Pub Date : 2023-09-05DOI: 10.5194/isprs-archives-xlviii-m-3-2023-21-2023
K. Ashritha, P. C. Deka
Abstract. Roads play a crucial role in the urban spatial structure. A place's development and growth depend on the road network connectivity and accessibility being the socio-economic and transportation carrier. It involves the mobility of people and goods from one place to another. The choice of mode of travel depends on the living standards, connectivity, and vicinity to the work area. The study uses satellite data to analyze road network connectivity using the connectivity indices of Mangalore City Corporation, a port city in India. The connectivity indices alpha, beta, gamma, and eta showed the Area's good connectivity with proper roads and interconnectivity. Using Dijkstra's algorithm, the least cost path is identified on which the spatial mapping of the travel information is made. The travel information raster served the commuter in knowing the time, distance, and cost of modes from all possible origins to each city center. Specifically, it serves as the base map for bus routes, their cost, and travel time for significant city bus stations. The cost of travel, Duration, and distance information is mapped for two-wheeler and four-wheeler commuters. The study used the Modis Land Use Land Cover Data to identify inaccessible road network areas.
{"title":"SPATIAL MAPPING OF TRAVEL INFORMATION AND ASSESSMENT OF ROAD CONNECTIVITY","authors":"K. Ashritha, P. C. Deka","doi":"10.5194/isprs-archives-xlviii-m-3-2023-21-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-21-2023","url":null,"abstract":"Abstract. Roads play a crucial role in the urban spatial structure. A place's development and growth depend on the road network connectivity and accessibility being the socio-economic and transportation carrier. It involves the mobility of people and goods from one place to another. The choice of mode of travel depends on the living standards, connectivity, and vicinity to the work area. The study uses satellite data to analyze road network connectivity using the connectivity indices of Mangalore City Corporation, a port city in India. The connectivity indices alpha, beta, gamma, and eta showed the Area's good connectivity with proper roads and interconnectivity. Using Dijkstra's algorithm, the least cost path is identified on which the spatial mapping of the travel information is made. The travel information raster served the commuter in knowing the time, distance, and cost of modes from all possible origins to each city center. Specifically, it serves as the base map for bus routes, their cost, and travel time for significant city bus stations. The cost of travel, Duration, and distance information is mapped for two-wheeler and four-wheeler commuters. The study used the Modis Land Use Land Cover Data to identify inaccessible road network areas.\u0000","PeriodicalId":30634,"journal":{"name":"The International Archives of the Photogrammetry Remote Sensing and Spatial Information Sciences","volume":" ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43401627","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-3-2023-249-2023
R. Verma, J. Zawadzka, P. Garg
Abstract. India is a magnanimous country having large population centres with different settlement characteristics in various states and Union Territories (UTs), which can affect climate and development of country in longer duration. As such spatio-temporal analysis of urban dynamics over different constituent land use/land cover (LU/LC) is performed in this study using open source data and software programs only. The study derives a pattern of 4 Landscape Metrics (LSMs) by mapping urban growth through continuity, complexity, centrality and compactness of built-up land use using a publically available classified Decadal Land use data of India for years 1985, 1995 and 2005, over a period of 20 years in 7 zones of India. Spatially, UTs are showing lowest values in all LSMs which may be attributed to comparatively smaller sizes of districts in UTs. Central zone of India is showing highest values of Largest Patch Index (LPI) indicating larger built-up patches in zone, as larger population resides in the central states of India. East zone is having most complex shape of urbanisation with highest Landscape Shape Index (LSI) value. West Zone is predominantly showing greater centrality values through Mean Euclidean Nearest Neighbor Distance (ENN_MN), as larger part of it comprises of dessert. Temporally, built-up patches are larger and more complex in shape but less centralized in year 2005 with Aggregation Index (AI) remaining almost same over the years. All the results are indicating a dispersed urban growth in zones of India with similar surroundings of past years.
