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Accurate and reliable information about land cover and land use is essential to carbon cycle and climate change modeling. While historical regional-to-global scale land cover and land use data products had been produced by AVHRR and MSS/TM, this task has been advanced by sensors such as MODIS and ETM since the latter 1990s. While the accuracies and reliabilities of these data products have been improved, there have been reports from the modeling community that additional work is needed to reduce errors so that the uncertainties associated with the global carbon cycle and climate change modeling can be addressed. Remotely sensed data collected in different wavelength regions, at different viewing geometries, usually provide complementary information. Their combination has the potential to enhance remote sensing capabilities in discriminating important land cover components. In this paper, we studied multi-angle data fusion, and optical-SAR data fusion for land cover classification at regional spatial scale in the temperate forests of the eastern United States. Data from EOS-MISR, Landsat-ETM+ and RadarSat-SAR were used. The results showed significantly improved land cover classification accuracy when using the data fusion approach. These results may benefit future land cover products for global change research.

Spatial and temporal variations in net primary production (NPP) are of great importance to ecological studies, natural resource management, and terrestrial carbon sink estimates. However, most of the existing estimates of interannual variation in NPP at regional and global scales were made at coarse resolutions with climate-driven process models. In this study, we quantified global NPP variation at an 8 km and 10-day resolution from 1981 to 2000 based on satellite observations. The high resolution was achieved using the GLObal Production Efficiency Model (GLO-PEM), which was driven with variables derived almost entirely from satellite remote sensing. The results show that there was an increasing trend toward enhanced terrestrial NPP that was superimposed on high seasonal and interannual variations associated with climate variability and that the increase was occurring in both northern and tropical latitudes. NPP generally decreased in El Nino season and increased in La Nina seasons, but the magnitude and spatial pattern of the response varied widely between individual events. Our estimates also indicate that the increases in NPP during the period were caused mainly by increases in atmospheric carbon dioxide and precipitation. The enhancement of NPP by warming was limited to northern high latitudes (above 50°N); in other regions, the interannual variations in NPP were correlated negatively with temperature and positively with precipitation.

The Boreal Ecosystem Atmosphere Study (BOREAS) was a large, multiyear internationally supported study designed to improve our understanding of the boreal forest biome and its interactions with the atmosphere, biosphere, and the carbon cycle in the face of global climate change. In the initial phase of this study (early 1990s), remote sensing played a key role by providing products needed for planning and modeling. During and after the main BOREAS field campaigns (1994 and 1996), innovative remote sensing approaches and analyses expanded our understanding of the boreal forest in four key areas: (1) definition of vegetation structure, (2) land-cover classification, (3) assessment of the carbon balance, and (4) links between surface properties, weather, and climate. In addition to six BOREAS special issues and over 500 journal papers, a principal legacy of BOREAS is its well-documented and publicly available database, which provides a lasting scientific resource and opportunity to further advance our understanding of this critical northern biome., Funding sources for BOREAS included NASA, the US National Science Foundation, the US Geological Survey, the US Forest Service, the US Environmental Protection Agency, the Canada Centre for Remote Sensing, Natural Resources Canada, Environment Canada, Natural Sciences and Engineering Research Council of Canada, Agriculture and Agri-Food Canada, National Research Council of Canada, Parks Canada, Canadian Forest Service, Institute for Space and Terrestrial Science (now CRESTech), Canadian Space Agency, and the Royal Society of Canada.

The Chesapeake Bay is the largest estuary in the United States, encompassed by a watershed extending 168,000 km 2 over portions of six states and Washington, D.C. Restoration of the Bay has been the focus of a two-decade regional partnership of local, state and federal agencies, including a network of scientists, politicians and activists interacting through various committees, working groups, and advisory panels. The effectiveness of the restoration effort has been mixed, with both notable successes and failures. The overall health of the Bay has not declined since the restoration was initiated in 1983, but many of the advances have been offset by the pressure of increasing population and exurban sprawl across the watershed. The needs of the Chesapeake Bay Program are many, but the greatest is accurate information on land cover and land use change, primarily to assess the implications for water quality, examine various restoration scenarios, and calibrate spatial models of the urbanization process. We report here on a number of new land cover and land use data products, and associated applications to assist vulnerability assessment, integrated ecosystem analysis, and ultimately Bay restoration. We provide brief overviews of applications to model new residential development, assess losses and vulnerability of resource lands, and identify the factors that disrupt the health of streams in small watersheds. These data products and approaches are being applied by a number of agencies involved with the restoration effort, including the Chesapeake Bay Program's activities focused on living resources, water quality, and sound land use.

