Pub Date : 2023-08-15DOI: 10.5194/isprs-archives-xlviii-m-1-2023-573-2023
M. Şahingöz, S. Berberoglu
Abstract. Thermal adaptation and thermal comfort indices are critical in determining the thermal comfort of the outdoor environment. They also play an essential role in research on heat stress, an environmental threat that can affect individuals' productivity, health and even survival. Urban growth and the resulting expansion of impervious surfaces affect the thermal characteristics of a landscape by raising Land Surface Temperatures (LST). The resulting warming can lead to thermal discomfort, the prevalence of heat-related health problems, air pollution, increased water use and energy demand for air conditioning, among others. Recently, efforts to understand the effects of urbanization and landscape changes on indoor and outdoor temperatures have increased significantly. Together with remote sensing technology, this study aims to understand human heat stress, and geographic information system (GIS) is a tool used in the research. In the estimation of heat stress, besides temperature, physiological status, environmental impact and relative humidity factors are also important. The discomfort index (DI) is a heat stress indicator proposed by Thom (1959), which expresses the contribution of air temperature and relative humidity to human thermal comfort. The discomfort index proposed by Thom (1959) was calculated as DI=0.5Ta+0.5Tw (Ta: dry bulb temperature, Tw: wet bulb temperature) modified by SOHAR, Adar and Laky (1963). In the study, the dry bulb temperature, assumed to be equal to the air temperature, was taken monthly from MODIS LST data at 1km resolution. Relative humidity was produced by interpolating 73 meteorological data in the study area at 1km resolution. Wet bulb temperature is difficult to measure, so it was calculated from dry bulb temperature and relative humidity data so that the discomfort index as a measure of heat stress in the study area was calculated with a resolution of 1 km. The discomfort index was calculated monthly and annually and classified according to Thom's 4 comfort classes. According to the calculation results, Antalya's average discomfort index value for the whole year is 24.9 °C, indicating that Antalya is a moderately comfortable place. This value varies monthly, especially in April and October when the heat stress is the highest.
{"title":"DETECTION OF DISCOMFORT INDEX WITH REMOTE SENSING TECHNOLOGY: THE CASE OF ANTALYA PROVINCE","authors":"M. Şahingöz, S. Berberoglu","doi":"10.5194/isprs-archives-xlviii-m-1-2023-573-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-573-2023","url":null,"abstract":"Abstract. Thermal adaptation and thermal comfort indices are critical in determining the thermal comfort of the outdoor environment. They also play an essential role in research on heat stress, an environmental threat that can affect individuals' productivity, health and even survival. Urban growth and the resulting expansion of impervious surfaces affect the thermal characteristics of a landscape by raising Land Surface Temperatures (LST). The resulting warming can lead to thermal discomfort, the prevalence of heat-related health problems, air pollution, increased water use and energy demand for air conditioning, among others. Recently, efforts to understand the effects of urbanization and landscape changes on indoor and outdoor temperatures have increased significantly. Together with remote sensing technology, this study aims to understand human heat stress, and geographic information system (GIS) is a tool used in the research. In the estimation of heat stress, besides temperature, physiological status, environmental impact and relative humidity factors are also important. The discomfort index (DI) is a heat stress indicator proposed by Thom (1959), which expresses the contribution of air temperature and relative humidity to human thermal comfort. The discomfort index proposed by Thom (1959) was calculated as DI=0.5Ta+0.5Tw (Ta: dry bulb temperature, Tw: wet bulb temperature) modified by SOHAR, Adar and Laky (1963). In the study, the dry bulb temperature, assumed to be equal to the air temperature, was taken monthly from MODIS LST data at 1km resolution. Relative humidity was produced by interpolating 73 meteorological data in the study area at 1km resolution. Wet bulb temperature is difficult to measure, so it was calculated from dry bulb temperature and relative humidity data so that the discomfort index as a measure of heat stress in the study area was calculated with a resolution of 1 km. The discomfort index was calculated monthly and annually and classified according to Thom's 4 comfort classes. According to the calculation results, Antalya's average discomfort index value for the whole year is 24.9 °C, indicating that Antalya is a moderately comfortable place. This value varies monthly, especially in April and October when the heat stress is the highest.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70624599","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-1-2023-471-2023
O. Bayrak, F. Erdem, M. Uzar
Abstract. Forest monitoring and tree species categorization has a vital importance in terms of biodiversity conservation, ecosystem health assessment, climate change mitigation, and sustainable resource management. Due to large-scale coverage of forest areas, remote sensing technology plays a crucial role in the monitoring of forest areas by timely and regular data acquisition, multi-spectral and multi-temporal analysis, non-invasive data collection, accessibility and cost-effectiveness. High-resolution satellite and airborne remote sensing technologies have supplied image data with rich spatial, color, and texture information. Nowadays, deep learning models are commonly utilized in image classification, object recognition, and semantic segmentation applications in remote sensing and forest monitoring as well. We, in this study, selected a popular CNN and object detection algorithm YOLOv8 variants for tree species classification from aerial images of TreeSatAI benchmark. Our results showed that YOLOv8-l outperformed benchmark’s initial release results, and other YOLOv8 variants with 71,55% and 72,70% for weighted and micro averaging scores, respectively.
