Bruce E. Borders

Taper equations for Pinus taeda plantations in Southern Brazil

Abstract Non-linear regression was used to fit taper functions with data from Pinus taeda plantations in Southern Brazil. Five models were evaluated: Kozak et al. (1969); a fifth-degree polynomial; Max and Burkhart (1976); Parresol et al. (1987); and Clark et al. (1991). Diameter prediction at 12 points along the stem was made to verify the accuracy and precision of these models. Furthermore, their ability to predict total and merchantable (7 cm and 25 cm top diameter) volumes was evaluated. Most of the statistics indicated that the segmented form-class model of Clark et al. (1991) was the best for estimating diameters along the stem and for predicting merchantable or total volume. However, the statistics also indicate regularity and good performance from the Max and Burkhart (1976); Parresol et al. (1987) and fifth-degree polynomial models.

Assessment of regression kriging for spatial interpolation – comparisons of seven GIS interpolation methods

As an important GIS function, spatial interpolation is one of the most often used geographic techniques for spatial query, spatial data visualization, and spatial decision-making processes in GIS and environmental science. However, less attention has been paid on the comparisons of available spatial interpolation methods, although a number of GIS models including inverse distance weighting, spline, radial basis functions, and the typical geostatistical models (i.e. ordinary kriging, universal kriging, and cokriging) are already incorporated in GIS software packages. In this research, the conceptual and methodological aspects of regression kriging and GIS built-in interpolation models and their interpolation performance are compared and evaluated. Regression kriging is the combination of multivariate regression and kriging. It takes into consideration the spatial autocorrelation of the variable of interest, the correlation between the variable of interest and auxiliary variables (e.g., remotely sensed images are often relatively easy to obtain as auxiliary variables), and the unbiased spatial estimation with minimized variance. To assess the efficiency of regression kriging and the difference between stochastic and deterministic interpolation methods, three case studies with strong, medium, and weak correlation between the response and auxiliary variables are compared to assess interpolation performances. Results indicate that regression kriging has the potential to significantly improve spatial prediction accuracy even when using a weakly correlated auxiliary variable.

Growth responses to planting density and management intensity in loblolly pine plantations in the southeastern USA Lower Coastal Plain

Abstract• BackgroundA culture/density study was established in 1995 in the Lower Coastal Plain of the southeastern USA to evaluate the effects of intensive silviculture and current operational practices on the growth and yield of loblolly pine plantations across a wide range of planting densities (741–4,448 trees/ha). The operational regime consisted of bedding and herbicide application in site preparation and fertilizer applications at planting and in the eighth and 12th growing seasons. The intensive management regime had additional complete competition control, tip moths control, and more repeated fertilization treatments.• MethodsThe data from 14 locations from this split-plot experiment design with repeated measurements were analyzed with a mixed-effects model approach in terms of average DBH, average height, average dominant height, survival, stand basal area, and stand volume.• ResultsIn the first few years after planting, there were no significant effects of management intensity and planting density. In later years, both management intensity and planting density significantly impacted response variables, and their interaction was only significant for average diameter at breast height (DBH). Responses to intensive management in DBH were greatest at the lowest planting densities. Intensive management resulted in larger average DBH, average height, dominant height, stand basal area, and volume. Intensively managed plots had more mortality at age 12. There were negative average DBH, average height, dominant height, and survival responses but positive stand basal area and volume responses to increasing planting density. However, there were no significant differences for planting densities above 2,224 trees/ha.• ConclusionsThe results demonstrate that both management intensity and planting density significantly affect loblolly pine productivity in the Lower Coastal Plain, and their effects are additive in nature due to the general lack of interactions.

K Nearest Neighbor Method for Forest Inventory Using Remote Sensing Data

The K nearest neighbor (KNN) method of image analysis is practical, relatively easy to implement, and is becoming one of the most popular methods for conducting forest inventory using remote sensing data. The KNN is often named K nearest neighbor classifier when it is used for classifying categorical variables, while KNN is called K nearest neighbor regression when it is applied for predicting noncategorical variables. As an instance-based estimation method, KNN has two problems: the selection of K values and computation cost. We address the problems of K selection by applying a new approach, which is the combination of the Kolmogorov-Smirnov (KS) test and cumulative distribution function (CDF) to determine the optimal K. Our research indicates that the KS tests and CDF are much more efficient for selecting K than cross-validation and bootstrapping, which are commonly used today. We use remote sensing data reduction techniques—such as principal components analysis, layer combination, and computation of a vegetation index—to save computation cost. We also consider the theoretical and practical implications of different K values in forest inventory.

