Satellite images have been widely used in the production of geospatial information such as land use and land cover mapping, as well as the generation of several thematic layers via image processing. Images acquired by sensors onboard various satellite platforms are influenced by systematic sensor and platform-induced geometry errors. Thus, geometric correction of satellite images is an important step of image pre-processing to extract accurate and reliable locational information. Geometric correction of satellite images obtained from two different satellites, Pleiades 1A (PHR) and SPOT-6, was performed within the scope of this study using empirical models and a physical model. The 2D polynomial model, 3D rational function model with calculated RPCs from GCPs, 3D rational function model with RPCs from satellite, RPC refinement model using GCPs, and Toutin's physical model were used within this scope. Several experiments were carried out to investigate the effects of various parameters on the performance of the geometric correction procedure, such as GCP reference data source, GCP number and distribution, DEM source, spatial resolution, and model. Our results showed that lower RMSE values can be achieved with the model that uses RPC from data providers for PHR and SPOT that is followed by the RPC refinement method for PHR and Toutin method for SPOT. In general, GCPs from the HGM data source and ALOS DEM combination provided better results. Lastly, lower RMSE values, thus better locational accuracies are observed with PHR image except for single test.
{"title":"A comprehensive analysis of different geometric correction methods for Pleiades -1A and Spot-6 satellite images","authors":"Buğrahan Özci̇han, Levent Doğukan Özlü, Mümin İlker Karakap, Halime Sürmeli̇, U. Alganci, Elif Sertel","doi":"10.26833/ijeg.1086861","DOIUrl":"https://doi.org/10.26833/ijeg.1086861","url":null,"abstract":"Satellite images have been widely used in the production of geospatial information such as land use and land cover mapping, as well as the generation of several thematic layers via image processing. Images acquired by sensors onboard various satellite platforms are influenced by systematic sensor and platform-induced geometry errors. Thus, geometric correction of satellite images is an important step of image pre-processing to extract accurate and reliable locational information. Geometric correction of satellite images obtained from two different satellites, Pleiades 1A (PHR) and SPOT-6, was performed within the scope of this study using empirical models and a physical model. The 2D polynomial model, 3D rational function model with calculated RPCs from GCPs, 3D rational function model with RPCs from satellite, RPC refinement model using GCPs, and Toutin's physical model were used within this scope. Several experiments were carried out to investigate the effects of various parameters on the performance of the geometric correction procedure, such as GCP reference data source, GCP number and distribution, DEM source, spatial resolution, and model. Our results showed that lower RMSE values can be achieved with the model that uses RPC from data providers for PHR and SPOT that is followed by the RPC refinement method for PHR and Toutin method for SPOT. In general, GCPs from the HGM data source and ALOS DEM combination provided better results. Lastly, lower RMSE values, thus better locational accuracies are observed with PHR image except for single test.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44578305","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}
Glaciers are retreating in the highest mountainous regions of the world as a result of climate change and global warming. This leads to the formation of different types of glacial lakes. These lakes are not only the source of fresh water but it also causes disaster in the form of Glacial Lake Outburst Flood (GLOF). Astore Drainage Basin is located in north eastern mountainous region of Himalayas. This area is prone to GLOFs because of the increasing number of glacial lakes and the growth of existing lakes as a result of global warming. To provide a detailed information about the spatial and temporal information of glacial lakes detailed inventories has been developed for the study area using Landsat images for the year 1989, 1999, 2009 and 2019. Glacial lakes were mapped and identified by using Normalized Different Water Index, Normalized Difference Snow Index and high resolution Google Earth images. It was found from the analysis that the number of the glacial lakes increased from 120 to 128 in a period of thirty years (i.e. from 1989 to 2019). During the study period two lakes disappeared whereas ten new lakes were formed. There were 21 lakes which show area expansion more than 100% representing high susceptibility for GLOF. The results also showed that smaller lakes expanded more rapidly in area than the larger lakes.