{"title":"DEEP LEARNING BASED AERIAL IMAGERY CLASSIFICATION FOR TREE SPECIES IDENTIFICATION","authors":"O. Bayrak, F. Erdem, M. Uzar","doi":"10.5194/isprs-archives-xlviii-m-1-2023-471-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-471-2023","url":null,"abstract":"Abstract. Forest monitoring and tree species categorization has a vital importance in terms of biodiversity conservation, ecosystem health assessment, climate change mitigation, and sustainable resource management. Due to large-scale coverage of forest areas, remote sensing technology plays a crucial role in the monitoring of forest areas by timely and regular data acquisition, multi-spectral and multi-temporal analysis, non-invasive data collection, accessibility and cost-effectiveness. High-resolution satellite and airborne remote sensing technologies have supplied image data with rich spatial, color, and texture information. Nowadays, deep learning models are commonly utilized in image classification, object recognition, and semantic segmentation applications in remote sensing and forest monitoring as well. We, in this study, selected a popular CNN and object detection algorithm YOLOv8 variants for tree species classification from aerial images of TreeSatAI benchmark. Our results showed that YOLOv8-l outperformed benchmark’s initial release results, and other YOLOv8 variants with 71,55% and 72,70% for weighted and micro averaging scores, respectively.\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":"41841249","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-1-2023-449-2023
T. Akın, S. Berberoglu
Abstract. Land degradation is a global barrier to ecological, economic and sustainable developments. Climate change, natural disasters, human activities may result changes in soil organic carbon content, land productivity and land use/cover. Climate change is accelerating and expanding these degraded areas. If land destruction is not minimized, cause increasing population, inappropriate land use, climate change and rapid depletion of natural resources etc. in the coming years. It is estimated that land degradation and desertification will be the most important environmental problems. Mapping of land degradation using remote sensing techniques; determining sensitive areas for land degradation and taking protection measures; sustainable management of natural resources, ensuring sustainable agricultural production, etc. are the key factors. This study was conducted in the Seyhan basin, which is suffer from soil loss processes, changes in land cover and land use. These indicators are; trends in land productivity dynamics, land cover change and change of soil organic carbon stocks. The data set utilized to reveal the land degradation was including; 1 km resolution Land Productivity from JRC GLOBAL (1999–2013) and 250 m resolution NDVI from MOD13Q1 (2000–2015), Land Cover ESA CCI's with 300 m resolution LC (2000–2015), SOC stock from LUCAS (JRC) with 250 m resolution, 2000–2018 data from CORINE. The land degradation of the Seyhan basin was mapped using the specified land degradation indicators together with the One Out All Out (1OAO) rule.
