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