Radar vision in the mapping of forest biodiversity from space

Bae, Soyeon; Levick, Shaun R.; Heidrich, Lea; Magdon, Paul; Leutner, Benjamin; Wöllauer, Stephan; Serebryanyk, Alla; Nauss, Thomas; Krzystek, Peter; Goßner, Martin M.; Schall, Peter; Heibl, Christoph; Bässler, Claus; Doerfler, Inken; Schulze, Ernst Detlef; Krah, Franz‐Sebastian; Culmsee, Heike; Jung, Kirsten; Heurich, Marco; Fischer, Markus; Seibold, Sebastian; Thorn, Simon; Gerlach, Tobias; Hothorn, Torsten; Weisser, Wolfgang W.; Müller, Jörg

doi:10.1038/s41467-019-12737-x

Cited by 75 publications

(61 citation statements)

References 58 publications

(65 reference statements)

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“…Therefore, we propose two future research avenues of interest: fusion with spectral and/or radar data and using an environmental framework. Both spectral data and radar data have previously been shown to predict some of the variance in tree species richness (Bae et al., 2019; Bongalov et al., 2019; Foody & Cutler, 2006; Marselis et al, 2019; Schäfer et al., 2016; Wolf et al., 2012) and may improve our models and allow for more accurate predictions of tree species richness across the tropics and the creation of wall‐to‐wall data products at higher spatial resolution. Especially data from the hyperspectral imager suite (Matsunaga et al., 2013) instrument, that is soon to be launched to the International Space Station, the radar BIOMASS mission (Le Toan et al, 2011), the Ice, Cloud and land Elevation Satellite 2 mission (Duncanson et al., 2020), the TerraSAR‐X add‐on for Digital Elevation Measurement mission (Qi, Saarela, Armston, Stahl, & Dubayah, 2019) and Landsat (Saarela et al., 2018), may be highly relevant for such applications.…”

Section: Discussionmentioning

confidence: 99%

Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness

Marselis

Abernethy

Alonso

et al. 2020

Global Ecol. Biogeogr.

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Aim Mapping tree species richness across the tropics is of great interest for effective conservation and biodiversity management. In this study, we evaluated the potential of full‐waveform lidar data for mapping tree species richness across the tropics by relating measurements of vertical canopy structure, as a proxy for the occupation of vertical niche space, to tree species richness. Location Tropics. Time period Present. Major taxa studied Trees. Methods First, we evaluated the characteristics of vertical canopy structure across 15 study sites using (simulated) large‐footprint full‐waveform lidar data (22 m diameter) and related these findings to in‐situ tree species information. Then, we developed structure–richness models at the local (within 25–50 ha plots), regional (biogeographical regions) and pan‐tropical scale at three spatial resolutions (1.0, 0.25 and 0.0625 ha) using Poisson regression. Results The results showed a weak structure–richness relationship at the local scale. At the regional scale (within a biogeographical region) a stronger relationship between canopy structure and tree species richness across different tropical forest types was found, for example across Central Africa and in South America [R2 ranging from .44–.56, root mean squared difference as a percentage of the mean (RMSD%) ranging between 23–61%]. Modelling the relationship pan‐tropically, across four continents, 39% of the variation in tree species richness could be explained with canopy structure alone (R2 = .39 and RMSD% = 43%, 0.25‐ha resolution). Main conclusions Our results may serve as a basis for the future development of a set of structure–richness models to map high resolution tree species richness using vertical canopy structure information from the Global Ecosystem Dynamics Investigation (GEDI). The value of this effort would be enhanced by access to a larger set of field reference data for all tropical regions. Future research could also support the use of GEDI data in frameworks using environmental and spectral information for modelling tree species richness across the tropics.

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Section: Discussionmentioning

confidence: 99%

Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness

Marselis

Abernethy

Alonso

et al. 2020

Global Ecol. Biogeogr.

