Forests 

About: Forests is an academic journal published by Multidisciplinary Digital Publishing Institute. The journal publishes majorly in the area(s): Biology & Environmental science. It has an ISSN identifier of 1999-4907. It is also open access. Over the lifetime, 9799 publications have been published receiving 92501 citations.

Assessment of Forest Structure Using Two UAV Techniques: A Comparison of Airborne Laser Scanning and Structure from Motion (SfM) Point Clouds

Abstract: This study investigates the potential of unmanned aerial vehicles (UAVs) to measure and monitor structural properties of forests. Two remote sensing techniques, airborne laser scanning (ALS) and structure from motion (SfM) were tested to capture three-dimensional structural information from a small multi-rotor UAV platform. A case study is presented through the analysis of data collected from a 30 × 50 m plot in a dry sclerophyll eucalypt forest with a spatially varying canopy cover. The study provides an insight into the capabilities of both technologies for assessing absolute terrain height, the horizontal and vertical distribution of forest canopy elements, and information related to individual trees. Results indicate that both techniques are capable of providing information that can be used to describe the terrain surface and canopy properties in areas of relatively low canopy closure. However, the SfM photogrammetric technique underperformed ALS in capturing the terrain surface under increasingly denser canopy cover, resulting in point density of less than 1 ground point per m2 and mean difference from ALS terrain surface of 0.12 m. This shortcoming caused errors that were propagated into the estimation of canopy properties, including the individual tree height (root mean square error of 0.92 m for ALS and 1.30 m for SfM). Differences were also seen in the estimates of canopy cover derived from the SfM (50%) and ALS (63%) pointclouds. Although ALS is capable of providing more accurate estimates of the vertical structure of forests across the larger range of canopy densities found in this study, SfM was still found to be an adequate low-cost alternative for surveying of forest stands.

Carbon Content of Tree Tissues: A Synthesis

Abstract: Assessing the potential for forest carbon (C) capture and storage requires accurate assessments of C in live tree tissues. In the vast majority of local, regional, and global assessments, C content has been assumed to be 50% of tree biomass; however, recent studies indicate that this assumption is not accurate, with substantial variation in C content among tree species as well as among tissue types. Here we conduct a comprehensive literature review to present a global synthesis of C content in tissues of live trees. We found a total of 253 species-specific stem wood C content records in 31 studies, and an additional 34 records of species with C content values of other tissues in addition to stem wood. In all biomes, wood C content varied widely across species ranging from 41.9–51.6% in tropical species, 45.7–60.7% in subtropical/Mediterranean species, and 43.4–55.6% in temperate/boreal species. Stem wood C content varied significantly as a function of biome and species type (conifer, angiosperm). Conifer species exhibited greater wood C content than angiosperm species (50.8 ± 0.7% (95% C.I.) and 47.7 ± 0.3%, respectively), a trend that was consistent among all biomes. Although studies have documented differences in C content among plant tissues, interspecific differences in stem wood appear to be of greater importance overall: among species, stem wood C content explained 37, 76, 48, 81, and 63% respectively of the variation in bark, branch, twig, coarse root, and fine root C content values, respectively. In each case, these intraspecific patterns approximated 1:1 linear relationships. Most published stem wood C content values (and all values for other tree tissues) are based on dried wood samples, and so neglect volatile C constituents that constitute on average 1.3–2.5% of total C in live wood. Capturing this volatile C fraction is an important methodological consideration for future studies. Our review, and associated data compilation, provides empirically supported wood

A Photogrammetric Workflow for the Creation of a Forest Canopy Height Model from Small Unmanned Aerial System Imagery

