The Relationship between Surface Temperature and Vegetation Cover Obtained Using Landsat 8 Satellite Imagery - A Case Study of Daegu Metropolitan City -

2021 ◽  
Vol 12 (6) ◽  
pp. 713-720
Author(s):  
Seonghee Eo ◽  
Hee-Sun Choi ◽  
Geunhan Kim ◽  
Gilsang Lee
Keyword(s):  
Surface Temperature ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Landsat 8 ◽  
Metropolitan City ◽  
The Relationship

scholarly journals Influence of Different Satellite Imagery on the Analysis of Riparian Leaf Density in a Mountain Stream

2020 ◽  
Vol 12 (20) ◽  
pp. 3376 ◽  
Author(s):  
Giovanni Romano ◽  
Giovanni Francesco Ricci ◽  
Francesco Gentile
Keyword(s):  
Winter Wheat ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Riparian Vegetation ◽  
Deciduous Forests ◽  
Sensor Data ◽  
Mountain Stream ◽  
Landsat 8 ◽  
Area Index ◽  
Sentinel 2

In recent decades, technological advancements in sensors have generated increasing interest in remote sensing data for the study of vegetation features. Image pixel resolution can affect data analysis and results. This study evaluated the potential of three satellite images of differing resolution (Landsat 8, 30 m; Sentinel-2, 10 m; and Pleiades 1A, 2 m) in assessing the Leaf Area Index (LAI) of riparian vegetation in two Mediterranean streams, and in both a winter wheat field and a deciduous forest used to compare the accuracy of the results. In this study, three different retrieval methods—the Caraux-Garson, the Lambert-Beer, and the Campbell and Norman equations—are used to estimate LAI from the Normalized Difference Vegetation Index (NDVI). To validate sensor data, LAI values were measured in the field using the LAI 2200 Plant Canopy Analyzer. The statistical indices showed a better performance for Pleiades 1A and Landsat 8 images, the former particularly in sites characterized by high canopy closure, such as deciduous forests, or in areas with stable riparian vegetation, the latter where stable reaches of riparian vegetation cover are almost absent or very homogenous, as in winter wheat fields. Sentinel-2 images provided more accurate results in terms of the range of LAI values. Considering the different types of satellite imagery, the Lambert-Beer equation generally performed best in estimating LAI from the NDVI, especially in areas that are geomorphologically stable or have a denser vegetation cover, such as deciduous forests.

scholarly journals Geospatial Monitoring on Land Surface Temperature and Vegetation Dynamics: A Case of a City Area in Khulna, Bangladesh

2020 ◽  
Vol 3 (2) ◽  
pp. a35-43
Author(s):  
MD. NAZMUL HAQUE ◽  
NOWRIN RAHMAN KHANAM ◽  
MEHNAZ NANJIBA
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Vegetation Index ◽  
Landsat 8 ◽  
Environmental Condition ◽  
Infrared Sensor ◽  
The Third ◽  
Canopy Area

Land surface temperature and vegetation cover are two important parameters to evaluate the climate change and environmental condition. The current study is carried out in respect of monitoring the changing phenomena of climate and environment. The area selected to conduct the study was ward number 1, 2 and 3 of Khulna City Corporation), from the third largest city of Bangladesh. This study is corresponding through the calculation of Land Surface Temperature (LST) and Normalized Differential Vegetation Index (NDVI) for two different years, 2010 and 2018. LST and NDVI are observed to realize the association between surface temperature and amount of vegetation. With the help of ArcGIS 10.5, LST and NDVI calculations are done using Landsat 5 Thermal Mapper, Landsat 8 Operational Land Imager and Thermal Infrared Sensor images (for 2010 and 2018, respectively) collected from USGS Earth Explorer. The findings of the study specify that the highest temperature in 2018 is 32.5˚C in ward 2 and in 2010 it was 27.5˚C in ward 3, though the overall vegetation amount decreased in 2018, About 18, 900 square meter of very low canopy area has increased in ward 3 from the period of 2010 to 2018 and in the same time 35, 100 square meter of low canopy area has been decreased for the overall study area. However, parts of the study area of ward no. 3 had faced a significant increase in vegetation cover which is the cause of low temperature compared to ward 1 and 2 in 2018.

scholarly journals Fractional Vegetation Cover Change Detection In Megacities Using Landsat Time-Series Images: A Case Study Of Hanoi City (Vietnam) During 1986-2019

2019 ◽  
Vol 12 (4) ◽  
pp. 175-187
Author(s):  
Thanh Tien Nguyen
Keyword(s):  
Rural Areas ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Classification Method ◽  
Landsat 8 ◽  
Landsat Images ◽  
Fractional Vegetation Cover ◽  
Multi Temporal

The objective of the study is to assess changes of fractional vegetation cover (FVC) in Hanoi megacity in period of 33 years from 1986 to 2016 based on a two endmember spectral mixture analysis (SMA) model using multi-spectral and multi-temporal Landsat-5 TM and -8 OLI images. Landsat TM/OLI images were first radiometrically corrected. FVC was then estimated by means of a combination of Normalized Difference Vegetation Index (NDVI) and classification method. The estimated FVC results were validated using the field survey data. The assessment of FVC changes was finally carried out using spatial analysis in GIS. A case study from Hanoi city shows that: (i) the proposed approach performed well in estimating the FVC retrieved from the Landsat-8 OLI data and had good consistency with in situ measurements with the statistically achieved root mean square error (RMSE) of 0.02 (R 2 =0.935); (ii) total FVC area of 321.6 km 2 (accounting for 9.61% of the total area) was slightly reduced in the center of the city, whereas, FVC increased markedly with an area of 1163.6 km 2 (accounting for 34.78% of the total area) in suburban and rural areas. The results from this study demonstrate the combination of NDVI and classification method using Landsat images are promising for assessing FVC change in megacities.

scholarly journals Retrieval of Land Surface Temperature of Lahore Through Landsat-8 TIRS Data

2019 ◽  
Vol 10 (1) ◽  
pp. 70-77
Author(s):  
Muhammad Nasar -u-Minallah
Keyword(s):  
Radiative Transfer ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Vegetation Cover ◽  
Land Surface ◽  
Global Climate ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Barren Land ◽  
Data Continuity

Land surface temperature (LST) is an important parameter in global climate change and urban thermalenvironmental studies. The significance of land surface temperature is being acknowledged gradually and interest isincreasing in developing methodologies for the retrieval of LST from Satellite Remote Sensing (SRS) data. ThermalInfrared Sensor (TIRS) of Landsat-8 is the newest TIR sensor for the Landsat Data Continuity Mission (LDCM),offering two adjacent thermal infrared bands (10, 11), having significant beneficiary for the land surface temperatureinversion. The spectral radiance can be estimated through TIR bands 10 and 11 of Landsat-8 OLI_TIRS satellite image.In the present study, the radiative transfer equation-based method has been employed in estimating LST of Lahore andthe analysis demonstrated that estimated LST has the highest accuracy from the radiative transfer method through band10. Land Surface Emissivity (LSE) was derived with the aid of the NDVI’s threshold technique. The present studyresults show that as the built-up area increases and vegetation cover decreases in urban surface, they are linked toincrease in urban land surface temperature and conversely larger vegetation cover associated with lower urbantemperature. The output exposed that LST was high in built-up and barren land, whereas it was low in the area wherethere were more vegetation cover and water.

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