سابِق|تال

Diversity features collaboration technology for monitoring forests before and after hurricanes by remote sensing

  • role： First author第一作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

  • Email：xzhong7@stu.xidian.edu.cn
  • Introduction：钟娴，研究方向为遥感图像处理。E-mail： xzhong7@stu.xidian.edu.cn
ZHONG Xian1,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

  • Email：wfeng@xidian.edu.cn
  • Introduction：冯伟，研究方向为遥感图像处理、机器学习。E-mail： wfeng@xidian.edu.cn
FENG Wei1*,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

ZHANG Yali1,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

QUAN Yinghui1,  
  • Affiliation：

    Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China

HUANG Wenjiang2,  
  • Affiliation：

    School of Electronic Engineering, Xidian University, Xi'an 710071, China

    Academy of Advanced Interdisciplinary Research, Xidian University, Xi'an 710071, China

XING Mengdao13

ملخص

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.

مفهوم

multi features;classification;rotation forest;forest monitoring;vegetation index;Formosat-2 satellite

References

  1. 1.
    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
  2. 2.
    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
  3. 3.
    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
  4. 4.
    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
  5. 5.
    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
  6. 6.
    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
  7. 7.
    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
  8. 8.
    FAO and UNEP. 2020. The State of the World’s Forests 2020: Forests, Biodiversity and People. Rome: FAO and UNEP.
  9. 9.
    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
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    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
  14. 14.
    Garcı́a-Sevilla P and Petrou M. 2001. Analysis of irregularly shaped texture regions. Computer Vision and Image Understanding, 84(1): 62-76
  15. 15.
    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
  16. 16.
    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
  17. 17.
    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
  18. 18.
    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
  19. 19.
    Lin Q H. 2012. Enhanced vegetation index using moderate resolution imaging spectroradiometers//2012 5th International Congress on Image and Signal Processing. IEEE: 1043-1046
  20. 20.
    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
  21. 21.
    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
  22. 22.
    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
  23. 23.
    Nelson R F. 1982. Detecting Forest Canopy Change Using Landsat. Technical Memorandum No. 83918. NASA Goddard Space Flight Center
  24. 24.
    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
  25. 25.
    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
  26. 26.
    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
  27. 27.
    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
  28. 28.
    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
  29. 29.
    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
  30. 30.
    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: ]
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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
  34. 34.
    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
  35. 35.
    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
  36. 36.
    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
  37. 37.
    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
  38. 38.
    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
  39. 39.
    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
  40. 40.
    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
  41. 41.
    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
  42. 42.
    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

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