Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China

Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation reduce greenhouse gas emissions. Consequently, many countries have been attempting generate electricity through PV plants over the last decade. Monitoring satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid precise locating with current status on a regional basis are crucial environmental impact assessment policy formulation. The effect of fusion spectral, textural different neighbor sizes, topographic features improve accuracy has not evaluated yet in plants’ mapping. This study mapped using random forest (RF) model Google Earth Engine (GEE) platform. We combined calculated from Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, Normalized Difference Vegetation Index (NDVI), Built-up (NDBI), Modified Water (MNDWI) Landsat-8 imagery elevation, slope, aspect Shuttle Radar Topography Mission (SRTM) as input variables. found GLCM prominent enhance identifying where size 30 pixels showed best performance. addition texture can Kappa statistic 0.904 ± 0.05 0.938 0.04 overall 97.45 0.14% 98.32 0.11%. contribute slight improvement modeling. extends knowledge various variables remote sensing data. characteristics at spatial resolutions deserve attention. findings our great significance collecting geographic information evaluating their impact.