A multimodel random forest ensemble method for an improved assessment of Chinese terrestrial vegetation carbon density

Author(s):  
Zhaosheng Wang ◽  
He Gong ◽  
Mei Huang ◽  
Fengxue Gu ◽  
Jie Wei ◽  
...  
Forests ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 880 ◽  
Author(s):  
Pan ◽  
Sun ◽  
Ouyang ◽  
Zang ◽  
Rao ◽  
...  

Carbon density is an important indicator of carbon sequestration capacity in forest ecosystems. We investigated the vegetation carbon density of Pinus massoniana Lamb. forest in the Jiangxi Province. Based on plots investigation and measurement of the carbon content of the samples, the influencing factors and spatial variation of vegetation carbon density (including the tree layer, understory vegetation layer and litter layer) were analysed. The results showed that the average vegetation carbon density value of P. massoniana forest was 52 Mg·ha−1. The vegetation carbon density was significantly (p < 0.01) and positively correlated with the stand age, mean annual precipitation, elevation and stand density and negatively correlated with the slope and mean annual temperature. Forest management had a significant impact on vegetation carbon density. To manage P. massoniana forest for carbon sequestration as the primary objective, near-natural forest management theory should be followed, e.g., replanting broadleaf trees. These measures would promote positive succession and improve the vegetation carbon sequestration capacity of forests. The results from the global Moran’s I showed that the vegetation carbon density of P. massoniana forest had significant positive spatial autocorrelation. The results of local Moran’s I showed that the high-high spatial clusters were mainly distributed in the southern, western and eastern parts of the province. The low-low spatial clusters were distributed in the Yushan Mountains and in the northern part of the province. The fitting results of the semivariogram models showed that the spherical model was the best fitting model for vegetation carbon density. The ratio of nugget to sill was 0.45, indicating a moderate spatial correlation of carbon density. The vegetation carbon density based on kriging spatial interpolation was mainly concentrated in the range of 32.5–69.8 Mg·ha−1. The spatial distribution of vegetation carbon density regularity was generally low in the middle region and high in the peripheral region, which was consistent with the terrain characteristics of the study area.


2016 ◽  
Vol 26 (03) ◽  
pp. 1750007 ◽  
Author(s):  
S. Dinakaran ◽  
P. Ranjit Jeba Thangaiah

This article introduces a novel ensemble method named eAdaBoost (Effective Adaptive Boosting) is a meta classifier which is developed by enhancing the existing AdaBoost algorithm and to handle the time complexity and also to produce the best classification accuracy. The eAdaBoost reduces the error rate when compared with the existing methods and generates the best accuracy by reweighing each feature for further process. The comparison results of an extensive experimental evaluation of the proposed method are explained using the UCI machine learning repository datasets. The accuracy of the classifiers and statistical test comparisons are made with various boosting algorithms. The proposed eAdaBoost has been also implemented with different decision tree classifiers like C4.5, Decision Stump, NB Tree and Random Forest. The algorithm has been computed with various dataset, with different weight thresholds and the performance is analyzed. The proposed method produces better results using random forest and NB tree as base classifier than the decision stump and C4.5 classifiers for few datasets. The eAdaBoost gives better classification accuracy, and prediction accuracy, and execution time is also less when compared with other classifiers.


2017 ◽  
Vol 29 (3) ◽  
pp. 164-170 ◽  
Author(s):  
Hao Wu

Purpose This paper aims to inspect the defects of solder joints of printed circuit board in real-time production line, simple computing and high accuracy are primary consideration factors for feature extraction and classification algorithm. Design/methodology/approach In this study, the author presents an ensemble method for the classification of solder joint defects. The new method is based on extracting the color and geometry features after solder image acquisition and using decision trees to guarantee the algorithm’s running executive efficiency. To improve algorithm accuracy, the author proposes an ensemble method of random forest which combined several trees for the classification of solder joints. Findings The proposed method has been tested using 280 samples of solder joints, including good and various defect types, for experiments. The results show that the proposed method has a high accuracy. Originality/value The author extracted the color and geometry features and used decision trees to guarantee the algorithm's running executive efficiency. To improve the algorithm accuracy, the author proposes using an ensemble method of random forest which combined several trees for the classification of solder joints. The results show that the proposed method has a high accuracy.


2020 ◽  
Author(s):  
A. Burt ◽  
M. Boni Vicari ◽  
A. C. L. da Costa ◽  
I. Coughlin ◽  
P. Meir ◽  
...  