摘要土地退化是生态、经济和可持续发展的全球性障碍。气候变化、自然灾害、人类活动可能导致土壤有机碳含量、土地生产力和土地利用/覆盖的变化。气候变化正在加速和扩大这些退化地区。如果不尽量减少土地破坏，在未来几年将导致人口增加、土地使用不当、气候变化和自然资源迅速枯竭等。据估计，土地退化和沙漠化将是最重要的环境问题。利用遥感技术绘制土地退化图;确定土地退化敏感区并采取保护措施;自然资源的可持续管理，确保可持续农业生产等是关键因素。研究对象为塞罕河流域，该流域的土壤流失过程、土地覆被和土地利用都发生了变化。这些指标是;土地生产力动态、土地覆被变化和土壤有机碳储量变化趋势。用于揭示土地退化的数据集包括:JRC GLOBAL的1公里分辨率土地生产力(1999-2013)和MOD13Q1的250米分辨率NDVI(2000-2015)，土地覆盖ESA CCI(300米分辨率LC) (2000-2015)， LUCAS (JRC)的250米分辨率SOC储量，2000-2018数据来自CORINE。采用指定的土地退化指标和“一出全出”(One Out All Out, 10ao)规则对塞汉流域土地退化进行了制图。
{"title":"MODELING LAND DEGRADATION USING REMOTE SENSING DATA: THE CASE OF SEYHAN BASIN","authors":"T. Akın, S. Berberoglu","doi":"10.5194/isprs-archives-xlviii-m-1-2023-449-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-449-2023","url":null,"abstract":"Abstract. Land degradation is a global barrier to ecological, economic and sustainable developments. Climate change, natural disasters, human activities may result changes in soil organic carbon content, land productivity and land use/cover. Climate change is accelerating and expanding these degraded areas. If land destruction is not minimized, cause increasing population, inappropriate land use, climate change and rapid depletion of natural resources etc. in the coming years. It is estimated that land degradation and desertification will be the most important environmental problems. Mapping of land degradation using remote sensing techniques; determining sensitive areas for land degradation and taking protection measures; sustainable management of natural resources, ensuring sustainable agricultural production, etc. are the key factors. This study was conducted in the Seyhan basin, which is suffer from soil loss processes, changes in land cover and land use. These indicators are; trends in land productivity dynamics, land cover change and change of soil organic carbon stocks. The data set utilized to reveal the land degradation was including; 1 km resolution Land Productivity from JRC GLOBAL (1999–2013) and 250 m resolution NDVI from MOD13Q1 (2000–2015), Land Cover ESA CCI's with 300 m resolution LC (2000–2015), SOC stock from LUCAS (JRC) with 250 m resolution, 2000–2018 data from CORINE. The land degradation of the Seyhan basin was mapped using the specified land degradation indicators together with the One Out All Out (1OAO) rule.\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":"43929895","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-1-2023-579-2023
A. Schneibel, M. Gähler, M. Halbgewachs, R. Berger, J. Brauchle, M. Gessner, V. Gstaiger, D. Hein, C. Henry, N. Merkle, D. Klein
Abstract. In the early morning hours on Tuesday, February 6, 2023, the southern part of Türkiye was struck by two large and several smaller earthquakes, causing destruction and casualties over a remarkably large area. In such cases, quick response and well-informed coordination is a key factor to successful first aid responses since damage and the number of people buried or in need often remain unclear in the hours after the disaster. The German Aerospace Center (DLR) responded to the earthquake by rapidly providing a number of information products, all above very high-resolution imagery in an easy-to-use web-based application. Next to satellite and drone imagery, damage information and pre-disaster imagery were provided to the users. Drone imagery was acquired in person for Kirikhan, a city in the south of the disaster area. Access to the viewer was granted to authorized users from public authorities, humanitarian aid organisations, and research institutes. Furthermore, DLR generated information products in the fields of settlement pattern, AI based damage assessment and tectonic movements. These data, as scientifically significant as they are, were not part of the web viewer. Within this paper, the reasons will be assessed as well as the general workflow of the activation. The paper will also discuss what steps need to be taken to ensure research outcomes being integrated into information products for users in future and how to prepare for the next disaster to still ensure a quick response but with an enriched product suite.
