scholarly journals Pemetaan Potensi Kekeringan Lahan se-pulau Batam menggunakan Teknik Sistem Informasi Geografis (SIG) dan Penginderaan Jauh

2017 ◽  
Vol 31 (1) ◽  
pp. 90
Author(s):  
Titi Aprilliyanti ◽  
Muhammad Zainuddin
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Negative Impact ◽  
Remote Sensing And Gis ◽  
Landsat 8 ◽  
Economic Activities ◽  
The Relationship

Kekeringan merupakan hubungan antara ketersediaan air yang jauh dibawah kebutuhan baik untuk kebutuhan hidup, pertanian, kegiatan ekonomi dan lingkungan. Informasi mengenai potensi kekeringan sangat diperlukan untuk pencegahan ataupun penanggulangan untuk mengurangi dampak negatif yang ditimbulkan. Adapun tujuan dari penelitian ini adalah menghasilkan peta potensi kekeringan lahan di Batam yang berbasis web. Dalam penelitian ini memanfaatkan teknik penginderaan jauh dan SIG. Penggunaan citra landsat 8 untuk menentukan nilai LST (Land Surface Temperature) dan penggunaan lahan kemudian di overlay dan dilakukan scoring. Tahap akhir penelitian yaitu melakukan validasi terhadap parameter-parameter yang mempengaruhi dengan mengambil beberapa sampel. Adapun hasil akhir dari penelitian ini adalah peta potensi kekeringan se-pulau Batam yang memiliki 5 kelas potensi kekeringan. Kelas potensi kekeringan sangat rendah dengan  luas area 2629.45 ha yang dominan terletak pada Kecamatan Sungai Beduk, Sekupang dan Batu Aji. Kelas potensi kekeringan rendah dengan luas area 9585.521 ha yang dominan terletak pada Kecamatan Sekupang. Kelas potensi kekeringan sedang dengan  luas area 9507.12 ha yang dominan terletak pada Kecamatan Sekupang. Kelas potensi kekeringan tinggi dengan  luas area 7081.392 ha yang dominan terletak pada Kecamatan Sekupang, Sagulung dan Nongsa. Kelas potensi kekeringan sangat tinggi dengan luas area 15600.12 ha yang dominan terletak pada Kecamatan Batam Kota dan Nongsa. Drought is the relationship between the availability of water is far below the need both for the necessities of life, agriculture, economic activities and the environment. Information about potential droughts is indispensable for the prevention or mitigation to reduce the negative impact caused. As for the purpose of this research is to produce a map of potential drought land in the Batam-based web. In this research utilising remote sensing and GIS techniques. The use of landsat 8 to determine the value of the LST (Land Surface Temperature) and land use overlay and then done the scoring. The final stage of research i.e. performs validation against parameters that influence by taking some samples. As for the end result of this research is to map the potential dryness in Island Batam which have 5 classes of potential drought. The class of potential drought is very low with an area of 2629.45 ha, the dominant River is located in Sungai Beduk , Sekupang and Batu Aji. The class of potential low drought with an area of 9,585,521 ha located on the dominant Sub Sekupang. The class of potential drought being with an area of 9507.12 ha located on the dominant Sub Sekupang. The class of potential high dryness with an area of 7,081,392 ha located on the dominant Sub Sekupang, Sagulung and Nongsa. The class of potential drought is extremely high with an area of 15600.12 ha located on the dominant sub Batam city and Nongsa.

scholarly journals Estimation and Validation of Land Surface Temperature by using Remote Sensing & GIS for Chittoor District, Andhra Pradesh

2021 ◽  
Vol 12 (5) ◽  
pp. 607-617
Author(s):  
A. Rajani, Dr. S.Varadarajan
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Andhra Pradesh ◽  
Temperature Data ◽  
Sensor Data ◽  
Spectral Radiance ◽  
Landsat 8 ◽  
Thermal Band

