Using Very-Large-Scale Aerial Imagery for Rangeland Monitoring and Assessment: Some Statistical Considerations

The availability of very-large-scale aerial (VLSA) imagery (typically less than 1 cm ground-sampling-distance spatial resolution) and techniques for processing those data into ecosystem indicators has opened the door for routinely using VLSA imagery in rangeland monitoring and assessment. However, f...

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Veröffentlicht in:Rangeland ecology & management 2012-07, Vol.65 (4), p.330-339
Hauptverfasser: Karl, Jason W., Duniway, Michael C., Nusser, Sarah M., Opsomer, Jean D., Unnasch, Robert S.
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container_end_page 339
container_issue 4
container_start_page 330
container_title Rangeland ecology & management
container_volume 65
creator Karl, Jason W.
Duniway, Michael C.
Nusser, Sarah M.
Opsomer, Jean D.
Unnasch, Robert S.
description The availability of very-large-scale aerial (VLSA) imagery (typically less than 1 cm ground-sampling-distance spatial resolution) and techniques for processing those data into ecosystem indicators has opened the door for routinely using VLSA imagery in rangeland monitoring and assessment. However, for VLSA imagery to provide defensible information for managers, it is crucial to understand the statistical implications of designing and implementing VLSA image studies, including consideration of image scale, sample design limitations, and the need for validation of estimates. A significant advantage of VLSA imaging is that the researcher can specify the scale (i.e., spatial resolution and extent) of the images. VLSA image programs should plan for scales that match monitoring questions, size of landscape elements to be measured, and spatial heterogeneity of the environment. Failure to plan for scale may result in images that are not optimal for answering management questions. Probability-based sampling guards against bias and ensures that inferences can be made to the desired study area. Often collected along flight transects, VLSA imagery lends itself well to certain probability-based sample designs, such as systematic sampling, not often used in field studies. With VLSA image programs, the sample unit can be an entire image or a portion of an image. It is critical to define the sampling unit and understand the relationship between measurements and estimates made from the imagery. Finally, it is important to statistically validate estimates produced from VLSA images at selected locations using quantitative data of the same scale and more precise and accurate than the VLSA image techniques. The extent to which VLSA imagery will be useful as a tool for understanding the status and trend of rangelands depends as much on the ability to build the imagery into robust programs as it does on the ability to quickly and relatively easily collect VLSA images over large landscapes. La disponibilidad de imágenes aéreas a gran escala (IAGE) (normalmente menos de un cm de de distancia de resolución espacial en el terreno) y técnicas que procesen esos datos dentro de indicadores del ecosistema han abierto la puerta para que de manera rutinaria se use IAGE en pastizales en monitoreo y evaluación. Sin embargo, para IAGE proveer información defendible para administradores es crucial para entender las implicaciones estadísticas para diseñar e implementar estudios de IAGE que inc
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However, for VLSA imagery to provide defensible information for managers, it is crucial to understand the statistical implications of designing and implementing VLSA image studies, including consideration of image scale, sample design limitations, and the need for validation of estimates. A significant advantage of VLSA imaging is that the researcher can specify the scale (i.e., spatial resolution and extent) of the images. VLSA image programs should plan for scales that match monitoring questions, size of landscape elements to be measured, and spatial heterogeneity of the environment. Failure to plan for scale may result in images that are not optimal for answering management questions. Probability-based sampling guards against bias and ensures that inferences can be made to the desired study area. Often collected along flight transects, VLSA imagery lends itself well to certain probability-based sample designs, such as systematic sampling, not often used in field studies. With VLSA image programs, the sample unit can be an entire image or a portion of an image. It is critical to define the sampling unit and understand the relationship between measurements and estimates made from the imagery. Finally, it is important to statistically validate estimates produced from VLSA