Landsat data are increasingly used for ecological monitoring and research. These data often require preprocessing prior to analysis to account for sensor, solar, atmospheric, and topographic effects. However, ecologists using these data are faced with a literature containing inconsistent terminology, outdated methods, and a vast number of approaches with contradictory recommendations. These issues can, at best, make determining the correct preprocessing workflow a difficult and time‐consuming task and, at worst, lead to erroneous results. We address these problems by providing a concise overview of the Landsat missions and sensors and by clarifying frequently conflated terms and methods. Preprocessing steps commonly applied to Landsat data are differentiated and explained, including georeferencing and co‐registration, conversion to radiance, solar correction, atmospheric correction, topographic correction, and relative correction. We then synthesize this information by presenting workflows and a decision tree for determining the appropriate level of imagery preprocessing given an ecological research question, while emphasizing the need to tailor each workflow to the study site and question at hand. We recommend a parsimonious approach to Landsat preprocessing that avoids unnecessary steps and recommend approaches and data products that are well tested, easily available, and sufficiently documented. Our focus is specific to ecological applications of Landsat data, yet many of the concepts and recommendations discussed are also appropriate for other disciplines and remote sensing platforms.

Demand for traditional medicine ingredients is causing species declines globally. Due to this trade, Himalayan caterpillar fungus ( Ophiocordyceps sinensis ) has become one of the world’s most valuable biological commodities, providing a crucial source of income for hundreds of thousands of collectors. However, the resulting harvesting boom has generated widespread concern over the sustainability of its collection. We investigate whether caterpillar fungus production is decreasing—and if so, why—across its entire range. To overcome the limitations of sparse quantitative data, we use a multiple evidence base approach that makes use of complementarities between local knowledge and ecological modeling. We find that, according to collectors across four countries, caterpillar fungus production has decreased due to habitat degradation, climate change, and especially overexploitation. Our statistical models corroborate that climate change is contributing to this decline. They indicate that caterpillar fungus is more productive under colder conditions, growing in close proximity to areas likely to have permafrost. With significant warming already underway throughout much of its range, we conclude that caterpillar fungus populations have been negatively affected by a combination of overexploitation and climate change. Our results underscore that harvesting is not the sole threat to economically valuable species, and that a collapse of the caterpillar fungus system under ongoing warming and high collection pressure would have serious implications throughout the Himalayan region.

Maximum flood extent—a key data need for disaster response and mitigation—is rarely quantified due to storm-related cloud cover and the low temporal resolution of optical sensors. While change detection approaches can circumvent these issues through the identification of inundated land and soil from post-flood imagery, their accuracy can suffer in the narrow and complex channels of increasingly developed and heterogeneous floodplains. This study explored the utility of the Operational Land Imager (OLI) and Independent Component Analysis (ICA) for addressing these challenges in the unprecedented 2013 Flood along the Colorado Front Range, USA. Pre- and post-flood images were composited and transformed with an ICA to identify change classes. Flooded pixels were extracted using image segmentation, and the resulting flood layer was refined with cloud and irrigated agricultural masks derived from the ICA. Visual assessment against aerial orthophotography showed close agreement with high water marks and scoured riverbanks, and a pixel-to-pixel validation with WorldView-2 imagery captured near peak flow yielded an overall accuracy of 87% and Kappa of 0.73. Additional tests showed a twofold increase in flood class accuracy over the commonly used modified normalized water index. The approach was able to simultaneously distinguish flood-related water and soil moisture from pre-existing water bodies and other spectrally similar classes within the narrow and braided channels of the study site. This was accomplished without the use of post-processing smoothing operations, enabling the important preservation of nuanced inundation patterns. Although flooding beneath moderate and sparse riparian vegetation canopy was captured, dense vegetation cover and paved regions of the floodplain were main sources of omission error, and commission errors occurred primarily in pixels of mixed land use and along the flood edge. Nevertheless, the unsupervised nature of ICA, in conjunction with the global availability of Landsat imagery, offers a straightforward, robust, and flexible approach to flood mapping that requires no ancillary data for rapid implementation. Finally, the spatial layer of flood extent and a summary of impacts were provided for use in the region’s ongoing hydrologic research and mitigation planning.

The Bale Mountains of Ethiopia contain the largest contiguous area of alpine habitat in Africa. The region provides critical water resources and other essential environmental services to highland communities, endemic wildlife, and millions of downstream people in East Africa. Increasing land use change has created concern over degradation to headwater wetlands and potential impacts on hydrologic regimes. Baseline understanding of wetland dynamics is lacking, however, and little is known about their function in the regional hydrologic system. We used remote sensing, machine learning, and field surveys to map the distribution of Afroalpine wetlands in the Bale Mountains. We developed a wetland typology based on hydrogeomorphic characteristics and a conceptual model of surface-groundwater flow. Our results show that wetland extent more than doubles between wet and dry seasons and that only 4 percent of the Afroalpine zone is saturated year-round. We also found evidence of a hydrologic continuum based on volcanic and glacial legacies, with wetlands at elevations above approximately 3,800 m asl likely to be ephemeral and wetlands at lower elevations tending to be perennial. Further interpretation suggests that local geology is a principal control on wetland distribution and hydrologic attenuation in the Bale Mountains. This lays the foundation for further research into surface–groundwater connectivity, climate change impacts, and conservation planning.