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Estimation of leaf color variances of Cotinus coggygria based on geographic and environmental variables

Journal of Forestry Research 2021年第2期32卷 609-622页

作者： Xing Tan Jiaojiao Wu Yun Liu Shixia Huang Lan Gao Wen Zhang College of Resources and Environment Southwest UniversityChongqing 400716People’s Republic of China

Capturing leaf color variances over space is important for diagnosing plant nutrient and health status,estimating water availability as well as improving ornamental and tourism values of *** this study,leaf color variances of the Eurasian smoke tree,Cotinus coggygria were estimated based on geographic and climate variables in a shrub community using generalized elastic net(GELnet)and support vector machine(svm)*** reveal that leaf color varied over space,and the variances were the result of geography due to its effect on solar radiation,temperature,illumination and moisture of the shrub environment,whereas the influence of climate were not *** svm and GELnet algorithm models were similar estimating leaf color indices based on geographic variables,and demonstrates that both techniques have the potential to estimate leaf color variances of *** in a shrubbery with a complex geographical environment in the absence of human activity.

关键词： RGB color space Digital elevation model Variable selection svm algorithm GELnet algorithm

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Application of SAM and svm Techniques to Burned Area Detection for Landsat TM Images in Forests of South Sumatra

Application of SAM and SVM Techniques to Burned Area Detecti...

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International Conference on Environmental Science and Technology（ICEST 2011）

作者： Ajeng Anggraeni Chinsu Lin Dept.of Forestry and Natural Resources National Chiayi University

Forest fires are an annual disaster which has become of international concern due to increased attention to environmental and economic issues in the last few *** fires cause damage that lead to annual decrease in *** is not only due to natural environmental conditions increasing the frequency of accidental forest fires（prolonged dry season or El Nino） but is also caused by clearing forest and peat land for palm oil plantation and HTI（timber production）.Lowland forests in Sumatra are now regarded as among the most threatened forests in the ***’s very important to detect and monitor forest *** sensing from space is especially suitable for forest fire *** data can be a very useful tool for mapping forest areas that have recently been *** TM images were used for target *** fire hot-spot data from MODIS were used as reference *** signature from burned area were then extracted and used for target detection analysis using a Spectral Angle Mapper（SAM） *** classification with Support Vector Machine（svm） algorithm was also used for classifying the image as a burned *** goal of this study was to measure fire induced deforestation in South Sumatra,produce a burned area map from a Landsat TM image and compare the efficiency of SAM and svm techniques for burned area *** showed that svm and SAM could reach an accuracy of 0.975 and 0.835 respectively,and it is suggested svm might be more suitable for burned area mapping to be utilized in forest monitoring.

关键词： forest fire deforestation Landsat TM image MODIS target detection burned area South Sumatra svm algorithm SAM algorithm

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Evaporation residue cross sections of superheavy nuclei based on optimized nuclear data

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Chinese Physics C 2023年第12期47卷 124-130页

作者：李佳星张鸿飞 School of Physics Xi’an Jiaotong UniversityXi’an 710049China School of Nuclear Science and Technology Lanzhou UniversityLanzhou 730000China

This study proposes an optimized method for estimating atomic nucleus masses by combining the finiterangedroplet model (FRDM) with the support vector machine algorithm. The optimization process significantly improvesthe accuracy of the FRDM by reducing the root mean square error from 0.606 to 0.253 MeV. The optimizedmass data obtained from this method are then used to calculate the evaporation residue cross-sections (ERCSs) forfusion-evaporation reactions, employing the di-nuclear system model. The experimental results for the 48Ca+238U reactionare relatively well reproduced using these optimized mass data. Additionally, the study investigates the impactof mass uncertainties on fusion and survival probabilities. By considering the mass uncertainties, the ERCSs fornew elements 119 and 120 are predicted based on the obtained optimized mass data.

关键词： superheavy nuclei svm algorithm DNS model evaporation residue cross-sections

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