Farmland shelterbelts play a positive role in ensuring food security and ecological safety. The absence or degradation of shelterbelt structures can lead to fragmentation of the remotely extracted results. Conversely, shelterbelt maintenance and management system considers these shelterbelts as entire units, even if they are divided into several parts by the gaps in them. It is essential to propose a remote extraction method to fill in fragmented results and accurately represent the distribution of farmland shelterbelts.
In this study, random forest algorithm was employed to classify land cover from ZY-3 (ZiYuan-3 satellite from China) imagery. Then, a thinning algorithm of mathematical morphology was applied to extract farmland shelterbelts, and the straight-line connection algorithm was used to connect central lines belonging to the same belt. Finally, the result was validated using nine uniformly distributed training sample areas across the entire region.
This method achieved a correct identification rate of 94.9% within the training areas. Among the different regions, the highest identification accuracy recorded was 98.4% and the lowest was 87.7%. In conjunction with cropland information and the shape index of forest patches, it was possible to remove information for non-farmland shelterbelts without introducing external information. This approach achieved a more refined extraction of forestland information. The combination of the thinning algorithm and straight-line connection algorithm addressed the issue of fragmented results in farmland shelterbelt extraction, compensating for the limitations of relying solely on mathematical morphology for belt connectivity. The research method can provide technical support for the monitoring and management of farmland shelterbelts.