{"title":"ZONAL PERSPECTIVE ON SPATIO-TEMPORAL LAND USE CHANGE IN INDIA THROUGH METRICS","authors":"R. Verma, J. Zawadzka, P. Garg","doi":"10.5194/isprs-archives-xlviii-m-3-2023-249-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-249-2023","url":null,"abstract":"Abstract. India is a magnanimous country having large population centres with different settlement characteristics in various states and Union Territories (UTs), which can affect climate and development of country in longer duration. As such spatio-temporal analysis of urban dynamics over different constituent land use/land cover (LU/LC) is performed in this study using open source data and software programs only. The study derives a pattern of 4 Landscape Metrics (LSMs) by mapping urban growth through continuity, complexity, centrality and compactness of built-up land use using a publically available classified Decadal Land use data of India for years 1985, 1995 and 2005, over a period of 20 years in 7 zones of India. Spatially, UTs are showing lowest values in all LSMs which may be attributed to comparatively smaller sizes of districts in UTs. Central zone of India is showing highest values of Largest Patch Index (LPI) indicating larger built-up patches in zone, as larger population resides in the central states of India. East zone is having most complex shape of urbanisation with highest Landscape Shape Index (LSI) value. West Zone is predominantly showing greater centrality values through Mean Euclidean Nearest Neighbor Distance (ENN_MN), as larger part of it comprises of dessert. Temporally, built-up patches are larger and more complex in shape but less centralized in year 2005 with Aggregation Index (AI) remaining almost same over the years. All the results are indicating a dispersed urban growth in zones of India with similar surroundings of past years.\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":"47380025","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-3-2023-197-2023
M. H. R. Putranto, I. Meilano, R. Virtriana, M. Abdurrachman, R. F. Adiwijaya
Abstract. Indonesia is located on the Ring of Fire with the most geologically active than any other countries, which makes it vulnerable due to the massive earthquakes and volcanic eruptions. Java Island has the most active volcano with high risks such as human risk and infrastructure from volcanic ash because of volcanic eruptions. The availability of the map of potential volcanic hazards is important to help mitigate the risk caused by volcanic eruptions. However, to the best of the author's knowledge, the distribution of volcanic ash has never been assessed in detail in the disaster-prone hazard map published by the Centre for Volcanology and Geological Hazard Mitigation (CVGHM), Indonesia. This research reported the potential distribution of volcanic ash due to volcanic eruptions in the future in Java island. Following the principles of Probabilistic Hazard Assessment and TephraProb software, the modeling of volcanic ash potential was performed using various parameters such as historical data, eruption source parameter, total grain-size distribution, tephra2 parameter, and the wind speed around the volcanoes as an input. The map shows the distribution of volcanic ash based on the volcanic ash accumulation (kg/m2) and the volcanic ash hazard map is classified into three classes. There are 19 models of volcanic ash distribution with various probabilities of exceedance based on 19 A-type volcanoes on Java Island. This volcano's distribution of volcanic ash tends to the southwest as the wind speed and direction.
