Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil




marshes, Sentinel 2A, remote sensing, CART method


Vast small inner marsh (SIM) areas have been lost in the past few decades through the conversion to agricultural, urban and industrial lands. The remaining marshes face several threats such as drainage for agriculture, construction of roads and port facilities, waste disposal, among others. This study integrates 17 remote sensing spectral indexes and decision tree (DT) method to map SIM areas using Sentinel 2A images from Summer and Winter seasons. Our results showed that remote sensing indexes, although not developed specifically for wetland delimitation, presented satisfactory results in order to classify these ecosystems. The indexes that showed to be more useful for marshes classification by DT techniques in the study area were NDTI, BI, NDPI and BI_2, with 25.9%, 17.7%, 11.1% and 0.8%, respectively. In general, the Proportion Correct (PC) found was 95.9% and 77.9% for the Summer and Winter images respectively. We hypothetize that this significant PC variation is related to the rice-planting period in the Summer and/or to the water level oscillation period in the Winter. For future studies, we recommend the use of active remote sensors (e.g., radar) and soil maps in addition to the remote sensing spectral indexes in order to obtain better results in the delimitation of small inner marsh areas.


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Author Biographies

J.P.D. Simioni, Federal University of Rio Grande do Sul

Research Center on Remote Sensing and Meteorology, Porto Alegre, Brazil(CEPSRM/UFRGS)

L.A. Guasselli, Federal University of Rio Grande do Sul

Research Center on Remote Sensing and Meteorology (CEPSRM/UFRGS) and Institute of Geosciences (IGEO)

L.F.C. Ruiz, Federal University of Rio Grande do Sul

Research Center on Remote Sensing and Meteorology, Porto Alegre, Brazil(CEPSRM/UFRGS)

V.F. Nascimento, Federal University of Rio Grande do Sul

Research Center on Remote Sensing and Meteorology, Porto Alegre, Brazil(CEPSRM/UFRGS)

G. de Oliveira, University of Kansas

Departament of Geography and Atmospheric Science


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