Pub Date : 2018-12-07DOI: 10.1201/9781315164151-13
Jessica J. Mitchell, N. Glenn, K. Dahlin, N. Ilangakoon, H. Dashti, Megan C. Maloney
{"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}
P. Thenkabail, M. Gumma, P. Teluguntla, Irshad A. Mohammed
There are now over 40 years of research in hyperspectral remote sensing (or imaging spectroscopy) of vegetation and agricultural crops (Thenkabail et al., 2011a). Even though much of the early research in hyperspectral remote sensing was overwhelmingly focused on minerals, now there is substantial literature in characterization, monitoring, modeling, and mapping of vegetation and agricultural crops using ground-based, platform-mounted, airborne, Unmanned Aerial Vehicle (UAV) mounted, and spaceborne hyperspectral remote sensing (Swatantran et al., 2011; Atzberger, 2013; Middleton et al., 2013; Schlemmer et al., 2013; Thenkabail et al., 2013; Udelhoven et al., 2013; Zhang et al., 2013). The state-of-the-art in hyperspectral remote sensing of vegetation and agriculture shows significant enhancement over conventional remote sensing, leading to improved and targeted modeling and mapping of specific agricultural characteristics such as: (a) biophysical and biochemical quantities (Galvao, 2011; Clark and Roberts, 2012), (b) crop typespecies (Thenkabail et al., 2013), (c) management and stress factors such as nitrogen deficiency, moisture deficiency, or drought conditions (Delalieux et al., 2009; Gitelson, 2013; Slonecker et al., 2013), and (d) water use and water productivities (Thenkabail et al., 2013). At the same time, overcoming Hughes’ phenomenon or curse of dimensionality of data and data redundancy (Plaza et al., 2009) is of great importance to make rapid advances in a much wider utilization of hyperspectral data. This is because, for a specific application, a large number of hyperspectral bands are redundant (Thenkabail et al., 2013). Selecting the relevant bands will require the use of data mining techniques (Burger and Gowen, 2011) to focus on utilizing the optimal or best ones to maximize the efficiency of data use and reduce unnecessary computing...
目前，对植被和农作物的高光谱遥感(或成像光谱)研究已有40多年的历史(Thenkabail et al.， 2011)。尽管高光谱遥感的早期研究绝大多数集中在矿物上，但现在有大量文献利用地面、平台、机载、无人机(UAV)和星载高光谱遥感对植被和农作物进行表征、监测、建模和制图(Swatantran等人，2011;Atzberger, 2013;Middleton et al.， 2013;Schlemmer et al.， 2013;Thenkabail et al.， 2013;Udelhoven et al.， 2013;Zhang等人，2013)。植被和农业高光谱遥感的最新技术比传统遥感有了显著提高，从而改进了特定农业特征的有针对性的建模和制图，例如:(a)生物物理和生化数量(Galvao, 2011;Clark和Roberts, 2012)， (b)作物类型和品种(Thenkabail等人，2013)，(c)管理和胁迫因素，如缺氮、缺水或干旱条件(Delalieux等人，2009;Gitelson, 2013;Slonecker et al.， 2013)和(d)水资源利用和水生产力(Thenkabail et al.， 2013)。同时，克服Hughes的数据维数和数据冗余现象或诅咒(Plaza et al.， 2009)对于快速推进高光谱数据的更广泛利用具有重要意义。这是因为，对于特定的应用，大量的高光谱波段是冗余的(Thenkabail et al.， 2013)。选择相关波段将需要使用数据挖掘技术(Burger和Gowen, 2011)，专注于利用最优或最好的数据来最大化数据使用效率并减少不必要的计算……
{"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}
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