Ground Profile Recovery from Aerial 3D LiDAR-based Maps

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc

Another dimension from LiDAR – Obtaining foliage density from full waveform data

LiDAR tells the user where surfaces are, not what they are. In this study we investigate the potential for waveform LiDAR to provide more information on the nature of the returns over forestry. Waveform LiDAR was acquired for ten Pinus radiata plots in a New Zealand plantation, along with comprehensive leaf area sampling in 2m vertical bands. The decay rate of each waveform peak was shown to be a useful tool for estimating foliage density, and has potential for identifying regions containing ground and understorey. Leaf Area Density (LAD) is an expression of foliage density per unit height, and a relationship between waveform decay rate and LAD was developed with an R2 of 56%. Incorporating the proportion of discrete LiDAR that fell in that band (which itself has an R2 of 50%) improves this model to explain 69% of the variation in LAD. This is a good result, especially given the costs and difficulties in measuring leaf area directly. As foliage density varies dramatically on a fine scale it was not possible to differentiate the nature of every single LiDAR return – but by averaging over a small area local variation in LAD could be easily mapped. Ground returns could be distinguished as having short decays, and broad leafed understorey typically had values between those of the canopy and ground, although surface roughness and slope make it impossible to robustly identify single returns. This study produced a useful model for estimating LAD in Pinus radiata which could easily be extended to other coniferous species

Comparison of forest attributes derived from two terrestrial lidar systems.

Abstract Terrestrial lidar (TLS) is an emerging technology for deriving forest attributes, including conventional inventory and canopy characterizations. However, little is known about the influence of scanner specifications on derived forest parameters. We compared two TLS systems at two sites in British Columbia. Common scanning benchmarks and identical algorithms were used to obtain estimates of tree diameter, position, and canopy characteristics. Visualization of range images and point clouds showed clear differences, even though both scanners were relatively high-resolution instruments. These translated into quantifiable differences in impulse penetration, characterization of stems and crowns far from the scan location, and gap fraction. Differences between scanners in estimates of effective plant area index were greater than differences between sites. Both scanners provided a detailed digital model of forest structure, and gross structural characterizations (including crown dimensions and position) were relatively robust; but comparison of canopy density metrics may require consideration of scanner attributes

Canopy height estimation from lidar data using open source software compared with commercial software

The goal of this study is to analyze the performance of Open Source Software (OSS) towards the generation of Digital Terrain Model (DTM) and Digital Surface Model (DSM), further on estimates the canopy height by using Light Detection and Ranging (LIDAR) data. Generation of DTM and DSM are very important in this research to ensure that better canopy height can be modeled. DTM and DSM commonly known as a digital representation of earth surface topography where DTM only represent the ground surface while DSM represent all the features including buildings and trees. Many software that have a function to generate DTM and DSM were developed recently. However, most software has been commercialized; therefore it requires a high expenditure to own the software. Advanced technology has lead to the emergence of the growing OSS. OSS is software that can be downloaded for free via the internet. By taking the forestry area of Pekan, Pahang for this research, LIDAR data for that particular area is processed by using the OSS Geographic Resources Analysis Support System (GRASS). To determine the effectiveness and capability of GRASS in the DTM and DSM generation, the same data were processed using commercial software which is TerraScan so that the result can be compared, further on better canopy height can be modele

Using Unmanned Aerial Systems for Deriving Forest Stand Characteristics in Mixed Hardwoods of West Virginia