images at selected locations using quantitative data of the same scale and more precise and accurate than the VLSA image techniques. The extent to which VLSA imagery will be useful as a tool for understanding the status and trend of rangelands depends as much on the ability to build the imagery into robust programs as it does on the ability to quickly and relatively easily collect VLSA images over large landscapes. La disponibilidad de imágenes aéreas a gran escala (IAGE) (normalmente menos de un cm de de distancia de resolución espacial en el terreno) y técnicas que procesen esos datos dentro de indicadores del ecosistema han abierto la puerta para que de manera rutinaria se use IAGE en pastizales en monitoreo y evaluación. Sin embargo, para IAGE proveer información defendible para administradores es crucial para entender las implicaciones estadísticas para diseñar e implementar estudios de IAGE que incluyan consideraciones de escala de la imagen, limitaciones en el diseño de muestreo y la necesidad de validación de los estimadores. Una ventaja significativa de IAGE es que el investigador puede definir la escala (ejm. resolución espacial y extensión) de la imagen. Los programas de IAGE deberían planear escalas que empaten preguntas de monitoreo, el tamaño de los elementos del paisaje a ser medidos y la heterogeneidad espacial del medioambiente. 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La amplitud a la cual IAGE será de utilidad como herramienta para entender el estatus y tendencia de los pastizales, depende en gran medida en la habilidad para construir imágenes en programas robustos sino también con la habilidad de recolectar imágenes IAGE rápidamente y relativamente fácil sobre grandes paisajes.</description><identifier>ISSN: 1550-7424</identifier><identifier>ISSN: 1551-5028</identifier><identifier>EISSN: 1551-5028</identifier><identifier>DOI: 10.2111/REM-D-11-00102.1</identifier><language>eng</language><publisher>Lawrence: the Society for Range Management</publisher><subject>Accuracy ; Bias ; Ecosystems ; Estimate reliability ; Estimation methods ; Forum ; Forums ; image analysis ; Image resolution ; Landscapes ; managers ; monitoring ; precision ; probability ; Random sampling ; Range management ; Rangeland ecology ; rangelands ; Remote sensing ; sample design ; Spatial resolution ; statistics ; Studies ; Survey sampling ; Trends ; very-large-scale aerial image</subject><ispartof>Rangeland ecology &amp; management, 2012-07, Vol.65 (4), p.330-339</ispartof><rights>2012 The Society for Range Management</rights><rights>Copyright 2012 Society for Range Management</rights><rights>Copyright Allen Press Publishing Services Jul 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b379t-4c8070ccda1b1bff33f59f5d28e7fb2eb02f078518d779b469945f78ee19a3be3</citedby><cites>FETCH-LOGICAL-b379t-4c8070ccda1b1bff33f59f5d28e7fb2eb02f078518d779b469945f78ee19a3be3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Karl, Jason W.</creatorcontrib><creatorcontrib>Duniway, Michael C.</creatorcontrib><creatorcontrib>Nusser, Sarah M.</creatorcontrib><creatorcontrib>Opsomer, Jean D.</creatorcontrib><creatorcontrib>Unnasch, Robert S.</creatorcontrib><title>Using Very-Large-Scale Aerial Imagery for Rangeland Monitoring and Assessment: Some Statistical Considerations</title><title>Rangeland ecology &amp; management</title><description>The availability of very-large-scale aerial (VLSA) imagery (typically less than 1 cm ground-sampling-distance spatial resolution) and techniques for processing those data into ecosystem indicators has opened the door for routinely using VLSA imagery in rangeland monitoring and assessment. However, for VLSA imagery to provide defensible information for managers, it is crucial to understand the statistical implications of designing and implementing VLSA image studies, including consideration of image scale, sample design limitations, and the need for validation of estimates. A significant advantage of VLSA imaging is that the researcher can specify the scale (i.e., spatial resolution and extent) of the images. VLSA image programs should plan for scales that match monitoring questions, size of landscape elements to be measured, and spatial heterogeneity of the environment. Failure to plan for scale may result in images that are not optimal for answering management questions. Probability-based sampling guards against bias and ensures that inferences can be made to the desired study area. Often collected along flight transects, VLSA imagery lends itself well to certain probability-based sample designs, such as systematic sampling, not often used in field studies. With VLSA image programs, the sample unit can be an entire image or a portion of an image. It is critical to define the sampling unit and