Enhanced Detection of Artisanal Small-Scale Mining with Spectral and Textural Segmentation of Landsat Time Series

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 10; p. 1749
• Main Authors: Fonseca, Alejandro, Marshall, Michael Thomas, Salama, Suhyb
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2024
• Subjects: Amazon; Application programming interface; Biodiversity; Biodiversity loss; Brazil; China; Data mining; Decision making; Decision trees; Deep learning; Deforestation; Dry season; Earth resources technology satellites; Emissions; Forest degradation; Geospatial data; Gold mines & mining; Gold mines and mining; Heterogeneity; Land clearance; Landsat; Landsat satellites; LandTrendr; machine learning; Mapping; Mercury; Mines; Monitoring; Natural vegetation; Normalized difference vegetative index; Pixels; Rain forests; Rainforests; Remote sensing; River basins; River networks; Rivers; Satellite imagery; Satellites; Sediments; Segmentation; Short wave radiation; Spatial heterogeneity; Time series; Topography; Vegetation; Brazil; China
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16101749

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 Tapajós River Basin of Brazil—an 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 “ensemble of ensembles” 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.