Pub Date : 2021-01-01DOI: 10.7494/GEOM.2021.15.2.89
O. Talbi, Belaïd Fatmi, K. Benhanifia, Djilali Talbi
Prolonged water erosion leads to severe soil degradation, with highly visible scars. Consequently, the quantitative and descriptive estimation by mapping of the phenomenon has become the main objective of a great deal of research. It is this perspective that this study takes, based on the Revised Universal Soil Losses Equation (RUSLE) for a relatively accurate estimate, by integrating Arc‐ GIS tools and remote sensing using high spatial resolution (10 m) image from the Sentinel ‐2A satellite. The model uses data on precipitation, soil, topography and vegetation cover management. The methodological approach taken imple‐ ments this model in order to optimize its use by the various potential users in their planning and decision ‐making studies. An application was carried out in the Oued Isser watershed (Tlemcen, Algeria). Soil loss maps were produced and the results indicate a high variation in soil losses in the study area and show that the highest values are concentrated on steep slopes, hence the great influ‐ ence of the topographic parameter relative to other factors in the model.
{"title":"Water Erosion Mapping by RUSLE: A Geomatic Approach by GIS and Remote Sensing in the Oued Isser Watershed, Tlemcen, Algeria","authors":"O. Talbi, Belaïd Fatmi, K. Benhanifia, Djilali Talbi","doi":"10.7494/GEOM.2021.15.2.89","DOIUrl":"https://doi.org/10.7494/GEOM.2021.15.2.89","url":null,"abstract":"Prolonged water erosion leads to severe soil degradation, with highly visible scars. Consequently, the quantitative and descriptive estimation by mapping of the phenomenon has become the main objective of a great deal of research. It is this perspective that this study takes, based on the Revised Universal Soil Losses Equation (RUSLE) for a relatively accurate estimate, by integrating Arc‐ GIS tools and remote sensing using high spatial resolution (10 m) image from the Sentinel ‐2A satellite. The model uses data on precipitation, soil, topography and vegetation cover management. The methodological approach taken imple‐ ments this model in order to optimize its use by the various potential users in their planning and decision ‐making studies. An application was carried out in the Oued Isser watershed (Tlemcen, Algeria). Soil loss maps were produced and the results indicate a high variation in soil losses in the study area and show that the highest values are concentrated on steep slopes, hence the great influ‐ ence of the topographic parameter relative to other factors in the model.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71332633","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 : 2021-01-01DOI: 10.7494/GEOM.2021.15.2.105
M. Taoufik, M. Laghlimi, A. Fekri
Land Surface Temperature (LST) is an important variable within global cli‐ mate change. With the appearance of remote sensing techniques and advanced GIS software, it is now possible to estimate LST. In this study, the effect of lock‐ down during COVID‐19 on the LST was assessed using Landsat 8 Imagery. LST dynamic was investigated for three different periods: Before, during and after the COVID‐19 lockdown. The study was conducted in Casablanca City. The results showed that during the emergence of COVID‐19 with lock‐ down policy applied, the LST decreases remarkably compared to the previ‐ ous 4‐years’ average LST. After the easing of restrictions, the LST increased to exceed the previous 4‐year mean LST. Furthermore, throughout all studied periods, the LST recorded its higher values in industrial zones and areas with high urban density and urban transportation, which indicates the conspicuous impact of anthropogenic activities on the LST variation. These findings indicate an ability to assess the feasibility of planned lockdowns intended as a potential preventive mechanism to reduce LST peaks and the loss of air quality in metro‐ politan environments in the future.
