Remote Sensing and Artificial Intelligence in Inland Waters Monitoring
Water, vital for life, confronts unprecedented challenges in aquatic ecosystems due to factors like scarcity and pollution. Monitoring at local to global scales is vital for effective management, aligning with sustainable development goals. To address these challenges, the integration of remote sens...
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| Formato: | Online |
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| Idioma: | inglês |
| Publicado em: |
MDPI - Multidisciplinary Digital Publishing Institute
2024
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| Acesso em linha: | ONIX_20240514_9783725805730_357 |
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| collection | Directory of Open Access Books |
| description | Water, vital for life, confronts unprecedented challenges in aquatic ecosystems due to factors like scarcity and pollution. Monitoring at local to global scales is vital for effective management, aligning with sustainable development goals. To address these challenges, the integration of remote sensing technologies with in situ data proves invaluable in unveiling the spatial distribution and dynamic variations in water quality and quantity. Leveraging the advantages of frequent data acquisition, expansive coverage, and diverse sensor types, coupled with the power of artificial intelligence and cloud computing, enables a profound understanding of intricate changes within aquatic environments. This Special Issue is dedicated to showcasing papers that elucidate strategies for enhancing inland water monitoring, emphasizing precision, frequency, and the augmentation of user value derived from remote sensing data. Specifically, the issue aims to spotlight ongoing research leveraging satellite imagery, UAV data, in situ instrumentation, GeoAI, as well as deep and machine learning algorithms. Additionally, cloud computing and big data processing applications are explored to comprehensively comprehend the existing state and proactively mitigate the deterioration of water resources. Encompassing a broad spectrum, topics include remote sensing monitoring of water quality parameters, artificial intelligence, GeoAI applications and time-series analysis techniques. |
| format | Online |
| id | doab-20.500.12854ir-137761 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1377612024-05-14T14:23:11Z Remote Sensing and Artificial Intelligence in Inland Waters Monitoring Govedarica, Miro Álvarez-Taboada, Flor Jakovljević, Gordana remote sensing water quality harmonize RS data machine learning global modeling model development linear regression LASSO regularization L1 coincident data Google Earth Engine cyanobacteria unmanned aerial systems multispectral imagery machine learning classification inland water multi-source satellite observation technology scientometrics CiteSpace few-shot learning underwater aquatic vegetation submerged vegetation foundation model Sentinel-2 VHR WorldView-2 UAV Segment Anything model water quality parameters spatiotemporal distribution Dianshan Lake Sentinel-1 water extraction flood disaster decision tree random forest improved U-Net deep learning hyperspectral imagery PRISMA satellite Chlorophyll-a lakes eutrophication machine learning algorithms in situ water quality data lakes Landsat-8 CMIP6 BCSD PLUS MOP flood risk assessment multi-scenario simulation climate change water quality monitoring Artificial Neural Network (ANN) artificial intelligence WISE sustainable water management genetic algorithm Extreme Gradient Boosting (XGBoost) water monitoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Water, vital for life, confronts unprecedented challenges in aquatic ecosystems due to factors like scarcity and pollution. Monitoring at local to global scales is vital for effective management, aligning with sustainable development goals. To address these challenges, the integration of remote sensing technologies with in situ data proves invaluable in unveiling the spatial distribution and dynamic variations in water quality and quantity. Leveraging the advantages of frequent data acquisition, expansive coverage, and diverse sensor types, coupled with the power of artificial intelligence and cloud computing, enables a profound understanding of intricate changes within aquatic environments. This Special Issue is dedicated to showcasing papers that elucidate strategies for enhancing inland water monitoring, emphasizing precision, frequency, and the augmentation of user value derived from remote sensing data. Specifically, the issue aims to spotlight ongoing research leveraging satellite imagery, UAV data, in situ instrumentation, GeoAI, as well as deep and machine learning algorithms. Additionally, cloud computing and big data processing applications are explored to comprehensively comprehend the existing state and proactively mitigate the deterioration of water resources. Encompassing a broad spectrum, topics include remote sensing monitoring of water quality parameters, artificial intelligence, GeoAI applications and time-series analysis techniques. 