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© 2021 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). NASA’s Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI’s footprint-level (~25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI’s waveform-to- biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g., RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available.

In Phase III of the North American Forest Dynamics (NAFD) study an automatic workflow has been developed for evaluating forest disturbance history using Landsat observations. It has four major components: an automated approach for image selection and preprocessing, the vegetation change tracker (VCT) forest disturbance analysis, postprocessing, and validation. This approach has been applied to the conterminous US (CONUS) to produce a comprehensive analysis of US forest disturbance history using the NASA Earth Exchange (NEX) cloud computing system. The resultant NAFD-NEX product includes 25 annual forest disturbance maps for 1986–2010 and two time-integrated maps to provide spatial-temporal synoptic view of disturbances over this time period. These maps were derived based on 24,000+ scenes selected from 350,000+ available Landsat images at 30-m resolution, and were validated using a visual assessment of Landsat time-series images in combination with high-resolution and other ancillary data sources over samples selected using a probability based sampling method. The validation revealed no major biases in the NAFD-NEX maps for disturbance events that resulted in at least 20% canopy cover loss. The average user's and producer's accuracies for the disturbance class were 53.6% and 53.3%, respectively, with the individual year's user's accuracy varying from 42.8% to 73.6% and producer's accuracy from 39.0% to 84.8% over the 25-year period. The NAFD-NEX disturbance maps are available from a web portal of the Oak Ridge National Laboratory Distributed Active Archive Center (ORNL-DAAC) at https://doi.org/10.3334/ORNLDAAC/1290 .

Population growth rates in Sub-Saharan East Africa are among the highest in the world, creating increasing pressure for land cover conversion. To date, however, there has been no comprehensive assessment of regional land cover change, and most long-term trends have not yet been quantified. Using a designed sample of satellite-based observations of historical land cover change, we estimate the areas and trends in nine land cover classes from 1998 to 2017 in Ethiopia, Kenya, Uganda, Malawi, Rwanda, Tanzania, and Zambia. Our analysis found an 18,154,000 (±1,580,000) ha, or 34.8%, increase in the area of cropland in East Africa. Conversion occurred primarily from Open Grasslands, Wooded Grasslands, and Open Forests, causing a large-scale reduction in woody vegetation classes. We observed far more conversion (by approximately 20 million hectares) of woody classes to less-woody classes than succession in the direction of increasing trees and shrubs. Spatial patterns within our sample highlight regional land cover conversion hotspots, such as the Central Zambezian Miombo Woodlands, as potential areas of concern related to the conservation of natural ecosystems. Our findings reflect a rapidly growing population that is moving into new areas, with a 43.5% increase in the area of Settlements over the three-decade period. Our results show the areas and ecoregions most impacted by three decades of human development, both spatially and statistically.

Improved monitoring of forest biomass and biomass change is needed to quantify natural and anthropogenic effects on the terrestrial carbon cycle. Landsat's temporal and spatial coverage, moderate spatial resolution, and long history of earth observations provide a unique opportunity for characterizing vegetation changes across large areas and long time scales. However, like with other multi-spectral passive optical sensors, Landsat's relationship of single-date reflectance with forest biomass diminishes under high leaf area and complex canopy conditions. Because the condition of a forest stand at any point in time is largely determined by its disturbance and recovery history, we conceived a method that enhances Landsat's spectral relationships with biomass by including information on vegetation trends prior to the date for which estimates are desired. With recently developed algorithms that characterize trends in disturbance (e.g. year of onset, duration, and magnitude) and post-disturbance regrowth, it should now be possible to realize improved Landsat-based mapping of current biomass across large regions. Moreover, given that we now have 40 years of Landsat data, it should also be possible to use this approach to map historic biomass densities. In this study, we developed regression tree models to predict current forest aboveground biomass (AGB) for a mixed-conifer region in eastern Oregon (USA) using Landsat-based disturbance and recovery (DR) metrics. We employed the trajectory-fitting algorithm LandTrendr to characterize DR trends from yearly Landsat time series between 1972 and 2010. The most important DR predictors of AGB were associated with magnitude of disturbance, post-disturbance condition and post-disturbance recovery, whereas time since disturbance and pre-disturbance trends showed only weak correlations with AGB. Including DR metrics substantially improved predictions of AGB (RMSE = 30.3 Mg ha − 1 , 27%) compared to models based on only single-date reflectance (RMSE = 39.6 Mg ha − 1 , 35%). To determine the number of years required to adequately capture the effect of DR on AGB, we explored the relationship between time-series length and model prediction accuracy. Prediction accuracy increased exponentially with increasing number of years across the entire observation period, suggesting that in this forest region the longer the historic record of disturbance and recovery metrics the more accurate the mapping of AGB. However, time series lengths of between 10 and 20 years were adequate to significantly improve model predictions, and lengths of as little as 5 years still had a meaningful impact. To test the concept of historic biomass prediction, we applied our model to Landsat time series from 1972–1993 and estimated AGB biomass change between 1993 and 2007. Our estimates compared well with historic inventory data, demonstrating that long-term Landsat observations of DR processes can aid in monitoring AGB and AGB change. Instead of directly linking Landsat data with the limited amount of available field-based AGB data, in this study we used the field data to map AGB with airborne lidar and then sampled the lidar data for model training and error assessment. By using lidar data to build and test our prediction model, this study illustrates that lidar data have great value for scaling between field measurements and Landsat data.

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.

Forest understory communities are important components in forest ecosystems providing wildlife habitat and influencing nutrient cycling, fuel loadings, fire behavior and tree species composition over time. One of the most widely utilized understory component metrics is understory vegetation cover, often used as a measure of vegetation abundance. To date, understory vegetation cover estimation and prediction has proven to be inherently difficult using traditional explanatory variables such as: leaf area index, basal area, slope, and aspect. We introduce airborne lidar-derived metrics into the modeling framework for understory vegetation cover. A new airborne lidar metric, understory lidar cover density, created by filtering understory lidar points using intensity values increased traditional explanatory power from non-lidar understory vegetation cover estimation models (non-lidar R 2 -values: 0.2–0.45 vs. lidar R 2 -values: 0.7–0.8). Beta regression, a relatively new modeling technique for this type of data, was compared with a traditional weighted linear regression model using a leave-one-out cross‐validation procedure. Both models provided similar understory vegetation cover accuracies (± 22%) and biases (~ 0%) using 40.5 m 2 circular plots (n = 154). The method presented in this paper provides the ability to accurately obtain census understory vegetation cover information at fine spatial resolutions over a broad range of stand conditions for the interior ponderosa pine forest type. Additional model enhancement and the extension of the method into other forest types warrant further investigation.

