Pub Date : 2023-04-28DOI: 10.58825/jog.2023.18.1.9
PRADNYA GOVEKAR, J. T. Gudagur, AJAYKUMAR N. ASODE
In the present study, basin morphometry of Hirehalla Sub-basin of Bagalkote District, Karnataka was carried out using remote sensing and geoinformatics techniques. Delineation and calculation of various morphometric parameters of the sub-basin was done in GIS environment. The study was categorized into– Linear, Aerial and Relief aspects. Result obtained from morphometric analysis confirms the highest order of fifth and showing sub-dendritic to dendritic drainage pattern. Values of shape parameters- form factor (0.16), elongation ratio (0.45) and circulatory ratio (0.38), suggests the sub-basin to be elongated in shape. Average bifurcation ratio value (Rb=3.34), indicates the influence of geomorphic features on the basin. From the values of Stream frequency (1.36) and drainage density (1.27) it indicates, permeable subsurface and low relief. Drainage texture value (1.74) suggests the basin is coarse. In addition, a low value of ruggedness number indicates the resistance of sub-basin to erosion.
{"title":"Morphometric analysis of Hirehalla Sub-basin of Malaprabha River, Northern Karnataka using geoinformatics techniques","authors":"PRADNYA GOVEKAR, J. T. Gudagur, AJAYKUMAR N. ASODE","doi":"10.58825/jog.2023.18.1.9","DOIUrl":"https://doi.org/10.58825/jog.2023.18.1.9","url":null,"abstract":"In the present study, basin morphometry of Hirehalla Sub-basin of Bagalkote District, Karnataka was carried out using remote sensing and geoinformatics techniques. Delineation and calculation of various morphometric parameters of the sub-basin was done in GIS environment. The study was categorized into– Linear, Aerial and Relief aspects. Result obtained from morphometric analysis confirms the highest order of fifth and showing sub-dendritic to dendritic drainage pattern. Values of shape parameters- form factor (0.16), elongation ratio (0.45) and circulatory ratio (0.38), suggests the sub-basin to be elongated in shape. Average bifurcation ratio value (Rb=3.34), indicates the influence of geomorphic features on the basin. From the values of Stream frequency (1.36) and drainage density (1.27) it indicates, permeable subsurface and low relief. Drainage texture value (1.74) suggests the basin is coarse. In addition, a low value of ruggedness number indicates the resistance of sub-basin to erosion.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46330233","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 : 2023-04-28DOI: 10.58825/jog.2023.17.1.77
K. N. Devi, R. Sarangi
Study has been carried out to monitor the phytoplankton biomass in Bay of Bengal (BoB) and Arabian Sea (AS) using Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite data. Cloud masking, geometric corrections and subsets generations were performed to retrieve chlorophyll images from MODIS-Aqua data during the periods January - December for the years 2007 and 2008. The two regions (BoB & AS) have been divided into four subsets; subset-1 (Northern Bay of Bengal), subset-2 (Southern Bay of Bengal), subset-3 (Northern Arabian Sea) and subset-4 (Southern Arabian Sea). The results were analyzed and confirmed that chlorophyll concentration mean range was high (0.97-1.89 mg m-3) in northern Arabian Sea during the months of July for both years 2007 and 2008 and low concentration range (0.12-0.35 mg m-3) was obtained during April month for both years in southern Bay of Bengal. This study found to be important as information about the chlorophyll concentration in the Northern Indian Ocean.
