately for the three sub-regions of South Asia, continental Southeast Asia, and insular Southeast Asia. Landsat TM images, field data and existing national maps served as references. We used the FAO (Food and Agriculture Organization) Land Cover Classification System (LCCS) for coding the sub-regional land-cover classes and for aggregating the latter to a uniform regional legend. A validation was performed based on a systematic grid of sample points, referring to visual interpretation from high-resolution Landsat imagery. Regional land-cover area estimates were obtained and compared with FAO statistics for the categories ‘forest’ and ‘cropland’.Results  The regional map displays 26 land-cover classes. The LCCS coding provided a standardized class description, independent from local class names; it also allowed us to maintain the link to the detailed sub-regional land-cover classes. The validation of the map displayed a mapping accuracy of 72% for the dominant classes of ‘forest’ and ‘cropland’; regional area estimates for these classes correspond reasonably well to existing regional statistics.Main conclusions  The land-cover map of South and Southeast Asia provides a synoptic view of the distribution of land cover of tropical and sub-tropical Asia, and it delivers reasonable thematic detail and quantitative estimates of the main land-cover proportions. The map may therefore serve for regional stratification or modelling of vegetation cover, but could also support the implementation of forest policies, watershed management or conservation strategies at regional scales.

EnviroAtlas - Memphis, TN - 51m Riparian Buffer Vegetated Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is vegetated. Vegetated cover is defined as Trees & Forest, Grass & Herbaceous, Woody Wetlands, and Emergent Wetlands. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the EnviroAtlas community area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

Predictive modeling and mapping of Malayan Sun Bear (Helarctos malayanus) distribution using maximum entropy.

PubMed

Nazeri, Mona; Jusoff, Kamaruzaman; Madani, Nima; Mahmud, Ahmad Rodzi; Bahman, Abdul Rani; Kumar, Lalit

2012-01-01

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear's population.

Predictive Modeling and Mapping of Malayan Sun Bear (Helarctos malayanus) Distribution Using Maximum Entropy

PubMed Central

Nazeri, Mona; Jusoff, Kamaruzaman; Madani, Nima; Mahmud, Ahmad Rodzi; Bahman, Abdul Rani; Kumar, Lalit

2012-01-01

One of the available tools for mapping the geographical distribution and potential suitable habitats is species distribution models. These techniques are very helpful for finding poorly known distributions of species in poorly sampled areas, such as the tropics. Maximum Entropy (MaxEnt) is a recently developed modeling method that can be successfully calibrated using a relatively small number of records. In this research, the MaxEnt model was applied to describe the distribution and identify the key factors shaping the potential distribution of the vulnerable Malayan Sun Bear (Helarctos malayanus) in one of the main remaining habitats in Peninsular Malaysia. MaxEnt results showed that even though Malaysian sun bear habitat is tied with tropical evergreen forests, it lives in a marginal threshold of bio-climatic variables. On the other hand, current protected area networks within Peninsular Malaysia do not cover most of the sun bears potential suitable habitats. Assuming that the predicted suitability map covers sun bears actual distribution, future climate change, forest degradation and illegal hunting could potentially severely affect the sun bear’s population. PMID:23110182

Agricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests

NASA Astrophysics Data System (ADS)

Vogels, M. F. A.; de Jong, S. M.; Sterk, G.; Addink, E. A.

2017-02-01

Land-use and land-cover (LULC) conversions have an important impact on land degradation, erosion and water availability. Information on historical land cover (change) is crucial for studying and modelling land- and ecosystem degradation. During the past decades major LULC conversions occurred in Africa, Southeast Asia and South America as a consequence of a growing population and economy. Most distinct is the conversion of natural vegetation into cropland. Historical LULC information can be derived from satellite imagery, but these only date back until approximately 1972. Before the emergence of satellite imagery, landscapes were monitored by black-and-white (B&W) aerial photography. This photography is often visually interpreted, which is a very time-consuming approach. This study presents an innovative, semi-automated method to map cropland acreage from B&W photography. Cropland acreage was mapped on two study sites in Ethiopia and in The Netherlands. For this purpose we used Geographic Object-Based Image Analysis (GEOBIA) and a Random Forest classification on a set of variables comprising texture, shape, slope, neighbour and spectral information. Overall mapping accuracies attained are 90% and 96% for the two study areas respectively. This mapping method increases the timeline at which historical cropland expansion can be mapped purely from brightness information in B&W photography up to the 1930s, which is beneficial for regions where historical land-use statistics are mostly absent.

An operational application of satellite snow cover observations, northwest United States. [using LANDSAT 1

NASA Technical Reports Server (NTRS)

Dillard, J. P.

1975-01-01

LANDSAT-1 imagery showing extent of snow cover was collected and is examined for the 1973 and 1974 snowmelt seasons for three Columbia River Basins. Snowlines were mapped and the aerial snow cover was determined using satellite data. Satellite snow mapping products were compared products from conventional information sources (computer programming and aerial photography was used). Available satellite data were successfully analyzed by radiance thresholding to determine snowlines and the attendant snow-covered area. Basin outline masks, contour elevation masks, and grid overlays were utilized as satellite data interpretation aids. Verification of the LANDSAT-1 data was generally good although there were exceptions. A major problem was lack of adequate cloud-free satellite imagery of high resolution and determining snowlines in forested areas.

EnviroAtlas - Paterson, NJ - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. EnviroAtlas defines tree buffer for this community as only trees and forest. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Biomass mission: a step forward in quantifying forest biomass and structure

NASA Astrophysics Data System (ADS)

LE Toan, T.

2015-12-01

The primary aim of the ESA BIOMASS mission is to determine, for the first time and in a consistent manner, the global distribution of above-ground forest biomass (AGB) in order to provide greatly improved quantification of the size and distribution of the terrestrial carbon pool, and improved estimates of terrestrial carbon fluxes. Specifically, BIOMASS will measure forest carbon stock, as well as forest height, from data provided by a single satellite giving a biomass map covering tropical, temperate and boreal forests at a resolution of around 200 m every 6 months throughout the five years of the mission. BIOMASS will use a long wavelength SAR (P-band) providing three mutually supporting measurement techniques, namely polarimetric SAR (PolSAR), polarimetric interferometric SAR (PolInSAR) and tomographic SAR (TomoSAR). The combination of these techniques will significantly reduce the uncertainties in biomass retrievals by yielding complementary information on biomass properties. Horizontal mapping: For a forest canopy, the P-band radar waves penetrate deep into the canopy, and their interaction with the structure of the forest will be exploited to map above ground biomass (AGB), as demonstrated from airborne data for temperate, boreal forests and tropical forest. Height mapping: By repeat revisits to the same location, the PolInSAR measurements will be used to estimate the height of scattering in the forest canopy. The long wavelength used by BIOMASS is crucial for the temporal coherence to be preserved over much longer timescales than at L-band, for example. 3D mapping: The P-band frequency used by BIOMASS is low enough to ensure penetration through the entire canopy, even in dense tropical forests. As a consequence, resolution of the vertical structure of the forest will be possible using tomographic methods from the multi-baseline acquisitions. This is the concept of SAR tomography, which will be implemented in the BIOMASS mission. The improvement in the quantification of the vegetation structure, will have an important impact in many aspects of ecosystem function, such as carbon cycling and biodiversity. For example, areas of forest loss or degradation and areas of growth or recovery, can be determined by the vegetation structure and its temporal change.

Baseline map of carbon emissions from deforestation in tropical regions.

PubMed

Harris, Nancy L; Brown, Sandra; Hagen, Stephen C; Saatchi, Sassan S; Petrova, Silvia; Salas, William; Hansen, Matthew C; Potapov, Peter V; Lotsch, Alexander

2012-06-22

Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

Baseline Map of Carbon Emissions from Deforestation in Tropical Regions

NASA Astrophysics Data System (ADS)

Harris, Nancy L.; Brown, Sandra; Hagen, Stephen C.; Saatchi, Sassan S.; Petrova, Silvia; Salas, William; Hansen, Matthew C.; Potapov, Peter V.; Lotsch, Alexander

2012-06-01

Policies to reduce emissions from deforestation would benefit from clearly derived, spatially explicit, statistically bounded estimates of carbon emissions. Existing efforts derive carbon impacts of land-use change using broad assumptions, unreliable data, or both. We improve on this approach using satellite observations of gross forest cover loss and a map of forest carbon stocks to estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year. This estimate is 25 to 50% of recently published estimates. By systematically matching areas of forest loss with their carbon stocks before clearing, these results serve as a more accurate benchmark for monitoring global progress on reducing emissions from deforestation.

Remote Sensing of The Carbon Stocks of the UNESCO World Heritage Site: Mt. Apo Natural Park, Philippines

NASA Astrophysics Data System (ADS)

Washington-Allen, R. A.; Rubas, L. C.; Conner, J. R.; Delgado, A.; Popescu, S. C.

