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Summary Human pressure mapping is important for understanding humanity's role in shaping Earth's patterns and processes. Our ability to map this influence has evolved, thanks to powerful computing, Earth-observing satellites, and new bottom-up census and crowd-sourced data. Here, we provide the latest temporally inter-comparable maps of the terrestrial human footprint and assessment of change in human pressure at global, biome, and ecoregional scales. In 2013, 42% of terrestrial Earth could be considered relatively free of direct anthropogenic disturbance, and 25% could be classed as “wilderness” (the least degraded end of the human footprint spectrum). Between 2000 and 2013, 1.9 million km2—an area the size of Mexico—of land relatively free of human disturbance became highly modified. The majority of this occurred within tropical and subtropical grasslands, savannah, and shrubland ecosystems, but the rainforests of Southeast Asia also underwent rapid modification. Our results show that humanity's footprint is eroding Earth's last intact ecosystems, and greater efforts are urgently needed to retain them.

Climate change‐driven droughts and insect outbreaks are becoming more frequent and widespread, increasing forest vulnerability to mortality. By addressing the impacts of climate and insects on tree growth preceding death, we can better understand tree mortality risk under a changing climate. Here, we used tree stature and interannual growth (basal area increment; BAI) to assess processes leading to trembling aspen (Populus tremuloides) survival or mortality during an unprecedented leaf miner (Phyllocnistis populiella) outbreak in boreal North America. We identified eight sites (22 plots) in the longest running forest monitoring network in Alaska, spanning ~350 km of latitude, that experienced ≥0.25 Mg·ha−1·yr−1 aspen mortality during the outbreak. We compared the size and canopy position, growth patterns, and sensitivity to climate and leaf mining of aspen that survived (living; n = 84) vs. died (dying; n = 76) and linked the normalized difference vegetation index (NDVI) to plot‐level aspen growth and stand biomass recruitment, growth, and mortality. Dying aspen were in the subcanopy, smaller in diameter, and after a drought in 1957 had lower growth than living aspen until death. Before the outbreak, growth of all trees was positively influenced by moisture and negatively by temperature, but only living trees maintained this climate response during the outbreak. Leaf mining reduced growth of both groups, exerting at least a twofold greater impact than climate. The NDVI captured plot‐level tree growth and stand biomass growth and mortality, yet it was nearly two times more strongly associated with living than dying tree growth and 12 times more strongly associated with biomass growth than mortality. These differences suggest that NDVI may inadequately detect insect‐driven dieback and dispersed mortality of aspen across the boreal biome. Our findings reveal that a historic drought triggered a multi‐decadal growth decline that predisposed aspen to mortality during the leaf miner outbreak and that while aspen growth is influenced by moisture and temperature, it is more strongly affected by P. populiella. We conclude that as the climate warms and insect outbreaks increase in frequency and magnitude at high latitudes, we should expect to see persistent and greater declines in aspen growth and increases in mortality.

Reducing deforestation underpins efforts to conserve global biodiversity. However, this focus on retaining forest cover overlooks the multitude of anthropogenic pressures that can degrade forest quality in ways that may imperil biodiversity. Here we use the latest remotely-sensed measures of forest structural condition and associated human pressures across the global humid tropics to provide the first estimates of the importance of forest quality, relative to forest cover, in mitigating extinction risk for rainforest vertebrates worldwide. We found tropical rainforests of intact structural condition and minimal human pressures played an outsized role in reducing the odds of species being threatened or having a declining population. Further, the effects of forest quality in mitigating extinction risk were stronger when small amounts of high quality forest remained within species geographic ranges, as opposed to when large extents were forested but of low quality. Our research underscores a critical need to focus global environmental policy and conservation strategies toward the targeted protection of the last remaining undisturbed forest landscapes, in concert with strategies aimed at preserving, restoring and reconnecting remnant forest fragments across the hyperdiverse humid tropics.

Many global environmental agendas, including halting biodiversity loss, reversing land degradation, and limiting climate change, depend upon retaining forests with high ecological integrity, yet the scale and degree of forest modification remain poorly quantified and mapped. By integrating data on observed and inferred human pressures and an index of lost connectivity, we generate a globally consistent, continuous index of forest condition as determined by the degree of anthropogenic modification. Globally, only 17.4 million km2 of forest (40.5%) has high landscape-level integrity (mostly found in Canada, Russia, the Amazon, Central Africa, and New Guinea) and only 27% of this area is found in nationally designated protected areas. Of the forest inside protected areas, only 56% has high landscape-level integrity. Ambitious policies that prioritize the retention of forest integrity, especially in the most intact areas, are now urgently needed alongside current efforts aimed at halting deforestation and restoring the integrity of forests globally., Mapping and quantifying degree of forest modification is critical to conserve and manage forests. Here the authors propose a new quantitative metric for landscape integrity and apply it to a global forest map, showing that less than half of the world’s forest cover has high integrity, most of which is outside nationally designed protected areas.

SummaryOur ability to map humanity’s influence across Earth has evolved, thanks to powerful computing, a network of earth observing satellites, and new bottom-up census and crowd-sourced data. Here, we provide the latest temporally inter-comparable maps of the terrestrial Human Footprint, and assessment of change in human pressure at global, biome, and ecoregional scales. In 2013, 42% of terrestrial Earth could be considered relatively free of anthropogenic disturbance, and 25% could be classed as ‘wilderness’ (the least degraded end of the human footprint spectrum). Between 2000 and 2013, 1.9 million km2 - an area the size of Mexico - of land relatively free of human disturbance became highly modified. The majority of this occurred within tropical and subtropical grasslands, savannah, and shrubland ecosystems, but the rainforests of Southeast Asia also underwent rapid modification. Our results show that humanity’s footprint is eroding Earth’s last intact ecosystems, and greater efforts are urgently needed to retain them.

