scholarly journals Deriving Tree Size Distributions of Tropical Forests from Lidar

2021 ◽  
Vol 13 (1) ◽  
pp. 131
Author(s):  
Franziska Taubert ◽  
Rico Fischer ◽  
Nikolai Knapp ◽  
Andreas Huth
Keyword(s):  
Size Distribution ◽  
Spatial Scales ◽  
Basal Area ◽  
Tree Height ◽  
Tree Size ◽  
Diameter Distribution ◽  
Forest Inventories ◽  
Allometric Relations ◽  
Lidar Measurements ◽  
Derived Data

Remote sensing is an important tool to monitor forests to rapidly detect changes due to global change and other threats. Here, we present a novel methodology to infer the tree size distribution from light detection and ranging (lidar) measurements. Our approach is based on a theoretical leaf–tree matrix derived from allometric relations of trees. Using the leaf–tree matrix, we compute the tree size distribution that fit to the observed leaf area density profile via lidar. To validate our approach, we analyzed the stem diameter distribution of a tropical forest in Panama and compared lidar-derived data with data from forest inventories at different spatial scales (0.04 ha to 50 ha). Our estimates had a high accuracy at scales above 1 ha (1 ha: root mean square error (RMSE) 67.6 trees ha−1/normalized RMSE 18.8%/R² 0.76; 50 ha: 22.8 trees ha−1/6.2%/0.89). Estimates for smaller scales (1-ha to 0.04-ha) were reliably for forests with low height, dense canopy or low tree height heterogeneity. Estimates for the basal area were accurate at the 1-ha scale (RMSE 4.7 tree ha−1, bias 0.8 m² ha−1) but less accurate at smaller scales. Our methodology, further tested at additional sites, provides a useful approach to determine the tree size distribution of forests by integrating information on tree allometries.

A longitudinal height–diameter model for Norway spruce in Finland

2004 ◽  
Vol 34 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Lauri Mehtätalo
Keyword(s):  
Norway Spruce ◽  
Mixed Model ◽  
Basal Area ◽  
Tree Height ◽  
Growth Patterns ◽  
Diameter Distribution ◽  
Stand Age ◽  
Canopy Layer ◽  
Mixed Model Approach ◽  
D Model

A height–diameter (H–D) model for Norway spruce (Picea abies (L.) Karst.) was estimated from longitudinal data. The Korf growth curve was used as the H–D curve. Firstly, H–D curves for each stand at each measurement time were fitted, and the trends in the parameters of the H–D curve were modeled. Secondly, the trends were included in the H–D model to estimate the whole model at once. To take the hierarchy of the data into account, a mixed-model approach was used. This makes it possible to calibrate the model for a new stand at a given point in time using sample tree height(s). The heights may be from different points in time and need not be from the point in time being predicted. The trends in the parameters of the H–D curve were not estimated as a function of stand age but as a function of the median diameter of basal area weighted diameter distribution (dGm). This approach was chosen because the stand ages may differ substantially among stands with similar current growth patterns. This is true especially with shade-tolerant tree species, which can regenerate and survive for several years beneath the dominant canopy layer and start rapid growth later. The growth patterns in stands with a given dGm, on the other hand, seem not to vary much. This finding indicates that the growth pattern of a stand does not depend on stand age but on mean tree size in the stand.

Effects of diameter distribution on the growth of simulated uneven-aged sugar maple stands

1987 ◽  
Vol 17 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Gerald D. Hansen ◽  
Ralph D. Nyland
Keyword(s):  
Sugar Maple ◽  
Basal Area ◽  
Tree Size ◽  
Diameter Distribution ◽  
Computer Simulation Model ◽  
Maximum Volume ◽  
Size Classes ◽  
Management Objectives ◽  
Stress Maximum ◽  
Volume Production

Effects of diameter distribution on the growth of simulated uneven-aged sugar maple (Acersaccharum Marsh.) stands are described using output from a computer simulation model. Results indicate that the combination of q ratio (a constant ratio between the numbers of trees present in adjacent diameters over the entire range of size classes present), maximum tree size, and basal area should vary depending on management objectives and cutting cycle length. A ratio of 1.2 appears best for describing a diameter distribution to maximize growth of large sawtimber, but larger q ratios are needed in the sapling and pole size classes to insure sufficient numbers of trees to sustain the distribution through the end of a cutting cycle. Retaining trees larger than a 40 cm diameter at breast height offers no advantage when objectives stress maximum volume production. A 50 cm maximum tree size appears better suited for maximizing large sawtimber volume and value growth, but a 40 cm maximum will yield a better compound rate of return on initial stand value. Under all options, longer cutting cycles require lower initial basal area levels.

