Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Sui, Lichun; Yang, Xiaomei; Wang, Zhihua; Huang, Chong; Wang, Jun

doi:10.3390/ijgi8050201

Cited by 24 publications

(18 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These classification systems are well suited for different application needs. But, the classification system of different datasets is not uniform [34,35]. Therefore, this study standardized the codes of the same land-cover types in different products to establish a unified classification system.…”

Section: Data Source and Preprocessingmentioning

confidence: 98%

Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

et al. 2020

Self Cite

View full text Add to dashboard Cite

Analyzing consistency of different land-cover data is significant to reasonably select land-cover data for regional development and resource survey. Existing consistency analysis of different datasets mainly focused on the phenomena of spatial consistency regional distribution or accuracy comparison to provide guidelines for choosing the land-cover data. However, few studies focused on the hidden inconsistency distribution rules of different datasets, which can provide guidelines not only for users to properly choose them but also for producers to improve their mapping strategies. Here, we zoned the Sindh province of Pakistan by the Terrestrial Ecoregions of the World as a case to analyze the inconsistency patterns of the following three datasets: GlobeLand30, FROM-GLC, and regional land cover (RLC). We found that the inconsistency of the three datasets was relatively low in areas having a dominant type and also showing homogeneity characteristics in remote sensing images. For example, cropland of the three datasets in the ecological zoning of Northwestern thorn scrub forests showed high consistency. In contrast, the inconsistency was high in areas with strong heterogeneity. For example, in the southeast of the Thar desert ecological zone where cropland, grassland, shrubland, and bareland were interleaved and the surface cover complexity was relatively high, the inconsistency of the three datasets was relatively high. We also found that definitions of some types in different classification systems are different, which also increased the inconsistency. For example, the definitions of grassland and bareland in GlobeLand30 and RLC were different, which seriously affects the consistency of these datasets. Hence, producers can use the existing land-cover products as reference in ecological zones with dominant types and strong homogeneity. It is necessary to pay more attention on ecological zoning with complex land types and strong heterogeneity. An effective way is standardizing the definitions of complex land types, such as forest, shrubland, and grassland in these areas.

show abstract

Section: Data Source and Preprocessingmentioning

confidence: 98%

Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…The area-based and pixel-based consistencies are widely used to assess the agreement between different global products. The area-based analysis mainly focuses on the consistency of the areal proportions of each LC type between the different GLC products [39,41], whereas the pixel-based analysis is concerned with the spatial (dis)agreement for each LC type between the different GLC products [34,41,42]. In this study, both an analysis of the consistency of the current 30 m GLC products and an analysis for different classes (impervious surface, forest, cropland, and water body) of current global 30 m thematic products were performed to give a more comprehensive understanding of the process of global land-cover mapping.…”

