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SummaryForest biodiversity is critical for many ecosystem functions and services at plot scale, but it is uncertain how biodiversity influences ecosystem functioning across environmental gradients and contiguous larger areas. We used remote sensing and process-based terrestrial biosphere modeling to explore functional diversity–productivity relationships at multiple scales for a heterogeneous forest site in Switzerland.We ran the biosphere model with empirical data about forest structure and composition derived from ground-based surveys, airborne laser scanning and imaging spectroscopy for the years 2006–2015 at 10×10-m spatial resolution. We then related the model outputs forest productivity to functional diversity under observed and experimental model conditions.Functional diversity increased productivity significantly (p < 0.001) across all simulations at 20×20-m to 30×30-m scale, but at 100×100-m scale positive relationships disappeared under homogeneous soil conditions.Whereas local functional diversity was an important driver of productivity, environmental context (especially soil depth, texture and water availability) underpinned the variation of productivity (and functional diversity) at larger spatial scales. Integration of remotely-sensed information on canopy composition and structure into terrestrial biosphere models helps fill the knowledge gap about how plant biodiversity affects carbon cycling and biosphere feedbacks onto climate over large contiguous areas.

Ecosystem heterogeneity has been widely recognized as a key ecological indicator of several ecological functions, diversity patterns and change, metapopulation dynamics, population connectivity or gene flow. In this paper, we present a new R package—rasterdiv—to calculate heterogeneity indices based on remotely sensed data. We also provide an ecological application at the landscape scale and demonstrate its power in revealing potentially hidden heterogeneity patterns. The rasterdiv package allows calculating multiple indices, robustly rooted in Information Theory, and based on reproducible open-source algorithms.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

International audience; Aim. The majority of work done to gather information on the Earth's biodiversity has been carried out using in-situ data, with known issues related to epistemology (e.g., species determination and taxonomy), spatial uncertainty, logistics (time and costs), among others. An alternative way to gather information about spatial ecosystem variability is the use of satellite remote sensing. It works as a powerful tool for attaining rapid and standardized information. Several metrics used to calculate remotely sensed diversity of ecosystems are based on Shannon’s information theory, namely on the differences in relative abundance of pixel reflectances in a certain area. Additional metrics like the Rao’s quadratic entropy allow the use of spectral distance beside abundance, but they are point descriptors of diversity, that is they can account only for a part of the whole diversity continuum. The aim of this paper is thus to generalize the Rao’s quadratic entropy by proposing its parameterization for the first time. Innovation. The parametric Rao’s quadratic entropy, coded in R, (a) allows the representation of the whole continuum of potential diversity indices in one formula, and (b) starting from the Rao’s quadratic entropy, allows the explicit use of distances among pixel reflectance values, together with relative abundances. Main conclusions. The proposed unifying measure is an integration between abundance- and distance-based algorithms to map the continuum of diversity given a satellite image at any spatial scale. Being part of the rasterdiv R package, the proposed method is expected to ensure high robustness and reproducibility.

Assessing functional diversity from space can help predict productivity and stability of forest ecosystems at global scale using biodiversity–ecosystem functioning relationships. We present a new spatially continuous method to map regional patterns of tree functional diversity using combined laser scanning and imaging spectroscopy. The method does not require prior taxonomic information and integrates variation in plant functional traits between and within plant species. We compare our method with leaf-level field measurements and species-level plot inventory data and find reasonable agreement. Morphological and physiological diversity show consistent change with topography and soil, with low functional richness at a mountain ridge under specific environmental conditions. Overall, functional richness follows a logarithmic increase with area, whereas divergence and evenness are scale invariant. By mapping diversity at scales of individual trees to whole communities we demonstrate the potential of assessing functional diversity from space, providing a pathway only limited by technological advances and not by methodology., As remote sensing technology improves, it is now possible to map fine-scale variation in plant functional traits. Schneider et al. remotely sense tree functional diversity, validate with field data, and reveal patterns of plant adaptation to the environment previously not retrievable from plot data

Remote sensing offers the potential to study forest ecosystems by providing spatially and temporally distributed information on key biophysical and biochemical variables. The estimation of biochemical constituents of leaves from remotely sensed data is of high interest revealing insight on photosynthetic processes, plant health, plant functional types, and species composition. However, upscaling leaf level observations to canopy level is not a trivial task, in particular due to the inherent structural complexity of forests. A common solution for scaling spectral information is the use of physically-based radiative transfer models. We parameterize the Discrete Anisotropic Radiative Transfer (DART) model based on airborne and in situ measurements. At-sensor radiances were simulated and compared with measurements of the Airborne Prism Experiment (APEX) imaging spectrometer. The study was performed on the Laegern site (47°28′43.0 N, 8°21′53.2 E, Switzerland), a temperate mixed forest characterized by steep slopes, a heterogeneous spectral background, and a high species diversity. Particularly the accurate 3D modeling of the complex canopy architecture is crucial to understand the interaction of photons with the vegetation canopy and its background. Two turbid medium based forest reconstruction approaches were developed and compared; namely based on a voxel grid and based on individual tree detection. Our study shows that the voxel grid based reconstruction yields better results. When using a pixel-wise comparison with the imaging spectrometer data, the voxel grid approach performed better (R 2 = 0.48, λ780 nm) than the individual tree approach (R 2 = 0.34, λ780 nm). Spatial patterns as compared to APEX data were similar, whereas absolute radiance values differed slightly, depending on wavelength. We provide a successful representation of a 3D radiative regime of a temperate mixed forest, suitable to simulate most spectral and spatial features of imaging spectrometer data. Limitations of the approach include the high spectral variability of leaf optical properties between and within species, which will be further addressed. The results also reveal the need of more accurate parameterizations of small-scale structures, such as needle clumping at shoot level as well as leaf angle.