The Palu-Koro Fault on Sulawesi Island possesses an extensive record of earthquake-related activity, notably the Palu earthquake on September 28, 2018, which was particularly destructive. This study investigates the evolution of this fault by using high-resolution PlanetScope and Landsat 8 Operational Land Imager/Therma Infrared Sensor (OLI/TIRS) images. By investigating the interseismic, coseismic, and postseismic stages of the earthquake's habits, this paper aims to obtain an in-depth understanding of its behavior. The coseismic displacement analysis, which was carried out alongside the optical image correlation technique, indicated major displacements throughout the Palu-Koro Fault, with the largest displacement of roughly 7 m. To ensure the accuracy of the results, internal verification standards, such as a reliability criterion of >30% and a mean structural similarity index (MSSIM) of 1, were used. Landsat 8 imagery was processed using the land surface temperature method to enhance the understanding of the earthquake phases. Prior to the earthquake, the results suggested a rise in temperature, which peaked during the coseismic phase and decreased progressively during the postseismic phase. Intriguingly, the temperature behavior revealed the possibility of using information from remote sensing as an alternative approach to identify the fault distribution in Palu City. Overall, this study demonstrates the utility of remote sensing data for analyzing the dynamics of the Palu-Koro Fault and understanding each stage of the 2018 Palu earthquake. By integrating high-resolution satellite imagery with sophisticated image processing techniques, this paper provides crucial insights into earthquake activity and its impact in this area.
{"title":"The Palu-Koro fault behaviour monitoring associated with the 2018 Palu earthquake based on the multi-temporal planetscope and Landsat 8 satellite images","authors":"Bondan Galih Dewanto , Calvin Wijaya , Ramadhan Priadi","doi":"10.1016/j.rsase.2024.101397","DOIUrl":"10.1016/j.rsase.2024.101397","url":null,"abstract":"<div><div>The Palu-Koro Fault on Sulawesi Island possesses an extensive record of earthquake-related activity, notably the Palu earthquake on September 28, 2018, which was particularly destructive. This study investigates the evolution of this fault by using high-resolution PlanetScope and Landsat 8 Operational Land Imager/Therma Infrared Sensor (OLI/TIRS) images. By investigating the interseismic, coseismic, and postseismic stages of the earthquake's habits, this paper aims to obtain an in-depth understanding of its behavior. The coseismic displacement analysis, which was carried out alongside the optical image correlation technique, indicated major displacements throughout the Palu-Koro Fault, with the largest displacement of roughly 7 m. To ensure the accuracy of the results, internal verification standards, such as a reliability criterion of >30% and a mean structural similarity index (MSSIM) of 1, were used. Landsat 8 imagery was processed using the land surface temperature method to enhance the understanding of the earthquake phases. Prior to the earthquake, the results suggested a rise in temperature, which peaked during the coseismic phase and decreased progressively during the postseismic phase. Intriguingly, the temperature behavior revealed the possibility of using information from remote sensing as an alternative approach to identify the fault distribution in Palu City. Overall, this study demonstrates the utility of remote sensing data for analyzing the dynamics of the Palu-Koro Fault and understanding each stage of the 2018 Palu earthquake. By integrating high-resolution satellite imagery with sophisticated image processing techniques, this paper provides crucial insights into earthquake activity and its impact in this area.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101397"},"PeriodicalIF":3.8,"publicationDate":"2024-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703693","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 : 2024-11-15DOI: 10.1016/j.rsase.2024.101388
Ricardo Gellert Paris , Andreas Rienow
The increased use of nighttime lights (NTL) to assess infrastructure implementation and socioeconomic development highlights the potential of this open data source, often used as a proxy indicator of economic dynamics. Many studies focus on supra-national levels and the quantification of light emissions, generating assumptions regarding development. However, fewer studies address the characterization of socio-spatial dynamics at the local level. This research analyses the Nacala corridor in Mozambique, aiming to challenge the assumption that increasing NTL levels equals local development. We qualify and contextualize the types of activities identified by nighttime light anomalies. Using data cubes with 10-year seasonal NTL emissions, we identified anomalies in the time series of 17 out of 74 settlements and subsequently analyzed them with very high-resolution images. Among these settlements, we identified soil extraction, quarrying, or industries in 13 cases. Finally, we compared the results with household surveys indicating that during the period, the population had no significant increase in access to energy. We conclude that the NTL time series can effectively portray infrastructure-driven activities, such as surface mining and industry, in the context of the Corridor. However, the assumption that local development is linked with an increase in NTL in non-urbanized areas can be misleading without qualitative analysis. The activities that are the source of radiance can be illicit, not socially adopted, economically concentrated, and/or environmentally harmful.