Many global environmental agendas, including halting biodiversity loss, reversing land degradation, and limiting climate change, depend upon retaining forests with high ecological integrity, yet the scale and degree of forest modification remains poorly quantified and mapped. By integrating data on observed and inferred human pressures and an index of lost connectivity, we generate the first globally-consistent, continuous index of forest condition as determined by degree of anthropogenic modification. Globally, only 17.4 million km2 of forest (40.5%) have high landscape level integrity (mostly found in Canada, Russia, the Amazon, Central Africa and New Guinea) and only 27% of this area is found in nationally-designated protected areas. Of the forest in protected areas, only 56% has high landscape level integrity. Ambitious policies that prioritize the retention of forest integrity, especially in the most intact areas, are now urgently needed alongside current efforts aimed at halting deforestation and restoring the integrity of forests globally.

Climate change is expected to alter the distribution of tree species because of critical environmental tolerances related to growth, mortality, reproduction, disturbances, and biotic interactions. How this is realized in 21st century remains uncertain, in large part due to limitations on plant migration and the impacts of landscape fragmentation. Understanding these changes is of particular concern for forest management, which requires information at an appropriately fine spatial resolution. Here we provide a framework and application for tree species vulnerability to climate change in the eastern United States that accounts for influential drivers of future distributions. We used species distribution models to project changes in habitat suitability at 800 m for 40 tree species that vary in physiology, range, and environmental niche. We then developed layers of adaptive capacity based on migration potential, forest fragmentation, and propagule pressure. These were combined into metrics of vulnerability, including an overall index and spatially explicit categories designed to inform management. Despite overall favorable changes in suitability, the majority of species and the landscape were considered vulnerable to climate change. Vulnerability was significantly exacerbated by projections of pests and pathogens for some species. Northern and high-elevation species tended to be the most vulnerable. There were, however, some notable areas of particular resilience, including most of West Virginia. Our approach combines some of the most important considerations for species vulnerability in a straightforward framework, and can be used as a tool for managers to prioritize species, areas, and actions.

Assessment of socioenvironmental benefits and feasibility identifies priority areas for restoring global tropical rainforests., Over 140 Mha of restoration commitments have been pledged across the global tropics, yet guidance is needed to identify those landscapes where implementation is likely to provide the greatest potential benefits and cost-effective outcomes. By overlaying seven recent, peer-reviewed spatial datasets as proxies for socioenvironmental benefits and feasibility of restoration, we identified restoration opportunities (areas with higher potential return of benefits and feasibility) in lowland tropical rainforest landscapes. We found restoration opportunities throughout the tropics. Areas scoring in the top 10% (i.e., restoration hotspots) are located largely within conservation hotspots (88%) and in countries committed to the Bonn Challenge (73%), a global effort to restore 350 Mha by 2030. However, restoration hotspots represented only a small portion (19.1%) of the Key Biodiversity Area network. Concentrating restoration investments in landscapes with high benefits and feasibility would maximize the potential to mitigate anthropogenic impacts and improve human well-being.

Roads facilitate development in remote forest regions, often with detrimental consequences for ecosystems. In the Congo Basin, unpaved logging roads used by timber firms, as well as paved and unpaved public roads, have expanded greatly. Comparing old (before 2003) and new (2003–2018) road datasets derived from Landsat imagery, we show that the total length of road networks inside logging concessions in Central Africa has doubled since 2003, whereas the total length of roads outside concessions has increased by 40%. We estimate that 44% of roads in logging concessions were abandoned by 2018, as compared to just 12% of roads outside concessions. Annual deforestation rates between 2000 and 2017 near (within 1 km) roads increased markedly and were highest for old roads, lowest for abandoned roads and generally higher outside logging concessions. The impact of logging on deforestation is partially ameliorated by the nearly fourfold higher rate of road abandonment inside concessions, but the overall expansion of logging roads in the Congo Basin is of broad concern for forest ecosystems, carbon storage and wildlife vulnerable to hunting. Road decommissioning after logging could play a crucial role in reducing the negative impacts of timber extraction on forest ecosystems. Road length has almost doubled in 2003–2018 within the Congo Basin forests. Deforestation within 1 km of roads has increased greatly. Almost half of the roads within logging concessions were subsequently abandoned, but most roads outside concessions endured.

Remotely sensed maps of global forest extent are widely used for conservation assessment and planning. Yet, there is increasing recognition that these efforts must now include elements of forest quality for biodiversity and ecosystem services. Such data are not yet available globally. Here we introduce two data products, the Forest Structural Condition Index (SCI) and the Forest Structural Integrity Index (FSII), to meet this need for the humid tropics. The SCI integrates canopy height, tree cover, and time since disturbance to distinguish short, open-canopy, or recently deforested stands from tall, closed-canopy, older stands typical of primary forest. The SCI was validated against estimates of foliage height diversity derived from airborne lidar. The FSII overlays a global index of human pressure on SCI to identify structurally complex forests with low human pressure, likely the most valuable for maintaining biodiversity and ecosystem services. These products represent an important step in maturation from conservation focus on forest extent to forest stands that should be considered “best of the last” in international policy settings., Measurement(s)forested areaTechnology Type(s)digital curationSample Characteristic - Environmentforest biomeSample Characteristic - LocationSouth America • Africa • Asia Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.9885341

As the Arctic warms, vegetation is responding, and satellite measures indicate widespread greening at high latitudes. This ‘greening of the Arctic’ is among the world’s most important large-scale ecological responses to global climate change. However, a consensus is emerging that the underlying causes and future dynamics of so-called Arctic greening and browning trends are more complex, variable and inherently scale-dependent than previously thought. Here we summarize the complexities of observing and interpreting high-latitude greening to identify priorities for future research. Incorporating satellite and proximal remote sensing with in-situ data, while accounting for uncertainties and scale issues, will advance the study of past, present and future Arctic vegetation change. acceptedVersion

