To celebrate our 10th Anniversary, we are highlighting a key article from each of our volumes. For Volume 6, we selected Nondestructive estimates of above‐ground biomass using terrestrial laser scanning by Calders et al. (2014).
In this post, two of our Associate Editors with expertise in remote sensing, Sarah Goslee and Hooman Latifi, share their favourite MEE papers in the fields of remote sensing and biomass estimation.
Sarah Goslee, USDA Agricultural Research Service
Trees are commercially valuable, offer habitat to many organisms (charismatic and otherwise), store a great deal of carbon, and are generally appealing in their own right. Some of the most interesting questions about forests involve quantifying both the amount of biomass and its horizontal and vertical distribution in space, by no means easy tasks. Methods in Ecology and Evolution is an excellent source to turn to for help.
The featured article for this volume, Calders et al. (2014), demonstrated the use of terrestrial laser scanning to estimate forest biomass. Atkins and colleagues (2018) built on that work to develop an R package, FORESTR, that uses terrestrial laser scanning to also characterize canopy structural complexity, including density and arrangement, and leaf area. This bottom-up approach is complemented by the top-down approach of Wilkes et al. (2015) in their use of airborne lasers to investigate the vertical structure of tree canopies.
All of this laser scanning is great, but sometimes you just need to get your hands on something. Youngentob and colleagues (2016) provide tips for consistently collecting leaves and seeds from tall trees without actually climbing them. Instead, they’ve worked out how to use a throw line launcher — a catapult on a rope — to safely retrieve particular branches. I haven’t had the opportunity to try this, but it looks like fun.
Hooman Latifi, K. N. Toosi University of Technology
Though the number of MEE-published papers on habitat structural analyses using 3D remote sensing approaches is relatively few compared to works published within other scopes in ecology and evolution, it is astonishing to see how highly qualitative and in particular application-oriented the MEE-published works are for a wide range of audience interested in ecosystem structure / remote sensing domain. Personally, I cherished a range of works stretching from the applied sensor types (terrestrial laser scanning (TLS), airborne laser scanning (ALS) and recently unmanned aerial systems (UAS)) to focal point of the studies (research articles, applications).
In the TLS domain, two application works by Atkins et al. (2018) and Lecigne et al. (2020) are amongst my absolute favorites. The “Forestr” R package (Atkins et al. 2018) enables structural complexity metrics (both lower and higher ones) including rugosity, gap fraction and rumple for TLS (and similar voxelized types of data) and provides useful inputs for habitat modelling in general as well as for other cause-and-effect analysis of ecological niches in particular. Most recently, “viewshed3D” (Lecigne et al. 2020) package in R addressed the well-known problem of tree visibility in dense stands for wildlife ecologists, by introducing a package dedicated to processing of dense TLS point cloud data from single or multiple viewpoints. The particular strength of the work lies in stepwise description of data analysis starting from initial data preparation for the functions.
In the field of ALS, I predominantly enjoyed reading the application developed by Silva et al. (2019) to the level that I almost swamped two of postgrad students with emails encouraging them to rapidly test the so-called “ForestGapR” package for calculating gap fractions from both
UAV and ALS data. The paper conveniently succeeds to describe how the package delivers multiple gap-relevant parameters (gap change detection, gap-size frequency distribution) in GIS-friendly formats based on canopy height models from airborne data. Alongside the horizontal structure, I treasured the innovative workflow suggested by Wilkes et al. (2015) for canopy vertical stratification based on ALS data. The workflow combines a probability-estimated gap presence with flexible cubic spline regressions to estimate the number of strata that, if estimated precisely, can indeed provide an essential “ecological biodiversity variable for characterizing habitat structure”, as claimed by the authors.
Last but not least, I would like to note my favorite selections in the domain of UAS, a highly progressive field with tremendous potentials for ecological and evolutionary research. Most recently, I appreciated reading the application published by Weinstein et al. (2020) on DeepForest Python package, a relatively rapid processing tool that combines an extremely popular deep learning approach with a richly trained crown delineation model that reduces the common need for numerous training samples when working in deep learning domain. I firmly believe that its robustness in training such models on RGB aerial data guarantees its widespread and boosted future applications in ecological research that require accurately delineated tree crowns from UAS data. A significant number of MEE application papers open new horizons for future ecological remote sensing research, in particular in open source domain. That´s why I always keep track of them.
Read about the article selected to highlight Volume 6: Nondestructive estimates of above-ground biomass using terrestrial laser scanning‘
Find out about the Methods in Ecology and Evolution articles selected to celebrate Volumes 1-5: