Pub Date : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-95-2023
A. Galodha, N. S. Kayithi, D. Sharma, P. Jain
Abstract. Groundwater depletion-related subsidence is a significant issue in many parts of the world. It can permanently reduce the amount of groundwater stored in an aquifer and even cause structural damage to the Earth’s surface. The Ganga Basin in the northwestern region of India is no exception, with around a meter of subsidence occurring between 2018 and 2023. However, understanding the connection between variations in groundwater quantities and ground deformation has been challenging. We used surface displacement measurements from InSAR and gravimetric terrestrial water storage estimates from the GRACE satellite pair to characterize the hydrological dynamics within the Ganga Basin. Sentinel-1 was used to map the entire Ganga River basin in the inundated zone. The InSAR time series shows coherent short-term changes that coincide with hydrological features when the long-term aquifer compaction is removed. For instance, an uplift is seen at the confluence of multiple rivers and streams that drain into the southeastern margin of the basin in the winters of 2018–2019 and 2021–2022. Imaging the monthly spatial variations in water volumes is based on these data and calculations of mass changes from the orbiting of Sentinel-1 and GRACE satellites. We even employ machine learning techniques as evaluative methods to make it simple to combine InSAR quickly and convincingly with gravimetric datasets, which will help advance global efforts to understand better and manage groundwater resources.
{"title":"MONITORING GROUNDWATER STORAGE BASINS AND HYDROLOGICAL CHANGES USING THE GRACE SATELLITE AND SENTINEL-1 FOR THE GANGA RIVER BASIN","authors":"A. Galodha, N. S. Kayithi, D. Sharma, P. Jain","doi":"10.5194/isprs-archives-xlviii-m-3-2023-95-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-95-2023","url":null,"abstract":"Abstract. Groundwater depletion-related subsidence is a significant issue in many parts of the world. It can permanently reduce the amount of groundwater stored in an aquifer and even cause structural damage to the Earth’s surface. The Ganga Basin in the northwestern region of India is no exception, with around a meter of subsidence occurring between 2018 and 2023. However, understanding the connection between variations in groundwater quantities and ground deformation has been challenging. We used surface displacement measurements from InSAR and gravimetric terrestrial water storage estimates from the GRACE satellite pair to characterize the hydrological dynamics within the Ganga Basin. Sentinel-1 was used to map the entire Ganga River basin in the inundated zone. The InSAR time series shows coherent short-term changes that coincide with hydrological features when the long-term aquifer compaction is removed. For instance, an uplift is seen at the confluence of multiple rivers and streams that drain into the southeastern margin of the basin in the winters of 2018–2019 and 2021–2022. Imaging the monthly spatial variations in water volumes is based on these data and calculations of mass changes from the orbiting of Sentinel-1 and GRACE satellites. We even employ machine learning techniques as evaluative methods to make it simple to combine InSAR quickly and convincingly with gravimetric datasets, which will help advance global efforts to understand better and manage groundwater resources.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42612477","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-161-2023
C. Okolie, J. Mills, A. Adeleke, J. Smit, I. Maduako
Abstract. Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science. However, the explainability of GBDT predictions especially with earth observation data is still an open issue requiring more focus by researchers. In this study, we investigate the explainability of Bayesian-optimised GBDT algorithms for modelling and prediction of the vertical error in Copernicus GLO-30 digital elevation model (DEM). Three GBDT algorithms are investigated (extreme gradient boosting - XGBoost, light boosting machine – LightGBM, and categorical boosting – CatBoost), and SHapley Additive exPlanations (SHAP) are adopted for the explainability analysis. The assessment sites are selected from urban/industrial and mountainous landscapes in Cape Town, South Africa. Training datasets are comprised of eleven predictor variables which are known influencers of elevation error: elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover, bare ground cover, and urban footprints. The target variable (elevation error) was calculated with respect to accurate airborne LiDAR. After model training and testing, the GBDTs were applied for predicting the elevation error at model implementation sites. The SHAP plots showed varying levels of emphasis on the parameters depending on the land cover and terrain. For example, in the urban area, the influence of vector ruggedness measure surpassed that of first-order derivatives such as slope and aspect. Thus, it is recommended that machine learning modelling procedures and workflows incorporate model explainability to ensure robust interpretation and understanding of model predictions by both technical and non-technical users.