2013-12-01

Tropical forest cover has reduced to 20% of the Philippines (6.1 M ha) by 1996 from 90% or 27 M ha in the 16th century. Land use is a major cause of deforestation including shifting cultivation, permanent agriculture, ranching, logging, fuel-wood gathering and charcoal-making. The UN's Reduction of Emissions from Deforestation and Degradation Program's (REDD) Tier 1 evaluation of the Philippines estimates that between 0.8 to 2.5 Pg C are emitted per year with high uncertainty levels. The purpose of this study was to reduce this uncertainty by implementing a Tier 3 high resolution field and satellite remote sensing approach to assess above-ground forest carbon stocks over time in the 54,975 ha UNESCO World Heritage site: Mt. Apo Natural Park (MANP) in Mindanao Island, Philippines. We established approximately 25 30-m X 30-m pixel resolution tree stands in MANP measuring species diversity, composition, height, crown area, and diameter-at-breast height (dbh) both manually and with a terrestrial laser scanner (TLS). Both these data were used to calibrate the tree heights of 2000 Shuttle Radar Topography Mission (SRTM) 90-m C-band and 2004 Intermap 5-m X-band IFSAR, and 2009 30-m ASTER Global digital elevation model (GDEM) digital surface models (DSM). The 5-m IFSAR also includes a 5-m last return DEM, where DSM - DEM = Tree Height. A tree density map was derived using a minima-maxima convolution filter in conjunction with a land cover map developed by the Philippines Department of Environment and Natural Resources (DENR). A 'universal allometric equation' for tropical forests that inputs crown diameter and tree height was then used to generate both Tropical forest biomass and forest carbon maps of MANP.

Application of two regression-based methods to estimate the effects of partial harvest on forest structure using Landsat data.

Treesearch

S.P. Healey; Z. Yang; W.B. Cohen; D.J. Pierce

2006-01-01

Although partial harvests are common in many forest types globally, there has been little assessment of the potential to map the intensity of these harvests using Landsat data. We modeled basal area removal and percentage cover change in a study area in central Washington (northwestern USA) using biennial Landsat imagery and reference data from historical aerial photos...

Predicting the effects of climate change on ecosystems and wildlife habitat in northwest Alaska: results from the WildCast project

Treesearch

Anthony R. DeGange; Bruce G. Marcot; James Lawler; Torre Jorgenson; Robert Winfree

2013-01-01

We used a modeling framework and a recent ecological land classification and land cover map to predict how ecosystems and wildlife habitat in northwest Alaska might change in response to increasing temperature. Our results suggest modest increases in forest and tall shrub ecotypes in Northwest Alaska by the end of this century thereby increasing habitat for forest-...

Spatial Conflict of Mining Land in Tolitoli District -Province of Central Sulawesi

NASA Astrophysics Data System (ADS)

Suwarno, Y.; Windiastuti, R.

2018-05-01

Spatial planning is supposed to be applied in the use of space, so there will be no overlapping space utilization. In fact, there are still overlapping uses of land, between the area of mining and plantation, as well as with forest areas. The purpose of this study was to find out the conflicts that occured due to overlapping permits given to mining and plantation companies, and also to forest status. The method used was by overlaying the maps of Mining Business Permit with that of Plantation Business Permit, and also with Forest Area Map. In Tolitoli District there were 23 mining business permit holders with 7 types of mining commodities, covering total areaof 81,503.54 Hectare. In addition, there were 5 companies holding plantation business permits, mostly on palm oil, and only 2 companies with rubber and sengon wood business commodities, with a total area of 80,005.35 Hectare. From the result of spatial analysis, it was found that there was an overlapping area of 22,869.70 Hectare, while the area of 118,072.93 Hectare did not overlap. The Mining Business Permit overlapped with the Plantation Business Permit covering an area of 18,853.32 Hectare, and 4,301.77 Hectare were located in Forest Protected Area and Nature Reserve.

Analysis of the Tanana River Basin using LANDSAT data

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Ambrosia, V. G.; Carson-Henry, C.

1981-01-01

Digital image classification techniques were used to classify land cover/resource information in the Tanana River Basin of Alaska. Portions of four scenes of LANDSAT digital data were analyzed using computer systems at Ames Research Center in an unsupervised approach to derive cluster statistics. The spectral classes were identified using the IDIMS display and color infrared photography. Classification errors were corrected using stratification procedures. The classification scheme resulted in the following eleven categories; sedimented/shallow water, clear/deep water, coniferous forest, mixed forest, deciduous forest, shrub and grass, bog, alpine tundra, barrens, snow and ice, and cultural features. Color coded maps and acreage summaries of the major land cover categories were generated for selected USGS quadrangles (1:250,000) which lie within the drainage basin. The project was completed within six months.

Exploration for fossil and nuclear fuels from orbital altitudes

NASA Technical Reports Server (NTRS)

Short, N. M.

1975-01-01

A review of satellite-based photographic (optical and infrared) and microwave exploration and large-area mapping of the earth's surface in the ERTS program. Synoptic cloud-free coverage of large areas has been achieved with planimetric vertical views of the earth's surface useful in compiling close-to-orthographic mosaics. Radar penetration of cloud cover and infrared penetration of forest cover have been successful to some extent. Geological applications include map editing (with corrections in scale and computer processing of images), landforms analysis, structural geology studies, lithological identification, and exploration for minerals and fuels. Limitations of the method are noted.

Developing Land Use Land Cover Maps for the Lower Mekong Basin to Aid SWAT Hydrologic Modeling

NASA Astrophysics Data System (ADS)

Spruce, J.; Bolten, J. D.; Srinivasan, R.

2017-12-01

This presentation discusses research to develop Land Use Land Cover (LULC) maps for the Lower Mekong Basin (LMB). Funded by a NASA ROSES Disasters grant, the main objective was to produce updated LULC maps to aid the Mekong River Commission's (MRC's) Soil and Water Assessment Tool (SWAT) hydrologic model. In producing needed LULC maps, temporally processed MODIS monthly NDVI data for 2010 were used as the primary data source for classifying regionally prominent forest and agricultural types. The MODIS NDVI data was derived from processing MOD09 and MYD09 8-day reflectance data with the Time Series Product Tool, a custom software package. Circa 2010 Landsat multispectral data from the dry season were processed into top of atmosphere reflectance mosaics and then classified to derive certain locally common LULC types, such as urban areas and industrial forest plantations. Unsupervised ISODATA clustering was used to derive most LULC classifications. GIS techniques were used to merge MODIS and Landsat classifications into final LULC maps for Sub-Basins (SBs) 1-8 of the LMB. The final LULC maps were produced at 250-meter resolution and delivered to the MRC for use in SWAT modeling for the LMB. A map accuracy assessment was performed for the SB 7 LULC map with 14 classes. This assessment was performed by comparing random locations for sampled LULC types to geospatial reference data such as Landsat RGBs, MODIS NDVI phenologic profiles, high resolution satellite data from Google Map/Earth, and other reference data from the MRC (e.g., crop calendars). LULC accuracy assessment results for SB 7 indicated an overall agreement to reference data of 81% at full scheme specificity. However, by grouping 3 deciduous forest classes into 1 class, the overall agreement improved to 87%. The project enabled updated LULC maps, plus more specific rice types were classified compared to the previous LULC maps. The LULC maps from this project should improve the use of SWAT for modeling hydrology in the LMB, plus improve water and disaster management in a region vulnerable to flooding, droughts, and anthropogenic change (e.g., from dam building and other LULC change).

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.

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.

Assessment of the vegetation cover in a burned area 22-years ago using remote sensing techniques and GIS analysis (Sierra de las Nieves, South of Spain).

NASA Astrophysics Data System (ADS)

Martínez-Murillo, Juan F.; Remond, Ricardo; Ruiz-Sinoga, José D.

2015-04-01

The study aim was to characterize the vegetation cover in a burned area 22-years ago considering the previous situation to wildfire in 1991 and the current one in 2013. The objectives were to: (i) compare the current and previous vegetation cover to widlfire; (ii) evaluate whether the current vegetation has recovered the previous cover to wildfire; and (iii) determine the spatial variability of vegetation recovery after 22-years since the wildfire. The study area is located in Sierra de las Nieves, South of Spain. It corresponds to an area affected by a wildfire in August 8th, 1991. The burned area was equal to 8156 ha. The burn severity was spatially very high. The main geographic features of the burned area are: mountainous topography (altitudes ranging from 250 m to 1500 m; slope gradient >25%; exposure mainly southfacing); igneous (peridotites), metamorphic (gneiss) and calcareous rocks (limestones); and predominant forest land use (Pinus pinaster sp. woodlands, 10%; pinus opened forest + shrubland, 40%; shrubland, 35%; and bare soil + grassland, 15%). Remote sensing techniques and GIS analysis has been applied to achieve the objectives. Landsat 5 and Landsat 8 images were used: July 13th, 1991 and July 1st, 2013, for the previous wildfire situation and 22-years after, respectively. The 1990 CORINE land cover was also considered to map 1991 land uses prior the wildfire. Likewise, the Andalucía Regional Government wildfire historic records were used to select the burned area and its geographical limit. 1991 and 2013 land cover map were obtained by means of object-oriented classifications. Also, NDVI and PVI1 vegetation indexes were calculated and mapped for both years. Finally, some images transformations and kernel density images were applied to determine the most recovered areas and to map the spatial concentration of bare soil and pine cover areas in 1991 and 2013, respectively. According to the results, the combination of remote sensing and GIS analysis let map the most recovered areas affected by the wildfire in 1991. The vegetation indexes indicated that the vegetation cover in 2013 was still lower than that mapped just before the 1991 widlfire in most of the burned area after 22-years. This result was also confirmed by other techniques applied. Finally, the kernel density surface let identify and locate the most recovered areas of pine cover as well as those areas that still remain totally or partially uncovered (bare soil.

Spatial and Temporal Habitat Use of an Asian Elephant in Sumatra

PubMed Central

Sitompul, Arnold F.; Griffin, Curtice R.; Rayl, Nathaniel D.; Fuller, Todd K.

