Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data

As wetlands are one of the world’s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, t...

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Main Authors: Gordana Kaplan, Ugur Avdan
Format: Article
Language:English
Published: MDPI AG 2018-10-01
Series:ISPRS International Journal of Geo-Information
Subjects:
UAV
Online Access:http://www.mdpi.com/2220-9964/7/10/411
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spelling doaj-32bffe17c71c462b874201847b5bfbed2020-11-25T00:23:59ZengMDPI AGISPRS International Journal of Geo-Information2220-99642018-10-0171041110.3390/ijgi7100411ijgi7100411Monthly Analysis of Wetlands Dynamics Using Remote Sensing DataGordana Kaplan0Ugur Avdan1Earth and Space Sciences Institute, Eskisehir Technical University, Eskisehir 26470, Turkey, <email>uavdan@anadolu.edu.tr</email>Earth and Space Sciences Institute, Eskisehir Technical University, Eskisehir 26470, Turkey, <email>uavdan@anadolu.edu.tr</email>As wetlands are one of the world&rsquo;s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time.http://www.mdpi.com/2220-9964/7/10/411remote sensingwetlandsUAVSentinel-1land surface temperature
collection DOAJ
language English
format Article
sources DOAJ
author Gordana Kaplan
Ugur Avdan
spellingShingle Gordana Kaplan
Ugur Avdan
Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
ISPRS International Journal of Geo-Information
remote sensing
wetlands
UAV
Sentinel-1
land surface temperature
author_facet Gordana Kaplan
Ugur Avdan
author_sort Gordana Kaplan
title Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
title_short Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
title_full Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
title_fullStr Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
title_full_unstemmed Monthly Analysis of Wetlands Dynamics Using Remote Sensing Data
title_sort monthly analysis of wetlands dynamics using remote sensing data
publisher MDPI AG
series ISPRS International Journal of Geo-Information
issn 2220-9964
publishDate 2018-10-01
description As wetlands are one of the world&rsquo;s most important ecosystems, their vulnerability necessitates the constant monitoring and mapping of their changes. Satellite-based remote sensing has become an essential data source for mapping and monitoring wetlands. As wetlands are dynamic ecosystems, their classification depends on many different parameters. However, considering their complex structure; wetlands tend to be challenging land cover for classification, which sometimes requires the use of multi-sensor remote sensing techniques. The objectives of this study were: (i) to investigate the monthly dynamics of several wetland classes using multi-sensor parameters; (ii) to find correlations between the investigated parameters. Thus, we extracted the Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) from Landsat 8, and extracted dual polarization backscatter values (VH-VV) from the Sentinel-1 satellite at a monthly period over a year. The results showed strong correlation between the LST and the NDVI values of 0.94, and strong correlation between the microwave (VH) and both thermal and optical parameters with a 0.81 correlation coefficient, while there was weak or no correlation between the VV and the other investigated parameters. We strongly recommend that future studies clarify the Sentinel-1 backscatter values in wetland areas, by taking multiple field measurements close to the image acquisition time.
topic remote sensing
wetlands
UAV
Sentinel-1
land surface temperature
url http://www.mdpi.com/2220-9964/7/10/411
work_keys_str_mv AT gordanakaplan monthlyanalysisofwetlandsdynamicsusingremotesensingdata
AT uguravdan monthlyanalysisofwetlandsdynamicsusingremotesensingdata
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