{"title":"USING EARTH OBSERVATION TO SUPPORT FIRST AID RESPONSE IN CRISIS SITUATIONS– LESSONS LEARNED FROM THE EARTHQUAKE IN TÜRKIYE/SYRIA (2023)","authors":"A. Schneibel, M. Gähler, M. Halbgewachs, R. Berger, J. Brauchle, M. Gessner, V. Gstaiger, D. Hein, C. Henry, N. Merkle, D. Klein","doi":"10.5194/isprs-archives-xlviii-m-1-2023-579-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-579-2023","url":null,"abstract":"Abstract. In the early morning hours on Tuesday, February 6, 2023, the southern part of Türkiye was struck by two large and several smaller earthquakes, causing destruction and casualties over a remarkably large area. In such cases, quick response and well-informed coordination is a key factor to successful first aid responses since damage and the number of people buried or in need often remain unclear in the hours after the disaster. The German Aerospace Center (DLR) responded to the earthquake by rapidly providing a number of information products, all above very high-resolution imagery in an easy-to-use web-based application. Next to satellite and drone imagery, damage information and pre-disaster imagery were provided to the users. Drone imagery was acquired in person for Kirikhan, a city in the south of the disaster area. Access to the viewer was granted to authorized users from public authorities, humanitarian aid organisations, and research institutes. Furthermore, DLR generated information products in the fields of settlement pattern, AI based damage assessment and tectonic movements. These data, as scientifically significant as they are, were not part of the web viewer. Within this paper, the reasons will be assessed as well as the general workflow of the activation. The paper will also discuss what steps need to be taken to ensure research outcomes being integrated into information products for users in future and how to prepare for the next disaster to still ensure a quick response but with an enriched product suite.\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":"41967316","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-1-2023-441-2023
H. B. Akdeniz
Abstract. The increase in impermeable surfaces within the urban areas contributes to local and regional-scale climate changes. This phenomenon, called "Urban Heat Island," is observed as the temperature in urban areas is higher than rural areas and natural landscape areas on the urban fringe. In recent years, advances in remote sensing and geographic information system technologies have enabled the urban heat island effect to be determined more quickly, economically, and accurately. In this study, the rapidly increasing urbanization in Konya, Türkiye and the resulting urban heat island effect have been analyzed. The study consists of four steps. In the first step, land surface temperatures for 1990 and 2022 of Konya city center were determined using the thermal band of Landsat-5 TM and Landsat-8 OLI satellite images. Then, satellite images were classified using the maximum likelihood method to determine land use and land cover in Konya. The effects of land use types and urban growth on urban heat island were examined. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-Up Index (NDBI) analyses were examined the statistical relationships between land surface temperature. The last step, the urban heat island effects of different types of regions in the city center of Konya were determined based on their urban form, texture, structure, landscape, and planning strategy. As a result of the study, measures that can be taken especially in spatial planning and design policies have been identified to reduce and prevent the urban heat island in Konya.
{"title":"SPATIO-TEMPORAL ANALYSIS OF THE EFFECTS OF URBAN GROWTH ON URBAN HEAT ISLAND: CASE OF KONYA, TURKIYE","authors":"H. B. Akdeniz","doi":"10.5194/isprs-archives-xlviii-m-1-2023-441-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-441-2023","url":null,"abstract":"Abstract. The increase in impermeable surfaces within the urban areas contributes to local and regional-scale climate changes. This phenomenon, called \"Urban Heat Island,\" is observed as the temperature in urban areas is higher than rural areas and natural landscape areas on the urban fringe. In recent years, advances in remote sensing and geographic information system technologies have enabled the urban heat island effect to be determined more quickly, economically, and accurately. In this study, the rapidly increasing urbanization in Konya, Türkiye and the resulting urban heat island effect have been analyzed. The study consists of four steps. In the first step, land surface temperatures for 1990 and 2022 of Konya city center were determined using the thermal band of Landsat-5 TM and Landsat-8 OLI satellite images. Then, satellite images were classified using the maximum likelihood method to determine land use and land cover in Konya. The effects of land use types and urban growth on urban heat island were examined. The Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built-Up Index (NDBI) analyses were examined the statistical relationships between land surface temperature. The last step, the urban heat island effects of different types of regions in the city center of Konya were determined based on their urban form, texture, structure, landscape, and planning strategy. As a result of the study, measures that can be taken especially in spatial planning and design policies have been identified to reduce and prevent the urban heat island in Konya.\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":"42440429","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-1-2023-509-2023
P. Ettehadi Osgouei, S. Kaya
