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Effects of climate change on potential habitats of the cold temperate coniferous forest in Yunnan province, southwestern China

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Journal of Mountain Science 2016年第8期13卷 1411-1422页

作者： LIWang-jun PENG Ming-chun Motoki HIGA Nobuyuki TANAKA Tetsuya MATSUI Cindy Q. TANG OU Xiao-kun ZHOU Rui-wu WANG Chong-yun YAN Hai-zhong Institute of Ecology and Geobotany Yunnan University Kunming 650091 China Kochi University Kochi-shi Kochi 780-8520 Japan Forestry and Forest Products Research Institute 1 Matsunosato Tsukuba Ibaraki 305-8687 Japan Tokyo University of Agriculture 1-1-1 Sakuragaoka Setagaya-ku Tokyo 156-8502 Japan

We built a classification tree （CT） model to estimate climatic factors controlling the cold temperate coniferous forest （CTCF） distributions in Yunnan province and to predict its potential habitats under the current and future climates, using seven climate change scenarios, projected over the years of 2070-2099. The accurate CT model on CTCFs showed that minimum temperature of coldest month （TMW） was the overwhelmingly potent factor among the six climate variables. The areas of TMW〈-4.05 were suitable habitats of CTCF, and the areas of -1.35 〈 TMW were non-habitats, where temperate conifer and broad-leaved mixed forests （TCBLFs） were distribute in lower elevation, bordering on the CTCF. Dominant species of Abies, Picea, and Larix in the CTCFs, are more tolerant to winter coldness than Tsuga and broad-leaved trees including deciduous broad-leaved Acer and Betula, evergreen broad- leaved Cyclobalanopsis and Lithocarpus in TCBLFs. Winter coldness may actually limit the cool-side distributions of TCBLFs in the areas between -1.35℃ and -4.05℃, and the warm-side distributions of CTCFs may be controlled by competition to the species of TCBLFs. Under future climate scenarios, the vulnerable area, where current potential （suitable ＋ marginal） habitats （80,749 km^2） shift to non-habitats, was predicted to decrease to 55.91% （45,053 km^2） of the current area. Inferring from the current vegetation distribution pattern, TCBLFs will replace declining CTCFs. Vulnerable areas predicted by models are important in determining priority of ecosystem conservation.

关键词： classification tree Climate scenarios Vulnerable area Abies Picea Larix Evergreenbroad-leaved tree ALOS remote-sensing images

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Knowledge-Based classification in Automated Soil Mapping

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Pedosphere 2003年第3期13卷 209-218页

作者： ZHOU BIN and WANG RENCHAOInstitute of Agricultural Remote Sensing and Information Technology Application, Zhejiang University, Hangzhou 310029 (China) Institute of Agricultural Remote Sensing and Information Technology Application Zhejiang University Hangzhou 310029

A machine-learning approach was developed for automated building of knowledgebases for soil resources mapping by using a classification tree to generate knowledge from trainingdata. With this method, building a knowledge base for automated soil mapping was easier than usingthe conventional knowledge acquisition approach. The knowledge base built by classification tree wasused by the knowledge classifier to perform the soil type classification of Longyou County,Zhejiang Province, China using Landsat TM bi-temporal images and CIS data. To evaluate theperformance of the resultant knowledge bases, the classification results were compared to existingsoil map based on a field survey. The accuracy assessment and analysis of the resultant soil mapssuggested that the knowledge bases built by the machine-learning method was of good quality formapping distribution model of soil classes over the study area.

关键词： classification classification tree knowledge-based rule extracting soilmapping

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The balance property in neural network modelling

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Statistical Theory and Related Fields 2022年第1期6卷 1-9页

作者： Mario V.Wüthrich RiskLab Department of MathematicsETH ZurichZurichSwitzerland

In estimation and prediction theory,considerable attention is paid to the question of hav-ing unbiased estimators on a global population *** developments in neural network modelling have mainly focused on accuracy on a granular sample level,and the question of unbi-asedness on the population level has almost completely been neglected by that *** discuss this question within neural network regression models,and we provide methods of receiving unbiased estimators for these models on the global population level.

关键词： Balance property unbiased binary classification logistic regression neural network classification tree

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High-resolution remote sensing data can predict household poverty in pastoral areas,Inner Mongolia,China

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Geography and Sustainability 2021年第4期2卷 254-263页

作者： Peng Han Qing Zhang Yanyun Zhao Frank Yonghong Li Ministry of Education Key Laboratory of Ecology and Resource Use of the Mongolian Plateau&Inner Mongolia Key Laboratory of Grassland Ecology School of Ecology and EnvironmentInner Mongolia UniversityHohhot 010021China

The accurate prediction of poverty is critical to efforts of poverty reduction,and high-resolution remote sensing(HRRS)data have shown great promise for facilitating such ***,the present study used HRRS with 1 m resolution and 238 households data to evaluate the utility and optimal scale of HRRS data for predicting household poverty in a grassland region of Inner Mongolia,*** prediction of household poverty was improved by using remote sensing indicators at multiple scales,instead of indicators at a single scale,and a model that combined indicators from four scales(building land,household,neighborhood,and regional)provided the most accurate prediction of household poverty,with testing and training accuracies of 48.57%and 70.83%,***,building area was the most efficient indicator of household *** compared to conducting household surveys,the analysis of HRRS data is a cheaper and more time-efficient method for predicting household poverty and,in this case study,it reduced study time and cost by about 75%and 90%,*** study provides the first evaluation of HRRS data for the prediction of household poverty in pastoral areas and thus provides technical support for the identification of poverty in pastoral areas around the world.

关键词： Weighted relative wealth index classification tree Inner Mongolia grassland Multi-scale

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基于改进S变换的电能质量扰动的研究

基于改进S变换的电能质量扰动的研究

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作者：杰弗斯（Jeffers Teargun Heptol）东北大学

学位级别：硕士

The growing interest in Power Quality Analysis (PQA) in recent years has brought a lot of tremendous changes to the supply of electrical energy. Many methods have been developed to analyze PQ to improve the quality for stable and efficiency. The S-Transform (ST) in the time frequency distribution was developed in 1994 for analyzing geophysics data. The S Transform is a generalization of the Short-time Fourier transform (STFT), extending the Continuous wavelet transform and overcoming some of its disadvantages.This paper proposes Improved S-Transform (IST) to detect and classify power quality (PQ) disturbances with time-domain analysis. To enhance the analytic power of S-Transform in different non-stationary signal processing,IST is achieved by adding an adjustable factor to the Gaussian window function of the normal S-Transform. The adjusted factor changes the velocity in which the width of the window function varies inversely with the *** possesses an adjustable time-frequency resolution and higher practicability and adaptability than ST in the actual *** analysis performed on the PQ disturbance signals can identify the magnitude and duration of the disturbances. The comparison between the Wavelet-transform-based method and the improved S-Transform-based method for power quality disturbance recognition is also provided.For the classification of power quality disturbance, the Support Vector Machine (SVM) is applied. The disturbance qualities were categorized into six groups known as SVM1, SVM2, SVM3, SVM4, SVM5 and SVM6 depending on the amplitude of the disturbances.The simulation results show that the proposed method is effective and immune against noise. The proposed method is feasible and promising for real applications.

关键词： S-Transform Wavelet power quality voltage disturbance SVM classification tree

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