Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Comparisons of the accuracy and consistency of different remote-sensing land cover datasets are important for the rational application of multi-source land cover datasets to regional development, or to studies of global or local environmental change. Existing comparisons of accuracy or spatial consistency among land cover datasets primarily use confusion or transfer matrices and focus on the type and area consistency of land cover. However, less attention has been paid to the consistency of spatial patterns, and quantitative analyses of spatial pattern consistency are rare. However, when proportions of land cover types are similar, spatial patterns are essential for studies of the ecological functions of a landscape system. In this study, we used classical landscape indices that quantifies spatial patterns to analyze the spatial pattern consistency among different land cover datasets, and chose three datasets (GlobeLand30-2010, FROM-GLC2010, and SERVIR MEKONG2010) in northern Laos as a case study. We also analyzed spatial pattern consistency at different scales after comparing the landscape indices method with the confusion matrix method. We found that the degree of consistency between GlobeLand30-2010 and SERVIR MEKONG2010 was higher than that of GlobeLand30-2010 and FROM-GLC2010, FROM-GLC2010, and SERVIR MEKONG2010 based on the confusion matrix, mainly because of the best forest consistency and then water. However, the spatial consistency results of the landscape indices analysis show that the three datasets have large differences in the number of patches (NP), patch density (PD), and landscape shape index (LSI) at the original scale of 30 m, and decrease with the increase of the scale. Meanwhile, the aggregation index (AI) shows different changes, such as the changing trend of the forest aggregation index increasing with the scale. Our results suggested that, when using or producing land cover datasets, it is necessary not only to ensure the consistency of landscape types and areas, but also to ensure that differences among spatial patterns are minimized, especially those exacerbated by scale. Attention to these factors will avoid larger deviations and even erroneous conclusions from these data products.

[1]  Daniel Simberloff,et al.  Experimental Zoogeography of Islands: Effects of Island Size , 1976 .

[2]  J. Townshend,et al.  Global land cover classi(cid:142) cation at 1 km spatial resolution using a classi(cid:142) cation tree approach , 2004 .

[3]  Limin Yang,et al.  Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data , 2000 .

[4]  P. Defourny,et al.  Land cover characterization and mapping of continental Southeast Asia using multi-resolution satellite sensor data , 2003 .

[5]  Rasim Latifovic,et al.  Accuracy assessment using sub-pixel fractional error matrices of global land cover products derived from satellite data , 2004 .

[6]  Bu Ren,et al.  A correlation analysis on landscape metrics , 2005 .

[7]  A. Belward,et al.  GLC2000: a new approach to global land cover mapping from Earth observation data , 2005 .

[8]  Chandra Giri,et al.  A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets , 2005 .

[9]  S. Nilsson,et al.  A spatial comparison of four satellite derived 1 km global land cover datasets , 2006 .

[10]  E. Lambin,et al.  The emergence of land change science for global environmental change and sustainability , 2007, Proceedings of the National Academy of Sciences.

[11]  J. Wickham,et al.  Completion of the 2001 National Land Cover Database for the conterminous United States , 2007 .

[12]  Christelle Vancutsem,et al.  GlobCover: ESA service for global land cover from MERIS , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Y. Zhang,et al.  Effects of land‐use categorization on landscape metrics: A case study in urban landscape of Shenzhen, China , 2007 .

[14]  Shannon J. Brines,et al.  A multi-scale assessment of hurricane impacts on agricultural landscapes based on land use and topographic features , 2008 .

[15]  Sue McIntyre,et al.  The role of plant leaf attributes in linking land use to ecosystem function in temperate grassy vegetation , 2008 .

[16]  P. Defourny,et al.  Accuracy Assessment of a 300 m Global Land Cover Map : The GlobCover Experience , 2009 .

[17]  R. Cowling,et al.  One Hundred Questions of Importance to the Conservation of Global Biological Diversity , 2009, Conservation biology : the journal of the Society for Conservation Biology.

[18]  Mario A. Gomarasca.  Land Use/Land Cover Classification Systems , 2009 .

[19]  G. Foody.  Assessing the Accuracy of Remotely Sensed Data: Principles and Practices , 2010 .

[20]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[21]  Armel Thibaut Kaptué Tchuenté,et al.  Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[22]  Yuji Murayama,et al.  Effect of Spatial Scale on Urban Land-Use Pattern Analysis , 2011 .