Time series analysis of a climate-driven model of malaria transmission shows limited evidence for an increase in suitability during the last century across Africa. Outside areas where climate was always or never suitable

A series of LANDSAT spectral vegetation index images, calibrated to a common reference image, were used to model the phenological dynamics of 30 quaking aspen (Populus tremuloides) and 29 black spruce (Picea mariana) stands in the boreal forest of northeast Minnesota. Phenology models of the stands were used in a simplified species-specific canopy modeling framework to estimate the amount of photosynthetically active radiation (PAR) intercepted by the canopy annually. The relationship between annual PAR interception (/spl Sigma/IPAR) and measured annual above-ground net primary production (AANPP), that is, the dry matter yield of /spl Sigma/IPAR, was examined. Age was a primary determinant of the relationship between /spl Sigma/IPAR and AANPP in aspen, with /spl Sigma/IPAR in young stands exhibiting a strong predictive capability relative to mature stands. This observation is consistent with increased maintenance respiration demands as the ratio of total to foliar biomass increases with stand age. /spl Sigma/IPAR was a good predictor of AANPP in spruce stands. Calculated values of the dry matter yield of /spl Sigma/IPAR were within the range of literature reports for other forest ecosystems, but further research to understand the sources of between-stand variations is required, and on-going. >

The mid-Atlantic Regional Earth Science Applications Center (RESAC) was established in the Geography Department at the University of Maryland (UMD) by NASA's Earth Science Applications Program. The mid-Atlantic RESAC is to provide improved land cover mapping and ecological modeling capabilities for a diverse consortium of partners in government, academia, industry and NGOs within the 178000 km/sup 2/ Chesapeake Bay watershed. It is one of 7 regional centers established nationwide and leverages expertise in satellite remote sensing to address applications of regional significance including land cover change, land use planning, carbon exchange modeling, and integrated environmental monitoring. Examples of issues that are being addressed include nutrient runoff to the Chesapeake Bay, urban sprawl, farm and forest productivity, landscape fragmentation effects on biodiversity, a land manager decision support system, and educational outreach. The mid-Atlantic RESAC provides an example of how scientific advances can be focused on practical applications that challenge our ability to manage resources sustainably. A brief overview of the RESAC is provided and specific applications are reviewed using examples that emphasize the utility of remote sensing and GIS capabilities. Results of field activities undertaken during the 1999 growing season, for example, are used with a fusion of multi-temporal Landsat-7 Enhanced Thematic Mapper and SPOT panchromatic imagery to classify vegetation types, and to characterize development of the severe drought that took place in the region.

The NOAA-AVHRR satellite record has provided a new view of terrestrial vegetation processes but has been exploited relatively little for the recovery of physical environment variables such as surface wetness, air temperature, or near-surface humidity. Recent advances in techniques to recover these variables over large areas has permitted the development of improved terrestrial primary production models. The authors report on the development of techniques to recover physical environment variables from 8-year time series of AVHRR observations. The recovery algorithms incorporate visible, near-infrared and thermal infrared radiation measurements in a contextual array. The accuracy of the recovered variables, when compared to surface meteorological stations over a broad range of environments, was shown to be within 95% confidence limits of /spl plusmn/6.8/spl deg/C for a 36/spl deg/C air temperature range; /spl plusmn/1.28 cm for a 3.6 cm atmospheric water vapor range; and /spl plusmn/11.2 mb for a 58 mb vapor pressure deficit range. Recovered values of surface soil moisture explained 77% of the observed variability at a temperate grassland site. Maps of retrieved variables for several study areas had good relative accuracy when compared to spatially interpolated surface observations. Multi-temporal global maps of these variables are presented and spatially analyzed relative to other information sources.

[1] Over the past 100 years, high northern latitude regions have experienced more rapid warming than elsewhere on Earth. This trend is expected to continue over the next century. Arctic tundra, boreal forests, and peatlands are already undergoing major changes, reinforced by the cascading effects of thawing permafrost, increasing disturbance (particularly fire and insect pests), and altered surface hydrology. These changes influence processes at the ecosystem and landscape scales, including energy balance and vegetation productivity, which feed back to regional and global climate in addition to affecting wildlife habitat and ecosystem resources available to local communities.