Closest Spectral Fit for Removing Clouds and Cloud Shadows

Completely cloud-free remotely sensed images are preferred, but they are not always available. Although the average cloud coverage for the entire planet is about 40 percent, the removal of clouds and cloud shadows is rarely studied. To address this problem, a closest spectral fit method is developed to replace cloud and cloud-shadow pixels with their most similar nonclouded pixel values. The objective of this paper is to illustrate the methodology of the closest spectral fit and test its performance for removing clouds and cloud shadows in images. The closest spectral fit procedures are summarized into six steps, in which two main conceptions, location-based one-to-one correspondence and spectral-based closest fit, are defined. The location-based one-to-one correspondence is applied to identify pixels with the same locations in both base image and auxiliary images. The spectral-based closest fit is applied to determine the most similar pixels in an image. Finally, this closest spectral fit approach is applied to remove cloud and cloud-shadow pixels and diagnostically checked using Landsat TM images. Additional examples using QuickBird and MODIS images also indicate the efficiency of the closest spectral fit for removing cloud pixels.

High-resolution satellite image fusion using regression kriging

Image fusion is an important component of digital image processing and quantitative image analysis. Image fusion is the technique of integrating and merging information from different remote sensors to achieve refined or improved data. A number of fusion algorithms have been developed in the past two decades, and most of these methods are efficient for applications especially for same-sensor and single-date images. However, colour distortion is a common problem for multi-sensor or multi-date image fusion. In this study, a new image fusion method of regression kriging is presented. Regression kriging takes consideration of correlation between response variable (i.e., the image to be fused) and predictor variables (i.e., the image with finer spatial resolutions), spatial autocorrelation among pixels in the predictor images, and the unbiased estimation with minimized variance. Regression kriging is applied to fuse multi-temporal (e.g., Ikonos, QuickBird, and OrbView-3) images. The significant properties of image fusion using regression kriging are spectral preservation and relatively simple procedures. The qualitative assessments indicate that there is no apparent colour distortion in the fused images that coincides with the quantitative checks, which show that the fused images are highly correlated with the initial data and the per-pixel differences are too small to be considered as significant errors. Besides a basic comparison of image fusion between a wavelet based approach and regression kriging, general comparisons with other published fusion algorithms indicate that regression kriging is comparable with other sophisticated techniques for multi-sensor and multi-date image fusion.

Site preparation and competing vegetation control affect loblolly pine long-term productivity in the southern Piedmont/Upper Coastal Plain of the United States

