Data Fusion and Accuracy Analysis of Multi-Source Land Use/Land Cover Datasets along Coastal Areas of the Maritime Silk Road

Wan Hou,Xiyong Hou

doi:10.3390/ijgi8120557

Abstract

High-precision land use/land cover classification mapping derived from remote sensing supplies essential datasets for scientific research on environmental assessment, climate change simulation, geographic condition monitoring, and environmental management at global and regional scales. It is an important issue in the study of earth system science, and the coastal area is a hot spot region in this field. In this paper, the coastal areas of the Maritime Silk Road were used as the research object and a fusion method based on agreement analysis and fuzzy-set theory was adopted to achieve the fusion of three land use/land cover datasets: MCD12Q1-2010, CCI-LC2010, and GlobeLand30-2010. The accuracy of the fusion results was analyzed using an error matrix, spatial confusion, average overall consistency, and average type-specific consistency. The main findings were as follows. (1) After the establishment of reference data based on Google Earth, both the producer accuracy and user accuracy of the fusion data were improved when compared with those of the three input data sources, and the fusion data had the highest overall accuracy and Kappa coefficient, with values of 90.37% and 0.8617, respectively. (2) Various input data sources differed in terms of the correctly classified contributions and misclassified influences of different land use/land cover types in the fusion data; furthermore, the overall accuracy and Kappa coefficient between the fusion data and any one of the input data sources were far higher than those between any two of the input data sources. (3) The average overall consistency of the fusion data was the highest at 89.29%, which was approximately 5% higher than that of the input data sources. (4) The average type-specific consistencies of cropland, forest, grassland, shrubland, wetland, artificial surfaces, bare land, and permanent snow and ice in the fusion data were the highest, with values of 69.95%, 74.41%, 21.24%, 34.22%, 97.62%, 51.83%, 84.39%, and 2.46%, respectively; compared with the input data sources, the average type-specific consistencies of the fusion data were 0.61–20.32% higher. This paper provides information and suggestions for the development and accuracy evaluation of future land use/land cover data in global and regional coastal areas.

Highlights

High-precision land use/land cover classification datasets at global and regional scales can provide important basic information to effectively support scientific research on global change and regional sustainable development, serving as a key information source with which to objectively describe the structure of terrestrial ecosystems and their ecological processes [1,2,3]
Numerous land use/land cover datasets have been formed at global and regional scales [4,5], for example, IGBP-DISCover established by the United States Geological Survey, UMD developed by the University of Maryland in the United States, GLC2000 established by the Joint Research Center of the European Union, MCD12Q1 produced by Boston University in the United States, GlobCover and CCI-LC prepared by the European Space Agency, FROM-GLC developed by Tsinghua University in China, and GlobeLand30 provided by the National Administration of Surveying, Mapping and Geoinformation of China [6,7,8,9,10,11,12,13,14]
Compared with the subjective cognition formed by visual observation of Google Earth images, it can be preliminarily determined that this dataset can accurately reflect the cropland that is widely distributed along the coasts of China’s Yellow and Bohai Seas, the Southeast Asian coast, the Indian east coast, and the Mediterranean coast

Summary

Introduction

High-precision land use/land cover classification datasets at global and regional scales can provide important basic information to effectively support scientific research on global change and regional sustainable development, serving as a key information source with which to objectively describe the structure of terrestrial ecosystems and their ecological processes [1,2,3]. Scholars and numerous international organizations are making full use of the advantages of data fusion technology to perform land use/land cover remote sensing classification mapping studies based on multi-source data fusion [18,19]. Jung et al proposed a fusion method based on the affinity index, which fuses GLCC, GLC2000, and MODIS data, and the fusion results were shown to better express land use/land cover types in heterogeneous regions [20]. Bai et al designed a decision fusion method based on fuzzy logic, which fuses multi-source datasets such as GLCC, UMD, GLC2000, MODIS LC, GlobCover, MODIS VCF, MODIS Cropland Probability, and AVHRR CFTC, and generated a set of land use/land cover fusion data with a spatial resolution of 1 km at the global scale [24]. Remote sensing classification mapping of land use/land cover based on multi-source data fusion is relatively mature for most terrestrial areas, while related research on macro-scale coastal areas is still insufficient

Results

Discussion

Conclusion