Coincidence Analysis of the Cropland Distribution of Multi-Sets of Global Land Cover Products

Modern global cropland products have been widely used to assess the impact of land use and cover change (LUCC) on carbon budgets, climate change, terrestrial ecosystems, etc. However, each product has its own uncertainty, and inconsistencies exist among different products. Understanding the reliability of these datasets is essential for knowing the uncertainties that exist in the study of global change impact forced by cropland reclamation. In this paper, we propose a set of coincidence assessments to identify where reliable cropland distribution is by overlaying ten widely used global land cover/cropland datasets around 2000 AD. A quantitative assessment for different spatial units is also performed. We further discuss the spatial distribution characteristics of different coincidence degrees and explain the reasons. The results show that the high-coincidence proportion is only 40.5% around the world, and the moderate-coincidence and low-coincidence proportion is 18.4% and 41.1%, respectively. The coincidence degrees among different continents and countries have large discrepancies. The coincidence is relatively higher in Europe, South Asia and North America, while it is very poor in Latin America and Africa. The spatial distribution of high and moderate coincidence roughly corresponds to the regions with suitable agricultural conditions and intensive reclamation. In addition to the random factors such as the product’s quality and the year it represented, the low coincidence is mainly caused by the inconsistent land cover classification systems and the recognition capability of cropland pixels with low fractions in different products.

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Analyzing the Uncertainty of Degree Confluence Project for Validating Global Land-Cover Maps Using Reference Data-Based Classification Schemes

Remote Sensing ◽

10.3390/rs12162589 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2589

Author(s):

Tana Qian ◽

Tsuguki Kinoshita ◽

Minoru Fujii ◽

Yuhai Bao

Keyword(s):

Land Cover ◽

Reference Data ◽

Data Uncertainty ◽

Validation Data ◽

Classification Schemes ◽

Reference Information ◽

Global Land Cover ◽

Global Land ◽

Land Cover Maps ◽

The Impact

Global land-cover products play an important role in assisting the understanding of climate-related changes and the assessment of progress in the implementation of international initiatives for the mitigation of, and adaption to, climate change. However, concerns over the accuracies of land-cover products remain, due to the issue of validation data uncertainty. The volunteer-based Degree Confluence Project (DCP) was created in 1996, and it has been used to provide useful ground-reference information. This study aims to investigate the impact of DCP-based validation data uncertainty and the thematic issues on map accuracies. We built a reference dataset based on the DCP-interpreted dataset and applied a comparison for three existing global land-cover maps and DCP dataset-based probability maps under different classification schemes. The results of the obtained confusion matrices indicate that the uncertainty, including the number of classes and the confusion in mosaic classes, leads to a decrease in map accuracy. This paper proposes an informative classification scheme that uses a matrix structure of unaggregated land-cover and land-use classes, and has the potential to assist in the land-cover interpretation and validation processes. The findings of this study can potentially serve as a guide to select reference data and choose/define appropriate classification schemes.

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Synergistic Modern Global 1 Km Cropland Dataset Derived from Multi-Sets of Land Cover Products

Remote Sensing ◽

10.3390/rs11192250 ◽

2019 ◽

Vol 11 (19) ◽

pp. 2250 ◽

Cited By ~ 1

Author(s):

Chengpeng Zhang ◽

Yu Ye ◽

Xiuqi Fang ◽

Hansunbai Li ◽

Xueqiong Wei

Keyword(s):

