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    基于多源遥感数据的福建省海岸带县域森林生态质量评价

    Evaluation of forest ecological quality in coastal zone counties of Fujian Province, eastern China based on multi-source remote sensing data

    • 摘要:
      目的 森林生态质量是从生态角度反映森林质量的内涵,对森林的生态功能和生态服务、生长状况以及自我调节功能进行综合测度,以期提高森林改善生态环境、维护生态平衡的能力。
      方法 利用中、高分辨率多源遥感数据,获取大范围尺度下能表征森林生态质量的关键指标信息,在此基础上,分析福建省海岸带40个县域的森林生态质量状况。首先,基于2016年2 m分辨率多源遥感数据为主要数据源,利用双层尺度集模型选定最佳分割尺度,多分类器集成算法集,自动选择最优分类算法进行森林类型提取,并结合2020年Sentinel遥感数据及森林分类产品,更新2020年福建省海岸带森林类型精细分布图;其次,利用LandTrendr算法衍生的干扰开始时间特征推算现存森林年龄,通过GEDI冠层高度产品获取海岸带森林冠层高度分布图;在以上关键森林质量指标提取基础上,对遥感手段获取的8项森林生态质量评价指标进行主成分分析,获得福建省海岸带县域森林生态质量综合评价结果。
      结果 2020年福建海岸带40个县域约50%的县域森林生态质量处于优良水平,其中仙游县、闽侯县、南安市、霞浦县、柘荣县及厦门海沧区、思明区、集美区、同安区等森林生态质量为优;森林生态质量较差的县域有惠安县、秀屿区、石狮市、福安市、平潭实验区、诏安县。
      结论 结合中、高分辨率多源遥感数据,能够发挥遥感大范围监测优点,客观评价福建省海岸带40个县域的森林生态质量;研究结果表明2020年福建沿海县域森林生态质量还存在较大的提升空间,需要针对存在的问题采取相应森林管理措施提升森林生态质量。

       

      Abstract:
      Objective Forest ecological quality reflects the ecological quality of forests from an ecological perspective, measuring the comprehensive capacity of forests to improve the ecological environment, maintain ecological balance, and provide ecological functions and services. Utilizing medium and high-resolution multisource remote sensing data, we aim to acquire crucial indicator information that characterizes forest ecological quality at a large-scale level. Based on this foundation, our objective is to analyze the forest ecological quality status in the 40 coastal counties of Fujian Province of eastern China.
      Method Firstly, the primary data source utilized was 2 m resolution multisource remote sensing data. The bi-level scale-sets model (BSM) was employed to determine the optimal segmentation scale, integrating multiple classifiers to create an algorithm set. The best classification algorithm for forest type extraction was automatically selected. This approach was complemented by the use of Sentinel remote sensing data in 2020 and fine forest classification products, resulting in an optimized fine-scale distribution map of coastal forests in Fujian Province for year 2020. Secondly, forest age was estimated using disturbance onset time features derived from the LandTrendr algorithm. Additionally, GEDI crown height products were used to obtain the canopy height distribution map of coastal forests. Building upon the extraction of key forest quality indicators through remote sensing methods, a principal component analysis was conducted on eight forest ecological quality assessment indicators obtained through remote sensing. This led to a comprehensive assessment of forest ecological quality for the coastal counties of Fujian Province.
      Result In 2020, approximately 50% of the 40 coastal counties in Fujian’s coastal zone exhibited a favorable level of forest ecological quality. Among them, Xianyou County, Minhou County, Nan’an City, Xiapu County, Zherong County, as well as the districts of Haicang, Siming, Jimei, and Tong’an in Xiamen, demonstrated excellent forest ecological quality. On the other hand, counties with relatively poor forest ecological quality included Huian County, Xiuyu District, Shishi City, Fu’an City, Pingtan Experimental Zone, and Zhao’an County.
      Conclusion Utilizing high and medium-resolution multisource remote sensing data, the advantages of remote sensing large-scale monitoring have been leveraged to provide an objective assessment of the forest ecological quality in the 40 coastal counties of Fujian Province. The research findings reveal that there is considerable room for improvement in the forest ecological quality of the coastal counties in Fujian Province in 2020. Addressing the existing issues calls for the implementation of appropriate forest management measures to enhance the forest ecological quality.

       

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