{"title":"THE EXPLAINABILITY OF GRADIENT-BOOSTED DECISION TREES FOR DIGITAL ELEVATION MODEL (DEM) ERROR PREDICTION","authors":"C. Okolie, J. Mills, A. Adeleke, J. Smit, I. Maduako","doi":"10.5194/isprs-archives-xlviii-m-3-2023-161-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-161-2023","url":null,"abstract":"Abstract. Gradient boosted decision trees (GBDTs) have repeatedly outperformed several machine learning and deep learning algorithms in competitive data science. However, the explainability of GBDT predictions especially with earth observation data is still an open issue requiring more focus by researchers. In this study, we investigate the explainability of Bayesian-optimised GBDT algorithms for modelling and prediction of the vertical error in Copernicus GLO-30 digital elevation model (DEM). Three GBDT algorithms are investigated (extreme gradient boosting - XGBoost, light boosting machine – LightGBM, and categorical boosting – CatBoost), and SHapley Additive exPlanations (SHAP) are adopted for the explainability analysis. The assessment sites are selected from urban/industrial and mountainous landscapes in Cape Town, South Africa. Training datasets are comprised of eleven predictor variables which are known influencers of elevation error: elevation, slope, aspect, surface roughness, topographic position index, terrain ruggedness index, terrain surface texture, vector roughness measure, forest cover, bare ground cover, and urban footprints. The target variable (elevation error) was calculated with respect to accurate airborne LiDAR. After model training and testing, the GBDTs were applied for predicting the elevation error at model implementation sites. The SHAP plots showed varying levels of emphasis on the parameters depending on the land cover and terrain. For example, in the urban area, the influence of vector ruggedness measure surpassed that of first-order derivatives such as slope and aspect. Thus, it is recommended that machine learning modelling procedures and workflows incorporate model explainability to ensure robust interpretation and understanding of model predictions by both technical and non-technical users.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48293441","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-121-2023
S. Kumari, A. Roy
Abstract. In this study geospatial forecast model WRF (Weather Research & Forecast) has been used to simulate weather variables over Western Himalaya, India. WRF produces simulation which is based on idealized condition or actual atmospheric conditions includes both observation and analyses. WRF Pre-Processing System setup is a collection of Fortran and C programs which requires static and meteorological input data having specific resolution and can be used for nested domain i.e., for more than one grid. For the simulation purpose of the model real time atmospheric data has been used and the result has been compared with existing products. The output generated was for a single time-period with 30 km and 10 km of spatial resolution for outer and inner nest respectively which cover the study area. Mid of May month has been preferred for this study and analysis of the result carried out. Accumulated precipitation and surface soil moisture is very less in lower region whereas as we move up, there is inflation of these two parameters. Similarly, the temperature is very high in lower region in both cases of surface temperature as well as temperature at 2 m above the earth surface.
{"title":"PARAMETRIZATION OF WEATHER RESEARCH FORECAST MODEL OVER WESTERN HIMALAYAN REGION – INDIA","authors":"S. Kumari, A. Roy","doi":"10.5194/isprs-archives-xlviii-m-3-2023-121-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-121-2023","url":null,"abstract":"Abstract. In this study geospatial forecast model WRF (Weather Research & Forecast) has been used to simulate weather variables over Western Himalaya, India. WRF produces simulation which is based on idealized condition or actual atmospheric conditions includes both observation and analyses. WRF Pre-Processing System setup is a collection of Fortran and C programs which requires static and meteorological input data having specific resolution and can be used for nested domain i.e., for more than one grid. For the simulation purpose of the model real time atmospheric data has been used and the result has been compared with existing products. The output generated was for a single time-period with 30 km and 10 km of spatial resolution for outer and inner nest respectively which cover the study area. Mid of May month has been preferred for this study and analysis of the result carried out. Accumulated precipitation and surface soil moisture is very less in lower region whereas as we move up, there is inflation of these two parameters. Similarly, the temperature is very high in lower region in both cases of surface temperature as well as temperature at 2 m above the earth surface.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45320116","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-273-2023
Q. Yuan
Abstract. The urban road network detection and extraction have significant applications in many domains, such as intelligent transportation and navigation, urban planning, and automatic driving. Although manual annotation methods can provide accurate road network maps, their low efficiency with high-cost consumption are insufficient for the current tasks. Traditional methods based on spectral or geometric information rely on shallow features and often struggle with low semantic segmentation accuracy in complex remote sensing backgrounds. In recent years, deep convolutional neural networks (CNN) have provided robust feature representations to distinguish complex terrain objects. However, these CNNs ignore the fusion of global-local contexts and are often confused with other types of features, especially buildings. In addition, conventional convolution operations use a fixed template paradigm to aggregate local feature information. The road features present complex linear-shape geometric relationships, which brings some obstacles to feature construction. To address the above issues, we proposed a hybrid network structure that combines the advantages of CNN and transformer models. Specifically, a multiscale deformable convolution module has been developed to capture local road context information adaptively. The Transformer model is introduced into the encoder to enhance semantic information to build the global context. Meanwhile, the CNN features are fused with the transformer features. Finally, the model outputs a road extraction prediction map in high spatial resolution. Quantitative analysis and visual expression confirm that the proposed model can effectively and automatically extract road features from complex remote sensing backgrounds, outperforming state-of-the-art methods with IOU by 86.5% and OA by 97.4%.
