scholarly journals Tree Species Traits Determine the Success of LiDAR-Based Crown Mapping in a Mixed Temperate Forest

2020 ◽  
Vol 12 (2) ◽  
pp. 309 ◽  
Author(s):  
Jack H. Hastings ◽  
Scott V. Ollinger ◽  
Andrew P. Ouimette ◽  
Rebecca Sanders-DeMott ◽  
Michael W. Palace ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Spectral Characteristics ◽  
Species Abundance ◽  
Remote Sensing Data ◽  
Parameter Tuning ◽  
Species Traits ◽  
Tree Level ◽  
Species Evenness ◽  
Individual Tree ◽  
Crown Delineation

The ability to automatically delineate individual tree crowns using remote sensing data opens the possibility to collect detailed tree information over large geographic regions. While individual tree crown delineation (ITCD) methods have proven successful in conifer-dominated forests using Light Detection and Ranging (LiDAR) data, it remains unclear how well these methods can be applied in deciduous broadleaf-dominated forests. We applied five automated LiDAR-based ITCD methods across fifteen plots ranging from conifer- to broadleaf-dominated forest stands at Harvard Forest in Petersham, MA, USA, and assessed accuracy against manual delineation of crowns from unmanned aerial vehicle (UAV) imagery. We then identified tree- and plot-level factors influencing the success of automated delineation techniques. There was relatively little difference in accuracy between automated crown delineation methods (51–59% aggregated plot accuracy) and, despite parameter tuning, none of the methods produced high accuracy across all plots (27—90% range in plot-level accuracy). The accuracy of all methods was significantly higher with increased plot conifer fraction, and individual conifer trees were identified with higher accuracy (mean 64%) than broadleaf trees (42%) across methods. Further, while tree-level factors (e.g., diameter at breast height, height and crown area) strongly influenced the success of crown delineations, the influence of plot-level factors varied. The most important plot-level factor was species evenness, a metric of relative species abundance that is related to both conifer fraction and the degree to which trees can fill canopy space. As species evenness decreased (e.g., high conifer fraction and less efficient filling of canopy space), the probability of successful delineation increased. Overall, our work suggests that the tested LiDAR-based ITCD methods perform equally well in a mixed temperate forest, but that delineation success is driven by forest characteristics like functional group, tree size, diversity, and crown architecture. While LiDAR-based ITCD methods are well suited for stands with distinct canopy structure, we suggest that future work explore the integration of phenology and spectral characteristics with existing LiDAR as an approach to improve crown delineation in broadleaf-dominated stands.

scholarly journals Including tree spatial extension in the evaluation of neighbourhood competition effects in Bornean rain forest

2020 ◽  
Author(s):  
David M. Newbery ◽  
Peter Stoll
Keyword(s):  
Alternative Hypothesis ◽  
Species Abundance ◽  
Abundant Species ◽  
Root Competition ◽  
Light Limitation ◽  
Tree Level ◽  
Model Parameters ◽  
Individual Tree ◽  
Stem Size ◽  
Tree Survival

AbstractClassical tree neighbourhood models use size variables acting at point distances. In a new approach here, trees were spatially extended as a function of their crown sizes, represented impressionistically as points within crown areas. Extension was accompanied by plasticity in the form of crown removal or relocation under the overlap of taller trees. Root systems were supposedly extended in a similar manner. For the 38 most abundant species in the focal size class (10 - <100 cm stem girth) in two 4-ha plots at Danum (Sabah), for periods P1 (1986-1996) and P2 (1996-2007), stem growth rate and tree survival were individually regressed against stem size, and neighbourhood conspecific (CON) and heterospecific (HET) basal areas within incremented steps in radius. Model parameters were critically assessed, and statistical robustness in the modelling set by randomization testing. Classical and extended models differed importantly in their outcomes. Crown extension weakened the relationship of CON effect on growth versus plot species’ abundance, showing that models without plasticity over-estimated negative density dependence. A significant negative trend of difference in CON effects on growth (P2 − P1) versus CON or HET effect on survival in P1 was strongest with crown extension. Model outcomes did not then support an explanation of CON and HET effects being due to (asymmetric) competition for light alone. An alternative hypothesis is that changes in CON effects on small trees, largely incurred by a drought phase (relaxing light limitation) in P2, and following the more shaded (suppressing) conditions in P1, were likely due to species-specific (symmetric) root competition and mycorrhizal processes. The very high variation in neighbourhood composition and abundances led to a strong ‘neighbourhood stochasticity’, and hence to largely idiosyncratic species’ responses. A need to much better understand the roles of rooting structure and processes at the individual tree level was highlighted.

