Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia

Wei, Xueqiong; Widgren, Mats; Li, Beibei; Ye, Yu; Fang, Xiuqi; Zhang, Chengpeng; Chen, Tiexi

doi:10.5194/essd-13-3035-2021

Cited by 13 publications

(6 citation statements)

References 68 publications

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“…For example, the historical cropland in Scandinavia reconstructed by Wei et al . (2021) was strictly in accordance with the FAO definition of ‘cropland’ and is therefore adopted without correction. Other collected records of ‘cropland’, defined as arable land and permanent cultures, including vineyards, orchards and gardens (Krausmann 2001; Kanianska et al .…”

Section: Methodsmentioning

confidence: 99%

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Reconstruction of cropland change in European countries using integrated multisource data since AD 1800

Fang

et al. 2022

Boreas

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Credible historical land use/cover data are very important for past global change research. This study generates a set of integrated reconstruction methods based on multisource data and produces a new set of improved historical cropland data sets in Europe over the past 200 years. For AD 2000, FAO data, existing research results and statistical data are integrated. For AD 1900, a method of integrating two sets of independent historical agricultural land data by correction and validation and supplemented by other historical cropland data are developed. For AD 1850 and 1800, a methodological scheme of diversified proxy integrative technology and methods based on multisource data is constructed. In this new data set, quantitative reconstructions for AD 1900, 1850 and 1800 are improved to account for 100, 78 and 57% of all European countries, respectively. The reconstruction results show that each region in Europe has been in different stage of historical agricultural development. More than 86% of the countries’ cropland area and its proportion peaked in AD 1900 or did not exceed the data for AD 2000. Specifically, a high reclamation zone gradually formed from France to Ukraine, in which every country’s cropland fraction was ≥40% during AD 1800–1900. From AD 1900–2000, the highly cultivated region contracted, and the centre of higher cropland proportions shifted to eastern Europe and Poland, Czechia and Hungary. The cropland area was systematically underestimated by HYDE3.2, with a relative difference ratio of −20 to −30% between HYDE3.2 and this study. Historical empirical data were used for only 32% of countries in HYDE3.2. This method of multiproxy integrated reconstruction is applicable to other regions of the world and it would be worth attempting to apply it to earlier historical European cropland data sets in the future.

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Section: Methodsmentioning

confidence: 99%

“…Denmark, Norway and Sweden adopted their national official statistics (Wei et al . 2021). Finland uses the cropland area from the statistical yearbook (Suomen Virallinen Tilasto 2001).…”

Section: Data Sourcesmentioning

confidence: 99%

Reconstruction of cropland change in European countries using integrated multisource data since AD 1800

Fang

et al. 2022

Boreas

Self Cite

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“…As a side note, we observe high levels of discrepancies between the GHSL built-up area and HYDE urban area estimates in some study areas, such as Birmingham. This is likely an effect of different definitions, as the GHSL includes all detected settlements (including rural settlements), whereas the urban areas in HYDE are likely to exclude those areas, but can also be attributed to the general difficulty of global models such as HYDE to estimate historical land use patterns at the regional or local level [92]. In the specific case of the London study area, this discrepancy could also be the result of edge effects due to the small study area in relation to the HYDE grid cells (i.e., 5 ), which may exclude partially overlapping grid cells from the study area.…”

Section: Cross-comparison To Hyde and Hind-casted Ghsl Trajectoriesmentioning

confidence: 99%

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Uhl

Leyk

et al. 2021

Remote Sensing

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Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature–human systems (e.g., the dynamics of the wildland–urban interface). Herein, we propose a framework that jointly uses remote-sensing-derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color space segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach against historical building density estimates from the Historical Settlement Data Compilation for the US (HISDAC-US), and against urban area estimates from the History Database of the Global Environment (HYDE). Our results achieve Area-under-the-Curve values >0.9 when comparing to HISDAC-US and are largely in agreement with model-based urban areas from the HYDE database, demonstrating that the integration of remote-sensing-derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization and long-term land cover change in countries where historical maps are available.

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“…As a side note, we observe high levels of discrepancies between the GHSL built-up area and HYDE urban area estimates in some study areas, such as Birmingham. This is likely an effect of different definitions, as the GHSL includes all detected settlements (including rural settlements), whereas the urban areas in HYDE are likely to exclude those areas, but can also be attributed to the general difficulty of global models such as HYDE to estimate historical land use patterns at the regional or local level [87]. In the specific case of the London study area, this discrepancy could also be the result of edge effects due to the small study area in relation to the HYDE grid cells (i.e., 5'), which may exclude partially overlapping grid cells from the study area.…”

Section: Cross-comparison To Hyde and Hind-casted Gshl Trajectoriesmentioning

confidence: 99%

Combining Remote Sensing Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Uhl¹,

Leyk²,

Li³

et al. 2021

Preprint

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Spatially explicit, fine-grained datasets describing historical urban extents are rarely available prior to the era of operational remote sensing. However, such data are necessary to better understand long-term urbanization and land development processes and for the assessment of coupled nature-human systems, e.g., the dynamics of the wildland-urban interface. Herein, we propose a framework that jointly uses remote sensing derived human settlement data (i.e., the Global Human Settlement Layer, GHSL) and scanned, georeferenced historical maps to automatically generate historical urban extents for the early 20th century. By applying unsupervised color segmentation to the historical maps, spatially constrained to the urban extents derived from the GHSL, our approach generates historical settlement extents for seamless integration with the multi-temporal GHSL. We apply our method to study areas in countries across four continents, and evaluate our approach for two U.S. study sites against historical settlement extents derived from the Historical Settlement Data Compilation for the US, HISDAC-US, achieving Area-under-the-Curve values >0.9. Our results are largely in agreement with model-based urban areas from the HYDE database, and demonstrate that the integration of remote sensing derived observations and historical cartographic data sources opens up new, promising avenues for assessing urbanization, and long-term land cover change in countries where historical maps are available.

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Dataset of 1 km cropland cover from 1690 to 1999 in Scandinavia

Cited by 13 publications

References 68 publications

Reconstruction of cropland change in European countries using integrated multisource data since AD 1800

Reconstruction of cropland change in European countries using integrated multisource data since AD 1800

Combining Remote-Sensing-Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

Combining Remote Sensing Derived Data and Historical Maps for Long-Term Back-Casting of Urban Extents

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