Keeping in the view of water conservation the Government of India has initiated the Jal Shakti Abhiyan, which needs the inventory of waterbodies for further planning and monitoring. Lakes and Ponds in year 2005 and year 2018 were mapped in Ranchi district at 1:5000 scale using high resolution satellite data and reservoirs at 1:50,000 scale using moderate resolution satellite data. Seasonal fluctuation in water extent of reservoirs was estimated using Pre- monsoon and post-monsoon satellite data. Total number of lakes and ponds mapped in Ranchi district in year 2005 and year 2018 were 2170 and 2556, respectively. Total area under lakes and ponds was 1425 ha and 1618 ha, respectively in year 2005 and year 2018, which was 0.29% and 0.32% of total geographical area of Ranchi district. A total of 19 and 20 reservoirs mapped in Ranchi district in year 2006-07 and year 2018-19, respectively. Area recorded under reservoir category in Ranchi district was 2854 ha and 2829 ha in pre-monsoon season in the year 2007 and 2019, respectively. In Post-monsoon season the area occupied by reservoirs was 3428 ha and 3352 ha in Year 2007 and 2018, respectively. It has been suggested to undertake large scale mapping of lakes and ponds at 1:500/1000 scale using seasonal satellite data for preparation of inventory and estimation of pre and post-monsoon variation in number and area of lakes and ponds. Maps and statistics generated in present study are useful for planning, monitoring and management of waterbodies in present scenario of urbanization, environmental degradation and climate change. Keywords: High resolution data, urbanization, pre-monsoon, post-monsoon, lakes and ponds
{"title":"Spatiotemporal Analysis of Waterbodies in Ranchi District, Jharkhand using Satellite Data: An Input for Jal Shakti Abhiyan","authors":"N. Sharma, N. J. Kullu, Kumari Anshula","doi":"10.37591/.V11I1.792","DOIUrl":"https://doi.org/10.37591/.V11I1.792","url":null,"abstract":"Keeping in the view of water conservation the Government of India has initiated the Jal Shakti Abhiyan, which needs the inventory of waterbodies for further planning and monitoring. Lakes and Ponds in year 2005 and year 2018 were mapped in Ranchi district at 1:5000 scale using high resolution satellite data and reservoirs at 1:50,000 scale using moderate resolution satellite data. Seasonal fluctuation in water extent of reservoirs was estimated using Pre- monsoon and post-monsoon satellite data. Total number of lakes and ponds mapped in Ranchi district in year 2005 and year 2018 were 2170 and 2556, respectively. Total area under lakes and ponds was 1425 ha and 1618 ha, respectively in year 2005 and year 2018, which was 0.29% and 0.32% of total geographical area of Ranchi district. A total of 19 and 20 reservoirs mapped in Ranchi district in year 2006-07 and year 2018-19, respectively. Area recorded under reservoir category in Ranchi district was 2854 ha and 2829 ha in pre-monsoon season in the year 2007 and 2019, respectively. In Post-monsoon season the area occupied by reservoirs was 3428 ha and 3352 ha in Year 2007 and 2018, respectively. It has been suggested to undertake large scale mapping of lakes and ponds at 1:500/1000 scale using seasonal satellite data for preparation of inventory and estimation of pre and post-monsoon variation in number and area of lakes and ponds. Maps and statistics generated in present study are useful for planning, monitoring and management of waterbodies in present scenario of urbanization, environmental degradation and climate change. Keywords: High resolution data, urbanization, pre-monsoon, post-monsoon, lakes and ponds","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"52 1-2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126942003","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The economy of most of the people in India is mainly based on rain fed agriculture. The agriculture yield primarily depends on the availability of good productive soil and sufficient quantity of availability of water. In the past, in many areas both were available but recently it is observed that due to various abnormal natural processes and excessive human interference in the natural processes, the soil layer with nutrient from fertile land is getting eroded, which resulted into reduced fertility and reduced crop yield. The soil loss from the catchment directly and indirectly affects the