Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Schumacher, Paul; Mislimshoeva, Bunafsha; Brenning, Alexander; Zandler, Harald; Brandt, Martin; Samimi, Cyrus; Koellner, Thomas

doi:10.3390/rs8070540

Cited by 42 publications

(20 citation statements)

References 74 publications

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“…The vegetation indices used in this study were chosen based on their optimal performance in literature [17,22]. Formulae for computing vegetation indices are detailed in Schumacher et al [38]. The window sizes for deriving the grey-level co-occurrence texture models used in this study were 3 × 3, 5 × 5 and 7 × 7 pixels [39,40].…”

Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

“…The texture models computed in this study were mean, variance, homogeneity, contrast, dissimilarity, entropy, second moment and correlation. More details about the formulae for computing these texture models are summarised in Dube and Mutanga [30], as well as Schumacher et al [38]. All the variables used in this study, and the formulae used to compute them, are detailed in Table 2.…”

Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

“…Wallis [31] Kelsey et al [53] Schumacher et al [38] Ouma et al [54] Salas et al [33] Zhao et al [21] Variance…”

Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

“…These combinations were derived from literature [30,38]. Analysis of variance (ANOVA) was used after the normality test and it indicated that the data did not significantly deviate from the normal distribution.…”

Section: Phases Of Estimating Above-ground Grass Biomassmentioning

confidence: 99%

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Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

et al. 2017

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Abstract:The ability of texture models and red-edge to facilitate the detection of subtle structural vegetation traits could aid in discriminating and mapping grass quantity, a challenge that has been longstanding in the management of grasslands in southern Africa. Subsequently, this work sought to explore the robustness of integrating texture metrics and red-edge in predicting the above-ground biomass of grass growing under different levels of mowing and burning in grassland management treatments. Based on the sparse partial least squares regression algorithm, the results of this study showed that red-edge vegetation indices improved above-ground grass biomass from a root mean square error of perdition (RMSEP) of 0.83 kg/m 2 to an RMSEP of 0.55 kg/m 2 . Texture models further improved the accuracy of grass biomass estimation to an RMSEP of 0.35 kg/m 2 . The combination of texture models and red-edge derivatives (red-edge-derived vegetation indices) resulted in an optimal prediction accuracy of RMSEP 0.2 kg/m 2 across all grassland management treatments. These results illustrate the prospect of combining texture metrics with the red-edge in predicting grass biomass across complex grassland management treatments. This offers the detailed spatial information required for grassland policy-making and sustainable grassland management in data-scarce regions such as southern Africa.

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Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

“…Wallis [31] Kelsey et al [53] Schumacher et al [38] Ouma et al [54] Salas et al [33] Zhao et al [21] Variance…”

Section: Modelling Above-ground Grass Biomassmentioning

confidence: 99%

Section: Phases Of Estimating Above-ground Grass Biomassmentioning

confidence: 99%

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Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

et al. 2017

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“…In our study, we applied VHR RapidEye data comprising a red edge channel that was centered at 710 nm. Recent studies have shown the performance of red edge bands in facilitating important information on the state of vegetation and their benefit for vegetation monitoring [29][30][31]. In 2015, Sentinel-2A satellite has been launched.…”

Section: Introductionmentioning

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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Dynamic assessment of restored ecosystem health based on pressure‐state‐response subsystem‐system model in a karst trough valley

Qiong,

Yongjun,

Yina

et al. 2023

Ecohydrology

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Limited soil and water resources in a sloping karst trough valley have negatively affected the local population and ecosystem health. A deeper understanding of karst ecosystem health and its driving mechanism are important for ecosystem management and restoration. In recent decades, the karst trough valley has been exposed to multiple threats from various natural and anthropogenic factors. Assessment of which is essential to understand the state of the karst ecosystem and to develop a suitable, scientifically proven management strategy. Based on the pressure‐state‐response‐subsystem‐system (PSR‐SS) model, the restored ecosystem health of karst trough valley was assessed from 2000 to 2015 at the grid scale. The principal component analysis (PCA) method was adopted to explore the factors driving ecosystem health change. It is found that from the years of 2000 to 2015, there was an improvement in ecosystem health in karst trough valley ecosystem. Land use analysis maps indicate a slight decrease in woodland and grassland and increase in cropland and urban buildup, and the Land coverage increased from 84.87% to 87.64%. The area of karst rock desertification decreased by 75.3 km2 from 2000 to 2015. The AEH indices of 0.27 (2000), 0.45 (2005), 0.46 (2010) and 0.68 (2015), which indicate moderate warning, pre‐warning, early warning and generally healthy, respectively. The 17 selected driving factors were grouped into two broad categories by PCA, one is national policy, and the other one is natural condition and local activities. National policy was to be found the most important influence on ecosystem health. Our findings emphasize the key role of government in ecosystem health change, which would give directions to the sustainable development of karst trough valley areas.

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Do Red Edge and Texture Attributes from High-Resolution Satellite Data Improve Wood Volume Estimation in a Semi-Arid Mountainous Region?

Cited by 42 publications

References 74 publications

Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

Estimating Biomass of Native Grass Grown under Complex Management Treatments Using WorldView-3 Spectral Derivatives

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

Dynamic assessment of restored ecosystem health based on pressure‐state‐response subsystem‐system model in a karst trough valley

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