Combining LiDAR and Spaceborne Multispectral Data for Mapping Successional Forest Stages in Subtropical Forests

• Published in: Remote sensing (Basel, Switzerland) Vol. 16; no. 9; p. 1523
• Main Authors: Ziegelmaier Neto, Bill Herbert, Schimalski, Marcos Benedito, Liesenberg, Veraldo, Sothe, Camile, Martins-Neto, Rorai Pereira, Floriani, Mireli Moura Pitz
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-05-2024
• Subjects: Algorithms; Atlantic Rainforest; Band spectra; Brazil; Cameras; CBERS-4; Classification; Datasets; Drone aircraft; Earth resources technology satellites; Ecological succession; Environmental management; Environmental protection; Evaluation; Flora; Forest management; Forests; Inventories; Landsat; Landsat satellites; Landsat-8; Lidar; Machine learning; Mixed forests; Open source software; Optical measuring instruments; Optical radar; planet; Protection and preservation; Public software; Rain forests; Rainforests; Remote sensing; Satellites; Sensors; Sentinel-2; Spectral bands; Stand structure; Statistical analysis; Support vector machines; Sustainable forestry; Tropical forests; Unmanned aerial vehicles; Vegetation; Vegetation index; Brazil; Atlantic Forest
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs16091523

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs.