Pub Date : 2023-10-31DOI: 10.58825/jog.2023.17.2.70
Kamal Pandey, None Sukirti, Abhishek Danodia, Harish Chandra Karnatak
The consequences of climate change have a substantial impact on agricultural crop production and management. Predicting or forecasting crop yields well in advance would help farmers, agriculture corporations and government agencies manage risk and design suitable crop insurance plans. Ground survey is the traditional way of determining yield, which is subjective, time-consuming, and expensive. While Machine learning techniques make yield prediction less expensive, less time taking and more efficient. In this study, thirteen years of meteorological parameters and wheat yield data (2001-2013) of Uttar Pradesh were used to train and analyze three machine learning regression models viz. Support Vector Regression, Ordinary Least Squares, and Random Forest. Each model's performance was assessed using Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. Results revealed that the Random Forest model with a MAE of 0.258 t/ha, MSE of 0.096 t/ha and RMSE of 0.311 t/ha proved to be the best model in the yield prediction of wheat when results are statistically compared with others. Researchers and decision-makers can use the findings to estimate pre-harvest yields and to ensure food security.
{"title":"Development of Machine Learning based Models for Multivariate Prediction of Wheat Crop Yield in Uttar Pradesh, India","authors":"Kamal Pandey, None Sukirti, Abhishek Danodia, Harish Chandra Karnatak","doi":"10.58825/jog.2023.17.2.70","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.70","url":null,"abstract":"The consequences of climate change have a substantial impact on agricultural crop production and management. Predicting or forecasting crop yields well in advance would help farmers, agriculture corporations and government agencies manage risk and design suitable crop insurance plans. Ground survey is the traditional way of determining yield, which is subjective, time-consuming, and expensive. While Machine learning techniques make yield prediction less expensive, less time taking and more efficient. In this study, thirteen years of meteorological parameters and wheat yield data (2001-2013) of Uttar Pradesh were used to train and analyze three machine learning regression models viz. Support Vector Regression, Ordinary Least Squares, and Random Forest. Each model's performance was assessed using Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error. Results revealed that the Random Forest model with a MAE of 0.258 t/ha, MSE of 0.096 t/ha and RMSE of 0.311 t/ha proved to be the best model in the yield prediction of wheat when results are statistically compared with others. Researchers and decision-makers can use the findings to estimate pre-harvest yields and to ensure food security.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863498","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}
Antarctic Ice Sheet (AIS) surface elevation change plays a crucial role in understanding the ice sheet mass balance. The present study focuses on improving AIS surface elevation estimations by incorporating slope correction methods called Direct Method (DM) using SARAL/AltiKa 40 Hz geophysical data record for 2013 (Exact Repeat Mission) and 2020 (Drifting Phase) with terrain slope ranges from 0° to 0.85°. The NASA's Ice, Cloud, and land Elevation Satellite (ICESat) Digital Elevation Model (DEM) has been utilized as a priori topography model to retrieve slope of the AIS terrain. While comparing the two direct methods (DM1 & DM2) based slope corrected elevations with Operation Ice Bridge (OIB) elevation data of November 2013, the RMSE resulted in 0.35 and 0.37 m and biases of the order of 0.26 m and 0.28 m for DM1 and DM2 respectively. Moreover, comparison with ICESat DEM showed the RMSE of the order of 1.81 and 2.09 m, and biases of the order of 0.95 and 0.99 m for DM1 and DM2, respectively. It has been observed that DM1 is the most suitable method for correcting terrain slope with the lowest RMSE and bias. Moreover, the slope induced error correction methods show utmost importance in estimating accurate elevation, especially over undulating terrain of AIS.
