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.

Normalized algorithm for mapping and dating forest disturbances and regrowth for the United States

Treesearch

Liming He; Jing M. Chen; Shaoliang Zhang; Gustavo Gomez; Yude Pan; Kevin McCullough; Richard Birdsey; Jeffrey G. Masek

2011-01-01

Forest disturbances such as harvesting, wildfire and insect infestation are critical ecosystem processes affecting the carbon cycle. Because carbon dynamics are related to time since disturbance, forest stand age that can be used as a surrogate for major clear-cut/fire disturbance information has recently been recognized as an important input to forest carbon cycle...

The impacts of disturbance on the spatial and temporal variations of carbon balance in forest ecosystems on Hokkaido, Japan

NASA Astrophysics Data System (ADS)

Hirata, R.; Ito, A.; Saigusa, N.

2013-12-01

Carbon balance in a forest ecosystem can be quite variable if the forest ecosystem structure and function change abruptly as a result of disturbance and subsequent recovery processes. A map of forest age is useful for upscaling carbon balance from the site level to a regional scale because it provides information about when disturbance occurred and how it spread over a wide area. In this study, we used maps of forest age to help evaluate spatial and temporal variations in the carbon balance of forest ecosystems with a process-based ecosystem model. Forests less than 60 years old account for more than 70% of Japanese forests because forest stands have been quickly replaced after disturbance caused by thinning, harvesting, plantations, fires, typhoons, and insect damage. However, few studies have attempted to quantify how much disturbance affects the spatial and temporal variations of carbon balance. In this study, we focused on how disturbance and subsequent re-growth affected the spatial and temporal variations of the carbon balance of forests. We adapted the Vegetation Integrative SImulator for Trace Gases (VISIT) model in order to simulate carbon balance on Hokkaido, which is the northernmost island of Japan. The model was validated with tower flux data obtained from forests with ages between 0 and 43 years. Simulations of the carbon balance were conducted for the period 1948-2010 after a 1000-year spin-up at a spatial resolution of 1 km × 1 km. We investigated two case studies of simulated carbon balance: one that took into account the spatial distribution of forest ages derived from forest inventory data, and another that ignored the impact of disturbance (i.e., no disturbance and a homogeneous distribution of ages). We first focused on the difference from 2000-2010 in the spatial distribution of net ecosystem production (NEP) between the disturbance and non-disturbance cases. In the non-disturbance case, the temporal and spatial changes in NEP were gradual and ranged from 0 to 1 t C ha-1 y-1, depending on meteorological conditions such as temperature or solar radiation. In the disturbance case, however, large NEP changes ranging from 3 to 5 t C ha-1 y-1 were distributed in patches like hotspots, because the forests in those spots ranged in age from 20 to 100 years and were younger than the forests in the non-disturbance case. In the 1970s, wood harvesting and tree planting were conducted intensively on Hokkaido. In the disturbance case during this period, there were many hotspots where NEP was negative. We next focused on the difference between the disturbance and non-disturbance cases of temporal variations of spatially averaged NEP on Hokkaido. Until 1970, the difference between the two cases of average NEP was less than 0.01 t C ha-1 y-1. After 1970, the difference became large and reached about 0.5 t C ha-1 y-1, the implication being that the regional NEP in the disturbance case increased to as much as 2-5 times the regional NEP of the non-disturbance case. Our results show the importance of considering forest age when simulating the carbon balance of forests. Carbon balance maps that take forest age into account are useful for carbon management and prediction of ecosystem feedbacks on climate change.

Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA

DOE PAGES

Parresol, B. R.; Scott, D. A.; Zarnoch, S. J.; ...

2017-12-15

Spatially explicit mapping of forest productivity is important to assess many forest management alternatives. We assessed the relationship between mapped variables and site index of forests ranging from southern pine plantations to natural hardwoods on a 74,000-ha landscape in South Carolina, USA. Mapped features used in the analysis were soil association, land use condition in 1951, depth to groundwater, slope and aspect. Basal area, species composition, age and height were the tree variables measured. Linear modelling identified that plot basal area, depth to groundwater, soils association and the interactions between depth to groundwater and forest group, and between land usemore » in 1951 and forest group were related to site index (SI) (R 2 =0.37), but this model had regression attenuation. We then used structural equation modeling to incorporate error-in-measurement corrections for basal area and groundwater to remove bias in the model. We validated this model using 89 independent observations and found the 95% confidence intervals for the slope and intercept of an observed vs. predicted site index error-corrected regression included zero and one, respectively, indicating a good fit. With error in measurement incorporated, only basal area, soil association, and the interaction between forest groups and land use were important predictors (R2 =0.57). Thus, we were able to develop an unbiased model of SI that could be applied to create a spatially explicit map based primarily on soils as modified by past (land use and forest type) and recent forest management (basal area).« less

Modeling forest site productivity using mapped geospatial attributes within a South Carolina Landscape, USA

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

Parresol, B. R.; Scott, D. A.; Zarnoch, S. J.

Spatially explicit mapping of forest productivity is important to assess many forest management alternatives. We assessed the relationship between mapped variables and site index of forests ranging from southern pine plantations to natural hardwoods on a 74,000-ha landscape in South Carolina, USA. Mapped features used in the analysis were soil association, land use condition in 1951, depth to groundwater, slope and aspect. Basal area, species composition, age and height were the tree variables measured. Linear modelling identified that plot basal area, depth to groundwater, soils association and the interactions between depth to groundwater and forest group, and between land usemore » in 1951 and forest group were related to site index (SI) (R 2 =0.37), but this model had regression attenuation. We then used structural equation modeling to incorporate error-in-measurement corrections for basal area and groundwater to remove bias in the model. We validated this model using 89 independent observations and found the 95% confidence intervals for the slope and intercept of an observed vs. predicted site index error-corrected regression included zero and one, respectively, indicating a good fit. With error in measurement incorporated, only basal area, soil association, and the interaction between forest groups and land use were important predictors (R2 =0.57). Thus, we were able to develop an unbiased model of SI that could be applied to create a spatially explicit map based primarily on soils as modified by past (land use and forest type) and recent forest management (basal area).« less

Stand Replacing Disturbance History from Object-Based Image Analysis (OBIA) of LiDAR Data

NASA Astrophysics Data System (ADS)

Sanchez Lopez, N.; Hudak, A. T.; Boschetti, L.

2016-12-01

Spatially explicit information on the location, the extent, and the time since a stand replacing forest disturbance occured all have the potential to improve the accuracy of carbon cycle models, and ultimately to reduce the uncertainties in the global carbon budget (Frolking et al., 2009). Earth observation optical satellite data offers a unique opportunity for systematic monitoring of stand-replacing disturbances (Hansen et al., 2013) by detecting the abrupt spectral changes induced by the disturbance, but discriminates poorly between stands of different age, as spectral response of optical data saturates on closed canopy forests. Thus, the potential of optical satellite data to reconstruct the disturbance history of a forest is limited by the short time series of suitable data (starting with the launch of Landsat-1 in 1972). In contrast, LIDAR data directly reflects stand characteristics such as height and density that can be correlated to the time since disturbance. In this study we focus on Object Based Image Analysis (OBIA) of LiDAR data to identify forest stands (objects) based on the age since the last disturbance, to test whether it is possible to extend the disturbance history of a forest beyond what is possible with Landsat data. The study area was located in the Clear Creek watershed and the Selway River & Elk Creek ( 54,000 ha) inside the Nez Perce-Clearwater National Forests (Idaho), using airborne LiDAR data collected in 2009 (Clear Creek watershed) and 2012 (Selway River & Elk Creek). Extensive datasets of disturbances are available over the study area: decadal maps of stand-replacing fires compiled from historical photographs are available from 1870 to 1940, and yearly clearcut maps compiled from timber harvest records are available from 1950 as part of the US Forest Service FACTS (Forest ACtivity Tracking System) dataset. Additionally, a field campaign was conducted in the summer of 2016 to collect additional measurements on plots of known disturbance age, in order to better understand regrowth patterns following a disturbance and to estimate the actual age of the regeneration. This paper will present the initial results of the comparison between LIDAR-estimated disturbance maps, and historical and field data.

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.

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.

Complex mountain terrain and disturbance history drive variation in forest aboveground live carbon density in the western Oregon Cascades, USA

PubMed Central

Zald, Harold S.J.; Spies, Thomas A.; Seidl, Rupert; Pabst, Robert J.; Olsen, Keith A.; Steel, E. Ashley

2016-01-01

Forest carbon (C) density varies tremendously across space due to the inherent heterogeneity of forest ecosystems. Variation of forest C density is especially pronounced in mountainous terrain, where environmental gradients are compressed and vary at multiple spatial scales. Additionally, the influence of environmental gradients may vary with forest age and developmental stage, an important consideration as forest landscapes often have a diversity of stand ages from past management and other disturbance agents. Quantifying forest C density and its underlying environmental determinants in mountain terrain has remained challenging because many available data sources lack the spatial grain and ecological resolution needed at both stand and landscape scales. The objective of this study was to determine if environmental factors influencing aboveground live carbon (ALC) density differed between young versus old forests. We integrated aerial light detection and ranging (lidar) data with 702 field plots to map forest ALC density at a grain of 25 m across the H.J. Andrews Experimental Forest, a 6369 ha watershed in the Cascade Mountains of Oregon, USA. We used linear regressions, random forest ensemble learning (RF) and sequential autoregressive modeling (SAR) to reveal how mapped forest ALC density was related to climate, topography, soils, and past disturbance history (timber harvesting and wildfires). ALC increased with stand age in young managed forests, with much greater variation of ALC in relation to years since wildfire in old unmanaged forests. Timber harvesting was the most important driver of ALC across the entire watershed, despite occurring on only 23% of the landscape. More variation in forest ALC density was explained in models of young managed forests than in models of old unmanaged forests. Besides stand age, ALC density in young managed forests was driven by factors influencing site productivity, whereas variation in ALC density in old unmanaged forests was also affected by finer scale topographic conditions associated with sheltered sites. Past wildfires only had a small influence on current ALC density, which may be a result of long times since fire and/or prevalence of non-stand replacing fire. Our results indicate that forest ALC density depends on a suite of multi-scale environmental drivers mediated by complex mountain topography, and that these relationships are dependent on stand age. The high and context-dependent spatial variability of forest ALC density has implications for quantifying forest carbon stores, establishing upper bounds of potential carbon sequestration, and scaling field data to landscape and regional scales. PMID:27041818

Southern pine beetle infestation probability mapping using weights of evidence analysis

Treesearch

Jason B. Grogan; David L. Kulhavy; James C. Kroll

2010-01-01

Weights of Evidence (WofE) spatial analysis was used to predict probability of southern pine beetle (Dendroctonus frontalis) (SPB) infestation in Angelina, Nacogdoches, San Augustine and Shelby Co., TX. Thematic data derived from Landsat imagery (1974–2002 Landsat 1–7) were used. Data layers included: forest covertype, forest age, forest patch size...

Testing random forest classification for identifying lava flows and mapping age groups on a single Landsat 8 image

NASA Astrophysics Data System (ADS)

Li, Long; Solana, Carmen; Canters, Frank; Kervyn, Matthieu

2017-10-01

Mapping lava flows using satellite images is an important application of remote sensing in volcanology. Several volcanoes have been mapped through remote sensing using a wide range of data, from optical to thermal infrared and radar images, using techniques such as manual mapping, supervised/unsupervised classification, and elevation subtraction. So far, spectral-based mapping applications mainly focus on the use of traditional pixel-based classifiers, without much investigation into the added value of object-based approaches and into advantages of using machine learning algorithms. In this study, Nyamuragira, characterized by a series of > 20 overlapping lava flows erupted over the last century, was used as a case study. The random forest classifier was tested to map lava flows based on pixels and objects. Image classification was conducted for the 20 individual flows and for 8 groups of flows of similar age using a Landsat 8 image and a DEM of the volcano, both at 30-meter spatial resolution. Results show that object-based classification produces maps with continuous and homogeneous lava surfaces, in agreement with the physical characteristics of lava flows, while lava flows mapped through the pixel-based classification are heterogeneous and fragmented including much "salt and pepper noise". In terms of accuracy, both pixel-based and object-based classification performs well but the former results in higher accuracies than the latter except for mapping lava flow age groups without using topographic features. It is concluded that despite spectral similarity, lava flows of contrasting age can be well discriminated and mapped by means of image classification. The classification approach demonstrated in this study only requires easily accessible image data and can be applied to other volcanoes as well if there is sufficient information to calibrate the mapping.

40 shades of black: regional differences in vegetation response to a changing human influence in the Low Countries during the Dark Ages (AD 300-1000).

NASA Astrophysics Data System (ADS)

Gouw-Bouman, Marjolein T. I. J.; Donders, Timme H.; Hoek, Wim Z.

2016-04-01

During the Dark Ages, which includes the Late Roman Period (LRP, AD 300-500) and the Early Middle Ages (EMA, AD 500-1000), large scale vegetation development in Northwestern Europe is generally characterized by a forest regeneration. This forest redevelopment phase was not uniformous across the Netherlands. A comparison between existing pollen records shows that forest redevelopment started earlier and was more severe in the southern part of the Netherlands than in the northeastern Netherlands. The prevailing view advocates that the forest redevelopment is the result of a diminishing human influence on the landscape due to the collapse of the Roman Empire. Following this view, regional changes in forest regeneration are explained by varying population densities. However, existing climate-records indicate a colder and wetter climate during the Dark Ages and the geomorphological record points to a changing landscape. How and to what extent these climatic and environmental changes contributed to the changes in vegetation development or even to the decline of the Roman Empire is largely unknown. To understand the relative importance of the factors (climate, environment, economy and demography) influencing vegetation development it is important to accurately map regional differences in vegetation both on a regional and extra-regional scale. For an extra-regional overview all available pollen records in the Netherlands from this period are compiled to show differences in amplitude of the vegetation development during the Dark Ages. On a regional scale, vegetation reconstruction maps have been produced reflecting the influence of geological/geomorphological factors.

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.

75 FR 16719 - Information Collection; Forest Landscape Value and Special Place Mapping for National Forest...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-02

... Collection; Forest Landscape Value and Special Place Mapping for National Forest Planning AGENCY: Forest... on the new information collection, Forest Landscape Value and Special Place Mapping for National... holidays. SUPPLEMENTARY INFORMATION: Title: Forest Landscape Value and Special Place Mapping for National...

Forest biomass carbon stocks and variation in Tibet's carbon-dense forests from 2001 to 2050.

PubMed

Sun, Xiangyang; Wang, Genxu; Huang, Mei; Chang, Ruiying; Ran, Fei

2016-10-05

Tibet's forests, in contrast to China's other forests, are characterized by primary forests, high carbon (C) density and less anthropogenic disturbance, and they function as an important carbon pool in China. Using the biomass C density data from 413 forest inventory sites and a spatial forest age map, we developed an allometric equation for the forest biomass C density and forest age to assess the spatial biomass C stocks and variation in Tibet's forests from 2001 to 2050. The results indicated that the forest biomass C stock would increase from 831.1 Tg C in 2001 to 969.4 Tg C in 2050, with a net C gain of 3.6 Tg C yr -1 between 2001 and 2010 and a decrease of 1.9 Tg C yr -1 between 2040 and 2050. Carbon tends to allocate more in the roots of fir forests and less in the roots of spruce and pine forests with increasing stand age. The increase of the biomass carbon pool does not promote significant augmentation of the soil carbon pool. Our findings suggest that Tibet's mature forests will remain a persistent C sink until 2050. However, afforestation or reforestation, especially with the larger carbon sink potential forest types, such as fir and spruce, should be carried out to maintain the high C sink capacity.

Forest biomass carbon stocks and variation in Tibet’s carbon-dense forests from 2001 to 2050

PubMed Central

Sun, Xiangyang; Wang, Genxu; Huang, Mei; Chang, Ruiying; Ran, Fei

2016-01-01

Tibet’s forests, in contrast to China’s other forests, are characterized by primary forests, high carbon (C) density and less anthropogenic disturbance, and they function as an important carbon pool in China. Using the biomass C density data from 413 forest inventory sites and a spatial forest age map, we developed an allometric equation for the forest biomass C density and forest age to assess the spatial biomass C stocks and variation in Tibet’s forests from 2001 to 2050. The results indicated that the forest biomass C stock would increase from 831.1 Tg C in 2001 to 969.4 Tg C in 2050, with a net C gain of 3.6 Tg C yr−1 between 2001 and 2010 and a decrease of 1.9 Tg C yr−1 between 2040 and 2050. Carbon tends to allocate more in the roots of fir forests and less in the roots of spruce and pine forests with increasing stand age. The increase of the biomass carbon pool does not promote significant augmentation of the soil carbon pool. Our findings suggest that Tibet’s mature forests will remain a persistent C sink until 2050. However, afforestation or reforestation, especially with the larger carbon sink potential forest types, such as fir and spruce, should be carried out to maintain the high C sink capacity. PMID:27703215

Impacts of disturbance history on annual carbon stocks and fluxes in southeastern US forests during 1986-2010 using remote sensing, forest inventory data, and a carbon cycle model

NASA Astrophysics Data System (ADS)

Gu, H.; Zhou, Y.; Williams, C. A.

2017-12-01

Accurate assessment of forest carbon storage and uptake is central to policymaking aimed at mitigating climate change and understanding the role forests play in the global carbon cycle. Disturbance events are highly heterogeneous in space and time, impacting forest carbon dynamics and challenging the quantification and reporting of carbon stocks and fluxes. This study documents annual carbon stocks and fluxes from 1986 and 2010 mapped at 30-m resolution across southeastern US forests, characterizing how they respond to disturbances and ensuing regrowth. Forest inventory data (FIA) are used to parameterize a carbon cycle model (CASA) to represent post-disturbance carbon trajectories of carbon pools and fluxes with time following harvest, fire and bark beetle disturbances of varying severity and across forest types and site productivity settings. Time since disturbance at 30 meters is inferred from two remote-sensing data sources: disturbance year (NAFD, MTBS and ADS) and biomass (NBCD 2000) intersected with FIA-derived curves of biomass accumulation with stand age. All of these elements are combined to map carbon stocks and fluxes at a 30-m resolution for the year 2010, and to march backward in time for continuous, annual reporting. Results include maps of annual carbon stocks and fluxes for forests of the southeastern US, and analysis of spatio-temporal patterns of carbon sources/sinks at local and regional scales.

High-resolution (30 m), annual (1986 - 2010) carbon stocks and fluxes for southeastern US forests derived from remote sensing, inventory data, and a carbon cycle model

NASA Astrophysics Data System (ADS)

Gu, H.; Zhou, Y.; Williams, C. A.

2016-12-01

Disturbance events are highly heterogeneous in space and time, impacting forest carbon dynamics and challenging the quantification and reporting of carbon stocks and flux. This study documents annual carbon stocks and fluxes from 1986 and 2010 mapped at 30-m resolution across southeastern US forests, characterizing how they respond to disturbances and ensuing regrowth. Forest inventory data (FIA) are used to parameterize a carbon cycle model (CASA) to represent post-disturbance carbon trajectories of carbon pools and fluxes for harvest, fire and bark beetle disturbances of varying severity and across forest types and site productivity settings. Time since disturbance at 30 meters is inferred from two remote-sensing data sources: disturbance year (NAFD, MTBS and ADS) and biomass (NBCD 2000) intersected with inventory-derived curves of biomass accumulation with stand age. All of these elements are combined to map carbon stocks and fluxes at a 30-m resolution for the year 2010, and to march backward in time for continuous, annual reporting. Results include maps of annual carbon stocks and fluxes for forests of the southeastern US, and analysis of spatio-temporal patterns of carbon sources/sinks at local and regional scales.

Predicting mosaics and wildlife diversity resulting from fire disturbance to a forest ecosystem

NASA Astrophysics Data System (ADS)

Potter, Meredith W.; Kessell, Stephen R.

1980-05-01

A model for predicting community mosaics and wildlife diversity resulting from fire disturbance to a forest ecosystem is presented. It applies an algorithm that delineates the size and shape of each patch from grid-based input data and calculates standard diversity measures for the entire mosaic of community patches and their included animal species. The user can print these diversity calculations, maps of the current community-type-age-class mosaic, and maps of habitat utilization by each animal species. Furthermore, the user can print estimates of changes in each resulting from natural disturbance. Although data and resolution level independent, the model is demonstrated and tested with data from the Lewis and Clark National Forest in Montana.

Simultaneous comparison and assessment of eight remotely sensed maps of Philippine forests

NASA Astrophysics Data System (ADS)

Estoque, Ronald C.; Pontius, Robert G.; Murayama, Yuji; Hou, Hao; Thapa, Rajesh B.; Lasco, Rodel D.; Villar, Merlito A.

2018-05-01

This article compares and assesses eight remotely sensed maps of Philippine forest cover in the year 2010. We examined eight Forest versus Non-Forest maps reclassified from eight land cover products: the Philippine Land Cover, the Climate Change Initiative (CCI) Land Cover, the Landsat Vegetation Continuous Fields (VCF), the MODIS VCF, the MODIS Land Cover Type product (MCD12Q1), the Global Tree Canopy Cover, the ALOS-PALSAR Forest/Non-Forest Map, and the GlobeLand30. The reference data consisted of 9852 randomly distributed sample points interpreted from Google Earth. We created methods to assess the maps and their combinations. Results show that the percentage of the Philippines covered by forest ranges among the maps from a low of 23% for the Philippine Land Cover to a high of 67% for GlobeLand30. Landsat VCF estimates 36% forest cover, which is closest to the 37% estimate based on the reference data. The eight maps plus the reference data agree unanimously on 30% of the sample points, of which 11% are attributable to forest and 19% to non-forest. The overall disagreement between the reference data and Philippine Land Cover is 21%, which is the least among the eight Forest versus Non-Forest maps. About half of the 9852 points have a nested structure such that the forest in a given dataset is a subset of the forest in the datasets that have more forest than the given dataset. The variation among the maps regarding forest quantity and allocation relates to the combined effects of the various definitions of forest and classification errors. Scientists and policy makers must consider these insights when producing future forest cover maps and when establishing benchmarks for forest cover monitoring.

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

Treesearch

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

2012-01-01

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

Methods used to parameterize the spatially-explicit components of a state-and-transition simulation model

USGS Publications Warehouse

Sleeter, Rachel; Acevedo, William; Soulard, Christopher E.; Sleeter, Benjamin M.

2015-01-01

Spatially-explicit state-and-transition simulation models of land use and land cover (LULC) increase our ability to assess regional landscape characteristics and associated carbon dynamics across multiple scenarios. By characterizing appropriate spatial attributes such as forest age and land-use distribution, a state-and-transition model can more effectively simulate the pattern and spread of LULC changes. This manuscript describes the methods and input parameters of the Land Use and Carbon Scenario Simulator (LUCAS), a customized state-and-transition simulation model utilized to assess the relative impacts of LULC on carbon stocks for the conterminous U.S. The methods and input parameters are spatially explicit and describe initial conditions (strata, state classes and forest age), spatial multipliers, and carbon stock density. Initial conditions were derived from harmonization of multi-temporal data characterizing changes in land use as well as land cover. Harmonization combines numerous national-level datasets through a cell-based data fusion process to generate maps of primary LULC categories. Forest age was parameterized using data from the North American Carbon Program and spatially-explicit maps showing the locations of past disturbances (i.e. wildfire and harvest). Spatial multipliers were developed to spatially constrain the location of future LULC transitions. Based on distance-decay theory, maps were generated to guide the placement of changes related to forest harvest, agricultural intensification/extensification, and urbanization. We analyze the spatially-explicit input parameters with a sensitivity analysis, by showing how LUCAS responds to variations in the model input. This manuscript uses Mediterranean California as a regional subset to highlight local to regional aspects of land change, which demonstrates the utility of LUCAS at many scales and applications.

Forest and wildlife habitat analysis using remote sensing and geographic information systems. M.S. Thesis, 26 May 1992 Abstract Only

NASA Technical Reports Server (NTRS)

Fiorella, Maria

1995-01-01

Forest and wildlife habitat analyses were conducted at the H.J. Andrews Experimental Forest in the Central Cascade Mountains of Oregon using remotely sensed data and a geographic information system (GIS). Landsat Thematic Mapper (TM) data were used to determine forest successional stages, and to analyze the structure of both old and young conifer forests. Two successional stage maps were developed. One was developed from six TM spectral bands alone, and the second was developed from six TM spectral bands and a relative sun incidence band. Including the sun incidence band in the classification improved the mapping accuracy in the two youngest successional stages, but did not improve overall accuracy or accuracy of the two oldest successional stages. Mean spectral values for old-growth and mature stands were compared in seven TM bands and seven band transformations. Differences between mature and old-growth successional stages were greatest for the band ratio of TM 4/5 (P = 0.00005) and the multiband transformation of wetness (P = 0.00003). The age of young conifer stands had the highest correlation to TM 4/5 values (r = 0.9559) of any of the TM band or band transformations used. TM 4/5 ratio values of poorly regenerated conifer stands were significantly different from well regenerated conifer stands after age 15 (P = 0.0000). TM 4/5 was named a 'Successional Stage Index' (SSI) because of its ability to distinguish forest successional stages. The forest successional stage map was used as input into a vertebrate richness model using GIS. The three variables of (1) successional stage, (2) elevation, and (3) site moisture were used in the GIS to predict the spatial occurrence of small mammal, amphibian, and reptile species based on primary and secondary habitat requirements. These occurrence or habitat maps were overlayed to tally the predicted number of vertebrate at any given point in the study area. Overall, sixty-three and sixty-seven percent of the model predictions for vertebrate occurrence matched the vertebrates that were trapped in the field in eight forested stands. Of the three model variables, site moisture appeared to have the greatest influence on the pattern of high vertebrate richness in all vertebrate classes.

Forest discrimination with multipolarization imaging radar

NASA Technical Reports Server (NTRS)

Ford, J. P.; Wickland, D. E.

1985-01-01

The use of radar polarization diversity for discriminating forest canopy variables on airborne synthetic-aperture radar (SAR) images is evaluated. SAR images were acquired at L-Band (24.6 cm) simultaneously in four linear polarization states (HH, HV, VH, and VV) in South Carolina on March 1, 1984. In order to relate the polarization signatures to biophysical properties, false-color composite images were compared to maps of forest stands in the timber compartment. In decreasing order, the most useful correlative forest data are stand basal area, forest age, site condition index, and forest management type. It is found that multipolarization images discriminate variation in tree density and difference in the amount of understory, but do not discriminate between evergreen and deciduous forest types.

Improving artificial forest biomass estimates using afforestation age information from time series Landsat stacks.

PubMed

Liu, Liangyun; Peng, Dailiang; Wang, Zhihui; Hu, Yong

2014-11-01

China maintains the largest artificial forest area in the world. Studying the dynamic variation of forest biomass and carbon stock is important to the sustainable use of forest resources and understanding of the artificial forest carbon budget in China. In this study, we investigated the potential of Landsat time series stacks for aboveground biomass (AGB) estimation in Yulin District, a key region of the Three-North Shelter region of China. Firstly, the afforestation age was successfully retrieved from the Landsat time series stacks in the last 40 years (from 1974 to 2013) and shown to be consistent with the surveyed tree ages, with a root-mean-square error (RMSE) value of 4.32 years and a determination coefficient (R (2)) of 0.824. Then, the AGB regression models were successfully developed by integrating vegetation indices and tree age. The simple ratio vegetation index (SR) is the best candidate of the commonly used vegetation indices for estimating forest AGB, and the forest AGB model was significantly improved using the combination of SR and tree age, with R (2) values from 0.50 to 0.727. Finally, the forest AGB images were mapped at eight epochs from 1985 to 2013 using SR and afforestation age. The total forest AGB in seven counties of Yulin District increased by 20.8 G kg, from 5.8 G kg in 1986 to 26.6 G kg in 2013, a total increase of 360 %. For the persistent forest area since 1974, the forest AGB density increased from 15.72 t/ha in 1986 to 44.53 t/ha in 2013, with an annual rate of about 0.98 t/ha. For the artificial forest planted after 1974, the AGB density increased about 1.03 t/ha a year from 1974 to 2013. The results present a noticeable carbon increment for the planted artificial forest in Yulin District over the last four decades.

Hydrological model uncertainty due to spatial evapotranspiration estimation methods

NASA Astrophysics Data System (ADS)

Yu, Xuan; Lamačová, Anna; Duffy, Christopher; Krám, Pavel; Hruška, Jakub

2016-05-01

Evapotranspiration (ET) continues to be a difficult process to estimate in seasonal and long-term water balances in catchment models. Approaches to estimate ET typically use vegetation parameters (e.g., leaf area index [LAI], interception capacity) obtained from field observation, remote sensing data, national or global land cover products, and/or simulated by ecosystem models. In this study we attempt to quantify the uncertainty that spatial evapotranspiration estimation introduces into hydrological simulations when the age of the forest is not precisely known. The Penn State Integrated Hydrologic Model (PIHM) was implemented for the Lysina headwater catchment, located 50°03‧N, 12°40‧E in the western part of the Czech Republic. The spatial forest patterns were digitized from forest age maps made available by the Czech Forest Administration. Two ET methods were implemented in the catchment model: the Biome-BGC forest growth sub-model (1-way coupled to PIHM) and with the fixed-seasonal LAI method. From these two approaches simulation scenarios were developed. We combined the estimated spatial forest age maps and two ET estimation methods to drive PIHM. A set of spatial hydrologic regime and streamflow regime indices were calculated from the modeling results for each method. Intercomparison of the hydrological responses to the spatial vegetation patterns suggested considerable variation in soil moisture and recharge and a small uncertainty in the groundwater table elevation and streamflow. The hydrologic modeling with ET estimated by Biome-BGC generated less uncertainty due to the plant physiology-based method. The implication of this research is that overall hydrologic variability induced by uncertain management practices was reduced by implementing vegetation models in the catchment models.

Application of mapped plots for single-owner forest surveys

Treesearch

Paul C. Van Deusen; Francis Roesch

2009-01-01

Mapped plots are used for the nation forest inventory conducted by the U.S. Forest Service. Mapped plots are also useful foro single ownership inventoires. Mapped plots can handle boundary overlap and can aprovide less variable estimates for specified forest conditions. Mapping is a good fit for fixed plot inventories where the fixed area plot is used for both mapping...

Mapping forest structure, species gradients and growth in an urban area using lidar and hyperspectral imagery

NASA Astrophysics Data System (ADS)

Gu, Huan

Urban forests play an important role in the urban ecosystem by providing a range of ecosystem services. Characterization of forest structure, species variation and growth in urban forests is critical for understanding the status, function and process of urban ecosystems, and helping maximize the benefits of urban ecosystems through management. The development of methods and applications to quantify urban forests using remote sensing data has lagged the study of natural forests due to the heterogeneity and complexity of urban ecosystems. In this dissertation, I quantify and map forest structure, species gradients and forest growth in an urban area using discrete-return lidar, airborne imaging spectroscopy and thermal infrared data. Specific objectives are: (1) to demonstrate the utility of leaf-off lidar originally collected for topographic mapping to characterize and map forest structure and associated uncertainties, including aboveground biomass, basal area, diameter, height and crown size; (2) to map species gradients using forest structural variables estimated from lidar and foliar functional traits, vegetation indices derived from AVIRIS hyperspectral imagery in conjunction with field-measured species data; and (3) to identify factors related to relative growth rates in aboveground biomass in the urban forests, and assess forest growth patterns across areas with varying degree of human interactions. The findings from this dissertation are: (1) leaf-off lidar originally acquired for topographic mapping provides a robust, potentially low-cost approach to quantify spatial patterns of forest structure and carbon stock in urban areas; (2) foliar functional traits and vegetation indices from hyperspectral data capture gradients of species distributions in the heterogeneous urban landscape; (3) species gradients, stand structure, foliar functional traits and temperature are strongly related to forest growth in the urban forests; and (4) high uncertainties in our ability to map forest structure, species gradient and growth rate occur in residential neighborhoods and along forest edges. Maps generated from this dissertation provide estimates of broad-scale spatial variations in forest structure, species distributions and growth to the city forest managers. The associated maps of uncertainty help managers understand the limitations of the maps and identify locations where the maps are more reliable and where more data are needed.

Forest/non-forest mapping using inventory data and satellite imagery

Treesearch

Ronald E. McRoberts

2002-01-01

For two study areas in Minnesota, USA, one heavily forested and one sparsely forested, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and two prediction techniques, logistic regression and a k-Nearest Neighbours technique. The maps were used to increase the precision of forest area estimates by...

Spatio-temporal Change Patterns of Tropical Forests from 2000 to 2014 Using MOD09A1 Dataset

NASA Astrophysics Data System (ADS)

Qin, Y.; Xiao, X.; Dong, J.

2016-12-01

Large-scale deforestation and forest degradation in the tropical region have resulted in extensive carbon emissions and biodiversity loss. However, restricted by the availability of good-quality observations, large uncertainty exists in mapping the spatial distribution of forests and their spatio-temporal changes. In this study, we proposed a pixel- and phenology-based algorithm to identify and map annual tropical forests from 2000 to 2014, using the 8-day, 500-m MOD09A1 (v005) product, under the support of Google cloud computing (Google Earth Engine). A temporal filter was applied to reduce the random noises and to identify the spatio-temporal changes of forests. We then built up a confusion matrix and assessed the accuracy of the annual forest maps based on the ground reference interpreted from high spatial resolution images in Google Earth. The resultant forest maps showed the consistent forest/non-forest, forest loss, and forest gain in the pan-tropical zone during 2000 - 2014. The proposed algorithm showed the potential for tropical forest mapping and the resultant forest maps are important for the estimation of carbon emission and biodiversity loss.

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.

Combining forest inventory, satellite remote sensing, and geospatial data for mapping forest attributes of the conterminous United States

Treesearch

Mark Nelson; Greg Liknes; Charles H. Perry

2009-01-01

Analysis and display of forest composition, structure, and pattern provides information for a variety of assessments and management decision support. The objective of this study was to produce geospatial datasets and maps of conterminous United States forest land ownership, forest site productivity, timberland, and reserved forest land. Satellite image-based maps of...

Determination of pumper truck intervention ratios in zones with high fire potential by using geographical information system

NASA Astrophysics Data System (ADS)

Aricak, Burak; Kucuk, Omer; Enez, Korhan

2014-01-01

Fighting forest fires not only depends on the forest type, topography, and weather conditions, but is also closely related to the technical properties of fire-fighting equipment. Firefighting is an important part of fire management planning. However, because of the complex nature of forests, creating thematic layers to generate potential fire risk maps is difficult. The use of remote sensing data has become an efficient method for the discrete classification of potential fire risks. The study was located in the Central District of the Kastamonu Regional Forest Directorate, covering an area of 24,320 ha, 15,685 ha of which is forested. On the basis of stand age, crown closure, and tree species, the sizes and distributions of potential fire risk zones within the study area were determined using high-resolution GeoEye satellite imagery and geographical information system data. The status of pumper truck intervention in zones with high fire risk and the sufficiency of existing forest roads within an existing forest network were discussed based on combustible matter characteristics. Pumper truck intervention was 83% for high-risk zones, 79% for medium-risk zones, and 78% for low-risk zones. A pumper truck intervention area map along existing roads was also created.

A 50-m forest cover map in Southeast Asia from ALOS/PALSAR and its application on forest fragmentation assessment.

PubMed

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Duong, Nguyen Dinh; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×10(6) km(2) (GlobCover) to 2.69×10(6) km(2) (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity.

A 50-m Forest Cover Map in Southeast Asia from ALOS/PALSAR and Its Application on Forest Fragmentation Assessment

PubMed Central

Dong, Jinwei; Xiao, Xiangming; Sheldon, Sage; Biradar, Chandrashekhar; Zhang, Geli; Dinh Duong, Nguyen; Hazarika, Manzul; Wikantika, Ketut; Takeuhci, Wataru; Moore, Berrien

2014-01-01

Southeast Asia experienced higher rates of deforestation than other continents in the 1990s and still was a hotspot of forest change in the 2000s. Biodiversity conservation planning and accurate estimation of forest carbon fluxes and pools need more accurate information about forest area, spatial distribution and fragmentation. However, the recent forest maps of Southeast Asia were generated from optical images at spatial resolutions of several hundreds of meters, and they do not capture well the exceptionally complex and dynamic environments in Southeast Asia. The forest area estimates from those maps vary substantially, ranging from 1.73×106 km2 (GlobCover) to 2.69×106 km2 (MCD12Q1) in 2009; and their uncertainty is constrained by frequent cloud cover and coarse spatial resolution. Recently, cloud-free imagery from the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) onboard the Advanced Land Observing Satellite (ALOS) became available. We used the PALSAR 50-m orthorectified mosaic imagery in 2009 to generate a forest cover map of Southeast Asia at 50-m spatial resolution. The validation, using ground-reference data collected from the Geo-Referenced Field Photo Library and high-resolution images in Google Earth, showed that our forest map has a reasonably high accuracy (producer's accuracy 86% and user's accuracy 93%). The PALSAR-based forest area estimates in 2009 are significantly correlated with those from GlobCover and MCD12Q1 at national and subnational scales but differ in some regions at the pixel scale due to different spatial resolutions, forest definitions, and algorithms. The resultant 50-m forest map was used to quantify forest fragmentation and it revealed substantial details of forest fragmentation. This new 50-m map of tropical forests could serve as a baseline map for forest resource inventory, deforestation monitoring, reducing emissions from deforestation and forest degradation (REDD+) implementation, and biodiversity. PMID:24465714

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.

Modeling forest site productivity using mapped geospatial attributes within a South Carolina landscape, USA

Treesearch

B.R. Parresol; D.A. Scott; S.J. Zarnoch; L.A. Edwards; J.I. Blake

2017-01-01

Spatially explicit mapping of forest productivity is important to assess many forest management alternatives. We assessed the relationship between mapped variables and site index of forests ranging from southern pine plantations to natural hardwoods on a 74,000-ha landscape in South Carolina, USA. Mapped features used in the analysis were soil association, land use...

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.

A technique for conducting point pattern analysis of cluster plot stem-maps

Treesearch

C.W. Woodall; J.M. Graham

2004-01-01

Point pattern analysis of forest inventory stem-maps may aid interpretation and inventory estimation of forest attributes. To evaluate the techniques and benefits of conducting point pattern analysis of forest inventory stem-maps, Ripley`s K(t) was calculated for simulated tree spatial distributions and for over 600 USDA Forest Service Forest...

Evaluating kriging as a tool to improve moderate resolution maps of forest biomass

Treesearch

Elizabeth A. Freeman; Gretchen G. Moisen

2007-01-01

The USDA Forest Service, Forest Inventory and Analysis program (FIA) recently produced a nationwide map of forest biomass by modeling biomass collected on forest inventory plots as nonparametric functions of moderate resolution satellite data and other environmental variables using Cubist software. Efforts are underway to develop methods to enhance this initial map. We...

Statewide LANDSAT inventory of California forests

NASA Technical Reports Server (NTRS)

Likens, W.; Peterson, D. (Principal Investigator)

1981-01-01

Six forest cover categories were mapped, along with 10 general land cover classes. To map the state's 100 million acres, 1.6 acre mapping units were utilized. Map products were created. Standing forest acreage for the state was computed to be 26.8 million acres.

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.

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.

Map of forest ownership in the conterminous United States. [Scale 1:7,500,000].

Treesearch

Mark D. Nelson; Greg C. Liknes; Brett J. Butler

2010-01-01

This map depicts the spatial distribution of forest land across the conterminous United States, in 2007, differentiated into public vs. private forest land, and the percentage of corporate ownership of private forest land. Notable differences between eastern and western United States are evident on the map. Over two-thirds of western forest land is publicly owned, the...

Mapping forest vegetation for the western United States using modified random forests imputation of FIA forest plots

Treesearch

Karin Riley; Isaac C. Grenfell; Mark A. Finney

2016-01-01

Maps of the number, size, and species of trees in forests across the western United States are desirable for many applications such as estimating terrestrial carbon resources, predicting tree mortality following wildfires, and for forest inventory. However, detailed mapping of trees for large areas is not feasible with current technologies, but statistical...

Landscape scale mapping of forest inventory data by nearest neighbor classification

Treesearch

Andrew Lister

2009-01-01

One of the goals of the Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program is large-area mapping. FIA scientists have tried many methods in the past, including geostatistical methods, linear modeling, nonlinear modeling, and simple choropleth and dot maps. Mapping methods that require individual model-based maps to be...

Mapping Forest Inventory and Analysis forest land use: timberland, reserved forest land, and other forest land

Treesearch

Mark D. Nelson; John Vissage

2007-01-01

The Forest Inventory and Analysis (FIA) program produces area estimates of forest land use within three subcategories: timberland, reserved forest land, and other forest land. Mapping these subcategories of forest land requires the ability to spatially distinguish productive from unproductive land, and reserved from nonreserved land. FIA field data were spatially...

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.

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.

USE OF ROAD MAPS IN NATIONAL ASSESSMENTS OF FOREST FRAGMENTATION IN THE UNITED STATES

EPA Science Inventory

Including road-mediated forest fragmentation is a contentious issue in United States national assessments. We compared fragmentation as calculated from national land-cover maps alone, and from land-cover maps in combination with road maps. The increment of forest edge from roads ...

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.

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.

Global forest cover mapping for the United Nations Food and Agriculture Organization forest resources assessment 2000 program

USGS Publications Warehouse

Zhu, Z.; Waller, E.

2003-01-01

Many countries periodically produce national reports on the status and changes of forest resources, using statistical surveys and spatial mapping of remotely sensed data. At the global level, the Food and Agriculture Organization (FAO) of the United Nations has conducted a Forest Resources Assessment (FRA) program every 10 yr since 1980, producing statistics and analysis that give a global synopsis of forest resources in the world. For the year 2000 of the FRA program (FRA2000), a global forest cover map was produced to provide spatial context to the extensive survey. The forest cover map, produced at the U.S. Geological Survey (USGS) EROS Data Center (EDC), has five classes: closed forest, open or fragmented forest, other wooded land, other land cover, and water. The first two forested classes at the global scale were delineated using combinations of temporal compositing, modified mixture analysis, geographic stratification, and other classification techniques. The remaining three FAO classes were derived primarily from the USGS global land cover characteristics database (Loveland et al. 1999). Validated on the basis of existing reference data sets, the map is estimated to be 77% accurate for the first four classes (no reference data were available for water), and 86% accurate for the forest and nonforest classification. The final map will be published as an insert to the FAO FRA2000 report.

