Vegetation growth monitoring based on ground-based visible light images from different views
Authors: Chen Yanli , Huang Lu , Chen Cheng , Xie Ying 
Published: February 2025

Multi-view real-life images taken by eco-meteorological observation stations can provide high-throughput visible light (RGB) image data for vegetation monitoring, but at present, there are few research reports on the vegetation monitoring effect of multi-view images and its difference from satellite remote sensing monitoring. In this study, with the underlying surface mixed with karst bare rock and vegetation as the research object, the far-view images and near-view images of 4 eco-meteorological stations were used to compare the segmentation effect of machine learning segmentation algorithm on images from far and near views, analyze the vegetation growth characteristics of visible images from far and near views, and investigate the differences between multi-view images and satellite remote sensing monitoring. The results showed that: (1) machine learning algorithm was suitable for green vegetation segmentation of multi-view images. The segmentation accuracy of machine learning algorithm for a near view image was higher than that for a far view image, with an accuracy rate of over 85%. Images captured under weak light conditions could obtain higher vegetation segmentation accuracy, and the proportion of bare rocks had no obvious influence on image segmentation accuracy. (2) The interannual variation trends of vegetation presented by different RGB vegetation indexes varied greatly, and the interannual variation difference of vegetation from a far view was greater than that from a near view. NDYI and RGBVI showed good consistency in vegetation changes from far and near views, and could also better show the interannual differences of vegetation. From the perspective of intra-year variation, various RGB vegetation indexes showed seasonal changes in different degrees. The vegetation in karst areas grew well from April to October, and the RGB vegetation indexes reached the peaks from May to June at most stations. The seasonal distribution of vegetation indexes were more obvious from a far view. (3) There was significant difference in the correlation between ground-based multi-view RGB vegetation indexes and NDVI of different satellites. The correlation with FY3D NDVI was weaker than that with MODIS NDVI. Most RGB vegetation indexes from a far view had a good correlation with MODIS NDVI, and the indexes with significant differences (P &lt; 0.05) accounted for 70.5%. The correlation of most RGB vegetation indexes with FY3D NDVI and MODIS NDVI from a far view was better than that from a near view, and there was a significant difference in the RGB indexes with the highest correlation with NDVI of the 2 satellites from both far and near views in different stations. The machine learning algorithm combined with NLM filtering optimization had great advantages in multi-view image segmentation. Different RGB vegetation indices had different responses to vegetation growth changes, which may be related to the band composition of vegetation index and vegetation morphology and location. The image shooting mode of satellite was closer to the far-view Angle, so the correlation and fitting degree between satellite NDVI and far-view NDVI were higher. The research results provided theoretical basis and technical support for improving the ability of multi-view remote sensing monitoring of vegetation growth in karst ecological station.