article i nfo Lidar is currently the most accurate method for remote estimation of forest structure, but it has limited spatial and temporal coverage. Conversely, Landsat data are more widely available, but exhibit a weaker relationship with structure under medium to high leaf area conditions. One potentially valuable means of enhancing the re- lationship between Landsat reflectance and forest structure is to incorporate Landsat spectral trends prior to a date of interest. Because the condition of a forest stand at any point in time is linked to the stand's disturbance history, an approach that directly leverages the temporal information of Landsat time series should improve estimates of forest structure. The main objective of this study was to test and demonstrate the utility of distur- bance and recovery metrics derived from spectral profiles of annual Landsat time series (LTS) to predict current forest structure attributes (as compared to more traditional approaches, including airborne, discrete return lidar and single-date Landsat). We estimated aboveground live biomass (AGBlive), dead woody biomass (AGBdead), basal area (live and dead), and Lorey's mean stand height for a mixed-conifer forest in eastern Oregon, USA, and compared the results with estimates from lidar and single, current-date Landsat imagery. Annual time- series stacks for the entire Landsat record (1972-2010) were obtained to characterize all long-term (insect, growth) and short-term (fire, harvest) vegetation changes that occurred during that period. This required the additional objective of integrating Landsat data from MSS and TM/ETM+sensors, and we describe here our approach. To extract spectral trajectories and change metrics associated with forest disturbances and recovery we applied a temporal segmentation to the calibrated time series. Lidar predicted forest structure of live trees most accurately (e.g. AGBlive :R

Understanding fine-grain patterns of forest disturbance and regrowth at the landscape scale is critical for effective management, particularly in forests in western Washington, Oregon, and California, U.S., where the policy known as the Northwest Forest Plan (NWFP) was imposed in 1994 over > 8 million ha of forest in an effort to balance environmental and economic tensions. We developed approaches to create disturbance and regrowth maps for forests within the area of the NWFP from the results of LandTrendr, a temporal segmentation algorithm described previously only at the pixel scale. Maps were developed from 674 Landsat Thematic Mapper and Enhanced Thematic Mapper + images distributed across 22 separate scene areas, and were assessed for validity at 2360 points using TimeSync, a time-series validation and interpretation tool. Unlike maps derived using other techniques, maps derived from the segmentation approach were unique in providing simultaneous detection of abrupt disturbance, chronic disturbance, and ongoing vegetative growth with consistency across large areas and across time. Maps were then used to address six core monitoring questions focusing on the distribution of disturbance across time, ownership categories, and ecoregions. Forest was disturbed at rates that varied by ownership category and state, ranging from 9% to > 39% of forest area over the period 1985 to 2008, with highest cumulative disturbed area on private and native lands in Washington and Oregon and lowest disturbed area on federal protected lands in Washington. Effects of court injunctions and subsequent implementation of the NWFP lowered forest disturbance rates, particularly in Oregon, and also caused decreases in the relative magnitude of disturbance on those lands relative to private lands. State-managed forests showed forest disturbance rates that varied considerably among the three states, with the highest rates in Washington state and lowest in California. Affected by large, stochastic fire events, protected lands in both Oregon and California showed disturbance rates similar to those found on actively managed federal lands. Protected lands also experienced high rates of chronic disturbance, often associated with insect-related mortality. As expected, moisture-limited ecoregions recovered vegetation more slowly than those where moisture was not limiting. Vegetative regrowth rates showed substantial variation among ownership categories, suggesting that differential forest policies may affect vegetative recovery rate. Taken together, these results emphasize that forest management policies do have manifestations at the landscape scale, but that detection of these manifestations is best achieved with mapping approaches that can detect both abrupt and longer-duration processes within the Landsat archive.

Our ability to assess the continental impacts of woody encroachment remains compromised by the paucity of studies quantifying regional encroachment rates. This knowledge gap is especially apparent when it comes to quantifying the impact of woody encroachment on large-scale carbon dynamics. In this study, we use a combination of aerial photography from 1985– 1986 and 2005 and near-annual Landsat satellite imagery over the same period to assess the rates of encroachment by western juniper, Juniperus occidentalis Hook., into the grasslands and shrublands of eastern Oregon. The approximately 20-yr Landsat reflectance trajectories identified for the juniper woodlands of eastern Oregon did not correlate well with changes in juniper crown cover over the same period, suggesting that systematic trends in reflectance are being driven by vegetation other than juniper. Using a random sample of 150 aerial photography plots, we estimate the average aboveground accumulation of carbon in undisturbed juniper woodlands to be 2.9 kg C ? m 22 ? yr 21 ; about 0.20 Tg C ? yr 21 across all of Oregon. However, juniper removal by cutting and or burning, occurring at a rate of , 1% yr 21 , counteracted regional encroachment by about 35%, bringing the net change in aboveground carbon down to 1.9 kg C ? m 22 ? yr 21 , about 0.13 Tg C ? yr 21 across all of Oregon. This study illustrates the capacity of woody removal, over very small areas, to offset encroachment over very large areas and cautions against scaling site-level encroachment studies over entire regions.

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 algorithm that exploits the spectral-temporal properties of summer Landsat time series stacks (LTSSs) to generate spatially explicit maps of forest and recent forest disturbances. VCT performs well in contiguous forest landscapes with closed or nearly closed canopies, but often incorrectly classifies large patches of land as forest or forest disturbance in the complex and spatially heterogeneous environments that typify fragmented forest landscapes. We introduce an improved version of VCT (dubbed VCTw) that incorporates a nonforest mask derived from snow-covered winter Landsat time series stacks ( LTSSw) and compare it with VCT across nearly 25 million ha of land in the Lake Superior (Canada, USA) and Lake Michigan (USA) drainage basins. Accuracy assessments relying on 87 primary sampling units (PSUs) and 2640 secondary sampling units (SSUs) indicated that VCT performed with an overall accuracy of 86.3%. For persisting forest, the commission error was 14.7% and the omission error was 4.3%. Commission and omission errors for the two forest disturbance classes fluctuated around 50%. VCTw produced a statistically significant increase in overall accuracy to 91.2% and denoted about 1.115 million ha less forest (- .371 million ha disturbed and -0.744 million ha persisting). For persisting forest, the commission error decreased to 9.3% and the omission error was relatively unchanged at 5.0%. Commission errors decreased considerably to near 22% and omission errors remained near 50% in both forest disturbance classes. Dividing the assessments into three geographic strata demonstrated that the most dramatic improvement occurred across the southern half of the Lake Michigan basin, which contains a highly fragmented agricultural landscape and relatively sparse deciduous forest, although substantial improvements occurred in other geographic strata containing little agricultural land, abundant wetlands, and extensive coniferous forest. Unlike VCT, VCTw also generally corresponded well with field-based estimates of forest cover in each stratum. Snow-covered winter imagery appears to be a valuable resource for improving automated disturbance mapping accuracy. About 34% of the world's forests receive sufficient snowfall to cover the ground and are potentially suitable for VCTw; other season-based techniques may be worth pursuing for the remaining 66%.