{"title":"SPATIAL ANALYSIS OF VOLCANIC ASH DISTRIBUTION DUE TO VOLCANIC ERUPTION IN JAVA ISLAND","authors":"M. H. R. Putranto, I. Meilano, R. Virtriana, M. Abdurrachman, R. F. Adiwijaya","doi":"10.5194/isprs-archives-xlviii-m-3-2023-197-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-197-2023","url":null,"abstract":"Abstract. Indonesia is located on the Ring of Fire with the most geologically active than any other countries, which makes it vulnerable due to the massive earthquakes and volcanic eruptions. Java Island has the most active volcano with high risks such as human risk and infrastructure from volcanic ash because of volcanic eruptions. The availability of the map of potential volcanic hazards is important to help mitigate the risk caused by volcanic eruptions. However, to the best of the author's knowledge, the distribution of volcanic ash has never been assessed in detail in the disaster-prone hazard map published by the Centre for Volcanology and Geological Hazard Mitigation (CVGHM), Indonesia. This research reported the potential distribution of volcanic ash due to volcanic eruptions in the future in Java island. Following the principles of Probabilistic Hazard Assessment and TephraProb software, the modeling of volcanic ash potential was performed using various parameters such as historical data, eruption source parameter, total grain-size distribution, tephra2 parameter, and the wind speed around the volcanoes as an input. The map shows the distribution of volcanic ash based on the volcanic ash accumulation (kg/m2) and the volcanic ash hazard map is classified into three classes. There are 19 models of volcanic ash distribution with various probabilities of exceedance based on 19 A-type volcanoes on Java Island. This volcano's distribution of volcanic ash tends to the southwest as the wind speed and direction.\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":"44300699","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-3-2023-7-2023
C. Apraku, Y. Twumasi, Z. H. Ning, M. Anokye, P. Loh, R. Armah, J. Oppong
Abstract. Urban sprawl has become a huge concern for cities like Los Angeles, New York, and Chicago in recent years. As urban sprawl pushes urbanization into city suburbs and outskirts, forest fragmentation becomes evidently prevalent and exposes forests to high temperatures, pollution, pests, and fires that threaten forest health. A 2021 report titled Rebuilding for a Resilient Recovery affirmed that the frequency and damage potential of wildfires have been exacerbated by climate change and urban sprawl especially in California. Globally, these fires can be attributed to both natural and anthropogenic drivers such as deforestation, agriculture, mining, and industrialization. Future projections predict that these incidences of fires will only worsen as the planet continues to warm further, with emphasis on the spread and intensities of the annual California wildfires over the decade. Quantifying the consequences of these fires on global climate change has become crucial and with the emergence of advanced GIS mapping tools, focus, visualization, and interpretation of fire and burn severity has become easier. However, knowledge and understanding of wildfire dynamics is limited especially in terms of fuel load, impacts on vegetation health, aerosol release and associated movement in the atmosphere. It is therefore important to address these gaps to make better and informed actions towards forest use, protection, management, and policies and broadly towards ambitious climate goals such as the UN’s Carbon Neutral goal by 2050. This study uses Sentinel 2A data from the Copernicus fleet between 2018 and 2022 to identify and assess the burn severity of affected areas in Sonoma County, California. The aim of the study is to understand the impacts of fires of fire on vegetation health and the post-fire recovery process. The Normalized Burn Ration Index (NBRI) was used to identify and measure the extent of the burnt areas within the county and their severity and Normalized Difference Vegetation Index (NDVI) was used as a measure of forest heath. The results show that Sonoma County has become a high burn severity area with a major decrease in unburned areas between 2018 and 2022. NDVI values recorded all decrease from January to December for all the years because of pre-fire season drought. The wildfire season begins in May and before then there are seasonal droughts that occur hence accounting for the initial decline in NDVI. The least values recorded were between 0.5 and 0.57 for September, indicating sparse and unhealthy vegetation because of sharp declines during the fire season.