Properties of the quadratic mean diameter in balanced diameter distributions

1985 ◽  
Vol 15 (2) ◽  
pp. 474-476
Author(s):  
Donald J. Weatherhead ◽  
Roger C. Chapman ◽  
John H. Bassman
Keyword(s):  
Stand Structure ◽  
Basal Area ◽  
Tree Size ◽  
Diameter Distribution ◽  
Quadratic Mean ◽  
Growing Stock ◽  
Mean Diameter ◽  
Quadratic Mean Diameter ◽  
Stand Basal Area ◽  
Diameter Distributions

Balanced diameter distributions are widely used to describe stand structure goals for residual growing stock in uneven-aged forests. The quadratic mean diameter is frequently used as a descriptor of a balanced diameter distribution. In this paper the quadratic mean diameter is shown to be independent of stand basal area for balanced diameter distributions with a common class width, maximum and minimum diameters, and de Liocourt's q ratio. Additionally it is shown that the quadratic mean diameter is relatively insensitive to changes in maximum tree size and q ratios for q ratios 1.5 and larger.

scholarly journals Models for diameter distribution and tree height in hybrid aspen plantations in southern Finland

Silva Fennica ◽  
2021 ◽  
Vol 55 (5) ◽  
Author(s):  
Daesung Lee ◽  
Jouni Siipilehto ◽  
Jari Hynynen
Keyword(s):  
Goodness Of Fit ◽  
Basal Area ◽  
Tree Height ◽  
Diameter Distribution ◽  
Weibull Function ◽  
Hybrid Aspen ◽  
Recovery Method ◽  
Parameter Recovery ◽  
Mean Diameter ◽  
Median Diameter

Hybrid aspen ( L. × Michx.) is known with outstanding growth rate and some favourable wood characteristics, but models for stand management have not yet been prepared in northern Europe. This study introduces methods and models to predict tree dimensions, diameter at breast height (dbh) and tree height for a hybrid aspen plantation using data from repeatedly measured permanent sample plots established in clonal plantations in southern Finland. Dbh distributions using parameter recovery method for the Weibull function was used with Näslund’s height curve to model tree heights. According to the goodness-of-fit statistics of Kolmogorov-Smirnov and the Error Index, the arithmetic mean diameter () and basal area-weighted mean diameter () provided more stable parameter recovery for the Weibull distribution than the median diameter () and basal area-weighted median diameter (), while showed the best overall fit. Thus, Näslund’s height curve was modelled using with Lorey’s height (), age, basal area (), and tree dbh (Model 1). Also, Model 2 was tested using all predictors of Model 1 with the number of trees per ha (). All predictors were shown to be significant in both Models, showing slightly different behaviour. Model 1 was sensitive to the mean characteristics, and , while Model 2 was sensitive to stand density, including both and as predictors. Model 1 was considered more reasonable to apply based on our results. Consequently, the parameter recovery method using and Näslund’s models were applicable for predicting tree diameter and height.Populus tremulaP. tremuloidesDDGDMDGMDGDGHGBATPHDGHGBATPHDG

Tree size distribution at increasing spatial scales converges to the rotated sigmoid curve in two old-growth beech stands of the Italian Apennines

2011 ◽  
Vol 262 (11) ◽  
pp. 1950-1962 ◽  
Author(s):  
Alfredo Alessandrini ◽  
Franco Biondi ◽  
Alfredo Di Filippo ◽  
Emanuele Ziaco ◽  
Gianluca Piovesan
Keyword(s):  
Size Distribution ◽  
Spatial Scales ◽  
Tree Size ◽  
Old Growth ◽  
Sigmoid Curve ◽  
Italian Apennines

scholarly journals Reuse of field data in ALS-assisted forest inventory

Silva Fennica ◽  
2020 ◽  
Vol 54 (5) ◽  
Author(s):  
Ana de Lera Garrido ◽  
Terje Gobakken ◽  
Hans Ørka ◽  
Erik Næsset ◽  
Ole Bollandsås
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Field Data ◽  
Reference Data ◽  
Growth Models ◽  
Basal Area ◽  
Tree Height ◽  
Cost Component ◽  
Forest Inventories ◽  
Forest Attributes