{"title":"DCTNET: HYBRID NETWORK MODEL FUSING WITH MULTISCALE DEFORMABLE CNN AND TRANSFORMER STRUCTURE FOR ROAD EXTRACTION FROM GAOFEN SATELLITE REMOTE SENSING IMAGE","authors":"Q. Yuan","doi":"10.5194/isprs-archives-xlviii-m-3-2023-273-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-273-2023","url":null,"abstract":"Abstract. The urban road network detection and extraction have significant applications in many domains, such as intelligent transportation and navigation, urban planning, and automatic driving. Although manual annotation methods can provide accurate road network maps, their low efficiency with high-cost consumption are insufficient for the current tasks. Traditional methods based on spectral or geometric information rely on shallow features and often struggle with low semantic segmentation accuracy in complex remote sensing backgrounds. In recent years, deep convolutional neural networks (CNN) have provided robust feature representations to distinguish complex terrain objects. However, these CNNs ignore the fusion of global-local contexts and are often confused with other types of features, especially buildings. In addition, conventional convolution operations use a fixed template paradigm to aggregate local feature information. The road features present complex linear-shape geometric relationships, which brings some obstacles to feature construction. To address the above issues, we proposed a hybrid network structure that combines the advantages of CNN and transformer models. Specifically, a multiscale deformable convolution module has been developed to capture local road context information adaptively. The Transformer model is introduced into the encoder to enhance semantic information to build the global context. Meanwhile, the CNN features are fused with the transformer features. Finally, the model outputs a road extraction prediction map in high spatial resolution. Quantitative analysis and visual expression confirm that the proposed model can effectively and automatically extract road features from complex remote sensing backgrounds, outperforming state-of-the-art methods with IOU by 86.5% and OA by 97.4%.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48850961","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-211-2023
C. J. Roros, R. Deshmukh, A. C. Kak
Abstract. There is a great deal of interest in fusing together the information provided by different satellites for real-time change detection on the surface of the earth. For detecting important types of changes on the ground, it is necessary to inject geometry into the data provided by low-res satellites using multi-view imaging satellites that record each point on the ground from multiple perspectives. Combining these perspectives and generating a DSM (Digital Surface Model) gives us the geometry needed for a more meaningful analysis of satellite data. Before such an analysis can be carried out, it is necessary to align the images from all available satellites. Automatic image alignment, however, requires features on the ground that can be identified and correctly matched across different images using computer vision algorithms. While such features are common in urban areas, that is not always the case in predominantly rural areas that present a more-or-less uniform texture to the sensors. In this paper we present methods for automatic identification and alignment of featureless regions. Featureless regions are identified using point spread maps, which are a byproduct of DSM generation. The subsequent strategy for aligning featureless regions depends on the proportion of featureless regions to feature-rich regions. If most of the AOI (Area of Interest) is feature-rich, we ignore featureless regions when estimating inter-satellite image alignment parameters and apply those parameters to the entire AOI. Finally, we present a technique to propagate and fuse parameters from feature-rich regions to featureless regions.