Earth-observing satellites have provided an unprecedented view of the land surface but have been exploited relatively little for the measurement of environmental variables of particular relevance to epidemiology. Recent advances in techniques to recover continuous fields of air temperature, humidity, and vapour pressure deficit from remotely sensed observations have significant potential for disease vector monitoring and related epidemiological applications. We report on the development of techniques to map environmental variables with relevance to the prediction of the relative abundance of disease vectors and intermediate hosts. Improvements to current methods of obtaining information on vegetation properties, canopy and surface temperature and soil moisture over large areas are also discussed. Algorithms used to measure these variables incorporate visible, near-infrared and thermal infrared radiation observations derived from time series of satellite-based sensors, focused here primarily but not exclusively on the Advanced Very High Resolution Radiometer (AVHRR) instruments. The variables compare favourably with surface measurements over a broad array of conditions at several study sites, and maps of retrieved variables captured patterns of spatial variability comparable to, and locally more accurate than, spatially interpolated meteorological observations. Application of multi-temporal maps of these variables are discussed in relation to current epidemiological research on the distribution and abundance of some common disease vectors.

Summary The practicality of ecological research at a global scale is increasing as a result of the development of satellite remote sensing which enables surface conditions to be inferred for large areas at high spatial and temporal resolutions. Remote sensing of canopy light absorption, for example, enables an important component of canopy carbon assimilation rates to be estimated over extensive areas. Modelling of carbon exchange and net storage may also be facilitated, owing to natural selection for a narrow range of light-use efficiencies among a wide range of plant functional types. This “functional convergence” arises from resource allocation strategies that appear to maximise the benefits of carbon (energy) and nutrients (e.g. mainly nitrogen) relative to the costs of acquisition. Convergence is most evident on a leaf mass per unit ground area basis, a measure that more closely reflects the costs of resource acquisition than does leaf area. We demonstrate, however, that light absorption and utilisation are decoupled so that convergence is to be expected on gross production rather than net production, owing to differences in respiratory costs associated with synthesis and maintenance of plant constituents and associated “payback intervals” on carbon investment in different functional types. A link between fitness and carbon gain is noted in relation to gross primary production. We conclude that, while functional convergence provides a basis for the use of remote sensing of light absorption in measurement of primary production, models driven with light absorption need to include terms that describe the actual respiratory costs of maintenance and synthesis. Quantification of these processes will improve large area primary production models, and enhance the value of the information that can be acquired by remote sensing.

Deforestation and forest degradation account for between 7% and 30% of total anthropogenic carbon emissions [Canadell et al., 2007; Denman et al., 2007]. This wide range of values results from three major uncertainties: rates of deforestation, carbon stocks (biomass and soils) in forests prior to deforestation, and changes in carbon stocks within forests (i.e., both increases from growth and decreases from degradation). Historically, rates of deforestation and reforestation, together with estimates of forest biomass, have been used to calculate the net flux of carbon between terrestrial ecosystems and the atmosphere [Woodwell et al., 1983; Detwiler and Hall, 1988; Hall and Uhlig, 1991; Fearnside, 2000; DeFries et al., 2002; Achard et al., 2004; Houghton, 20037]. This net flux is the difference between the sinks of carbon in growing and recovering forests and the sources from burning and decay associated with deforestation. Satellite imagery, particularly from Landsat, has long been used to sample deforestation rates [DeFries et al., 2002; Achard et al., 2004; Skole and Tucker, 1993; Hansen et al., 2008]. Obtaining estimates of biomass, reforestation, and forest growth and degradation, however, has proven more difficult.

Recent advances in remote sensing enable the mapping and monitoring of carbon stocks without relying on extensive in situ measurements. The Democratic Republic of the Congo (DRC) is among the countries where national forest inventories (NFI) are either non-existent or out of date. Here we demonstrate a method for estimating national-scale gross forest aboveground carbon (AGC) loss and associated uncertainties using remotely sensed-derived forest cover loss and biomass carbon density data. Lidar data were used as a surrogate for NFI plot measurements to estimate carbon stocks and AGC loss based on forest type and activity data derived using time-series multispectral imagery. Specifically

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.

