基于多样性特征协同技术的飓风前后森林破坏遥感监测
Diversity features collaboration technology for monitoring forests before and after hurricanes by remote sensing
- 2022年26卷第9期 页码：1838-1848
收稿：2020-06-29，
纸质出版：2022-09-07
DOI： 10.11834/jrs.20210230
移动端阅览
摘要
对被飓风破坏的森林进行变化监测与灾害评估是遥感技术的一个重要应用，遥感影像的特征信息提取对森林遥感监测的效果至关重要。多样性特征结合可以有效提高对森林变化的监测精度。然而，当前的空间信息如纹理特征的获取算法依旧保留着传统的固定式计算模式，一直面临着特征数量和邻域参考范围之间难以均衡的问题。为了解决以上问题，本文提出了基于多样性特征协同技术的飓风前后森林破坏遥感监测方法，首先计算出森林遥感影像变化前后的归一化植被指数差值和增强植被指数差值，并提出了基于复合窗口技术的来提取纹理特征，然后建立了多样性特性结合模型；其次提出了一种基于特征分离的旋转森林改进算法，最终，实现了内泽尔森林在暴风前后的高精度变化监测；另外，还测试了新模型在不同训练样本数量下的分类性能。实验结果表明，相对传统的基于光谱特征和单纯的纹理特征的变化监测方法，本文所提出的方法的整体精度、对变化区域和未变化区域的检测精度至多分别提高了3.68%、6.53%和3.46%。本文的研究方法可以有效提高森林变化监测的性能，为森林灾害评估与森林资源保护提供参考依据。
Abstract
Change monitoring and disaster assessment of hurricane-damaged forests are important applications of remote sensing technology. The extraction of feature information from remote sensing images is very important to forest remote sensing monitoring. The combination of diversity features can effectively improve the accuracy of forest change monitoring. However
the current spatial information acquisition algorithms
such as texture features
still retain the traditional fixed computing model and do not fully consider the diversity of the spatial distribution of ground objects. When extracting texture features
the accuracy of texture features is affected when the sliding window is extremely large or small. Therefore
the calculation method of this kind of texture feature is always faced with the problem of hard balance between the number of features and the neighborhood reference range. Therefore
this paper focuses on forest change monitoring technology. To solve the above problems
a remote sensing monitoring method of forest destruction before and after hurricanes are proposed based on diversity features collaborative technology. First
the difference between the normalized vegetation index (NDVI) and the Enhanced Vegetation Index (EVI) before and after forest remote sensing image change is calculated. Second
the compound window technology is proposed to extract the texture features. Then
the texture features extracted from the remote sensing image and the spectrum of the remote sensing image are used to build a diverse characteristic combination model. This model can enhance the diversity of features. Finally
an improved rotating forest algorithm based on feature separation is proposed to reduce the direct feature correlation and improve the accuracy of the classifier. The study area is the remote sensing images of the Nezer forest in France before and after the hurricane
which were obtained from the Formosat-2 satellite. In the experimental part
the classification performance of the proposed algorithm was compared with those of six other methods. Experimental results show that compared with the traditional change detection methods based on spectral and texture features
the overall accuracy of the proposed method
the detection accuracy of the changed area
and the unchanged area improved by 3.68%
6.53% and 3.46%
respectively. In addition
the sensitivity of the features extracted by different methods to the number of training samples was tested. The results show that the proposed method maintains high classification accuracy on different training sample numbers
and its overall accuracy and Kappa coefficient are better than those of the comparison methods. After the number of training samples reaches 50
the accuracy of the proposed method tends to flatten. The proposed method can effectively improve the performance of forest change monitoring. This method can be used to monitor the change of and damage to forests in real time and efficiently obtain information on forest disaster areas
providing important reference data for the emergency decision-making of forest resource management departments. Therefore
this method has a high practical value.
