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

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 stu...

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Main Authors: J. P. D. Simioni, L. A. Guasselli, L. F. C. Ruiz, V. F. Nascimento, G. de Oliveira
Format: Article
Language:English
Published: Universitat Politécnica de Valencia 2018-12-01
Series:Revista de Teledetección
Subjects:
Online Access:https://polipapers.upv.es/index.php/raet/article/view/10366
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spelling doaj-e3859c979255414ba5593c9965038a932020-11-24T21:25:54ZengUniversitat Politécnica de ValenciaRevista de Teledetección1133-09531988-87402018-12-01052556610.4995/raet.2018.103667225Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern BrazilJ. P. D. Simioni0L. A. Guasselli1L. F. C. Ruiz2V. F. Nascimento3G. de Oliveira4Federal University of Rio Grande do SulFederal University of Rio Grande do SulFederal University of Rio Grande do SulFederal University of Rio Grande do SulUniversity of KansasVast 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.https://polipapers.upv.es/index.php/raet/article/view/10366marshesSentinel 2Aremote sensingCART method
collection DOAJ
language English
format Article
sources DOAJ
author J. P. D. Simioni
L. A. Guasselli
L. F. C. Ruiz
V. F. Nascimento
G. de Oliveira
spellingShingle J. P. D. Simioni
L. A. Guasselli
L. F. C. Ruiz
V. F. Nascimento
G. de Oliveira
Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil
Revista de Teledetección
marshes
Sentinel 2A
remote sensing
CART method
author_facet J. P. D. Simioni
L. A. Guasselli
L. F. C. Ruiz
V. F. Nascimento
G. de Oliveira
author_sort J. P. D. Simioni
title Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil
title_short Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil
title_full Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil
title_fullStr Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil
title_full_unstemmed Small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern Brazil
title_sort small inner marsh area delimitation using remote sensing spectral indexes and decision tree method in southern brazil
publisher Universitat Politécnica de Valencia
series Revista de Teledetección
issn 1133-0953
1988-8740
publishDate 2018-12-01
description 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.
topic marshes
Sentinel 2A
remote sensing
CART method
url https://polipapers.upv.es/index.php/raet/article/view/10366
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