{"title":"Evaluation of Slope Correction Methods to Improve Surface Elevation Change Estimation over Antarctic Ice Sheet using SARAL/AltiKa","authors":"Priyanka Patel, Purvee Joshi, Tarang Patadiya, Sushil Kumar Singh, Kunvar Yadav, Sandip Oza","doi":"10.58825/jog.2023.17.2.23","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.23","url":null,"abstract":"Antarctic Ice Sheet (AIS) surface elevation change plays a crucial role in understanding the ice sheet mass balance. The present study focuses on improving AIS surface elevation estimations by incorporating slope correction methods called Direct Method (DM) using SARAL/AltiKa 40 Hz geophysical data record for 2013 (Exact Repeat Mission) and 2020 (Drifting Phase) with terrain slope ranges from 0° to 0.85°. The NASA's Ice, Cloud, and land Elevation Satellite (ICESat) Digital Elevation Model (DEM) has been utilized as a priori topography model to retrieve slope of the AIS terrain. While comparing the two direct methods (DM1 & DM2) based slope corrected elevations with Operation Ice Bridge (OIB) elevation data of November 2013, the RMSE resulted in 0.35 and 0.37 m and biases of the order of 0.26 m and 0.28 m for DM1 and DM2 respectively. Moreover, comparison with ICESat DEM showed the RMSE of the order of 1.81 and 2.09 m, and biases of the order of 0.95 and 0.99 m for DM1 and DM2, respectively. It has been observed that DM1 is the most suitable method for correcting terrain slope with the lowest RMSE and bias. Moreover, the slope induced error correction methods show utmost importance in estimating accurate elevation, especially over undulating terrain of AIS.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863494","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 : 2023-10-31DOI: 10.58825/jog.2023.17.2.59
None D. Asenso-Gyambibi, N. Lamkai, E. K. Larbi, M.S. Peprah, B. Asamoah, P. Okantey
Petroleum Infrastructure is indispensable considering the current local and global energy demands, however explosions from such establishments often cause loss of lives and properties within the surrounding communities thereby posing great concern to government and the citizenry. This condition calls for research that would provide meaningful solutions to mitigate this menace. Unlawful siting of oil refineries, petrochemical plants, berthing terminals, pipelines, storage terminals, oil and gas retail assets results from non-consideration of environmental impact on growing human population, competition for customers and lack of enforcement of energy standards. The study aims to employ a multifaceted approach comprising of suitability, proximity and spatial statistical analysis in assessing viable areas for developing petroleum hubs in the district. This study further investigated the efficiency of the method and level of compliance to standards set by the Ministry of Energy, Environmental Protection Agency (EPA) and the Town and Country Planning Department through validation using the newly acquired land for petroleum hub and existing filling stations in the study area. Primary and secondary data were used for the study. The primary data consist of locations of oil and gas filling stations picked with the Garmin handheld GPS and surveyed boundary of the land. The secondary data was obtained from the Survey and Mapping division of the land commission of Ghana. It comprises of topographic data, geology and soil maps from which soil types, lithology, roads networks, water bodies, terrain slope and land use features of the area were extracted and used. The dataset was reclassified and weighted using Fuzzy AHP and VIKOR. Spatial analyses were carried out using ArcGIS software to show areas suitable or otherwise for siting petroleum hubs in the study area. Results shows 67.44% of the area are highly suitable for establishment of petroleum hubs, 32.33% of the area falls within moderate suitability zones whereas the least suitability zones occupied 0.23% of the total area. The newly acquired government land for the petroleum hub project fell within the highly suitable zone confirming the validity of the studies in comparison with studies from field experts via environmental impact assessment. The proposed petroleum hub covered areas dominated by very high and high area suitability for its establishment constituting 75.9 km2 (90.3%) of its entire area whereas the moderate suitability zones constituted 8.2 km2 (9.7%) of the remaining areas. Towns situated in very high areas includes; Bakakole Nkwanta, Ahobre, Nawule, Allowule, Tikobo No.1, Edu, Damofu, Ave lenu and Ebonloa, Mpatabo. High areas comprises of Kengen Kpokezo, Alenda wharf, Tekyinta. Anwonakrom, Nkwamta, Elubo and Agege are among the moderate and low area zones for hub and oil retail assets establishment 75% of the oil retail assets complied with the required set standards whiles 25% defaulted.
