Pub Date : 2023-10-31DOI: 10.58825/jog.2023.17.2.63
Sukalpa Changmai
The broad availability of geospatial data has changed how we think about solving problems. There are numerous uses for GIS and Remote Sensing in a variety of fields. Such a field includes Supply Chain Management where GIS is used to map various stages like producers, consumers, processing facilities, suppliers, distribution centres, and transportation routes for better decision making and enhanced flow of goods. In this study we are integrating geospatial technologies to map raw material source, routes taken for the delivery of processed items, areas served by a processing unit and finally model relationship between different factors of a dairy industry. Parameters taken involve import-export data of milk and milk products, roads network, service areas, and Normalized Difference Vegetation Index (NDVI). Aanchal Dairy in Dehradun district of Uttarakhand is considered for this study. The primary data was collected through field visits and the raw data was structured for further analysis. Secondary data was obtained from various verified internet sources. Results indicate certain regions with high quantity of raw milk supply and areas where processed products are delivered. Optimised routes and the areas where Aanchal Dairy provides its services are also defined. NDVI shows that places with high raw milk supply have better fodder for dairy cows. Finally the results are geospatially mapped and various relationships are presented in graphical form.
{"title":"Geospatial Application for Dairy Supply Chain Management","authors":"Sukalpa Changmai","doi":"10.58825/jog.2023.17.2.63","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.63","url":null,"abstract":"The broad availability of geospatial data has changed how we think about solving problems. There are numerous uses for GIS and Remote Sensing in a variety of fields. Such a field includes Supply Chain Management where GIS is used to map various stages like producers, consumers, processing facilities, suppliers, distribution centres, and transportation routes for better decision making and enhanced flow of goods. In this study we are integrating geospatial technologies to map raw material source, routes taken for the delivery of processed items, areas served by a processing unit and finally model relationship between different factors of a dairy industry. Parameters taken involve import-export data of milk and milk products, roads network, service areas, and Normalized Difference Vegetation Index (NDVI). Aanchal Dairy in Dehradun district of Uttarakhand is considered for this study. The primary data was collected through field visits and the raw data was structured for further analysis. Secondary data was obtained from various verified internet sources. Results indicate certain regions with high quantity of raw milk supply and areas where processed products are delivered. Optimised routes and the areas where Aanchal Dairy provides its services are also defined. NDVI shows that places with high raw milk supply have better fodder for dairy cows. Finally the results are geospatially mapped and various relationships are presented in graphical form.
","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":"2023 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135872515","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.2023.17.2.33
Anuradha Chahar, None Arun S H, Charan Singh, Sebin John
The reduction in visibility due to fog leads to cancellation of flights, delays and diversions at IGI Airport, New Delhi during the winter season. Accurate prediction of fog/ visibility is required within sufficient lead time to make the flight operations safer, economical and more convenient. This study attempts to improve the fog/visibility forecast at IGI airport for the months of December and January during the winter seasons of 2020-21 and 2021-22. Various meteorological parameters required to predict the fog/visibility are obtained from IMD GFS forecasted meteorological information which are valid for next 24 and 48-hours. Forecast verification was performed using the METAR and synoptic observations. The results for 2020-21 fog/visibility forecast for 24-hours are promising with a POD of 0.92, critical success index as 0.68, and false alarm ratio as 0.28. Moreover, the 2021-22 results for 24 -hours fog /visibility forecast are also observed to be promising with a POD of 0.73, critical success index of 0.54 and false alarm ratio at 0.32. This method gives fairly accurate predictions in point locations and can also be used for a larger spatial area. However, the 48-hours forecast performance needs further improvement. The method also predicted the wind speed and relative humidity which were found to be in agreement with the observed data to a great extent. In the upcoming years, the fog/visibility forecast method will be more robust with better prediction accuracy of meteorological parameters from model outputs and observations.
