The area used for mineral extraction is a key indicator for understanding and mitigating the environmental impacts caused by the extractive sector. To date, worldwide data products on mineral extraction do not report the area used by mining activities. In this paper, we contribute to filling this gap by presenting a new data set of mining extents derived by visual interpretation of satellite images. We delineated mining areas within a 10 km buffer from the approximate geographical coordinates of more than six thousand active mining sites across the globe. The result is a global-scale data set consisting of 21,060 polygons that add up to 57,277 km². The polygons cover all mining above-ground features that could be identified from the satellite images, including open cuts, tailings dams, waste rock dumps, water ponds, and processing infrastructure. The data set is available for download from https://doi.org/10.1594/PANGAEA.910894 and visualization at www.fineprint.global/viewer.

This paper presents a time-weighted version of the dynamic time warping (DTW) method for land-use and land-cover classification using remote sensing image time series. Methods based on DTW have achieved significant results in time-series data mining. The original DTW method works well for shape matching, but is not suited for remote sensing time-series classification. It disregards the temporal range when finding the best alignment between two time series. Since each land-cover class has a specific phenological cycle, a good time-series land-cover classifier needs to balance between shape matching and temporal alignment. To that end, we adjusted the original DTW method to include a temporal weight that accounts for seasonality of land-cover types. The resulting algorithm improves on previous methods for land-cover classification using DTW. In a case study in a tropical forest area, our proposed logistic time-weighted version achieves the best overall accuracy of 87.32%. The accuracy of a version with maximum time delay constraints is 84.66%. A time-warping method without time constraints has a 70.14% accuracy. To get good results with the proposed algorithm, the spatial and temporal resolutions of the data should capture the properties of the landscape. The pattern samples should also represent well the temporal variation of land cover.

The growing demand for minerals has pushed mining activities into new areas increasingly affecting biodiversity-rich natural biomes. Mapping the land use of the global mining sector is, therefore, a prerequisite for quantifying, understanding and mitigating adverse impacts caused by mineral extraction. This paper updates our previous work mapping mining sites worldwide. Using visual interpretation of Sentinel-2 images for 2019, we inspected more than 34,000 mining locations across the globe. The result is a global-scale dataset containing 44,929 polygon features covering 101,583 km² of large-scale as well as artisanal and small-scale mining. The increase in coverage is substantial compared to the first version of the dataset, which included 21,060 polygons extending over 57,277 km². The polygons cover open cuts, tailings dams, waste rock dumps, water ponds, processing plants, and other ground features related to the mining activities. The dataset is available for download from https://doi.org/10.1594/PANGAEA.942325 and visualisation at www.fineprint.global/viewer.

Growing demand for minerals continues to drive deforestation worldwide. Tropical forests are particularly vulnerable to the environmental impacts of mining and mineral processing. Many local- to regional-scale studies document extensive, long-lasting impacts of mining on biodiversity and ecosystem services. However, the full scope of deforestation induced by industrial mining across the tropics is yet unknown. Here, we present a biome-wide assessment to show where industrial mine expansion has caused the most deforestation from 2000 to 2019. We find that 3,264 km2 of forest was directly lost due to industrial mining, with 80% occurring in only four countries: Indonesia, Brazil, Ghana, and Suriname. Additionally, controlling for other nonmining determinants of deforestation, we find that mining caused indirect forest loss in two-thirds of the investigated countries. Our results illustrate significant yet unevenly distributed and often unmanaged impacts on these biodiverse ecosystems. Impact assessments and mitigation plans of industrial mining activities must address direct and indirect impacts to support conservation of the world’s tropical forests.

Land cover is of fundamental importance to many environmental applications and serves as critical baseline information for many large scale models e.g. in developing future scenarios of land use and climate change. Although there is an ongoing movement towards the development of higher resolution global land cover maps, medium resolution land cover products (e.g. GLC2000 and MODIS) are still very useful for modelling and assessment purposes. However, the current land cover products are not accurate enough for many applications so we need to develop approaches that can take existing land covers maps and produce a better overall product in a hybrid approach. This paper uses geographically weighted regression (GWR) and crowdsourced validation data from Geo-Wiki to create two hybrid global land cover maps that use medium resolution land cover products as an input. Two different methods were used: (a) the GWR was used to determine the best land cover product at each location; (b) the GWR was only used to determine the best land cover at those locations where all three land cover maps disagree, using the agreement of the land cover maps to determine land cover at the other cells. The results show that the hybrid land cover map developed using the first method resulted in a lower overall disagreement than the individual global land cover maps. The hybrid map produced by the second method was also better when compared to the GLC2000 and GlobCover but worse or similar in performance to the MODIS land cover product depending upon the metrics considered. The reason for this may be due to the use of the GLC2000 in the development of GlobCover, which may have resulted in areas where both maps agree with one another but not with MODIS, and where MODIS may in fact better represent land cover in those situations. These results serve to demonstrate that spatial analysis methods can be used to improve medium resolution global land cover information with existing products