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This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (

This study investigates the potential of object-based texture parameters extracted from 15m spatial resolution ASTER imagery for estimating tree size diversity in a Mediterranean forested landscape in Turkey. Tree size diversity based on tree basal area was determined using the Shannon index and Gini Coefficient at the sampling plot level. Image texture parameters were calculated based on the grey level co-occurrence matrix (GLCM) for various image segmentation levels. Analyses of relationships between tree size diversity and texture parameters found that relationships between the Gini Coefficient and the GLCM values were the most statistically significant, with the highest correlation (r=0.69) being with GLCM Homogeneity values. In contrast, Shannon Index values were weakly correlated with image derived texture parameters. The results suggest that 15m resolution Aster imagery has considerable potential in estimating tree size diversity based on the Gini Coefficient for heterogeneous Mediterranean forests.

Biodiversity conservation has become an increasingly important issue in forest management, with forest managers now having to include biodiversity considerations within existing management plans [

Differences in tree size diversity may be due to a variety of factors including species composition, age differences and disturbance history. Management activities and the effects of insect and fungal pathogens may also affect tree size diversity. Wood oriented management systems have a homogenizing effect on stand structures as the goal is to grow even-aged stands. Furthermore shrubs and deciduous trees are removed by thinning to favor conifer trees, which are economically more valuable. Consequently, wood oriented silvicultural treatments have decreased the variability of size and tree species distributions in comparison with unmanaged stands [

Diameter at breast height (DBH), tree height, and crown depth and width can all be used to describe tree size [

Mapping and monitoring tree size diversity over large areas is crucial to forestry. The use of ground-based methods to determine and map tree size diversity for all stands in a landscape is, however, both expensive and time consuming. Satellite remote sensing technologies provide a powerful alternative for providing such information. Satellite image data are a cost efficient source of information especially for large-area forest inventories, and have been widely applied in forest mapping and monitoring [

Object oriented image analysis is a widely used tool for mapping and monitoring forests [

The study area (centered on 37°18′50″N, 30°44′50″E, 350 – 1200 m a.s.l.) is mostly composed pure

The analyses were based on the three bands with 15 m spatial resolution of Aster (Advanced Spaceborne Thermal Emission and Reflection Radiometer) satellite data with an image acquisition date of May 5th 2007. The spectral ranges of the three bands were 0.52-0.60 μm (green), 0.63-0.69 μm (red) and 0.76-0.86 μm (near infrared). Image pre-processing including atmospheric correction and orthorectification were applied by the supplier in order to correct distortions and degradations resulting from the image acquisition process and no further image pre-processing was undertaken.

The texture features were extracted from the image segments generated by the multi-resolution segmentation approach developed by Definiens image processing software. Segmentation is an algorithm which creates meaningful objects in an image by grouping the individual pixels according to their spatial and spectral properties and is performed using the scale parameter and homogeneity criteria. The scale parameter is an abstract term which determines the maximum allowed heterogeneity of the resulting image objects. While the scale parameter is increased, the homogeneity of segments decreases and the standard deviation within the resulting image objects increase [

The homogeneity criterions are the color, shape, compactness and smoothness in Definiens. It is recommended that the color criterion should be used as much as possible while keeping the shape criterion as high as necessary to produce image objects that suit the purpose. In other words, the color criterion is the most important for forming meaningful objects because the spectral information is the primary information contained in an image [

The segmentations at eleven different scales were conducted by modifying the scale parameter in order to determine the optimal segment scale that allows the best modeling of tree size diversity. The eleven scale parameters tested in this study were 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, and 60. We accepted this range based on the existing forest map. The stand borders in the forest map were visually compared with the resulting segments after each segmentation process. We did not repeat the segmentation process because we understood that the segments created using the scale parameters which are less than 10 and more than 60 did not form meaningful objects; segment scales >60 delineated objects capturing both fruit orchards and forests while segment scales <10 generated objects that were too small to match image features with the ground sampling plot data.

A large number of diversity indices can be used to characterize tree size diversity within a stand [

The Shannon index [

Since the sensitivity of the Shannon index to the change in class width is uncertain, we also used the Gini coefficient as it does not require arbitrary classified diameter classes and has been proposed for calculating tree size diversity [

Diversity indices were determined for 541 geo-referenced circular sampling plots that had been measured as part of forest management plan development by forest inventory teams in 2007. In the Turkish forest inventory system, the sampling plots are systematically distributed at 300 m intervals over the forested areas of a planning unit with plot sizes of 0.04 ha, 0.06 ha and 0.08 ha depending on the crown closure degrees of stands. The sampling plots as point data were overlaid with the eleven segmented images using GIS. A 20 m buffer zone was created from segment boundaries in order to reduce the spatial error resulting from GPS measurement. The texture information derived from a segment was then matched with the diversity indices of sampling plots which are situated in that segment. If a segment has more than one sampling plot, the arithmetic mean of these plots was used in the correlation analysis.

