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Open AccessArticle

Geospatial Monitoring of Land Surface Temperature Effects on Vegetation Dynamics in the Southeastern Region of Bangladesh from 2001 to 2016

by Shahidul Islam 1,2,3 and Mingguo Ma 1,2,3,*
1
Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China
2
Research Base of Karst Eco-environments at Nanchuan in Chongqing, Ministry of Nature Resources, School of Geographical Sciences, Southwest University, Chongqing 400715, China
3
Chongqing Jinfo Mountain Field Scientific Observation and Research Station for Kaster Ecosystem, School of Geographical Sciences, Southwest University, Chongqing 400715, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(12), 486; https://doi.org/10.3390/ijgi7120486
Received: 8 November 2018 / Revised: 14 December 2018 / Accepted: 15 December 2018 / Published: 19 December 2018
Land surface temperature (LST) can significantly alter seasonal vegetation phenology which in turn affects the global and regional energy balance. These are the most important parameters of surface–atmosphere interactions and climate change. Methods for retrieving LSTs from satellite remote-sensing data are beneficial for modeling hydrological, ecological, agricultural and meteorological processes on the Earth’s surface. This paper assesses the geospatial patterns of LST using correlations of the seasonally integrated normalized difference vegetation index (SINDVI) in the southeastern region of Bangladesh from 2001 to 2016. Moderate Resolution Imaging Spectroradiometer (MODIS) time series datasets for LST and SINDVI were used for estimations in the study. From 2001 to 2016, the MODIS-based land surface temperature in the southeastern region of Bangladesh was found to have gently increased by 0.2 °C (R2 = 0.030), while the seasonally integrated normalized difference vegetation index also increased by 0.43 (R2 = 0.268). The interannual average LSTs mostly increased across the study areas, except in some coastal plain and tidal floodplain areas of the study. However, the SINDVI increased in the floodplain and coastal plain regions, except for in hilly areas. Physiographically, the study area is a combination of low lying alluvial floodplains, river basin wetlands, tidal floodplains, tertiary hills, terraced lands and coastal plains in nature. The hilly areas are mostly covered by dense forests, with the exception of agricultural areas. The impacts of increased LSTs were inversely correlated for the hilly areas and areas with forest coverage; LSTs were conversely correlated for the floodplain region, and tree cover outside of the forest and agricultural crops. This study will be very helpful for the protection and restoration of the natural environment. View Full-Text
Keywords: LST; seasonally integrated normalized difference vegetation index (SINDVI); correlation analysis; MODIS; remote sensing LST; seasonally integrated normalized difference vegetation index (SINDVI); correlation analysis; MODIS; remote sensing
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Islam, S.; Ma, M. Geospatial Monitoring of Land Surface Temperature Effects on Vegetation Dynamics in the Southeastern Region of Bangladesh from 2001 to 2016. ISPRS Int. J. Geo-Inf. 2018, 7, 486.

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