TREE CROWN DELINEATION ON VHR AERIAL IMAGERY WITH SVM CLASSIFICATION TECHNIQUE OPTIMIZED BY TAGUCHI METHOD: A CASE STUDY IN ZAGROS WOODLANDS

The Support Vector Machine (SVM) is a theoretically superior machine learning methodology with great results in classification of remotely sensed datasets. Determination of optimal parameters applied in SVM is still vague to some scientists. In this research, it is suggested to use the Taguchi method to optimize these parameters. The objective of this study was to detect tree crowns on very high resolution (VHR) aerial imagery in Zagros woodlands by SVM optimized by Taguchi method. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The VHR aerial imagery of the plot with 0.06 m spatial resolution was obtained from National Geographic Organization (NGO), Iran, to extract the crowns of Persian oak trees in this study. The SVM parameters were optimized by Taguchi method and thereafter, the imagery was classified by the SVM with optimal parameters. The results showed that the Taguchi method is a very useful approach to optimize the combination of parameters of SVM. It was also concluded that the SVM method could detect the tree crowns with a KHAT coefficient of 0.961 which showed a great agreement with the observed samples and overall accuracy of 97.7% that showed the accuracy of the final map. Finally, the authors suggest applying this method to optimize the parameters of classification techniques like SVM

MODELING THE SPATIAL DISTRIBUTION OF ESHNAN (<i>SEIDLITZIA ROSMARINUS</i>) SHRUBS TO EXPLORING THEIR ECOLOGICAL INTERACTIONS IN DRYLANDS OF CENTRAL IRAN

Evaluating the interactions of woody plants has been a major research topic of ecological investigations in arid ecosystems. Plant-plant interactions can shift from positive (facilitation) to negative (competition) depending on levels of environmental stress and determine the spatial pattern of plants. The spatial distribution analysis of plants via different summary statistics can reveal the interactions of plants and how they influence one another. An aggregated distribution indicates facilitative interactions among plants, while dispersion of species reflects their competition for scarce resources. This study was aimed to explore the intraspecific interactions of eshnan (Seidlitzia rosmarinus) shrubs in arid lands, central Iran, using different summary statistics (i.e., pair correlation function g(r), O-ring function O(r), nearest neighbour distribution function D(r), spherical contact distribution function Hs(r)). The observed pattern of shrubs showed significant spatial heterogeneity as compared to inhomogeneous Poisson process (α=0.05). The results of g(r) and O(r) revealed the significant aggregation of eshnan shrubs up to scale of 3 m (α=0.05). The results of D(r) and Hs(r) also showed that maximum distance to nearest shrub was 6 m and the distribution of the sizes of gaps was significantly different from random distribution up to this spatial scale. In general, it was concluded that there were positive interactions between eshnan shrubs at small scales and they were aggregated due to their intraspecific facilitation effects in the study area

Suitable Methods in Spatial Pattern Analysis of Heterogeneous Wild Pistachio (Pistacia atlantica Desf.) Woodlands in Zagros, Iran

Spatial pattern of trees in forests reveals how trees interact with each other and their environment. Spatial structure of trees in forest ecosystems is affected by environmental heterogeneity that leads to their heterogeneous distribution. This study was aimed to investigate the appropriate methods to analyze spatial pattern of heterogeneous wild pistachio woodlands in Zagros, Iran. A 40-ha pure stand of wild pistachio trees (Pistacia atlantica Desf.) was selected in Wild Pistachio Research Forest in Fars Province for this purpose. The Kolmogrov-Smirnov test of goodness-of-fit of inhomogeneous Poisson point process showed that the distribution of wild pistachio trees was significantly heterogeneous (α=0.05). Inhomogeneous Ripley's K-, L-, and G-functions were applied beside their homogeneous forms. Inhomogeneous Ripley's K- and L-functions showed that wild pistachio trees were primarily clumped and dispersedly distributed thereafter, while g(r) not only showed these results but also well expressed the detailed changes in spatial scale. The results of inappropriate homogeneous functions in the study area showed that all three functions expressed the primary clumping of the trees more than it was and their dispersed pattern as clumped. In general, it was concluded that inhomogeneous functions should be applied to analyze the spatial pattern of heterogeneous wild pistachio trees in the study area and it is recommended to develop g(r) applications due to its more detailed informatio

EFFECTS OF HETEROGENIETY ON SPATIAL PATTERN ANALYSIS OF WILD PISTACHIO TREES IN ZAGROS WOODLANDS, IRAN

Vegetation heterogeneity biases second-order summary statistics, e.g., Ripley's K-function, applied for spatial pattern analysis in ecology. Second-order investigation based on Ripley's K-function and related statistics (i.e., L- and pair correlation function g) is widely used in ecology to develop hypothesis on underlying processes by characterizing spatial patterns of vegetation. The aim of this study was to demonstrate effects of underlying heterogeneity of wild pistachio (Pistacia atlantica Desf.) trees on the second-order summary statistics of point pattern analysis in a part of Zagros woodlands, Iran. The spatial distribution of 431 wild pistachio trees was accurately mapped in a 40 ha stand in the Wild Pistachio & Almond Research Site, Fars province, Iran. Three commonly used second-order summary statistics (i.e., K-, L-, and g-functions) were applied to analyse their spatial pattern. The two-sample Kolmogorov-Smirnov goodness-of-fit test showed that the observed pattern significantly followed an inhomogeneous Poisson process null model in the study region. The results also showed that heterogeneous pattern of wild pistachio trees biased the homogeneous form of K-, L-, and g-functions, demonstrating a stronger aggregation of the trees at the scales of 0&ndash;50 m than actually existed and an aggregation at scales of 150&ndash;200 m, while regularly distributed. Consequently, we showed that heterogeneity of point patterns may bias the results of homogeneous second-order summary statistics and we also suggested applying inhomogeneous summary statistics with related null models for spatial pattern analysis of heterogeneous vegetations

CANOPY DENSITY MAPPING ON ULTRACAM-D AERIAL IMAGERY IN ZAGROS WOODLANDS, IRAN

Canopy density maps express different characteristics of forest stands, especially in woodlands. Obtaining such maps by field measurements is so expensive and time-consuming. It seems necessary to find suitable techniques to produce these maps to be used in sustainable management of woodland ecosystems. In this research, a robust procedure was suggested to obtain these maps by very high spatial resolution aerial imagery. It was aimed to produce canopy density maps by UltraCam-D aerial imagery, newly taken in Zagros woodlands by Iran National Geographic Organization (NGO), in this study. A 30 ha plot of Persian oak (Quercus persica) coppice trees was selected in Zagros woodlands, Iran. The very high spatial resolution aerial imagery of the plot purchased from NGO, was classified by kNN technique and the tree crowns were extracted precisely. The canopy density was determined in each cell of different meshes with different sizes overlaid on the study area map. The accuracy of the final maps was investigated by the ground truth obtained by complete field measurements. The results showed that the proposed method of obtaining canopy density maps was efficient enough in the study area. The final canopy density map obtained by a mesh with 30 Ar (3000 m2) cell size had 80% overall accuracy and 0.61 KHAT coefficient of agreement which shows a great agreement with the observed samples. This method can also be tested in other case studies to reveal its capability in canopy density map production in woodlands

Effects of Spatial Distribution of Trees on Density Estimation by Nearest Individual Sampling Method: Case Studies in Zagros Wild Pistachio Woodlands and Simulated Stands

Distance methods and their estimators of density may have biased measurements unless the studied stand of trees has a random spatial pattern. This study aimed at assessing the effect of spatial arrangement of wild pistachio trees on the results of density estimation by using the nearest individual sampling method in Zagros woodlands, Iran, and applying a correction factor based on the spatial pattern of trees. A 45 ha clumped stand of wild pistachio trees was selected in Zagros woodlands and two random and dispersed stands with similar density and area were simulated. Distances from the nearest individual and neighbour at 40 sample points in a 100 × 100 m grid were measured in the three stands. The results showed that the nearest individual method with Batcheler estimator could not calculate density correctly in all stands. However, applying the correction factor based on the spatial pattern of the trees, density was measured with no significant difference in terms of the real density of the stands. This study showed that considering the spatial arrangement of trees can improve the results of the nearest individual method with Batcheler estimator in density measurement

Evaluating the Intraspecific Interactions of Indian Rosewood (Dalbergia sissoo Roxb.) Trees in Indian Rosewood Reserveof Khuzestan Province

