利用多信息源提高半干旱地区TM影像的森林类型制图精度：以北京西部山区为例

doi:10.11849/zrzyxb.20160159

自然资源学报 ›› 2017, Vol. 32 ›› Issue (7): 1217-1228.doi: 10.11849/zrzyxb.20160159

利用多信息源提高半干旱地区TM影像的森林类型制图精度：以北京西部山区为例

王晓学^{1, 2}, 沈会涛³, 林田苗⁴, 景峰², 李叙勇^{1*, *}, 孔凡利⁵

1. 中国科学院生态环境研究中心城市与区域生态国家重点实验室,北京 100085;
2. 中国国际工程咨询公司,北京 100048;
3. 河北省科学院地理科学研究所,石家庄 050021;
4. 水利部水土保持植物开发管理中心,北京 100038;
5. 国家林业局调查规划设计院,北京 100714

收稿日期:2016-02-22 修回日期:2017-04-25 出版日期:2017-08-02 发布日期:2017-08-02
通讯作者: 於方（1972- ）,博士,研究员,主要从事环境经济核算与环境风险评估。E-mail:yufang @caep.org.cn
作者简介:王晓学（1983- ）,男,博士,主要研究方向为森林水文等。E-mail: wxc_8787@126.com *通信作者简介：李叙勇（1965- ）,男,新疆乌鲁木齐人,研究员,主要研究领域为流域生态学。E-mail: xyli@rcees.ac.cn
基金资助:
国家重点研发计划项目（2016YFC0503007,2016YFD0201206）

Improving the Forest Type Mapping Accuracy in Semiarid Mountainous Areas Based on TM Images—Take the West Mountain of Beijing as an Example

WANG Xiao-xue^{1, 2}, SHEN Hui-tao³, LIN Tian-miao⁴, JING Feng², LI Xu-yong¹, KONG Fan-li⁵

1. State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, CAS, Beijing 100085, China;
2. China International Engineering Consulting Corporation, Beijing 100048, China;
3. Institute of Geographical Sciences, Hebei Academy of Sciences, Shijiazhuang 050021, China;
4. Plant Development and Management Center for Soil and Water Conservation, Ministry of Water Resource, Beijng 100038, China;
5. Academy of Forest Inventory and Planning, State Forestry Administration, Beijing 100714, China

Received:2016-02-22 Revised:2017-04-25 Online:2017-08-02 Published:2017-08-02
Supported by:
National Key Research and Development Plan Programs, No. 2016YFC0503007 and 2016YFD0201206.

摘要/Abstract

摘要： 以地处半干旱地区的北京西部山区为例,利用研究区森林类型的季相特征、已有的少部分林相图、Google Earth免费影像数据等信息选择不同坡向的相同森林类型做训练样本,通过加入其他辅助数据（海拔和坡向数据）,来提高Landsat TM影像的森林类型分类精度,同时对比了基于像元和面向对象方法提取森林类型的效果。结果表明：1）就半干旱山区的森林类型划分来说,TM影像的TM4、TM5、TM4-TM2及辅助数据DEM和坡向可作为TM影像森林类型划分的最佳数据源。2）单独加入海拔信息,阔叶林的提取精度提高23%,针叶林和混交林的分类精度只提高了4%~5%;单独加入坡向信息,阔叶林的提取精度只提高21%,但是针叶林和混交林的分类精度则分别提高了13%、18%,显著优于单独加入海拔信息的效果。同时加入海拔信息和坡向信息,至少可以准确区分出约70%以上的针叶林、阔叶林和混交林。3）就本研究区而言,坡向比海拔更有效地辅助提高森林分类精度。4）就混淆矩阵数据而言,面向对象的分类方法比基于像元分类结果总体精度低3%,Kappa系数低4%,但面向对象的分类结果更加符合研究区实际情况。该研究对中分辨率影像应用于半干旱山区森林类型划分具有一定的借鉴意义。