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.

Estimating mapped-plot forest attributes with ratios of means

Treesearch

S.J. Zarnoch; W.A. Bechtold

2000-01-01

The mapped-plot design utilized by the U.S. Department of Agriculture (USDA) Forest Inventory and Analysis and the National Forest Health Monitoring Programs is described. Data from 2458 forested mapped plots systematically spread across 25 States reveal that 35 percent straddle multiple conditions. The ratio-of-means estimator is developed as a method to obtain...

How similar are forest disturbance maps derived from different Landsat time series algorithms?

Treesearch

Warren B. Cohen; Sean P. Healey; Zhiqiang Yang; Stephen V. Stehman; C. Kenneth Brewer; Evan B. Brooks; Noel Gorelick; Chengqaun Huang; M. Joseph Hughes; Robert E. Kennedy; Thomas R. Loveland; Gretchen G. Moisen; Todd A. Schroeder; James E. Vogelmann; Curtis E. Woodcock; Limin Yang; Zhe Zhu

2017-01-01

Disturbance is a critical ecological process in forested systems, and disturbance maps are important for understanding forest dynamics. Landsat data are a key remote sensing dataset for monitoring forest disturbance and there recently has been major growth in the development of disturbance mapping algorithms. Many of these algorithms take advantage of the high temporal...

Improving Post-Hurricane Katrina Forest Management with MODIS Time Series Products

NASA Technical Reports Server (NTRS)

Lewis, Mark David; Spruce, Joseph; Evans, David; Anderson, Daniel

2012-01-01

Hurricane damage to forests can be severe, causing millions of dollars of timber damage and loss. To help mitigate loss, state agencies require information on location, intensity, and extent of damaged forests. NASA's MODerate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series data products offers a potential means for state agencies to monitor hurricane-induced forest damage and recovery across a broad region. In response, a project was conducted to produce and assess 250 meter forest disturbance and recovery maps for areas in southern Mississippi impacted by Hurricane Katrina. The products and capabilities from the project were compiled to aid work of the Mississippi Institute for Forest Inventory (MIFI). A series of NDVI change detection products were computed to assess hurricane induced damage and recovery. Hurricane-induced forest damage maps were derived by computing percent change between MODIS MOD13 16-day composited NDVI pre-hurricane "baseline" products (2003 and 2004) and post-hurricane NDVI products (2005). Recovery products were then computed in which post storm 2006, 2007, 2008 and 2009 NDVI data was each singularly compared to the historical baseline NDVI. All percent NDVI change considered the 16-day composite period of August 29 to September 13 for each year in the study. This provided percent change in the maximum NDVI for the 2 week period just after the hurricane event and for each subsequent anniversary through 2009, resulting in forest disturbance products for 2005 and recovery products for the following 4 years. These disturbance and recovery products were produced for the Mississippi Institute for Forest Inventory's (MIFI) Southeast Inventory District and also for the entire hurricane impact zone. MIFI forest inventory products were used as ground truth information for the project. Each NDVI percent change product was classified into 6 categories of forest disturbance intensity. Stand age and stand type raster data, also provided by MIFI, were used along with the forest disturbance/recovery products to create forest damage stratification products integrating 3 stand type classes, 6 stand age classes, and 6 forest disturbance intensity classes. This stratification product will be used to aid MIFI timber inventory planning and to prepare for damage assessments due to future hurricane events. Validation of MODIS percent NDVI change products was performed by comparing the MODIS percent NDVI change products to those from Landsat data for the same time and MIFI inventory district area.

Using Moran's I and GIS to study spatial pattern of forest litter carbon density in typical subtropical region, China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2013-12-01

The spatial variation of forest litter carbon (FLC) density in the typical subtropical forests in southeast China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (South-North) × 6 km (East-West) grid system in Zhejiang Province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using Local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas. While Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns in distribution map were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS could be used to study spatial patterns of environmental variables related to forest ecosystem.

The effects of forest fragmentation on forest stand attributes

Treesearch

Ronald E. McRoberts; Greg C. Liknes

2002-01-01

For two study areas in Minnesota, USA, one heavily forested and one sparsely forested, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and a logistic regression model. The maps were used to estimate quantitative indices of forest fragmentation. Correlations between the values of the indices and...

An application of quantile random forests for predictive mapping of forest attributes

Treesearch

E.A. Freeman; G.G. Moisen

2015-01-01

Increasingly, random forest models are used in predictive mapping of forest attributes. Traditional random forests output the mean prediction from the random trees. Quantile regression forests (QRF) is an extension of random forests developed by Nicolai Meinshausen that provides non-parametric estimates of the median predicted value as well as prediction quantiles. It...

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

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

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

1995-12-01

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

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies.

PubMed

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1-98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting.

Forest Cover Estimation in Ireland Using Radar Remote Sensing: A Comparative Analysis of Forest Cover Assessment Methodologies

PubMed Central

Devaney, John; Barrett, Brian; Barrett, Frank; Redmond, John; O`Halloran, John

2015-01-01

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting. PMID:26262681

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).

An imputed forest composition map for New England screened by species range boundaries

Treesearch

Matthew J. Duveneck; Jonathan R. Thompson; B. Tyler Wilson

2015-01-01

Initializing forest landscape models (FLMs) to simulate changes in tree species composition requires accurate fine-scale forest attribute information mapped continuously over large areas. Nearest-neighbor imputation maps, maps developed from multivariate imputation of field plots, have high potential for use as the initial condition within FLMs, but the tendency for...

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.

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.

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.

Use of a forest sapwood area index to explain long-term variability in mean annual evapotranspiration and streamflow in moist eucalypt forests

NASA Astrophysics Data System (ADS)

Benyon, Richard G.; Lane, Patrick N. J.; Jaskierniak, Dominik; Kuczera, George; Haydon, Shane R.

2015-07-01

Mean sapwood thickness, measured in fifteen 73 year old Eucalyptus regnans and E. delegatensis stands, correlated strongly with forest overstorey stocking density (R2 0.72). This curvilinear relationship was used with routine forest stocking density and basal area measurements to estimate sapwood area of the forest overstorey at various times in 15 research catchments in undisturbed and disturbed forests located in the Great Dividing Range, Victoria, Australia. Up to 45 years of annual precipitation and streamflow data available from the 15 catchments were used to examine relationships between mean annual loss (evapotranspiration estimated as mean annual precipitation minus mean annual streamflow), and sapwood area. Catchment mean sapwood area correlated strongly (R2 0.88) with catchment mean annual loss. Variation in sapwood area accounted for 68% more variation in mean annual streamflow than precipitation alone (R2 0.90 compared with R2 0.22). Changes in sapwood area accounted for 96% of the changes in mean annual loss observed after forest thinning or clear-cutting and regeneration. We conclude that forest inventory data can be used reliably to predict spatial and temporal variation in catchment annual losses and streamflow in response to natural and imposed disturbances in even-aged forests. Consequently, recent advances in mapping of sapwood area using airborne light detection and ranging will enable high resolution spatial and temporal mapping of mean annual loss and mean annual streamflow over large areas of forested catchment. This will be particularly beneficial in management of water resources from forested catchments subject to disturbance but lacking reliable long-term (years to decades) streamflow records.

A multicriteria framework for producing local, regional, and national insect and disease risk maps

Treesearch

Frank J. Jr. Krist; Frank J. Sapio

2010-01-01

The construction of the 2006 National Insect and Disease Risk Map, compiled by the USDA Forest Service, State and Private Forestry Area, Forest Health Protection Unit, resulted in the development of a GIS-based, multicriteria approach for insect and disease risk mapping that can account for regional variations in forest health concerns and threats. This risk mapping...

Landsat for practical forest type mapping - A test case

NASA Technical Reports Server (NTRS)

Bryant, E.; Dodge, A. G., Jr.; Warren, S. D.

1980-01-01

Computer classified Landsat maps are compared with a recent conventional inventory of forest lands in northern Maine. Over the 196,000 hectare area mapped, estimates of the areas of softwood, mixed wood and hardwood forest obtained by a supervised classification of the Landsat data and a standard inventory based on aerial photointerpretation, probability proportional to prediction, field sampling and a standard forest measurement program are found to agree to within 5%. The cost of the Landsat maps is estimated to be $0.065/hectare. It is concluded that satellite techniques are worth developing for forest inventories, although they are not yet refined enough to be incorporated into current practical inventories.

Advancing the quantification of humid tropical forest cover loss with multi-resolution optical remote sensing data: Sampling & wall-to-wall mapping

NASA Astrophysics Data System (ADS)

Broich, Mark

Humid tropical forest cover loss is threatening the sustainability of ecosystem goods and services as vast forest areas are rapidly cleared for industrial scale agriculture and tree plantations. Despite the importance of humid tropical forest in the provision of ecosystem services and economic development opportunities, the spatial and temporal distribution of forest cover loss across large areas is not well quantified. Here I improve the quantification of humid tropical forest cover loss using two remote sensing-based methods: sampling and wall-to-wall mapping. In all of the presented studies, the integration of coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data enable advances in quantifying forest cover loss in the humid tropics. Imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used as the source of coarse spatial resolution, high temporal resolution data and imagery from the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor are used as the source of moderate spatial, low temporal resolution data. In a first study, I compare the precision of different sampling designs for the Brazilian Amazon using the annual deforestation maps derived by the Brazilian Space Agency for reference. I show that sampling designs can provide reliable deforestation estimates; furthermore, sampling designs guided by MODIS data can provide more efficient estimates than the systematic design used for the United Nations Food and Agricultural Organization Forest Resource Assessment 2010. Sampling approaches, such as the one demonstrated, are viable in regions where data limitations, such as cloud contamination, limit exhaustive mapping methods. Cloud-contaminated regions experiencing high rates of change include Insular Southeast Asia, specifically Indonesia and Malaysia. Due to persistent cloud cover, forest cover loss in Indonesia has only been mapped at a 5-10 year interval using photo interpretation of single best Landsat images. Such an approach does not provide timely results, and cloud cover reduces the utility of map outputs. In a second study, I develop a method to exhaustively mine the recently opened Landsat archive for cloud-free observations and automatically map forest cover loss for Sumatra and Kalimantan for the 2000-2005 interval. In a comparison with a reference dataset consisting of 64 Landsat sample blocks, I show that my method, using per pixel time-series, provides more accurate forest cover loss maps for multiyear intervals than approaches using image composites. In a third study, I disaggregate Landsat-mapped forest cover loss, mapped over a multiyear interval, by year using annual forest cover loss maps generated from coarse spatial, high temporal resolution MODIS imagery. I further disaggregate and analyze forest cover loss by forest land use, and provinces. Forest cover loss trends show high spatial and temporal variability. These results underline the importance of annual mapping for the quantification of forest cover loss in Indonesia, specifically in the light of the developing Reducing Emissions from Deforestation and Forest Degradation in Developing Countries policy framework (REDD). All three studies highlight the advances in quantifying forest cover loss in the humid tropics made by integrating coarse spatial, high temporal resolution data with moderate spatial, low temporal resolution data. The three methods presented can be combined into an integrated monitoring strategy.

Clearcut mapping and forest type mapping in eastern forests with LANDSAT data

NASA Technical Reports Server (NTRS)

Sutherland, K.

1981-01-01

The development and use of signature packages which provide a forest type map and which identify clearcut areas is discussed. The type map divides the forest land into three categories: softwood, mixed wood, and hardwood. The user defines each of these categories and adjusts the signature package to fit his needs. Success in identifying clearcuts and their stage of regrowth was demonstrated in New Hampshire where clearcuts range in size from 5 to 100 acres with between 30 and 40 acres being the most common.

Mapping forest height in Alaska using GLAS, Landsat composites, and airborne LiDAR

USGS Publications Warehouse

Peterson, Birgit; Nelson, Kurtis

2014-01-01

Vegetation structure, including forest canopy height, is an important input variable to fire behavior modeling systems for simulating wildfire behavior. As such, forest canopy height is one of a nationwide suite of products generated by the LANDFIRE program. In the past, LANDFIRE has relied on a combination of field observations and Landsat imagery to develop existing vegetation structure products. The paucity of field data in the remote Alaskan forests has led to a very simple forest canopy height classification for the original LANDFIRE forest height map. To better meet the needs of data users and refine the map legend, LANDFIRE incorporated ICESat Geoscience Laser Altimeter System (GLAS) data into the updating process when developing the LANDFIRE 2010 product. The high latitude of this region enabled dense coverage of discrete GLAS samples, from which forest height was calculated. Different methods for deriving height from the GLAS waveform data were applied, including an attempt to correct for slope. These methods were then evaluated and integrated into the final map according to predefined criteria. The resulting map of forest canopy height includes more height classes than the original map, thereby better depicting the heterogeneity of the landscape, and provides seamless data for fire behavior analysts and other users of LANDFIRE data.

Assessing the Effects of Forest Fragmentation Using Satellite Imagery and Forest Inventory Data

Treesearch

Ronald E. McRoberts; Greg C. Liknes

2005-01-01

For a study area in the North Central region of the USA, maps of predicted proportion forest area were created using Landsat Thematic Mapper imagery, forest inventory plot data, and a logistic regression model. The maps were used to estimate quantitative indices of forest fragmentation. Correlations between the values of the indices and forest attributes observed on...

Mapping change of older forest with nearest-neighbor imputation and Landsat time-series

Treesearch

Janet L. Ohmann; Matthew J. Gregory; Heather M. Roberts; Warren B. Cohen; Robert E. Kennedy; Zhiqiang Yang

2012-01-01

The Northwest Forest Plan (NWFP), which aims to conserve late-successional and old-growth forests (older forests) and associated species, established new policies on federal lands in the Pacific Northwest USA. As part of monitoring for the NWFP, we tested nearest-neighbor imputation for mapping change in older forest, defined by threshold values for forest attributes...

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.

Comparing five modelling techniques for predicting forest characteristics

Treesearch

Gretchen G. Moisen; Tracey S. Frescino

2002-01-01

Broad-scale maps of forest characteristics are needed throughout the United States for a wide variety of forest land management applications. Inexpensive maps can be produced by modelling forest class and structure variables collected in nationwide forest inventories as functions of satellite-based information. But little work has been directed at comparing modelling...

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites.

PubMed

Mitchard, Edward T A; Feldpausch, Ted R; Brienen, Roel J W; Lopez-Gonzalez, Gabriela; Monteagudo, Abel; Baker, Timothy R; Lewis, Simon L; Lloyd, Jon; Quesada, Carlos A; Gloor, Manuel; Ter Steege, Hans; Meir, Patrick; Alvarez, Esteban; Araujo-Murakami, Alejandro; Aragão, Luiz E O C; Arroyo, Luzmila; Aymard, Gerardo; Banki, Olaf; Bonal, Damien; Brown, Sandra; Brown, Foster I; Cerón, Carlos E; Chama Moscoso, Victor; Chave, Jerome; Comiskey, James A; Cornejo, Fernando; Corrales Medina, Massiel; Da Costa, Lola; Costa, Flavia R C; Di Fiore, Anthony; Domingues, Tomas F; Erwin, Terry L; Frederickson, Todd; Higuchi, Niro; Honorio Coronado, Euridice N; Killeen, Tim J; Laurance, William F; Levis, Carolina; Magnusson, William E; Marimon, Beatriz S; Marimon Junior, Ben Hur; Mendoza Polo, Irina; Mishra, Piyush; Nascimento, Marcelo T; Neill, David; Núñez Vargas, Mario P; Palacios, Walter A; Parada, Alexander; Pardo Molina, Guido; Peña-Claros, Marielos; Pitman, Nigel; Peres, Carlos A; Poorter, Lourens; Prieto, Adriana; Ramirez-Angulo, Hirma; Restrepo Correa, Zorayda; Roopsind, Anand; Roucoux, Katherine H; Rudas, Agustin; Salomão, Rafael P; Schietti, Juliana; Silveira, Marcos; de Souza, Priscila F; Steininger, Marc K; Stropp, Juliana; Terborgh, John; Thomas, Raquel; Toledo, Marisol; Torres-Lezama, Armando; van Andel, Tinde R; van der Heijden, Geertje M F; Vieira, Ima C G; Vieira, Simone; Vilanova-Torre, Emilio; Vos, Vincent A; Wang, Ophelia; Zartman, Charles E; Malhi, Yadvinder; Phillips, Oliver L

2014-08-01

The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1. Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%. Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.

Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

PubMed Central

Mitchard, Edward T A; Feldpausch, Ted R; Brienen, Roel J W; Lopez-Gonzalez, Gabriela; Monteagudo, Abel; Baker, Timothy R; Lewis, Simon L; Lloyd, Jon; Quesada, Carlos A; Gloor, Manuel; ter Steege, Hans; Meir, Patrick; Alvarez, Esteban; Araujo-Murakami, Alejandro; Aragão, Luiz E O C; Arroyo, Luzmila; Aymard, Gerardo; Banki, Olaf; Bonal, Damien; Brown, Sandra; Brown, Foster I; Cerón, Carlos E; Chama Moscoso, Victor; Chave, Jerome; Comiskey, James A; Cornejo, Fernando; Corrales Medina, Massiel; Da Costa, Lola; Costa, Flavia R C; Di Fiore, Anthony; Domingues, Tomas F; Erwin, Terry L; Frederickson, Todd; Higuchi, Niro; Honorio Coronado, Euridice N; Killeen, Tim J; Laurance, William F; Levis, Carolina; Magnusson, William E; Marimon, Beatriz S; Marimon Junior, Ben Hur; Mendoza Polo, Irina; Mishra, Piyush; Nascimento, Marcelo T; Neill, David; Núñez Vargas, Mario P; Palacios, Walter A; Parada, Alexander; Pardo Molina, Guido; Peña-Claros, Marielos; Pitman, Nigel; Peres, Carlos A; Poorter, Lourens; Prieto, Adriana; Ramirez-Angulo, Hirma; Restrepo Correa, Zorayda; Roopsind, Anand; Roucoux, Katherine H; Rudas, Agustin; Salomão, Rafael P; Schietti, Juliana; Silveira, Marcos; de Souza, Priscila F; Steininger, Marc K; Stropp, Juliana; Terborgh, John; Thomas, Raquel; Toledo, Marisol; Torres-Lezama, Armando; van Andel, Tinde R; van der Heijden, Geertje M F; Vieira, Ima C G; Vieira, Simone; Vilanova-Torre, Emilio; Vos, Vincent A; Wang, Ophelia; Zartman, Charles E; Malhi, Yadvinder; Phillips, Oliver L

2014-01-01

Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi.org/10.5521/FORESTPLOTS.NET/2014_1 Methods Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%. Main conclusions Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space. PMID:26430387

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.

Area change reporting using the desktop FIADB

Treesearch

Patrick D. Miles; Mark H. Hansen

2012-01-01

The estimation of area change between two FIA inventories is complicated by the "mapping" of subplots. Subplots can be subdivided or mapped into forest and nonforest conditions, and forest conditions can be further mapped based on distinct changes in reserved status, owner group, forest type, stand-size class, regeneration status, and stand density. The...

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast‑growing Eucalyptus forest plantation using airborne LiDAR data

Treesearch

Carlos Alberto Silva; Andrew Thomas Hudak; Carine Klauberg; Lee Alexandre Vierling; Carlos Gonzalez‑Benecke; Samuel de Padua Chaves Carvalho; Luiz Carlos Estraviz Rodriguez; Adrian Cardil

2017-01-01

LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m− 2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations...

Stand-level variation in evapotranspiration in non-water-limited eucalypt forests

NASA Astrophysics Data System (ADS)

Benyon, Richard G.; Nolan, Rachael H.; Hawthorn, Sandra N. D.; Lane, Patrick N. J.

2017-08-01

To better understand water and energy cycles in forests over years to decades, measurements of spatial and long-term temporal variability in evapotranspiration (Ea) are needed. In mountainous terrain, plot-level measurements are important to achieving this. Forest inventory data including tree density and size measurements, often collected repeatedly over decades, sample the variability occurring within the geographic and topographic range of specific forest types. Using simple allometric relationships, tree stocking and size data can be used to estimate variables including sapwood area index (SAI), which may be strongly correlated with annual Ea. This study analysed plot-level variability in SAI and its relationship with overstorey and understorey transpiration, interception and evaporation over a 670 m elevation gradient, in non-water-limited, even-aged stands of Eucalyptus regnans F. Muell. to determine how well spatial variation in annual Ea from forests can be mapped using SAI. Over the 3 year study, mean sap velocity in five E. regnans stands was uncorrelated with overstorey sapwood area index (SAI) or elevation: annual transpiration was predicted well by SAI (R2 0.98). Overstorey and total annual interception were positively correlated with SAI (R2 0.90 and 0.75). Ea from the understorey was strongly correlated with vapour pressure deficit (VPD) and net radiation (Rn) measured just above the understorey, but relationships between understorey Ea and VPD and Rn differed between understorey types and understorey annual Ea was not correlated with SAI. Annual total Ea was also strongly correlated with SAI: the relationship being similar to two previous studies in the same region, despite differences in stand age and species. Thus, spatial variation in annual Ea can be reliably mapped using measurements of SAI.

Mapping forest functional type in a forest-shrubland ecotone using SPOT imagery and predictive habitat distribution modelling

USGS Publications Warehouse

Assal, Timothy J.; Anderson, Patrick J.; Sibold, Jason

2015-01-01

The availability of land cover data at local scales is an important component in forest management and monitoring efforts. Regional land cover data seldom provide detailed information needed to support local management needs. Here we present a transferable framework to model forest cover by major plant functional type using aerial photos, multi-date Système Pour l’Observation de la Terre (SPOT) imagery, and topographic variables. We developed probability of occurrence models for deciduous broad-leaved forest and needle-leaved evergreen forest using logistic regression in the southern portion of the Wyoming Basin Ecoregion. The model outputs were combined into a synthesis map depicting deciduous and coniferous forest cover type. We evaluated the models and synthesis map using a field-validated, independent data source. Results showed strong relationships between forest cover and model variables, and the synthesis map was accurate with an overall correct classification rate of 0.87 and Cohen’s kappa value of 0.81. The results suggest our method adequately captures the functional type, size, and distribution pattern of forest cover in a spatially heterogeneous landscape.

Controls over aboveground forest carbon density on Barro Colorado Island, Panama

NASA Astrophysics Data System (ADS)

Mascaro, J.; Asner, G. P.; Muller-Landau, H. C.; van Breugel, M.; Hall, J.; Dahlin, K.

2011-06-01

Despite the importance of tropical forests to the global carbon cycle, ecological controls over landscape-level variation in live aboveground carbon density (ACD) in tropical forests are poorly understood. Here, we conducted a spatially comprehensive analysis of ACD variation for a continental tropical forest - Barro Colorado Island, Panama (BCI) - and tested site factors that may control such variation. We mapped ACD over 1256 ha of BCI using airborne Light Detection and Ranging (LiDAR), which was well-correlated with ground-based measurements of ACD in Panamanian forests of various ages (r2 = 0.84, RMSE = 17 Mg C ha-1, P < 0.0001). We used multiple regression to examine controls over LiDAR-derived ACD, including slope angle, forest age, bedrock, and soil texture. Collectively, these variables explained 14 % of the variation in ACD at 30-m resolution, and explained 33 % at 100-m resolution. At all resolutions, slope (linked to underlying bedrock variation) was the strongest driving factor; standing carbon stocks were generally higher on steeper slopes. This result suggests that physiography may be more important in controlling ACD variation in Neotropical forests than currently thought. Although BCI has been largely undisturbed by humans for a century, past land-use over approximately half of the island still influences ACD variation, with younger forests (80-130 years old) averaging ~15 % less carbon storage than old-growth forests (>400 years old). If other regions of relatively old tropical secondary forests also store less carbon aboveground than primary forests, the effects on the global carbon cycle could be substantial and difficult to detect with traditional satellite monitoring.

Modeling the height of young forests regenerating from recent disturbances in Mississippi using Landsat and ICESat data

USGS Publications Warehouse

Li, Ainong; Huang, Chengquan; Sun, Guoqing; Shi, Hua; Toney, Chris; Zhu, Zhiliang; Rollins, Matthew G.; Goward, Samuel N.; Masek, Jeffery G.

2011-01-01

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat–lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, “young” forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS–VCT disturbance products. The height of “young” forest is then mapped based on the derived relationships and the LTSS–VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS–VCT method yielded the lowest cross validation error. The R2 and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS–VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.

Modeling the Height of Young Forests Regenerating from Recent Disturbances in Mississippi using Landsat and ICESat data

NASA Technical Reports Server (NTRS)

Li, Ainong; Huang, Chengquan; Sun, Guoqing; Shi, Hua; Toney, Chris; Zhu, Zhiliang; Rollins, Matthew G.; Goward, Samuel N.; Masek, Jeffrey G.

2011-01-01

Many forestry and earth science applications require spatially detailed forest height data sets. Among the various remote sensing technologies, lidar offers the most potential for obtaining reliable height measurement. However, existing and planned spaceborne lidar systems do not have the capability to produce spatially contiguous, fine resolution forest height maps over large areas. This paper describes a Landsat-lidar fusion approach for modeling the height of young forests by integrating historical Landsat observations with lidar data acquired by the Geoscience Laser Altimeter System (GLAS) instrument onboard the Ice, Cloud, and land Elevation (ICESat) satellite. In this approach, "young" forests refer to forests reestablished following recent disturbances mapped using Landsat time-series stacks (LTSS) and a vegetation change tracker (VCT) algorithm. The GLAS lidar data is used to retrieve forest height at sample locations represented by the footprints of the lidar data. These samples are used to establish relationships between lidar-based forest height measurements and LTSS-VCT disturbance products. The height of "young" forest is then mapped based on the derived relationships and the LTSS-VCT disturbance products. This approach was developed and tested over the state of Mississippi. Of the various models evaluated, a regression tree model predicting forest height from age since disturbance and three cumulative indices produced by the LTSS-VCT method yielded the lowest cross validation error. The R(exp 2) and root mean square difference (RMSD) between predicted and GLAS-based height measurements were 0.91 and 1.97 m, respectively. Predictions of this model had much higher errors than indicated by cross validation analysis when evaluated using field plot data collected through the Forest Inventory and Analysis Program of USDA Forest Service. Much of these errors were due to a lack of separation between stand clearing and non-stand clearing disturbances in current LTSS-VCT products and difficulty in deriving reliable forest height measurements using GLAS samples when terrain relief was present within their footprints. In addition, a systematic underestimation of about 5 m by the developed model was also observed, half of which could be explained by forest growth that occurred between field measurement year and model target year. The remaining difference suggests that tree height measurements derived using waveform lidar data could be significantly underestimated, especially for young pine forests. Options for improving the height modeling approach developed in this study were discussed.

78 FR 56650 - Boundary Description and Final Map for Roaring Wild and Scenic River, Mount Hood National Forest...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-09-13

... DEPARTMENT OF AGRICULTURE Forest Service Boundary Description and Final Map for Roaring Wild and... availability. SUMMARY: In accordance with section 3(b) of the Wild and Scenic Rivers Act, the USDA Forest Service, Washington Office, is transmitting the final boundary description and map of the Roaring Wild and...

Mapping Martinique's forests and other natural lands for land planning and development

Treesearch

Remi Teissier du Cros; Claude Vidal

2009-01-01

The Regional Council of Martinique has chosen the French national forest inventory to realize Martinique's forest and other natural lands map. The project is divided into the three following steps: (1) nomenclature proposal and study area delineation; (2) mapping of the vegetation based on 2005 airborne orthophotographs, Geographic Information System-based slope...

Strong Gradients in Forest Sensitivity to Climate Change Revealed by Dynamics of Forest Fire Cycles in the Post Little Ice Age Era

NASA Astrophysics Data System (ADS)

Drobyshev, Igor; Bergeron, Yves; Girardin, Martin P.; Gauthier, Sylvie; Ols, Clémentine; Ojal, John

2017-10-01

The length of the fire cycle is a critical factor affecting the vegetation cover in boreal and temperate regions. However, its responses to climate change remain poorly understood. We reanalyzed data from earlier studies of forest age structures at the landscape level, in order to map the evolution of regional fire cycles across Eastern North American boreal and temperate forests, following the termination of the Little Ice Age (LIA). We demonstrated a well-defined spatial pattern of post-LIA changes in the length of fire cycles toward lower fire activity during the 1800s and 1900s. The western section of Eastern North America (west of 77°W) experienced a decline in fire activity as early as the first half of the 1800s. By contrast, the eastern section showed these declines as late as the early 1900s. During a regionally fire-prone period of the 1910s-1920s, forests in the western section of Eastern boreal North America burned more than forests in the eastern section. The climate appeared to dominate over vegetation composition and human impacts in shaping the geographical pattern of the post-LIA change in fire activity. Changes in the atmospheric circulation patterns following the termination of the LIA, specifically changes in Arctic Oscillation and the strengthening of the Continental Polar Trough, were likely drivers of the regional fire dynamics.

Next-generation forest change mapping across the United States: the landscape change monitoring system (LCMS)

Treesearch

Sean P. Healey; Warren B. Cohen; Yang Zhiqiang; Ken Brewer; Evan Brooks; Noel Gorelick; Mathew Gregory; Alexander Hernandez; Chengquan Huang; Joseph Hughes; Robert Kennedy; Thomas Loveland; Kevin Megown; Gretchen Moisen; Todd Schroeder; Brian Schwind; Stephen Stehman; Daniel Steinwand; James Vogelmann; Curtis Woodcock; Limin Yang; Zhe Zhu

2015-01-01

Forest change information is critical in forest planning, ecosystem modeling, and in updating forest condition maps. The Landsat satellite platform has provided consistent observations of the world’s ecosystems since 1972. A number of innovative change detection algorithms have been developed to use the Landsat archive to identify and characterize forest change. The...

Forest type mapping with satellite data

NASA Technical Reports Server (NTRS)

Dodge, A. G., Jr.; Bryant, E. S.

1976-01-01

Computer classification of data from Landsat, an earth-orbiting satellite, has resulted in measurements and maps of forest types for two New Hampshire counties. The acreages of hardwood and softwood types and total forested areas compare favorably with Forest Service figures for the same areas. These techniques have advantages for field application, particularly in states having forest taxation laws based on general productivity.

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.

Life on the Edge - Improved Forest Cover Mapping in Mixed-Use Tropical Regions

NASA Astrophysics Data System (ADS)

Anderson, C.; Mendenhall, C. D.; Daily, G.

2016-12-01

Tropical ecosystems and biodiversity are experiencing rapid change, primarily due to conversion of forest habitat to agriculture. Protected areas, while effective for conservation, only manage 15% of terrestrial area, whereas approximately 58% is privately owned. To incentivize private forest management and slow the loss of biodiversity, payments for ecosystem services (PES) programs were established in Costa Rica that pay landowners who maintain trees on their property. While this program is effective in improving livelihoods and preventing forest conversion, it is only managing payments to landowners on 1% of eligible, non-protected forested land.A major bottleneck for this program is access to accurate, national-scale tree cover maps. While the remote sensing community has made great progress in global-scale tree cover mapping, these maps are not sufficient to guide investments for PES programs. The major limitations of current global-scale tree-cover maps are that they a) do not distinguish between forest and agriculture and b) overestimate tree cover in mixed land-use areas (e.g. Global Forest Change overestimates by 20% on average in this region). This is especially problematic in biodiversity-rich Costa Rica, where small patches of forest intermix with agricultural production, and where the conservation value of tree-cover is high. To address this problem, we are developing a new forest cover mapping method that a) performs a least-squares spectral mixture analysis (SMA) using repeat Landsat imagery and canopy radiative transfer modeling: b) combines Landsat data, SMA results, and radar backscatter data using multi-sensor fusion techniques and: c) trains tree-cover classification models using high resolution data sets along a land use-intensity gradient. Our method predicted tree cover with 85% accuracy when compared to a fine-scale map of tree cover in a tropical, agricultural landscape, whereas the next-best method, the Global Forest Change map, predicted tree cover with 72% accuracy. Next steps will aim to test, improve, and apply this method globally to guide investments in nature in agricultural landscapes where forest stewardship will sustain biodiversity.

Geologic Map of the Middle East Rift Geothermal Subzone, Kilauea Volcano, Hawaii

USGS Publications Warehouse

Trusdell, Frank A.; Moore, Richard B.

2006-01-01

K'lauea is an active shield volcano in the southeastern part of the Island of Hawai'i. The middle east rift zone (MERZ) map includes about 27 square kilometers of the MERZ and shows the distribution of the products of 37 separate eruptions during late Holocene time. Lava flows erupted during 1983-96 have reached the mapped area. The subaerial part of the MERZ is 3-4 km wide and about 18 km long. It is a constructional ridge, 50-150 m above the adjoining terrain, marked by low spatter ramparts and cones as high as 60 m. Lava typically flowed either northeast or southeast, depending on vent location relative to the topographic crest of the rift zone. The MERZ receives more than 100 in. of rainfall annually and is covered by tropical rain forest. Vegetation begins to grow on lava a few months after its eruption. Relative heights of trees can be a guide to relative ages of underlying lava flows, but proximity to faults, presence of easily weathered cinders, and human activity also affect the rate of growth. The rocks have been grouped into five basic age groups. The framework for the ages assigned is provided by eight radiocarbon ages from previous mapping by the authors and a single date from the current mapping effort. The numerical ages are supplemented by observations of stratigraphic relations, degree of weathering, soil development, and vegetative cover.

A Project to Map and Monitor Baldcypress Forests in Coastal Louisiana, Using Landsat, MODIS, and ASTER Satellite Data

NASA Technical Reports Server (NTRS)

Spruce, Joseph; Sader, Steven; Smoot, James

2012-01-01

Cypress swamp forests of Louisiana offer many important ecological and economic benefits: wildlife habitat, forest products, storm buffers, water quality, and recreation. Such forests are also threatened by multiple factors: subsidence, salt water intrusion, sea level rise, persistent flooding, hydrologic modification, hurricanes, insect and nutria damage, timber harvesting, and land use conversion. Unfortunately, there are many information gaps regarding the type, location, extent, and condition of these forests. Better more up to date swamp forest mapping products are needed to aid coastal forest conservation and restoration work (e.g., through the Coastal Forest Conservation Initiative or CFCI). In response, a collaborative project was initiated to develop, test and demonstrate cypress swamp forest mapping products, using NASA supported Landsat, ASTER, and MODIS satellite data. Research Objectives are: Develop, test, and demonstrate use of Landsat and ASTER data for computing new cypress forest classification products and Landsat, ASTER, and MODIS satellite data for detecting and monitoring swamp forest change

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.

Field methods and data processing techniques associated with mapped inventory plots

Treesearch

William A. Bechtold; Stanley J. Zarnoch

1999-01-01

The U.S. Forest Inventory and Analysis (FIA) and Forest Health Monitoring (FHM) programs utilize a fixed-area mapped-plot design as the national standard for extensive forest inventories. The mapped-plot design is explained, as well as the rationale for its selection as the national standard. Ratio-of-means estimators am presented as a method to process data from...

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.

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...

Localizing National Fragmentation Statistics with Forest Type Maps

Treesearch

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

2003-01-01

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

Mapping U.S. forest biomass using nationwide forest inventory data and moderate resolution information

Treesearch

J. A. Blackard; M. V. Finco; E. H. Helmer; G. R. Holden; M. L. Hoppus; D.M. Jacobs; A. J. Lister; G. G. Moisen; M. D. Nelson; R. Riemann; B. Ruefenacht; D. Salajanu; D. L. Weyermann; K. C. Winterberger; T. J. Brandeis; R. L. Czaplewski; R. E. McRoberts; P. L. Patterson; R. P. Tymcio

2008-01-01

A spatially explicit dataset of aboveground live forest biomass was made from ground measured inventory plots for the conterminous U.S., Alaska and Puerto Rico. The plot data are from the USDA Forest Service Forest Inventory and Analysis (FIA) program. To scale these plot data to maps, we developed models relating field-measured response variables to plot attributes...

Mapping timing, extent, type and magnitude of disturbances across the national forest system, 1990–2011

Treesearch

Alexander Hernandez; Sean P. Healey; Chenquan Huang; R. Douglas Ramsey

2015-01-01

As part of the U.S. Forest Service (USFS), National Forest System (NFS) comprehensive plan for carbon monitoring, a detailed temporal mapping of forest disturbances across all National Forests in the United States has been conducted. A long-term annual time series of data layers that show the timing, extent, type, and magnitude of disturbance beginning in 1990 and...

Mapping land cover and estimating forest structure using satellite imagery and coarse resolution lidar in the Virgin Islands

Treesearch

T.A. Kennaway; E.H. Helmer; M.A. Lefsky; T.A. Brandeis; K.R. Sherill

2008-01-01

Current information on land cover, forest type and forest structure for the Virgin Islands is critical to land managers and researchers for accurate forest inventory and ecological monitoring. In this study, we use cloud free image mosaics of panchromatic sharpened Landsat ETM+ images and decision tree classification software to map land cover and forest type for the...

Mapping land cover and estimating forest structure using satellite imagery and coarse resolution lidar in the Virgin Islands

Treesearch

Todd Kennaway; Eileen Helmer; Michael Lefsky; Thomas Brandeis; Kirk Sherrill

2009-01-01

Current information on land cover, forest type and forest structure for the Virgin Islands is critical to land managers and researachers for accurate forest inverntory and ecological monitoring. In this study, we use cloud free image mosaics of panchromatic sharpened Landsat ETM+ images and decision tree classification software to map land cover and forest type for the...

Forest cover of North America in the 1970s mapped using Landsat MSS data

NASA Astrophysics Data System (ADS)

Feng, M.; Sexton, J. O.; Channan, S.; Townshend, J. R.

2015-12-01

The distribution and changes in Earth's forests impact hydrological, biogeochemical, and energy fluxes, as well as ecosystems' capacity to support biodiversity and human economies. Long-term records of forest cover are needed across a broad range of investigation, including climate and carbon-cycle modeling, hydrological studies, habitat analyzes, biological conservation, and land-use planning. Satellite-based observations enable mapping and monitoring of forests at ecologically and economically relevant resolutions and continental or even global extents. Following early forest-mapping efforts using coarser resolution remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) and MODerate-resolution Imaging Spectroradiometer (MODIS), forests have been mapped regionally at < 100-m resolution using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+). These "Landsat-class" sensors offer precise calibration, but they provide observations only over the past three decades—a relatively short period for delineating the long-term changes of forests. Starting in 1971, the Multispectral Scanner (MSS) was the first generation of sensors aboard the Landsat satellites. MSS thus provides a unique resource to extend observations by at least a decade longer in history than records based on Landsat TM and ETM+. Leveraging more recent Landsat-based forest-cover products developed by the Global Land Cover Facility (GLCF) as reference, we developed an automated approach to detect forests using MSS data by leveraging the multispectral and phenological characteristics of forests observed in MSS time-series. The forest-cover map is produced with layers representing the year of observation, detection of forest-cover change relative to 1990, and the uncertainty of forest-cover and -change layers. The approach has been implemented with open-source libraries to facilitate processing large volumes of Landsat MSS images on high-performance computing machines. As the first result of our global mapping effort, we present the forest cover for North America. More than 25,000 Landsat MSS scenes were processed to provide a 120-meter resolution forest cover for North America, which will be made publicly available on the GLCF website (http://www.landcover.org).

Mapping snow depth distribution in forested terrain using unmanned aerial vehicles and structure-from-motion

NASA Astrophysics Data System (ADS)

Webster, C.; Bühler, Y.; Schirmer, M.; Stoffel, A.; Giulia, M.; Jonas, T.

2017-12-01

Snow depth distribution in forests exhibits strong spatial heterogeneity compared to adjacent open sites. Measurement of snow depths in forests is currently limited to a) manual point measurements, which are sparse and time-intensive, b) ground-penetrating radar surveys, which have limited spatial coverage, or c) airborne LiDAR acquisition, which are expensive and may deteriorate in denser forests. We present the application of unmanned aerial vehicles in combination with structure-from-motion (SfM) methods to photogrammetrically map snow depth distribution in forested terrain. Two separate flights were carried out 10 days apart across a heterogeneous forested area of 900 x 500 m. Corresponding snow depth maps were derived using both, LiDAR-based and SfM-based DTM data, obtained during snow-off conditions. Manual measurements collected following each flight were used to validate the snow depth maps. Snow depths were resolved at 5cm resolution and forest snow depth distribution structures such as tree wells and other areas of preferential melt were represented well. Differential snow depth maps showed maximum ablation in the exposed south sides of trees and smaller differences in the centre of gaps and on the north side of trees. This new application of SfM to map snow depth distribution in forests demonstrates a straightforward method for obtaining information that was previously only available through manual spatially limited ground-based measurements. These methods could therefore be extended to more frequent observation of snow depths in forests as well as estimating snow accumulation and depletion rates.

A mangrove forest map of China in 2015: Analysis of time series Landsat 7/8 and Sentinel-1A imagery in Google Earth Engine cloud computing platform

NASA Astrophysics Data System (ADS)

Chen, Bangqian; Xiao, Xiangming; Li, Xiangping; Pan, Lianghao; Doughty, Russell; Ma, Jun; Dong, Jinwei; Qin, Yuanwei; Zhao, Bin; Wu, Zhixiang; Sun, Rui; Lan, Guoyu; Xie, Guishui; Clinton, Nicholas; Giri, Chandra

2017-09-01

Due to rapid losses of mangrove forests caused by anthropogenic disturbances and climate change, accurate and contemporary maps of mangrove forests are needed to understand how mangrove ecosystems are changing and establish plans for sustainable management. In this study, a new classification algorithm was developed using the biophysical characteristics of mangrove forests in China. More specifically, these forests were mapped by identifying: (1) greenness, canopy coverage, and tidal inundation from time series Landsat data, and (2) elevation, slope, and intersection-with-sea criterion. The annual mean Normalized Difference Vegetation Index (NDVI) was found to be a key variable in determining the classification thresholds of greenness, canopy coverage, and tidal inundation of mangrove forests, which are greatly affected by tide dynamics. In addition, the integration of Sentinel-1A VH band and modified Normalized Difference Water Index (mNDWI) shows great potential in identifying yearlong tidal and fresh water bodies, which is related to mangrove forests. This algorithm was developed using 6 typical Regions of Interest (ROIs) as algorithm training and was run on the Google Earth Engine (GEE) cloud computing platform to process 1941 Landsat images (25 Path/Row) and 586 Sentinel-1A images circa 2015. The resultant mangrove forest map of China at 30 m spatial resolution has an overall/users/producer's accuracy greater than 95% when validated with ground reference data. In 2015, China's mangrove forests had a total area of 20,303 ha, about 92% of which was in the Guangxi Zhuang Autonomous Region, Guangdong, and Hainan Provinces. This study has demonstrated the potential of using the GEE platform, time series Landsat and Sentine-1A SAR images to identify and map mangrove forests along the coastal zones. The resultant mangrove forest maps are likely to be useful for the sustainable management and ecological assessments of mangrove forests in China.