scholarly journals From LiDAR waveforms to Hyper Point Clouds: a novel data product to characterize vegetation structure

2018 ◽  
Author(s):  
Tan Zhou ◽  
Sorin Popescu ◽  
Lonesome Malambo ◽  
Kaiguang Zhao ◽  
Keith Krause
Keyword(s):  
Vegetation Structure ◽  
Point Clouds ◽  
Structure Characterization ◽  
Tree Level ◽  
Surface Model ◽  
Lidar Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Segment Tree ◽  
Crown Delineation

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as lack of available handy processing tools and technical intricacy of waveform processing. This study introduces a new product, named the Hyper Point Cloud (HPC) derived from FW LiDAR data, and explore its potential applications such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which show a promising solution to the constraints of using FW LiDAR data. Results of the HPC present a new direction to handle FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~ 182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has larger maximum intensity (MAXI) and mean intensity (MI) than the vegetation region while having smaller total intensity (TI) and number of intensities (NI) at the given grid cell. Our analysis of intensity distribution contours at individual tree level exhibit similar patterns, indicating that the MAXI and MI are decreasing from the tree crown center to tree boundary while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives to use FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

scholarly journals From LiDAR Waveforms to Hyper Point Clouds: A Novel Data Product to Characterize Vegetation Structure

2018 ◽  
Vol 10 (12) ◽  
pp. 1949 ◽  
Author(s):  
Tan Zhou ◽  
Sorin Popescu ◽  
Lonesome Malambo ◽  
Kaiguang Zhao ◽  
Keith Krause
Keyword(s):  
Vegetation Structure ◽  
Point Clouds ◽  
Structure Characterization ◽  
Tree Level ◽  
Surface Model ◽  
Lidar Data ◽  
Tree Crown ◽  
Individual Tree ◽  
Segment Tree ◽  
Crown Delineation

Full waveform (FW) LiDAR holds great potential for retrieving vegetation structure parameters at a high level of detail, but this prospect is constrained by practical factors such as the lack of available handy processing tools and the technical intricacy of waveform processing. This study introduces a new product named the Hyper Point Cloud (HPC), derived from FW LiDAR data, and explores its potential applications, such as tree crown delineation using the HPC-based intensity and percentile height (PH) surfaces, which shows promise as a solution to the constraints of using FW LiDAR data. The results of the HPC present a new direction for handling FW LiDAR data and offer prospects for studying the mid-story and understory of vegetation with high point density (~182 points/m2). The intensity-derived digital surface model (DSM) generated from the HPC shows that the ground region has higher maximum intensity (MAXI) and mean intensity (MI) than the vegetation region, while having lower total intensity (TI) and number of intensities (NI) at a given grid cell. Our analysis of intensity distribution contours at the individual tree level exhibit similar patterns, indicating that the MAXI and MI decrease from the tree crown center to the tree boundary, while a rising trend is observed for TI and NI. These intensity variable contours provide a theoretical justification for using HPC-based intensity surfaces to segment tree crowns and exploit their potential for extracting tree attributes. The HPC-based intensity surfaces and the HPC-based PH Canopy Height Models (CHM) demonstrate promising tree segmentation results comparable to the LiDAR-derived CHM for estimating tree attributes such as tree locations, crown widths and tree heights. We envision that products such as the HPC and the HPC-based intensity and height surfaces introduced in this study can open new perspectives for the use of FW LiDAR data and alleviate the technical barrier of exploring FW LiDAR data for detailed vegetation structure characterization.

