Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests

Özdemir, İbrahim

doi:10.1080/01431160802082155

Cited by 72 publications

(43 citation statements)

References 22 publications

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“…Panchromatic imagery, with fine spatial resolution (< 1 m) is particularly well suited for analysis of spatial relations through image texture measures [15,16]. Texture measures enable the combination of spatial detail of panchromatic imagery with unique spectral information conferred by multispectral imagery serving to leverage complementary information [17] that can be employed separately or with a pan-sharpening approach [18,19]. Spectral measures may be understood to inform on vegetation status, type, and condition with textural measures informing on vegetation structure.…”

Section: High Spatial Resolution (Hsr) Imagerymentioning

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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Section: High Spatial Resolution (Hsr) Imagerymentioning

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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“…In high spatial resolution images, shadows have completely different spectral signatures with leaves or braches, as shown in Figure 8a,c, resulting in high heterogeneity in the spectral signatures within the same forest cover. The shadows from different tall objects have various spectral signatures due to the impacts of different tree heights and crown sizes, topography, and sun elevation angles [68]. In order to reduce the shadow problem, different approaches such as vegetation indices and textures may be used, but shadow areas from high spatial resolution images are so large that we have to select shadows as special classes to handle.…”

Section: Discussionmentioning

confidence: 99%

“…The shadows from different tall objects have various spectral signatures due to the impacts of different tree heights and crown sizes, topography, and sun elevation angles [68]. In order to reduce the shadow problem, different approaches such as vegetation indices and textures may be used, but shadow areas from high spatial resolution images are so large that we have to select shadows as special classes to handle.…”

Section: Discussionmentioning

confidence: 99%

Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

Wang

2017

Remote Sensing

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Rapid expansion of Torreya forests in the mountainous region in Zhejiang Province in the past three decades has produced many environmental problems such as soil erosion and poor water quality, requiring an update of its spatial distribution in a timely way. However, to date there are no suitable approaches available for mapping Torreya forest distribution, especially the new Torreya plantations, due to the complex landscapes. This research used high spatial resolution Chinese Gaofen (GF-1) and Ziyuan (ZY-3) satellite images and digital elevation model (DEM) data to extract old Torreya forests and new Torreya plantations using a newly proposed expert rules-based approach. Different variables such as spectral bands, vegetation indices, textural images, and DEM-derived variables were examined, and separability analyses of different land covers were explored. An expert rules-based approach was developed for the extraction of old Torreya forests and new Torreya plantations. The accuracy assessment using field survey data and Google Earth images indicates that this newly-proposed approach can effectively distinguish both old Torreya forests and new Torreya plantations from other land covers with producer's accuracies of 84% and 92%, and user's accuracies of 77% and 85%, respectively, much better classification accuracies than the maximum likelihood classifier. This new approach may be used for other study area for extracting Torreya forest distribution. This research provides valuable data sources for better managing existing Torreya forests and planning potential Torreya expansions in this region in the near future.

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“…For example, Ozdemir [24] reports a RMSE of approximately 13% for the estimation of stem volume of sparse vegetation dominated by Juniperus spp. on the basis of QuickBird imagery in western Turkey.…”

Section: Discussionmentioning

confidence: 99%

“…The single-tree approach based on canopy delineation can be used as a predictor to estimate AGB, but this procedure is complex and computing intensive when implemented on large regions [24,25]. For this reason, it is particularly interesting to investigate if a more simple area-based approach can lead to similar results.…”

Section: Introductionmentioning

confidence: 99%

Biomass Estimation of Xerophytic Forests Using Visible Aerial Imagery: Contrasting Single-Tree and Area-Based Approaches

2017

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A large part of arid areas in tropical and sub-tropical regions are dominated by sparse xerophytic vegetation, which are essential for providing products and services for local populations. While a large number of researches already exist for the derivation of wall-to-wall estimations of above ground biomass (AGB) with remotely sensed data, only a few of them are based on the direct use of non-photogrammetric aerial photography. In this contribution we present an experiment carried out in a study area located in the Santiago Island in the Cape Verde archipelago where a National Forest Inventory (NFI) was recently carried out together with a new acquisition of a visible high-resolution aerial orthophotography. We contrasted two approaches: single-tree, based on the automatic delineation of tree canopies; and area-based, on the basis of an automatic image classification. Using 184 field plots collected for the NFI we created parametric models to predict AGB on the basis of the crown projection area (CPA) estimated from the two approaches. Both the methods produced similar root mean square errors (RMSE) at pixel level 45% for the single-tree and 42% for the area-based. However, the latest was able to better predict the AGB along all the variable range, limiting the saturation problem which is evident when the CPA tends to reach the full coverage of the field plots. These findings demonstrate that in regions dominated by sparse vegetation, a simple aerial orthophoto can be used to successfully create AGB wall-to-wall predictions. The level of these estimations' uncertainty permits the derivation of small area estimations useful for supporting a more correct implementation of sustainable management practices of wood resources.

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Estimating stem volume by tree crown area and tree shadow area extracted from pan‐sharpened Quickbird imagery in open Crimean juniper forests

Cited by 72 publications

References 22 publications

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

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

Mapping Torreya grandis Spatial Distribution Using High Spatial Resolution Satellite Imagery with the Expert Rules-Based Approach

Biomass Estimation of Xerophytic Forests Using Visible Aerial Imagery: Contrasting Single-Tree and Area-Based Approaches

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