Predicting temperate forest stand types using only structural profiles from discrete return airborne lidar

NASA Astrophysics Data System (ADS)

Fedrigo, Melissa; Newnham, Glenn J.; Coops, Nicholas C.; Culvenor, Darius S.; Bolton, Douglas K.; Nitschke, Craig R.

2018-02-01

Light detection and ranging (lidar) data have been increasingly used for forest classification due to its ability to penetrate the forest canopy and provide detail about the structure of the lower strata. In this study we demonstrate forest classification approaches using airborne lidar data as inputs to random forest and linear unmixing classification algorithms. Our results demonstrated that both random forest and linear unmixing models identified a distribution of rainforest and eucalypt stands that was comparable to existing ecological vegetation class (EVC) maps based primarily on manual interpretation of high resolution aerial imagery. Rainforest stands were also identified in the region that have not previously been identified in the EVC maps. The transition between stand types was better characterised by the random forest modelling approach. In contrast, the linear unmixing model placed greater emphasis on field plots selected as endmembers which may not have captured the variability in stand structure within a single stand type. The random forest model had the highest overall accuracy (84%) and Cohen's kappa coefficient (0.62). However, the classification accuracy was only marginally better than linear unmixing. The random forest model was applied to a region in the Central Highlands of south-eastern Australia to produce maps of stand type probability, including areas of transition (the 'ecotone') between rainforest and eucalypt forest. The resulting map provided a detailed delineation of forest classes, which specifically recognised the coalescing of stand types at the landscape scale. This represents a key step towards mapping the structural and spatial complexity of these ecosystems, which is important for both their management and conservation.

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.

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...

37. Photo copy of map, (original in Forest Service Office, ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

37. Photo copy of map, (original in Forest Service Office, Elkins, WV, 'Blister Rust Survey Map), 1930. PARSONS NURSERY SITE PLAN - Parsons Nursery, South side of U.S. Route 219, Parsons, Tucker County, WV

Airborne laser-guided imaging spectroscopy to map forest trait diversity and guide conservation.

PubMed

Asner, G P; Martin, R E; Knapp, D E; Tupayachi, R; Anderson, C B; Sinca, F; Vaughn, N R; Llactayo, W

2017-01-27

Functional biogeography may bridge a gap between field-based biodiversity information and satellite-based Earth system studies, thereby supporting conservation plans to protect more species and their contributions to ecosystem functioning. We used airborne laser-guided imaging spectroscopy with environmental modeling to derive large-scale, multivariate forest canopy functional trait maps of the Peruvian Andes-to-Amazon biodiversity hotspot. Seven mapped canopy traits revealed functional variation in a geospatial pattern explained by geology, topography, hydrology, and climate. Clustering of canopy traits yielded a map of forest beta functional diversity for land-use analysis. Up to 53% of each mapped, functionally distinct forest presents an opportunity for new conservation action. Mapping functional diversity advances our understanding of the biosphere to conserve more biodiversity in the face of land use and climate change. Copyright © 2017, American Association for the Advancement of Science.

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...

Regression and Geostatistical Techniques: Considerations and Observations from Experiences in NE-FIA

Treesearch

Rachel Riemann; Andrew Lister

2005-01-01

Maps of forest variables improve our understanding of the forest resource by allowing us to view and analyze it spatially. The USDA Forest Service's Northeastern Forest Inventory and Analysis unit (NE-FIA) has used geostatistical techniques, particularly stochastic simulation, to produce maps and spatial data sets of FIA variables. That work underscores the...

Simultaneous reproduction of global carbon exchange and storage of terrestrial forest ecosystems

NASA Astrophysics Data System (ADS)

Kondo, M.; Ichii, K.

2012-12-01

Understanding the mechanism of the terrestrial carbon cycle is essential for assessing the impact of climate change. Quantification of both carbon exchange and storage is the key to the understanding, but it often associates with difficulties due to complex entanglement of environmental and physiological factors. Terrestrial ecosystem models have been the major tools to assess the terrestrial carbon budget for decades. Because of its strong association with climate change, carbon exchange has been more rigorously investigated by the terrestrial biosphere modeling community. Seeming success of model based assessment of carbon budge often accompanies with the ill effect, substantial misrepresentation of storage. In practice, a number of model based analyses have paid attention solely on terrestrial carbon fluxes and often neglected carbon storage such as forest biomass. Thus, resulting model parameters are inevitably oriented to carbon fluxes. This approach is insufficient to fully reduce uncertainties about future terrestrial carbon cycles and climate change because it does not take into account the role of biomass, which is equivalently important as carbon fluxes in the system of carbon cycle. To overcome this issue, a robust methodology for improving the global assessment of both carbon budget and storage is needed. One potentially effective approach to identify a suitable balance of carbon allocation proportions for each individual ecosystem. Carbon allocations can influence the plant growth by controlling the amount of investment acquired from photosynthesis, as well as carbon fluxes by controlling the carbon content of leaves and litter, both are active media for photosynthesis and decomposition. Considering those aspects, there may exist the suitable balance of allocation proportions enabling the simultaneous reproduction of carbon budget and storage. The present study explored the existence of such suitable balances of allocation proportions, and examines the performance of carbon fluxes and biomass simulations with them. An experiment was performed with a widely used model, Biome-BGC, and effects of disturbance and forest age were considered in the model run. As for disturbance, human influence index map derived by CIESIN was used. A global forest age map was prepared with model inversion method using CIESIN human influence index, GFED fire burnt area, and IIASA global forest biomass maps. To validate model GPP and RE, we prepared the global GPP map estimated with support vector machine and the global RE map derived by downscaling the carbon budget product (L4A) of Greenhouse gases Observing SATellite (GOSAT) in conjunction with IIASA biomass and soil carbon products. Through a process of testing the simultaneous reproducibility of the Biome-BGC model, it will be determined whether the current terrestrial ecosystem model is sophisticated enough for clarifying the mechanism of carbon cycle.

Sound-mapping a coniferous forest—Perspectives for biodiversity monitoring and noise mitigation

PubMed Central

Fischer, Michael; Tzanopoulos, Joseph

2018-01-01

Acoustic diversity indices have been proposed as low-cost biodiversity monitoring tools. The acoustic diversity of a soundscape can be indicative of the richness of an acoustic community and the structural/vegetation characteristics of a habitat. There is a need to apply these methods to landscapes that are ecologically and/or economically important. We investigate the relationship between the acoustic properties of a coniferous forest with stand-age and structure. We sampled a 73 point grid in part of the UK’s largest man-made lowland coniferous plantation forest, covering a 320ha mosaic of different aged stands. Forest stands ranged from 0–85 years old providing an age-gradient. Short soundscape recordings were collected from each grid point on multiple mornings (between 6am-11am) to capture the dawn chorus. We repeated the study during July/August in 2014 and again in 2015. Five acoustic indices were calculated for a total of 889 two minute samples. Moderate relationships between acoustic diversity with forest stand-age and vegetation characteristics (canopy height; canopy cover) were observed. Ordinations suggest that as structural complexity and forest age increases, the higher frequency bands (4-10KHz) become more represented in the soundscape. A strong linear relationship was observed between distance to the nearest road and the ratio of anthropogenic noise to biological sounds within the soundscape. Similar acoustic patterns were observed in both years, though acoustic diversity was generally lower in 2014, which was likely due to differences in wind conditions between years. Our results suggest that developing these relatively low-cost acoustic monitoring methods to inform adaptive management of production landscapes, may lead to improved biodiversity monitoring. The methods may also prove useful for modelling road noise, landscape planning and noise mitigation. PMID:29320514

Controls over aboveground forest carbon density on Barro Colorado Island, Panama

NASA Astrophysics Data System (ADS)

Mascaro, J.; Asner, G. P.; Muller-Landau, H. C.; van Breugel, M.; Hall, J.; Dahlin, K.

2010-12-01

Despite the importance of tropical forests to the global carbon cycle, ecological controls over landscape-level variation in live aboveground carbon density (ACD) in tropical forests are poorly understood. Here, we conducted a spatially comprehensive analysis of ACD variation for a mainland tropical forest - Barro Colorado Island, Panama (BCI) - and tested site factors that may control such variation. We mapped ACD over 98% of BCI (~1500 ha) using airborne Light Detection and Ranging (LiDAR), which was well-correlated with ground-based measurements of ACD in Panamanian forests of various ages (r2 = 0.77, RMSE = 29 Mg C ha-1, P < 0.0001). We used multiple regression to examine controls over LiDAR-derived ACD, including slope angle, bedrock, soil texture, and forest age. Collectively, these variables explained 14% of the variation in ACD at 30-m resolution, and explained 33% at 100-m resolution. At all resolutions, slope (linked to underlying bedrock variation) was the strongest driving factor; standing carbon stocks were generally higher on steeper slopes, where erosion rates tend to exceed weathering rates, compared to gentle slopes, where weathering in place produces deep, oxic soils. This result suggests that physiography may be more important in controlling ACD variation in Neotropical forests than currently thought. Although BCI has been largely undisturbed by humans for a century, past land-use over approximately half of the island still influences ACD variation, with younger forests (80-130 years old) averaging ~15% less carbon storage than old-growth forests (>400 years old). If other regions of relatively old tropical secondary forests also store less carbon aboveground than primary forests, the effects on the global carbon cycle could be substantial and difficult to detect with satellite monitoring.

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

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.

A framework for mapping tree species combining hyperspectral and LiDAR data: Role of selected classifiers and sensor across three spatial scales

NASA Astrophysics Data System (ADS)

Ghosh, Aniruddha; Fassnacht, Fabian Ewald; Joshi, P. K.; Koch, Barbara

2014-02-01

Knowledge of tree species distribution is important worldwide for sustainable forest management and resource evaluation. The accuracy and information content of species maps produced using remote sensing images vary with scale, sensor (optical, microwave, LiDAR), classification algorithm, verification design and natural conditions like tree age, forest structure and density. Imaging spectroscopy reduces the inaccuracies making use of the detailed spectral response. However, the scale effect still has a strong influence and cannot be neglected. This study aims to bridge the knowledge gap in understanding the scale effect in imaging spectroscopy when moving from 4 to 30 m pixel size for tree species mapping, keeping in mind that most current and future hyperspectral satellite based sensors work with spatial resolution around 30 m or more. Two airborne (HyMAP) and one spaceborne (Hyperion) imaging spectroscopy dataset with pixel sizes of 4, 8 and 30 m, respectively were available to examine the effect of scale over a central European forest. The forest under examination is a typical managed forest with relatively homogenous stands featuring mostly two canopy layers. Normalized digital surface model (nDSM) derived from LiDAR data was used additionally to examine the effect of height information in tree species mapping. Six different sets of predictor variables (reflectance value of all bands, selected components of a Minimum Noise Fraction (MNF), Vegetation Indices (VI) and each of these sets combined with LiDAR derived height) were explored at each scale. Supervised kernel based (Support Vector Machines) and ensemble based (Random Forest) machine learning algorithms were applied on the dataset to investigate the effect of the classifier. Iterative bootstrap-validation with 100 iterations was performed for classification model building and testing for all the trials. For scale, analysis of overall classification accuracy and kappa values indicated that 8 m spatial resolution (reaching kappa values of over 0.83) slightly outperformed the results obtained from 4 m for the study area and five tree species under examination. The 30 m resolution Hyperion image produced sound results (kappa values of over 0.70), which in some areas of the test site were comparable with the higher spatial resolution imagery when qualitatively assessing the map outputs. Considering input predictor sets, MNF bands performed best at 4 and 8 m resolution. Optical bands were found to be best for 30 m spatial resolution. Classification with MNF as input predictors produced better visual appearance of tree species patches when compared with reference maps. Based on the analysis, it was concluded that there is no significant effect of height information on tree species classification accuracies for the present framework and study area. Furthermore, in the examined cases there was no single best choice among the two classifiers across scales and predictors. It can be concluded that tree species mapping from imaging spectroscopy for forest sites comparable to the one under investigation is possible with reliable accuracies not only from airborne but also from spaceborne imaging spectroscopy datasets.

Mapping the Distribution of Cloud Forests Using MODIS Imagery

NASA Astrophysics Data System (ADS)

Douglas, M. W.; Mejia, J.; Murillo, J.; Orozco, R.

2007-05-01

Tropical cloud forests - those forests that are frequently immersed in clouds or otherwise very humid, are extremely difficult to map from the ground, and are not easily distinguished in satellite imagery from other forest types, but they have a very different flora and fauna than lowland rainforest. Cloud forests, although found in many parts of the tropics, have a very restricted vertical extent and thus are also restricted horizontally. As a result, they are subject to both human disturbance (coffee growing for example) and the effects of possible climate change. Motivated by a desire to seek meteorological explanations for the distribution of cloud forests, we have begun to map cloudiness using MODIS Terra and Aqua visible imagery. This imagery, at ~1030 LT and 1330 LT, is an approximation for mid-day cloudiness. In tropical regions the amount of mid-day cloudiness strongly controls the shortwave radiation and thus the potential for evaporation (and aridity). We have mapped cloudiness using a simple algorithm that distinguishes between the cloud-free background brightness and the generally more reflective clouds to separate clouds from the underlying background. A major advantage of MODIS imagery over many other sources of satellite imagery is its high spatial resolution (~250m). This, coupled with precisely navigated images, means that detailed maps of cloudiness can be produced. The cloudiness maps can then be related to the underlying topography to further refine the location of the cloud forests. An advantage of this technique is that we are mapping the potential cloud forest, based on cloudiness, rather than the actual cloud forest, which are commonly based on forest estimates from satellite and digital elevation data. We do not derive precipitation, only estimates of daytime cloudiness. Although only a few years of MODIS imagery has been used in our studies, we will show that this is sufficient to describe the climatology of cloudiness with acceptable accuracy for its intended purposes. Even periods as short as one month are sufficient for depicting the location of most cloud forest environments. However, we are proceeding to distinguish different characteristics of cloud forests, depending on the overall frequency of cloudiness, the seasonality of cloudiness, and the interannual variability of cloudiness. These results should be useful to those seeking to describe relationships between the physical characteristics of the cloud forests and their biological environment.

Combining MODIS and Landsat imagery to estimate and map boreal forest cover loss

USGS Publications Warehouse

Potapov, P.; Hansen, Matthew C.; Stehman, S.V.; Loveland, Thomas R.; Pittman, K.

2008-01-01

Estimation of forest cover change is important for boreal forests, one of the most extensive forested biomes, due to its unique role in global timber stock, carbon sequestration and deposition, and high vulnerability to the effects of global climate change. We used time-series data from the MODerate Resolution Imaging Spectroradiometer (MODIS) to produce annual forest cover loss hotspot maps. These maps were used to assign all blocks (18.5 by 18.5 km) partitioning the boreal biome into strata of high, medium and low likelihood of forest cover loss. A stratified random sample of 118 blocks was interpreted for forest cover and forest cover loss using high spatial resolution Landsat imagery from 2000 and 2005. Area of forest cover gross loss from 2000 to 2005 within the boreal biome is estimated to be 1.63% (standard error 0.10%) of the total biome area, and represents a 4.02% reduction in year 2000 forest cover. The proportion of identified forest cover loss relative to regional forest area is much higher in North America than in Eurasia (5.63% to 3.00%). Of the total forest cover loss identified, 58.9% is attributable to wildfires. The MODIS pan-boreal change hotspot estimates reveal significant increases in forest cover loss due to wildfires in 2002 and 2003, with 2003 being the peak year of loss within the 5-year study period. Overall, the precision of the aggregate forest cover loss estimates derived from the Landsat data and the value of the MODIS-derived map displaying the spatial and temporal patterns of forest loss demonstrate the efficacy of this protocol for operational, cost-effective, and timely biome-wide monitoring of gross forest cover loss.

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.

Genetic subpopulation structuring and its implications in a mature eastern white pine stand

Treesearch

Samuel E. Nijensohn; Paul G. Schaberg; Gary J. Hawley; Donald H. DeHayes; Donald H. DeHayes

2005-01-01

We examined patterns of genetic structuring within a mature eastern white pine (Pinus strobus L.) forest, using geographic information system (GIS)-based data and maps that combined genetic (isozyme analysis of 46 loci) and other tree-specific information (e.g., size, growth, age, and location) for 220 trees in Jericho, Vermont. Interconnections between genotypic...

Mapping of past stand-level forest disturbances and estimation of time since disturbance using simulated spaceborne LiDAR data

NASA Astrophysics Data System (ADS)

Sanchez Lopez, N.; Hudak, A. T.; Boschetti, L.

2017-12-01

Explicit information on the location, the size or the time since disturbance (TSD) at the forest stand level complements field inventories, improves the monitoring of forest attributes and the estimation of biomass and carbon stocks. Even-aged stands display homogenous structural parameters that have often been used as a proxy of stand age. Consequently, performing object-oriented analysis on Light Detection and Ranging (LiDAR) data has potential to detect historical stand-replacing disturbances. Recent research has shown good results in the delineation of forest stands as well as in the prediction of disturbance occurrence and TSD using airborne LiDAR data. Nevertheless, the use of airborne LiDAR for systematic monitoring of forest stands is limited by the sporadic availability of data and its high cost compared to satellite instruments. NASA's forthcoming Global Ecosystem Dynamics Investigations (GEDI) mission will provide systematically data on the vertical structure of the vegetation, but its use presents some challenges compared to the common discrete-return airborne LiDAR. GEDI will be a waveform instrument, hence the summary metrics will be different to those obtained with airborne LiDAR, and the sampling configuration could limit the utility of the data, especially on heterogeneous landscapes. The potential use of GEDI data for forest characterization at the stand level would therefore depend on the predictive power of the GEDI footprint metrics, and on the density of point samples relative to forest stand size (i.e. the number of observation/footprints per stand).In this study, we assess the performance of simulated GEDI-derived metrics for stand characterization and estimation of TSD, and the point density needed to adequately identify forest stands, which translates - due to the fixed sampling configuration - into the minimum temporal interval needed to collect a sufficient number of points. The study area was located in the Clear Creek, Selway River, and Elk Creek watersheds ( 54,000 ha) within the Nez Perce-Clearwater National Forest in Idaho, where airborne LiDAR and reference maps on TSD were available. Simulated GEDI footprints and waveforms were obtained from airborne LiDAR point clouds and the results were compared to similar analysis performed with airborne LiDAR.

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

20th century Betula pubescens subsp. czerepanovii tree- and forest lines in Norway.

PubMed

Bryn, Anders; Potthoff, Kerstin

2017-01-01

Georeferenced tree- and forest line data has a wide range of applications and are increasingly used for e.g. monitoring of climate change impacts and range shift modelling. As part of a research project, registrations of previously re-mapped tree- and forest lines have been georeferenced. The data described in this paper contains 100 re-mapped registrations of Betula pubescens subsp. czerepanovii throughout Norway. All of the re-mapped tree- and forest line localities are georeferenced, elevation and aspect are given, elevational and spatial uncertainty are provided, and the re-mapping methods are explained. The published data weremapped for the first time between 1819 and 1963. The same sites were re-mapped between 1928 and 1996, but have until now been missing spatial coordinates. The entries contain 40 x 2 tree lines and 60 x 2 forest lines, most likely presenting the regionally highest registered tree- and forest lines at the given time. The entire material is stored and available for download through the GBIF server. Previously, the entries have been published in journals or reports, partly in Norwegian or German only. Without the provision of the spatial coordinates, the specific locations have been unknown. The material is now available for modelling and monitoring of tree- and forest line range shifts: The recordings are useful for interpretation of climate change impacts on tree- and forest lines, and the locations of re-mapped tree- and forest lines can be implemented in future monitoring projects. Since the recordings most likely provide the highest registered Betula pubescens subsp. czerepanovii locations within their specific regions, they are probably representing the contemporary physiognomic range limits.

Revisions to the 1995 map of ecological subregions that affect users of the southern variant of the Forest Vegetation Simulator

Treesearch

W. Henry McNab; Chad E. Keyser

2011-01-01

The Southern Variant of the Forest Vegetation Simulator utilizes ecological units mapped in 1995 by the Forest Service, U.S. Department of Agriculture, to refine tree growth models for the Southern United States. The 2007 revision of the 1995 map resulted in changes of identification and boundary delineation for some ecoregion units. In this report, we summarize the...

Rates and patterns of deforestation in the Philippines: application of geographic information system analysis

Treesearch

Dawning S. Lui; Louis R. Iverson; Sandra Brown

1993-01-01

Land-use maps for 1934 and 1988, and a 1941 road map of the Philippines were digitized into a geographic information system. These maps were then analyzed to determine the rates of deforestation and their relationship with factors such as the distance of forests to roads and forest fragmentation (measured by perimeter-to-area ratio (P/A ratio) of forest patches) for...

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.

Data Base Design with GIS in Ecosystem Based Multiple Use Forest Management in Artvin, Turkey: A Case Study in Balcı Forest Management Planning Unit.

PubMed

Yolasığmaz, Hacı Ahmet; Keleş, Sedat

2009-01-01

In Turkey, the understanding of planning focused on timber production has given its place on Multiple Use Management (MUM). Because the whole infrastructure of forestry with inventory system leading the way depends on timber production, some cases of bottle neck are expected during the transition period. Database design, probably the most important stage during the transition to MUM, together with the digital basic maps making up the basis of this infrastructure constitute the main point of this article. Firstly, the forest management philosophy of Turkey in the past was shortly touched upon in the article. Ecosystem Based Multiple Use Forest Management (EBMUFM) approaches was briefly introduced. The second stage of the process of EBMUFM, database design was described by examining the classical planning infrastructure and the coverage to be produced and consumed were suggested in the form of lists. At the application stage, two different geographical databases were established with GIS in Balcı Planning Unit of the years 1984 and 2006. Following that the related basic maps are produced. Timely diversity of the planning unit of 20 years is put forward comparatively with regard to the stand parameters such as tree types, age class, development stage, canopy closure, mixture, volume and increment.

Digital Bedrock Compilation: A Geodatabase Covering Forest Service Lands in California

NASA Astrophysics Data System (ADS)

Elder, D.; de La Fuente, J. A.; Reichert, M.

2010-12-01

This digital database contains bedrock geologic mapping for Forest Service lands within California. This compilation began in 2004 and the first version was completed in 2005. Second publication of this geodatabase was completed in 2010 and filled major gaps in the southern Sierra Nevada and Modoc/Medicine Lake/Warner Mountains areas. This digital map database was compiled from previously published and unpublished geologic mapping, with source mapping and review from California Geological Survey, the U.S. Geological Survey and others. Much of the source data was itself compilation mapping. This geodatabase is huge, containing ~107,000 polygons and ~ 280,000 arcs. Mapping was compiled from more than one thousand individual sources and covers over 41,000,000 acres (~166,000 km2). It was compiled from source maps at various scales - from ~ 1:4,000 to 1:250,000 and represents the best available geologic mapping at largest scale possible. An estimated 70-80% of the source information was digitized from geologic mapping at 1:62,500 scale or better. Forest Service ACT2 Enterprise Team compiled the bedrock mapping and developed a geodatabase to store this information. This geodatabase supports feature classes for polygons (e.g, map units), lines (e.g., contacts, boundaries, faults and structural lines) and points (e.g., orientation data, structural symbology). Lookup tables provide detailed information for feature class items. Lookup/type tables contain legal values and hierarchical groupings for geologic ages and lithologies. Type tables link coded values with descriptions for line and point attributes, such as line type, line location and point type. This digital mapping is at the core of many quantitative analyses and derivative map products. Queries of the database are used to produce maps and to quantify rock types of interest. These include the following: (1) ultramafic rocks - where hazards from naturally occurring asbestos are high, (2) granitic rocks - increased erosion hazards, (3) limestone, chert, sedimentary rocks - paleontological resources (Potential Fossil Yield Classification maps), (4) calcareous rocks (cave resources, water chemistry), and (5) lava flows - lava tubes (more caves). Map unit groupings (e.g., belts, terranes, tectonic & geomorphic provinces) can also be derived from the geodatabase. Digital geologic mapping was used in ground water modeling to predict effects of tunneling through the San Bernardino Mountains. Bedrock mapping is used in models that characterize watershed sediment regimes and quantify anthropogenic influences. When combined with digital geomorphology mapping, this geodatabase helps to assess landslide hazards.

Predicting a roadkill hotspots based on spatial distribution of Korean water deer (Hydropotes inermis argyropus) using Maxent model in South Korea Expressway : In Case of Cheongju-Sangju Expressway

NASA Astrophysics Data System (ADS)

Park, Hyomin; Lee, Sangdon

2016-04-01

Road construction has direct and indirect effects on ecosystems. Especially wildlife-vehicle conflicts (roadkills) caused by roads are a considerable threat for population of many species. This study aims to identify the effects of topographic characteristics and spatial distribution of Korean water deer (Hydropotes inermis). Korean water deer is indigenous and native species in Korea that listed LC (least concern) by IUCN redlist categories. Korean water deer population is growing every year occupying for most of roadkills (>70%) in Korean express highway. In order to predict a distribution of the Korean water deer, we selected factors that most affected water deer's habitat. Major habitats of waterdeer are known as agricultural area, forest area and water. Based on this result, eight factors were selected (land cover map, vegetation map, age class of forest, diameter class of tree, population, slope of study site, elevation of study site, distance of river), and made a thematic map by using GIS program (ESRI, Arc GIS 10.3.1 ver.). To analyze the affected factors of waterdeer distribution, GPS data and thematic map of study area were entered into Maxent model (Maxent 3.3.3.k.). Results of analysis were verified by the AUC (Area Unit Curve) of ROC (Receiver Operating Characteristic). The ROC curve used the sensitivity and specificity as a reference for determining the prediction efficiency of the model and AUC area of ROC curve was higher prediction efficiency closer to '1.' Selecting factors that affected the distribution of waterdeer were land cover map, diameter class of tree and elevation of study site. The value of AUC was 0.623. To predict the water deer's roadkills hot spot on Cheongju-Sangju Expressway, the thematic map was prepared based on GPS data of roadkill spots. As a result, the topographic factors that affected waterdeer roadkill were land cover map, actual vegetation map and age class of forest and the value of AUC was 0.854. Through this study, we could identify the site and hot spots that water deer frequently expected to use based on quantitative data on the spatial and topographic factors. Therefore, we can suggest ways to minimize roadkills by selecting the hot spots and by suggesting construction of eco-corridors. This study will significantly enhance human-wildlife conflicts by identifying key habitat areas for wild mammals.

Map of distribution of six forest ownership types in the conterminous United States

Treesearch

Jaketon H. Hewes; Brett J. Butler; Greg C. Liknes; Mark D. Nelson; Stephanie A. Snyder

2014-01-01

This map depicts the spatial distribution of ownership types across forest land in the conterminous United States circa 2009. The distribution is derived, in part, from Forest Inventory and Analysis (FIA) data that are collected at a sample intensity of approximately one plot per 2400 ha across the United States (U.S. Forest Service 2012). Ownership categories were...

Forest Fire Advanced System Technology (FFAST): A Conceptual Design for Detection and Mapping

Treesearch

J. David Nichols; John R. Warren

1987-01-01

The Forest Fire Advanced System Technology (FFAST) project is developing a data system to provide near-real-time forest fire information to fire management at the fire Incident Command Post (ICP). The completed conceptual design defined an integrated forest fire detection and mapping system that is based upon technology available in the 1990's. System component...

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...

Mapping Biomass for REDD in the Largest Forest of Central Africa: the Democratic Republic of Congo

NASA Astrophysics Data System (ADS)

Shapiro, Aurelie; Saatchi, Sassan

2014-05-01

With the support of the International Climate Initiative (ICI) of the Federal Ministry of the Environment, Conservation, and Nuclear Security, the implementation of the German Development Bank KfW, the World Wide Fund for Nature (WWF) Germany, the University of California Los Angeles (UCLA) and local DRC partners will produce a national scale biomass map for the entire forest coverage of the Democratic Republic of Congo (DRC) along with feasibility assessments of different forest protection measures within a framework of a REDD+ model project. The « Carbon Map and Model (CO2M&M) » project will produce a national forest biomass map for the DRC, which will enable quantitative assessments of carbon stocks and emissions in the largest forest of the Congo Basin. This effort will support the national REDD (Reducing Emissions from Deforestation and Degradation) program in DRC, which plays a major role in sustainable development and poverty alleviation. This map will be developed from field data, complemented by airborne LiDAR (Light Detection and Ranging) and aerial photos, systematically sampled throughout the forests of the DRC and up-scaled to satellite images to accurately estimate carbon content in all forested areas. The second component of the project is to develop specific approaches for model REDD projects in key landscapes. This project represents the largest LiDAR-derived mapping effort in Africa, under unprecedented logistical constraints, which will provide one of the poorest nations in the world with the richest airborne and satellites derived datasets for analyzing forest structure, biomass and biodiversity.

Global Boreal Forest Mapping with JERS-1: North America

NASA Technical Reports Server (NTRS)

Williams, Cynthia L.; McDonald, Kyle; Chapman, Bruce

2000-01-01

Collaborative effort is underway to map boreal forests worldwide using L-band, single polarization Synthetic Aperture Radar (SAR) imagery from the Japanese Earth Resources (JERS-1) satellite. Final products of the North American Boreal Forest Mapping Project will include two continental scale radar mosaics and supplementary multitemporal mosaics for Alaska, central Canada, and eastern Canada. For selected sites, we are also producing local scale (100 km x 100 km) and regional scale maps (1000 km x 1000 km). As with the nearly completed Amazon component of the Global Rain Forest Mapping project, SAR imagery, radar image mosaics and SAR-derived texture image products will be available to the scientific community on the World Wide Web. Image acquisition for this project has been completed and processing and image interpretation is underway at the Alaska SAR Facility.

Forest host mapping: Implications for pest survey and modeling

Treesearch

James R. Ellenwood

2010-01-01

The Forest Health Technology Enterprise Team has developed forest-health risk maps for the past decade. Each subsequent rendition has improved upon previous renditions by acquiring and developing better data. Part of the improvement has focused upon host data of individual tree species.

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.

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.

Accuracy Assessment of Satellite Derived Forest Cover Products in South and Southeast Asia

NASA Astrophysics Data System (ADS)

Gilani, H.; Xu, X.; Jain, A. K.

2017-12-01

South and Southeast Asia (SSEA) region occupies 16 % of worlds land area. It is home to over 50% of the world's population. The SSEA's countries are experiencing significant land-use and land-cover changes (LULCCs), primarily in agriculture, forest, and urban land. For this study, we compiled four existing global forest cover maps for year 2010 by Gong et al.(2015), Hansen et al. (2013), Sexton et al.(2013) and Shimada et al. (2014), which were all medium resolution (≤30 m) products based on Landsat and/or PALSAR satellite images. To evaluate the accuracy of these forest products, we used three types of information: (1) ground measurements, (2) high resolution satellite images and (3) forest cover maps produced at the national scale. The stratified random sampling technique was used to select a set of validation data points from the ground and high-resolution satellite images. Then the confusion matrix method was used to assess and rank the accuracy of the forest cover products for the entire SSEA region. We analyzed the spatial consistency of different forest cover maps, and further evaluated the consistency with terrain characteristics. Our study suggests that global forest cover mapping algorithms are trained and tested using limited ground measurement data. We found significant uncertainties in mountainous areas due to the topographical shadow effect and the dense tree canopies effects. The findings of this study will facilitate to improve our understanding of the forest cover dynamics and their impacts on the quantities and pathways of terrestrial carbon and nitrogen fluxes. Gong, P., et al. (2012). "Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data." International Journal of Remote Sensing 34(7): 2607-2654. Hansen, M. C., et al. (2013). "High-Resolution Global Maps of 21st-Century Forest Cover Change." Science 342(6160): 850-853. Sexton, J. O., et al. (2013). "Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error." International Journal of Digital Earth: 1-22. Shimada, M., et al. (2014). "New global forest/non-forest maps from ALOS PALSAR data (2007-2010)." Remote Sensing of Environment 155: 13-31.

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.

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.

Improved estimation of forest area in tropical Africa through ALOS/PALSAR 50-m orthorectified mosaic images

NASA Astrophysics Data System (ADS)

Dong, J.; Xiao, X.; Li, L.; Tenku, S. N.; Zhang, G.; Biradar, C. M.

2013-12-01

Tropical and moist Africa has one of the largest rainforests in the world. However, our knowledge about its forest area and spatial extent is still very limited. Forest area datasets from the Food and Agriculture Organization (FAO) Forest Resource Assessment (FRA) and the analyses of optical images (e.g., MODIS and MERIS) had a significant discrepancy, and they cannot meet the requirements to support the studies of forest carbon cycle and biodiversity, as well as the implementation of reducing emissions from deforestation and forest degradation (REDD+). The reasons for the large data discrepancy are complex and may attribute to the frequent cloud cover, coarse spatial resolution of images (MODIS, MERIS), diverse forest definition and classification approaches. In this study we generated a forest cover map in central Africa at 50-m resolution through the use of the Phased Array Type L-band Synthetic Aperture Radar (PALSAR) 50-m orthorectified mosaic imagery in 2009. The resultant forest map was evaluated by the ground-reference data collected from the Geo-referenced Field Photo Library and Google Earth, and it has a reasonably high accuracy (producer's accuracy 83% and user's accuracy 94%). We also compared the PALSAR-based forest map with other three forest cover products (MCD12Q1 2009, GlobCover 2009 and VCF tree cover 2009) at the scales of (1) entire study domain and (2) selected sample regions. This new PALSAR-based 50-m forest cover map is likely to help reduce the uncertainty in forest area estimation, and better quantify and track deforestation, REDD+ implementation, and biodiversity conservation in central Africa.

Forest Cover Mapping in Iskandar Malaysia Using Satellite Data

NASA Astrophysics Data System (ADS)

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

2016-09-01

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

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.

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

USGS Publications Warehouse

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

2012-01-01

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

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.

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

Treesearch

Karen Schleeweis; Samuel N. Goward; Chengquan Huang; John L. Dwyer; Jennifer L. Dungan; Mary A. Lindsey; Andrew Michaelis; Khaldoun Rishmawi; Jeffery G. Masek

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...

Integrating Landsat-derived disturbance maps with FIA inventory data: Applications for state-Level forest resource assessments

Treesearch

Sonja Oswalt; Chengquan Huang; Hua Shi; James Vogelmann; Zhiliang Zhu; Samuel N. Goward; John Coulston

2009-01-01

Landsat images have been widely used for assessing forest characteristics and dynamics. Recently, significant progress has been made towards indepth exploration of the rich Landsat archive kept by the U.S. Geological Survey to improve our under standing of forest disturbance and recovery processes. In this study, we used Landsat images to map forest disturbances at...

A forestry GIS-based study on evaluating the potential of imaging spectroscopy in mapping forest land fertility

NASA Astrophysics Data System (ADS)

Mõttus, Matti; Takala, Tuure

2014-12-01

Fertility, or the availability of nutrients and water, controls forest productivity. It affects its carbon sequestration, and thus the forest's effect on climate, as well as its commercial value. Although the availability of nutrients cannot be measured directly using remote sensing methods, fertility alters several vegetation traits detectable from the reflectance spectra of the forest stand, including its pigment content and water stress. However, forest reflectance is also influenced by other factors, such as species composition and stand age. Here, we present a case study demonstrating how data obtained using imaging spectroscopy is correlated with site fertility. The study was carried out in Hyytiälä, Finland, in the southern boreal forest zone. We used a database of state-owned forest stands including basic forestry variables and a site fertility index. To test the suitability of imaging spectroscopy with different spatial and spectral resolutions for site fertility mapping, we performed two airborne acquisitions using different sensor configurations. First, the sensor was flown at a high altitude with high spectral resolution resulting in a pixel size in the order of a tree crown. Next, the same area was flown to provide reflectance data with sub-meter spatial resolution. However, to maintain usable signal-to-noise ratios, several spectral channels inside the sensor were combined, thus reducing spectral resolution. We correlated a number of narrowband vegetation indices (describing canopy biochemical composition, structure, and photosynthetic activity) on site fertility. Overall, site fertility had a significant influence on the vegetation indices but the strength of the correlation depended on dominant species. We found that high spatial resolution data calculated from the spectra of sunlit parts of tree crowns had the strongest correlation with site fertility.

Geologic map of the Cucamonga Peak 7.5' quadrangle, San Bernardino County, California

USGS Publications Warehouse

Morton, D.M.; Matti, J.C.; Digital preparation by Koukladas, Catherine; Cossette, P.M.

2001-01-01

a. This Readme; includes in Appendix I, data contained in fif_met.txt b. The same graphic as plotted in 2 above. (Test plots have not produced 1:24,000-scale map sheets. Adobe Acrobat pagesize setting influences map scale.) The Correlation of Map Units and Description of Map Units is in the editorial format of USGS Miscellaneous Investigations Series (I-series) maps but has not been edited to comply with I-map standards. Within the geologic map data package, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. Even though this is an author-prepared report, every attempt has been made to closely adhere to the stratigraphic nomenclature of the U. S. Geological Survey. Descriptions of units can be obtained by viewing or plotting the .pdf file (3b above) or plotting the postscript file (2 above). If roads in some areas, especially forest roads that parallel topographic contours, do not show well on plots of the geologic map, we recommend use of the USGS Cucamonga Peak 7.5’ topographic quadrangle in conjunction with the geologic map.

Geologic map of the Telegraph Peak 7.5' quadrangle, San Bernardino County, California

USGS Publications Warehouse

Morton, D.M.; Woodburne, M.O.; Foster, J.H.; Morton, Gregory; Cossette, P.M.

2001-01-01

a. This Readme; includes in Appendix I, data contained in fif_met.txt b. The same graphic as plotted in 2 above. Test plots have not produced 1:24,000-scale map sheets. Adobe Acrobat pagesize setting influences map scale. The Correlation of Map Units and Description of Map Units is in the editorial format of USGS Miscellaneous Investigations Series (I-series) maps but has not been edited to comply with I-map standards. Within the geologic map data package, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. Even though this is an author-prepared report, every attempt has been made to closely adhere to the stratigraphic nomenclature of the U. S. Geological Survey. Descriptions of units can be obtained by viewing or plotting the .pdf file (3b above) or plotting the postscript file (2 above). If roads in some areas, especially forest roads that parallel topographic contours, do not show well on plots of the geologic map, we recommend use of the USGS Telegraph Peak 7.5’ topographic quadrangle in conjunction with the geologic map.

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.

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.

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.

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

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...

Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Treesearch

Wenli Huang; Anu Swatantran; Kristofer Johnson; Laura Duncanson; Hao Tang; Jarlath O' Neil Dunne; George Hurtt; Ralph Dubayah

2015-01-01

Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-...

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...

Combined effect of pulse density and grid cell size on predicting and mapping aboveground carbon in fast-growing Eucalyptus forest plantation using airborne LiDAR data.

PubMed

Silva, Carlos Alberto; Hudak, Andrew Thomas; Klauberg, Carine; Vierling, Lee Alexandre; Gonzalez-Benecke, Carlos; de Padua Chaves Carvalho, Samuel; Rodriguez, Luiz Carlos Estraviz; Cardil, Adrián

2017-12-01

LiDAR remote sensing is a rapidly evolving technology for quantifying a variety of forest attributes, including aboveground carbon (AGC). Pulse density influences the acquisition cost of LiDAR, and grid cell size influences AGC prediction using plot-based methods; however, little work has evaluated the effects of LiDAR pulse density and cell size for predicting and mapping AGC in fast-growing Eucalyptus forest plantations. The aim of this study was to evaluate the effect of LiDAR pulse density and grid cell size on AGC prediction accuracy at plot and stand-levels using airborne LiDAR and field data. We used the Random Forest (RF) machine learning algorithm to model AGC using LiDAR-derived metrics from LiDAR collections of 5 and 10 pulses m -2 (RF5 and RF10) and grid cell sizes of 5, 10, 15 and 20 m. The results show that LiDAR pulse density of 5 pulses m -2 provides metrics with similar prediction accuracy for AGC as when using a dataset with 10 pulses m -2 in these fast-growing plantations. Relative root mean square errors (RMSEs) for the RF5 and RF10 were 6.14 and 6.01%, respectively. Equivalence tests showed that the predicted AGC from the training and validation models were equivalent to the observed AGC measurements. The grid cell sizes for mapping ranging from 5 to 20 also did not significantly affect the prediction accuracy of AGC at stand level in this system. LiDAR measurements can be used to predict and map AGC across variable-age Eucalyptus plantations with adequate levels of precision and accuracy using 5 pulses m -2 and a grid cell size of 5 m. The promising results for AGC modeling in this study will allow for greater confidence in comparing AGC estimates with varying LiDAR sampling densities for Eucalyptus plantations and assist in decision making towards more cost effective and efficient forest inventory.

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...

Geographic Information System (GIS) | Virginia Department of Forestry

Science.gov Websites

relationships, making GIS a valuable tool to explore management and policy alternatives. GIS at the Virginia in combination in various VDOF applications. InFOREST can be used to map your property. Foresters can Property Inventory and Stand Mapping Riparian Buffer Tax Credit Program Management Forest Sustainability

Improving the precision of dynamic forest parameter estimates using Landsat

Treesearch

Evan B. Brooks; John W. Coulston; Randolph H. Wynne; Valerie A. Thomas

2016-01-01

The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is wellestablished.When reducing the variance of post-stratification estimates for forest change parameters such as forestgrowth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is

Cartographic standards to improve maps produced by the Forest Inventory and Analysis program

Treesearch

Charles H. (Hobie) Perry; Mark D. Nelson

2009-01-01

The Forest Service, U.S. Department of Agriculture's Forest Inventory and Analysis (FIA) program is incorporating an increasing number of cartographic products in reports, publications, and presentations. To create greater quality and consistency within the national FIA program, a Geospatial Standards team developed cartographic design standards for FIA map...

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.

