Abstract
Studies designed to discriminate different successional forest stages play a strategic role in forest management, forest policy and environmental conservation in tropical environments. The discrimination of different successional forest stages is still a challenge due to the spectral similarity among the concerned classes. Considering this, the objective of this paper was to investigate the performance of Sentinel-2 and Landsat-8 data for discriminating different successional forest stages of a patch located in a subtropical portion of the Atlantic Rain Forest in Southern Brazil with the aid of two machine learning algorithms and relying on the use of spectral reflectance data selected over two seasons and attributes thereof derived. Random Forest (RF) and Support Vector Machine (SVM) were used as classifiers with different subsets of predictor variables (multitemporal spectral reflectance, textural metrics and vegetation indices). All the experiments reached satisfactory results, with Kappa indices varying between 0.9, with Landsat-8 spectral reflectance alone and the SVM algorithm, and 0.98, with Sentinel-2 spectral reflectance alone also associated with the SVM algorithm. The Landsat-8 data had a significant increase in accuracy with the inclusion of other predictor variables in the classification process besides the pure spectral reflectance bands. The classification methods SVM and RF had similar performances in general. As to the RF method, the texture mean of the red-edge and SWIR bands were considered the most important ranked attributes for the classification of Sentinel-2 data, while attributes resulting from multitemporal bands, textural metrics of SWIR bands and vegetation indices were the most important ones in the Landsat-8 data classification.
Highlights
Tropical forests are among the most complex ecosystems on Earth, playing crucial roles in biodiversity conservation and in ecology dynamics at global scale [1]
The minimum overall accuracy (OA) (91.9%) and Kappa index (0.90) refer refer to the G1-L8 experiment associated with the Support Vector Machine (SVM) classifier; and the maximum OA (98.4%) and to the G1-L8 experiment associated with the SVM classifier; and the maximum OA (98.4%) and Kappa
The OA of G1-S2 was significantly significantly superior to that of all experiments using Landsat-8 data, with the exception of superior to that of all experiments using Landsat-8 data, with the exception of experiments G2-L8 and experiments G2-L8 and G4-L8 associated with Random Forest (RF), and G3-L8 with SVM
Summary
Tropical forests are among the most complex ecosystems on Earth, playing crucial roles in biodiversity conservation and in ecology dynamics at global scale [1]. Forest remnants are sparsely found and present different degradation levels and successional forest stages Despite these threats, the Atlantic Forest and its associated ecosystems (sandbanks and mangroves) are still rich with respect to biodiversity, accounting for a meaningful share of the national biodiversity, Remote Sens. 2017, 9, 838 with high rates of endemism [4] As reported by these authors, this complex biome contains greater species diversity than that observed in the Amazon Forest. This species richness, the extremely high rates of endemism and the small percentage of this forest remnants led Myers et al [5] to classify the Atlantic Forest among the main global biodiversity hot spots
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
