Pub Date : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-35-2023
Samarth Y. Bhatia, .. Gopal, R. Patil, K. Buddhiraju
Abstract. Urban growth in developing countries like India is happening more than twice as fast as the population increase. Such rapid urban growth has resulted in urban sprawl characterized by low-density scattered development. Urban planners require a timely updated dataset and suitable tools to monitor the urban sprawl and ensure sustainable development. The present study uses Landsat data from 1999, 2009 and 2019 and socioeconomic data to study the urban sprawl characteristic of the Mumbai Metropolitan Region (MMR) over two decades. The analyses show that MMR's built-up areas have expanded from 400 sq. km in 1999 to 761 sq. km in 2019, implying a 90% growth in the past two decades. While most municipal corporations have more than 60% of land covered by built-up areas, municipal councils are less saturated, with <30% built-up covers. With saturated land spaces within municipal corporations, higher growth rates are observed in the municipal councils. Also, the urban growth rates in these municipal councils outpace the population growth rate. The urban sprawl indices computed also suggest a continuous compact development within the municipal corporations while a continued sprawling within these fast-developing municipal councils. Mira Bhayandar is the most compact, while Bhiwandi Special Notified Area is the most sprawled urban area in MMR. The analyses show a clear indication of urban sprawl characteristics of the MMR. Many of these municipal councils are in the initial stages of development and lack appropriate governance to tackle rapid urbanization. Suitable policy measures that result in balanced urban growth can help ensure sustainable development.
{"title":"ANALYSING URBAN SPRAWL OF THE MUMBAI METROPOLITAN REGION USING REMOTE SENSING AND SOCIOECONOMIC DATA","authors":"Samarth Y. Bhatia, .. Gopal, R. Patil, K. Buddhiraju","doi":"10.5194/isprs-archives-xlviii-m-3-2023-35-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-35-2023","url":null,"abstract":"Abstract. Urban growth in developing countries like India is happening more than twice as fast as the population increase. Such rapid urban growth has resulted in urban sprawl characterized by low-density scattered development. Urban planners require a timely updated dataset and suitable tools to monitor the urban sprawl and ensure sustainable development. The present study uses Landsat data from 1999, 2009 and 2019 and socioeconomic data to study the urban sprawl characteristic of the Mumbai Metropolitan Region (MMR) over two decades. The analyses show that MMR's built-up areas have expanded from 400 sq. km in 1999 to 761 sq. km in 2019, implying a 90% growth in the past two decades. While most municipal corporations have more than 60% of land covered by built-up areas, municipal councils are less saturated, with <30% built-up covers. With saturated land spaces within municipal corporations, higher growth rates are observed in the municipal councils. Also, the urban growth rates in these municipal councils outpace the population growth rate. The urban sprawl indices computed also suggest a continuous compact development within the municipal corporations while a continued sprawling within these fast-developing municipal councils. Mira Bhayandar is the most compact, while Bhiwandi Special Notified Area is the most sprawled urban area in MMR. The analyses show a clear indication of urban sprawl characteristics of the MMR. Many of these municipal councils are in the initial stages of development and lack appropriate governance to tackle rapid urbanization. Suitable policy measures that result in balanced urban growth can help ensure sustainable development.\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":"42601862","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-3-2023-227-2023
E. Sari, N. Musaoglu
Abstract. There is an urbanization process on a global scale with population growth and technological developments. Especially in metropolitan cities, mega projects located on the city periphery lead to environmental pressure on natural areas and forests. Istanbul Northern Forests, the transition region of the Black Sea and Mediterranean flora, host various ecosystems due to its pseudomaki structure. However, the effects of big projects recently realized such as Istanbul Airport, Northern Marmara Motorway, and Yavuz Sultan Selim Bridge on the Istanbul Northern Forests can be noticed even with the naked eye. This study investigates land use/cover changes in Istanbul Northern Forests which are under intense urbanization pressure using LANDSAT and SENTINEL-2 satellite images in a time series of thirty-nine years (1984–2023). As a result of the analysis, it is seen that the class of impervious surfaces increased in Istanbul Northern Forests between 1984–2023, while the class of barren land, natural areas and agricultural areas decreased. Landscape metrics such as Total Class Area, Percentage of Landscape, and Interspersion Juxtaposition index were used to better understand the change in the study area between 2017 and 2023.
