利用密集大地遥感卫星时间序列跟踪红树林状况变化

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES
Xiucheng Yang , Zhe Zhu , Kevin D. Kroeger , Shi Qiu , Scott Covington , Jeremy R. Conrad , Zhiliang Zhu
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引用次数: 0

摘要

热带和亚热带海岸的红树林会受到偶发性干扰,特别是来自强风暴的干扰,可能导致大面积植被死亡。植被的恢复能力各不相同,而随着干扰越来越频繁和严重,跟踪和预测植被的反应以支持管理和政策决策至关重要。之前的研究主要集中在二元红树林绘图(即存在或不存在)上,而对状况和状况变化的跟踪则没有得到足够的重视。在本文中,我们展示了一种基于密集时间序列陆地卫星图像的红树林状况连续监测方法,我们跟踪三种干扰后的红树林状况,包括受干扰(受干扰,在一个生长季内恢复到之前的状态)、恢复(在超过一个生长季的时间内自然恢复)和衰退(受干扰后出现长期衰退)。该方法首先使用 "潮汐湿地变化的检测和特征描述(DECODE)"算法进行扰动检测,这是一种现有的密集时间序列模型,旨在检测潮汐湿地中的扰动,并适应潮汐波动。该算法非常适合潮汐湿地扰动的检测,但由于扰动后大地遥感卫星观测数据存在巨大差异,因此无法提供令人满意的扰动后监测结果。为了更好地监测扰动后的状况,建议在恢复阶段采用一种新的时间序列拟合方法 DECODER(DECODE 和恢复)。此外,对于按干扰事件划分的时间片段,我们利用从时间序列模型中得出的时间-光谱变量建立了一个随机森林分类器,以描述红树林的状况。在佛罗里达州的红树林中采用这种方法,我们生成了枯萎和恢复等状况图,总体准确率约为 97.96 ± 0.86- [95 % 置信区间]。比较佛罗里达州飓风后的状况发现,干扰频率和严重程度的增加对红树林的恢复能力提出了挑战,可能会削弱其恢复和维持生态系统功能的能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tracking mangrove condition changes using dense Landsat time series
Mangroves in tropical and subtropical coasts are subject to episodic disturbances, notably from severe storms, leading to potential widespread vegetation mortality. The ability of vegetation to recover varies, and with disturbances becoming more frequent and severe, it is vital to track and project vegetation responses to support management and policy decisions. Prior studies have largely focused on binary mangrove mapping (i.e., presence or absence), while tracking conditions and condition change have not received sufficient attention. In this paper, we demonstrate a method based on dense time series Landsat images for continuous monitoring of mangrove conditions, where we track three kinds of post-disturbance mangrove conditions, including disturbed (disturbed, with rebound to the previous state within one growing season), recovering (undergoing natural recovery in longer than one growing season), and declining (showing long-term decline after disturbance). The method starts with disturbance detection using the DEtection and Characterization Of the tiDal wEtland change (DECODE) algorithm, an existing dense time series model designed to detect disturbances in tidal wetlands with adaptation to tidal fluctuations. This algorithm is well suited for the detection of tidal wetland disturbances but does not provide satisfactory post-disturbance monitoring results, due to the substantial variability in post-disturbance Landsat observations. To better monitor post-disturbance conditions, a new time series fitting approach, DECODER (DECODE and Recovery), is proposed for the recovery stage. Additionally, for temporal segments divided by disturbance events, we built a random forest classifier with temporal-spectral variables derived from the time series model to characterize mangrove conditions. Employing this approach in Florida's mangroves, we generated condition maps, such as dieback and recovery, with an overall accuracy of approximately 97.96 ± 0.86- [95 % confidence intervals]. Comparing post-hurricane conditions in Florida revealed that the increased frequency and severity of disturbances are challenging mangrove resilience, potentially diminishing their ability to recover and sustain ecosystem functions.
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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