Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation最新文献

{"title":"Integrating Hyperspectral and LiDAR Data in the Study of Vegetation","authors":"Jessica J. Mitchell, N. Glenn, K. Dahlin, N. Ilangakoon, H. Dashti, Megan C. Maloney","doi":"10.1201/9781315164151-13","DOIUrl":"https://doi.org/10.1201/9781315164151-13","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116462090","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}

{"title":"Monitoring Vegetation Diversity and Health through Spectral Traits and Trait Variations Based on Hyperspectral Remote Sensing","authors":"A. Lausch, P. Leitão","doi":"10.1201/9781315164151-4","DOIUrl":"https://doi.org/10.1201/9781315164151-4","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"33 4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123617688","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}

{"title":"Linking Online Spectral Libraries with Hyperspectral Test Data through Library Building Tools and Code","authors":"M. A. Hoque, S. Phinn","doi":"10.1201/9781315164151-6","DOIUrl":"https://doi.org/10.1201/9781315164151-6","url":null,"abstract":"","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"571 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127791481","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}

{"title":"Advances in Hyperspectral Remote Sensing of Vegetation and Agricultural Crops","authors":"P. Thenkabail, M. Gumma, P. Teluguntla, Irshad A. Mohammed","doi":"10.1201/9781315164151-1","DOIUrl":"https://doi.org/10.1201/9781315164151-1","url":null,"abstract":"There are now over 40 years of research in hyperspectral remote sensing (or \u0000imaging spectroscopy) of vegetation and agricultural crops (Thenkabail et \u0000al., 2011a). Even though much of the early research in hyperspectral remote \u0000sensing was overwhelmingly focused on minerals, now there is substantial \u0000literature in characterization, monitoring, modeling, and mapping of vegetation \u0000and agricultural crops using ground-based, platform-mounted, airborne, \u0000Unmanned Aerial Vehicle (UAV) mounted, and spaceborne hyperspectral \u0000remote sensing (Swatantran et al., 2011; Atzberger, 2013; Middleton et al., 2013; \u0000Schlemmer et al., 2013; Thenkabail et al., 2013; Udelhoven et al., 2013; Zhang \u0000et al., 2013). The state-of-the-art in hyperspectral remote sensing of vegetation \u0000and agriculture shows significant enhancement over conventional remote \u0000sensing, leading to improved and targeted modeling and mapping of specific \u0000agricultural characteristics such as: (a) biophysical and biochemical quantities \u0000(Galvao, 2011; Clark and Roberts, 2012), (b) crop typespecies (Thenkabail \u0000et al., 2013), (c) management and stress factors such as nitrogen deficiency, \u0000moisture deficiency, or drought conditions (Delalieux et al., 2009; Gitelson, \u00002013; Slonecker et al., 2013), and (d) water use and water productivities \u0000(Thenkabail et al., 2013). At the same time, overcoming Hughes’ phenomenon \u0000or curse of dimensionality of data and data redundancy (Plaza et al., 2009) \u0000is of great importance to make rapid advances in a much wider utilization of \u0000hyperspectral data. This is because, for a specific application, a large number \u0000of hyperspectral bands are redundant (Thenkabail et al., 2013). Selecting the \u0000relevant bands will require the use of data mining techniques (Burger and \u0000Gowen, 2011) to focus on utilizing the optimal or best ones to maximize the \u0000efficiency of data use and reduce unnecessary computing...","PeriodicalId":304529,"journal":{"name":"Fundamentals, Sensor Systems, Spectral Libraries, and Data Mining for Vegetation","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126526585","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}