{"title":"FOG/VISIBILITY FORECAST AND VERIFICATION AT IGI AIRPORT, NEW DELHI DURING THE WINTER SEASONS OF 2020-21 & 2021-22","authors":"Anuradha Chahar, None Arun S H, Charan Singh, Sebin John","doi":"10.58825/jog.2023.17.2.33","DOIUrl":"https://doi.org/10.58825/jog.2023.17.2.33","url":null,"abstract":"The reduction in visibility due to fog leads to cancellation of flights, delays and diversions at IGI Airport, New Delhi during the winter season. Accurate prediction of fog/ visibility is required within sufficient lead time to make the flight operations safer, economical and more convenient. This study attempts to improve the fog/visibility forecast at IGI airport for the months of December and January during the winter seasons of 2020-21 and 2021-22. Various meteorological parameters required to predict the fog/visibility are obtained from IMD GFS forecasted meteorological information which are valid for next 24 and 48-hours. Forecast verification was performed using the METAR and synoptic observations. The results for 2020-21 fog/visibility forecast for 24-hours are promising with a POD of 0.92, critical success index as 0.68, and false alarm ratio as 0.28. Moreover, the 2021-22 results for 24 -hours fog /visibility forecast are also observed to be promising with a POD of 0.73, critical success index of 0.54 and false alarm ratio at 0.32. This method gives fairly accurate predictions in point locations and can also be used for a larger spatial area. However, the 48-hours forecast performance needs further improvement. The method also predicted the wind speed and relative humidity which were found to be in agreement with the observed data to a great extent. In the upcoming years, the fog/visibility forecast method will be more robust with better prediction accuracy of meteorological parameters from model outputs and observations.
","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135871345","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.2023.17.1.83
Sharmistha Bhowmik, Bindu Bhatt
The expeditious extension of LULC in the name of development is the root cause of global warming. Replacement of natural resources due to the expansion of manmade erections is accountable for the increase in LST of Earth’s topography. The impression of change in LULC is reflected in LST. To seize the rising temperature, the lamentation of a new plan of action for urbanization is of utmost requisite. This paper examines the change in LULC and its spatiotemporal impact on LST in Vadodara, which is situated on the bank of river Vishmamitri river. Vadodara an arid region has three main seasons and these are summer, monsoon, and winter. The climate is characterized by hot summer and dryness in the non-rainy seasons. May is the hottest month while January is the coldest month. The annual rainfall of the district is 475.2 mm. Hence, to analyze we used multi-spectral and multi thermal Landsat TM and ETM+ satellite images to monitor the evaluation of LULC and its impact on LST from 2001(pre-monsoon) to 2021(pre- monsoon). The study explores to what extent observed LST can be examined by vegetation cover measured through NDVI from2001 to 2021. To achieve this an analysis of co-relation is performed between LULC and LST and by using spectral indices comprising NDVI with the help of software like ArcGIS 10.2 and Erdas Imagine 2014. It had been observed that a considerable increase in LST in Vadodara was 58.338°C (Max) and 21.9014°C (Min) in 2001(pre-monsoon) to 60.844 °C (Max) and 24.6784 °C (Min) in 2021(pre-monsoon) which is just about 2°C increase in Max and 3 degrees increase in Min for LST in past 20 years. It was also observed that there is an inverse relationship between LST and NDVI. The value of NDVI is observed that change from 0.380711(H) and -0.59322(L) in 2001 to 0.551(H) to -0.351193(L)in 2021. Moreover, SMI places a vital role to investigate and verify the relation between LST and NDVI. Henceforth, to verify SMI was also calculated and it was noticed that places with high LST value and low NDVI value contained less soil moisture and places with less LST value and high NDVI values contained more soil moisture. Thus, it can be concluded that, if urban planners and decision-makers implement suitable land-use strategies then Earth’s topography can be protected from adverse effects of urban heat by planting adequate and appropriate trees in bare soil and beside the impervious areas, thus the expansion of UHI can also be controlled. Moreover, with the help of SMI values, it will also be beneficial for the agricultural sector.