关键词: DEM, Landsat TM, 半干旱山区, 面向对象分类, 坡向, 森林类型制图

Abstract: Since forest is an important indicator of global climate change, the way to extract forest changing should be top priority in forest management and utilization. Especially, the extraction of sub-categories of forest vegetation has always been a difficult point in remote sensing image classification. Therefore, it is important to find a suitable method for forest type mapping, especially in regions with diverse climatic conditions and complex terrain. The present study discussed various methods that could be used to improve the accuracy of forest type classification using Landsat Thematic Mapper (TM) imagery data, taking a semiarid mountainous area in Beijing, China as an example. All classification results were compared with confusion matrices and Kappa statistics. The results showed that: 1) The combination of a digital elevation model (DEM), aspect data, TM4 and TM5, and a synthetic band (TM4-TM2) comprised an optimal dataset when using pixel-based classification. 2) Elevation alone could increase the accuracy by 23% in broad-leaved forest, whereas by 4%-5% in coniferous and mixed forest. Meanwhile, aspect alone could increase the accuracy by 21% in broad-leaved forest, whereas by 13% in coniferous forest and 18% in mixed forest, respectively. Aspect can provide more valuable information for forest mapping than elevation. 3) According to the confusion matrices, the accuracy of pixel-based classifications was slightly higher than that of object-based classification. 4) However, the latter seemed to consist with field investigations better. Our findings implied that integrating distributional characteristics of forests in semiarid regions with Landsat TM imagery could improve the accuracy of forest stand mapping at a regional scale.

Key words: aspect, DEM, forest type mapping, Landsat TM imagery, object-oriented classification, semiarid region

中图分类号:

S771.8

王晓学, 沈会涛, 林田苗, 景峰, 李叙勇, 孔凡利. 利用多信息源提高半干旱地区TM影像的森林类型制图精度：以北京西部山区为例[J]. 自然资源学报, 2017, 32(7): 1217-1228.

WANG Xiao-xue, SHEN Hui-tao, LIN Tian-miao, JING Feng, LI Xu-yong, KONG Fan-li. Improving the Forest Type Mapping Accuracy in Semiarid Mountainous Areas Based on TM Images—Take the West Mountain of Beijing as an Example[J]. JOURNAL OF NATURAL RESOURCES, 2017, 32(7): 1217-1228.

