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  1. Palms play an outsized role in tropical forests and are important resources for humans and wildlife. A central question in tropical ecosystems is understanding palm distribution and abundance. However, accurately identifying and localizing palms in geospatial imagery presents significant challenges due to dense vegetation, overlapping canopies, and variable lighting conditions in mixed-forest landscapes. Addressing this, we introduce PalmProbNet, a probabilistic approach utilizing transfer learning to analyze high-resolution UAV-derived orthomosaic imagery, enabling the detection of palm trees within the dense canopy of the Ecuadorian Rainforest. This approach represents a substantial advancement in automated palm detection, effectively pinpointing palm presence and locality in mixed tropical rainforests. Our process begins by generating an orthomosaic image from UAV images, from which we extract and label palm and non-palm image patches in two distinct sizes. These patches are then used to train models with an identical architecture, consisting of an unaltered pre-trained ResNet-18 and a Multilayer Perceptron (MLP) with specifically trained parameters. Subsequently, PalmProbNet employs a sliding window technique on the landscape orthomosaic, using both small and large window sizes to generate a probability heatmap. This heatmap effectively visualizes the distribution of palms, showcasing the scalability and adaptability of our approach in various forest densities. Despite the challenging terrain, our method demonstrated remarkable performance, achieving an accuracy of 97.32% and a Cohen's κ of 94.59% in testing. 
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    Free, publicly-accessible full text available April 18, 2025
  2. Abstract

    Artisanal and small‐scale gold mining (ASGM), a wealth‐generating industry in many regions, is nonetheless a global challenge for governance and a threat to biodiversity, public health, and ecosystem integrity. In 2019, the Peruvian government mobilized a targeted, large‐scale armed intervention against illegal ASGM, which has caused deforestation and water resource degradation in this Tropical Biodiversity Hotspot. Before the intervention, the extent of waterbodies created by mining (mining ponds) was increasing by 33%–90%/year; after, they decreased by 4%–5%/year in targeted zones. Mining activity indicators showed 70%–90% abandonment. New mining activity accelerated in nearby areas outside the targeted area (pond area increases: 42%–83%; deforestation increases +3–5 km2/year). Far from intervention zones, mining remained stable during the study period. Our analysis demonstrates that targeted, large‐scale government intervention can have positive effects on conservation by stopping illegal mining activity and shifting it to permitted areas, thereby setting the stage for governance. Continued conservation efforts must further address the impacts of informal mining while (1) limiting environmental degradation by legal mining; (2) remediating former mining areas to reduce erosion and enable reforestation or alternative uses of the landscape; and (3) sustaining such efforts, as some miners began to return to intervention areas when enforcement relaxed in 2022.

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