D. Stampoulis, H. G. Damavandi, D. Boscovic, J. Sabo
{"title":"利用卫星遥感和机器学习技术进行降水预测和植被分类","authors":"D. Stampoulis, H. G. Damavandi, D. Boscovic, J. Sabo","doi":"10.3808/jei.202000427","DOIUrl":null,"url":null,"abstract":"The spatial distribution, magnitude and timing of precipitation events are being altered globally, often leading to extreme hydrologic conditions with serious implications to ecosystem services, water, food and energy security, as well as the welfare of billions of people. Motivated by the pressing need to understand, from a hydro-ecological perspective, how the dynamic nature of the hydrologic cycle will impact the environment in water-stressed regions, we implemented a novel approach that predicts precipitation spatio-temporal trends over the drought-burdened region of East Africa, based on other major hydrological components, such as vegetation water content (VWC), soil moisture (SM) and surface temperature (ST). The spatial patterns and characteristics of the inter-relations among the four aforementioned hydrologic variables were investigated over regions of East Africa characterized by different vegetation types and for various precipitation intensity rates during 2003-2011. To this end, we analyzed multi-year satellite microwave remote sensing observations of SM, ST, and VWC (derived from Naval Research Laboratory's WindSat radiometer) as well as their response to precipitation patterns (derived from NASA's TRMM 3B42 V7). We categorized precipitation into four bins (ranges) of intensity and trained five different state-of-the-art machine learning models for each of these categories. The models were then applied to predict the spatiotemporal precipitation dynamics over this complex region. Specifically, the Random Forest and Linear Regression models outperformed the others with the normalized mean absolute error being less than 27% for all of the categories. The characteristics of the predicted precipitation were in turn used to classify vegetation regimes in East Africa. Our results indicate significant discrepancies in the performance of the models with varying values in the predicting skill as well as their ability to accurately classify vegetation into different types. Our predictive models were able to forecast the three vegetation regimes, i.e., Forest/Woody Savanna, Savanna/Grasslands and Shrubland, with precision rate of at least 81% for all of the aforementioned precipitation bins.","PeriodicalId":54840,"journal":{"name":"Journal of Environmental Informatics","volume":null,"pages":null},"PeriodicalIF":6.0000,"publicationDate":"2020-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Using Satellite Remote Sensing and Machine Learning Techniques Towards Precipitation Prediction and Vegetation Classification\",\"authors\":\"D. 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The spatial patterns and characteristics of the inter-relations among the four aforementioned hydrologic variables were investigated over regions of East Africa characterized by different vegetation types and for various precipitation intensity rates during 2003-2011. To this end, we analyzed multi-year satellite microwave remote sensing observations of SM, ST, and VWC (derived from Naval Research Laboratory's WindSat radiometer) as well as their response to precipitation patterns (derived from NASA's TRMM 3B42 V7). We categorized precipitation into four bins (ranges) of intensity and trained five different state-of-the-art machine learning models for each of these categories. The models were then applied to predict the spatiotemporal precipitation dynamics over this complex region. Specifically, the Random Forest and Linear Regression models outperformed the others with the normalized mean absolute error being less than 27% for all of the categories. The characteristics of the predicted precipitation were in turn used to classify vegetation regimes in East Africa. Our results indicate significant discrepancies in the performance of the models with varying values in the predicting skill as well as their ability to accurately classify vegetation into different types. 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Using Satellite Remote Sensing and Machine Learning Techniques Towards Precipitation Prediction and Vegetation Classification
The spatial distribution, magnitude and timing of precipitation events are being altered globally, often leading to extreme hydrologic conditions with serious implications to ecosystem services, water, food and energy security, as well as the welfare of billions of people. Motivated by the pressing need to understand, from a hydro-ecological perspective, how the dynamic nature of the hydrologic cycle will impact the environment in water-stressed regions, we implemented a novel approach that predicts precipitation spatio-temporal trends over the drought-burdened region of East Africa, based on other major hydrological components, such as vegetation water content (VWC), soil moisture (SM) and surface temperature (ST). The spatial patterns and characteristics of the inter-relations among the four aforementioned hydrologic variables were investigated over regions of East Africa characterized by different vegetation types and for various precipitation intensity rates during 2003-2011. To this end, we analyzed multi-year satellite microwave remote sensing observations of SM, ST, and VWC (derived from Naval Research Laboratory's WindSat radiometer) as well as their response to precipitation patterns (derived from NASA's TRMM 3B42 V7). We categorized precipitation into four bins (ranges) of intensity and trained five different state-of-the-art machine learning models for each of these categories. The models were then applied to predict the spatiotemporal precipitation dynamics over this complex region. Specifically, the Random Forest and Linear Regression models outperformed the others with the normalized mean absolute error being less than 27% for all of the categories. The characteristics of the predicted precipitation were in turn used to classify vegetation regimes in East Africa. Our results indicate significant discrepancies in the performance of the models with varying values in the predicting skill as well as their ability to accurately classify vegetation into different types. Our predictive models were able to forecast the three vegetation regimes, i.e., Forest/Woody Savanna, Savanna/Grasslands and Shrubland, with precision rate of at least 81% for all of the aforementioned precipitation bins.
期刊介绍:
Journal of Environmental Informatics (JEI) is an international, peer-reviewed, and interdisciplinary publication designed to foster research innovation and discovery on basic science and information technology for addressing various environmental problems. The journal aims to motivate and enhance the integration of science and technology to help develop sustainable solutions that are consensus-oriented, risk-informed, scientifically-based and cost-effective. JEI serves researchers, educators and practitioners who are interested in theoretical and/or applied aspects of environmental science, regardless of disciplinary boundaries. The topics addressed by the journal include:
- Planning of energy, environmental and ecological management systems
- Simulation, optimization and Environmental decision support
- Environmental geomatics - GIS, RS and other spatial information technologies
- Informatics for environmental chemistry and biochemistry
- Environmental applications of functional materials
- Environmental phenomena at atomic, molecular and macromolecular scales
- Modeling of chemical, biological and environmental processes
- Modeling of biotechnological systems for enhanced pollution mitigation
- Computer graphics and visualization for environmental decision support
- Artificial intelligence and expert systems for environmental applications
- Environmental statistics and risk analysis
- Climate modeling, downscaling, impact assessment, and adaptation planning
- Other areas of environmental systems science and information technology.