2024-05-14T14:23:05Z 2024-05-14T14:23:05Z 2024 book ONIX_20240514_9783725805730_357 9783725805730 9783725805747 https://directory.doabooks.org/handle/20.500.12854/137761 eng application/octet-stream Attribution-NonCommercial-NoDerivatives 4.0 International https://mdpi.com/books/pdfview/book/8994 https://mdpi.com/books/pdfview/book/8994 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-0574-7 10.3390/books978-3-7258-0574-7 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725805730 9783725805747 294 open access |
| spellingShingle | remote sensing water quality harmonize RS data machine learning global modeling model development linear regression LASSO regularization L1 coincident data Google Earth Engine cyanobacteria unmanned aerial systems multispectral imagery machine learning classification inland water multi-source satellite observation technology scientometrics CiteSpace few-shot learning underwater aquatic vegetation submerged vegetation foundation model Sentinel-2 VHR WorldView-2 UAV Segment Anything model water quality parameters spatiotemporal distribution Dianshan Lake Sentinel-1 water extraction flood disaster decision tree random forest improved U-Net deep learning hyperspectral imagery PRISMA satellite Chlorophyll-a lakes eutrophication machine learning algorithms in situ water quality data lakes Landsat-8 CMIP6 BCSD PLUS MOP flood risk assessment multi-scenario simulation climate change water quality monitoring Artificial Neural Network (ANN) artificial intelligence WISE sustainable water management genetic algorithm Extreme Gradient Boosting (XGBoost) water monitoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues Remote Sensing and Artificial Intelligence in Inland Waters Monitoring |
| title | Remote Sensing and Artificial Intelligence in Inland Waters Monitoring |
| title_full | Remote Sensing and Artificial Intelligence in Inland Waters Monitoring |
| title_fullStr | Remote Sensing and Artificial Intelligence in Inland Waters Monitoring |
| title_full_unstemmed | Remote Sensing and Artificial Intelligence in Inland Waters Monitoring |
| title_short | Remote Sensing and Artificial Intelligence in Inland Waters Monitoring |
| title_sort | remote sensing and artificial intelligence in inland waters monitoring |
| topic | remote sensing water quality harmonize RS data machine learning global modeling model development linear regression LASSO regularization L1 coincident data Google Earth Engine cyanobacteria unmanned aerial systems multispectral imagery machine learning classification inland water multi-source satellite observation technology scientometrics CiteSpace few-shot learning underwater aquatic vegetation submerged vegetation foundation model Sentinel-2 VHR WorldView-2 UAV Segment Anything model water quality parameters spatiotemporal distribution Dianshan Lake Sentinel-1 water extraction flood disaster decision tree random forest improved U-Net deep learning hyperspectral imagery PRISMA satellite Chlorophyll-a lakes eutrophication machine learning algorithms in situ water quality data lakes Landsat-8 CMIP6 BCSD PLUS MOP flood risk assessment multi-scenario simulation climate change water quality monitoring Artificial Neural Network (ANN) artificial intelligence WISE sustainable water management genetic algorithm Extreme Gradient Boosting (XGBoost) water monitoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| topic_facet | remote sensing water quality harmonize RS data machine learning global modeling model development linear regression LASSO regularization L1 coincident data Google Earth Engine cyanobacteria unmanned aerial systems multispectral imagery machine learning classification inland water multi-source satellite observation technology scientometrics CiteSpace few-shot learning underwater aquatic vegetation submerged vegetation foundation model Sentinel-2 VHR WorldView-2 UAV Segment Anything model water quality parameters spatiotemporal distribution Dianshan Lake Sentinel-1 water extraction flood disaster decision tree random forest improved U-Net deep learning hyperspectral imagery PRISMA satellite Chlorophyll-a lakes eutrophication machine learning algorithms in situ water quality data lakes Landsat-8 CMIP6 BCSD PLUS MOP flood risk assessment multi-scenario simulation climate change water quality monitoring Artificial Neural Network (ANN) artificial intelligence WISE sustainable water management genetic algorithm Extreme Gradient Boosting (XGBoost) water monitoring thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issues |
| url | ONIX_20240514_9783725805730_357 |