A Range of Earth Observation Techniques for Assessing Plant Diversity

Abstract

Vegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS.

Measuring Alpha and Beta Diversity by Field and Remote-Sensing Data: A Challenge for Coastal Dunes Biodiversity Monitoring

“…In 2002, Palmer and coauthors postulated the spectral variability hypothesis (hereafter SVH), stating that the larger the spectral heterogeneity of an environment the higher its biodiversity [3]. Since then, several research efforts have been devoted to explore the relationship between remotely sensed and field-collected data [12,13] accounting for both alpha diversity (e.g., within sample/pixel variability) [14] and beta diversity (e.g., between samples/pixels variability) [15]. The potential of SVH to depict alpha diversity was tested on several ecosystems covering large areas as evergreen forests [10], tropical forests [16], wetlands [17], grasslands [18], savannah woodlands [19].…”

Extending vegetation site data and ensemble models to predict patterns of foliage cover and species richness for plant functional groups

“…By focussing on different growth forms, we implicitly acknowledge that different disturbances influence different functional groups in different ways. Advances in data availability (such as routinely updated land use information, or remotely sensed data, including hyperspectral, LiDAR, IKONOS, Quickbird, Landsat ETM and Sentinel-2) may offer opportunities to dynamically and iteratively update and refine categorical variables such as land use (Leitão and Santos 2019), to better predict characteristics of vegetation at a regional scale (Lausch et al 2020). At a global scale, predictors that relate to land use are often inferred from remotely sensed land cover data (see Socioeconomic Data and Applications Center http://sedac.ciesin.columbia.edu).…”

“…Continuous vegetation models recognise that patterns in vegetation often present as gradients and typically rely on remote sensing. Remote sensing data have advanced the development and production of continuous vegetation models by integrating biochemical, physiological and structural quantities of vegetation across a range of spatial and temporal scales (Houborg et al 2015;Lausch et al 2020). However, due to confounding and complex interactions between leaf, canopy, atmosphere and reflectance, integrating remote sensing data in ecology remains challenging with large uncertainties (Schimel et al 2020;Schrodt et al 2020), especially when used for informing models that target species or aggregated functional groups (Ustin and Gamon 2010).…”

Flower Mapping in Grasslands With Drones and Deep Learning

“…Plant diversity can be estimated on different scales and granularity-from space-borne sensors down to in-situ measurements (Lausch et al, 2016(Lausch et al, , 2020Wang and Gamon, 2019). Remote sensing based approaches cannot offer the same number of measurable traits as in-site measurements (Homolov et al, 2013).…”

“…Many remote sensing technologies exist to assess plant diversity (Wang and Gamon, 2019 ; Lausch et al, 2020 ). In the last 10 years, rapid developments in sensor technology and robotics have enhanced the capabilities of unmanned aerial vehicles (UAVs) (Anderson and Gaston, 2013 ; Pajares, 2015 ; Sanchez-Azofeifa et al, 2017 ; Aasen et al, 2018b ).…”