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

Abstract

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 = 0.76 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.

Challenges and opportunities for biogeography—What can we still learn from von Humboldt?

“…Recent advances in remote sensing technology and data storage offer unprecedented opportunities to assess change in both living and non‐living nature. For example, remote sensing has been used to assess changes in land cover (Amici, Marcantonio, La Porta, & Rocchini, ), species abundance (Paganini, Leidner, Geller, Turner, & Wegmann, ), functional traits (van Cleemput, Vanierschot, Fernández‐Castilla, Honnay, & Somers, ; Lausch et al, ) and even phylogenetic composition of plant communities (Schweiger et al, ). It is increasingly used for abiotic aspects, such as soil (Rogge et al, ) and hydrological features (Bierkens et al, ).…”

Retrieving Foliar Traits of Quercus garryana var. garryana across a Modified Landscape Using Leaf Spectroscopy and LiDAR

“…The application of remote sensing techniques to evaluate relevant ecosystem characteristics, such as biodiversity and plant health, provide opportunities to achieve these goals [4] and improvements in sensor technology, deployment platforms and computing are enabling new monitoring methodologies at multiple spatial scales [5][6][7]. One technique that has benefited from these recent developments is the use of spectroscopy to identify plant functional traits [8][9][10][11]. These traits, which relate to plant growth and development strategies, provide biologically relevant information relating to photosynthesis and environmental drivers such as nutrient, water and stress regimes [12][13][14][15].…”

A novel procedure for measuring functional traits of herbaceous species through field spectroscopy

“…The biochemical and morphological properties of leaves, as well as the 3D structure of vegetation, actually determine how light is reflected, transmitted and absorbed, and as such shape the spectral response of vegetation (Ollinger, ). A variety of plant traits can be estimated from hyperspectral observations with reasonable to high accuracies (Van Cleemput, Vanierschot, Fernández‐Castilla, Honnay, & Somers, ; Homolová, Malenovský, Clevers, García‐Santos, & Schaepman, ), and such traits are referred to as ‘spectral traits’ (Lausch et al, ). Consequently, this technology could form a valuable data source to complement existing functional trait libraries (e.g.…”