{"title":"MULTI-SATELLITE IMAGE ALIGNMENT OVER LARGE AREAS WITH FEATURELESS REGIONS","authors":"C. J. Roros, R. Deshmukh, A. C. Kak","doi":"10.5194/isprs-archives-xlviii-m-3-2023-211-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-211-2023","url":null,"abstract":"Abstract. There is a great deal of interest in fusing together the information provided by different satellites for real-time change detection on the surface of the earth. For detecting important types of changes on the ground, it is necessary to inject geometry into the data provided by low-res satellites using multi-view imaging satellites that record each point on the ground from multiple perspectives. Combining these perspectives and generating a DSM (Digital Surface Model) gives us the geometry needed for a more meaningful analysis of satellite data. Before such an analysis can be carried out, it is necessary to align the images from all available satellites. Automatic image alignment, however, requires features on the ground that can be identified and correctly matched across different images using computer vision algorithms. While such features are common in urban areas, that is not always the case in predominantly rural areas that present a more-or-less uniform texture to the sensors. In this paper we present methods for automatic identification and alignment of featureless regions. Featureless regions are identified using point spread maps, which are a byproduct of DSM generation. The subsequent strategy for aligning featureless regions depends on the proportion of featureless regions to feature-rich regions. If most of the AOI (Area of Interest) is feature-rich, we ignore featureless regions when estimating inter-satellite image alignment parameters and apply those parameters to the entire AOI. Finally, we present a technique to propagate and fuse parameters from feature-rich regions to featureless regions.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42251051","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-49-2023
M. Bondada, K. Gupta, A. Danodia, M. Bhatt, N. R. Patel
Abstract. The rapid urbanization and population growth in Delhi have led to significant changes in the land and land cover, resulting in increased emissions and alterations in the urban energy balance. To understand these long-term trends and identify contributions factors, a study was conducted using Landsat series data and meteorological data from the ECMWF ERA-5 reanalysis. The study focused on estimating urban energy fluxes, including Net Radiation, Sensible heat flux, Latent heat flux and Ground heat flux with anthropogenic heat considered as the residual. The findings reveal a substantial increase in the anthropogenic heat flux, rising from 172 W/m2 in 1990 to 281 W/m2 in 2022. Seasonal variations were also observed, with the highest energy flux values occurring during the summer season, followed by post monsoon and winter season. Net radiation ranged from 650 to 700 W/m2, sensible heat flux ranged between 250–300 W/m2, latent heat flux ranged between 250–300 W/m2 and ground heat flux ranged 30–120 W/m2. Urban areas exhibited higher energy fluxes, emphasizing the importance of effective planning interventions to mitigate emissions in such areas. The study highlights the potential of Earth observation based approaches in estimating and balancing urban energy fluxes, while also emphasising the need to consider seasonal and spatial variations in the land use pattern when formulating strategies to mitigate emissions in the urban areas.
{"title":"LONG TERM SPATIO-TEMPORAL VARIATIONS OF URBAN ENERGY FLUXES USING EARTH OBSERVATION DATA FOR DELHI","authors":"M. Bondada, K. Gupta, A. Danodia, M. Bhatt, N. R. Patel","doi":"10.5194/isprs-archives-xlviii-m-3-2023-49-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-49-2023","url":null,"abstract":"Abstract. The rapid urbanization and population growth in Delhi have led to significant changes in the land and land cover, resulting in increased emissions and alterations in the urban energy balance. To understand these long-term trends and identify contributions factors, a study was conducted using Landsat series data and meteorological data from the ECMWF ERA-5 reanalysis. The study focused on estimating urban energy fluxes, including Net Radiation, Sensible heat flux, Latent heat flux and Ground heat flux with anthropogenic heat considered as the residual. The findings reveal a substantial increase in the anthropogenic heat flux, rising from 172 W/m2 in 1990 to 281 W/m2 in 2022. Seasonal variations were also observed, with the highest energy flux values occurring during the summer season, followed by post monsoon and winter season. Net radiation ranged from 650 to 700 W/m2, sensible heat flux ranged between 250–300 W/m2, latent heat flux ranged between 250–300 W/m2 and ground heat flux ranged 30–120 W/m2. Urban areas exhibited higher energy fluxes, emphasizing the importance of effective planning interventions to mitigate emissions in such areas. The study highlights the potential of Earth observation based approaches in estimating and balancing urban energy fluxes, while also emphasising the need to consider seasonal and spatial variations in the land use pattern when formulating strategies to mitigate emissions in the urban areas.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41458116","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 : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-77-2023
