Linking Foliar Traits to Belowground Processes

Above- and belowground systems are linked via plant chemistry. In forested systems, leaf litter chemistry and quality mirror that of green foliage and have important afterlife effects. In systems where belowground inputs dominate, such as grasslands, or in ecosystems where aboveground biomass is frequently removed by burning or harvesting, foliar traits may provide important information regarding belowground inputs via exudates and fine-root turnover. Many, if not most, of the plant traits that drive variation in belowground processes are also measurable via remote sensing technologies. The ability of remote sensing techniques to measure fine-scale biodiversity and plant chemistry over large spatial scales can help researchers address ecological questions that were previously prohibitively expensive to address. Key to these potential advances is the idea that remotely sensed vegetation spectra and plant chemistry can provide detailed information about the function of belowground processes beyond what traditional field sampling can provide.

Spectral Field Campaigns: Planning and Data Collection

This chapter focuses on planning field campaigns and data collection relevant to plant biodiversity. Particular emphasis is placed on sampling spectra of plants across scales, from the leaf to the canopy and airborne level, considering the issue of matching ecological data with spectra. The importance of planning is highlighted from the perspective of the long-term sustainability of a project, which includes using and contributing to the development of standards for project documentation and archiving. These issues are critical to biodiversity researchers involved in data collection in situ and via remote sensing (RS).

Lessons Learned from Spectranomics: Wet Tropical Forests

One of the major struggles for biodiversity science is how to measure biodiversity at scales relevant for conservation and management, particularly in wet tropical forests where vast, largely inaccessible landscapes and enormous taxonomic variation make field-based approaches alone infeasible, and current Earth-observing satellites are unable to detect compositional differences or forest functional changes over time. The Spectranomics approach was developed to link plant canopy functional traits to their spectral properties with the objective of providing time-varying, scalable methods for remote sensing (RS) of forest biodiversity. In this chapter we explain key components of Spectranomics and highlight some of the major lessons learned over the past decade as we developed the program in tropical forests sites around the world.

How the Optical Properties of Leaves Modify the Absorption and Scattering of Energy and Enhance Leaf Functionality

Leaves absorb, scatter, and transmit sunlight at all wavelengths across the visible, near-infrared, and shortwave-infrared spectrum. The optical properties of a leaf are determined by its biochemical and biophysical characteristics, including its 3-D cellular organization. The absorption and scattering properties of leaves together create the shape of their reflectance spectra. Terrestrial seed plant species share similar physiological and metabolic processes for fluxes of gases (CO2, O2, H2O), nutrients, and energy, while differences are primarily consequences of how these properties are distributed and their physical structures. Related species generally share biochemical and biophysical traits, and their optical properties are also similar, providing a mechanism for identification. However, it is often the minor differences in spectral properties throughout the wavelengths of the solar spectrum that define a species or groups of related species. This chapter provides a review and summary of the most common interactions between leaf properties and light and the physical processes that regulate the outcomes of these interactions.

Using Remote Sensing for Modeling and Monitoring Species Distributions

Interpolated climate surfaces have been widely used to predict species distributions and develop environmental niche models. However, the spatial coverage and density of meteorological sites used to develop these surfaces vary among countries and regions, such that the most biodiverse regions often have the most sparsely sampled climatic data. We explore the potential of satellite remote sensing (S-RS) products—which have consistently high spatial and temporal resolution and nearly global coverage—to quantify species-environment relationships that predict species distributions. We propose several new environmental metrics that take advantage of high temporal resolution in S-RS data and compare these approaches to classic climate-only approaches using the live oaks (Quercus section Virentes) as a case study. We show that models perform similarly but for some species, particularly in understudied regions, show less precision in predicting spatial distribution. These results provide evidence supporting efforts to enhance environmental niche models and species distribution models (ENMs/SDMs) with S-RS data and, when combined with other approaches for species detection, will likely enhance our ability to monitor biodiversity globally.

Essential Biodiversity Variables: Integrating In-Situ Observations and Remote Sensing Through Modeling

Essential biodiversity variables (EBVs) are designed to support the detection and quantification of biodiversity change and to define priorities in biodiversity monitoring. Unlike most primary observations of biodiversity phenomena, EBV products should provide information readily available to produce policy-relevant biodiversity indicators, ideally at multiple spatial scales, from global to subnational. This information is typically complex to produce from a single set of data or type of observation, thus requiring approaches that integrate multiple sources of in situ and remote sensing (RS) data. Here we present an up-to-date EBV concept for biodiversity data integration and discuss the critical components of workflows for EBV production. We argue that open and reproducible workflows for data integration are critical to ensure traceability and reproducibility so that each EBV endures and can be updated as novel biodiversity models are adopted, new observation systems become available, and new data sets are incorporated. Fulfilling the EBV vision requires strengthening efforts to mobilize massive amounts of in situ biodiversity data that are not yet publicly available and taking full advantage of emerging RS technologies, novel biodiversity models, and informatics infrastructures, in alignment with the development of a globally coordinated system for biodiversity monitoring.

