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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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’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’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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