Leena Dhruwa, Pradeep Kumar Garg, Ph.D. Scholar
Abstract. Nowadays, Light Detection and Ranging (LiDAR) data acquisition technology is gaining popularity due to its accuracy, precision, and rapid data collection. In recent years, many applications have demanded 3-D models and 3-D mapping for fly-through views of cities. LiDAR data is used to map topographic features as well as the height and density of high-rise objects, such as trees and buildings, on the earth's surface. Although there are numerous traditional surveying and space-based technologies existing to determine the elevation or height of any object are time-consuming, inaccurate, and require additional effort. Therefore, the present study focused on developing a large-scale 3D map and accuracy assessment for existing high-rise features in the study area using a Terrestrial Laser Scanner (TLS). Further, LiDAR point cloud data has been used to estimate the position and elevation of the building. It can acquire data anytime, i.e., day and night, and collects more than 1.5 million points per second. The FARO Scene software has been used to process the data, and the processed data is then automatically registered and verified. The point cloud data's overall registration RMSE error is 36 mm. This file with an extension *.LAS format contains the positional coordinates of the features.The approach provided here for positional accuracy of features with improved accuracy will be helpful for identifying and monitoring the shift and deformations in the buildings and other features. It may also be used for site analysis, planning, and building information modeling.
{"title":"POSITIONAL ACCURACY ASSESSMENT OF FEATURES USING LIDAR POINT CLOUD","authors":"Leena Dhruwa, Pradeep Kumar Garg, Ph.D. Scholar","doi":"10.5194/isprs-archives-xlviii-m-3-2023-77-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-77-2023","url":null,"abstract":"Abstract. Nowadays, Light Detection and Ranging (LiDAR) data acquisition technology is gaining popularity due to its accuracy, precision, and rapid data collection. In recent years, many applications have demanded 3-D models and 3-D mapping for fly-through views of cities. LiDAR data is used to map topographic features as well as the height and density of high-rise objects, such as trees and buildings, on the earth's surface. Although there are numerous traditional surveying and space-based technologies existing to determine the elevation or height of any object are time-consuming, inaccurate, and require additional effort. Therefore, the present study focused on developing a large-scale 3D map and accuracy assessment for existing high-rise features in the study area using a Terrestrial Laser Scanner (TLS). Further, LiDAR point cloud data has been used to estimate the position and elevation of the building. It can acquire data anytime, i.e., day and night, and collects more than 1.5 million points per second. The FARO Scene software has been used to process the data, and the processed data is then automatically registered and verified. The point cloud data's overall registration RMSE error is 36 mm. This file with an extension *.LAS format contains the positional coordinates of the features.The approach provided here for positional accuracy of features with improved accuracy will be helpful for identifying and monitoring the shift and deformations in the buildings and other features. It may also be used for site analysis, planning, and building information modeling.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42956315","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 : 2023-08-15DOI: 10.5194/isprs-archives-xlviii-m-1-2023-595-2023
F. Sunar, A. Dervisoglu, N. Yagmur, E. Aslan, M. Ozguven
Abstract. Water quality monitoring has a key role in maintaining a sustainable ecosystem and environmental health. To ensure consistent monitoring, remote sensing provides regular data acquisition with varying spatial resolutions. However, more accurate, and effective solutions can be achieved by integrating remote sensing data with in-situ measurements. This study investigates the integration of in-situ measurements with satellite data, which have different spectral and spatial resolutions, using linear and exponential regression models for four optically active components in the Gulf of Izmit. In this context, Sentinel-2 (S2) and PlanetScope SuperDove (PS) multispectral images, which were acquired on the same date, were used for the comparative analysis of the accurate mapping of chlorophyll-a (Chl-a), turbidity, Secchi disk depth (SDD) and total suspended matter (TSM) water quality parameters combined with simultaneously collected in-situ measurements. The models were evaluated using validation data, along with visual comparison, to assess their accuracy. The results indicate that, overall, exponential models provide more accurate results than linear models, except for the SDD parameter. Furthermore, models created with S2 data demonstrate better performance in retrieving water quality parameters for Chl-a, turbidity, and TSM, with R2 values of 0.71, 0.84, and 0.91, respectively. The linear model created with PS data stands out in the accurately mapping of SDD parameter. Nevertheless, the spatial distribution of these parameters using both satellite dataset exhibits a similar pattern throughout the gulf, which is under threat from significant terrestrial pollution sources, particularly in the eastern part.