Understanding spatial patterns of diversity in tropical forests is indispensable for their sustainable use and conservation. Recent studies have reported relationships between forest structure and α-diversity. While tree α-diversity is difficult to map via remote sensing, large-scale forest structure models are becoming more common, which would facilitate mapping the relationship between tree α-diversity and forest structure, contributing to our understanding of biogeographic patterns in the tropics. We developed a methodology to map tree α-diversity in tropical forest regions at 50 m spatial resolution using α-diversity estimates from forest inventories as response variables and forest structural metrics and environmental variables as predictors. To include forest structural metrics in our modelling, we first developed a method to map seven of these metrics integrating discrete light detection and ranging (LiDAR), multispectral, and synthetic aperture radar imagery (SAR). We evaluated this methodology in the Chocó region of Colombia, a tropical forest with high tree diversity and complex forest structure. The relative errors (REs) of the random forest models used to map the seven forest structural variables ranged from low (6%) to moderate (35%). The α-diversity maps had moderate RE; the maps of Simpson and Shannon diversity indices had the lowest RE (9% and 13%), followed by richness (17%), while Shannon and Simpson effective number of species indices had the highest RE, 27% and 47%, respectively. The highest concentrations of tree α-diversity are located along the Pacific Coast from the centre to the northwest of the Chocó Region and in non-flooded forest along the boundary between the Chocó region and the Andes. Our results reveal strong relationships between canopy structure and tree α-diversity, providing support for ecological theories that link structure to diversity via niche partitioning and environmental conditions. With modification, our methods could be applied to assess tree α-diversity of any tropical forest where tree α-diversity field observations coincident with LiDAR data.

Previous research indicates that the effects of climate warming, including shrub expansion and increased fire frequency may lead to declining lichen abundance in arctic tundra and northern alpine areas. Lichens are important forage for caribou (Rangifer tarandus), whose populations are declining throughout most of North America. To clarify how lichen cover might affect caribou resource selection, ecologists require better data on the spatial distribution and abundance of lichen. Here, we use a combination of field data and satellite imagery to model lichen cover for a 583 200 km2 area that fully encompasses nine caribou ranges in interior Alaska and Yukon. We aggregated data from in situ vegetation plots, aerial survey polygons and unmanned aerial vehicle (UAV) imagery to align with 30 m resolution Landsat pixels. We used these data to train a random forest model with a suite of environmental and spectral predictors to estimate lichen cover. We validated our lichen cover model using reserved training data and existing external datasets, and found that reserved data from aerial survey polygons (R 2 = 0.77) and UAV imagery (R 2 = 0.71) provided the best fit. We used our lichen cover map to evaluate the influence of estimated lichen cover on caribou resource selection in the Fortymile Herd from 2012 to 2018 during summer and winter. In both seasons, caribou avoided lichen-poor areas (0%–5% lichen cover) and showed stronger selection as lichen cover increased to ∼30%, above which selection leveled off. Our results suggest that terrestrial lichen cover is an important factor influencing caribou resource selection in northern boreal forests across seasons. Our lichen cover map goes beyond existing maps of lichen abundance and distribution because it incorporates extensive field data for model training and validation and estimates lichen cover over a much larger spatial extent. We expect our landscape-scale map will be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management and conservation.

Current rates of global environmental and climate change pose potential challenges for migratory species that must cope with or adapt to new conditions and different rates of change across broad spatial scales throughout their annual life cycle. North American migratory hummingbirds may be especially sensitive to changes in environment and climate due to their extremely small body size, high metabolic rates, and dependence on nectar as a main resource. We used occurrence information from the eBird citizen-science database to track migratory movements of five North American hummingbird species (Archilochus alexandri, A. colubris, Selasphorus calliope, S. platycercus, and S. rufus) across 6 years (2008–2013) at a daily temporal resolution to describe annual and seasonal variation in migration patterns. Our findings suggest that the timing of the onset of spring migration generally varies less than the arrival on the wintering grounds. Species follow similar routes across years, but exhibit more variation in...

Increasing tree mortality from global change drivers such as drought and biotic infestations is a widespread phenomenon, including in the boreal zone where climate changes and feedbacks to the Earth system are relatively large. Despite the importance for science and management communities, our ability to forecast tree mortality at landscape to continental scales is limited. However, two independent information streams have the potential to inform and improve mortality forecasts: repeat forest inventories and satellite remote sensing. Time series of tree-level growth patterns indicate that productivity declines and related temporal dynamics often precede mortality years to decades before death. Plot-level productivity, in turn, has been related to satellite-based indices such as the Normalized difference vegetation index (NDVI). Here we link these two data sources to show that early warning signals of mortality are evident in several NDVI-based metrics up to 24 years before death. We focus on two repeat forest inventories and three NDVI products across western boreal North America where productivity and mortality dynamics are influenced by periodic drought. These data sources capture a range of forest conditions and spatial resolution to highlight the sensitivity and limitations of our approach. Overall, results indicate potential to use satellite NDVI for early warning signals of mortality. Relationships are broadly consistent across inventories, species, and spatial resolutions, although the utility of coarse-scale imagery in the heterogeneous aspen parkland was limited. Longer-term NDVI data and annually remeasured sites with high mortality levels generate the strongest signals, although we still found robust relationships at sites remeasured at a typical 5 year frequency. The approach and relationships developed here can be used as a basis for improving forest mortality models and monitoring systems.

The degree to which tree species will shift in response to climate change is uncertain yet critical to understand for assessing ecosystem vulnerability. We analyze results from recent studies that model potential tree species habitat across the eastern United States during the coming century. Our goals were to quantify and spatially analyze habitat projections and their congruence under multiple climate scenarios and to assess the implications of habitat change for forest vulnerability and adaptation to climate change in and around protected areas. We assessed habitat projections of species

The boreal zone of Alaska is dominated by interactions between disturbances, vegetation, and soils. These interactions are likely to change in the future through increasing permafrost thaw, more frequent and intense wildfires, and vegetation change from drought and competition. We utilize an individual tree-based vegetation model, the University of Virginia Forest Model Enhanced (UVAFME), to estimate current and future forest conditions across sites within interior Alaska. We updated UVAFME for application within interior Alaska, including improved simulation of permafrost dynamics, litter decay, nutrient dynamics, fire mortality, and post-fire regrowth. Following these updates, UVAFME output on species-specific biomass and stem density was comparable to inventory measurements at various forest types within interior Alaska. We then simulated forest response to climate change at specific inventory locations and across the Tanana Valley River Basin on a 2 × 2 km2 grid. We derived projected temperature and precipitation from a five-model average taken from the CMIP5 archive under the RCP 4.5 and 8.5 scenarios. Results suggest that climate change and the concomitant impacts on wildfire and permafrost dynamics will result in overall decreases in biomass (particularly for spruce (Picea spp.)) within the interior Tanana Valley, despite increases in quaking aspen (Populus tremuloides) biomass, and a resulting shift towards higher deciduous fraction. Simulation results also predict increases in biomass at cold, wet locations and at high elevations, and decreases in biomass in dry locations, under both moderate (RCP 4.5) and extreme (RCP 8.5) climate change scenarios. These simulations demonstrate that a highly detailed, species interactive model can be used across a large region within Alaska to investigate interactions between vegetation, climate, wildfire, and permafrost. The vegetation changes predicted here have the capacity to feed back to broader scale climate-forest interactions in the North American boreal forest, a region which contributes significantly to the global carbon and energy budgets.

