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Remote Sensing
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Validation on Global Land Cover Datasets
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Validation on Global Land Cover Datasets

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2017) | Viewed by 32338

Special Issue Editors

Prof. Dr. Yifang Ban

E-Mail Website
Guest Editor
Division of Geoinformatics and Department of Urban Planning and Environment at KTH Royal Institute of Technology in Stockholm, Stockholm, Sweden
Interests: EO big data analytics; multitemporal remote sensing; SAR-based classification and change detection; urban mapping and wildfire monitoring
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Xiaohua Tong

E-Mail Website
Guest Editor
College of Surveying and Geo-Informatics, Tongji University, 1239 Siping Rd., Shanghai 200092, China
Interests: spatial data quality; validation of global land covers dataset
Special Issues, Collections and Topics in MDPI journals
Prof. Lijun Chen

E-Mail Website
Guest Editor
National Geomatics Center of China, 28 Lianhuachi West Road, Haidian District, Beijing 100830, China
Interests: land cover mapping based-on remote sensing; updating and related application analysis

Special Issue Information

Dear Colleagues,

Validation is an indispensible component of global land cover (GLC) datasets for ensuring their quality assurance. New challenges arise in global validation of GLC datasets, especially in global sampling plan, allocation of global samples, judgment and assessment methods, and web-based validation supporting tools. Currently, The Group of Earth Observation GEO is leading an international campaign on Data Validation ofGLC Datasets, with the aiming of developing generic and coordinated approaches and toolset for validating land cover data and thus employing the approaches and tools for validation of global and regional GLC datasets. The focus of the Special Issue is to present the new concepts and outputs of GLC datasets validation, including algorithms and models of validation schemes, sample collection and accuracy assessment, as well as on-line validation and tagging tools. Additionally, the Special Issue is not limited to the GEO led international validation compaign. Researchers from all disciplines are welcome to contribute to the special issue, but the application areas should primarily come from global or large regional scale, which are typically suited to output a generic and coordinated approach for the validation of GLC datasets.

Prof. Jun Chen
Prof. Xiaohua Tong
Prof. Lijun Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Global and Regional Land Cover Datasets
  • Remote Sensing
  • Sampling, allocation and judgment
  • Accuracy and Validation
  • Online Validation tools

Published Papers (4 papers)