{"title":"Shedding light on local development: Unveiling spatial dynamics from infrastructure implementation through nighttime lights in the Nacala corridor, Mozambique","authors":"Ricardo Gellert Paris , Andreas Rienow","doi":"10.1016/j.rsase.2024.101388","DOIUrl":"10.1016/j.rsase.2024.101388","url":null,"abstract":"<div><div>The increased use of nighttime lights (NTL) to assess infrastructure implementation and socioeconomic development highlights the potential of this open data source, often used as a proxy indicator of economic dynamics. Many studies focus on supra-national levels and the quantification of light emissions, generating assumptions regarding development. However, fewer studies address the characterization of socio-spatial dynamics at the local level. This research analyses the Nacala corridor in Mozambique, aiming to challenge the assumption that increasing NTL levels equals local development. We qualify and contextualize the types of activities identified by nighttime light anomalies. Using data cubes with 10-year seasonal NTL emissions, we identified anomalies in the time series of 17 out of 74 settlements and subsequently analyzed them with very high-resolution images. Among these settlements, we identified soil extraction, quarrying, or industries in 13 cases. Finally, we compared the results with household surveys indicating that during the period, the population had no significant increase in access to energy. We conclude that the NTL time series can effectively portray infrastructure-driven activities, such as surface mining and industry, in the context of the Corridor. However, the assumption that local development is linked with an increase in NTL in non-urbanized areas can be misleading without qualitative analysis. The activities that are the source of radiance can be illicit, not socially adopted, economically concentrated, and/or environmentally harmful.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101388"},"PeriodicalIF":3.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-15DOI: 10.1016/j.rsase.2024.101398
Kassiel Trajano da Luz , Antonio Henrique Cordeiro Ramalho , Edna Santos de Souza , Cristiano Bento da Silva
The Urban Solid Waste sector is one of the main contributors to methane emissions. Despite specific legislation, many Brazilian municipalities still maintain outdated waste dumps. Geotechnological tools, such as Fuzzy logic, can provide a viable and efficient solution. This research aimed to evaluate the current location and identify optimal sites for the implementation of sanitary landfills, using Fuzzy logic. We considered were slope, proximity to water bodies, urban areas, roads, and airports, land use and occupation, geology, and pedology. The results showed that the current dump location is inadequate due to its proximity to the airport, roads, and urban center. The suitability map revealed that 35.38% of the studied area has high to very high suitability. The new selected location to landfill having sufficient area, being distant from the airport and urban center, and complying with operational and logistical standards of proximity to highways and water bodies. The research confirms that the current Urban Solid Waste structure is not in compliance with regulations and that Fuzzy logic is effective in selecting sites for new sanitary landfills. This model can serve as a reference for other municipalities, contributing to more efficient and responsible waste management.
