Methodology for credibility assessment of historical global LUCC datasets

Fang, Xiuqi; Wan-yi, Zhao; Zhang, Chengpeng; Zhang, Diyang; Wei, Xueqiong; Qiu, Wei-Li; Yu, Ye

doi:10.1007/s11430-019-9555-3

Cited by 39 publications

(25 citation statements)

References 30 publications

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“…The method we proposed in this paper to assess the coincidence of cropland spatial distributions among these different products is focused on the circumstance that there are plenty of products, but they lack an objective reference dataset for accuracy assessment. This method assumes that the original satellite images and interpretation methods adopted in each product have their own rationality and reliability (e.g., each product executes its own accuracy validation) [53]. However, there are still certain errors and uncertainties.…”

Section: Methodsmentioning

confidence: 99%

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

Zhang

Fang

et al. 2020

IJERPH

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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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Section: Methodsmentioning

confidence: 99%

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

Zhang

Fang

et al. 2020

IJERPH

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“…https://doi.org/10.5194/essd-2020-187 1690, 1750, 1810, 1875, 1910, 1930, 1950, 1980, 1999 and 2015 (Wei et al, 2020).…”

Section: Uncertainties In Cropland Area Allocationmentioning

confidence: 99%

“…Therefore, the PAGES LandCover6k and related projects aim to improve ALCC history at both regional and global scales based on empirical 10 data (Gaillard et al, 2015a;Widgren, 2018a). Errors can be assessed or corrected by using the regional quantitative reconstructed land cover data and regional agrarian history maps (Widgren, 2018b;Fang et al, 2020).…”

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confidence: 99%

Dataset of cropland cover from 1690 to 2015 in Scandinavia

Wei¹,

Widgren²,

Li³

et al. 2020

Preprint

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Abstract. High-resolution historical land cover datasets are essential not only for simulations of climate and environmental dynamics, but also for projections of future land use, food security, climate and biodiversity. However, widely used global datasets are developed for continental-to-global scale analysis and simulations and the accuracy of global datasets depends on the verification of more regional reconstruction results. In this study, based on the collected statistics of cropland area of each administrative unit (Parish/ Municipality/ County/ Province) in Scandinavia from 1690 to 2015, the cropland area at the administrative unit level was allocated into 30-arc second grid cells. The results indicated that the cropland area increased from 1.81 million ha in 1690 to 7.10 million ha in 1950, then decreased to 6.02 million ha in 2015. Before 1810, cropland cover expanded in southern Scandinavia and remained stable in northern. From 1810 to 1910, northern Scandinavia experienced slight expansion and the cropland area increased rapidly in the southern part of the study area. Then, cropland area changed gently. After 1950, cropland area began to decrease in most regions, especially in the east of Scandinavia. When comparing HYDE3.2 with this study, differences in cropland distribution over the past 300 years are mainly observed in regions which were highly cultivated. Our dataset can be used by future environment and climate models in Scandinavia. The dataset can be downloaded from https://doi.pangaea.de/10.1594/PANGAEA.919929 (Wei et al., 2020).

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“…There are disagreements among the different global datasets because of differences in the historical data sources and reconstruction methods as well as the limitations in the allocation models regarding the gradation applied in different datasets (Gaillard et al, 2010; Klein Goldewijk and Verburg, 2013). Many regional reconstructions that are based on the historical archives have demonstrated inaccuracies in cropland cover data recorded in these global datasets (Fang et al, 2020; Kaplan et al, 2009; Leite et al, 2012; Li et al, 2019a; Zumkehr and Campbell, 2013). Therefore, improving the accuracy of gridded cropland datasets is a widely discussed topic within the research literature on historical land use and land cover.…”

Section: Introductionmentioning

confidence: 99%

“…Regional land cover reconstructions based on the available historical archives at high spatial resolutions are critical for improving the accuracy of global datasets (Bender et al, 2005; Benitez and Fisher, 2004; Galatowitsch, 1990; Skaloš et al, 2011). Accuracy could be improved by adopting regional cropland cover reconstructions based on more detailed regional historical source data (Fang et al, 2020; Klein Goldewijk et al, 2010, 2017). For the reconstructions of cropland area on national and sub-national scales based on the historical population, the estimated values would vary from the per capita cropland area employed by different global land cover datasets.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of cropland cover using historical literature and settlement relics in farming areas of Shangjing Dao during the Liao Dynasty, China, around 1100 AD

Fang

Dan

et al. 2020

The Holocene

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To improve the accuracy of historical cropland data, we reconstructed cropland cover in the northern China’s farming–pastoral zone during the Liao dynasty using historical literature and settlement relics. We first reconstructed the total cropland area using historical household data based on the cropland area per household. Next, we allocated the cropland area into 5′ grid cells weighted by settlement density to generate a cropland data. Our main findings were as follows: (1) Data on settlement relics enabled not only the identification of ancient farming areas but also the allocation of cropland cover within high resolution grids in the study area. (2) In the flourishing period of the Liao dynasty, the total cropland area was 0.39 × 104 km2. The cropland grids comprised 28.30% of the total grids, with average and maximum values of cropland fractions of 6.61% and 31.18%, respectively. (3) Compared with our result, we found that the PJ overestimate the cropland area of the study area in 1100 AD, whereas the HYDE 3.2 underestimate it. The area of anthropogenic land use in KK 10 was larger than the cropland area in this study too. None of the three global datasets revealed the mosaic spatial distribution pattern of cropland cover in this study area. (4) The orders of land suitability for cultivation during the Liao dynasty and the present period were almost the identical, even in the farming–pastoral zone of northern China, which has fragile ecological environment.

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Methodology for credibility assessment of historical global LUCC datasets

Cited by 39 publications

References 30 publications

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

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

Dataset of cropland cover from 1690 to 2015 in Scandinavia

Reconstruction of cropland cover using historical literature and settlement relics in farming areas of Shangjing Dao during the Liao Dynasty, China, around 1100 AD

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