understand the relationship between measurements and estimates made from the imagery. Finally, it is important to statistically validate estimates produced from VLSA images at selected locations using quantitative data of the same scale and more precise and accurate than the VLSA image techniques. The extent to which VLSA imagery will be useful as a tool for understanding the status and trend of rangelands depends as much on the ability to build the imagery into robust programs as it does on the ability to quickly and relatively easily collect VLSA images over large landscapes. La disponibilidad de imágenes aéreas a gran escala (IAGE) (normalmente menos de un cm de de distancia de resolución espacial en el terreno) y técnicas que procesen esos datos dentro de indicadores del ecosistema han abierto la puerta para que de manera rutinaria se use IAGE en pastizales en monitoreo y evaluación. Sin embargo, para IAGE proveer información defendible para administradores es crucial para entender las implicaciones estadísticas para diseñar e implementar estudios de IAGE que incluyan consideraciones de escala de la imagen, limitaciones en el diseño de muestreo y la necesidad de validación de los estimadores. Una ventaja significativa de IAGE es que el investigador puede definir la escala (ejm. resolución espacial y extensión) de la imagen. Los programas de IAGE deberían planear escalas que empaten preguntas de monitoreo, el tamaño de los elementos del paisaje a ser medidos y la heterogeneidad espacial del medioambiente. Fallas en planear la escala puede resultar en imágenes que no son optimas en resolver las preguntas del administrador. Muestreos basados en probabilidad protegen contra sesgo y aseguran que la inferencia puede ser hecha para la area de estudio deseada. Seguido, recolección a lo largo de vuelos en transectos, IAGE permite bien a cierto diseño de muestra basado en probabilidad como diseño sistemático no usado a menudo en estudios de campo. Con programas IAGE la unidad de muestreo puede ser la imagen completa o una porción de ésta. Es fundamental definir la unidad de muestreo y entender la relación entre medidas y estimaciones hechas de la imagen. Finalmente, es importante validar estadísticamente los estimadores producidos de IAGE es lugares seleccionados usando datos cuantitativos de la misma escala y más precisos y certeros que las técnicas de IAGE. La amplitud a la cual IAGE será de utilidad como herramienta para entender el estatus y tendencia de los pastizales, depende en gran medida en la habilidad para construir imágenes en programas robustos sino también con la habilidad de recolectar imágenes IAGE rápidamente y relativamente fácil sobre grandes paisajes.</description><subject>Accuracy</subject><subject>Bias</subject><subject>Ecosystems</subject><subject>Estimate reliability</subject><subject>Estimation methods</subject><subject>Forum</subject><subject>Forums</subject><subject>image analysis</subject><subject>Image resolution</subject><subject>Landscapes</subject><subject>managers</subject><subject>monitoring</subject><subject>precision</subject><subject>probability</subject><subject>Random sampling</subject><subject>Range management</subject><subject>Rangeland ecology</subject><subject>rangelands</subject><subject>Remote sensing</subject><subject>sample design</subject><subject>Spatial resolution</subject><subject>statistics</subject><subject>Studies</subject><subject>Survey sampling</subject><subject>Trends</subject><subject>very-large-scale aerial image</subject><issn>1550-7424</issn><issn>1551-5028</issn><issn>1551-5028</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><recordid>eNqFkUFr3DAQhU1poWmSey8FQS-9aKOxpZWc27JJ28CGQjbpVcj2aNFiS6nkDeTfV16XHnJIThppvvcYzSuKz8AWJQBc3F3f0isKQBkDVi7gXXECQgAVrFTvjzWjkpf8Y_EppT1j1RJAnhT-ITm_I78xPtONiTuk29b0SFYYnenJzWB2uUVsiOTO-B32xnfkNng3hjgJp-sqJUxpQD9ekm0YkGxHM7o0uuxE1sEn12HML7k6Kz5Y0yc8_3eeFg_fr-_XP-nm14-b9WpDm0rWI-WtYpK1bWeggcbaqrKitqIrFUrblNiw0jKpBKhOyrrhy7rmwkqFCLWpGqxOi2-z72MMfw6YRj241GKfx8dwSBpEVXMueK3eRlnFFXClJvTrC3QfDtHnjxwpWQomIVNsptoYUopo9WN0g4nPGdJTVjpnpa90Lo5Z6UnyZZbsU97rf57DUk0h5v7l3Me8syeHUafWoW-xcxHbUXfBvWa-mMWNC8Hj29P8BfCHsjU</recordid><startdate>201207</startdate><enddate>201207</enddate><creator>Karl, Jason W.</creator><creator>Duniway, Michael C.</creator><creator>Nusser, Sarah M.</creator><creator>Opsomer, Jean D.</creator><creator>Unnasch, Robert S.</creator><general>the Society for Range Management</general><general>Elsevier Inc</general><general>Allen Press Publishing Services</general><general>Elsevier Limited</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>C1K</scope><scope>SOI</scope><scope>7S9</scope><scope>L.6</scope></search><sort><creationdate>201207</creationdate><title>Using Very-Large-Scale Aerial Imagery for Rangeland Monitoring and Assessment: Some Statistical Considerations</title><author>Karl, Jason W. ; 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management</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karl, Jason W.