{"title":"Spatiotemporal analysis of land surface temperature owing to NDVI: A case study of Vadodara District, Gujarat","authors":"Sharmistha Bhowmik, Bindu Bhatt","doi":"10.58825/jog.2023.17.1.83","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.83","url":null,"abstract":"The expeditious extension of LULC in the name of development is the root cause of global warming. Replacement of natural resources due to the expansion of manmade erections is accountable for the increase in LST of Earth’s topography. The impression of change in LULC is reflected in LST. To seize the rising temperature, the lamentation of a new plan of action for urbanization is of utmost requisite. This paper examines the change in LULC and its spatiotemporal impact on LST in Vadodara, which is situated on the bank of river Vishmamitri river. Vadodara an arid region has three main seasons and these are summer, monsoon, and winter. The climate is characterized by hot summer and dryness in the non-rainy seasons. May is the hottest month while January is the coldest month. The annual rainfall of the district is 475.2 mm. Hence, to analyze we used multi-spectral and multi thermal Landsat TM and ETM+ satellite images to monitor the evaluation of LULC and its impact on LST from 2001(pre-monsoon) to 2021(pre- monsoon). The study explores to what extent observed LST can be examined by vegetation cover measured through NDVI from2001 to 2021. To achieve this an analysis of co-relation is performed between LULC and LST and by using spectral indices comprising NDVI with the help of software like ArcGIS 10.2 and Erdas Imagine 2014. It had been observed that a considerable increase in LST in Vadodara was 58.338°C (Max) and 21.9014°C (Min) in 2001(pre-monsoon) to 60.844 °C (Max) and 24.6784 °C (Min) in 2021(pre-monsoon) which is just about 2°C increase in Max and 3 degrees increase in Min for LST in past 20 years. It was also observed that there is an inverse relationship between LST and NDVI. The value of NDVI is observed that change from 0.380711(H) and -0.59322(L) in 2001 to 0.551(H) to -0.351193(L)in 2021. Moreover, SMI places a vital role to investigate and verify the relation between LST and NDVI. Henceforth, to verify SMI was also calculated and it was noticed that places with high LST value and low NDVI value contained less soil moisture and places with less LST value and high NDVI values contained more soil moisture. Thus, it can be concluded that, if urban planners and decision-makers implement suitable land-use strategies then Earth’s topography can be protected from adverse effects of urban heat by planting adequate and appropriate trees in bare soil and beside the impervious areas, thus the expansion of UHI can also be controlled. Moreover, with the help of SMI values, it will also be beneficial for the agricultural sector.","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":"47212506","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.2023.18.1.71
Ram Sharan Chaurasia, S. N. Mohapatra
Stone quarrying and crushing (SQC) activities under mining operations normally produce a destructive landscaping impression during the period from start to end. Spatio-temporal data, downloaded from Google Earth Pro s/w, coupled with remote sensing, GIS, and GPS has been used to evaluate time series (2003, 2008, 2015 and 2021) analysis of stone quarrying and crushing activities in a part of Jhansi Tehsil. Results shows that the variability and pressure on land resources due to stone quarry and crushing activities were found linear increments during the time. There was annual heavy exploitation record during 2003-2008 (30.83%) of stone from quarry sites and total increments of SQC activities were found 277.64% since last 18 years. This study exhibited the prospective advantage of annual monitoring over period and support to make the possible preparations/ management for destructive mining operations. This study exhibited the prospective advantage of annual monitoring over period and support to make the possible preparations/ management for destructive mining operations.
{"title":"Temporal status and change detection of stone quarrying and crushing activities using multi-temporal google earth images","authors":"Ram Sharan Chaurasia, S. N. Mohapatra","doi":"10.58825/jog.2023.18.1.71","DOIUrl":"https://doi.org/10.58825/jog.2023.18.1.71","url":null,"abstract":"Stone quarrying and crushing (SQC) activities under mining operations normally produce a destructive landscaping impression during the period from start to end. Spatio-temporal data, downloaded from Google Earth Pro s/w, coupled with remote sensing, GIS, and GPS has been used to evaluate time series (2003, 2008, 2015 and 2021) analysis of stone quarrying and crushing activities in a part of Jhansi Tehsil. Results shows that the variability and pressure on land resources due to stone quarry and crushing activities were found linear increments during the time. There was annual heavy exploitation record during 2003-2008 (30.83%) of stone from quarry sites and total increments of SQC activities were found 277.64% since last 18 years. This study exhibited the prospective advantage of annual monitoring over period and support to make the possible preparations/ management for destructive mining operations. This study exhibited the prospective advantage of annual monitoring over period and support to make the possible preparations/ management for destructive mining operations.","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":"47293655","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.2023.17.1.74
Dency V. Panicker, Bhasha Vachharajani, R. Srivastava, S. Oza
