摘要:
随着我国社会经济的快速发展和对自然资源需求的日益增加,自然保护区面临的压力越来越重。在对于红树林扰动和恢复的监测当中,应用时间序列分析法对其进行遥感研究还处于起步阶段,并且时间序列算法本身都十分复杂。文章基于谷歌地球引擎(Google Earth Engine, GEE)云平台的LandTrendr时间分割算法和Landsat影像时序数据,研究了东寨港红树林自然保护区1990—2020年期间红树林的扰动情况。研究结果表明: 1990—2020年间,共有42.39 hm2的红树林发生了扰动,其中2014年保护区内红树林扰动面积最大,为12.78 hm2; 1990—2020年间,轻微扰动和中度扰动所占比例较大,分别为65.39%和30.78%,严重扰动所占比例最少,只有3.83%; 红树林变化像元的总体识别精度为89.50%,对扰动年份检测的总体精度为88%,Kappa系数为0.79。本研究基于LandTrendr算法解析了30 a间东寨港保护区内红树林发生扰动的年份和面积,结合实际情况分析了导致扰动的因素,认为人类活动是红树林扰动的主要原因,自然因素(如病虫害和极端天气等)是导致扰动的次要原因。研究结果能够为红树林保护区的管理提供科学依据和决策参考。
Abstract:
With the rapid socio-economic development and the increasing demand for natural resources in China, the protection of natural reserves is facing increasing difficulties. The remote sensing-based research on monitoring the disturbance and the restoration of mangrove forests through time series analysis is still in its initial stage. Moreover, time series algorithms are highly complex. Based on the LandTrendr time segmentation algorithm of Google Earth Engine (GEE) and the Landsat image time-series data, this study investigated the disturbance to mangrove forests in the Dongzhaigang Mangrove Nature Reserve during 1990—2020. The results are as follows: ① A total of 42.39 hm2 of mangrove forests were disturbed during 1990—2020, among which the largest disturbance area of 12.78 hm2 occurred in 2014; ② During 1990—2020, minor, moderate, and severe disturbances accounted for 65.39%, 30.78%, and 3.83%, respectively; ③ The overall identification accuracy of the pixels of mangrove forests subject to changes was 89.50%, and the overall detection accuracy of years witnessing disturbance was 88%, with a Kappa coefficient of 0.79. This study analyzed the years and areas of the disturbance to mangrove forests in the Dongzhaigang Mangrove Nature Reserve over 30 years based on LandTrendr. Moreover, this study analyzed the disturbance factors according to the actual situation and concluded that human activities are the main disturbance factor, followed by natural factors, such as diseases, pests, and extreme weather events. This study will provide a scientific basis and a decision reference for the management of the mangrove forest reserve.
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