Abstract• A site preparation study was established in 1986 to evaluate the effect of different site preparation treatments on growth and yield of loblolly pine (Pinus taeda L.) plantations on the Piedmont and Upper Coastal Plain regions of the southern United States. Site preparation treatments included: (1) burn only, (2) chop-burn, (3) shear-pile-disk, (4) chop-herbicide-burn, (5) herbicide-burn, and (6) herbicide-burn-herbicide.• The data from the available 19 installations at age 21 were analyzed with separate analysis of variance and a multilevel nonlinear mixed-effects modeling approach.• The herbicide-burn-herbicide treatment significantly increased average Dbh, height, basal area and volume per hectare compared to all other treatments. The burn only treatment consistently ranked worst and was followed by the chop-burn treatment. The shear-pile-disk and chop-herbicide-burn treatments had similar overall growth pattern, and will approach the same level of pine volume as the herbicide-burn treatment.• Loblolly pine mean annual increment in volume (m3 ha−1 y−1) at age 21 by treatment were: herbicide-burn-herbicide (17.9), shear-pile-disk (16.1), herbicide-burn (15.9), chop-herbicide-burn (15.4), chop-burn (14.3), and burn (11.2).• An additional chop or herbicide treatment to the burn treatment significantly increased loblolly pine yield. Complete control of both herbaceous and woody completion enhanced long-term pine productivity.Résumé• Une expérimentation destine à tester la préparation du terrain a été installée en 1986 pour évaluer l’impact de différents traitements de préparation du terrain sur la croissance et la productivité de Pinus taeda L. dans les plaines du Sud des États-Unis d’Amérique. Les traitements comprenaient : (1) un brûlis, (2) un débroussaillage suivi de brûlis, (3) un andainage automatique, (4) un débroussaillage suivi d’un traitement herbicide et d’un brûlis, (5) un traitement herbicide suivi de brûlis, (6) un herbicide suivi d’un brûlis et d’un second traitement herbicide.• Les données issues de 19 blocs ont été analysées après 21 ans de croissance, en utilisant des analyses de variance séparées ainsi qu’un modèle mixte non linéaire plusieurs niveaux.• Le traitement herbicide-brûlis-herbicide a augmenté de manière significative le diamètre à 1 m 30, la hauteur, la surface terrière et le volume sur pied par rapport à tous les autres traitements. Le brûlis seul a produit les résultats les plus médiocres, suivi par le débroussaillage-brûlis. Les traitements avec andainage et broyage et celui avec débroussaillage-herbicide-brûlis ont présenté des croissances comparables, and se rapprochent du traitement herbicide-brûlis.• L’accroissement annuel moyen à 21 ans (m3ha−1 y−1) était de 17,9 (herbicide-brûlis-herbicide), 16,1 (débroussaillage- and ainage-broyage), 15,9 (herbicide-brûlis), 15,4 (broyage-herbicide-brûlis), 14,3 (broyage-brûlis) et enfin 11,2 (brûlis seul).• En conclusion, compléter le brûlis par un traitement herbicide ou un broyage des rémanents a permis d’augmenter de manière très significative le rendement de Pinus taerda. Un contrôle précis de la competition par les herbacées et les plantes ligneuses a permis d’augmenter la productivité de Pinus taeda sur le long terme.

A volume equation for mangrove trees in northeast Brazil

Abstract An individual tree volume equation was developed for mangrove trees in northeast Brazil. On each of 50 felled sample trees, the volume of the main stem, branches and stilt roots with diameters greater than 3 cm was determined using Smalians formula. The volume model VOL = [λ(β 0 + β 1 DAR + β 2 HDAR + e i ) + 1] 1 2 , where VOL is volume of the tree (m3), DAR is diameter at the height of the highest stilt root (cm), HDAR is distance from the DAR to the ground at low-tide level (m), λ is transformation parameter, β0 and β1 are coefficients of the model, and ei is error component, provides good estimates of the volume. The final equation, as well as a volume table generated with the equation, are presented.

An iterative state-space growth and yield modeling approach for unthinned loblolly pine plantations

A mathematical iteration-based projection approach is applied in developing a management-oriented tree growth model for unthinned loblolly pine (Pinus taeda L.) plantations according to the biomass balances among the physical compartments of a tree. In this study, the biomass of each tree compartment is used as a state variable. Tree growth is characterized as a dynamic system in which both inputs and outputs are functions of time. This dynamic system can be updated from one state to another by updating state variables, which is based on a hypothesis from tree physiology that suggests the biomass productivity of a tree is proportional to the foliage biomass. The dynamic processes of the system are described using a transition function and an output function of state variables. This approach evaluates the individual growth response to external forces by predicting some coefficients that regulate the growth response based on the input and output of the system. Compared with the traditional empirical growth and yield modeling approach, this method is more easily adjusted to model growth patterns of plantations managed under different growth conditions.

Spacing effects in an unthinned 11-year-old Terminalia superba plantation in the dry lowland rainforest area of Nigeria

Abstract Data from a spacing study in an 11-year-old plantation of Terminalia superba were examined with response models. Analysis of variance of the latin-square design with four spacings indicated significant effects of spacing on survival and mean diameter, as well as basal area, height and volume growth. Response model analysis showed that a planting density of 2391 trees ha −1 maximizes basal area growth and 2331 trees ha −1 maximizes volume growth, but the largest mean diameter is produced by planting 232 trees ha −1 . Guides are provided for the forest managers use in determining trade-offs between maximum timber production and space between trees to grow agricultural crops within the Taungya system.