article Insects are important forest disturbance agents, and mapping their effects on tree mortality and surface fuels represents a critical research challenge. Although various remote sensing approaches have been developed to monitor insect impacts, most studies have focused on single insect agents or single locations and have not related observed changes to ground-based measurements. This study presents a remote sensing framework to (1) characterize spectral trajectories associated with insect activity of varying duration and severity and (2) relate those trajectories to ground-based measurements of tree mortality and surface fuels in the Cascade Range, Oregon, USA. We leverage a Landsat time series change detection algorithm (LandTrendr), annual forest health aerial detection surveys (ADS), and field measurements to investigate two study landscapes broadly applicable to conifer forests and dominant insect agents of western North America. We distributed 38 plots across multiple forest types (ranging from mesic mixed-conifer to xeric lodgepole pine) and insect agents (defoliator (western spruce budworm) and bark beetle (mountain pine beetle)). Insect effects were evident in the Landsat time series as combinations of both short- and long-duration changes in the Normal- ized Burn Ratio spectral index. Western spruce budworm trajectories appeared to show a consistent tempo- ral evolution of long-duration spectral decline (loss of vegetation) followed by recovery, whereas mountain pine beetle plots exhibited both short- and long-duration spectral declines and variable recovery rates. Although temporally variable, insect-affected stands generally conformed to four spectral trajectories: short- duration decline then recovery, short- then long-duration decline, long-duration decline, long-duration decline then recovery. When comparing remote sensing data with field measurements of insect impacts, we found that spectral changes were related to cover-based estimates (tree basal area mortality (R 2

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67–0.74, Kappa: 0.41–0.52) and increased by 0.09–0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%–20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.

article Accurate estimation of live and dead biomass in forested ecosystems is important for studies of carbon dynamics, biodiversity, wildfire behavior, and for forest management. Lidar remote sensing has been used successfully to estimate live biomass, but studies focusing on dead biomass are rare. We used lidar data, in conjunction with field measurements from 58 plots to distinguish between and map standing live and dead tree biomass in the mixed coniferous forest of the North Rim of Grand Canyon National Park, USA. Lidar intensity and canopy volume were key variables for estimating live biomass, whereas for dead biomass, lidar intensity alone was critical for accurate estimation. Regression estimates of both live and dead biomass ranged between 0 and 600 Mg ha −1

[EN] Spatially explicit maps of wildlife habitat relationships have proven to be valuable tools for conservation and management applications including evaluating how and which species may be impacted by large scale climate change, ongoing fragmentation of habitat, and local land-use practices. Studies have turned to remote sensing datasets as a way to characterize vegetation for the examination of habitat selection and for mapping realized relationships across the landscape. Potentially one of the more difficult habitat types to try to characterize with remote sensing are the vertically and horizontally complex forest systems. Characterizing this complexity is needed to explore which aspects may represent driving and/or limiting factors for wildlife species. Active remote sensing data from lidar and radar sensors has thus caught the attention of the forest wildlife research and management community in its potential to represent three dimensional habitat features. The purpose of this review was to examine the applications of active remote sensing for characterizing forest in wildlife habitat studies through a keyword search within Web of Science. We present commonly used active remote sensing metrics and methods, discuss recent advances in characterizing aspects of forest habitat, and provide suggestions for future research in the area of new remote sensing data/techniques that could benefit forest wildlife studies that are currently not represented or may be underutilized within the wildlife literature. We also highlight the potential value in data fusion of active and passive sensor data for representing multiple dimensions and scales of forest habitat. While the use of remote sensing has increased in recent years within wildlife habitat studies, continued communication between the remote sensing, forest management, and wildlife communities is vital to ensure appropriate data sources and methods are understood and utilized, and so that creators of mapping products may better realize the needs of secondary users., [ES] Se ha probado que los mapas que muestran explícitamente las relaciones especie-hábitat constituyen herramientas valiosas en aplicaciones de conservación y gestión, incluyendo la evaluación sobre qué especies y de qué forma se pueden ver afectadas por el cambio climático a gran escala, la fragmentación progresiva del hábitat y los usos del suelo a nivel local. Diversos estudios se han centrado en utilizar la teledetección como herramienta que permite caracterizar la vegetación para el análisis de la selección del hábitat y para cartografiar las relaciones con el entorno natural. Uno de los tipos de hábitats más difíciles de caracterizar mediante teledetección son los sistemas forestales verticales y horizontales complejos. Su caracterización es necesaria para estudiar los aspectos determinantes y/o limitantes para las especies. El uso de la teledetección activa mediante sensores LiDAR y RADAR ha suscitado gran interés en el ámbito de la investigación de especies de fauna silvestre en áreas forestales así como su gestión, dado el potencial de esta tecnología para representar características tridimensionales de estos hábitats. El objetivo de este artículo de revisión es analizar las aplicaciones de teledetección activa en los estudios de hábitat de fauna silvestre en zonas forestales a través de búsquedas de palabras claves en la WebofScience. Se presentan las métricas y métodos comúnmente utilizados, los avances recientes en la caracterización de hábitats forestales y se recomiendan líneas futuras de investigación en el área de teledetección que podrían beneficiar estudios sobre fauna silvestre en ámbitos forestales que actualmente o no existen o están infrautilizados. También se destaca el valor potencial de la fusión de datos de sensores activos y pasivos para la representación de múltiples dimensiones y escalas del hábitat forestal. Si bien el uso de la teledetección en estudios de hábitat de fauna silvestre se ha incrementado en los últimos años, la comunicación fluida entre las comunidades científicas relacionadas con la teledetección, la gestión forestal y la ecología es vital para garantizar el uso y comprensión adecuados de los datos, permitiendo un mejor conocimiento de las necesidades de los usuarios., Our review was funded by the National Aeronautics and Space Administration, Carbon Cycle Program (NASA Grant 10-CARBON10-45).