{"title":"Monitoring of monthly scale chlorophyll concentration variability in the Bay of Bengal and Arabian Sea using MODIS Aqua Satellite Data","authors":"K. N. Devi, R. Sarangi","doi":"10.58825/jog.2023.17.1.77","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.77","url":null,"abstract":"Study has been carried out to monitor the phytoplankton biomass in Bay of Bengal (BoB) and Arabian Sea (AS) using Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua satellite data. Cloud masking, geometric corrections and subsets generations were performed to retrieve chlorophyll images from MODIS-Aqua data during the periods January - December for the years 2007 and 2008. The two regions (BoB & AS) have been divided into four subsets; subset-1 (Northern Bay of Bengal), subset-2 (Southern Bay of Bengal), subset-3 (Northern Arabian Sea) and subset-4 (Southern Arabian Sea). The results were analyzed and confirmed that chlorophyll concentration mean range was high (0.97-1.89 mg m-3) in northern Arabian Sea during the months of July for both years 2007 and 2008 and low concentration range (0.12-0.35 mg m-3) was obtained during April month for both years in southern Bay of Bengal. This study found to be important as information about the chlorophyll concentration in the Northern Indian Ocean.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43550768","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 : 2023-04-28DOI: 10.58825/jog.2023.17.1.80
A. Farah
Precise Point Positioning (PPP) is relatively modern GNSS positioning technique that proved its efficiency comparing with traditional Differential positioning technique for more than three decades. PPP requires only one receiver collecting observations at unknown station, while Differential technique requires two receivers collecting observations simultaneously one at known-position station and the other at unknown station. Extensive mitigation of different GNSS errors is essential for PPP-collected observations. Static-PPP accuracy depends on different factors such as; used GNSS system; single (GPS(G) or GLONASS(R) or BeiDou(C) or Galileo(E)) or mixed-GNSS systems (GPS/GLONASS or GPS/GLONASS/BeiDou or GPS/GLONASS/BeiDou/Galileo), observations type (single or dual or triple frequency), satellites geometry and observations duration. This research investigates static-PPP accuracy variation on three different-latitude IGS stations based on different factors; used GNSS system (single or mixed), observations type (single or dual or triple frequency) and satellites geometry. It can be concluded that GRCE combination provides 3D-accuracy of (8 cm) using single frequency observations, (1.5 mm) using dual frequency observations and (1 mm) using triple frequency observations. GRCE combination provides a convergence time of only four minutes (8 epochs) for dual frequency observations.
{"title":"Static-PPP Performance using Multi-GNSS (Single, Dual and Triple) Frequency Observations","authors":"A. Farah","doi":"10.58825/jog.2023.17.1.80","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.80","url":null,"abstract":"Precise Point Positioning (PPP) is relatively modern GNSS positioning technique that proved its efficiency comparing with traditional Differential positioning technique for more than three decades. PPP requires only one receiver collecting observations at unknown station, while Differential technique requires two receivers collecting observations simultaneously one at known-position station and the other at unknown station. Extensive mitigation of different GNSS errors is essential for PPP-collected observations. Static-PPP accuracy depends on different factors such as; used GNSS system; single (GPS(G) or GLONASS(R) or BeiDou(C) or Galileo(E)) or mixed-GNSS systems (GPS/GLONASS or GPS/GLONASS/BeiDou or GPS/GLONASS/BeiDou/Galileo), observations type (single or dual or triple frequency), satellites geometry and observations duration. This research investigates static-PPP accuracy variation on three different-latitude IGS stations based on different factors; used GNSS system (single or mixed), observations type (single or dual or triple frequency) and satellites geometry. It can be concluded that GRCE combination provides 3D-accuracy of (8 cm) using single frequency observations, (1.5 mm) using dual frequency observations and (1 mm) using triple frequency observations. GRCE combination provides a convergence time of only four minutes (8 epochs) for dual frequency observations. ","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45903531","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 : 2023-04-28DOI: 10.58825/jog.2023.17.1.75
Zubair Ahmed, P.P. Nageswara Rao, P. Srikanth
Timely and accurate estimation of acreage and production of horticulture crops is necessary for deciding how much, where and when to export these commodities in the national and global markets. Remote sensing has been one of the methods adopted, in addition to conventional sampling methods, for improving the estimates. Parametric image classification algorithms have been used by many researchers for identification and area estimation of horticulture crops. But these algorithms result in several unclassified pixels leading to over/underestimates. This study has been undertaken to estimate the area of two horticulture crops (i.e., mango and coconut) of Hesaraghatta hobli of Bengaluru urban district using Convolutional Neural Network (CNN) on Google Colaband Random Forest (RF) algorithms on Google Earth Engine (GEE). Remotely sensed data acquired by the Multi-Spectral Instrument (MSI) onboard Sentiel-2A satellite was used. Spectral signatures of horticulture crops and other associated cover types have been generated to identify the cover types and for selecting appropriate band combinations. Two different band combinations were used for area estimation of selected horticulture crops: i) Near-InfraRed (NIR), Red, and Green all three having a spatial resolution of 10 m, ii) Red edge-3, Short-Wave InfraRed1 (SWIR1) and Short-Wave InfraRed2 (SWIR2) having 20 m spatial resolution. Area estimates of horticulture crops and associated cover types were validated with respect to ground truth and statistical reports from Karnataka State Directorate of Horticulture (KSDH). It was found that the CNN model performed better than RF using NIR, Red, and Green band combination with an overall accuracy of 84%, but it failed to give similar accuracies with Red edge 3, SWIR1, and SWIR2 band combination. We attempted transfer learning using the trained CNN model at two different study areas far away from the study area and found encouraging results.
