Fusion of airborne hyperspectral and LiDAR canopy-height data for estimating fractional cover of tall woody plants, herbaceous vegetation, and other soil cover types in a semi-arid savanna ecosystem
Creators: name: Pervin, Rubaya
name: Robeson, Scott M.
name: MacBean, Natasha
Publisher: name: Taylor & Francis
Year: 2022
Subjects: subject: Ecology
subject: FOS: Biological sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: Plant Biology

description: Vegetation in semi-arid regions is often a spatially heterogeneous mix of bare soil, herbaceous vegetation, and woody plants. Detecting the fractional cover of woody plants versus herbaceous vegetation, in addition to non-photosynthetically active cover types such as biological soil crusts, dry grasses, and bare ground, is crucial for understanding spatial patterns and temporal changes in semi-arid savanna ecosystems. With and without the inclusion of LiDAR-derived canopy height information, we tested different configurations of unsupervised linear unmixing classification of hyperspectral data to estimate fractional cover of tall woody plants, herbaceous vegetation, and other non-photosynthetically active cover types. To perform this analysis, we used 1 m resolution hyperspectral and LiDAR data collected by the NEON airborne observation platform at the Santa Rita Experimental Range in Arizona. Our results show that both hyperspectral and canopy height information are needed in the linear unmixing algorithm to correctly identify tall woody plants versus herbaceous vegetation. However, including height data is not always sufficient to be able to separate the two vegetation types: selecting the optimum number of endmembers is also crucial for separating these two types of photosynthetically active vegetation. In comparison to our reference dataset, fractional cover accuracy was highest for the woody plant class based on two accuracy assessment methods, although the fractional cover of dense woody plant is likely underestimated because linear unmixing also detects sub-canopy elements. The accuracy of the herbaceous vegetation class was lower than the other classes, likely because of the presence of both active and senesced grasses. Our study presents the first use of both hyperspectral and canopy height information to separately detect fractional cover of woody plants versus herbaceous vegetation in addition to other bare soil cover types that should be useful for fractional cover classification in other semi-arid savanna ecosystems where hyperspectral and LiDAR data are available. Unsupervised linear unmixing using high spatial resolution hyperspectral data alone could not identify tall woody plants versus herbaceous vegetation in a semiarid savanna ecosystem; instead, canopy height information was needed to separate the two vegetation types.Even when including canopy height information, accurate separation of the two vegetation types was dependent on the number of endmembers included in the unmixing algorithm due to the presence of additional dominant spectral signatures that did not correspond to photosynthetically active vegetation.Fractional cover estimates for the woody plant class were the most accurate, although dense woody plant patches are likely underestimated. Unsupervised linear unmixing using high spatial resolution hyperspectral data alone could not identify tall woody plants versus herbaceous vegetation in a semiarid savanna ecosystem; instead, canopy height information was needed to separate the two vegetation types. Even when including canopy height information, accurate separation of the two vegetation types was dependent on the number of endmembers included in the unmixing algorithm due to the presence of additional dominant spectral signatures that did not correspond to photosynthetically active vegetation. Fractional cover estimates for the woody plant class were the most accurate, although dense woody plant patches are likely underestimated.