living standard resulting into various social problems. In order to take the preventive and curative measures against this soil erosion, it is necessary to estimate the quantity of the soil eroded and the major causes for the erosion. The soil erosion for the study area is estimated with a good degree of accuracy by integrating Geographical Information System technique and Universal Soil Loss Equation (USLE) with supportive Soil and Water Assessment Tool. In USLE model the soil loss is estimated using the parameters like rainfall erosivity (R), soil erodibility (K), slope length-steepness (LS), cover management (C) and conservation practice factor (P). The average annual soil loss estimated for the study area by QSWAT technique is 72.42 ton ha-1 year-1and using arc GIS soil loss estimated is 67.87 ton ha-1 year-1. The spatial distribution of USLE parameter indicates mild and moderate risk due to soil erosion in the study area. Keywords: USLE, arc GIS 10.2.2, QSWAT, Erosion, Thematic
印度大多数人的经济主要依靠雨养农业。农业产量主要取决于肥沃的土壤和充足的水分。过去，在许多地区，这两种情况都是可以得到的，但最近人们发现，由于各种异常的自然过程和人类对自然过程的过度干预，肥沃土地的养分层正在被侵蚀，导致肥力下降，作物减产。流域水土流失直接或间接地影响着人们的生活水平，从而引发各种社会问题。为了采取防治水土流失的措施，有必要对水土流失的数量和主要原因进行估算。利用地理信息系统技术、通用水土流失方程(USLE)和辅助水土评价工具，对研究区土壤侵蚀进行了较为准确的估算。在USLE模型中，利用降雨侵蚀力(R)、土壤可蚀性(K)、坡长-陡度(LS)、覆盖管理(C)和保护实践因子(P)等参数估算了土壤流失量。QSWAT技术估算的研究区年平均土壤流失量为72.42 t ha-1 -1, arcgis估算的土壤流失量为67.87 t ha-1 -1。USLE参数的空间分布表明研究区存在轻度和中度土壤侵蚀风险。关键词:USLE, arcgis 10.2.2, QSWAT，侵蚀，专题
{"title":"Erosion and Sediment Yield Modeling with USLE, Arc GIS and SWAT","authors":"Nagesh Madhukar Kukade","doi":"10.37591/.V11I1.812","DOIUrl":"https://doi.org/10.37591/.V11I1.812","url":null,"abstract":"The economy of most of the people in India is mainly based on rain fed agriculture. The agriculture yield primarily depends on the availability of good productive soil and sufficient quantity of availability of water. In the past, in many areas both were available but recently it is observed that due to various abnormal natural processes and excessive human interference in the natural processes, the soil layer with nutrient from fertile land is getting eroded, which resulted into reduced fertility and reduced crop yield. The soil loss from the catchment directly and indirectly affects the living standard resulting into various social problems. In order to take the preventive and curative measures against this soil erosion, it is necessary to estimate the quantity of the soil eroded and the major causes for the erosion. The soil erosion for the study area is estimated with a good degree of accuracy by integrating Geographical Information System technique and Universal Soil Loss Equation (USLE) with supportive Soil and Water Assessment Tool. In USLE model the soil loss is estimated using the parameters like rainfall erosivity (R), soil erodibility (K), slope length-steepness (LS), cover management (C) and conservation practice factor (P). The average annual soil loss estimated for the study area by QSWAT technique is 72.42 ton ha-1 year-1and using arc GIS soil loss estimated is 67.87 ton ha-1 year-1. The spatial distribution of USLE parameter indicates mild and moderate risk due to soil erosion in the study area. Keywords: USLE, arc GIS 10.2.2, QSWAT, Erosion, Thematic","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"175 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115050735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Runoff is one of the significant hydrologic variables used in generally of the water resources applications. The Soil Conservation Service–Curve Number (SCS–CN) method is adopted for the evaluation of surface runoff in the Karur District, Tamil Nadu, India using multispectral remote sensing data, rainfall data and curve number approach. The weighted curve number is determined based on antecedent moisture condition (AMC)-II with an integration of Hydrologic Soil Groups(HSGs) and land use/ land cover categories. The daily runoff was estimated for rainfall Period September 2018 to November 2018. The results of the present study shows that the runoff depth for the study area are ranging in between 106.57 and 713.65 mm and runoff volume are ranging in between 2.75 and 126.21 mcm. In the present study, the methodology for determination of runoff for study area using remote sensing, GIS and SCS–CN method was described. Keywords: Surface runoff estimation, sub-watershed, Remote Sensing, GIS, Curve number.
