基于ZY-3影像的农田防护林自动提取

摘要

【目的】利用遥感技术分析东北地区农田防护林的光谱特征和空间几何特征，探究基于农田防护林特征的高精度农田防护林自动提取方法，为大范围提取东北地区农田防护林及后续遥感监测研究提供基础数据支持。〗【方法】以吉林省中部农田防护林典型建设区的德惠、农安境内部分区域为研究区，基于资源三号(ZY-3)多光谱遥感影像，分析农田防护林的植被指数和空间几何特征；以冬天有雪覆盖地表的 Landsat8 OLI影像为辅助数据，提取居民地边界作为掩膜数据；提出利用面向对象方法处理二值图像，并结合数学形态学方法、GIS技术来提取农田防护林矢量化结果。【结果】在研究区50 km ×50 km范围内，农田防护林总长度为304.46 km，其中正确提取的农田防护林286.42 km，多余提取18.05 km，遗漏提取14.19 km。结合该区已有的成果数据、野外观测数据、GeoEye 数据及 ZY-3影像，采用全区验证、野外实地验证和高分辨率影像验证3种精度验证方式。经全区验证，准确度为89.89%，冗余误差为5.66%，遗漏误差为4.45%；野外采集的22条林带，全部正确提取，长度提取精度为93.93%。【结论】在提取高植被覆盖度的农田防护林时，比值植被指数( RVI)比归一化植被指数( NDVI)更佳；数学形态学方法和面向对象方法在处理具有一定间断距离的线性特征地物特别是农田防护林时，效果较好；利用遥感技术进行农田防护林自动提取时，应充分考虑农田防护林的物候信息、光谱信息及空间几何信息。基于5.8 m空间分辨率的 ZY-3多光谱影像，结合数学形态学、面向对象方法和 GIS 技术自动提取农田防护林能获得较高精度，可以为东北地区农田防护林的大范围自动提取提供参考，为后续的农田防护林景观生态空间分析及动态监测与管理提供技术支持。%Objective] This paper was to explore a high precision automatic extraction method of farmland shelterbelt in northeast China based on analyzing its spectral and spatial geometric characteristics. And the results will provide basic data support for a wide range of farmland shelterbelt extraction and remote sensing monitoring.[Method]In this paper, part zones of Dehui City and Nong’an County of Jilin Province were took as the study area. We analyzed the vegetation index and spatial geometric features of the farmland shelterbelt based on ZY-3 multi-spectral image. The residential boundary was extracted from Landsat8 OLI data. Then we put forward using the object-oriented method to deal with binary image data. The vector results of farmland shelterbelt were extracted in combination with the mathematical morphology and the GIS technology.[Result]The total length of farmland shelterbelt is 304. 46 km within the 50 km × 50 km study area. The correct extraction of farmland shelterbelt is 286. 42 km,the excess extraction of 18. 05 km and missing extraction is 14. 19 km. In this study,we used the region verification,filed verification and high resolution images verification based on existing outcome data,filed observation data,GeoEye image and ZY-3 image. As for reqion verification,the extraction accuracy is 89. 89%,the redundancy error is 5. 66% and the missing error is 4. 45%. All 22 belts collected in field were extracted correctly and the extraction accuracy of length is 93. 93%.[Conclusion]The ratio vegetation index( RVI) is better than the normalized difference vegetation index( NDVI) when extracting the farmland shelterbelts in high vegetation coverage. Mathematical morphology method and object-oriented method have their unique advantages in processing linear characteristic features which has a certain gap,especially for the farmland shelterbelt. It should be given full consideration to the phenology information,spectral information and spatial geometry information of farmland shelterbelt when extracting farmland shelterbelt automatically with remote sensing technology. Accuracy verification results show that the combination of morphology,object-oriented methods and GIS technology to extract farmland shelterbelts can obtain higher accuracy based on ZY-3 image. This method can give a reference for extracting the farmland shelterbelt automatically and widely in northeast China. It also can provide technical support for the spatial analysis of landscape and the dynamic monitoring and management in the future.