Mapping Forest Fire Susceptibility in Temperate Mountain Areas with Expert Knowledge. A Case Study from Iezer Mountains, Romanian Carpathians

NASA Astrophysics Data System (ADS)

Mihai, Bogdan; Savulescu, Ionut

2014-05-01

Forest fires in Romanian Carpathians became a frequent phenomenon during the last decade, although local climate and other environmental features did not create typical conditions. From 2004, forest fires affect in Romania more than 100 hectares/year of different forest types (deciduous and coniferous). Their magnitude and frequency are not known, since a historical forest fire inventory does not exist (only press papers and local witness for some selected events). Forest fires features the summer dry periods but there are dry autumns and early winter periods with events of different magnitudes. The application we propose is based on an empirical modeling of forest fire susceptibility in a typical mountain area from the Southern Carpathians, the Iezer Mountains (2462 m). The study area features almost all the altitudinal vegetation zones of the European temperate mountains, from the beech zone, to the coniferous zone, the subalpine and the alpine zones (Mihai et al., 2007). The analysis combines GIS and remote sensing models (Chuvieco et al., 2012), starting from the ideas that forest fires are featured by the ignition zones and then by the fire propagation zones. The first data layer (ignition zones) is the result of the crossing between the ignition factors: lightning - points of multitemporal occurence and anthropogenic activities (grazing, tourism and traffic) and the ignition zones (forest fuel zonation - forest stands, soil cover and topoclimatic factor zonation). This data is modelled from different sources: the MODIS imagery fire product (Hantson et al., 2012), detailed topographic maps, multitemporal orthophotos at 0.5 m resolution, Landsat multispectral imagery, forestry cadastre maps, detailed soil maps, meteorological data (the WorldClim digital database) as well as the field survey (mapping using GPS and local observation). The second data layer (fire propagation zones) is the result of the crossing between the forest fuel zonation, obtained with the help of forestry data, the wind regime data and the topographic features of the mountain area (elevation, slope declivity, slope aspect). The analysis also consider the insolation degree of mountain slopes, that creates favourable conditions for fire propagation between different canopies. These data layers are integrated within a simple GIS analysis in order to intersect the ignition zones with the fire propagation zones in order to obtain the potential areas to be affected by fire. The digital map show three levels of forest fire susceptibility, differenced on the basis of expert knowledge. The map can be validated from the statistical point of view with the polygons of the forest fire affected areas mapped from Landsat TM, ETM+ and OLI satellite imagery. The mapping results could be integrated within the forest management strategies and especially within the forest cadastre and development maps (updated every ten years). The result can confirm that the data gap in terms of forest fire events can be filled with expert knowledge. References Chuvieco, E, Aguado, I., Jurdao, S., Pettinari, M., Yebra, M., Salas, J., Hantson, S., de la Riva, J., Ibarra, P., Rodrigues, M., Echeverria, M., Azqueta, D., Roman, M., Bastarrika, A., Martinez, S., Recondo, C., Zapico, E., Martinez-Vega F.J. (2012) Integrating geospatial information into fire risk assessment, International Journal of Wildland Fire, 2,2, 69-86. Hantson, S., Padilla, M., Corti., D, Chuvieco, E. (2013) Strenghts and weaknesses of MODIS hotspots to characterize Global fire occurence, Remote Sensing of Environment, 131, 1, 152-159. Mihai, B., Savulescu, I.,Sandric, I. (2007) Change detection analysis (1986/2002) for the alpine, subalpine and forest landscape in Iezer Mountains (Southern Carpathians, Romania), Mountain Research and Development, 27, 250-258.

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.

Analysis of Radarsat-2 Full Polarimetric Data for Forest Mapping

NASA Astrophysics Data System (ADS)

Maghsoudi, Yasser

Forests are a major natural resource of the Earth and control a wide range of environmental processes. Forests comprise a major part of the planet's plant biodiversity and have an important role in the global hydrological and biochemical cycles. Among the numerous potential applications of remote sensing in forestry, forest mapping plays a vital role for characterization of the forest in terms of species. Particularly, in Canada where forests occupy 45% of the territory, representing more than 400 million hectares of the total Canadian continental area. In this thesis, the potential of polarimetric SAR (PolSAR) Radarsat-2 data for forest mapping is investigated. This thesis has two principle objectives. First is to propose algorithms for analyzing the PolSAR image data for forest mapping. There are a wide range of SAR parameters that can be derived from PolSAR data. In order to make full use of the discriminative power offered by all these parameters, two categories of methods are proposed. The methods are based on the concept of feature selection and classifier ensemble. First, a nonparametric definition of the evaluation function is proposed and hence the methods NFS and CBFS. Second, a fast wrapper algorithm is proposed for the evaluation function in feature selection and hence the methods FWFS and FWCBFS. Finally, to incorporate the neighboring pixels information in classification an extension of the FWCBFS method i.e. CCBFS is proposed. The second objective of this thesis is to provide a comparison between leaf-on (summer) and leaf-off (fall) season images for forest mapping. Two Radarsat-2 images acquired in fine quad-polarized mode were chosen for this study. The images were collected in leaf-on and leaf-off seasons. We also test the hypothesis whether combining the SAR parameters obtained from both images can provide better results than either individual datasets. The rationale for this combination is that every dataset has some parameters which may be useful for forest mapping. To assess the potential of the proposed methods their performance have been compared with each other and with the baseline classifiers. The baseline methods include the Wishart classifier, which is a commonly used classification method in PolSAR community, as well as an SVM classifier with the full set of parameters. Experimental results showed a better performance of the leaf-off image compared to that of leaf-on image for forest mapping. It is also shown that combining leaf-off parameters with leaf-on parameters can significantly improve the classification accuracy. Also, the classification results (in terms of the overall accuracy) compared to the baseline classifiers demonstrate the effectiveness of the proposed nonparametric scheme for forest mapping.

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.

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

Treesearch

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

2007-01-01

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

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.

Mapping Tropical Forest Change in the Greater Marañón and Ucayali regions of Peru using CLASlite

NASA Astrophysics Data System (ADS)

Perez-Leiva, P.; Knapp, D. E.; Clark, J. K.; Asner, G. P.

2012-12-01

The Carnegie Landsat Analysis System-lite (CLASlite) was used to map and monitor tropical forest change in two large tropical watersheds in Peru: Greater Marañón and Ucayali. CLASlite uses radiometric and atmospheric correction algorithms as well as an Automated Monte Carlo Unmixing (AutoMCU) to obtain consistent fractional land cover per-pixel at high spatial resolution. Fractional land cover is automatically extracted from universal spectral libraries which allow for a differentiation between live photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV) and bare substrate (S). Fractional cover information is directly translated to maps of forest cover based in the physical characteristics of the forest canopy. Rates of deforestation and disturbance are estimated through analysis of change in fractional land cover over time. The Greater Marañón and Ucayali watersheds were studied over the period 1985 to 2012, through analysis of 1900 multi-spectral images from Landsat 4, 5 and 7. These images were processed and analyzed using CLASlite to obtain fractional cover and forest cover information for each year within the period. Annualization of the collected maps provided detailed information on the gross rates of disturbance and deforestation throughout the region. Further, net deforestation and disturbance maps were used to show the general forest change in these watersheds over the past 25 years. We found that deforestation accounts for just ~50% of the total forest losses, and that forest disturbance (degradation) is critically important to consider when making forest change estimates associated with losses in habitat and carbon in the region. These results also provide spatially-detailed, temporally-specific information on forest change for nearly three decades. Information provided by this study will assist decision-makers in Peru to improve their regional environmental management. The results, unprecedented in spatial and temporal scope, are another example showing the fidelity of tropical deforestation and forest degradation monitoring made routine using the CLASlite system.

Application of satellite data and LARS's data processing techniques to mapping vegetation of the Dismal Swamp. M.S. Thesis - Old Dominion Univ.

NASA Technical Reports Server (NTRS)

Messmore, J. A.

1976-01-01

The feasibility of using digital satellite imagery and automatic data processing techniques as a means of mapping swamp forest vegetation was considered, using multispectral scanner data acquired by the LANDSAT-1 satellite. The site for this investigation was the Dismal Swamp, a 210,000 acre swamp forest located south of Suffolk, Va. on the Virginia-North Carolina border. Two basic classification strategies were employed. The initial classification utilized unsupervised techniques which produced a map of the swamp indicating the distribution of thirteen forest spectral classes. These classes were later combined into three informational categories: Atlantic white cedar (Chamaecyparis thyoides), Loblolly pine (Pinus taeda), and deciduous forest. The subsequent classification employed supervised techniques which mapped Atlantic white cedar, Loblolly pine, deciduous forest, water and agriculture within the study site. A classification accuracy of 82.5% was produced by unsupervised techniques compared with 89% accuracy using supervised techniques.

Historical harvests reduce neighboring old-growth basal area across a forest landscape.

PubMed

Bell, David M; Spies, Thomas A; Pabst, Robert

2017-07-01

While advances in remote sensing have made stand, landscape, and regional assessments of the direct impacts of disturbance on forests quite common, the edge influence of timber harvesting on the structure of neighboring unharvested forests has not been examined extensively. In this study, we examine the impact of historical timber harvests on basal area patterns of neighboring old-growth forests to assess the magnitude and scale of harvest edge influence in a forest landscape of western Oregon, USA. We used lidar data and forest plot measurements to construct 30-m resolution live tree basal area maps in lower and middle elevation mature and old-growth forests. We assessed how edge influence on total, upper canopy, and lower canopy basal area varied across this forest landscape as a function of harvest characteristics (i.e., harvest size and age) and topographic conditions in the unharvested area. Upper canopy, lower canopy, and total basal area increased with distance from harvest edge and elevation. Forests within 75 m of harvest edges (20% of unharvested forests) had 4% to 6% less live tree basal area compared with forest interiors. An interaction between distance from harvest edge and elevation indicated that elevation altered edge influence in this landscape. We observed a positive edge influence at low elevations (<800 m) and a negative edge influence at moderate to high elevations (>800 m). Surprisingly, we found no or weak effects of harvest age (13-60 yr) and harvest area (0.2-110 ha) on surrounding unharvested forest basal area, implying that edge influence was relatively insensitive to the scale of disturbance and multi-decadal recovery processes. Our study indicates that the edge influence of past clearcutting on the structure of neighboring uncut old-growth forests is widespread and persistent. These indirect and diffuse legacies of historical timber harvests complicate forest management decision-making in old-growth forest landscapes by broadening the traditional view of stand boundaries. Furthermore, the consequences of forest harvesting may reach across ownership boundaries, highlighting complex governance issues surrounding landscape management of old-growth forests. © 2017 by the Ecological Society of America.

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.

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.

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).

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.

Application of China's National Forest Continuous Inventory database.

PubMed

Xie, Xiaokui; Wang, Qingli; Dai, Limin; Su, Dongkai; Wang, Xinchuang; Qi, Guang; Ye, Yujing

2011-12-01

The maintenance of a timely, reliable and accurate spatial database on current forest ecosystem conditions and changes is essential to characterize and assess forest resources and support sustainable forest management. Information for such a database can be obtained only through a continuous forest inventory. The National Forest Continuous Inventory (NFCI) is the first level of China's three-tiered inventory system. The NFCI is administered by the State Forestry Administration; data are acquired by five inventory institutions around the country. Several important components of the database include land type, forest classification and ageclass/ age-group. The NFCI database in China is constructed based on 5-year inventory periods, resulting in some of the data not being timely when reports are issued. To address this problem, a forest growth simulation model has been developed to update the database for years between the periodic inventories. In order to aid in forest plan design and management, a three-dimensional virtual reality system of forest landscapes for selected units in the database (compartment or sub-compartment) has also been developed based on Virtual Reality Modeling Language. In addition, a transparent internet publishing system for a spatial database based on open source WebGIS (UMN Map Server) has been designed and utilized to enhance public understanding and encourage free participation of interested parties in the development, implementation, and planning of sustainable forest management.

A synoptic climatology for forest fires in the NE US and future implications for GCM simulations

Treesearch

Yan Qing; Ronald Sabo; Yiqiang Wu; J.Y. Zhu

1994-01-01

We studied surface-pressure patterns corresponding to reduced precipitation, high evaporation potential, and enhanced forest-fire danger for West Virginia, which experienced extensive forest-fire damage in November 1987. From five years of daily weather maps we identified eight weather patterns that describe distinctive flow situations throughout the year. Map patterns...

Testing a Landsat-based approach for mapping disturbance causality in U.S. forests

Treesearch

Todd A. Schroeder; Karen G. Schleeweis; Gretchen G. Moisen; Chris Toney; Warren B. Cohen; Elizabeth A. Freeman; Zhiqiang Yang; Chengquan Huang

2017-01-01

In light of Earth's changing climate and growing human population, there is an urgent need to improve monitoring of natural and anthropogenic disturbanceswhich effect forests' ability to sequester carbon and provide other ecosystem services. In this study, a two-step modeling approach was used to map the type and timing of forest disturbances occurring...

Implications of allometric model selection for county-level biomass mapping

Treesearch

Laura Duncanson; Wenli Huang; Kristofer Johnson; Anu Swatantran; Ronald E. McRoberts; Ralph Dubayah

2017-01-01

Background: Carbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend...

Mapping forest soil organic matter on New Jersey's coastal plain

Treesearch

Brian J. Clough; Edwin J. Green; Richard B. Lathrop

2012-01-01

Managing forest soil organic matter (SOM) stocks is a vital strategy for reducing the impact of anthropogenic carbon dioxide emissions. However, the SOM pool is highly variable, and developing accurate estimates to guide management decisions has remained a difficult task. We present the results of a spatial model designed to map soil organic matter for all forested...

Forest mapping of Central America and Mexico with AVHRR data

Treesearch

Keith B. Lannom

2001-01-01

Concerns over changes in global forest resource distributions have prompted a number of studies to examine and map forest areas at continental scales with various types of satillite data. The work described here details the use of Advanced Very High Resolution Radiometer (AVHRR) data in concert with Landsat Thematic Mapper (TM) and Systeme Probatoire d'...

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

Treesearch

Sally. Duncan

2003-01-01

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

Forest type mapping of the Interior West

Treesearch

Bonnie Ruefenacht; Gretchen G. Moisen; Jock A. Blackard

2004-01-01

This paper develops techniques for the mapping of forest types in Arizona, New Mexico, and Wyoming. The methods involve regression-tree modeling using a variety of remote sensing and GIS layers along with Forest Inventory Analysis (FIA) point data. Regression-tree modeling is a fast and efficient technique of estimating variables for large data sets with high accuracy...

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...

Digital data collection in forest dynamics plots

Treesearch

Faith Inman-Narahari; Christian Giardina; Rebecca Ostertag; Susan Cordell; Lawren Sack

2010-01-01

Summary 1. Computers are widely used in all aspects of research but their application to in-field data collection for forest plots has rarely been evaluated. 2. We developed digital data collection methods using ESRI mapping software and ruggedized field computers to map and measure ~30 000 trees in two 4-ha forest dynamics plots in wet and dry...

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.

Comparison of Stem Map Developed from Crown Geometry Allometry Linked Census Data to Airborne and Terrestrial Lidar at Harvard Forest, MA

NASA Astrophysics Data System (ADS)

Sullivan, F.; Palace, M. W.; Ducey, M. J.; David, O.; Cook, B. D.; Lepine, L. C.

2014-12-01

Harvard Forest in Petersham, MA, USA is the location of one of the temperate forest plots established by the Center for Tropical Forest Science (CTFS) as a joint effort with Harvard Forest and the Smithsonian Institute's Forest Global Earth Observatory (ForestGEO) to characterize ecosystem processes and forest dynamics. Census of a 35 ha plot on Prospect Hill was completed during the winter of 2014 by researchers at Harvard Forest. Census data were collected according to CTFS protocol; measured variables included species, stem diameter, and relative X-Y locations. Airborne lidar data were collected over the censused plot using the high spatial resolution Goddard LiDAR, Hyperspectral, and Thermal sensor package (G-LiHT) during June 2012. As part of a separate study, 39 variable radius plots (VRPs) were randomly located and sampled within and throughout the Prospect Hill CTFS/ForestGEO plot during September and October 2013. On VRPs, biometric properties of trees were sampled, including species, stem diameter, total height, crown base height, crown radii, and relative location to plot centers using a 20 Basal Area Factor prism. In addition, a terrestrial-based lidar scanner was used to collect one lidar scan at plot center for 38 of the 39 VRPs. Leveraging allometric equations of crown geometry and tree height developed from 374 trees and 16 different species sampled on 39 VRPs, a 3-dimensional stem map will be created using the Harvard Forest ForestGEO Prospect Hill census. Vertical and horizontal structure of 3d field-based stem maps will be compared to terrestrial and airborne lidar scan data. Furthermore, to assess the quality of allometric equations, a 2d canopy height raster of the field-based stem map will be compared to a G-LiHT derived canopy height model for the 35 ha census plot. Our automated crown delineation methods will be applied to the 2d representation of the census stem map and the G-LiHT canopy height model. For future work related to this study, high quality field-based stem maps with species and crown geometry information will allow for better comparisons and interpretations of individual tree spectra from the G-LiHT hyperspectral sensor as estimated by automated crown delineation of the G-LiHT lidar canopy height model.

An evaluation of satellite data for estimating the area of small forestland in the southern lower peninsula of Michigan. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Karteris, M. A. (Principal Investigator)

1980-01-01

A winter black and white band 5, a winter color, a fall color, and a diazo color composite of the fall scene were used to assess the use and potential of LANDSAT images for mapping and estimating acreage of small scattered forest tracts in Barry County, Michigan. Forests as small as 2.5 acres were mapped from each LANDSAT data source. The maps for each image were compared with an available forest-type map. Mapping errors detected were categorized as boundary and identification errors. The most frequently misclassified areas were agriculture lands, treed-bogs, brushlands and lowland and mixed hardwood stands. Stocking level affected interpretation more than stand size. The overall level of the interpretation performance was expressed through the estimation of classification, interpretation, and mapping accuracies. These accuracies ranged from 74 between 74% and 98%. Considering errors, accuracy, and cost, winter color imagery is the best LANDSAT alternative for mapping small forest tracts. However, since the availability of cloud-free winter images of the study area is significantly lower than images for other seasons, a diazo enhanced image of a fall scene is recommended as the best next best alternative.

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

NASA Technical Reports Server (NTRS)

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

1973-01-01

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

Assessing error sources for Landsat time series analysis for tropical test sites in Viet Nam and Ethiopia

NASA Astrophysics Data System (ADS)

Schultz, Michael; Verbesselt, Jan; Herold, Martin; Avitabile, Valerio

2013-10-01

Researchers who use remotely sensed data can spend half of their total effort analysing prior data. If this data preprocessing does not match the application, this time spent on data analysis can increase considerably and can lead to inaccuracies. Despite the existence of a number of methods for pre-processing Landsat time series, each method has shortcomings, particularly for mapping forest changes under varying illumination, data availability and atmospheric conditions. Based on the requirements of mapping forest changes as defined by the United Nations (UN) Reducing Emissions from Forest Degradation and Deforestation (REDD) program, the accurate reporting of the spatio-temporal properties of these changes is necessary. We compared the impact of three fundamentally different radiometric preprocessing techniques Moderate Resolution Atmospheric TRANsmission (MODTRAN), Second Simulation of a Satellite Signal in the Solar Spectrum (6S) and simple Dark Object Subtraction (DOS) on mapping forest changes using Landsat time series data. A modification of Breaks For Additive Season and Trend (BFAST) monitor was used to jointly map the spatial and temporal agreement of forest changes at test sites in Ethiopia and Viet Nam. The suitability of the pre-processing methods for the occurring forest change drivers was assessed using recently captured Ground Truth and high resolution data (1000 points). A method for creating robust generic forest maps used for the sampling design is presented. An assessment of error sources has been performed identifying haze as a major source for time series analysis commission error.

The mysterious demise of an ice-age relic: exposing the cause of yellow-cedar decline.

Treesearch

Jonathan Thompson

2007-01-01

For more than a century, yellow-cedar has been inexplicably dying throughout the northern coastal rain forests of the Pacific Northwest. Yellow-cedar mortality has been mapped on more than a half million acres in southeastern Alaska, yet until recently the cause of death was a stubborn mystery. Researchers are hopeful, after several decades of investigation, that they...

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.

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.

Mapping forest inventory and analysis data attributes within the framework of double sampling for stratification design

Treesearch

David C. Chojnacky; Randolph H. Wynne; Christine E. Blinn

2009-01-01

Methodology is lacking to easily map Forest Inventory and Analysis (FIA) inventory statistics for all attribute variables without having to develop separate models and methods for each variable. We developed a mapping method that can directly transfer tabular data to a map on which pixels can be added any way desired to estimate carbon (or any other variable) for a...

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).

Advancing reference emission levels in subnational and national REDD+ initiatives: a CLASlite approach

PubMed Central

Asner, Gregory P; Joseph, Shijo

2015-01-01

Conservation and monitoring of tropical forests requires accurate information on their extent and change dynamics. Cloud cover, sensor errors and technical barriers associated with satellite remote sensing data continue to prevent many national and sub-national REDD+ initiatives from developing their reference deforestation and forest degradation emission levels. Here we present a framework for large-scale historical forest cover change analysis using free multispectral satellite imagery in an extremely cloudy tropical forest region. The CLASlite approach provided highly automated mapping of tropical forest cover, deforestation and degradation from Landsat satellite imagery. Critically, the fractional cover of forest photosynthetic vegetation, non-photosynthetic vegetation, and bare substrates calculated by CLASlite provided scene-invariant quantities for forest cover, allowing for systematic mosaicking of incomplete satellite data coverage. A synthesized satellite-based data set of forest cover was thereby created, reducing image incompleteness caused by clouds, shadows or sensor errors. This approach can readily be implemented by single operators with highly constrained budgets. We test this framework on tropical forests of the Colombian Pacific Coast (Chocó) – one of the cloudiest regions on Earth, with successful comparison to the Colombian government’s deforestation map and a global deforestation map. PMID:25678933

Comparison of interferometric and stereo-radargrammetric 3D metrics in mapping of forest resources

NASA Astrophysics Data System (ADS)

Karila, K.; Karjalainen, M.; Yu, X.; Vastaranta, M.; Holopainen, M.; Hyyppa, J.

2015-04-01

Accurate forest resources maps are needed in diverse applications ranging from the local forest management to the global climate change research. In particular, it is important to have tools to map changes in forest resources, which helps us to understand the significance of the forest biomass changes in the global carbon cycle. In the task of mapping changes in forest resources for wide areas, Earth Observing satellites could play the key role. In 2013, an EU/FP7-Space funded project "Advanced_SAR" was started with the main objective to develop novel forest resources mapping methods based on the fusion of satellite based 3D measurements and in-situ field measurements of forests. During the summer 2014, an extensive field surveying campaign was carried out in the Evo test site, Southern Finland. Forest inventory attributes of mean tree height, basal area, mean stem diameter, stem volume, and biomass, were determined for 91 test plots having the size of 32 by 32 meters (1024 m2). Simultaneously, a comprehensive set of satellite and airborne data was collected. Satellite data also included a set of TanDEM-X (TDX) and TerraSAR-X (TSX) X-band synthetic aperture radar (SAR) images, suitable for interferometric and stereo-radargrammetric processing to extract 3D elevation data representing the forest canopy. In the present study, we compared the accuracy of TDX InSAR and TSX stereo-radargrammetric derived 3D metrics in forest inventory attribute prediction. First, 3D data were extracted from TDX and TSX images. Then, 3D data were processed as elevations above the ground surface (forest canopy height values) using an accurate Digital Terrain Model (DTM) based on airborne laser scanning survey. Finally, 3D metrics were calculated from the canopy height values for each test plot and the 3D metrics were compared with the field reference data. The Random Forest method was used in the forest inventory attributes prediction. Based on the results InSAR showed slightly better performance in forest attribute (i.e. mean tree height, basal area, mean stem diameter, stem volume, and biomass) prediction than stereo-radargrammetry. The results were 20.1% and 28.6% in relative root mean square error (RMSE) for biomass prediction, for TDX and TSX respectively.

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.

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index

PubMed Central

Pu, Ruiliang; Gong, Peng; Yu, Qian

2008-01-01

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data. PMID:27879906

Comparative Analysis of EO-1 ALI and Hyperion, and Landsat ETM+ Data for Mapping Forest Crown Closure and Leaf Area Index.

PubMed

Pu, Ruiliang; Gong, Peng; Yu, Qian

2008-06-06

In this study, a comparative analysis of capabilities of three sensors for mapping forest crown closure (CC) and leaf area index (LAI) was conducted. The three sensors are Hyperspectral Imager (Hyperion) and Advanced Land Imager (ALI) onboard EO-1 satellite and Landsat-7 Enhanced Thematic Mapper Plus (ETM+). A total of 38 mixed coniferous forest CC and 38 LAI measurements were collected at Blodgett Forest Research Station, University of California at Berkeley, USA. The analysis method consists of (1) extracting spectral vegetation indices (VIs), spectral texture information and maximum noise fractions (MNFs), (2) establishing multivariate prediction models, (3) predicting and mapping pixel-based CC and LAI values, and (4) validating the mapped CC and LAI results with field validated photo-interpreted CC and LAI values. The experimental results indicate that the Hyperion data are the most effective for mapping forest CC and LAI (CC mapped accuracy (MA) = 76.0%, LAI MA = 74.7%), followed by ALI data (CC MA = 74.5%, LAI MA = 70.7%), with ETM+ data results being least effective (CC MA = 71.1%, LAI MA = 63.4%). This analysis demonstrates that the Hyperion sensor outperforms the other two sensors: ALI and ETM+. This is because of its high spectral resolution with rich subtle spectral information, of its short-wave infrared data for constructing optimal VIs that are slightly affected by the atmosphere, and of its more available MNFs than the other two sensors to be selected for establishing prediction models. Compared to ETM+ data, ALI data are better for mapping forest CC and LAI due to ALI data with more bands and higher signal-to-noise ratios than those of ETM+ data.

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...

Determining subcanopy Psidium cattleianum invasion in Hawaiian forests using imaging spectroscopy

Treesearch

Jomar Barbosa; Gregory Asner; Roberta Martin; Claire Baldeck; Flint Hughes; Tracy Johnson

2016-01-01

High-resolution airborne imaging spectroscopy represents a promising avenue for mapping the spread of invasive tree species through native forests, but for this technology to be useful to forest managers there are two main technical challenges that must be addressed: (1) mapping a single focal species amongst a diverse array of other tree species; and (2) detecting...

Approximating prediction uncertainty for random forest regression models

Treesearch

John W. Coulston; Christine E. Blinn; Valerie A. Thomas; Randolph H. Wynne

2016-01-01

Machine learning approaches such as random forest have increased for the spatial modeling and mapping of continuous variables. Random forest is a non-parametric ensemble approach, and unlike traditional regression approaches there is no direct quantification of prediction error. Understanding prediction uncertainty is important when using model-based continuous maps as...

The effects of rectification and Global Positioning System errors on satellite image-based estimates of forest area

Treesearch

Ronald E. McRoberts

2010-01-01

Satellite image-based maps of forest attributes are of considerable interest and are used for multiple purposes such as international reporting by countries that have no national forest inventory and small area estimation for all countries. Construction of the maps typically entails, in part, rectifying the satellite images to a geographic coordinate system, observing...

Deconstructing the timber volume paradigm in management of the Tongass National Forest.

Treesearch

John P. Caouette; Marc G. Kramer; Gregory J. Nowacki

2000-01-01

Timber volume information and associated maps have been widely used in the Tongass National Forest for land-use planning and timber and wildlife management. Although considerable effort has been expended to improve timber volume maps, little has been done to evaluate the suitability of timber volume as a descriptor of forest character.We established a rough indicator...

Sampling and mapping forest volume and biomass using airborne LIDARs

Treesearch

Erik Naesset; Terje Gobakken; Ross Nelson

2009-01-01

Since around 1995, extensive research efforts have been made in Scandinavia to develop airborne Light Detection and Ranging (LIDAR) as an operational tool for wall-to-wall mapping of forest stands for planning purposes. Scanning LIDAR has the ability to capture the entire three-dimensional structure of forest canopies and has therefore proved to be a very efficient...

Seeing the forest beyond the trees

Treesearch

Sassan Saatchi; Joseph Mascaro; Liang Xu; Michael Keller; Yan Yang; Paul Duffy; Fernando Espirito-Santo; Alessandro Baccini; Jeffery Chambers; David Schimel

2014-01-01

In a recent paper (Mitchard et al. 2014, Global Ecology and Biogeography, 23,935-946) a new map of forest biomass based on a geostatistical model of field data for the Amazon (and surrounding forests) was presented and contrasted with two earlier maps based on remote sensing data Saatchi et al. (2011; RS1) and Baccini et al. (2012; RS2). Mitchard et al....

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 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.

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)

Weighted analysis methods for mapped plot forest inventory data: Tables, regressions, maps and graphs

Treesearch

Paul C. Van Deusen; Linda S. Heath

2010-01-01

Weighted estimation methods for analysis of mapped plot forest inventory data are discussed. The appropriate weighting scheme can vary depending on the type of analysis and graphical display. Both statistical issues and user expectations need to be considered in these methods. A weighting scheme is proposed that balances statistical considerations and the logical...

A Simulation Algorithm to Approximate the Area of Mapped Forest Inventory Plots

Treesearch

William A. Bechtold; Naser E. Heravi; Matthew E. Kinkenon

2003-01-01

Calculating the area of polygons associated with mapped forest inventory plots can be mathematically cumbersome, especially when computing change between inventories. We developed a simulation technique that utilizes a computer-generated dot grid and geometry to estimate the area of mapped polygons within any size circle. The technique also yields a matrix of change in...

36 CFR 9.42 - Well records and reports, plots and maps, samples, tests and surveys.

Code of Federal Regulations, 2010 CFR

2010-07-01

... Well records and reports, plots and maps, samples, tests and surveys. Any technical data gathered... 36 Parks, Forests, and Public Property 1 2010-07-01 2010-07-01 false Well records and reports, plots and maps, samples, tests and surveys. 9.42 Section 9.42 Parks, Forests, and Public Property...

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.

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.

Terrestrial Carbon Fluxes from Deforestation in the Brazilian Amazon and Cerrado Regions Predicted from MODIS Satellite Data and Ecosystem Modeling

NASA Astrophysics Data System (ADS)

Klooster, S.; Potter, C.; Genovese, V.

2008-12-01

The NASA-CASA (Carnegie Ames Stanford Approach) simulation model based on satellite observations of monthly vegetation cover from the Moderate Resolution Imaging Spectroradiometer (MODIS) was used to estimate tropical forest and savanna (Cerrado) carbon pools for the Brazilian Amazon region over the period 2000-2004. Adjustments for mean age of forest stands were carried out across the region, resulting in a new mapping of aboveground biomass pools based on MODIS satellite data. Yearly maps of newly deforested lands from the Brazilian PRODES (Programa de calculo do desflorestamento da Amazonia ) project were combined with these NASA-CASA biomass predictions to generate seasonal budgets of potential carbon and nitrogen trace gas losses from biomass burning events. Simulations of plant residue and soil carbon decomposition were conducted in the NASA-CASA model during and following deforestation events to track the fate of aboveground biomass pools that were cut and burned each year across the region.

Urban forest topographical mapping using UAV LIDAR

NASA Astrophysics Data System (ADS)

Putut Ash Shidiq, Iqbal; Wibowo, Adi; Kusratmoko, Eko; Indratmoko, Satria; Ardhianto, Ronni; Prasetyo Nugroho, Budi

2017-12-01

Topographical data is highly needed by many parties, such as government institution, mining companies and agricultural sectors. It is not just about the precision, the acquisition time and data processing are also carefully considered. In relation with forest management, a high accuracy topographic map is necessary for planning, close monitoring and evaluating forest changes. One of the solution to quickly and precisely mapped topography is using remote sensing system. In this study, we test high-resolution data using Light Detection and Ranging (LiDAR) collected from unmanned aerial vehicles (UAV) to map topography and differentiate vegetation classes based on height in urban forest area of University of Indonesia (UI). The semi-automatic and manual classifications were applied to divide point clouds into two main classes, namely ground and vegetation. There were 15,806,380 point clouds obtained during the post-process, in which 2.39% of it were detected as ground.

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.

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.

Digital mapping of soil properties in Canadian managed forests at 250 m of resolution using the k-nearest neighbor method

NASA Astrophysics Data System (ADS)

Mansuy, N. R.; Paré, D.; Thiffault, E.

2015-12-01

Large-scale mapping of soil properties is increasingly important for environmental resource management. Whileforested areas play critical environmental roles at local and global scales, forest soil maps are typically at lowresolution.The objective of this study was to generate continuous national maps of selected soil variables (C, N andsoil texture) for the Canadian managed forest landbase at 250 m resolution. We produced these maps using thekNN method with a training dataset of 538 ground-plots fromthe National Forest Inventory (NFI) across Canada,and 18 environmental predictor variables. The best predictor variables were selected (7 topographic and 5 climaticvariables) using the Least Absolute Shrinkage and Selection Operator method. On average, for all soil variables,topographic predictors explained 37% of the total variance versus 64% for the climatic predictors. Therelative root mean square error (RMSE%) calculated with the leave-one-out cross-validation method gave valuesranging between 22% and 99%, depending on the soil variables tested. RMSE values b 40% can be considered agood imputation in light of the low density of points used in this study. The study demonstrates strong capabilitiesfor mapping forest soil properties at 250m resolution, compared with the current Soil Landscape of CanadaSystem, which is largely oriented towards the agricultural landbase. The methodology used here can potentiallycontribute to the national and international need for spatially explicit soil information in resource managementscience.

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 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.

Forest resources of the Clearwater National Forest

Treesearch

Ryan P. Hughes

2011-01-01

The Interior West Forest Inventory and Analysis (IWFIA) Program of the USDA Forest Service, Rocky Mountain Research Station, as part of our National Forest System cooperative inventories, conducted a forest resource inventory on the Clearwater National Forest using a nationally standardized mapped-plot design (for more details see section "Inventory methods...

Forest resources of the Medicine Bow National Forest

Treesearch

Jim Steed

2008-01-01

The Interior West Forest Inventory and Analysis (IWFIA) Program of the USDA Forest Service, Rocky Mountain Research Station, as part of our National Forest System cooperative inventories, conducted a forest resource inventory on the Medicine Bow National Forest using a nationally standardized mapped-plot design (for more details see "Inventory methods"...

High-resolution forest carbon stocks and emissions in the Amazon.

PubMed

Asner, Gregory P; Powell, George V N; Mascaro, Joseph; Knapp, David E; Clark, John K; Jacobson, James; Kennedy-Bowdoin, Ty; Balaji, Aravindh; Paez-Acosta, Guayana; Victoria, Eloy; Secada, Laura; Valqui, Michael; Hughes, R Flint

2010-09-21

Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at 0.1-ha resolution over 4.3 million ha of the Peruvian Amazon, an area twice that of all forests in Costa Rica, to reveal the determinants of forest carbon density and to demonstrate the feasibility of mapping carbon emissions for REDD. We discovered previously unknown variation in carbon storage at multiple scales based on geologic substrate and forest type. From 1999 to 2009, emissions from land use totaled 1.1% of the standing carbon throughout the region. Forest degradation, such as from selective logging, increased regional carbon emissions by 47% over deforestation alone, and secondary regrowth provided an 18% offset against total gross emissions. Very high-resolution monitoring reduces uncertainty in carbon emissions for REDD programs while uncovering fundamental environmental controls on forest carbon storage and their interactions with land-use change.

Forest Aboveground Biomass Estimation in the Greater Mekong, Subregion and Russian Siberia

NASA Astrophysics Data System (ADS)

Pang, Yong; Li, Zengyuan; Sun, Gouqing; Zhang, Zhiyu; Schmullius, Christiane; Meng, Shili; Ma, Zhenyu; Lu, Hao; Li, Shiming; Liu, Qingwang; Bai, Lina; Tian, Xin

2016-08-01

Forests play a vital role in sustainable development and provide a range of economic, social and environmental benefits, including essential ecosystem services such as climate change mitigation and adaptation. We summarized works in forest aboveground biomass estimation in Greater Mekong Subregion (GMS) and Russian Siberia (RuS). Both regions are rich in forest resources. These mapping and estimation works were based on multiple-source remote sensing data and some field measurements. Biomass maps were generated at 500 m and 30 m pixel size for RuS and GMS respectively. With the available of the 2015 PALSAR-2 mosaic at 25 m spacing, Sentinel-2 data at 20 m, we will work on the biomass mapping and dynamic study at higher spatial resolution.

Combining mapped and statistical data in forest ecological inventory and monitoring - supplementing an existing system

Treesearch

H. T. Schreuder; R. Czaplewski; R. G. Bailey

1999-01-01

 forest ecological inventory and monitoring system combining information derived from maps and samples is proposed based on ecosystem regions (Bailey, 1994). The system extends the design of the USDA Forest Service Region 6 Inventory and Monitoring System (R6IMS) in the Pacific Northwest of the United States. The key uses of the information are briefly discussed and...

Linking Fuel Inventories With Atmospheric Data for Assessment of Fire Danger

Treesearch

Christopher W. Woodall; Joseph Charney; Greg Liknes; Brian Potter

2006-01-01

Combining forest fuel maps and real-time atmospheric data may enable creation of more dynamic and comprehensive fire danger assessments. The goal of this study was to combine fuel maps, based on data from the Forest Inventory and Analysis (FIA) program of the U.S. Department of Agriculture Forest Service, with real-time atmospheric data to create a more dynamic index...

New approaches to forest planning: inventorying and mapping place values in the Pacific Northwest Region

Treesearch

Troy E. Hall; Jennifer O. Farnum; Terry C. Slider; Kathy Ludlow

2009-01-01

This report chronicles a large-scale effort to map place values across the Pacific Northwest Region (Washington and Oregon) of the U.S. Forest Service. Through workshops held with Forest Service staff, 485 socioculturally meaningful places were identified. Staff also generated corresponding descriptions of the places’ unique social and biophysical elements—in other...

Use of Forest Inventory and Analysis information in wildlife habitat modeling: a process for linking multiple scales

Treesearch

Thomas C. Edwards; Gretchen G. Moisen; Tracey S. Frescino; Joshua L. Lawler

2002-01-01

We describe our collective efforts to develop and apply methods for using FIA data to model forest resources and wildlife habitat. Our work demonstrates how flexible regression techniques, such as generalized additive models, can be linked with spatially explicit environmental information for the mapping of forest type and structure. We illustrate how these maps of...

What's the fire danger now? Linking fuel inventories with atmospheric data

Treesearch

Christopher W. Woodall; Joseph J. Charney; Greg C. Liknes; Brian E. Potter

2005-01-01

The combination of forest fuel maps with real-time atmospheric data may enable the creation of more dynamic and comprehensive assessments of fire danger. The goal of this study was to combine fuel maps, based on data from the Forest Inventory and Analysis (FIA) program of the USDA Forest Service, with real-time atmospheric data for the creation of a more dynamic index...

Geologic map of the eastern part of the Challis National Forest and vicinity, Idaho

USGS Publications Warehouse

Wilson, A.B.; Skipp, B.A.

1994-01-01

The paper version of the Geologic Map of the eastern part of the Challis National Forest and vicinity, Idaho was compiled by Anna Wilson and Betty Skipp in 1994. The geology was compiled on a 1:250,000 scale topographic base map. TechniGraphic System, Inc. of Fort Collins Colorado digitized this map under contract for N.Shock. G.Green edited and prepared the digital version for publication as a GIS database. The digital geologic map database can be queried in many ways to produce a variety of geologic maps.

Mapping snags and understory shrubs for LiDAR based assessment of wildlife habitat suitability

Treesearch

Sebastian Martinuzzi; Lee A. Vierling; William A. Gould; Michael J. Falkowski; Jeffrey S. Evans; Andrew T. Hudak; Kerri T. Vierling

2009-01-01

The lack of maps depicting forest three-dimensional structure, particularly as pertaining to snags and understory shrub species distribution, is a major limitation for managing wildlife habitat in forests. Developing new techniques to remotely map snags and understory shrubs is therefore an important need. To address this, we first evaluated the use of LiDAR data for...

Softwood distribution maps for the South

Treesearch

Paul L. Janssen; Melvin R. Weiland

1960-01-01

The maps in this report describe the relative concentration as well as the approximate range of 11 softwoods in 12 southern states--extending from the Atlantic Coast westward to about the 96th meridian in Oklahoma and Texas. The data upon which the maps are based were gathered during 1947-57 by the Forest Surveys of the Southern and Southeastern Forest Experiment...

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...

Characterizing and mapping forest fire fuels using ASTER imagery and gradient modeling

Treesearch

Michael J. Falkowski; Paul E. Gessler; Penelope Morgan; Andrew T. Hudak; Alistair M. S. Smith

2005-01-01

Land managers need cost-effective methods for mapping and characterizing forest fuels quickly and accurately. The launch of satellite sensors with increased spatial resolution may improve the accuracy and reduce the cost of fuels mapping. The objective of this research is to evaluate the accuracy and utility of imagery from the advanced spaceborne thermal emission and...

Landscape Heterogeneity mapping for Access to Tribal health care in Nilgiris District of Tamil Nadu, India

NASA Astrophysics Data System (ADS)

Brindha, B.; Prashanthi Devi, M.

2014-11-01

The Nilgiris district in Tamilnadu has a rich biodiversity in terms of flora, fauna and ethnic population. The district is basically a mountainous region, situated at an elevation of 2000 to 2,600 meters above MSL and constituting of several hill and Steep Mountain valleys. This region houses six tribes who are mainly forest dwellers and live in close settlements depending on the forest resources for their livelihood. The Tribes of Nilgiris have been diagnosed and monitored for Sickle cell Anemia which is a disease of major concern among these ethnic populations. This genetic disorder developed due to the sickling of Red Blood Cells has increased during the past few decades. The Tribes, as they live in close encounter with the forest regions and have strict social cultural barriers, face difficulty in availing treatment or counseling from the Sickle Cell Research Center (SCRC) and other NGOs like NAWA and AHWINI in the region. It was observed that many factors such as landscape terrain, climatic conditions and improper roads tend to hinder the access to appropriate health care. The SCRC in Gudalur region is a facility established to monitor the disease cases inspite of these influencing factors. On analyzing the year bound age wise classification among male and female patients, certain dropouts in cases were observed which may be due to inaccessible condition or migration of the patient. In our study, Landscape heterogeneity mapping for different climatic seasons was done in ArcGIS 10.1. For this, contour and terrain maps, road networks and villages were prepared and factors that determine Terrain Difficulty were assessed. Vegetation mapping using IRS satellite images for the study region was attempted and associated with the landscape map. A risk analysis was proposed based on terrain difficulty and access to the nearest Health care Center. Based on this, the above factors alternate routes were suggested to access the difficult areas.

