{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T05:22:25Z","timestamp":1772601745323,"version":"3.50.1"},"reference-count":70,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T00:00:00Z","timestamp":1715731200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Artisanal small-scale mines (ASMs) in the Amazon Rainforest are an important cause of deforestation, forest degradation, biodiversity loss, sedimentation in rivers, and mercury emissions. Satellite image data are widely used in environmental decision-making to monitor changes in the land surface, but ASMs are difficult to map from space. ASMs are small, irregularly shaped, unevenly distributed, and confused (spectrally) with other land clearance types. To address this issue, we developed a reliable and efficient ASM detection method for the Tapaj\u00f3s River Basin of Brazil\u2014an important gold mining region of the Amazon Rainforest. We enhanced detection in three key ways. First, we used the time-series segmentation (LandTrendr) Google Earth Engine (GEE) Application Programming Interface to map the pixel-wise trajectory of natural vegetation disturbance and recovery on an annual basis with a 2000 to 2019 Landsat image time series. Second, we segmented 26 textural features in addition to 5 spectral features to account for the high spatial heterogeneity in ASM pixels. Third, we trained and tested a Random Forest model to detect ASMs after eliminating irrelevant and redundant features with the Variable Selection Using Random Forests \u201censemble of ensembles\u201d technique. The out-of-bag error and overall accuracy of the final Random Forest was 3.73 and 92.6%, which are comparable to studies mapping large industrial mines with the normalized difference vegetation index (NDVI) and LandTrendr. The most important feature in our study was NDVI, followed by textural features in the near and shortwave infrared. Our work paves the way for future ASM regulation through large area monitoring from space with free and open-source GEE and operational satellites. Studies with sufficient computational resources can improve ASM monitoring with advanced sensors consisting of spectral narrow bands (Sentinel-2, Environmental Mapping and Analysis Program, PRecursore IperSpettrale della Missione Applicativa) and deep learning.<\/jats:p>","DOI":"10.3390\/rs16101749","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T06:14:42Z","timestamp":1715753682000},"page":"1749","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-1566-6263","authenticated-orcid":false,"given":"Alejandro","family":"Fonseca","sequence":"first","affiliation":[{"name":"IABG Industrieanlagen-Betriebsgesellschaft, Hermann-Reicheltelt-Stra\u00dfe 3, 01109 Dresden, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9738-5036","authenticated-orcid":false,"given":"Michael Thomas","family":"Marshall","sequence":"additional","affiliation":[{"name":"Department of Natural Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6670-6853","authenticated-orcid":false,"given":"Suhyb","family":"Salama","sequence":"additional","affiliation":[{"name":"Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.foreco.2015.06.013","article-title":"Forest Resources Assessment of 2015 shows positive global trends but forest loss and degradation persist in poor tropical countries","volume":"352","author":"Sloan","year":"2015","journal-title":"For. Ecol. 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