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5米光学02星的成功发射与运行为林草行业各主体业务提供了丰富的遥感数据应用需求，为林业管理和生态服务提供可靠的信息保障。为测试5米光学02星多光谱数据在农田防护林监测这一林业主体业务中的应用能力，研究以河北省张北县为研究区，基于5米光学02星多光谱数据，构建了光谱、植被指数和纹理特征集，并设计4种分类信息提取方案：(1)光谱特征，(2)光谱特征+植被指数，(3)光谱特征+纹理特征，( 4 )光谱特征+植被指数+纹理特征。采用随机森林算法进行特征选择和分类信息提取、验证，最后评价了5米光学02星农田防护林信息提取的应用潜力和效果。研究结果表明：(1)基于5米光学02星多光谱数据能够准确提取研究区农田防护林信息，在较好地反映研究区农田防护林的实际分布情况。其中，方案1农田防护林分类信息提取结果总体精度、Kappa系数分别为0.8371、0.7760；方案2农田防护林分类信息提取结果总体精度、Kappa系数分别为0.8440、07855；方案3于对农田防护林信息提取总体精度、Kappa系数分别达到0.8839、0.8403；方案4分类结果精度最高，其总体精度、Kappa系数分别为0.8908、0.8499。(2)使用多种特征变量可有效提高农田防护林信息提取精度，就不同特征对农田防护林信息提取的贡献程度而言，光谱特征>纹理特征>植被指数。(3)5米光学02星多光谱数据对农田防护林信息提取精度较高，结果可靠，能够较好地满足防护林监测业务的需求，在森林调查和监测主体业务中具有巨大的应用潜力。
The successful launch and operation of the 5m Optical Satellite 02 (ZY-1-02E) have provided a wealth of remote sensing data for various main businesses within the forestry and grass industry, offering reliable information guarantees for forestry management and ecological services. This study aims to test the application capability of the ZY-1-02E multispectral data in farmland shelterbelt monitoring, a primary forestry business. Zhangbei County, Hebei Province, serves as the study area. Based on the ZY-1-02E multispectral data, spectral, vegetation index, and texture feature sets are constructed, and four classification information extraction schemes are designed: (1) spectral features, (2) spectral features + vegetation index, (3) spectral features + texture features, and (4) spectral features + vegetation index + texture features. The random forest algorithm was employed for feature selection, classification information extraction, and validation to evaluate the application potential and effectiveness of the ZY-1-02E multispectral data in farmland shelterbelt information extraction. The results show that (1) the ZY-1-02E multispectral data allows for the accurate extraction of farmland shelterbelt information in the study area, reflecting the actual distribution of farmland shelterbelts to a high degree. Among them, the overall accuracy and Kappa coefficient of Scheme 1 are 0.8371 and 0.7760; the overall accuracy and Kappa coefficient of Scheme 2 are 0.8440 and 0.7855; the overall accuracy and Kappa coefficient of Scheme 3 reach 0.8839 and 0.8403; and Scheme 4 has the highest accuracy, with its overall accuracy and Kappa coefficient being 0.8908 and 0.8499. (2) The effective use of multiple feature variables can significantly improve the accuracy of farmland shelterbelt information extraction. Regarding the contribution of different features to farmland shelterbelt information extraction, the order of importance is spectral features > texture features > vegetation indices. (3) The ZY-1-02E multispectral data exhibit high accuracy and reliable results for farmland shelterbelt information extraction, which can better meet the needs of protection forest monitoring operations and has considerable potential for application in forest surveys and monitoring thematic operations. In conclusion, this study demonstrates the potential and effectiveness of the ZY-1-02E multispectral data for extracting farmland shelterbelt information. Using multiple feature variables and the random forest algorithm enables accurate extraction and validation of farmland shelterbelt information, providing valuable insights for future forest monitoring and management. As more data become available and the application capabilities of the ZY-1-02E are further explored, future work can consider integrating multispectral data from different periods and linear features of farmland shelterbelt to enhance the accuracy of information extraction, ultimately achieving more efficient and precise extraction of farmland shelterbelt information.