{"title":"Geotechnological multicriteria analysis applied to identify optimal locations for the installation of sanitary landfills","authors":"Kassiel Trajano da Luz , Antonio Henrique Cordeiro Ramalho , Edna Santos de Souza , Cristiano Bento da Silva","doi":"10.1016/j.rsase.2024.101398","DOIUrl":"10.1016/j.rsase.2024.101398","url":null,"abstract":"<div><div>The Urban Solid Waste sector is one of the main contributors to methane emissions. Despite specific legislation, many Brazilian municipalities still maintain outdated waste dumps. Geotechnological tools, such as Fuzzy logic, can provide a viable and efficient solution. This research aimed to evaluate the current location and identify optimal sites for the implementation of sanitary landfills, using Fuzzy logic. We considered were slope, proximity to water bodies, urban areas, roads, and airports, land use and occupation, geology, and pedology. The results showed that the current dump location is inadequate due to its proximity to the airport, roads, and urban center. The suitability map revealed that 35.38% of the studied area has high to very high suitability. The new selected location to landfill having sufficient area, being distant from the airport and urban center, and complying with operational and logistical standards of proximity to highways and water bodies. The research confirms that the current Urban Solid Waste structure is not in compliance with regulations and that Fuzzy logic is effective in selecting sites for new sanitary landfills. This model can serve as a reference for other municipalities, contributing to more efficient and responsible waste management.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101398"},"PeriodicalIF":3.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142720416","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 : 2024-11-15DOI: 10.1016/j.rsase.2024.101402
Chih-Yuan Huang , Hsuan-Chi Ho , Tang-Huang Lin
With the growth in industrialization and urban development, air pollution has become an increasing serious health concern. Although ground stations can effectively monitor air quality, they generally observe only located phenomena and limited in the spatial distribution. Remote-sensing approaches have thus been employed by many scholars for air quality monitoring in an entire region. However, no single satellite equips with sufficient spatial and temporal resolutions for detecting rapidly changing local phenomena, such as air quality variations. A top-of-atmosphere reflectance–based spatial–temporal image fusion model (TOA-STFM) is proposed in this paper to solve this problem. The proposed TOA-STFM is modified based on the spatial–temporal adaptive reflectance fusion model (STARFM) and yields fused images in which atmospheric properties are retained. A key process in the TOA-STFM is blurring effect adjustment (BEA), which is performed to match the atmospheric effects caused by aerosols in images with different spatial resolutions. The feasibility of fusing Himawari-8 images with SPOT-6 images was evaluated in this study. We used the proposed model to extract aerosol optical depths (AODs) from images produced by fusing Himawari-8 and SPOT-6 images and compared the extracted AODs with corresponding in-situ observations made by the AErosol RObotic NETwork (AERONET). The AOD relative errors of the proposed TOA-STFM were 2.3%–7.6%, which is a significant improvement comparing to a relative error of 8.4%–13.5% from Himawari-8 images and existing AOD products.
{"title":"High spatial–temporal image fusion model for retrieving aerosol optical depth based on top-of-atmosphere reflectance","authors":"Chih-Yuan Huang , Hsuan-Chi Ho , Tang-Huang Lin","doi":"10.1016/j.rsase.2024.101402","DOIUrl":"10.1016/j.rsase.2024.101402","url":null,"abstract":"<div><div>With the growth in industrialization and urban development, air pollution has become an increasing serious health concern. Although ground stations can effectively monitor air quality, they generally observe only located phenomena and limited in the spatial distribution. Remote-sensing approaches have thus been employed by many scholars for air quality monitoring in an entire region. However, no single satellite equips with sufficient spatial and temporal resolutions for detecting rapidly changing local phenomena, such as air quality variations. A top-of-atmosphere reflectance–based spatial–temporal image fusion model (TOA-STFM) is proposed in this paper to solve this problem. The proposed TOA-STFM is modified based on the spatial–temporal adaptive reflectance fusion model (STARFM) and yields fused images in which atmospheric properties are retained. A key process in the TOA-STFM is blurring effect adjustment (BEA), which is performed to match the atmospheric effects caused by aerosols in images with different spatial resolutions. The feasibility of fusing Himawari-8 images with SPOT-6 images was evaluated in this study. We used the proposed model to extract aerosol optical depths (AODs) from images produced by fusing Himawari-8 and SPOT-6 images and compared the extracted AODs with corresponding in-situ observations made by the AErosol RObotic NETwork (AERONET). The AOD relative errors of the proposed TOA-STFM were 2.3%–7.6%, which is a significant improvement comparing to a relative error of 8.4%–13.5% from Himawari-8 images and existing AOD products.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101402"},"PeriodicalIF":3.8,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702971","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 : 2024-11-13DOI: 10.1016/j.rsase.2024.101396
Ahmed Mutasim Abdalla Mahmoud , Nichole Sheldrick , Muftah Ahmed
Archaeological sites across the globe are facing significant threats and heritage managers are under increasing pressure to monitor and preserve these sites. Since 2015, the EAMENA project has documented more than 200,000 archaeological sites and the disturbances and threats affecting them across the Middle East and North Africa (MENA) region, using a combination of remote sensing, digitization, and fieldwork methodologies. The large number of sites and their often remote or otherwise difficult to access locations makes consistent and regular monitoring of these sites for disturbances and threats a daunting task. Combined with the increasing frequency and severity of threats to archaeological sites, the need to develop novel tools and methods that can rapidly monitor the changes at and around archaeological sites and provide accurate and consistent monitoring has never been more urgent. In this paper, we introduce the EAMENA Machine Learning Automated Change Detection tool (EAMENA MLACD). This newly-developed online tool uses bespoke machine learning algorithms to process sequential satellite images and create land classification maps to detect and identify disturbances and threats in the vicinity of known archaeological sites for the purposes of heritage monitoring and preservation. Initial testing and validation of results from the EAMENA MLACD in a case study in Bani Walid, Libya, demonstrate how it can be used to identify disturbances and potential threats to heritage sites, and increase the speed and efficiency of monitoring activities undertaken by heritage professionals.