Peatlands play a major role in the global carbon cycle but are largely overlooked in current large-scale vegetation mapping efforts. In this study, we investigated the potential of the Moderate Resolution Imaging Spectroradiometer (MODIS) to capture the extent and distributionofpeatlandsintheSt.PetersburgregionofRussiabyanalyzingtherelationshipsbetweenpeatlandcoverfractionsderived from reference maps and ∼1-km resolution MODIS Nadir BRDF-Adjusted Reflectance (NBAR) data from year 2002. First, we characterized and mapped 50 peatlands from forest inventory and peat deposit inventory data. The peatlands represent three major nutritional types (oligotrophic, mesotrophic, eutrophic) and different sizes (0.6–7800 ha). In addition, parts of 6 peatlands were mined for peat and these were mapped separately. The reference maps provided information on peatland cover for 1105 MODIS pixels. We performed regression analysis on 50% of the pixels and reserved the remainder for model validation. Canonical correlation analysis on the MODIS reflectance bands and the peatland cover fractions produced a multi-spectral peatland cover index (PCI), which served as the predictor in a reduced major axis (RMA) regression model. The results suggest a high potential for mapping peatlands with MODIS. The RMA regression models explained much of the variance in the PCI (r 2 =0.74 for mined and r 2 =0.81 for unmined peatlands). Model validation showed high correlation between observed versus predicted peatland cover (mined: r=0.87; unmined: r=0.92). We used the models to derive peatland cover estimates for the St. Petersburg region and compared the results to current MODIS land cover maps. © 2006 Elsevier B.V. All rights reserved.

This article describes the development of a methodology for scaling observations of changes in tropical forest cover to large areas at high temporal frequency from coarse resolution satellite imagery. The approach for estimating proportional forest cover change as a continuous variable is based on a regression model that relates multispectral, multitemporal MODIS data, transformed to optimize the spectral detection of vegetation changes, to reference change data sets derived from a Landsat data record for a study site in Central America. A number of issues involved in model development are addressed here by exploring the spatial, spectral and temporal patterns of forest cover change as manifested in a time-series of multi-scale satellite imagery. The analyses highlighted the distinct spectral change patterns from year-to-year in response to the possible land cover trajectories of forest clearing, regeneration and changes in climatic and land cover conditions. Spectral response in the MODIS Calibrated Radiances Swath data set followed more closely with the expected patterns of forest cover change than did the spectral response in the Gridded Surface Reflectance product. With forest cover change patterns relatively invariant to the spatial grain size of the analysis, the model results indicate that the best spectral metrics for detecting tropical forest clearing and regeneration are those that incorporate shortwave infrared information from the MODIS calibrated radiances data set at 500-m resolution, with errors ranging from 7.4 to 10.9% across the time periods of analysis.

Global maps of land cover and leaf area index (LAI) derived from the Moderate Resolution Imaging Spectrometer (MODIS) reflectance data are an important resource in studies of global change, but errors in these must be characterized and well understood. Product validation requires careful scaling from ground and related measurements to a grain commensurate with MODIS products. We present an updated BigFoot project protocol for developing 25-m validation data layers over 49-km2 study areas. Results from comparisons of MODIS and BigFoot land cover and LAI products at nine contrasting sites are reported. In terms of proportional coverage, MODIS and BigFoot land cover were in close agreement at six sites. The largest differences were at low tree cover evergreen needleleaf sites and at an Arctic tundra site where the MODIS product overestimated woody cover proportions. At low leaf biomass sites there was reasonable agreement between MODIS and BigFoot LAI products, but there was not a particular MODIS LAI algorithm pathway that consistently compared most favorably. At high leaf biomass sites, MODIS LAI was generally overpredicted by a significant amount. For evergreen needleleaf sites, LAI seasonality was exaggerated by MODIS. Our results suggest incremental improvement from Collection 3 to Collection 4 MODIS products, with some remaining problems that need to be addressed

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, carbon storage in moderate to high biomass forests is difficult to estimate with conventional optical or radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidarmeasured canopy structure and coincident field measurements of forest stand structure at five locations in the Pacific Northwest of the U.S.A. with contrasting composition. Coefficient of determination values (r 2 ) ranged between 41% and 96%. Correlations for two important variables, LAI (81%) and aboveground biomass (92%), were noteworthy, as was the fact that neither variable showed an asymptotic response. Of the 17 stand structure variables considered in this study, we were able to develop eight equations that were valid for all sites, including equations for two variables generally considered to be highly important (aboveground biomass and leaf area index). The other six equations that were valid for all sites were either related to height (which is most directly measured by lidar) or diameter at breast height (which should be closely related to height). Four additional equations (a total of 12) were applicable to all sites where either Douglas-fir (Pseudotsuga menziesii), western hemlock (Tsuga heterophylla) or Sitka spruce (Picea sitchensi) were dominant. Stand structure variables in sites dominated by true firs (Abies sp.) or ponderosa pine (Pinus ponderosa) had biases when predicted by these four additional equations. Productivity-related variables describing the edaphic, climatic and topographic environment of the sites where available for every regression, but only two of the 17 equations (maximum diameter at breast height, stem density) incorporated them. Given the wide range of these environmental conditions sampled, we conclude that the prediction of stand structure is largely independent of environmental conditions in this study area. Most studies of lidar remote sensing for predicting stand structure have depended on intensive data collections within a relatively small study area. This study indicates that the relationships between many stand structure indices and lidar measured canopy structure have generality at the regional scale. This finding, if replicated in other regions, would suggest that mapping of stand structure using lidar may be accomplished by distributing field sites extensively over a region, thus reducing the overall inventory effort required.

In the past decade, lidar (light detection and ranging) has emerged as a powerful tool for remotely sensing forest canopy and stand structure, including the estimation of aboveground biomass and carbon storage. Numerous papers have documented the use of lidar measurements to predict important aspects of forest stand structure, including aboveground biomass. Other papers have documented the ability to transform lidar measurements to approximate common field measures, such as cover, stand height, and vertical distributions of foliage density and light transmittance. However, only a small number of existing works have thoroughly examined relationships between comprehensive assemblages of forest canopy and forest stand structure indices. In this work, canonical correlation analysis of coincident lidar and field datasets in western Oregon and Washington is used to define seven statistically significant pairs of canonical variables, each defining an axis of variation that stand and canopy structure have in common. The first major axis relates mean stand height, and related variables, to aboveground biomass. The second relates canopy cover and volume to leaf area index and stem density. The third relates canopy height variability to mean stem diameter and the basal area of deciduous species. Of the four remaining axes, three are related to contrasts between mature and old-growth stands. Canonical correlation analysis provides a method for ranking the importance of these effects, and for placing both canopy and stand structure indices within the overall covariance structure of the two datasets. In this sense, and for the study area involved, the first three factors (mean height, cover or leaf index area, height variability) represent the same kind of enhancement of lidar data that the tasseled cap indices [Crist, C.P., R.C. Cicone, 1984. A physically-based transformation of thematic mapper data—the TM tasseled cap. IEEE Transactions on Geoscience and Remote Sensing 22, 256–263.] represent for optical remote sensing. D 2005 Elsevier B.V. All rights reserved.