As marginal, the Barents Sea plays a major role in the process of Atlantification, and large seasonal variability in sea ice is observed over the region. Current sea ice concentration and thickness obtained from satellite help one understand the variation in sea ice is seasonal. During summer, the concentration and thickness of sea ice are seen to fall, and during winters, it is seen to rise. In order to understand the difference in these variabilities and to analyse the future state of sea ice, a standard logistic curve model is considered. The standard logistic curve model is applied to sea ice parameters during summer and winter to quantify the sea ice growth and decay processes over the Barents Sea.The model yields predicted values based on the adjustment parameter (b) used.Results show that the predicted sea ice concentration performs well with the satellite sea ice concentration values. The model is run on the timeframe grouped into two, with each set having an average of ten years from 2000-2020. For the decay process, the fitted sea ice concentration decay curves derived from the standard logistic curve model are in good agreement with the observed data for the two timelines, with r2 = 0.88 and 0.87, respectively. Similarly, for the growth process, the relevant fitted decay curves derived from the standard logistic curve model are also in good agreement with the observed data during the above different time periods withr2= 0.80 and 0.78, respectively. Further, the model is implied to sea ice thickness, and the result obtained by the logistic curve model is found to be consistent with the satellite sea ice thickness with r2 = 0.75 for the years 2011–2020. Particularly, both the rapid sea ice increase pattern during the growth process and the remarkable decrease pattern during the decay process are successfully characterized by the corresponding fitted curves. The introduction of calculated adjustment parameters into the model helps in accurately determining the sea ice variables, which brings us closer to conservation tools that mitigate therisks associated with rapid sea ice loss.
{"title":"Analysis of sea ice concentration and thickness over Barents Sea using standard logistic curve model","authors":"Dency V. Panicker, Bhasha Vachharajani, R. Srivastava, S. Oza","doi":"10.58825/jog.2023.17.1.74","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.74","url":null,"abstract":"As marginal, the Barents Sea plays a major role in the process of Atlantification, and large seasonal variability in sea ice is observed over the region. Current sea ice concentration and thickness obtained from satellite help one understand the variation in sea ice is seasonal. During summer, the concentration and thickness of sea ice are seen to fall, and during winters, it is seen to rise. In order to understand the difference in these variabilities and to analyse the future state of sea ice, a standard logistic curve model is considered. The standard logistic curve model is applied to sea ice parameters during summer and winter to quantify the sea ice growth and decay processes over the Barents Sea.The model yields predicted values based on the adjustment parameter (b) used.Results show that the predicted sea ice concentration performs well with the satellite sea ice concentration values. The model is run on the timeframe grouped into two, with each set having an average of ten years from 2000-2020. For the decay process, the fitted sea ice concentration decay curves derived from the standard logistic curve model are in good agreement with the observed data for the two timelines, with r2 = 0.88 and 0.87, respectively. Similarly, for the growth process, the relevant fitted decay curves derived from the standard logistic curve model are also in good agreement with the observed data during the above different time periods withr2= 0.80 and 0.78, respectively. Further, the model is implied to sea ice thickness, and the result obtained by the logistic curve model is found to be consistent with the satellite sea ice thickness with r2 = 0.75 for the years 2011–2020. Particularly, both the rapid sea ice increase pattern during the growth process and the remarkable decrease pattern during the decay process are successfully characterized by the corresponding fitted curves. The introduction of calculated adjustment parameters into the model helps in accurately determining the sea ice variables, which brings us closer to conservation tools that mitigate therisks associated with rapid sea ice loss.","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":"46050329","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.2023.17.1.78
G.P. Naniwadekar, S. Gurubaran, A.P. Jadhav, R. Ghodpage, P. Patil, D. Burud
We present the study of mesospheric winds in the 78–98 km height range using observations by a partial reflection radar station (MF–radar) situated at Kolhapur (16.8° N, 74.2° E), India. The sequential wind profiles over the period of 2014–2019 obtained from this radar operated at 1.98 MHz are used for this study. To delineate the behaviour of the winds in the mesosphere and lower thermosphere (MLT) region, we use wind data providing horizontal wind velocities averaged for an hour. Details of the seasonal, annual, and inter-annual variations and also the climatology of mean motion in zonal (East-West) and meridional (North-South) components in the MLT region over the aforementioned period are presented. The zonal wind below 90 km has been observed with eastward flow for the period of solstices and westward flow at equinoxes, showing strong semi-annual oscillations (SAO). While above 90 km, annual oscillations (AO) are seen to be dominant. Annual oscillations (AO) are observed in the mean meridional wind, with poleward motion during winter and equatorward motion during the remaining seasons. At higher altitudes (above 92 km), the poleward motion weakens and the equatorward wind flow becomes strong.