{"title":"Quantification of Sub-Watershed Wise Surface Runoff Using Remote Sensing, GIS and Soil Conservation Services – Curve Number Method, Karur District, Tamil Nadu, India","authors":"J. Muralitharan, K. Palanivel","doi":"10.37591/.V11I1.781","DOIUrl":"https://doi.org/10.37591/.V11I1.781","url":null,"abstract":"Runoff is one of the significant hydrologic variables used in generally of the water resources applications. The Soil Conservation Service–Curve Number (SCS–CN) method is adopted for the evaluation of surface runoff in the Karur District, Tamil Nadu, India using multispectral remote sensing data, rainfall data and curve number approach. The weighted curve number is determined based on antecedent moisture condition (AMC)-II with an integration of Hydrologic Soil Groups(HSGs) and land use/ land cover categories. The daily runoff was estimated for rainfall Period September 2018 to November 2018. The results of the present study shows that the runoff depth for the study area are ranging in between 106.57 and 713.65 mm and runoff volume are ranging in between 2.75 and 126.21 mcm. In the present study, the methodology for determination of runoff for study area using remote sensing, GIS and SCS–CN method was described. Keywords: Surface runoff estimation, sub-watershed, Remote Sensing, GIS, Curve number.","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130018468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The energy consumption of most developing countries like Ethiopia highly depends on biomass and imported fuels. These sources of energy have imposed severe impacts on the environment.Recently, Ethiopia has begun to harness electricity from renewable energies mainly from hydro- dams to meet its energy need. However, the existing energy production of the hydro-dams declines during dry season due to shortage of river waters. So, it is important to look for other renewable energy sources like wind to compliment the energy decline. This study presents an identification of suitable site for wind farm development in selected districts of Tigray Region. Ten major biophysical, environmental and socio-political factors were identified that influence the wind farm development. These major factors were delineated from the different spatial datasets which were collected from various sources. ArcGIS 10.3 and ERDAS 9.2 software were used to prepare factor maps for all variables. The pair wise comparison associated with Analytical Hierarchy Process was used to weigh the factors then weighted overlay model was applied. The findings of the overlay showed that there are sufficient lands suitable for grid- based and off-grid wind farms development in the study area. About 140.5 km2 i.e. 6.5% is highly suitable and about 1103.1 km2 which means 51.0% is moderately suitable for wind farm development. A land of area 25 km2 entirely laid under highly suitable category was identified for wind farm design. The site has excellent wind speed and is located entirely in close proximity to the main roads and grid transmission lines. Keywords: Wind farm, GIS, multi-criteria analysis, renewable energies, AHP, Ethiopia
{"title":"Potential Site Suitability Analysis for Wind Farm Development Using GIS and Multi-Criteria Analysis","authors":"Fessehaye Gebremeskel Abera, Vanum Govindu, Yechale Kebede Bizuneh","doi":"10.37591/.V11I1.780","DOIUrl":"https://doi.org/10.37591/.V11I1.780","url":null,"abstract":"The energy consumption of most developing countries like Ethiopia highly depends on biomass and imported fuels. These sources of energy have imposed severe impacts on the environment.Recently, Ethiopia has begun to harness electricity from renewable energies mainly from hydro- dams to meet its energy need. However, the existing energy production of the hydro-dams declines during dry season due to shortage of river waters. So, it is important to look for other renewable energy sources like wind to compliment the energy decline. This study presents an identification of suitable site for wind farm development in selected districts of Tigray Region. Ten major biophysical, environmental and socio-political factors were identified that influence the wind farm development. These major factors were delineated from the different spatial datasets which were collected from various sources. ArcGIS 10.3 and ERDAS 9.2 software were used to prepare factor maps for all variables. The pair wise comparison associated with Analytical Hierarchy Process was used to weigh the factors then weighted overlay model was applied. The findings of the overlay showed that there are sufficient lands suitable for grid- based and off-grid wind farms development in the study area. About 140.5 km2 i.e. 6.5% is highly suitable and about 1103.1 km2 which means 51.0% is moderately suitable for wind farm development. A land of area 25 km2 entirely laid under highly suitable category was identified for wind farm design. The site has excellent wind speed and is located entirely in close proximity to the main roads and grid transmission lines. Keywords: Wind farm, GIS, multi-criteria analysis, renewable energies, AHP, Ethiopia","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130198822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The term morphometry senses the measurement and analysis of form and its properties. In context of geomorphology, which