{"title":"ANALYSIS OF TEMPORAL AND SPATIAL CHANGES IN ISTANBUL NORTHERN FORESTS WITH SATELLITE IMAGES AND LANDSCAPE METRICS","authors":"E. Sari, N. Musaoglu","doi":"10.5194/isprs-archives-xlviii-m-3-2023-227-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-227-2023","url":null,"abstract":"Abstract. There is an urbanization process on a global scale with population growth and technological developments. Especially in metropolitan cities, mega projects located on the city periphery lead to environmental pressure on natural areas and forests. Istanbul Northern Forests, the transition region of the Black Sea and Mediterranean flora, host various ecosystems due to its pseudomaki structure. However, the effects of big projects recently realized such as Istanbul Airport, Northern Marmara Motorway, and Yavuz Sultan Selim Bridge on the Istanbul Northern Forests can be noticed even with the naked eye. This study investigates land use/cover changes in Istanbul Northern Forests which are under intense urbanization pressure using LANDSAT and SENTINEL-2 satellite images in a time series of thirty-nine years (1984–2023). As a result of the analysis, it is seen that the class of impervious surfaces increased in Istanbul Northern Forests between 1984–2023, while the class of barren land, natural areas and agricultural areas decreased. Landscape metrics such as Total Class Area, Percentage of Landscape, and Interspersion Juxtaposition index were used to better understand the change in the study area between 2017 and 2023.\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":"49205843","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-3-2023-183-2023
A. Poudel, S. Bhatti, E. Bevilacqua
Abstract. The identification, delineation, and mapping of landcover is integral for resource management and planning as it establishes a baseline for thematic mapping and change detection analysis. The availability of high-resolution satellite imagery and the development of machine learning algorithms have significantly improved the prediction and accuracy of landcover classification. In this study, landcover classification is performed on seven-band Landsat 9 imagery and eight-band PlanetScope imagery for the village of Tully, NY, with an area of 900 square kilometers. The resolution of Landsat imagery is 30 meters, whereas the resolution of PlanetScope imagery is 3 meters. Classification schema is developed in ArcGIS Pro with five classification levels: conifer forest, hardwood forest, agriculture, developed, and water. Pixel-based supervised classification is performed using Support Vector Machine (SVM), Random Tress (RT), K-Nearest Neighbor (K-NN), and Maximum Likelihood Classifier (MLC). The reference dataset is acquired by an image interpreter using high-resolution imagery for map accuracy assessment. All the classification methods for Landsat imagery have more than 78% accuracy, but SVM performed best with 82% accuracy. For PlanetScope imagery, SVM performed best with 85% accuracy, whereas MLC had the lowest accuracy of 77%.