Abstract. This study investigates the spatiotemporal variability of the aerosol optical depth (AOD) in the atmosphere over the Marmara region, Turkiye. Long-term satellite observations from MODIS MAIAC AOD data spanning the period from 2000 to 2021 are utilized. Examining the temporal variations in AOD in the Marmara region, it is observed that AOD reaches its peak during spring (May) and summer (August) months, while lower AOD values are observed in winter. Specifically, between August and December, there is a significant decline in monthly mean AOD which is majorly due to particulate removal from the atmosphere via precipitation scavenging. The findings reveal that the inter-annual variability of monthly AOD variations in the Marmara region is primarily influenced by temporary Saharan dust transportation with highest deviations from 22 year averaged AOD in late winters and early springs. The findings from the analysis of seasonal spatial variation of high AOD values revealed that the high AOD area is largest in the summer with about 54% of the total area and then spring (45%) and autumn (26%). Winter has the lowest HVA with 17% of the total area. The seasonal percentage rates of HVA are due to atmospheric conditions and aerosol sources. Larger HVA in summer is due to the increase of farming practices and biomass residue burnings combined with high moisture absorption effects and high temperature. The heating-specific emissions are the main source of anthropogenic emissions over the high AOD areas during the autumn and winter and aerosols are concentrated over the urbanized centres and industrialized zones.
{"title":"A COMPREHENSIVE ANALYSIS OF THE SPATIO-TEMPORAL VARIATION OF SATELLITE-BASED AEROSOL OPTICAL DEPTH IN MARMARA REGION OF TURKIYE DURING 2000–2021","authors":"P. Ettehadi Osgouei, S. Kaya","doi":"10.5194/isprs-archives-xlviii-m-1-2023-509-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-509-2023","url":null,"abstract":"Abstract. This study investigates the spatiotemporal variability of the aerosol optical depth (AOD) in the atmosphere over the Marmara region, Turkiye. Long-term satellite observations from MODIS MAIAC AOD data spanning the period from 2000 to 2021 are utilized. Examining the temporal variations in AOD in the Marmara region, it is observed that AOD reaches its peak during spring (May) and summer (August) months, while lower AOD values are observed in winter. Specifically, between August and December, there is a significant decline in monthly mean AOD which is majorly due to particulate removal from the atmosphere via precipitation scavenging. The findings reveal that the inter-annual variability of monthly AOD variations in the Marmara region is primarily influenced by temporary Saharan dust transportation with highest deviations from 22 year averaged AOD in late winters and early springs. The findings from the analysis of seasonal spatial variation of high AOD values revealed that the high AOD area is largest in the summer with about 54% of the total area and then spring (45%) and autumn (26%). Winter has the lowest HVA with 17% of the total area. The seasonal percentage rates of HVA are due to atmospheric conditions and aerosol sources. Larger HVA in summer is due to the increase of farming practices and biomass residue burnings combined with high moisture absorption effects and high temperature. The heating-specific emissions are the main source of anthropogenic emissions over the high AOD areas during the autumn and winter and aerosols are concentrated over the urbanized centres and industrialized zones.\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":"45393007","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-1-2023-491-2023
E. Çolak, F. Sunar
Abstract. For the management of the forest and the assessment of impacts on ecosystems, post-fire burned area mapping is crucial for sustainable environment and forestry. While optical remote sensing data has been extensively used for monitoring forest fires due to its spatial and temporal resolutions, it is susceptible to limitations imposed by poor weather conditions. To overcome this challenge, the complementary use of optical and Synthetic Aperture Radar (SAR) data is beneficial, as SAR can penetrate clouds and capture images in all-weather conditions. However, SAR lacks the necessary spectral features for comprehensive forest fire monitoring and burned area mapping. To overcome these limitations, this study proposes a Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) based deep feature translation method for burned area mapping by combining optical and SAR data. This approach allows for the retrieval of precise information of interest with a level of precision that cannot be achieved by either optical or SAR data alone. The Cycle-GAN uses a cyclic structure to transfer data from one domain (optical) to another domain (SAR) into the same feature space. As a result, it can maintain its spectral characteristics while providing ongoing and current information for monitoring forest fires. For this purpose, Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalised Burn Ratio (NBR) were determined using optical data and image translation was performed with Cycle-GAN on SAR data. The accuracy of the fake BAI, MIRBI and NBR spectral burn indices determined from the SAR was established by correlating the original spectral burn indices determined from the optical data. The results demonstrate a significant correlation between the real and generated fake burn indices, particularly with a noteworthy correlation coefficient of 0.93 observed for the NBR index. In addition, the findings validate the effectiveness of the generated indices in accurately representing and quantifying the extent of burned areas.
