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A Machine Learning Approach for Mapping Forest Vegetation in Riparian Zones in an Atlantic Biome Environment Using Sentinel-2 Imagery.

Authors :
Furuya, Danielle Elis Garcia
Aguiar, João Alex Floriano
Estrabis, Nayara V.
Pinheiro, Mayara Maezano Faita
Furuya, Michelle Taís Garcia
Pereira, Danillo Roberto
Gonçalves, Wesley Nunes
Liesenberg, Veraldo
Li, Jonathan
Marcato Junior, José
Prado Osco, Lucas
Ramos, Ana Paula Marques
Source :
Remote Sensing; Dec2020, Vol. 12 Issue 24, p4086, 1p
Publication Year :
2020

Abstract

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20724292
Volume :
12
Issue :
24
Database :
Complementary Index
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
147778977
Full Text :
https://doi.org/10.3390/rs12244086