参考文献

[1] GRONDIN P, GAUTHIER S, BORCARD D, et al. A new approach to ecological land classification for the Canadian boreal forest that integrates disturbances [J]. Landscape Ecology, 2014, 29(1): 1-16.
[2] FFOLLIOTT P, BROOKS K, GREGERSEN H. Dryland Forestry: Planning and Management [M]. John Wiley and Sons, 1995.
[3] 张超, 黄清麟, 朱雪林, 等. 基于ETM+和DEM的西藏灌木林遥感分类技术 [J]. 林业科学, 2011, 47(1): 15-21. [ZHANG C, HUANG Q L, ZHU X L et al. Remote sensing classification technique of Shrub in Tibet based on ETM+ and DEM. Scientia Silvae Sinicae, 2011, 47(1): 15-21. ]
[4] 成晓英. 基于特征库的信息提取在森林资源调查中的应用研究 [D]. 武汉: 中国地质大学, 2012. [CHENG X Y. Based on the Characteristics of Library Information Extraction and its Application in Forest Resource Survey Research. Wuhan: China University of Geosciences, 2012. ]
[5] GIRI C, ZHU Z, TIESZEN L, et al. Mangrove forest distributions and dynamics (1975-2005) of the tsunami-affected region of Asia [J]. Journal of Biogeography, 2007, 35(3): 519-528.
[6] KUEMMERLE T, CHASKOVSKYY O, KNORN J, et al. Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007 [J]. Remote Sensing of Environment, 2009, 113(6): 1194-1207.
[7] TOWNSEND P, HELMERS D, KINGDON C, et al. Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976-2006 Landsat time series [J]. Remote Sensing of Environment, 2009, 113(1): 62-72.
[8] GAVIER-PIZARRO G, KUEMMERLE T, HOYOS L, et al. Monitoring the invasion of an exotic tree ( Ligustrum lucidum ) from 1983 to 2006 with Landsat TM/ETM+ satellite data and support vector machines in Córdoba, Argentina [J]. Remote Sensing of Environment, 2012, 122: 134-145.
[9] SINGH S, SRIVASTAVA P, GUPTA M, et al. Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine [J]. Environmental Earth Sciences, 2014, 71(5): 2245-2255.
[10] FOODY G, BOYD D, CUTLER M. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions [J]. Remote Sensing of Environment, 2003, 85(4): 463-474.
[11] HILL R. Image segmentation for humid tropical forest classification in Landsat TM data [J]. International Journal of Remote Sensing, 1999, 20(5): 1039-1044.
[12] SESNIE S E, GESSLER P E, FINEGAN B, et al. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments [J]. Remote Sensing of Environment, 2008, 112(5): 2145-2159.
[13] SADER S A, AHL D, LIOU W S. Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine [J]. Remote Sensing of Environment, 1995, 53(3): 133-144.
[14] KOVACS J M, LIU Y, ZHANG C, et al. A field based statistical approach for validating a remotely sensed mangrove forest classification scheme [J]. Wetlands Ecology and Management, 2011, 19(5): 409-421.
[15] BRANDT J S, KUEMMERLE T, LI H, et al. Using Landsat imagery to map forest change in southwest China in response to the national logging ban and ecotourism development [J]. Remote Sensing of Environment, 2012, 121: 358-369.
[16] 陈艳华, 张万昌. 地理信息系统支持下的山区遥感影像决策树分类 [J]. 国土资源遥感, 2006(1): 69-74. [CHEN Y H, ZHANG W C. GIS supported decision tree classification of remote sensing images in mountainous areas. Remote Sensing for Land & Resources, 2006(1): 69-74. ]
[17] DORREN L K A, MAIER B, SEIJMONSBERGEN A C. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification [J]. Forest Ecology and Management, 2003, 183(1/3): 31-46.
[18] 游晓斌, 王蕾. 应用辅助信息提高森林分类和森林区划能力的研究 [J]. 北京林业大学学报, 2003, 25(S1): 41-42. [YOU X B, WANG L. Application of auxiliary information to improve forest classification capability and forest division. Journal of Beijing Forestry University, 2003, 25(S1): 41-42. ]
[19] 李纯, 吴俐民, 左小清. 一种基于像元和面向对象的库塘信息提取方法 [J]. 昆明理工大学学报(自然科学版), 2011, 36(1): 7-11. [LI C, WU L M, ZUO X Q. An approach of reservoirs and ponds extraction based on pixels and object-oriented method. Journal of Kunming University of Science and Technology (Natural Science Edition), 2011, 36(1): 7-11. ]
[20] 郭亚鸽, 于信芳, 江东, 等. 面向对象的森林植被图像识别分类方法 [J]. 地球信息科学学报, 2012, 14(4): 514-522. [GUO Y G, YU X F, JIANG D, et al. Study on forest classification based on object oriented techniques. Journal of Geo-Information Science, 2012, 14(4): 514-522. ]
[21] 马婷婷. 基于最佳尺度的面向对象高分辨率遥感影像分类及应用 [D]. 成都: 西南交通大学, 2012. [MA T T. The Object-oriented Classification and Application of High-resolution Remote Sensing Image Based on the Optimal Scale. Chengdu: Southwest Jiaotong University, 2012. ]