{"title":"CYCLE-GAN BASED FEATURE TRANSLATION FOR OPTICAL-SAR DATA IN BURNED AREA MAPPING","authors":"E. Çolak, F. Sunar","doi":"10.5194/isprs-archives-xlviii-m-1-2023-491-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-491-2023","url":null,"abstract":"Abstract. For the management of the forest and the assessment of impacts on ecosystems, post-fire burned area mapping is crucial for sustainable environment and forestry. While optical remote sensing data has been extensively used for monitoring forest fires due to its spatial and temporal resolutions, it is susceptible to limitations imposed by poor weather conditions. To overcome this challenge, the complementary use of optical and Synthetic Aperture Radar (SAR) data is beneficial, as SAR can penetrate clouds and capture images in all-weather conditions. However, SAR lacks the necessary spectral features for comprehensive forest fire monitoring and burned area mapping. To overcome these limitations, this study proposes a Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) based deep feature translation method for burned area mapping by combining optical and SAR data. This approach allows for the retrieval of precise information of interest with a level of precision that cannot be achieved by either optical or SAR data alone. The Cycle-GAN uses a cyclic structure to transfer data from one domain (optical) to another domain (SAR) into the same feature space. As a result, it can maintain its spectral characteristics while providing ongoing and current information for monitoring forest fires. For this purpose, Burn Area Index (BAI), Mid Infrared Burn Index (MIRBI), Normalised Burn Ratio (NBR) were determined using optical data and image translation was performed with Cycle-GAN on SAR data. The accuracy of the fake BAI, MIRBI and NBR spectral burn indices determined from the SAR was established by correlating the original spectral burn indices determined from the optical data. The results demonstrate a significant correlation between the real and generated fake burn indices, particularly with a noteworthy correlation coefficient of 0.93 observed for the NBR index. In addition, the findings validate the effectiveness of the generated indices in accurately representing and quantifying the extent of burned areas.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49434847","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-1-2023-565-2023
P. Russo, A. Laguarda, G. Abal, L. Doppler
Abstract. Aerosols are liquid or solid particles with diameters between 2.5 and 10 µm suspended in the lower layers of the atmosphere. Aerosol Optical Depth (AOD) is a relevant parameter that quantifies their concentration in the atmosphere. It is usually estimated from sun photometer measurements at specific wavelengths. The objective of this work is to implement a simple inversion algorithm to retrieve AOD at six different wavelengths (340, 380, 440, 500, 675 and 870 nm) using solar direct normal spectral irradiance ground measurements from a relatively low cost collimated spectroradiometer (EKO MS-711) at a low-altitude site in Montevideo, Uruguay. The results obtained are compared with AERONET products for the same site, including AOD and Angström coefficient. The results of AOD for all wavelengths show a consistent negative mean bias (MBD, unitless), between −0.005 and −0.015, and dispersion (RMSD, unitless) between 0.021 and 0.015 (to be compared to a mean reference AOD of 0.097). These metrics improve considerably for very clear days, MBD up to ± 0.001 and RMSD under 0.007 (to be compared to a mean reference AOD of 0.058). These results are considered to be a first step in implementing the methodology and acquiring local knowledge about AOD retrievals using relatively simple instrumentation.
