[1]张倩宁,黄泽纯 *,徐 柱,等.一种自适应定权地形复杂度模型[J].山地学报,2017,(02):230-237.[doi:10.16089/j.cnki.1008-2786.000216]
　ZHANG Qianning,HUANG Zechun *,XU Zhu,et al.An Adaptive Weighting Terrain Complexity Model[J].Mountain Research,2017,(02):230-237.[doi:10.16089/j.cnki.1008-2786.000216]
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一种自适应定权地形复杂度模型()
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《山地学报》[ISSN:1008-2186/CN:51-1516]

卷:
期数:
2017年02期
页码:
230-237
栏目:
山地技术
出版日期:
2017-03-30

文章信息/Info

Title:
An Adaptive Weighting Terrain Complexity Model
文章编号:
1008-2786-(2017)2-230-08
作者:
张倩宁1; 黄泽纯1; 2 *; 徐 柱1; 2; 洪安东1; 张瑞芳1
1.西南交通大学 地球科学与环境工程学院,四川 成都 611756;
2.西南交通大学 高速铁路运营安全空间信息技术国家地方联合工程实验室,四川 成都 611756
Author(s):
ZHANG Qianning1; HUANG Zechun1; 2 *; XU Zhu1; 2; Hong Andong1; ZHANG Ruifang1
1.Faculty of Geosciences and Environmental Engineering,Southwest Jiaotong University,Chengdu,Sichuan,611756,China;
2.State-province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety,Southwest Jiaotong University,Chengdu,Sichuan,610031,China
关键词:
地形复杂度指标; 主因子分析; 经验公式; 地形特征采样
Keywords:
integrated terrain complexity index(ITCI);  principle component analysis;  empirical formula;  terrain sampling
分类号:
P237
DOI:
10.16089/j.cnki.1008-2786.000216
文献标志码:
A
摘要:
地形复杂度指标是数字地形分析中重要的参数。地形复杂度的定量表达能够为地形特征采样理论、数字地形分析(Digitalterrainanalysis,DTA)的不确定性分析、水文分析等方面提供重要依据。本文基于多因素地形因子指标,采用主成分分析方法构建一个自适应定权地形复杂度模型,以实现地形特征的综合描述:选取坡度(Slope,S)、全曲率(TotalCurvature,Cur)、地形起伏度(Relief,Rel)和地形粗糙度(Rough,Rou)四个单一地形因子指标构建综合地形复杂度指标(Integratedterraincomplexityindex,ITCI,后文简称C);通过实验训练数据实现地形复杂度指标的解算,得到C与S、Cur、Rel、Rou之间的经验公式;根据经验公式计算实验区域的地形复杂度指标值,并结合实验区域的地形类型,得到地形复杂度指标值与地形类型之间的对应关系;选取了平原、丘陵和山区三个实验区域,计算C值对其对应关系进行验证。实验结果表明,本文构建的地形复杂度指标与地形特征之间的对应关系是合理的,能够有效描述地形特征。本文研究对于地形特征采样理论和DTA的不确定性分析等方面具有重要的参考价值和应用价值。
Abstract:
Terrain Complexity Index(TCI)is an important parameter in digital terrain analysis. Proper determination of TCI would well serve terrain feature sampling theory,uncertainty analysis of digital terrain and hydrological analysis,etc. Most available topographic feature indexes scarcely arrive at an appreciable approximation for terrain due to poor inclusion of necessary physical factors into TCI.In this study,an integrated terrain complexity index(ITCI)was introduced to define terrain using multiple topographic factors. The procedure of ITCI construction is described consecutively as below:Principle component analysis was conducted to develop an adaptive weighting terrain complexity model,in which four single topographic feature indexes,including slope,total curvature,terrain roughness and relief,were collected to build ITCI; Based on training data from experiments,ITCI was examined and an empirical formula of ITCI was shaped accordingly; According to the empirical formula,ITCI values at experimental areas were computed for formulating connections between ITCI and landform classification; In order to verify our suggested formulation of ITCI with landform types,three experimental regions with characteristic feature attributes,were selected and then calculated. Results showed that ITCI could effectively describe topographic features and the formulation of ITCI with landform types was agreeably matched. The study has great value for terrain sampling and uncertainty analysis in digital terrain analysis.

参考文献/References:

[1] 孟宪宇. 测树学[M]. 北京:中国林业出版社,1996.[MENG Xianyu. Forest mensuration[M]. Beijing:China Forestry Publishing House,1996.]
[2] 刘志华,常禹,陈宏伟.基于遥感、地理信息系统和人工神经网络的呼中林区森林蓄积量估测[J]. 应用生态学报,2008,19(9):1891-1896. [LIU Zhihua,CHANG Yu,CHEN Hongwei. Estimation of forest volume in Huzhong forest area based on RS,GIS,and ANN[J]. Chinese Journal of Applied Ecology,2008,19(9):1891-1896.]
[3] 贺鹏,张会儒,雷相东,等.基于地统计学的森林地上生物量估计[J]. 林业科学,2013,49(5):101-109.[HE Peng,ZHANG Huiru,LEI Xiangdong,et al. Estimation of forest above-ground biomass based on geostatistics[J]. Scientia Silvae Sinicae,2013,49(5):101-109.]
[4] ZHENG Sheng,CAO Chunxiang,DANG Yongfeng,et al. Retrieval of forest growing stock volume by two different methods using Landsat TM images[J]. International Journal of Remote Sensing,2014,35(1):29-43.
[5] 琚存勇,蔡体久.用泛化改进的BP神经网络估测森林蓄积量[J]. 林业科学,2006,42(12):59～62.[JU Cunyong CAI Tijiu. Forest volume estimate based on bayesian regularization back propagation neural network[J]. Scientia Silvae Sinicae,2006,42(12):59～62.]
[6] TOMPPO E,NILSSON M,ROSENGREN M,et al. Simultaneous use of Landsat-TM and IRS-1 CWiFS data in estimating large area tree stem volume and aboveground biomass. Remote Sensing of Environment,2002,82(1):156～171.
[7] SCOTT A. Soenen,RONALD J. Hall,CRAIG A. Coburn. Estimating aboveground forest biomass from canopy reflectance model inversion in mountainous terrain. Remote Sensing of Environment,2010,114(7):1325～1337.
[8] 张友静,方有清,陈钦峦.南方山地森林蓄积量遥感估算研究[J]. 国土资源遥感,1993,4(2):39～47.[ZHANG Youjing,FANG Youqing,Chen Qinluan. Study on remotely sensed estimate model of stand volume in the south-China mountain[J]. Remote sensing for land & resource,1993,4(2):39～47.]
[9] 董斌,冯仲科,杜林芳,等.山东省黄河流域森林蓄积量遥感定量估测模型研究[J]. 遥感技术与应用,2010,25(4):520～524.[DONG Bin,FENG Zhongke,DU Linfang,et al. Study on Quantitative estimation modeling of forest volume based on remote sensing in the Yellow River basin of Shandong Province[J]. Remote sensing technology and application,2010,25(4):520～524.]
[10] 王佳,宋珊芸,刘霞,等.结合影像光谱与地形因子的森林蓄积量估测模型[J]. 农业机械学报,2014,45(5):216～220.[WANG Jia,SONG Shanyun,Liu Xia,et al. Forest volume estimation model using spectra and topographic factors of ZY-3 image[J]. Transactions of the Chinese Society for Agriculture Machinery,2014,45(5):216～220.]
[11] ATKINSON P M,LEWIS P. Geostatistical classification for remote sensing:an introduction. Computer & Geosciences,2000,26(4):361～371.
[12] CHUBEY M S,FRANKLIN S E,WULDER M A. Object-based analysis of IKONOS-2 imagery for extraction of forest inventory parameters. Photogrammetric Engineering & Remote Sensing,2006,72(4):383～394.
[13] 吴飏,张登荣,张汉奎,等.结合图像纹理特征的森林郁闭度遥感估测[J]. 林业科学,2012,48(2):48～53.[WU Yang,ZHANG Dengrong,ZHANG Hankui,et al. Remote sensing estimation of forest canopy density combined with texture features[J]. Scientia Silvae Sinicae,2012,48(2):48～53.]
[14] 徐菲楠,祁元,王建华,等.面向对象的黑河下游河岸林植被覆盖信息分类. 遥感技术与应用,2015,30(5):996-1005.[XU Feinan,QI Yuan,WANG Jianhua,et al. Riparian forest vegetation coverage information classification based on Object-oriented method in Heihe River[J]. Remote sensing technology and application,2015,30(5):996-1005.]
[15] 薛薇.统计分析与SPSS的应用[M]. 北京:中国人民大学出版社,2011.[XUE Wei. Stastistical analysis and application of SPSS[M]. Beijing:China Renmin University Press,2011.]
[16] 高明亮,赵文吉,宫兆宁,等.基于环境卫星数据的黄河湿地植被生物量反演研究[J]. 生态学报,2013,33(2):542～553.[GAO Mingliang,ZHAO Wenji,GONG Zhaoning,et al. The study of vegetation biomass based on HJ satellite data in Yellow river wetland[J]. Acta Ecologica Sinica,2013,33(2):542～553.]
[17] 张超,彭道黎,涂云燕,等.利用TM影像和偏最小二乘回归方法估测三峡库区森林蓄积量[J]. 北京林业大学学报,2013,35(3):11～17.[ZHANG Chao,PENG Daoli,TU Yunyan,et al. Predicting forest volume in Three Gorges Reservoir region using TM images and partial squares regression[J]. Journal of Beijing Forestry university,2013,35(3):11～17.]

备注/Memo

备注/Memo:
基金项目(Foundation item):测绘地理信息公益性行业科研专项项目(201512028); 中央高校基本科研业务费专项资金(2682014CX017); 西南交通大学研究生创新实验实践项目(YC201514103)[Mapping Geographic Information Public Service Industry Research and Special Funds(201512028); Central Universities Fundamental Research Funds(2682014CX017); Graduate Innovative Experimental Practice Program Of Southwest Jiaotong University(YC201514103)]。
作者简介(Biography):张倩宁(1992-),女,山西夏县人,博士研究生,主要研究方向:数字地形分析,GIS时空数据挖掘与分析研究。[Zhang Qianning(1992-),female, born in Xiaxian, Shanxi province, China, Ph.D. candidate, research on digital terrain analysis and GIS spatial-temporal data analysis and mining] E-mail: zhangqianning1020@163.com
*通信作者(Corresponding author): 黄泽纯(1974-),男,副教授,主要研究方向:GIS时空数据挖掘与分析研究。[Huang Zechun(1974-), male, associate professor, research on GIS spatial-temporal data analysis and mining] E-mail: zchuang2005@qq.com
更新日期/Last Update: 2017-03-30