The textural properties of the segments from the eleven segmented images were determined based on the approach of “Texture after Haralick”. The texture parameters were calculated for all pixels of an image object based on the grey level co-occurrence matrix (GLCM) that is a tabulation of how often different combinations of pixel grey levels occur, in a given direction, in an image object. The grey-level co-occurrence matrix can reveal certain attributes pertaining to the spatial distribution of the grey levels in an image object. Several statistical measures can be also derived from the GLCM. Hall-Beyer (2007) separated these into three main groups; i) contrast, ii) orderliness and iii) descriptive statistics [

Three GLCM contrast parameters were calculated for each band (0.52-0.60 μm, 0.63-0.69 μm and 0.76-0.86 μm) separately. Since a single GLCM using one direction might not be enough to describe the textural features of an image object, the three GLMC operations were performed based on the four directions as horizontally (90°), vertically (0°), and two diagonally (45° and 135°) [

Every GLCM is normalized according to the formula below [

Where; i: the row number; j: the column number; Vij; the value in the cell i,j of the matrix; Pij: the normalized value in the cell i,j; N: the number of rows or columns.

The GLCM homogeneity measures the closeness of the distribution of elements in the GLCM to the GLCM diagonal. The GLCM homogeneity of an image object is high if GLCM concentrates along the diagonal. It decreases exponentially according to their distance to the diagonal. The formula of GLCM homogeneity is [

GLMC Contrast is a measure of the amount of local variations in the grey-level co-occurrence matrix. It is the opposite of the GLMC Homogeneity and increases exponentially when i-j increases. The formula of GLMC Contrast is [

The Dissimilarity is similar to GLMC Contrast, however increases linearly as i-j increases. It is high if the local region has a high contrast. The formula of GLMC Dissimilarity is [

Correlation analysis was performed to determine if there was a significant relationship between the texture parameters and the diversity indices. Pearson's product moment correlation coefficient (r) was used to assess the relationship between the variables. A two-tailed p value was used to calculate statistical significance; a value of P<0.05 was taken to be significant. Therefore, the variables that show the highest correlation were identified to establish a regression model for mapping tree size diversity. The Gini coefficient was used as the dependent variable while the texture parameter was used as an explanatory variable for the regression model. Since the linear regression analysis assumes all variables have a normal distribution, the Kolmogorov-Simirnof Z test was applied to the variables. If P>0.05, a variable was accepted as having a normal distribution. The significance of the slope of the regression line was determined by the t-statistic.

An initial examination of the relationship between Gini coefficient and Shannon index values for the sample plots showed this to be non-linear. Therefore a Spearman's correlation test was used to determine the strength of the relationships between these two variables, with a correlation to rs = 0.55 (P<0.01).

Statistically significant linear relationships occurred between the textural parameters and the diversity indices, with the Gini coefficient more strongly correlated (highest r = 0.69, P < 0.01) than the Shannon index (highest r = 0.35, P < 0.01) with image derived texture parameters, and for this reason the rest of this paper focuses on the Gini coefficient. For the Gini coefficient, the strongest correlations were with a scale parameter of 40 (

The texture parameters of the GLCM Homogeneity and the GLCM Dissimilarity derived from the green band (0.52-0.60 μm) were most strongly correlated with the Gini coefficient, while for GLCM Contrast derived from the red band (0.63-0.69 μm) were most strongly correlated with the Gini coefficient (

As a result, the GLCM Homogeneity values of band 1 (0.52-0.60) derived from the segments generated by scale parameter of 40 were chosen as the explanatory variable for predicting the Gini coefficient because the highest r value were obtained from this relation. Therefore, a regression analysis was performed to determine the ability of GLCM Homogeneity to predict Gini coefficient values and be used as a tool for mapping tree size diversity. Least squares linear regression analysis was used because visual examination of the scatter plot suggested that relations between the GLCM Homogeneity and the Gini coefficient were linear (

The regression equation is;

This regression equation was then used to produce a spatial map of tree size diversity (

Lexerod and Eid (2006) observed a stronger correlation between the Shannon index and Gini coefficient than observed here (rs = 0.73 cf. 0.55). This difference may result from differences in the tree species and forest conditions of the two study regions. For example, the typical Mediterranean forests we studied have degraded stands because of long-standing anthropogenic impacts, with crown closure of less than 40 %. In the area studied here, the average tree density in degraded stands is 150 individuals ha-1 which results in a small number of trees inside a sampling plot. Thus, Shannon index values calculated from these sampling points may be influenced by sample size thus weakening the relation between Gini coefficient and Shannon index. The other reason may be differences in size classes used. Lexerod and Eid (2006) used 2 cm diameter class width while 4 cm was used here, which might also influence the association between the Gini coefficient and Shannon index [

The most appropriate diameter class width to use for calculating the Shannon index is still under debate [

The greater utility of Gini coefficient in determining tree size diversity based on basal area measurements in comparison to other indices such as Shannon index, Simpson, Margalef, McIntosh, Berger-Parker, Shannon evenness and McIntosh evenness has been emphasized in other studies [e.g., 7]. Consequently, it can be concluded that the Gini coefficient which does not require an arbitrary classified diameter classes is a more reliable parameter for quantifying tree size diversity. Furthermore, existing sample plot data measured during timber management plan inventories can be used in the calculation of the Gini coefficient because it this coefficient is less sensitive to the number of trees in a sampling plot.

