Details

Autor(en) / Beteiligte

• Van Cleemput, Elisa
• Vanierschot, Laura
• Fernández-Castilla, Belén
• Honnay, Olivier
• Somers, Ben

Titel

The functional characterization of grass- and shrubland ecosystems using hyperspectral remote sensing: trends, accuracy and moderating variables

Ist Teil von

• Remote sensing of environment, 2018-05, Vol.209, p.747-763

Ort / Verlag

New York: Elsevier Inc

Erscheinungsjahr

2018

Link zum Volltext

Quelle

Elsevier ScienceDirect Journals Complete

Beschreibungen/Notizen

• Hyperspectral remote sensing is increasingly being recognized as a powerful tool to map ecosystem properties and functions through time and space. However, general information on the accuracy of this technology to assess the vegetation's biophysical and -chemical trait composition, and on the variables which are mediating this accuracy, is often lacking so far. Here, we addressed this knowledge gap for grass- and shrubland ecosystems and applied novel three-level meta-analytical regression equations to 77 studies that validated hyperspectral remote sensing data with field observations. Our results showed that the accuracy of hyperspectral sensors is generally high, but strongly depends on the trait being studied (leaf area index: R2 = 0.79 and nRMSE = 0.19, chlorophyll: R2 = 0.77 and nRMSE = 0.21, carotenoids: R2 = 0.80 and nRMSE = 0.29, phosphorus: R2 = 0.75 and nRMSE = 0.14, nitrogen: R2 = 0.74 and nRMSE = 0.09, water: R2 = 0.69 and nRMSE = 0.13, and lignin content: R2 = 0.64 and nRMSE = 0.26). Moreover, they indicated that the use of multivariate signal processing techniques could improve these estimation accuracies (adjusted p < 0.06 for LAI, chlorophyll and nitrogen). Finally, estimations from air- and spaceborne imaging spectrometers, allowing for functional mapping at broader spatial scales, were found to be as accurate as estimations from ground-based spectral measurements. Despite these promising findings, we revealed that leaf morphological properties (e.g. specific leaf area and leaf dry matter content) and biochemical traits which are not growth-related (e.g. lignin and cellulose) remain underexplored in grass- and shrublands. Moreover there was a strong publication bias towards R2 for assessing model performance. Our findings foster and direct further methodological and technological developments for a more accurate and complete functional characterization of these ecosystems worldwide. •Meta-analysis of 77 hyperspectral grass- and shrubland studies quantifying plant traits.•Overall trait estimation R2's reach from 0.64 to 0.80 depending on the trait.•Overall trait estimation nRMSE ranges between 0.09 and 0.29 depending on the trait.•LAI, pigments, phosphorus and nitrogen can be estimated with highest R2.•Multivariate signal processing techniques perform better than univariate regressions.•Air- and spaceborne imaging spectroscopy holds great potential.