Section: Consistency and Accuracy Assessment Metricsmentioning

confidence: 99%

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

et al. 2021

View full text Add to dashboard Cite

Land-cover mapping is one of the foundations of Earth science. As a result of the combined efforts of many scientists, numerous global land-cover (GLC) products with a resolution of 30 m have so far been generated. However, the increasing number of fine-resolution GLC datasets is imposing additional workloads as it is necessary to confirm the quality of these datasets and check their suitability for user applications. To provide guidelines for users, in this study, the recent developments in currently available 30 m GLC products (including three GLC products and thematic products for four different land-cover types, i.e., impervious surface, forest, cropland, and inland water) were first reviewed. Despite the great efforts toward improving mapping accuracy that there have been in recent decades, the current 30 m GLC products still suffer from having relatively low accuracies of between 46.0% and 88.9% for GlobeLand30-2010, 57.71% and 80.36% for FROM_GLC-2015, and 65.59% and 84.33% for GLC_FCS30-2015. The reported accuracies for the global 30 m thematic maps vary from 67.86% to 95.1% for the eight impervious surface products that were reviewed, 56.72% to 97.36% for the seven forest products, 32.73% to 98.3% for the six cropland products, and 15.67% to 99.7% for the six inland water products. The consistency between the current GLC products was then examined. The GLC maps showed a good overall agreement in terms of spatial patterns but a limited agreement for some vegetation classes (such as shrub, tree, and grassland) in specific areas such as transition zones. Finally, the prospects for fine-resolution GLC mapping were also considered. With the rapid development of cloud computing platforms and big data, the Google Earth Engine (GEE) greatly facilitates the production of global fine-resolution land-cover maps by integrating multisource remote sensing datasets with advanced image processing and classification algorithms and powerful computing capability. The synergy between the spectral, spatial, and temporal features derived from multisource satellite datasets and stored in cloud computing platforms will definitely improve the classification accuracy and spatiotemporal resolution of fine-resolution GLC products. In general, up to now, most land-cover maps have not been able to achieve the maximum (per class or overall) error of 5%–15% required by many applications. Therefore, more efforts are needed toward improving the accuracy of these GLC products, especially for classes for which the accuracy has so far been low (such as shrub, wetland, tundra, and grassland) and in terms of the overall quality of the maps.

show abstract

“…The pixel-based analysis concerned the spatial (dis)agreement for each land-cover type between the different GLC products [18,20,23]. In order to intuitively illustrate the spatial consistency for the different GLC products, the spatial superposition method [23] was employed to obtain the pixel-by-pixel spatial correspondence for the different products analyzed in this study.…”

Section: Pixel-based Consistency Analysismentioning

confidence: 99%

Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset

et al. 2020

View full text Add to dashboard Cite

Land-cover plays an important role in the Earth's energy balance, the hydrological cycle, and the carbon cycle. Therefore, it is important to evaluate the current global land-cover (GLC) products and to understand the differences between these products so that they can be used effectively in different applications. In this study, three 30-m GLC products, namely GlobeLand30-2010, GLC_FCS30-2015, and FROM_GLC30-2015, were evaluated in terms of areal consistency and spatial consistency using the Land Use/Cover Area frame statistical Survey (LUCAS) reference dataset over the European Union (EU). Given the limitations of the traditional confusion matrix used in accuracy assessment, we adjusted the confusion matrices from sample counts by accounting for the class proportions of the map and reported the standard errors of the descriptive accuracy measures in the accuracy assessment. The results revealed the following. (1) The overall accuracy of the GlobeLand30-2010 product was the highest at 88.90 ± 0.68%; this was followed by GLC_FCS30-2015 (84.33 ± 0.80%) and FROM_GLC2015 (65.31 ± 1.0%). (2) The consistency between the GLC_FCS30-2015 and GlobeLand30-2010 is higher than the consistency between other products, with an area correlation coefficient of 0.930 and a proportion of consistent pixels of 52.41%, respectively. (3) Across the area of the EU, the dominant land-cover types such as forest and cropland are the most consistent across the three products, whereas the spatial consistency for bare land, grassland, shrubland, and wetland is relatively low. (4) The proportion of pixels for which the consistency is low accounts for less than 16.17% of pixels, whereas the proportion of pixels for which the consistency is high accounts for about 39.12%. The disagreement between these products primarily occurs in transitional zones with mixed land cover types or in mountain areas. Overall, the GlobeLand30 and GLC-FCS30 products were found to be the most consistent and to have good classification accuracy in the EU, with the disagreement between the three 30-m GLC products mainly occurring in heterogeneous regions.

show abstract

Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Cited by 24 publications

References 45 publications

Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

Inconsistency distribution patterns of different remote sensing land-cover data from the perspective of ecological zoning

Finer-Resolution Mapping of Global Land Cover: Recent Developments, Consistency Analysis, and Prospects

Consistency Analysis and Accuracy Assessment of Three Global 30-m Land-Cover Products over the European Union using the LUCAS Dataset

Contact Info

Product

Resources

About