Next Article in Journal
Types of Crime, Poverty, Population Density and Presence of Police in the Metropolitan District of Quito
Previous Article in Journal
Geo-Tagged Photo Metadata Processing Method for Beijing Inbound Tourism Flow
Open AccessArticle

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

by Wan Hou 1,2,3,4 and Xiyong Hou 1,3,4,*
1
Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
2
College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
3
Key Laboratory of Coastal Environmental Processes and Ecological Remediation, CAS, Yantai 264003, China
4
Center for Ocean Mega-Science, CAS, Qingdao 266071, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(12), 557; https://doi.org/10.3390/ijgi8120557
Received: 4 November 2019 / Revised: 24 November 2019 / Accepted: 2 December 2019 / Published: 4 December 2019
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. View Full-Text
Keywords: remote sensing; land use/land cover; data fusion; agreement analysis; fuzzy-set theory; accuracy analysis; the coastal areas of the Maritime Silk Road remote sensing; land use/land cover; data fusion; agreement analysis; fuzzy-set theory; accuracy analysis; the coastal areas of the Maritime Silk Road
Show Figures

Figure 1

MDPI and ACS Style

Hou, W.; Hou, X. Data Fusion and Accuracy Analysis of Multi-Source Land Use/Land Cover Datasets along Coastal Areas of the Maritime Silk Road. ISPRS Int. J. Geo-Inf. 2019, 8, 557.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop