Evaluation of environmental impacts produced by gold mining areas on the surrounding forest in southwestern Ecuador using multispectral satellite and uas imagery

Mining is a dangerous activity that can cause environmental damage to flora and fauna due to the utilization of heavy metals. Ecuador has a long history of mineral extractions and nowadays the activity is increasing in many parts of the country. Environmentalists state that chemicals, such as cyanide and mercury, could cause alterations in vegetation health. This study utilizes satellite and Unmanned Aircraft System (UAS) based remote sensing to analyze impacts to vegetation health around a mining area located in Bella Rica within the El Oro province of the southwestern zone of Ecuador. Vegetation can be analyzed and identified through many remote sensing techniques, one of them is the Normalized Difference Vegetation Index (NDVI). This band ratio index ranges from +1 to -1 and uses red and near-infrared (NIR) bands to identify the presence of healthy or stressed vegetation. In this study, a small rotary UAS equipped with a two-band sensor recording red and NIR reflectance and a separate red-green-blue (RGB) digital camera was used to gather data and determine if vegetation closer to the mine exhibited different NDVI patterns compared to vegetation located farther away. Spatial differences in NDVI patterns may indicate potential impacts of waste from mining operations. To provide a time series assessment of vegetation changes around the mine, satellite imagery from PlanetScope was acquired and analyzed to measure changes in NDVI throughout the years 2017, 2018, and 2019. PlanetScope uses an array of miniaturized satellites, called CubeSats, equipped with four-band multispectral sensors providing imagery at a resolution of 3 m ground sample distance (GSD). In comparison, spatial resolution of the UAS products, which is dependent on flying height, range from 2.97 cm GSD for the RGB camera to 11.4 cm GSD for the multispectral sensor. Satellite derived NDVI was vi statistically compared to UAS derived NDVI values to assess the impact of spatial resolution and sensor quality on NDVI measurement. Furthermore, the UAS acquired RGB imagery was processed using Structure from Motion (SfM) photogrammetry to derive a 3D reconstruction of the scene, referred to as a point cloud. Properties of the point cloud data were analyzed to determine if relationships exist between land cover structure and NDVI patterns captured in the UAS multispectral imagery. From UAS based multispectral data, significant differences in NDVI values were found between vegetation close to the mining area and vegetation at longer distances (p < 0.05), indicating that mining waste could be altering NDVI values in the region. Satellite imagery analysis suggests that changes in NDVI are related to different human activities that have been developed inside the study area. UAS derived NDVI shows a strong linear relationship with PlanetScope derived NDVI (R = 0.91), suggesting that the low cost and light-weight sensor onboard the UAS was able to capture similar reflectance information but at much higher resolution. UAS-SfM point cloud data was applied to measure spatial variation in point density and canopy height, and determine if these measures could serve as a proxy for NDVI to assess vegetation health impacts from the mining operation. Results varied with NDVI and point cloud density exhibiting a weak relationship (R = 0.04). This relationship held at multiple resolutions suggesting that scene texture and uniformity in the densification stage of SfM does not correlate well with variation in NDVI due to differences in canopy cover. Interestingly, point cloud density changes did show a connection to the type of vegetation with high values of point density occurring over the more densely canopied forest areas. In contrast to point cloud density, UASSfM derived canopy height measures exhibited much stronger correlation to the UAS multispectral NDVI values (R = 0.69).