[23]  Dirk Pflugmacher,et al.  Comparison and assessment of coarse resolution land cover maps for Northern Eurasia , 2011 .

[24]  Bojie Fu,et al.  Landscape ecology: Coupling of pattern, process, and scale , 2011 .

[25]  Zhang Jinghua,et al.  Progress on Studies of Land Use/Land Cover Classification Systems , 2011 .

[26]  Jin Yin,et al.  Impact of Climate and Land-Use Changes on Water Security for Agriculture in Northern China , 2012 .

[27]  Hankui K. Zhang,et al.  Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data , 2013 .

[28]  Russell G. Congalton,et al.  Global Land Cover Mapping: A Review and Uncertainty Analysis , 2014, Remote. Sens..

[29]  Martin Herold,et al.  Utilizing the Global Land Cover 2000 reference dataset for a comparative accuracy assessment of 1 km global land cover maps , 2015 .

[30]  Jin Chen,et al.  Global land cover mapping at 30 m resolution: A POK-based operational approach , 2015 .

[31]  Marinos Kavouras,et al.  An overview of 21 global and 43 regional land-cover mapping products , 2015 .

[32]  M. Herold,et al.  UTILIZING THE GLOBAL LAND COVER 2000 REFERENCE DATASET FOR A COMPARATIVE ACCURACY ASSESSMENT OF GLOBAL 1 KM LAND COVER MAPS , 2015 .

[33]  Jonathan R. Thompson,et al.  Land‐use impacts on the quantity and configuration of ecosystem service provisioning in Massachusetts, USA , 2015 .

[34]  R. Gardner,et al.  Landscape Ecology in Theory and Practice , 2015, Springer New York.

[35]  Xiaodong Na,et al.  Comparison of land use/land cover change and landscape patterns in Honghe National Nature Reserve and the surrounding Jiansanjiang Region, China , 2015 .

[36]  S. de Bruin,et al.  Assessing global land cover reference datasets for different user communities , 2015 .

[37]  Yuanyong Dian,et al.  Responses of landscape metrics to altering grain size in the Three Gorges Reservoir landscape in China , 2016, Environmental Earth Sciences.

[38]  Shengyan Ding,et al.  Landscape pattern changes at a county scale: A case study in Fengqiu, Henan Province, China from 1990 to 2013 , 2016 .

[39]  Qiming Zhou,et al.  Expressing the spatio-temporal pattern of farmland change in arid lands using landscape metrics , 2016 .

[40]  Lingmei Jiang,et al.  Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China , 2017 .

[41]  Shu Fang,et al.  Analysis of Landscape Patterns of Arid Valleys in China, Based on Grain Size Effect , 2017 .

[42]  M. Hereher,et al.  Effects of land use/cover change on regional land surface temperatures: severe warming from drying Toshka lakes, the Western Desert of Egypt , 2017, Natural Hazards.

[43]  Qingyan Meng,et al.  Assessing the impacts of grain sizes on landscape pattern of urban green space , 2017, Applied Optics and Photonics China.

[44]  Xuezhi Feng,et al.  Accuracy assessment of seven global land cover datasets over China , 2017 .

[45]  Zhihua Wang,et al.  A scale self-adapting segmentation approach and knowledge transfer for automatically updating land use/cover change databases using high spatial resolution images , 2018, Int. J. Appl. Earth Obs. Geoinformation.

[46]  Jonathan R. Rhodes,et al.  The Scale-Dependent Role of Biological Traits in Landscape Ecology: A Review , 2018 .

[47]  Ayinuer Yushanjiang,et al.  Ecological risk assessment due to land use/cover changes (LUCC) in Jinghe County, Xinjiang, China from 1990 to 2014 based on landscape patterns and spatial statistics , 2018, Environmental Earth Sciences.

[48]  Shuai Wang,et al.  Spatial Consistency Assessments for Global Land-Cover Datasets: A Comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO , 2018, Remote. Sens..

[49]  Jing Wang,et al.  Surface metrics for landscape ecology: a comparison of landscape models across ecoregions and scales , 2018, Landscape Ecology.

[50]  Elif Sertel,et al.  Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps , 2018, ISPRS Int. J. Geo Inf..