Forest inventory information is a principle driver for forest management decisions. Information gathered through these inventories provides a summary of the condition of forested stands. The method by which remote sensing aids land managers is changing rapidly. Imagery produced from unmanned aerial systems (UAS) offer high temporal and spatial resolutions to small-scale forest management. UAS imagery is less expensive and easier to coordinate to meet project needs compared to traditional manned aerial imagery. This study focused on producing an efficient and approachable work flow for producing forest stand board volume estimates from UAS imagery in mixed hardwood stands of West Virginia. A supplementary aim of this project was to evaluate which season was best to collect imagery for forest inventory. True color imagery was collected with a DJI Phantom 3 Professional UAS and was processed in Agisoft Photoscan Professional. Automated tree crown segmentation was performed with Trimble eCognition Developer’s multi-resolution segmentation function with manual optimization of parameters through an iterative process. Individual tree volume metrics were derived from field data relationships and volume estimates were processed in EZ CRUZ forest inventory software. The software, at best, correctly segmented 43% of the individual tree crowns. No correlation between season of imagery acquisition and quality of segmentation was shown. Volume and other stand characteristics were not accurately estimated and were faulted by poor segmentation. However, the imagery was able to capture gaps consistently and provide a visualization of forest health. Difficulties, successes and time required for these procedures were thoroughly noted

Predicting growing stock volume of Eucalyptus plantations using 3-D point clouds derived from UAV imagery and ALS data

Estimating forest inventory variables is important in monitoring forest resources and mitigating climate change. In this respect, forest managers require flexible, non-destructive methods for estimating volume and biomass. High-resolution and low-cost remote sensing data are increasingly available to measure three-dimensional (3D) canopy structure and to model forest structural attributes. The main objective of this study was to evaluate and compare the individual tree volume estimates derived from high-density point clouds obtained from airborne laser scanning (ALS) and digital aerial photogrammetry (DAP) in Eucalyptus spp. plantations. Object-based image analysis (OBIA) techniques were applied for individual tree crown (ITC) delineation. The ITC algorithm applied correctly detected and delineated 199 trees from ALS-derived data, while 192 trees were correctly identified using DAP-based point clouds acquired fromUnmannedAerialVehicles(UAV), representing accuracy levels of respectively 62% and 60%. Addressing volume modelling, non-linear regression fit based on individual tree height and individual crown area derived from the ITC provided the following results: Model E ciency (Mef) = 0.43 and 0.46, Root Mean Square Error (RMSE) = 0.030 m3 and 0.026 m3, rRMSE = 20.31% and 19.97%, and an approximately unbiased results (0.025 m3 and 0.0004 m3) using DAP and ALS-based estimations, respectively. No significant di erence was found between the observed value (field data) and volume estimation from ALS and DAP (p-value from t-test statistic = 0.99 and 0.98, respectively). The proposed approaches could also be used to estimate basal area or biomass stocks in Eucalyptus spp. plantationsinfo:eu-repo/semantics/publishedVersio

Serendipitous Data Following a Severe Windstorm in an Old-Growth Pine Stand

Reliable dimensional data for old-growth pine-dominated forests in the Gulf Coastal Plain of Arkansas are hard to find, but sometimes unfortunate circumstances provide good opportunities to acquire this information. On July 11, 2013, a severe thunderstorm with high winds struck the Levi Wilcoxon Demonstration Forest (LWDF) near Hamburg, Arkansas. This storm uprooted or snapped dozens of large pines and hardwoods and provided an opportunity to more closely inspect these rare specimens. For instance, the largest tree killed in this event, a loblolly pine (Pinus taeda), was 105 cm in diameter at breast height, 39.3 m tall, and if the tree had been sound would have yielded 3,803 board feet (Doyle log rule) of lumber. Gross board foot volume yield was also estimated from two other recently toppled large pines, an 85-cm-DBH loblolly and an 86-cm-DBH shortleaf pine (Pinus echinata), which tallied 2,430 and 2,312 board feet Doyle, respectively. A number of the other wind thrown pines on the LWDF were sound enough to count their rings for a reasonable (± 2-5 years) estimate of their ages. The stump of the fallen national champion shortleaf pine had 168 rings, and counts from other pines toppled by this storm had from 68 to 198 rings. We also searched for a new champion shortleaf pine using a LiDAR canopy height model of the LWDF to narrow our search. This preliminary assessment produced a number of targets that exceeded 40 m in height; further field checking of the tallest of these trees found that these were loblolly pines up to about 44 m. We eventually found shortleaf pines between 37 and 41 m tall, with diameters of up to 85 cm, indicating that the LWDF could still contain the Arkansas state champion