{"title":"Site Suitability Assessment for Petroleum hubs and Oil retail assets in the Jomoro District: A Hybrid Approach using Fuzzy AHP and VIKOR Method","authors":"None D. Asenso-Gyambibi, N. Lamkai, E. K. Larbi, M.S. Peprah, B. Asamoah, P. Okantey","doi":"10.58825/jog.2023.17.2.59","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.59","url":null,"abstract":"Petroleum Infrastructure is indispensable considering the current local and global energy demands, however explosions from such establishments often cause loss of lives and properties within the surrounding communities thereby posing great concern to government and the citizenry. This condition calls for research that would provide meaningful solutions to mitigate this menace. Unlawful siting of oil refineries, petrochemical plants, berthing terminals, pipelines, storage terminals, oil and gas retail assets results from non-consideration of environmental impact on growing human population, competition for customers and lack of enforcement of energy standards. The study aims to employ a multifaceted approach comprising of suitability, proximity and spatial statistical analysis in assessing viable areas for developing petroleum hubs in the district. This study further investigated the efficiency of the method and level of compliance to standards set by the Ministry of Energy, Environmental Protection Agency (EPA) and the Town and Country Planning Department through validation using the newly acquired land for petroleum hub and existing filling stations in the study area. Primary and secondary data were used for the study. The primary data consist of locations of oil and gas filling stations picked with the Garmin handheld GPS and surveyed boundary of the land. The secondary data was obtained from the Survey and Mapping division of the land commission of Ghana. It comprises of topographic data, geology and soil maps from which soil types, lithology, roads networks, water bodies, terrain slope and land use features of the area were extracted and used. The dataset was reclassified and weighted using Fuzzy AHP and VIKOR. Spatial analyses were carried out using ArcGIS software to show areas suitable or otherwise for siting petroleum hubs in the study area. Results shows 67.44% of the area are highly suitable for establishment of petroleum hubs, 32.33% of the area falls within moderate suitability zones whereas the least suitability zones occupied 0.23% of the total area. The newly acquired government land for the petroleum hub project fell within the highly suitable zone confirming the validity of the studies in comparison with studies from field experts via environmental impact assessment. The proposed petroleum hub covered areas dominated by very high and high area suitability for its establishment constituting 75.9 km2 (90.3%) of its entire area whereas the moderate suitability zones constituted 8.2 km2 (9.7%) of the remaining areas. Towns situated in very high areas includes; Bakakole Nkwanta, Ahobre, Nawule, Allowule, Tikobo No.1, Edu, Damofu, Ave lenu and Ebonloa, Mpatabo. High areas comprises of Kengen Kpokezo, Alenda wharf, Tekyinta. Anwonakrom, Nkwamta, Elubo and Agege are among the moderate and low area zones for hub and oil retail assets establishment 75% of the oil retail assets complied with the required set standards whiles 25% defaulted. ","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863088","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 : 2023-10-31DOI: 10.58825/jog.2023.17.2.65
Yamini Agrawal, None Hina Pandey, None Poonam S. Tiwari
Rapid urbanization is the major cause for Land Use and Land Cover changes globally. The urbanization alters the land surface dynamics and affects the surface temperature, which gives rise to urban heat island effect. In the present study, spatial correlation analysis has been done between Land Surface Temperature (LST) and Land Use and Land Cover (LULC) for the city of Chandigarh. The LST is retrieved from Landsat-8 thermal band using Mono-Window algorithm and shows 2.5°C increase of temperature from 2016 to 2022. The LULC has been derived using Maximum Likelihood Classifier (MLC) which shows an increase in built-up of 7.56% and decrease in forest cover by 32%. Spectral indices belonging to major LULC classes have been derived using Sentinel-2 optical bands and spatially correlated with LST using linear regression analysis. The results show a strong positive correlation (r=0.988) between built-up and LST and a negative correlation (r=-0.625) between urban vegetation cover and LST. The mean correlation coefficient for LST-NDVI for vegetation and forest cover, LST-NDWI for water bodies, LST-NDBI for built-up and LST-NBLI for bare land is -0.3, 0.116, 0.51 and 0.392 respectively. The results indicate that vegetation and water bodies mitigate the rise of LST, whereas built-up areas and bare lands sustain in the rise of LST. The statistical analysis will be helpful for policy makers and urban planners for prevention of further degradation of urban environment and surface dynamics.