{"title":"MAPPING FIRE SEVERITY FROM RECENT CALIFORNIA WILDFIRES USING SATELLITE IMAGERY","authors":"C. Apraku, Y. Twumasi, Z. H. Ning, M. Anokye, P. Loh, R. Armah, J. Oppong","doi":"10.5194/isprs-archives-xlviii-m-3-2023-7-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-7-2023","url":null,"abstract":"Abstract. Urban sprawl has become a huge concern for cities like Los Angeles, New York, and Chicago in recent years. As urban sprawl pushes urbanization into city suburbs and outskirts, forest fragmentation becomes evidently prevalent and exposes forests to high temperatures, pollution, pests, and fires that threaten forest health. A 2021 report titled Rebuilding for a Resilient Recovery affirmed that the frequency and damage potential of wildfires have been exacerbated by climate change and urban sprawl especially in California. Globally, these fires can be attributed to both natural and anthropogenic drivers such as deforestation, agriculture, mining, and industrialization. Future projections predict that these incidences of fires will only worsen as the planet continues to warm further, with emphasis on the spread and intensities of the annual California wildfires over the decade. Quantifying the consequences of these fires on global climate change has become crucial and with the emergence of advanced GIS mapping tools, focus, visualization, and interpretation of fire and burn severity has become easier. However, knowledge and understanding of wildfire dynamics is limited especially in terms of fuel load, impacts on vegetation health, aerosol release and associated movement in the atmosphere. It is therefore important to address these gaps to make better and informed actions towards forest use, protection, management, and policies and broadly towards ambitious climate goals such as the UN’s Carbon Neutral goal by 2050. This study uses Sentinel 2A data from the Copernicus fleet between 2018 and 2022 to identify and assess the burn severity of affected areas in Sonoma County, California. The aim of the study is to understand the impacts of fires of fire on vegetation health and the post-fire recovery process. The Normalized Burn Ration Index (NBRI) was used to identify and measure the extent of the burnt areas within the county and their severity and Normalized Difference Vegetation Index (NDVI) was used as a measure of forest heath. The results show that Sonoma County has become a high burn severity area with a major decrease in unburned areas between 2018 and 2022. NDVI values recorded all decrease from January to December for all the years because of pre-fire season drought. The wildfire season begins in May and before then there are seasonal droughts that occur hence accounting for the initial decline in NDVI. The least values recorded were between 0.5 and 0.57 for September, indicating sparse and unhealthy vegetation because of sharp declines during the fire season.\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":"41461425","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}
Abstract. Frequency of extreme weather events such as cloudbursts, heatwaves etc. have increased as an outcome of changing climate. Identification of the pattern of extreme temperature events is important since it governs various events such as heatwaves, wildfires, droughts, storms, coldwaves etc. Moderate Resolution Imaging Spectroradiometer (MODIS) provides Land Surface Temperature (LST) data at 1 kilometre of spatial resolution at daily interval that can help in the identification and mapping of the anomalies in the temperature at pixel level. This study proposes a global-scale daily long-term thermal anomaly detection tool made using Google Earth Engine (GEE) App. This open source tool with the name of ‘Deviation from Mean’ uses the MODIS LST data available from 2000 till date to detect temperature anomaly based on the deviation of temperature of any day (chosen by the user) from the long-term climatological mean. It also generates a time-series plot of temperature values of any pixel for any date for last 24 years i.e. 2000–2023 in the graphical form to analyze the variation in the temperature over the time. A case study has also been done using the tool to highlight the thermal anomaly experienced over the Indian sub-continent during March-April, 2022 and 2023. This tool is capable of providing thermal anomaly information at global, regional as well as local level that can help in taking region-specific mitigation measures.
摘要由于气候变化，诸如暴雨、热浪等极端天气事件的频率有所增加。识别极端温度事件的模式很重要，因为它控制着各种事件，如热浪、野火、干旱、风暴、寒潮等。中分辨率成像光谱辐射计(MODIS)提供每日1公里空间分辨率的地表温度(LST)数据，有助于在像元水平上识别和绘制温度异常。本研究提出了一个使用谷歌Earth Engine (GEE) App制作的全球尺度日长期热异常检测工具。这个名为“Deviation from Mean”的开源工具使用2000年至今的MODIS LST数据，根据任意一天(用户选择)的温度与长期气候平均值的偏差来检测温度异常。它亦会以图形形式生成过去24年(即2000-2023年)任何日期任何像元的温度值的时间序列图，以分析温度随时间的变化。利用该工具还进行了一个案例研究，以突出2022年3月至4月、2023年印度次大陆经历的热异常。该工具能够提供全球、区域和地方各级的热异常信息，有助于采取针对特定区域的缓解措施。