AbstractA large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly due to the difficulty of making direct, whole-tree measurements. We harvested four large tropical rainforest trees (stem diameter: 0.6–1.2 m, height: 30–46 m, AGB: 3960–18 584 kg) in a natural closed forest stand in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We found approximately 40 % of green mass was water, and the majority of AGB was most often found in the crown, but varied from 42–62 %. Woody tissue density varied substantially intra-tree, with both height and radius, but variations were not systematic inter-tree. Terrestrial lidar data were collected pre-harvest, from which volume-derived AGB estimates were retrieved. These estimates were more accurate than traditional allometric counterparts (mean tree-scale relative error: 3 % vs. 15 %). Error in lidar-derived estimates remained constant across tree size, whilst error in allometric-derived estimates increased up to 4 −fold over the diameter range. Further, unlike allometric estimates, the error in lidar estimates decreased when up-scaling to the cumulative AGB of the four trees. Terrestrial lidar methods therefore can help reduce uncertainty in tree- and stand-scale AGB estimates, which would substantially advance our understanding of the role of tropical forests in the global carbon cycle.


2019 ◽  
Vol 7 (1) ◽  
Author(s):  
Wanlong Sun ◽  
Xuehua Liu

Abstract Background The accuracy in estimating forest ecosystem carbon storage has drawn extensive attention of researchers in the field of global climate change. However, incomparable data sources and various estimation methods have led to significant differences in the estimation of forest carbon storage at large scales. Methods In this study, we reviewed fundamental types of forest carbon storage estimation methods and their applications in China. Results Results showed that the major forest carbon storage estimation methods were classified into 3 major categories and 15 subcategories focusing on vegetation carbon storage estimation, soil carbon storage estimation, and litter carbon storage estimation, respectively. The application in China showed that there have been 3 development stages of research in China since the 1990s. Studies of forest carbon storage estimation in province scales were conducted more frequently in the northeastern, eastern and southwestern provinces such as Zhejiang, Heilongjiang and Sichuan with high forest coverage or large forest area. Inventory-based methods, soil type method, and biomass model were the main forest estimation methods used in China, focusing on vegetation, soil and litter carbon storage estimation respectively. Total forest carbon storage of China was approximate 28.90 Pg C, and the average vegetation carbon density (42.04 ± 5.39 Mg·ha− 1) was much lower than that of the whole world (71.60 Mg·ha− 1). Vegetation carbon density from average biomass method was the highest (57.07 Mg·ha− 1) through comparing nine types of vegetation carbon storage estimation methods applied during 1989 to 1993. Conclusions Many studies on forest carbon storages have been carried out in China at patch scales or regional scales. These efforts enabled the research of forest carbon storage to reach a relatively advanced stage. Meanwhile, the accumulation of massive research data provides the basis for subsequent research work. Some challenges are also existing. This review could provide a reference for more accurate estimation of forest carbon storage in the future.


2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Haiyan Li ◽  
Yi Qu ◽  
Xingyu Zeng ◽  
Hongqiang Zhang ◽  
Ling Cui ◽  
...  

AbstractLarge-scale human activities especially the destruction of forest land, grassland, and unused land result in a large amount of carbon release into the atmosphere and cause drastic changes in land use/cover in the Sanjiang Plain. As a climate change-sensitive and ecologically vulnerable area, the Sanjiang Plain ecosystem’s carbon cycle is affected by significant climate change. Therefore, it is important that studying the impact of the changes in land use/cover and climate on vegetation carbon storage in the Sanjiang Plain. Remote sensing, temperature, and precipitation data in four periods from 2001 to 2015 are used as bases in conducting an analysis of land use/cove types and spatio-temporal variation of vegetation carbon density and carbon storage in growing season using model and related analysis methods. Moreover, the impact of land use/cover change and climate change on vegetation carbon density and carbon storage is discussed. The findings are as follows. (1) Cultivated land in the Sanjiang Plain increased, while forest land, grassland and unused land generally decreased. (2) Vegetation carbon density increased, in which the average carbon density of cultivated land, grassland, and unused land varied insignificantly, while that of forest land increased continuously from 4.18 kg C/m2 in 2001 to 7.65 kg C/m2 in 2015. Vegetation carbon storage increased from 159.18 Tg C in 2001 to 256.83 Tg C in 2015, of which vegetation carbon storage of forest land contributed 94% and 97%, respectively. (3) Conversion of land use/cover types resulted in a 22.76-TgC loss of vegetation carbon storage. Although the forest land area decreased by 3389.5 km2, vegetation carbon storage in the research area increased by 97.65 Tg C owing to the increase of forest carbon density. (4) Pixel-by-pixel analysis showed that vegetation carbon storage in the majority of the areas of the Sanjiang Plain are negatively correlated with temperature and positively correlated with precipitation. The results showed that changes of land use/cover types and vegetation carbon density directly lead to a change in vegetation carbon storage, with the change of forest vegetation carbon density being the main driver affecting vegetation carbon storage variation. The increase of temperature mainly suppresses the vegetation carbon density, and the increase of precipitation mainly promotes it.


