Estimating woody above-ground biomass in an arid zone of central Australia using Landsat imagery

Wang, Zhihui; Bastin, Gary; Liu, Liangyun; Caccetta, Peter; Peng, Dailiang

doi:10.1117/1.jrs.9.096036

Cited by 8 publications

(8 citation statements)

References 15 publications

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“…It has been reported that the woody canopy coverage derived from high-resolution aerial or satellite imagery has a promising potential for accurately estimating the woody AGB of sparse woody vegetation in arid areas [6,30,47,48]. However, in this study, the accuracy of the woody coverage-AGB model was very low for the sparse mixed forest (Figure 8).…”

Section: Applicability Of the Woody Cover-agb Model To Sparse Mixed Forestsmentioning

confidence: 58%

“…However, the experimental results of this study indicated that the inversion accuracy of canopy coverage-AGB model would be reduced in sparse tree-shrub mixed forest. The surface background of herbaceous vegetation affects AGB estimation accuracy of VI-AGB models in dryland areas [30]. From comparing the different common AGB inversion methods, the stratification-based VI-AGB model was proposed to improve the accuracy of biomass inversion in sparse mixed forests in this study.…”

Section: Potential Of Using High-resolution Remote Sensing Images For Agb Estimation Of Sparse Mixed Forestmentioning

confidence: 99%

“…In this situation, the problem of mixed pixels in satellite observations with a medium resolution (e.g., Landsat at the 30 m scale) inevitably led to an unsatisfactory woody AGB estimation accuracy in sparse artificial forests [6,29]. Consequently, linear spectral mixture analysis (LSMA) was used to retrieve the woody canopy coverage, and then, the woody AGB was estimated based on the relationship between the woody canopy coverage and the AGB [13,30]. However, this method is not suitable for sparse forests with woody and dense green herbaceous vegetation because the coverage of such forests cannot be accurately identified using LSMA because their endmember spectra are very similar.…”

Section: Introductionmentioning

confidence: 99%

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Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

et al. 2021

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Sparse mixed forest with trees, shrubs, and green herbaceous vegetation is a typical landscape in the afforestation areas in northwestern China. It is a great challenge to accurately estimate the woody aboveground biomass (AGB) of a sparse mixed forest with heterogeneous woody vegetation types and background types. In this study, a novel woody AGB estimation methodology (VI-AGB model stratified based on herbaceous vegetation coverage) using a combination of Landsat-8, GaoFen-2, and unmanned aerial vehicle (UAV) images was developed. The results show the following: (1) the woody and herbaceous canopy can be accurately identified using the object-based support vector machine (SVM) classification method based on UAV red-green-blue (RGB) images, with an average overall accuracy and kappa coefficient of 93.44% and 0.91, respectively; (2) compared with the estimation uncertainties of the woody coverage-AGB models without considering the woody vegetation types (RMSE = 14.98 t∙ha−1 and rRMSE = 96.31%), the woody coverage-AGB models stratified based on five woody species (RMSE = 5.82 t∙ha−1 and rRMSE = 37.46%) were 61.1% lower; (3) of the six VIs used in this study, the near-infrared reflectance of pure vegetation (NIRv)-AGB model performed best (RMSE = 7.91 t∙ha−1 and rRMSE = 50.89%), but its performance was still seriously affected by the heterogeneity of the green herbaceous coverage. The normalized difference moisture index (NDMI)-AGB model was the least sensitive to the background. The stratification-based VI-AGB models considering the herbaceous vegetation coverage derived from GaoFen-2 and UAV images can significantly improve the accuracy of the woody AGB estimated using only Landsat VIs, with the RMSE and rRMSE of 6.6 t∙ha−1 and 42.43% for the stratification-based NIRv-AGB models. High spatial resolution information derived from UAV and satellite images has a great potential for improving the woody AGB estimated using only Landsat images in sparsely vegetated areas. This study presents a practical method of estimating woody AGB in sparse mixed forest in dryland areas.