{"title":"LONG-TERM THERMAL ANOMALY DETECTION AND MAPPING AT PIXEL LEVEL USING A GOOGLE EARTH ENGINE TOOL","authors":"S. Mamgain, Kshama Gupta, Harish Arijit Roy, Chandra Karnatak, Raghavendra Pratap Singh","doi":"10.5194/isprs-archives-xlviii-m-3-2023-147-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-147-2023","url":null,"abstract":"Abstract. Frequency of extreme weather events such as cloudbursts, heatwaves etc. have increased as an outcome of changing climate. Identification of the pattern of extreme temperature events is important since it governs various events such as heatwaves, wildfires, droughts, storms, coldwaves etc. Moderate Resolution Imaging Spectroradiometer (MODIS) provides Land Surface Temperature (LST) data at 1 kilometre of spatial resolution at daily interval that can help in the identification and mapping of the anomalies in the temperature at pixel level. This study proposes a global-scale daily long-term thermal anomaly detection tool made using Google Earth Engine (GEE) App. This open source tool with the name of ‘Deviation from Mean’ uses the MODIS LST data available from 2000 till date to detect temperature anomaly based on the deviation of temperature of any day (chosen by the user) from the long-term climatological mean. It also generates a time-series plot of temperature values of any pixel for any date for last 24 years i.e. 2000–2023 in the graphical form to analyze the variation in the temperature over the time. A case study has also been done using the tool to highlight the thermal anomaly experienced over the Indian sub-continent during March-April, 2022 and 2023. This tool is capable of providing thermal anomaly information at global, regional as well as local level that can help in taking region-specific mitigation measures.\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":"45236018","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}
Abstract. Training Deep Learning (DL) algorithms for segmenting features require hundreds to thousands of input data and corresponding labels. Generating thousands of input images and labels requires considerable resources and time. Hence, it is common practice to use opensource imagery data and labels available online. Most of these open-source data have little or no metadata describing their quality or suitability making it problematic for training or evaluating DL models. This study evaluated the effect of data quality on training DeepLabV3+, using Sentinel 2 A/B RGB images and labels obtained from Kaggle. We generated subsets of 256 × 256 pixels, and 10% of these images (802) were set aside for testing. First, we trained and validated the DeepLabV3+ model with the remaining images. Second, we removed images with incorrect labels and trained another DeepLabV3+ network. Finally, we trained the third DeepLabV3+ network after removing images with turbid water or with floating vegetation. All three trained models were evaluated with test images and then we calculated accuracy metrics. As the quality of the input images improved, accuracy of the predicted masks generated from the first model increased from 92.8% to 94.3% in the second model. The third model’s accuracy was 96.4%, demonstrating the network’s ability to better learn and predict water bodies when the input data had fewer class variations. Based on the results we recommend assessing the quality of open-source data for incorrect labels and variations in the target class prior to training DeepLabV3+ or any other DL network.
{"title":"EFFECT OF DATA QUALITY ON WATER BODY SEGMENTATION WITH DEEPLABV3+ ALGORITHM","authors":"Anirudh Edpuganti, Pillalamarri Akshaya, Jangala Gouthami, Sajith Variyar, Sowmya, R. Sivanpillai","doi":"10.5194/isprs-archives-xlviii-m-3-2023-81-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-81-2023","url":null,"abstract":"Abstract. Training Deep Learning (DL) algorithms for segmenting features require hundreds to thousands of input data and corresponding labels. Generating thousands of input images and labels requires considerable resources and time. Hence, it is common practice to use opensource imagery data and labels available online. Most of these open-source data have little or no metadata describing their quality or suitability making it problematic for training or evaluating DL models. This study evaluated the effect of data quality on training DeepLabV3+, using Sentinel 2 A/B RGB images and labels obtained from Kaggle. We generated subsets of 256 × 256 pixels, and 10% of these images (802) were set aside for testing. First, we trained and validated the DeepLabV3+ model with the remaining images. Second, we removed images with incorrect labels and trained another DeepLabV3+ network. Finally, we trained the third DeepLabV3+ network after removing images with turbid water or with floating vegetation. All three trained models were evaluated with test images and then we calculated accuracy metrics. As the quality of the input images improved, accuracy of the predicted masks generated from the first model increased from 92.8% to 94.3% in the second model. The third model’s accuracy was 96.4%, demonstrating the network’s ability to better learn and predict water bodies when the input data had fewer class variations. Based on the results we recommend assessing the quality of open-source data for incorrect labels and variations in the target class prior to training DeepLabV3+ or any other DL network.\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":"46153971","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-3-2023-15-2023