2013-01-01

Simple Summary A wild Sumatran elephant radio-monitored near a conservation center from August 2007–May 2008 used medium- and open-canopy land cover more than expected, but closed canopy forests were used more during the day than at night. When in closed canopy forests, elephants spent more time near the forest edge. Effective elephant conservation strategies in Sumatra need to focus on forest restoration of cleared areas and providing a forest matrix that includes various canopy types. Abstract Increasingly, habitat fragmentation caused by agricultural and human development has forced Sumatran elephants into relatively small areas, but there is little information on how elephants use these areas and thus, how habitats can be managed to sustain elephants in the future. Using a Global Positioning System (GPS) collar and a land cover map developed from TM imagery, we identified the habitats used by a wild adult female elephant (Elephas maximus sumatranus) in the Seblat Elephant Conservation Center, Bengkulu Province, Sumatra during 2007–2008. The marked elephant (and presumably her 40–60 herd mates) used a home range that contained more than expected medium canopy and open canopy land cover. Further, within the home range, closed canopy forests were used more during the day than at night. When elephants were in closed canopy forests they were most often near the forest edge vs. in the forest interior. Effective elephant conservation strategies in Sumatra need to focus on forest restoration of cleared areas and providing a forest matrix that includes various canopy types. PMID:26479527

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.

On Clear-Cut Mapping with Time-Series of Sentinel-1 Data in Boreal Forest

NASA Astrophysics Data System (ADS)

Rauste, Yrjo; Antropov, Oleg; Mutanen, Teemu; Hame, Tuomas

2016-08-01

Clear-cutting is the most drastic and wide-spread change that affects the hydrological and carbon-balance proper- ties of forested land in the Boreal forest zone1.A time-series of 36 Sentinel-1 images was used to study the potential for mapping clear-cut areas. The time series covered one and half year (2014-10-09 ... 2016-03-20) in a 200-km-by-200-km study site in Finland. The Sentinel- 1 images were acquired in Interferometric Wide-swath (IW), dual-polarized mode (VV+VH). All scenes were acquired in the same orbit configuration. Amplitude im- ages (GRDH product) were used. The Sentinel-1 scenes were ortho-rectified with in-house software using a digi- tal elevation model (DEM) produced by the Land Survey of Finland. The Sentinel-1 amplitude data were radio- metrically corrected for topographic effects.The temporal behaviour of C-band backscatter was stud- ied for areas representing 1) areas clear-cut during the ac- quisition of the Sentinel-1 time-series, 2) areas remaining forest during the acquisition of the Sentinel-1 time-series, and 3) areas that had been clear-cut before the acquisition of the Sentinel-1 time-series.The following observations were made:1. The separation between clear-cut areas and forest was generally low;2. Under certain acquisition conditions, clear-cut areas were well separable from forest;3. The good scenes were acquired: 1) in winter during thick snow cover, and 2) in late summer towards the end of a warm and dry period;4. The separation between clear-cut and forest was higher in VH polarized data than in VV-polarized data.5. The separation between clear-cut and forest was higher in the winter/snow scenes than in the dry summer scenes.

Comparison of stratified and non-stratified most similar neighbour imputation for estimating stand tables

Treesearch

Bianca N. I. Eskelson; Hailemariam Temesgen; Tara M. Barrett

2008-01-01

Many growth and yield simulators require a stand table or tree-list to set the initial condition for projections in time. Most similar neighbour (MSN) approaches can be used for estimating stand tables from information commonly available on forest cover maps (e.g. height, volume, canopy cover, and species composition). Simulations were used to compare MSN (using an...

The hydrological modeling in terms of determining the potential European beaver effect

NASA Astrophysics Data System (ADS)

Szostak, Marta; Jagodzińska, Jadwiga

2017-06-01

The objective of the paper was the hydrological analysis, in terms of categorizing main watercourses (based on coupled catchments) and marking areas covered by potential impact of the occurrence and activities of the European beaver Castor fiber. At the analysed area - the Forest District Głogów Małopolski there is a population of about 200 beavers in that Forest District. Damage inflicted by beavers was detected on 33.0 ha of the Forest District, while in the area of 13.9 ha the damage was small (below 10%). The monitoring of the beavers' behaviour and the analysis of their influence on hydrology of the area became an important element of using geoinformationtools in the management of forest areas. ArcHydro ArcGIS Esri module was applied, as an integrated set of tools for hydrographical analysis and modelling. Further steps of the procedure are hydrologic analyses such as: marking river networks on the DTM, filling holes, making maps of the flow direction, making the map of the accumulation flow, defining and segmentation of streams, marking elementary basins, marking coupled basins, making dams in the places, where beavers occur and localization of the area with a visible impact of damming. The result of the study includes maps prepared for the Forest District: the map of main rivers and their basins, categories of watercourses and compartments particularly threatened by beaver's foraging.

An interdisciplinary analysis of Colorado Rocky Mountain environments using ADP techniques. [San Juan Mts. and Indian Peaks

NASA Technical Reports Server (NTRS)

Hoffer, R. M. (Principal Investigator)

1974-01-01

The author has identified the following significant results. Good ecological, classification accuracy (90-95%) can be achieved in areas of rugged relief on a regional basis for Level 1 cover types (coniferous forest, deciduous forest, grassland, cropland, bare rock and soil, and water) using computer-aided analysis techniques on ERTS/MSS data. Cost comparisons showed that a Level 1 cover type map and a table of areal estimates could be obtained for the 443,000 hectare San Juan Mt. test site for less than 0.1 cent per acre, whereas photointerpretation techniques would cost more than 0.4 cent per acre. Results of snow cover mapping have conclusively proven that the areal extent of snow in mountainous terrain can be rapidly and economically mapped by using ERTS/MSS data and computer-aided analysis techniques. A distinct relationship between elevation and time of freeze or thaw was observed, during mountain lake mapping. Basic lithologic units such as igneous, sedimentary, and unconsolidated rock materials were successfully identified. Geomorphic form, which is exhibited through spatial and textual data, can only be inferred from ERTS data. Data collection platform systems can be utilized to produce satisfactory data from extremely inaccessible locations that encounter very adverse weather conditions, as indicated by results obtained from a DCP located at 3,536 meters elevation that encountered minimum temperatures of -25.5 C and wind speeds of up to 40.9m/sec (91 mph), but which still performed very reliably.

Monitoring and assessment of soil erosion at micro-scale and macro-scale in forests affected by fire damage in northern Iran.

PubMed

Akbarzadeh, Ali; Ghorbani-Dashtaki, Shoja; Naderi-Khorasgani, Mehdi; Kerry, Ruth; Taghizadeh-Mehrjardi, Ruhollah

2016-12-01

Understanding the occurrence of erosion processes at large scales is very difficult without studying them at small scales. In this study, soil erosion parameters were investigated at micro-scale and macro-scale in forests in northern Iran. Surface erosion and some vegetation attributes were measured at the watershed scale in 30 parcels of land which were separated into 15 fire-affected (burned) forests and 15 original (unburned) forests adjacent to the burned sites. The soil erodibility factor and splash erosion were also determined at the micro-plot scale within each burned and unburned site. Furthermore, soil sampling and infiltration studies were carried out at 80 other sites, as well as the 30 burned and unburned sites, (a total of 110 points) to create a map of the soil erodibility factor at the regional scale. Maps of topography, rainfall, and cover-management were also determined for the study area. The maps of erosion risk and erosion risk potential were finally prepared for the study area using the Revised Universal Soil Loss Equation (RUSLE) procedure. Results indicated that destruction of the protective cover of forested areas by fire had significant effects on splash erosion and the soil erodibility factor at the micro-plot scale and also on surface erosion, erosion risk, and erosion risk potential at the watershed scale. Moreover, the results showed that correlation coefficients between different variables at the micro-plot and watershed scales were positive and significant. Finally, assessment and monitoring of the erosion maps at the regional scale showed that the central and western parts of the study area were more susceptible to erosion compared with the western regions due to more intense crop-management, greater soil erodibility, and more rainfall. The relationships between erosion parameters and the most important vegetation attributes were also used to provide models with equations that were specific to the study region. The results of this paper can be useful for better understanding erosion processes at the micro-scale and macro-scale in any region having similar vegetation attributes to the forests of northern Iran.

Determination of Destructed and Infracted Forest Areas with Multi-temporal High Resolution Satellite Images

NASA Astrophysics Data System (ADS)

Seker, D. Z.; Unal, A.; Kaya, S.; Alganci, U.

2015-12-01

Migration from rural areas to city centers and their surroundings is an important problem of not only our country but also the countries that under development stage. This uncontrolled and huge amount of migration brings out urbanization and socio - economic problems. The demand on settling the industrial areas and commercial activities nearby the city centers results with a negative change in natural land cover on cities. Negative impacts of human induced activities on natural resources and land cover has been continuously increasing for decades. The main human activities that resulted with destruction and infraction of forest areas can be defined as mining activities, agricultural activities, industrial / commercial activities and urbanization. Temporal monitoring of the changes in spatial distribution of forest areas is significantly important for effective management and planning progress. Changes can occur as spatially large destructions or small infractions. Therefore there is a need for reliable, fast and accurate data sources. At this point, satellite images proved to be a good data source for determination of the land use /cover changes with their capability of monitoring large areas with reasonable temporal resolutions. Spectral information derived from images provides discrimination of land use/cover types from each other. Developments in remote sensing technology in the last decade improved the spatial resolution of satellites and high resolution images were started to be used to detect even small changes in the land surface. As being the megacity of Turkey, Istanbul has been facing a huge migration for the last 20 years and effects of urbanization and other human based activities over forest areas are significant. Main focus of this study is to determine the destructions and infractions in forest areas of Istanbul, Turkey with 2.5m resolution SPOT 5 multi-temporal satellite imagery. Analysis was mainly constructed on threshold based classification of multi-temporal vegetation index data derived from satellite images. Determined changes were exported to GIS environment and spatial overlay and intersection analyses were performed with use of forest type maps and authorized area maps in order to demonstrate the actual situation of destructions and infractions.

