Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe

Pérez-Hoyos, Ana; Garcı́a-Haro, Francisco Javier; San-Miguel-Ayanz, Jesús

doi:10.1016/j.isprsjprs.2012.09.006

Cited by 80 publications

(57 citation statements)

References 48 publications

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“…This agreement map was produced by Boolean or crisp comparison [23], revealed areas of uncertainty in global maps [47], and identified patterns of agreement. To minimize geolocalization errors, the agreement among datasets was computed in a 3 × 3 window instead of a pixel-by-pixel basis [18]. Furthermore, an agreement analysis in a pair-wise basis was performed to assess the discrepancies and similarities between pairs of land cover datasets.…”

Section: Comparison Of Datasetsmentioning

confidence: 99%

Comparison of Global Land Cover Datasets for Cropland Monitoring

et al. 2017

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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. (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cropland areas, whereas LC-CCI2010 and GLC2000 are less unsuitable due to large overestimations in the former and out of date information and low accuracy in the latter.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).

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Section: Comparison Of Datasetsmentioning

confidence: 99%

Comparison of Global Land Cover Datasets for Cropland Monitoring

et al. 2017

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“…To accomplish this task a land cover database was applied. Since the widely used IGBP land cover classification (part of the official MCD12 datasets) suffers from uncertainties and errors [74,75] with 75% overall accuracy [76], which includes misclassification of the mixed forest types [77], we used the CORINE 2012 database (Coordination of information on the environment [78]) which represents a higher reliability of 87.0% ± 0.7% [79]. The CORINE database covers our study area with the exception of Ukraine.…”

Section: Land Cover Databasementioning

confidence: 99%

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

2016

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Abstract:Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate-NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe.

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“…At the European scale, extensive use of the European wide Corine Land Cover (CLC) map is made by landscape planners, ecologists, researchers, and policy makers (Pérez-hoyos et al 2012;Pekkarinen et al 2009;Neumann et al 2007;Grekousis et al 2015). The CLC was produced for four years: 1990, 2000, 2006 and more recently for 2012 but CLC2012 is still in the validation phase and therefore not yet available.…”

Section: Introductionmentioning

confidence: 99%

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

Arsanjani

See

Tayyebi

2016

International Journal of Digital Earth

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Global land cover maps have been widely employed as the base layer for a number of applications including climate change, food security, water quality, biodiversity, change detection and environmental planning. Due to the importance of land cover, there is a pressing need to increase the temporal and spatial resolution of global land cover maps. A recent advance in this direction has been the GlobeLand30 dataset derived from Landsat imagery, which has been developed by the National Geomatics Center of China (NGCC). Although overall accuracy is greater than 80%, the NGCC would like help in assessing the accuracy of the product in different regions of the world. To assist in this process, this study compares the GlobeLand30 product with existing public and online datasets, i.e. CORINE, Urban Atlas, OpenStreetMap and ATKIS for Germany in order to assess overall and per class agreement. The results of the analysis reveal high agreement of up to 92% between these datasets and GlobeLand30 but that large disagreements for certain classes are evident, in particular wetlands. However, overall, GlobeLand30 is shown to be a useful product for characterizing land cover in Germany, and paves the way for further regional and national validation efforts.

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Conventional and fuzzy comparisons of large scale land cover products: Application to CORINE, GLC2000, MODIS and GlobCover in Europe

Cited by 80 publications

References 48 publications

Comparison of Global Land Cover Datasets for Cropland Monitoring

Comparison of Global Land Cover Datasets for Cropland Monitoring

Evaluation of the Quality of NDVI3g Dataset against Collection 6 MODIS NDVI in Central Europe between 2000 and 2013

Assessing the suitability of GlobeLand30 for mapping land cover in Germany

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