Forests of the Appalachian Landscape Conservation Cooperative provide critical ecological and management functions. The moist climate of the eastern United States fosters productive stands that store relatively high amounts of carbon; for example, the Appalachian Landscape Conservation Cooperative (Appalachian LCC) accounts for only 7.6 percent of the contiguous United States but contains 18.8 percent of its aboveground forest biomass (derived from Kellndorfer et al. 2012). The Appalachian Mountains create substantial topographic and microclimatic diversity, and forests in the southern Appalachian LCC have some of the highest levels of endemic mammal, bird, amphibian, reptile, freshwater fish, and tree species biodiversity in the conterminous United States (Jenkins et al. 2015). Forest types vary from commercial pine plantations in the south to temperate hardwoods in the central Appalachians to high-elevation spruce-fir forests in the north.

Forests in the eastern United States have a long history of change related to climate and land use. Eighteen thousand years ago, temperatures were considerably lower and glaciers covered much of the area where deciduous forests currently grow. As glaciers retreated and temperatures rose, tree species advanced from southern areas (Delcourt and Delcourt 1988) and may also have dispersed from low-density populations near the edge of the Laurentide ice sheet (McLachlan, Clark, and Manos 2005). A variety of other processes have also influenced the distribution of tree species. Derechos, tornadoes, and fires cause frequent, small- to intermediate-scale disturbances that are important influences on canopy structure and species composition, while larger disturbances, such as hurricanes, cause less frequent but more extensive changes (Dale et al. 2001).

Global temperatures have risen over the last few decades, and even the most conservative climate models project these trends to continue over the next eighty-five years (IPCC 2013). As climate changes, flora and fauna will be forced to adapt or migrate (Aitken et al. 2008). Many species have been able to adapt to past changes in climate, moving south during glacial periods and north during interglacial periods. However, anthropogenic climate change in most areas is occurring much faster than previous climatic shifts. Flora, in particular, may be unable to adapt or disperse quickly enough to track suitable climate conditions (Corlett and Westcott 2013). Understanding historical and projected future trends in temperature, precipitation, and other climate variables is important for evaluating the current context and likely consequences of climate changes in national parks, and in developing effective strategies for climate adaptation.

Park managers realized more than 130 years ago that protected areas are often subsets of larger ecosystems and are vulnerable to change in the unprotected portions of the ecosystem. We illustrate the need to delineate protected area—centered ecosystems (PACEs) by using comprehensive scientific methods to map and analyze land-use change within PACEs around 13 US national park units. The resulting PACEs were on average 6.7 times larger than the parks in upper watersheds and 44.6 times larger than those in middle watersheds. The sizes of these PACEs clearly emphasized the long-term reliance of park biodiversity on surrounding landscapes. PACEs in the eastern United States were dominated by private lands with high rates of land development, suggesting that they offer the greatest challenge for management. Delineating PACEs more broadly will facilitate monitoring, condition assessment, and conservation of the large number of protected areas worldwide that are being degraded by human activities in the areas tha...

Drought exerts a strong influence on tropical forest metabolism, carbon stocks, and ultimately the flux of carbon to the atmosphere. Satellite-based studies have suggested that Amazon forests green up during droughts because of increased sunlight, whereas field studies have reported increased tree mortality during severe droughts. In an effort to reconcile these apparently conflicting findings, we conducted an analysis of climate data, field measurements, and improved satellite-based measures of forest photosynthetic activity. Wet-season precipitation and plant-available water (PAW) decreased over the Amazon Basin from 1996−2005, and photosynthetically active radiation (PAR) and air dryness (expressed as vapor pressure deficit, VPD) increased from 2002–2005. Using improved enhanced vegetation index (EVI) measurements (2000–2008), we show that gross primary productivity (expressed as EVI) declined with VPD and PAW in regions of sparse canopy cover across a wide range of environments for each year of the study. In densely forested areas, no climatic variable adequately explained the Basin-wide interannual variability of EVI. Based on a site-specific study, we show that monthly EVI was relatively insensitive to leaf area index (LAI) but correlated positively with leaf flushing and PAR measured in the field. These findings suggest that production of new leaves, even when unaccompanied by associated changes in LAI, could play an important role in Basin-wide interannual EVI variability. Because EVI variability was greatest in regions of lower PAW, we hypothesize that drought could increase EVI by synchronizing leaf flushing via its effects on leaf bud development.

A topic of recurring interest in ecological research is the degree to which vegetation structure influences the distribution and abundance of species. Here we test the applicability of remote sensing, particularly novel use of waveform lidar measurements, for quantifying the habitat heterogeneity of a contiguous northern hardwoods forest in the northeastern United States. We apply these results to predict the breeding habitat quality, an indicator of reproductive output of a well-studied Neotropical migrant songbird, the Black-throated Blue Warbler (Dendroica caerulescens). We found that using canopy vertical structure metrics provided unique information for models of habitat quality and spatial patterns of prevalence. An ensemble decision tree modeling approach (random forests) consistently identified lidar metrics describing the vertical distribution and complexity of canopy elements as important predictors of habitat use over multiple years. Although other aspects of habitat were important, including the seasonality of vegetation cover, the canopy structure variables provided unique and complementary information that systematically improved model predictions. We conclude that canopy structure metrics derived from waveform lidar, which will be available on future satellite missions, can advance multiple aspects of biodiversity research, and additional studies should be extended to other organisms and regions.