AVIRIS spectral trajectories for forested areas of the Gifford Pinchot National Forest

NASA Technical Reports Server (NTRS)

Sabol, Donald E., Jr.; Smith, Milton O.; Adams, John B.; Zukin, Janet H.; Tucker, Compton J.; Roberts, Dar A.; Gillespie, Alan R.

1995-01-01

A simple mixing model employing reference endmembers (green vegetation, non-photosynthetic vegetation, soil and shade), and using 180 AVIRIS bands, was used to establish an interpretive framework for a forested area in the Pacific Northwest. A regrowth trend, based on changes in the endmember proportions, was defined for conifers that extends from clearcuts to mature forest, and by implication to old growth. Deciduous species within replanted forest plots caused the fractions to be displaced from the main coniferous regrowth trend and to move toward the green vegetation fraction. The results indicate that the spectral information in AVIRIS can be inverted to estimate approximate stand age and relative proportion of deciduous species in the context of the area studied. Using AVIRIS we measured a 3 to 5 percent increase in woody material in old-growth forest, as distinct from other mature forest. This result is consistent with a predicted increase in NPV in old-growth forest, based on field observations. Previous application of the mixing analysis to a TM image of the same area separated old growth based solely on the shade fraction; however the approach required successful removal of shade introduced by topography. Our new results suggest that with the high spectral resolution and high signal-to-noise of AVIRIS images it may be possible to characterize and map old-growth forests in the Northwest using both the NPV fraction and shade.

Forest Biomass Mapping from Stereo Imagery and Radar Data

NASA Astrophysics Data System (ADS)

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

2013-12-01

Both InSAR and lidar data provide critical information on forest vertical structure, which are critical for regional mapping of biomass. However, the regional application of these data is limited by the availability and acquisition costs. Some researchers have demonstrated potentials of stereo imagery in the estimation of forest height. Most of these researches were conducted on aerial images or spaceborne images with very high resolutions (~0.5m). Space-born stereo imagers with global coverage such as ALOS/PRISM have coarser spatial resolutions (2-3m) to achieve wider swath. The features of stereo images are directly affected by resolutions and the approaches use by most of researchers need to be adjusted for stereo imagery with lower resolutions. This study concentrated on analyzing the features of point clouds synthesized from multi-view stereo imagery over forested areas. The small footprint lidar and lidar waveform data were used as references. The triplets of ALOS/PRISM data form three pairs (forward/nadir, backward/nadir and forward/backward) of stereo images. Each pair of the stereo images can be used to generate points (pixels) with 3D coordinates. By carefully co-register these points from three pairs of stereo images, a point cloud data was generated. The height of each point above ground surface was then calculated using DEM from National Elevation Dataset, USGS as the ground surface elevation. The height data were gridded into pixel of different sizes and the histograms of the points within a pixel were analyzed. The average height of the points within a pixel was used as the height of the pixel to generate a canopy height map. The results showed that the synergy of point clouds from different views were necessary, which increased the point density so the point cloud could detect the vertical structure of sparse and unclosed forests. The top layer of multi-layered forest could be captured but the dense forest prevented the stereo imagery to see through. The canopy height map exhibited spatial patterns of roads, forest edges and patches. The linear regression showed that the canopy height map had a good correlation with RH50 of LVIS data at 30m pixel size with a gain of 1.04, bias of 4.3m and R2 of 0.74 (Fig. 1). The canopy height map from PRISM and dual-pol PALSAR data were used together to map biomass in our study area near Howland, Maine, and the results were evaluated using biomass map generated from LVIS waveform data independently. The results showed that adding CHM from PRISM significantly improved biomass accuracy and raised the biomass saturation level of L-band SAR data in forest biomass mapping.

Toward a National Early Warning System for Forest Disturbances Using Remotely Sensed Land-Surface Phenology

NASA Astrophysics Data System (ADS)

Hargrove, W. W.; Spruce, J.

2010-12-01

A prototype National Early Warning System (EWS) for Forest Disturbances was established in 2010 by producing national maps showing potential forest disturbance across the conterminous United States at 231m resolution every 8 days. Each map is based on Land-Surface Phenology (LSP), calculated using temporally smoothed MODIS MOD13 imagery obtained over the preceding 24-day analysis window. Potential disturbance maps are generated by comparing a spatially and temporally specific historical expectation of normal NDVI "greenness" with NDVI "greenness" from a series of current satellite views. Three different disturbance products are produced using differing lengths of historical baseline periods to calculate the expected normal greenness. The short-term baseline products show only disturbances newer than one year ago, while the intermediate baseline products show disturbances since the prior three years, and the long-term baseline products show all disturbances over the MODIS historical period. A Forest Change Assessment Viewer website, http://ews.forestthreats.org/NPDE/NPDE.html, showcases the three most recent national disturbance maps in full spatial context. Although 2010 was a wet el Nino year without major forest problems, disturbances in 2010 in MI, NY, CO and LA will be highlighted. Forest disturbances caused by wildfire, hurricanes, tornadoes, hail, ice storms, and defoliating insects, including fall cankerworms, forest tent caterpillars, gypsy moths, baldcypress leafrollers and winter moths were successfully detected during the 2009 and 2010 field seasons. The EWS was used in 2010 to detect and alert Forest Health Monitoring (FHM) Aerial Disturbance Survey personnel to an otherwise-unknown outbreak of forest tent caterpillar and baldcypress leafroller in the Atchafalaya and Pearl River regions of southern Louisiana. A local FHM Program Coordinator verified these EWS-detected outbreaks. Many defoliator-induced disturbances were ephemeral, and were followed by recovery in LSP, presumably due to refoliation. 2009 Vegetation Disturbances mapped as percent change in max NDVI from June 10 - July 27 2000-2008

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.

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.

Application of an imputation method for geospatial inventory of forest structural attributes across multiple spatial scales in the Lake States, U.S.A

NASA Astrophysics Data System (ADS)

Deo, Ram K.

Credible spatial information characterizing the structure and site quality of forests is critical to sustainable forest management and planning, especially given the increasing demands and threats to forest products and services. Forest managers and planners are required to evaluate forest conditions over a broad range of scales, contingent on operational or reporting requirements. Traditionally, forest inventory estimates are generated via a design-based approach that involves generalizing sample plot measurements to characterize an unknown population across a larger area of interest. However, field plot measurements are costly and as a consequence spatial coverage is limited. Remote sensing technologies have shown remarkable success in augmenting limited sample plot data to generate stand- and landscape-level spatial predictions of forest inventory attributes. Further enhancement of forest inventory approaches that couple field measurements with cutting edge remotely sensed and geospatial datasets are essential to sustainable forest management. We evaluated a novel Random Forest based k Nearest Neighbors (RF-kNN) imputation approach to couple remote sensing and geospatial data with field inventory collected by different sampling methods to generate forest inventory information across large spatial extents. The forest inventory data collected by the FIA program of US Forest Service was integrated with optical remote sensing and other geospatial datasets to produce biomass distribution maps for a part of the Lake States and species-specific site index maps for the entire Lake State. Targeting small-area application of the state-of-art remote sensing, LiDAR (light detection and ranging) data was integrated with the field data collected by an inexpensive method, called variable plot sampling, in the Ford Forest of Michigan Tech to derive standing volume map in a cost-effective way. The outputs of the RF-kNN imputation were compared with independent validation datasets and extant map products based on different sampling and modeling strategies. The RF-kNN modeling approach was found to be very effective, especially for large-area estimation, and produced results statistically equivalent to the field observations or the estimates derived from secondary data sources. The models are useful to resource managers for operational and strategic purposes.

Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe.

PubMed

Senf, Cornelius; Pflugmacher, Dirk; Hostert, Patrick; Seidl, Rupert

2017-08-01

Remote sensing is a key information source for improving the spatiotemporal understanding of forest ecosystem dynamics. Yet, the mapping and attribution of forest change remains challenging, particularly in areas where a number of interacting disturbance agents simultaneously affect forest development. The forest ecosystems of Central Europe are coupled human and natural systems, with natural and human disturbances affecting forests both individually and in combination. To better understand the complex forest disturbance dynamics in such systems, we utilize 32-year Landsat time series to map forest disturbances in five sites across Austria, the Czech Republic, Germany, Poland, and Slovakia. All sites consisted of a National Park and the surrounding forests, reflecting three management zones of different levels of human influence (managed, protected, strictly protected). This allowed for a comparison of spectral, temporal, and spatial disturbance patterns across a gradient from natural to coupled human and natural disturbances. Disturbance maps achieved overall accuracies ranging from 81% to 93%. Disturbance patches were generally small, with 95% of the disturbances being smaller than 10 ha. Disturbance rates ranged from 0.29% yr -1 to 0.95% yr -1 , and differed substantially among management zones and study sites. Natural disturbances in strictly protected areas were longer in duration (median of 8 years) and slightly less variable in magnitude compared to human-dominated disturbances in managed forests (median duration of 1 year). However, temporal dynamics between natural and human-dominated disturbances showed strong synchrony, suggesting that disturbance peaks are driven by natural events affecting managed and unmanaged areas simultaneously. Our study demonstrates the potential of remote sensing for mapping forest disturbances in coupled human and natural systems, such as the forests of Central Europe. Yet, we also highlight the complexity of such systems in terms of agent attribution, as many natural disturbances are modified by management responding to them outside protected areas.

Mapping aboveground woody biomass using forest inventory, remote sensing and geostatistical techniques.

PubMed

Yadav, Bechu K V; Nandy, S

2015-05-01

Mapping forest biomass is fundamental for estimating CO₂ emissions, and planning and monitoring of forests and ecosystem productivity. The present study attempted to map aboveground woody biomass (AGWB) integrating forest inventory, remote sensing and geostatistical techniques, viz., direct radiometric relationships (DRR), k-nearest neighbours (k-NN) and cokriging (CoK) and to evaluate their accuracy. A part of the Timli Forest Range of Kalsi Soil and Water Conservation Division, Uttarakhand, India was selected for the present study. Stratified random sampling was used to collect biophysical data from 36 sample plots of 0.1 ha (31.62 m × 31.62 m) size. Species-specific volumetric equations were used for calculating volume and multiplied by specific gravity to get biomass. Three forest-type density classes, viz. 10-40, 40-70 and >70% of Shorea robusta forest and four non-forest classes were delineated using on-screen visual interpretation of IRS P6 LISS-III data of December 2012. The volume in different strata of forest-type density ranged from 189.84 to 484.36 m(3) ha(-1). The total growing stock of the forest was found to be 2,024,652.88 m(3). The AGWB ranged from 143 to 421 Mgha(-1). Spectral bands and vegetation indices were used as independent variables and biomass as dependent variable for DRR, k-NN and CoK. After validation and comparison, k-NN method of Mahalanobis distance (root mean square error (RMSE) = 42.25 Mgha(-1)) was found to be the best method followed by fuzzy distance and Euclidean distance with RMSE of 44.23 and 45.13 Mgha(-1) respectively. DRR was found to be the least accurate method with RMSE of 67.17 Mgha(-1). The study highlighted the potential of integrating of forest inventory, remote sensing and geostatistical techniques for forest biomass mapping.

Using Landsat time series for characterizing forest disturbance dynamics in the coupled human and natural systems of Central Europe

NASA Astrophysics Data System (ADS)

Senf, Cornelius; Pflugmacher, Dirk; Hostert, Patrick; Seidl, Rupert

2017-08-01

Remote sensing is a key information source for improving the spatiotemporal understanding of forest ecosystem dynamics. Yet, the mapping and attribution of forest change remains challenging, particularly in areas where a number of interacting disturbance agents simultaneously affect forest development. The forest ecosystems of Central Europe are coupled human and natural systems, with natural and human disturbances affecting forests both individually and in combination. To better understand the complex forest disturbance dynamics in such systems, we utilize 32-year Landsat time series to map forest disturbances in five sites across Austria, the Czech Republic, Germany, Poland, and Slovakia. All sites consisted of a National Park and the surrounding forests, reflecting three management zones of different levels of human influence (managed, protected, strictly protected). This allowed for a comparison of spectral, temporal, and spatial disturbance patterns across a gradient from natural to coupled human and natural disturbances. Disturbance maps achieved overall accuracies ranging from 81% to 93%. Disturbance patches were generally small, with 95% of the disturbances being smaller than 10 ha. Disturbance rates ranged from 0.29% yr-1 to 0.95% yr-1, and differed substantially among management zones and study sites. Natural disturbances in strictly protected areas were longer in duration (median of 8 years) and slightly less variable in magnitude compared to human-dominated disturbances in managed forests (median duration of 1 year). However, temporal dynamics between natural and human-dominated disturbances showed strong synchrony, suggesting that disturbance peaks are driven by natural events affecting managed and unmanaged areas simultaneously. Our study demonstrates the potential of remote sensing for mapping forest disturbances in coupled human and natural systems, such as the forests of Central Europe. Yet, we also highlight the complexity of such systems in terms of agent attribution, as many natural disturbances are modified by management responding to them outside protected areas.

Imputing forest carbon stock estimates from inventory plots to a nationally continuous coverage

PubMed Central

2013-01-01

The U.S. has been providing national-scale estimates of forest carbon (C) stocks and stock change to meet United Nations Framework Convention on Climate Change (UNFCCC) reporting requirements for years. Although these currently are provided as national estimates by pool and year to meet greenhouse gas monitoring requirements, there is growing need to disaggregate these estimates to finer scales to enable strategic forest management and monitoring activities focused on various ecosystem services such as C storage enhancement. Through application of a nearest-neighbor imputation approach, spatially extant estimates of forest C density were developed for the conterminous U.S. using the U.S.’s annual forest inventory. Results suggest that an existing forest inventory plot imputation approach can be readily modified to provide raster maps of C density across a range of pools (e.g., live tree to soil organic carbon) and spatial scales (e.g., sub-county to biome). Comparisons among imputed maps indicate strong regional differences across C pools. The C density of pools closely related to detrital input (e.g., dead wood) is often highest in forests suffering from recent mortality events such as those in the northern Rocky Mountains (e.g., beetle infestations). In contrast, live tree carbon density is often highest on the highest quality forest sites such as those found in the Pacific Northwest. Validation results suggest strong agreement between the estimates produced from the forest inventory plots and those from the imputed maps, particularly when the C pool is closely associated with the imputation model (e.g., aboveground live biomass and live tree basal area), with weaker agreement for detrital pools (e.g., standing dead trees). Forest inventory imputed plot maps provide an efficient and flexible approach to monitoring diverse C pools at national (e.g., UNFCCC) and regional scales (e.g., Reducing Emissions from Deforestation and Forest Degradation projects) while allowing timely incorporation of empirical data (e.g., annual forest inventory). PMID:23305341

Forest resources of the Idaho Panhandle National Forest

Treesearch

Joshua C. Holte

2012-01-01

The Interior West Forest Inventory and Analysis (IWFIA) Program of the USDA Forest Service, Rocky Mountain Research Station, as part of our National Forest System cooperative inventories, conducted a forest resource inventory on the Idaho Panhandle National Forest (IPNF) using a nationally standardized mapped-plot design (for more details see "The inventory...

Forest resources of the Black Hills National Forest

Treesearch

Larry T. DeBlander

2002-01-01

The Interior West Forest Inventory and Analysis (IWFIA) Program of the USDA Forest Service, Rocky Mountain Research Station, as part of our National Forest System cooperative inventories, conducted a forest resource inventory on the Black Hills National Forest using a nationally standardized mapped-plot design (for more details see section "How was the inventory...

Forest resources of the Nez Perce National Forest

Treesearch

Michele Disney

2010-01-01

As part of a National Forest System cooperative inventory, the Interior West Forest Inventory and Analysis (IWFIA) Program of the USDA Forest Service conducted a forest resource inventory on the Nez Perce National Forest using a nationally standardized mapped-plot design (for more details see the section "Inventory methods"). This report presents highlights...

Forest resources of the Bighorn National Forest

Treesearch

Christopher Witt

2008-01-01

The Interior West Forest Inventory and Analysis (IWFIA) Program of the USDA Forest Service, Rocky Mountain Research Station, as part of our National Forest System cooperative inventories, conducted a forest resource inventory on the Bighorn National Forest (Bighorn) using a nationally standardized mapped-plot design. This report presents the highlights of this 2000...

Forest resources of the Shoshone National Forest

Treesearch

James Menlove

2008-01-01

The Interior West Forest Inventory and Analysis (IWFIA) Program of the USDA Forest Service, Rocky Mountain Research Station, as part of our National Forest System cooperative inventories, conducted a forest resource inventory on the Shoshone National Forest using a nationally standardized mapped-plot design. This report presents the highlights of this 1999 inventory...

Japanese national forest inventory and its spatial extension by remote sensing

Treesearch

Yasumasa Hirata; Mitsuo Matsumoto; Toshiro Iehara

2009-01-01

Japan has two independent forest inventory systems. One forest inventory is required by the forest planning system based on the Forest Law, in which forest registers and forest planning maps are prepared. The other system is a forest resource monitoring survey, in which systematic sampling is done at 4-km grid intervals. Here, we present these national forest inventory...

[Automated mapping of urban forests' disturbance and recovery in Nanjing, China].

PubMed

Lyu, Ying-ying; Zhuang, Yi-lin; Ren, Xin-yu; Li, Ming-shi; Xu, Wang-gu; Wang, Zhi

2016-02-01

Using Landsat TM/ETM dense time series observations spanning from 1987 to 2011, taking Laoshan forest farm and Purple Mountain as the research objects, the landsat ecosystem disturbance adaptive processing system (Ledaps) algorithm was used to generate surface reflectance datasets, which were fed to the vegetation change tracker model (VCT) model to derive urban forest disturbance and recovery products over Nanjing, followed by an intensive validation of the products. The results showed that there was a relatively high spatial agreement for forest disturbance products mapped by VCT, ranging from 65.4% to 95.0%. There was an apparent fluctuating forest disturbance and recovery rate over time, and the change trend of forest disturbance occurring at the two sites was roughly similar, but forest recovery was obviously different. Forest coverage in Purple Mountain was less than that in Laoshan forest farm, but the forest disturbance and recovery rates in Laoshan forest farm were larger than those in Purple Mountain.

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

Treesearch

Paul L. Patterson; Mark Finco

2011-01-01

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

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-...

Using widely spaced observations of land use, forest attributes, and intrusions to map resource potential and human impact probability

Treesearch

Victor A. Rudis

2000-01-01

Scant information exists about the spatial extent of human impact on forest resource supplies, i.e., depreciative and nonforest uses. I used observations of ground-sampled land use and intrusions on forest land to map the probability of resource use and human impact for broad areas. Data came from a seven-state survey region (Alabama, Arkansas, Louisiana, Mississippi,...

Using widely spaced observations of land use, forest attributes, and intrusions to map resource potential and human impact probability

Treesearch

Victor A. Rudis

2000-01-01

Scant information exists about the spatial extent of human impact on forest resource supplies, i.e., depreciative and nonforest uses. I used observations of ground-sampled land use and intrusions on forest land to map the probability of resource use and human impact for broad areas. Data came from a seven State survey region (Alabama, Arkansas, Louisiana, Mississippi,...

Mapping of forested wetland: use of Seasat radar images to complement conventional sources ( USA).

USGS Publications Warehouse

Place, J.L.

1985-01-01

Distinguishing forested wetland from dry forest using aerial photographs is handicapped because photographs often do not reveal the presence of water below tree canopies. Radar images obtained by the Seasat satellite reveal forested wetland as highly reflective patterns on the coastal plain between Maryland and Florida. Seasat radar images may complement aerial photographs for compiling maps of wetland. A test with experienced photointerpreters revealed that interpretation accuracy was significantly higher when using Seasat radar images than when using only conventional sources.-Author

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.

Generating Broad-Scale Forest Ownership Maps: A Closest-Neighbor Approach

Treesearch

Brett J. Butler

2005-01-01

A closest-neighbor method for producing a forest ownership map using remotely sensed imagery and point-based ownership information is presented for the Northeastern United States. Based on a validation data set, this method had an accuracy rate of 58 percent.

[Spatial pattern of sub-alpine forest restoration in west Sichuan].

PubMed

Zhang, Yuandong; Liu, Shirong; Zhao, Changming

2005-09-01

West Sichuan sub-alpine is an extension of Qinghai-Tibet Plateau to southeast China, which is covered mainly with dark coniferous forest. As a result of long-term large scale over-logging, the forests have been greatly reduced and degraded. Nowadays, the forest restoration and regeneration in the region are being highlighted. Selecting Miyaluo as a case study area and employing the methods of plot investigation, ETM image interpretation, and overlaying vegetation map with digital topography, this paper analyzed the relations between the appearance and origin of four forest vegetation types, along with their topography differentiation and spatial patterns after a large scale logging and regeneration. The results showed that the appearance of forest vegetations was significantly correlated with their origin. Old coniferous forests (OC) were primitive ones, middle-aged and young coniferous forests (MYC) were from artificial regeneration, deciduous broadleaf forests (DB) were natural secondary ones, while mixed coniferous and deciduous forests (MCD) were partly from natural secondary ones and others from the conjunct action of artificial and natural regeneration. The main cut area in Miyaluo located in the sites with elevation from 2 800 to 3 600 m, where forest restoration appeared difference among different aspects. MYC was mainly distributed on sunny and half-sunny slope, DB and MCD were distributed on shady and half-shady slope, and OC were reserved on the sites with elevation more than 3 600 m. In the process of forest restoration, the four forest vegetation types were in mosaic pattern, and the landscape was seriously fragmentized.

Identifying grain-size dependent errors on global forest area estimates and carbon studies

Treesearch

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

2008-01-01

Satellite-derived coarse-resolution data are typically used for conducting global analyses. But the forest areas estimated from coarse-resolution maps (e.g., 1 km) inevitably differ from a corresponding fine-resolution map (such as a 30-m map) that would be closer to ground truth. A better understanding of changes in grain size on area estimation will improve our...

An application of LANDSAT digital technology to forest fire fuel type mapping

NASA Technical Reports Server (NTRS)

Kourtz, P. H.

1977-01-01

The role of digital classifications suitable as fuel maps was examined. A Taylor enhancement was produced for an 8 million hectare fire control region showing water, muskeg, coniferous, deciduous and mixed stands, clearcut logging, burned areas, regeneration areas, nonforested areas and large forest roads. Use of the map by fire control personnel demonstrated its usefulness for initial attack decision making.

Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Treesearch

Harold S.J. Zald; Janet L. Ohmann; Heather M. Roberts; Matthew J. Gregory; Emilie B. Henderson; Robert J. McGaughey; Justin Braaten

2014-01-01

This study investigated how lidar-derived vegetation indices, disturbance history from Landsat time series (LTS) imagery, plot location accuracy, and plot size influenced accuracy of statistical spatial models (nearest-neighbor imputation maps) of forest vegetation composition and structure. Nearest-neighbor (NN) imputation maps were developed for 539,000 ha in the...

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Treesearch

Ronald E. McRoberts; Warren B. Cohen; Erik Naesset; Stephen V. Stehman; Erkki O. Tomppo

2010-01-01

Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic...

The Greek National Observatory of Forest Fires (NOFFi)

NASA Astrophysics Data System (ADS)

Tompoulidou, Maria; Stefanidou, Alexandra; Grigoriadis, Dionysios; Dragozi, Eleni; Stavrakoudis, Dimitris; Gitas, Ioannis Z.

2016-08-01

Efficient forest fire management is a key element for alleviating the catastrophic impacts of wildfires. Overall, the effective response to fire events necessitates adequate planning and preparedness before the start of the fire season, as well as quantifying the environmental impacts in case of wildfires. Moreover, the estimation of fire danger provides crucial information required for the optimal allocation and distribution of the available resources. The Greek National Observatory of Forest Fires (NOFFi)—established by the Greek Forestry Service in collaboration with the Laboratory of Forest Management and Remote Sensing of the Aristotle University of Thessaloniki and the International Balkan Center—aims to develop a series of modern products and services for supporting the efficient forest fire prevention management in Greece and the Balkan region, as well as to stimulate the development of transnational fire prevention and impacts mitigation policies. More specifically, NOFFi provides three main fire-related products and services: a) a remote sensing-based fuel type mapping methodology, b) a semi-automatic burned area mapping service, and c) a dynamically updatable fire danger index providing mid- to long-term predictions. The fuel type mapping methodology was developed and applied across the country, following an object-oriented approach and using Landsat 8 OLI satellite imagery. The results showcase the effectiveness of the generated methodology in obtaining highly accurate fuel type maps on a national level. The burned area mapping methodology was developed as a semi-automatic object-based classification process, carefully crafted to minimize user interaction and, hence, be easily applicable on a near real-time operational level as well as for mapping historical events. NOFFi's products can be visualized through the interactive Fire Forest portal, which allows the involvement and awareness of the relevant stakeholders via the Public Participation GIS (PPGIS) tool.

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.

Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verification (MRV) systems for REDD.

PubMed

Mitchell, Anthea L; Rosenqvist, Ake; Mora, Brice

2017-12-01

Forest degradation is a global phenomenon and while being an important indicator and precursor to further forest loss, carbon emissions due to degradation should also be accounted for in national reporting within the frame of UN REDD+. At regional to country scales, methods have been progressively developed to detect and map forest degradation, with these based on multi-resolution optical, synthetic aperture radar (SAR) and/or LiDAR data. However, there is no one single method that can be applied to monitor forest degradation, largely due to the specific nature of the degradation type or process and the timeframe over which it is observed. The review assesses two main approaches to monitoring forest degradation: first, where detection is indicated by a change in canopy cover or proxies, and second, the quantification of loss (or gain) in above ground biomass (AGB). The discussion only considers degradation that has a visible impact on the forest canopy and is thus detectable by remote sensing. The first approach encompasses methods that characterise the type of degradation and track disturbance, detect gaps in, and fragmentation of, the forest canopy, and proxies that provide evidence of forestry activity. Progress in these topics has seen the extension of methods to higher resolution (both spatial and temporal) data to better capture the disturbance signal, distinguish degraded and intact forest, and monitor regrowth. Improvements in the reliability of mapping methods are anticipated by SAR-optical data fusion and use of very high resolution data. The second approach exploits EO sensors with known sensitivity to forest structure and biomass and discusses monitoring efforts using repeat LiDAR and SAR data. There has been progress in the capacity to discriminate forest age and growth stage using data fusion methods and LiDAR height metrics. Interferometric SAR and LiDAR have found new application in linking forest structure change to degradation in tropical forests. Estimates of AGB change have been demonstrated at national level using SAR and LiDAR-assisted approaches. Future improvements are anticipated with the availability of next generation LiDAR sensors. Improved access to relevant satellite data and best available methods are key to operational forest degradation monitoring. Countries will need to prioritise their monitoring efforts depending on the significance of the degradation, balanced against available resources. A better understanding of the drivers and impacts of degradation will help guide monitoring and restoration efforts. Ultimately we want to restore ecosystem service and function in degraded forests before the change is irreversible.

Preliminary work of mangrove ecosystem carbon stock mapping in small island using remote sensing: above and below ground carbon stock mapping on medium resolution satellite image

NASA Astrophysics Data System (ADS)

Wicaksono, Pramaditya; Danoedoro, Projo; Hartono, Hartono; Nehren, Udo; Ribbe, Lars

2011-11-01

Mangrove forest is an important ecosystem located in coastal area that provides various important ecological and economical services. One of the services provided by mangrove forest is the ability to act as carbon sink by sequestering CO2 from atmosphere through photosynthesis and carbon burial on the sediment. The carbon buried on mangrove sediment may persist for millennia before return to the atmosphere, and thus act as an effective long-term carbon sink. Therefore, it is important to understand the distribution of carbon stored within mangrove forest in a spatial and temporal context. In this paper, an effort to map carbon stocks in mangrove forest is presented using remote sensing technology to overcome the handicap encountered by field survey. In mangrove carbon stock mapping, the use of medium spatial resolution Landsat 7 ETM+ is emphasized. Landsat 7 ETM+ images are relatively cheap, widely available and have large area coverage, and thus provide a cost and time effective way of mapping mangrove carbon stocks. Using field data, two image processing techniques namely Vegetation Index and Linear Spectral Unmixing (LSU) were evaluated to find the best method to explain the variation in mangrove carbon stocks using remote sensing data. In addition, we also tried to estimate mangrove carbon sequestration rate via multitemporal analysis. Finally, the technique which produces significantly better result was used to produce a map of mangrove forest carbon stocks, which is spatially extensive and temporally repetitive.

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.

Uncertainties in mapping forest carbon in urban ecosystems.

PubMed

Chen, Gang; Ozelkan, Emre; Singh, Kunwar K; Zhou, Jun; Brown, Marilyn R; Meentemeyer, Ross K

2017-02-01

Spatially explicit urban forest carbon estimation provides a baseline map for understanding the variation in forest vertical structure, informing sustainable forest management and urban planning. While high-resolution remote sensing has proven promising for carbon mapping in highly fragmented urban landscapes, data cost and availability are the major obstacle prohibiting accurate, consistent, and repeated measurement of forest carbon pools in cities. This study aims to evaluate the uncertainties of forest carbon estimation in response to the combined impacts of remote sensing data resolution and neighborhood spatial patterns in Charlotte, North Carolina. The remote sensing data for carbon mapping were resampled to a range of resolutions, i.e., LiDAR point cloud density - 5.8, 4.6, 2.3, and 1.2 pt s/m 2 , aerial optical NAIP (National Agricultural Imagery Program) imagery - 1, 5, 10, and 20 m. Urban spatial patterns were extracted to represent area, shape complexity, dispersion/interspersion, diversity, and connectivity of landscape patches across the residential neighborhoods with built-up densities from low, medium-low, medium-high, to high. Through statistical analyses, we found that changing remote sensing data resolution introduced noticeable uncertainties (variation) in forest carbon estimation at the neighborhood level. Higher uncertainties were caused by the change of LiDAR point density (causing 8.7-11.0% of variation) than changing NAIP image resolution (causing 6.2-8.6% of variation). For both LiDAR and NAIP, urban neighborhoods with a higher degree of anthropogenic disturbance unveiled a higher level of uncertainty in carbon mapping. However, LiDAR-based results were more likely to be affected by landscape patch connectivity, and the NAIP-based estimation was found to be significantly influenced by the complexity of patch shape. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

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 Plant Diversity and Composition Across North Carolina Piedmont Forest Landscapes Using Lidar-Hyperspectral Remote Sensing

NASA Astrophysics Data System (ADS)

Hakkenberg, Christopher R.

Forest modification, from local stress to global change, has given rise to efforts to model, map, and monitor critical properties of forest communities like structure, composition, and diversity. Predictive models based on data from spatially-nested field plots and LiDAR-hyperspectral remote sensing systems are one particularly effective means towards the otherwise prohibitively resource-intensive task of consistently characterizing forest community dynamics at landscape scales. However, to date, most predictive models fail to account for actual (rather than idealized) species and community distributions, are unsuccessful in predicting understory components in structurally and taxonomically heterogeneous forests, and may suffer from diminished predictive accuracy due to incongruity in scale and precision between field plot samples, remotely-sensed data, and target biota of varying size and density. This three-part study addresses these and other concerns in the modeling and mapping of emergent properties of forest communities by shifting the scope of prediction from the individual or taxon to the whole stand or community. It is, after all, at the stand scale where emergent properties like functional processes, biodiversity, and habitat aggregate and manifest. In the first study, I explore the relationship between forest structure (a proxy for successional demographics and resource competition) and tree species diversity in the North Carolina Piedmont, highlighting the empirical basis and potential for utilizing forest structure from LiDAR in predictive models of tree species diversity. I then extend these conclusions to map landscape pattern in multi-scale vascular plant diversity as well as turnover in community-continua at varying compositional resolutions in a North Carolina Piedmont landscape using remotely-sensed LiDAR-hyperspectral estimates of topography, canopy structure, and foliar biochemistry. Recognizing that the distinction between correlation and causation mirrors that between knowledge and understanding, all three studies distinguish between prediction of pattern and inference of process. Thus, in addition to advancing mapping methodologies relevant to a range of forest ecosystem management and monitoring applications, all three studies are noteworthy for assessing the ecological relationship between environmental predictors and emergent landscape patterns in plant composition and diversity in North Carolina Piedmont forests.

Mapping the montane cloud forest of Taiwan using 12 year MODIS-derived ground fog frequency data.

PubMed

Schulz, Hans Martin; Li, Ching-Feng; Thies, Boris; Chang, Shih-Chieh; Bendix, Jörg

2017-01-01

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as "MCF" or "non-MCF". This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest's location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF.

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%).

Characterization of Canopy Layering in Forested Ecosystems Using Full Waveform Lidar

NASA Technical Reports Server (NTRS)

Whitehurst, Amanda S.; Swatantran, Anu; Blair, J. Bryan; Hofton, Michelle A.; Dubayah, Ralph

2013-01-01

Canopy structure, the vertical distribution of canopy material, is an important element of forest ecosystem dynamics and habitat preference. Although vertical stratification, or "canopy layering," is a basic characterization of canopy structure for research and forest management, it is difficult to quantify at landscape scales. In this paper we describe canopy structure and develop methodologies to map forest vertical stratification in a mixed temperate forest using full-waveform lidar. Two definitions-one categorical and one continuous-are used to map canopy layering over Hubbard Brook Experimental Forest, New Hampshire with lidar data collected in 2009 by NASA's Laser Vegetation Imaging Sensor (LVIS). The two resulting canopy layering datasets describe variation of canopy layering throughout the forest and show that layering varies with terrain elevation and canopy height. This information should provide increased understanding of vertical structure variability and aid habitat characterization and other forest management activities.

Quantifying forest vertical structure to determine bird habitat quality in the Greenbelt Corridor, Denton, TX

NASA Astrophysics Data System (ADS)

Matsubayashi, Shiho

This study presents the integration of light detection and range (LiDAR) and hyperspectral remote sensing to create a three-dimensional bird habitat map in the Greenbelt Corridor of the Elm Fork of the Trinity River. This map permits to examine the relationship between forest stand structure, landscape heterogeneity, and bird community composition. A biannual bird census was conducted at this site during the breeding seasons of 2009 and 2010. Census data combined with the three-dimensional map suggest that local breeding bird abundance, community structure, and spatial distribution patterns are highly influenced by vertical heterogeneity of vegetation surface. For local breeding birds, vertical heterogeneity of canopy surface within stands, connectivity to adjacent forest patches, largest forest patch index, and habitat (vegetation) types proved to be the most influential factors to determine bird community assemblages. Results also highlight the critical role of secondary forests to increase functional connectivity of forest patches. Overall, three-dimensional habitat descriptions derived from integrated LiDAR and hyperspectral data serve as a powerful bird conservation tool that shows how the distribution of bird species relates to forest composition and structure at various scales.

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

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

A second look a North Dakota's timber lands, 1980.

Treesearch

Pamela J. Jakes; W. Brad Smith

1982-01-01

The second inventory of North Dakota forest resources shows a decline in commercial forest area between 1954 and 1980. Presented are text and statistics on forest area and timber volume, growth, mortality, ownership, stocking, future timber supply, timber use, forest management opportunities, and nontimber forest resources. A forest type map is included.

Sustainable Forest Management Support Based on the Spatial Distribution of Fuels for Fire Management

Treesearch

José Germán Flores Garnica; Juan de Dios Benavides Solorio; David Arturo Moreno Gonzalez

2006-01-01

Fire behavior simulation is based mainly on the fuel model-concept. However, there are great difficulties to develop the corresponding maps, therefore it is suggested the generation of four fuel maps (1-hour, 10-hours, 100-hours and alive). These maps will allow a better definition of the spatial variation of forest fuels, even within a zone classified as a given fuel...

Benchmark map of forest carbon stocks in tropical regions across three continents.

PubMed

Saatchi, Sassan S; Harris, Nancy L; Brown, Sandra; Lefsky, Michael; Mitchard, Edward T A; Salas, William; Zutta, Brian R; Buermann, Wolfgang; Lewis, Simon L; Hagen, Stephen; Petrova, Silvia; White, Lee; Silman, Miles; Morel, Alexandra

2011-06-14

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a "benchmark" map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ± 6% to ± 53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ± 5% and ca. ± 1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete.

ForWarn: A Cross-Cutting Forest Resource Management and Decision Support System Providing the Capacity to Identify and Track Forest Disturbances Nationally

NASA Astrophysics Data System (ADS)

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

2012-12-01

The Eastern Forest Environmental Threat Assessment Center and Western Wildland Environmental Assessment Center of the USDA Forest Service have collaborated with NASA Stennis Space Center to develop ForWarn, a forest monitoring tool that uses MODIS satellite imagery to produce weekly snapshots of vegetation conditions across the lower 48 United States. Forest and natural resource managers can use ForWarn to rapidly detect, identify, and respond to unexpected changes in the nation's forests caused by insects, diseases, wildfires, severe weather, or other natural or human-caused events. ForWarn detects most types of forest disturbances, including insects, disease, wildfires, frost and ice damage, tornadoes, hurricanes, blowdowns, harvest, urbanization, and landslides. It also detects drought, flood, and temperature effects, and shows early and delayed seasonal vegetation development. Operating continuously since January 2010, results show ForWarn to be a robust and highly capable tool for detecting changes in forest conditions. ForWarn is the first national-scale system of its kind based on remote sensing developed specifically for forest disturbances. It has operated as a prototype since January 2010 and has provided useful information about the location and extent of disturbances detected during the 2011 growing season, including tornadoes, wildfires, and extreme drought. The ForWarn system had an official unveiling and rollout in March 2012, initiated by a joint NASA and USDA press release. The ForWarn home page has had 2,632 unique visitors since rollout in March 2012, with 39% returning visits. ForWarn was used to map tornado scars from the historic April 27, 2011 tornado outbreak, and detected timber damage within more than a dozen tornado tracks across northern Mississippi, Alabama, and Georgia. ForWarn is the result of an ongoing, substantive cooperation among four different government agencies: USDA, NASA, USGS, and DOE. Disturbance maps are available on the web through the ForWarn Change Assessment Viewer at http://forwarn.forestthreats.org/fcav. No user id or password is required, and there is no cost. The Assessment Viewer operates within any popular web browser using nearly any type of computer. It lets users pan, zoom, and scroll around within ForWarn maps, and also contains an up-to-date library of co-registered, near real-time ancillary maps from diverse sources that allows users to assess the nature of particular forest disturbances and ascribe their most-likely causes. Users can check the current week's U.S. Drought Monitor, USGS VegDRI maps, FHM Historical Aerial Disturbance Surveys, MODIS Cumulative Current Year Fire Detections, and many others. A "Share this map" feature lets users save the current map view and extent into a web URL, so that users can easily share what they are looking at inside the Assessment Viewer with others via an email, a document, or a web page. The ForWarn Rapid National Assessment Team examined more than 60 ForWarn forest disturbance events in 2011-2012, and issued over 30 alerts. We hope to automate forest disturbance alerts and supply them through various subscription services. Forest owners and managers would only be alerted to disturbances occurring near their own forest resources.

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.

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.

Geologic map of the San Bernardino North 7.5' quadrangle, San Bernardino County, California

USGS Publications Warehouse

Miller, F.K.; Matti, J.C.

2001-01-01

3. Portable Document Format (.pdf) files of: a. This Readme; includes an Appendix, containing data found in sbnorth_met.txt . b. The Description of Map Units identical to that found on the plot of the PostScript file. c. The same graphic as plotted in 2 above. (Test plots from this .pdf do not produce 1:24,000-scale maps. Use Adobe Acrobat pagesize setting to control map scale.) The Correlation of Map Units and Description of Map Units is in the editorial format of USGS Miscellaneous Investigations Series (I-series) maps. Within the geologic map data package, map units are identified by standard geologic map criteria such as formation-name, age, and lithology. Even though this is an author-prepared report, every attempt has been made to closely adhere to the stratigraphic nomenclature of the U. S. Geological Survey. Descriptions of units can be obtained by viewing or plotting the .pdf file (3b above) or plotting the postscript file (2 above). If roads in some areas, especially forest roads that parallel topographic contours, do not show well on plots of the geologic map, we recommend use of the USGS San Bernardino North 7.5’ topographic quadrangle in conjunction with the geologic map.

Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India.

PubMed

Sowmya, S V; Somashekar, R K

2010-11-01

Fire is the most spectacular natural disturbance that affects the forest ecosystem composition and diversity. Fire has a devastating effect on the landscape and its impact is felt at every level of the ecosystem and it is possible to map forest fire risk zone and thereby minimize the frequency of fire. There is a need for supranational approaches that analyze wide scenarios of factors involved and global fire effects. Fires can be monitored and analyzed over large areas in a timely and cost effective manner by using satellite imagery. Also Geographical Information System (GIS) can be used effectively to demarcate the fire risk zone map. Bhadra wildlife Sanctuary located in Kamataka, India was selected for this study. Vegetation, slope, distance from roads, settlements parameters were derived for a study area using topographic maps and field information. The Remote Sensing (RS) and Geographical Information System (GIS)-based forest fire risk model of the study area appeared to be highly compatible with the actual fire-affected sites. The temporal satellite data from 1989 to2006 have been analyzed to map the burnt areas. These classes were weighted according to their influence on forest fire. Four categories of fire risk regions such as Low, Moderate, High and Very high fire intensity zones were identified. It is predicted that around 10.31% of the area falls undermoderate risk zone.

Forest Biomass Mapping from Prism Triplet, Palsar and Landsat Data

NASA Astrophysics Data System (ADS)

Ranson, J.; Sun, G.; Ni, W.

