Using hyperspectral imagery to characterize rangeland vegetation composition at process-relevant scales

Authors: Gaffney, Rowan; Augustine, David; Kearney, Sean; Porensky, Lauren

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/5/2021
Publication Date: 11/16/2021
Citation: Gaffney, R.M., Augustine, D.J., Kearney, S.P., Porensky, L.M. 2021. Using hyperspectral imagery to characterize rangeland vegetation composition at process-relevant scales. Remote Sensing. 13. Article 4603. https//doi.org/10.3390/rs13224603.
DOI: https://doi.org/10.3390/rs13224603

Interpretive Summary: Rangelands are composed of patchy vegetation that is difficult to quantify across broad spatial scales. Furthermore, the spectral profile of many species within these systems are similar, making it difficult to differentiate between them using traditional remote sensing datasets and methods. We analyzed high resolution hyperspectral data produced by the National Ecological Observatory Network (NEON) at an experimental station (Central Plains Experimental Range) to map vegetation composition and change over a 5-year period. The spatial resolution (1 m) of the data was able to resolve plant community type at a suitable scale and the information-rich spectral resolution (426 bands) was able to differentiate plant community classes. The resulting plant community class map showed strong accuracy both formal quantitative measurements and informal qualitative assessments. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use the resultant maps to inform rangeland ecology and management.

Technical Abstract: 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 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 83%) and informal qualitative assessments. Over a 5-year period, we found that plant community composition was impacted more strongly by weather than by rangeland management regime. Our work displays the potential to map plant community classes across extensive areas of herbaceous vegetation and use the resultant maps to inform rangeland ecology and management. Critical to the success of the research was developing computational methods that allowed us to implement efficient and flexible analyses on the large and complex data.