Abstract: The recent development of operational small unmanned aerial systems (UASs) opens the door for their extensive use in forest mapping, as both the spatial and temporal resolution of UAS imagery better suit local-scale investigation than traditional remote sensing tools. This article focuses on the use of combined photogrammetry and “Structure from Motion” approaches in order to model the forest canopy surface from low-altitude aerial images. An original workflow, using the open source and free photogrammetric toolbox, MICMAC (acronym for Multi Image Matches for Auto Correlation Methods), was set up to create a digital canopy surface model of deciduous stands. In combination with a co-registered light detection and ranging (LiDAR) digital terrain model, the elevation of vegetation was determined, and the resulting hybrid photo/LiDAR canopy height model was compared to data from a LiDAR canopy height model and from forest inventory data. Linear regressions predicting dominant height and individual height from plot metrics and crown metrics showed that the photogrammetric canopy height model was of good quality for deciduous stands. Although photogrammetric reconstruction significantly smooths the canopy surface, the use of this workflow has the potential to take full advantage of the flexible revisit period of drones in order to refresh the LiDAR canopy height model and to collect dense multitemporal canopy height series.

Small Drones for Community-Based Forest Monitoring: An Assessment of Their Feasibility and Potential in Tropical Areas

Abstract: Data gathered through community-based forest monitoring (CBFM) programs may be as accurate as those gathered by professional scientists, but acquired at a much lower cost and capable of providing more detailed data about the occurrence, extent and drivers of forest loss, degradation and regrowth at the community scale. In addition, CBFM enables greater survey repeatability. Therefore, CBFM should be a fundamental component of national forest monitoring systems and programs to measure, report and verify (MRV) REDD+ activities. To contribute to the development of more effective approaches to CBFM, in this paper we assess: (1) the feasibility of using small, low-cost drones (i.e., remotely piloted aerial vehicles) in CBFM programs; (2) their potential advantages and disadvantages for communities, partner organizations and forest data end-users; and (3) to what extent their utilization, coupled with ground surveys and local ecological knowledge, would improve tropical forest monitoring. To do so, we reviewed the existing literature regarding environmental applications of drones, including forest monitoring, and drew on our own firsthand experience flying small drones to map and monitor tropical forests and training people to operate them. We believe that the utilization of small drones can enhance CBFM and that this approach is feasible in many locations throughout the tropics if some degree of external assistance and funding is provided to communities. We suggest that the use of small drones can help tropical communities to better manage and conserve their forests whilst benefiting partner organizations, governments and forest data end-users, particularly those engaged in forestry, biodiversity conservation and climate change mitigation projects such as REDD+.

The Utility of Image-Based Point Clouds for Forest Inventory: A Comparison with Airborne Laser Scanning

Abstract: Airborne Laser Scanning (ALS), also known as Light Detection and Ranging (LiDAR) enables an accurate three-dimensional characterization of vertical forest structure. ALS has proven to be an information-rich asset for forest managers, enabling the generation of highly detailed bare earth digital elevation models (DEMs) as well as estimation of a range of forest inventory attributes (including height, basal area, and volume). Recently, there has been increasing interest in the advanced processing of high spatial resolution digital airborne imagery to generate image-based point clouds, from which vertical information with similarities to ALS can be produced. Digital airborne imagery is typically less costly to acquire than ALS, is well understood by inventory practitioners, and in addition to enabling the derivation of height information, allows for visual interpretation of attributes that are currently problematic to estimate from ALS (such as species, health status, and maturity). At present, there are two limiting factors associated with the use of image-based point clouds. First, a DEM is required to normalize the image-based point cloud heights to aboveground heights; however DEMs with sufficient spatial resolution and vertical accuracy, particularly in forested areas, are usually only available from ALS data. The use of image-based point clouds may therefore be limited to those forest areas that already have an ALS-derived DEM. Second, image-based point clouds primarily characterize the outer envelope of the forest canopy, whereas ALS pulses penetrate the canopy and provide information on sub-canopy forest structure. The impact of these limiting factors on the estimation of forest inventory attributes has not been extensively researched and is not yet well understood. In this paper, we review the key similarities and differences between ALS data and image-based point clouds, summarize the results of current research related to the comparative use of these data for forest inventory attribute estimation, and highlight some outstanding research questions that should be addressed before any definitive recommendation can be made regarding the use of image-based point clouds for this application.