Plot Edge Bias in Forest Stand Growth Simulation Models

1974 ◽  
Vol 4 (4) ◽  
pp. 419-423 ◽  
Author(s):  
Robert A. Monserud ◽  
Alan R. Ek
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Simulation Models ◽  
Bias Reduction ◽  
Forest Stand ◽  
Tree Size ◽  
Individual Tree ◽  
Growth Simulation ◽  
Relative Simplicity ◽  
Stand Growth

The problem of edge bias arising in the computation of individual tree competition in stand growth simulation models is discussed. The problem arises from difficulty in characterizing tree size and spatial patterns beyond the edge of the simulation plot. Various methods for reducing this bias are reviewed along with related assumptions and major sources of error. Methods which involve shifting the simulation plot image to form a set of border plots are judged best on the basis of likely bias reduction and the relative simplicity of introduced spatial pattern periodicity.

Spatial Pattern of Trees in the Major Forest Types of Northern Ontario

1974 ◽  
Vol 4 (1) ◽  
pp. 8-14 ◽  
Author(s):  
Bijan Payandeh
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Simulation Modelling ◽  
Species Group ◽  
Stand Development ◽  
Forest Types ◽  
Northern Ontario ◽  
Growth Simulation ◽  
Natural Stands ◽  
Mixedwood Stands

This paper describes the spatial pattern, expressed by Pielou's nonrandomness index, of trees within 13 sampled tracts from the major forest types of northern Ontario. Results indicate that: (a) the majority of natural coniferous or mixedwood stands have highly clustered patterns; (b) hardwood stands or the hardwood component of the mixedwood stands show nearly 'random' spatial patterns; and (c) uniform spacing in natural stands is very rare. Results also indicate that spatial patterns vary considerably during stand development for the various species group – size class combinations within a stand and between different forest types. The importance of spatial pattern and stand population dynamics is pointed out with regard to growth simulation modelling and mechanized harvesting and thinning studies.

Trend surface analysis of spatial patterns of tree size, microsite effects, and competitive stress

1986 ◽  
Vol 16 (2) ◽  
pp. 279-282 ◽  
Author(s):  
A. J. Thomson
Keyword(s):  
Surface Analysis ◽  
Spatial Patterns ◽  
Three Dimensional ◽  
Tree Size ◽  
Dimensional Representation ◽  
Trend Surface Analysis ◽  
Individual Tree ◽  
Spatial Trend ◽  
Trend Surface ◽  
Three Dimensional Representation

Trend surface analysis was used to determine the spatial patterns of tree size, competitive stress, and effects of microsite on growth. A three-dimensional representation of the trend surface facilitated interpretation. Gradients of competitive stress depended on the competition index used. Microsite effects have a spatial trend and individual tree genetic effects are represented by the residuals from this trend.

scholarly journals Spatial Pattern Analysis of Sharka Disease (Plum pox virus Strain M) in Peach Orchards of Southern France

2003 ◽  
Vol 93 (12) ◽  
pp. 1543-1552 ◽  
Author(s):  
Sylvie Dallot ◽  
Tim Gottwald ◽  
Gérard Labonne ◽  
Jean-Bernard Quiot
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Disease Incidence ◽  
Point Pattern ◽  
Plum Pox Virus ◽  
Spatial Pattern Analysis ◽  
Individual Tree ◽  
Southern France ◽  
Wide Range ◽  
Sharka Disease

The spatial pattern of Sharka disease, caused by Plum pox virus (PPV) strain M, was investigated in 18 peach plots located in two areas of southern France. PPV infections were monitored visually for each individual tree during one to three consecutive years. Point pattern and correlation-type approaches were undertaken using the binary data directly or after parsing them in contiguous quadrats of 4, 9, and 16 trees. Ordinary runs generally revealed a low but variable proportion of rows with adjacent symptomatic trees. Aggregation of disease incidence was indicated by the θ parameter of the beta-binomial distribution and related indices in 15 of the 18 plots tested for at least one assessment date of each. When aggregation was detected, it was indicated at all quadrat sizes and tended to be a function of disease incidence, as shown by the binary form of Taylor's power law. Spatial analysis by distance indices (SADIE) showed a nonrandom arrangement of quadrats with infected trees in 14 plots. The detection of patch clusters enclosing quadrats with above-average density of symptomatic trees, ellipsoidal in shape and generally extending from 4 to 14 trees within rows and from 4 to 10 trees perpendicular to the rows, could be interpreted as local areas of influence of PPV spread. Spatial patterns at the plot scale were often characterized by the occurrence of several clusters of infected trees located up to 90 m apart in the direction of the rows. When several time assessments were available, increasing clustering over time was generally evidenced by stronger values of the clustering index and by increasing patch cluster size. The combination of the different approaches revealed a wide range of spatial patterns of PPV-M, from no aggregation to high aggregation of symptomatic trees at all spatial scales investigated. Such patterns suggested that aphid transmission to neighboring trees occurred frequently but was not systematic. The mechanism of primary virus introduction, the age and structure of the orchards when infected, and the diversity of vector species probably had a strong influence on the secondary spread of the disease. This study provides a more complete understanding of PPV-M patterns which could help to improve targeting of removal of PPV-infected trees for more effective disease control.

