A remote sensing-guided forest inventory concept using multispectral 3D and height information from ZiYuan-3 satellite data

Abstract Increased frequencies of storms and droughts due to climate change are changing central European forests more rapidly than in previous decades. To monitor these changes, multispectral 3D remote sensing (RS) data can provide relevant information for forest management inventory. In this case study, the 3D-capable satellite system ZiYuan-3 (ZY-3) were used a RS-guided inventory concept reduce field sample size compared standard grid We first pre-stratified area via ZY-3 dataset into coniferous, broadleaved mixed types using object-based image analysis. Each type was then split three height strata stereo module-derived digital canopy model (CHM). Due limited sizes, we reduced nine six strata. Then, each strata, randomly selected representative segments plot placement. conducted inventories plots. The collected calculate attributes, such as tree species composition, timber volume at level (terrestrially measured from CHM). Subsequently, resulting attributes with reference based only on difference mean volumes +30.21 m3ha?1 (8.99 per cent) terrestrial ?11.32 (?3.37 data. relative efficiency (RE) indicator compare different sampling schemes. RE random reduction 1.22 measurements 1.85 results show that presented workflow is suitable support by reducing hence potentially increase frequency.