关键词
Keywords
references
Bar S , Parida B R and Pandey A C . 2020 . Landsat-8 and Sentinel-2 based forest fire burn area mapping using machine learning algorithms on GEE cloud platform over Uttarakhand, Western Himalaya . Remote Sensing Applications: Society and Environment , 18 : 100324 [ DOI: 10.1016/j.rsase.2020.100324 http://dx.doi.org/10.1016/j.rsase.2020.100324 ]
Boutet Jr J C and Weishampel J F . 2003 . Spatial pattern analysis of pre-and post-hurricane forest canopy structure in North Carolina, USA . Landscape Ecology , 18 ( 6 ): 553 - 559 [ DOI: 10.1023/A:1026058312853 http://dx.doi.org/10.1023/A:1026058312853 ]
Camarretta N , Harrison P A , Bailey T , Potts B , Lucieer A , Davidson N and Hunt M . 2020 . Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches . New Forests , 51 ( 4 ): 573 - 596 [ DOI: 10.1007/s11056-019-09754-5 http://dx.doi.org/10.1007/s11056-019-09754-5 ]
Chen K , Peng Z P and Ke W D . 2013 . Remote sensing image classification based on improvement of neural network HSV modle . Computer Simulation , 30 ( 1 ): 423 - 426
陈珂 , 彭志平 , 柯文德 . 2013 . 遥感图像异物同谱干扰消除技术研究与仿真 . 计算机仿真 , 30 ( 1 ): 423 - 426 [ DOI: 10.3969/j.issn.1006-9348.2013.01.099 http://dx.doi.org/10.3969/j.issn.1006-9348.2013.01.099 ]
Christopoulou A , Mallinis G , Vassilakis E , Farangitakis G P , Fyllas N M , Kokkoris G D and Arianoutsou M . 2019 . Assessing the impact of different landscape features on post-fire forest recovery with multitemporal remote sensing data: the case of Mount Taygetos (Southern Greece) . International Journal of Wildland Fire , 28 ( 7 ): 521 - 532 [ DOI: 10.1071/WF18153 http://dx.doi.org/10.1071/WF18153 ]
Dong L P , Li X Z , Wang G L and Sun Q Q . 2017 . Change detection method of construction land based on multiple feature fusion . Remote Sensing Information , 32 ( 5 ): 152 - 156
董丽萍 , 李新芝 , 王广亮 , 孙茜茜 . 2017 . 融合多特征的建设用地变化检测方法 . 遥感信息 , 32 ( 5 ): 152 - 156 [ DOI: 10.3969/j.issn.1000-3177.2017.05.022 http://dx.doi.org/10.3969/j.issn.1000-3177.2017.05.022 ]
Du X J , Zhang C , Yang J Y and Su W . 2013 . A new multi-feature approach to object-oriented change detection based on fuzzy classification . Intelligent Automation and Soft Computing , 18 ( 8 ): 1063 - 1073 [ DOI: 10.1080/10798587.2008.10643311 http://dx.doi.org/10.1080/10798587.2008.10643311 ]
FAO and UNEP . 2020 . The State of the World’s Forests 2020: Forests, Biodiversity and People . Rome : FAO and UNEP . https://doi.org/10.4060/ca8642en. 2020. 978-92-5-132419-6 https://doi.org/10.4060/ca8642en.2020.978-92-5-132419-6
Feng W and Bao W X . 2017 . Weight-based rotation forest for hyperspectral image classification . IEEE Geoscience and Remote Sensing Letters , 14 ( 11 ): 2167 - 2171 [ doi: 10.1109/LGRS.2017.2757043 http://dx.doi.org/10.1109/LGRS.2017.2757043 ]