Land Surface Temperature (LST) quantification is needed in various applications like temporal analysis, identification of global warming, land use or land cover, water management, soil moisture estimation and natural disasters. The objective of this study is estimation as well as validation of temperature data at 14 Automatic Weather Stations (AWS) in Chittoor District of Andhra Pradesh with LST extracted by using remote sensing as well as Geographic Information System (GIS). Satellite data considered for estimation purpose is LANDSAT 8. Sensor data used for assessment of LST are OLI (Operational Land Imager) and TIR (Thermal Infrared). Thermal band  contains spectral bands of 10 and 11 were considered for evaluating LST independently by using algorithm called Mono Window Algorithm (MWA). Land Surface Emissivity (LSE) is the vital parameter for calculating LST. The LSE estimation requires NDVI (Normalized Difference Vegetation Index) which is computed by using Band 4 (visible Red band) and band 5 (Near-Infra Red band) spectral radiance bands. Thermal band images having wavelength 11.2 µm and 12.5 µm of 30th May, 2015 and 21st October, 2015 were processed for the analysis of LST. Later on validation of estimated LST through in-suite temperature data obtained from 14 AWS stations in Chittoor district was carried out. The end results showed that, the LST retrieved by using proposed method achieved 5 per cent greater correlation coefficient (r) compared to LST retrieved by using existing method which is based on band 10.

scholarly journals The Effects of Land Use Zoning and Densification on Changes in Land Surface Temperature in Seoul

2019 ◽  
Vol 11 (24) ◽  
pp. 7056 ◽  
Author(s):  
Jae-Ik Kim ◽  
Myung-Jin Jun ◽  
Chang-Hwan Yeo ◽  
Ki-Hyun Kwon ◽  
Jun Yong Hyun
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Landsat 8 ◽  
Land Use Type ◽  
Coverage Ratio

This study investigated how changes in land surface temperature (LST) during 2004 and 2014 were attributable to zoning-based land use type in Seoul in association with the building coverage ratio (BCR), floor area ratio (FAR), and a normalized difference vegetation index (NDVI). We retrieved LSTs and NDVI data from satellite images, Landsat TM 5 for 2004 and Landsat 8 TIRS for 2014 and combined them with parcel-based land use information, which contained data on BCR, FAR, and zoning-based land use type. The descriptive analysis results showed a rise in LST for the low- and medium-density residential land, whereas significant LST decreases were found in high-density residential, semi-residential, and commercial areas over the time period. Statistical results further supported these findings, yielding statistically significant negative coefficient values for all interaction variables between higher-density land use types and a year-based dummy variable. The findings appear to be related to residential densification involving the provision of more high-rise apartment complexes and government efforts to secure more parks and green spaces through urban redevelopment and renewal projects.

scholarly journals Thermal Remote Sensing: A tool to Determine Temporal Land Surface Temperature in Hawassa City, Ethiopia

2020 ◽  
Author(s):  
Mikias Biazen Molla
Keyword(s):  
Remote Sensing ◽  
Standard Deviation ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Thermal Remote Sensing ◽  
The City

Abstract This investigation was conducted for the estimation of the temporal land surface temperature value using thermal remote sensing of Landsat-8 (OLI) Data in Hawassa City Administration, Ethiopia. Satellite datasets of Landsat-7 (ETM+) for 22nd March 2002 and Landsat-8 (OLI) of 22nd March 2019 were taken for this study. Different algorisms were used to estimate the Normalized Difference Vegetation Index threshold from the Red and Near-Infrared band and the ground earth's surface emissivity esteem is legitimately recovered from the thermal infrared by coordinating with the outcome got from MODIS information. The land use land cover map of the city was prepared with better accuracy using the on-screen classification technique. The spatial distribution of surface temperature of the city range from 6.62°C to 22.54°C with a mean of 14.58°C and a standard deviation of 11.25 in the year of march 22nd 2002. The LST result derived from Landsat 8 for March 22nd, 2019, ranges from 11.97°C to 35.5°C with a mean of 23.735 °C and a standard deviation of 16.64. In both years the higher LST values correspond to built-up/settlement and bare/open lands of the city; whereas, lower LST values were observed in vegetation (trees/woodlot, shrubs, and grass forested) area. Urban expansion (built-up area roads, and another impervious surface), decline in vegetation levels due to deforestation and increasing population density. Increasing an evergreen tree and green space coverage, design and develop city parks and rehabilitate the existing degraded natural environments are among the recommended strategy to reduce the rate of LST.

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