Recent large and frequent fires above the Alaskan arctic circle have forced a reassessment of the ecological and climatological importance of fire in arctic tundra ecosystems. Here we provide a general overview of the occurrence, distribution, and ecological and climate implications of Alaskan tundra fires over the past half-century using spatially explicit climate, fire, vegetation and remote sensing datasets for Alaska. Our analyses highlight the importance of vegetation biomass and environmental conditions in regulating tundra burning, and demonstrate that most tundra ecosystems are susceptible to burn, providing the environmental conditions are right. Over the past two decades, fire perimeters above the arctic circle have increased in size and importance, especially on the North Slope, indicating that future wildfire projections should account for fire regime changes in these regions. Remote sensing data and a literature review of thaw depths indicate that tundra fires have both positive and negative implications for climatic feedbacks including a decadal increase in albedo radiative forcing immediately after a fire, a stimulation of surface greenness and a persistent long-term (>10 year) increase in thaw depth. In order to address the future impact of tundra fires on climate, a better understanding of the control of tundra fire occurrence as well as the long-term impacts on ecosystem carbon cycling will be required.

Corrigendum to “Cajander larch (Larix cajanderi) biomass distribution, fire regime and post-fire recovery in northeastern Siberia” published in Biogeosciences, 9, 3943–3959, 2012 L. T. Berner1, P. S. A. Beck1, M. M. Loranty1,*, H. D. Alexander2,**, M. C. Mack2, and S. J. Goetz1 1The Woods Hole Research Center, 149 Woods Hole Road, Falmouth, MA 02540-1644, USA 2University of Florida, Department of Biology, P.O. Box 118525, Gainesville, FL 32611, USA *currently at: Colgate University, Department of Geography, 13 Oak Drive, Hamilton, NY 13346, USA **currently at: University of Texas – Brownsville, Department of Biological Sciences, 80 Fort Brown, Brownsville, TX 78520, USA Correspondence to: L. T. Berner (lberner@whrc.org)

As reported by FAO (2005 State of the World’s Forests (Rome: UNFAO), 2010 Forest Resource Assessment (FRA) 2010/095 (Rome: UNFAO)), Indonesia experiences the second highest rate of deforestation among tropical countries. Hence, timely and accurate forest data are required to combat deforestation and forest degradation in support of climate change mitigation and biodiversity conservation policy initiatives. Within Indonesia, Sumatra Island stands out due to the intensive forest clearing that has resulted in the conversion of 70% of the island’s forested area through 2010. We present here a hybrid approach for quantifying the extent and change of primary forest in Sumatra in terms of primary intact and primary degraded classes using a per-pixel supervised classification mapping followed by a Geographic Information System (GIS)-based fragmentation analysis. Loss of Sumatra’s primary intact and primary degraded forests was estimated to provide suitable information for the objectives of the United Nations Framework on Climate Change (UNFCCC) Reducing Emission from Deforestation and Forest Degradation (REDD and REDD+) program. Results quantified 7.54 Mha of primary forest loss in Sumatra during the last two decades (1990‐2010). An additional 2.31 Mha of primary forest was degraded. Of the 7.54 Mha cleared, 7.25 Mha was in a degraded state when cleared, and 0.28 Mha was in a primary state. The rate of primary forest cover change for both forest cover loss and forest degradation slowed over the study period, from 7.34 Mha from 1990 to 2000, to 2.51 Mha from 2000 to 2010. The Geoscience Laser Altimeter System (GLAS) data set was employed to evaluate results. GLAS-derived tree canopy height indicated a significant structural difference between primary intact and primary degraded forests (mean height 28 m 8.7 m and 19 m 8.2 m, respectively). The results demonstrate a method for quantifying primary forest cover stand-replacement disturbance and degradation that can be replicated across the tropics in support of REDDC initiatives.

