Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock

Immitzer, Markus; Stepper, Christoph; Böck, Sebastian; Straub, Christoph; Atzberger, Clement

doi:10.1016/j.foreco.2015.10.018

Cited by 82 publications

(61 citation statements)

References 58 publications

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“…In relative terms, it is also lower than Ozdemir and Karnieli [17], who reported 44% RMSE (27 m 3 ·ha −1 ) for VOL, based on WorldView-2 imagery of an Israeli pine plantation forest. Immitzer et al [21] estimated the VOL with 120 m 3 ·ha −1 (32%) from WorldView-2 data in a German forest. Moreover, Maack et al [22] used Pléiades data to estimate AGB at a test site in Chile, and achieved 59 tons·ha −1 (36%).…”

Section: Discussionmentioning

confidence: 99%

“…A few studies have shown these to be of similar importance as the image-matched height metrics [21,22] and some other are based purely on textural analysis [15,17,19]. The literature honour co-occurrence analysis, and specifically the grey-level co-occurrence matrix (GLCM) method with its related attributes.…”

Section: Textural Metricsmentioning

confidence: 99%

“…The model combining height metrics and spectral derivatives had 47 tons·ha −1 (24%) RMSE at the German test site and 59 tons·ha −1 (36%) at the test site in Chile. Both Immitzer et al [21] and Maack et al [22] obtained their main model contributions from the height metrics, while the spectral/textural contributions were rather limited. In Finland, Yu et al [7] used WorldView-2 data to evaluate forest attributes in boreal forest.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Persson

2016

Remote Sensing

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Abstract:In this study, the potential of using very high resolution Pléiades imagery to estimate a number of common forest attributes for 10-m plots in boreal forest was examined, when a high-resolution terrain model was available. The explanatory variables were derived from three processing alternatives. Height metrics were extracted from image matching of the images acquired from different incidence angles. Spectral derivatives were derived by performing principal component analysis of the spectral bands and lastly, second order textural metrics were extracted from a gray-level co-occurrence matrix, computed with an 11 × 11 pixels moving window. The analysis took place at two Swedish test sites, Krycklan and Remningstorp, containing boreal and hemi-boreal forest. The lowest RMSE was estimated with 1.4 m (7.7%) for Lorey's mean height, 1.7 m (10%) for airborne laser scanning height percentile 90, 5.1 m 2 ·ha −1 (22%) for basal area, 66 m 3 ·ha −1 (27%) for stem volume, and 26 tons·ha −1 (26%) for above-ground biomass, respectively. It was found that the image-matched height metrics were most important in all models, and that the spectral and textural metrics contained similar information. Nevertheless, the best estimations were obtained when all three explanatory sources were used. To conclude, image-matched height metrics should be prioritised over spectral metrics when estimation of forest attributes is concerned.

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Section: Discussionmentioning

confidence: 99%

Section: Textural Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Persson

2016

Remote Sensing

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“…For the classification and feature selection we used the Random Forest (RF) classifier [47]. This machine learning algorithm consists of an ensemble of decision trees and is currently widely used in remote sensing [25,27,[48][49][50]. RF was chosen for classification as the algorithm can deal with few training data, multi-modal classes, and non-normal data distributions [47,51].…”

Section: Combinationsmentioning

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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“…Straub et al (2013) used Cartosat-1 and WorldView-2 (WV2) stereo images to estimate growing stock in a mixed German forest. Immitzer et al (2016) used WV2 stereo images in combination with national forest inventory data to map growing stock wall-to-wall in Bavaria, Germany.…”

Section: Introductionmentioning

confidence: 99%

Assessment of boreal forest height from WorldView-2 satellite stereo images

Persson

Perko

2016

Remote Sensing Letters

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WorldView-2 data are sometimes unstable for canopy top height estimations (ALS height percentile 100, p100) and that the reconstructed heights are generally located below the actual top height. The WorldView-2 p60 was found to correlate best with ALS p70 in Remningstorp, while WorldView-2 p95 was found to correlate best with ALS p70 in Krycklan, and it moreover reached the highest correlation for all other estimated variables, at both test sites. It was concluded, that WorldView-2 p95 height data overall represents approximately the forest height ALS p70. The overall high correlation coefficients above 0.90 at both test sites, with different forest conditions, indicate that stereo matching of WorldView-2 satellite images is suitable for forest height mapping.

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Use of WorldView-2 stereo imagery and National Forest Inventory data for wall-to-wall mapping of growing stock

Cited by 82 publications

References 58 publications

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

Estimation of Boreal Forest Attributes from Very High Resolution Pléiades Data

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

Assessment of boreal forest height from WorldView-2 satellite stereo images

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