Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

Autor(es)

Martins-Neto, Rorai Pereira ^[1] ; Garcia Tommaselli, Antonio Maria ^{[2, 1]} ; Imai, Nilton Nobuhiro ^{[2, 1]} ; David, Hassan Camil ^[3] ; Miltiadou, Milto ^{[4, 5]} ; Honkavaara, Eija ^[6]

Afiliação do(s) autor(es)

^[1] Sao Paulo State Univ UNESP, Grad Program Cartog Sci, Roberto Simonsen 305, BR-19060900 Presidente Prudente, SP - Brazil

^[2] Sao Paulo State Univ UNESP, Dept Cartog, Roberto Simonsen 305, BR-19060900 Presidente Prudente, SP - Brazil

^[3] Fed Rural Univ Amazonia UFRA, Dept Forestry, Tv Pau Amarelo S-N, BR-68650000 Capitao Poco, PA - Brazil

^[4] ERATOSTHENES Ctr Excellence, CY-3036 Limassol - Cyprus

^[5] Cyprus Univ Technol, Sch Engn & Technol, Dept Civil Engn & Geomat, Lab Remote Sensing & Geoenvironm, CY-3036 Limassol - Cyprus

^[6] Natl Land Survey Finland, Finnish Geospatial Res Inst FGI, Geodeetinrinne 2, Masala 02430 - Finland

• Tipo de documento: Artigo Científico
• Fonte: REMOTE SENSING; v. 13, n. 13 JUL 2021.
• Citações Web of Science: 1

Resumo

Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon-Waver (H') and Simpson's diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H'and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H'and D, the RMSE was 5.2-10% with a bias between -1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2-27.6% and the bias between -12.4% and -0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data. (AU)

• Processo FAPESP: 13/50426-4 - Unmanned airborne vehicle-based 4D remote sensing for mapping Rain Forest biodiversity and its change in Brazil (UAV_4D_Bio)
• Beneficiário: Antonio Maria Garcia Tommaselli
• Modalidade de apoio: Auxílio à Pesquisa - Regular