{"title":"ANALYZING THE RETRIEVAL ACCURACY OF OPTICALLY ACTIVE WATER COMPONENTS FROM SATELLITE DATA UNDER VARYING IMAGE RESOLUTIONS","authors":"F. Sunar, A. Dervisoglu, N. Yagmur, E. Aslan, M. Ozguven","doi":"10.5194/isprs-archives-xlviii-m-1-2023-595-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-595-2023","url":null,"abstract":"Abstract. Water quality monitoring has a key role in maintaining a sustainable ecosystem and environmental health. To ensure consistent monitoring, remote sensing provides regular data acquisition with varying spatial resolutions. However, more accurate, and effective solutions can be achieved by integrating remote sensing data with in-situ measurements. This study investigates the integration of in-situ measurements with satellite data, which have different spectral and spatial resolutions, using linear and exponential regression models for four optically active components in the Gulf of Izmit. In this context, Sentinel-2 (S2) and PlanetScope SuperDove (PS) multispectral images, which were acquired on the same date, were used for the comparative analysis of the accurate mapping of chlorophyll-a (Chl-a), turbidity, Secchi disk depth (SDD) and total suspended matter (TSM) water quality parameters combined with simultaneously collected in-situ measurements. The models were evaluated using validation data, along with visual comparison, to assess their accuracy. The results indicate that, overall, exponential models provide more accurate results than linear models, except for the SDD parameter. Furthermore, models created with S2 data demonstrate better performance in retrieving water quality parameters for Chl-a, turbidity, and TSM, with R2 values of 0.71, 0.84, and 0.91, respectively. The linear model created with PS data stands out in the accurately mapping of SDD parameter. Nevertheless, the spatial distribution of these parameters using both satellite dataset exhibits a similar pattern throughout the gulf, which is under threat from significant terrestrial pollution sources, particularly in the eastern part.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43829794","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 : 2023-08-15DOI: 10.5194/isprs-archives-xlviii-m-1-2023-463-2023
D. Arıkan, F. Yildiz
Abstract. Environmental pollution, particularly air pollution, is one of the foremost problems we face today. Air pollution has become a global issue that affects not only regional areas but also the entire planet. The increase in the amount and concentration of pollutants or harmful substances in the atmosphere, such as various gases, particulate matter, and water vapor, causes air pollution. The rise in these substances can be due to human activities or natural environmental factors. It is crucial to examine air quality to reduce the harm inflicted on living and non-living entities. In this study, the spatial and temporal analysis of air pollutants (CO, NO2, UV_AER) in the Western Black Sea region was conducted using the Sentinel-5 TROPOMI satellite sent to monitor climate change and air quality. The Google Earth Engine platform was used to obtain the data. Monthly pollution maps were created for the year 2022, and the primary sources of pollutants were analysed. As a result, it was observed that pollutants changed on a monthly and seasonal basis, and areas with high pollutant concentrations in the region were identified. Mining, industrial activities, transportation networks, and domestic activities were determined to be the primary sources of air pollution in the study area.