Eliminating the effect of overlapping crowns from aerial inventory estimates

2006 ◽  
Vol 36 (7) ◽  
pp. 1649-1660 ◽  
Author(s):  
Lauri Mehtätalo
Keyword(s):  
Spatial Autocorrelation ◽  
Forest Stand ◽  
Tree Size ◽  
Aerial Photographs ◽  
Data Sets ◽  
Relative Height ◽  
Individual Tree ◽  
Mathematical Formula ◽  
Free Data ◽  
Laser Scanners

When a forest stand is inventoried from aerial photographs or with the use of laser scanners, only those trees that are not covered by a crown of a taller tree can be observed. This means that the probability of a tree being observable from the air depends on its relative height among the trees in the stand and on the crown areas of the taller trees. This study derives a mathematical formula for the probability of a tree being observable from the air according to certain assumptions about the censoring process in a forest stand where trees are randomly located without spatial autocorrelation in tree size. After presenting this formula, different approaches are proposed for the correction of the censoring effect upon the observed distribution of crown areas. The methods could change the aim of individual-tree recognition from finding all trees of the stand to finding a set of trees for which the stated assumptions about the censoring process are valid as far as possible. The methods performed well in a demonstration with simulated, error-free data sets, for which all stated assumptions were valid.

Modelling growing stock volume of forest stands with various ALS area-based approaches

2021 ◽  
Author(s):  
Karolina Parkitna ◽  
Grzegorz Krok ◽  
Stanisław Miścicki ◽  
Krzysztof Ukalski ◽  
Marek Lisańczuk ◽  
...  
Keyword(s):  
Laser Scanning ◽  
Mean Squared Error ◽  
Prediction Method ◽  
Forest Stand ◽  
Boosted Regression Trees ◽  
Forest Stands ◽  
Individual Tree ◽  
Growing Stock ◽  
Modelling Techniques ◽  
Stock Volume

Abstract Airborne laser scanning (ALS) is one of the most innovative remote sensing tools with a recognized important utility for characterizing forest stands. Currently, the most common ALS-based method applied in the estimation of forest stand characteristics is the area-based approach (ABA). The aim of this study was to analyse how three ABA methods affect growing stock volume (GSV) estimates at the sample plot and forest stand levels. We examined (1) an ABA with point cloud metrics, (2) an ABA with canopy height model (CHM) metrics and (3) an ABA with aggregated individual tree CHM-based metrics. What is more, three different modelling techniques: multiple linear regression, boosted regression trees and random forest, were applied to all ABA methods, which yielded a total of nine combinations to report. An important element of this work is also the empirical verification of the methods for estimating the GSV error for individual forest stand. All nine combinations of the ABA methods and different modelling techniques yielded very similar predictions of GSV for both sample plots and forest stands. The root mean squared error (RMSE) of estimated GSV ranged from 75 to 85 m3 ha−1 (RMSE% = 20.5–23.4 per cent) and from 57 to 64 m3 ha−1 (RMSE% = 16.4–18.3 per cent) for plots and stands, respectively. As a result of the research, it can be concluded that GSV modelling with the use of different ALS processing approaches and statistical methods leads to very similar results. Therefore, the choice of a GSV prediction method may be more determined by the availability of data and competences than by the requirement to use a particular method.

scholarly journals Using a Groundwater Adjusted Water Balance Approach and Copulas to Evaluate Spatial Patterns and Dependence Structures in Remote Sensing Derived Evapotranspiration Products

2021 ◽  
Vol 13 (5) ◽  
pp. 853
Author(s):  
Mohsen Soltani ◽  
Julian Koch ◽  
Simon Stisen
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Principal Component ◽  
Small Scale ◽  
Dependence Structure ◽  
Test Case ◽  
Catchment Scale ◽  
Balance Approach

