基于高分1号影像的森林植被信息提取
Extraction of Forest Vegetation Information Using GF-1 Imagery
查看参考文献23篇
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文摘
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实时最新森林植被信息的提取是林业航空植保作业的必要前提。论文以安徽省蚌埠市为研究区域,探讨了基于高分1号卫星遥感数据在亚热带农林植被混合地区的森林植被信息提取。根据植被物候信息差异选择了提取森林植被信息的5个关键时期高分影像,采用分区决策树方法监测森林植被的空间分布和面积信息,并与未分区决策树法的提取结果进行比较。结果表明:采用分区决策树法和未分区决策树法对于大中尺度森林植被信息提取的总体精度均优于85%。但分区决策树森林植被提取总体精度达到90.72%,较未分区决策树法提高3.80%、4.65%,Kappa系数达到0.81,较未分区决策树法提高约0.07~0.10,结合植被物候信息的分区决策树森林植被提取法好于未分区决策树法,能够满足林业航空植保作业的精度需求。具有较高空间分辨率、宽覆盖、短重访周期的高分1号影像,对于大区域的林业航空植保当年最新森林植被信息的提取表现出较大的潜力。 |
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其他语种文摘
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Accurate and up-to-date forest vegetation mapping can provide a better understanding of forest resources and support decision-makers in implementing sustainable forest management. Unfortunately, the distribution information of forests plantation with high accuracy and fine spatial resolution is still not yet conveniently available. Remote- sensing technologies are common used in mapping forest vegetation owing to their real- time data acquisition ability. However, extraction of forest vegetation information using single date remote- sensing imagery has been unsuccessful since the existence of similarity in spectrum feature between forest and field crops. Combination of seasonal variations of spectral response and phenological differences between forest vegetation and field crops presents a unique opportunity for forest mapping. Therefore, a method for extracting forest vegetation using multi- temporal GF- 1 imagery was proposed and validated in Bengbu City. Based on the phenological changes of forest and dominant field crops in the study area, the whole region was separated into 2 sub-regions (subregion A and sub-region B), and 5 phases of GF-1 imagery were utilized. Then, 2 sets of decision rules were built and applied to the corresponding sub-regions. In addition, we implemented forest extraction by non- partitioned decision tree for comparative analysis. The results show that the overall accuracy of both partitioned and non- partitioned decision trees are over 85%, which means that decision tree method using multi-temporal GF-1 imagery can acquire good accuracy when extracting forest vegetation in the large scale and mesoscale. Partitioned decision tree achieves overall accuracy of 90.72% and kappa coefficient of 0.81, which are 3.80%-4.65% and 0.07-0.10 higher than the overall accuracy and kappa coefficient of non-partitioned decision tree, respectively. Free GF-1 imageries with a fine spatial resolution, wide coverage, and low revisit period have great potentials in forests extraction which can benefit forestry aviation plant protection. |
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来源
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自然资源学报
,2018,33(6):1068-1079 【核心库】
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DOI
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10.31497/zrzyxb.20170570
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关键词
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航空植保
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森林植被
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高分1号
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决策树
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多时相
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地址
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1.
北京农业信息技术研究中心, 北京, 100097
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山东瑞达有害生物防控有限公司, 济南, 250101
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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1000-3037 |
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学科
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林业 |
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基金
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国家自然科学基金资助项目
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北京市农林科学院青年科研基金
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文献收藏号
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CSCD:6261428
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参考文献 共
23
共2页
|
|
1.
冯仲科. 森林调查装备与信息化技术发展分析.
农业机械学报,2015,46(9):257-265
|
被引
19
次
|
|
|
|
|
2.
李涛. 遥感影像判读地类在贵州省林地变更调查中的应用.
贵州农业科学,2013,41(9):172-175
|
被引
1
次
|
|
|
|
|
3.
庞国锦. “三北”防护林建设以来河西走廊林地变化的遥感监测.
中国沙漠,2012,32(2):539-544
|
被引
5
次
|
|
|
|
|
4.
张兴余. 乌兰布和沙漠高分辨率遥感影像梭梭林解译方法探讨.
遥感技术与应用,2010,25(6):828-835
|
被引
2
次
|
|
|
|
|
5.
杨存建. 基于多时相MODIS数据的四川省森林植被类型信息提取.
自然资源学报,2014,29(3):507-515
|
被引
9
次
|
|
|
|
|
6.
张颖. 基于Landsat-8影像森林植被信息计算机自动提取研究.
中国农学通报,2014,30(28):61-66
|
被引
3
次
|
|
|
|
|
7.
雷光斌. 基于阈值法的山区森林常绿、落叶特征遥感自动识别方法——以贡嘎山地区为例.
生态学报,2014,34(24):7210-7221
|
被引
15
次
|
|
|
|
|
8.
Chen B Q. Mapping tropical forests and deciduous rubber plantations in Hainan Island, China by integrating PALSAR 25-m and multi-temporal Landsat images.
International Journal of Applied Earth Observation and Geoinformation,2016,50:117-130
|
被引
11
次
|
|
|
|
|
9.
雷光斌. 基于多源多时相遥感影像的山地森林分类决策树模型研究.
遥感技术与应用,2016,31(1):31-41
|
被引
10
次
|
|
|
|
|
10.
贾明明. 基于环境星与MODIS时序数据的面向对象森林植被分类.
生态学报,2014,34(24):7167-7174
|
被引
30
次
|
|
|
|
|
11.
中华人民共和国国家统计局.
中国统计年鉴,2015
|
被引
89
次
|
|
|
|
|
12.
陈信文.
蚌埠市农业产业结构优化研究,2015
|
被引
1
次
|
|
|
|
|
13.
Hadjimitsis D G. Atmospheric correction for satellite remotely sensed data intended for agricultural applications: Impact on vegetation indices.
Natural Hazards & Earth System Sciences,2010,10(1):89-95
|
被引
9
次
|
|
|
|
|
14.
Rouse J W Jr. Monitoring vegetation systems in the great plains with ERTS.
Third Earth Resources Technology Satellite-1 Symposium Volume I: Technical Presentations. NASA SP-351,1974:309-317
|
被引
1
次
|
|
|
|
|
15.
陈利.
基于混合像元分解方法的MODIS森林类型识别研究,2014
|
被引
3
次
|
|
|
|
|
16.
贺鹏. 基于多时相GF - 1遥感影像的作物分类提取.
河南农业科学,2016,45(1):152-159
|
被引
7
次
|
|
|
|
|
17.
竞霞. 基于分区和多时相遥感数据的山区植被分类研究.
遥感技术与应用,2008,23(4):394-397
|
被引
21
次
|
|
|
|
|
18.
徐伟燕. 基于资源三号卫星影像的茶树种植区提取.
农业工程学报,2016,32(S1):161-168
|
被引
21
次
|
|
|
|
|
19.
Coburn C A. A multiscale texture analysis procedure for improved forest stand classification.
International Journal of Remote Sensing,2004,25(20):4287-4308
|
被引
27
次
|
|
|
|
|
20.
陈文倩. 基于高分一号影像的土地覆被分类方法初探.
干旱区地理,2016,39(1):182-189
|
被引
11
次
|
|
|
|
|
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