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When characterizing the processes that shape ecosystems, ecologists increasingly use the unique perspective offered by repeat observations of remotely sensed imagery. However, the concept of change embodied in much of the traditional remote-sensing literature was primarily limited to capturing large or extreme changes occurring in natural systems, omitting many more subtle processes of interest to ecologists. Recent technical advances have led to a fundamental shift toward an ecological view of change. Although this conceptual shift began with coarser-scale global imagery, it has now reached users of Landsat imagery, since these datasets have temporal and spatial characteristics appropriate to many ecological questions. We argue that this ecologically relevant perspective of change allows the novel characterization of important dynamic processes, including disturbances, longterm trends, cyclical functions, and feedbacks, and that these improvements are already facilitating our understanding of critical driving forces, such as climate change, ecological interactions, and economic pressures.

The development of conservation plans for Northern Spotted Owls (NSO) (Strix occidentalis caurina) in disturbance-prone landscapes requires evaluation of multiple threats and careful consideration of the consequences of management actions intended to reduce risk. Hanson et al. (2009) used downwardly revised estimates of recent old-forest losses to high-severity wildfire to argue that the recent NSO recovery plan (USDI 2008) overestimates fire risk to the NSO in dry, fire-prone forests. We believe their analysis is erroneous and deficient and does not support their conclusions. Furthermore, they show a bias against active management by ignoring recent science and risk factors in dry forests that do not support their opinions.

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

We used a spatially nested hierarchy of field and remote-sensing observations and a process model, Biome-BGC, to produce a carbon budget for the forested region of Oregon, and to determine the relative influence of differences in climate and disturbance among the ecoregions on carbon stocks and fluxes. The simulations suggest that annual net uptake (net ecosystem production (NEP)) for the whole forested region (8.2 million hectares) was 13.8 Tg C (168 g C m−2 yr−1), with the highest mean uptake in the Coast Range ecoregion (226 g C m−2 yr−1), and the lowest mean NEP in the East Cascades (EC) ecoregion (88 g C m−2 yr−1). Carbon stocks totaled 2765 Tg C (33 700 g C m−2), with wide variability among ecoregions in the mean stock and in the partitioning above- and belowground. The flux of carbon from the land to the atmosphere that is driven by wildfire was relatively low during the late 1990s (∼0.1 Tg C yr−1), however, wildfires in 2002 generated a much larger C source (∼4.1 Tg C). Annual harvest removals from the study area over the period 1995–2000 were ∼5.5 Tg C yr−1. The removals were disproportionately from the Coast Range, which is heavily managed for timber production (approximately 50% of all of Oregon's forest land has been managed for timber in the past 5 years). The estimate for the annual increase in C stored in long-lived forest products and land fills was 1.4 Tg C yr−1. Net biome production (NBP) on the land, the net effect of NEP, harvest removals, and wildfire emissions indicates that the study area was a sink (8.2 Tg C yr−1). NBP of the study area, which is the more heavily forested half of the state, compensated for ∼52% of Oregon's fossil carbon dioxide emissions of 15.6 Tg C yr−1 in 2000. The Biscuit Fire in 2002 reduced NBP dramatically, exacerbating net emissions that year. The regional total reflects the strong east–west gradient in potential productivity associated with the climatic gradient, and a disturbance regime that has been dominated in recent decades by commercial forestry.

As a source of atmospheric carbon, biomass burning emissions associated with deforestation in the Amazon are globally significant. Once deforested, these lands continue to be sources of substantial burning emissions for many years due to frequent pasture burning. The objective of this research was to quantify biomass-burning emissions at a local scale. We estimated carbon emissions from three sources: fires associated with primary forest slash, regenerating forest slash, and pastures. The study was conducted on a 94370-ha section of land surrounding Jamari, RondGnia, Brazil. In the emissions com- putation, we integrated site-specific, ground-based data (biomass, emission factors for flam- ing and smoldering combustion, and combustion factors for land-cover types) with a Landsat TM land cover change map of the study area for 1984-1992. This map was used to ascertain changes in land cover based on TM image dates during a period of early colonization and rapid deforestation in Rond8nia. Emissions of CO, CO,, CH,, and other hydrocarbon trace gases were calculated. Between 1984 and 1992, we found CO, emissions generated by primary forest slash burning were 694 379 Mg C (920 kg C-ha-I-yr-I) and regenerating forest slash burning contributed 23 436 Mg C (31 kg C.ha-'.yr-l), whereas cumulative pasture burning produced 238 180 Mg C (316 kg C.ha-l.yr-l). CO and CH, folIowed the same trends by land cover type. Primary forest slash burns contributed 73% of the total C emissions whereas burning regenerating forest slash and pasture produced 2% and 2596, respectively. The major finding of this work is the identification of pasture burning as a potentially important source of pyrogenic emissions, but better data on biomass and com- bustion efficiency could confirm this finding. Further we demonstrated a novel method for estimating area burned that links land cover type and change to burn frequency.

Shifts in the demographic and economic character of the Greater Yellowstone Ecosystem (GYE) are driving patterns of land cover and land use change in the region. Such changes may have important consequences for ecosystem functioning. The objective of this paper is to quantify the trajectories and rates of change in land cover and use across the GYE for the period 1975-1995 using satellite imagery. Spectral and geographic variables were used as inputs to classification tree regression analysis (CART) to find ''rules'' which defined land use and land cover classes on the landscape. The resulting CART functions were used to map land cover and land use across seven Landsat TM scenes for 1995. We then used a thresholding technique to identify locations that differed in spectral properties between the 1995 and 1985 time periods. These ''changed'' locations were classified using CART functions derived from spectral and geographic data from 1985. This was similarly done for the year 1975 based on Landsat MSS data. Differences between the 1975, 1985, and 1995 maps were considered change in land cover and use. We calibrated and tested the accuracy of our models using data acquired through manual interpretation of aerial photos. Elevation and vegetative indices derived from the remotely sensed satellite imagery explained the most variance in the land use and land cover classes (i.e., defined the ''rules'' most often). Overall accuracies from our study were good, ranging from 94% at the coarsest level of detail to 74% at the finest. The largest changes over the study period were the increases in burned, urban, and mixed conifer-herbaceous classes and decreases in woody deciduous, mixed woody deciduous-herbaceous, and conifer habitats. These changes have important implications for ecological function and biodiversity. The expansion of mixed conifer classes may increase fuel loads and enhance risk to the growing number of rural homes. The reduction of woody deciduous cover types is likely reducing population sizes for the numerous plant and animal species that specialize on this habitat type. Some of these species are also negatively influenced by the increase of rural homes in and near woody deciduous habitats.

Estimation of the amount of carbon stored in forests is a key challenge for understanding the global carbon cycle, one which remote sensing is expected to help address. However, estimation of carbon storage in moderate to high biomass forests is difficult for conventional optical and radar sensors. Lidar (light detection and ranging) instruments measure the vertical structure of forests and thus hold great promise for remotely sensing the quantity and spatial organization of forest biomass. In this study, we compare the relationships between lidar-measured canopy structure and coincident field measurements of above-ground biomass at sites in the temperate deciduous, temperate coniferous, and boreal coniferous biomes. A single regression for all three sites is compared with equations derived for each site individually. The single equation explains 84% of variance in above-ground biomass (P < 0.0001) and shows no statistically significant bias in its predictions for any individual site.

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