基于Landsat时序影像和LandTrendr算法的森林保护区植被扰动研究——以陕西柴松和太白山保护区为例
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国家重点研发计划(2016YFC0501101,2016YFC0503603)


Detecting dynamics of vegetation disturbance in forest natural reserve using Landsat imagery and LandTrendr algorithm: the case of Chaisong and Taibaishan Natural Reserves in Shaanxi, China
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    摘要:

    跟踪森林自然保护区的森林变化及其时空特征,可为保护区生态保护评价提供科学依据。使用LandTrendr算法和谷歌地球引擎(GEE)的Landsat卫星时间序列数据,描述了柴松和太白山保护区的长期(1984-2018年)森林变化模式。对森林变化像元和稳定像元的总体识别精度达到93%,对扰动年探测的总体精度为89%。在柴松保护区,扰动年发生在2008年左右,大部分扰动由人类活动引起。在太白山保护区,扰动年主要发生在2013年,由自然因素造成。柴松和太白山保护区的森林扰动面积分别为42.74 hm2和23.68 hm2。柴松保护区的扰动斑块数多于太白山,表明柴松保护区自2004年建立后干扰频繁。本研究可以帮助研究人员和决策者了解这两个保护区的森林状况,可为森林生态系统的自然保护区保护评估提供基线信息。

    Abstract:

    China is now paying much attention to ecological conservation when developing economy. "Green Shield Action" started in 2017 is one of these efforts in evaluating natural reserves protection and management. Forest ecological system is one kind of important parts in natural reserves. Remote sensing can supply historic and present images in nature reserves and can help analysis time series information to find out surface changes in these areas. The state of forest change, its spatial and temporal characteristics and attribution can provide a scientific basis for assessment of ecological protection in protected areas. Landsat is the only satellite that can supply long time series images since 1980s. This study characterized the long-term (1984-2018) forest change patterns in Chaisong and Taibaishan Natural Reserves, Shaanxi using the LandTrendr (Landsat-based Detection of Trends in Disturbance and Recovery) algorithm with Landsat time-series data acquired from the Google Earth Engine (GEE). LandTrendr is an approach to extract spectral trajectories of land surface change from yearly Landsat time-series stacks (LTS) and it takes two parts in time-series analysis of LTS: capture of short-duration events and smoothing of long-term trends. Algorithm users do not need to download all Landsat images to their own computer, but process data and execute the algorithm in cloud platform-GEE. In this study, the overall accuracy for classifying changed pixels and stable pixels is 93% and the overall accuracy for disturbance year is 89%. This high accuracy can give policy-makers reliable information for the natural reserves management. In Chaisong Natural Reserve, the disturbance year happened around 2008 and those disturbance were mainly caused by human activities (oil and gas fields). These activities lasted 9 years until "Green Shield Action" began in 2017 and human activities decreased after this action. In Taibaishan Natural Reserve, the disturbance year happened mostly in 2013 and those disturbance were caused by natural factors (glacier melting). The disturbance areas are higher in Chaisong Natural Reserve than that in Taibaishan Natural Reserve with 42.74 ha and 23.68 ha, respectively. The number of disturbance patches in Chaisong is more than Taibaishan, which indicates that Chaisong Natural Reserve was frequently disturbed after its establishment in 2004 and higher attention should be paid in Chaisong. Satellite remote sensing monitoring in natural reserves can supply valuable surface information especially in those areas where people are hard to get in. Our study can help researchers and policy makers understand the forest status in the protected areas and provide the benchmark state for evaluation of the ecological protection in natural reserves.

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殷崎栋,柳彩霞,田野.基于Landsat时序影像和LandTrendr算法的森林保护区植被扰动研究——以陕西柴松和太白山保护区为例.生态学报,2020,40(20):7343~7352

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