{"title":"Studies on the variability of mean winds in the mesosphere and lower thermosphere region (MLT) over Kolhapur (16.8oN, 74.2oE)","authors":"G.P. Naniwadekar, S. Gurubaran, A.P. Jadhav, R. Ghodpage, P. Patil, D. Burud","doi":"10.58825/jog.2023.17.1.78","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.78","url":null,"abstract":"We present the study of mesospheric winds in the 78–98 km height range using observations by a partial reflection radar station (MF–radar) situated at Kolhapur (16.8° N, 74.2° E), India. The sequential wind profiles over the period of 2014–2019 obtained from this radar operated at 1.98 MHz are used for this study. To delineate the behaviour of the winds in the mesosphere and lower thermosphere (MLT) region, we use wind data providing horizontal wind velocities averaged for an hour. Details of the seasonal, annual, and inter-annual variations and also the climatology of mean motion in zonal (East-West) and meridional (North-South) components in the MLT region over the aforementioned period are presented. The zonal wind below 90 km has been observed with eastward flow for the period of solstices and westward flow at equinoxes, showing strong semi-annual oscillations (SAO). While above 90 km, annual oscillations (AO) are seen to be dominant. Annual oscillations (AO) are observed in the mean meridional wind, with poleward motion during winter and equatorward motion during the remaining seasons. At higher altitudes (above 92 km), the poleward motion weakens and the equatorward wind flow becomes strong.","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":"41758318","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}
Spatio-Temporal Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and biophysical studies and for monitoring and management of watersheds. This study explored the spatio-temporal distribution of land surface temperature (LST), Normalised Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Vishwamitri watershed of Gujarat for the Pre Monsoon and Post Monsoon of the Year 2001 and 2016 using Landsat dataset. The findings of the study showed that the LST of the Vishwamitri watershed. The mean LST value of the year 2001 was 46.19°C in the pre-monsoon season and 39.27°C in the post-monsoon season. Mean LST values for the year 2016 were 49.34°C in pre-monsoon and 35.21°C in the post-monsoon season as observed. The spatial distribution of NDVI and LST reflects an inverse relationship. A strong positive correlation between LST with NDVI is observed over highly dense built-up areas. In summary, the LST is greatly controlled by surface characteristics. The results of this study illustrate there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in the studied years. The study concludes that there has been a degradation of vegetation and intensification of LST in the year 2016 as compared with the year 2001. This study can be used as a reference for land use and environmental planning in a tropical city.
{"title":"Quantifying Spatio-Temporal Land Surface Temperature and Biophysical Indices for Sustainable Management of Watershed: A Study of Vishwamitri Watershed of Gujarat","authors":"Bindu Bhatt, Shashikant Sharma, Janak Joshi, Shashikant Patel","doi":"10.58825/jog.2023.17.1.82","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.82","url":null,"abstract":"Spatio-Temporal Analysis of the nexus between vegetation dynamics and climatic parameters like surface temperature is essential in environmental and biophysical studies and for monitoring and management of watersheds. This study explored the spatio-temporal distribution of land surface temperature (LST), Normalised Difference Water Index (NDWI), and Normalized Difference Vegetation Index (NDVI) and the relationship between them in the Vishwamitri watershed of Gujarat for the Pre Monsoon and Post Monsoon of the Year 2001 and 2016 using Landsat dataset. The findings of the study showed that the LST of the Vishwamitri watershed. The mean LST value of the year 2001 was 46.19°C in the pre-monsoon season and 39.27°C in the post-monsoon season. Mean LST values for the year 2016 were 49.34°C in pre-monsoon and 35.21°C in the post-monsoon season as observed. The spatial distribution of NDVI and LST reflects an inverse relationship. A strong positive correlation between LST with NDVI is observed over highly dense built-up areas. In summary, the LST is greatly controlled by surface characteristics. The results of this study illustrate there has been a dynamic change in vegetation cover of the watershed in all seasons. There was also a negative correlation between LST and NDVI in the studied years. The study concludes that there has been a degradation of vegetation and intensification of LST in the year 2016 as compared with the year 2001. This study can be used as a reference for land use and environmental planning in a tropical city.","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":"46600117","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.2023.17.1.73
