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In the context of China\u2019s industrialization and urbanization, mining activities have caused huge damage to the forest ecology. In the Ulan Mulun River Basin (Ordos, China), afforestation is standard method for reclamation of coal mine degraded land. In order to understand, manage and utilize forests, it is necessary to collect local mining area\u2019s tree information. This paper proposed an improved Faster R-CNN model to identify individual trees. There were three major improved parts in this model. First, the model applied supervised multi-policy data augmentation (DA) to address the unmanned aerial vehicle (UAV) sample label size imbalance phenomenon. Second, we proposed Dense Enhance Feature Pyramid Network (DE-FPN) to improve the detection accuracy of small sample. Third, we modified the state-of-the-art Alpha Intersection over Union (Alpha-IoU) loss function. In the regression stage, this part effectively improved the bounding box accuracy. Compared with the original model, the improved model had the faster effect and higher accuracy. The result shows that the data augmentation strategy increased AP by 1.26%, DE-FPN increased AP by 2.82%, and the improved Alpha-IoU increased AP by 2.60%. Compared with popular target detection algorithms, our improved Faster R-CNN algorithm had the highest accuracy for tree detection in mining areas. AP was 89.89%. It also had a good generalization, and it can accurately identify trees in a complex background. Our algorithm detected correct trees accounted for 91.61%. In the surrounding area of coal mines, the higher the stand density is, the smaller the remote sensing index value is. Remote sensing indices included Green Leaf Index (GLI), Red Green Blue Vegetation Index (RGBVI), Visible Atmospheric Resistance Index (VARI), and Normalized Green Red Difference Index (NGRDI). In the drone zone, the western area of Bulianta Coal Mine (Area A) had the highest stand density, which was 203.95 trees ha\u22121. GLI mean value was 0.09, RGBVI mean value was 0.17, VARI mean value was 0.04, and NGRDI mean value was 0.04. The southern area of Bulianta Coal Mine (Area D) was 105.09 trees ha\u22121 of stand density. Four remote sensing indices were all the highest. GLI mean value was 0.15, RGBVI mean value was 0.43, VARI mean value was 0.12, and NGRDI mean value was 0.09. This study provided a sustainable development theoretical guidance for the Ulan Mulun River Basin. It is crucial information for local ecological environment and economic development.<\/jats:p>","DOI":"10.3390\/rs14215545","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T04:00:51Z","timestamp":1667534451000},"page":"5545","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Individual Tree Detection in Coal Mine Afforestation Area Based on Improved Faster RCNN in UAV RGB Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0703-2633","authenticated-orcid":false,"given":"Meng","family":"Luo","sequence":"first","affiliation":[{"name":"College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanan","family":"Tian","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"},{"name":"Key Laboratory of Water Resources Protection and Utilization of Inner Mongolia Autonomous Region, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9153-4398","authenticated-orcid":false,"given":"Shengwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"},{"name":"Key Laboratory of Water Resources Protection and Utilization of Inner Mongolia Autonomous Region, Hohhot 010018, China"},{"name":"Key Laboratory of Large Data Research and Application of Agriculture and Animal Husbandry in Inner Mongolia Autonomous Region, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"},{"name":"Key Laboratory of Water Resources Protection and Utilization of Inner Mongolia Autonomous Region, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huiqiang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"},{"name":"Key Laboratory of Water Resources Protection and Utilization of Inner Mongolia Autonomous Region, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqiang","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lin","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.envpol.2015.05.041","article-title":"Detecting the effects of hydrocarbon pollution in the Amazon forest using hyperspectral satellite images","volume":"205","author":"Arellano","year":"2015","journal-title":"Environ. 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