遥感技术在受损红树林资源调查与监测中的应用

红树林目前还受到不可持续利用方式的严重威胁。为快速掌握广西北海市合浦县铁山港白沙镇榄根村近岸海域监测范围内红树林受损及其周边景观演变情况,采用无人机（UAV）技术和多时期高分辨率卫星影像,结合野外地面群落调查,利用目视解译方法,对榄根村近岸海域红树林及周边景观进行遥感监测与分析。结果显示,该区域红树林2017年12月、2018年10月、2019年6月、2020年4月受损面积分别为0.55,4.77,9.42,17.18 hm²,4年间红树林受损面积增长约30.23倍。将2020年4月红树林受损区按照受损程度不同分为死亡区和严重受损区,面积分别为7.81,9.37 hm²,斑块数分别为35,106个。研究期内榄根村红树林受损面积呈迅速增加趋势,主要位于潮沟两侧;死亡及严重退化区红树林群落形态上呈灰白色不规则斑块状;红树林受损斑块呈“多点开花”特点,有从斑块林缘向内部蔓延的趋势。遥感技术可有效提高红树林生态监测的工作效率和准确性,应用前景广阔。

Application of Remote Sensing Technology in Investigation and Monitoring of Damaged Mangrove Resources

Mangroves are still being seriously threatened by unsustainable utilization pattern at present. In order to quickly grasp the mangrove damage and the surrounding landscape evolution in the coastal waters of Lan''gen Village, Baisha Town, Tieshan Bay, Hepu County, Beihai City of Guangxi, using unmanned aerial vehicle (UAV) technology and multi-period high resolution satellite images, combined with field ground community survey and visual interpretation method, remote sensing monitoring and analysis of mangrove forest and surrounding landscape in the coastal waters of Lan''gen Village were carried out. The results showed that the damaged area of mangrove in December 2017, October 2018, June 2019 and April 2020 were 0.55, 4.77, 9.42 and 17.18 hm², respectively. The damaged area of mangrove had increased by about 30.23 times during the four years. The damaged mangrove areas in April 2020 were divided into dead area and severely damaged area according to different degrees, the area was 7.81 and 9.37 hm² respectively, and the number of plaques was 35 and 106 respectively. During the study period, the damaged area of mangroves in Lan''gen Village showed a rapid increasing trend, mainly located on both sides of the tidal gully. Morphological characteristics of dead and severely degraded mangrove areas were grayish white irregular plaques. The damaged plaques of mangroves showed "multi-point blooms" characteristics and tended to spread from edge to interior. Remote sensing technology can effectively improve the efficiency and accuracy of mangrove ecological monitoring, and its application prospects are broad.