GIS based mapping of land cover changes utilizing multi-temporal remotely sensed image data in Lake Hawassa Watershed, Ethiopia.

PubMed

Nigatu Wondrade; Dick, Øystein B; Tveite, Havard

2014-03-01

Classifying multi-temporal image data to produce thematic maps and quantify land cover changes is one of the most common applications of remote sensing. Mapping land cover changes at the regional level is essential for a wide range of applications including land use planning, decision making, land cover database generation, and as a source of information for sustainable management of natural resources. Land cover changes in Lake Hawassa Watershed, Southern Ethiopia, were investigated using Landsat MSS image data of 1973, and Landsat TM images of 1985, 1995, and 2011, covering a period of nearly four decades. Each image was partitioned in a GIS environment, and classified using an unsupervised algorithm followed by a supervised classification method. A hybrid approach was employed in order to reduce spectral confusion due to high variability of land cover. Classification of satellite image data was performed integrating field data, aerial photographs, topographical maps, medium resolution satellite image (SPOT 20 m), and visual image interpretation. The image data were classified into nine land cover types: water, built-up, cropland, woody vegetation, forest, grassland, swamp, bare land, and scrub. The overall accuracy of the LULC maps ranged from 82.5 to 85.0 %. The achieved accuracies were reasonable, and the observed classification errors were attributable to coarse spatial resolution and pixels containing a mixture of cover types. Land cover change statistics were extracted and tabulated using the ERDAS Imagine software. The results indicated an increase in built-up area, cropland, and bare land areas, and a reduction in the six other land cover classes. Predominant land cover is cropland changing from 43.6 % in 1973 to 56.4 % in 2011. A significant portion of land cover was converted into cropland. Woody vegetation and forest cover which occupied 21.0 and 10.3 % in 1973, respectively, diminished to 13.6 and 5.6 % in 2011. The change in water body was very peculiar in that the area of Lake Hawassa increased from 91.9 km(2) in 1973 to 95.2 km(2) in 2011, while that of Lake Cheleleka whose area was 11.3 km(2) in 1973 totally vanished in 2011 and transformed into mud-flat and grass dominated swamp. The "change and no change" analysis revealed that more than one third (548.0 km(2)) of the total area was exposed to change between 1973 and 2011. This study was useful in identifying the major land cover changes, and the analysis pursued provided a valuable insight into the ongoing changes in the area under investigation.

Bird conservation would complement landslide prevention in the Central Andes of Colombia

PubMed Central

Ocampo-Peñuela, Natalia

2015-01-01

Conservation and restoration priorities often focus on separate ecosystem problems. Inspired by the November 11th (2011) landslide event near Manizales, and the current poor results of Colombia’s Article 111 of Law 99 of 1993 as a conservation measure in this country, we set out to prioritize conservation and restoration areas where landslide prevention would complement bird conservation in the Central Andes. This area is one of the most biodiverse places on Earth, but also one of the most threatened. Using the case of the Rio Blanco Reserve, near Manizales, we identified areas for conservation where endemic and small-range bird diversity was high, and where landslide risk was also high. We further prioritized restoration areas by overlapping these conservation priorities with a forest cover map. Restoring forests in bare areas of high landslide risk and important bird diversity yields benefits for both biodiversity and people. We developed a simple landslide susceptibility model using slope, forest cover, aspect, and stream proximity. Using publicly available bird range maps, refined by elevation, we mapped concentrations of endemic and small-range bird species. We identified 1.54 km2 of potential restoration areas in the Rio Blanco Reserve, and 886 km2 in the Central Andes region. By prioritizing these areas, we facilitate the application of Article 111 which requires local and regional governments to invest in land purchases for the conservation of watersheds. PMID:25737819

Bird conservation would complement landslide prevention in the Central Andes of Colombia.

PubMed

Ocampo-Peñuela, Natalia; Pimm, Stuart L

2015-01-01

Conservation and restoration priorities often focus on separate ecosystem problems. Inspired by the November 11th (2011) landslide event near Manizales, and the current poor results of Colombia's Article 111 of Law 99 of 1993 as a conservation measure in this country, we set out to prioritize conservation and restoration areas where landslide prevention would complement bird conservation in the Central Andes. This area is one of the most biodiverse places on Earth, but also one of the most threatened. Using the case of the Rio Blanco Reserve, near Manizales, we identified areas for conservation where endemic and small-range bird diversity was high, and where landslide risk was also high. We further prioritized restoration areas by overlapping these conservation priorities with a forest cover map. Restoring forests in bare areas of high landslide risk and important bird diversity yields benefits for both biodiversity and people. We developed a simple landslide susceptibility model using slope, forest cover, aspect, and stream proximity. Using publicly available bird range maps, refined by elevation, we mapped concentrations of endemic and small-range bird species. We identified 1.54 km(2) of potential restoration areas in the Rio Blanco Reserve, and 886 km(2) in the Central Andes region. By prioritizing these areas, we facilitate the application of Article 111 which requires local and regional governments to invest in land purchases for the conservation of watersheds.

EnviroAtlas - Portland, OR - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (http:/www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Woodbine, Iowa - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Milwaukee, WI - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Tampa, FL - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Durham, NC - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

EnviroAtlas - Phoenix, AZ - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Green Bay, WI - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

EnviroAtlas - New Bedford, MA - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Fresno, CA - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area.This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Portland, Maine - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

EnviroAtlas - Pittsburgh, PA - 51m Riparian Buffer Forest Cover

EPA Pesticide Factsheets

This EnviroAtlas dataset describes the percentage of a 51-m riparian buffer that is forested. There is a potential for decreased water quality in areas where the riparian buffer is less forested. The displayed line represents the center of the analyzed riparian buffer. The water bodies analyzed include hydrologically connected streams, rivers, connectors, reservoirs, lakes/ponds, ice masses, washes, locks, and rapids within the Atlas Area. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The Price of Precision: Large-Scale Mapping of Forest Structure and Biomass Using Airborne Lidar

NASA Astrophysics Data System (ADS)

Dubayah, R.

2015-12-01

Lidar remote sensing provides one of the best means for acquiring detailed information on forest structure. However, its application over large areas has been limited largely because of its expense. Nonetheless, extant data exist over many states in the U.S., funded largely by state and federal consortia and mainly for infrastructure, emergency response, flood plain and coastal mapping. These lidar data are almost always acquired in leaf-off seasons, and until recently, usually with low point count densities. Even with these limitations, they provide unprecedented wall-to-wall mappings that enable development of appropriate methodologies for large-scale deployment of lidar. In this talk we summarize our research and lessons learned in deriving forest structure over regional areas as part of NASA's Carbon Monitoring System (CMS). We focus on two areas: the entire state of Maryland and Sonoma County, California. The Maryland effort used low density, leaf-off data acquired by each county in varying epochs, while the on-going Sonoma work employs state-of-the-art, high density, wall-to-wall, leaf-on lidar data. In each area we combine these lidar coverages with high-resolution multispectral imagery from the National Agricultural Imagery Program (NAIP) and in situ plot data to produce maps of canopy height, tree cover and biomass, and compare our results against FIA plot data and national biomass maps. Our work demonstrates that large-scale mapping of forest structure at high spatial resolution is achievable but products may be complex to produce and validate over large areas. Furthermore, fundamental issues involving statistical approaches, plot types and sizes, geolocation, modeling scales, allometry, and even the definitions of "forest" and "non-forest" must be approached carefully. Ultimately, determining the "price of precision", that is, does the value of wall-to-wall forest structure data justify their expense, should consider not only carbon market applications, but the other ways the underlying lidar data may be used.

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.

Permafrost thaw and wildfire: Equally important drivers of boreal tree cover changes in the Taiga Plains, Canada

NASA Astrophysics Data System (ADS)

Helbig, M.; Pappas, C.; Sonnentag, O.

2016-02-01

Boreal forests cover vast areas of the permafrost zones of North America, and changes in their composition and structure can lead to pronounced impacts on the regional and global climate. We partition the variation in regional boreal tree cover changes between 2000 and 2014 across the Taiga Plains, Canada, into its main causes: permafrost thaw, wildfire disturbance, and postfire regrowth. Moderate Resolution Imaging Spectroradiometer Percent Tree Cover (PTC) data are used in combination with maps of historic fires, and permafrost and drainage characteristics. We find that permafrost thaw is equally important as fire history to explain PTC changes. At the southern margin of the permafrost zone, PTC loss due to permafrost thaw outweighs PTC gain from postfire regrowth. These findings emphasize the importance of permafrost thaw in controlling regional boreal forest changes over the last decade, which may become more pronounced with rising air temperatures and accelerated permafrost thaw.

A Multi-Index Integrated Change detection method for updating the National Land Cover Database

USGS Publications Warehouse

Jin, Suming; Yang, Limin; Xian, George Z.; Danielson, Patrick; Homer, Collin G.