Forest inventories assisted by wall-to-wall airborne laser scanning (ALS), have become common practice in many countries. One major cost component in these inventories is the measurement of field sample plots used for constructing models relating biophysical forest attributes to metrics derived from ALS data. In areas where ALS-assisted forest inventories are planned, and in which the previous inventories were performed with the same method, reusing previously acquired field data can potentially reduce costs, either by (1) temporally transferring previously constructed models or (2) projecting field reference data using growth models that can serve as field reference data for model construction with up-to-date ALS data. In this study, we analyzed these two approaches of reusing field data acquired 15 years prior to the current ALS acquisition to estimate six up-to-date forest attributes (dominant tree height, mean tree height, stem number, stand basal area, volume, and aboveground biomass). Both approaches were evaluated within small stands with sizes of approximately 0.37 ha, assessing differences between estimates and ground reference values. The estimates were also compared to results from an up-to-date forest inventory relying on concurrent field- and ALS data. The results showed that even though the reuse of historical information has some potential and could be beneficial for forest inventories, systematic errors may appear prominent and need to be overcome to use it operationally. Our study showed systematic trends towards the overestimation of lower-range ground references and underestimation of the upper-range ground references.

Spray Volume, Canopy Density, and Other Factors Involved in Thinner Efficacy

HortScience ◽  
1998 ◽  
Vol 33 (3) ◽  
pp. 553d-553
Author(s):  
C.R. Unrath
Keyword(s):  
Tree Height ◽  
Tree Canopy ◽  
Tree Size ◽  
Water Volume ◽  
Canopy Density ◽  
The North ◽  
Run Off ◽  
Tree Canopies ◽  
Average Water ◽  
Water Volumes

Historically, most airblast chemical applications to apple orchards used a single “average” water volume, resulting in variability of coverage with tree size and also the greatest variable in chemical thinning. This coverage variability can be eliminated by properly quantifying the tree canopy, as tree row volume (TRV), and relating that volume to airblast water rate for adequate coverge. Maximum typical tree height, cross-row limb spread, and between-row spacing are used to quantify the TRV. Further refinement is achieved by adjusting the water volume for tree canopy density. The North Carolina TRV model allows a density adjustment from 0.7 gal/1000 ft3 of TRV for young, very open tree canopies to 1.0 gal/1000 ft3 of TRV for large, thick tree canopies to deliver a full dilute application for maximum water application (to the point of run-off). Most dilute pesticide applications use 70% of full dilute to approach the point of drip (pesticide dilute) to not waste chemicals and reduce non-target environmental exposure. From the “chemical load” (i.e., lb/acre) calculated for the pesticide dilute application, the proper chemical load for lower (concentrate) water volumes can be accurately determined. Another significant source of variability is thinner application response is spray distribution to various areas of the tree. This variability is related to tree configuration, light, levels, fruit set, and natural thinning vs. the need for chemical thinning. Required water delivery patterns are a function of tree size, form, spacing, and density, as well as sprayer design (no. of nozzles and fan size). The TRV model, density adjustments, and nozzle patterns to effectively hit the target for uniform crop load will be addressed.

scholarly journals A protocol for sampling vascular epiphyte richness and abundance

2009 ◽  
Vol 25 (2) ◽  
pp. 107-121 ◽  
Author(s):  
Jan H. D. Wolf ◽  
S. Robbert Gradstein ◽  
Nalini M. Nadkarni
Keyword(s):  
Tree Height ◽  
Dry Weight ◽  
Mantel Test ◽  
Tree Size ◽  
Tree Level ◽  
Trunk Diameter ◽  
Host Trees ◽  
Epiphyte Communities ◽  
Vascular Epiphyte

Abstract:The sampling of epiphytes is fraught with methodological difficulties. We present a protocol to sample and analyse vascular epiphyte richness and abundance in forests of different structure (SVERA). Epiphyte abundance is estimated as biomass by recording the number of plant components in a range of size cohorts. Epiphyte species biomass is estimated on 35 sample-trees, evenly distributed over six trunk diameter-size cohorts (10 trees with dbh > 30 cm). Tree height, dbh and number of forks (diameter > 5 cm) yield a dimensionless estimate of the size of the tree. Epiphyte dry weight and species richness between forests is compared with ANCOVA that controls for tree size. SChao1 is used as an estimate of the total number of species at the sites. The relative dependence of the distribution of the epiphyte communities on environmental and spatial variables may be assessed using multivariate analysis and Mantel test. In a case study, we compared epiphyte vegetation of six Mexican oak forests and one Colombian oak forest at similar elevation. We found a strongly significant positive correlation between tree size and epiphyte richness or biomass at all sites. In forests with a higher diversity of host trees, more trees must be sampled. Epiphyte biomass at the Colombian site was lower than in any of the Mexican sites; without correction for tree size no significant differences in terms of epiphyte biomass could be detected. The occurrence of spatial dependence, at both the landscape level and at the tree level, shows that the inclusion of spatial descriptors in SVERA is justified.

scholarly journals Adjudicating Perspectives on Forest Structure: How Do Airborne, Terrestrial, and Mobile Lidar-Derived Estimates Compare?