STUDYING OF TERSKO-SULAKSKAYA LOWLAND VEGETATION UNDER THE RESULTS OF GROUND-BASED RESEARCH AND AUTOMATED INTERPRETATION

1970 ◽  
pp. 29-32
Author(s):  
M. I. Dzhalalova ◽  
A. B. Biarslanov ◽  
D. B. Asgerova
Keyword(s):  
Plant Communities ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Anthropogenic Impacts ◽  
Species Abundance ◽  
Remote Sensing Data ◽  
Indigenous Communities ◽  
The State ◽  
Anthropogenic Factors ◽  
Internet Resource

The state of plant communities in areas located in the Tersko-Sulak lowland was studied by assessing phytocenotic indicators: the structure of vegetation cover, projective cover, species diversity, species abundance and elevated production, as well as automated decoding methods. There are almost no virgin soils and natural phytocenoses here; all of them have been transformed into agrocenoses (irrigated arable lands and hayfields, rice-trees and pastures). The long-term impact on pasture ecosystems of natural and anthropogenic factors leads to significant changes in the indigenous communities of this region. Phytocenoses are formed mainly by dry-steppe types of cereals with the participation of feather grass, forbs and ephemera, a semi-desert haloxerophytic shrub - Taurida wormwood. At the base of the grass stand is common coastal wormwood and Taurida wormwood - species resistant to anthropogenic influences. Anthropogenic impacts have led to a decrease in the number of species of feed-rich grain crops and a decrease in the overall productivity of pastures. Plant communities in all areas are littered with ruderal species. The seasonal dynamics of the land cover of the sites was estimated by the methods of automatic decoding of satellite images of the Landsat8 OLI series satellite for 2015, dated by the periods: spring - May 20, summer - July 23, autumn - October 20. Satellite imagery data obtained by Landsat satellite with a resolution in the multispectral image of 30 m per pixel, and in the panchromatic image - 10 m per pixel, which correspond to the requirements for satellite imagery to assess the dynamics of soil and vegetation cover. Lower resolution data, for example, NDVI MODIS, does not provide a reliable reflection of the state of soil and vegetation cover under arid conditions. In this regard, remote sensing data obtained from the Internet resource https://earthexplorer.usgs.gov/ was used.

scholarly journals Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications

2020 ◽  
Vol 13 (1) ◽  
pp. 77
Author(s):  
Tianyu Hu ◽  
Xiliang Sun ◽  
Yanjun Su ◽  
Hongcan Guan ◽  
Qianhui Sun ◽  
...  
Keyword(s):  
Forest Inventory ◽  
Low Cost ◽  
Canopy Cover ◽  
Tree Height ◽  
Flying Height ◽  
Tree Level ◽  
Forest Site ◽  
Lidar System ◽  
Individual Tree ◽  
Gap Fraction

Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

scholarly journals Remotely Sensed Single Tree Data Enable the Determination of Habitat Thresholds for the Three-Toed Woodpecker (Picoides tridactylus)

2018 ◽  
Vol 10 (12) ◽  
pp. 1972 ◽  
Author(s):  
Katarzyna Zielewska-Büttner ◽  
Marco Heurich ◽  
Jörg Müller ◽  
Veronika Braunisch
Keyword(s):  
Bark Beetles ◽  
Laser Scanning ◽  
Positive Relationship ◽  
Spatial Scales ◽  
Keystone Species ◽  
Remote Sensing Data ◽  
Individual Tree ◽  
Substrate Quality ◽  
Dead Trees ◽  
Negative Effect

