呼包鄂榆城市群遥感土地覆被产品评估分析与集成学习

doi:10.12118/j.issn.1000-6060.2023.718

Abstract

Abstract:

In recent years, numerous large-scale, medium to high-resolution land-cover products have been produced both domestically and internationally. However, the reliability of these products in arid region urban agglomerations has not been adequately validated. Assessing the spatial consistency and classification accuracy of different products in arid urban agglomerations, and exploring ways to utilize existing data to produce more precise integrated products, can advance and refine relevant research in this region. Based on the five major existing land-cover products [GlobeLand30, GLC_FCS30, CLCD, FROM_GLC30, and ESA WorldCover (ESAWC)], this study analyzed the spatial differences and reliability of these products within the Hohhot-Baotou-Ordos-Yulin (HBOY) urban agglomeration, north China in terms of the similarity of composition type, spatial consistency, and classification accuracy. An effective multi-source product ensemble learning method was proposed, involving the fusion of five land-cover products to generate a novel, integrated land-cover product. The results show that: (1) The overall composition and spatial distribution of land-cover in the HBOY urban agglomeration is similar among the five products. The areas that are completely consistent and highly consistent among different products account for 68.79% of the total area, with grassland having the highest consistency. (2) The overall accuracy of the five products ranged from 67.83% to 76.96%, with the ESAWC product exhibiting the highest overall accuracy (76.96%), followed by GLC_FCS30 (75.34%). All the products demonstrated significant spatial accuracy in depicting ground reality in forests, grasslands, water bodies, and bare ground, with relatively lower accuracy observed in scrubland and wetlands. (3) The proposed product integration scheme harnesses the strengths of existing products and prior knowledge, enabling the integrated application of multiple land-cover products. The new product achieves an overall accuracy of 83.36%, representing an improvement of 6.40% to 15.53% compared to the accuracy of the original products. The results of the study provide valuable references for the selection and production of land-cover products for thematic studies in arid region urban agglomerations.

Key words: land-cover products, spatial consistency analysis, accuracy assessment, integrated application, HBOY urban agglomeration

XIA Zilong, DU Peijun, GUO Shanchuan, FANG Hong, ZHANG Wei, PAN Xiaoquan. Evaluation and ensemble learning of remote sensing land-cover products in the Hohhot-Baotou-Ordos-Yulin urban agglomeration[J].Arid Land Geography, 2024, 47(8): 1367-1379.

Figures/Tables 12

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References 36

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土地覆被类型	GLC_FCS30		GlobeLand30			CLCD			FROM_GLC30			ESAWC （30 m）			ESAWC （10 m）		集成产品
土地覆被类型	PA	UA	PA		UA	PA		UA	PA		UA	PA		UA	PA	UA	PA	UA
耕地	60.49	72.59	48.46		81.48	63.98		76.30	56.07		71.85	73.33		76.74	79.34	71.11	78.47	83.70
林地	74.36	58.00	59.18		58.00	72.00		72.00	62.69		84.00	62.00		86.11	85.29	58.00	82.93	68.00
灌木	6.25	2.00	28.30		30.00	100.00		34.00	40.00		8.00	80.56		82.48	60.00	24.00	100.00	14.00
草地	78.55	84.55	75.00		73.26	73.98		90.70	80.19		71.93	22.00		47.83	82.72	81.89	82.40	93.36
湿地	76.19	64.00	80.95		34.00	0.00		22.00	53.85		14.00	36.00		100.00	100.00	36.00	100.00	42.00
水体	89.36	84.00	79.63		86.00	82.46		94.00	81.63		80.00	94.00		79.66	82.76	96.00	74.63	100.00
不透水面	97.22	70.00	65.57		80.00	74.42		64.00	50.00		52.00	76.00		95.00	97.56	80.00	93.02	80.00
裸地	75.27	74.05	84.03		54.05	86.32		44.32	61.94		89.73	87.03		57.71	58.45	89.73	89.82	81.08
OA	75.34			67.83			74.57			69.54			75.94		76.96		83.36

Evaluation and ensemble learning of remote sensing land-cover products in the Hohhot-Baotou-Ordos-Yulin urban agglomeration

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数据名称	数据源	研发单位	空间分辨率/m	年份	地类	分类方法
GLC_FCS30	Landsat TM/ETM+/OLI	中科院	30	2020	29	随机森林
GlobeLand30	Landsat TM/ETM+，HJ-1	国家基础信息中心	30	2020	10	POK法
CLCD	Landsat TM/ETM+/OLI	武汉大学	30	2019	9	随机森林
FROM_GLC30	Landsat TM/ETM+	清华大学	30	2017	10	随机森林
ESAWC	Sentinel-1/2	欧洲航天局	10	2020	11	深度学习

土地覆被类型	GLC_FCS30	GlobeLand30	CLCD	FROM_GLC30	ESAWC
耕地	10、11、20	10	1	1	40
林地	50~100	20	2	2	10
灌木	120、121、122	40	3	4	20
草地	130、150	30	4	3	30
湿地	180	50	9	5	90
水体	210	60	5	6	80
不透水面	190	80	8	8	50
裸地	200	90	7	9	60

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