Assessing Across-Scale Optical Diversity and Productivity Relationships in Grasslands of the Italian Alps

Abstract

The linearity and scale-dependency of ecosystem biodiversity and productivity relationships (BPRs) have been under intense debate. In a changing climate, monitoring BPRs within and across different ecosystem types is crucial, and novel remote sensing tools such as the Sentinel-2 (S2) may be adopted to retrieve ecosystem diversity information and to investigate optical diversity and productivity patterns. But are the S2 spectral and spatial resolutions suitable to detect relationships between optical diversity and productivity? In this study, we implemented an integrated analysis of spatial patterns of grassland productivity and optical diversity using optical remote sensing and Eddy Covariance data. Across-scale optical diversity and ecosystem productivity patterns were analyzed for different grassland associations with a wide range of productivity. Using airborne optical data to simulate S2, we provided empirical evidence that the best optical proxies of ecosystem productivity were linearly correlated with optical diversity. Correlation analysis at increasing pixel sizes proved an evident scale-dependency of the relationships between optical diversity and productivity. The results indicate the strong potential of S2 for future large-scale assessment of across-ecosystem dynamics at upper levels of observation.

VIS-NIR, Red-Edge and NIR-Shoulder Based Normalized Vegetation Indices Response to Co-Varying Leaf and Canopy Structural Traits in Heterogeneous Grasslands

“…S-2 bands 5, 6 and 7 (respectively, at 705, 740 and 783 nm) are considered to be part of the RE region [38,39] although band 7 is well beyond the threshold between RE and NIR and is in the NIR-shoulder region (750-900 nm [20][21][22]). In addition, a few authors [9,40,41] using normalized difference indices (NDIs) based on S-2 bands 7 and 8 (or 8a) refer to such SVIs as RENDVI indices, although they only make use of NIR-shoulder bands. The definition of two bands NDIs used in this study is presented in Section 2.3.1.…”

“…In its central part, the meadow hosts a Fluxnet Eddy Covariance (EC) tower (IT-MBo, Italy). Due to its heterogeneous management and orography, the plateau is characterized by the presence of different grassland types characterized by extremely varying LAI and biomass [9]). Such heterogeneous meadows include several different vegetation types (two most abundant associations, one of them including two variants [9]).…”

“…Due to its heterogeneous management and orography, the plateau is characterized by the presence of different grassland types characterized by extremely varying LAI and biomass [9]). Such heterogeneous meadows include several different vegetation types (two most abundant associations, one of them including two variants [9]). The Sieverso-Nardetum strictae association (average aboveground biomass: 236 g•m −2 [9]) coves a high portion of the plateau, including the EC footprint, and is characterized by short canopies and not intensively managed.…”

“…Remote sensing can provide fundamental spatial and temporal information, which can be used in monitoring PTs related to plant biochemistry, photosynthetic processes and canopy structure. During the last years, the proximal sensing approach was used to fill the scaling gap between leaf and satellite measurements, linking vegetation characteristics and spectral responses from the leaf level to increasing pixel sizes [7][8][9]. While the visible (VIS) and shortwave infrared (SWIR, 1100-2500 nm) parts of the reflectance spectrum are mainly determined by pigments and water content absorption, respectively, in the near infrared (NIR, 750-1400), reflectance is high compared to the VIS domain because individual leaves and whole plant canopies strongly scatter NIR, and the degree of NIR scattering is driven by the internal leaf structure alongside with canopy structure and the ratio between green and non-photosynthetic components [5].…”

“…In herbaceous plants, LAI is a spatially-and temporally-dynamic key trait related to ecosystem functions (e.g., productivity and evapotranspiration), and remote sensing data have been widely used to capture its variability at various scales [10,11]. However, the structural and biochemistry variability among leaves, plants and ecosystems-particularly in natural grasslands, characterized by extreme heterogeneity [9]-is strongly affecting our ability to link spectral variation and LAI. Simultaneously with LAI, factors such as leaf anatomy and LAD are also varying in space and time (e.g., across heterogeneous canopies or due to phenological changes), and this has a significant and often unpredictable impact on scattering across the spectrum.…”