Spatial Distribution ◽

Land Cover ◽

Expert Knowledge ◽

Global Changes ◽

Moderate Amount ◽

Global Land Cover ◽

Spatial Consistency ◽

Sensing Technology ◽

Global Land ◽

Pixel Scale

The quality of global cropland products could affect our understanding of the impacts of cropland reclamation on global changes. With the advancement of remote sensing technology, several global land cover products and synergistic datasets have been developed in recent decades. However, there are still some disagreements among the global cropland datasets. In this paper, we proposed a new synergistic method that integrates the reliability of spatial distribution and cropland fraction on a pixel scale, and developed a modern (around 2000 C.E.) fractional cropland dataset with a 1 km × 1 km spatial resolution on the basis of the spatial consistency of cropland reclamation intensity derived from multi-sets of global land cover products. The main conclusions are shown as follows: (1) The accuracy of spatial distribution assessed by validation samples in this synergistic dataset reaches 87.6%, and the dataset also has a moderate amount of cropland pixels when compared with other products. (2) The reliability of cropland fraction on the pixel scale had been highly improved, and most cropland pixel has a higher fraction (over 90%) in this dataset. The “L” shape of the histogram of pixel numbers with different reclamation intensities is reasonable because it is consistent with the up-scaling results derived from satellite-derived products with high spatial resolutions and the expert knowledge on cultivation. (3) The cropland areas in this non-calibrated result are generally closer to that of FAOSTAT on scales from global to national when compared to other non-calibrated synergistic datasets and original satellite-derived products. (4) The reliability of the synergistic result developed by this method might be decreased to some degree in the regions with high discrepancies among the original multi-sets of cropland datasets.

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The impact of global land-cover change on the terrestrial water cycle

Nature Climate Change ◽

10.1038/nclimate1690 ◽

2012 ◽

Vol 3 (4) ◽

pp. 385-390 ◽

Cited By ~ 238

Author(s):

Shannon M. Sterling ◽

Agnès Ducharne ◽

Jan Polcher

Keyword(s):

Land Cover ◽

Land Cover Change ◽

Water Cycle ◽

Global Land Cover ◽

Global Land ◽

Terrestrial Water ◽

The Impact

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Revisiting land cover observations to address the needs of the climate modelling community

Biogeosciences Discussions ◽

10.5194/bgd-8-7713-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 7713-7740 ◽

Cited By ~ 10

Author(s):

S. Bontemps ◽

M. Herold ◽

L. Kooistra ◽

A. van Groenestijn ◽

A. Hartley ◽

...

Keyword(s):

Land Cover ◽

Classification Systems ◽

Climate Modelling ◽

Political Discussion ◽

Global Land Cover ◽

Global Land ◽

Develop Land ◽

Mapping Techniques ◽

Key Aspects ◽

Stability And Consistency

Abstract. One of the relevant processes driven by political discussion under the United Framework Convention on Climate Change is the monitoring of Essential Climate Variables. Land Cover is one of those variables and efforts are therefore to be made to develop land cover observation approaches which meet the climate modelling community expectations. This paper aims at contributing to this necessity. First, consultation mechanisms were established with the climate modelling community to identify its specific requirements in terms of satellite-based global land cover products. This assessment highlighted specific needs in terms of land cover characterization and products accuracy, stability and consistency that are currently not met. Based on this outcome, the paper calls into question the current land cover representation and for revisiting global land cover mapping approaches. Increasing the flexibility of current classification systems and making the mapping techniques less sensitive to the period of observation are proposed as two key aspects to enhance the usability of global land cover dataset.

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Integrating Land-Cover Products Based on Ontologies and Local Accuracy

Information ◽

10.3390/info12060236 ◽

2021 ◽

Vol 12 (6) ◽

pp. 236

Author(s):

Ling Zhu ◽

Guangshuai Jin ◽

Dejun Gao

Keyword(s):

Land Cover ◽

Semantic Similarity ◽

Mixed Forest ◽

Global Scale ◽

Semantic Mapping ◽

Global Land Cover ◽

Resolution Observation ◽

Local Accuracy ◽

Global Land ◽

Broad Leaf

Freely available satellite imagery improves the research and production of land-cover products at the global scale or over large areas. The integration of land-cover products is a process of combining the advantages or characteristics of several products to generate new products and meet the demand for special needs. This study presents an ontology-based semantic mapping approach for integration land-cover products using hybrid ontology with EAGLE (EIONET Action Group on Land monitoring in Europe) matrix elements as the shared vocabulary, linking and comparing concepts from multiple local ontologies. Ontology mapping based on term, attribute and instance is combined to obtain the semantic similarity between heterogeneous land-cover products and realise the integration on a schema level. Moreover, through the collection and interpretation of ground verification points, the local accuracy of the source product is evaluated using the index Kriging method. Two integration models are developed that combine semantic similarity and local accuracy. Taking NLCD (National Land Cover Database) and FROM-GLC-Seg (Finer Resolution Observation and Monitoring-Global Land Cover-Segmentation) as source products and the second-level class refinement of GlobeLand30 land-cover product as an example, the forest class is subdivided into broad-leaf, coniferous and mixed forest. Results show that the highest accuracies of the second class are 82.6%, 72.0% and 60.0%, respectively, for broad-leaf, coniferous and mixed forest.

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