{"title":"ASSESSMENT OF IMAGE CLASSIFICATION ALGORITHMS FOR LAND COVER CLASSIFICATIONS IN TULLY, NY","authors":"A. Poudel, S. Bhatti, E. Bevilacqua","doi":"10.5194/isprs-archives-xlviii-m-3-2023-183-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-183-2023","url":null,"abstract":"Abstract. The identification, delineation, and mapping of landcover is integral for resource management and planning as it establishes a baseline for thematic mapping and change detection analysis. The availability of high-resolution satellite imagery and the development of machine learning algorithms have significantly improved the prediction and accuracy of landcover classification. In this study, landcover classification is performed on seven-band Landsat 9 imagery and eight-band PlanetScope imagery for the village of Tully, NY, with an area of 900 square kilometers. The resolution of Landsat imagery is 30 meters, whereas the resolution of PlanetScope imagery is 3 meters. Classification schema is developed in ArcGIS Pro with five classification levels: conifer forest, hardwood forest, agriculture, developed, and water. Pixel-based supervised classification is performed using Support Vector Machine (SVM), Random Tress (RT), K-Nearest Neighbor (K-NN), and Maximum Likelihood Classifier (MLC). The reference dataset is acquired by an image interpreter using high-resolution imagery for map accuracy assessment. All the classification methods for Landsat imagery have more than 78% accuracy, but SVM performed best with 82% accuracy. For PlanetScope imagery, SVM performed best with 85% accuracy, whereas MLC had the lowest accuracy of 77%.\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":"43893350","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-3-2023-115-2023
Kalingga Titon, Nur Ihsan, A. Sakti, Atsushi Higuchi, Hideaki Takenaka, D. Suwardhi, K. Wikantika
Abstract. The potential for future energy crises is a problem the world is currently facing. Many countries are switching from fossil to renewable energy to prevent an energy crisis. One of the most developed renewable energy today is solar energy. Easy installation makes solar energy installation not only on a large scale but also on a home scale. Urban areas will be very suitable for building solar photovoltaic (PV) roofs due to minimal open areas. In installing rooftop solar PV, sound planning is needed to predict the energy potential that can be provided by solar energy on the rooftop of a building. Spatial modeling can be done to determine the energy potential and suitable location for rooftop solar PV installation. In building rooftop solar PV modeling, the level of detail of the building will affect the results of the model. The rooftop's shape and the building's height will affect the amount of solar radiation going into the building. However, the higher the level of detail of the building, the higher the cost and processing time will be. This study will review the differences in modeling the potential of rooftop solar PV using different levels of detail. This research will integrate solar radiation data from remote sensing to determine the energy potential of solar radiation and digital surface model data from photogrammetry to create a level of detail for buildings. Integration of solar radiation data and the level of detail of the building will use hillshade analysis. Hillshade analysis can review the shadow effect on the rooftop of a building which will be directly related to the potential of solar energy on the rooftop of the building. This study determines the energy potential on the rooftop of the building with different levels of detail, namely 0, 1, and actual shape, to determine the difference in energy potential in the three scenarios. Hopefully, this research will determine the best level of detail for modeling rooftop solar PV. The best model that can show high accuracy value but at a lower price. Hopefully, this research can also assist policymakers and the public in planning for rooftop solar PV installations to develop renewable energy.
{"title":"COMPARISON OF POTENTIAL ENERGY OF SOLAR RADIATION IN ROOFTOP MODELING USING DIFFERENT BUILDING LEVELS OF DETAIL","authors":"Kalingga Titon, Nur Ihsan, A. Sakti, Atsushi Higuchi, Hideaki Takenaka, D. Suwardhi, K. Wikantika","doi":"10.5194/isprs-archives-xlviii-m-3-2023-115-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-115-2023","url":null,"abstract":"Abstract. The potential for future energy crises is a problem the world is currently facing. Many countries are switching from fossil to renewable energy to prevent an energy crisis. One of the most developed renewable energy today is solar energy. Easy installation makes solar energy installation not only on a large scale but also on a home scale. Urban areas will be very suitable for building solar photovoltaic (PV) roofs due to minimal open areas. In installing rooftop solar PV, sound planning is needed to predict the energy potential that can be provided by solar energy on the rooftop of a building. Spatial modeling can be done to determine the energy potential and suitable location for rooftop solar PV installation. In building rooftop solar PV modeling, the level of detail of the building will affect the results of the model. The rooftop's shape and the building's height will affect the amount of solar radiation going into the building. However, the higher the level of detail of the building, the higher the cost and processing time will be. This study will review the differences in modeling the potential of rooftop solar PV using different levels of detail. This research will integrate solar radiation data from remote sensing to determine the energy potential of solar radiation and digital surface model data from photogrammetry to create a level of detail for buildings. Integration of solar radiation data and the level of detail of the building will use hillshade analysis. Hillshade analysis can review the shadow effect on the rooftop of a building which will be directly related to the potential of solar energy on the rooftop of the building. This study determines the energy potential on the rooftop of the building with different levels of detail, namely 0, 1, and actual shape, to determine the difference in energy potential in the three scenarios. Hopefully, this research will determine the best level of detail for modeling rooftop solar PV. The best model that can show high accuracy value but at a lower price. Hopefully, this research can also assist policymakers and the public in planning for rooftop solar PV installations to develop renewable energy.\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":"46517599","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-3-2023-135-2023
D. Lee, J. Riechel
Abstract. Auburn is a small but famous gold mining and railroad town in the Sierra Nevada Mountains of Placer County, California. The city has seen recent and historically reoccurring wildfires. In 2021, 1.5 million acres burned in the Sierra Nevada Mountains, a new record. Auburn consists of three (3) nonadjacent and nonoverlapping sections or parcels of land. The three (3) centroids of each section of Auburn are identified and used in three (3) evacuation scenarios: First, Auburn is evacuated away from the three (3) centroids. We look at how far evacuees can reach in 5, 10, 15, and 30 minutes using emergency vehicle travel times, so signal lights, one-way streets, etc., are ignored. Second, we evacuate away from the three (3) centroids while also avoiding previous burn areas. Third, we evacuate Auburn toward the only area hospital, a structure likely to be defended from fire by first responders. Also, some evacuees might require medical attention. Future work includes looking at how far emergency vehicles from places like fire stations can reach in 5, 10, 15, and 30 minutes, providing a coverage area for first responders.