2010-01-01

Land cover change is typically captured by comparing two or more dates of imagery and associating spectral change with true thematic change. A new change detection method, Multi-Index Integrated Change (MIIC), has been developed to capture a full range of land cover disturbance patterns for updating the National Land Cover Database (NLCD). Specific indices typically specialize in identifying only certain types of disturbances; for example, the Normalized Burn Ratio (NBR) has been widely used for monitoring fire disturbance. Recognizing the potential complementary nature of multiple indices, we integrated four indices into one model to more accurately detect true change between two NLCD time periods. The four indices are NBR, Normalized Difference Vegetation Index (NDVI), Change Vector (CV), and a newly developed index called the Relative Change Vector (RCV). The model is designed to provide both change location and change direction (e.g. biomass increase or biomass decrease). The integrated change model has been tested on five image pairs from different regions exhibiting a variety of disturbance types. Compared with a simple change vector method, MIIC can better capture the desired change without introducing additional commission errors. The model is particularly accurate at detecting forest disturbances, such as forest harvest, forest fire, and forest regeneration. Agreement between the initial change map areas derived from MIIC and the retained final land cover type change areas will be showcased from the pilot test sites.

Inventory of forest and rangeland and detection of forest stress

NASA Technical Reports Server (NTRS)

Heller, R. C.; Aldrich, R. C.; Weber, F. P.; Driscoll, R. S. (Principal Investigator)

1972-01-01

There are no author-identified significant results in this report. Some ERTS-1 imagery has been received for each of the test sites: Black Hills, Atlanta, and Manitou. Only small portions of each site are covered and clouds have precluded capturing good imagery over the center of each site. Discoloration infestations of ponderosa pine are being located and sized on CIR transparencies. A computer program was completed from microdensitometer scans of CIR photos which maps areas of an image which are spectrally similar. Decided differences between forest types are present as well as differences between forest and other vegetative and nonvegetative land classes.

Geochemical map of the Chama River Canyon Wilderness and contiguous roadless area, Rio Arriba County, New Mexico

USGS Publications Warehouse

Ridgley, Jennie L.

1986-01-01

The Chama River Canyon Wilderness, in Rio Arriba County, north-central New Mexico, covers 50,300 acres (20,364 hectares) within the Coyote and Cuba Ranger Districts of the Santa Fe National Forest and the Canjilon Ranger District of the Carson National Forest. In 1979 the U.S. Forest Service, under the Forest Service Roadless Area Review and Evaluation (RARE II) program, designated three additional areas, contiguous to the wilderness, for further planning to assess wilderness characteristics. These areas, totaling 4,800 acres (1,945 hectares), were collectively designated Roadless area 03098; they have since been dropped from consideration. 

An integrated analysis of the effects of past land use on forest herb colonization at the landscape scale

USGS Publications Warehouse

Verheyen, K.; Guntenspergen, Glenn R.; Biesbrouck, B.; Hermy, M.

2003-01-01

A framework that summarizes the direct and indirect effects of past land use on forest herb recolonization is proposed, and used to analyse the colonization patterns of forest understorey herbaceous species in a 360-ha mixed forest, grassland and arable landscape in the Dijle river valley (central Belgium).Fine-scale distribution maps were constructed for 14 species. The species were mapped in 15 946 forest plots and outside forests (along parcel margins) in 5188 plots. Forest stands varied in age between 1 and more than 224 years. Detailed land-use history data were combined with the species distribution maps to identify species-specific colonization sources and to calculate colonization distances.The six most frequent species were selected for more detailed statistical analysis.Logistic regression models indicated that species frequency in forest parcels was a function of secondary forest age, distance from the nearest colonization source and their interaction. Similar age and distance effects were found within hedgerows.In 199 forest stands, data about soils, canopy structure and the cover of competitive species were collected. The relative importance of habitat quality and spatio-temporal isolation for the colonization of the forest herb species was quantified using structural equation modelling (SEM), within the framework proposed for the effects of past land use.The results of the SEM indicate that, except for the better colonizing species, the measured habitat quality variables are of minor importance in explaining colonization patterns, compared with the combination of secondary forest age and distance from colonization sources.Our results suggest the existence of a two-stage colonization process in which diaspore availability determines the initial pattern, which is affected by environmental sorting at later stages.

Characterizing channel change along a multithread gravel-bed river using random forest image classification

NASA Astrophysics Data System (ADS)

Overstreet, B. T.; Legleiter, C. J.

2012-12-01

The Snake River in Grand Teton National Park is a dam-regulated but highly dynamic gravel-bed river that alternates between a single thread and a multithread planform. Identifying key drivers of channel change on this river could improve our understanding of 1) how flow regulation at Jackson Lake Dam has altered the character of the river over time; 2) how changes in the distribution of various types of vegetation impacts river dynamics; and 3) how the Snake River will respond to future human and climate driven disturbances. Despite the importance of monitoring planform changes over time, automated channel extraction and understanding the physical drivers contributing to channel change continue to be challenging yet critical steps in the remote sensing of riverine environments. In this study we use the random forest statistical technique to first classify land cover within the Snake River corridor and then extract channel features from a sequence of high-resolution multispectral images of the Snake River spanning the period from 2006 to 2012, which encompasses both exceptionally dry years and near-record runoff in 2011. We show that the random forest technique can be used to classify images with as few as four spectral bands with far greater accuracy than traditional single-tree classification approaches. Secondly, we couple random forest derived land cover maps with LiDAR derived topography, bathymetry, and canopy height to explore physical drivers contributing to observed channel changes on the Snake River. In conclusion we show that the random forest technique is a powerful tool for classifying multispectral images of rivers. Moreover, we hypothesize that with sufficient data for calculating spatially distributed metrics of channel form and more frequent channel monitoring, this tool can also be used to identify areas with high probabilities of channel change. Land cover maps of a portion of the Snake River produced from digital aerial photography from 2010 and a 2011 WorldView2 satellite image. This pair of maps thus captures changes that occurred during the 2011 runoff

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

USGS Publications Warehouse

Schleeweis, Karen; Goward, Samuel N.; Huang, Chengquan; Dwyer, John L.; Dungan, Jennifer L.; Lindsey, Mary A.; Michaelis, Andrew; Rishmawi, Khaldoun; Masek, Jeffery G.

2016-01-01

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.

Viable contribution of Tibetan sacred mountains in southwestern China to forest conservation.

PubMed

Shen, Xiaoli; Li, Sheng; Wang, Dajun; Lu, Zhi

2015-12-01

The Tibetan sacred mountains (TSMs) cover a large area and may represent a landscape-scale conservation opportunity. We compared the conservation value of forests in these mountains with the conservation value of government-established nature reserves and unmanaged open-access areas in Danba County, southwestern China. We used Landsat satellite images to map forest cover and to estimate forest loss in 1974-1989, 1989-1999, and 1999-2013. The TSMs (n = 41) and nature reserves (n = 4) accounted for 21.6% and 29.7% of the county's land area, respectively. Remaining land was open-access areas (i.e., areas without any restrictions on resource use) (56.2%) and farmlands (2.2%). Within the elevation range suitable for forests, forest cover did not differ significantly between nature reserves (58.8%) and open-access areas (58.4%), but was significantly higher in TSMs (65.5%) after controlling for environmental factors such as aspect, slope, and elevation. The TSMs of great cultural importance had higher forest cover, but patrols by monastery staff were not necessarily associated with increased forest cover. The annual deforestation rate in nonsacred areas almost tripled in 1989-1999 (111.4 ha/year) relative to 1974-1989 (40.4 ha/year), whereas the rate in TSMs decreased in the later period (19.7 ha/year vs. 17.2 ha/year). The reduced forest loss in TSMs in 1989-1999 was possibly due to the renaissance of TSM worship and strengthened management by the local Buddhist community since late 1980s. The annual deforestation rate in Danba decreased dramatically to 4.4 ha/year in 1999-2013, which coincided with the implementation of a national ban on logging in 1998. As the only form of protected area across the Tibetan region during much of its history, TSMs have positively contributed to conserving forest at a landscape scale. Conservation of TSM forests largely relied on the strength of local religious institutions. Integrating community-based conservation of TSMs within the government conservation network would benefit the conservation of the Tibetan region. © 2015 Society for Conservation Biology.

Mapping resource use over a Russian landscape: an integrated look at harvesting of a non-timber forest product in central Kamchatka

NASA Astrophysics Data System (ADS)

Hitztaler, Stephanie K.; Bergen, Kathleen M.

2013-12-01

Small-scale resource use became an important adaptive mechanism in remote logging communities in Russia at the onset of the post-Soviet period in 1991. We focused on harvesting of a non-timber forest product, lingonberry (Vaccinium vitis-idaea), in the forests of the Kamchatka Peninsula (Russian Far East). We employed an integrated geographical approach to make quantifiable connections between harvesting and the landscape, and to interpret these relationships in their broader contexts. Landsat TM images were used for a new classification; the resulting land-cover map was the basis for linking non-spatial data on harvesters’ gathering behaviors to spatial data within delineated lingonberry gathering sites. Several significant relationships emerged: (1) mature forests negatively affected harvesters’ initial choice to gather in a site, while young forests had a positive effect; (2) land-cover type was critical in determining how and why gathering occurred: post-disturbance young and maturing forests were significantly associated with higher gathering intensity and with the choice to market harvests; and (3) distance from gathering sites to villages and main roads also mattered: longer distances were significantly correlated to more time spent gathering and to increased marketing of harvests. We further considered our findings in light of the larger ecological and social dynamics at play in central Kamchatka. This unique study is an important starting point for conservation- and sustainable development-based work, and for additional research into the drivers of human-landscape interactions in the Russian Far East.

