干旱区生态环境知识资源中心(Arid): A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region

Arid

DOI	10.3390/rs14040961
	A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region
	Meng, Yuanyuan; Wei, Caiyong; Guo, Yanpei; Tang, Zhiyao
通讯作者	Tang, ZY (corresponding author)，Peking Univ, Inst Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China. ; Tang, ZY (corresponding author)，Peking Univ, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China.
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:4
英文摘要	Planted forests provide a variety of meaningful ecological functions and services, which is a major approach for ecological restoration, especially in arid areas. However, mapping planted forests with remote-sensed data remains challenging due to the similarities in canopy spectral and structure characteristics and associated phenology features between planted forests and other vegetation types. In this study, taking advantage of the Google Earth Engine (GEE) platform and taking the Ningxia Hui Autonomous Region in northwestern China as an example, we developed an approach to map planted forests in an arid region by applying long-term features of the NDVI derived from dense Landsat time series. Our land cover map achieved a satisfactory accuracy and relatively low uncertainty, with an overall accuracy of 93.65% and a kappa value of 0.92. Specifically, the producer (PA) and user accuracies (UA) were 92.48% and 91.79% for the planted forest class, and 93.88% and 95.83% for the natural forest class, respectively. The total planted forest area was estimated as 3608.72 km(2) in 2020, accounting for 20.60% of the study area. The proposed mapping approach can facilitate assessment of the restoration effects of ecological engineering and research on ecosystem services and stability of planted forests.
英文关键词	planted forests long-term change trend features Landsat time series Google Earth Engine random forest NDVI
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:000765161700001
WOS关键词	DECIDUOUS RUBBER PLANTATIONS ; GOOGLE EARTH ENGINE ; WATER INDEX NDWI ; CHINA ; CLASSIFICATION ; DYNAMICS ; IMAGERY ; AFFORESTATION ; PERFORMANCE
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/376360
作者单位	[Meng, Yuanyuan; Guo, Yanpei; Tang, Zhiyao] Peking Univ, Inst Ecol, Coll Urban & Environm Sci, Beijing 100871, Peoples R China; [Meng, Yuanyuan; Guo, Yanpei; Tang, Zhiyao] Peking Univ, Key Lab Earth Surface Proc, Beijing 100871, Peoples R China; [Wei, Caiyong] China Univ Geosci, Sch Informat Engn, Beijing 100083, Peoples R China; [Wei, Caiyong] Ningxia Inst Remote Sensing Survey, Yinchuan 750021, Ningxia, Peoples R China
推荐引用方式 GB/T 7714	Meng, Yuanyuan,Wei, Caiyong,Guo, Yanpei,et al. A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region[J],2022,14(4).
APA	Meng, Yuanyuan,Wei, Caiyong,Guo, Yanpei,&Tang, Zhiyao.(2022).A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region.REMOTE SENSING,14(4).
MLA	Meng, Yuanyuan,et al."A Planted Forest Mapping Method Based on Long-Term Change Trend Features Derived from Dense Landsat Time Series in an Ecological Restoration Region".REMOTE SENSING 14.4(2022).