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“…The BIOKLIM Project was conducted in the Bavarian Forest National Park (BAY) and the Steigerwald Project in Northern Bavaria (STE) (for details of the five regions, see Figure and Bae et al. (2019)). Not all functional groups were sampled in each plot.…”

Section: Methodsmentioning

confidence: 99%

Dispersal ability, trophic position and body size mediate species turnover processes: Insights from a multi‐taxa and multi‐scale approach

Bae

Heidrich

Levick

et al. 2020

Diversity and Distributions

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Aim Despite increasing interest in β‐diversity, that is the spatial and temporal turnover of species, the mechanisms underlying species turnover at different spatial scales are not fully understood, although they likely differ among different functional groups. We investigated the relative importance of dispersal limitations and the environmental filtering caused by vegetation for local, multi‐taxa forest communities differing in their dispersal ability, trophic position and body size. Location Temperate forests in five regions across Germany. Methods In the inter‐region analysis, the independent and shared effects of the regional spatial structure (regional species pool), landscape spatial structure (dispersal limitation) and environmental factors on species turnover were quantified with a 1‐ha grain across 11 functional groups in up to 495 plots by variation partitioning. In the intra‐region analysis, the relative importance of three environmental factors related to vegetation (herb and tree layer composition and forest physiognomy) and spatial structure for species turnover was determined. Results In the inter‐region analysis, over half of the explained variation in community composition (23% of the total explained 35%) was explained by the shared effects of several factors, indicative of spatially structured environmental filtering. Among the independent effects, environmental factors were the strongest on average over 11 groups, but the importance of landscape spatial structure increased for less dispersive functional groups. In the intra‐region analysis, the independent effect of plant species composition had a stronger influence on species turnover than forest physiognomy, but the relative importance of the latter increased with increasing trophic position and body size. Main conclusions Our study revealed that the mechanisms structuring assemblage composition are associated with the traits of functional groups. Hence, conservation frameworks targeting biodiversity of multiple groups should cover both environmental and biogeographical gradients. Within regions, forest management can enhance β‐diversity particularly by diversifying tree species composition and forest physiognomy.

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“…A further area of development for the RSDB will be to enable users to manually set parameters for particular indices. This parametrization will lead to more complexity for users because currently users just need to select desired indices and index variants are covered by naming that variants (which have been utilized in synthesis studies (Bae et al 2019, Meyer et al 2019)). However, for some indices, for example AGB, parameterization is indispensable for adapting to different climate zones.…”

Section: Discussionmentioning

confidence: 99%

RSDB: an easy to deploy open‐source web platform for remote sensing raster and point cloud data management, exploration and processing

et al. 2020

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Monitoring biodiversity and its change over space and time is a key challenge for ecological research. Utilizing remote sensing data in ecological research has been shown to be a powerful complement to labor‐intensive field work to derive area‐wide biodiversity indices, which can also capture larger spatiotemporal areas. However, preprocessing different types of remote sensing data for specific studies often requires extensive computational skills as the algorithms for creating final data products are scattered across the literature. Our remote sensing database (RSDB, ) serves as an integrative storage, management and processing solution for several types of remote sensing data. Transparent data processing capabilities for more than 200 biodiversity‐related indices are included and can be used to calculate ready‐to‐use data products for a variety of ecological studies. These data products, for example, leaf area, vertical distribution of biomass and foliage height derived from light detection and ranging (LiDAR) point clouds, are individually calculated for user‐specified study areas and can serve as proxies for ecological variables, such as the availability of resources or habitat heterogeneity. The primary RSDB user interface is web browser based on management, exploration, visualization and product processing capabilities. The RSDB stores data in layers that are discoverable by textual metadata and mapping on overview maps. Individual data layers can be explored using interactive maps and 3‐D visualizations. We additionally provide a seamless connection of the RSDB's functionality with the R environment through our accompanying R package for scripting and advanced processing tasks. We designed the RSDB to be an easily accessible solution for on‐demand processing of comprehensive biodiversity indices. To date, the RSDB has been used and tested in several studies, and we are convinced that it will continue to facilitate biodiversity monitoring via its application to ecological research.

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Radar vision in the mapping of forest biodiversity from space

Cited by 75 publications

References 58 publications

Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness

Evaluating the potential of full‐waveform lidar for mapping pan‐tropical tree species richness

Dispersal ability, trophic position and body size mediate species turnover processes: Insights from a multi‐taxa and multi‐scale approach

RSDB: an easy to deploy open‐source web platform for remote sensing raster and point cloud data management, exploration and processing

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