{"title":"AEROSOL OPTICAL DEPTH FROM SPECTRAL DIRECT NORMAL IRRADIANCE MEASUREMENTS IN MONTEVIDEO, URUGUAY","authors":"P. Russo, A. Laguarda, G. Abal, L. Doppler","doi":"10.5194/isprs-archives-xlviii-m-1-2023-565-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-565-2023","url":null,"abstract":"Abstract. Aerosols are liquid or solid particles with diameters between 2.5 and 10 µm suspended in the lower layers of the atmosphere. Aerosol Optical Depth (AOD) is a relevant parameter that quantifies their concentration in the atmosphere. It is usually estimated from sun photometer measurements at specific wavelengths. The objective of this work is to implement a simple inversion algorithm to retrieve AOD at six different wavelengths (340, 380, 440, 500, 675 and 870 nm) using solar direct normal spectral irradiance ground measurements from a relatively low cost collimated spectroradiometer (EKO MS-711) at a low-altitude site in Montevideo, Uruguay. The results obtained are compared with AERONET products for the same site, including AOD and Angström coefficient. The results of AOD for all wavelengths show a consistent negative mean bias (MBD, unitless), between −0.005 and −0.015, and dispersion (RMSD, unitless) between 0.021 and 0.015 (to be compared to a mean reference AOD of 0.097). These metrics improve considerably for very clear days, MBD up to ± 0.001 and RMSD under 0.007 (to be compared to a mean reference AOD of 0.058). These results are considered to be a first step in implementing the methodology and acquiring local knowledge about AOD retrievals using relatively simple instrumentation.\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":"48065131","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-1-2023-601-2023
B. Tavus
Abstract. Efforts on flood mapping from active and passive satellite Earth Observation sensors increased in the last decade especially due to the availability of free datasets from European Space Agency’s Sentinel-1 and Sentinel-2 platforms. Regular data acquisition scheme also allows observing areas prone to natural hazards with a small temporal interval (within a week). Thus, before and after datasets are often available for detecting surface changes caused by flooding. This study investigates the contribution of textural variables to the predictive performance of a data-driven machine learning algorithm for detecting the effects of a flooding caused by Sardoba Dam break in Uzbekistan. In addition to the spectral channels of Sentinel-2 and polarization bands of Sentinel-1, two spectral indices (normalized difference vegetation index and modified normalized difference water index), and textural features of gray-level co-occurrence matrix (GLCM) were used with the Random Forest. Due to high dimensionality of input variables, principal component (PC) analysis was applied to the GLCM features and only the most significant PCs were used for modeling. The feature stacks used for learning were derived from both pre- and post-event Sentinel-1 and Sentinel-2 images. The models were validated through model test measures and external reference data obtained from PlanetScope imagery. The results show that the GLCM features improve the classification of flooded areas (from 82% to 93%) and flooded vegetation (from 17% to 78%) expressed in user’s accuracy. As an outcome of the study, the use of textural features is recommended for accurate mapping of flooded areas and flooded vegetation.
{"title":"GLCM FEATURES FOR LEARNING FLOODED VEGETATION FROM SENTINEL-1 AND SENTINEL-2 DATA","authors":"B. Tavus","doi":"10.5194/isprs-archives-xlviii-m-1-2023-601-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-601-2023","url":null,"abstract":"Abstract. Efforts on flood mapping from active and passive satellite Earth Observation sensors increased in the last decade especially due to the availability of free datasets from European Space Agency’s Sentinel-1 and Sentinel-2 platforms. Regular data acquisition scheme also allows observing areas prone to natural hazards with a small temporal interval (within a week). Thus, before and after datasets are often available for detecting surface changes caused by flooding. This study investigates the contribution of textural variables to the predictive performance of a data-driven machine learning algorithm for detecting the effects of a flooding caused by Sardoba Dam break in Uzbekistan. In addition to the spectral channels of Sentinel-2 and polarization bands of Sentinel-1, two spectral indices (normalized difference vegetation index and modified normalized difference water index), and textural features of gray-level co-occurrence matrix (GLCM) were used with the Random Forest. Due to high dimensionality of input variables, principal component (PC) analysis was applied to the GLCM features and only the most significant PCs were used for modeling. The feature stacks used for learning were derived from both pre- and post-event Sentinel-1 and Sentinel-2 images. The models were validated through model test measures and external reference data obtained from PlanetScope imagery. The results show that the GLCM features improve the classification of flooded areas (from 82% to 93%) and flooded vegetation (from 17% to 78%) expressed in user’s accuracy. As an outcome of the study, the use of textural features is recommended for accurate mapping of flooded areas and flooded vegetation.\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":"47248856","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-1-2023-485-2023
E. Çolak, B. V. Patel, A. Vyas, R. Zichner, M. Chandra
Abstract. As a result of increasing use of wind energy as a sustainable source of electricity, large Wind Parks with numerous Wind Turbines have been constructed. Wind turbines are extremely tall objects consisting of stationary and moving parts. The presence of wind turbines in the vicinity of weather radar systems can significantly impact their performance, leading to false alarms and errors in radar measurements. Accurate weather forecasting is challenging in this circumstance. Large Radar Cross Section (RCS) of wind turbines results in interference, also known asWind Turbine Clutter (WTC) orWind Turbine Interference (WTI), within and beyond the radar main beam, Multipath Interference (MPI), and phenomena referred to as ”shadowing effects” behind the wind turbines. These effects vary significantly in both time and space as a result of various wind turbine operations and meteorological conditions. It can often be difficult to distinguish wind turbine returns from weather-like signals. For the assessment of WTC or WTI, it is essential to understand the scattering properties of these wind turbines. In this paper, the bistatic scattering characteristics of a wind park turbine using a Unmanned Aerial Vehicle (UAV)-mounted receiver recording C-band weather radar signals were investigated by determining the average received power (PRxAvg (θs)) and RCS of wind turbine as a function of the scattering angle. For this purpose, the measurements and data provided by the German Meteorological Service (DWD, DeutscherWetterdienst) were utilised. The average received power as a function of scattering angle (θs) was calculated by using I-Q (In-phase and Quadrature) signals. Forward, back and side scattering of the calculated average received power were analysed separately. Moreover, Front-to-Back ratio, Front-to-Right side ratio and Front-to-Left side ratio were calculated and compared using forward, back and side scatter values. RCS values were also calculated depending on the scattering angle (θs) of the wind turbine.
{"title":"BISTATIC SCATTERING CHARACTERISTICS OF A WIND PARK TURBINE DERIVED FROM AN UAV-MOUNTED RECEIVER RECORDING C-BAND WEATHER RADAR SIGNALS","authors":"E. Çolak, B. V. Patel, A. Vyas, R. Zichner, M. Chandra","doi":"10.5194/isprs-archives-xlviii-m-1-2023-485-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-1-2023-485-2023","url":null,"abstract":"Abstract. As a result of increasing use of wind energy as a sustainable source of electricity, large Wind Parks with numerous Wind Turbines have been constructed. Wind turbines are extremely tall objects consisting of stationary and moving parts. The presence of wind turbines in the vicinity of weather radar systems can significantly impact their performance, leading to false alarms and errors in radar measurements. Accurate weather forecasting is challenging in this circumstance. Large Radar Cross Section (RCS) of wind turbines results in interference, also known asWind Turbine Clutter (WTC) orWind Turbine Interference (WTI), within and beyond the radar main beam, Multipath Interference (MPI), and phenomena referred to as ”shadowing effects” behind the wind turbines. These effects vary significantly in both time and space as a result of various wind turbine operations and meteorological conditions. It can often be difficult to distinguish wind turbine returns from weather-like signals. For the assessment of WTC or WTI, it is essential to understand the scattering properties of these wind turbines. In this paper, the bistatic scattering characteristics of a wind park turbine using a Unmanned Aerial Vehicle (UAV)-mounted receiver recording C-band weather radar signals were investigated by determining the average received power (PRxAvg (θs)) and RCS of wind turbine as a function of the scattering angle. For this purpose, the measurements and data provided by the German Meteorological Service (DWD, DeutscherWetterdienst) were utilised. The average received power as a function of scattering angle (θs) was calculated by using I-Q (In-phase and Quadrature) signals. Forward, back and side scattering of the calculated average received power were analysed separately. Moreover, Front-to-Back ratio, Front-to-Right side ratio and Front-to-Left side ratio were calculated and compared using forward, back and side scatter values. RCS values were also calculated depending on the scattering angle (θs) of the wind turbine.\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":"48274292","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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