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 theWest Mountain of Beijing as an Example
查看参考文献39篇
|
文摘
|
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。 |
|
其他语种文摘
|
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. |
|
来源
|
自然资源学报
,2017,32(7):1217-1228 【核心库】
|
|
DOI
|
10.11849/zrzyxb.20160159
|
|
关键词
|
森林类型制图
;
Landsat TM
;
半干旱山区
;
DEM
;
坡向
;
面向对象分类
|
|
地址
|
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.
河北省科学院地理科学研究所, 石家庄, 050021
3.
水利部水土保持植物开发管理中心, 北京, 100038
4.
中国国际工程咨询公司, 北京, 100048
5.
国家林业局调查规划设计院, 北京, 100714
|
|
语种
|
中文 |
|
文献类型
|
研究性论文 |
|
ISSN
|
1000-3037 |
|
学科
|
林业 |
|
基金
|
国家重点研发计划项目
|
|
文献收藏号
|
CSCD:6029531
|
参考文献 共
39
共2页
|
|
1.
Grondin P. A new approach to ecological land classification for the Canadian boreal forest that integrates disturbances.
Landscape Ecology,2014,29(1):1-16
|
CSCD被引
11
次
|
|
|
|
|
2.
Ffolliott P.
Dryland Forestry: Planning and Management,1995
|
CSCD被引
1
次
|
|
|
|
|
3.
张超. 基于ETM+和DEM的西藏灌木林遥感分类技术.
林业科学,2011,47(1):15-21
|
CSCD被引
7
次
|
|
|
|
|
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,2012
|
CSCD被引
1
次
|
|
|
|
|
5.
Giri C. Mangrove forest distributions and dynamics (1975-2005) of the tsunami-affected region of Asia.
Journal of Biogeography,2007,35(3):519-528
|
CSCD被引
13
次
|
|
|
|
|
6.
Kuemmerle T. Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007.
Remote Sensing of Environment,2009,113(6):1194-1207
|
CSCD被引
8
次
|
|
|
|
|
7.
Townsend P. Changes in the extent of surface mining and reclamation in the Central Appalachians detected using a 1976-2006 Landsat time series.
Remote Sensing of Environment,2009,113(1):62-72
|
CSCD被引
14
次
|
|
|
|
|
8.
Gavier-Pizarro G. Monitoring the invasion of an exotic tree (Ligustrum lucidum) from 1983 to 2006 with Landsat TM/ETM+ satellite data and support vector machines in Cordoba, Argentina.
Remote Sensing of Environment,2012,122:134-145
|
CSCD被引
5
次
|
|
|
|
|
9.
Singh S. Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine.
Environmental Earth Sciences,2014,71(5):2245-2255
|
CSCD被引
4
次
|
|
|
|
|
10.
Foody G. Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions.
Remote Sensing of Environment,2003,85(4):463-474
|
CSCD被引
63
次
|
|
|
|
|
11.
Hill R. Image segmentation for humid tropical forest classification in Landsat TM data.
International Journal of Remote Sensing,1999,20(5):1039-1044
|
CSCD被引
4
次
|
|
|
|
|
12.
Sesnie S E. Integrating Landsat TM and SRTM-DEM derived variables with decision trees for habitat classification and change detection in complex neotropical environments.
Remote Sensing of Environment,2008,112(5):2145-2159
|
CSCD被引
12
次
|
|
|
|
|
13.
Sader S A. Accuracy of Landsat-TM and GIS rule-based methods for forest wetland classification in Maine.
Remote Sensing of Environment,1995,53(3):133-144
|
CSCD被引
27
次
|
|
|
|
|
14.
Kovacs J M. A field based statistical approach for validating a remotely sensed mangrove forest classification scheme.
Wetlands Ecology and Management,2011,19(5):409-421
|
CSCD被引
1
次
|
|
|
|
|
15.
Brandt J S. Using Landsat imagery to map forest change in southwest China in response to the national logging ban and ecotourism development.
Remote Sensing of Environment,2012,121:358-369
|
CSCD被引
7
次
|
|
|
|
|
16.
陈艳华. 地理信息系统支持下的山区遥感影像决策树分类.
国土资源遥感,2006(1):69-74
|
CSCD被引
7
次
|
|
|
|
|
17.
Dorren L K A. Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification.
Forest Ecology and Management,2003,183(1/3):31-46
|
CSCD被引
20
次
|
|
|
|
|
18.
游晓斌. 应用辅助信息提高森林分类和森林区划能力的研究.
北京林业大学学报,2003,25(S1):41-42
|
CSCD被引
7
次
|
|
|
|
|
19.
李纯. 一种基于像元和面向对象的库塘信息提取方法.
昆明理工大学学报(自然科学版),2011,36(1):7-11
|
CSCD被引
1
次
|
|
|
|
|
20.
郭亚鸽. 面向对象的森林植被图像识别分类方法.
地球信息科学学报,2012,14(4):514-522
|
CSCD被引
24
次
|
|
|
|
|
了解气象卫星更多信息，请访问