{"title":"A novel machine learning automated change detection tool for monitoring disturbances and threats to archaeological sites","authors":"Ahmed Mutasim Abdalla Mahmoud , Nichole Sheldrick , Muftah Ahmed","doi":"10.1016/j.rsase.2024.101396","DOIUrl":"10.1016/j.rsase.2024.101396","url":null,"abstract":"<div><div>Archaeological sites across the globe are facing significant threats and heritage managers are under increasing pressure to monitor and preserve these sites. Since 2015, the EAMENA project has documented more than 200,000 archaeological sites and the disturbances and threats affecting them across the Middle East and North Africa (MENA) region, using a combination of remote sensing, digitization, and fieldwork methodologies. The large number of sites and their often remote or otherwise difficult to access locations makes consistent and regular monitoring of these sites for disturbances and threats a daunting task. Combined with the increasing frequency and severity of threats to archaeological sites, the need to develop novel tools and methods that can rapidly monitor the changes at and around archaeological sites and provide accurate and consistent monitoring has never been more urgent. In this paper, we introduce the EAMENA Machine Learning Automated Change Detection tool (EAMENA MLACD). This newly-developed online tool uses bespoke machine learning algorithms to process sequential satellite images and create land classification maps to detect and identify disturbances and threats in the vicinity of known archaeological sites for the purposes of heritage monitoring and preservation. Initial testing and validation of results from the EAMENA MLACD in a case study in Bani Walid, Libya, demonstrate how it can be used to identify disturbances and potential threats to heritage sites, and increase the speed and efficiency of monitoring activities undertaken by heritage professionals.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101396"},"PeriodicalIF":3.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142702972","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-13DOI: 10.1016/j.rsase.2024.101399
Rajkumar Guria , Manoranjan Mishra , Richarde Marques da Silva , Carlos Antonio Costa dos Santos , Celso Augusto Guimarães Santos
Recurrent droughts in India have severely impacted the economy and the quality of life. The agricultural drought from June to October 2023 in Odisha (the Kharif season), India, highlighted the urgent need for precise monitoring and assessment due to its significant effects on crop yield and food security. This study develops and validates the multisensor Integrated Drought Severity Index (IDSI) to accurately assess agricultural drought severity using multiple remote sensing indices, including optical, thermal, and microwave sensors. Ten indices were computed and combined using the Analytic Hierarchy Process (AHP) to assign weights, aiming to establish a new agricultural drought index that can monitor severity, identify critical indices, and assess uncertainties in affected areas. Validation results from ROC-AUC indicate that the IDSI model achieved a precision exceeding 85% using empirical weights. The study area's mapping shows that approximately 8.91% experience extreme drought conditions, with significant impacts in specific districts of Odisha. This comprehensive tool provides critical insights for policymakers and farmers, enhancing global drought preparedness and response strategies through its adaptable methodology.
