干旱区生态环境知识资源中心(Arid): A novel space-spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China

Arid

DOI	10.1016/j.ecoinf.2024.102607
	A novel space-spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China
	Guo, Fuchen; Fan, Liangxin; Zhang, Chengkang; Xue, Sha
通讯作者	Fan, LX
来源期刊	ECOLOGICAL INFORMATICS
ISSN	1574-9541
EISSN	1878-0512
出版年	2024
卷号	81
英文摘要	The rapid renewal of land use and land cover (LULC) maps using remote-sensing technologies constitutes a sine qua non for judicious land resource management at both regional and national scales. Existing research conducted on the Google Earth Engine (GEE) platform has overwhelmingly focused on pixel-based LULC classification techniques, often neglecting the role of spatial context via neighbouring valuable pixel information. Remarkably, little attention has been paid to the amalgamation of 3 x 3 neighbouring pixels into a threedimensional space -spectrum array that can emulate the functionalities of object-based image analysis. In this study, we developed a novel integrated model consisting of a space -spectrum array (SSA) model based on 3 x 3 neighbouring pixels, a tile model based on random forest, and a multiple probabilistic classification model (SSATPRF) on the GEE platform to generate a LULC map with high overall accuracy (OA) for Ordos in 2020. Three bimonthly median value images were synthesised and feature collections, including spectral bands and vegetation indices, were constructed. Five experimental groups (EXP1 -EXP5) were used to assess the different model combinations. Subsequent validation procedures employed abundant reference samples and compared the results with those of the three extant LULC mapping products. The results showed that EXP2, which was grounded in the tile-based model, yielded an OA of 87.53%, surpassing that of EXP1 (84.99%), which employed a traditional overall model. Furthermore, EXP3, which integrated the multiple probabilistic classification model with the traditional overall model, exhibited an OA of 85.19%, exceeding that of EXP1. A comparison of the five experimental groups using the four regional spatial subtlety features revealed that the EXP5, employing the SSATPRF model, successfully decreased the salt and pepper noise. The OA of six tile sizes ranging from 10 km to 100 km were compared, and the highest OA (88.35%) was achieved at a tile size of 25 km. The resultant LULC map in Ordos, derived from the SSA-TPRF model, showed superior OA compared with the extant LULC products. This study thus contributes to a versatile and scalable model within the GEE framework, offering avenues for facile adaptation and recurrent application across disparate geographical locations and temporal settings. The adaptability of this model is particularly advantageous for developing nations and regions typified by diverse landscapes, thereby catalysing the iterative updating of LULC maps through advanced remote-sensing paradigms.
英文关键词	Arid zone LULC Tile Multiple probabilistic classification Space-spectrum array Random forest Google earth engine
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:001232683300001
WOS关键词	LAND-COVER ; TIME-SERIES ; TRAINING DATA ; PER-PIXEL ; CLASSIFICATION ; IMAGES ; MAP ; PERFORMANCE ; AREA
WOS类目	Ecology
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/403434
推荐引用方式 GB/T 7714	Guo, Fuchen,Fan, Liangxin,Zhang, Chengkang,et al. A novel space-spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China[J],2024,81.
APA	Guo, Fuchen,Fan, Liangxin,Zhang, Chengkang,&Xue, Sha.(2024).A novel space-spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China.ECOLOGICAL INFORMATICS,81.
MLA	Guo, Fuchen,et al."A novel space-spectrum array tile probability random-forest model enhances LULC mapping accuracy on Google Earth Engine: An experiment in Ordos, China".ECOLOGICAL INFORMATICS 81(2024).