Defining and Mapping Tropical Dryland Woody Cover: A Comparative Study using Drone Data in Senegal

Dryland woody cover plays an important role in terrestrial ecosystems while undergoing transformation due to the changing climate and human impacts. Dryland woody cover mapping is thus critical but challenging given the great variations in dryland vegetation life forms, structure, and spatial distribution. Thus, the clear thresholds defining the structural limits of woody vegetation cover class are needed, to map woody cover and to validate the map accuracy and usability. The Land Cover Classification System (LCCS) of Food and Agriculture Organization (FAO) provides a detailed operational framework for applying classifiers in building legends for mapping land cover. The physiognomic-structural traits - height and cover, have been used by a number of global classification schemes to define the extent of woody cover. However, in the existing studies on dryland woody cover mappings, the definitions of “tree” or “forest” are often vague or lacking in terms of definitive physiognomic structural perspectives of woody cover. The objective of this study is to apply classifiers in mapping the woody cover, and compare with existing large-scale products to illustrate the need for clear definitions. The study was implemented in Senegal which features a corresponding high variation in height and cover of woody vegetation. We present spatially explicit woody cover structure mapping using field-based unmanned aerial vehicles (UAV) of passive optical photogrammetry and lidar-derived point cloud. We employed Harmonized Landsat Sentinel-2 (HLS) and Synthetic Aperture Radar (SAR) data from Sentinel-1 as mapping inputs. Specifically, we generated a set of 30-m cover maps for woody cover derived using different height definitions. We validated the cover maps using set aside field data and achieved a set of R-square ranging from 0.67 to 0.86, which demonstrates the capability of HLS and SAR in mapping different definitions of woody cover. We also validated the existing large-area maps to examine what structural thresholds these products best match using the aerial lidar collections. The study emphasizes the need to relate remote sensing-based observations and derived products with the explicit structure of woody cover, illustrating that imprecise definitions preclude robust validation and the informed use of such products.

Li, X., Hansen, M., Potapov, P., Li, H. and Poulson, A.J. (2024). Defining and Mapping Tropical Dryland Woody Cover: A Comparative Study using Drone Data in Senegal. AGU Fall Meeting, Abstract B11J-1445, December 9-13, Washington, D.C., USA.