{"title":"Multisensor Integrated Drought Severity Index (IDSI) for assessing agricultural drought in Odisha, India","authors":"Rajkumar Guria , Manoranjan Mishra , Richarde Marques da Silva , Carlos Antonio Costa dos Santos , Celso Augusto Guimarães Santos","doi":"10.1016/j.rsase.2024.101399","DOIUrl":"10.1016/j.rsase.2024.101399","url":null,"abstract":"<div><div>Recurrent droughts in India have severely impacted the economy and the quality of life. The agricultural drought from June to October 2023 in Odisha (the Kharif season), India, highlighted the urgent need for precise monitoring and assessment due to its significant effects on crop yield and food security. This study develops and validates the multisensor Integrated Drought Severity Index (IDSI) to accurately assess agricultural drought severity using multiple remote sensing indices, including optical, thermal, and microwave sensors. Ten indices were computed and combined using the Analytic Hierarchy Process (AHP) to assign weights, aiming to establish a new agricultural drought index that can monitor severity, identify critical indices, and assess uncertainties in affected areas. Validation results from ROC-AUC indicate that the IDSI model achieved a precision exceeding 85% using empirical weights. The study area's mapping shows that approximately 8.91% experience extreme drought conditions, with significant impacts in specific districts of Odisha. This comprehensive tool provides critical insights for policymakers and farmers, enhancing global drought preparedness and response strategies through its adaptable methodology.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101399"},"PeriodicalIF":3.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142703689","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 : 2024-11-10DOI: 10.1016/j.rsase.2024.101392
Courtney A. Di Vittorio , Melita Wiles , Yasin W. Rabby , Saeed Movahedi , Jacob Louie , Lily Hezrony , Esteban Coyoy Cifuentes , Wes Hinchman , Alex Schluter
The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing reflectance with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The algorithm uses data and tools within the Google Earth Engine platform, making it accessible and scalable. The average overall accuracy is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsatderived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the new products’ severe change magnitude matches that of the existing US inventory and the moderate change magnitude matches that of the Northeastern Coast product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.
{"title":"Mapping coastal wetland changes from 1985 to 2022 in the US Atlantic and Gulf Coasts using Landsat time series and national wetland inventories","authors":"Courtney A. Di Vittorio , Melita Wiles , Yasin W. Rabby , Saeed Movahedi , Jacob Louie , Lily Hezrony , Esteban Coyoy Cifuentes , Wes Hinchman , Alex Schluter","doi":"10.1016/j.rsase.2024.101392","DOIUrl":"10.1016/j.rsase.2024.101392","url":null,"abstract":"<div><div>The areal extent of coastal wetlands is declining rapidly worldwide, and scientists and land managers need land cover maps that show the magnitude and severity of changes over time to assess impacts and develop effective conservation strategies. Within the United States (US), widely-used, continental-scale wetland land cover data products are either static in time (The National Wetlands Inventory) or have a course temporal resolution and do not distinguish between different types of change (the NOAA Coastal Change Analysis Program, C-CAP). This study presents a new coastal wetland geospatial data product that leverages the Landsat database and maps annual land cover across the US Atlantic and Gulf Coasts from 1985 to 2022. The algorithm was trained on the existing US wetland inventories to make the final maps compatible with products that are used in operational management. A multi-stage classification approach was designed that uses the Continuous Change Detection and Classification (CCDC) algorithm to characterize time series of remote sensing reflectance with fitted harmonic functions and identify when changes likely occurred. The fitted time series models are then input into a random forest classifier to make a class prediction. An annual-scale random forest classification is performed in parallel, and results from both algorithms are combined and analysed to detect both gradual and abrupt changes and to identify transitional time series segments. A time series smoothing procedure is subsequently applied to ensure class transitions are logical and consistent and extract a summative change characterization map that shows the severity and spatial density of change. The final maps distinguish between four homogenous classes and six mixed classes, representing areas that are transitioning between classes and where the boundaries between classes are unstable. The algorithm uses data and tools within the Google Earth Engine platform, making it accessible and scalable. The average overall accuracy is 93.7%, and the average class omission and commission errors are 6.7% and 6.4%, respectively. A variety of change detection comparisons were performed, using the existing wetland inventory that employed a fundamentally different change detection approach, and a more comparable annual-scale, Landsatderived product that estimated changes across the Northeastern Atlantic Coast. These comparisons show that the new products’ severe change magnitude matches that of the existing US inventory and the moderate change magnitude matches that of the Northeastern Coast product. The 2019 Wetland Status and Trends Report estimated that net loss rates in emergent wetlands from 2010 to 2019 amount to 1.7%, and the new maps show an equivalent loss rate of 1.6%, again showing close agreement.