Vegetation Canopy Structure from NASA EOS Multiangle Imaging

NASA Astrophysics Data System (ADS)

Chopping, M.; Martonchik, J. V.; Bull, M.; Rango, A.; Schaaf, C. B.; Zhao, F.; Wang, Z.

2008-12-01

We used red band bidirectional reflectance data from the NASA Multiangle Imaging SpectroRadiometer (MISR) and the MODerate resolution Imaging Spectroradiometer (MODIS) mapped onto a 250 m grid in a multiangle approach to obtain estimates of woody plant fractional cover and crown height through adjustment of the mean radius and mean crown aspect ratio parameters of an hybrid geometric-optical (GO) model. We used a technique to rapidly obtain MISR surface reflectance estimates at 275 m resolution through regression on 1 km MISR land surface estimates previously corrected for atmospheric attenuation using MISR aerosol estimates. MISR data were used to make end of dry season maps from 2000-2007 for parts of southern New Mexico, while MODIS data were used to replicate previous results obtained using MISR for June 2002 over large parts of New Mexico and Arizona. We also examined the applicability of this method in Alaskan tundra and forest by adjusting the GO model against MISR data for winter (March 2000) and summer (August 2008) scenes. We found that the GO model crown aspect ratio from MISR followed dominant shrub species distributions in the USDA, ARS Jornada Experimental Range, enabling differentiation of the more spherical crowns of creosotebush (Larrea tridentata) from the more prolate crowns of honey mesquite (Prosopis glandulosa). The measurement limits determined from 2000-2007 maps for a large part of southern New Mexico are ~0.1 in fractional shrub crown cover and ~3 m in mean canopy height (results obtained using data acquired shortly after precipitation events that radically darkened and altered the structure and angular response of the background). Typical standard deviations over the period for 12 sites covering a range of cover types are on the order of 0.05 in crown cover and 2 m in mean canopy height. We found that the GO model can be inverted to retrieve reasonable distributions of canopy parameters in southwestern environments using MODIS V005 red band surface reflectance estimates at ~250 m spatial resolution accumulated over 16 day periods. The MODIS (N=895) and MISR (N=576) estimates of forest height and cover both showed agreement with USDA, Forest Service estimates, with MODIS mean absolute errors (MAE) of 0.09 and 8.4 m respectively; and MISR MAE of 0.10 and 2.2 m, respectively, noting that a sub-optimal background was used for the MODIS inversions. The MODIS and MISR MAE for estimates of aboveground woody biomass via regression against Forest Service estimates were both 10.1 Mg.ha-1. We found that red band MISR data for central Alaska can be used to obtain first-order estimates of forest cover and height using a snow-free summer scene and shrub cover using a winter scene with full snow cover. The GO model inversion results are often physically unrealistic but spatial distributions correspond to high resolution images and reflect the potential for the multiangle/GO method to retrieve meaningful information that is qualitatively different to that obtained using vegetation indices.

Spatial dynamics of deforestation and forest fragmentation (1930-2013) in Eastern Ghats, India

NASA Astrophysics Data System (ADS)

Sudhakar Reddy, C.; Jha, C. S.; Dadhwal, V. K.

2014-11-01

The tropical forests are the most unique ecosystems for their potential economic value. Eastern Ghats, a phytogeographical region of India has rugged hilly terrain distributed in parts of five states, viz. Odisha, Andhra Pradesh, Telangana, Karnataka and Tamil Nadu. The present study is mainly aimed to analyse the trends in deforestation and its role in forest fragmentation of Eastern Ghats. The long term changes in forest cover with its spatial pattern over time has been assessed by analyzing a set of topographical maps and satellite remote sensing datasets. The multi-source and multi-date mapping has been carried out using survey of India topographical maps (1930's), Landsat MSS (1975 and 1985), IRS 1B LISS-I (1995), IRS P6 AWiFS (2005) and Resourcesat-2 AWiFS (2013) satellite images. The classified spatial data for 1930, 1975, 1985, 1995, 2005 and 2013 showed that the forest cover for the mentioned years are 102213 km2 (45.6 %), 76630 (34.2 %), 73416 km2 (32.7 %), 71730 km2 (32 %), 71305 km2 (31.8 %) and 71186 km2 (31.7 %) of the geographical area of Eastern Ghats respectively. A spatial statistical analysis of the deforestation rates and forest cover change were carried out based on distinctive time phases, i.e. 1930-1975, 1975-1985, 1985-1995, 1995-2005 and 2005-2013. The spatial analysis was carried out first by segmenting the study area into grid cells of 5 km x 5 km for time series assessment and determining spatial changes in forests. The distribution of loss and gain of forest was calculated across six classes i.e. <1 km2, 1-5 km2, 5-10 km2, 10-15 km2, 15-20 km2 and >20 km2. Landscape metrics were used to quantify spatial variability of landscape structure and composition. The results of study on net rate of deforestation was found to be 0.64 during 1935 to 1975, 0.43 during 1975-1985, 0.23 during 1985-1995, 0.06 during 1995-2005 and 0.02 during 2005-2013. The number of forest patches increased from 2688 (1930) to 13009 (2013). The largest forest patch in 1930 represents area of 41669 km2 that has reduced to 27800 km2 by 2013. Thus, it is evident that there is a substantial reduction in the size of the very large forest patches due to deforestation. According to spatial analysis, among the different land use change drivers, agriculture occupies highest area, followed by degradation to scrub and conversion to orchards. The dominant forest type was dry deciduous which comprises 37192 km2 (52.2 %) of the total forest area of Eastern Ghats, followed by moist deciduous forest (39.2 %) and semievergreen forest (4.8 %) in 2013. The change analysis showed that the large scale negative changes occurred in deciduous forests and semi-evergreen forests compared to wet evergreen forests due to high economic potential and accessibility. This study has quantified the deforestation that has taken place over the last eight decades in the Eastern Ghats. The decline in overall rate of deforestation in recent years indicates increased measures of conservation. The change analysis of deforestation and forest fragmentation provides a decisive component for conservation and helpful in long term management of forests of Eastern Ghats.

Losing a jewel—Rapid declines in Myanmar’s intact forests from 2002-2014

PubMed Central

Horning, Ned; Khaing, Thiri; Thein, Zaw Min; Aung, Kyaw Moe; Aung, Kyaw Htet; Phyo, Paing; Tun, Ye Lin; Oo, Aung Htat; Neil, Anthony; Thu, Win Myo; Songer, Melissa; Huang, Qiongyu; Connette, Grant; Leimgruber, Peter

2017-01-01

New and rapid political and economic changes in Myanmar are increasing the pressures on the country’s forests. Yet, little is known about the past and current condition of these forests and how fast they are declining. We mapped forest cover in Myanmar through a consortium of international organizations and environmental non-governmental groups, using freely-available public domain data and open source software tools. We used Landsat satellite imagery to assess the condition and spatial distribution of Myanmar’s intact and degraded forests with special focus on changes in intact forest between 2002 and 2014. We found that forests cover 42,365,729 ha or 63% of Myanmar, making it one of the most forested countries in the region. However, severe logging, expanding plantations, and degradation pose increasing threats. Only 38% of the country’s forests can be considered intact with canopy cover >80%. Between 2002 and 2014, intact forests declined at a rate of 0.94% annually, totaling more than 2 million ha forest loss. Losses can be extremely high locally and we identified 9 townships as forest conversion hotspots. We also delineated 13 large (>100,000 ha) and contiguous intact forest landscapes, which are dispersed across Myanmar. The Northern Forest Complex supports four of these landscapes, totaling over 6.1 million ha of intact forest, followed by the Southern Forest Complex with three landscapes, comprising 1.5 million ha. These remaining contiguous forest landscape should have high priority for protection. Our project demonstrates how open source data and software can be used to develop and share critical information on forests when such data are not readily available elsewhere. We provide all data, code, and outputs freely via the internet at (for scripts: https://bitbucket.org/rsbiodiv/; for the data: http://geonode.themimu.info/layers/geonode%3Amyan_lvl2_smoothed_dec2015_resamp) PMID:28520726

Losing a jewel-Rapid declines in Myanmar's intact forests from 2002-2014.

PubMed

Bhagwat, Tejas; Hess, Andrea; Horning, Ned; Khaing, Thiri; Thein, Zaw Min; Aung, Kyaw Moe; Aung, Kyaw Htet; Phyo, Paing; Tun, Ye Lin; Oo, Aung Htat; Neil, Anthony; Thu, Win Myo; Songer, Melissa; LaJeunesse Connette, Katherine; Bernd, Asja; Huang, Qiongyu; Connette, Grant; Leimgruber, Peter

2017-01-01

New and rapid political and economic changes in Myanmar are increasing the pressures on the country's forests. Yet, little is known about the past and current condition of these forests and how fast they are declining. We mapped forest cover in Myanmar through a consortium of international organizations and environmental non-governmental groups, using freely-available public domain data and open source software tools. We used Landsat satellite imagery to assess the condition and spatial distribution of Myanmar's intact and degraded forests with special focus on changes in intact forest between 2002 and 2014. We found that forests cover 42,365,729 ha or 63% of Myanmar, making it one of the most forested countries in the region. However, severe logging, expanding plantations, and degradation pose increasing threats. Only 38% of the country's forests can be considered intact with canopy cover >80%. Between 2002 and 2014, intact forests declined at a rate of 0.94% annually, totaling more than 2 million ha forest loss. Losses can be extremely high locally and we identified 9 townships as forest conversion hotspots. We also delineated 13 large (>100,000 ha) and contiguous intact forest landscapes, which are dispersed across Myanmar. The Northern Forest Complex supports four of these landscapes, totaling over 6.1 million ha of intact forest, followed by the Southern Forest Complex with three landscapes, comprising 1.5 million ha. These remaining contiguous forest landscape should have high priority for protection. Our project demonstrates how open source data and software can be used to develop and share critical information on forests when such data are not readily available elsewhere. We provide all data, code, and outputs freely via the internet at (for scripts: https://bitbucket.org/rsbiodiv/; for the data: http://geonode.themimu.info/layers/geonode%3Amyan_lvl2_smoothed_dec2015_resamp).