{"title":"Comparison of Land Surface Temperature Before, During and After the Covid‑19 Lockdown Using Landsat Imagery: A Case Study of Casablanca City, Morocco","authors":"M. Taoufik, M. Laghlimi, A. Fekri","doi":"10.7494/GEOM.2021.15.2.105","DOIUrl":"https://doi.org/10.7494/GEOM.2021.15.2.105","url":null,"abstract":"Land Surface Temperature (LST) is an important variable within global cli‐ mate change. With the appearance of remote sensing techniques and advanced GIS software, it is now possible to estimate LST. In this study, the effect of lock‐ down during COVID‐19 on the LST was assessed using Landsat 8 Imagery. LST dynamic was investigated for three different periods: Before, during and after the COVID‐19 lockdown. The study was conducted in Casablanca City. The results showed that during the emergence of COVID‐19 with lock‐ down policy applied, the LST decreases remarkably compared to the previ‐ ous 4‐years’ average LST. After the easing of restrictions, the LST increased to exceed the previous 4‐year mean LST. Furthermore, throughout all studied periods, the LST recorded its higher values in industrial zones and areas with high urban density and urban transportation, which indicates the conspicuous impact of anthropogenic activities on the LST variation. These findings indicate an ability to assess the feasibility of planned lockdowns intended as a potential preventive mechanism to reduce LST peaks and the loss of air quality in metro‐ politan environments in the future.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71332886","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 : 2021-01-01DOI: 10.7494/GEOM.2021.15.2.17
M. Birylo, Z. Rzepecka
The Venezia Islands are a very special area from the hydrological point of view due to its water mass changes. Regular floods results in the need for the regular monitoring of water mass changes. For this purpose, a Gravity Recovery and Climate Experiment mission (GRACE) can be used as a source of data. The aim of the paper is to compare the latest results of the new GRACE FO observations. The comparisons were carried out all over Venezia Island using the L3 level, RL06 release data obtained with spherical harmonics degree and order extension of up to 120, by the three most important computational cen‐ tres: JPL, GFZ, CSR. Results are compared to an average month values of pre‐ cipitation and evapotranspiration and tide gauge data in the nearby area. Based on the research, no dependence between TWS and evapotranspiration and evapotranspiration change were found
{"title":"An Analysis of Total Water Storage Changes Obtained from GRACE FO Observations over the Venezia Islands Area Supported with Additional Data","authors":"M. Birylo, Z. Rzepecka","doi":"10.7494/GEOM.2021.15.2.17","DOIUrl":"https://doi.org/10.7494/GEOM.2021.15.2.17","url":null,"abstract":"The Venezia Islands are a very special area from the hydrological point of view due to its water mass changes. Regular floods results in the need for the regular monitoring of water mass changes. For this purpose, a Gravity Recovery and Climate Experiment mission (GRACE) can be used as a source of data. The aim of the paper is to compare the latest results of the new GRACE FO observations. The comparisons were carried out all over Venezia Island using the L3 level, RL06 release data obtained with spherical harmonics degree and order extension of up to 120, by the three most important computational cen‐ tres: JPL, GFZ, CSR. Results are compared to an average month values of pre‐ cipitation and evapotranspiration and tide gauge data in the nearby area. Based on the research, no dependence between TWS and evapotranspiration and evapotranspiration change were found","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71332895","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 : 2021-01-01DOI: 10.7494/GEOM.2021.15.1.5
N. Aziz, I. Alwan
Land cover mapping of marshland areas from satellite images data is not a sim‐ ple process, due to the similarity of the spectral characteristics of the land cov‐ er. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Senti‐ nel 2B by ESA (European Space Agency) were used to classify the land cover of Al ‐Hawizeh marsh/Iraq ‐Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built ‐up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B imag‐ es provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.