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101392"},"PeriodicalIF":3.8,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-11-04DOI: 10.1016/j.rsase.2024.101393
Priyanshu Gupta, Neeti Singh, R.K. Giri, A.K. Mitra
Dry microbursts can generate severe meteorological conditions including turbulence and strong winds even in the absence of precipitation. Present study evaluate the performance of Indian geostationary satellite, INSAT-3DR in capturing Dry Microburst Index (DMI) and validated against the radiosonde dataset. Data is validated across 14 selected stations across the India for 3 year (2020–2022). However, radiosonde data is very limited but spatial and temporal resolution of INSAT-3DR is good to analyse and predict the atmospheric phenomena. Different statistics have been used to validate INSAT-3DR against radiosonde observation. A Taylor plot confirm strong correlation and low RMSE between INSAT-3DR and radiosonde data. Spatial distribution depicts annual mean DMI values, it is influence by diurnal variation, regional weather pattern, and seasonal factors. Seasonal analysis indicates lower DMI during winter (5–45) due to reduced instability and moisture, while post-monsoon season witness increased DMI owing to warmer, humid conditions. The pre-monsoon season shows rising DMI as temperature increase. Study also analyses the co-occurrence of thunderstorm during DMI events, revealing a Probability of Detection (POD) of 0.75 for the INSAT-3DR DMI product, indicating 75% correct identification of thunderstorms. However, the False Alarm Rate (FAR) suggest false alarms occurred in approximately 55.2% of cases. Overall, study underscores the importance of considering local factors and conditions in interpreting INSAT-3DR satellite-based DMI data. Understanding and accurately predicting dry microbursts are crucial for enhancing aviation safety and improving the resilience of infrastructure in regions prone to these phenomena.
{"title":"Assessment of Dry Microburst Index over India derived from INSAT-3DR satellite","authors":"Priyanshu Gupta, Neeti Singh, R.K. Giri, A.K. Mitra","doi":"10.1016/j.rsase.2024.101393","DOIUrl":"10.1016/j.rsase.2024.101393","url":null,"abstract":"<div><div>Dry microbursts can generate severe meteorological conditions including turbulence and strong winds even in the absence of precipitation. Present study evaluate the performance of Indian geostationary satellite, INSAT-3DR in capturing Dry Microburst Index (DMI) and validated against the radiosonde dataset. Data is validated across 14 selected stations across the India for 3 year (2020–2022). However, radiosonde data is very limited but spatial and temporal resolution of INSAT-3DR is good to analyse and predict the atmospheric phenomena. Different statistics have been used to validate INSAT-3DR against radiosonde observation. A Taylor plot confirm strong correlation and low RMSE between INSAT-3DR and radiosonde data. Spatial distribution depicts annual mean DMI values, it is influence by diurnal variation, regional weather pattern, and seasonal factors. Seasonal analysis indicates lower DMI during winter (5–45) due to reduced instability and moisture, while post-monsoon season witness increased DMI owing to warmer, humid conditions. The pre-monsoon season shows rising DMI as temperature increase. Study also analyses the co-occurrence of thunderstorm during DMI events, revealing a Probability of Detection (POD) of 0.75 for the INSAT-3DR DMI product, indicating 75% correct identification of thunderstorms. However, the False Alarm Rate (FAR) suggest false alarms occurred in approximately 55.2% of cases. Overall, study underscores the importance of considering local factors and conditions in interpreting INSAT-3DR satellite-based DMI data. Understanding and accurately predicting dry microbursts are crucial for enhancing aviation safety and improving the resilience of infrastructure in regions prone to these phenomena.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101393"},"PeriodicalIF":3.8,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142652920","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 : 2024-11-01DOI: 10.1016/j.rsase.2024.101376
Dini Andriani , Supriyadi , Muhammad Aufaristama , Asep Saepuloh , Alamta Singarimbun , Wahyu Srigutomo
This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m2 and 115.13 W/m2, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.