A random forest approach for predicting the presence of Echinococcus multilocularis intermediate host Ochotona spp. presence in relation to landscape characteristics in western China

PubMed Central

Marston, Christopher G.; Danson, F. Mark; Armitage, Richard P.; Giraudoux, Patrick; Pleydell, David R.J.; Wang, Qian; Qui, Jiamin; Craig, Philip S.

2014-01-01

Understanding distribution patterns of hosts implicated in the transmission of zoonotic disease remains a key goal of parasitology. Here, random forests are employed to model spatial patterns of the presence of the plateau pika (Ochotona spp.) small mammal intermediate host for the parasitic tapeworm Echinococcus multilocularis which is responsible for a significant burden of human zoonoses in western China. Landsat ETM+ satellite imagery and digital elevation model data were utilized to generate quantified measures of environmental characteristics across a study area in Sichuan Province, China. Land cover maps were generated identifying the distribution of specific land cover types, with landscape metrics employed to describe the spatial organisation of land cover patches. Random forests were used to model spatial patterns of Ochotona spp. presence, enabling the relative importance of the environmental characteristics in relation to Ochotona spp. presence to be ranked. An index of habitat aggregation was identified as the most important variable in influencing Ochotona spp. presence, with area of degraded grassland the most important land cover class variable. 71% of the variance in Ochotona spp. presence was explained, with a 90.98% accuracy rate as determined by ‘out-of-bag’ error assessment. Identification of the environmental characteristics influencing Ochotona spp. presence enables us to better understand distribution patterns of hosts implicated in the transmission of Em. The predictive mapping of this Em host enables the identification of human populations at increased risk of infection, enabling preventative strategies to be adopted. PMID:25386042

Environmental Controls on Above-Ground Biomass in the Taita Hills, Kenya

NASA Astrophysics Data System (ADS)

Adhikari, H.; Heiskanen, J.; Siljander, M.; Maeda, E. E.; Heikinheimo, V.; Pellikka, P.

2016-12-01

Tropical forests are globally significant ecosystems which maintain high biodiversity and provide valuable ecosystem services, including carbon sink, climate change mitigation and adaptation. This ecosystem has been severely degraded for decades. However, the magnitude and spatial patterns of the above ground biomass (AGB) in the tropical forest-agriculture landscapes is highly variable, even under the same climatic condition and land use. This work aims 1) to generate wall-to-wall map of AGB density for the Taita Hills in Kenya based on field measurements and airborne laser scanning (ALS) and 2) to examine environmental controls on AGB using geospatial data sets on topography, soils, climate and land use, and statistical modelling. The study area (67000 ha) is located in the northernmost part of the Eastern Arc Mountains of Kenya and Tanzania, and the highest hilltops reach over 2200 m in elevation. Most of the forest area has been cleared for croplands and agroforestry, and hills are surrounded by the semi-arid scrublands and dry savannah at an elevation of 600-900 m a.s.l. As a result, the current land cover is a mosaic of various types of land cover and land use. The field measurements were carried out in total of 216 plots in 2013-2015 for AGB computations and ALS flights were conducted in 2014-2015. AGB map at 30 m x 30 m resolution was implemented using multiple linear regression based on ALS variables derived from the point cloud, namely canopy cover and 25 percentile height of ALS returns (R2 = 0.88). Boosted regression trees (BRT) was used for examining the relationship between AGB and explanatory variables, which were derived from ALS-based high resolution DEM (2 m resolution), soil database, downscaled climate data and land cover/use maps based on satellite image analysis. The results of these analyses will be presented in the conference.

Effect of land cover change on runoff curve number estimation in Iowa, 1832-2001

USGS Publications Warehouse

Wehmeyer, Loren L.; Weirich, Frank H.; Cuffney, Thomas F.

2011-01-01

Within the first few decades of European-descended settlers arriving in Iowa, much of the land cover across the state was transformed from prairie and forest to farmland, patches of forest, and urbanized areas. Land cover change over the subsequent 126 years was minor in comparison. Between 1832 and 1859, the General Land Office conducted a survey of the State of Iowa to aid in the disbursement of land. In 1875, an illustrated atlas of the State of Iowa was published, and in 2001, the US Geological Survey National Land Cover Dataset was compiled. Using these three data resources for classifying land cover, the hydrologic impact of the land cover change at three points in time over a period of 132+ years is presented in terms of the effect on the area-weighted average curve number, a term commonly used to predict peak runoff from rainstorms. In the four watersheds studied, the area-weighted average curve number associated with the first 30 years of settlement increased from 61·4 to 77·8. State-wide mapped forest area over this same period decreased 19%. Over the next 126 years, the area-weighted average curve number decreased to 76·7, despite an additional forest area reduction of 60%. This suggests that degradation of aquatic resources (plants, fish, invertebrates, and habitat) arising from hydrologic alteration was likely to have been much higher during the 30 years of initial settlement than in the subsequent period of 126 years in which land cover changes resulted primarily from deforestation and urbanization. 

Characterization of post-fire surface cover, soils, and burn severity at the Cerro Grande Fire, New Mexico, using hyperspectral and multispectral remote sensing

USGS Publications Warehouse

Kokaly, R.F.; Rockwell, B.W.; Haire, S.L.; King, T.V.V.

2007-01-01

Forest fires leave behind a changed ecosystem with a patchwork of surface cover that includes ash, charred organic matter, soils and soil minerals, and dead, damaged, and living vegetation. The distributions of these materials affect post-fire processes of erosion, nutrient cycling, and vegetation regrowth. We analyzed high spatial resolution (2.4??m pixel size) Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) data collected over the Cerro Grande fire, to map post-fire surface cover into 10 classes, including ash, soil minerals, scorched conifer trees, and green vegetation. The Cerro Grande fire occurred near Los Alamos, New Mexico, in May 2000. The AVIRIS data were collected September 3, 2000. The surface cover map revealed complex patterns of ash, iron oxide minerals, and clay minerals in areas of complete combustion. Scorched conifer trees, which retained dry needles heated by the fire but not fully combusted by the flames, were found to cover much of the post-fire landscape. These scorched trees were found in narrow zones at the edges of completely burned areas. A surface cover map was also made using Landsat Enhanced Thematic Mapper plus (ETM+) data, collected September 5, 2000, and a maximum likelihood, supervised classification. When compared to AVIRIS, the Landsat classification grossly overestimated cover by dry conifer and ash classes and severely underestimated soil and green vegetation cover. In a comparison of AVIRIS surface cover to the Burned Area Emergency Rehabilitation (BAER) map of burn severity, the BAER high burn severity areas did not capture the variable patterns of post-fire surface cover by ash, soil, and scorched conifer trees seen in the AVIRIS map. The BAER map, derived from air photos, also did not capture the distribution of scorched trees that were observed in the AVIRIS map. Similarly, the moderate severity class of Landsat-derived burn severity maps generated from the differenced Normalized Burn Ratio (dNBR) calculation had low agreement with the AVIRIS classes of scorched conifer trees. Burn severity and surface cover images were found to contain complementary information, with the dNBR map presenting an image of degree of change caused by fire and the AVIRIS-derived map showing specific surface cover resulting from fire.

Multi-Scale Mapping of Vegetation Biomass

NASA Astrophysics Data System (ADS)

Hudak, A. T.; Fekety, P.; Falkowski, M. J.; Kennedy, R. E.; Crookston, N.; Smith, A. M.; Mahoney, P.; Glenn, N. F.; Dong, J.; Kane, V. R.; Woodall, C. W.

2016-12-01

Vegetation biomass mapping at multiple scales is important for carbon inventory and monitoring, reporting, and verification (MRV). Project-level lidar collections allow biomass estimation with high confidence where associated with field plot measurements. Predictive models developed from such datasets are customarily used to generate landscape-scale biomass maps. We tested the feasibility of predicting biomass in landscapes surveyed with lidar but without field plots, by withholding plot datasets from a reduced model applied to the landscapes, and found support for a generalized model in the northern Idaho ecoregion. We are also upscaling a generalized model to all forested lands in Idaho. Our regional modeling approach is to sample the 30-m biomass predictions from the landscape-scale maps and use them to train a regional biomass model, using Landsat time series, topographic derivatives, and climate variables as predictors. Our regional map validation approach is to aggregate the regional, annual biomass predictions to the county level and compare them to annual county-level biomass summarized independently from systematic, field-based, annual inventories conducted by the US Forest Inventory and Analysis (FIA) Program nationally. A national-scale forest cover map generated independently from 2010 PALSAR data at 25-m resolution is being used to mask non-forest pixels from the aggregations. Effects of climate change on future regional biomass stores are also being explored, using biomass estimates projected from stand-level inventory data collected in the National Forests and comparing them to FIA plot data collected independently on public and private lands, projected under the same climate change scenarios, with disturbance trends extracted from the Landsat time series. Our ultimate goal is to demonstrate, focusing on the ecologically diverse Northwest region of the USA, a carbon monitoring system (CMS) that is accurate, objective, repeatable, and transparent.