{"title":"Analytical study of relation between Land surface temperature and Land Use/Land Cover using spectral indices: A case study of Chandigarh","authors":"Yamini Agrawal, None Hina Pandey, None Poonam S. Tiwari","doi":"10.58825/jog.2023.17.2.65","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.65","url":null,"abstract":"Rapid urbanization is the major cause for Land Use and Land Cover changes globally. The urbanization alters the land surface dynamics and affects the surface temperature, which gives rise to urban heat island effect. In the present study, spatial correlation analysis has been done between Land Surface Temperature (LST) and Land Use and Land Cover (LULC) for the city of Chandigarh. The LST is retrieved from Landsat-8 thermal band using Mono-Window algorithm and shows 2.5°C increase of temperature from 2016 to 2022. The LULC has been derived using Maximum Likelihood Classifier (MLC) which shows an increase in built-up of 7.56% and decrease in forest cover by 32%. Spectral indices belonging to major LULC classes have been derived using Sentinel-2 optical bands and spatially correlated with LST using linear regression analysis. The results show a strong positive correlation (r=0.988) between built-up and LST and a negative correlation (r=-0.625) between urban vegetation cover and LST. The mean correlation coefficient for LST-NDVI for vegetation and forest cover, LST-NDWI for water bodies, LST-NDBI for built-up and LST-NBLI for bare land is -0.3, 0.116, 0.51 and 0.392 respectively. The results indicate that vegetation and water bodies mitigate the rise of LST, whereas built-up areas and bare lands sustain in the rise of LST. The statistical analysis will be helpful for policy makers and urban planners for prevention of further degradation of urban environment and surface dynamics.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135863226","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}
Guwahati city is the highest order urban center of Assam and is an important gateway to the north eastern region of India. In this study, a 50km buffer from the master plan boundary of Guwahati Metropolitan Development Authority (GMDA) is selected for identifying potential urban centers and rural growth centers (URGC) of different order for decentralized planning and inter and intra-administrative cooperation around the city using multi-parametric criteria. This includes central place theory, nested hexagon method and thematic information on groundwater potential zones, land use/land cover, flood prone and landslide susceptible zones. Out of the 32 identified potential villages, 15 are proposed for new urban centers and 17 are proposed for development as rural growth centers. 9 towns are also proposed for up-gradation to higher order for proper spatio-functional interaction. However, several suggestions and preventive measures were made before initiating developmental expansion which needs to be considered. The findings of this study would be useful for decentralized planning to minimize the economic imbalances, rural migration and sustainable development of the region.
{"title":"Identification of Urban Centre and Rural Growth Centres Around Guwahati and Its Surrounding Rural Region Using Hierarchical Settlements, Nested Hexagons, Remote Sensing and GIS","authors":"Jeni Bhattacharjee, Swapna Acharjee, Sudisht Mishra","doi":"10.58825/jog.2023.17.2.67","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.67","url":null,"abstract":"Guwahati city is the highest order urban center of Assam and is an important gateway to the north eastern region of India. In this study, a 50km buffer from the master plan boundary of Guwahati Metropolitan Development Authority (GMDA) is selected for identifying potential urban centers and rural growth centers (URGC) of different order for decentralized planning and inter and intra-administrative cooperation around the city using multi-parametric criteria. This includes central place theory, nested hexagon method and thematic information on groundwater potential zones, land use/land cover, flood prone and landslide susceptible zones. Out of the 32 identified potential villages, 15 are proposed for new urban centers and 17 are proposed for development as rural growth centers. 9 towns are also proposed for up-gradation to higher order for proper spatio-functional interaction. However, several suggestions and preventive measures were made before initiating developmental expansion which needs to be considered. The findings of this study would be useful for decentralized planning to minimize the economic imbalances, rural migration and sustainable development of the region.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135870879","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 : 2023-10-31DOI: 10.58825/jog.2023.17.2.96
Abhijit Patil, Sachin Panhalkar
This study evaluates different machine learning algorithms for land use and land cover classification using Sentinel-2 Level-1C data with 10-meter spatial resolution. The algorithms include Random Forest (RF), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naive Bayes (NB), and Gradient Boosting (GTB). The classification was performed on the Google Earth Engine (GEE) platform. Results highlight variations in land cover classification among algorithms, with RF and CART identifying cropland as dominant, SVM indicating fallow land presence, NB revealing significant forest cover, and GTB emphasizing cropland importance. Accuracy assessment was performed to evaluate the performance of the algorithms, considering metrics such as producer accuracy, consumer accuracy, overall accuracy, and Kappa coefficient. SVM demonstrates the highest overall accuracy and agreement with reference data. The study contributes insights for land management and planning, and GEE proves valuable for LULC classification.
