Pub Date : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469828
R. Birk
From the 1980s through 2004, NASA developed and deployed the Earth Observing System to conduct global research using measurements from the Terra, Aqua, and Aura spacecraft. The research results from these and other NASA space-based observatories are pathfinders for next-generation operational systems and are information sources for evolving computer models used to improve predictions of weather, climate, and natural hazards. Improved understanding of climate change and the prediction and preparedness associated with disasters are two additional societal benefit areas of the GEO. One of NASA’s goals is to extend benefits of space research to improve scientific understanding of the Earth system and to demonstrate new technologies with the potential to improve future operational systems. NASA focuses on applications of national priority to transition these benefits systematically, enabling and improving integrated system solutions that inform decisions to serve society. Management of energy, coastal and biological ecosystems, agriculture, water, and human health are applications served by integrating NASA research results into solutions that are consistent with GEO societal benefit areas. NASA and the GEO share a common framework architecture to systematically apply Earth observations and predictions to enable decision support for specific applications areas. Over the next 10 years, NASA plans to continue collaborations with its U.S. and international partners to develop and deploy innovative research spacecraft and instruments. These systems can demonstrate the capacity for space systems to address targets identified in The Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan. I. OBJECTIVES AND CONTEXT National Aeronautics and Space Administration (NASA) goals include extending the benefits of space research to improve scientific understanding of the Earth system and demonstrating new technologies with the potential to improve future operational systems. NASA’s objectives include focusing on applications of national priority to systematically transition the benefits of scientific research results and innovative technologies, enabling and improving integrated system solutions that inform decisions to serve society. NASA’s pursuit of improved understanding and prediction of weather, climate, and natural hazards is consistent with the societal benefit areas identified by the international Group on Earth Observations (GEO), along with applying Earth observation system capacity to the management of energy, coastal and biological ecosystems, agriculture, water, and human health. The societal benefit areas (Fig. 1) are described in the Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan [1] and in the NASA Earth Science Applications Plan [2].
{"title":"Extending NASA earth-sun system research results to serve GEOSS societal benefits","authors":"R. Birk","doi":"10.1109/AMTRSI.2005.1469828","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469828","url":null,"abstract":"From the 1980s through 2004, NASA developed and deployed the Earth Observing System to conduct global research using measurements from the Terra, Aqua, and Aura spacecraft. The research results from these and other NASA space-based observatories are pathfinders for next-generation operational systems and are information sources for evolving computer models used to improve predictions of weather, climate, and natural hazards. Improved understanding of climate change and the prediction and preparedness associated with disasters are two additional societal benefit areas of the GEO. One of NASA’s goals is to extend benefits of space research to improve scientific understanding of the Earth system and to demonstrate new technologies with the potential to improve future operational systems. NASA focuses on applications of national priority to transition these benefits systematically, enabling and improving integrated system solutions that inform decisions to serve society. Management of energy, coastal and biological ecosystems, agriculture, water, and human health are applications served by integrating NASA research results into solutions that are consistent with GEO societal benefit areas. NASA and the GEO share a common framework architecture to systematically apply Earth observations and predictions to enable decision support for specific applications areas. Over the next 10 years, NASA plans to continue collaborations with its U.S. and international partners to develop and deploy innovative research spacecraft and instruments. These systems can demonstrate the capacity for space systems to address targets identified in The Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan. I. OBJECTIVES AND CONTEXT National Aeronautics and Space Administration (NASA) goals include extending the benefits of space research to improve scientific understanding of the Earth system and demonstrating new technologies with the potential to improve future operational systems. NASA’s objectives include focusing on applications of national priority to systematically transition the benefits of scientific research results and innovative technologies, enabling and improving integrated system solutions that inform decisions to serve society. NASA’s pursuit of improved understanding and prediction of weather, climate, and natural hazards is consistent with the societal benefit areas identified by the international Group on Earth Observations (GEO), along with applying Earth observation system capacity to the management of energy, coastal and biological ecosystems, agriculture, water, and human health. The societal benefit areas (Fig. 1) are described in the Global Earth Observation System of Systems (GEOSS) 10-Year Implementation Plan [1] and in the NASA Earth Science Applications Plan [2].","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132310024","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469870
R. Lunetta, J. Knight, J. Ediriwickrema
Land-cover (LC) composition and conversions are important factors that affect ecosystem condition and function. These data are frequently used as a primary data source to generate landscape-based metrics to assess landscape condition at multiple assessment scales. The use of satellite-based remote sensor data has been widely applied to provide a cost-effective means to develop LC coverages over large geographic regions. Past and ongoing efforts for generating LC data for the United States have been implemented using an interagency consortium to share the substantial costs associated satellite data acquisition, processing and analysis. The first moderate resolution National Land-Cover Data (NLCD) set was developed for the conterminous United States using Landsat Thematic Mapper (TM) imagery collected between1991-1992 (Vogelmann et al., 1998). Currently, the 2001 NLCD is under development for all 50 States and the Commonwealth of Puerto Rico (Homer et al., 2004). The 2001 effort, building on the lessons learned from the 1991 NLCD, promises to provide a relatively high quality baseline LC product.