The Use of Remote Sensing to Enhance Biodiversity Monitoring and Detection: A Critical Challenge for the Twenty-First Century

Improved detection and monitoring of biodiversity is critical at a time when the Earth’s biodiversity loss due to human activities is accelerating at an unprecedented rate. We face the largest loss of biodiversity in human history, a loss which has been called the “sixth mass extinction” (Leakey 1996; Kolbert 2014), given that its magnitude is in proportion to past extinction episodes in Earth history detectable from the fossil record. International efforts to conserve biodiversity (United Nations 2011) and to develop an assessment process to document changes in the status and trends of biodiversity globally through the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (Díaz et al. 2015) have raised awareness about the critical need for continuous monitoring of biodiversity at multiple spatial scales across the globe. Biodiversity itself—the variation in life found among ecosystems and organisms at any level of biological organization—cannot practically be observed everywhere. However, if habitats, functional traits, trait diversity, and the spatial turnover of plant functions can be remotely sensed, the potential exists to globally inventory the diversity of habitats and traits associated with terrestrial biodiversity. To face this challenge, there have been recent calls for a global biodiversity monitoring system (Jetz et al. 2016; Proença et al. 2017; The National Academy of Sciences 2017). A central theme of this volume is that remote sensing (RS) will play a key role in such a system.

Predicting Patterns of Plant Diversity and Endemism in the Tropics Using Remote Sensing Data: A Study Case from the Brazilian Atlantic Forest

We combine remote sensing (RS) measurements of temperature and precipitation with phylogenetic and distribution data from three plant clades with different life forms, i.e., shrubs and treelets (tribe Miconieae, Melastomes), epiphytes (Ronnbergia-Wittmackia alliance, Bromeliaceae), and lianas (“Fridericia and Allies” clade, Bignoniaceae), to predict the distribution of biodiversity in a tropical hot spot: the Brazilian Atlantic Forest. We assess (i) how well RS-derived climate estimates predict the spatial distribution of species richness (SR), phylogenetic diversity (PD), and phylogenetic endemism (PE) and (ii) how they compare to predictions based on interpolated weather station information. We find that environmental descriptors derived from RS sources can predict the distribution of SR and PD, performing as well as or better than weather station-based data. Yet performance is lower for endemism and for clades with a high number of species of small ranges. We argue that this approach can provide an alternative to remotely monitor megadiverse groups or biomes for which species identification through RS are not yet feasible or available.

The Laegeren Site: An Augmented Forest Laboratory

Given the increased pressure on forests and their diversity in the context of global change, new ways of monitoring diversity are needed. Remote sensing has the potential to inform essential biodiversity variables on the global scale, but validation of data and products, particularly in remote areas, is difficult. We show how radiative transfer (RT) models, parameterized with a detailed 3-D forest reconstruction based on laser scanning, can be used to upscale leaf-level information to canopy scale. The simulation approach is compared with actual remote sensing data, showing very good agreement in both the spectral and spatial domains. In addition, we compute a set of physiological and morphological traits from airborne imaging spectroscopy and laser scanning data and show how these traits can be used to estimate the functional richness of a forest at regional scale. The presented RT modeling framework has the potential to prototype and validate future spaceborne observation concepts aimed at informing variables of biodiversity, while the trait-based mapping of diversity could augment in situ networks of diversity, providing effective spatiotemporal gap filling for a comprehensive assessment of changes to diversity.

Scaling Functional Traits from Leaves to Canopies

In this chapter, we begin by exploring the relationship between plant functional traits and functional diversity and how this relates to the characterization and monitoring of global plant biodiversity. We then discuss the connection between leaf functional traits and their resulting optical properties (i.e., reflectance, transmittance, and absorption) and how this related to remote sensing (RS) of functional diversity. Building on this, we briefly discuss the history of RS of functional traits using spectroscopy and imaging spectroscopy data. We include a discussion of the key considerations with the use of imaging spectroscopy data for scaling and mapping plant functional traits across diverse landscapes. From here we provide a review of the general methods for scaling and mapping functional traits, including empirical and radiative transfer model (RTM) approaches. We complete the chapter with a discussion of other key considerations, such as field sampling protocols, as well as current caveats and future opportunities.