How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?

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 s...

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Autores principales:	Yuanyuan Meng, Xiangnan Liu, Zheng Wang, Chao Ding, Lihong Zhu
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Spatial structural metrics LandTrendr algorithm Disturbance and recovery detection Dense Landsat time series Google Earth Engine Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/8c7491347bab42ab9d344ee3fecd6293
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id	oai:doaj.org-article:8c7491347bab42ab9d344ee3fecd6293
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spelling	oai:doaj.org-article:8c7491347bab42ab9d344ee3fecd62932021-12-01T05:02:55ZHow can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?1470-160X10.1016/j.ecolind.2021.108336https://doaj.org/article/8c7491347bab42ab9d344ee3fecd62932021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X21010013https://doaj.org/toc/1470-160XForest 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.Yuanyuan MengXiangnan LiuZheng WangChao DingLihong ZhuElsevierarticleSpatial structural metricsLandTrendr algorithmDisturbance and recovery detectionDense Landsat time seriesGoogle Earth EngineEcologyQH540-549.5ENEcological Indicators, Vol 132, Iss , Pp 108336- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Spatial structural metrics LandTrendr algorithm Disturbance and recovery detection Dense Landsat time series Google Earth Engine Ecology QH540-549.5
spellingShingle	Spatial structural metrics LandTrendr algorithm Disturbance and recovery detection Dense Landsat time series Google Earth Engine Ecology QH540-549.5 Yuanyuan Meng Xiangnan Liu Zheng Wang Chao Ding Lihong Zhu How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
description	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.
format	article
author	Yuanyuan Meng Xiangnan Liu Zheng Wang Chao Ding Lihong Zhu
author_facet	Yuanyuan Meng Xiangnan Liu Zheng Wang Chao Ding Lihong Zhu
author_sort	Yuanyuan Meng
title	How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
title_short	How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
title_full	How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
title_fullStr	How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
title_full_unstemmed	How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?
title_sort	how can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense landsat time series?
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/8c7491347bab42ab9d344ee3fecd6293
work_keys_str_mv	AT yuanyuanmeng howcanspatialstructuralmetricsimprovetheaccuracyofforestdisturbanceandrecoverydetectionusingdenselandsattimeseries AT xiangnanliu howcanspatialstructuralmetricsimprovetheaccuracyofforestdisturbanceandrecoverydetectionusingdenselandsattimeseries AT zhengwang howcanspatialstructuralmetricsimprovetheaccuracyofforestdisturbanceandrecoverydetectionusingdenselandsattimeseries AT chaoding howcanspatialstructuralmetricsimprovetheaccuracyofforestdisturbanceandrecoverydetectionusingdenselandsattimeseries AT lihongzhu howcanspatialstructuralmetricsimprovetheaccuracyofforestdisturbanceandrecoverydetectionusingdenselandsattimeseries
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How can spatial structural metrics improve the accuracy of forest disturbance and recovery detection using dense Landsat time series?

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