Fire activity in boreal forests has increased recently with climate warming, altering stand structure and composition in many areas. Changes in stand dynamics have the potential to alter C cycling and biophysical processes, with feedbacks to global and regional climate. Here, we assess the interactions between fire, stand structure, and aboveground C accumulation and storage within boreal forests of interior Alaska, where increased fire severity is predicted to shift forest composition from predominantly black spruce (Picea mariana) to greater deciduous cover. We measured aboveground biomass and net primary productivity (ANPP) of trees and large shrubs, snags, and downed woody debris across 44 mid-successional (20–59 years since fire) stands of varying deciduous importance value (IV), determined by relative density, basal area, and frequency of deciduous trees and large shrubs within each stand. Aboveground biomass, ANPP, and deciduous snag biomass increased significantly with increased deciduous IV and years since fire. Deciduous IV had little influence on evergreen snag biomass and downed woody debris, but both C pools decreased with years since fire. Forest type also affected stand structure and C pools. Black spruce stands had shorter trees with less basal area and aboveground biomass and slower rates of biomass accumulation and ANPP compared to those dominated by trembling aspen (Populus tremuloides) or Alaska birch (Betula neoalaskana). These parameters in black spruce stands were similar to mixed stands of black spruce and aspen but were often lower than mixed stands of black spruce and Alaska birch. Much of the biomass accumulation in deciduous stands was attributed to higher tree-level ANPP, allowing individual stems of deciduous species to accumulate more stemwood/bark faster than black spruce trees. If increased fire activity shifts stand composition from black spruce to increased deciduous cover, ANPP, aboveground tree/large shrub biomass, and deciduous snag biomass will increase, leading to increased aboveground C pools in mid-successional forest stands of interior Alaska. While species dominance shifts like these will impact aboveground patterns of landscape-level C cycling in boreal forests, variations in soil C pools and forest properties like albedo must also be assessed to accurately determine implications for global and regional climate.

Biomass mapping using satellite imagery is a rapidly evolving field that has been greatly facilitated in recent years by the advent of LiDAR remote sensing coupled with co-located field measurements. The biomass map of Africa that we published in 2008 did not take direct advantage of coincident field and LiDAR measurements, as our more recent efforts have. The criticisms of our earlier map by Mitchard et al (2011 Environ. Res. Lett. 6 049001) are duly noted and worthwhile, although they are also limited in several respects that we describe. Most notably, they assess our map with field data sets that are only representative of a subset of conditions across the study domain, thus they not only inadequately characterize undisturbed tropical forest regions but also the diverse disturbance dynamics that are captured in satellite imagery. We point out the limitations of their assessment and focus on a way forward, moving beyond both inadequate field sampling and remote sensing to an approach the captures the full range of dynamics by directly coupling field and satellite measurements.

The response of boreal forests to anthropogenic climate change remains uncertain, with potentially significant impacts for the global carbon cycle, albedo, canopy evapotranspiration and feedbacks into further climate change. Here, we focus on tree-ring data from the Firth River site at treeline in northeastern Alaska, in a tundra‐forest transition region where pronounced warming has already occurred. Both tree-ring width (TRW) and maximum latewood density (MXD) chronologies were developed to identify the nature of tree growth and density responses to climatic and environmental changes in white spruce (Picea glauca), a dominant Arctic treeline species. Good agreement was found between the interannual fluctuations in the TRW chronology and summer temperatures from 1901 to 1950, whereas no significant relationships were found from 1951 to 2001, supporting evidence of significant divergence between TRW and summer temperature in the second half of the 20th century. In contrast to this unstable climatic response in the TRW record, the high frequency July‐August temperature signal in the MXD series seems reasonably stable through the 20th century. Wider and denser rings were more frequent during the 20th century, particularly after 1950, than in previous centuries. Finally, comparison between the tree-ring proxies and a satellite-derived vegetation index suggests that TRW and MXD correlate with vegetation productivity at the landscape level at different times of the growing season.

To assess ongoing changes in high latitude vegetation productivity we compared spatiotemporal patterns in remotely sensed vegetation productivity in the tundra and boreal zones of North America and Eurasia. We compared the long-term GIMMS (Global Inventory Modeling and Mapping Studies) NDVI (Normalized Difference Vegetation Index) to the more recent and advanced MODIS (Moderate Resolution Imaging Spectroradiometer) NDVI data set, and mapped circumpolar trends in a gross productivity metric derived from the former. We then analyzed how temporal changes in productivity differed along an evergreen–deciduous gradient in boreal Alaska, along a shrub cover gradient in Arctic Alaska, and during succession after fire in boreal North America and northern Eurasia. We find that the earlier reported contrast between trends of increasing tundra and decreasing boreal forest productivity has amplified in recent years, particularly in North America. Decreases in boreal forest productivity are most prominent in areas of denser tree cover and, particularly in Alaska, evergreen forest stands. On the North Slope of Alaska, however, increases in tundra productivity do not appear restricted to areas of higher shrub cover, which suggests enhanced productivity across functional vegetation types. Differences in the recovery of post-disturbance vegetation productivity between North America and Eurasia are described using burn chronosequences, and the potential factors driving regional differences are discussed.