2014-12-01

The loss of sensitivity at higher biomass levels is a common problem in biomass mapping using optical multi-spectral data or radar backscattering data due to the lack of information on canopy vertical structure. Studies have shown that adding implicit information of forest vertical structure improves the performance of forest biomass mapping from optical reflectance and radar backscattering data. LiDAR, InSAR and stereo imager are the data sources for obtaining forest structural information. The potential of providing information on forest vertical structure by stereoscopic imagery data has drawn attention recently due to the availability of high-resolution digital stereo imaging from space and the advances of digital stereo image processing software. The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) onboard the Advanced Land Observation Satellite (ALOS) has acquired multiple global coverage from June 2006 to April 2011 providing a good data source for regional/global forest studies. In this study, five PRISM triplets acquired on June 14, 2008, August 19 and September 5, 2009; PALSAR dual-pol images acquired on July 12, 2008 and August 30, 2009; and LANDSAT 5 TM images acquired on September 5, 2009 and the field plot data collected in 2009 and 2010 were used to map forest biomass at 50m pixel in an area of about 4000 km2in Maine, USA ( 45.2 deg N 68.6 deg W). PRISM triplets were used to generate point cloud data at 2m pixel first and then the average height of points above NED (National Elevation Dataset) within a 50m by 50m pixel was calculated. Five images were mosaicked and used as canopy height information in the biomass estimation along with the PALSAR HH, HV radar backscattering and optical reflectance vegetation indices from L-5 TM data. A small portion of this region was covered by the Land Vegetation and Ice Sensor (LVIS) in 2009. The biomass maps from the LVIS data was used to evaluate the results from combined use of PRISM, PALSAR and LANDSAT data. The results show that the canopy height index from PRISM stereo images significantly improves the biomass mapping accuracy and extends the saturation level of biomass, and results in a biomass map comparable with those generated from LVIS data.

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.

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.

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

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

Zhao, Feng R.; Meng, Ran; Huang, Chengquan

Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. Here in this study, the LTSS-VCT approach was applied to examine long-term (up to 24more » years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of ~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results also indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. Lastly, with the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S.« less

Long-Term Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack

DOE PAGES

Zhao, Feng R.; Meng, Ran; Huang, Chengquan; ...

2016-10-29

Forest recovery from past disturbance is an integral process of ecosystem carbon cycles, and remote sensing provides an effective tool for tracking forest disturbance and recovery over large areas. Although the disturbance products (tracking the conversion from forest to non-forest type) derived using the Landsat Time Series Stack-Vegetation Change Tracker (LTSS-VCT) algorithm have been validated extensively for mapping forest disturbances across the United States, the ability of this approach to characterize long-term post-disturbance recovery (the conversion from non-forest to forest) has yet to be assessed. Here in this study, the LTSS-VCT approach was applied to examine long-term (up to 24more » years) post-disturbance forest spectral recovery following stand-clearing disturbances (fire and harvests) in the Greater Yellowstone Ecosystem (GYE). Using high spatial resolution images from Google Earth, we validated the detectable forest recovery status mapped by VCT by year 2011. Validation results show that the VCT was able to map long-term post-disturbance forest recovery with overall accuracy of ~80% for different disturbance types and forest types in the GYE. Harvested areas in the GYE have higher percentages of forest recovery than burned areas by year 2011, and National Forests land generally has higher recovery rates compared with National Parks. The results also indicate that forest recovery is highly related with forest type, elevation and environmental variables such as soil type. Findings from this study can provide valuable insights for ecosystem modeling that aim to predict future carbon dynamics by integrating fine scale forest recovery conditions in GYE, in the face of climate change. Lastly, with the availability of the VCT product nationwide, this approach can also be applied to examine long-term post-disturbance forest recovery in other study regions across the U.S.« less

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...

Implications of allometric model selection for county-level biomass mapping.

PubMed

Duncanson, Laura; Huang, Wenli; Johnson, Kristofer; Swatantran, Anu; McRoberts, Ronald E; Dubayah, Ralph

2017-10-18

Carbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend on the application of allometric models, which often have unknown and unreported uncertainties outside of the size class or environment in which they were developed. Here, we test three popular allometric approaches to field biomass estimation, and explore the implications of allometric model selection for county-level biomass mapping in Sonoma County, California. We test three allometric models: Jenkins et al. (For Sci 49(1): 12-35, 2003), Chojnacky et al. (Forestry 87(1): 129-151, 2014) and the US Forest Service's Component Ratio Method (CRM). We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates. Further, we show that discrepancies are greater in high biomass areas with high canopy covers and relatively moderate heights (25-45 m). The CRM models, although on average ~ 20% lower than Jenkins and Chojnacky, produce higher estimates in the tallest forests samples (> 60 m), while Jenkins generally produces higher estimates of biomass in forests < 50 m tall. Discrepancies do not continually increase with increasing forest height, suggesting that inclusion of height in allometric models is not primarily driving discrepancies. Models developed using all three allometric models underestimate high biomass and overestimate low biomass, as expected with random forest biomass modeling. However, these deviations were generally larger using the Jenkins and Chojnacky allometries, suggesting that the CRM approach may be more appropriate for biomass mapping with lidar. These results confirm that allometric model selection considerably impacts biomass maps and estimates, and that allometric model errors remain poorly understood. Our findings that allometric model discrepancies are not explained by lidar heights suggests that allometric model form does not drive these discrepancies. A better understanding of the sources of allometric model errors, particularly in high biomass systems, is essential for improved forest biomass mapping.

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.

STS-55 Earth observation of agricultural development in northern Argentina

NASA Technical Reports Server (NTRS)

1993-01-01

STS-55 Earth observation taken aboard Columbia, Orbiter Vehicle (OV) 102, is of agricultural development in northern Argentina. This photograph is from a mapping strip of photographs acquired by the STS-55 crew. This mapping strip runs from the 'eyelash forests' of the Bolivian Andes, southeast across the Chaco Plains, and into the upper Parana River Basin of north-central Argentina. The formerly densely forested areas between the upper Rio Pilcomayo and the Rio Teuco of NW Argentina rest on deep, rich alluvial and loess deposits. These modern soils were carried into the region by rivers from the Andes and by dust storms from large playa areas of the Altiplano (high plains) of Peru and Boliva. In this scene, representative of the long mapping strip, the process of converting forests to agriculture is far advanced. The original road network, a series of grids laid out in the forest, has nearly coalesced into a farm and ranch landscape. Some few relict forests are still visible as distin

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...

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).

Assessment of the postagrogenic transformation of soddy-podzolic soils: Cartographic and analytic support

NASA Astrophysics Data System (ADS)

Sorokina, N. P.; Kozlov, D. N.; Kuznetsova, I. V.

2013-10-01

The results of experimental studies of the postagrogenic transformation of loamy soddy-podzolic soils on the southern slope of the Klin-Dmitrov Moraine Ridge are discussed. A chronosequence of soils (arable soils (cropland)-soils under fallow with meadow vegetation-soils under secondary forests of different ages-soils under a conventionally initial native forest) was examined, and the stages of the postagrogenic transformation of the automorphic soddy-podzolic soils were identified. The differentiation of the former plow horizon into the A1 and A1A2 horizons (according to the differences in the humus content, texture, and acidity) served as the major criterion of the soil transformation. A stage of textural differentiation with clay depletion from the uppermost layer was identified in the soils of the 20- to 60-year-old fallows. The specificity of the postagrogenic transformation of the soils on the slopes was demonstrated. From the methodological point of view, it was important to differentiate between the chronosequences of automorphic and semihydromorphic soils of the leveled interfluves and the soils of the slopes. For this purpose, a series of maps reflecting the history of the land use and the soil cover pattern was analyzed. The cartographic model included the attribute data of the soil surveys, the cartographic sources (a series of historical maps of the land use, topographic maps, remote sensing data, and a digital elevation model), and two base maps: (a) the integral map of the land use and (b) the map of the soil combinations with the separation of the zonal automorphic, semihydromorphic, and erosional soil combinations. This scheme served as a matrix for the organization and analysis of the already available and new materials.

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data

NASA Astrophysics Data System (ADS)

Kandare, Kaja; Ørka, Hans Ole; Dalponte, Michele; Næsset, Erik; Gobakken, Terje

2017-08-01

Site productivity is essential information for sustainable forest management and site index (SI) is the most common quantitative measure of it. The SI is usually determined for individual tree species based on tree height and the age of the 100 largest trees per hectare according to stem diameter. The present study aimed to demonstrate and validate a methodology for the determination of SI using remotely sensed data, in particular fused airborne laser scanning (ALS) and airborne hyperspectral data in a forest site in Norway. The applied approach was based on individual tree crown (ITC) delineation: tree species, tree height, diameter at breast height (DBH), and age were modelled and predicted at ITC level using 10-fold cross validation. Four dominant ITCs per 400 m2 plot were selected as input to predict SI at plot level for Norway spruce (Picea abies (L.) Karst.) and Scots pine (Pinus sylvestris L.). We applied an experimental setup with different subsets of dominant ITCs with different combinations of attributes (predicted or field-derived) for SI predictions. The results revealed that the selection of the dominant ITCs based on the largest DBH independent of tree species, predicted the SI with similar accuracy as ITCs matched with field-derived dominant trees (RMSE: 27.6% vs 23.3%). The SI accuracies were at the same level when dominant species were determined from the remotely sensed or field data (RMSE: 27.6% vs 27.8%). However, when the predicted tree age was used the SI accuracy decreased compared to field-derived age (RMSE: 27.6% vs 7.6%). In general, SI was overpredicted for both tree species in the mature forest, while there was an underprediction in the young forest. In conclusion, the proposed approach for SI determination based on ITC delineation and a combination of ALS and hyperspectral data is an efficient and stable procedure, which has the potential to predict SI in forest areas at various spatial scales and additionally to improve existing SI maps in Norway.

A Spectral Mapping Signature for the Rapid Ohia Death (ROD) Pathogen in Hawaiian Forests

USDA-ARS?s Scientific Manuscript database

Pathogenic invasions are a major disruptive source of change in both agricultural and natural ecosystems. In forests, fungal pathogens can kill habitat-generating plant species such as canopy trees, but methods for remote detection, mapping and monitoring of such outbreaks are poorly developed. Cera...

Mapping the defoliation potential of gypsy moth

Treesearch

David A. Gansner; Stanford L. Arner; Rachel Riemann Hershey; Susan L. King

1993-01-01

A model that uses forest stand characteristics to estimate the likelihood of gypsy moth (Lymantria dispar) defoliation has been developed. It was applied to recent forest inventory plot data to produce susceptibility ratings and a map showing defoliation potential for counties in Pennsylvania and six adjacent states on new frontiers of infestation.

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,...

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.

A tale of two "forests": random forest machine learning AIDS tropical forest carbon mapping.

PubMed

Mascaro, Joseph; Asner, Gregory P; Knapp, David E; Kennedy-Bowdoin, Ty; Martin, Roberta E; Anderson, Christopher; Higgins, Mark; Chadwick, K Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including--in the latter case--x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called "out-of-bag"), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha(-1) when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation.

Aboveground Biomass Variability Across Intact and Degraded Forests in the Brazilian Amazon

NASA Technical Reports Server (NTRS)

Longo, Marcos; Keller, Michael; Dos-Santos, Maiza N.; Leitold, Veronika; Pinage, Ekena R.; Baccini, Alessandro; Saatchi, Sassan; Nogueira, Euler M.; Batistella, Mateus; Morton, Douglas C.

2016-01-01

Deforestation rates have declined in the Brazilian Amazon since 2005, yet degradation from logging, re, and fragmentation has continued in frontier forests. In this study we quantified the aboveground carbon density (ACD) in intact and degraded forests using the largest data set of integrated forest inventory plots (n 359) and airborne lidar data (18,000 ha) assembled to date for the Brazilian Amazon. We developed statistical models relating inventory ACD estimates to lidar metrics that explained70 of the variance across forest types. Airborne lidar-ACD estimates for intact forests ranged between 5.0 +/- 2.5 and 31.9 +/- 10.8 kg C m(exp -2). Degradation carbon losses were large and persistent. Sites that burned multiple times within a decade lost up to 15.0 +/- 0.7 kg C m(-2)(94%) of ACD. Forests that burned nearly15 years ago had between 4.1 +/- 0.5 and 6.8 +/- 0.3 kg C m(exp -2) (22-40%) less ACD than intact forests. Even for low-impact logging disturbances, ACD was between 0.7 +/- 0.3 and 4.4 +/- 0.4 kg C m(exp -2)(4-21%) lower than unlogged forests. Comparing biomass estimates from airborne lidar to existing biomass maps, we found that regional and pan-tropical products consistently overestimated ACD in degraded forests, under-estimated ACD in intact forests, and showed little sensitivity to res and logging. Fine-scale heterogeneity in ACD across intact and degraded forests highlights the benefits of airborne lidar for carbon mapping. Differences between airborne lidar and regional biomass maps underscore the need to improve and update biomass estimates for dynamic land use frontiers, to better characterize deforestation and degradation carbon emissions for regional carbon budgets and Reduce Emissions from Deforestation and forest Degradation(REDD+).

Simulation Studies of the Effect of Forest Spatial Structure on InSAR Signature

NASA Technical Reports Server (NTRS)

Sun, Guoqing; Liu, Dawei; Ranson, K. Jon; Koetz, Benjamin

2007-01-01

The height of scattering phase retrieved from InSAR data is considered being correlated with the tree height and the spatial structure of the forest stand. Though some researchers have used simple backscattering models to estimate tree height from the height of scattering center, the effect of forest spatial structure on InSAR data is not well understood yet. A three-dimensional coherent radar backscattering model for forest canopies based on realistic three-dimensional scene was used to investigate the effect in this paper. The realistic spatial structure of forest canopies was established either by field measurements (stem map) or through use of forest growth model. Field measurements or a forest growth model parameterized using local environmental parameters provides information of forest species composition and tree sizes in certain growth phases. A fractal tree model (L-system) was used to simulate individual 3- D tree structure of different ages or heights. Trees were positioned in a stand in certain patterns resulting in a 3-D medium of discrete scatterers. The radar coherent backscatter model took the 3-D forest scene as input and simulates the coherent radar backscattering signature. Interferometric SAR images of 3D scenes were simulated and heights of scattering phase centers were estimated from the simulated InSAR data. The effects of tree height, crown cover, crown depth, and the spatial distribution patterns of trees on the scattering phase center were analyzed. The results will be presented in the paper.

High-Throughput DNA sequencing of ancient wood.

PubMed

Wagner, Stefanie; Lagane, Frédéric; Seguin-Orlando, Andaine; Schubert, Mikkel; Leroy, Thibault; Guichoux, Erwan; Chancerel, Emilie; Bech-Hebelstrup, Inger; Bernard, Vincent; Billard, Cyrille; Billaud, Yves; Bolliger, Matthias; Croutsch, Christophe; Čufar, Katarina; Eynaud, Frédérique; Heussner, Karl Uwe; Köninger, Joachim; Langenegger, Fabien; Leroy, Frédéric; Lima, Christine; Martinelli, Nicoletta; Momber, Garry; Billamboz, André; Nelle, Oliver; Palomo, Antoni; Piqué, Raquel; Ramstein, Marianne; Schweichel, Roswitha; Stäuble, Harald; Tegel, Willy; Terradas, Xavier; Verdin, Florence; Plomion, Christophe; Kremer, Antoine; Orlando, Ludovic

2018-03-01

Reconstructing the colonization and demographic dynamics that gave rise to extant forests is essential to forecasts of forest responses to environmental changes. Classical approaches to map how population of trees changed through space and time largely rely on pollen distribution patterns, with only a limited number of studies exploiting DNA molecules preserved in wooden tree archaeological and subfossil remains. Here, we advance such analyses by applying high-throughput (HTS) DNA sequencing to wood archaeological and subfossil material for the first time, using a comprehensive sample of 167 European white oak waterlogged remains spanning a large temporal (from 550 to 9,800 years) and geographical range across Europe. The successful characterization of the endogenous DNA and exogenous microbial DNA of 140 (~83%) samples helped the identification of environmental conditions favouring long-term DNA preservation in wood remains, and started to unveil the first trends in the DNA decay process in wood material. Additionally, the maternally inherited chloroplast haplotypes of 21 samples from three periods of forest human-induced use (Neolithic, Bronze Age and Middle Ages) were found to be consistent with those of modern populations growing in the same geographic areas. Our work paves the way for further studies aiming at using ancient DNA preserved in wood to reconstruct the micro-evolutionary response of trees to climate change and human forest management. © 2018 John Wiley & Sons Ltd.

El Yunque National Forest Atlas

Treesearch

Maya Quiñones; Isabel K. Parés-Ramos; William A. Gould; Grizelle Gonzalez; Kathleen McGinley; Pedro Ríos

2018-01-01

El Yunque National Forest Atlas is a collaborative effort by the International Institute of Tropical Forestry and El Yunque National Forest to provide upto-date maps and analyses of spatial information of an important natural reserve in Puerto Rico and the only tropical forest in the National Forest System of the United States. El Yunque National Forest Atlas serves as...

78 FR 17632 - Caribou-Targhee National Forest; Idaho and Wyoming; Amendment to the Targhee Revised Forest Plan...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-22

...; Amendment to the Targhee Revised Forest Plan--Canada Lynx Habitat AGENCY: Forest Service, USDA. ACTION... Forest proposes to amend the Targhee Revised Forest Plan (1997) to include a map identifying specific... Administrative Review Process: The decision on this proposed plan amendment will be subject to the objection...

Classification of Forest Regrowth Stage using Polarimetric Decomposition and Foliage Projective Cover

NASA Astrophysics Data System (ADS)

Clewley, D.; Lucas, R.; Bunting, P.; Moghaddam, M.

2012-12-01

Within Queensland, Australia extensive clearing of vegetation for agriculture has occurred within the Brigalow Belt Bioregion (BBB), reducing forests dominated by Acacia harpophylla (brigalow) to 10 % of their former extent. Where cleared land is left abandoned or unmanaged regeneration is rapid, Regenerating vegetation represents a more efficient and cost effective method for carbon sequestration than direct planting and offers a number of benefits over plantation forest, particularly in terms of provision of habitat for native fauna. To effectively protect regenerating vegetation, maps of the distribution of forests at different stages of regeneration are required. Whilst mapping approaches have traditionally focused on optical data, the high canopy cover of brigalow regrowth in all but the very early stages limits discrimination of forests at different stages of growth. The combination of optical data, namely Landsat derived Foliage Projective Cover (FPC) and Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (SAR) backscatter data have previously been investigated for mapping regrowth. This study therefore aimed to investigate the potential of the alpha-Entropy (α/H) decomposition (S Cloude and E Pottier, "An entropy based classification scheme for land applications of polarimetric SAR," 1997, IEEE Transactions on Geoscience and Remote Sensing, vol. 35, no. 1, pp. 68-78) applied to polarimetric ALOS PALSAR backscatter for mapping regrowth stage combined with FPC data to account for canopy variations. The study focused on the Tara Downs subregion, located in the Western Darling Downs, within the south of the BBB. PALSAR data were acquired over the study site in fully-polarimetric mode (incidence angle mid swath ~ 26 degrees). From these data α/H layers were generated and stacked with FPC data. Considering only those areas known to contain brigalow prior to clearing and with an FPC > 9 %, k-means clustering was applied, with the number of clusters set to three. The position of each cluster, within α/H space was then used to determine the appropriate regrowth stage, based on the zones defined by Cloude and Pottier (1997). The classification was compared to an existing regrowth stage classification of the area derived from time-series interpretation of aerial photography and high resolution satellite data. The overall accuracy of the classification was 47 %, with confusion attributed to the differing methods of classification in that the separation of regrowth stage based on age did not account for variation in structure, associated with differences in soil, topography and clearing history. Conversely, the proposed classification method is based on scattering properties, which vary as a function of forest structure. The approach has demonstrated the potential of α/H layers derived from PALSAR data and FPC for discriminating and mapping different stages of regrowth. A particular advantage of the technique is that regrowth stages are assigned based on scattering characteristics, placing less reliance on field data which is not always available. Further work is being undertaken to evaluate alternative supervised and rule-based approaches to classification, such that a more consistent mapping methodology can be developed.

Sustainability assessment in forest management based on individual preferences.

PubMed

Martín-Fernández, Susana; Martinez-Falero, Eugenio

2018-01-15

This paper presents a methodology to elicit the preferences of any individual in the assessment of sustainable forest management at the stand level. The elicitation procedure was based on the comparison of the sustainability of pairs of forest locations. A sustainability map of the whole territory was obtained according to the individual's preferences. Three forest sustainability indicators were pre-calculated for each point in a study area in a Scots pine forest in the National Park of Sierra de Guadarrama in the Madrid Region in Spain to obtain the best management plan with the sustainability map. We followed a participatory process involving fifty people to assess the sustainability of the forest management and the methodology. The results highlighted the demand for conservative forest management, the usefulness of the methodology for managers, and the importance and necessity of incorporating stakeholders into forestry decision-making processes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Comparing the efficiency of digital and conventional soil mapping to predict soil types in a semi-arid region in Iran

NASA Astrophysics Data System (ADS)

Zeraatpisheh, Mojtaba; Ayoubi, Shamsollah; Jafari, Azam; Finke, Peter

2017-05-01

The efficiency of different digital and conventional soil mapping approaches to produce categorical maps of soil types is determined by cost, sample size, accuracy and the selected taxonomic level. The efficiency of digital and conventional soil mapping approaches was examined in the semi-arid region of Borujen, central Iran. This research aimed to (i) compare two digital soil mapping approaches including Multinomial logistic regression and random forest, with the conventional soil mapping approach at four soil taxonomic levels (order, suborder, great group and subgroup levels), (ii) validate the predicted soil maps by the same validation data set to determine the best method for producing the soil maps, and (iii) select the best soil taxonomic level by different approaches at three sample sizes (100, 80, and 60 point observations), in two scenarios with and without a geomorphology map as a spatial covariate. In most predicted maps, using both digital soil mapping approaches, the best results were obtained using the combination of terrain attributes and the geomorphology map, although differences between the scenarios with and without the geomorphology map were not significant. Employing the geomorphology map increased map purity and the Kappa index, and led to a decrease in the 'noisiness' of soil maps. Multinomial logistic regression had better performance at higher taxonomic levels (order and suborder levels); however, random forest showed better performance at lower taxonomic levels (great group and subgroup levels). Multinomial logistic regression was less sensitive than random forest to a decrease in the number of training observations. The conventional soil mapping method produced a map with larger minimum polygon size because of traditional cartographic criteria used to make the geological map 1:100,000 (on which the conventional soil mapping map was largely based). Likewise, conventional soil mapping map had also a larger average polygon size that resulted in a lower level of detail. Multinomial logistic regression at the order level (map purity of 0.80), random forest at the suborder (map purity of 0.72) and great group level (map purity of 0.60), and conventional soil mapping at the subgroup level (map purity of 0.48) produced the most accurate maps in the study area. The multinomial logistic regression method was identified as the most effective approach based on a combined index of map purity, map information content, and map production cost. The combined index also showed that smaller sample size led to a preference for the order level, while a larger sample size led to a preference for the great group level.

Mapping permafrost in the boreal forest with Thematic Mapper satellite data

NASA Technical Reports Server (NTRS)

Morrissey, L. A.; Strong, L. L.; Card, D. H.

1986-01-01

A geographic data base incorporating Landsat TM data was used to develop and evaluate logistic discriminant functions for predicting the distribution of permafrost in a boreal forest watershed. The data base included both satellite-derived information and ancillary map data. Five permafrost classifications were developed from a stratified random sample of the data base and evaluated by comparison with a photo-interpreted permafrost map using contingency table analysis and soil temperatures recorded at sites within the watershed. A classification using a TM thermal band and a TM-derived vegetation map as independent variables yielded the highest mapping accuracy for all permafrost categories.

Identification of stand age in rubber plantations using time series Landsat and PALSAR-2 data

NASA Astrophysics Data System (ADS)

Chen, B.; Wu, Z.; Xiao, X.; Li, X.; Ma, J.; Lan, G.; Yang, C.; Xie, G.; Dong, J.; Qin, Y.

2016-12-01

Stand age of rubber plantation is vital for optimal plantation management such as fertilization, prediction of latex yield and timber production. It is also an important variable for biomass estimation and determining the distribution of carbon pools and fluxes in rubber plantation ecosystem. Benefit from the traits of large coverage, high speed, and low-cost, satellite remote sensing techniques have been serviced as a major approach to map acreage and stand age of forest and plantations. Despite a number of studies working on acreage and stand age mapping, the stand age information of rubber plantation is still poorly available at regional scale. In this study, the 25-m cloud-free Phased Array type L-band Synthetic Aperture Radar 2 (PALSAR-2) mosaic product, together with the 30-m time series images of Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI), were used to map stand age of rubber plantation in China under Google Earth Engine computing platform. Rubber plantation in 2015 were first identified by structural information in PALSAR-2 and phenological and spectral signatures (deciduous, rapid change of canopies during rubber defoliation and foliation periods, and dense canopy in growing season) that derived from time series Landsat ETM+/OLI images. Based on the resultant rubber plantation map, we then successfully identified the stand age of rubber plantation using land cover transfer information during rubber seeding cultivation period, specifically, by yearly composited Land Surface Water Index (LSWI) of Landsat TM/ETM+/OLI images since 1985. The estimated stand age has very high accuracy with Root Square Mean Error (RMSE) less than 2 years. The resultant rubber stand age information are likely to be useful for sustainable plantation management and ecological assessment, and the methodology can be extendable for applications in other regions.

Mapping the distribution of tick-borne encephalitis in mainland China.

PubMed

Sun, Ruo-Xi; Lai, Sheng-Jie; Yang, Yang; Li, Xin-Lou; Liu, Kun; Yao, Hong-Wu; Zhou, Hang; Li, Yu; Wang, Li-Ping; Mu, Di; Yin, Wen-Wu; Fang, Li-Qun; Yu, Hong-Jie; Cao, Wu-Chun

2017-06-01

Tick-borne encephalitis (TBE) has become an increasing public health threat in recent years, ranging from Europe, through far-eastern Russia to Japan and northern China. However, the neglect of its expansion and scarce analyses of the dynamics have made the overall disease burden and the risk distribution of the disease being unclear in mainland China. In this study, we described epidemiological characteristics of 2117 reported human TBE cases from 2006 to 2013 in mainland China. About 99% of the cases were reported in forest areas of northeastern China, and 93% of reported infections occurred during May-July. Cases were primarily male (67%), mostly in 30-59 years among all age-gender groups. Farmers (31.6%), domestic workers (20.1%) and forest workers (17.9%) accounted for the majority of the patients, and the proportions of patients from farmers and domestic workers were increasing in recent years. The epidemiological features of TBE differed slightly across the affected regions. The distribution and features of the disease in three main endemic areas of mainland China were also summarized. Using the Boosted Regression Trees (BRT) model, we found that the presence of TBE was significantly associated with a composite meteorological index, altitude, the coverage of broad-leaved forest, the coverage of mixed broadleaf-conifer forest, and the distribution of Ixodes persulcatus (I. persulcatus) ticks. The model-predicted probability of presence of human TBE cases in mainland China was mapped at the county level. The spatial distribution of human TBE in China was largely driven by the distributions of forests and I. persulcatus ticks, altitude, and climate. Enhanced surveillance and intervention for human TBE in the high-risk regions, particularly on the forest areas in north-eastern China, is necessary to prevent human infections. Copyright © 2017 The Authors. Published by Elsevier GmbH.. All rights reserved.

Assimilation of high resolution satellite imagery into the 3D-CMCC forest ecosystem model

NASA Astrophysics Data System (ADS)

Natali, S.; Collalti, A.; Candini, A.; Della Vecchia, A.; Valentini, R.

2012-04-01

The use of satellite observations for the accurate monitoring of the terrestrial biosphere has been carried out since the very early stage of remote sensing applications. The possibility to observe the ground surface with different wavelengths and different observation modes (namely active and passive observations) has given to the scientific community an invaluable tool for the observation of wide areas with a resolution down to the single tree. On the other hand, the continuous development of forest ecosystem models has permitted to perform simulations of complex ("natural") forest scenarios to evaluate forest status, forest growth and future dynamics. Both remote sensing and modelling forest assessment methods have advantages and disadvantages that could be overcome by the adoption of an integrated approach. In the framework of the European Space Agency Project KLAUS, high resolution optical satellite data has been integrated /assimilated into a forest ecosystem model (named 3D-CMCC) specifically developed for multi-specie, multi-age forests. 3D-CMCC permits to simulate forest areas with different forest layers, with different trees at different age on the same point. Moreover, the model permits to simulate management activities on the forest, thus evaluating the carbon stock evolution following a specific management scheme. The model has been modified including satellite data at 10m resolution, permitting the use of directly measured information, adding to the model the real phenological cycle of each simulated point. Satellite images have been collected by the JAXA ALOS-AVNIR-2 sensor. The integration schema has permitted to identify a spatial domain in which each pixel is characterised by a forest structure (species, ages, soil parameters), meteo-climatological parameters and estimated Leaf Area Index from satellite. The resulting software package (3D-CMCC-SAT) is built around 3D-CMCC: 2D / 3D input datasets are processed iterating on each point of the analysed domain to create a set of monthly/ yearly output maps. The integrated approach has been tested on the "Parco Nazionale dei Monti Sibillini, Italy". The high correlation showed between observed and computed data can be considered statistically meaningful and hence the model can be deemed a good predictor both for high resolution and for short period of simulation. Moreover the coupling satellite data at high resolution and field information as input data have shown that these data can be used in the 3D-CMCC Forest Model run. These data can be also successfully used to simulate the main physiological processes at regional scale and to produce with good accordance with measured and literature data, reliable output to better investigate forest growth, dynamic and carbon stock.

A Tale of Two “Forests”: Random Forest Machine Learning Aids Tropical Forest Carbon Mapping

PubMed Central

Mascaro, Joseph; Asner, Gregory P.; Knapp, David E.; Kennedy-Bowdoin, Ty; Martin, Roberta E.; Anderson, Christopher; Higgins, Mark; Chadwick, K. Dana

2014-01-01

Accurate and spatially-explicit maps of tropical forest carbon stocks are needed to implement carbon offset mechanisms such as REDD+ (Reduced Deforestation and Degradation Plus). The Random Forest machine learning algorithm may aid carbon mapping applications using remotely-sensed data. However, Random Forest has never been compared to traditional and potentially more reliable techniques such as regionally stratified sampling and upscaling, and it has rarely been employed with spatial data. Here, we evaluated the performance of Random Forest in upscaling airborne LiDAR (Light Detection and Ranging)-based carbon estimates compared to the stratification approach over a 16-million hectare focal area of the Western Amazon. We considered two runs of Random Forest, both with and without spatial contextual modeling by including—in the latter case—x, and y position directly in the model. In each case, we set aside 8 million hectares (i.e., half of the focal area) for validation; this rigorous test of Random Forest went above and beyond the internal validation normally compiled by the algorithm (i.e., called “out-of-bag”), which proved insufficient for this spatial application. In this heterogeneous region of Northern Peru, the model with spatial context was the best preforming run of Random Forest, and explained 59% of LiDAR-based carbon estimates within the validation area, compared to 37% for stratification or 43% by Random Forest without spatial context. With the 60% improvement in explained variation, RMSE against validation LiDAR samples improved from 33 to 26 Mg C ha−1 when using Random Forest with spatial context. Our results suggest that spatial context should be considered when using Random Forest, and that doing so may result in substantially improved carbon stock modeling for purposes of climate change mitigation. PMID:24489686

Forest and Range Inventory and Mapping

NASA Technical Reports Server (NTRS)

Aldrich, R. C.

1971-01-01

The state of the art in remote sensing for forest and range inventories and mapping has been discussed. There remains a long way to go before some of these techniques can be used on an operational basis. By the time that the Earth Resources Technology Satellite and Skylab space missions are flown, it should be possible to tell what kind and what quality of information can be extracted from remote sensors and how it can be used for surveys of forest and range resources.

Forest resources of the United States, 1992

Treesearch

Douglas S. Powell; Joanne L. Faulkner; David R. Darr; Zhiliang Zhu; Douglas W. MacCleery

1993-01-01

The 1987 Resources Planning Act (RPA) Assessment forest resources statistics are updated to 1992, to provide current information on the Nation's forests. Resource tables present estimates of forest area, volume, mortality, growth, removals, and timber products output. Resource data are analyzed, and trends since 1987 are noted. A forest type map produced from...

Predictive mapping for tree sizes and densities in southeast Alaska.

Treesearch

John P. Caouette; Eugene J. DeGayner

2005-01-01

The Forest Service has relied on a single forest measure, timber volume, to meet many management and planning information needs in southeast Alaska. This economic-based categorization of forest types tends to mask critical information relevant to other contemporary forest-management issues, such as modeling forest structure, ecosystem diversity, or wildlife habitat. We...

K-Nearest Neighbor Estimation of Forest Attributes: Improving Mapping Efficiency

Treesearch

Andrew O. Finley; Alan R. Ek; Yun Bai; Marvin E. Bauer

2005-01-01

This paper describes our efforts in refining k-nearest neighbor forest attributes classification using U.S. Department of Agriculture Forest Service Forest Inventory and Analysis plot data and Landsat 7 Enhanced Thematic Mapper Plus imagery. The analysis focuses on FIA-defined forest type classification across St. Louis County in northeastern Minnesota. We outline...

Application of AIS Technology to Forest Mapping

NASA Technical Reports Server (NTRS)

Yool, S. R.; Star, J. L.

1985-01-01

Concerns about environmental effects of large scale deforestation have prompted efforts to map forests over large areas using various remote sensing data and image processing techniques. Basic research on the spectral characteristics of forest vegetation are required to form a basis for development of new techniques, and for image interpretation. Examination of LANDSAT data and image processing algorithms over a portion of boreal forest have demonstrated the complexity of relations between the various expressions of forest canopies, environmental variability, and the relative capacities of different image processing algorithms to achieve high classification accuracies under these conditions. Airborne Imaging Spectrometer (AIS) data may in part provide the means to interpret the responses of standard data and techniques to the vegetation based on its relatively high spectral resolution.

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.

Benchmark map of forest carbon stocks in tropical regions across three continents

PubMed Central

Saatchi, Sassan S.; Harris, Nancy L.; Brown, Sandra; Lefsky, Michael; Mitchard, Edward T. A.; Salas, William; Zutta, Brian R.; Buermann, Wolfgang; Lewis, Simon L.; Hagen, Stephen; Petrova, Silvia; White, Lee; Silman, Miles; Morel, Alexandra

2011-01-01

Developing countries are required to produce robust estimates of forest carbon stocks for successful implementation of climate change mitigation policies related to reducing emissions from deforestation and degradation (REDD). Here we present a “benchmark” map of biomass carbon stocks over 2.5 billion ha of forests on three continents, encompassing all tropical forests, for the early 2000s, which will be invaluable for REDD assessments at both project and national scales. We mapped the total carbon stock in live biomass (above- and belowground), using a combination of data from 4,079 in situ inventory plots and satellite light detection and ranging (Lidar) samples of forest structure to estimate carbon storage, plus optical and microwave imagery (1-km resolution) to extrapolate over the landscape. The total biomass carbon stock of forests in the study region is estimated to be 247 Gt C, with 193 Gt C stored aboveground and 54 Gt C stored belowground in roots. Forests in Latin America, sub-Saharan Africa, and Southeast Asia accounted for 49%, 25%, and 26% of the total stock, respectively. By analyzing the errors propagated through the estimation process, uncertainty at the pixel level (100 ha) ranged from ±6% to ±53%, but was constrained at the typical project (10,000 ha) and national (>1,000,000 ha) scales at ca. ±5% and ca. ±1%, respectively. The benchmark map illustrates regional patterns and provides methodologically comparable estimates of carbon stocks for 75 developing countries where previous assessments were either poor or incomplete. PMID:21628575

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).

Evaluating and mapping sources and temporary storage areas of sediment

Treesearch

Leslie M. Reid

1982-01-01

Legislation to regulate forest practices, water quality, and management of federal lands has increased the land managers' need for efficient methods of identifying and mapping sources of sediment in forested basins. At the same time, theoretical analysis of landscape evolution has led research geomorphologists to the consideration of many of the same...

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...

Going beyond the biophysical when mapping national forests

Treesearch

Geoff Koch; Lee Cerveny

2018-01-01

Resource managers have long mapped biophysical forest data. Often lacking, however, is relevant social science data for understanding the variety of human needs a given landscape fulfills.For nearly a decade, Lee Cerveny has been exploring how to provide this data on public lands around the Pacific Northwest. Cerveny is a research...

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.

Tree age, disturbance history, and carbon stocks and fluxes in subalpine Rocky Mountain forests

Treesearch

J.B. Bradford; R.A. Birdsey; L.A. Joyce; M.G. Ryan

2008-01-01

Forest carbon stocks and fluxes vary with forest age, and relationships with forest age are often used to estimate fluxes for regional or national carbon inventories. Two methods are commonly used to estimate forest age: observed tree age or time since a known disturbance. To clarify the relationships between tree age, time since disturbance and forest carbon storage...

Object-based class modelling for multi-scale riparian forest habitat mapping

NASA Astrophysics Data System (ADS)

Strasser, Thomas; Lang, Stefan

2015-05-01

Object-based class modelling allows for mapping complex, hierarchical habitat systems. The riparian zone, including forests, represents such a complex ecosystem. Forests within riparian zones are biologically high productive and characterized by a rich biodiversity; thus considered of high community interest with an imperative to be protected and regularly monitored. Satellite earth observation (EO) provides tools for capturing the current state of forest habitats such as forest composition including intermixture of non-native tree species. Here we present a semi-automated object based image analysis (OBIA) approach for the mapping of riparian forests by applying class modelling of habitats based on the European Nature Information System (EUNIS) habitat classifications and the European Habitats Directive (HabDir) Annex 1. A very high resolution (VHR) WorldView-2 satellite image provided the required spatial and spectral details for a multi-scale image segmentation and rule-base composition to generate a six-level hierarchical representation of riparian forest habitats. Thereby habitats were hierarchically represented within an image object hierarchy as forest stands, stands of homogenous tree species and single trees represented by sunlit tree crowns. 522 EUNIS level 3 (EUNIS-3) habitat patches with a mean patch size (MPS) of 12,349.64 m2 were modelled from 938 forest stand patches (MPS = 6868.20 m2) and 43,742 tree stand patches (MPS = 140.79 m2). The delineation quality of the modelled EUNIS-3 habitats (focal level) was quantitatively assessed to an expert-based visual interpretation showing a mean deviation of 11.71%.

Mapping Species Composition of Forests and Tree Plantations in Northeastern Costa Rica with an Integration of Hyperspectral and Multitemporal Landsat Imagery

NASA Technical Reports Server (NTRS)

Fagan, Matthew E.; Defries, Ruth S.; Sesnie, Steven E.; Arroyo-Mora, J. Pablo; Soto, Carlomagno; Singh, Aditya; Townsend, Philip A.; Chazdon, Robin L.

2015-01-01

An efficient means to map tree plantations is needed to detect tropical land use change and evaluate reforestation projects. To analyze recent tree plantation expansion in northeastern Costa Rica, we examined the potential of combining moderate-resolution hyperspectral imagery (2005 HyMap mosaic) with multitemporal, multispectral data (Landsat) to accurately classify (1) general forest types and (2) tree plantations by species composition. Following a linear discriminant analysis to reduce data dimensionality, we compared four Random Forest classification models: hyperspectral data (HD) alone; HD plus interannual spectral metrics; HD plus a multitemporal forest regrowth classification; and all three models combined. The fourth, combined model achieved overall accuracy of 88.5%. Adding multitemporal data significantly improved classification accuracy (p less than 0.0001) of all forest types, although the effect on tree plantation accuracy was modest. The hyperspectral data alone classified six species of tree plantations with 75% to 93% producer's accuracy; adding multitemporal spectral data increased accuracy only for two species with dense canopies. Non-native tree species had higher classification accuracy overall and made up the majority of tree plantations in this landscape. Our results indicate that combining occasionally acquired hyperspectral data with widely available multitemporal satellite imagery enhances mapping and monitoring of reforestation in tropical landscapes.

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.

Detection of aspen-conifer forest mixes from LANDSAT digital data. [Utah-Idaho Bear River Range

NASA Technical Reports Server (NTRS)

Jaynes, R. A.; Merola, J. A.

1982-01-01

Aspen, conifer and mixed aspen/conifer forests were mapped for a 15-quadrangle study area in the Utah-Idaho Bear River Range using LANDSAT multispectral scanner data. Digital classification and statistical analysis of LANDSAT data allowed the identification of six groups of signatures which reflect different types of aspen/conifer forest mixing. Photo interpretations of the print symbols suggest that such classes are indicative of mid to late seral aspen forests. Digital print map overlays and acreage calculations were prepared for the study area quadrangles. Further field verification is needed to acquire additional information about the nature of the forests. Single date LANDSAT analysis should be a cost effective means to index aspen forests which are at least in the mid seral phase of conifer invasion. Since aspen canopies tend to obscure understory conifers for early seral forests, a second date analysis, using data taken when aspens are leafless, could provide information about early seral aspen forests.

Remote sensing for conservation of tropical moist forests: A study in Indonesia

NASA Astrophysics Data System (ADS)

Warwick-Smith, Robert Myles

The Indonesian archipelago extends in a great 6000km arc from the northern tip of Sumatra to the eastern border of Irian Jaya. It includes a wide diversity of ecosystems ranging from the floristically rich and economically important lowland tropical rain forests to the 'moss' and sub-alpine meadows of the higher mountains and from fresh-water swamp forest to the dry monsoon forest and savanna woodlands of the lesser Sunda islands. These forests are of importance for the protection of watersheds and catchment areas, for the maintenance of water supplies, and for their general and local influence upon climate. They are the habitat of a large number of rare, endangered and endemic plant and animal species; also many other birds, mammals, reptiles and insects which form a colourful, scientifically valuable and irreplaceable part of the national heritage and world genetic resources. This study examines an area of great ecological importance in Sulawesi, and an attempt is made to map a number of ecosystems in the area. Landsat multispectral imagery (1972) was the basis of the mapping and field work was completed in 1980. The satellite imagery proved to be a satisfactory mapping tool in these tropical moist forest conditions.

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

Estimating release of carbon from 1990 and 1991 forest fires in Alaska

NASA Technical Reports Server (NTRS)

Kaisischke, Eric S.; French, Nancy H. F.; Bourgeau-Chavez, Laura L.; Christensen, N. L., Jr.

1995-01-01

An improved method to estimate the amounts of carbon released during fires in the boreal forest zone of Alaska in 1990 and 1991 is described. This method divides the state into 64 distinct physiographic regions and estimates areal extent of five different land covers: two forest types, peat land, tundra, and nonvegetated. The areal extent of each cover type was estimated from a review of topographic maps of each region and observations on the distribution of foreat types within the state. Using previous observations and theoretical models for the two forest types found in interior Alaska, models of biomass accumulation as a function of stand age were developed. Stand age distributions for each region were determined using a statistical distribution based on fire frequency, which was from available long-term historical records. Estimates of the degree of biomass combusted were based on recent field observations as well as research reported in the literature. The location and areal extent of fires in this region for 1990 and 1991 were based on both field observations and analysis of satellite (advanced very high resolution radiometer (AVHRR)) data sets. Estimates of average carbon release for the two study years ranged between 2.54 and 3.00 kg/sq m, which are 2.2 to 2.6 times greater than estimates used in other studies of carbon release through biomass burning in boreal forests. Total average annual carbon release for the two years ranged between 0.012 and 0.018 Pg C/yr, with the lower value resulting from the AVHRR estimates of fire location and area.