{"title":"SPATIAL AND TEMPORAL ANALYSIS OF POLLUTANT GASES IN WESTERN BLACK SEA OF TURKIYE","authors":"D. Arıkan, F. Yildiz","doi":"10.5194/isprs-archives-xlviii-m-1-2023-463-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-463-2023","url":null,"abstract":"Abstract. Environmental pollution, particularly air pollution, is one of the foremost problems we face today. Air pollution has become a global issue that affects not only regional areas but also the entire planet. The increase in the amount and concentration of pollutants or harmful substances in the atmosphere, such as various gases, particulate matter, and water vapor, causes air pollution. The rise in these substances can be due to human activities or natural environmental factors. It is crucial to examine air quality to reduce the harm inflicted on living and non-living entities. In this study, the spatial and temporal analysis of air pollutants (CO, NO2, UV_AER) in the Western Black Sea region was conducted using the Sentinel-5 TROPOMI satellite sent to monitor climate change and air quality. The Google Earth Engine platform was used to obtain the data. Monthly pollution maps were created for the year 2022, and the primary sources of pollutants were analysed. As a result, it was observed that pollutants changed on a monthly and seasonal basis, and areas with high pollutant concentrations in the region were identified. Mining, industrial activities, transportation networks, and domestic activities were determined to be the primary sources of air pollution in the study area.","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44532694","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 : 2023-08-15DOI: 10.5194/isprs-archives-xlviii-m-1-2023-515-2023
M. Gurbuz, A. Çilek
Abstract. This study aims to use remote sensing techniques to map the urban region of Ankara from the past to the present, assessing the nature, magnitude and direction of changes within the area, including the transformation of LULC classes and explaining the driving forces behind these transformations. The study encompasses three stages. Firstly, Landsat 7 ETM+ images from 2000 and Sentinel-2 satellite images from 2020 were obtained for Ankara city and surroundings through the Google Earth Engine (GEE) platform. Image classification was conducted for both 2000 and 2020 using 'Blue', 'Green', 'Red', 'Vegetation Red Edge1', 'Vegetation Red Edge2', 'Vegetation Red Edge3', 'NIR', 'Vegetation Red Edge4', 'Water vapour', ' SWIR1', 'SWIR2' bands, as well as 'NDWI', 'NDVI', 'NDBI' indices on the GEE platform. LULC was classified using the Random Forest (RF) classifier, which included six classes: urban area, forest, water surfaces, open areas, agricultural areas and roads. Secondly, the LULC maps of the 2000 and 2020 images were classified using RF. The study employed the 'Categorical Change, Pixel Value Change and Time Series Change' methods to determine the transformations between LULC categories. Specifically, the urban change within the study area increased by 70% between 2000 and 2020. Over the past 20 years, from 2000 to 2020, the urban areas in Ankara expanded by 170%. Consequently, accurately determining the nature, magnitude and direction of urban development using remote sensing data offers valuable baseline information for various disciplines related to spatial planning at local and national scales.
{"title":"ANALYSIS OF URBAN LAND USE CHANGE USING REMOTE SENSING AND DIFFERENT CHANGE DETECTION TECHNIQUES: THE CASE OF ANKARA PROVINCE","authors":"M. Gurbuz, A. Çilek","doi":"10.5194/isprs-archives-xlviii-m-1-2023-515-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-515-2023","url":null,"abstract":"Abstract. This study aims to use remote sensing techniques to map the urban region of Ankara from the past to the present, assessing the nature, magnitude and direction of changes within the area, including the transformation of LULC classes and explaining the driving forces behind these transformations. The study encompasses three stages. Firstly, Landsat 7 ETM+ images from 2000 and Sentinel-2 satellite images from 2020 were obtained for Ankara city and surroundings through the Google Earth Engine (GEE) platform. Image classification was conducted for both 2000 and 2020 using 'Blue', 'Green', 'Red', 'Vegetation Red Edge1', 'Vegetation Red Edge2', 'Vegetation Red Edge3', 'NIR', 'Vegetation Red Edge4', 'Water vapour', ' SWIR1', 'SWIR2' bands, as well as 'NDWI', 'NDVI', 'NDBI' indices on the GEE platform. LULC was classified using the Random Forest (RF) classifier, which included six classes: urban area, forest, water surfaces, open areas, agricultural areas and roads. Secondly, the LULC maps of the 2000 and 2020 images were classified using RF. The study employed the 'Categorical Change, Pixel Value Change and Time Series Change' methods to determine the transformations between LULC categories. Specifically, the urban change within the study area increased by 70% between 2000 and 2020. Over the past 20 years, from 2000 to 2020, the urban areas in Ankara expanded by 170%. Consequently, accurately determining the nature, magnitude and direction of urban development using remote sensing data offers valuable baseline information for various disciplines related to spatial planning at local and national scales.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46833097","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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