is science of land forms, it is concerned with the various geometrical aspects of the landforms. It is inevitable in the study of fluvial geomorphology. Morphometric analysis particularly deals with quantitative measurements of different aspects like linear, aerial and relief, for instance stream order, stream length, drainage density, drainage frequency, bifurcation ratio, constant channel maintenance etc. Kandra River one of the major tributaries of Warana River flows on southern part of Deccan Trap of Maharashtra and occupies an area of about 148sq. km. The stream orders of this basin range from first order to fifth order. The dendritic drainage pattern indicates the homogeneity in texture and lack of structural control on the rocks of the area. The average drainage density of the river is 2.12; however it varies within the basin which detects the minor changes in the terrain configuration and gradient intensity and its aspects. Drainage frequency is also dynamic however, the average drainage frequency is 2.02 which is an outcome of the undulating and rugged terrain supported by the constant of channel maintenance of basin that is 0.47 km2/km. Keywords: Stream orders, Dendritic drainage pattern, Drainage density, Drainage frequency.
{"title":"Estimation of Various Geomorphometric Aspects of Kandra River Basin of Panhala Tehsil, Kolhapur District (M.S.)","authors":"J. V. Khanapurkar","doi":"10.37591/.v11i1.818","DOIUrl":"https://doi.org/10.37591/.v11i1.818","url":null,"abstract":"The term morphometry senses the measurement and analysis of form and its properties. In context of geomorphology, which is science of land forms, it is concerned with the various geometrical aspects of the landforms. It is inevitable in the study of fluvial geomorphology. Morphometric analysis particularly deals with quantitative measurements of different aspects like linear, aerial and relief, for instance stream order, stream length, drainage density, drainage frequency, bifurcation ratio, constant channel maintenance etc. Kandra River one of the major tributaries of Warana River flows on southern part of Deccan Trap of Maharashtra and occupies an area of about 148sq. km. The stream orders of this basin range from first order to fifth order. The dendritic drainage pattern indicates the homogeneity in texture and lack of structural control on the rocks of the area. The average drainage density of the river is 2.12; however it varies within the basin which detects the minor changes in the terrain configuration and gradient intensity and its aspects. Drainage frequency is also dynamic however, the average drainage frequency is 2.02 which is an outcome of the undulating and rugged terrain supported by the constant of channel maintenance of basin that is 0.47 km2/km. Keywords: Stream orders, Dendritic drainage pattern, Drainage density, Drainage frequency.","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124376721","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-06-12DOI: 10.46610/jorsgt.2020.v06i02.001
M N Sumaiy, V. Sneha, V. Tejaswini, L. Vani, J. L. Vidya
{"title":"WATER QUALITY ASSESSMENTS USING REMOTE SENSING IMAGES AND ANALOG SENSORS","authors":"M N Sumaiy, V. Sneha, V. Tejaswini, L. Vani, J. L. Vidya","doi":"10.46610/jorsgt.2020.v06i02.001","DOIUrl":"https://doi.org/10.46610/jorsgt.2020.v06i02.001","url":null,"abstract":"","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"274 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131404695","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The reduce in the storage volume due to sediment deposits in the reservoir at different level over a period of time can be related with the decrease in water diffusion zone at different elevation. This study shows the use of RS and GIS to estimate reservoir sediment deposits. The 2007 regional capacity curve (water storage) is now used due to the fact basis for the 2017-18 sediment assessment. This will help us assess the precipitation in the long run. The digital processing was performed using Arc-GIS software. NDWI (normalized difference water index) is used to describe the features of water and progress the existence of water pixels in the Landsat-8 satellite images of the Morbe dam. The water diffusion zone of the water storage basin on the specific height of the day of the satellite passes which help to form an elevation capacity curve. The linear interpolation/ extrapolation method is used to evaluation of the water diffusion zone of the Morbe dam on the different heights in 2007. The different elevations are taken to estimate the storage capacity in live zone of the water basin between the contour lines form by the water body with the help of Prizmoidal formula. After that revised reservoir capacity is differentiated with the capacity at the begin in 2007 to achieve capacity loss from 2007 to 2018 (i.e. 11 years). The loss in capacity of the reservoir is the sediment deposited at that elevation. As a result, the show the capacity of live storage zone is reduced from 190.89 Mm3 to 186.251 Mm3, indicating a total capacity loss 4.6390 Mm3.The loss of capacity 2.4302% in 11 years. If the sedimentation occurs linearly then the rate of sedimentation is estimated to be 0.4217 Mm3 year-1. Keywords: Arc-GIS, Prizmoidal Formula, Sedimentation, RS and GIS, Normalized Difference Water Index (NDWI).