Operational monitoring of global terrestrial gross primary production (GPP) and net primary production (NPP) is now underway using imagery from the satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Evaluation of MODIS GPP and NPP products will require site-level studies across a range of biomes, with close attention to numerous scaling issues that must be addressed to link ground measurements to the satellite-based carbon flux estimates. Here, we report results of a study aimed at evaluating MODIS NPP/GPP products at six sites varying widely in climate, land use, and vegetation physiognomy. Comparisons were made for twenty-five 1km 2 cells at each site, with 8-day averages for GPP and an annual value for NPP. The validation data layers were made with a combination of ground measurements, relatively high resolution satellite data (Landsat Enhanced Thematic Mapper Plus at � 30m resolution), and process-based modeling. There was strong seasonality in the MODIS GPP at all sites, and mean NPP ranged from 80gCm � 2 yr � 1 at an arctic tundra site to 550gCm � 2 yr � 1 at a temperate deciduous forest site. There was not a consistent over- or underprediction of NPP across sites relative to the validation estimates. The closest agreements in NPP and GPP were at the temperate deciduous forest, arctic tundra, and boreal forest sites. There was moderate underestimation in the MODIS products at the agricultural field site, and strong overestimation at the desert grassland and at the dry coniferous forest sites. Analyses of specific inputs to the MODIS NPP/ GPP algorithm ‐ notably the fraction of photosynthetically active radiation absorbed by the vegetation canopy, the maximum light use efficiency (LUE), and the climate data ‐ revealed the causes of the over- and underestimates. Suggestions for algorithm improvement include selectively altering values for maximum LUE (based on observations at eddy covariance flux towers) and parameters regulating autotrophic respiration.

Extensive estimates of forest productivity are required to understand the relationships between shifting land use, changing climate and carbon storage and fluxes. Aboveground net primary production of wood (NPPAw) is a major component of total NPP and of net ecosystem production (NEP). Remote sensing of NPP and NPPAw is generally based on light use efficiency or process-based biogeochemistry models. However, validating these large area flux estimates remains a major challenge. In this study we develop an independent approach to estimating NPPAw, based on stand age and biomass, that could be implemented over a large area and used in validation efforts. Stand age is first mapped by iterative unsupervised classification of a multi-temporal sequence of images from a passive optical sensor (e.g. Landsat TM). Stand age is then cross-tabulated with estimates of stand height and aboveground biomass from lidar remote sensing. NPPAw is then calculated as the average increment in lidar-estimated biomass over the time period determined using change detection. In western Oregon, productivity estimates made using this method compared well with forest inventory estimates and were significantly different than estimates from a spatially distributed biogeochemistry model. The generality of the relationship between lidar-based canopy characteristics and stand biomass means that this approach could potentially be widely applicable to landscapes with stand replacing disturbance regimes, notably in regions where forest inventories are not routinely maintained.

The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach. In this study, the 2001 MODIS GPP product was compared with scaled GPP estimates (25 km 2 ) based on ground measurements at two forested sites. The ground-based GPP scaling approach relied on a carbon cycle process model run in a spatially distributed mode. Land cover classification and maximum annual leaf area index, as derived from Landsat ETM+ imagery, were used in model initiation. The model was driven by daily meteorological observations from an eddy covariance flux tower situated at the center of each site. Model simulated GPPs were corroborated with daily GPP estimates from the flux tower. At the hardwood forest site, the MODIS GPP phenology started earlier than was indicated by the scaled GPP, and the summertime GPP from MODIS was generally lower than the scaled GPP values. The fall-off in production at the end of the growing season was similar to the validation data. At the boreal forest site, the GPP phenologies generally agreed because both responded to the strong signal associated with minimum temperature. The midsummer MODIS GPP there was generally higher than the ground-based GPP. The differences between the MODIS GPP products and the ground-based GPPs were driven by differences in the timing of FPAR and the magnitude of light use efficiency as well as by differences in other inputs to the MODIS GPP algorithm—daily incident PAR, minimum temperature, and vapor pressure deficit. Ground-based scaling of GPP has the potential to improve the parameterization of light use efficiency in satellite-based GPP monitoring algorithms.

Light detection and ranging (lidar) data provide accurate measurements of forest canopy structure in the vertical plane; however, current lidar sensors have limited coverage in the horizontal plane. Landsat data provide extensive coverage of generalized forest structural classes in the horizontal plane but are relatively insensitive to variation in forest canopy height. It would, therefore, be desirable to integrate lidar and Landsat data to improve the measurement, mapping, and monitoring of forest structural attributes. We tested five aspatial and spatial methods for predicting canopy height, using an airborne lidar system (Aeroscan) and Landsat Enhanced Thematic Mapper (ETM+) data: regression, kriging, cokriging, and kriging and cokriging of regression residuals. Our 200-km 2 study area in western Oregon encompassed Oregon State University’s McDonald–Dunn Research Forest, which is broadly representative of the age and structural classes common in the region. We sampled a spatially continuous lidar coverage in eight systematic patterns to determine which lidar sampling strategy would optimize lidar– Landsat integration in western Oregon forests: transects sampled at 2000, 1000, 500, and 250 m frequencies, and points sampled at these same spatial frequencies. The aspatial regression model results, regardless of sampling strategy, preserved actual vegetation pattern, but underestimated taller canopies and overestimated shorter canopies. The spatial models, kriging and cokriging, produced less biased results than regression but poorly reproduced vegetation pattern, especially at the sparser (2000 and 1000 m) sampling frequencies. The spatial model predictions were more accurate than the regression model predictions at locations

Light use efficiency (LUE) algorithms are a potentially effective approach to monitoring global net primary production (NPP) using satellite-borne sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS). However, these algorithms are applied at relatively coarse spatial resolutions ( � 1 km), which may subsume significant heterogeneity in vegetation LUE (en, gM J � 1 ) and, hence, introduce error. To examine the effects of spatial heterogeneity on a LUE algorithm, imagery from the Advanced Very High Resolution Radiometer (AVHRR) at � 1-km resolution was used to implement a LUE approach for NPP estimation over a 25-km 2 area of corn (Zea mays L.) and soybean (Glycine max Merr.) in central Illinois, USA. Results from several en formulations were compared with a NPP reference surface based on measured NPPs and a high spatial resolution land cover surface derived from Landsat ETM+. Determination of en based on measurements of biomass production and monitoring of absorbed photosynthetically active radiation (APAR) revealed that en of soybean was 68% of that for corn. When a LUE algorithm for estimating NPP was implemented in the study area using the assumption of homogeneous cropland and the en for corn, the estimate for total biomass production was 126% of that from the NPP reference surface. Because of counteracting errors, total biomass production using the soybean en was closer (86%) to that from the NPP reference surface. Retention of high spatial resolution land cover to assign en resulted in a total NPP very similar to the reference NPP because differences in leaf phenology between the crop types were small except early in the growing season. These results suggest several alternative approaches to accounting for land cover heterogeneity in en when implementing LUE algorithms at coarse resolution. D 2002 Elsevier Science Inc. All rights reserved.

