干旱区生态环境知识资源中心(Arid): Object-based change detection for vegetation disturbance and recovery using Landsat time series

Arid

DOI	10.1080/15481603.2022.2129870
	Object-based change detection for vegetation disturbance and recovery using Landsat time series
	Wang, Zheng; Wei, Caiyong; Liu, Xiangnan; Zhu, Lihong; Yang, Qin; Wang, Qinyu; Zhang, Qian; Meng, Yuanyuan
通讯作者	Liu, XN
来源期刊	GISCIENCE & REMOTE SENSING
ISSN	1548-1603
EISSN	1943-7226
出版年	2022
卷号	59 期号:1 页码:1706-1721
英文摘要	Accurate characterization of historical trends in vegetation change at the landscape scale is necessary for resource management and ecological assessment. Vegetation disturbance and recovery are coherent spatial and temporal processes. Pixel-based change detection methods often struggle to provide reliable estimates of change events because they neglect spatial contextual information and are affected by salt-and-pepper noise. To address such problems, we propose a new approach, object-based change detection of trends in disturbance and recovery (Object-LT), which introduces object-based image analysis (OBIA) into the current framework of LandTrendr algorithm. We then applied this approach to detect vegetation changes during 2000-2020 in the ecologically fragile region of Guyuan, Ning Xia, China. Accuracy assessment indicated that Object-LT could accurately identify disturbance and recovery trends in vegetation with overall accuracies of 90.05% and 87.50%, respectively. Compare with pixel-based LandTrendr algorithm, Object-LT significantly improved user's accuracy and removed salt-and-pepper noise. Spatial-temporal maps of vegetation change showed that the recovery area was 571.27 km(2) while the disturbed area was 297.65 km(2), accounting for 5.44% and 2.83% of the study area, respectively. This indicates a general vegetation recovery trend in the study area. Object-LT allowed for an accurate and comprehensive characterization of vegetation change over large areas, which contributes to a better understanding of change processes of vegetation landscape over time.
英文关键词	object-based change detection vegetation disturbance and recovery LandTrendr time series
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000863509000001
WOS关键词	FOREST DISTURBANCE ; CHINA ; NDVI ; DYNAMICS ; DEFORESTATION ; DESERTIFICATION ; FREQUENCIES ; LANDTRENDR ; LANDSCAPE ; DIVERSITY
WOS类目	Geography, Physical ; Remote Sensing
WOS研究方向	Physical Geography ; Remote Sensing
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392963
推荐引用方式 GB/T 7714	Wang, Zheng,Wei, Caiyong,Liu, Xiangnan,et al. Object-based change detection for vegetation disturbance and recovery using Landsat time series[J],2022,59(1):1706-1721.
APA	Wang, Zheng.,Wei, Caiyong.,Liu, Xiangnan.,Zhu, Lihong.,Yang, Qin.,...&Meng, Yuanyuan.(2022).Object-based change detection for vegetation disturbance and recovery using Landsat time series.GISCIENCE & REMOTE SENSING,59(1),1706-1721.
MLA	Wang, Zheng,et al."Object-based change detection for vegetation disturbance and recovery using Landsat time series".GISCIENCE & REMOTE SENSING 59.1(2022):1706-1721.