Feng W , Dauphin G , Huang W J , Quan Y H , Bao W X , Wu M Q and Li Q . 2019a . Dynamic synthetic minority over-sampling technique-based rotation forest for the classification of imbalanced hyperspectral data . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 12 ( 7 ): 2159 - 2169 [ DOI: 10.1109/JSTARS.2019.2922297 http://dx.doi.org/10.1109/JSTARS.2019.2922297 ]
Feng W , Dauphin G , Huang W J , Quan Y H and Liao W Z . 2019b . New margin-based subsampling iterative technique in modified random forests for classification . Knowledge-Based Systems , 182 : 104845 [ DOI: 10.1016/j.knosys.2019.07.016 http://dx.doi.org/10.1016/j.knosys.2019.07.016 ]
Feng W , Quan Y , Dauphin G , Wu P X , Bie B W , Tong Y P , Yuan X G , Li J and Xing M D . 2020 . Feature Separation Based Rotation Forest for Hyperspectral Image Classification // 2020 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE : 489 - 492 [ DOI: 10.1109/IGARSS39084.2020.9323854 http://dx.doi.org/10.1109/IGARSS39084.2020.9323854 ]
Franklin S E , Hall R J , Moskal L M , Maudie A J and Lavigne M B . 2000 . Incorporating texture into classification of forest species composition from airborne multispectral images . International Journal of Remote Sensing , 21 ( 1 ): 61 - 79 [ DOI: 10.1080/014311600210993 http://dx.doi.org/10.1080/014311600210993 ]
Garcı́a-Sevilla P and Petrou M . 2001 . Analysis of irregularly shaped texture regions . Computer Vision and Image Understanding , 84 ( 1 ): 62 - 76 [ DOI: 10.1006/cviu.2001.0941 http://dx.doi.org/10.1006/cviu.2001.0941 ]
Kulkarni A . 2004 . Evaluation of the Impacts of Hurricane Hugo on the Land Cover of Francis Marion National Forest, South Carolina Using Remote Sensing . Baton Rouge : Louisiana State University [ DOI: 10.31390/gradschool_theses.1345 http://dx.doi.org/10.31390/gradschool_theses.1345 ]
Gong P , Chen Z , Tang H , and Zhang F . 2006 . Land cover classification based on multi-temporal MODIS NDVI and LST in Northeastern China // 2006 IEEE International Symposium on Geoscience and Remote Sensing . IEEE : 1149 - 1152 [ DOI: 10.1109/IGARSS.2006.297 http://dx.doi.org/10.1109/IGARSS.2006.297 ]
Huang C B , Dian Y Y , Zhou Z X , Wang D , and Chen R D . 2015 . Forest change detection based on time series images with statistical properties . J Remote Sens , 19 ( 4 ), 657 - 668
黄春波 , 佃袁勇 , 周志翔 , 王娣 , 陈瑞冬 . 2015 . 基于时间序列统计特性的森林变化监测 . 遥感学报 , 19 ( 4 ), 657 - 668 [ DOI: 10.11834/jrs.20154104 http://dx.doi.org/10.11834/jrs.20154104 ]
Li L , Shu N , Wang K and Gong Y . 2014 . Change detection method for remote sensing images based on multi-features fusion . Acta Geodaetica et Cartographica Sinica , 43 ( 9 ): 945 - 953
李亮 , 舒宁 , 王凯 , 龚龑 . 2014 . 融合多特征的遥感影像变化检测方法 . 测绘学报 , 43 ( 9 ): 945 - 953 [ DOI: 10.13485/j.cnki.11-2089.2014.0138 http://dx.doi.org/10.13485/j.cnki.11-2089.2014.0138 ]