及时准确地估计园艺作物的种植面积和产量对于决定向国内和全球市场出口这些商品的数量、地点和时间是必要的。遥感是除常规抽样方法外，用以改进估算的方法之一。参数化图像分类算法已被许多研究者用于园艺作物的识别和面积估计。但是这些算法会导致一些未分类的像素导致高估或低估。本研究利用谷歌Colaband Random Forest (RF)算法在谷歌Earth Engine (GEE)上对班加罗尔市区Hesaraghatta hobli的两种园艺作物(即芒果和椰子)的面积进行了估算。利用sentinel - 2a卫星上的多光谱仪(MSI)获取的遥感数据。已经生成了园艺作物和其他相关覆盖类型的光谱特征，以确定覆盖类型并选择适当的波段组合。采用两种不同的波段组合对选定的园艺作物进行面积估算:i)近红外(NIR)、红色和绿色，这三种波段的空间分辨率均为10 m; ii)红边-3、短波红外d1 (SWIR1)和短波红外d2 (SWIR2)的空间分辨率为20 m。根据卡纳塔克邦园林局(KSDH)的实际情况和统计报告，对园艺作物和相关覆盖类型的面积估计进行了验证。研究发现，CNN模型在使用NIR、Red和Green波段组合时的表现优于RF，总体准确率为84%，但在使用Red edge 3、SWIR1和SWIR2波段组合时未能给出相似的精度。我们在远离研究区域的两个不同的研究区域尝试使用训练好的CNN模型进行迁移学习，并获得了令人鼓舞的结果。
{"title":"Area Estimation of Mango and Coconut Crops using Machine Learning in Hesaraghatta Hobli of Bengaluru Urban District, Karnataka","authors":"Zubair Ahmed, P.P. Nageswara Rao, P. Srikanth","doi":"10.58825/jog.2023.17.1.75","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.75","url":null,"abstract":"Timely and accurate estimation of acreage and production of horticulture crops is necessary for deciding how much, where and when to export these commodities in the national and global markets. Remote sensing has been one of the methods adopted, in addition to conventional sampling methods, for improving the estimates. Parametric image classification algorithms have been used by many researchers for identification and area estimation of horticulture crops. But these algorithms result in several unclassified pixels leading to over/underestimates. This study has been undertaken to estimate the area of two horticulture crops (i.e., mango and coconut) of Hesaraghatta hobli of Bengaluru urban district using Convolutional Neural Network (CNN) on Google Colaband Random Forest (RF) algorithms on Google Earth Engine (GEE). Remotely sensed data acquired by the Multi-Spectral Instrument (MSI) onboard Sentiel-2A satellite was used. Spectral signatures of horticulture crops and other associated cover types have been generated to identify the cover types and for selecting appropriate band combinations. Two different band combinations were used for area estimation of selected horticulture crops: i) Near-InfraRed (NIR), Red, and Green all three having a spatial resolution of 10 m, ii) Red edge-3, Short-Wave InfraRed1 (SWIR1) and Short-Wave InfraRed2 (SWIR2) having 20 m spatial resolution. Area estimates of horticulture crops and associated cover types were validated with respect to ground truth and statistical reports from Karnataka State Directorate of Horticulture (KSDH). It was found that the CNN model performed better than RF using NIR, Red, and Green band combination with an overall accuracy of 84%, but it failed to give similar accuracies with Red edge 3, SWIR1, and SWIR2 band combination. We attempted transfer learning using the trained CNN model at two different study areas far away from the study area and found encouraging results.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44815881","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 : 2022-10-31DOI: 10.58825/jog.2022.16.2.46
Mragank Snighal, A. Payal, Ashok Kumar