由于土地覆盖光谱特征的相似性，利用卫星图像数据绘制沼泽地区的土地覆盖地图并不是一个简单的过程。这导致在一些土地覆盖类中遇到挑战，特别是在湿地类中。在这项研究中，利用ESA(欧洲航天局)sentinel - nel 2B卫星图像对Al - Hawizeh沼泽/伊拉克-伊朗边境的土地覆盖进行了分类。利用空间分辨率为10 m的多光谱卫星图像，采用三种分类方法比较其精度。分类过程使用三种不同的算法进行，即:最大似然分类(MLC)，人工神经网络(ANN)和支持向量机(SVM)。利用ENVI 5.1软件进行分类算法，对6类土地覆盖进行分类:深水沼泽、浅水沼泽、沼泽植被(水生植被)、城区(建成区)、农业区和贫瘠土壤。结果表明，与人工神经网络和支持向量机方法相比，MLC方法应用于Sentinel 2B图像具有更高的整体精度和kappa系数。MLC、ANN和SVM方法的总体准确率分别为85.32%、70.64%和77.01%。
{"title":"An Accuracy Analysis Comparison of Supervised Classification Methods for Mapping Land Cover Using Sentinel 2 Images in the Al‑Hawizeh Marsh Area, Southern Iraq","authors":"N. Aziz, I. Alwan","doi":"10.7494/GEOM.2021.15.1.5","DOIUrl":"https://doi.org/10.7494/GEOM.2021.15.1.5","url":null,"abstract":"Land cover mapping of marshland areas from satellite images data is not a sim‐ ple process, due to the similarity of the spectral characteristics of the land cov‐ er. This leads to challenges being encountered with some land covers classes, especially in wetlands classes. In this study, satellite images from the Senti‐ nel 2B by ESA (European Space Agency) were used to classify the land cover of Al ‐Hawizeh marsh/Iraq ‐Iran border. Three classification methods were used aimed at comparing their accuracy, using multispectral satellite images with a spatial resolution of 10 m. The classification process was performed using three different algorithms, namely: Maximum Likelihood Classification (MLC), Artificial Neural Networks (ANN), and Support Vector Machine (SVM). The classification algorithms were carried out using ENVI 5.1 software to detect six land cover classes: deep water marsh, shallow water marsh, marsh vegetation (aquatic vegetation), urban area (built ‐up area), agriculture area, and barren soil. The results showed that the MLC method applied to Sentinel 2B imag‐ es provides a higher overall accuracy and the kappa coefficient compared to the ANN and SVM methods. Overall accuracy values for MLC, ANN, and SVM methods were 85.32%, 70.64%, and 77.01% respectively.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71332876","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 : 2021-01-01DOI: 10.7494/GEOM.2021.15.2.33
Magdalena Karabin-Zych
From the year 2014 to July 31, 2020, setting out a building was surveying work subject to the obligation to report to the locally competent district governor (starost) before it was commenced. After completion of the building setting out, the contractor of surveying works was obliged to notify the authorities of the completion of the surveying works and to submit the results of survey‐ ing works connected with the building setting out. Since July 31, 2020, follow‐ ing the amended Geodesic and Cartographic Law, the obligation to report the building setting out has been repealed. Despite that, the real estate owner will still be obliged to submit an appropriate application to the starost to dis‐ close the new land use in accordance with Article 22 of the Geodesic and Car‐ tographic Law. The author has analysed 31 documentation sets, being the results of building setting out procedures. The analysis verified the size of a land parcel on which the buildings were set out, what land use was presented before setting out and what land use was presented after the building setting out. In addition, the current state of development of the land parcel (as of July 2020) was ex‐ amined using map portals, and an orthophotomap was used to check whether the building was constructed, in order to verify whether the real estate cadastre was updated further.
