Identifying tropical dry forests extent and succession via the use of machine learning techniques

Publicado en International Journal of Applied Earth Observation and Geoinformation, v. 63:196-205

Wei Li, Sen Cao, Campos-Vargas, Carlos, Sanchez-Azofeifa, G.A.

Año de publicación 2017

College of Information and Communication Engineering, Harbin Engineering University, Harbin, 150001, China Alberta Centre for Earth Observation Sciences, Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, T6G 2E3, Canada



Proyecto CRN3025


•This study mapped secondary tropical dry forest succession by integrating hyperspectral data (HyMap) with LIDAR data (LVIS).

•Our paper recognizes that the process of ecological succession is not deterministic but follows a stochastic path.

•Our study highlights the use of multiple ecological variables and hyperspectral features to identify secondary succession.


Information on ecosystem services as a function of the successional stage for secondary tropical dry forests (TDFs) is scarce and limited. Secondary TDFs succession is defined as regrowth following a complete forest clearance for cattle growth or agriculture activities. In the context of large conservation initiatives, the identification of the extent, structure and composition of secondary TDFs can serve as key elements to estimate the effectiveness of such activities. As such, in this study we evaluate the use of a Hyperspectral MAPper (HyMap) dataset and a waveform LIDAR dataset for characterization of different levels of intra-secondary forests stages at the Santa Rosa National Park (SRNP) Environmental Monitoring Super Site located in Costa Rica. Specifically, a multi-task learning based machine learning classifier (MLC-MTL) is employed on the first shortwave infrared (SWIR1) of HyMap in order to identify the variability of aboveground biomass of secondary TDFs along a successional gradient. Our paper recognizes that the process of ecological succession is not deterministic but a combination of transitional forests types along a stochastic path that depends on ecological, edaphic, land use, and micro-meteorological conditions, and our results provide a new way to obtain the spatial distribution of three main types of TDFs successional stages.