Convolution Neural Network (CNN) is a deep learning approach that has become an area of interest to the researchers for solving complex problems. With the evaluation of CNN, extraction of deep features for accurate classification of remotely sensed images has gained lot of momentum. This research work uses CNN deep learning model for mapping rabi crops (mustard and wheat) using temporal remote sensing data. The mappings of mustard and wheat crops have been conducted using multispectral temporal images obtained from Sentinel 2A/2B between the dates 1st Nov 2019 and 24th Feb 2020 of Banasthali, Rajasthan region. The CNN model created in this research work uses several layers along with 5 activation functions (relu, sigmoid, tanh, elu and selu) for finding out which activation function gave the best result for the proposed study. Batch size has been examined from 1 to 50 in the multiple of 5 and epochs have been tested from 1 to 10 for a training data of 200 samples for each class. The optimal value with a batch size of 5 and epochs of 30 has been calculated as best suited in this study as the accuracy was getting constant. The implementation of CNN model for classification shows better results as compared to the traditional approach as the CNN algorithms are learning algorithms. This also helps in handling the heterogeneity within a class. A comparison has been conducted using Modified Possibilistic c-Means (MPCM) fuzzy algorithm for the classification of the same set of classes. F-Score, Kappa and Overall Accuracy have been calculated to show how the proposed approach has been outperformed and the level of classification accuracy achieved.
{"title":"Study of CNN deep learning model for temporal remote sensing data processing to map rabi crops","authors":"Mragank Snighal, A. Payal, Ashok Kumar","doi":"10.58825/jog.2022.16.2.46","DOIUrl":"https://doi.org/10.58825/jog.2022.16.2.46","url":null,"abstract":"Convolution Neural Network (CNN) is a deep learning approach that has become an area of interest to the researchers for solving complex problems. With the evaluation of CNN, extraction of deep features for accurate classification of remotely sensed images has gained lot of momentum. This research work uses CNN deep learning model for mapping rabi crops (mustard and wheat) using temporal remote sensing data. The mappings of mustard and wheat crops have been conducted using multispectral temporal images obtained from Sentinel 2A/2B between the dates 1st Nov 2019 and 24th Feb 2020 of Banasthali, Rajasthan region. The CNN model created in this research work uses several layers along with 5 activation functions (relu, sigmoid, tanh, elu and selu) for finding out which activation function gave the best result for the proposed study. Batch size has been examined from 1 to 50 in the multiple of 5 and epochs have been tested from 1 to 10 for a training data of 200 samples for each class. The optimal value with a batch size of 5 and epochs of 30 has been calculated as best suited in this study as the accuracy was getting constant. The implementation of CNN model for classification shows better results as compared to the traditional approach as the CNN algorithms are learning algorithms. This also helps in handling the heterogeneity within a class. A comparison has been conducted using Modified Possibilistic c-Means (MPCM) fuzzy algorithm for the classification of the same set of classes. F-Score, Kappa and Overall Accuracy have been calculated to show how the proposed approach has been outperformed and the level of classification accuracy achieved.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47225102","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 : 2022-10-31DOI: 10.58825/jog.2022.16.2.42
K. Lata, Anil Sood, K. Kaur, Amanpreet Kaur Benipal, B. Pateriya