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Section: Applicability Of the Woody Cover-agb Model To Sparse Mixed Forestsmentioning

confidence: 58%

Section: Potential Of Using High-resolution Remote Sensing Images For Agb Estimation Of Sparse Mixed Forestmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

et al. 2021

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“…Time series derived from satellite data can identify both rapid and longer-term changes in vegetation cover [ 36 , 37 ]. Spectral information from optical satellite images allows for the quantitative estimation of biophysical vegetation characteristics, such as vegetation cover [ 38 ], aboveground biomass [e.g., 38 , 39 ], leaf area, and leaf chlorophyll concentration [e.g., 39 ] among others. This remotely sensed information can be used to quantify, map, and monitor provisioning, regulating, and (to a lesser extent) cultural ecosystem services [ 40 ].…”

Section: Introductionmentioning

confidence: 99%

Long-term assessment of ecosystem services at ecological restoration sites using Landsat time series

et al. 2021

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Reversing ecological degradation through restoration activities is a key societal challenge of the upcoming decade. However, lack of evidence on the effectiveness of restoration interventions leads to inconsistent, delayed, or poorly informed statements of success, hindering the wise allocation of resources, representing a missed opportunity to learn from previous experiences. This study contributes to a better understanding of spatial and temporal dynamics of ecosystem services at ecological restoration sites. We developed a method using Landsat satellite images combined with a Before-After-Control-Impact (BACI) design, and applied this to an arid rural landscape, the Baviaanskloof in South Africa. Since 1990, various restoration projects have been implemented to halt and reverse degradation. We applied the BACI approach at pixel-level comparing the conditions of each intervened pixel (impact) with 20 similar control pixels. By evaluating the conditions before and after the restoration intervention, we assessed the effectiveness of long-term restoration interventions distinguishing their impact from environmental temporal changes. The BACI approach was implemented with Landsat images that cover a 30-year period at a spatial resolution of 30 meter. We evaluated the impact of three interventions (revegetation, livestock exclusion, and the combination of both) on three ecosystem services; forage provision, erosion prevention, and presence of iconic vegetation. We also evaluated whether terrain characteristics could partially explain the variation in impact of interventions. The resulting maps showed spatial patterns of positive and negative effects of interventions on ecosystem services. Intervention effectiveness differed across vegetation conditions, terrain aspect, and soil parent material. Our method allows for spatially explicit quantification of the long-term restoration impact on ecosystem service supply, and for the detailed visualization of impact across an area. This pixel-level analysis is specifically suited for heterogeneous landscapes, where restoration impact not only varies between but also within restoration sites.

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“…Spectral information from optical satellite images allows for the quantitative estimation of biophysical vegetation characteristics, such as vegetation cover [e.g. 40], aboveground biomass [e.g. 41,42], leaf area, and leaf chlorophyll concentration [e.g.…”

Section: Introductionmentioning

confidence: 99%

Long-term assessment of ecosystem services at ecological restoration sites using Landsat time series

Rio

Willemen

Vrieling

et al. 2020

Preprint

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Reversing ecological degradation through restoration activities is a key societal challenge of the upcoming decade. However, lack of evidence on the effectiveness of restoration interventions leads to inconsistent, delayed, or poorly informed statements of success, hindering the wise allocation of resources, representing a missed opportunity to learn from previous experiences. This study contributes to a better understanding of spatial and temporal dynamics of ecosystem services at ecological restoration sites. We developed a method using Landsat satellite images combined with a Before-After-Control-Impact (BACI) design, and applied this to an arid rural landscape, the Baviaanskloof in South Africa. Since 1990, various restoration projects have been implemented to halt and reverse degradation. We applied the BACI approach at pixel-level comparing the conditions of each intervened pixel (impact) with 20 similar control pixels. By evaluating the conditions before and after the intervention, we assessed the effectiveness of long-term restoration interventions distinguishing their impact from environmental temporal changes. The BACI approach was implemented with Landsat images that cover a 30-year period at a spatial resolution of 30 m. We evaluated the impact of three interventions (revegetation, livestock exclusion, and the combination of both) on three ecosystem services; forage provision, erosion prevention, and presence of iconic vegetation. We also evaluated whether terrain characteristics could partially explain the variation in impact of interventions. The resulting maps showed spatial patterns of positive and negative effects of interventions on ecosystem services. Intervention effectiveness differed between land cover vegetation clusters, terrain aspect, and soil parent material. Our method allows for spatially explicit quantification of the long-term restoration impact on ecosystem service supply, and for the detailed visualization of impact across an area. This pixel-level analysis is specifically suited for heterogeneous landscapes, where restoration impact not only varies between but also within restoration sites.

show abstract

Estimating woody above-ground biomass in an arid zone of central Australia using Landsat imagery

Cited by 8 publications

References 15 publications

Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

Improving Estimation of Woody Aboveground Biomass of Sparse Mixed Forest over Dryland Ecosystem by Combining Landsat-8, GaoFen-2, and UAV Imagery

Long-term assessment of ecosystem services at ecological restoration sites using Landsat time series

Long-term assessment of ecosystem services at ecological restoration sites using Landsat time series

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