{"title":"The Issue of Updating the Real Estate Cadastre in the Field of Land Use in Connection with the Construction of a Building","authors":"Magdalena Karabin-Zych","doi":"10.7494/GEOM.2021.15.2.33","DOIUrl":"https://doi.org/10.7494/GEOM.2021.15.2.33","url":null,"abstract":"From the year 2014 to July 31, 2020, setting out a building was surveying work subject to the obligation to report to the locally competent district governor (starost) before it was commenced. After completion of the building setting out, the contractor of surveying works was obliged to notify the authorities of the completion of the surveying works and to submit the results of survey‐ ing works connected with the building setting out. Since July 31, 2020, follow‐ ing the amended Geodesic and Cartographic Law, the obligation to report the building setting out has been repealed. Despite that, the real estate owner will still be obliged to submit an appropriate application to the starost to dis‐ close the new land use in accordance with Article 22 of the Geodesic and Car‐ tographic Law. The author has analysed 31 documentation sets, being the results of building setting out procedures. The analysis verified the size of a land parcel on which the buildings were set out, what land use was presented before setting out and what land use was presented after the building setting out. In addition, the current state of development of the land parcel (as of July 2020) was ex‐ amined using map portals, and an orthophotomap was used to check whether the building was constructed, in order to verify whether the real estate cadastre was updated further.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71332923","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 : 2021-01-01DOI: 10.7494/GEOM.2021.15.2.67
Marta Róg, A. Rzonca
This research attempted to determine the optimal photo overlap, number of con‐ trol points and method of camera calibration for a photogrammetric 3D model reconstruction of an object of cultural heritage value. Terrestrial images of the object were taken with a hand‐held digital camera and processed in the Con‐ textCapture software using the Structure‐from‐Motion (SfM) algorithm. A total station was used to measure ground control points (GCPs) and check points. Here, the research workflow, methodology, and various analyses concerning different configurations of the aforementioned factors are described. An at‐ tempt to assess the parameters which should be implemented in order to pro‐ vide a high degree of accuracy of the model and reduce time‐consumption both during fieldwork and data processing was taken. The manuscript discusses the results of the analyses and compares them with other studies presented by dif‐ ferent authors and indicates further potential directions of studies within this scope. Based on the authors’ experience with this research, some general con‐ clusions and remarks concerning the planning of photo acquisition from the terrestrial level for the purpose of 3D model reconstruction were formulated.
{"title":"The Impact of Photo Overlap, the Number of Control Points and the Method of Camera Calibration on the Accuracy of 3D Model Reconstruction","authors":"Marta Róg, A. Rzonca","doi":"10.7494/GEOM.2021.15.2.67","DOIUrl":"https://doi.org/10.7494/GEOM.2021.15.2.67","url":null,"abstract":"This research attempted to determine the optimal photo overlap, number of con‐ trol points and method of camera calibration for a photogrammetric 3D model reconstruction of an object of cultural heritage value. Terrestrial images of the object were taken with a hand‐held digital camera and processed in the Con‐ textCapture software using the Structure‐from‐Motion (SfM) algorithm. A total station was used to measure ground control points (GCPs) and check points. Here, the research workflow, methodology, and various analyses concerning different configurations of the aforementioned factors are described. An at‐ tempt to assess the parameters which should be implemented in order to pro‐ vide a high degree of accuracy of the model and reduce time‐consumption both during fieldwork and data processing was taken. The manuscript discusses the results of the analyses and compares them with other studies presented by dif‐ ferent authors and indicates further potential directions of studies within this scope. Based on the authors’ experience with this research, some general con‐ clusions and remarks concerning the planning of photo acquisition from the terrestrial level for the purpose of 3D model reconstruction were formulated.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71332567","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 : 2021-01-01DOI: 10.7494/geom.2021.15.4.101
L. G. Taha, R. Ibrahim
The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.