This paper presents a fine-scale (30 meter resolution) regional land cover modeling system, based on the SLEUTH cellular automata model, that was developed for a 257000 km 2 area comprising the Chesapeake Bay drainage basin in the eastern United States. As part of this effort, we developed a new version of the SLEUTH model (SLEUTH-3r), which introduces new functionality and fit metrics that substantially increase the performance and applicability of the model. In addition, we developed methods that expand the capability of SLEUTH to incorporate economic, cultural and policy information, opening up new avenues for the integration of SLEUTH with other land-change models. SLEUTH-3r is also more computationally efficient (by a factor of 5) and uses less memory (reduced 65%) than the original software. With the new version of SLEUTH, we were able to achieve high accuracies at both the aggregate level of 15 subregional modeling units and at finer scales. We present forecasts to 2030 of urban development under a current trends scenario across the entire Chesapeake Bay drainage basin, and three alternative scenarios for a sub-region within the Chesapeake Bay watershed to illustrate the new ability of SLEUTH-3r to generate forecasts across a broad range of conditions.

This paper examined the effects of simulated land cover/land use (LC/LU) change from 2000 to 2030 on nutrient loadings to the Chesapeake Bay. The SPAtially Referenced Regression On Watershed Attributes (SPARROW) model was used with anticipated watershed-wide LC/LU change from a growth forecast model that provides spatially explicit probabilities of conversion to impervious surface. The total nitrogen (TN) and total phosphorus (TP) loadings estimated to enter the Chesapeake Bay were reduced by 20% and 19%, respectively. In general, as development replaced other LC/LUs from 2000 to 2030, TN and TP runoff was significantly reduced by losses of non-point, non-urban source loadings, yields, and land-to-water delivery. The simulation results suggest future changes in landscape composition and configuration at catchment and riparian stream buffer width scales could lower TN and TP runoff to the estuary.

article i nfo The exurbanization process, particularly rural residential development, is reducing the amount of roadless areas and remote habitat across the nation, with implications for biodiversity and ecosystem integrity of parks and protected areas. The need for connecting protected areas via existing habitat centers, or relatively undisturbed core areas, is greater than ever as exurbanization expands. Our objective was to make use of nationally available data sets on roads as well as information derived from satellite imagery, including impervious cover of the built environment and forest canopy density, to identify core habitat of the northeastern and mid-Atlantic USA. The identified core habitat areas, which covered 73,730 km 2 across 1177 discrete units, were stratified in terms of land ownership and management, and then analyzed in a landscape context using connectivity metrics derived from graph theory. The connectivity analysis made use of a suitability surface, derived from the land cover information, which approximated the costs incurred by hypothetical animals traversing the landscape. We show that protected areas are frequently identified as core habitat but are typically isolated, albeit sometimes buffered by adjacent multi-use lands (such as state or national forests). Over one third of the core habitat we identified has no protection, and another 42% is subject to motorized recreation or timber extraction. We provide maps showing the relative importance of core habitat areas for potentially connecting existing protected areas, and also provide an example of the vulnerability of connectivity to projected future residential development around one greater park ecosystem.

The amount of human altered land surface within a watershed has long been known to influence the biota of receiving streams and waterways. In contrast, vegetation in riparian zones can filter pollutants and reduce flow velocities that incise stream channels. We examined the relationship between the built environment and water quality for streams (1st through 4th order) across three physiographic provinces of the State of Maryland, USA, including 59 watersheds with some 865 stream sampling locations. We used image data products capable of discriminating fine-scale information of the land surface, including proportional impervious, tree, grass and crop cover. Stepwise multiple linear regression and decision tree statistical approaches were used to assess the relationship between the land cover predictors and benthic indices of biological integrity (BIBI) and number of sensitive invertebrate taxa (NEPT), response variables derived from the Maryland Biological Stream Survey (MBSS). Impervious and tree cover were found to be the best predictors of stream biota, although this varied with physiographic province and the response variable of interest. The best multivariate models predicted 65% of variability in BIBI and 62% of NEPT (p

Land development pressures that threaten habitat core areas and connectivity are intensifying across the nation and extending beyond urbanized areas in the form of rural residential development. This is particularly true in the temperate forests of the northeastern United States. We used a suite of nationally available data sets derived from satellite imagery to identify core habitat areas of the northeastern United States, including impervious cover (urbanized and developed areas) and forest cover (canopy density). These were augmented with road network extent and density. We analyzed the influence of different types of unimproved roads and amount of forest cover on identification of the extent and configuration of roadless areas, and then assessed these core habitat areas in terms of land ownership (public, private) and management (parks, refuges, multi-use). We also derived patch connectivity metrics using a graph theory approach, making use of cost surfaces that accounted for the above variab...

The Chesapeake Bay is the largest estuary in the United States, and was once one of its most productive. Historically, the Chesapeake supported thousands of migrant and resident species, including oysters, blue crab, shad, herring, and waterfowl. Today the estuary's productivity is a shadow of what it once was. In 1880, for example, the oyster harvest was 120 million pounds, but this declined to a mere 3 million pounds by 2000—and that actually represented an increase from a historic low in 1993. While the native Chesapeake oyster has fallen victim to disease, a major contributing factor to its decline, along with the health of the estuary, has been the loss of habitat from dredging, sediment loading, and increased pollutant levels. Nearly all of the sediment and pollution originates from human activity on the land surface of the Chesapeake's watershed (Figure 1). As population and development continue to grow in the region, pollution from urbanized areas has become an issue of primary concern. Using examples primarily from the Chesapeake Bay region, we discuss how smart growth strategies can contribute to ecosystem restoration, and provide examples of how geospatial technologies have been developed to serve as decision support tools, ending with a summary of some of the challenges that remain for sustainable urban development.