[22] 马克平, 陈灵芝, 于顺利, 等. 北京东灵山地区植物群落的基本类型 [M]// 陈灵芝. 暖温带森林生态系统结构与功能的研究 [C]. 北京: 科学出版社, 1997: 56-75. [MA K P, CHEN L Z, YU S L, et al. Basic types of plant community in Donglingshan Mountain, Beijing // CHEN L Z. Study on the Structure and Function of Forest Ecosystem in Warm Temperate Zone. Beijing: Science Press, 1997: 56-75. ]
[23] PEKKARINEN A, REITHMAIER L, STROBL P. Pan-European forest/non-forest mapping with Landsat ETM+ and CORINE Land Cover 2000 data [J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(2): 171-183.
[24] POTAPOV P, TURUBANOVA S, HANSEN M. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia [J]. Remote Sensing of Environment, 2011, 115(2): 548-561.
[25] ZHANG Z, DE WULF R R, VAN COILLIE F M B, et al. Influence of different topographic correction strategies on mountain vegetation classification accuracy in the Lancang Watershed China [J]. Journal of Applied Remote Sensing, 2011, 5(1), 053512. doi:10.1117/1.3569124.
[26] HUANG C, DAVIS L, TOWNSHEND J. An assessment of support vector machines for land cover classification [J]. International Journal of Remote Sensing, 2002, 23(4): 725-749.
[27] DIXON B, CANDADE N. Multispectral landuse classification using neural networks and support vector machines: One or the other, or both? [J]. International Journal of Remote Sensing, 2008, 29(4): 1185-1206.
[28] VAN NOORD H. The Role of Geomorphological Information in Ecological Forest Site Typology in Mountainous Areas: A Methodological Study in the E-Räetikon and NW-Montafon Mountains (Vorarlberg, Austria) [D]. Amsterdam: University of Amsterdam, 1996.
[29] 林超, 李昌文. 阴阳坡在山地地理研究中的意义 [J]. 地理学报, 1985, 40(1): 20-28. [LIN C, LI C W. The significance of aspect in mountain geography. Acta Geographica Sinica, 1985, 40(1): 20-28. ]
[30] 郭晋平, 张芸香. 森林景观恢复过程中景观要素空间分布格局及其动态研究 [J]. 生态学报, 2002, 11(22): 2022-2029. [GUO J P, ZHANG Y X. Studies on the dynamics and distribution pattern of landscape elements in the forest landscape restoration process in Guandishan Forest region. Acta Ecologica Sinica, 2002, 11(22): 2022-2029. ]
[31] 马克明, 傅伯杰, 周华锋. 北京东灵山地区森林的物种多样性和景观格局多样性研究 [J]. 生态学报, 1999, 19(1): 1-7. [MA K M, FU B J, ZHOU H F. Studies on species and pattern diversities of the forest landscapes of Donglingshan Mountain region, Beijing, China. Acta Ecologica Sinica, 1999, 19(1): 1-7. ]
[32] 章皖秋, 李先华, 罗庆州, 等. 基于RS, GIS 的天目山自然保护区植被空间分布规律研究 [J]. 生态学杂志, 2003, 22(6): 21-27. [ZHANG W Q, LI X H, LUO Q Z, et al. Spatial distribution of vegetation in Tianmu Mountain Nature Reserve based on RS and GIS data. Chinese Journal of Ecology, 2003, 22(6): 21-27. ]
[33] 竞霞, 王锦地, 王纪华, 等. 基于分区和多时相遥感数据的山区植被分类研究 [J]. 遥感技术与应用, 2008, 23(4): 394-397. [JING X, WANG J D, WANG J H, et al. Classifying forest vegetation using sub-region classification based on multi-temporal remote sensing images. Remote Sensing Technology and Application, 2008, 23(4): 394-397. ]
[34] 莫源富, 周立新. 分区分类法针对山区遥感图像的一种有效的分类方法 [J]. 中国岩溶, 2000, 19(4): 360-365. [MO Y F, ZHOU L X. Sub-region classification method—A new classification method to remote sensing image in mountain areas. Carsologica Sinica, 2000, 19(4): 360-365. ]
[35] 刘健, 许章华, 余坤勇, 等. 山地丘陵区遥感影像阴影检测与去除方法 [J]. 农业机械学报, 2013, 44(10): 238-241. [LIU J, XU Z H, YU K Y, et al. Shadow detection and removal method for remote sensing image in mountainous and hilly area. Transactions fo the CSAM, 2013, 44(10): 238-241. ]
[36] YU Q, GONG P, CLINTON N, et al. Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery [J]. Photogrammetric Engineering and Remote Sensing, 2006, 72(7): 799-811.
[37] GAO Y, MAS J. A comparison of the performance of pixel based and object based classifications over images with various spatial resolutions [J]. Online Journal of Earth Sciences, 2008, 2(1): 27-35.
[38] 牟智慧, 杨广斌. 基于TM影像面向对象的桉树信息提取 [J]. 林业资源管理, 2014(2): 119-125. [MOU Z H, YANG G B. The extraction of Eucalyptus information based on TM data using object-oriented method. Forest Resources Management, 2014(2): 119-125. ]
[39] 李秀瑞, 孙林, 朱金山, 等. 多尺度遥感数据协同的干旱地区植被覆盖度提取 [J]. 生态学杂志, 2016, 35(5): 1394-1402. [LI X R, SUN L, ZHU J S, et al. Extraction of vegetation coverage in arid regions using multi-scale remote sensing data synergistically. Chinese Journal of Ecology, 2016, 35(5): 1394-1402. ]