{"title":"Assessment of Approaches for the Extraction of Building Footprints from Pléiades Images","authors":"L. G. Taha, R. Ibrahim","doi":"10.7494/geom.2021.15.4.101","DOIUrl":"https://doi.org/10.7494/geom.2021.15.4.101","url":null,"abstract":"The Marina area represents an official new gateway of entry to Egypt and the development of infrastructure is proceeding rapidly in this region. The objective of this research is to obtain building data by means of automated extraction from Pléiades satellite images. This is due to the need for efficient mapping and updating of geodatabases for urban planning and touristic development. It compares the performance of random forest algorithm to other classifiers like maximum likelihood, support vector machines, and backpropagation neural networks over the well-organized buildings which appeared in the satellite images. Images were subsequently classified into two classes: buildings and non-buildings. In addition, basic morphological operations such as opening and closing were used to enhance the smoothness and connectedness of the classified imagery.The overall accuracy for random forest, maximum likelihood, support vector machines, and backpropagation were 97%, 95%, 93% and 92% respectively. It was found that random forest was the best option, followed by maximum likelihood, while the least effective was the backpropagation neural network. The completeness and correctness of the detected buildings were evaluated. Experiments confirmed that the four classification methods can effectively and accurately detect 100% of buildings from very high-resolution images. It is encouraged to use machine learning algorithms for object detection and extraction from very high-resolution images.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71333007","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 : 2021-01-01DOI: 10.7494/geom.2021.15.1.41
Adetola Olufunmilayo Gbopa, E. Ayodele, C. Okolie, A. O. Ajayi, Chima J. Iheaturu
Unmanned Aerial Vehicles (UAVs), commonly known as drones are increas‐ ingly being used for three ‐dimensional (3D) mapping of the environment. This study utilised UAV technology to produce a revised 3D map of the University of Lagos as well as land cover change detection analysis. A DJI Phantom 4 UAV was used to collect digital images at a flying height of 90 m, and 75% fore and 65% side overlaps. Ground control points (GCPs) for orthophoto rectifica‐ tion were coordinated with a Trimble R8 Global Navigation Satellite System. Pix4D Mapper was used to produce a digital terrain model and an orthophoto at a ground sampling distance of 4.36 cm. The change detection analysis, using the 2015 base map as reference, revealed a significant change in the land cover such as an increase of 16,306.7 m2 in buildings between 2015 and 2019. The root mean square error analysis performed using 7 GCPs showed a horizontal and vertical accuracy of 0.183 m and 0.157 m respectively. This suggests a high level of accuracy, which is adequate for 3D mapping and change detection analysis at a sustainable cost.
{"title":"Unmanned Aerial Vehicles for Three‑dimensional Mapping and Change Detection Analysis","authors":"Adetola Olufunmilayo Gbopa, E. Ayodele, C. Okolie, A. O. Ajayi, Chima J. Iheaturu","doi":"10.7494/geom.2021.15.1.41","DOIUrl":"https://doi.org/10.7494/geom.2021.15.1.41","url":null,"abstract":"Unmanned Aerial Vehicles (UAVs), commonly known as drones are increas‐ ingly being used for three ‐dimensional (3D) mapping of the environment. This study utilised UAV technology to produce a revised 3D map of the University of Lagos as well as land cover change detection analysis. A DJI Phantom 4 UAV was used to collect digital images at a flying height of 90 m, and 75% fore and 65% side overlaps. Ground control points (GCPs) for orthophoto rectifica‐ tion were coordinated with a Trimble R8 Global Navigation Satellite System. Pix4D Mapper was used to produce a digital terrain model and an orthophoto at a ground sampling distance of 4.36 cm. The change detection analysis, using the 2015 base map as reference, revealed a significant change in the land cover such as an increase of 16,306.7 m2 in buildings between 2015 and 2019. The root mean square error analysis performed using 7 GCPs showed a horizontal and vertical accuracy of 0.183 m and 0.157 m respectively. This suggests a high level of accuracy, which is adequate for 3D mapping and change detection analysis at a sustainable cost.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71332996","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 : 2021-01-01DOI: 10.7494/geom.2021.15.4.117
Zubairul Islam, Sudhir Kumar Singh
The main objective was to explore the connection between flood and drought hazards and their impact on crop land and human migration. The Flood and Drought effect on Cropland Index (FDCI), hot spot analysis and the Global Regression Analysis method was applied for the identification of the relationship between human migration and flood and drought hazards. The spatial pattern and hot and cold spots of FDCI, spatial autocorrelation and Getis-OrdGi* statistic techniques were used respectively. The FDCI was taken as an explanatory variable and human migration was taken as a dependent variable in the environment of the geographically weighted regression (GWR) model which was applied to measure the impact of flood and drought hazards on human migration. FDCI suggests a z-score of 4.9, which shows that the impact of flood and drought frequency on crop land is highly clustered. In the case of the hot spots analysis, out of seventy districts in Uttar Pradesh twenty-one were classified as hot spot and eight were classified as cold spots with a confidence level of 90 to 99%. Hot spot indicate maximum and cold spots show minimum impact of flood and drought hazards on crop land. The impact of flood and drought hazards on human migration show that there are fourteen districts where migration out is far more than predicted while there are ten districts where migration out is far lower.