2021 ◽  
Vol 13 (12) ◽  
pp. 2297
Author(s):  
Jonathon J. Donager ◽  
Andrew J. Sánchez Meador ◽  
Ryan C. Blackburn
Keyword(s):  
Ponderosa Pine ◽  
Laser Scanning ◽  
Canopy Cover ◽  
Basal Area ◽  
Tree Height ◽  
Absolute Error ◽  
Forest Monitoring ◽  
Individual Tree ◽  
Structural Configurations ◽  
Individual Trees

Applications of lidar in ecosystem conservation and management continue to expand as technology has rapidly evolved. An accounting of relative accuracy and errors among lidar platforms within a range of forest types and structural configurations was needed. Within a ponderosa pine forest in northern Arizona, we compare vegetation attributes at the tree-, plot-, and stand-scales derived from three lidar platforms: fixed-wing airborne (ALS), fixed-location terrestrial (TLS), and hand-held mobile laser scanning (MLS). We present a methodology to segment individual trees from TLS and MLS datasets, incorporating eigen-value and density metrics to locate trees, then assigning point returns to trees using a graph-theory shortest-path approach. Overall, we found MLS consistently provided more accurate structural metrics at the tree- (e.g., mean absolute error for DBH in cm was 4.8, 5.0, and 9.1 for MLS, TLS and ALS, respectively) and plot-scale (e.g., R2 for field observed and lidar-derived basal area, m2 ha−1, was 0.986, 0.974, and 0.851 for MLS, TLS, and ALS, respectively) as compared to ALS and TLS. While TLS data produced estimates similar to MLS, attributes derived from TLS often underpredicted structural values due to occlusion. Additionally, ALS data provided accurate estimates of tree height for larger trees, yet consistently missed and underpredicted small trees (≤35 cm). MLS produced accurate estimates of canopy cover and landscape metrics up to 50 m from plot center. TLS tended to underpredict both canopy cover and patch metrics with constant bias due to occlusion. Taking full advantage of minimal occlusion effects, MLS data consistently provided the best individual tree and plot-based metrics, with ALS providing the best estimates for volume, biomass, and canopy cover. Overall, we found MLS data logistically simple, quickly acquirable, and accurate for small area inventories, assessments, and monitoring activities. We suggest further work exploring the active use of MLS for forest monitoring and inventory.

Machine learning analyses of remote sensing measurements establish strong relationships between vegetation and snow depth in the boreal forest of Interior Alaska

2021 ◽  
Author(s):  
Thomas Douglas ◽  
Caiyun Zhang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Snow Depth ◽  
Spatial Scales ◽  
Critical Role ◽  
Winter Season ◽  
Thermal Conditions ◽  
Near Surface ◽  
Lidar Measurements ◽  
Remote Sensing Methods

The seasonal snowpack plays a critical role in Arctic and boreal hydrologic and ecologic processes. Though snow depth can be different from one season to another there are repeated relationships between ecotype and snowpack depth. Alterations to the seasonal snowpack, which plays a critical role in regulating wintertime soil thermal conditions, have major ramifications for near-surface permafrost. Therefore, relationships between vegetation and snowpack depth are critical for identifying how present and projected future changes in winter season processes or land cover will affect permafrost. Vegetation and snow cover areal extent can be assessed rapidly over large spatial scales with remote sensing methods, however, measuring snow depth remotely has proven difficult. This makes snow depth–vegetation relationships a potential means of assessing snowpack characteristics. In this study, we combined airborne hyperspectral and LiDAR data with machine learning methods to characterize relationships between ecotype and the end of winter snowpack depth. Our results show hyperspectral measurements account for two thirds or more of the variance in the relationship between ecotype and snow depth. An ensemble analysis of model outputs using hyperspectral and LiDAR measurements yields the strongest relationships between ecotype and snow depth. Our results can be applied across the boreal biome to model the coupling effects between vegetation and snowpack depth.

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