A remarkable result from this study is that the relation between the Gini coefficient and the GLCM Homogeneity and Dissimilarity values of the green band (0.52-0.60) is stronger than the other bands, while the texture parameters of the NIR band (0.76-0.86 μm) are the least strongly correlated. Surprisingly, the NIR band yields extreme GLCM Contrast texture values in the segments which are adjacent to the border of study area. The superiority of the green band might arise from its sensitiveness to the bare soil and exposed rocks. Therefore, it can be concluded that the unmanaged stands with high tree size diversity and less crown closure yield a more textured image in the green part of electromagnetic spectrum due to its distinction capability. In visually inspection on the image, it is easily noticed that the NIR band in some segments exhibits uncongenial texture values compared with bands 1 and 2. The two image segments belonging to both an unmanaged mixed stand consisted of

The results presented here indicate that Aster imagery with 15 m resolution is promising for estimating the Gini coefficient as an indicator of tree size diversity for heterogeneous Mediterranean landscapes. The highest correlation coefficient (r=0.69) was found between the GLCM Homogeneity of band 1 and the Gini coefficient using the segments generated by the scale parameter of 40. This relation can allow mapping tree size diversity based on Gini coefficient over large geographical areas. Such satellite derived maps can then be used to evaluate changes in tree size diversity over time and to determine the impact of different management interventions on tree size diversity at the landscape scale. We are not aware of any other research that has investigated the relationship between tree size diversity and remote sensing imagery. Therefore, this study suggests another and potentially very important application of remote sensing in forest management and conservation.

When the extreme values in the scatter plots of GLCM Homogeneity of band 1 against Gini coefficient are inspected, two stand types are evident as outliers. These are; i) mature

This study has used the available forest survey data measured as part of forest management plan preparation. It can be expected that a stronger relation might be found if a more targeted sampling approach [e.g., 37] was undertaken aimed at measuring the Gini coefficient. We recommend the scale parameter of 40 for mapping the Gini coefficient if the existing sampling plot data is used. However, if a specific sampling strategy apart from the traditional one is undertaken as the basis for determining the Gini coefficient, then smaller segment scales might be more suitable for modeling the Gini coefficient.

The satellite data used in this project was acquired in spring (May). As the spectral differences among plant communities are dependent on the season, estimation of Gini coefficient by means of satellite data might be more reliable if data from other seasons was also used. In addition to the textural parameters belonging to “contrast” group, the measures of “orderliness” and “descriptive statistics” groups can be also investigated in any further studies.

The author wish to thank the forest engineers in the forest management teams of Isparta who are Cahit Sahin, Hayrettin Kucuk, Mehmet Yigici, Barıs Cicek and Tefik Buyukgebiz for supplying the sampling plot data.

The Pearson's correlation coefficients of the relations between the Gini coefficient and the texture parameters for the eleven segment level with regard to the bands.

The scatter plot of Gini Coefficient values against the corresponding GLCM Homogeneity values of band 1.

A tree size diversity map based on the Gini coefficient values estimated from GLCM Homogeneity values of the image segments of band 1 generated from scale parameter of 40.

The spectral responses and Gini coefficient of the typical both managed and unmanaged stands and their corresponding texture values with regard to the bands.

Two example stands with low tree size diversity, however, some types of maquis vegetation enter into the brutian stands because they reach enough light. Therefore, they creates more textured image due to their varying spectral characteristics.

The Gini and Shannon values calculated from the three example plot data which represent low, moderate and high tree size diversity.

DBH (cm) | GINI coefficient | Shannon Index | ||||
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Diameter Class Width (cm) | ||||||

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1 | 2 | 3 | 4 | |||

Low tree size diversity | 20;20;21;22;23;24;25;26;26;26;27;28;28 29;30;30;31;31;31;32;32;32;32;33;33;33 | 0.167 | 2.367 | 1.705 | 1.462 | 1.293 |

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Moderate tree size diversity | 20;20;21;22;23;24;25;27;27;27;29;30;32 32;32;34;35;36;38;38;39;40;40;40;45;45 | 0.270 | 2.553 | 2.304 | 1.883 | 1.875 |

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High tree size diversity | 09;10;10;18;20;20;28;28;28;37;37;38;45 45;45;53;53;53;60;61;61;61;82;82;83;83 | 0.496 | 2.058 | 1.637 | 1.928 | 1.593 |