Real-time visualization is the requirement for immediacy of decision making, which tends to be role-based. Using maps to visualize data can enable quicker interpretation of complex geographical phenomena, identify patterns, and aid in planning, resource allocations for policy and decision making. In present study, an interactive Web GIS Dashboard is developed with the objectives to display the work progress of Department of Soil & Water Conservation. The data includes activities and schemes undergoing in the department which was validated and geo-tagged with district & block boundary. For real-time data visualization, the graphs for different year, activities and schemes are developed for number of beneficiaries and area benefitted in ha. Various filters i.e. Year, Scheme and District are provided for viewing map. Different levels of User Authentication are provided for uploading new data and updating data
{"title":"Web-GIS based Dashboard for Real-Time Data Visualization & Analysis using Open Source Technologies","authors":"K. Lata, Anil Sood, K. Kaur, Amanpreet Kaur Benipal, B. Pateriya","doi":"10.58825/jog.2022.16.2.42","DOIUrl":"https://doi.org/10.58825/jog.2022.16.2.42","url":null,"abstract":"Real-time visualization is the requirement for immediacy of decision making, which tends to be role-based. Using maps to visualize data can enable quicker interpretation of complex geographical phenomena, identify patterns, and aid in planning, resource allocations for policy and decision making. In present study, an interactive Web GIS Dashboard is developed with the objectives to display the work progress of Department of Soil & Water Conservation. The data includes activities and schemes undergoing in the department which was validated and geo-tagged with district & block boundary. For real-time data visualization, the graphs for different year, activities and schemes are developed for number of beneficiaries and area benefitted in ha. Various filters i.e. Year, Scheme and District are provided for viewing map. Different levels of User Authentication are provided for uploading new data and updating data","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41643809","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 : 2022-10-31DOI: 10.58825/jog.2022.16.2.40
Raaed Mohamed Kamel Hassouna
Different types of radii of curvature were assessed for the geoid based on the GECO geopotential model, up to degree and order 2190. The route values of gravity and the three horizontal gravity gradients were computed based on such geopotential model and the angular velocity of the Earth. The investigation was performed on coarse global grids and finer grids covering the Egyptian territory. Respective latitudinal and longitudinal profiles for the geoidal radii were extracted. Comparisons were held with the radii of curvature on the WGS-84 ellipsoid, and with the geoidal radii derived from other models of lower resolutions. Unlike the ellipsoid, the values of the geoidal radii exhibited a rather irregular behaviour that is far from any geographical symmetry. The principal radii of the geoid do not generally occur along the meridian and prime-vertical directions. Such irregularities were found to be more exaggerated with higher degrees. At all investigated resolution levels, the signs of the principal radii assured the convexity of the geoid surface. This enabled to define, compute and compare the Gaussian mean radii for the geoid. The local residual geoidal radii showed a decaying spectral tendency. Also, the results implied that the utilized algorithm proved to be convergent.