{"title":"Geospatial Analysis of the Impact of Flood and Drought Hazards on Crop Land and Its Relationship with Human Migration at the District Level in Uttar Pradesh, India","authors":"Zubairul Islam, Sudhir Kumar Singh","doi":"10.7494/geom.2021.15.4.117","DOIUrl":"https://doi.org/10.7494/geom.2021.15.4.117","url":null,"abstract":"The main objective was to explore the connection between flood and drought hazards and their impact on crop land and human migration. The Flood and Drought effect on Cropland Index (FDCI), hot spot analysis and the Global Regression Analysis method was applied for the identification of the relationship between human migration and flood and drought hazards. The spatial pattern and hot and cold spots of FDCI, spatial autocorrelation and Getis-OrdGi* statistic techniques were used respectively. The FDCI was taken as an explanatory variable and human migration was taken as a dependent variable in the environment of the geographically weighted regression (GWR) model which was applied to measure the impact of flood and drought hazards on human migration. FDCI suggests a z-score of 4.9, which shows that the impact of flood and drought frequency on crop land is highly clustered. In the case of the hot spots analysis, out of seventy districts in Uttar Pradesh twenty-one were classified as hot spot and eight were classified as cold spots with a confidence level of 90 to 99%. Hot spot indicate maximum and cold spots show minimum impact of flood and drought hazards on crop land. The impact of flood and drought hazards on human migration show that there are fourteen districts where migration out is far more than predicted while there are ten districts where migration out is far lower.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71333015","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-19DOI: 10.7494/geom.2020.14.4.69
M. Stupen, N. Stupen, Z. Ryzhok, O. Stupen
The authors applied satellite monitoring data of agricultural lands of the geographic information system of International Production Assessment Division of the United States Department of Agriculture on the example of winter cereal cultivation. The authors did so according to the indices of vegetation index NDVI, information on atmospheric precipitation, soil moisture, and air temperature compared to Earth observations to estimate the condition of their sowing area. According to the research results, one can use remote sensing data of the IPAD USDA geographic information system to monitor agricultural land, yield capacity prediction and the estimation of gross agricultural products.
{"title":"Application of Satellite Monitoring Data for Winter Cereals Growing in the Lviv Region","authors":"M. Stupen, N. Stupen, Z. Ryzhok, O. Stupen","doi":"10.7494/geom.2020.14.4.69","DOIUrl":"https://doi.org/10.7494/geom.2020.14.4.69","url":null,"abstract":"The authors applied satellite monitoring data of agricultural lands of the geographic information system of International Production Assessment Division of the United States Department of Agriculture on the example of winter cereal cultivation. The authors did so according to the indices of vegetation index NDVI, information on atmospheric precipitation, soil moisture, and air temperature compared to Earth observations to estimate the condition of their sowing area. According to the research results, one can use remote sensing data of the IPAD USDA geographic information system to monitor agricultural land, yield capacity prediction and the estimation of gross agricultural products.","PeriodicalId":36672,"journal":{"name":"Geomatics and Environmental Engineering","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46803206","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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