{"title":"AUBURN, CA, EVACUATION PLAN","authors":"D. Lee, J. Riechel","doi":"10.5194/isprs-archives-xlviii-m-3-2023-135-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-135-2023","url":null,"abstract":"Abstract. Auburn is a small but famous gold mining and railroad town in the Sierra Nevada Mountains of Placer County, California. The city has seen recent and historically reoccurring wildfires. In 2021, 1.5 million acres burned in the Sierra Nevada Mountains, a new record. Auburn consists of three (3) nonadjacent and nonoverlapping sections or parcels of land. The three (3) centroids of each section of Auburn are identified and used in three (3) evacuation scenarios: First, Auburn is evacuated away from the three (3) centroids. We look at how far evacuees can reach in 5, 10, 15, and 30 minutes using emergency vehicle travel times, so signal lights, one-way streets, etc., are ignored. Second, we evacuate away from the three (3) centroids while also avoiding previous burn areas. Third, we evacuate Auburn toward the only area hospital, a structure likely to be defended from fire by first responders. Also, some evacuees might require medical attention. Future work includes looking at how far emergency vehicles from places like fire stations can reach in 5, 10, 15, and 30 minutes, providing a coverage area for first responders.\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":"48397873","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-3-2023-71-2023
D. B. Demir, N. Musaoglu
Abstract. In this study, deep learning-based semantic segmentation is used to automatically generate CORINE land cover (CLC) Level 2 classes for a test region in Türkiye. This is accomplished by utilizing new datasets and models created from a pilot region in Italy, which exhibits similar land use/land cover (LU/LC) characteristics to the test region in Canakkale/Türkiye. The training and validation datasets for Italy were generated by employing Sentinel-2 images from various months and different band combinations, along with CLC 2018 vector data for labelling. Different datasets were created to investigate the impact of patch sizes (128 and 256 pixels) and seasonal changes in LU/LC. For the semantic segmentation task, the U-Net architecture was selected as the primary deep learning model. Furthermore, the U-Net architecture was used in conjunction with ResNet50 and ResNet101 for transfer learning, enabling the replacement of the encoder section of the U-Net. These models were tested in the Italy region, and the best-performing ones were subsequently applied to the Canakkale test region to automatically generate CLC 2018. The results were compared with published CLC 2018 Level 2 data for the same region, and the accuracy was assessed using the Intersection over Union (IoU) metric. The findings were presented both visually and statistically.