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14302 KiB  
Article
Comparison of Global Land Cover Datasets for Cropland Monitoring
by Ana Pérez-Hoyos, Felix Rembold, Hervé Kerdiles and Javier Gallego
Remote Sens. 2017, 9(11), 1118; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9111118 - 03 Nov 2017
Cited by 121 | Viewed by 12347
Abstract
Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing food security crises and anticipating response planning. In this paper, we compared some of the most widely used global land [...] Read more.
Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing food security crises and anticipating response planning. In this paper, we compared some of the most widely used global land cover datasets to examine their comparative advantages for cropland monitoring. Cropland class areas are compared for the following datasets: FAO-GLCshare (FAO Global Land Cover Network), Geowiki IIASA-Hybrid (Hybrid global land cover map from the International Institute of Applied System Analysis), GLC2000 (Global Land Cover 2000), GLCNMO2008 (Global Land Cover by National Mapping Organizations), GlobCover, Globeland30, LC-CCI (Land Cover Climate Change Initiative) 2010 and 2015, and MODISLC (MODIS Land Cover product). The methodology involves: (1) highlighting discrepancies in the extent and spatial distribution of cropland, (2) comparing the areas with FAO agricultural statistics at the country level, and (3) providing accuracy assessment through freely available reference datasets. Recommendations for crop monitoring at the country level are based on a priority ranking derived from the results obtained from analyses 2 and 3. Our results revealed that cropland information varies substantially among the analyzed land cover datasets. FAO-GLCshare and Globeland30 generally provided adequate results to monitor cropland areas, whereas LC-CCI2010 and GLC2000 are less unsuitable due to large overestimations in the former and out of date information and low accuracy in the latter. The recently launched LC-CCI datasets (i.e., LC-CCI2015) show a higher potential for cropland monitoring uses than the previous version (i.e., LC-CCI2010). Full article
(This article belongs to the Special Issue Validation on Global Land Cover Datasets)
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8746 KiB  
Article
Validation of Automatically Generated Global and Regional Cropland Data Sets: The Case of Tanzania
by Juan Carlos Laso Bayas, Linda See, Christoph Perger, Christina Justice, Catherine Nakalembe, Jan Dempewolf and Steffen Fritz
Remote Sens. 2017, 9(8), 815; https://doi.org/10.3390/rs9080815 - 09 Aug 2017
Cited by 12 | Viewed by 4525
Abstract
There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme [...] Read more.
There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme II) using a validation data set that was collected by students through the Geo-Wiki tool. The ultimate aim was to understand the usefulness of these products for agricultural monitoring. Data were collected wall-to-wall for Kilosa district and for a sample across Tanzania. The results show that the amount of and spatial extent of cropland in the different products differs considerably from 8% to 42% for Tanzania, with similar values for Kilosa district. The agreement of the validation data with the four different products varied between 36% and 54% and highlighted that cropland is overestimated by the ESA-CCI and underestimated by FROM-GC. The validation data were also analyzed for consistency between the student interpreters and also compared with a sample interpreted by five experts for quality assurance. Regarding consistency between the students, there was more than 80% agreement if one difference in cropland category was considered (e.g., between low and medium cropland) while most of the confusion with the experts was also within one category difference. In addition to the validation of current cropland products, the data set collected by the students also has potential value as a training set for improving future cropland products. Full article
(This article belongs to the Special Issue Validation on Global Land Cover Datasets)
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2667 KiB  
Article
Optimized Sample Selection in SVM Classification by Combining with DMSP-OLS, Landsat NDVI and GlobeLand30 Products for Extracting Urban Built-Up Areas
by Xiaolong Ma, Xiaohua Tong, Sicong Liu, Xin Luo, Huan Xie and Chengming Li
Remote Sens. 2017, 9(3), 236; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9030236 - 04 Mar 2017
Cited by 51 | Viewed by 6696
Abstract
The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized [...] Read more.
The accuracy of training samples used for data classification methods, such as support vector machines (SVMs), has had a considerable positive impact on the results of urban area extractions. To improve the accuracy of urban built-up area extractions, this paper presents a sample-optimized approach for classifying urban area data using a combination of the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS) for nighttime light data, Landsat images, and GlobeLand30, which is a 30-m global land cover data product. The proposed approach consists of three main components: (1) initial sample generation and data classification into built-up and non-urban built-up areas based on the maximum and minimum intervals of digital numbers from the DMSP-OLS data, respectively; (2) refined sample selection and optimization by the probability threshold of each pixel based on vegetation-cover, using the Landsat-derived normalized differential vegetation index (NDVI) and artificial surfaces extracted from the GlobeLand30 product as the constraints; (3) iterative classification and urban built-up area data extraction using the relationship between these three aspects of data collection together with the training sets. Experiments were conducted for several cities in western China using this proposed approach for the extraction of built-up areas, which were classified using urban construction statistical yearbooks and Landsat images and were compared with data obtained from traditional data collection methods, such as the threshold dichotomy method and the improved neighborhood focal statistics method. An analysis of the empirical results indicated that (1) the sample training process was improved using the proposed method, and the overall accuracy (OA) increased from 89% to 96% for both the optimized and non-optimized sample selection; (2) the proposed method had a relative error of less than 10%, as calculated by an accuracy assessment; (3) the overall and individual class accuracy were higher for artificial surfaces in GlobeLand30; and (4) the average OA obviously improved and the Kappa coefficient in the case of Chengdu increased from 0.54 to 0.80. Therefore, the experimental results demonstrated that our proposed approach is a reliable solution for extracting urban built-up areas with a high degree of accuracy. Full article
(This article belongs to the Special Issue Validation on Global Land Cover Datasets)
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3578 KiB  
Technical Note
LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya
by Linda See, Juan Carlos Laso Bayas, Dmitry Schepaschenko, Christoph Perger, Christopher Dresel, Victor Maus, Carl Salk, Juergen Weichselbaum, Myroslava Lesiv, Ian McCallum, Inian Moorthy and Steffen Fritz
Remote Sens. 2017, 9(7), 754; https://0-doi-org.brum.beds.ac.uk/10.3390/rs9070754 - 22 Jul 2017
Cited by 31 | Viewed by 7852
Abstract
Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set [...] Read more.
Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement. Full article
(This article belongs to the Special Issue Validation on Global Land Cover Datasets)
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