Recheal Naa, Dedei Armah, Z. H. Ning, M. Anokye, Y. Twumasi, D. B. Frimpong, A. Asare-Ansah, P. Loh, F. Owusu
Abstract. The Sam Houston National Forest is a large, forested area in Texas that has experienced significant land-use changes over the past few decades. The study area replicates plentiful climatic, physiographic, and edaphic differences in the country and this forest faces a serious problem of degradation and disturbance of different nature. In this study, we utilized remote sensing technology specifically Landsat 4 ETM and Landsat 8 from USGS Earth Explorer with spatial resolution 30 m, to analyze forest cover change in Sam Houston National Forest from 2001 to 2020. We also employed the Hansen Global Forest Cover Data from the Google Earth Engine Catalogue to assess the forest cover loss and gain within the study period. Also, the i-Tree software was used to estimate carbon sequestration in the forest and assess the potential benefits of forest management practices. Results of the study showed that the Sam Houston National Forest has experienced a net loss of forest cover over the past few decades, primarily due to agricultural expansion and urbanization. However, the forest has also shown signs of regrowth and recovery in certain areas, highlighting the potential for effective forest management practices to promote carbon sequestration and conservation. Overall, our study highlights the importance of remote sensing technology for understanding forest cover change and its implications for carbon sequestration and climate change mitigation.
{"title":"USING REMOTE SENSING TO DETECT FOREST COVER CHANGE IN SAM HOUSTON NATIONAL FOREST, TEXAS","authors":"Recheal Naa, Dedei Armah, Z. H. Ning, M. Anokye, Y. Twumasi, D. B. Frimpong, A. Asare-Ansah, P. Loh, F. Owusu","doi":"10.5194/isprs-archives-xlviii-m-3-2023-15-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-15-2023","url":null,"abstract":"Abstract. The Sam Houston National Forest is a large, forested area in Texas that has experienced significant land-use changes over the past few decades. The study area replicates plentiful climatic, physiographic, and edaphic differences in the country and this forest faces a serious problem of degradation and disturbance of different nature. In this study, we utilized remote sensing technology specifically Landsat 4 ETM and Landsat 8 from USGS Earth Explorer with spatial resolution 30 m, to analyze forest cover change in Sam Houston National Forest from 2001 to 2020. We also employed the Hansen Global Forest Cover Data from the Google Earth Engine Catalogue to assess the forest cover loss and gain within the study period. Also, the i-Tree software was used to estimate carbon sequestration in the forest and assess the potential benefits of forest management practices. Results of the study showed that the Sam Houston National Forest has experienced a net loss of forest cover over the past few decades, primarily due to agricultural expansion and urbanization. However, the forest has also shown signs of regrowth and recovery in certain areas, highlighting the potential for effective forest management practices to promote carbon sequestration and conservation. Overall, our study highlights the importance of remote sensing technology for understanding forest cover change and its implications for carbon sequestration and climate change mitigation.\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":"44536759","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-3-2023-1-2023
T. S. Anggraini, C. Santoso
Abstract. Renewable energy is a solution for reducing environmental damage caused by greenhouse gas emissions from fossil fuels. Energy consumption in Indonesia involves sectors such as industry, households, transportation, and agriculture, which still heavily rely on non-renewable energy sources. Indonesia possesses significant maritime potential, boasting the second longest coastline in the world, a water area covering 71%, and abundant marine biological resources that can contribute to maritime economic growth. This research aims to leverage remote sensing technology and geographic information science to harness Indonesia's maritime potential. In the realm of renewable energy, this study emphasizes the potential of wave energy and current energy in Indonesia, with the objective of establishing an Ocean Renewable Energy (ORE) model. Additionally, the research will consider marine habitat suitability to mitigate any negative impact on biodiversity from power generation activities. Based on the research findings, the ocean current energy in Lombok Strait has a high energy potential reaching 1,035 Watts and Maluku Sea has 1,536 Watts, while the southwest region exhibits wave energy potential of Panaitan Island has a wave energy potential of 23,051 kW/m, while Sangiang Island has a potential of 12,842 kW/m. Furthermore, potential energy locations will be identified through the overlaying of potential fish zones. The ultimate goal of this research is to fulfill sustainable energy needs, decrease dependence on fossil fuels, and promote sustainable economic growth in Indonesia.
