Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

Jia, Zhu; Huang, Zhihong; Sun, Hua; Wang, Guangxing

doi:10.3390/rs9030241

Cited by 39 publications

(35 citation statements)

References 78 publications

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“…In Kangbao County, there were only 134 sample plots available, and thus a leave-one-out cross validation was utilized for the accuracy assessment and comparison of the methods. The measures used to quantify the accuracy of the predictions included coefficient of determination (R 2 ), mean PVC predictions (MPVC), relative bias (RBias), RMSE and relative RMSE (RRMSE) for the test plots, and coefficient of variation (CV r ), mean value (μ map ), and variance of the prediction maps (VAR map ) [34,58]. Because the field observations of PVC for the plots were obtained by averaging the values of five 1 m × 1 m subplots within each of the 30 m × 30 m plots, the field observations contained uncertainties and were thus regarded as referenced values.…”

Section: Evaluation and Comparison Of Predictionsmentioning

confidence: 99%

“…More importantly, the parametric methods model a global trend of spatial variability of variables and ignore their local variability. Thus, the methods often lead to overestimations and underestimations for the small and large values of a dependent variable, respectively [33,34]. GWR is also a parametric method but Remote Sens.…”

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confidence: 99%

“…In addition, the accuracy of estimating a dependent variable using kNN varies greatly depending on several factors, including the number of nearest neighbors (that is, k value), distance metric, weighting function, and feature weighting parameters [34,55]. Katila et al [56] analyzed the effects of the factors on estimation of forest parameters for Finnish multisource national forest inventory using kNN and a leave-one-out cross-validation method.…”

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confidence: 99%

“…Tomppo et al [57] and McRoberts et al [58] improved the Euclidean distance metric using a genetic algorithm to increase estimation accuracy. Zhu et al [34] developed a spectral correlation-weighted kNN algorithm to predict forest ecosystem biomass density in Xiangjiang River Basin, China.…”

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confidence: 99%

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Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

Sun

Wang

et al. 2018

Remote Sensing

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Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas. IntroductionLand degradation and desertification is a serious ecological and environmental problems and has received worldwide attention [1][2][3]. Percentage vegetation cover (PVC) represented with the range of values from 0.0 to 1.0 in this study is one of the effective indicators for assessing land degradation and desertification in arid and semi-arid areas and has been widely used. However, collecting field measurements of PVC in remote and sparsely populated arid and semi-arid areas is labor-intensive and time-consuming [4]. This method works for small areas only, which cannot provide the detailed information of spatial characteristics and temporal trend of PVC at a regional or global scale. Compared with the traditional method, remote sensing technologies can repeatedly offer images that cover a same region and quantify the spatial variability and temporal dynamics of PVC. Moreover, mapping PVC using remotely sensed images al...

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Section: Evaluation and Comparison Of Predictionsmentioning

confidence: 99%

mentioning

confidence: 99%

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confidence: 99%

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confidence: 99%

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Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

Sun

Wang

et al. 2018

Remote Sensing

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“…Stepwise regression screening variables method, one of the most widely used methods in regression models [67,68], was used to establish a remote sensing information model of AGC in bamboo forests. 70% of the sample plots were randomly selected for developing the model, and the others for evaluating the established model.…”

Section: Methods Of Model Constructionmentioning

confidence: 99%

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Han

et al. 2018

Remote Sensing

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China is one of the countries with the most abundant bamboo forest resources in the world, and Zhejiang province is among the top-3 Chinese provinces with richest bamboo forests. For rational bamboo forests management, it is of great significance to study the spatiotemporal dynamic changes of Aboveground Carbon (AGC) stocks of bamboo forest in Zhejiang. In this study, remote sensing variables, such as spectral, vegetation indices and texture features of bamboo forest in Zhejiang, were extracted from 32 Landsat TM and OLI images got from four different years (2000, 2004, 2008 and 2014). These variables were subsequently selected with stepwise regression method to build an estimation model of AGC of the bamboo forests. The results showed that (1) the accuracy of bamboo forest remote sensing information extracted from the four different years was high with a classification accuracy of >76.26% and an accuracy of users of >91.62%. The classification area of bamboo forest was highly consistent with the area from forest resource inventory, and the area accuracy was over 96.50%; (2) the estimation model performed well in predicting the AGC in Zhejiang for different years. The correlation coefficient for estimated and measured AGC was between 63% and 72% with low root mean square error; (3) the derived AGC of the bamboo forests in Zhejiang province increased gradually from 2000 to 2014, with the AGC density of 6.75 Mg·ha −1 , 10.95 Mg·ha −1 , 15.25 Mg·ha −1 and 19.07 Mg·ha −1 respectively, and the average annual growth of 0.88 Mg·ha −1 . The spatiotemporal evolution of bamboo forest AGC in Zhejiang province had a close relationship with the gradual expansion of bamboo forest in the province and the differentiation of management levels in different regions.

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Influence of environmental predispositions on temperate mountain forest damage at different spatial scales during alternating drought and flood periods: case study in Hrubý Jeseník Mts. (Czech Republic)

Samec

Kudĕlková²,

Lukeš³

et al. 2022

J. Mt. Sci.

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Mapping Forest Ecosystem Biomass Density for Xiangjiang River Basin by Combining Plot and Remote Sensing Data and Comparing Spatial Extrapolation Methods

Cited by 39 publications

References 78 publications

Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

Optimizing kNN for Mapping Vegetation Cover of Arid and Semi-Arid Areas Using Landsat images

Spatiotemporal Estimation of Bamboo Forest Aboveground Carbon Storage Based on Landsat Data in Zhejiang, China

Influence of environmental predispositions on temperate mountain forest damage at different spatial scales during alternating drought and flood periods: case study in Hrubý Jeseník Mts. (Czech Republic)

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