Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran

Wetland Surface Temperature (WST) maps are an increasingly important parameter to understand the extensive range of existing processes in wetlands. The Wetlands placed in neighborhoods of agricultural and industrial lands are exposed to more chemical pollutants and pesticides that can lead to spatia...

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Main Authors: Vahid Eisavi, Ahmad Maleknezhad Yazdi, Seyeed Ali Niknezhad
Format: Article
Language:English
Published: İstanbul University 2016-01-01
Series:İstanbul Üniversitesi Orman Fakültesi Dergisi
Subjects:
Online Access:http://dx.doi.org/10.17099/jffiu.26733
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author Vahid Eisavi
Ahmad Maleknezhad Yazdi
Seyeed Ali Niknezhad
author_facet Vahid Eisavi
Ahmad Maleknezhad Yazdi
Seyeed Ali Niknezhad
author_sort Vahid Eisavi
collection DOAJ
description Wetland Surface Temperature (WST) maps are an increasingly important parameter to understand the extensive range of existing processes in wetlands. The Wetlands placed in neighborhoods of agricultural and industrial lands are exposed to more chemical pollutants and pesticides that can lead to spatial and temporal variations of their surface temperature. Therefore, more studies are required for temperature modeling and the management and conservation of these variations in their ecosystem. Landsat 8 time series data of Sulduz region, Western Azerbaijan province, Iran were used in this study. The WST was derived using a mono-window algorithm after implementation of atmospheric correction. The NDVI (Normalized Differential Vegetation Index) threshold method was also employed to determine the surface emissivity. Our findings show that the WST experienced extensive spatial and temporal variations. It reached its maximum value in June and also experienced the highest mean in the same month. In this research, August (2013.12.08) had a lowest spatial standard deviation regarding surface temperature and June (2013.06.28) had the highest one. Wetlands' watersides adjacent to industrial zones have a higher surface temperature than the middle lands of these places. The map obtained from the WST variance over time can be exploited to reveal thermal stable and unstable zones. The outcome demonstrates that land use, land cover effectively contribute to wetland ecosystem health. The results are useful in the water management, preventive efforts against drying of wetland and evapotranspiration modeling. The approach employed in this research indicates that remote sensing is a valuable, low-cost and stable tool for thermal monitoring of wetlands health.
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spelling doaj.art-66be1af3e4074852bda0ae44f6afc20e2023-02-15T16:09:23Zengİstanbul Universityİstanbul Üniversitesi Orman Fakültesi Dergisi0535-84180535-84182016-01-01661465810.17099/jffiu.26733Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, IranVahid Eisavi0Ahmad Maleknezhad Yazdi1Seyeed Ali NiknezhadTarbiat Modares University, Remote Sensing and GIS Department, M.Sc. in Remote Sensing, Tehran, IranUniversity of Tehran, Natural Resources, M.Sc. in Combating Desertification Department, Tehran, IranWetland Surface Temperature (WST) maps are an increasingly important parameter to understand the extensive range of existing processes in wetlands. The Wetlands placed in neighborhoods of agricultural and industrial lands are exposed to more chemical pollutants and pesticides that can lead to spatial and temporal variations of their surface temperature. Therefore, more studies are required for temperature modeling and the management and conservation of these variations in their ecosystem. Landsat 8 time series data of Sulduz region, Western Azerbaijan province, Iran were used in this study. The WST was derived using a mono-window algorithm after implementation of atmospheric correction. The NDVI (Normalized Differential Vegetation Index) threshold method was also employed to determine the surface emissivity. Our findings show that the WST experienced extensive spatial and temporal variations. It reached its maximum value in June and also experienced the highest mean in the same month. In this research, August (2013.12.08) had a lowest spatial standard deviation regarding surface temperature and June (2013.06.28) had the highest one. Wetlands' watersides adjacent to industrial zones have a higher surface temperature than the middle lands of these places. The map obtained from the WST variance over time can be exploited to reveal thermal stable and unstable zones. The outcome demonstrates that land use, land cover effectively contribute to wetland ecosystem health. The results are useful in the water management, preventive efforts against drying of wetland and evapotranspiration modeling. The approach employed in this research indicates that remote sensing is a valuable, low-cost and stable tool for thermal monitoring of wetlands health.http://dx.doi.org/10.17099/jffiu.26733WSTspatial variationstemporal variationsNDVI
spellingShingle Vahid Eisavi
Ahmad Maleknezhad Yazdi
Seyeed Ali Niknezhad
Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran
İstanbul Üniversitesi Orman Fakültesi Dergisi
WST
spatial variations
temporal variations
NDVI
title Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran
title_full Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran
title_fullStr Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran
title_full_unstemmed Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran
title_short Spatial and temporal modeling of wetland surface temperature using Landsat-8 imageries in Sulduz, Iran
title_sort spatial and temporal modeling of wetland surface temperature using landsat 8 imageries in sulduz iran
topic WST
spatial variations
temporal variations
NDVI
url http://dx.doi.org/10.17099/jffiu.26733
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AT ahmadmaleknezhadyazdi spatialandtemporalmodelingofwetlandsurfacetemperatureusinglandsat8imageriesinsulduziran
AT seyeedaliniknezhad spatialandtemporalmodelingofwetlandsurfacetemperatureusinglandsat8imageriesinsulduziran