{"title":"comparative analysis of machine learning algorithms for land use and land cover classification using google earth engine platform","authors":"Abhijit Patil, Sachin Panhalkar","doi":"10.58825/jog.2023.17.2.96","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.96","url":null,"abstract":"This study evaluates different machine learning algorithms for land use and land cover classification using Sentinel-2 Level-1C data with 10-meter spatial resolution. The algorithms include Random Forest (RF), Classification and Regression Trees (CART), Support Vector Machines (SVM), Naive Bayes (NB), and Gradient Boosting (GTB). The classification was performed on the Google Earth Engine (GEE) platform. Results highlight variations in land cover classification among algorithms, with RF and CART identifying cropland as dominant, SVM indicating fallow land presence, NB revealing significant forest cover, and GTB emphasizing cropland importance. Accuracy assessment was performed to evaluate the performance of the algorithms, considering metrics such as producer accuracy, consumer accuracy, overall accuracy, and Kappa coefficient. SVM demonstrates the highest overall accuracy and agreement with reference data. The study contributes insights for land management and planning, and GEE proves valuable for LULC classification.
 
 
","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135869390","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 : 2023-10-31DOI: 10.58825/jog.2023.17.2.6
Ritesh Agrawal
SAR Interferometry is one of the techniques used for generating three-dimensional information about the Earth’s surface, which converts the absolute interferometric phase data of complex radar signal into topographic information. The prime objective of the study was to explore the potential of the RISAT-1 data for interferometric analysis. In this study, an attempt has made to generate the DEM of the part of Bharatpur region, Rajasthan using InSAR techniques using FFT based instead of the conventional approach due to non-availability of precise orbits. The analysis was carried out using FRS-1 data of 3 m resolution and 25 km swath corresponding to 21 February 2015 and 18 March 2015 having temporal separation of 25 days. The accuracy assessment of the generated DEM was compared with the extracted reference elevation information over 53 points from the Cartosat-1 DEM. The accuracy of the Generated DEM observed as 11.8 m and mean error of 2.3 m.
{"title":"Generation and Validation of Digital Elevation Model Using RISAT-1 SAR Interferometry","authors":"Ritesh Agrawal","doi":"10.58825/jog.2023.17.2.6","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.6","url":null,"abstract":"SAR Interferometry is one of the techniques used for generating three-dimensional information about the Earth’s surface, which converts the absolute interferometric phase data of complex radar signal into topographic information. The prime objective of the study was to explore the potential of the RISAT-1 data for interferometric analysis. In this study, an attempt has made to generate the DEM of the part of Bharatpur region, Rajasthan using InSAR techniques using FFT based instead of the conventional approach due to non-availability of precise orbits. The analysis was carried out using FRS-1 data of 3 m resolution and 25 km swath corresponding to 21 February 2015 and 18 March 2015 having temporal separation of 25 days. The accuracy assessment of the generated DEM was compared with the extracted reference elevation information over 53 points from the Cartosat-1 DEM. The accuracy of the Generated DEM observed as 11.8 m and mean error of 2.3 m.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871963","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 : 2023-10-31DOI: 10.58825/jog.2023.17.2.29
None M S Saran, None Manju V S, None Vishnu V P
Road crashes in India is showing progressive growth since COVID time, despite many road safety measures and program the rate of crashes are not declining. Many a times the road safety measures are not implemented in proper geographical locations owing to lack of proper crash information, which include crash information of the past. Road crash information is thus a vital support for the road safety assessment programs that eyes for a reduction in the road crashes. In India, as in other developing countries, very little effort is taken to providing enough road crash information. The identification of road crash location, analysis and treatment of road accident black spots are widely regarded as one of the most effective approaches to road accident prevention. A user friendly web Geographic Information System (GIS) based Road Crash Information System (RCIS) is developed in the present study for Kerala State, India. An online platform to add, update and maintain the database of road accident black spots is offered by the system, including analysis functionalities. Database maintains a standard guideline for road crash reporting thereby reducing data redundancy. Integration of all crash data from accident locations and to filter data based on different criteria are the core objectives of this study. The study also focused on systematically sharing the accident black spot details to the public user through an online platform.
