Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

The attribution of forest disturbances to disturbance agents is a critical challenge for remote sensing-based forest monitoring, promising important insights into drivers and impacts of forest disturbances. Previous studies have used spectral-temporal metrics derived from annual Landsat time series to identify disturbance agents. Here, we extend this approach to new predictors derived from intra-annual time series and test it at three sites in Central Europe, including managed and protected forests. The two newly tested predictors are: (1) intra-annual timing of disturbance events and (2) temporal proximity to windstorms based on prior knowledge. We estimated the intra-annual timing of disturbances using a breakpoint detection algorithm and all available Landsat observations between 1984 and 2016. Using spectral, temporal, and topography-related metrics, we then mapped four disturbance classes: windthrow, cleared windthrow, bark beetles, and other harvest. Disturbance agents were identified with overall accuracies of 76-86%. Temporal proximity to storm events was among the most important predictors, while intra-annual timing itself was less important. Moreover, elevation information was very effective for discriminating disturbance agents. Our results demonstrate the potential of incorporating dense, intra-annual Landsat time series information and prior knowledge of disturbance events for monitoring forest ecosystem change at the disturbance agent level.

Aim Several large‐mammal species in Europe have recovered and recolonized parts of their historical ranges. Knowing where suitable habitat exists, and thus where range expansions are possible, is important for proactively promoting coexistence between people and large mammals in shared landscapes. We aimed to assess the opportunities and limitations for range expansions of Europe's two largest herbivores, the European bison (Bison bonasus) and moose (Alces alces). Location Central Europe. Methods We used large occurrence datasets from multiple populations and species distribution models to map environmentally suitable habitats for European bison and moose across Central Europe, and to assess human pressure inside the potential habitat. We then used circuit theory modeling to identify potential recolonization corridors. Results We found widespread suitable habitats for both European bison (>120,000 km2) and moose (>244,000 km2), suggesting substantial potential for range expansions. However, much habitat was associated with high human pressure (37% and 43% for European bison and moose, respectively), particularly in the west of Central Europe. We identified a strong east–west gradient of decreasing connectivity, with major barriers likely limiting natural recolonization in many areas. Main conclusions We identify major potential for restoring large herbivores and their functional roles in Europe's landscapes. However, we also highlight considerable challenges for conservation planning and wildlife management, including areas where recolonization likely leads to human–wildlife conflict and where barriers to movement prevent natural range expansion. Conservation measures restoring broad‐scale connectivity are needed in order to allow European bison and moose to recolonize their historical ranges. Finally, our analyses and maps indicate suitable but isolated habitat patches that are unlikely to be colonized but are candidate locations for reintroductions to establish reservoir populations. More generally, our work emphasizes that transboundary cooperation is needed for restoring large herbivores and their ecological roles, and to foster coexistence with people in Europe's landscapes.

Satellite-based habitat monitoring reveals long-term dynamics of deer habitat in response to forest disturbances. Ecological

Agricultural expansion into tropical and subtropical forests often leads to major social-ecological trade-offs. Yet, despite ever-more detailed information on where deforestation occurs, how agriculture expands into forests remains unclear, which is hampered by a lackof spatially and temporally detailed reconstruction of agricultural expansion. Here, we developed and mapped a novel set of metrics that quantify agricultural frontier processes at unprecedented spatial and temporal detail. Specifically, we first derived consistent annual time series of land-use/cover to, second, describe archetypical patterns of frontier expansion, pertaining to speed, diffusion and activity of deforestation, as well as post-deforestation land use. We exemplify this approach for understanding agricultural frontier expansion across the entire South American Chaco, a global deforestation hotspot. Our study provides three major insights. First, agricultural expansion has been rampant in the Chaco, with more than 19.3 million ha of woodlands converted between 1985 and 2020, including a surge in deforestation after 2019. Second, land-use trajectories connected to frontier processes have changed in major ways over the 35-year study period we studied, including substantial regional variations. For instance, while ranching expansion drove most of the deforestation in the 1980s and 1990s, cropland expansion dominated during the mid-2000s in Argentina, but not in Paraguay. Similarly, 40% of all areas deforested were initially used for ranching, but later on converted to cropping. Accounting for post-deforestation land-use change is thus needed to properly attribute deforestation and associated environmental impacts, such as carbon emissions or biodiversity loss, to commodities. Finally, we identified major, recurrent frontier types that may be a useful spatial template for land governance to match policies to specific frontier situations. Collectively, our study reveals the diversity of frontier processes and how frontier metrics can capture and structure this diversity to uncover major patterns of human-nature interactions, which can be used to guide spatially-targeted policies.