Can we set a global threshold age to define mature forests?

PubMed

Martin, Philip; Jung, Martin; Brearley, Francis Q; Ribbons, Relena R; Lines, Emily R; Jacob, Aerin L

2016-01-01

Globally, mature forests appear to be increasing in biomass density (BD). There is disagreement whether these increases are the result of increases in atmospheric CO2 concentrations or a legacy effect of previous land-use. Recently, it was suggested that a threshold of 450 years should be used to define mature forests and that many forests increasing in BD may be younger than this. However, the study making these suggestions failed to account for the interactions between forest age and climate. Here we revisit the issue to identify: (1) how climate and forest age control global forest BD and (2) whether we can set a threshold age for mature forests. Using data from previously published studies we modelled the impacts of forest age and climate on BD using linear mixed effects models. We examined the potential biases in the dataset by comparing how representative it was of global mature forests in terms of its distribution, the climate space it occupied, and the ages of the forests used. BD increased with forest age, mean annual temperature and annual precipitation. Importantly, the effect of forest age increased with increasing temperature, but the effect of precipitation decreased with increasing temperatures. The dataset was biased towards northern hemisphere forests in relatively dry, cold climates. The dataset was also clearly biased towards forests <250 years of age. Our analysis suggests that there is not a single threshold age for forest maturity. Since climate interacts with forest age to determine BD, a threshold age at which they reach equilibrium can only be determined locally. We caution against using BD as the only determinant of forest maturity since this ignores forest biodiversity and tree size structure which may take longer to recover. Future research should address the utility and cost-effectiveness of different methods for determining whether forests should be classified as mature.

Mapping young forest in Wisconsin

Treesearch

Mark Nelson; Kirk Stueve; Charles Perry; Dale Gormanson; Chengquan Huang; Sean. Healey

2012-01-01

Population declines of early successional forest-associated wildlife species have been linked to declines in habitat abundance. Forest Inventory and Analysis (FIA) data can be used to estimate composition and change in 'young' forest, but such information typically lacks spatial specificity for determining landscape patterns that also affect habitat...

Regression tree modeling of forest NPP using site conditions and climate variables across eastern USA

NASA Astrophysics Data System (ADS)

Kwon, Y.

2013-12-01

As evidence of global warming continue to increase, being able to predict forest response to climate changes, such as expected rise of temperature and precipitation, will be vital for maintaining the sustainability and productivity of forests. To map forest species redistribution by climate change scenario has been successful, however, most species redistribution maps lack mechanistic understanding to explain why trees grow under the novel conditions of chaining climate. Distributional map is only capable of predicting under the equilibrium assumption that the communities would exist following a prolonged period under the new climate. In this context, forest NPP as a surrogate for growth rate, the most important facet that determines stand dynamics, can lead to valid prediction on the transition stage to new vegetation-climate equilibrium as it represents changes in structure of forest reflecting site conditions and climate factors. The objective of this study is to develop forest growth map using regression tree analysis by extracting large-scale non-linear structures from both field-based FIA and remotely sensed MODIS data set. The major issue addressed in this approach is non-linear spatial patterns of forest attributes. Forest inventory data showed complex spatial patterns that reflect environmental states and processes that originate at different spatial scales. At broad scales, non-linear spatial trends in forest attributes and mixture of continuous and discrete types of environmental variables make traditional statistical (multivariate regression) and geostatistical (kriging) models inefficient. It calls into question some traditional underlying assumptions of spatial trends that uncritically accepted in forest data. To solve the controversy surrounding the suitability of forest data, regression tree analysis are performed using Software See5 and Cubist. Four publicly available data sets were obtained: First, field-based Forest Inventory and Analysis (USDA, Forest Service) data set for the 31 eastern most United States. Second, 8-day composite of MODIS Land Cover, FPAR, LAI and GPP/NPP data were obtained from Jan 2001 to Dec 2004 (total 182 composite) and each product were filtered by pixel-level quality assurance data to select best quality pixels. Third, 30-year averaged climate data were collected from National Oceanic and Atmospheric Administration (NOAA) and five climatic variables were obtained: Monthly temperature, precipitation, annual heating and cooling days, and annual frost-free days. Forth, topographic data were obtained from digital elevation model (1km by 1km). This research will provide a better understanding of large-scale forest responses to environmental factors that will be beneficial for the development of important forest management applications.

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.

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

Utilization of geoinformation tools for the development of forest fire hazard mapping system: example of Pekan fire, Malaysia

NASA Astrophysics Data System (ADS)

Mahmud, Ahmad Rodzi; Setiawan, Iwan; Mansor, Shattri; Shariff, Abdul Rashid Mohamed; Pradhan, Biswajeet; Nuruddin, Ahmed

2009-12-01

A study in modeling fire hazard assessment will be essential in establishing an effective forest fire management system especially in controlling and preventing peat fire. In this paper, we have used geographic information system (GIS), in combination with other geoinformation technologies such as remote sensing and computer modeling, for all aspects of wild land fire management. Identifying areas that have a high probability of burning is an important component of fire management planning. The development of spatially explicit GIS models has greatly facilitated this process by allowing managers to map and analyze variables contributing to fire occurrence across large, unique geographic units. Using the model and its associated software engine, the fire hazard map was produced. Extensive avenue programming scripts were written to provide additional capabilities in the development of these interfaces to meet the full complement of operational software considering various users requirements. The system developed not only possesses user friendly step by step operations to deliver the fire vulnerability mapping but also allows authorized users to edit, add or modify parameters whenever necessary. Results from the model can support fire hazard mapping in the forest and enhance alert system function by simulating and visualizing forest fire and helps for contingency planning.

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.

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.

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.

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...

Effects of satellite image spatial aggregation and resolution on estimates of forest land area

Treesearch

M.D. Nelson; R.E. McRoberts; G.R. Holden; M.E. Bauer

2009-01-01

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We...

Forests on the edge: evaluating contributions of and threats to America's private forest lands

Treesearch

Mark Hatfield; Ronald E. McRoberts; Dacia M. Meneguzzo; Mike Dechter; < i> et al< /i>

2007-01-01

The Forests on the Edge project, sponsored by the U.S. Department of Agriculture Forest Service, uses geographic information systems to construct and analyze maps depicting ecological, social, and economic contributions of America's private forest lands and threats to those contributions. Watersheds across the conterminous United States are ranked relative to the...

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...

Harmonizing estimates of forest land area from national-level forest inventory and satellite imagery

Treesearch

Bonnie Ruefenacht; Mark D. Nelson; Mark Finco

2009-01-01

Estimates of forest land area are derived both from national-level forest inventories and satellite image-based map products. These estimates can differ substantially within subregional extents (e.g., states or provinces) primarily due to differences in definitions of forest land between inventory- and image-based approaches. We present a geospatial modeling approach...

Evaluation of open source data mining software packages

Treesearch

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

2009-01-01

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

Assessing the Impact of Asian Longhorned Beetle in Worcester, MA: Thermal Effects, Community Responses, and Future Vulnerability

NASA Astrophysics Data System (ADS)

Elmes, Arthur Francis Marett

Urban environments experience air, water, noise, and heat pollution as a consequence of their composition, structure, and function. These adverse environmental conditions, which have direct and indirect effects on human health and ecological stability, can be mitigated or partially offset by healthy and extensive urban forests. Additionally, urban trees provide a number of cultural, aesthetic, and property value benefits. However, to provide maximum ecosystem service benefits, an urban forest must be carefully planned and maintained, so that average tree lifespan is maximized and ecological vulnerability is minimized. Maximal urban forest resilience is best achieved via taxonomic and age-diversity, such that no one genus or age-cohort dominates. This diversity enhances overall urban forest resilience, which in turn facilitates maximum ecosystem service provision. The Asian Longhorned Beetle (ALB, Anoplophora glabripennis) infestation in Worcester, MA represents a case example of taxonomic monoculture vulnerability, but also an excellent opportunity to reinvest and diversify the urban forest. As a result of the ALB infestation, which was first documented in 2008, approximately 35,000 host trees were removed from residential and public property, substantially reducing urban tree canopy coverage and uncovering impervious surfaces. Chapter two quantifies this Urban Tree Canopy (UTC) loss, and shows that in loss areas Land Surface Temperature (LST) increases range from 1 - 6° C. The research investigates multi-scale effects of this relationship, particularly in the hard-hit Burncoat and Greendale neighborhoods of Worcester, MA. In response to the large quantity of UTC loss suffered due to ALB, the Massachusetts Department of Conservation and Recreation (DCR), the City of Worcester, and the Worcester Tree Initiative (WTI) have together planted over 30,000 trees, primarily via tree giveaway programs to local residents. Chapter three assesses the effectiveness of this initiative in terms of juvenile tree mortality rates - a critical indicator of long-term urban forest success. Results indicate that tree stewardship variables such as renter proportion and number of trees planted per property are strong predictors of tree mortality. Tree species was shown to be strong predictor of mortality, with ornamental trees showing lower mortality rates than shade deciduous or conifers. Finally, Chapter four investigates the potential risk of further ALB infestation in the Worcester area, using a circuit theory dispersal model, which uses an analogy with electrical circuits to predict the dispersal of random-walkers across a heterogeneous landscape. The results indicate that impervious surfaces such as roads, sidewalks, and parking lots, as well as proximity to existing trees are facilitators of ALB movement. Circuit-based dispersal maps highlight the importance of narrow dispersal corridors connecting larger areas of potential dispersal. Dispersal potential was combined with ALB habitat suitability measured with Mahalanobis typicality, yielding a hybrid map of ALB infestation risk. These map products are valuable both as contributions to the understanding of invasive species movement in novel environments, and as tools for land managers attempting to eradicate ALB, such as the USDA Animal and Plant Health Inspection Service. This dissertation investigates three elements of the ALB infestation of Worcester, providing a holistic explanation of the impacts, recovery, and vulnerability of Worcester's urban forest.

Towards lidar-based mapping of tree age at the Arctic forest tundra ecotone.

NASA Astrophysics Data System (ADS)

Jensen, J.; Maguire, A.; Oelkers, R.; Andreu-Hayles, L.; Boelman, N.; D'Arrigo, R.; Griffin, K. L.; Jennewein, J. S.; Hiers, E.; Meddens, A. J.; Russell, M.; Vierling, L. A.; Eitel, J.

2017-12-01

Climate change may cause spatial shifts in the forest-tundra ecotone (FTE). To improve our ability to study these spatial shifts, information on tree demography along the FTE is needed. The objective of this study was to assess the suitability of lidar derived tree heights as a surrogate for tree age. We calculated individual tree age from 48 tree cores collected at basal height from white spruce (Picea glauca) within the FTE in northern Alaska. Tree height was obtained from terrestrial lidar scans (<1cm spatial resolution). The relationship between age and height was examined using a linear regression model forced through the origin. We found a very strong predictive relationship between tree height and age (R2 = 0.90, RMSE = 19.34 years) for trees that ranged between 14 to 230 years. Separate regression models were also developed for small (height < 3 m) and large trees (height >= 3 m), yielding strong predictive relationships between height and age (R2 = 0.86, RMSE 12.21 years, and R2 = 0.93, RMSE = 25.16 years, respectively). The slope coefficient for small and large tree models (16.83 and 12.98 years/m, respectively) indicate that small trees grow 1.3 times faster than large trees at these FTE study sites. Although a strong, predictive relationship between age and height is uncommon in light-limited forest environments, our findings suggest that the sparseness of trees within the FTE may explain the strong tree height-age relationships found herein. Further analysis of 36 additional tree cores recently collected within the FTE near Inuvik, Canada will be performed. Our preliminary analysis suggests that lidar derived tree height could be a reliable proxy for tree age at the FTE, thereby establishing a new technique for scaling tree structure and demographics across larger portions of this sensitive ecotone.

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)

Characteristics of Forests in Western Sayani Mountains, Siberia from SAR Data

NASA Technical Reports Server (NTRS)

Ranson, K. Jon; Sun, Guoqing; Kharuk, V. I.; Kovacs, Katalin

1998-01-01

This paper investigated the possibility of using spaceborne radar data to map forest types and logging in the mountainous Western Sayani area in Siberia. L and C band HH, HV, and VV polarized images from the Shuttle Imaging Radar-C instrument were used in the study. Techniques to reduce topographic effects in the radar images were investigated. These included radiometric correction using illumination angle inferred from a digital elevation model, and reducing apparent effects of topography through band ratios. Forest classification was performed after terrain correction utilizing typical supervised techniques and principal component analyses. An ancillary data set of local elevations was also used to improve the forest classification. Map accuracy for each technique was estimated for training sites based on Russian forestry maps, satellite imagery and field measurements. The results indicate that it is necessary to correct for topography when attempting to classify forests in mountainous terrain. Radiometric correction based on a DEM (Digital Elevation Model) improved classification results but required reducing the SAR (Synthetic Aperture Radar) resolution to match the DEM. Using ratios of SAR channels that include cross-polarization improved classification and

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).

Conterminous U.S. and Alaska Forest Type Mapping Using Forest Inventory and Analysis Data

Treesearch

B. Ruefenacht; M.V. Finco; M.D. Nelson; R. Czaplewski; E.H. Helmer; J. A. Blackard; G.R. Holden; A.J. Lister; D. Salajanu; D. Weyermann; K. Winterberger

2008-01-01

Classification-trees were used to model forest type groups and forest types for the conterminous United States and Alaska. The predictor data were a geospatial data set with a spatial resolution of 250 m developed by the U.S. Department of Agriculture Forest Service (USFS). The response data were plot data from the USFS Forest Inventory and Analysis program. Overall...

ERTS-1 data applications to Minnesota forest land use classification

NASA Technical Reports Server (NTRS)

Sizer, J. E. (Principal Investigator); Eller, R. G.; Meyer, M. P.; Ulliman, J. J.

1973-01-01

The author has identified the following significant results. Color-combined ERTS-1 MSS spectral slices were analyzed to determine the maximum (repeatable) level of meaningful forest resource classification data visually attainable by skilled forest photointerpreters for the following purposes: (1) periodic updating of the Minnesota Land Management Information System (MLMIS) statewide computerized land use data bank, and (2) to provide first-stage forest resources survey data for large area forest land management planning. Controlled tests were made of two forest classification schemes by experienced professional foresters with special photointerpretation training and experience. The test results indicate it is possible to discriminate the MLMIS forest class from the MLMIS nonforest classes, but that it is not possible, under average circumstances, to further stratify the forest classification into species components with any degree of reliability with ERTS-1 imagery. An ongoing test of the resulting classification scheme involves the interpretation, and mapping, of the south half of Itasca County, Minnesota, with ERTS-1 imagery. This map is undergoing field checking by on the ground field cooperators, whose evaluation will be completed in the fall of 1973.

Mapping wetland and forest landscapes in Siberia with Landsat data

NASA Astrophysics Data System (ADS)

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

2014-05-01

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

Mapping Fuels on the Okanogan and Wenatchee National Forests

Treesearch

Crystal L. Raymond; Lara-Karena B. Kellogg; Donald McKenzie

2006-01-01

Resource managers need spatially explicit fuels data to manage fire hazard and evaluate the ecological effects of wildland fires and fuel treatments. For this study, fuels were mapped on the Okanogan and Wenatchee National Forests (OWNF) using a rule-based method and the Fuels Characteristic Classification System (FCCS). The FCCS classifies fuels based on their...

12. Photographic copy of original scale map and sections of ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

12. Photographic copy of original scale map and sections of upper and lower dams at Loletta by Paul Wakefield, 1934 (original in possession of United States Department of Agriculture-Forest Service-Allegheny National Forest). - Loleta Recreation Area, Lower Dam, 6 miles Southeast of interesection of State Route 24041 & State Route 66, Loleta, Elk County, PA

North American forest disturbance mapped from a decadal Landsat record

Treesearch

Jeffrey G. Masek; Chengquan Huang; Robert Wolfe; Warren Cohen; Forrest Hall; Jonathan Kutler; Peder Nelson

2008-01-01

Forest disturbance and recovery are critical ecosystem processes, but the spatial pattern of disturbance has never been mapped across North America. The LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) project has assembled a wall-to-wall record of stand-clearing disturbance (clearcut harvest, fire) for the United States and Canada for the period 1990-...

Utilizing inventory information to calibrate a landscape simulation model

Treesearch

Steven R. Shifley; Frank R., III Thompson; David R. Larsen; David J. Mladenoff; Eric J. Gustafson

2000-01-01

LANDIS is a spatially explicit model that uses mapped landscape conditions as a starting point and projects the patterns in forest vegetation that will result from alternative harvest practices, alternative fire regimes, and wind events. LANDIS was originally developed for Lake States forests, but it is capable of handling the input, output, bookkeeping, and mapping...

AWPA biodeterioration hazard map revisited

Treesearch

Grant T. Kirker; Amy B. Bishell; William J. Hickey

2017-01-01

The fungal decay hazard map used by the American Wood Protection Association (AWPA) currently describes regional decay hazards in ground contact for North America and is based on condition assessments of utility poles from the 1970’s. Current work at the USDA Forest Service, Forest Products Laboratory is underway to analyze soil and wood samples from several National...

Aerial detection of Ailanthus altissima: a cost-effective method to map an invasive tree in forested landscapes

Treesearch

Joanne Rebbeck; Aaron Kloss; Michael Bowden; Cheryl Coon; Todd F. Hutchinson; Louis Iverson; Greg Guess

2015-01-01

We present an aerial mapping method to efficiently and effectively identify seed clusters of the invasive tree, Ailanthus altissima (Mill.) Swingle across deciduous forest landscapes in the eastern United States. We found that the ideal time to conduct aerial digital surveys is early to middle winter, when Ailanthus seed...

Foreword

Treesearch

Robert E. Keane

2011-01-01

In the mid 1980s I was asked to create a fire regime map of the Selway-Bitterroot Wilderness Area for the Bitterroot National Forest fire management staff. The well known fire historian Steve Barrett had already completed most of the work by synthesizing all available fire history results by forest habitat type, so I figured it would be easy to create a map of habitat...

Habitat mapping and interpretation in New England

Treesearch

William B. Leak

1982-01-01

Recommendations are given on the classification of forest land in New England on the basis of physiographic region, climate (elevation, latitude), mineralogy, and habitat. A habitat map for the Bartlett Experimental Forest in New Hampshire is presented based on land form, vegetation, and soil materials. For each habitat or group of habitats, data are presented on stand...

Distribution maps for Midsouth tree species

Treesearch

Roy C. Beltz; Daniel F. Bertelson

1990-01-01

The Midsouth is an important timber-producing region, with a wide variety of sites and species. In addition to timber production, increasing demands for non-timber amenities are placed on the region’s forests. These maps indicate the distribution of individual species recorded in surveys of the Midsouth conducted by the U.S. Department of Agriculture, Forest Service....

High-resolution forest carbon stocks and emissions in the Amazon

Treesearch

G. P. Asner; George V. N. Powell; Joseph Mascaro; David E. Knapp; John K. Clark; James Jacobson; Ty Kennedy-Bowdoin; Aravindh Balaji; Guayana Paez-Acosta; Eloy Victoria; Laura Secada; Michael Valqui; R. Flint. Hughes

2010-01-01

Efforts to mitigate climate change through the Reduced Emissions from Deforestation and Degradation (REDD) depend on mapping and monitoring of tropical forest carbon stocks and emissions over large geographic areas. With a new integrated use of satellite imaging, airborne light detection and ranging, and field plots, we mapped aboveground carbon stocks and emissions at...

[Carbon storage of forest stands in Shandong Province estimated by forestry inventory data].

PubMed

Li, Shi-Mei; Yang, Chuan-Qiang; Wang, Hong-Nian; Ge, Li-Qiang

2014-08-01

Based on the 7th forestry inventory data of Shandong Province, this paper estimated the carbon storage and carbon density of forest stands, and analyzed their distribution characteristics according to dominant tree species, age groups and forest category using the volume-derived biomass method and average-biomass method. In 2007, the total carbon storage of the forest stands was 25. 27 Tg, of which the coniferous forests, mixed conifer broad-leaved forests, and broad-leaved forests accounted for 8.6%, 2.0% and 89.4%, respectively. The carbon storage of forest age groups followed the sequence of young forests > middle-aged forests > mature forests > near-mature forests > over-mature forests. The carbon storage of young forests and middle-aged forests accounted for 69.3% of the total carbon storage. Timber forest, non-timber product forest and protection forests accounted for 37.1%, 36.3% and 24.8% of the total carbon storage, respectively. The average carbon density of forest stands in Shandong Province was 10.59 t x hm(-2), which was lower than the national average level. This phenomenon was attributed to the imperfect structure of forest types and age groups, i. e., the notably higher percentage of timber forests and non-timber product forest and the excessively higher percentage of young forests and middle-aged forest than mature forests.

Multi-aged Forest: an Optimal Management Strategy for Carbon Sequestration

NASA Astrophysics Data System (ADS)

Yao, L.; Tang, X.; Ma, M.

2017-12-01

Disturbances and climatic changes significantly affect forest ecosystem productivity, water use efficiency (WUE) and carbon (C) flux dynamics. A deep understanding of terrestrial feedbacks to such effects and recovery mechanisms in forests across contrasting climatic regimes is essential to predict future regional/global C and water budgets, which are also closely related to the potential forest management decisions. However, the resilience of multi-aged and even-aged forests to disturbances has been debated for more than 60 years because of technical measurement constraints. Here we evaluated 62 site-years of eddy covariance measurements of net ecosystem production (NEP), evapotranspiration (ET), the estimates of gross primary productivity (GPP), ecosystem respiration (Re) and ecosystem-level WUE, as well as the relationships with environmental controls in three chronosequences of multi- and even-aged coniferous forests covering the Mediterranean, temperate and boreal regions. Age-specific dynamics in multi-year mean annual NEP and WUE revealed that forest age is a key variable that determines the sign and magnitude of recovering forest C source-sink strength from disturbances. However, the trends of annual NEP and WUE across succession stages between two stand structures differed substantially. The successional patterns of NEP exhibited an inverted-U trend with age at the two even-aged chronosequences, whereas NEP of the multi-aged chronosequence increased steadily through time. Meanwhile, site-level WUE of even-aged forests decreased gradually from young to mature, whereas an apparent increase occurred for the same forest age in multi-aged stands. Compared with even-aged forests, multi-aged forests sequestered more CO2 with forest age and maintained a relatively higher WUE in the later succession periods. With regard to the available flux measurements in this study, these behaviors are independent of tree species, stand ages and climate conditions . We also found that distinctly different environmental factors controlled forest C and water fluxes under three climatic regimes.These findings will provide important implications for forest management strategies to mitigate global climate change.

Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China

NASA Astrophysics Data System (ADS)

Fu, W. J.; Jiang, P. K.; Zhou, G. M.; Zhao, K. L.

2014-04-01

Spatial pattern information of carbon density in forest ecosystem including forest litter carbon (FLC) plays an important role in evaluating carbon sequestration potentials. The spatial variation of FLC density in the typical subtropical forests in southeastern China was investigated using Moran's I, geostatistics and a geographical information system (GIS). A total of 839 forest litter samples were collected based on a 12 km (south-north) × 6 km (east-west) grid system in Zhejiang province. Forest litter carbon density values were very variable, ranging from 10.2 kg ha-1 to 8841.3 kg ha-1, with an average of 1786.7 kg ha-1. The aboveground biomass had the strongest positive correlation with FLC density, followed by forest age and elevation. Global Moran's I revealed that FLC density had significant positive spatial autocorrelation. Clear spatial patterns were observed using local Moran's I. A spherical model was chosen to fit the experimental semivariogram. The moderate "nugget-to-sill" (0.536) value revealed that both natural and anthropogenic factors played a key role in spatial heterogeneity of FLC density. High FLC density values were mainly distributed in northwestern and western part of Zhejiang province, which were related to adopting long-term policy of forest conservation in these areas, while Hang-Jia-Hu (HJH) Plain, Jin-Qu (JQ) Basin and coastal areas had low FLC density due to low forest coverage and intensive management of economic forests. These spatial patterns were in line with the spatial-cluster map described by local Moran's I. Therefore, Moran's I, combined with geostatistics and GIS, could be used to study spatial patterns of environmental variables related to forest ecosystem.

A Computer Program for Displaying Forest Survey Type Information

Treesearch

B. Bruce Bare; Robert N. Stone

1968-01-01

Presents a computerized procedure for displaying forest type information from inventory plots. Although the development of general forest type maps in emphasized, the program can be used to display any locational data having rectangular coordinates

Northwest Forest Plan—the first 10 years (1994-2003): status and trend of late-successional and old-growth forest.

Treesearch

Melinda Moeur; Thomas A. Spies; Miles Hemstrom; Jon R. Martin; James Alegria; Julie Browning; John Cissel; Warren B. Cohen; Thomas E. Demeo; Sean Healey; Ralph Warbington

2005-01-01

We monitored the status and trend of late-successional and old-growth forest (older forest) on 24 million ac of land managed by the Forest Service, Bureau of Land Management, and National Park Service in the Northwest Forest Plan (the Plan) area between 1994 and 2003. We developed baseline maps from satellite imagery of older forest conditions at the start of the Plan...

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.

Remote sensing applied to forest resources

NASA Technical Reports Server (NTRS)

Hernandezfilho, P. (Principal Investigator)

1984-01-01

The development of methodologies to classify reforested areas using remotely sensed data is discussed. A preliminary study was carried out in northeast of the Sao Paulo State in 1978. The reforested areas of Pinus spp and Eucalyptus spp were based on the spectral, spatial and temporal characteristics fo LANDSAT imagery. Afterwards, a more detailed study was carried out in the Mato Grosso do Sul State. The reforested areas were mapped in functions of the age (from: 0 to 1 year, 1 to 2 years, 2 to 3 years, 3 to 4 years, 4 to 5 years and 5 to 6 years) and of the heterogeneity stand (from: 0 to 20%, 20 to 40%, 40 to 60%, 60 to 80% and 80 to 100%). The relative differences between the artificial forest areas, estimated from LANDSAT data and ground information, varied from -8.72 to +9.49%. The estimation of forest volume through a multistage sampling technique, with probability proportional to size, is also discussed.

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.

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.

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.

Mapping Migratory Bird Prevalence Using Remote Sensing Data Fusion

PubMed Central

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G.; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Background Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. Methodology and Principal Findings A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy (“fusion”) models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Conclusion and Significance Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level. PMID:22235254

Mapping migratory bird prevalence using remote sensing data fusion.

PubMed

Swatantran, Anu; Dubayah, Ralph; Goetz, Scott; Hofton, Michelle; Betts, Matthew G; Sun, Mindy; Simard, Marc; Holmes, Richard

2012-01-01

Improved maps of species distributions are important for effective management of wildlife under increasing anthropogenic pressures. Recent advances in lidar and radar remote sensing have shown considerable potential for mapping forest structure and habitat characteristics across landscapes. However, their relative efficacies and integrated use in habitat mapping remain largely unexplored. We evaluated the use of lidar, radar and multispectral remote sensing data in predicting multi-year bird detections or prevalence for 8 migratory songbird species in the unfragmented temperate deciduous forests of New Hampshire, USA. A set of 104 predictor variables describing vegetation vertical structure and variability from lidar, phenology from multispectral data and backscatter properties from radar data were derived. We tested the accuracies of these variables in predicting prevalence using Random Forests regression models. All data sets showed more than 30% predictive power with radar models having the lowest and multi-sensor synergy ("fusion") models having highest accuracies. Fusion explained between 54% and 75% variance in prevalence for all the birds considered. Stem density from discrete return lidar and phenology from multispectral data were among the best predictors. Further analysis revealed different relationships between the remote sensing metrics and bird prevalence. Spatial maps of prevalence were consistent with known habitat preferences for the bird species. Our results highlight the potential of integrating multiple remote sensing data sets using machine-learning methods to improve habitat mapping. Multi-dimensional habitat structure maps such as those generated from this study can significantly advance forest management and ecological research by facilitating fine-scale studies at both stand and landscape level.

Vegetation survey in Amazonia using LANDSAT data. [Brazil

NASA Technical Reports Server (NTRS)

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

1982-01-01

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

Variations in soil N cycling and trace gas emissions in wet tropical forests.

PubMed

Holtgrieve, Gordon W; Jewett, Peter K; Matson, Pamela A

2006-01-01

We used a previously described precipitation gradient in a tropical montane ecosystem of Hawai'i to evaluate how changes in mean annual precipitation (MAP) affect the processes resulting in the loss of N via trace gases. We evaluated three Hawaiian forests ranging from 2200 to 4050 mm year-1 MAP with constant temperature, parent material, ecosystem age, and vegetation. In situ fluxes of N2O and NO, soil inorganic nitrogen pools (NH4+ and NO3-), net nitrification, and net mineralization were quantified four times over 2 years. In addition, we performed 15N-labeling experiments to partition sources of N2O between nitrification and denitrification, along with assays of nitrification potential and denitrification enzyme activity (DEA). Mean NO and N2O emissions were highest at the mesic end of the gradient (8.7+/-4.6 and 1.1+/-0.3 ng N cm-2 h-1, respectively) and total oxidized N emitted decreased with increased MAP. At the wettest site, mean trace gas fluxes were at or below detection limit (

A study of forest fire danger district division in Lushan Mountain based on RS and GIS

NASA Astrophysics Data System (ADS)

Xiao, Jinxiang; Huang, Shu-E.; Zhong, Anjian; Zhu, Biqin; Ye, Qing; Sun, Lijun

2009-09-01

The study selected 9 factors, average maximum temperature, average temperature, average precipitation, average the longest days of continuous drought and average wind speed during fire prevention period, vegetation type, altitude, slope and aspect as the index of forest fire danger district division, which has taken the features of Lushan Mountain's forest fire history into consideration, then assigned subjective weights to each factor according to their sensitivity to fire or their fire-inducing capability. By remote sensing and GIS, vegetation information layer were gotten from Landsat TM image and DEM with a scale of 1:50000 was abstracted from the digital scanned relief map. Topography info. (elevation, slope, aspect) layers could be gotten after that. A climate resource databank that contained the data from the stations of Lushan Mountain and other nearby 7 stations was built up and extrapolated through the way of grid extrapolation in order to make the distribution map of climate resource. Finally synthetical district division maps were made by weighing and integrating all the single factor special layers,and the study area were divided into three forest fire danger district, include special fire danger district, I-fire danger district and II-fire danger district. It could be used as a basis for developing a forest fire prevention system, preparing the annual investment plan, allocating reasonably the investment of fire prevention, developing the program of forest fire prevention and handle, setting up forest fire brigade, leaders' decisions on forest fire prevention work.

Current state and projection of the probable original vegetation of the São Carlos region of São Paulo State, Brazil.

PubMed

Soares, J J; da Silva, D W; Lima, M I

2003-08-01

A map of the native vegetation remaining in São Carlos County was built based on aerial images, satellite images, and field observations, and a projection of the probable original vegetation was made by checking it against soil and relief surveys. The existing vegetation is very fragmented and improverished, consisting predominantly of cerrados (savanna vegetation of various physiognomies), semideciduous and riparian forest, and regeneration areas. Araucaria angustifolia (Bertol.) Kuntze, found in patches inside the semideciduous forest beginning at a minimum altitude of 850 m, has practically disappeared. By evaluating areas on the map for different forms of vegetation, we obtained the following results for original coverage: 27% cerrado (sparsely arboreal and short-shrub savanna, and wet meadows); 16% cerradão (arboreal savanna); 55% semideciduous and riparian forests; and 2% forest with A. angustifolia. There are now 2% cerrados; 2.5% cerradão; 1% semideciduous forest and riparian forests; 1.5% regeneration areas; and 0% forest with A. angustifolia.

Identifying baldcypress-water tupelo regeneration classes in forested wetlands of the Atchafalaya Basin, Louisiana

USGS Publications Warehouse

Faulkner, Stephen P.; Bhattarai, Prajwol; Allen, Yvonne C.; Barras, John A.; Constant, Glenn C.

2009-01-01

Baldcypress-water tupelo (cypress-tupelo) swamps are critically important coastal forested wetlands found throughout the southeastern U.S. The long-term survival and sustainability of these swamp forests is unknown due to large-scale changes in hydrologic regimes that prevent natural regeneration following logging or mortality. We used NWI wetland maps and remotely sensed hydrologic data to map cypress-tupelo communities, surface water, and the extent and location of proposed regeneration condition classes for cypress-tupelo swamps in the Atchafalaya Basin, LA. Only 6,175 ha (5.8%) of the 106,227 ha of cypress-tupelo forest in the Lower Atchafalaya Basin Floodway was classified as capable of naturally regenerating. Over 23% (24,525 ha) of the forest area was mapped as unable to regenerate either naturally or artificially. The loss and conversion of nearly 25,000 ha of cypress-tupelo forest would have significant and long-lasting impacts on ecosystem services such as wildlife habitat for birds and Louisiana black bears. Given the landscape-scale changes in surface elevations and flooding depths and durations throughout southern Louisiana, similar conditions and impacts are likely applicable to all coastal cypress-tupelo forests in Louisiana. Better data on flooding during the growing season are needed to more accurately identify and refine the location and spatial extent of the regeneration condition classes.

Does increasing rotation length lead to greater forest carbon storage?

NASA Astrophysics Data System (ADS)

Ter-Mikaelian, M. T.; Colombo, S. J.; Chen, J.

2016-12-01

Forest management is a key factor affecting climate change mitigation by forests. Increasing the age of harvesting (also referred to as rotation length) is a management practice that has been proposed as a means of increasing forest carbon sequestration and storage. However, studies of the effects of increasing harvest age on forest carbon stocks have mostly been limited to forest plantations. In contrast, this study assesses the effects of increased harvest age of managed natural forests of Ontario (Canada) at two scales. At the stand level, we assess merchantable volume yield curves to differentiate those for which increasing the age of harvest results in an increase in total forest carbon stocks versus those for which increased harvest age reduces carbon stocks. The stand level results are then applied to forest landscapes to demonstrate that the effect of increasing the age of harvest on forest carbon storage is specific to the forest growth rates for a given forest landscape and depends on the average age at which forests are harvested under current (business-as-usual) management practice. We discuss the implications of these results for forest management aimed at mitigating climate change.

Evaluation of alternative approaches for landscape-scale biomass estimation in a mixed-species northern forest

Treesearch

Coeli M. Hoover; Mark J. Ducey; R. Andy Colter; Mariko Yamasaki

2018-01-01

There is growing interest in estimating and mapping biomass and carbon content of forests across large landscapes. LiDAR-based inventory methods are increasingly common and have been successfully implemented in multiple forest types. Asner et al. (2011) developed a simple universal forest carbon estimation method for tropical forests that reduces the amount of required...

The 1930s survey of forest resources in Washington and Oregon.

Treesearch

Constance A. Harrington

2003-01-01

Forest resources in Washington and Oregon were surveyed in the early 1930s by employees of the Pacific Northwest Forest Experiment Station (the original name of the current Pacific Northwest Research Station). This was the first of many periodic forest surveys conducted nationwide by the USDA Forest Service. Many publications and maps were produced from the Washington...

Invasive plants found in Georgia’s forests, 2010 - forest inventory and analysis factsheet

Treesearch

Sonja N. Oswalt; Christopher M. Oswalt

2014-01-01

This science update provides an overview of invasive plants found in forests of the State of Georgia based on an annual inventory conducted by the Forest Inventory and Analysis (FIA) Program at the Southern Research Station (SRS) of the U.S. Department of Agriculture, Forest Service in cooperation with the Georgia Forestry Commission. These estimates and coverage maps...

Invasive plants found in Oklahoma’s forests, 2010 - forest inventory and analysis factsheet

Treesearch

Sonja N. Oswalt; Christopher M. Oswalt

2014-01-01

This science update provides an overview of nonnative invasive plants found in Oklahoma forests based on an annual inventory conducted by the Forest Inventory and Analysis (FIA) Program at the Southern Research Station (SRS) of the U.S. Department of Agriculture Forest Service in cooperation with the Oklahoma Forestry Services. These estimates and coverage maps will be...

Net change in forest density, 1873-2001. Using historical maps to monitor long-term forest trends.

Treesearch

Greg C. Liknes; Mark D. Nelson; Daniel J. Kaisershot

2013-01-01

European settlement of the United States and utilization of forests are inextricably linked. Forest products fueled development, providing the building blocks for railroads, bridges, ships, and homes. Perhaps because of the importance of its forests, the United States has a rich cartographic history documenting its resources. Long-term, broad-scale monitoring efforts...

Forest resources of the United States, 2002: mapping the renewable resource planning act data

Treesearch

Cassandra M. Kurtz; Daniel J. Kaisershot; Dale D. Gormanson; Jeffery S. Wazenegger

2009-01-01

Forest Inventory and Analysis (FIA), a national program of the Forest Service, U.S. Department of Agriculture conducts and maintains comprehensive inventories of the forest resources in the United States. The Forest and Rangeland Renewable Resources Planning Act (RPA) of 1974 mandates a comprehensive assessment of past trends, current status, and the future potential...

Invasive plants found in Mississippi forests, 2009 forest inventory and analysis factsheet

Treesearch

Sonja N. Oswalt; Christopher M. Oswalt

2011-01-01

This science update provides information on nonnative invasive plants in Mississippi’s forest land based on an annual inventory conducted by the U.S. Department of Agriculture Forest Service, Forest Inventory and Analysis (FIA) program at the Southern Research Station in cooperation with the Mississippi Forestry Commission. These estimates and coverage maps will be...

Regional Assessment of Remote Forests and Black Bear Habitat from Forest Resource Surveys

Treesearch

Victor A. Rudis; John B. Tansey

1995-01-01

We developed a spatially explicit modeling approach, using a county-scaled remote forest (i.e., forested area reserved from or having no direct human interference) assessment derived from 1984-1990 forest resource inventory data and a 1984 black bear (Ursus americantus) range map for 12 states in the southern United States.We defined minimum suitable and optimal black...

Bark beetle conditions in western forests and formation of the Western Bark Beetle Research Group

Treesearch

Robert J. Cain; Jane L. Hayes

2009-01-01

The recent dramatic impacts of bark beetle outbreaks across conifer forests of the West have been mapped and reported by entomology and pathology professionals with Forest Health Protection (FHP), a component of USDA Forest Service's State and Private Forestry, and their state counterparts. These forest conditions set the stage for the formation of the Western...

Invasive plants found in Alabama forests, 2009 forest inventory and analysis factsheet

Treesearch

Christopher M. Oswalt; Sonja N. Oswalt

2012-01-01

This publication provides an overview of nonnative invasive plants found in forests of the State of Alabama based on an annual inventory conducted by the Forest Inventory and Analysis (FIA) Program at the Southern Research Station of the U.S. Department of Agriculture Forest Service in cooperation with the Alabama Forestry Commission. These estimates and coverage maps...

Use of LIDAR for forest inventory and forest management application

Treesearch

Birgit Peterson; Ralph Dubayah; Peter Hyde; Michelle Hofton; J. Bryan Blair; JoAnn Fites-Kaufman

2007-01-01

A significant impediment to forest managers has been the difficulty in obtaining large-area forest structure and fuel characteristics at useful resolutions and accuracies. This paper demonstrates how LIDAR data were used to predict canopy bulk density (CBD) and canopy base height (CBH) for an area in the Sierra National Forest. The LIDAR data were used to generate maps...

Can a Forest/Nonforest Change Map Improve the Precision of Forest Area, Volume, Growth, Removals, and Mortality Estimates?

Treesearch

Dale D. Gormanson; Mark H. Hansen; Ronald E. McRoberts

2005-01-01

In an extensive forest inventory, stratifications that use dual-date forest/nonforest classifications of Landsat Thematic Mapper data approximately 10 years apart are tested against similar classifications that use data from only one date. Alternative stratifications that further define edge strata as pixels adjacent to a forest/nonforest boundary are included in the...

Forest loss maps from regional satellite monitoring systematically underestimate deforestation in two rapidly changing parts of the Amazon

NASA Astrophysics Data System (ADS)

Milodowski, D. T.; Mitchard, E. T. A.; Williams, M.

2017-09-01

Accurate, consistent reporting of changing forest area, stratified by forest type, is required for all countries under their commitments to the Paris Agreement (UNFCCC 2015 Adoption of the Paris Agreement (Paris: UNFCCC)). Such change reporting may directly impact on payments through comparisons to national Reference (Emissions) Levels under the Reducing Emissions from Deforestation and forest Degradation (REDD+) framework. The emergence of global, satellite-based forest monitoring systems, including Global Forest Watch (GFW) and FORMA, have great potential in aiding this endeavour. However, the accuracy of these systems has been questioned and their uncertainties are poorly constrained, both in terms of the spatial extent of forest loss and timing of change. Here, using annual time series of 5 m optical imagery at two sites in the Brazilian Amazon, we demonstrate that GFW more accurately detects forest loss than the coarser-resolution FORMA or Brazil’s national-level PRODES product, though all underestimate the rate of loss. We conclude GFW provides robust indicators of forest loss, at least for larger-scale forest change, but under-predicts losses driven by small-scale disturbances (< 2 ha), even though these are much larger than its minimum mapping unit (0.09 ha).

Forest loss maps from regional satellite monitoring systematically underestimate deforestation in two rapidly changing parts of the Amazon

NASA Astrophysics Data System (ADS)

Milodowski, D. T.; Mitchard, E. T. A.; Williams, M.

2016-09-01

Accurate, consistent reporting of changing forest area, stratified by forest type, is required for all countries under their commitments to the Paris Agreement (UNFCCC 2015 Adoption of the Paris Agreement (Paris: UNFCCC)). Such change reporting may directly impact on payments through comparisons to national Reference (Emissions) Levels under the Reducing Emissions from Deforestation and forest Degradation (REDD+) framework. The emergence of global, satellite-based forest monitoring systems, including Global Forest Watch (GFW) and FORMA, have great potential in aiding this endeavour. However, the accuracy of these systems has been questioned and their uncertainties are poorly constrained, both in terms of the spatial extent of forest loss and timing of change. Here, using annual time series of 5 m optical imagery at two sites in the Brazilian Amazon, we demonstrate that GFW more accurately detects forest loss than the coarser-resolution FORMA or Brazil’s national-level PRODES product, though all underestimate the rate of loss. We conclude GFW provides robust indicators of forest loss, at least for larger-scale forest change, but under-predicts losses driven by small-scale disturbances (< 2 ha), even though these are much larger than its minimum mapping unit (0.09 ha).

