Producing global land cover maps consistent over time to respond the needs of the climate modelling community

Bontemps, Sophie; Defourny, Pierre; Eric, Van Bogaert; Herold, Martin; Lammert, Kooistra; Kalogirou, Vasileios; Arino, Olivier

doi:10.1109/multi-temp.2011.6005073

Cited by 7 publications

(7 citation statements)

References 2 publications

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“…After the training, RF can assign a quality measure to each feature. In this study, the Gini index [12] is used as the feature selection measure in the decision tree design. The Gini index measures the impurity of a feature with respect to the classes:…”

Section: Classificationmentioning

confidence: 99%

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Urban Area Extraction by Regional and Line Segment Feature Fusion and Urban Morphology Analysis

2017

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Urban areas are a complex combination of various land-cover types, and show a variety of land-use structures and spatial layouts. Furthermore, the spectral similarity between built-up areas and bare land is a great challenge when using high spatial resolution remote sensing images to map urban areas, especially for images obtained in dry and cold seasons or high-latitude regions. In this study, a new procedure for urban area extraction is presented based on the high-level, regional, and line segment features of high spatial resolution satellite data. The urban morphology is also analyzed. Firstly, the primitive features-the morphological building index (MBI), the normalized difference vegetation index (NDVI), and line segments-are extracted from the original images. Chessboard segmentation is then used to segment the image into the same-size objects. In each object, advanced features are then extracted based on the MBI, the NDVI, and the line segments. Subsequently, object-oriented classification is implemented using the above features to distinguish urban areas from non-urban areas. In general, the boundaries of urban and non-urban areas are not very clear, and each urban area has its own spatial structure characteristic. Hence, in this study, an analysis of the urban morphology is carried out to obtain a clear regional structure, showing the main city, the surrounding new development zones, etc. The experimental results obtained with six WorldView-2 and Gaofen-2 images obtained from different regions and seasons demonstrate that the proposed method outperforms the current state-of-the-art methods.

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Section: Classificationmentioning

confidence: 99%

“…In the early years, only low or medium spatial resolution images were available to the general public and researchers [11,12]. In these images, the characteristics of urban areas are mainly identified by impervious surfaces [9] or built-up areas [10].…”

Section: Introductionmentioning

confidence: 99%

Urban Area Extraction by Regional and Line Segment Feature Fusion and Urban Morphology Analysis

2017

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“…Among scientists, LUC maps are frequently used as a basis for modelling exercises (Tsendbazar et al 2015;Herold et al 2016). At a global scale, climate change models require global LUC maps (Sophie et al 2011). At regional and local scales, land use and cover change models have emerged as valuable tools for policy support (Van Delden et al 2011;White et al 2015).…”

Section: Uses Of Luc Datamentioning

confidence: 99%

“…LUC change analyses are widely used in climate change studies (Sophie et al 2011), the study of hydrological systems (Carlson and Traci Arthur 2000;Cuo et al 2009), weather conditions (Marshall et al 2004), soil erosion (Cebecauer and Hofierka 2008), loss of biodiversity (Cebecauer and Hofierka 2008), as well as in research into ecosystem services (Hu et al 2008) or animal habitats (Lawler et al 2004). The utility of LUC data increases when historical information is available, as it allows us to track LUC changes over time (Verburg et al 2011;García-Álvarez and Camacho Olmedo 2017).…”

Section: Land Change Sciencementioning

confidence: 99%

Land Use Cover Mapping, Modelling and Validation. A Background

García-Álvarez

Olmedo

Mas

et al. 2022

Land Use Cover Datasets and Validation Tools

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In this chapter, we offer a brief introduction to the main concepts associated with Land Use Cover (LUC) mapping, Land Use Cover Change (LUCC) modelling and the uncertainty and validation of LUC and LUCC data and model outputs. The chapter summarizes the theoretical fundamentals required to understand the rest of the book. First, we define Land Use and Land Cover concepts that have been extensively discussed and debated in the literature (Sect. 2). Second, we review the history of LUC mapping, from the first manually produced maps to the advent of aerial and satellite imagery and the production of new datasets with much greater detail and accuracy (Sect. 3). Third, we address the usefulness of LUC data and LUCC analysis for society (Sect. 4), contextualizing all these studies and efforts within the framework of Land Change Science (Sect. 5). Fourth, we offer a brief introduction to LUCC modelling, its purpose, uses and the different stages that make up a LUCC modelling exercise (Sect. 6). We also offer a brief introduction to the different types of LUCC models currently available. Finally, we present the concepts of uncertainty and validation and offer a brief introduction to the topic (Sect. 7). The chapter also includes a short list of recommendations for further reading for those who wish to explore the theory presented here in more depth.

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“…GlobCover is generated by the ESA and adopts a hierarchical legend based on the Food and Agriculture Organization (FAO) of the United Nations Land Cover Classification System (UN FAO LCCS). The spatial resolution is 300 m, and the overall accuracy is 60% to 70% [10,38]. The dataset is produced for epochs 2005 and 2009.…”

Section: Datasets 221 Global Land Cover Datasetsmentioning

confidence: 99%

Evaluating the Accuracy and Spatial Agreement of Five Global Land Cover Datasets in the Ecologically Vulnerable South China Karst

et al. 2022

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Accurate and reliable land cover information is vital for ecosystem management and regional sustainable development, especially for ecologically vulnerable areas. The South China Karst, one of the largest and most concentrated karst distribution areas globally, has been undergoing large-scale afforestation projects to combat accelerating land degradation since the turn of the new millennium. Here, we assess five recent and widely used global land cover datasets (i.e., CCI-LC, MCD12Q1, GlobeLand30, GlobCover, and CGLS-LC) for their comparative performances in land dynamics monitoring in the South China Karst during 2000–2020 based on the reference China Land Use/Cover Database. The assessment proceeded from three aspects: areal comparison, spatial agreement, and accuracy metrics. Moreover, divergent responses of overall accuracy with regard to varying terrain and geomorphic conditions have also been quantified. The results reveal that obvious discrepancies exist amongst land cover maps in both area and spatial patterns. The spatial agreement remains low in the Yunnan–Guizhou Plateau and heterogeneous mountainous karst areas. Furthermore, the overall accuracy of the five datasets ranges from 40.3% to 52.0%. The CGLS-LC dataset, with the highest accuracy, is the most accurate dataset for mountainous southern China, followed by GlobeLand30 (51.4%), CCI-LC (50.0%), MCD12Q1 (41.4%), and GlobCover (40.3%). Despite the low overall accuracy, MCD12Q1 has the best accuracy in areas with an elevation above 1200 m or a slope greater than 25°. With regard to geomorphic types, accuracy in non-karst areas is evidently higher than in karst areas. Additionally, dataset accuracy declines significantly (p < 0.05) with an increase in landscape heterogeneity in the region. These findings provide useful guidelines for future land cover mapping and dataset fusion.

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Producing global land cover maps consistent over time to respond the needs of the climate modelling community

Cited by 7 publications

References 2 publications

Urban Area Extraction by Regional and Line Segment Feature Fusion and Urban Morphology Analysis

Urban Area Extraction by Regional and Line Segment Feature Fusion and Urban Morphology Analysis

Land Use Cover Mapping, Modelling and Validation. A Background

Evaluating the Accuracy and Spatial Agreement of Five Global Land Cover Datasets in the Ecologically Vulnerable South China Karst

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