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Objective: Change monitoring and disaster assessment of hurricane-damaged forests is an important application of remote sensing technology. The extraction of feature information from remote sensing images is very important to the effect of forest remote sensing monitoring. The combination of diversity features can effectively improve the monitoring accuracy of forest change. 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 will be affected if the sliding window is too large or too small. Therefore, the calculation method of this kind of texture feature is always faced with the problem of a hard balance between the number of features and the neighborhood reference range. Therefore, this paper focuses on forest change monitoring technology. Method: To solve the above problems, in this paper, a remote sensing monitoring method for forest destruction before and after hurricanes is proposed based on diversity feature collaborative technology. Firstly, the difference between normalized vegetation index (NDVI) and enhanced vegetation index (NDVI) before and after forest remote sensing image change was calculated. Secondly, 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 were used to build a diversity characteristic combination model. This 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. Result: The study area used in this study is the remote sensing image of the Nezer forest in France before and after the hurricane, which is derived from the Formosat-2 satellite. In the experimental part, the classification performance of the proposed algorithm is compared with the other six methods. The experimental results show that compared with the traditional change detection methods based on spectral features and texture features, the overall accuracy of the proposed method, the detection accuracy of the changed area and the unchanged area are improved by 3.68%, 5.82% and 3.46%, respectively. In addition, this paper tested the sensitivity of features extracted by different methods to the number of training samples. The results show that the proposed method still maintains high classification accuracy in different training sample numbers, and the overall accuracy and Kappa coefficient are better than the comparison methods. And after the number of training samples reaches 50, the accuracy of the proposed method tends to flatten out. Conclusion: The proposed method can effectively improve the performance of forest change monitoring. This method can be used to monitor the change and damage of forests in real-time, and obtain the information of forest disaster areas efficiently, which can provide important reference data for the emergency decision-making of forest resource management departments. Therefore, this method has high practical value.