EnviroAtlas -Durham, NC- One Meter Resolution Urban Area Land Cover Map (2010)

EPA Pesticide Factsheets

The EnviroAtlas Durham, NC land cover map was generated from USDA NAIP (National Agricultural Imagery Program) four band (red, green, blue and near infrared) aerial photography from July 2010 at 1 m spatial resolution. Five land cover classes were mapped: impervious surface, soil and barren, grass and herbaceous, trees and forest, and water. An accuracy assessment using a stratified random sampling of 500 samples yielded an overall accuracy of 83 percent using a minimum mapping unit of 9 pixels (3x3 pixel window). The area mapped is defined by the US Census Bureau's 2010 Urban Statistical Area for Durham, and includes the cities of Durham, Chapel Hill, Carrboro and Hillsborough, NC. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas ) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets ).

BOREAS TE-20 Soils Data Over the NSA-MSA and Tower Sites in Vector Format

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Veldhuis, Hugo; Knapp, David

2000-01-01

The BOREAS TE-20 team collected several data sets for use in developing and testing models of forest ecosystem dynamics. This data set contains vector layers of soil maps that were received from Dr. Hugo Veldhuis, who did the original mapping in the field during 1994. The vector layers were converted to ARCANFO EXPORT files. These data cover 1-kilometer diameters around each of the NSA tower sites, and another layer covers the NSA-MSA. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Center (DAAC).

Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery

USGS Publications Warehouse

Helmer, E.H.; Kennaway, T.A.; Pedreros, D.H.; Clark, M.L.; Marcano-Vega, H.; Tieszen, L.L.; Ruzycki, T.R.; Schill, S.R.; Carrington, C.M.S.

2008-01-01

Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius, testing a more detailed classification than earlier work in the latter three islands. Secondly, we estimate the extents of land cover and protected forest by formation for five islands and ask how land cover has changed over the second half of the 20th century. The image interpretation approach combines image mosaics and ancillary geographic data, classifying the resulting set of raster data with decision tree software. Cloud-free image mosaics for one or two seasons were created by applying regression tree normalization to scene dates that could fill cloudy areas in a base scene. Such mosaics are also known as cloud-filled, cloud-minimized or cloud-cleared imagery, mosaics, or composites. The approach accurately distinguished several classes that more standard methods would confuse; the seamless mosaics aided reference data collection; and the multiseason imagery allowed us to separate drought deciduous forests and woodlands from semi-deciduous ones. Cultivated land areas declined 60 to 100 percent from about 1945 to 2000 on several islands. Meanwhile, forest cover has increased 50 to 950%. This trend will likely continue where sugar cane cultivation has dominated. Like the island of Puerto Rico, most higher-elevation forest formations are protected in formal or informal reserves. Also similarly, lowland forests, which are drier forest types on these islands, are not well represented in reserves. Former cultivated lands in lowland areas could provide lands for new reserves of drier forest types. The land-use history of these islands may provide insight for planners in countries currently considering lowland forest clearing for agriculture. Copyright 2008 College of Arts and Sciences.

Strategies for detection of floodplain inundation with multi-frequency polarimetric SAR

NASA Technical Reports Server (NTRS)

Hess, Laura L.; Melack, John M.

1992-01-01

Mapping of floodplain inundation patterns is a key element in developing hydrological and biogeochemical models for large tropical river basins such as the Amazon. Knowledge of the time sequence of inundation is necessary to determine both water routing and biogenic gas fluxes. Synthetic Aperture Radar (SAR) is uniquely suited for this application because of its ability to penetrate cloud cover and, in many cases, to detect flooding beneath a forest or herbaceous canopy. A procedure for discriminating flooded forest, flooded herbaceous vegetation, and open water from other cover types for a coastal wetland site on the lower Altamaha floodplain, Georgia, emphasizing robust classifiers that are not site-specific is currently being developed.

The spatial pattern and dominant drivers of woody cover change in Latin America and Caribbean from 2001 to 2010

NASA Astrophysics Data System (ADS)

Clark, M.; Aide, T.; Riner, G.; Redo, D.; Grau, H.; Bonilla-Moheno, M.; Lopez-Carr, D.; Levy, M.

2011-12-01

Change in woody vegetation (i.e., forests, shrublands) is a major component of global environmental change: it directly affects biodiversity, the global carbon budget, and ecosystem function. For several decades, remote sensing technology has been used to document deforestation in Latin America and the Caribbean (LAC), although mostly at local to regional scales (e.g., moist forests of the Amazon basin). Most studies have focused on forest loss, some local-scale studies have mapped forest recovery, with contrasting forest dynamics attributed to shifting demographic and socio-economic factors. For example, local population change (rural-urban migration) can stimulate forest recovery on abandoned land, while increasing global food demand may drive regional expansion of mechanized agriculture. However, there are no studies in LAC that simultaneously map both loss and gain in woody vegetation at continental, national, and municipality scales with consistent data sources, methods and accuracy; and thus, we lack a comprehensive assessment of the spatial distribution of woody vegetation change and the relative importance of the multi-scale drivers of this change. We overcame this limitation by producing annual land-cover maps between 2001 and 2010 for each of the >16,000 municipalities in LAC. We focused on mapping municipality-scale trends in three broad classes: woody vegetation, mixed woody/plantations, and agriculture/herbaceous vegetation. Our area estimates show that woody vegetation change during the past decade was dominated by deforestation, or loss (-541,830 km2), particularly in the Amazon basin moist forest and the tropical-subtropical Cerrado and Chaco ecoregions, where large swaths of forest have been transformed to pastures and agricultural lands. Extensive areas (362,431 km2) in LAC also gained woody vegetation, particularly in regions too dry or too steep for modern agriculture, including the desert/xeric shrub biome in NE Brazil and northern Mexico, the conifer forest and tropical dry forest biomes in Central America, and Andean montane areas. We used Random Forests regression, a non-linear and non-parametric analytical technique, as a means to assess the relative importance of demographic and environmental variables in explaining trends in woody vegetation at the municipality scale. We found no association between population change and woody vegetation change at this scale, suggesting that global demand for food (e.g., soybean production for export to China) is a more important driver of deforestation than local population change. Our results emphasize that both loss and gain (i.e., deforestation and reforestation) need to be addressed in a research framework that links multiple spatial scales of land change with global drivers of change.

Evaluating differences in forest fragmentation and restoration between western natural forests and southeastern plantation forests in the United States.

PubMed

Ren, Xinyu; Lv, Yingying; Li, Mingshi

2017-03-01

Changes in forest ecosystem structure and functions are considered some of the research issues in landscape ecology. In this study, advancing Forman's theory, we considered five spatially explicit processes associated with fragmentation, including perforation, dissection, subdivision, shrinkage, and attrition, and two processes associated with restoration, i.e., increment and expansion processes. Following this theory, a forest fragmentation and restoration process model that can detect the spatially explicit processes and ecological consequences of forest landscape change was developed and tested in the current analysis. Using the National Land Cover Databases (2001, 2006 and 2011), the forest fragmentation and restoration process model was applied to US western natural forests and southeastern plantation forests to quantify and classify forest patch losses into one of the four fragmentation processes (the dissection process was merged into the subdivision process) and to classify the newly gained forest patches based on the two restoration processes. At the same time, the spatio-temporal differences in fragmentation and restoration patterns and trends between natural forests and plantations were further compared. Then, through overlaying the forest fragmentation/restoration processes maps with targeting year land cover data and land ownership vectors, the results from forest fragmentation and the contributors to forest restoration in federal and nonfederal lands were identified. Results showed that, in natural forests, the forest change patches concentrated around the urban/forest, cultivated/forest, and shrubland/forest interfaces, while the patterns of plantation change patches were scattered sparsely and irregularly. The shrinkage process was the most common type in forest fragmentation, and the average size was the smallest. Expansion, the most common restoration process, was observed in both natural forests and plantations and often occurred around the previous expansion or covered the previous subdivision or shrinkage processes. The overall temporal fragmentation pattern of natural forests had a "perforation-subdivision/shrinkage-attrition" pathway, which corresponded to Forman's landscape fragmentation rule, while the plantation forests did not follow the rule strictly. The main land cover types resulted from forest fragmentation in natural forests and plantation forests were shrubland and herbaceous, mainly through subdivision and shrinkages process. The processes and effects of restoration of plantation forests were more diverse and efficient, compared to the natural forest, which were simpler with a lower regrowth rate. The fragmentation mostly occurred in nonfederal lands. In natural forests, forest fragmentation pattern differed in different land tenures, yet plantations remained the same in federal and nonfederal lands. Copyright © 2016 Elsevier Ltd. All rights reserved.

Land cover and forest formation distributions for St. Kitts, Nevis, St. Eustatius, Grenada and Barbados from decision tree classification of cloud-cleared satellite imagery. Caribbean Journal of Science. 44(2):175-198.

Treesearch

E.H. Helmer; T.A. Kennaway; D.H. Pedreros; M.L. Clark; H. Marcano-Vega; L.L. Tieszen; S.R. Schill; C.M.S. Carrington

2008-01-01

Satellite image-based mapping of tropical forests is vital to conservation planning. Standard methods for automated image classification, however, limit classification detail in complex tropical landscapes. In this study, we test an approach to Landsat image interpretation on four islands of the Lesser Antilles, including Grenada and St. Kitts, Nevis and St. Eustatius...