Multi-Sensor Remote Sensing of Forest Dynamics in Central Siberia

NASA Technical Reports Server (NTRS)

Ransom, K. J.; Sun, G.; Kharuk, V. I.; Howl, J.

2011-01-01

The forested regions of Siberia, Russia are vast and contain about a quarter of the world's forests that have not experienced harvesting. However, many Siberian forests are facing twin pressures of rapidly changing climate and increasing timber harvest activity. Monitoring the dynamics and mapping the structural parameters of the forest is important for understanding the causes and consequences of changes observed in these areas. Because of the inaccessibility and large extent of this forest, remote sensing data can play an important role for observing forest state and change. In Central Siberia, multi-sensor remote sensing data have been used to monitor forest disturbances and to map above-ground biomass from the Sayan Mountains in the south to the taiga-tundra boundaries in the north. Radar images from the Shuttle Imaging Radar-C (SIR-C)/XSAR mission were used for forest biomass estimation in the Sayan Mountains. Radar images from the Japanese Earth Resources Satellite-1 (JERS-1), European Remote Sensing Satellite-1 (ERS-1) and Canada's RADARSAT-1, and data from ETM+ on-board Landsat-7 were used to characterize forest disturbances from logging, fire, and insect damage in Boguchany and Priangare areas.

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...

Threats to private forest lands in the U.S.A.: a forests on the edge study

Treesearch

Mark H. Hatfield; Ronald E. McRoberts; Dacia M. Meneguzzo; Sara Comas

2010-01-01

The Forests on the Edge project, sponsored by the USDA Forest Service, uses geographic information systems to construct and analyze maps depicting threats to the contributions of America’s private forest lands. For this study, watersheds across the conterminous United States are evaluated with respect to the amount of their private forest land. Watersheds with at least...

Does the precipitation redistribution of the canopy sense in the moisture pattern of the forest litter?

NASA Astrophysics Data System (ADS)

Zagyvai-Kiss, Katalin Anita; Kalicz, Péter; Csáfordi, Péter; Kucsara, Mihály; Gribovszki, Zoltán

2013-04-01

Precipitation is trapped and temporarily stored by the surfaces of forest crown (canopy interception) and forest litter (litter interception). The stemflow and throughfall reach the litter, thus theoretically the litter moisture content depends on these parts of precipitation. Nowadays the moisture pattern of the forest floor, both spatial and temporal scale, have growing respect for the forestry. The transition to the continuous cover forestry induce much higher variability compared to the even aged, more-less homogeneous, monocultural stands. The gap cutting is one of the key methods in the Hungarian forestry. There is an active discussion among the forest professionals how to determine the optimal gap size to maintain the optimal conditions for the seedlings. Among the open questions is how to modify surrounding trees the moisture pattern of the forest floor in the gap? In the early steps of a multidisciplinary project we processed some available data, to estimate the spatial dependency between the water content of forest litter and the spatial pattern of the canopy represented by the tree trunk. The maximum water content depends on dry weight of litter, thus we also analysed that parameter. Data were measured in three different forest ecosystems: a middle age beech (Fagus sylvatica), a sessile oak (Quercus petraea) and a spruce (Picea abies) stand. The study site (Hidegvíz Valley Research Cathcment) is located in Sopron Hills at the eastern border of the Alps. Litter samples were collected under each stand (occasionally 10-10 pieces from 40?40 cm area) and locations of the samples and neighbouring trees were mapped. We determined dry weight and the water content of litter in laboratory. The relationship between water content and the distance of tree trunks in case of spruce and oak stands were not significant and in case of the beech stand was weakly significant. Climate change effects can influence significantly forest floor moisture content, therefore this factor has also taken into account. Acknowledgement: The research was financially supported by the TÁMOP-4.2.2.A-11/1/KONV-2012-0004 and TÁMOP-4.2.2.A-11/1/KONV-2012-0013 joint EU-national research projects.

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.

Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae).

PubMed

Riley, Kathryn N; Browne, Robert A

2011-01-01

We examined diversity, community composition, and wing-state of Carabidae as a function of forest age in Piedmont North Carolina. Carabidae were collected monthly from 396 pitfall traps (12×33 sites) from March 2009 through February 2010, representing 5 forest age classes approximately 0, 10, 50, 85, and 150 years old. A total of 2,568 individuals, representing 30 genera and 63 species, were collected. Carabid species diversity, as estimated by six diversity indices, was significantly different between the oldest and youngest forest age classes for four of the six indices. Most carabid species were habitat generalists, occurring in all or most of the forest age classes. Carabid species composition varied across forest age classes. Seventeen carabid species were identified as potential candidates for ecological indicators of forest age. Non-metric multidimensional scaling (NMDS) showed separation among forest age classes in terms of carabid beetle community composition. The proportion of individuals capable of flight decreased significantly with forest age.

Changes in ground beetle diversity and community composition in age structured forests (Coleoptera, Carabidae)

PubMed Central

Riley, Kathryn N.; Browne, Robert A.

2011-01-01

Abstract We examined diversity, community composition, and wing-state of Carabidae as a function of forest age in Piedmont North Carolina. Carabidae were collected monthly from 396 pitfall traps (12×33 sites) from March 2009 through February 2010, representing 5 forest age classes approximately 0, 10, 50, 85, and 150 years old. A total of 2,568 individuals, representing 30 genera and 63 species, were collected. Carabid species diversity, as estimated by six diversity indices, was significantly different between the oldest and youngest forest age classes for four of the six indices. Most carabid species were habitat generalists, occurring in all or most of the forest age classes. Carabid species composition varied across forest age classes. Seventeen carabid species were identified as potential candidates for ecological indicators of forest age. Non-metric multidimensional scaling (NMDS) showed separation among forest age classes in terms of carabid beetle community composition. The proportion of individuals capable of flight decreased significantly with forest age. PMID:22371677

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.

How do disturbances and climate effects on carbon and water fluxes differ between multi-aged and even-aged coniferous forests?

PubMed

Tang, Xuguang; Li, Hengpeng; Ma, Mingguo; Yao, Li; Peichl, Matthias; Arain, Altaf; Xu, Xibao; Goulden, Michael

2017-12-01

Disturbances and climatic changes significantly affect forest ecosystem productivity, water use efficiency (WUE) and carbon (C) flux dynamics. A deep understanding of terrestrial feedbacks to such effects and recovery mechanisms in forests across contrasting climatic regimes is essential to predict future regional/global C and water budgets, which are also closely related to the potential forest management decisions. However, the resilience of multi-aged and even-aged forests to disturbances has been debated for >60years because of technical measurement constraints. Here we evaluated 62site-years of eddy covariance measurements of net ecosystem production (NEP), evapotranspiration (ET), the estimates of gross primary productivity (GPP), ecosystem respiration (R e ) and ecosystem-level WUE, as well as the relationships with environmental controls in three chronosequences of multi- and even-aged coniferous forests covering the Mediterranean, temperate and boreal regions. Age-specific dynamics in multi-year mean annual NEP and WUE revealed that forest age is a key variable that determines the sign and magnitude of recovering forest C source-sink strength from disturbances. However, the trends of annual NEP and WUE across succession stages between two stand structures differed substantially. The successional patterns of NEP exhibited an inverted-U trend with age at the two even-aged chronosequences, whereas NEP of the multi-aged chronosequence increased steadily through time. Meanwhile, site-level WUE of even-aged forests decreased gradually from young to mature, whereas an apparent increase occurred for the same forest age in multi-aged stands. Compared with even-aged forests, multi-aged forests sequestered more CO 2 with forest age and maintained a relatively higher WUE in the later succession periods. With regard to the available flux measurements in this study, these behaviors are independent of tree species, stand ages and climate conditions. We also found that distinctly different environmental factors controlled forest C and water fluxes under three climatic regimes. Typical weather events such as temperature anomalies or drying-wetting cycles severely affected forest functions. Particularly, a summer drought in the boreal forest resulted in an increased NEP owing to a considerable decrease in R e , but at the cost of greater water loss from deeper groundwater resources. These findings will provide important implications for forest management strategies to mitigate global climate change. Copyright © 2017 Elsevier B.V. All rights reserved.

Quantifying and mapping spatial variability in simulated forest plots

Treesearch

Gavin R. Corral; Harold E. Burkhart

2016-01-01

We used computer simulations to test the efficacy of multivariate statistical methods to detect, quantify, and map spatial variability of forest stands. Simulated stands were developed of regularly-spaced plantations of loblolly pine (Pinus taeda L.). We assumed no affects of competition or mortality, but random variability was added to individual tree characteristics...

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

Treesearch

Janet L. Ohmann

2003-01-01

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

Mapping host-species abundance of three major exotic forest pests

Treesearch

Randall S. Morin; Andrew M. Liebhold; Eugene R. Luzader; Andrew J. Lister; Kurt W. Gottschalk; Daniel B. Twardus

2005-01-01

Periodically over the last century, forests of the Eastern United States devastated by invasive pests. We used existing data to predict the geographical extent of future damage from beech bark disease (BBD), hemlock woolly adelgid (HWA), and gypsy moth. The distributions of host species of these alien pests were mapped in 1-km2 cells by interpolating host basal area/ha...

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),...

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...

Comparison of Programs Used for FIA Inventory Information Dissemination and Spatial Representation

Treesearch

Roger C. Lowe; Chris J. Cieszewski

2005-01-01

Six online applications developed for the interactive display of Forest Inventory and Analysis (FIA) data in which FIA database information and query results can be viewed as or selected from interactive geographic maps are compared. The programs evaluated are the U.S. Department of Agriculture Forest Service?s online systems; a SAS server-based mapping system...

Hand pose estimation in depth image using CNN and random forest

NASA Astrophysics Data System (ADS)

Chen, Xi; Cao, Zhiguo; Xiao, Yang; Fang, Zhiwen

2018-03-01

Thanks to the availability of low cost depth cameras, like Microsoft Kinect, 3D hand pose estimation attracted special research attention in these years. Due to the large variations in hand`s viewpoint and the high dimension of hand motion, 3D hand pose estimation is still challenging. In this paper we propose a two-stage framework which joint with CNN and Random Forest to boost the performance of hand pose estimation. First, we use a standard Convolutional Neural Network (CNN) to regress the hand joints` locations. Second, using a Random Forest to refine the joints from the first stage. In the second stage, we propose a pyramid feature which merges the information flow of the CNN. Specifically, we get the rough joints` location from first stage, then rotate the convolutional feature maps (and image). After this, for each joint, we map its location to each feature map (and image) firstly, then crop features at each feature map (and image) around its location, put extracted features to Random Forest to refine at last. Experimentally, we evaluate our proposed method on ICVL dataset and get the mean error about 11mm, our method is also real-time on a desktop.

Effects of stand age on the demography of a temperate forest herb in post-agricultural forests.

PubMed

Jacquemyn, Hans; Brys, Rein

2008-12-01

Changes in land use have been shown to have profound effects on forest plant community structure and diversity. Dispersal limitation has been invoked as a major factor hampering colonization of forest plant species, while seed-sowing experiments and performance observations have provided some evidence for recruitment limitation determining forest plant distribution in post-agricultural forests. However, most of these studies were relatively short-term, and very few studies have investigated long-term growth rates of populations occurring in recent and ancient forests. In this study, matrix models using demographic data collected for four consecutive years were used to study the effect of forest age on population dynamics of the temperate forest herb Primula elatior. A life table response experiment (LTRE) and elasticity analysis were used to analyze the effect of forest age on population growth rate (lambda) and to decompose the effect of forest age on lambda into contributions from each matrix element. Population growth increased logarithmically with increasing forest age. Bootstrap analyses showed that populations located in very recent forests (< 50-years-old) had growth rates that were significantly < 1, whereas populations located in forests > 150-years-old had growth rates that were significantly > 1. Summed elasticities for individual growth significantly decreased with increasing forest age, whereas summed elasticities for survival and fertility significantly increased with increasing forest age. The LTRE analysis showed that the increase in lambda with increasing forest age was mainly due to increased seedling and juvenile growth and increased juvenile and adult survival. Our results indicate that past agricultural land use has long-lasting effects on the demography of forest herbs and may provide an additional mechanistic explanation for the poor colonization capacity of many forest herbs in post-agricultural forests.

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 growing stock volume and forest live biomass: a case study of the Polissya region of Ukraine

NASA Astrophysics Data System (ADS)

Bilous, Andrii; Myroniuk, Viktor; Holiaka, Dmytrii; Bilous, Svitlana; See, Linda; Schepaschenko, Dmitry

2017-10-01

Forest inventory and biomass mapping are important tasks that require inputs from multiple data sources. In this paper we implement two methods for the Ukrainian region of Polissya: random forest (RF) for tree species prediction and k-nearest neighbors (k-NN) for growing stock volume and biomass mapping. We examined the suitability of the five-band RapidEye satellite image to predict the distribution of six tree species. The accuracy of RF is quite high: ~99% for forest/non-forest mask and 89% for tree species prediction. Our results demonstrate that inclusion of elevation as a predictor variable in the RF model improved the performance of tree species classification. We evaluated different distance metrics for the k-NN method, including Euclidean or Mahalanobis distance, most similar neighbor (MSN), gradient nearest neighbor, and independent component analysis. The MSN with the four nearest neighbors (k = 4) is the most precise (according to the root-mean-square deviation) for predicting forest attributes across the study area. The k-NN method allowed us to estimate growing stock volume with an accuracy of 3 m3 ha-1 and for live biomass of about 2 t ha-1 over the study area.

Monitoring of wildfires in boreal forests using large area AVHRR NDVI composite image data

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

Kasischke, E.S.; French, N.H.F.; Harrell, P.

1993-06-01

Normalized difference vegetation index (NDVI) composite image data, produced from AVHRR data collected in 1990, were evaluated for locating and mapping the areal extent of wildfires in the boreal forests of Alaska during that year. A technique was developed to map forest fire boundaries by subtracting a late-summer AVHRR NDVI image from an early summer scene. The locations and boundaries of wildfires within the interior region of Alaska were obtained from the Alaska Fire Service, and compared to the AVHRR-derived fire-boundary map. It was found that AVHRR detected 89.5% of all fires with sizes greater than 2,000ha with no falsemore » alarms and that, for most cases, the general shape of the fire boundary detected by AVHRR matched those mapped by field observers. However, the total area contained within the fire boundaries mapped by AVHRR were only 61% of those mapped by the field observers. However, the AVHRR data used in this study did not span the entire time period during which fires occurred, and it is believed the areal estimates could be improved significantly if an expanded AVHRR data set were used.« less

Monitoring of wildfires in boreal forests using large area AVHRR NDVI composite image data

NASA Technical Reports Server (NTRS)

Kasischke, Eric S.; French, Nancy H. F.; Harrell, Peter; Christensen, Norman L., Jr.; Ustin, Susan L.; Barry, Donald

1993-01-01

Normalized difference vegetation index (NDVI) composite image data, produced from AVHRR data collected in 1990, were evaluated for locating and mapping the areal extent of wildfires in the boreal forests of Alaska during that year. A technique was developed to map forest fire boundaries by subtracting a late-summer AVHRR NDVI image from an early summer scene. The locations and boundaries of wildfires within the interior region of Alaska were obtained from the Alaska Fire Service, and compared to the AVHRR-derived fire-boundary map. It was found that AVHRR detected 89.5 percent of all fires with sizes greater than 2000 ha with no false alarms and that, for most cases, the general shape of the fire boundary detected by AVHRR matched those mapped by field observers. However, the total area contained within the fire boundaries mapped by AVHRR were only 61 percent of those mapped by the field observers. However, the AVHRR data used in this study did not span the entire time period during which fires occurred, and it is believed the areal estimates could be improved significantly if an expanded AVHRR data set were used.

Mega-fire Recovery in Dry Conifer Forests of the Interior West

NASA Astrophysics Data System (ADS)

Malone, S. L.; Fornwalt, P.; Chambers, M. E.; Battaglia, M.

2015-12-01

Wildfire is a complex landscape process with great uncertainty in whether trends in size and severity are shifting trajectories for ecosystem recovery that are outside of the historical range of variability. Considering that wildfire size and severity is likely to increase into the future with a drier climate, it is important that we understand wildfire effects and ecosystem recovery. To evaluate how ecosystems recover from wildfire we measured spatial patterns in regeneration and mapped tree refugia within mega-fire perimeters (Hayman, Jasper, Bobcat, and Grizzly Gulch) in ponderosa pine (Pinus ponderosa) dominated forest. On average, high severity fire effects accounted for > 15% of burned area and increased with fire size. Areas with high severity fire effects contained 1 - 15% tree refugia cover, compared to 37 - 70% observed in low severity areas . Large high severity patches with low coverage of tree refugia, were more frequent in larger fires and regeneration distances required to initiate forest recovery far exceeded 1.5 canopy height or 200 m, distances where the vast majority of regeneration is likely to arise. Using a recovery model driven by distance, we estimate recovery times between 300 to > 1000 years for these mega-fires. In Western dry conifer forests, large patches of stand replacing fire are likely to lead to uneven aged forest and very long recovery times.

Optical remote sensing for forest area estimation

Treesearch

Randolph H. Wynne; Richard G. Oderwald; Gregory A. Reams; John A. Scrivani

2000-01-01

The air photo dot-count method is now widely and successfully used for estimating operational forest area in the USDA Forest Inventory and Analysis (FIA) program. Possible alternatives that would provide for more frequent updates, spectral change detection, and maps of forest area include the AVHRR calibration center technique and various Landsat TM classification...

A framework for identifying carbon hotspots and forest management drivers

Treesearch

Nilesh Timilsina; Francisco J. Escobedo; Wendell P. Cropper; Amr Abd-Elrahman; Thomas Brandeis; Sonia Delphin; Samuel Lambert

2013-01-01

Spatial analyses of ecosystem system services that are directly relevant to both forest management decision making and conservation in the subtropics are rare. Also, frameworks that identify and map carbon stocks and corresponding forest management drivers using available regional, national, and international-level forest inventory datasets could provide insights into...

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...

Mapping and imputing potential productivity of Pacific Northwest forests using climate variables

Treesearch

Gregory Latta; Hailemariam Temesgen; Tara Barrett

2009-01-01

Regional estimation of potential forest productivity is important to diverse applications, including biofuels supply, carbon sequestration, and projections of forest growth. Using PRISM (Parameter-elevation Regressions on Independent Slopes Model) climate and productivity data measured on a grid of 3356 Forest Inventory and Analysis plots in Oregon and Washington, we...

Vegetation mapping as a guide to better silviculture

Treesearch

Marinus. Westveld

1951-01-01

Difficult and challenging problems confront the forester today. Disastrous floods, critical water shortages, and the uninterrupted trend of forest depletion have focused attention on the important role forests play in the strength, economy, and prosperity of the Nation. Forests are increasingly prized for recreation, the protection they afford sources of water, and...

Water availability predicts forest canopy height at the global scale.

PubMed

Klein, Tamir; Randin, Christophe; Körner, Christian

2015-12-01

The tendency of trees to grow taller with increasing water availability is common knowledge. Yet a robust, universal relationship between the spatial distribution of water availability and forest canopy height (H) is lacking. Here, we created a global water availability map by calculating an annual budget as the difference between precipitation (P) and potential evapotranspiration (PET) at a 1-km spatial resolution, and in turn correlated it with a global H map of the same resolution. Across forested areas over the globe, Hmean increased with P-PET, roughly: Hmean (m) = 19.3 + 0.077*(P-PET). Maximum forest canopy height also increased gradually from ~ 5 to ~ 50 m, saturating at ~ 45 m for P-PET > 500 mm. Forests were far from their maximum height potential in cold, boreal regions and in disturbed areas. The strong association between forest height and P-PET provides a useful tool when studying future forest dynamics under climate change, and in quantifying anthropogenic forest disturbance. © 2015 John Wiley & Sons Ltd/CNRS.

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).

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).

Evaluating horizontal positional accuracy of low-cost UAV orthomosaics over forest terrain using ground control points extracted from different sources

NASA Astrophysics Data System (ADS)

Patias, Petros; Giagkas, Fotis; Georgiadis, Charalampos; Mallinis, Giorgos; Kaimaris, Dimitris; Tsioukas, Vassileios

2017-09-01

Within the field of forestry, forest road mapping and inventory plays an important role in management activities related to wood harvesting industry, sentiment and water run-off modelling, biodiversity distribution and ecological connectivity, recreation activities, future planning of forest road networks and wildfire protection and fire-fighting. Especially in countries of the Mediterranean Rim, knowledge at regional and national scales regarding the distribution and the characteristics of rural and forest road network is essential in order to ensure an effective emergency management and rapid response of the fire-fighting mechanism. Yet, the absence of accurate and updated geodatabases and the drawbacks related to the use of traditional cartographic methods arising from the forest environment settings, and the cost and efforts needed, as thousands of meters need to be surveyed per site, trigger the need for new data sources and innovative mapping approaches. Monitoring the condition of unpaved forest roads with unmanned aerial vehicle technology is an attractive option for substituting objective, laboursome surveys. Although photogrammetric processing of UAV imagery can achieve accuracy of 1-2 centimeters and dense point clouds, the process is commonly based on the establishment of control points. In the case of forest road networks, which are linear features, there is a need for a great number of control points. Our aim is to evaluate low-cost UAV orthoimages generated over forest areas with GCP's captured from existing national scale aerial orthoimagery, satellite imagery available through a web mapping service (WMS), field surveys using Mobile Mapping System and GNSS receiver. We also explored the direct georeferencing potential through the GNSS onboard the low cost UAV. The results suggest that the GNSS approach proved to most accurate, while the positional accuracy derived using the WMS and the aerial orthoimagery datasets deemed satisfactory for the specific task at hand. The direct georeferencing procedure seems to be insufficient unless an onboard GNSS with improved specifications or Real-Time Kinematic (RTK) capabilities is used.

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.

PDF Weaving - Linking Inventory Data and Monte Carlo Uncertainty Analysis in the Study of how Disturbance Affects Forest Carbon Storage

NASA Astrophysics Data System (ADS)

Healey, S. P.; Patterson, P.; Garrard, C.

2014-12-01

Altered disturbance regimes are likely a primary mechanism by which a changing climate will affect storage of carbon in forested ecosystems. Accordingly, the National Forest System (NFS) has been mandated to assess the role of disturbance (harvests, fires, insects, etc.) on carbon storage in each of its planning units. We have developed a process which combines 1990-era maps of forest structure and composition with high-quality maps of subsequent disturbance type and magnitude to track the impact of disturbance on carbon storage. This process, called the Forest Carbon Management Framework (ForCaMF), uses the maps to apply empirically calibrated carbon dynamics built into a widely used management tool, the Forest Vegetation Simulator (FVS). While ForCaMF offers locally specific insights into the effect of historical or hypothetical disturbance trends on carbon storage, its dependence upon the interaction of several maps and a carbon model poses a complex challenge in terms of tracking uncertainty. Monte Carlo analysis is an attractive option for tracking the combined effects of error in several constituent inputs as they impact overall uncertainty. Monte Carlo methods iteratively simulate alternative values for each input and quantify how much outputs vary as a result. Variation of each input is controlled by a Probability Density Function (PDF). We introduce a technique called "PDF Weaving," which constructs PDFs that ensure that simulated uncertainty precisely aligns with uncertainty estimates that can be derived from inventory data. This hard link with inventory data (derived in this case from FIA - the US Forest Service Forest Inventory and Analysis program) both provides empirical calibration and establishes consistency with other types of assessments (e.g., habitat and water) for which NFS depends upon FIA data. Results from the NFS Northern Region will be used to illustrate PDF weaving and insights gained from ForCaMF about the role of disturbance in carbon storage.

Estimating Forest Canopy Heights and Aboveground Biomass with Simulated ICESat-2 Data

NASA Astrophysics Data System (ADS)

Malambo, L.; Narine, L.; Popescu, S. C.; Neuenschwander, A. L.; Sheridan, R.

2016-12-01

The Ice, Cloud and Land Elevation Satellite (ICESat) 2 is scheduled for launch in 2017 and one of its overall science objectives will be to measure vegetation heights, which can be used to estimate and monitor aboveground biomass (AGB) over large spatial scales. This study serves to develop a methodology for utilizing vegetation data collected by ICESat-2 that will be on a five-year mission from 2017, for mapping forest canopy heights and estimating aboveground forest biomass (AGB). The specific objectives are to, (1) simulate ICESat-2 photon-counting lidar (PCL) data, (2) utilize simulated PCL data to estimate forest canopy heights and propose a methodology for upscaling PCL height measurements to obtain spatially contiguous coverage and, (3) estimate and map AGB using simulated PCL data. The laser pulse from ICESat-2 will be divided into three pairs of beams spaced approximately 3 km apart, with footprints measuring approximately 14 m in diameter and with 70 cm along-track intervals. Using existing airborne lidar data (ALS) for Sam Houston National Forest (SHNF) and known ICESat-2 beam locations, footprints are generated along beam locations and PCL data are then simulated from discrete return lidar points within each footprint. By applying data processing algorithms, photons are classified into top of canopy points and ground surface elevation points to yield tree canopy height values within each ICESat-2 footprint. AGB is then estimated using simple linear regression that utilizes AGB from a biomass map generated with ALS data for SHNF and simulated PCL height metrics for 100 m segments along ICESat-2 tracks. Two approaches also investigated for upscaling AGB estimates to provide wall-to-wall coverage of AGB are (1) co-kriging and (2) Random Forest. Height and AGB maps, which are the outcomes of this study, will demonstrate how data acquired by ICESat-2 can be used to measure forest parameters and in extension, estimate forest carbon for climate change initiatives.

National-scale aboveground biomass geostatistical mapping with FIA inventory and GLAS data: Preparation for sparsely sampled lidar assisted forest inventory

NASA Astrophysics Data System (ADS)

Babcock, C. R.; Finley, A. O.; Andersen, H. E.; Moskal, L. M.; Morton, D. C.; Cook, B.; Nelson, R.

2017-12-01

Upcoming satellite lidar missions, such as GEDI and IceSat-2, are designed to collect laser altimetry data from space for narrow bands along orbital tracts. As a result lidar metric sets derived from these sources will not be of complete spatial coverage. This lack of complete coverage, or sparsity, means traditional regression approaches that consider lidar metrics as explanatory variables (without error) cannot be used to generate wall-to-wall maps of forest inventory variables. We implement a coregionalization framework to jointly model sparsely sampled lidar information and point-referenced forest variable measurements to create wall-to-wall maps with full probabilistic uncertainty quantification of all inputs. We inform the model with USFS Forest Inventory and Analysis (FIA) in-situ forest measurements and GLAS lidar data to spatially predict aboveground forest biomass (AGB) across the contiguous US. We cast our model within a Bayesian hierarchical framework to better model complex space-varying correlation structures among the lidar metrics and FIA data, which yields improved prediction and uncertainty assessment. To circumvent computational difficulties that arise when fitting complex geostatistical models to massive datasets, we use a Nearest Neighbor Gaussian process (NNGP) prior. Results indicate that a coregionalization modeling approach to leveraging sampled lidar data to improve AGB estimation is effective. Further, fitting the coregionalization model within a Bayesian mode of inference allows for AGB quantification across scales ranging from individual pixel estimates of AGB density to total AGB for the continental US with uncertainty. The coregionalization framework examined here is directly applicable to future spaceborne lidar acquisitions from GEDI and IceSat-2. Pairing these lidar sources with the extensive FIA forest monitoring plot network using a joint prediction framework, such as the coregionalization model explored here, offers the potential to improve forest AGB accounting certainty and provide maps for post-model fitting analysis of the spatial distribution of AGB.

Estimates of Forest Biomass Carbon Storage in Liaoning Province of Northeast China: A Review and Assessment

PubMed Central

Yu, Dapao; Wang, Xiaoyu; Yin, You; Zhan, Jinyu; Lewis, Bernard J.; Tian, Jie; Bao, Ye; Zhou, Wangming; Zhou, Li; Dai, Limin

2014-01-01

Accurate estimates of forest carbon storage and changes in storage capacity are critical for scientific assessment of the effects of forest management on the role of forests as carbon sinks. Up to now, several studies reported forest biomass carbon (FBC) in Liaoning Province based on data from China's Continuous Forest Inventory, however, their accuracy were still not known. This study compared estimates of FBC in Liaoning Province derived from different methods. We found substantial variation in estimates of FBC storage for young and middle-age forests. For provincial forests with high proportions in these age classes, the continuous biomass expansion factor method (CBM) by forest type with age class is more accurate and therefore more appropriate for estimating forest biomass. Based on the above approach designed for this study, forests in Liaoning Province were found to be a carbon sink, with carbon stocks increasing from 63.0 TgC in 1980 to 120.9 TgC in 2010, reflecting an annual increase of 1.9 TgC. The average carbon density of forest biomass in the province has increased from 26.2 Mg ha−1 in 1980 to 31.0 Mg ha−1 in 2010. While the largest FBC occurred in middle-age forests, the average carbon density decreased in this age class during these three decades. The increase in forest carbon density resulted primarily from the increased area and carbon storage of mature forests. The relatively long age interval in each age class for slow-growing forest types increased the uncertainty of FBC estimates by CBM-forest type with age class, and further studies should devote more attention to the time span of age classes in establishing biomass expansion factors for use in CBM calculations. PMID:24586881

Estimates of forest biomass carbon storage inLiaoning Province of Northeast China: a review and assessment.

PubMed

Yu, Dapao; Wang, Xiaoyu; Yin, You; Zhan, Jinyu; Lewis, Bernard J; Tian, Jie; Bao, Ye; Zhou, Wangming; Zhou, Li; Dai, Limin

2014-01-01

Accurate estimates of forest carbon storage and changes in storage capacity are critical for scientific assessment of the effects of forest management on the role of forests as carbon sinks. Up to now, several studies reported forest biomass carbon (FBC) in Liaoning Province based on data from China's Continuous Forest Inventory, however, their accuracy were still not known. This study compared estimates of FBC in Liaoning Province derived from different methods. We found substantial variation in estimates of FBC storage for young and middle-age forests. For provincial forests with high proportions in these age classes, the continuous biomass expansion factor method (CBM) by forest type with age class is more accurate and therefore more appropriate for estimating forest biomass. Based on the above approach designed for this study, forests in Liaoning Province were found to be a carbon sink, with carbon stocks increasing from 63.0 TgC in 1980 to 120.9 TgC in 2010, reflecting an annual increase of 1.9 TgC. The average carbon density of forest biomass in the province has increased from 26.2 Mg ha(-1) in 1980 to 31.0 Mg ha(-1) in 2010. While the largest FBC occurred in middle-age forests, the average carbon density decreased in this age class during these three decades. The increase in forest carbon density resulted primarily from the increased area and carbon storage of mature forests. The relatively long age interval in each age class for slow-growing forest types increased the uncertainty of FBC estimates by CBM-forest type with age class, and further studies should devote more attention to the time span of age classes in establishing biomass expansion factors for use in CBM calculations.

LOCALIZING NATIONAL FRAGMENTATION STATISTICS WITH FOREST TYPE MAPS

EPA Science Inventory

Fragmmentation of forest types is an indicator of biodiversity in the Montreal Process, but the available national data permit assessment of only overall forestland fragmentation, not forest type fragmentation. Here we illustrate how to localize national statistics from the 2003...

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.

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.

Identification of understory invasive exotic plants with remote sensing in urban forests

NASA Astrophysics Data System (ADS)

Shouse, Michael; Liang, Liang; Fei, Songlin

2013-04-01

Invasive exotic plants (IEP) pose a significant threat to many ecosystems. To effectively manage IEP, it is important to efficiently detect their presences and determine their distribution patterns. Remote sensing has been a useful tool to map IEP but its application is limited in urban forests, which are often the sources and sinks for IEP. In this study, we examined the feasibility and tradeoffs of species level IEP mapping using multiple remote sensing techniques in a highly complex urban forest setting. Bush honeysuckle (Lonicera maackii), a pervasive IEP in eastern North America, was used as our modeling species. Both medium spatial resolution (MSR) and high spatial resolution (HSR) imagery were employed in bush honeysuckle mapping. The importance of spatial scale was also examined using an up-scaling simulation from the HSR object based classification. Analysis using both MSR and HSR imagery provided viable results for IEP distribution mapping in urban forests. Overall mapping accuracy ranged from 89.8% to 94.9% for HSR techniques and from 74.6% to 79.7% for MSR techniques. As anticipated, classification accuracy reduces as pixel size increases. HSR based techniques produced the most desirable results, therefore is preferred for precise management of IEP in heterogeneous environment. However, the use of MSR techniques should not be ruled out given their wide availability and moderate accuracy.

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...

Seeing the forest for the trees: utilizing modified random forests imputation of forest plot data for landscape-level analyses

Treesearch

Karin L. Riley; Isaac C. Grenfell; Mark A. Finney

2015-01-01

Mapping the number, size, and species of trees in forests across the western United States has utility for a number of research endeavors, ranging from estimation of terrestrial carbon resources to tree mortality following wildfires. For landscape fire and forest simulations that use the Forest Vegetation Simulator (FVS), a tree-level dataset, or “tree list”, is a...

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...

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.

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.

Assessing the resources and mitigation potential of European forests

NASA Astrophysics Data System (ADS)

Hasenauer, Hubert; Neumann, Mathias; Moreno, Adam; Running, Steve

2017-04-01

Today 40 % of the European land area is covered with forests managed for the provision of ecosystem services including timber production. Forests store large amounts of carbon and are the main resource for the growing demand of a bio-based economy. They are also a major source for biodiversity. Thus a consistent pan-European gridded data set on the state of forest resources is essential for researchers, policy makers and conservationists to study and understand the European forests for the global carbon cycle independent of political boundaries. The purpose of this study is to use existing European data to develop a consistent pan-European data set for Net Primary Production (NPP), live tree carbon per hectare, volume per hectare, mean tree height and mean tree age by integrating remotely sensed satellite data and harmonized NFI data from 13 different European countries. We provide new NPP estimates using the MOD17 algorithm by collating a newly down-scaled daily climate dataset across Europe. By consolidating these two independent productivity data sources (top down satellite versus bottom up terrestrial forest NFI data) for assessing forest resources in Europe, we are able to detect and quantify forest management impacts. We produce a pan-European map for each of the five key variables on a 0.133° grid representing the time period 2000-2010. The results show distinct differences in the carbon storage of European forests due to biophysical limits and regional historic drivers in forest management, which directly affect the carbon mitigation option of European forests. We use this data to assess the state of forest resources across Europe showing that mountainous regions have the highest carbon and volume per hectare values, central Europe has the tallest mean tree heights and Austria and Northern Scandinavia have the oldest mean tree ages. Cross-validation of the data indicates that the error varies by forest characteristic but shows negligible biases for all. We also use this data to examine climate limitations on potential forest structure, relevant for assessing potential timber assortments or suitability as wildlife habitat. The results suggest: (i) Boreal forests are limited by minimum temperature, the Mediterranean forests by maximum temperature and temperate forests by both temperature and precipitation. As a result of changing climate during the last 50 years, the potential average diameter at breast height, which can be achieved in Europe, has decreased by 5.0 %. A similar result is evident for the potential basal area per hectare with a decrease of 6.5 %. (ii) European forests exhibit an annual average carbon uptake of 577 gC/m2/year, which can be considered as the carbon sequestration potential and/or available resource for the increasing demand of a growing bio-economy.

Diverse responses of different structured forest to drought in Southwest China through remotely sensed data

NASA Astrophysics Data System (ADS)

Xu, Peipei; Zhou, Tao; Zhao, Xiang; Luo, Hui; Gao, Shan; Li, Zheng; Cao, Leyao

2018-07-01

Global climate change leads to gradual increases in the frequency, intensity, and duration of extreme drought events. Human activities such as afforestation and deforestation have led to spatial variation in forest structure, causing forests to exhibit an age-spatial structure relationship. Thus, it is of great importance to accurately evaluate the effects of drought stress on forest ecosystems with different forest age structures. Because the spatial heterogeneity varies with drought stress intensity, forest age, there are still a lot of uncertainties in current studies. In this study, based on the field measurement, and the proxy index of stand age (based on forest canopy height from LiDAR and stock volume from inventory) at the regional scale, we analyzed the different drought responses of forest ecosystems with various forest ages across different scales in Yunnan province, southwest China from 2001 to 2014. At the local scale, significant differences in the effects of drought stress were found among forests with various ages, suggesting that older forests suffer more under drought stress than younger forests. At the regional scale, the investigation statistics of forest damage indicated a maximum damage ratio in the forest with tall trees (>32 m), whereas damage was minimal in the forest with short trees (<25 m). The stock volume of the forest exhibited the same pattern, that is, the forest damage ratio increased as the stock volume increased. These data demonstrate that the responses of forest drought could be affected by forest age. Under drought stress, older forests show greater vulnerability and risk of damage, which will require special attention for forest managers, as well as improved risk assessments, in the context of future climate change.

Climate change induced effects on the predisposition of forests of the water protection zone Wildalpen to disturbances by bark beetles

NASA Astrophysics Data System (ADS)

Baier, P.; Pennerstorfer, J.; Schopf, A.

2012-04-01

The provision of drinking water of high quality is a precious service of forests. Large-scale disturbances like forest fires, wind throws, pest outbreaks and subsequent clear cutting may lead to changes in hydrology (runoff as well as percolation). Furthermore, water quality can be negatively influenced by increased erosion, increased decomposition of litter and humus and leaching of nitrate. Large-scale epidemics of forest pests may induce forest decline at landscape scale with subsequent long-lasting negative effects on water quality. The European spruce bark beetle, Ips typographus (L.), is one of the most significant sources of mortality in mature spruce forest ecosystems in Eurasia. The objective of this study was to apply a complex predisposition assessment system for hazard rating and for the evaluation of climate change impacts for the water protection forests of the City of Vienna in Wildalpen. The following steps have been done to adapt/apply the bark beetle phenology model and the hazard rating system: -application, adaptation and validation of the bark beetle phenology model PHENIPS concerning start of dispersion, brood initiation, duration of development, beginning of sister broods, voltinism and hibernation - spatial/temporal modelling of the phenology and voltinism of I. typographus using past, present as well as projected climate data - application and validation of the stand- and site related long-term predisposition assessment system using forest stand/site data, annual damage reports and outputs of phenology modelling as data input - mapping of endangered areas and assessment of future susceptibility to infestations by I. typographus and other disturbing agents based on climate scenarios using GIS. The assessment of site- and stand-related predisposition revealed that the forest stands in Wildalpen are highly susceptible to bark beetle infestation. More than 65% of the stands were assigned to the predisposition classes high/very high. Only 10% of the stands showed a low stand-related predisposition to bark beetle infestations. This high susceptibility to bark beetle damage is related to the dominance of even-aged, spruce-dominated mature stands. The projected change in voltinism of I. typographus will further increase total predisposition of the stands to bark beetle infestations. Mapping the projected predisposition of present-day forest stands at the end of the century clearly indicated that stands with high/very high susceptibility to bark beetle infestation form large continuous areas, a pre-requisite for extended epidemics of I. typographus.

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....

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.

Relationships between net primary productivity and stand age for several forest types and their influence on China's carbon balance.

PubMed

Wang, Shaoqiang; Zhou, Lei; Chen, Jingming; Ju, Weimin; Feng, Xianfeng; Wu, Weixing

2011-06-01

Affected by natural and anthropogenic disturbances such as forest fires, insect-induced mortality and harvesting, forest stand age plays an important role in determining the distribution of carbon pools and fluxes in a variety of forest ecosystems. An improved understanding of the relationship between net primary productivity (NPP) and stand age (i.e., age-related increase and decline in forest productivity) is essential for the simulation and prediction of the global carbon cycle at annual, decadal, centurial, or even longer temporal scales. In this paper, we developed functions describing the relationship between national mean NPP and stand age using stand age information derived from forest inventory data and NPP simulated by the BEPS (Boreal Ecosystem Productivity Simulator) model in 2001. Due to differences in ecobiophysical characteristics of different forest types, NPP-age equations were developed for five typical forest ecosystems in China (deciduous needleleaf forest (DNF), evergreen needleleaf forest in tropic and subtropical zones (ENF-S), deciduous broadleaf forest (DBF), evergreen broadleaf forest (EBF), and mixed broadleaf forest (MBF)). For DNF, ENF-S, EBF, and MBF, changes in NPP with age were well fitted with a common non-linear function, with R(2) values equal to 0.90, 0.75, 0.66, and 0.67, respectively. In contrast, a second order polynomial was best suitable for simulating the change of NPP for DBF, with an R(2) value of 0.79. The timing and magnitude of the maximum NPP varied with forest types. DNF, EBF, and MBF reached the peak NPP at the age of 54, 40, and 32 years, respectively, while the NPP of ENF-S maximizes at the age of 13 years. The highest NPP of DBF appeared at 122 years. NPP was generally lower in older stands with the exception of DBF, and this particular finding runs counter to the paradigm of age-related decline in forest growth. Evaluation based on measurements of NPP and stand age at the plot-level demonstrates the reliability and applicability of the fitted NPP-age relationships. These relationships were used to replace the normalized NPP-age relationship used in the original InTEC (Integrated Terrestrial Ecosystem Carbon) model, to improve the accuracy of estimated carbon balance for China's forest ecosystems. With the revised NPP-age relationship, the InTEC model simulated a larger carbon source from 1950-1980 and a larger carbon sink from 1985-2001 for China's forests than the original InTEC model did because of the modification to the age-related carbon dynamics in forests. This finding confirms the importance of considering the dynamics of NPP related to forest age in estimating regional and global terrestrial carbon budgets. Copyright © 2011 Elsevier Ltd. All rights reserved.

A comparison of selected parametric and non-parametric imputation methods for estimating forest biomass and basal area

Treesearch

Donald Gagliasso; Susan Hummel; Hailemariam Temesgen

2014-01-01

Various methods have been used to estimate the amount of above ground forest biomass across landscapes and to create biomass maps for specific stands or pixels across ownership or project areas. Without an accurate estimation method, land managers might end up with incorrect biomass estimate maps, which could lead them to make poorer decisions in their future...