V. Kadam, A. Tejankar, S. K. Sirsat
The main objective of the present study is to assess the level of contamination, source identification, and health risk assessment of heavy metals in the industrial soils of Aurangabad. A total of 15 Soil samples were collected with a sampling density of 3–5 composite soil samples from 0–10 cm surface soil, analyzed heavy metals (Ni, Pb, Cd, Zn, Cr, Mn, Fe, and Cu) using atomic absorption spectroscopy. The geographical information system (GIS) technology like Kriging and inverse distance weighted interpolation (IDW) was used for the preparation of spatial distribution maps. A significant spatial relationship was found for Ni, Cd, Zn, Pb, and Cu in the soils using a GIS-based analysis, suggesting that these metal contaminants in the industrial area had common sources. Assess the risks of contamination for heavy metals in the soil were assessed based on a geo-accumulation index (Igeo) and contamination factor (CF). According to the Igeo and CF, most of the samples vary between 0 to 1, unpolluted to moderately polluted except Cd values. Most of the measured heavy metals showed the highest availability in top soils collected from around the steel and metal industries of the Waluj MIDC area. Also, based on the outcomes of the health risk assessment, particular attention should be paid to Ni, Pb, Cd, Zn, Cr and Cu in the industrial soils of Aurangabad. This study is socially beneficial for prevailing human health hazards in such industrially populated regions.
{"title":"Study of Heavy Metal Contamination in Industrial Soils of Aurangabad Using GIS Techniques","authors":"V. Kadam, A. Tejankar, S. K. Sirsat","doi":"10.58825/jog.2023.17.1.73","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.73","url":null,"abstract":"The main objective of the present study is to assess the level of contamination, source identification, and health risk assessment of heavy metals in the industrial soils of Aurangabad. A total of 15 Soil samples were collected with a sampling density of 3–5 composite soil samples from 0–10 cm surface soil, analyzed heavy metals (Ni, Pb, Cd, Zn, Cr, Mn, Fe, and Cu) using atomic absorption spectroscopy. The geographical information system (GIS) technology like Kriging and inverse distance weighted interpolation (IDW) was used for the preparation of spatial distribution maps. A significant spatial relationship was found for Ni, Cd, Zn, Pb, and Cu in the soils using a GIS-based analysis, suggesting that these metal contaminants in the industrial area had common sources. Assess the risks of contamination for heavy metals in the soil were assessed based on a geo-accumulation index (Igeo) and contamination factor (CF). According to the Igeo and CF, most of the samples vary between 0 to 1, unpolluted to moderately polluted except Cd values. Most of the measured heavy metals showed the highest availability in top soils collected from around the steel and metal industries of the Waluj MIDC area. Also, based on the outcomes of the health risk assessment, particular attention should be paid to Ni, Pb, Cd, Zn, Cr and Cu in the industrial soils of Aurangabad. This study is socially beneficial for prevailing human health hazards in such industrially populated regions.","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":"43106786","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.2023.17.1.79
Shilpa Suman, Ashok Kumar, Dheeraj Kumar
The accuracy of satellite image classification and the computational complexity is reduced due to the image's noisy pixels. Therefore, spatial contextual information-based classifiers are required to handle the noisy pixels and obtain the neighborhood information. This paper represents Noise clustering (NC) based Markov Random Field (MRF) models (SP, DA (H1, H2, H3, and H4)) that handle the noisy pixels and provide the information. The Smoothing Prior (SP) and Discontinuity Adaptive (DA) models are useful for reducing noise by smoothing the images and showing the boundary of classes, respectively. This study has carried out a comparative study among MRF model-based NC classifiers SP and DA for different distance measures and parameters. MRF models based on NC classifiers were tested for classifying Eucalyptus, Water, Riverine sand, Grassland, Dense Forest, and Wheat classes using the Formosat-2 and Landsat-8 multispectral images of the Haridwar area. The DA (H1) model provides the best overall accuracy (85.09%) for m=1.3, λ=0.2, δ=104,γ=0.8, and Mean Absolute Difference.