{"title":"Determination of radii of curvature for high resolution geoid models using the harmonic synthesis algorithm","authors":"Raaed Mohamed Kamel Hassouna","doi":"10.58825/jog.2022.16.2.40","DOIUrl":"https://doi.org/10.58825/jog.2022.16.2.40","url":null,"abstract":"Different types of radii of curvature were assessed for the geoid based on the GECO geopotential model, up to degree and order 2190. The route values of gravity and the three horizontal gravity gradients were computed based on such geopotential model and the angular velocity of the Earth. The investigation was performed on coarse global grids and finer grids covering the Egyptian territory. Respective latitudinal and longitudinal profiles for the geoidal radii were extracted. Comparisons were held with the radii of curvature on the WGS-84 ellipsoid, and with the geoidal radii derived from other models of lower resolutions. Unlike the ellipsoid, the values of the geoidal radii exhibited a rather irregular behaviour that is far from any geographical symmetry. The principal radii of the geoid do not generally occur along the meridian and prime-vertical directions. Such irregularities were found to be more exaggerated with higher degrees. At all investigated resolution levels, the signs of the principal radii assured the convexity of the geoid surface. This enabled to define, compute and compare the Gaussian mean radii for the geoid. The local residual geoidal radii showed a decaying spectral tendency. Also, the results implied that the utilized algorithm proved to be convergent. ","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49194737","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 : 2022-10-31DOI: 10.58825/jog.2022.16.2.47
Seema Jalan, D. Chouhan, Shailesh Chaure
Groundwater is one the major sources of natural water being exploited excessively for various uses in India. Thus, it is very essential to monitor the spatial and temporal variability of groundwater quality. Geo-Statistical Interpolation using GIS has been considered as the best and most advanced method for the interpolation and prediction studies of groundwater pollution and quality, and is adopted universally. In this paper, ordinary Kriging with logarithmic data transformation has been used to interpolate and predict the spatial variation of groundwater quality parameters - EC, TDS, pH, Na+, Ca2+, Bi-Carbonate, Fluoride, Chloride, Sulphate and Nitrate using data pertaining to 48 well locations in the Dungarpur tehsil. Data was transformed and normalized using Logarithmic Transformation Method and Semivariograms were drawn and analyzed for selecting the suitable model. The best Semivariogram model was obtained based upon cross validation and on the lesser RMSE criterion and Coefficient of Determination. The results show that the best semivariogram model based on RMSE varied for each water quality parameter. For log transformed data Exponential model was found suitable for EC, TDS, Na+, TH etc.; Spherical model for Ca2+ ; Chloride Gaussian Model for Chloride. For original or raw for non-transformed data Exponential Model was found suitable for Fluoride, Sulphate and Nitrate; and Gaussian Model for pH and Bi-Carbonates.
{"title":"Geospatial Analysis of Spatial Variability of Groundwater Quality Using Ordinary Kriging: A Case Study of Dungarpur Tehsil, Rajasthan, India","authors":"Seema Jalan, D. Chouhan, Shailesh Chaure","doi":"10.58825/jog.2022.16.2.47","DOIUrl":"https://doi.org/10.58825/jog.2022.16.2.47","url":null,"abstract":"Groundwater is one the major sources of natural water being exploited excessively for various uses in India. \u0000Thus, it is very essential to monitor the spatial and temporal variability of groundwater quality. Geo-Statistical Interpolation using GIS has been considered as the best and most advanced method for the interpolation and prediction studies of groundwater pollution and quality, and is adopted universally. In this paper, ordinary Kriging with logarithmic data transformation has been used to interpolate and predict the spatial variation of groundwater quality parameters - EC, TDS, pH, Na+, Ca2+, Bi-Carbonate, Fluoride, Chloride, Sulphate and Nitrate using data pertaining to 48 well locations in the Dungarpur tehsil. Data was transformed and normalized using Logarithmic Transformation Method and Semivariograms were drawn and analyzed for selecting the suitable model. The best Semivariogram model was obtained based upon cross validation and on the lesser RMSE criterion and Coefficient of Determination. The results show that the best semivariogram model based on RMSE varied for each water quality parameter. For log transformed data Exponential model was found suitable for EC, TDS, Na+, TH etc.; Spherical model for Ca2+ ; Chloride Gaussian Model for Chloride. For original or raw for non-transformed data Exponential Model was found suitable for Fluoride, Sulphate and Nitrate; and Gaussian Model for pH and Bi-Carbonates.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48185847","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 : 2022-10-31DOI: 10.58825/jog.2022.16.2.50
Pradeep S Arya, Rajani M B
Human interaction with the landscape they inhabit leaves imprints that are largely inconspicuous on the ground. Yet, these remain the most thought provoking among the sets of clues available to us in fuelling the quest to understand our past. Spatial view provided by satellite imagery plays a pivotal part in enabling us to identify these imprints in the form of patterns. In this study, a paleo-environmental reconstruction of a particular cultural landscape using geo-spatial tools has been attempted. A range of historical documents (textual records such as traveller’s accounts dating to 5th and 7th centuries CE, maps of the Indian sub-continent published in the 18th and 19th centuries) and satellite imagery of the last 50 years (CORONA, Google Earth Digital Globe ) are used to identify and analyse the distribution of cultural sites that are hitherto unexplored to understand the impact of past changes to the landscape of these ancient sites located along one of the most dynamic regions of the Indian subcontinent: the Gangetic Delta. The identification of numerous sites of a distinct pattern and their distribution, analysed along with the morphological signature of the landscape on which they remain almost undetected has led to certain inferences on the possible location and extents of the ancient port of Tamralipti. The study also reveals the coastal and deltaic changes in the vicinity of the ancient port.