{"title":"Genesis and Spatio-Temporal Analysis of Glacial Lakes in the Peri-Glacial Environment of Western Himalayas","authors":"Fareeha Si̇ddi̇que, A. Rahman","doi":"10.26833/ijeg.1097912","DOIUrl":"https://doi.org/10.26833/ijeg.1097912","url":null,"abstract":"Glaciers are retreating in the highest mountainous regions of the world as a result of climate change and global warming. This leads to the formation of different types of glacial lakes. These lakes are not only the source of fresh water but it also causes disaster in the form of Glacial Lake Outburst Flood (GLOF). Astore Drainage Basin is located in north eastern mountainous region of Himalayas. This area is prone to GLOFs because of the increasing number of glacial lakes and the growth of existing lakes as a result of global warming. To provide a detailed information about the spatial and temporal information of glacial lakes detailed inventories has been developed for the study area using Landsat images for the year 1989, 1999, 2009 and 2019. Glacial lakes were mapped and identified by using Normalized Different Water Index, Normalized Difference Snow Index and high resolution Google Earth images. It was found from the analysis that the number of the glacial lakes increased from 120 to 128 in a period of thirty years (i.e. from 1989 to 2019). During the study period two lakes disappeared whereas ten new lakes were formed. There were 21 lakes which show area expansion more than 100% representing high susceptibility for GLOF. The results also showed that smaller lakes expanded more rapidly in area than the larger lakes.","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46129737","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}
{"title":"Performance analysis of rule-based classification and deep learning method for automatic road extraction","authors":"Z. Bayramoğlu, M. Uzar","doi":"10.26833/ijeg.1062250","DOIUrl":"https://doi.org/10.26833/ijeg.1062250","url":null,"abstract":"","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45470596","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}
{"title":"Investigation and modeling of physical development of urban areas and its effects on light pollution using night light data","authors":"S. Bagheri, S. Karimzadeh, B. Feizizadeh","doi":"10.26833/ijeg.976495","DOIUrl":"https://doi.org/10.26833/ijeg.976495","url":null,"abstract":"","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47790225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The hyperspectral images have so far been widely utilized in monitoring and detecting the changes in a broad range of environmentally related matters. The hyperspectral image analysis yields maps that show spatial dispersion of physical and ecological characteristics of the terrain. Within the scope of the current study, an integrated Fuzzy-MCDM in a Geographic Information Systems (GIS) platform was used to map the health condition of Ramsar forest. Spectral indices can provide different methods for identifying vegetation coverings. For forest health analysis, spectral indices such as NDWI, CRI1, PSRI, PRI
{"title":"A Fuzzy Multi-Criteria Decision-Making approach for the assessment of forest health applying Hyper Spectral Imagery: A case study from Ramsar forest, North of Iran.","authors":"Behnam Khorrami, Khalil Valizadeh Kamran","doi":"10.26833/ijeg.940166","DOIUrl":"https://doi.org/10.26833/ijeg.940166","url":null,"abstract":"The hyperspectral images have so far been widely utilized in monitoring and detecting the changes in a broad range of environmentally related matters. The hyperspectral image analysis yields maps that show spatial dispersion of physical and ecological characteristics of the terrain. Within the scope of the current study, an integrated Fuzzy-MCDM in a Geographic Information Systems (GIS) platform was used to map the health condition of Ramsar forest. Spectral indices can provide different methods for identifying vegetation coverings. For forest health analysis, spectral indices such as NDWI, CRI1, PSRI, PRI","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44922000","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}
{"title":"Using Multiple Linear Regression To Analyze Changes in Forest Area: The case study of Akdeniz Region","authors":"N. Basaran, Dilek Küçük Matcı, U. Avdan","doi":"10.26833/ijeg.976418","DOIUrl":"https://doi.org/10.26833/ijeg.976418","url":null,"abstract":"","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46530189","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}
{"title":"A linear approach for wheat yield prediction by using different spectral vegetation indices","authors":"Y. Kaya, N. Polat","doi":"10.26833/ijeg.1035037","DOIUrl":"https://doi.org/10.26833/ijeg.1035037","url":null,"abstract":"","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43328650","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}
{"title":"Synoptic Analysis of The January 2004 Snowstorm: Example of Çanakkale","authors":"Z. Acar, Mahmut Eşsi̇z","doi":"10.26833/ijeg.988115","DOIUrl":"https://doi.org/10.26833/ijeg.988115","url":null,"abstract":"","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43827853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Y. Nofrizal, Rei Sonobe, Yamashita Hiroto, A. Morita, Takashi Ikka
{"title":"Estimating Chlorophyll Content of Zizania latifolia with Hyperspectral data and Random Forest","authors":"A. Y. Nofrizal, Rei Sonobe, Yamashita Hiroto, A. Morita, Takashi Ikka","doi":"10.26833/ijeg.953188","DOIUrl":"https://doi.org/10.26833/ijeg.953188","url":null,"abstract":"","PeriodicalId":42633,"journal":{"name":"International Journal of Engineering and Geosciences","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46222666","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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