Analysis of Spatio-Temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia

Alemu, Melkamu Meseret

doi:10.5814/j.issn.1674-764x.2019.01.010

Cited by 25 publications

(25 citation statements)

References 42 publications

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“…It reveals that High land surface temperature more related with low green spaces (vegetation cover) and vice versa. The result is agreement with (Abhijit et al, 2018;Alemu, 2019;Wedajo et al, 2019). The correlation between (LST) and NDBaIThe result shows that LST and NDBaI have positive strong relationship with (R 2 = 0.96).…”

Section: = (Eq 3)supporting

confidence: 90%

“…Land surface temperature (LST) defined as skin temperature of the earth's temperature is a key factor for land surface processing studies at both a regional and global scale (Thanh et al, 2018). According to Alemu (2019) stated that land surface temperature is calculated from emitted energy by earth surface or satellite based devices. Relationships of land surface temperatures with Normalized Difference Index, Normalized Barren Index and Modified Normalized Difference Water Index were examined (Qinqin, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Analysis of Land Surface Temperature due to Dynamics of Green Spaces and Water Bodies Using Geospatial Techniques in Gida Kiremu, Limu and Amuru Districts, Western Ethiopia

Badasa¹,

Busha²

2021

Preprint

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Analysis of the correlation between indices (Normalized Difference Vegetation Index, Normalized Difference Barren Index and Modified Normalized Difference Water Index) and land surface temperature is used to natural resources and environmental studies. This research aimed to analysis of Land Surface Temperature due to dynamics of Different Indices (NDVI, NDBaI and MNDWI) Using Remote Sensing Data in three selected districts (Gida Kiremu, Limu and Amuru), western Ethiopia. From thermal and multispectral bands of landsat imageries (Landsat TM of 1990, landsat ETM+ of 2003 and landsat OLI/TIRS of 2020) Land surface temperature and NDVI, NDBaI and MNDWI were calculated. Correlation analysis was used to indicate relationships between LST with NDVI, NDBaI and MNDWI. The study found that Land Surface Temperature was increased by 50C from 1990 to 2020. Vegetation areas (NDVI) and Water bodies (MNDWI) have strong negative relationship with Land Surface Temperature (R2= 0.99, 0.95) whereas Barren land (NDBaI) has positive relationship with Land Surface Temperature (R2= 0.96). Finally, we recommend the decision makers and environmental analyst to emphasis the importance of vegetation cover and water body to minimize the potential impacts of land surface temperature.

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Section: = (Eq 3)supporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Analysis of Land Surface Temperature due to Dynamics of Green Spaces and Water Bodies Using Geospatial Techniques in Gida Kiremu, Limu and Amuru Districts, Western Ethiopia

Badasa¹,

Busha²

2021

Preprint

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“…According to the figure, the low R 2 between LST and NDVI in 2013 could be due to an unusually low temperature in this year (May) as compared to the 25-year average. However, due to the inverse correlation, it can be stated that decreasing vegetation cover results in an increased surface temperature (Alemu 2019;Ning, Gao, and Chen 2017;Deng et al 2018;Balew and Korme 2020).…”

Section: Relationship Between Ndvi and Lstmentioning

confidence: 99%

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

Maskooni

Hashemi

Berndtsson

et al. 2020

International Journal of Digital Earth

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Many factors are involved in urban heat island development, such as lack of green spaces, improper choice of building materials, densification, and other human activities. The aim of this research was to quantify the effects of land-use/land-cover (LU/LC) changes on urban land surface temperature (LST) during a 25-year period (1993-2018) for the semiarid Shiraz City in southern Iran using Landsat data (TM, ETM + , and OLI/TIRS) and machine learning algorithms. Five main LU/LC classes, such as orchard, vegetation, bare surface, asphalt cover, and built-up areas, were identified using a support vector machine algorithm. Landsat images were used to retrieve normalized difference vegetation index (NDVI) and normalized difference built-up index (NDBI). The results showed that the mean LST over the entire study domain increased considerably between 1993 and 2018, due to urbanization, decrease of green areas, and increasing industrial areas. Built-up areas increased considerably by 25.8% from 80 to 100.6 km 2 between 1993 and 2018, while vegetation cover decreased dramatically by 69.3%. Mean LST increased from 38.4 to 40.2°C during the 25-year period with a significant increase of 3.9°C between 2013 and 2018. In addition, the Urban heat island Ratio Index (URI) displayed a substantial upward trend during the 25-year period.

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“…NDVI is a straightforward graphical indicator that analyses remote sensing measurements and assesses whether or not an area contains green vegetation. Studies show that NDVI provides accurate estimations of surface radiant temperatures [23][24][25][26]. NDVI is one of the outputs that was entered into the model to retrieve LST by using (5):…”

Section: Calculation Ndvimentioning

confidence: 99%

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

Yuhendra¹,

Sumantyo²

2020

TELKOMNIKA

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West Sumatra is one of has big geothermal energy resources potential. Remote sensing technology can have a role in geothermal exploration activity to measure the distribution of land surface temperatures (LST) and predict the geothermal potential area. Main study to obtain the assessment of Landsat 8 TIRS (Landsat`s Thermal Infrared Sensor) data capability for geothermal energy resources estimation. Mono-window algorithms were used to generate the LST maps. Data set was combined with a digital elevation model (DEM) to identify the potential geothermal energy based on the variation in surface temperature. The result that were derived from LST map of West Sumatra shows that ranged from -8.6 C 0 to 32.59 C 0 and the different temperatures are represented by a graduated pink to brown shading. A calculated result clearly identifies the hot areas in the dataset, which are brown in colour images. Lima Puluh Kota, Tanah Datar, Solok, and South Solok areas showed the high-temperature value (Brown) in the range of 28.1 C 0 to 32.59 C 0 color in images which means that they possess high potential for generating thermal energy. In contrast, the temperatures were lower (Pink) in the north-eastern areas and the range distribution was from -8.5 C 0 to 5 C 0 .

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Analysis of Spatio-Temporal Land Surface Temperature and Normalized Difference Vegetation Index Changes in the Andassa Watershed, Blue Nile Basin, Ethiopia

Cited by 25 publications

References 42 publications

Analysis of Land Surface Temperature due to Dynamics of Green Spaces and Water Bodies Using Geospatial Techniques in Gida Kiremu, Limu and Amuru Districts, Western Ethiopia

Analysis of Land Surface Temperature due to Dynamics of Green Spaces and Water Bodies Using Geospatial Techniques in Gida Kiremu, Limu and Amuru Districts, Western Ethiopia

Impact of spatiotemporal land-use and land-cover changes on surface urban heat islands in a semiarid region using Landsat data

Assessment of Landsat 8 TIRS data capability for the preliminary study of geothermal energy resources in West Sumatra

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