Standardizing Ecosystem Morphological Traits from 3D Information Sources. (August 2020)

• Record Type: Journal Article
• Authors: Valbuena, R.; O'Connor, B.; Zellweger, F.; Simonson, W.; Vihervaara, P.; Maltamo, M.; Silva, C.A.; Almeida, D.R.A.; Danks, F.; Morsdorf, F.; Chirici, G.; Lucas, R.; Coomes, D.A.; Coops, N.C.
• Is Part Of: Trends in ecology & evolution. Volume 35:Number 8(2020)
• Page Start: 656
• Page End: 667
• Publication Date: 2020-08
• Subjects: Essential biodiversity variables (EBVs) -- Sustainable Development Goals (SDG) -- light detection and ranging (LIDAR) -- synthetic aperture radar (SAR) -- digital photogrammetry; Ecology -- Periodicals; Evolution (Biology) -- Periodicals; 576.8
• DOI: 10.1016/j.tree.2020.03.006
• Languages: English
• ISSNs: 0169-5347

Abstract

Abstract : 3D-imaging technologies provide measurements of terrestrial and aquatic ecosystems' structure, key for biodiversity studies. However, the practical use of these observations globally faces practical challenges. First, available 3D data are geographically biased, with significant gaps in the tropics. Second, no data source provides, by itself, global coverage at a suitable temporal recurrence. Thus, global monitoring initiatives, such as assessment of essential biodiversity variables (EBVs), will necessarily have to involve the combination of disparate data sets. We propose a standardized framework of ecosystem morphological traits – height, cover, and structural complexity – that could enable monitoring of globally consistent EBVs at regional scales, by flexibly integrating different information sources – satellites, aircrafts, drones, or ground data – allowing global biodiversity targets relating to ecosystem structure to be monitored and regularly reported. Highlights: 3D-imaging data acquired from a variety of platforms have become critical for ecological and environmental management. However, the use of disparate information sources to produce comprehensive and standardized global products is hindered by a lack of harmonization and terminology around ecosystem structure. We propose a sensor- and platform-independent framework which effectively distils the wealth of 3D information into concise ecosystem morphological traits – height, cover, and structural complexity – easy to conceptualize by ecologists and conservation stakeholders lacking remote sensing background. The conceptual disaggregation of ecosystem structure would contribute to defining and monitoring essential biodiversity variables obtained from 3D imaging that can be used to inform progress towards the UN 2030 Sustainable Development Goals and other international policy targets.