利用多信息源提高半干旱地区TM影像的森林类型制图精度：以北京西部山区为例

Improving the Forest Type Mapping Accuracy in Semiarid Mountainous Areas Based on TM Images—Take the West Mountain of Beijing as an Example

RichHTML

PDF (PC)

赞

可视化

被引次数

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 14

Metrics

本文评价

推荐阅读 0

[1]	徐春海, 李忠勤, 王飞腾, 王林. 基于LiDAR、SRTM DEM的祁连山黑河流域十一冰川2000—2012年物质平衡估算[J]. 自然资源学报, 2017, 32(1): 88-100.
[2]	李娅芸, 刘雷, 安韶山, 曾全超, 李鑫. 应用Le Bissonnais法研究黄土丘陵区不同植被区及坡向对土壤团聚体稳定性和可蚀性的影响[J]. 自然资源学报, 2016, 31(2): 287-298.
[3]	汝海丽, 张海东, 焦峰, 郭美丽, 薛超玉. 黄土丘陵区微地形梯度下草地群落植物与土壤碳、氮、磷化学计量学特征[J]. 自然资源学报, 2016, 31(10): 1752-1763.
[4]	程乾, 陈金凤. 基于高分1号杭州湾南岸滨海陆地土地覆盖信息提取方法研究[J]. 自然资源学报, 2015, 30(2): 350-360.
[5]	王彩艳, 王瑷玲, 王介勇, 王瑞燕, 姜峥嵘. 基于面向对象的海岸带土地利用信息提取研究[J]. 自然资源学报, 2014, 29(9): 1589-1597.
[6]	沈恬, 李璐, 陈远生, 窦闻. 基于IKONOS影像的城市住宅区中水回用空间分布估算[J]. 自然资源学报, 2014, 29(2): 344-354.
[7]	刘焱序, 薛亮, 刘宪锋. 生态恢复背景下关中地区城镇与植被空间优化协调研究[J]. 自然资源学报, 2013, 28(9): 1515-1525.
[8]	林凯荣,郭生练,熊立华,牛存稳. DEM栅格分辨率对TOPMODEL模拟不确定性的影响研究[J]. 自然资源学报, 2010, 25(6): 1022-1032.
[9]	李飞, 王春, 赵军, 郑佳佳. 中国陆地多年平均积温空间化研究[J]. 自然资源学报, 2010, 25(5): 778-784.
[10]	张海龙, 刘高焕, 叶宇, 黄翀. 青藏高原短波辐射分布式模拟及其时空分布[J]. 自然资源学报, 2010, 25(5): 811-821.
[11]	穆振侠, 姜卉芳, 彭亮. 冰川积雪融水补给为主河流水库蓄水期起始时间的判定[J]. 自然资源学报, 2010, 25(2): 200-206.
[12]	程晨, 蔡喆, 闫维, 李洪远, 孟伟庆, 郝翠, 莫训强. 基于Landsat TM/ETM+的天津城区及滨海新区热岛效应时空变化研究[J]. 自然资源学报, 2010, 25(10): 1727-1737.
[13]	吴险峰, 刘昌明, 王中根, . 栅格DEM的水平分辨率对流域特征的影响分析[J]. 自然资源学报, 2003, 18(2): 148-154.
[14]	王中根, 刘昌明, 吴险峰, . 基于DEM的分布式水文模型研究综述[J]. 自然资源学报, 2003, 18(2): 168-173.