{"title":"AUTOMATIC CLASSIFICATION OF SELECTED CORINE CLASSES USING DEEP LEARNING BASED SEMANTIC SEGMENTATION","authors":"D. B. Demir, N. Musaoglu","doi":"10.5194/isprs-archives-xlviii-m-3-2023-71-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-71-2023","url":null,"abstract":"Abstract. In this study, deep learning-based semantic segmentation is used to automatically generate CORINE land cover (CLC) Level 2 classes for a test region in Türkiye. This is accomplished by utilizing new datasets and models created from a pilot region in Italy, which exhibits similar land use/land cover (LU/LC) characteristics to the test region in Canakkale/Türkiye. The training and validation datasets for Italy were generated by employing Sentinel-2 images from various months and different band combinations, along with CLC 2018 vector data for labelling. Different datasets were created to investigate the impact of patch sizes (128 and 256 pixels) and seasonal changes in LU/LC. For the semantic segmentation task, the U-Net architecture was selected as the primary deep learning model. Furthermore, the U-Net architecture was used in conjunction with ResNet50 and ResNet101 for transfer learning, enabling the replacement of the encoder section of the U-Net. These models were tested in the Italy region, and the best-performing ones were subsequently applied to the Canakkale test region to automatically generate CLC 2018. The results were compared with published CLC 2018 Level 2 data for the same region, and the accuracy was assessed using the Intersection over Union (IoU) metric. The findings were presented both visually and statistically.\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":"42414584","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-3-2023-57-2023
Temitope H. Dauda, Zhu Ning, Y. Twumasi, Opeyemi I. Oladigbolu
Abstract. Louisiana coastal wetlands contain about 37 percent of the estuarine herbaceous marshes in the conterminous United States. However, the combined effect of sea level rise and other anthropogenic factors have altered land use land cover over the last few years. This is true for two wetlands in coastal Louisiana, Barataria bay and Wax Lake delta. Barataria Bay, Louisiana, USA has experienced significant land loss. Updated information on the dynamics of change in these wetlands is limited and poorly documented. This information is necessary to develop strategies that will contribute to reversing and halting degradation. Thus, this study employed the Maximum Likelihood classifier on Landsat satellite imagery to assess land use and land cover changes in Barataria Bay and Wax Lake Delta, southeastern Louisiana, USA. The analysis revealed notable alterations in the land cover patterns over the study period. In Barataria Bay, there was a decrease in salt marsh areas with a corresponding increase in open water and Built-up area. In contrast, Wax Lake Delta demonstrated substantial land/wetland growth, with significant expansion of vegetation cover. The Maximum Likelihood classifier demonstrated high accuracy in classifying the land cover types, with an overall accuracy of 86% for Barataria Bay and 92% for Wax Lake Delta. These results highlight the effectiveness of the classifier in accurately identifying and mapping land cover changes in coastal environments. The findings contribute valuable insights for understanding the dynamics of coastal ecosystems and can inform decision-making processes for coastal management and conservation efforts.
{"title":"MAXIMUM LIKELIHOOD ALGORITHM DETECTS COASTAL WETLAND CHANGES IN TWO CONTRASTING COASTAL WETLANDS IN LOUISIANA","authors":"Temitope H. Dauda, Zhu Ning, Y. Twumasi, Opeyemi I. Oladigbolu","doi":"10.5194/isprs-archives-xlviii-m-3-2023-57-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-57-2023","url":null,"abstract":"Abstract. Louisiana coastal wetlands contain about 37 percent of the estuarine herbaceous marshes in the conterminous United States. However, the combined effect of sea level rise and other anthropogenic factors have altered land use land cover over the last few years. This is true for two wetlands in coastal Louisiana, Barataria bay and Wax Lake delta. Barataria Bay, Louisiana, USA has experienced significant land loss. Updated information on the dynamics of change in these wetlands is limited and poorly documented. This information is necessary to develop strategies that will contribute to reversing and halting degradation. Thus, this study employed the Maximum Likelihood classifier on Landsat satellite imagery to assess land use and land cover changes in Barataria Bay and Wax Lake Delta, southeastern Louisiana, USA. The analysis revealed notable alterations in the land cover patterns over the study period. In Barataria Bay, there was a decrease in salt marsh areas with a corresponding increase in open water and Built-up area. In contrast, Wax Lake Delta demonstrated substantial land/wetland growth, with significant expansion of vegetation cover. The Maximum Likelihood classifier demonstrated high accuracy in classifying the land cover types, with an overall accuracy of 86% for Barataria Bay and 92% for Wax Lake Delta. These results highlight the effectiveness of the classifier in accurately identifying and mapping land cover changes in coastal environments. The findings contribute valuable insights for understanding the dynamics of coastal ecosystems and can inform decision-making processes for coastal management and conservation efforts.\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":"42851423","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-3-2023-101-2023