{"title":"Paleo-topographic Reconstruction of Cultural Landscapes using Remote Sensing and GIS: A case study of the ancient port of Tamralipti","authors":"Pradeep S Arya, Rajani M B","doi":"10.58825/jog.2022.16.2.50","DOIUrl":"https://doi.org/10.58825/jog.2022.16.2.50","url":null,"abstract":"Human interaction with the landscape they inhabit leaves imprints that are largely inconspicuous on the ground. Yet, these remain the most thought provoking among the sets of clues available to us in fuelling the quest to understand our past. Spatial view provided by satellite imagery plays a pivotal part in enabling us to identify these imprints in the form of patterns. In this study, a paleo-environmental reconstruction of a particular cultural landscape using geo-spatial tools has been attempted. A range of historical documents (textual records such as traveller’s accounts dating to 5th and 7th centuries CE, maps of the Indian sub-continent published in the 18th and 19th centuries) and satellite imagery of the last 50 years (CORONA, Google Earth Digital Globe ) are used to identify and analyse the distribution of cultural sites that are hitherto unexplored to understand the impact of past changes to the landscape of these ancient sites located along one of the most dynamic regions of the Indian subcontinent: the Gangetic Delta. The identification of numerous sites of a distinct pattern and their distribution, analysed along with the morphological signature of the landscape on which they remain almost undetected has led to certain inferences on the possible location and extents of the ancient port of Tamralipti. The study also reveals the coastal and deltaic changes in the vicinity of the ancient port.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46199258","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 : 2022-10-31DOI: 10.58825/jog.2022.16.2.49
A. Mathur, Nishant Tiwari, Vipan Kumar, B. Pateriya
Maintenance of roads is a key concern for smooth flow of traffic and goods for any economy to thrive. The maintenance part has been limited in GIS to information collected through ground-based surveys, GPS location of affected road area, uploading geo-tagged photos shared by public through mobile app on GIS data. The present work focuses on one such innovative approach using geo-tagging of videos of road surface with Road layer in GIS. Stretches of road in and around Ludhiana city of Punjab to reflect different scenarios such as roads located in open village area, highways, and broad and congested city roads have been used for understanding condition of surface of road in a contiguous fashion.
{"title":"Monitoring Road maintenance using video-geotagging in geographical information system: an innovative approach","authors":"A. Mathur, Nishant Tiwari, Vipan Kumar, B. Pateriya","doi":"10.58825/jog.2022.16.2.49","DOIUrl":"https://doi.org/10.58825/jog.2022.16.2.49","url":null,"abstract":"Maintenance of roads is a key concern for smooth flow of traffic and goods for any economy to thrive. The maintenance part has been limited in GIS to information collected through ground-based surveys, GPS location of affected road area, uploading geo-tagged photos shared by public through mobile app on GIS data. The present work focuses on one such innovative approach using geo-tagging of videos of road surface with Road layer in GIS. Stretches of road in and around Ludhiana city of Punjab to reflect different scenarios such as roads located in open village area, highways, and broad and congested city roads have been used for understanding condition of surface of road in a contiguous fashion.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44417272","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：