Nech Sar National Park (NSNP) is one of the most important biodiversity centers in Ethiopia. In recent years, a widespread decline of the terrestrial ecosystems has been reported, yet to date there is no comprehensive assessment on degradation across the park. In this study, changes in landcover were analyzed using 30 m spatial resolution Landsat imagery. Interannual variations of normalized difference vegetation index (NDVI) were examined and compared with climatic variables. The result presented seven landcover classes and five of the seven landcover classes (forest, bush/shrubland, wooded grassland, woodland and grassland) were related to natural vegetation and two landcover types (cultivated land and area under encroaching plants) were direct results of anthropogenic alterations of the landscape. The forest, grassland, and wooded grassland are the most threatened habitat types. A considerable area of the grassland has been replaced by encroaching plants, prominently by Dichrostachys cinerea, Acacia mellifera, A. nilotica, A. oerfota, and A. seyal and is greatly affected by expansion of herbaceous plants, most commonly the species of the family Malvaceae which include Abutilon anglosomaliae, A.bidentatum and A.figarianu. Thus, changes in vegetation of NSNP may be attributed to (i) degradation of existing vegetation through deforestation and (ii) replacement of existing vegetation by encroaching plants. While limited in local meteorological station, NDVI analysis indicated that climate related changes did not have major effects on park vegetation degradation, which suggests anthropogenic impacts as a major driver of observed disturbances.

When mapping land cover with satellite imagery in montane tropical regions, varying illumination angles and ecological zones can obscure the differences between spectral responses of old-growth forest, secondary forest and agricultural lands. We used multi-date, Landsat Thematic Mapper (TM) imagery to map secondary forests, agricultural lands and old-growth forests in the Talamanca Mountain Range in southern Costa Rica. With stratification by illumination and ecological zone, the overall accuracy for this classification was 87% with a Kappa coefficient of 0.83. We also examined spectral responses to forest successional stage, ecological zone and aspect illumination for the TM Tasselled Cap indices, TM (2 x 6)/7, TM 4/5 and TM difference bands, and whether using digital data from multiple decades improved classification accuracy. Digital maps of ecological zones should be useful for large-scale mapping of land use and forest successional stage in complex montane regions such as those in Central America.

21 of biomass and an LAI of 12, with 90% and like conventional microwave and optical sensors, lidar 75% of variance explained, respectively. Furthermore, we sensors directly measure the distribution of vegetation were able to make accurate estimates of other stand material along the vertical axis and can be used to pro- structure attributes, including the mean and standard devide three-dimensional, or volumetric, characterizations viation of diameter at breast height, the number of stems of vegetation structure. Ecological applications of scan- greater than 100 cm in diameter, and independent estining lidar have hitherto used one-dimensional indices to mates of the basal area of Douglas-fir and western hemcharacterize canopy height. A novel three-dimensional lock. These measurements can be directly related to indianalysis of lidar waveforms was developed to character- ces of forest stand structural complexity, such as those ize the total volume and spatial organization of vegeta- developed for old-growth forest characterization. Indices tion material and empty space within the forest canopy. of canopy structure developed using the novel, threeThese aspects of the physical structure of canopies have dimensional analysis accounted for most of the variables been infrequently measured, from either field or remote used in predictive equations generated by the stepwise methods. We applied this analysis to 22 plots in Douglas- multiple regression. Published by Elsevier Science Inc. fir/western hemlock stands on the west slope of the Cascades Range in Oregon. Each plot had coincident lidar data and field measurements of stand structure. We com- INTRODUCTION pared results from the novel analysis to two earlier methCharacterization of structure in moderate to high bioods of canopy description. Using the indices of canopy mass forests is a major challenge in remote sensing. structure from all three methods of description as inde

Mapping and monitoring of leaf area index (LAI) is important for spatially distributed modeling of vegetation productivity, evapotranspiration, and surface energy balance. Global LAI surfaces will be an early product of the MODIS Land Science Team, and the requirements for LAI validation at selected sites have prompted interest in accurate LAI mapping at a more local scale. While spectral vegetation indices (SVIs) derived from satellite remote sensing have been used to map LAI, vegetation type, and related optical properties, and effects of Sun–surface–sensor geometry, background reflectance, and atmospheric quality can limit the strength and generality of empirical LAI–SVI relationships. In the interest of a preliminary assessment of the variability in LAI–SVI relationships across vegetation types, we compared Landsat 5 Thematic Mapper imagery from three temperate zone sites with on-site LAI measurements. The sites differed widely in location, vegetation physiognomy (grass, shrubs, hardwood forest, and conifer forest), and topographic complexity. Comparisons were made using three different red and near-infrared-based SVIs (NDVI, SR, SAVI). Several derivations of the SVIs were examined, including those based on raw digital numbers (DN), radiance, top of the atmosphere reflectance, and atmospherically corrected reflectance. For one of the sites, which had extreme topographic complexity, additional corrections were made for Sun–surface–sensor geometry. Across all sites, a strong general relationship was preserved, with SVIs increasing up to LAI values of 3 to 5. For all but the coniferous forest site, sensitivity of the SVIs was low at LAI values above 5. In coniferous forests, the SVIs decreased at the highest LAI values because of decreasing near-infrared reflectance associated with the complex canopy in these mature to old-growth stands. The cross-site LAI–SVI relationships based on atmospherically corrected imagery were stronger than those based on DN, radiance, or top of atmosphere reflectance. Topographic corrections at the conifer site altered the SVIs in some cases but had little effect on the LAI–SVI relationships. Significant effects of vegetation properties on SVIs, which were independent of LAI, were evident. The variability between and around the best fit LAI–SVI relationships for this dataset suggests that for local accuracy in development of LAI surfaces it will be desirable to stratify by land cover classes (e.g., physiognomic type and successional stage) and to vary the SVI.