Observations from the moderate resolution imaging spectroradiometer (MODIS) were used in combination with a large data set of field measurements to map woody above-ground biomass (AGB) across tropical Africa. We generated a best-quality cloud-free mosaic of MODIS satellite reflectance observations for the period 2000‐2003 and used a regression tree model to predict AGB at 1 km resolution. Results based on a cross-validation approach show that the model explained 82% of the variance in AGB, with a root mean square error of 50.5 Mg ha −1 for a range of biomass between 0 and 454 Mg ha −1 . Analysis of lidar metrics from the Geoscience Laser Altimetry System (GLAS), which are sensitive to vegetation structure, indicate that the model successfully captured the regional distribution of AGB. The results showed a strong positive correlation (R 2 = 0.90) between the GLAS height metrics and predicted AGB.

Vegetation composition at high latitudes plays a critical role in the climate and, in turn, is strongly affected by the climate. The increased frequency of fires expected as a result of climate warming at high latitudes will feedback positively to further warming by releasing carbon to the atmosphere, but will also feedback negatively by increasing the surface albedo. The net effect is complex because the severity of fire affects the trajectory of both carbon stocks and albedo change following a fire, and these are likely to differ between high latitude ecosystems in North America and northern Eurasia. Here we use growth trajectories, productivity trends and regional carbon fluxes to characterize these fire- and climate-driven changes.

Satellite sensors have been imaging Earth9s land surface, oceans, and ice fields since the early 1970s. The data sets derived from these observations have chronicled transformations on the planet9s surface, ranging from urban sprawl to tropical deforestation, covering even the most remote regions of the globe. Scientists in the United States, supported by the National Aeronautics and Space Administration (NASA) and other agencies, have demonstrated the utility and societal benefits of the data in a wide range of applications, including community planning, crop monitoring, coral reef mapping, water-quality assessment, disaster management, and homeland security. Sadly, this is about to change.

The extent, density, and configuration of the built environment—such as buildings, roads, parking lots, and other materials constructed for human use—have an impact on a wide range of biogeochemical and hydrological processes. These built areas, which are impervious to water infiltration, modify hydrology through the combined influence of increased peak flows, reduced base flows, flashier stream hydrographs (decreased lag times between storm events and peak discharge), and changes in bank and streambed erosion [Nilsson et al., 2003]. Additionally, increasing impervious cover has long been known to amplify point source pollution discharges into streams, including chemical runoff from parking lots and roads [Schuelen 1994]. Two maps of the built environment, expressed in terms of impervious surface area, have been derived for areas that encompass the 168,000-square-kilometer Chesapeake Bay watershed (Figure l), a region that has been highly altered by human land use [Goetz et al., 2004; Jantz et al., 2005]. One map was developed for the region at fine (30-square-meter) spatial resolution, and the other covers the extent of the conterminous United States at one-square-kilometer resolution [Elvidge et al., 2004].A finer-resolution regional map was used to assess the quality of the national map, demonstrating the utility the latter map for a range of applications related to monitoring land transformation and assessing watershed impacts.

Radiometric surface temperatures retrieved at various spatial resolutions from aircraft and satellite measurements at the FIFE site in eastern Kansas were compared with near-surface temperature measurements to determine the accuracy of the retrieval techniques and consistency between the various sensors. Atmospheric characterizations based on local radiosonde profiles of temperature, pressure, and water vapor were used with the LOWTRAN-7 and MODTRAN atmospheric radiance models to correct measured thermal radiances of water and grassland targets for atmospheric attenuation. Comparison of retrieved surface temperatures from a helicopter-mounted modular multispectral radiometer (MMR) (∼5-m “pixel”), C-130 mounted thematic mapper simulator (TMS) (NS001, ∼20-m pixel), and the Landsat 5 thematic mapper (TM) (120-m pixel) was done. Differences between atmospherically corrected radiative temperatures and near-surface measurements ranged from less than 1°C to more than 8°C. Corrected temperatures from helicopter-MMR and NS001-TMS were in general agreement with near-surface infrared radiative thermometer (IRT) measurements collected from automated meteorological stations, with mean differences of 3.2°C and 1.7°C for grassland targets. Much better agreement (within 1°C) was found between the retrieved aircraft surface temperatures and near-surface measurements acquired with a hand-held mast equipped with a MMR and IRT. The NS001-TMS was also in good agreement with near-surface temperatures acquired over water targets. In contrast, the Landsat 5 TM systematically overestimated surface temperature in all cases. This result has been noted previously but not consistently. On the basis of the results reported here, surface measurements were used to provide a calibration of the TM thermal channel. Further evaluation of the in-flight radiometric calibration of the TM thermal channel is recommended.

