Mapping of forest structure: the potential of combining optical and radar imageries with on-site measurements

Land cover maps constitute a major information source for many spatial analyses of ecological and socioeconomic relevance. Typically, however, classified maps depict the spatial arrangement of classes within a landscape without considering variability within each class, although structure and variation within the classes may well influence landscape’s potential and functions. For different “forest classes”, for example, their specific functions depend very much on vertical and horizontal structure. As a consequence, to better understand the multiple functions of “forest classes”, their within class structure should also be analyzed. Nevertheless, comprehensively assessing “forest structure” requires either time intensive fieldwork or cost intensive LiDAR campaigns, or both. By the term of “forest structure”, we are here referring to the horizontal and vertical distribution of layers of the vegetation. In this study, we investigate the potential of combining different sources of information, optical and radar imageries with on-site measurements, to relate indicators of forest structure to canopy surface roughness, as one strongly dependent but remotely observable structural indicator. The study is conducted in Harapan Rainforest in Jambi Province, Sumatra, Indonesia, which is one of the last areas dominated by a diverse lowland rainforest and habitat for many endemic species. Indicator variables for forest structure were recorded on 70 field plots on transects following defined UAV flight paths where multispectral images were taken. We hypothesize correlations between indices from three-dimensional photogrammetric point clouds based on UAV captured images, radar backscatter from Sentinel-1 C- and ALOS L-band SAR, and field observed indicators of forest structure as well as texture indices derived from optical data. The goal will be to distinguish different structural forest types by means of characterizing their canopy surface roughness from the mentioned remote sensing data sources. Different studies have shown the potential of photogrammetric point clouds derived from UAV captured images to analyze canopy structure with high resolution (Wallace et al. 2016; Morgenroth and Gomez, 2014). By combining such information with crown penetrating C- and L-band SAR and with on-site data, we expect to produce more detailed information on forest structure and provide a more cost-efficient approach to forest structure mapping.