Abstract
Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.
Highlights
By comparing the model trained with variables from reflectance to the model trained with additional textural variables, we discovered that the additional textural variables positively impact the model’s performance in identifying PV power plants (Figure 3)
Similar to the variations of kappa and overall accuracy (OA), the user’s accuracy (UA) and producer’s accuracy (PA) of PV power plants or non-PV power plants all reached their maximum value at the neighbor size of 30 to 40 (Figure 3d–g)
We evaluated the thermal spectra as brightness temperature (BT) of L-8 on the model’s performance since the land surface temperature (LST) of PV power plants is different from their adjacent regions [13,52]
Summary
Solar energy is the most commonly available renewable energy source with a great potential to replace fossil fuels while reducing greenhouse gas (GHG) emissions to limit climate change [1,2]. Photovoltaic (PV) technology can convert solar energy directly into electricity with large arrays of solar panels [3]. With PV technology and industry development, the cost of electricity generated by PV power plants has declined to the same level as that generated by traditional fossil-fuel power plants [4]. According to the International Energy Agency (IEA), the global installed PV capacity has increased from about 1.25 GW in 2000 to more than 627 GW in 2019. With the establishment of the carbon neutrality goal of the majority of countries worldwide, the generation capacity of PV power
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