Development of forest prediction model using Individual Tree Crown method and Gray theory in an old-growth Chamaecyparis obtusa stand, in the Akazawa Forest Reserve, central Japan （Individual Tree Crown 法と灰色理論を利用した赤沢ヒノキ老齢林の森林予測システムの開発）

信州大学(Shinshu university)博士（農学）Thesis王　楠. Development of forest prediction model using Individual Tree Crown method and Gray theory in an old-growth Chamaecyparis obtusa stand, in the Akazawa Forest Reserve, central Japan （Individual Tree Crown 法と灰色理論を利用した赤沢ヒノキ老齢林の森林予測システムの開発）. 信州大学, 2014, 博士論文. 博士（農学）, 甲第46号, 平成26年3月20日授与.doctoral thesi

Individual Tree Crown Delineation Using Multispectral LiDAR Data

In this study, an improved treetop detection and a region-based segmentation algorithm were developed to delineate Individual Tree Crowns (ITCs) using multispectral Light Detection and Ranging (LiDAR) data. The dataset used for this research was acquired from Teledyne Optechs Titan LiDAR sensor which was operated at three wavelengths: 1550 nm, 1064 nm, and 532 nm. An improved multi-scale method was developed to identify treetops for different crown sizes and merge them via Gaussian fitting. With the improved region growing segmentation method, neutrosophic logic was extensively used to incorporate contextual intensity information in the region merging decision heuristics. The LiDAR positional data was uniquely exploited, in this research, to generate refine crown boundary approximations. The results from the proposed method were compared with manually delineated ITCs to highlight the performance improvements. A 12% increase in the accuracy was observed with the proposed method over the popular Marker Controlled Watershed segmentation technique

Predicting species composition in an eastern hardwood forest with the use of digitally derived terrain variables

This thesis addresses the need for improved classification of remotely sensed imagery in the complex hardwood forests of West Virginia. A geographic information system (GIS) was used in conjunction with forest plot data to develop a model to predict species composition in the eastern hardwood forest of West Virginia. The study area was located on the West Virginia University Research Forest (WVURF) in northern West Virginia. Terrain variables including aspect, curvature and slope change drastically at a local scale within the forest to greatly influence species composition. Light Detection and Ranging (LiDAR) data was collected for the entire WVURF, which produced an extremely detailed digital elevation model (DEM), with 1 m spatial resolution. Individual tree crown polygons were created from the LiDAR data so that individual trees could be co-registered to the DEM eliminating the bias of misplaced inventory points. Forest-plot data was collected and each individual tree crown polygon that was created from the LiDAR was assigned actual ground data. Terrain variable values were then sampled for each plot. The data was analyzed using a classification and regression tree (CART) to produce a binomial decision tree that was used within GIS to create a prediction grid of species distribution based on terrain variables. With the decreasing price of data acquisition and with new technology this method is likely to become more widespread and useful to various management agencies

Forest Remote Sensing in Canada and the Individual Tree Crown (ITC) Approach to Forest Inventories

After a brief description of Canada’s forest situation and the role of the federal government in forestry, some Natural Resources Canada’country-wide project will be introduced. These include the National Forest Inventories (past and present), the National Forest Information System, the EOSD programs to map land cover, monitor change and evaluate biomass, mostly from Canada-wide coverages with Landsat images. The accounting of carbon and the monitoring of deforestation at a map scale level will also be introduced. The second and most significant part of this paper will describe our Individual Tree Crown (ITC) approach to forest inventories used with high spatial resolution images (better than 1m/pixel). Techniques for individual crown delineation, species classification and regrouping into forest stands that are leading to a semi-automatic production of forest inventories will be described.A locally adaptive technique for tree counts, mostly reserved for young regenerating areas, will also be presented. The synergy of multispectral and LIDAR data (atmany levels) will be examined and, the normalization of spectral values within and among aerial images will be considered.Article信州大学農学部紀要. 46(1-2): 85-92 (2010)departmental bulletin pape

Assessing Biodiversity in Boreal Forests with UAV-Based Photogrammetric Point Clouds and Hyperspectral Imaging

Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring

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

Segmentation and classification of individual tree crowns

By segmentation and classification of individual tree crowns in high spatial resolution aerial images, information about the forest can be automatically extracted. Segmentation is about finding the individual tree crowns and giving each of them a unique label. Classification, on the other hand, is about recognising the species of the tree. The information of each individual tree in the forest increases the knowledge about the forest which can be useful for managements, biodiversity assessment, etc. Different algorithms for segmenting individual tree crowns are presented and also compared to each other in order to find their strengths and weaknesses. All segmentation algorithms developed in this thesis focus on preserving the shape of the tree crown. Regions, representing the segmented tree crowns, grow according to certain rules from seed points. One method starts from many regions for each tree crown and searches for the region that fits the tree crown best. The other methods start from a set of seed points, representing the locations of the tree crowns, to create the regions. The segmentation result varies from 73 to 95 % correctly segmented visual tree crowns depending on the type of forest and the method. The former value is for a naturally generated mixed forest and the latter for a non-mixed forest. The classification method presented uses shape information of the segments and colour information of the corresponding tree crown in order to decide the species. The classification method classifies 77 % of the visual trees correctly in a naturally generated mixed forest, but on a forest stand level the classification is over 90 %

Feasibility of Bi-Temporal Airborne Laser Scanning Data in Detecting Species-Specific Individual Tree Crown Growth of Boreal Forests

The tree crown, with its functionality of assimilation, respiration, and transpiration, is a key forest ecosystem structure, resulting in high demand for characterizing tree crown structure and growth on a spatiotemporal scale. Airborne laser scanning (ALS) was found to be useful in measuring the structural properties associated with individual tree crowns. However, established ALS-assisted monitoring frameworks are still limited. The main objective of this study was to investigate the feasibility of detecting species-specific individual tree crown growth by means of airborne laser scanning (ALS) measurements in 2009 (T1) and 2014 (T2). Our study was conducted in southern Finland over 91 sample plots with a size of 32 × 32 m. The ALS crown metrics of width (WD), projection area (A2D), volume (V), and surface area (A3D) were derived for species-specific individually matched trees in T1 and T2. The Scots pine (Pinus sylvestris), Norway spruce (Picea abies (L.) H. Karst), and birch (Betula sp.) were the three species groups that studied. We found a high capability of bi-temporal ALS measurements in the detection of species-specific crown growth (Δ), especially for the 3D crown metrics of V and A3D, with Cohen’s D values of 1.09–1.46 (p-value < 0.0001). Scots pine was observed to have the highest relative crown growth (rΔ) and showed statistically significant differences with Norway spruce and birch in terms of rΔWD, rΔA2D, rΔV, and rΔA3D at a 95% confidence interval. Meanwhile, birch and Norway spruce had no statistically significant differences in rΔWD, rΔV, and rΔA3D (p-value < 0.0001). However, the amount of rΔ variability that could be explained by the species was only 2–5%. This revealed the complex nature of growth controlled by many biotic and abiotic factors other than species. Our results address the great potential of ALS data in crown growth detection that can be used for growth studies at large scales

LiDAR evaluation of the structural complexity of multi-cropped white oak (Quercus alba) and pine (Pinus spp.) plantings in east Tennessee, USA

Structural complexity has an important influence on wildlife habitat and several other ecosystem services. Establishment of white oak (Quercus alba) intercropped with loblolly pine (Pinus taeda), shortleaf pine (Pinus echinata), or eastern white pine (Pinus strobus), in 2014 provided the opportunity to investigate effects of planting species mixtures in different spatial arrangements on structural complexity. Terrestrial LiDAR was used to evaluate the structure of each intercropped treatment and monoculture control. The measures of complexity included: 1) rumple 2) top rugosity 3) standard deviation of individual tree crown area, 4) standard deviation of maximum tree heights, 5) standard deviation of total number returns associated with trees, 6) standard deviation of LiDAR returns associated with trees across 0.5m vertical layers, and 7) standard deviation of 0.5 x 0.5 x 0.5m voxel by the number of returns at 0.5m vertical intervals. In addition, mean maximum tree height, individual tree crown area, mean of 95th percentile of returns, and the mean number of returns by tree height were analyzed. The following three hypotheses were tested: 1) oak and pine mixtures would have greater structural complexity than monocultures, 2) white oak and loblolly pine would have greater structural complexity than other mixtures, and 3) complexity would be greater in treatments with a 0.31m spacing than in those with a 1.74m spacing. Significantly greater complexity in the mixtures than in oak monocultures partially supported the hypothesis that oak and pine mixtures would have greater structural complexity. The lack of significant differences between the complexity of mixtures and pine monocultures, however, suggests that the pines were more important in contributing to complexity than white oak. According to most measures of variability, mixtures with loblolly pine and loblolly pine monocultures had the greatest structural complexity; supporting the hypothesis that white oak and loblolly pine would have greater structural complexity. The hypothesis that complexity would be greater in treatments with a 0.31m spacing was not supported. The importance of loblolly pine in this study suggests that fast-growing species can influence structural complexity as much or more than the number of species planted