Estimation of Tree Size Diversity Using Object Oriented Texture Analysis and Aster Imagery

Özdemir, İbrahim; Norton, David A.; Özkan, Ulaş Yunus; Mert, Ahmet; Şentürk, Özdemir

doi:10.3390/s8084709

Cited by 48 publications

(26 citation statements)

References 24 publications

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“…Only a few studies have investigated the extraction of the more complex structural variables such as tree size diversity and tree position diversity [13,76,77]. However, these complex structural variables are significant in the development of management plans, especially for multipurpose forests, and are usually more expensive and time-consuming to collect in a field survey.…”

Section: Discussionmentioning

confidence: 99%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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Uneven-aged forest management has received increasing attention in the past few years. Compared with even-aged plantations, the complex structure of uneven-aged forests complicates the formulation of management strategies. Forest structural diversity is expected to provide considerable significant information for uneven-aged forest management planning. In the present study, we investigated the potential of using SPOT-5 satellite images for extracting forest structural diversity. Forest stand variables were calculated from the field plots, whereas spectral and textural measures were derived from the corresponding satellite images. We firstly employed Pearson's correlation analysis to examine the relationship between the forest stand variables and the image-derived measures. Secondly, we performed all possible subsets multiple linear regression to produce models by including the image-derived measures, which showed significant correlations with the forest stand variables, used as independent variables. The produced models were evaluated with the adjusted coefficient of determination (R 2 adj ) and the root mean square error (RMSE). Furthermore, a ten-fold cross-validation approach was used to validate the best-fitting models (R 2 adj > 0.5). The results indicated that basal area, stand volume, the Shannon index, Simpson index, Pielou index, standard deviation of DBHs, diameter differentiation index and species intermingling index could be reliably predicted using the spectral or textural measures extracted from SPOT-5 satellite images.

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Section: Discussionmentioning

confidence: 99%

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Meng

Wang

et al. 2016

Remote Sensing

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“…Frequent techniques to obtain information from HSR images include crown isolation [26,27], shadow analysis [18,28], texture analysis [13,29,30], and geostatistical approaches [31][32][33]. The capacity to characterize forest structural attributes typically decreases as crown closure increases [6], with an asymptotic relationship predictably emerging for vertically distributed attributes of forest structure [34].…”

Section: Hsr Related To Forest Structurementioning

confidence: 99%

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Gómez

Wulder

Montes³

et al. 2012

Remote Sensing

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Abstract:Forest structural parameters such as quadratic mean diameter, basal area, and number of trees per unit area are important for the assessment of wood volume and biomass and represent key forest inventory attributes. Forest inventory information is required to support sustainable management, carbon accounting, and policy development activities. Digital image processing of remotely sensed imagery is increasingly utilized to assist traditional, more manual, methods in the estimation of forest structural attributes over extensive areas, also enabling evaluation of change over time. Empirical attribute estimation with remotely sensed data is frequently employed, yet with known limitations, especially over complex environments such as Mediterranean forests. In this study, the capacity of high spatial resolution (HSR) imagery and related techniques to model structural parameters at the stand level (n = 490) in Mediterranean pines in Central Spain is tested using data from the commercial satellite QuickBird-2. Spectral and spatial information derived from multispectral and panchromatic imagery (2.4 m and 0.68 m sided pixels, respectively) served to model structural parameters. Classification and Regression Tree Analysis (CART) was selected for the modeling of attributes. Accurate models were produced of quadratic mean diameter (QMD) (R 2 = 0.8; RMSE = 0.13 m) with an average error of 17% while basal area (BA) models produced an average error of 22% (RMSE = 5.79 m 2 /ha). OPEN ACCESS Remote Sens. 2012, 4 136When the measured number of trees per unit area (N) was categorized, as per frequent forest management practices, CART models correctly classified 70% of the stands, with all other stands classified in an adjacent class. The accuracy of the attributes estimated here is expected to be better when canopy cover is more open and attribute values are at the lower end of the range present, as related in the pattern of the residuals found in this study. Our findings indicate that attributes derived from HSR imagery captured from space-borne platforms have capacity to inform on local structural parameters of Mediterranean pines. The nascent program for annual national coverages of HSR imagery over Spain offers unique opportunities for forest structural attribute estimation; whereby, depletions can be readily captured and successive annual collections of data can support or enable refinement of attributes. Further, HSR imagery and associated attribute estimation techniques can be used in conjunction, not necessarily in competition to, more traditional forest inventory with synergies available through provision of data within an inventory cycle and the capture of forest disturbance or depletions.

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“…In image segmentation, firstly the spectral bands to use must be selected and weights of such bands in the segmentation must be defined. Then, the scale parameter defining the average size of image objects to be generated and colour and shape criteria, being the homogeneity criteria must be identified (Rego, 2003;Ozdemir et al, 2008). Four bands of Ikonos satellite images (Blue, Green, Red and Near Infra-red) and NDVI Vegetation index were used for image segmentation in study.…”

Section: Classification Of Satellite Imagementioning

confidence: 99%

Producing forest stand maps from Ikonos image based on object-based classification

Özkan¹,

Yeşil²

2016

J FAC FOR ISTANBUL U

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Estimation of Tree Size Diversity Using Object Oriented Texture Analysis and Aster Imagery

Cited by 48 publications

References 24 publications

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Estimation of Forest Structural Diversity Using the Spectral and Textural Information Derived from SPOT-5 Satellite Images

Modeling Forest Structural Parameters in the Mediterranean Pines of Central Spain using QuickBird-2 Imagery and Classification and Regression Tree Analysis (CART)

Producing forest stand maps from Ikonos image based on object-based classification

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