Lin Q H . 2012 . Enhanced vegetation index using moderate resolution imaging spectroradiometers // 2012 5th International Congress on Image and Signal Processing . IEEE : 1043 - 1046 [ DOI: 10.1109/CISP.2012.6470008 http://dx.doi.org/10.1109/CISP.2012.6470008 ]
Lyon J G , Yuan D , Lunetta R and Elvidge C . 1998 . A change detection experiment using vegetation indices . Photogrammetric Engineering and Remote Sensing , 64 ( 2 ): 143 - 150 [ DOI: 10.1.1.462.2056 http://dx.doi.org/10.1.1.462.2056 ]
Murray H , Lucieer A and Williams R . 2010 . Texture-based classification of sub-antarctic vegetation communities on heard island . International Journal of Applied Earth Observation and Geoinformation , 12 ( 3 ): 138 - 149 [ DOI: 10.1016/j.jag.2010.01.006 http://dx.doi.org/10.1016/j.jag.2010.01.006 ]
Myers R K , Van Lear D H . 1998 . Hurricane-fire interactions in coastal forests of the south: a review and hypothesis . Forest Ecology and Management , 103 ( 2-3 ): 265 - 276 [ DOI: 10.1016/S0378-1127(97)00223-5 http://dx.doi.org/10.1016/S0378-1127(97)00223-5 ]
Nelson R F . 1982 . Detecting Forest Canopy Change Using Landsat . Technical Memorandum No . 83918 . NASA Goddard Space Flight Center [ DOI: http://dx.doi.org/ http://dx.doi.org/http://dx.doi.org/ ]
Peña-Barragán J M , Ngugi M K , Plant R E and Six J . 2011 . Object-based crop identification using multiple vegetation indices, textural features and crop phenology . Remote Sensing of Environment , 115 ( 6 ): 1301 - 1316 [ DOI: 10.1016/j.rse.2011.01.009 http://dx.doi.org/10.1016/j.rse.2011.01.009 ]
Puig D and García M A . 2001 . Determining optimal window size for texture feature extraction methods // Proceedings of the IX Spanish Symposium on Pattern Recognition and Image Analysis . Castellon, Spain: 237 - 242
Puissant A , Hirsch J and Weber C . 2005 . The utility of texture analysis to improve per-pixel classification for high to very high spatial resolution imagery . International Journal of Remote Sensing , 26 ( 4 ): 733 - 745 [ DOI: 10.1080/01431160512331316838 http://dx.doi.org/10.1080/01431160512331316838 ]
Rich R L , Frelich L , Reich P B and Bauer M E . 2010 . Detecting wind disturbance severity and canopy heterogeneity in boreal forest by coupling high-spatial resolution satellite imagery and field data . Remote Sensing of Environment , 114 ( 2 ): 299 - 308 [ DOI: 10.1016/j.rse.2009.09.005 http://dx.doi.org/10.1016/j.rse.2009.09.005 ]
Rodriguez J J , Kuncheva L I and Alonso C J . 2006 . Rotation forest: a new classifier ensemble method . IEEE Transactions on Pattern Analysis and Machine Intelligence , 28 ( 10 ): 1619 - 1630 [ DOI: 10.1109/TPAMI.2006.211 http://dx.doi.org/10.1109/TPAMI.2006.211 ]