This study aims to improve the standard water balance evapotranspiration (WB ET) estimate, which is typically used as benchmark data for catchment-scale ET estimation, by accounting for net intercatchment groundwater flow in the ET calculation. Using the modified WB ET approach, we examine errors and shortcomings associated with the long-term annual mean (2002–2014) spatial patterns of three remote-sensing (RS) MODIS-based ET products from MODIS16, PML_V2, and TSEB algorithms at 1 km spatial resolution over Denmark, as a test case for small-scale, energy-limited regions. Our results indicate that the novel approach of adding groundwater net in water balance ET calculation results in a more trustworthy ET spatial pattern. This is especially relevant for smaller catchments where groundwater net can be a significant component of the catchment water balance. Nevertheless, large discrepancies are observed both amongst RS ET datasets and compared to modified water balance ET spatial pattern at the national scale; however, catchment-scale analysis highlights that difference in RS ET and WB ET decreases with increasing catchment size and that 90%, 87%, and 93% of all catchments have ∆ET < ±150 mm/year for MODIS16, PML_V2, and TSEB, respectively. In addition, Copula approach captures a nonlinear structure of the joint relationship with multiple densities amongst the RS/WB ET products, showing a complex dependence structure (correlation); however, among the three RS ET datasets, MODIS16 ET shows a closer spatial pattern to the modified WB ET, as identified by a principal component analysis also. This study will help improve the water balance approach by the addition of groundwater net in the ET estimation and contribute to better understand the true correlations amongst RS/WB ET products especially over energy-limited environments.

scholarly journals Using Airborne Light Detection and Ranging (LIDAR) to Characterize Forest Stand Condition on the Kenai Peninsula of Alaska

2009 ◽  
Vol 24 (2) ◽  
pp. 95-102 ◽  
Author(s):  
Hans-Erik Andersen
Keyword(s):  
Forest Inventory ◽  
Laser Scanning ◽  
Three Dimensional ◽  
Grid Cell ◽  
Forest Stand ◽  
Light Detection And Ranging ◽  
Inventory Analysis ◽  
Light Detection ◽  
Individual Tree ◽  
Kenai Peninsula

Abstract Airborne laser scanning (also known as light detection and ranging or LIDAR) data were used to estimate three fundamental forest stand condition classes (forest stand size, land cover type, and canopy closure) at 32 Forest Inventory Analysis (FIA) plots distributed over the Kenai Peninsula of Alaska. Individual tree crown segment attributes (height, area, and species type) were derived from the three-dimensional LIDAR point cloud, LIDAR-based canopy height models, and LIDAR return intensity information. The LIDAR-based crown segment and canopy cover information was then used to estimate condition classes at each 10-m grid cell on a 300 × 300-m area surrounding each FIA plot. A quantitative comparison of the LIDAR- and field-based condition classifications at the subplot centers indicates that LIDAR has potential as a useful sampling tool in an operational forest inventory program.

Spatial-Pattern-Induced Evolution of a Self-Replicating Loop Network

2006 ◽  
Vol 12 (4) ◽  
pp. 461-485 ◽  
Author(s):  
Keisuke Suzuki ◽  
Takashi Ikegami
Keyword(s):  
Pattern Formation ◽  
Spatial Pattern ◽  
Spatial Patterns ◽  
Spiral Structure ◽  
Scale Interactions ◽  
Micro Scale ◽  
Macro Scale ◽  
Loop Network ◽  
Spatial Pattern Formation

We study a system of self-replicating loops in which interaction rules between individuals allow competition that leads to the formation of a hypercycle-like network. The main feature of the model is the multiple layers of interaction between loops, which lead to both global spatial patterns and local replication. The network of loops manifests itself as a spiral structure from which new kinds of self-replicating loops emerge at the boundaries between different species. In these regions, larger and more complex self-replicating loops live for longer periods of time, managing to self-replicate in spite of their slower replication. Of particular interest is how micro-scale interactions between replicators lead to macro-scale spatial pattern formation, and how these macro-scale patterns in turn perturb the micro-scale replication dynamics.

scholarly journals Forest stand growth dynamics in Central Europe have accelerated since 1870

2014 ◽  
Vol 5 (1) ◽  
Author(s):  
Hans Pretzsch ◽  
Peter Biber ◽  
Gerhard Schütze ◽  
Enno Uhl ◽  
Thomas Rötzer
Keyword(s):  
Central Europe ◽  
Growth Dynamics ◽  
Forest Stand ◽  
Stand Growth

Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

2021 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Erjie Hu ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Distribution Pattern ◽  
Spatial Patterns ◽  
Spillover Effect ◽  
Tertiary Industry ◽  
Innovation Capacity ◽  
Structure Index ◽  
Secondary Industry ◽  
Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

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