{"title":"Study the effect of MRF Model based NC classifier with different distance measures and parameters","authors":"Shilpa Suman, Ashok Kumar, Dheeraj Kumar","doi":"10.58825/jog.2023.17.1.79","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.79","url":null,"abstract":"The accuracy of satellite image classification and the computational complexity is reduced due to the image's noisy pixels. Therefore, spatial contextual information-based classifiers are required to handle the noisy pixels and obtain the neighborhood information. This paper represents Noise clustering (NC) based Markov Random Field (MRF) models (SP, DA (H1, H2, H3, and H4)) that handle the noisy pixels and provide the information. The Smoothing Prior (SP) and Discontinuity Adaptive (DA) models are useful for reducing noise by smoothing the images and showing the boundary of classes, respectively. This study has carried out a comparative study among MRF model-based NC classifiers SP and DA for different distance measures and parameters. MRF models based on NC classifiers were tested for classifying Eucalyptus, Water, Riverine sand, Grassland, Dense Forest, and Wheat classes using the Formosat-2 and Landsat-8 multispectral images of the Haridwar area. The DA (H1) model provides the best overall accuracy (85.09%) for m=1.3, λ=0.2, δ=104,γ=0.8, and Mean Absolute Difference.","PeriodicalId":53688,"journal":{"name":"测绘地理信息","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41645101","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.2023.17.1.35
Rahul Das, Mohit Singh, Soumyadeep Roy, Vipul Malhotra, Kamal Pandey, H. Karnatak
With the ever-increasing need to manage and conserve the ecosystem, as well as the growing business potential for tourism, it is becoming increasingly important to address and align these two domains. Ecotourism enters the scene to provide a solution for anthropogenic interruptions at ecological tourist sites, with its potential to provide sustainable maintenance and development of both the environment and the local communities closely related with natural ecosystems. Tourism is one of the most valuable industries in India and the world, accounting for a considerable portion of most countries' economies. Chamoli is one of the tourist hotspots districts in India. Along with being an ecologically rich zone residing in the Himalayas, it possesses some of the highest peaks and national parks like Nanda Devi and valley of flowers. The study area is also prone to various natural disasters like floods, earthquakes, landslides, and the recent one being the rishi-ganga landslide of February 2021. The use of GIS tools in conjunction with AHP allows for a more streamlined and holistic approach to making scientifically calculated conclusions. We attempted to establish the prospective zones of ecotourism sites in our study region by considering a variety of factors that influence those sites of sustainable zones, such as slope, topographic roughness, elevation, road closeness, river proximity, and proximity to a protected area. The study area's data and information were geospatially analysed to build an ecotourism potential map that can be used as a guide for planning sustainable resource management and development operations in the Chamoli district. Keywords: - Ecotourism (ET), Analytical Hierarchy Process (AHP), Site-Suitability, Chamoli.
{"title":"Site suitability analysis for Ecotourism potential using Analytical Hierarchy Process (AHP) and GIS in Chamoli district, Uttarakhand","authors":"Rahul Das, Mohit Singh, Soumyadeep Roy, Vipul Malhotra, Kamal Pandey, H. Karnatak","doi":"10.58825/jog.2023.17.1.35","DOIUrl":"https://doi.org/10.58825/jog.2023.17.1.35","url":null,"abstract":"With the ever-increasing need to manage and conserve the ecosystem, as well as the growing business potential for tourism, it is becoming increasingly important to address and align these two domains. Ecotourism enters the scene to provide a solution for anthropogenic interruptions at ecological tourist sites, with its potential to provide sustainable maintenance and development of both the environment and the local communities closely related with natural ecosystems. Tourism is one of the most valuable industries in India and the world, accounting for a considerable portion of most countries' economies. Chamoli is one of the tourist hotspots districts in India. Along with being an ecologically rich zone residing in the Himalayas, it possesses some of the highest peaks and national parks like Nanda Devi and valley of flowers. The study area is also prone to various natural disasters like floods, earthquakes, landslides, and the recent one being the rishi-ganga landslide of February 2021. The use of GIS tools in conjunction with AHP allows for a more streamlined and holistic approach to making scientifically calculated conclusions. We attempted to establish the prospective zones of ecotourism sites in our study region by considering a variety of factors that influence those sites of sustainable zones, such as slope, topographic roughness, elevation, road closeness, river proximity, and proximity to a protected area. The study area's data and information were geospatially analysed to build an ecotourism potential map that can be used as a guide for planning sustainable resource management and development operations in the Chamoli district. \u0000Keywords: - Ecotourism (ET), Analytical Hierarchy Process (AHP), Site-Suitability, Chamoli.","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":"44372727","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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