{"title":"Analysis of Sedimentation Deposition in Morbe Reservoir by Using RS and GIS","authors":"Tanvi Nijampurkar, R. Narwade, K. Nagarajan","doi":"10.37591/.v11i1.813","DOIUrl":"https://doi.org/10.37591/.v11i1.813","url":null,"abstract":"The reduce in the storage volume due to sediment deposits in the reservoir at different level over a period of time can be related with the decrease in water diffusion zone at different elevation. This study shows the use of RS and GIS to estimate reservoir sediment deposits. The 2007 regional capacity curve (water storage) is now used due to the fact basis for the 2017-18 sediment assessment. This will help us assess the precipitation in the long run. The digital processing was performed using Arc-GIS software. NDWI (normalized difference water index) is used to describe the features of water and progress the existence of water pixels in the Landsat-8 satellite images of the Morbe dam. The water diffusion zone of the water storage basin on the specific height of the day of the satellite passes which help to form an elevation capacity curve. The linear interpolation/ extrapolation method is used to evaluation of the water diffusion zone of the Morbe dam on the different heights in 2007. The different elevations are taken to estimate the storage capacity in live zone of the water basin between the contour lines form by the water body with the help of Prizmoidal formula. After that revised reservoir capacity is differentiated with the capacity at the begin in 2007 to achieve capacity loss from 2007 to 2018 (i.e. 11 years). The loss in capacity of the reservoir is the sediment deposited at that elevation. As a result, the show the capacity of live storage zone is reduced from 190.89 Mm3 to 186.251 Mm3, indicating a total capacity loss 4.6390 Mm3.The loss of capacity 2.4302% in 11 years. If the sedimentation occurs linearly then the rate of sedimentation is estimated to be 0.4217 Mm3 year-1. Keywords: Arc-GIS, Prizmoidal Formula, Sedimentation, RS and GIS, Normalized Difference Water Index (NDWI).","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133002807","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Moradi, H. Babaei, A. Alimohammadi, Soheil Radiom
{"title":"Estimation of Crop Coefficients in the Moghan Cultivation Industry and the Study of Relationship between Evapotranspiration and Yield Performance","authors":"A. Moradi, H. Babaei, A. Alimohammadi, Soheil Radiom","doi":"10.52547/GISJ.11.4.11","DOIUrl":"https://doi.org/10.52547/GISJ.11.4.11","url":null,"abstract":"","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129125371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Land surface temperature (LST) is an important indicator for the study of climate change, urban environment, heat balance studies, hydrological and agricultural process, and urban land use and land cover as well as user input for climate models. Landsat data is utilized for the number of applications such as environment study, digester and resource management. Land surface temperate is estimated by the help of ArcGIS through Landsat 8 and 5 satellites images. The normalized difference vegetation index (NDVI) thresholds method was used for land surface emissivity (LSE) estimation; NDVI is calculated by near infrared (NIR) and red spectral bands in its formula. NDVI = (NIR-RED)/(NIR-RED). The thermal infrared band is the source of LSE in this study area. The aim of this study is to calculate the LST and NDVI of Agra city, India, and to accomplish this task, ArcGIS Raster calculator is utilized. The empirical determine value of NDVI, LSE, and LST with appropriate accuracy help to achieve the aim of this study. In the last drive out the temperature variance in different land use and land cover area of Agra city, India. Keywords: ArcGIS, Landsat satellite images, land surface emissivity, land surface temperature, normalized difference vegetation index, raster calculator, Landsat 8
