Inventory-based estimation of forest biomass in Shitai County, China: A comparison of five methods

Tang, Xiaolu; Fehrmann, Lutz; Guan, Fengying; Forrester, David I.; Guisasola, Rubén; Kleinn, Christoph

doi:10.15287/afr.2016.574

Cited by 13 publications

(12 citation statements)

References 34 publications

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“…The basin has complicated topographic features, and is surrounded by mountains and hills in the eastern, southern, and western parts of the basin and dominated by flat terrains in the central and northern parts. The basin has a humid mid-subtropical monsoon climate with distinct seasonality [53,54]. The mean annual temperature is approximately 17.5 • C with a mean annual precipitation of 1400 mm.…”

Section: Study Areamentioning

confidence: 99%

“…In this study, several allometric equations were used to estimate the values of AGB of trees by species; however, the uncertainty from the used allometric equations was unknown. In addition, Tang et al [54] demonstrated that selecting spatial extrapolation methods is also vital for estimation and mapping of forest ecosystem AGB. In this study, three kinds of methods were compared using the same dataset and spectral variables.…”

Section: Uncertainty Analysis Of Forest Biomassmentioning

confidence: 99%

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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

et al. 2017

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Abstract:The distribution of forest biomass in a river basin usually has obvious spatial heterogeneity in relation to the locations of the upper and lower reaches of the basin. In the subtropical region of China, a large amount of forest biomass, comprising diverse forest types, plays an important role in maintaining the balance of the regional carbon cycle. However, accurately estimating forest ecosystem aboveground biomass density (AGB) and mapping its spatial variability at a scale of river basin remains a great challenge. In this study, we attempted to map the current AGB in the Xiangjiang River Basin in central southern China. Three approaches, including a multivariate linear regression (MLR) model, a logistic regression (LR) model, and an improved k-nearest neighbors (kNN) algorithm, were compared to generate accurate estimates and their spatial distribution of forest ecosystem AGB in the basin. Forest inventory data from 782 field plots across the basin and remote sensing images from Landsat 5 in the same period were combined. A stepwise regression method was utilized to select significant spectral variables and a leave-one-out cross-validation (LOOCV) technique was employed to compare their predictions and assess the methods. Results demonstrated the high spatial heterogeneity in the distribution of AGB across the basin. Moreover, the improved kNN algorithm with 10 nearest neighbors showed stronger ability of spatial interpolation than other two models, and provided greater potential of accurately generating population and spatially explicit predictions of forest ecosystem AGB in the complicated basin.

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Section: Study Areamentioning

confidence: 99%

Section: Uncertainty Analysis Of Forest Biomassmentioning

confidence: 99%

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

et al. 2017

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“…Deforestation, forest fires, and natural regeneration can all affect the applicability of the volume formula [ 27 ]. It is therefore crucial for the forest survey program in Wangyedian Forest Farm to obtain updated forest resource data annually while checking and calibrating volume equations periodically [ 9 , 27 ]. This would help to substantially reduce the error of the plot level GSV estimates.…”

Section: Discussionmentioning

confidence: 99%

“…Accurate estimation of the GSV, therefore, plays an important role in both forest resource management and the monitoring of ecosystem dynamics [ 4 , 5 , 6 ]. Compared with traditional field surveys, remote sensing technology allows for a more efficient approach to the monitoring and management of forest resources in real-time [ 7 , 8 , 9 ]. However, the accuracy of GSV estimation using remotely sensed data is determined together by data sources, feature variable selection methods, and estimation models.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

Jiang

Kutia²,

Sarkissian³

et al. 2020

Sensors

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Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2’s red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.

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“…The FMI plays a very important role in improving provincial forest planning, efficient management, and policy decision-making. Therefore, FMI data are widely used in many types of research, including forest health assessment and land cover change monitoring [54,55]. The FMI data of Ganzhou and Kangbao collected in 2017 and 2013, respectively, show six land cover types: unused land, water, forest, farmland, building, and grassland.…”

Section: Sampling Design and Leaf Area Index Measurementmentioning

confidence: 99%

A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China

Jiang

Smith

Kutia³

et al. 2020

Remote Sensing

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As an important vegetation canopy parameter, the leaf area index (LAI) plays a critical role in forest growth modeling and vegetation health assessment. Estimating LAI is helpful for understanding vegetation growth and global ecological processes. Machine learning methods such as k-nearest neighbors (kNN) and random forest (RF) with remote sensing images have been widely used for mapping LAI. However, the accuracy of mapping LAI in arid and semi-arid areas using these methods is limited due to remote and large areas, the high cost of collecting field data, and the great spatial variability of the vegetation canopy. Here, a novel and modified kNN method was presented for mapping LAI in arid and semi-arid areas of China using Sentinel-2 and Landsat 8 images with field data collected in Ganzhou and Kangbao of China. The modified kNN was developed by integrating the traditional kNN estimation and RF classification. The results were compared with those from kNN and RF regression alone using three sets of input predictors: (i) spectral reflectance bands (input 1); (ii) vegetation indices (input 2); and (iii) a combination of spectral reflectance bands and vegetation indices (input 3). Our analysis showed that in Ganzhou, the red-edge bands of the Sentinel-2 image had a high correlation with LAI. Using the red-edge band-derived vegetation indices increased the accuracy of mapping LAI compared with using other spectral variables. Among the three sets of input predictors, input 3 resulted in the highest prediction accuracy. Based on the combination, the values of RMSE obtained by the traditional kNN, RF, and modified kNN were 0.526, 0.523, and 0.372, respectively, and the modified kNN significantly improved the accuracy of LAI prediction by 29.3% and 28.9% compared with the kNN and RF alone, respectively. A similar improvement was achieved for input 1 and input 2. In Kangbao, the improvement of the prediction accuracy obtained by the modified kNN was 31.4% compared with both the kNN and RF. Therefore, this study implied that the modified kNN provided the potential to improve the accuracy of mapping LAI in arid and semi-arid regions using the images.

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Inventory-based estimation of forest biomass in Shitai County, China: A comparison of five methods

Cited by 13 publications

References 34 publications

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

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

Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method

A Modified KNN Method for Mapping the Leaf Area Index in Arid and Semi-Arid Areas of China

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