Scientists have developed a multi-scale method to assess vegetation conditions in PV energy plants. The research was aimed at nine PV plants in China in different climate zones. In comparison with only satellite estimates, the method reduces the distortion by 16.98%.
Scientists from Chinese Zhejiang University have developed a multi-scale method to assess vegetation conditions in PV energy plants.
The new approach combines field data, unmanned Aerial Vehicle (UAV) images and Sentinel -2 satellite data. It is intended to correct the biased normalized difference of vegetation index (NDVI) results that have been delivered by an analysis of only satellite images.
“For quantitative assessments of the effects of PV panel installations on the vegetation on a regional scale, it is crucial to accurately pick up the actual vegetation conditions within PV energy plants from satellite-picked up signals,” said the academics. “In this study we propose a solution that is aimed at NDVI from Sentinel-2 data by using UAV images to mediate field and satellite data integration.
The research was aimed at nine PV facilities on mainland China, on different climate zones and types of land use.
In general, 76 visits to the various parks were performed to establish the ground truth, whereby 3,295 pair of underpaneel and interim panel images were collected to determine whether there is a correlation. In addition, the DJI Phantom 4 Multispectral Quadcopter and the DJI Mavic 3 Multispectral Quadcopter were used to collect ultra-high-resolution teleclasses. They flow on days with a good view of a height of 200 m. These were linked to level-2 Sentinel-2 data of high spatial and temporary resolution.
“We have adjusted a U-network model to calibrate the Sentinel-2 NDVI to better record the complex relationship between UAV and satellite data,” the researchers explains. “We selected 5.054 patches or 64 × 64 pixels as inputs for model training, with 80% used for training and 20% for validation. To further Evaluate the Model’s generalization capability, we tested it on six solar plants across china. We then’s modified usete useyted useyted un-net Used to calibrate the Sentinel-2 NDVI and Mosaicked the Predicted NDVI for Each Plant for further analyzes.
Their analysis showed that the corrected output considerably reduced the discrepancy between Sentinel-2 NDVI and fundamental truth, which was measured on the field days. However, no perfect agreement has been reached. In general, they discovered that before the correction, Sentinel-2 NDVI tended to overestimate values in low-NDVI plants and to underestimate them in High-NDVI.
“Our results show that vegetation underneath and between the photovoltaic panels is strongly correlated and the absorption of vegetation under the panel increases the average normalized difference in the vegetation index from 0.248 ± 0.158 to 0.298 ± 0.193,” the results showed. “In comparison with only satellite estimates, our method reduces the bias by 16.98%. On a regional scale, about 61.59% of the power plants did not suppress vegetation growth. This approach makes more accurate environmental assessments possible of the development of photovoltaic power plants and supports a better-informed plan.”
Their findings were presented in “Using unmanned antenna vehicle images improves the mapping of vegetation in photovoltaic power plants“Published in Communication Earth & Environment. Scientists from Chinese Zhejiang University, the Chinese Academy of Sciences and the Beijing Institute of Space Mechanics and Electricity participated in the study.
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