Globalland30 Mapping Capacity of Land Surface Water in Thessaly, Greece

Manakos, Ioannis; Chatzopoulos-Vouzoglanis, Konstantinos; Petrou, Zisis I.; Filchev, Lachezar; Apostolakis, A.

doi:10.3390/land4010001

Cited by 21 publications

(16 citation statements)

References 40 publications

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“…Since its release for open access at the end of September 2014, GlobeLand30 has been downloaded by more than 1000 scientists from 70 countries. Since then, it has been also used in some analysis on spatial distribution pattern and temporal fluctuation of global land surface water [13,14].…”

Section: Introductionmentioning

confidence: 99%

The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

et al. 2015

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As result of the "Global Land Cover Mapping at Finer Resolution" project led by National Geomatics Center of China (NGCC), one of the first global land cover datasets at 30-meters resolution (GlobeLand30) has been produced for the years 2000 and 2010. The first comprehensive accuracy assessment at a national level of these data (excluding some comparisons in China) has been performed on the Italian area by means of a benchmarking with the more detailed land cover datasets available for some Italian regions. The accuracy evaluation was based on the cell-by-cell comparison between Italian maps and the GlobeLand30 in order to obtain the confusion matrix and its derived statistics (overall accuracy, allocation and quantity disagreements, user and producer accuracy), which help to understand the classification quality. This paper illustrates the adopted methodology and procedures for assessing GlobeLand30 and reports the obtained statistics. The analysis has been performed in eight regions across Italy and shows very good results: the comparison of the datasets according to the first level of Corine Land Cover nomenclature highlights overall accuracy values generally higher than 80%.

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Section: Introductionmentioning

confidence: 99%

The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

et al. 2015

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“…Manakos et al [30] found that land surface water of GlobeLand30 overlaps 91.9% of the reference data in Thessaly, Greece, whereas the coarser-resolution products are restricted to lower accuracies. Lu et al [37] compared cropland class of five global cropland datasets in circa 2010 of China, including GlobeLand30, Finer Resolution Observation and Monitoring GLC dataset (FROM-GLC), GlobCover, MODIS Collection 5, and MODIS Cropland.…”

Section: Accuracy Analysismentioning

confidence: 99%

“…The accuracy of GlobeLand30 had been evaluated by third-party researchers from more than ten countries for its all classes or one single class via sample-based validation or comparison with existing land cover products [28][29][30]. At the country/region level, a satisfactory overall accuracy was estimated as 82.4% for China [31], 80% for Italy [29], 77.90% for all of Iran [14], 80.1% for Nepal [32], and 89.7% for Kyiv Oblast, Ukraine [33].…”

Section: Accuracy Analysismentioning

confidence: 99%

Analysis and Applications of GlobeLand30: A Review

Chen

Cao

Peng

et al. 2017

IJGI

147

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GlobeLand30, donated to the United Nations by China in September 2014, is the first wall-to-wall 30 m global land cover (GLC) data product. GlobeLand30 is widely used by scientists and users around the world. This paper provides a review of the analysis and applications of GlobeLand30 based on its data-downloading statistics and published studies. An average accuracy of 80% for full classes or one single class is achieved by third-party researchers from more than 10 countries through sample-based validation or comparison with existing data. GlobeLand30 has users from more than 120 countries on five continents, and from all five Social Benefit Areas. The significance of GlobeLand30 is demonstrated by a number of published papers dealing with land-cover status and change analysis, cause-and-consequence analysis, and the environmental parameterization of Earth system models. Accordingly, scientific data sharing in the field of geosciences and Earth observation is promoted, and fine-resolution GLC mapping and applications worldwide are stimulated. The future development of GlobeLand30, including comprehensive validation, continuous updating, and monitoring of sustainable development goals, is also discussed.

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“…Furthermore, the overall accuracy (OA) of GlobeLand30-2010 data is 83.50% and the Kappa coefficient (K) is 0.78. Some research on the analysis and application of the GlobeLand30 data has been made and their results have shown good performance [39,40]. In the present study, the GlobeLand30 product for the 2010 reference year is selected, with the time period ranging from 1 January 2010 to 31 December 2010, and map numbers N50_25 and N50_30.…”

Section: Globeland30 Datamentioning

confidence: 99%

Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

Liang

Zuo

Huang

et al. 2015

IJGI

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Abstract:Land cover plays an important role in the climate and biogeochemistry of the Earth system. It is of great significance to produce and evaluate the global land cover (GLC) data when applying the data to the practice at a specific spatial scale. The objective of this study is to evaluate and validate the consistency of the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover product (MCD12Q1) at a provincial scale (Anhui Province, China) based on the Chinese 30 m GLC product (GlobeLand30). A harmonization method is firstly used to reclassify the land cover types between five classification schemes (International Geosphere Biosphere Programme (IGBP) global vegetation classification, University of Maryland (UMD), MODIS-derived Leaf Area Index and Fractional Photosynthetically Active Radiation (LAI/FPAR), MODIS-derived Net Primary Production (NPP), and Plant Functional Type (PFT)) of MCD12Q1 and ten classes of GlobeLand30, based on the knowledge rule (KR) and C4.5 decision tree (DT) classification algorithm. A total of five harmonized land cover types are derived including woodland, grassland, cropland, wetland and artificial surfaces, and four evaluation indicators are selected including the area consistency, spatial consistency, classification accuracy and landscape diversity in the three sub-regions of Wanbei, Wanzhong and Wannan. The results indicate that the consistency of IGBP is the best among the five schemes of MCD12Q1 according to the correlation coefficient (R). The "woodland" LAI/FPAR is the worst, with a spatial OPEN ACCESS ISPRS Int. J. Geo-Inf. 2015, 4 2520 similarity (O) of 58.17% due to the misclassification between "woodland" and "others". The consistency of NPP is the worst among the five schemes as the agreement varied from 1.61% to 56.23% in the three sub-regions. Furthermore, with the biggest difference of diversity indices between LAI/FPAR and GlobeLand30, the consistency of LAI/FPAR is the weakest. This study provides a methodological reference for evaluating the consistency of different GLC products derived from multi-source and multi-resolution remote sensing datasets on various spatial scales.

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Globalland30 Mapping Capacity of Land Surface Water in Thessaly, Greece

Cited by 21 publications

References 40 publications

The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

The First Comprehensive Accuracy Assessment of GlobeLand30 at a National Level: Methodology and Results

Analysis and Applications of GlobeLand30: A Review

Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China

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