Riparian buffer zone management is an area of increasing relevance as human modification of the landscape continues unabated. Land and water resource managers are continually challenged to maintain stream ecosystem integrity and water quality in the context of rapidly changing land use, which often offsets management gains. Approaches are needed not only to map vegetation cover in riparian zones, but also to monitor the changes taking place, target restoration activities, and assess the success of previous management actions. To date, these objectives have been difficult to meet using traditional techniques based on aerial photos and field visits, particularly over large areas. Recent advances in remote sensing have the potential to substantially aid buffer zone management. Very high resolution imagery is now available that allows detailed mapping and monitoring of buffer zone vegetation and provides a basis for consistent assessments using moderately high resolution remote sensing (e.g., Landsat). Laser-based remote sensing is another advance that permits even more detailed information on buffer zone properties, such as refined topographic derivatives and multidimensional vegetation structure. These sources of image data and map information are reviewed in this paper, examples of their application to riparian buffer mapping and stream health assessment are provided, and future prospects for improved buffer monitoring are discussed.

We made use of land cover maps, and land use change associated with urbanization, to provide estimates of the loss of natural resource lands (forest, agriculture, and wetland areas) across the 168,000 km2 Chesapeake Bay watershed. We conducted extensive accuracy assessments of the satellite-derived maps, most of which were produced by us using widely available multitemporal Landsat imagery. The change in urbanization was derived from impervious surface area maps (the built environment) for 1990 and 2000, from which we estimated the loss of resource lands that occurred during this decade. Within the watershed, we observed a 61% increase in developed land (from 5,177 to 8,363 km2). Most of this new development (64%) occurred on agricultural and grasslands, whereas 33% occurred on forested land. Some smaller municipalities lost as much as 17% of their forest lands and 36% of their agricultural lands to development, although in the outlying counties losses ranged from 0% to 1.4% for forests and 0% to 2.6% for agriculture. Fast-growing urban areas surrounded by forested land experienced the most loss of forest to impervious surfaces. These estimates could be used for the monitoring of the impacts of development across the Chesapeake Bay watershed, and the approach has utility for other regions nationwide. In turn, the results and the approach can help jurisdictions set goals for resource land protection and acquisition that are consistent with regional restoration goals.

Land cover and land use change have long been known to influence the chemical, physical, and biological character- istics of streams. This study makes use of land cover maps derived from fine resolution satellite imagery and an extensive stream quality dataset to determine the relationship between small water- shed health rankings and land cover composition and configura- tion. Landscape metrics were derived from digital impervious surface area (ISA), tree cover (percent), and agricultural crop maps within Montgomery County, Maryland. Watershed rankings were developed by state and county collaborators (MD-DNR and MCDEP) using extensive biological and chemical measurements. In stepwise logistic regression models the factors accounting for the most variation in stream health ranking were the percent ISA, fol- lowed by the percent of tree cover. Riparian buffer zone tree cover was also a significant predictor. Of the metrics that considered the spatial configuration of the landscape, a contagion index and the percent of ISA in the flow path from the ISA to the stream were also found to be significant predictors of stream health. Despite limited ability to characterize landscape configuration or narrow riparian buffer zone vegetation with coarser resolution imagery (from Landsat), model results were not significantly different from those based on the use of fine-resolution ISA information, suggest- ing that broader area applications of the approach are possible. The results indicate that management practices designed to improve stream water quality should focus on the amount of ISA and tree cover in both the watershed and within the buffer zone. (KEY TERMS: land use planning; remote sensing; restoration; riparian buffers; stream health; urbanization; water quality; water- shed management.)

Different processes shaping land‐use patterns are observed at different scales. In land‐use modelling, scale can influence the measurement and quantitative description of land‐use patterns and can therefore significantly impact the behaviour of model parameters that describe land‐use change processes. We present results of a rigorous sensitivity analysis of a cellular urban land‐use‐change model, SLEUTH, testing its performance in response to varying cell resolutions. Specifically, we examine the behaviour of each type of urban growth rule across different cell sizes, and explore the model's ability to capture growth rates and patterns across scales. Our findings suggest that SLEUTH's sensitivity to scale extend beyond issues of calibration. While the model was able to capture the rate of growth reliably across all cell sizes, differences in its ability to simulate growth patterns across scales were substantial. We also observed significant differences in the sensitivity of the growth rules across cell si...

Accurate measurement of impervious surface (IS) cover is an essential indicator of downstream water quality and a critical input variable for many water quality and quantity models. This study compares IS estimates from a recently developed satellite imagery/land cover approach with a more traditional aerial photography/land use approach. Both approaches are evaluated against a high-quality validation set consisting of planimetric data merged with manually-delineated areas of soil disturbance. The study area is the rapidly urbanizing 127 km 2 Cub Run watershed in northern Virginia, located on the fringe of the Washington, D.C. metropolitan region. Results show that photo-interpreted IS estimates of land class are higher than satellite-derived IS estimates by 100 percent or more, even in land uses conservatively assigned high IS values. Satellite-derived IS estimates by land class correlate well with planimetric reference data (r � 0.95) and with published ranges for similar sites in the region. Basin-wide mean IS values, difference grids, and regression and density plots validate the use of satellite-derived/land cover-based IS estimates over photo-interpreted/land use-based estimates. Results of this site-specific study support the use of automated, satellite-derived IS estimates for planning and management within rapidly urbanizing watersheds where a GIS system is in place, but where time-sensitive, high quality planimetric data is unavailable.

Natural resource lands in the Chesapeake Bay watershed are increasingly susceptible to conversion into developed land uses, particularly as the demand for residential development grows. We assessed development pressure in the Baltimore-Washington, DC region, one of the major urban and suburban centers in the watershed. We explored the utility of two modeling approaches for forecasting future development trends and patterns by comparing results from a cellular automata model, SLEUTH (slope, land use, excluded land, urban extent, transportation), and a supply/demand/allocation model, the Western Futures Model. SLEUTH can be classified as a land-cover change model and produces projections on the basis of historic trends of changes in the extent and patterns of developed land and future land protection scenarios. The Western Futures Model derives forecasts from historic trends in housing units, a U.S. Census variable, and exogenously supplied future population projections. Each approach has strengths and weaknesses, and combining the two has advantages and limitations.