{"title":"WebGIS Based Road Crash Information System: A Case Study","authors":"None M S Saran, None Manju V S, None Vishnu V P","doi":"10.58825/jog.2023.17.2.29","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.29","url":null,"abstract":"Road crashes in India is showing progressive growth since COVID time, despite many road safety measures and program the rate of crashes are not declining. Many a times the road safety measures are not implemented in proper geographical locations owing to lack of proper crash information, which include crash information of the past. Road crash information is thus a vital support for the road safety assessment programs that eyes for a reduction in the road crashes. In India, as in other developing countries, very little effort is taken to providing enough road crash information. The identification of road crash location, analysis and treatment of road accident black spots are widely regarded as one of the most effective approaches to road accident prevention. A user friendly web Geographic Information System (GIS) based Road Crash Information System (RCIS) is developed in the present study for Kerala State, India. An online platform to add, update and maintain the database of road accident black spots is offered by the system, including analysis functionalities. Database maintains a standard guideline for road crash reporting thereby reducing data redundancy. Integration of all crash data from accident locations and to filter data based on different criteria are the core objectives of this study. The study also focused on systematically sharing the accident black spot details to the public user through an online platform.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135862945","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 : 2023-10-31DOI: 10.58825/jog.2023.17.2.87
Durgesh Kurmi
Sprawl refers to the horizontal expansion of the city, from core towards outskirts of the city Data extracted from Landsat imagery was utilized to quantify urban expansion of the Bhopal city with respect to direction and pattern from 1991-2021. A direct relationship between population and built-up area is established, which reflects urban sprawl. Statistical study and spatio-temporal analysis of the data is done to account for these changes. The research revealed that Bhopal City has majorly spread towards south and south-east directions in uncontrolled manner, engulfing used productive cropped areas. Sprawling pattern has evolved from radial to leap-frogging, with time.
{"title":"Monitoring Dynamics of Sprawling Bhopal “An Emerging Metropolitan”","authors":"Durgesh Kurmi","doi":"10.58825/jog.2023.17.2.87","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.87","url":null,"abstract":"Sprawl refers to the horizontal expansion of the city, from core towards outskirts of the city Data extracted from Landsat imagery was utilized to quantify urban expansion of the Bhopal city with respect to direction and pattern from 1991-2021. A direct relationship between population and built-up area is established, which reflects urban sprawl. Statistical study and spatio-temporal analysis of the data is done to account for these changes. The research revealed that Bhopal City has majorly spread towards south and south-east directions in uncontrolled manner, engulfing used productive cropped areas. Sprawling pattern has evolved from radial to leap-frogging, with time.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871328","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 : 2023-10-31DOI: 10.58825/jog.2023.17.2.7
Diksha Karapurkar, Venkatraman Hegde
Sediment yield is the possible volume of sediments that a basin is capable of delivering to its watershed outlet. It is a function of the topography of the drainage basin, climate, including precipitation, land use- land cover, soil characteristics, and other factors associated with the rate of soil formation and its transportation. Modeling sediment yield from a watershed enables computing quantitative estimates of sediments generated from a watershed. The Revised Universal Soil Loss Equation (RUSLE) is an efficient model for the assessment of annual soil loss from a basin using remotely sensed data in the Geographical Information System (GIS) platform. In the present study, the assessment of sediment yield from the Gangolli river basin of Karnataka, located on the central west coast of India, is carried out based on satellite data, processed in the GIS platform following the RUSLE model. The basin has a relief of 1200 m and a total catchment area of 1513.04km2, spread on the western face of the Western Ghat region of the South Kanara district. The basin is located in a tropical environment and experiences a hot humid climate and annual precipitation of ~ 355 cm. Physiographically, the basin is divided into three subdivisions; the high-relief mountainous region of the Western Ghats, the residual hilly region with low relief, and the coastal plains. The basin has a high circularity Index (0.25) and a moderately high elongation ratio (0.51). The total actual sediment yield from the basin has been estimated to be 6,32,976.38 tons/yr-1 and the potential yield is 23,26,047.61 tons/yr-1. implying high sediment flux into the estuarine system. The results of this study help to strategize inland soil conservation planning as well as estuarine management.
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