土地覆被的组成和转换是影响生态系统状况和功能的重要因素。这些数据经常被用作主要数据源，生成基于景观的指标，以在多个评估尺度上评估景观状况。基于卫星的遥感数据的使用已被广泛应用，为在大地理区域发展LC覆盖提供了一种具有成本效益的手段。过去和正在进行的为美国生成LC数据的工作已经通过一个机构间联盟来实施，以分担与卫星数据获取、处理和分析相关的大量费用。第一个中等分辨率的国家土地覆盖数据(NLCD)集是利用1991-1992年期间收集的Landsat Thematic Mapper (TM)图像为美国周边地区开发的(Vogelmann等，1998年)。目前，2001年全国人口统计正在为所有50个州和波多黎各联邦制定(Homer et al.， 2004)。2001年的努力以1991年NLCD的经验教训为基础，承诺提供相对高质量的基准LC产品。
{"title":"Land-cover characterization and change detection using multitemporal MODIS NDVI data","authors":"R. Lunetta, J. Knight, J. Ediriwickrema","doi":"10.1109/AMTRSI.2005.1469870","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469870","url":null,"abstract":"Land-cover (LC) composition and conversions are important factors that affect ecosystem condition and function. These data are frequently used as a primary data source to generate landscape-based metrics to assess landscape condition at multiple assessment scales. The use of satellite-based remote sensor data has been widely applied to provide a cost-effective means to develop LC coverages over large geographic regions. Past and ongoing efforts for generating LC data for the United States have been implemented using an interagency consortium to share the substantial costs associated satellite data acquisition, processing and analysis. The first moderate resolution National Land-Cover Data (NLCD) set was developed for the conterminous United States using Landsat Thematic Mapper (TM) imagery collected between1991-1992 (Vogelmann et al., 1998). Currently, the 2001 NLCD is under development for all 50 States and the Commonwealth of Puerto Rico (Homer et al., 2004). The 2001 effort, building on the lessons learned from the 1991 NLCD, promises to provide a relatively high quality baseline LC product.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132447207","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 : 2005-05-16DOI: 10.1109/AMTRSI.2005.1469867
E. LeDrew
Analysis of processes forcing temporal change in climate has fostered the development of new procedures for identifying significant patterns and episodes from sequential satellite imagery. Particularly rewarding results have been derived from sea ice concentration and snow water equivalent derived from passive microwave imagery. We have a remarkable archive of such data that extends back to 1978. These data can be used to highlight factors that may contribute to the anomalously warm years that have been identified within the past decade. In this study we report on the use of correlations of wavelets of the Principal Component temporal loadings for sea ice concentration and concurrent patterns of atmospheric data. This approach will provide insight beyond that evident in traditional linear correlations of trend patterns.
{"title":"The temporal signal of sea ice variability in the polar basin from wavelet analysis of passive microwave sea ice concentrations","authors":"E. LeDrew","doi":"10.1109/AMTRSI.2005.1469867","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469867","url":null,"abstract":"Analysis of processes forcing temporal change in climate has fostered the development of new procedures for identifying significant patterns and episodes from sequential satellite imagery. Particularly rewarding results have been derived from sea ice concentration and snow water equivalent derived from passive microwave imagery. We have a remarkable archive of such data that extends back to 1978. These data can be used to highlight factors that may contribute to the anomalously warm years that have been identified within the past decade. In this study we report on the use of correlations of wavelets of the Principal Component temporal loadings for sea ice concentration and concurrent patterns of atmospheric data. This approach will provide insight beyond that evident in traditional linear correlations of trend patterns.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125759842","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 : 1900-01-01DOI: 10.1109/AMTRSI.2005.1469868
H. J. Carter, D. Eslinger, M. Vanderwilt
The National Oceanic and Atmospheric Administration’s (NOAA) Coastal Remote Sensing Program at the Coastal Services Center (the Center) runs a Coastal Water Quality project. The primary goal of this project is to investigate the complex nature of the impacts of terrestrial land management practices on coastal water quality and the capability of remote sensing to monitor and measure those impacts. The complex interactions between terrestrial and aquatic systems pose challenges to coastal zone managers who need to understand the relationships between land cover and water quality. The Center developed two GIS based tools to allow managers to explore these linkages using easily obtainable remote sensing data and GIS layers. The Impervious Surface Analysis Tool (ISAT) calculates the percentage of impervious surface area of user-selected geographic areas. The Nonpoint-Source Pollution and Erosion Comparison Tool (N-SPECT) examines the relationships between land cover, soil characteristics, topography, and precipitation in order to assess spatial and temporal patterns of surface water runoff, nonpoint-source pollution, and erosion. Two eras of Coastal Change Analysis Program (C-CAP) land cover data are used to model potential water quality change trends in Myrtle Beach, South Carolina.
{"title":"GIS management tools for estimating change trends in surface water quality: an application of multi-temporal land cover data","authors":"H. J. Carter, D. Eslinger, M. Vanderwilt","doi":"10.1109/AMTRSI.2005.1469868","DOIUrl":"https://doi.org/10.1109/AMTRSI.2005.1469868","url":null,"abstract":"The National Oceanic and Atmospheric Administration’s (NOAA) Coastal Remote Sensing Program at the Coastal Services Center (the Center) runs a Coastal Water Quality project. The primary goal of this project is to investigate the complex nature of the impacts of terrestrial land management practices on coastal water quality and the capability of remote sensing to monitor and measure those impacts. The complex interactions between terrestrial and aquatic systems pose challenges to coastal zone managers who need to understand the relationships between land cover and water quality. The Center developed two GIS based tools to allow managers to explore these linkages using easily obtainable remote sensing data and GIS layers. The Impervious Surface Analysis Tool (ISAT) calculates the percentage of impervious surface area of user-selected geographic areas. The Nonpoint-Source Pollution and Erosion Comparison Tool (N-SPECT) examines the relationships between land cover, soil characteristics, topography, and precipitation in order to assess spatial and temporal patterns of surface water runoff, nonpoint-source pollution, and erosion. Two eras of Coastal Change Analysis Program (C-CAP) land cover data are used to model potential water quality change trends in Myrtle Beach, South Carolina.","PeriodicalId":302923,"journal":{"name":"International Workshop on the Analysis of Multi-Temporal Remote Sensing Images, 2005.","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114219697","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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