How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
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空间结构指标如何使用密集的陆地卫星时间序列提高森林干扰和恢复检测的准确性？
DOI：
10.1016/j.ecolind.2021.108336
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发表时间：
2021-12
影响因子：
6.9
通讯作者：
Lihong Zhu
中科院分区：
文献类型：
--
作者：
Yuanyuan Meng;Xiangnan Liu;Zheng Wang;Chao Ding;Lihong Zhu
森林干扰与恢复监测是评估生态系统恢复力和服务能力的重要手段，有助于进一步建立可持续的生态系统发展。遥感数据的时间序列分析为此类研究提供了必要和有效的方法。一些研究将空间结构特征纳入森林突变探测的空间精度，但很少关注生态系统动态过程中恢复的探测。为了更全面地检测森林扰动和恢复，并探索将空间结构指标纳入Landsat密集时间分析的有效性，本研究采用归一化燃烧比（NBR）和基于NBR的空间结构指标时间序列进行了LandTrendr算法。利用基于NBR空间邻域的灰度共生矩阵（GLCM）计算空间结构度量（即纹理度量）。在谷歌地球引擎平台上，使用1986年至2018年中国亚热带地区所有可用的Landsat图像对该方法进行了测试。与使用基于像素的NBR时间序列相比，采用基于glcm的纹理度量后，恢复检测的时间精度从大约20%提高到63%。此外，在密集时间分析中结合空间度量可以很好地描述森林组成和结构的变化模式（封闭林到灌木或封闭林到农田）以及景观斑块边缘像元的变化。我们的研究结果强调，空间结构指标可以整合为森林动态监测提供更可靠的检测指标，并确定对生态评估和管理有意义的特征。
Forest disturbance and recovery detection is vital for assessing ecosystem resilience and service to further establish the sustainable ecosystem development. Time series analyses of remote sensing data provide essential and effective methods in such research. Some studies have incorporated spatial structural characteristics to improve the spatial accuracy of detecting forest abrupt disturbances, however, few of them paid attention to the detection of recovery during ecosystem dynamics. To more comprehensively detect forest disturbance and recovery and explore the effectiveness of incorporating spatial structural metrics in dense Landsat temporal analysis, this study performed the LandTrendr algorithm using the normalized burn ratio (NBR) and the NBR -based spatial structural metrics time series. The spatial structural metrics (i.e., texture metrics) were calculated using the grey-level co-occurrence matrix (GLCM) based on the spatial neighbor of NBR. The methodology was tested using all available Landsat images in a subtropical region in China from 1986 to 2018 on the Google Earth Engine platform. The temporal accuracy of the recovery detection was improved from approximately 20% to 63% after incorporating the GLCM-based texture metrics compared to that using the pixel-based NBR time series. Additionally, the change patterns of forest composition and structure (closed forest to shrub or closed forest to cropland) and changes in the edge pixels in landscape patches can be well depicted by incorporating spatial metrics in dense temporal analyses. Our results highlight that the spatial structural metrics can be integrated to develop more robust detection indicators for the monitoring of forest dynamics and to determine the characteristics that are meaningful to ecological assessment and management.
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DOI：
10.3390/rs11091056
发表时间：
2019-05
期刊：
Remote. Sens.
影响因子：
--
作者：
Xiao Zhang;Liangyun Liu;Xidong Chen;Shuai Xie;Yuan Gao
通讯作者：
Xiao Zhang;Liangyun Liu;Xidong Chen;Shuai Xie;Yuan Gao
影响因子：
13.5
作者：
S. Hislop;S. Jones;M. Soto‐Berelov;A. Skidmore;A. Haywood;T. Nguyen
通讯作者：
S. Hislop;S. Jones;M. Soto‐Berelov;A. Skidmore;A. Haywood;T. Nguyen
DOI：
10.1016/j.isprsjprs.2017.05.013
发表时间：
2017-08
影响因子：
12.7
作者：
Fabio Güttler;D. Ienco;Jordi Nin;M. Teisseire;P. Poncelet
通讯作者：
Fabio Güttler;D. Ienco;Jordi Nin;M. Teisseire;P. Poncelet
影响因子：
5.5
作者：
T. Sankey;Adam Belmonte;R. Massey;J. Leonard
通讯作者：
T. Sankey;Adam Belmonte;R. Massey;J. Leonard
影响因子：
3.7
作者：
U. Pimple;D. Simonetti;Isabella Hinks;J. Oszwald;U. Berger;S. Pungkul;Kumron Leadprathom;Tamanai Pravinvongvuthi;Pasin Maprasoap;V. Gond
通讯作者：
U. Pimple;D. Simonetti;Isabella Hinks;J. Oszwald;U. Berger;S. Pungkul;Kumron Leadprathom;Tamanai Pravinvongvuthi;Pasin Maprasoap;V. Gond