Shao Y K , Wang L , Zhu C M , Fang H , Zhang X , Huang D and Tao L . 2020 . Forest survey and spatio-temporal analysis in West Tianshan Mountains supported by Google Earth Engine . Bulletin of Surveying and Mapping , ( 8 ): 13 - 17
邵亚奎 , 王蕾 , 朱长明 , 方晖 , 张新 , 黄端 , 陶莉 . 2020 . GEE云平台支持下的西天山森林遥感监测与时空变化分析 . 测绘通报 , ( 8 ): 13 - 17 [ DOI: 10.13474/j.cnki.11-2246.2020.0240 http://dx.doi.org/10.13474/j.cnki.11-2246.2020.0240 ]
Shen W J , Li M S , and Huang C Q . 2018 . Review of Remote Sensing algorithms for monitoring forest disturbance from time series and multi-source data fusion . J. Remote Sens , 22 , 1005 - 1022 . (沈文娟, 李明诗, 黄成全. 2018. 长时间序列多源遥感数据的森林干扰监测算法研究进展. 遥感学报, 22 ( 06 ), 1005-1022 ) [ DOI: 10.11834/jrs.20187089 http://dx.doi.org/10.11834/jrs.20187089 ]
Sun X X , Zhang J X , Yan Q and Gao J X . 2011 . A summary on current techniques and prospects of remote sensing change detection . Remote Sensing Information , 26 ( 1 ): 119 - 123
孙晓霞 , 张继贤 , 燕琴 , 高井祥 . 2011 . 遥感影像变化检测方法综述及展望 . 遥感信息 , 26 ( 1 ): 119 - 123 [ doi: 10.3969/j.issn.1000-3177.2011.01.023 http://dx.doi.org/10.3969/j.issn.1000-3177.2011.01.023 ]
Tarabalka Y , Fauvel M , Chanussot J and Benediktsson J A . 2010 . SVM- and MRF-based method for accurate classification of hyperspectral images . IEEE Geoscience and Remote Sensing Letters , 7 ( 4 ): 736 - 740 [ DOI: 10.1109/LGRS.2010.2047711 http://dx.doi.org/10.1109/LGRS.2010.2047711 ]
Tong G F , Li Y , Ding W L and Yue X Y . 2015 . Review of remote sensing image change detection . Journal of Image and Graphics , 20 ( 12 ): 1561 - 1571
佟国峰 , 李勇 , 丁伟利 , 岳晓阳 . 2015 . 遥感影像变化检测算法综述 . 中国图象图形学报 , 20 ( 12 ): 1561 - 1571 [ DOI: 10.11834/jig.20151201 http://dx.doi.org/10.11834/jig.20151201 ]
Wan L , Cen H Y , Zhu J P , Zhang J F , Du X Y and He Y . 2020 . Using fusion of texture features and vegetation indices from water concentration in rice crop to UAV remote sensing monitor . Smart Agriculture , 2 ( 1 ): 58 - 67
万亮 , 岑海燕 , 朱姜蓬 , 张佳菲 , 杜晓月 , 何勇 . 2020 . 基于纹理特征与植被指数融合的水稻含水量无人机遥感监测 . 智慧农业 , 2 ( 1 ): 58 - 67 [ DOI: 10.12133/j.smartag.2020.2.1.201911-SA002 http://dx.doi.org/10.12133/j.smartag.2020.2.1.201911-SA002 ]
Wang J , Yu K , Tian M , and Wang Z M . 2019 . Estimation of rice key phenology date using Chinese HJ-1 vegetation index time-series images // 2019 8th International Conference on Agro-Geoinformatics (Agro-Geoinformatics) . IEEE : 1 - 4 [ DOI: 10.1109/Agro-Geoinformatics.2019.8820262 http://dx.doi.org/10.1109/Agro-Geoinformatics.2019.8820262 ]
Wu J , Huang R L , Xu Z Y and Han N . 2011 . Forest fire smog feature extraction based on Pulse-Coupled neural network // Proceedings of the 6th IEEE Joint International Information Technology and Artificial Intelligence Conference . Chongqing, China : IEEE: 186 - 189 [ DOI: 10.1109/ITAIC.2011.6030182 http://dx.doi.org/10.1109/ITAIC.2011.6030182 ]
