Mapping and dynamic monitoring of military training-induced vegetation cover loss using Sentinel-2 images and method comparison

• Published in: Environmental monitoring and assessment Vol. 195; no. 2; p. 320
• Main Authors: Xu, Xiaoyu, Ban, Bibek, Howard, Heidi R., Chen, Shu, Wang, Guangxing
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.02.2023; Springer Nature B.V
• Subjects: Algorithms; Atmospheric Protection/Air Quality Control/Air Pollution; Correlation; Earth and Environmental Science; Ecology; Ecotoxicology; Environment; Environmental Management; Environmental monitoring; Environmental Monitoring - methods; Environmental science; Humans; Machine learning; Methods; Military; Military Personnel; Military preparedness; Military training; Modelling; Monitoring; Monitoring systems; Monitoring/Environmental Analysis; Neural networks; Plant cover; regression analysis; Remote sensing; Remote Sensing Technology - methods; Satellite Imagery; Spatial discrimination; Spatial resolution; Support vector machines; Sustainability management; Training; Vegetation; Vegetation cover; Sentinel-2 imagery; Installation; Spectral variable selection; Military training-induced disturbance; Percentage vegetation cover; Spatial modeling
• ISSN: 0167-6369; 1573-2959; 1573-2959
• DOI: 10.1007/s10661-023-10918-2

Sustainable management of the US Army installations is critical for military training and readiness of forces. However, monitoring military training-induced vegetation cover disturbances using remote sensing data is challenging due to the lack of methodology for optimizing the selection of spectral variables or predictors and spatial modeling methods. This study aimed to propose and demonstrate a methodological solution for this purpose. The study was conducted in the Fort Riley installation in which three training areas were selected to map and monitor the training-induced vegetation cover loss. Sentinel-2 images and field observations of percentage vegetation cover (PVC) were combined at a spatial resolution of 10 m by 10 m to map PVC and its dynamics by comparison of two predictor selection methods and five spatial modeling algorithms based on a total of 304 spectral variables from the images before and after the training. Results showed that overall, the correlation-based predictor selection method reduced the relative root mean square error (RRMSE) of PVC predictions by 4.44% than the random forest (RF)-based predictor selection. Machine learning methods including RF, neural network, and support vector machine overall reduced the RRMSE of PVC predictions by 42.83% compared with multiple linear regression and k-nearest neighbors. Combining correlation-based predictor selection and RF modeling, coupled with leave one out cross validation, provided the greatest potential of increasing the accuracy of monitoring the vegetation cover loss. The findings provided useful implications to develop a near real-time system of monitoring military training-induced vegetation cover loss.