B. Ghale, K. Gupta, A. Roy
Abstract. Greenspaces (GSs) available to the public for recreational, environmental, and aesthetic purposes are termed Public Urban Green Spaces (PUGS). Accessibility to PUGS is one of the main pre-requisite for their frequent use. With rising urbanization and inequitable distribution of GSs, a significant portion of the population remains inaccessible to the benefits provided by PUGS. Therefore, it is essential to have tools to evaluate these GSs. This study evaluates the accessibility and spatial quality of various hierarchies of PUGS using GIS-based analysis in Dehradun, India. Accessibility is assessed using network analysis, aesthetics is determined by the presence of bird population and waterbody, the surface index is determined based on NDVI thresholding, and affordability, and spaciousness are computed based on survey and GIS data. The indices are combined to form a composite green space index (CGSI) using analytical hierarchy process. CGSI shows that most of the PUGS in Dehradun have relatively poor accessibility and quality. As per World Health Organization (WHO) guidelines for providing a minimum of 9m2 of GS for each person, Dehradun lies way behind, providing 2.02m2/person. The lower hierarchy PUGS, notably totlots, which are crucial for young children’s physical, mental, and cognitive development, is quite limited. On the contrary, city parks are well distributed with moderate to good accessibility and quality. CGSI is a comprehensive index encompassing different characteristics of GSs and serves as a valuable tool for setting goals, prioritizing investments, identifying areas in need of improvement, and potential locations for future GS development.
{"title":"EVALUATING PUBLIC URBAN GREEN SPACES: A COMPOSITE GREEN SPACE INDEX FOR MEASURING ACCESSIBILITY AND SPATIAL QUALITY","authors":"B. Ghale, K. Gupta, A. Roy","doi":"10.5194/isprs-archives-xlviii-m-3-2023-101-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-101-2023","url":null,"abstract":"Abstract. Greenspaces (GSs) available to the public for recreational, environmental, and aesthetic purposes are termed Public Urban Green Spaces (PUGS). Accessibility to PUGS is one of the main pre-requisite for their frequent use. With rising urbanization and inequitable distribution of GSs, a significant portion of the population remains inaccessible to the benefits provided by PUGS. Therefore, it is essential to have tools to evaluate these GSs. This study evaluates the accessibility and spatial quality of various hierarchies of PUGS using GIS-based analysis in Dehradun, India. Accessibility is assessed using network analysis, aesthetics is determined by the presence of bird population and waterbody, the surface index is determined based on NDVI thresholding, and affordability, and spaciousness are computed based on survey and GIS data. The indices are combined to form a composite green space index (CGSI) using analytical hierarchy process. CGSI shows that most of the PUGS in Dehradun have relatively poor accessibility and quality. As per World Health Organization (WHO) guidelines for providing a minimum of 9m2 of GS for each person, Dehradun lies way behind, providing 2.02m2/person. The lower hierarchy PUGS, notably totlots, which are crucial for young children’s physical, mental, and cognitive development, is quite limited. On the contrary, city parks are well distributed with moderate to good accessibility and quality. CGSI is a comprehensive index encompassing different characteristics of GSs and serves as a valuable tool for setting goals, prioritizing investments, identifying areas in need of improvement, and potential locations for future GS development.\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":"49081286","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-3-2023-267-2023
B. Vivek, P. K. Garg
Abstract. Experiments using an SVC spectroradiometer ranging from 0.35 to 2.5 µm are being conducted in the field as part of the current research in order to gain a better understanding of how contamination affects spectral characteristics. Utilizing a spectroradiometer (0.35–2.5 µm), studies were carried out in the field to determine the linear mixing of snow pollutants (such as coal, ash, wood, and soil) with snow in terms of concentration of contaminants in order to simulate and comprehend the spectrum response of real-world scenarios. Present studies contribute to mapping snow and contaminated snow pixels in remote sensing RS data based on of linear mixing, and to identifying and discriminating between different types of snow contaminants in respect of linear mixing manner, via appropriate wavelength selections for future studies.