Yan W , Zhou W , Yi L L and Tian X . 2019 . Research progress of remote sensing classification and change monitoring on forest types . Remote Sensing Technology and Application , 34 ( 3 ): 445 - 454
颜伟 , 周雯 , 易利龙 , 田昕 . 2019 . 森林类型遥感分类及变化监测研究进展 . 遥感技术与应用 , 34 ( 3 ): 445 - 454 [ DOI: 10.11873/j.issn.1004-0323.2019.3.0445 http://dx.doi.org/10.11873/j.issn.1004-0323.2019.3.0445 ]
Yang Q , Wang T T , Chen H and Wang Y D . 2015 . Characteristics of vegetation cover change in Xilin Gol League based on MODIS EVI data . Transactions of the Chinese Society of Agricultural Engineering , 31 ( 22 ): 191 - 198
杨强 , 王婷婷 , 陈昊 , 王运动 . 2015 . 基于MODIS EVI数据的锡林郭勒盟植被覆盖度变化特征 . 农业工程学报 , 31 ( 22 ): 191 - 198 [ DOI: 10.11975/j.issn.1002-6819.2015.22.026 http://dx.doi.org/10.11975/j.issn.1002-6819.2015.22.026 ]
Zarco-Tejada P J , Hornero A , Hernández-Clemente R and Beck P S A . 2018 . Understanding the temporal dimension of the red-edge spectral region for forest decline detection using high-resolution hyperspectral and Sentinel-2a imagery . ISPRS Journal of Photogrammetry and Remote Sensing , 137 : 134 - 148 [ DOI: 10.1016/j.isprsjprs.2018.01.017 http://dx.doi.org/10.1016/j.isprsjprs.2018.01.017 ]
Zhang H X , Li Q Z , Liu J G , Shang J L , Du X , McNairn H , Champagne C , Dong T F and Liu M X . 2017 . Image classification using rapideye data: integration of spectral and textual features in a random forest classifier . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 10 ( 12 ): 5334 - 5349 [ DOI: 10.1109/JSTARS.2017.2774807 http://dx.doi.org/10.1109/JSTARS.2017.2774807 ]
Zheng L L , Liu Y B , Bai H Y , Jiao J Y , and Tang K L . 1993 . Effect of forest vegetation destruction on eco-environment . Research of Soil and Water Conservation , ( 1 ): 99 - 106
郑粉莉 , 刘元保 , 白红英 , 焦菊英 , 唐克丽 . 1993 . 森林植被破坏对生态环境的影响 . 水土保持研究 , ( 1 ): 99 - 106 [ DOI： CNKI:SUN:STBY.0.1993-01-017 http://dx.doi.org/CNKI:SUN:STBY.0.1993-01-017 ]
Zhuang H F , Deng K Z and Fan H D . 2016 . SAR images unsupervised change detection based on combination of texture feature vector with maximum entropy principle . Acta Geodaetica et Cartographica Sinica , 45 ( 3 ): 339 - 346
庄会富 , 邓喀中 , 范洪冬 . 2016 . 纹理特征向量与最大化熵法相结合的SAR影像非监督变化检测 . 测绘学报 , 45 ( 3 ): 339 - 346 [ DOI： CNKI:SUN:CHXB.0.2016-03-015 http://dx.doi.org/CNKI:SUN:CHXB.0.2016-03-015 ]
文章被引用时，请邮件提醒。
提交
相关作者
余龙 中山大学 地理科学与规划学院
卓莉 中山大学 地理科学与规划学院
李军 中国地质大学(武汉) 计算机学院, 智能地学信息处理湖北省重点实验室
段依琳 中国地质大学(武汉) 计算机学院, 智能地学信息处理湖北省重点实验室
翁谦 福州大学 计算机与大数据学院;空间数据挖掘与信息共享教育部重点实验室
安远 福州大学 计算机与大数据学院
陈光剑 福建省地质测绘院
吴瑞姣 福建省地质测绘院
相关机构
中山大学 地理科学与规划学院
中国地质大学(武汉) 计算机学院, 智能地学信息处理湖北省重点实验室
福州大学 计算机与大数据学院
空间数据挖掘与信息共享教育部重点实验室
福建省地质测绘院
选择翻译语言
- 地址：北京市海淀区北四环西路19号 《遥感学报》编辑部 邮编：100190
- 联系电话：010-58887052 Email：nrsb@aircas.ac.cn
- 技术支持由北京北大方正电子有限公司提供 京ICP备20021838号-9
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies
0
批量引用
导出至
BibTeXEndnoteRefManNoteExpressNoteFirst
回到顶部