Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales

Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is com...

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Autores principales:	Rowan Gaffney, David J. Augustine, Sean P. Kearney, Lauren M. Porensky
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	plant community composition hyperspectral grasslands HPC computing NEON AOP machine learning Science Q
Acceso en línea:	https://doaj.org/article/f31fefcd964e4a9f972ed32b054f2862
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id	oai:doaj.org-article:f31fefcd964e4a9f972ed32b054f2862
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spelling	oai:doaj.org-article:f31fefcd964e4a9f972ed32b054f28622021-11-25T18:54:42ZUsing Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales10.3390/rs132246032072-4292https://doaj.org/article/f31fefcd964e4a9f972ed32b054f28622021-11-01T00:00:00Zhttps://www.mdpi.com/2072-4292/13/22/4603https://doaj.org/toc/2072-4292Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is complicated by their similar spectral absorption profiles. To better quantify the impacts of land management and weather variability on rangeland vegetation change, we analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at a 6500-ha experimental station (Central Plains Experimental Range) to map vegetation composition and change over a 5-year timescale. The spatial resolution (1 m) of the data was able to resolve the plant community type at a suitable scale and the information-rich spectral resolution (426 bands) was able to differentiate closely related plant community classes. The resulting plant community class map showed strong accuracy results from both formal quantitative measurements (F1 75% and Kappa 0.83) and informal qualitative assessments. Over a 5-year period, we found that plant community composition was impacted more strongly by weather than by the rangeland management regime. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use resultant maps to inform rangeland ecology and management. Critical to the success of the research was the development of computational methods that allowed us to implement efficient and flexible analyses on the large and complex data.Rowan GaffneyDavid J. AugustineSean P. KearneyLauren M. PorenskyMDPI AGarticleplant community compositionhyperspectralgrasslandsHPC computingNEON AOPmachine learningScienceQENRemote Sensing, Vol 13, Iss 4603, p 4603 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	plant community composition hyperspectral grasslands HPC computing NEON AOP machine learning Science Q
spellingShingle	plant community composition hyperspectral grasslands HPC computing NEON AOP machine learning Science Q Rowan Gaffney David J. Augustine Sean P. Kearney Lauren M. Porensky Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales
description	Rangelands are composed of patchy, highly dynamic herbaceous plant communities that are difficult to quantify across broad spatial extents at resolutions relevant to their characteristic spatial scales. Furthermore, differentiation of these plant communities using remotely sensed observations is complicated by their similar spectral absorption profiles. To better quantify the impacts of land management and weather variability on rangeland vegetation change, we analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at a 6500-ha experimental station (Central Plains Experimental Range) to map vegetation composition and change over a 5-year timescale. The spatial resolution (1 m) of the data was able to resolve the plant community type at a suitable scale and the information-rich spectral resolution (426 bands) was able to differentiate closely related plant community classes. The resulting plant community class map showed strong accuracy results from both formal quantitative measurements (F1 75% and Kappa 0.83) and informal qualitative assessments. Over a 5-year period, we found that plant community composition was impacted more strongly by weather than by the rangeland management regime. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use resultant maps to inform rangeland ecology and management. Critical to the success of the research was the development of computational methods that allowed us to implement efficient and flexible analyses on the large and complex data.
format	article
author	Rowan Gaffney David J. Augustine Sean P. Kearney Lauren M. Porensky
author_facet	Rowan Gaffney David J. Augustine Sean P. Kearney Lauren M. Porensky
author_sort	Rowan Gaffney
title	Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales
title_short	Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales
title_full	Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales
title_fullStr	Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales
title_full_unstemmed	Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales
title_sort	using hyperspectral imagery to characterize rangeland vegetation composition at process-relevant scales
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/f31fefcd964e4a9f972ed32b054f2862
work_keys_str_mv	AT rowangaffney usinghyperspectralimagerytocharacterizerangelandvegetationcompositionatprocessrelevantscales AT davidjaugustine usinghyperspectralimagerytocharacterizerangelandvegetationcompositionatprocessrelevantscales AT seanpkearney usinghyperspectralimagerytocharacterizerangelandvegetationcompositionatprocessrelevantscales AT laurenmporensky usinghyperspectralimagerytocharacterizerangelandvegetationcompositionatprocessrelevantscales
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Using Hyperspectral Imagery to Characterize Rangeland Vegetation Composition at Process-Relevant Scales

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