{"title":"DEVELOPMENT OF OCEAN RENEWABLE ENERGY MODEL IN INDONESIA TO SUPPORT ECO-FRIENDLY ENERGY","authors":"T. S. Anggraini, C. Santoso","doi":"10.5194/isprs-archives-xlviii-m-3-2023-1-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-1-2023","url":null,"abstract":"Abstract. Renewable energy is a solution for reducing environmental damage caused by greenhouse gas emissions from fossil fuels. Energy consumption in Indonesia involves sectors such as industry, households, transportation, and agriculture, which still heavily rely on non-renewable energy sources. Indonesia possesses significant maritime potential, boasting the second longest coastline in the world, a water area covering 71%, and abundant marine biological resources that can contribute to maritime economic growth. This research aims to leverage remote sensing technology and geographic information science to harness Indonesia's maritime potential. In the realm of renewable energy, this study emphasizes the potential of wave energy and current energy in Indonesia, with the objective of establishing an Ocean Renewable Energy (ORE) model. Additionally, the research will consider marine habitat suitability to mitigate any negative impact on biodiversity from power generation activities. Based on the research findings, the ocean current energy in Lombok Strait has a high energy potential reaching 1,035 Watts and Maluku Sea has 1,536 Watts, while the southwest region exhibits wave energy potential of Panaitan Island has a wave energy potential of 23,051 kW/m, while Sangiang Island has a potential of 12,842 kW/m. Furthermore, potential energy locations will be identified through the overlaying of potential fish zones. The ultimate goal of this research is to fulfill sustainable energy needs, decrease dependence on fossil fuels, and promote sustainable economic growth in Indonesia.\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":"43514383","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-3-2023-127-2023
A. Ládai, C. Toth
Abstract. Airborne and ground mobile mapping platforms are increasingly used in group formations to increase productivity or complement each other in terms of improving observation capacity and efficiency in the surveyed area. Similarly, the navigation of assisted and autonomous vehicles presents a similar problem of sharing a small place where, for example, accurate relative positioning is essential to avoid collisions. Estimating platform relative positions from inter-platform range measurements is important in these applications, as they can provide valuable information to improve individual platform navigation or can potentially detect anomalies in these solutions that could be caused by unintentional or intentional disturbances. Free-network adjustment based on ranges forms the baseline solution to obtain relative positions. The challenge is to provide adequate platform position approximations for the least squares adjustment to achieve both quick convergence and fast execution time. Here we propose a preprocessing method that creates suitable approximations based on range values.