The MODIS sensor to be launched on the EOS-AM platform will be the most important sensor for global vegetation mapping. Among the programmatic goals for the MODIS sensor are to assess and track changes in land use/landcover, leaf area index (LAI), and net primary productivity (NPP). For these products to be used in global models, they must be rigorously validated with site-specific data products. This article presents a review of some of the problems facing a regional- to global-scale validation effort and presents strategies for coordinating the land-cover classification process across multiple sites. We suggest the Enhanced Thematic Mapper (ETM+) as the source of remotely sensed data for validation, and that the IGBP 17-class land-cover classification system be used to provide a link between more complex site-specific systems and global-scale data products. We further recommend that the best site-specific land-cover classifications be obtained, using whatever ancillary data are found to be useful, as a basis for validation. In addition, we propose ways in which ambiguities in translation of classes, from specific to general systems, may be identified. Finally, we stress that even though standardization of methodology among sites may not be appropriate to the goal of obtaining the best possible land-cover products, there should be standardization of error analysis and metadata reporting.

A method of predicting two forest stand structure attributes, basal area and aboveground biomass, from measurements of forest vertical structure was developed and tested using field and remotely sensed canopy structure measurements. Coincident estimates of the vertical distribution of canopy surface area (the canopy height profile), and field-measured stand structure attributes were acquired for two data sets. The chronosequence data set consists of 48 plots in stands distributed within 25 miles of Annapolis, MD, with canopy height profiles measured in the field using the optical-quadrat method. The stem-map data set consists of 75 plots subsetted from a single 32 ha stem-mapped stand, with measurements of their canopy height profiles made using the SLICER (Scanning Lidar Imager of Canopies by Echo Recovery) instrument, an airborne surface lidar system. Four height indices, maximum, median, mean, and quadratic mean canopy height (QMCH) were calculated from the canopy height profiles. Regressions between the indices and stand basal area and biomass were developed using the chronosequence data set. The regression equations developed from the chronosequence data set were then applied to height indices calculated from the remotely sensed canopy height profiles from the stem map data set, and the ability of the regression equations to predict the stem map plot’s stand structure attributes was then evaluated. The QMCH was found to explain the most variance in the chronosequence data set’s stand structure attributes, and to most accurately predict the values of the same attributes in the stem map data set. For the chronosequence data set, the QMCH predicted 70% of variance in stand basal area, and 80% of variance in aboveground biomass, and remained nonasymptotic with basal areas up to 50 m2 ha−1, and aboveground biomass values up to 450 Mg ha−1. When applied to the stem-map data set, the regression equations resulted in basal areas that were, on average, underestimated by 2.1 m2 ha−1, and biomass values were underestimated by 16 Mg ha−1, and explained 37% and 33% of variance, respectively. Differences in the magnitude of the coefficients of determination were due to the wider range of stand conditions found in the chronosequence data set; the standard deviation of residual values were lower in the stem map data set than on the chronosequence data sets. Stepwise multiple regression was performed to predict the two stand structure attributes using the canopy height profile data directly as independent variables, but they did not improve the accuracy of the estimates over the height index approach.

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.

A 34 year time series (1972–2006) of Landsat imagery for a portion of Snohomish and King Counties, Washington (the Snohomish Water Resource Inventory Area (WRIA)) was analyzed to estimate the amount of land that was converted into impervious surface as a result of urban and residential development. Spectral unmixing was used to determine the fractional composition of vegetation, open, and shadow for each pixel. Unsupervised and supervised classification techniques were then used to derive preliminary land cover maps for each time period. Digital orthophotos were used to create agricultural, forest management, high elevation, and riparian masks. In conjunction with established Urban Growth Areas (UGAs), these masks were utilized for the application of spatial rules that identified impervious surface as a surrogate for urban and residential development. Temporal rules, that minimized classification error, were developed based on each pixel's classified trajectory over the time series of imagery. Overall cross-date classification accuracy for impervious v. non-impervious surface was 95%. The results of the analysis indicate that the area of impervious surface in the Snohomish WRIA increased by 255% over 34 years, from 3285 ha in 1972 to 11,652 ha in 2006. This approach demonstrates the unique value of the 35 year Landsat archive for monitoring impervious surface trends in rapidly urbanizing areas.

Forest disturbances are thought to play a major role in controlling land-atmosphere fluxes of carbon. Under the auspices of the North American Carbon Program, the LEDAPS (Landsat Ecosystem Disturbance Adaptive Processing System) and NACP-FIA projects have been analyzing the Landsat satellite record to assess rates of forest disturbance across North America. In the LEDAPS project, wall-to-wall Landsat imagery for the period 1975-2000 has been converted to surface reflectance and analyzed for decadal losses (disturbance) or gains (regrowth) in biomass using a spectral "disturbance index". The NACP-FIA project relies on a geographic sample of dense Landsat image time series, allowing both disturbance rates and recovery trends to be characterized. Preliminary results for the 1990's indicate high rates of harvest within the southeastern US, Eastern Canada, and the Pacific Northwest, with spatially averaged (~50times50 km) turnover periods as low as 25-40 years. Lower rates of disturbance are found in the Rockies and Northeastern US.

[1] This paper discusses the quality and the accuracy of the Joint Research Center (JRC) fraction of absorbed photosynthetically active radiation (FAPAR) products generated from an analysis of Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The FAPAR value acts as an indicator of the presence and state of the vegetation and it can be estimated from remote sensing measurements using a physically based approach. The quality of the SeaWiFS FAPAR products assessed in this paper capitalizes on the availability of a 6-year FAPAR time series over the full globe. This evaluation exercise is performed in two phases involving, first, an analysis of the verisimilitude of the FAPAR products under documented environmental conditions and, second, a direct comparison of the FAPAR values with ground-based estimations where and when the latter are available. This second phase is conducted following a careful analysis of problems arising for performing such a comparison. This results in the grouping of available field information into broad categories representing different radiative transfer regimes. This strategy greatly helps the interpretation of the results since it recognizes the various levels of difficulty and sources of uncertainty associated with the radiative sampling of different types of vegetation canopies.

Scanning lidar remote sensing systems have become generally available for use in ecological applications. Unlike microwave and conventional optical sensors, lidar sensors directly measure the distribution of vegetation material along a vertical axis and can be used to provide three-dimensional characterizations of vegetation structure. Ecological applications of scanning lidar have previously used uni-dimensional indices of canopy height. A new, three-dimensional, approach to interpreting lidar waveforms was developed to characterize the total volume of both vegetation and empty space within the forest canopy, and their spatial organization. These aspects of the physical structure of canopies have been infrequently measured, either from field or remote methods. Applying this approach to 21 plots with coincident lidar measurements and field surveys, we were able to predict both biomass and leaf area index from the volumes of four classes of canopy structure. These predictions were non-asymptotic over a wide range, up to 1200 Mg ha/sup -1/ of biomass and an LAI of 12, with 90% and 88% of variance explained, respectively.