{"title":"Estimation of Land Surface Temperature using Landsat Data: a Case Study of Agra City, India","authors":"Kamal S. Bisht","doi":"10.37591/.V10I3.793","DOIUrl":"https://doi.org/10.37591/.V10I3.793","url":null,"abstract":"Land surface temperature (LST) is an important indicator for the study of climate change, urban environment, heat balance studies, hydrological and agricultural process, and urban land use and land cover as well as user input for climate models. Landsat data is utilized for the number of applications such as environment study, digester and resource management. Land surface temperate is estimated by the help of ArcGIS through Landsat 8 and 5 satellites images. The normalized difference vegetation index (NDVI) thresholds method was used for land surface emissivity (LSE) estimation; NDVI is calculated by near infrared (NIR) and red spectral bands in its formula. NDVI = (NIR-RED)/(NIR-RED). The thermal infrared band is the source of LSE in this study area. The aim of this study is to calculate the LST and NDVI of Agra city, India, and to accomplish this task, ArcGIS Raster calculator is utilized. The empirical determine value of NDVI, LSE, and LST with appropriate accuracy help to achieve the aim of this study. In the last drive out the temperature variance in different land use and land cover area of Agra city, India. Keywords: ArcGIS, Landsat satellite images, land surface emissivity, land surface temperature, normalized difference vegetation index, raster calculator, Landsat 8","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"30 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116405857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The aim of the present is to identifying the land use and cover analysis of Sathyavedu area, chittoor district, Andhra Pradesh, India by using Geographical Information System. The present study have been employed with survey of India toposheet No's. 57 O/14, O/15, 66 C/2, and C/3 and Remote sensing LISS-III data. On the basis of field study, topographical maps and satellite data have been determined 15 categories’ in the study area. These are Built up (Rural), Mining / industrial, Hamlets and dispersed household, Reservoir / Tanks, River / Stream / Drain, River / Stream / Drain, Lakes /Ponds, Canal, Scrubland Open, Scrubland Dense, Barren rocky, Cropland, Aquaculture/pisciculture, Agriculture plantation, Forest plantation and Forest. In overall, the majorly occupied with forest land followed by cropland. Keywords: Landuse, Landcover, Satellite Imageries, Remote sensing and GIS
{"title":"Landuse/Landcover Classification in parts of the Sathyavedu Area, Chittoor District (Andhra Pradesh), India by using Remote sensing and GIS Techniques","authors":"V. Golla, M. Rajasekhar, E. Balaji, P. Harish","doi":"10.37591/.V10I3.693","DOIUrl":"https://doi.org/10.37591/.V10I3.693","url":null,"abstract":"The aim of the present is to identifying the land use and cover analysis of Sathyavedu area, chittoor district, Andhra Pradesh, India by using Geographical Information System. The present study have been employed with survey of India toposheet No's. 57 O/14, O/15, 66 C/2, and C/3 and Remote sensing LISS-III data. On the basis of field study, topographical maps and satellite data have been determined 15 categories’ in the study area. These are Built up (Rural), Mining / industrial, Hamlets and dispersed household, Reservoir / Tanks, River / Stream / Drain, River / Stream / Drain, Lakes /Ponds, Canal, Scrubland Open, Scrubland Dense, Barren rocky, Cropland, Aquaculture/pisciculture, Agriculture plantation, Forest plantation and Forest. In overall, the majorly occupied with forest land followed by cropland. Keywords: Landuse, Landcover, Satellite Imageries, Remote sensing and GIS","PeriodicalId":427440,"journal":{"name":"Journal of Remote Sensing & GIS","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131096327","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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