StrucNet: a global network for automated vegetation structure monitoring

Abstract

Climate change and increasing human activities are impacting ecosystems and their biodiversity. Quantitative measurements of essential biodiversity variables (EBV) and essential climate variables are used to monitor biodiversity and carbon dynamics and evaluate policy and management interventions. Ecosystem structure is at the core of EBVs and carbon stock estimation and can help to inform assessments of species and species diversity. Ecosystem structure is also used as an indirect indicator of habitat quality and expected species richness or species community composition. Spaceborne measurements can provide large‐scale insight into monitoring the structural dynamics of ecosystems, but they generally lack consistent, robust, timely and detailed information regarding their full three‐dimensional vegetation structure at local scales. Here we demonstrate the potential of high‐frequency ground‐based laser scanning to systematically monitor structural changes in vegetation. We present a proof‐of‐concept high‐temporal ecosystem structure time series of 5 years in a temperate forest using terrestrial laser scanning (TLS). We also present data from automated high‐temporal laser scanning that can allow upscaling of vegetation structure scanning, overcoming the limitations of a typically opportunistic TLS measurement approach. Automated monitoring will be a critical component to build a network of field monitoring sites that can provide the required calibration data for satellite missions to effectively monitor the structural dynamics of vegetation over large areas. Within this perspective, we reflect on how this network could be designed and discuss implementation pathways.

Innovations in plant science from integrative remote sensing research: an introduction to a Virtual Issue

“…The PhenoCam Network; Richardson, 2019), but networks are still in development for newer platforms such as drones and TLS (e.g. the recently proposed StrucNet for frequent TLS observations; Calders et al, 2023). Additional effort is also needed to improve representation of some geographic regions in networks.…”

Charting the Future of the FLUXNET Network

“…While MODIS data are no longer being collected, the successful pairing of FLUXNET data with MODIS has proven the value in consistent ground truth data and opens the opportunities for the next generation of remote sensing products (Chu et al 2021;Running et al 1999;Walther et al 2022). New technologies, such as the high spatial/temporal resolutions of the Sentinel fleet, new physiological parameters such as spaceborne sun-induced fluorescence (SIF), and up-and-coming hyperspectral and laser data will allow new insights into ecosystem diversity and functioning (Calders et al 2023;Shirkey et al 2022). All of these new data streams will need not only data from eddy covariance, but also the knowledge and expertise the community brings with it.…”