Positive and negative (facilitative and competitive) interactions of plants are important issues in autecology and can be evaluated by the spatial pattern analysis in plant ecosystems. This study investigates the intraspecific interactions of Indian rosewood (Dalbergia sissoo Roxb.) trees in Indian rosewood Reserve of Khuzestan province. Three 150 m &times; 200 m plots were selected and the spatial locations of all Indian rosewoods (239 trees) were specified. Structurally different summary statistics (nearest neighbour distribution function D(r), K2-index K2(r), pair correlation function g(r), and O-ring O(r)) were also implemented to analyze the spatial pattern of the trees. The distribution of Indian rosewood trees significantly followed inhomogeneous Poisson process (&alpha;=0.05). The results of D(r) and K2(r) showed that the maximum distance to nearest tree was 12 m and density was decreased to this scale. The results of g(r) and O(r) also revealed the significant aggregation of Indian rosewood trees at scales of 1.5 to 4 m (&alpha;=0.05). In general, it was concluded that Indian rosewood trees had positive intraspecific interactions in Indian rosewood Reserve of Khuzestan province and their aggregation showed their facilitative effects on one another

Application of Nearest Neighbor Indices in Persian Oak (Quercus brantii var. persica) Coppice Stands of Zagros Forests

The ecological relationship between trees is important in the sustainable management of forests. Studying this relationship in spatial ecology, different indices are applied that are based on distance to nearest neighbor. The aim of this research was introduction of important indices based on nearest neighbor analysis and their application in the investigation of ecological relationship between Persian oak coppice trees in Zagros forests. A 9 ha plot of these forests in Kohgilouye - BoyerAhmad province was selected that was completely homogeneous. This plot was covered with Persian oak coppice trees that their point map was obtained after registering their spatial location. Five nearest neighbor indices of G(r), F(r), J(r), GF(r) and CE were then applied to study the spatial pattern and relationship of these trees. The results showed that Persian oak coppice trees were located regularly in the homogeneous plot and they were not dependent ecologically. These trees were independent and did not affect the establishment of each other

Analyzing Spatial Pattern of Fagus orientalis Lipsky. Species in Hyrcanian Forests by Angular Indicators (Case Study: Nave Asalem- Guilan Forests)

It is so important to know about ecological characteristics of trees of a stand, in forest management. The first step to achieve this knowledge is to recognize the spatial pattern of trees. Therefore, regarding the enviro-economic&nbsp; importance of F. orientalis in hyrcanian forests, this study checked spatial pattern of these trees. In order to do this research, 5 one-hectare plots with homogeneous environmental conditions were inventoried in natural stands of Fagetum in Nave Asalem, Guilan province accidentally. Then, measuring the angle between fagus trees and using indicators of uniform angle index (Wi), Mean directional inedx (Ri), Mean of angles index and Clark-Evans (CE), the spatial pattern in Fagus orientalis was analyzed. The results showed clumpy pattern of the Fagus orientalis trees which is also tended to be random, and using Wi, Ri and CE indicators together provides better results to determine the spatial pattern of trees. Also, using angular indicators, besides the high accuracy due to the lack of need to measure distances between trees, speeds up spatial pattern determination of forest stands

WOODLAND MAPPING AT SINGLE-TREE LEVELS USING OBJECT-ORIENTED CLASSIFICATION OF UNMANNED AERIAL VEHICLE (UAV) IMAGES

Remotely sensed datasets offer a reliable means to precisely estimate biophysical characteristics of individual species sparsely distributed in open woodlands. Moreover, object-oriented classification has exhibited significant advantages over different classification methods for delineation of tree crowns and recognition of species in various types of ecosystems. However, it still is unclear if this widely-used classification method can have its advantages on unmanned aerial vehicle (UAV) digital images for mapping vegetation cover at single-tree levels. In this study, UAV orthoimagery was classified using object-oriented classification method for mapping a part of wild pistachio nature reserve in Zagros open woodlands, Fars Province, Iran. This research focused on recognizing two main species of the study area (i.e., wild pistachio and wild almond) and estimating their mean crown area. The orthoimage of study area was consisted of 1,076 images with spatial resolution of 3.47 cm which was georeferenced using 12 ground control points (RMSE=8 cm) gathered by real-time kinematic (RTK) method. The results showed that the UAV orthoimagery classified by object-oriented method efficiently estimated mean crown area of wild pistachios (52.09±24.67 m2) and wild almonds (3.97±1.69 m2) with no significant difference with their observed values (α=0.05). In addition, the results showed that wild pistachios (accuracy of 0.90 and precision of 0.92) and wild almonds (accuracy of 0.90 and precision of 0.89) were well recognized by image segmentation. In general, we concluded that UAV orthoimagery can efficiently produce precise biophysical data of vegetation stands at single-tree levels, which therefore is suitable for assessment and monitoring open woodlands