On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation

Ouma, Yashon O.; Ngigi, Thomas G.; Tateishi, Ryosuke

doi:10.1080/01431160500295885

Cited by 48 publications

(16 citation statements)

References 46 publications

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“…We applied a discrete stationary wavelet transformation with Coiflets (coif1) [53] at four different scales. After the fourth scale a natural convergence occurs, therefore only four transformation passages were performed [103]. To reduce the data amount, the mean of the three directions was calculated for each level, assuming no preferred direction of windthrow patterns.…”

Section: Index Equation Referencementioning

confidence: 99%

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann

Immitzer

Böck

et al. 2017

Forests

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With climate change, extreme storms are expected to occur more frequently. These storms can cause severe forest damage, provoking direct and indirect economic losses for forestry. To minimize economic losses, the windthrow areas need to be detected fast to prevent subsequent biotic damage, for example, related to beetle infestations. Remote sensing is an efficient tool with high potential to cost-efficiently map large storm affected regions. Storm Niklas hit South Germany in March 2015 and caused widespread forest cover loss. We present a two-step change detection approach applying commercial very high-resolution optical Earth Observation data to spot forest damage. First, an object-based bi-temporal change analysis is carried out to identify windthrow areas larger than 0.5 ha. For this purpose, a supervised Random Forest classifier is used, including a semi-automatic feature selection procedure; for image segmentation, the large-scale mean shift algorithm was chosen. Input features include spectral characteristics, texture, vegetation indices, layer combinations and spectral transformations. A hybrid-change detection approach at pixel-level subsequently identifies small groups of fallen trees, combining the most important features of the previous processing step with Spectral Angle Mapper and Multivariate Alteration Detection. The methodology was evaluated on two test sites in Bavaria with RapidEye data at 5 m pixel resolution. The results regarding windthrow areas larger than 0.5 ha were validated with reference data from field visits and acquired through orthophoto interpretation. For the two test sites, the novel object-based change detection approach identified over 90% of the windthrow areas (≥0.5 ha). The red edge channel was the most important for windthrow identification. Accuracy levels of the change detection at tree level could not be calculated, as it was not possible to collect field data for single trees, nor was it possible to perform an orthophoto validation. Nevertheless, the plausibility and applicability of the pixel-based approach is demonstrated on a second test site.

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Section: Index Equation Referencementioning

confidence: 99%

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Einzmann

Immitzer

Böck

et al. 2017

Forests

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“…The panchromatic band is reported to be particularly well suited for the analysis of spatial relationships using image textural measures [22,[50][51][52]. As a result, we only extracted the second-order textural measures from the panchromatic band for each plot in comparison to the spectral and first-order textural measures.…”

Section: Textural Measuresmentioning

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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“…HSR imagery facilitates, for instance, the detection of individual tree characteristics [14], providing improved estimates of forest structural attributes [7]. 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].…”

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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On the optimization and selection of wavelet texture for feature extraction from high‐resolution satellite imagery with application towards urban‐tree delineation

Cited by 48 publications

References 46 publications

Windthrow Detection in European Forests with Very High-Resolution Optical Data

Windthrow Detection in European Forests with Very High-Resolution Optical Data

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)

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