Utilizing single-view-angle visible and NIR satellite observations, it was possible to make the necessary radiometric corrections to produce reliable estimates of the simple ratio and the normalized difference vegetation index as inputs to surface evaporation models. Agreement was achieved between helicopter and satellite measures of surface reflectance to within +/-1 percent absolute for both the visible and the NIR bands. A theoretical calculation utilizing radiative transfer models indicates that this translates into a +/-10 percent error in the estimation in Fpar.

Visible to shortwave infrared radiometric data collected by a number of remote sensing instruments on aircraft and satellite platforms were compared over common areas in the First International Satellite Land Surface Climatology Project (ISLSCP) Field Experiment (FIFE) site on August 4, 1989, to assess their radiometric consistency and the adequacy of atmospheric correction algorithms. The instruments in the study included the Landsat 5 thematic mapper (TM), the SPOT 1 high-resolution visible (HRV) 1 sensor, the NS001 thematic mapper simulator, and the modular multispectral radiometers (MMRs). Atmospheric correction routines analyzed were an algorithm developed for FIFE, LOWTRAN 7, and 5S. A comparison between corresponding bands of the SPOT 1 HRV 1 and the Landsat 5 TM sensors indicated that the two instruments were radiometrically consistent to within about 5%. Retrieved surface reflectance factors using the FIFE algorithm over one site under clear atmospheric conditions indicated a capability to determine near-nadir surface reflectance factors to within about 0.01 at a reflectance of 0.06 in the visible (0.4–0.7 μm) and about 0.30 in the near infrared (0.7–1.2 μm) for all but the NS001 sensor. All three atmospheric correction procedures produced absolute reflectances to within 0.005 in the visible and near infrared. In the shortwave infrared (1.2–2.5 μm) region the three algorithms differed in the retrieved surface reflectances primarily owing to differences in predicted gaseous absorption. Although uncertainties in the measured surface reflectance in the shortwave infrared precluded definitive results, the 5S code appeared to predict gaseous transmission marginally more accurately than LOWTRAN 7.

Landsat time-series multi-spectral data, GLAS (Geoscience Laser Altimeter System) height data and a regression tree model were used to estimate tree height for a transect in Sub-Saharan Africa ranging from the Sahara Desert through the Congo Basin to the Kalahari Desert (+22 to −22° latitude and 23 to 24° longitude). Objectives included comparing the performance of Landsat 7- and 8-derived inputs separately and combined in mapping tree height at a regional scale, assessing the relative value of good observation counts and different Landsat spectral inputs for tree height estimation across a range of environments, and describing tree height distributions and discontinuities in Sub-Saharan Africa. A total of 5371 images were processed and per pixel quality assessed to create a set of multi-temporal metrics for the 2013 and 2014 calendar years for Landsat 7 only, Landsat 8 only and both Landsat 7 and 8 combined. Differences in performance were slight between different sensor inputs. However, performance generally improved with increasing numbers of good observations. Metrics derived from red reflectance data contributed most in estimating tree height. The regression tree algorithm accurately reproduced the LiDAR-derived height training data with an overall mean absolute error (MAE) for tree height estimation of 2.45m using integrated Landsat 7 and 8 data. Significant underestimations were quantified for tall tree cover (MAE of 4.65m for >20m heights) and overestimations for low/no tree cover (MAE 1.61 for

The ability to map and monitor the spatial extent of the built environment, and associated temporal changes, has important societal and economic relevance. Multitemporal satellite data now provide the potential for mapping and monitoring urban land use change, but require the development of accurate and repeatable techniques that can be extended to a broad range of conditions and environments. We have developed an approach using Landsat imagery, trained with the high resolution data sets, that identifies impervious surface areas (buildings, roads, etc) at subpixel resolution. We report on application of the approach over a range of scales, from the local to the entire Chesapeake Bay Watershed (168,000 km 2 ). We also developed maps of past changes in the built environment, used them to calibrate a spatial predictive model, and generated maps of expected future change under various policy scenarios out to year 2030. We believe these techniques have applicability to a wide range of applications.

BackgroundMapping 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.ResultsWe 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.ConclusionsBiomass 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.