Estimation of aboveground biomass of vegetation in the desert-oasis ecotone on the northeastern edge of the Ulan Buh Desert

Jingyun, 叶静芸 Ye; Bo, 吴波 Wu; Minghu, 刘明虎 Liu; Ying, 高莹 Gao; Junliang, 高君亮 Gao; Yuancai, 雷渊才 Lei

doi:10.5846/stxb201612312719

Cited by 4 publications

(2 citation statements)

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“…The location of the shrub community was measured using the Real-time Kine Matic (RTK) system. The above ground biomass of the shrubs were obtained based on the allometric equation of shrubs in the area (Table 1) [34]. The AGB for 228 PSPs was obtained by adding up the plot-level biomass that ranged from 0.0054 ton ha −1 (0.54 g m −2 ) to 5.428 ton ha −1 (542.80 g m −2 ).…”

Section: Study Areamentioning

confidence: 99%

Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

et al. 2023

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Aboveground biomass (AGB) of shrub communities in the desert is a basic quantitative characteristic of the desert ecosystem and an important index to measure ecosystem productivity and monitor desertification. An accurate and efficient method of predicting the AGB of a shrub community is essential for studying the spatial patterns and ecological functions of the desert region. Even though there are several entries in the literature on the AGB prediction of desert shrub communities using remote sensing data, the applicability and accuracy of airborne LiDAR data and prediction methods have not been well studied. We first extracted the elevation, density and intensity variables based on the airborne LiDAR, and then sample plot-level AGB prediction models were constructed using the parametric regression (nonlinear regression) and nonparametric methods (Random Forest, Support Vector Machine, K-Nearest Neighbor, Gradient Boosting Machine, and Multivariate adaptive regression splines). We evaluated accuracies of all the AGB prediction models we developed based on the fit statistics. Results showed that: (1) the elevation, density and intensity variables obtained from LiDAR point cloud data effectively predicted the AGB of the desert shrub community at a sample plot level, (2) the kappa coefficient of nonlinear mixed-effects (NLME) model obtained was 0.6977 with an improvement by 13% due to the random effects included into the model, and (3) the nonparametric model, such as Support Vector Machine showed the best fit statistics (R2 = 0.8992), which is 28% higher than the NLME-model, and effectively reduced the heteroscedasticity. The AGB prediction model presented in this paper, which is based on the airborne LiDAR data and machine learning algorithm, will provide a valuable tool to the managers and researchers for evaluating desert ecosystem productivity and monitoring desertification.

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Section: Study Areamentioning

confidence: 99%

Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

et al. 2023

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“…vegetation coverage is often less than 15% [33]. Therefore, allometric equation method is becoming the most pragmatic method to estimate [34] the above-ground biomass of species growing in the Gobi ecosystems.…”

Section: Plos Onementioning

confidence: 99%

Allometric equations for estimating above-ground biomass of Nitraria sibirica Pall. in Gobi Desert of Mongolia

Nyamjav

Batsaikhan

et al. 2020

PLoS ONE

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Nitraria sibirica Pall. is a shrub species belonging to the family of Nitrariaceae. It plays pivotal role in arid ecosystems since it is tolerant to high salinity and drought. This species is widely distributed throughout Mongolia and it is mostly found in arid ecosystems of Mongolian Gobi Desert. In this study, we developed allometric equations for estimating aboveground biomass of N. sibirica using various structural descriptors and pinpointed the best models. Variables that precisely predicted above-ground biomass were a combination of basal diameter, crown area, and height. The allometric growth equation constructed is not merely helpful to achieve accurate estimations of the above-ground biomass in shrub vegetation in the Gobi Desert of Mongolia, but also can provide a reference for the above-ground biomass of Nitraria species growing in analogous habitats worldwide. Therefore, our research purposes an important advance for biomass estimation in Gobi ecosystems and complements previous studies of shrub biomass worldwide. This study provides reasonable estimates of biomass of N. sibirica, which will be valuable in evaluations of biological resources, especially for quantifying the main summer diet of Gobi bears, and also can be an alternative tool for assessing carbon cycling in Gobi Desert.

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Estimating carbon storage of desert ecosystems in China

Xiaohong,

Jia,

Jingyun

et al. 2023

International Journal of Digital Earth

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Estimation of aboveground biomass of vegetation in the desert-oasis ecotone on the northeastern edge of the Ulan Buh Desert

Cited by 4 publications

References 0 publications

Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

Aboveground Biomass Prediction of Arid Shrub-Dominated Community Based on Airborne LiDAR through Parametric and Nonparametric Methods

Allometric equations for estimating above-ground biomass of Nitraria sibirica Pall. in Gobi Desert of Mongolia

Estimating carbon storage of desert ecosystems in China

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