Collaborative Vegetation Management of Photovoltaic Power Plants Based on UAV and Satellite Imagery
As a new mode of modern ecological agriculture development,agrophotovoltaic power plants urgently need a high-precision and whole-process remote sensing technology to achieve accurate monitoring and real-time control of crop conditions in local-scale.Providing accurate remote sensing information can help optimize the environmental management of power plants,promote ecological restoration,and gain insights on the environmental impacts of photovoltaic power plants.It can not only provide technical support for the sustainable development of agrophotovoltaic power plants,but also provide an important scientific basis for future intelligent agricultural management decisions and ecological construction of power plants.However,significant uncertainties and biases exist in monitoring of agrophotovoltaic power plants due to the scale mismatch and spatiotemporal topological errors between the coarse resolution of satellite images and the limited area.The emergence of Unmanned Aerial Vehicle(UAV)makes up for this disadvantage,but it is difficult to promote to large-scale applications.This study took various typical agrophotovoltaic power plants in Zhejiang Province as the research object.The ultra-high spatial resolution multi-spectral remote sensing data was obtained by UAV to accurately estimate the Normalized Difference Vegetation Index(NDVI)of different land use types within PV power plants.Combining the contemporaneous images of Sentinel-2 MSI and Landsat-8/9 OLI satellites,the ability of the three types of images to invert vegetation conditions was quantitatively analyzed using three indicators:mean,dynamic range and standard deviation.A regression analysis was conducted to establish a conversion equation for UAV and satellite image NDVI data across the entire extent of PV power plants.This equation allowed for the application of UAV-derived NDVI data in different scales,and validated its regional applicability within Zhejiang Province.The results showed that there were large heterogeneity of physical space and significant differences in vegetation growth inside the PV power plants.And agricultural planting was of great significance to improve NDVI at PV power plants.UAV showed better vegetation detection capabilities at both overall and zonal scales,while satellite underestimated the NDVI value.Compared to Landsat,the NDVI conversion equation between Sentinel-2 and UAV was more conducive to complementing the monitoring results of the two.The goodness of fit(R2)between Sentinel-2 and UAV was 0.86,the Root Mean Square Error(RMSE)was 0.10,which was higher than Landsat(R2=0.76,RMSE=0.11).The results might extend the collaborative application scenarios of UAV and satellite images at agrophotovoltaic power plants,and provide a technical reference for the accurate monitoring and management of vegetation status of medium-and high-resolution satellites to achieve fragmented land use at the local scale.
agrophotovoltaic power plantecological restorationenvironmental managementphotovoltaic+vegetation index
NETL
NSTL
万方数据