</au><au>Duniway, Michael C.</au><au>Nusser, Sarah M.</au><au>Opsomer, Jean D.</au><au>Unnasch, Robert S.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Very-Large-Scale Aerial Imagery for Rangeland Monitoring and Assessment: Some Statistical Considerations</atitle><jtitle>Rangeland ecology &amp; management</jtitle><date>2012-07</date><risdate>2012</risdate><volume>65</volume><issue>4</issue><spage>330</spage><epage>339</epage><pages>330-339</pages><issn>1550-7424</issn><issn>1551-5028</issn><eissn>1551-5028</eissn><abstract>The availability of very-large-scale aerial (VLSA) imagery (typically less than 1 cm ground-sampling-distance spatial resolution) and techniques for processing those data into ecosystem indicators has opened the door for routinely using VLSA imagery in rangeland monitoring and assessment. However, for VLSA imagery to provide defensible information for managers, it is crucial to understand the statistical implications of designing and implementing VLSA image studies, including consideration of image scale, sample design limitations, and the need for validation of estimates. A significant advantage of VLSA imaging is that the researcher can specify the scale (i.e., spatial resolution and extent) of the images. VLSA image programs should plan for scales that match monitoring questions, size of landscape elements to be measured, and spatial heterogeneity of the environment. Failure to plan for scale may result in images that are not optimal for answering management questions. Probability-based sampling guards against bias and ensures that inferences can be made to the desired study area. Often collected along flight transects, VLSA imagery lends itself well to certain probability-based sample designs, such as systematic sampling, not often used in field studies. With VLSA image programs, the sample unit can be an entire image or a portion of an image. It is critical to define the sampling unit and understand the relationship between measurements and estimates made from the imagery. Finally, it is important to statistically validate estimates produced from VLSA images at selected locations using quantitative data of the same scale and more precise and accurate than the VLSA image techniques. The extent to which VLSA imagery will be useful as a tool for understanding the status and trend of rangelands depends as much on the ability to build the imagery into robust programs as it does on the ability to quickly and relatively easily collect VLSA images over large landscapes. La disponibilidad de imágenes aéreas a gran escala (IAGE) (normalmente menos de un cm de de distancia de resolución espacial en el terreno) y técnicas que procesen esos datos dentro de indicadores del ecosistema han abierto la puerta para que de manera rutinaria se use IAGE en pastizales en monitoreo y evaluación. Sin embargo, para IAGE proveer información defendible para administradores es crucial para entender las implicaciones estadísticas para diseñar e implementar estudios de IAGE que incluyan consideraciones de escala de la imagen, limitaciones en el diseño de muestreo y la necesidad de validación de los estimadores. Una ventaja significativa de IAGE es que el investigador puede definir la escala (ejm. resolución espacial y extensión) de la imagen. Los programas de IAGE deberían planear escalas que empaten preguntas de monitoreo, el tamaño de los elementos del paisaje a ser medidos y la heterogeneidad espacial del medioambiente. 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La amplitud a la cual IAGE será de utilidad como herramienta para entender el estatus y tendencia de los pastizales, depende en gran medida en la habilidad para construir imágenes en programas robustos sino también con la habilidad de recolectar imágenes IAGE rápidamente y relativamente fácil sobre grandes paisajes.</abstract><cop>Lawrence</cop><pub>the Society for Range Management</pub><doi>10.2111/REM-D-11-00102.1</doi><tpages>10</tpages></addata></record>
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subjects Accuracy
Bias
Ecosystems
Estimate reliability
Estimation methods
Forum
Forums
image analysis
Image resolution
Landscapes
managers
monitoring
precision
probability
Random sampling
Range management
Rangeland ecology
rangelands
Remote sensing
sample design
Spatial resolution
statistics
Studies
Survey sampling
Trends
very-large-scale aerial image
title Using Very-Large-Scale Aerial Imagery for Rangeland Monitoring and Assessment: Some Statistical Considerations
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