Declining water quality in the Chesapeake Bay estuary is in part the result of disruptions in the hydrological system caused by urban and suburban development throughout its 167000 km2 watershed. A modeling system that could provide regional assessments of future development and explore the potential impacts of different regional management scenarios would be useful for a wide range of applications relevant to the future health of the Bay and its tributaries. We describe and test a regional predictive modeling system that could be used to meet these needs. An existing cellular automaton model, SLEUTH, was applied to a 23 700 km2 area centered on the Washington-Baltimore metropolitan region, which has experienced rapid land-use change in recent years. The model was calibrated using a historic time series of developed areas derived from remote sensing imagery, and future growth was projected out to 2030 assuming three different policy scenarios: (1) current trends, (2) managed growth, and (3) ecologically sustainable growth. The current trends scenario allowed areas on the urban fringe that are currently rural or forested to be developed, which would have implications for water quality in the Chesapeake Bay and its tributaries. The managed growth and ecologically sustainable scenarios produced growth patterns that were more constrained and which consumed less natural resource land. This application of the SLEUTH model demonstrates an ability to address a range of regional planning issues, but spatial accuracy and scale sensitivity are among the factors that must be further considered for practical application.

High-resolution imagery from the IKONOS satellite may be useful for many resource management applications. We assessed the utility of IKONOS imagery for applications in the mid-Atlantic region, including mapping of tree cover, impervious surface areas, and riparian buffer zone variables in relation to stream health ratings. We focused on a 1313-km2 area in central Maryland using precision-georeferenced IKONOS products. We found the IKONOS imagery to be a valuable resource for these applications, and were able to achieve map accuracies comparable to manual aerial photo interpretation. We were also able to use derived data sets for consistent assessments over areas that would be difficult to accomplish with traditional photographic mapping methods. For example, we found that a stream health rating of excellent required no more than 6% impervious cover in the watershed, and at least 65% tree cover in the riparian zone. A rating of good required less than 10% impervious and 60% tree cover. A number of issues associated with application of the IKONOS data arose, however, including logistics of image acquisition related to phenological and atmospheric conditions, shadowing within canopies and between scene elements, and limited spectral discrimination of cover types. Cost per unit area was also a nontrivial consideration for the image data products we used, but allowed us to provide valuable derived products to agencies in support of their planning and regulatory decision-making processes. We report on both the capabilities and limitations of IKONOS imagery for these varied applications.

Climate warming is affecting the Arctic in multiple ways, including via increased dominance of deciduous shrubs. Although many studies have focused on how this vegetation shift is altering nutrient cycling and energy balance, few have explicitly considered effects on tundra fauna, such as the millions of migratory songbirds that breed in northern regions every year. To understand how increasing deciduous shrub dominance may alter breeding songbird habitat, we quantified vegetation and arthropod community characteristics in both graminoid and shrub dominated tundra. We combined measurements of preferred nest site characteristics for Lapland longspurs (Calcarius lapponicus) and Gambel's White-crowned sparrows (Zonotrichia leucophrys gambelii) with modeled predictions for the distribution of plant community types in the Alaskan arctic foothills region for the year 2050. Lapland longspur nests were found in sedge-dominated tussock tundra where shrub height does not exceed 20 cm, whereas White-crowned sparrows nested only under shrubs between 20 cm and 1 m in height, with no preference for shrub species. Shrub canopies had higher canopy-dwelling arthropod availability (i.e. small flies and spiders) but lower ground-dwelling arthropod availability (i.e. large spiders and beetles). Since flies are the birds' preferred prey, increasing shrubs may result in a net enhancement in preferred prey availability. Acknowledging the coarse resolution of existing tundra vegetation models, we predict that by 2050 there will be a northward shift in current White-crowned sparrow habitat range and a 20-60% increase in their preferred habitat extent, while Lapland longspur habitat extent will be equivalently reduced. Our findings can be used to make first approximations of future habitat change for species with similar nesting requirements. However, we contend that as exemplified by this study's findings, existing tundra modeling tools cannot yet simulate the fine-scale habitat characteristics that are critical to accurately predicting future habitat extent for many wildlife species.

Avian diversity is under increasing pressures. It is thus critical to understand the ecological variables that contribute to large scale spatial distribution of avian species diversity. Traditionally, studies have relied primarily on two-dimensional habitat structure to model broad scale species richness. Vegetation vertical structure is increasingly used at local scales. However, the spatial arrangement of vegetation height has never been taken into consideration. Our goal was to examine the efficacies of three-dimensional forest structure, particularly the spatial heterogeneity of vegetation height in improving avian richness models across forested ecoregions in the U.S. We developed novel habitat metrics to characterize the spatial arrangement of vegetation height using the National Biomass and Carbon Dataset for the year 2000 (NBCD). The height-structured metrics were compared with other habitat metrics for statistical association with richness of three forest breeding bird guilds across Breeding Bird Survey (BBS) routes: a broadly grouped woodland guild, and two forest breeding guilds with preferences for forest edge and for interior forest. Parametric and non-parametric models were built to examine the improvement of predictability. Height-structured metrics had the strongest associations with species richness, yielding improved predictive ability for the woodland guild richness models (r(2) = ∼ 0.53 for the parametric models, 0.63 the non-parametric models) and the forest edge guild models (r(2) = ∼ 0.34 for the parametric models, 0.47 the non-parametric models). All but one of the linear models incorporating height-structured metrics showed significantly higher adjusted-r2 values than their counterparts without additional metrics. The interior forest guild richness showed a consistent low association with height-structured metrics. Our results suggest that height heterogeneity, beyond canopy height alone, supplements habitat characterization and richness models of forest bird species. The metrics and models derived in this study demonstrate practical examples of utilizing three-dimensional vegetation data for improved characterization of spatial patterns in species richness.

