How ecologists think about above‐ground forest structure and dynamics is fundamentally shaped by the data we can collect. This has historically been limited to what is possible with simple equipment such as a tape measure, which has often led to the three‐dimensional complexity of the form of trees being reduced to the diameter of a trunk, and perhaps the height of the tree. While simple and pragmatic, this trunk‐centric framework has some obvious limitations, as many of the major processes that influence how trees grow and interact with one another happen up in the canopy. For instance, the structural complexity of individual trees' leaf and branch arrangements and how trees fill canopy space are direct drivers of individual tree and whole‐forest productivity and dynamics, but remain poorly studied because they have traditionally been challenging to measure. However, recent advances in remote sensing and data processing are revolutionising our ability to accurately measure tree and forest structure from leaves to landscapes. Not only do we have access to more accurate data on structure, but we also have data spanning a much broader range of spatial, temporal and ecological scales, and new avenues of research that challenge how we understand forests are emerging. Synthesis. Here, we review the new opportunities these technologies bring us to measure the physical structure of trees and highlight the technological developments needed to maximise their value to the field of forest ecology. Today, perhaps for the first time, how ecologists choose to study above‐ground forest structure and dynamics is limited more by creativity than by what we can measure.

https://www.repository.cam.ac.uk/bitstream/1810/339196/2/1365-2745.13944.pdf

The shape of trees: Reimagining forest ecology in three dimensions with remote sensing

Abstract Remote sensing techniques are increasingly used for studying ecosystem dynamics, delivering spatially explicit information on the properties of Earth over large spatial and multi-decadal temporal extents. Yet, there is still a gap between the more technology-driven development of novel remote sensing techniques and their applications for studying ecosystem dynamics. Here, I review the existing literature to explore how addressing these gaps might enable recent methods to overcome longstanding challenges in ecological research. First, I trace the emergence of remote sensing as a major tool for understanding ecosystem dynamics. Second, I examine recent developments in the field of remote sensing that are of particular importance for studying ecosystem dynamics. Third, I consider opportunities and challenges for emerging open data and software policies and suggest that remote sensing is at its most powerful when it is theoretically motivated and rigorously ground-truthed. I close with an outlook on four exciting new research frontiers that will define remote sensing ecology in the upcoming decade. 

/pdf/seeing-the-system-from-above-the-use-and-potential-of-remote-2tytgfaq.pdf

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

To understand time‐lag dynamics in the response of biodiversity to contemporary environmental changes (e.g. macroclimate warming and atmospheric pollution), we need to consider former anthropogenic forcing factors such as past land uses and management practices that can have both compounding and confounding effects. This is especially true in European temperate forests, where legacies from past human activities have left strong imprints on today’s understorey plant species composition, generating long‐term lagging effects which can be mistakenly attributed to more recent macro‐environmental changes. By combining the expertise of plant, soil and historical ecologists together with remote sensing scientists, we review the potential of light detection and ranging (LiDAR) to unveil ghosts from the past in terms of former land uses and management practices. We show that imprints from past land uses and management practices can still be captured today through well‐chosen LiDAR‐derived variables describing, at sub‐decimetre scale, the vertical and horizontal micro‐variations of vegetation and terrain structure hidden below treetops. Synthesis. We encourage plant and soil ecologists to use LiDAR data and to work with historians, archaeologists and remote sensing scientists in order to select meaningful LiDAR‐derived variables to account for time‐lagged biotic responses to long‐term macro‐environmental changes.

/pdf/unveil-the-unseen-using-lidar-to-capture-time-lag-dynamics-3a4v42i6.pdf

Unveil the unseen: Using LiDAR to capture time‐lag dynamics in the herbaceous layer of European temperate forests