Aerial photography has been routinely used for several decades by natural resource scientists and managers to map and monitor the condition of forested landscapes. Recently, along with the emergence of concepts in managing forests as ecosystems, has come a significant shift in emphasis from smaller to larger spatial scales and the widespread use of geographic information systems. These developments have precipitated an increasing need for vegetation information derived from other remote sensing imagery, especially digital data acquired from high-elevation aircraft and satellite platforms. This paper introduces fundamental concepts in digital remote sensing and describes numerous applications of the technology. The intent is to provide a balanced, nontechnical view, discussing the shortcomings, successes, and future potential for digital remote sensing of forested ecosystems.

Exogenous disturbances are critical agents of change in temperate forests capable of damaging trees and influencing forest structure, composition, demography, and ecosystem processes. Forest disturbances of intermediate magnitude and intensity receive relatively sparse attention, particularly at landscape scales, despite influencing most forests at least once per generation. Contextualizing the spatial extent and heterogeneity of such damage is of paramount importance to increasing our understanding of forested ecosystems. We investigated patterns of intermediate wind disturbance across a forested landscape in the northern Great Lakes, USA. A vegetation change tracker (VCT) algorithm was utilized for processing near-biennial Landsat data stacks (1984–2009) spanning forests sustaining damage from four recent windstorms. VCT predominantly maps stand-clearing disturbance and regrowth patterns, which were used to identify forest boundaries, young stands, and disturbance patterns across space and time. To map wind damage severity, we compared satellite-derived normalized difference vegetation index (NDVI) values calculated from pre- and post-storm Landsat imagery. A geographic information system (GIS) was used to derive wind damage predictor variables from VCT, digital terrain, soils/landform, land cover, and storm tracking data. Hierarchical and random forests regressions were applied to rank the relative importance of predictor variables in influencing wind damage. A conservative estimate of aggregate damage from the intermediate windstorms (extrapolated to ∼150,000 ha, ∼25,500 severe) rivaled individual large, infrequent disturbances in the region. Damage patterns were relatively congruent among storms and became more spatially heterogeneous with increasing disturbance intensity. Proximity to forest-nonforest edge, stand age, and soils/landform were consistently important damage predictors. The spatial extent and distribution of the first two damage predictors are extremely sensitive to anthropogenic modifications of forested landscapes, the most important disturbance agent in the northern Great Lakes. This provides circumstantial evidence suggesting anthropogenic activities are augmenting and/or diminishing the ecological effects of the natural wind disturbance regime. Natural disturbances of intermediate size and intensity are significant agents of change in this region, and likely in other regions, deserving more attention from ecologists and biogeographers.

Direct estimation of aboveground biomass with spectral reflectance data has proven challenging for high biomass forests of the Pacific Northwestern United States. We present an alternative modeling strategy which uses Landsat's spatial, spectral and temporal characteristics to predict live forest carbon through integration of stand age and site index maps and locally calibrated Chapman-Richards curves. Predictions from the curve-fit model were evaluated at the local and landscape scales using two periods of field inventory data. At the pixel-level, the curve-fit model had large positive bias statistics and at the landscape scale over-predicted study area carbon for both inventory periods. Despite the over-estimation, the change in forest carbon estimated by the curve-fit model was well within the standard error of the inventory estimates. In addition to validating the curve-fit models carbon predictions we used Landsat data to evaluate the degree to which the field inventory plots captured the forest conditions of the study area. Landsat-based frequency histograms revealed the systematic sample of inventory plots effectively captured the broad range of forest conditions found in the study area, whereas stand age trajectories revealed a temporally punctuated shift in land- use which was not spectrally detected by the inventory sample.

Net ecosystem production (NEP) was estimated over a 10.9 × 10 4 km 2 forested region in western Oregon USA for 2 yr (2002–2003) using a combination of remote sensing, distributed meteorological data, and a carbon cycle model (CFLUX). High spatial resolution satellite data (Landsat, 30 m) provided information on land cover and the disturbance regime. Coarser resolution satellite imagery (MODIS, 1 km) provided estimates of vegetation absorption of photosynthetically active radiation. A spatially distributed (1 km) daily time step meteorology was generated for model input by interpolation of meteorological station data. The model employed a light use efficiency approach for photosynthesis. It was run over a 1 km grid. This approach captured spatial patterns in NEP associated with climatic gradients, ecoregional differences in NEP generated by different management histories, temporal variation in NEP associated with interannual variation in climate and changes in NEP associated with recovery from disturbances such as the large forest fire in southern Oregon in 2002. Regional NEP averaged 174 gC m −2 yr −1 in 2002 and 142 gC m −2 yr −1 in 2003. A diagnostic modelling approach of this type can provide independent estimates of regional NEP for comparison with results of inversion or boundary layer budget approaches. DOI: 10.1111/j.1600-0889.2006.00221.x

Remote sensing, geographic information systems, and modeling have combined to produce a virtual explosion of growth in ecological investigations and applications that are explicitly spatial and temporal. Of all remotely sensed data, those acquired by Landsat sensors have played the most pivotal role in spatial and temporal scaling. Modern terrestrial ecology relies on remote sensing for modeling biogeochemical cycles and for characterizing land cover, vegetation biophysical attributes, forest structure, and fragmentation in relation to biodiversity. Given the more than 30-year record of Landsat data, mapping land and vegetation cover change and using the derived surfaces in ecological models is becoming commonplace. In this article, we summarize this large body of work, highlighting the unique role of Landsat.

Boreal peatlands play a major role in carbon and water cycling and other global environmental processes but understanding this role is constrained by inconsistent representation of peatlands on, or omission from, many global land cover maps. The comparison of several widely used global and continental-scale databases on peatland distribution with a detailed map for the St. Petersburg region of Russia showed significant under-reporting of peatland area, or even total omission. Analysis of the spatial agreement and disagreement with the detailed regional map indicated that the error of comission (overestimation) was significantly lower than the error of omission (underestimation) which means, that overall, peatlands were correctly classified as such in coarse resolution datasets but a large proportion (74–99%) was overlooked. The coarse map resolution alone caused significant omission of peatlands in the study region. In comparison to categorical maps, continuous field mapping approach utilizing MODIS sensor data showed potential for a greatly improved representation of peatlands on coarse resolution maps. Analysis of spectral signatures of peatlands with different types of surface vegetation suggested that improved mapping of boreal peatlands on categorical maps is feasible. The lower reflectance of treeless peatlands in the near- and shortwave-infrared parts of the electromagnetic spectrum is consistent with the spectral signature of sphagnum mosses. However, when trees are present, the canopy architecture appears to be more important in defining the overall spectral reflectance of peatlands. A research focus on developing remote sensing methods for boreal peatlands is needed for adequate characterization of their global distribution.