{"title":"OBTAINING APPROXIMATIONS FOR RANGE-BASED FREE-NETWORK ADJUSTMENT","authors":"A. Ládai, C. Toth","doi":"10.5194/isprs-archives-xlviii-m-3-2023-127-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-127-2023","url":null,"abstract":"Abstract. Airborne and ground mobile mapping platforms are increasingly used in group formations to increase productivity or complement each other in terms of improving observation capacity and efficiency in the surveyed area. Similarly, the navigation of assisted and autonomous vehicles presents a similar problem of sharing a small place where, for example, accurate relative positioning is essential to avoid collisions. Estimating platform relative positions from inter-platform range measurements is important in these applications, as they can provide valuable information to improve individual platform navigation or can potentially detect anomalies in these solutions that could be caused by unintentional or intentional disturbances. Free-network adjustment based on ranges forms the baseline solution to obtain relative positions. The challenge is to provide adequate platform position approximations for the least squares adjustment to achieve both quick convergence and fast execution time. Here we propose a preprocessing method that creates suitable approximations based on range values.\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":"42554043","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-3-2023-279-2023
Q. Yuan, X. Tang
Abstract. The intelligent interpretation of land-use change has become a research frontier. Reasonably and effectively utilizing limited land resources and making scientific predictions to promote sustainable utilization of land resources is significant for establishing a resource-saving and environmentally friendly society. Remote sensing technology can efficiently complete multi-temporal and dynamic land-use change detection, especially using high-spatial resolution remote sensing images. However, the existing land-use change and prediction have not been combined. In addition, land-use change detection mainly relies on shallow feature design, resulting in low prediction accuracy and weak generalization performance. To solve the above problems, we proposed a CNN-based CA Markov model using multi-temporal GaoFen satellite remote sensing images for the change detection and prediction of land cover. Taking the city of Panzhihua in China as an example, the study constructed training sample data that includes a multi-temporal remote sensing training dataset from 2006, 2010, 2015, and 2021 using GaoFen satellite remote sensing images. Meanwhile, a multitemporal CNN land-use detection model was constructed to generate a land-use transfer matrix by training the dataset. Furthermore, the comprehensive driving factors were selected, including terrain factors (height and slope) and social factors (economic and population density). Then, the CA-Markov model was constructed to predict the land-use development trend in Panzhihua City after ten years. Compared with the traditional methods, experimental results demonstrate that the proposed model can improve the model's automatic interpretation ability and prediction accuracy with an increase of 24.6% in the FoM index and 4.37% in the Kappa coefficient.
{"title":"MULTI-TEMPORAL URBAN LAND-USE CHANGE DETECTION AND PREDICTION USING CNN-BASED CA-MARKOV MODEL FROM GAOFEN SATELLITE IMAGES","authors":"Q. Yuan, X. Tang","doi":"10.5194/isprs-archives-xlviii-m-3-2023-279-2023","DOIUrl":"https://doi.org/10.5194/isprs-archives-xlviii-m-3-2023-279-2023","url":null,"abstract":"Abstract. The intelligent interpretation of land-use change has become a research frontier. Reasonably and effectively utilizing limited land resources and making scientific predictions to promote sustainable utilization of land resources is significant for establishing a resource-saving and environmentally friendly society. Remote sensing technology can efficiently complete multi-temporal and dynamic land-use change detection, especially using high-spatial resolution remote sensing images. However, the existing land-use change and prediction have not been combined. In addition, land-use change detection mainly relies on shallow feature design, resulting in low prediction accuracy and weak generalization performance. To solve the above problems, we proposed a CNN-based CA Markov model using multi-temporal GaoFen satellite remote sensing images for the change detection and prediction of land cover. Taking the city of Panzhihua in China as an example, the study constructed training sample data that includes a multi-temporal remote sensing training dataset from 2006, 2010, 2015, and 2021 using GaoFen satellite remote sensing images. Meanwhile, a multitemporal CNN land-use detection model was constructed to generate a land-use transfer matrix by training the dataset. Furthermore, the comprehensive driving factors were selected, including terrain factors (height and slope) and social factors (economic and population density). Then, the CA-Markov model was constructed to predict the land-use development trend in Panzhihua City after ten years. Compared with the traditional methods, experimental results demonstrate that the proposed model can improve the model's automatic interpretation ability and prediction accuracy with an increase of 24.6% in the FoM index and 4.37% in the Kappa coefficient.\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":"42437983","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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