Hemispherical photography is a relevant tool to estimate canopy attributes such as leaf area index (LAI). Advancements in digital photography and image processing tools have supported long-lasting use of digital hemispherical photography (DHP). While some open-source tools have been made available for DHP, very few solutions have been made available in R programming packages, and none of these allows a full processing workflow to retrieve LAI and other canopy attributes from fisheye images. To fill this gap, we developed an R package (hemispheR) to support the whole processing of DHP images in an automated, fast, and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline, while allowing inspecting the quality of each image processing step. The package allows to analyze both circular and fullframe fisheye images, collected either with upward facing (forest canopies) or downward facing (short canopies and crops) camera orientation. In addition, the package allows to implement two consolidated LAI methods (LAI-2000/2200 and 57° method). A case study is presented to demonstrate the reliability of canopy attributes derived from hemispheR in temperate deciduous forests with variable canopy density and structure. Canopy attributes were validated against either results obtained from a reference proprietary software, either by benchmarking measurements obtained from terrestrial laser scanning. Results indicated hemispheR provide reliable openness and leaf area index values in forest canopies as compared with reference values. By providing a simple, transparent, and flexible image processing procedure, hemispheR supported the use of DHP for routine measurements and monitoring of forest canopy attributes. Hosting the package in a Git repository will further support development of the package, through either collaborative coding or forking projects.

hemispheR: an R package for fisheye canopy image analysis

Urban forests play an important role in urban ecosystems. They can not only beautify the urban environment but also help protect biodiversity and maintain ecological balance. Effective urban forest management is a basic requirement to ensure sustainable development. Traditional urban forest management usually requires the investment of a lot of materials and labor to conduct field research. RGB high-resolution aerial images have emerged as an efficient source of data for use in the detection and mapping of individual trees in urban areas. In recent years, there has been impressive progress in the field of deep learning methods for use in object detection. Semi-supervised learning is an effective way to deal with the problem that deep learning requires a large amount of labeled data. In this paper, we proposed an improved faster region-based convolutional neural network (Faster R-CNN) with Swin transformer method. Based on existing datasets, the model was trained and then transferred to new datasets. The method was evaluated within three distinct urban areas: a green space, a residential area and a suburban area. The experimental results indicate that our method achieved higher performance than other Faster R-CNN models. This method provides a reference in automated individual tree detection based on high-resolution images in urban areas for urban forestry managers. 

/pdf/individual-tree-detection-based-on-high-resolution-rgb-1klayskv.pdf

Individual Tree Detection Based on High-Resolution RGB Images for Urban Forestry Applications

http://manuscript.elsevier.com/S037811272100904X/pdf/S037811272100904X.pdf

Open-Source tools in R for forestry and forest ecology

https://www.srs.fs.usda.gov/pubs/ja/2022/ja_2022_atkins_001.pdf

Lidar-derived forest structural diversity (FSD) metrics—including measures of forest canopy height, vegetation arrangement, canopy cover (CC), structural complexity and leaf area and density—are increasingly used to describe forest structural characteristics and can be used to infer many ecosystem functions. Despite broad adoption, the importance of spatial resolution (grain and extent) over which these structural metrics are calculated remains largely unconsidered. Often researchers will quantify FSD at the spatial grain size of the process of interest without considering the scale dependency or statistical behaviour of the FSD metric employed. We investigated the appropriate scale of inference for eight lidar-derived spatial metrics—CC, canopy relief ratio, foliar height diversity, leaf area index, mean and median canopy height, mean outer canopy height, and rugosity (RT)--representing five FSD categories—canopy arrangement, CC, canopy height, leaf area and density, and canopy complexity. Optimal scale was determined using the representative elementary area (REA) concept whereby the REA is the smallest grain size representative of the extent. Structural metrics were calculated at increasing canopy spatial grain (from 5 to 1000 m) from aerial lidar data collected at nine different forested ecosystems including sub-boreal, broadleaf temperate, needleleaf temperate, dry tropical, woodland and savanna systems, all sites are part of the National Ecological Observatory Network within the conterminous United States. To identify the REA of each FSD metric, we used changepoint analysis via segmented or piecewise regression which identifies significant changepoints for both the magnitude and variance of each metric. We find that using a spatial grain size between 25 and 75 m sufficiently captures the REA of CC, canopy arrangement, canopy leaf area and canopy complexity metrics across multiple forest types and a grain size of 30–150 m captures the REA of canopy height metrics. However, differences were evident among forest types with higher REA necessary to characterize CC in evergreen needleleaf forests, and canopy height in deciduous broadleaved forests. These findings indicate the appropriate range of spatial grain sizes from which inferences can be drawn from this set of FSD metrics, informing the use of lidar-derived structural metrics for research and management applications. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/2041-210X.14040

Michael T. Hirschmann

Papers

Open-Source tools in R for forestry and forest ecology

Open-Source tools in R for forestry and forest ecology

Scale dependency of lidar‐derived forest structural diversity