{"title":"Analysis of radiative heat flux using ASTER thermal images: Climatological and volcanological factors on Java Island, Indonesia","authors":"Dini Andriani , Supriyadi , Muhammad Aufaristama , Asep Saepuloh , Alamta Singarimbun , Wahyu Srigutomo","doi":"10.1016/j.rsase.2024.101376","DOIUrl":"10.1016/j.rsase.2024.101376","url":null,"abstract":"<div><div>This study focuses on analysing natural Radiative Heat Flux (RHF) anomalies to map out the heat distribution across the Java Island. Leveraging remote sensing techniques, we calculated natural RHF anomalies using Land Surface Temperature (LST) and Land Surface Emissivity (LSE) data obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. A key aspect of our approach was distinguishing between natural and anthropogenic heat sources by cross-referencing the LST Map with the Land Use Land Cover (LULC) map of Java Island. The study interprets natural RHF anomalies by examining regional trends in non-volcanic areas and local trends within volcanic regions, considering climatological and volcanological factors. Relation with climatological factors involves assessing soil moisture parameters from Soil Moisture Active Passive (SMAP) data, precipitation from monthly Global Precipitation Measurement (GPM) data, and classifications according to the Köppen-Geiger climate schema. Our regional analysis reveals high natural RHF anomalies in the northern regions of West Java, parts of Central Java, and most of East Java, attributed to low soil moisture and low precipitation in savanna and monsoon climates. On a more localised scale, RHF values are significantly high in volcanic areas, particularly around Central and East Java's volcanoes, such as Mt. Merapi, Mt. Slamet, Mt. Semeru, the Sidoarjo Mud Volcano, and Mt. Ijen. The Natural RHF anomalies at volcanoes in West Java were identified as not being high except at Mt Patuha. These areas exhibit average natural RHF anomalies ranging between 32.22 W/m<sup>2</sup> and 115.13 W/m<sup>2</sup>, indicating strong and intense volcanic activity. The insights obtained from these findings explain the overall thermal characteristics of Java Island and highlight the presence of subsurface thermal zones associated with volcanic activity and geothermal potential.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101376"},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The detrimental impacts of the Urban Heat Island (UHI) effect are widely recognized in cities globally. Despite the natural cooling capacity of urban cold islands (UCIs), their fragmented state diminishes overall effectiveness. Previous research focused on identifying corridors to connect these isolated UCIs, aiming to enhance cooling networks. However, optimal connection strategies remained elusive. This study introduces a novel framework to address this gap. Utilizing ArcGIS Pro's optimal region connection tools alongside Morphological Spatial Pattern Analysis (MSPA) and ecological parameters, corridors in Ghaemshahr, Iran were meticulously planned and assessed. Through minimum cumulative resistance and gravity models, 63 potential corridors totaling 153 km were identified. Optimization procedures then refined this selection to 27 key corridors spanning 22 km, with 67% measuring less than 0.5 km and strategically positioned near UCIs. This prioritizes adjacency, maximizing corridor protection and construction likelihood. This cost-effective approach fosters stronger connectivity between adjacent UCIs, ultimately linking all UCIs within the region. This innovative methodology provides a holistic solution for mitigating UHI effects, promoting sustainable urban development.
{"title":"Effective cooling networks: Optimizing corridors for Urban Heat Island mitigation","authors":"Teimour Rezaei, Xinyuan Shen, Rattanawat Chaiyarat, Nathsuda Pumijumnong","doi":"10.1016/j.rsase.2024.101372","DOIUrl":"10.1016/j.rsase.2024.101372","url":null,"abstract":"<div><div>The detrimental impacts of the Urban Heat Island (UHI) effect are widely recognized in cities globally. Despite the natural cooling capacity of urban cold islands (UCIs), their fragmented state diminishes overall effectiveness. Previous research focused on identifying corridors to connect these isolated UCIs, aiming to enhance cooling networks. However, optimal connection strategies remained elusive. This study introduces a novel framework to address this gap. Utilizing ArcGIS Pro's optimal region connection tools alongside Morphological Spatial Pattern Analysis (MSPA) and ecological parameters, corridors in Ghaemshahr, Iran were meticulously planned and assessed. Through minimum cumulative resistance and gravity models, 63 potential corridors totaling 153 km were identified. Optimization procedures then refined this selection to 27 key corridors spanning 22 km, with 67% measuring less than 0.5 km and strategically positioned near UCIs. This prioritizes adjacency, maximizing corridor protection and construction likelihood. This cost-effective approach fosters stronger connectivity between adjacent UCIs, ultimately linking all UCIs within the region. This innovative methodology provides a holistic solution for mitigating UHI effects, promoting sustainable urban development.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"36 ","pages":"Article 101372"},"PeriodicalIF":3.8,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142663076","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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