2021 ◽  
Vol 8 (2) ◽  
pp. 201458
Author(s):  
Andrew Burt ◽  
Matheus Boni Vicari ◽  
Antonio C. L. da Costa ◽  
Ingrid Coughlin ◽  
Patrick Meir ◽  
...  

A large portion of the terrestrial vegetation carbon stock is stored in the above-ground biomass (AGB) of tropical forests, but the exact amount remains uncertain, partly owing to the lack of measurements. To date, accessible peer-reviewed data are available for just 10 large tropical trees in the Amazon that have been harvested and directly measured entirely via weighing. Here, we harvested four large tropical rainforest trees (stem diameter: 0.6–1.2 m, height: 30–46 m, AGB: 3960–18 584 kg) in intact old-growth forest in East Amazonia, and measured above-ground green mass, moisture content and woody tissue density. We first present rare ecological insights provided by these data, including unsystematic intra-tree variations in density, with both height and radius. We also found the majority of AGB was usually found in the crown, but varied from 42 to 62%. We then compare non-destructive approaches for estimating the AGB of these trees, using both classical allometry and new lidar-based methods. Terrestrial lidar point clouds were collected pre-harvest, on which we fitted cylinders to model woody structure, enabling retrieval of volume-derived AGB. Estimates from this approach were more accurate than allometric counterparts (mean tree-scale relative error: 3% versus 15%), and error decreased when up-scaling to the cumulative AGB of the four trees (1% versus 15%). Furthermore, while allometric error increased fourfold with tree size over the diameter range, lidar error remained constant. This suggests error in these lidar-derived estimates is random and additive. Were these results transferable across forest scenes, terrestrial lidar methods would reduce uncertainty in stand-scale AGB estimates, and therefore advance our understanding of the role of tropical forests in the global carbon cycle.


2018 ◽  
Vol 17 (7) ◽  
pp. 1657-1666 ◽  
Author(s):  
Kelin Wang ◽  
Mingyang Zhang ◽  
Huiyu Liu ◽  
Weijian Luo ◽  
Jing Wang ◽  
...  

Author(s):  
Deepika Bansal ◽  
Kavita Khanna ◽  
Rita Chhikara ◽  
Rakesh Kumar Dua ◽  
Rajeev Malhotra

Objective: Dementia is a progressive neurodegenerative brain disease emerging as a global health problem in adults aged 65 years or above, resulting in the death of nerve cells. The elimination of redundant and irrelevant features from the datasets is however very necessary for accurate detection and thus the timely treatment of dementia. Methods: For this purpose, an ensemble approach of univariate and multivariate feature selection methods has been proposed in this study. A comparison of four univariate feature selection techniques (t-Test, Wilcoxon, Entropy and ROC) and six multivariate feature selection approaches (ReliefF, Bhattacharyya, CFSSubsetEval, ClassifierAttributeEval, CorrelationAttributeEval, OneRAttributeEval) has been performed. The ensemble of best univariate & multivariate filter algorithms is proposed which helps in acquiring a subset of features that includes only relevant and non-redundant features. The classification is performed using Naïve Bayes, k-NN, and Random Forest algorithms. Results: Experimental Results show that t-Test & ReliefF feature selection is capable of selecting 10 relevant features that give the same accuracy as when all features are considered. In addition to it, the accuracy obtained using k-NN with an ensemble approach is 99.96%. The statistical significance of the method has been established using Friedman’s statistical test. Conclusion: The new ranking criteria computed by the ensemble method efficiently eliminate the insignificant features and reduces the computational cost of the algorithm. The ensemble method has been compared to the other approaches for ensuring the superiority of the proposed model. Discussion: The percentage gain in accuracy for all three classifiers, Naïve Bayes, k-NN, and Random Forest is shown in There is a remarkable difference noted down for the percentage gain in the accuracies after applying feature selection using Naïve Bayes and k-NN. Using univariate filter selection methods, the t-test is outshining among all the methods while selecting only 10 feature subsets.


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