Forest biodiversity conservation requires precise, area-wide information on the abundance and distribution of key habitat structures at multiple spatial scales. We combined airborne laser scanning (ALS) data with color-infrared (CIR) aerial imagery for identifying individual tree characteristics and quantifying multi-scale habitat requirements using the example of the three-toed woodpecker (Picoides tridactylus) (TTW) in the Bavarian Forest National Park (Germany). This bird, a keystone species of boreal and mountainous forests, is highly reliant on bark beetles dwelling in dead or dying trees. While previous studies showed a positive relationship between the TTW presence and the amount of deadwood as a limiting resource, we hypothesized a unimodal response with a negative effect of very high deadwood amounts and tested for effects of substrate quality. Based on 104 woodpecker presence or absence locations, habitat selection was modelled at four spatial scales reflecting different woodpecker home range sizes. The abundance of standing dead trees was the most important predictor, with an increase in the probability of TTW occurrence up to a threshold of 44–50 dead trees per hectare, followed by a decrease in the probability of occurrence. A positive relationship with the deadwood crown size indicated the importance of fresh deadwood. Remote sensing data allowed both an area-wide prediction of species occurrence and the derivation of ecological threshold values for deadwood quality and quantity for more informed conservation management.

scholarly journals An integrated system for modeling tree and stand survival

2017 ◽  
Vol 47 (10) ◽  
pp. 1405-1409 ◽  
Author(s):  
Quang V. Cao
Keyword(s):  
Survival Probability ◽  
Mathematical Structure ◽  
Survival Model ◽  
Integrated System ◽  
Survival Models ◽  
Tree Level ◽  
Individual Tree ◽  
Tree Survival ◽  
Different Levels ◽  
Model Approach

Traditionally, separate models have been used to predict the number of trees per unit area (stand-level survival) and the survival probability of an individual tree (tree-level survival) at a certain age. This study investigated the development of integrated systems in which survival models at different levels of resolution are related in a mathematical structure. Two approaches for modeling tree and stand survival were considered: deriving a stand-level survival model from a tree-level survival model (approach 1) and deriving a tree survival model from a stand survival model (approach 2). Both approaches rely on finding a tree diameter that yields a tree survival probability equal to the stand-level survival probability. The tree and stand survival models from either approach are conceptually compatible with each other but not numerically compatible. Parameters of these models can be estimated either sequentially or simultaneously. Results indicated that approach 2, with parameters estimated sequentially (first from the stand survival model and then from the derived tree survival model), performed best in predicting both tree- and stand-level survival. Although disaggregation did not help improve prediction of tree-level survival, this method can be used when numerical consistency between stand and tree survival is desired.

scholarly journals SISFLOR: A COMPUTATIONAL SYSTEM TO DETERMINE THE OPTIMAL TREE BUCKING

2020 ◽  
Vol 28 ◽  
pp. 192-201
Author(s):  
Rodrigo Freitas Silva ◽  
Marcelo Otone Aguiar ◽  
Mayra Luiza Marques Da Silva ◽  
Gilson Fernandes Da Silva ◽  
Adriano Ribeiro De Mendonça
Keyword(s):  
Optimization Technique ◽  
Wood Products ◽  
Tree Level ◽  
Economic Losses ◽  
Computational System ◽  
Individual Tree ◽  
Optimal Tree ◽  
Cutting Pattern ◽  
Forest Enterprises ◽  
The Individual

A continuously competitive forest market and tied to the demands for wood products promotes the study and development of applications that increase the revenue of the forest enterprises. At harvesting, the cutting pattern (forest assortment) in which the trees are traced is traditionally determined by the experience of the chainsaw operator without using any optimization technique, which may result in economic losses in relation to the commercialized products. In general, there are numerous distinct assortments that can be chosen and hardly processed by a brute-force algorithm. This is the forest assortment problem at the individual tree level with the objetice of maximizing the commercial values of the felled trees. stem-level bucking optimization problem. The aim is to maximize the sales value of harvested trees. Dynamic Programming (DP) is an efficient optimization technique to determine the optimum bucking tree as it significantly reduces the number of calculations to be made. Thus, the objective of this work was to develop a modern and intuitive computational system that is able to find the optimum tree stem bucking through DP to help companies over the bole tracing, therefore, characterizing itself as a tool that supports decision making. After the execution of the system, the optimum assortment is shown by sequentially detailing all products that should be removed from the analyzed bole as well as their respective volumes and revenue.

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