Disturbance of mangrove forests in Guangxi Beilun Estuary during 1990—2020
- Vol. 26, Issue 6, Pages: 1112-1120(2022)
Published： 07 June 2022
DOI： 10.11834/jrs.20221579
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摘要
监测国家级自然保护区内红树林的扰动，可为滨海湿地生态系统的管理和保护提供数据支撑和决策支持。本研究使用谷歌云平台GEE（Google Earth Engine）建立Landsat长时间序列卫星数据集，结合LandTrendr算法研究了广西北仑河口红树林自然保护区1990年—2020年红树林的扰动情况。结果表明：（1）1990年—2020年，共有45.94 ha的红树林发生了扰动，其中2001年保护区内红树林扰动面积最大，为12.91 ha；（2）1990年—2020年，轻微扰动和中度扰动所占比例较大，分别为57.5%和29.17%，严重扰动所占比例最少，只有13.33%；（3）红树林变化像元的总体识别精度达到88.56%，对扰动年份检测的总体精度达到87%，Kappa系数为0.76。本研究基于LandTrendr算法解析了30年间北仑河口保护区内红树林发生扰动的年份、频率和面积，结合实际情况分析了导致扰动的因素，认为人类活动是北仑河口红树林扰动的次要原因，自然因素，如虫灾，台风等是导致扰动的主要原因。本研究的结论和方法可为红树林保护区管理处制定科学合理的保护和恢复政策提供重要的决策支持。
Abstract
Mangrove forests are highly productive ecosystems that maintain coastal ecological balance and biodiversity by providing breeding and nursing grounds for waterfowl
marine
and pelagic species. Mangroves are highly subjected to natural and anthropogenic disturbances
owing to their intermediate position between the terrestrial and marine environments. This study used Landsat imagery to track the temporal and spatial changes of mangrove forests in Guangxi Beilun Estuary National Nature Reserve. The objectives of this study are (1) to monitor spatial distributions and intensities of mangrove forest disturbances during the past 30 years
and (2) to analyze the natural and anthropogenic factors that cause these disturbances in the reserve.
This study used the Google Earth Engine (GEE) platform to establish a time series Landsat dataset during 1990—2020. And then
GEE constructed the image dataset stack using the Medoid method for annual best pixel composition. Based on LandTrendr algorithm and the dataset
we studied disturbances of mangrove forests in Guangxi Beilun Estuary National Nature Reserve from 1990 to 2020. GEE enables quick access and processes a massive number of Landsat images in a paralleled process. Specifically
the GEE synchronizes all the Landsat data and provides different levels of processed products
including the top of atmosphere and surface reflectance data. LandTrendr algorithm can be used to detect changes in the time series of satellite images pixel by pixel and capture pixel-level subtle disturbances.
The results show that (1) during 1990—2020
the total area of mangrove forest disturbances in the Beilun Estuary Reserve in Guangxi was 45.94 ha. Most disturbances occurred near pearl Bay
and a small amount of disturbances occurred in Beilun estuary; (2) the maximum disturbed area occurred in 2001
which was 12.91 ha
and the minimum disturbed area occurred in 2007
which was 0.09 ha; (3) slight
moderate
and severe disturbances accounted for 57.5%
29.17%
and 13.33% of the total disturbance
respectively
and the areas are 26.42
13.40
and 6.13 ha.
According to our results and literature reviews
the following conclusions can be drawn: natural and anthropogenic factors cause the disturbance of mangrove forest in Guangxi Beilun Estuary National Nature Reserve. In terms of natural factors
sea level rise
extreme weather
pests and diseases
and invasion of spartina alterniflora have seriously threatened the growth environment of mangroves. In terms of human factors
cultivation ponds and farmland reclamation directly occupy the growth environment of mangroves. Mangroves are also threatened by wastewater from aquaculture ponds and pesticide residues in cultivated land. Parts of the terrigenous mangroves are developed as dikes or other artificial surfaces to attract residents or visitors. This condition has also led to an increase in wastewater from domestic production; not only does it hinder the growth of mangroves
but it also hinders the flow of matter and energy between land and sea. In addition
results of this study can serve as an important scientific basis and fundamental data for formulating mangrove protection and restoration strategies.
关键词
遥感红树林保护区谷歌地球引擎GEE（Google Earth Engine）LandTrendr算法
Keywords
remote sensingmangrovesprotected areasGoogle Earth Engine (GEE)Land Trendr algorithm
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Department of Geography and Spatial Information Techniques, Ningbo University
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Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University
Institute of Remote Sensing Science and Engineering, Faculty of Geographical Science, Beijing Normal University
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