Using freely-available data and open-source software, we developed a remote sensing methodology to identify mining areas and assess recent mining expansion in Myanmar. Our country-wide analysis used Landsat 8 satellite data from a select number of mining areas to create a raster layer of potential mining areas. We used this layer to guide a systematic scan of freely-available fine-resolution imagery, such as Google Earth, in order to digitize likely mining areas. During this process, each mining area was assigned a ranking indicating our certainty in correct identification of the mining land use. Finally, we identified areas of recent mining expansion based on the change in albedo, or brightness, between Landsat images from 2002 and 2015. We identified 90,041 ha of potential mining areas in Myanmar, of which 58% (52,312 ha) was assigned high certainty, 29% (26,251 ha) medium certainty, and 13% (11,478 ha) low certainty. Of the high-certainty mining areas, 62% of bare ground was disturbed (had a large increase in albedo) since 2002. This four-month project provides the first publicly-available database of mining areas in Myanmar, and it demonstrates an approach for large-scale assessment of mining extent and expansion based on freely-available data.

McCarter, J., E. J. Sterling, S. D. Jupiter, G. D. Cullman, S. Albert, M. Basi, E. Betley, D. Boseto, E. S. Bulehite, R. Harron, P. S. Holland, N. Horning, A. Hughes, N. Jino, C. Malone, S. Mauli, B. Pae, R. Papae, F. Rence, O. Revo, E. Taqala, M. Taqu, H. Woltz, and C. E. Filardi. 2018. Biocultural approaches to developing well-being indicators in Solomon Islands. Ecology and Society 23(1):32. https://doi.org/10.5751/ES-09867-230132

The use of unmanned aerial vehicles (UAVs) to map and monitor the environment has increased sharply in the last few years. Many individuals and organizations have purchased consumer‐grade UAVs, and commonly acquire aerial photographs to map land cover. The resulting ultra‐high‐resolution (sub‐decimeter‐resolution) imagery has high information content, but automating the extraction of this information to create accurate, wall‐to‐wall land‐cover maps is quite difficult. We introduce image‐processing workflows that are based on open‐source software and can be used to create land‐cover maps from ultra‐high‐resolution aerial imagery. We compared four machine‐learning workflows for classifying images. Two workflows were based on random forest algorithms. Of these, one used a pixel‐by‐pixel approach available in ilastik, and the other used image segments and was implemented with R and the Orfeo ToolBox. The other two workflows used fully connected neural networks and convolutional neural networks implemented with Nenetic. We applied the four workflows to aerial photographs acquired in the Great Basin (western USA) at flying heights of 10 m, 45 m and 90 m above ground level. Our focal cover type was cheatgrass (Bromus tectorum), a non‐native invasive grass that changes regional fire dynamics. The most accurate workflow for classifying ultra‐high‐resolution imagery depends on diverse factors that are influenced by image resolution and land‐cover characteristics, such as contrast, landscape patterns and the spectral texture of the land‐cover types being classified. For our application, the ilastik workflow yielded the highest overall accuracy (0.82–0.89) as assessed by pixel‐based accuracy.

Ecological niche models (ENMs) can project changes in species’ distributions under climate change and thus inform conservation efforts and further our understanding of patterns of change. Predictions of species’ distribution shifts under climate change in topographically and geologically complex landscapes, such as karst landforms, should be improved by better integration of non-climate abiotic variables, such as karst geology or habitat structure, into model projections. We built ENMs for one of the limestone langurs, a group of leaf monkeys adapted to forests on the Sino-Vietnamese limestone karst landform. We collected occurrence localities for François’ leaf monkeys (Trachypithecus francoisi) and thinned them to avoid sampling bias. We included as environmental parameters a global dataset for karst geology and 19 bioclimatic variables derived from monthly temperature and precipitation at 30 arc-second resolution. ENMs including karst geology and climatic variables outperformed and differed spatially from climate-only models. Across six future-climate scenario projections, the optimal karst+climate model differed from the best climate-only model and predicted more spatial overlap with karst in the future, a contraction in total area of suitable habitat by the 2070s, and a small loss in the amount of suitable habitat in existing conservation areas. This study shows the importance of considering other abiotic factors beyond climate in projections of suitable habitat under climate change for species in complex landscapes. Because our results show that karst and climate interact to explain the distribution of a karst-adapted species, the results also suggest that, under climate change, these interactions are likely to produce altered networks of species into novel biological communities. Finally, our results support the need for conservation of limestone habitats and cross-border collaboration to maintain refuges and movement connectivity for endangered species in the face of climate change.

Linking animal movements to landscape features is critical to identify factors that shape the spatial behaviour of animals. Habitat selection is led by behavioural decisions and is shaped by the environment, therefore the landscape is crucial for the analysis. Land cover classification based on ground survey and remote sensing data sets are an established approach to define landscapes for habitat selection analysis. We investigate an approach for analysing habitat use using continuous land cover information and spatial metrics. This approach uses a continuous representation of the landscape using percentage cover of a chosen land cover type instead of discrete classes. This approach, fractional cover, captures spatial heterogeneity within classes and is therefore capable to provide a more distinct representation of the landscape. The variation in home range sizes is analysed using fractional cover and spatial metrics in conjunction with mixed effect models on red deer position data in the Bohemian Forest, compared over multiple spatio–temporal scales. We analysed forest fractional cover and a texture metric within each home range showing that variance of fractional cover values and texture explain much of variation in home range sizes. The results show a hump–shaped relationship, leading to smaller home ranges when forest fractional cover is very homogeneous or highly heterogeneous, while intermediate stages lead to larger home ranges. The application of continuous land cover information in conjunction with spatial metrics proved to be valuable for the explanation of home-range sizes of red deer.