The spatial local accuracy of land cover datasets over the Qiangtang Plateau, High Asia

doi:10.1007/s11442-019-1992-0

Abstract

Abstract:

We analyzed the spatial local accuracy of land cover (LC) datasets for the Qiangtang Plateau, High Asia, incorporating 923 field sampling points and seven LC compilations including the International Geosphere Biosphere Programme Data and Information System (IGBPDIS), Global Land cover mapping at 30 m resolution (GlobeLand30), MODIS Land Cover Type product (MCD12Q1), Climate Change Initiative Land Cover (CCI-LC), Global Land Cover 2000 (GLC2000), University of Maryland (UMD), and GlobCover 2009 (GlobCover). We initially compared resultant similarities and differences in both area and spatial patterns and analyzed inherent relationships with data sources. We then applied a geographically weighted regression (GWR) approach to predict local accuracy variation. The results of this study reveal that distinct differences, even inverse time series trends, in LC data between CCI-LC and MCD12Q1 were present between 2001 and 2015, with the exception of category areal discordance between the seven datasets. We also show a series of evident discrepancies amongst the LC datasets sampled here in terms of spatial patterns, that is, high spatial congruence is mainly seen in the homogeneous southeastern region of the study area while a low degree of spatial congruence is widely distributed across heterogeneous northwestern and northeastern regions. The overall combined spatial accuracy of the seven LC datasets considered here is less than 70%, and the GlobeLand30 and CCI-LC datasets exhibit higher local accuracy than their counterparts, yielding maximum overall accuracy (OA) values of 77.39% and 61.43%, respectively. Finally, 5.63% of this area is characterized by both high assessment and accuracy (HH) values, mainly located in central and eastern regions of the Qiangtang Plateau, while most low accuracy regions are found in northern, northeastern, and western regions.

Key words: land cover datasets, spatial accuracy assessment, remote sensing, Qiangtang Plateau, High Asia

LIU Qionghuan, ZHANG Yili, LIU Linshan, LI Lanhui, QI Wei. The spatial local accuracy of land cover datasets over the Qiangtang Plateau, High Asia[J].Journal of Geographical Sciences, 2019, 29(11): 1841-1858.

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LC type	Number of sample points	Definition	LC type	Number of sample points	Definition
Grassland	219	Region mainly covered with a community of cold-tolerant perennial herbaceous plants. In this region, this LC type mainly comprises meadows covered with Kobresia littledalei and K. pygmaea, a plant coverage area dominated by taxa with some cold tolerance, especially xerophytic perennial herbaceous species.	Wetland	80	Broad areas covered with herbs or woody plants, usually transitional zones between land and water.
Sparse vegetation	53	Plants comprise continuous vegetation that extends up to the permanent snow line, as well as a zone encompassing coverage between 5% and 40% of total surface that consists of cold-adapted plants such as cold habitat perennial axis-shaft root grasses, cushion plants, and lichens. One area, for example, contains cushion plants such as Arenaria serpyllifolia and Androsace tapete.	Urban area	26	Land covered with buildings.
Desert	45	Desert areas are widely distributed in this region and are characterized by the presence of semi-shrubs and their dwarf counterparts (e.g., Ceratocarpus latens, Ajania pallasiana) as well as C. compacta.	Barren land	142	Barren land, sand, rock, and saline areas with vegetation coverage less than, or equal to, 10%.
Water bodies	152	Long-strip depressions that naturally form along the ground surface as well as land below the perennial water level developed under natural conditions.	Glacier and snow	110	Land that is perennially covered with snow or ice.
Total	923

Dataset	OA (%)	Verification method	Sensor	Classification method	Resolution	Time	Classification system (Number of types)	URL for download	Reference
GLC2000	68.6	Confidence value statistical sampling	SPOT4 VEGETATION	Unsupervised classification	1 km	1999-2000	FAO LCCS (23 classes)	http://bioval.jrc.ec.europa.eu/products/glc2000/ products.php	Bartholom et al., 2005
IGBPDIS	66.9	Statistical sampling by validation working group	AVHRR	Unsupervised classification	1 km	1992-1993	USGS IGBP (17 classes)	http://edc2.usgs.gov/glcc/tabgoode_globe.php	Loveland et al., 2000
UMD	65.0	Evaluated using other digital datasets	AVHRR	Unsupervised classification, decision tree classification	1 km	1992-1993	Simplified IGBP (14 classes)	http://www.landcover.org/data/landcover/index.shtml	Hansen et al., 2000
MCD12Q1	74.8	Cross-validation	MODIS	Supervised classification, decision tree classification, neural network	500 m	2001-2016	IGBP (17 classes)	http://e4ftl01.cr.usgs.gov/MOTA/MCD12Q1.006/	Friedl et al., 2010,2011
GlobCover	67.5	Statistical sampling expert’s judgement	MERIS FR	Supervised classification, un- supervised classification	300 m	2009	UN LCCS (22 classes)	http://due.esrin.esa.int/globcover/	Bontemps et al., 2011
CCI-LC	74.1	Sampling-based labeling approach	MERIS Full and Reduced Resolution/ SPOT	Unsupervised classification	300 m	1992-2015	UN LCCS (22 classes)	http://maps.elie.ucl.ac.be/CCI/viewer/index.php	Belgium et al., 2016
GlobeLand30	80.0	Knowledge-based interactive verification	Landsat TM, ETM7, HJ-1A/b/	Integration of pixel- and object-based methods with knowledge (pok-based)	30 m	2000, 2010	11 classes	http://www.globallandcover.com	Chen et al., 2015

Datasets	Mean OA	Max OA	Min OA	Mean kappa	Max kappa
GLC2000	34.55%	66.12%	12.27%	0.11	0.32
IGBPDIS	31.42%	50.76%	9.07%	0.04	0.26
UMD	26.47%	47.07%	6.53%	0.04	0.18
MCD12Q1	28.12%	59.76%	3.69%	0.05	0.37
GlobCover	27.62%	74.11%	3.52%	0.08	0.40
CCI-LC	34.92%	61.43%	2.9%	0.15	0.45
GlobeLand30	42.08%	77.39%	9.83%	0.21	0.61
Summary	32.17%	52.05%	12.82%	0.10	0.31