Russia's boreal (taiga) biome will likely contract sharply and shift northward in response to 21st century climatic change, yet few studies have examined plant response to climatic variability along the northern margin. We quantified climate dynamics, trends in plant growth, and growth-climate relationships across the tundra shrublands and Cajander larch (Larix cajanderi Mayr.) woodlands of the Kolyma river basin (657 000 km(2) ) in northeastern Siberia using satellite-derived normalized difference vegetation indices (NDVI), tree ring-width measurements, and climate data. Mean summer temperatures (Ts ) increased 1.0 °C from 1938 to 2009, though there was no trend (P 0.05) in growing year precipitation or climate moisture index (CMIgy ). Mean summer NDVI (NDVIs ) increased significantly from 1982 to 2010 across 20% of the watershed, primarily in cold, shrub-dominated areas. NDVIs positively correlated (P 0.05) with Ts across 56% of the watershed (r = 0.52 ± 0.09, mean ± SD), principally in cold areas, and with CMIgy across 9% of the watershed (r = 0.45 ± 0.06), largely in warm areas. Larch ring-width measurements from nine sites revealed that year-to-year (i.e., high-frequency) variation in growth positively correlated (P 0.05) with June temperature (r = 0.40) and prior summer CMI (r = 0.40) from 1938 to 2007. An unexplained multi-decadal (i.e., low-frequency) decline in annual basal area increment (BAI) occurred following the mid-20th century, but over the NDVI record there was no trend in mean BAI (P 0.05), which significantly correlated with NDVIs (r = 0.44, P 0.05, 1982-2007). Both satellite and tree-ring analyses indicated that plant growth was constrained by both low temperatures and limited moisture availability and, furthermore, that warming enhanced growth. Impacts of future climatic change on forests near treeline in Arctic Russia will likely be influenced by shifts in both temperature and moisture, which implies that projections of future forest distribution and productivity in this area should take into account the interactions of energy and moisture limitations.

Earth-observing satellites have only recently been exploited for the measurement of environmental variables of relevance to epidemiology and public health. Such work has relied on sensors with spatial, spectral and geometric constraints that have allowed large-area questions associated with the epidemiology of vector-borne diseases to be addressed. Moving from pretty maps to pragmatic control tools requires a suite of satellite-derived environmental data of higher fidelity, spatial resolution, spectral depth and at similar temporal resolutions to existing meteorological satellites. Information derived from sensors onboard the next generation of moderate-resolution Earth-observing sensors may provide the key. The MODIS and ASTER sensors onboard the Terra and Aqua platforms provide substantial improvements in spatial resolution, number of spectral channels, choices of bandwidths, radiometric calibration and a much-enhanced set of pre-processed and freely available products. These sensors provide an important advance in moderate-resolution remote sensing and the data available to those concerned with improving public health.

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

Research activities focused on estimating the direct emissions of carbon from wildland fires across North America are reviewed as part of the North American Carbon Program disturbance synthesis. A comparison of methods to estimate the loss of carbon from the terrestrial biosphere to the atmosphere from wildland fires is presented. Published studies on emissions from recent and historic time periods and five specific cases are summarized, and new emissions estimates are made using contemporary methods for a set of specific fire events. Results from as many as six terrestrial models are compared. We find that methods generally produce similar results within each case, but estimates vary based on site location, vegetation (fuel) type, and fire weather. Area normalized emissions range from 0.23 kg C m−2 for shrubland sites in southern California/NW Mexico to as high as 6.0 kg C m−2 in northern conifer forests. Total emissions range from 0.23 to 1.6 Tg C for a set of 2003 fires in chaparral-dominated landscapes of California to 3.9 to 6.2 Tg C in the dense conifer forests of western Oregon. While the results from models do not always agree, variations can be attributed to differences in model assumptions and methods, including the treatment of canopy consumption and methods to account for changes in fuel moisture, one of the main drivers of variability in fire emissions. From our review and synthesis, we identify key uncertainties and areas of improvement for understanding the magnitude and spatial-temporal patterns of pyrogenic carbon emissions across North America.

[1] Our knowledge of the distribution and amount of terrestrial biomass is based almost entirely on ground measurements over an extremely small, and possibly biased sample, with many regions still unmeasured. Our understanding of changes in terrestrial biomass is even more rudimentary, although changes in land use, largely tropical deforestation, are estimated to have reduced biomass, globally. At the same time, however, the global carbon balance requires that terrestrial carbon storage has increased, albeit the exact magnitude, location, and causes of this residual terrestrial sink are still not well quantified. A satellite mission capable of measuring aboveground woody biomass could help reduce these uncertainties by delivering three products. First, a global map of aboveground woody biomass density would halve the uncertainty of estimated carbon emissions from land use change. Second, an annual, global map of natural disturbances could define the unknown but potentially large proportion of the residual terrestrial sink attributable to biomass recovery from such disturbances. Third, direct measurement of changes in aboveground biomass density (without classification of land cover or carbon modeling) would indicate the magnitude and distribution of at least the largest carbon sources (from deforestation and degradation) and sinks (from woody growth). The information would increase our understanding of the carbon cycle, including better information on the magnitude, location, and mechanisms responsible for terrestrial sources and sinks of carbon. This paper lays out the accuracy, spatial resolution, and coverage required for a satellite mission that would generate these products.

Tropical forests provide global climate regulation ecosystem services and their clearing is a significant source of anthropogenic greenhouse gas (GHG) emissions and resultant radiative forcing of climate change. However, consensus on pan-tropical forest carbon dynamics is lacking. We present a new estimate that employs recommended good practices to quantify gross tropical forest aboveground carbon (AGC) loss from 2000 to 2012 through the integration of Landsat-derived tree canopy cover, height, intactness and forest cover loss and GLAS-lidar derived forest biomass. An unbiased estimate of forest loss area is produced using a stratified random sample with strata derived from a wall-to-wall 30 m forest cover loss map. Our sample-based results separate the gross loss of forest AGC into losses from natural forests (0.59 PgC yr−1) and losses from managed forests (0.43 PgC yr−1) including plantations, agroforestry systems and subsistence agriculture. Latin America accounts for 43% of gross AGC loss and 54% of natural forest AGC loss, with Brazil experiencing the highest AGC loss for both categories at national scales. We estimate gross tropical forest AGC loss and natural forest loss to account for 11% and 6% of global year 2012 CO2 emissions, respectively. Given recent trends, natural forests will likely constitute an increasingly smaller proportion of tropical forest GHG emissions and of global emissions as fossil fuel consumption increases, with implications for the valuation of co-benefits in tropical forest conservation.

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.

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

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.