{"title":"INFLUENCE OF LINEAR MIXING ON CONTAMINATED SNOW SPECTRAL SIGNATURES","authors":"B. Vivek, P. K. Garg","doi":"10.5194/isprs-archives-xlviii-m-3-2023-267-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-267-2023","url":null,"abstract":"Abstract. Experiments using an SVC spectroradiometer ranging from 0.35 to 2.5 µm are being conducted in the field as part of the current research in order to gain a better understanding of how contamination affects spectral characteristics. Utilizing a spectroradiometer (0.35–2.5 µm), studies were carried out in the field to determine the linear mixing of snow pollutants (such as coal, ash, wood, and soil) with snow in terms of concentration of contaminants in order to simulate and comprehend the spectrum response of real-world scenarios. Present studies contribute to mapping snow and contaminated snow pixels in remote sensing RS data based on of linear mixing, and to identifying and discriminating between different types of snow contaminants in respect of linear mixing manner, via appropriate wavelength selections for future studies.\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":"49309287","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-3-2023-155-2023
C. Okolie, C. Iheaturu, B. Ojegbile, C. Ndu, A. Tella
Abstract. African cities are growing fast, and this rapid urbanisation has tremendously increased air pollution and greenhouse gas emissions. Despite this disturbing reality, the deleterious impacts of air pollution on livelihoods and the environment are often overlooked. Recently, the link between air quality and meteorological parameters has received attention from researchers and understanding this relationship could significantly improve our understanding of the spatial and temporal dynamics of air quality. This study focuses on analysing the spatiotemporal variation of three key air quality parameters, namely nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10), in Cape Town between 2020 and 2021. The study also aims to assess the relationship between air quality and meteorological parameters during this period, and the compliance with national and international air quality guidelines. Air quality data were collected from five monitoring stations in the City of Cape Town. A preliminary analysis of the data reveals high increases in the concentration of air pollutants from 2020 to 2021. For instance, the average monthly concentration of NO2 and SO2 at Bellville South station more than doubled during this period (from 6.7–14.8 µg/m3 and 3.4–8.1 µg/m3, respectively). This is worrisome as the air quality index (AQI) exceeded the safe limits at several sites. There is a need for urgent action by national and city governments in Africa to invest in air quality monitoring systems to enhance the well-being of citizens and promote the long-term sustainability of cities and infrastructure.
{"title":"SPATIO-TEMPORAL VARIABILITY OF AIR QUALITY AND RELATIONSHIP WITH METEOROLOGICAL PARAMETERS IN CAPE TOWN, SOUTH AFRICA","authors":"C. Okolie, C. Iheaturu, B. Ojegbile, C. Ndu, A. Tella","doi":"10.5194/isprs-archives-xlviii-m-3-2023-155-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-155-2023","url":null,"abstract":"Abstract. African cities are growing fast, and this rapid urbanisation has tremendously increased air pollution and greenhouse gas emissions. Despite this disturbing reality, the deleterious impacts of air pollution on livelihoods and the environment are often overlooked. Recently, the link between air quality and meteorological parameters has received attention from researchers and understanding this relationship could significantly improve our understanding of the spatial and temporal dynamics of air quality. This study focuses on analysing the spatiotemporal variation of three key air quality parameters, namely nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10), in Cape Town between 2020 and 2021. The study also aims to assess the relationship between air quality and meteorological parameters during this period, and the compliance with national and international air quality guidelines. Air quality data were collected from five monitoring stations in the City of Cape Town. A preliminary analysis of the data reveals high increases in the concentration of air pollutants from 2020 to 2021. For instance, the average monthly concentration of NO2 and SO2 at Bellville South station more than doubled during this period (from 6.7–14.8 µg/m3 and 3.4–8.1 µg/m3, respectively). This is worrisome as the air quality index (AQI) exceeded the safe limits at several sites. There is a need for urgent action by national and city governments in Africa to invest in air quality monitoring systems to enhance the well-being of citizens and promote the long-term sustainability of cities and infrastructure.\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":"43841531","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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