Multi-agent model-based historical cropland spatial pattern reconstruction for 1661–1952, Shandong Province, China

Abstract

To advance the research of global land use/cover change (LUCC), biodiversity, global carbon cycle, and other aspects of the earth system, it is essential to reconstruct changes in historical cropland cover with long time series and high-resolution grid. Currently, it is a general approach which is based on the view of combining the overall control of cropland area, selecting grid of high land suitability, and 'top-down' decision-making behaviors to reconstruct the historical cropland. Considering various factors that influenced cropland distribution, including behavioral agent's selection by itself and the limitation of nature and human factors, a spatiotemporal dynamical reconstruction model of historical cropland based on the multi-agent systems has been developed from the perspective of 'bottom-up', which combine macroscopic and microscopic decision-making behaviors of agents to simulate the government and farmer autonomously implementing the selection behaviors of farming area. Taking Shandong Province as the study area, this model was used to imitate its cropland spatiotemporal pattern with 1km grid-resolution from 1661 combining the contemporary pattern and reconstructed amount of historical cropland as a maximum potential scope and control variable of reconstruction model, respectively, furthermore, followed the accuracy valuation and comparative analysis. The reconstructed results show that: 1) It is properly suitable for Multi-Agent to simulate and reconstruct the spatial distribution of historical cropland; 2) Compared with historical map datasets (1930s) from the view of point to point, the correctly classified producer accuracy, user accuracy and overall accuracy of reconstructed result totally up to 59.09%, 80.62% and 62.31%,

Quantitative estimation and spatial reconstruction of urban and rural construction land in Jiangsu Province, 1820–1985

“…Historically, the development of most rural settlements was slow and many rural settlements' centers had remained stable for a long time (Jin, 1988;Yang et al, 2016). In the early modern period, the development of agricultural economy was lagging because of little advances in agricultural technologies and the deficiency of policy incentives.…”

“…Global climate change and human activities act as the dual stressors which drastically change the Earth's landscapes and increase the fragility of the Earth's ecosystem. Western industrial revolution, rapid development of science and technology, substantial increase of productivity, population growth, and the expansion of modern cities all lead to the remarkable phenomenon that construction land has become one of the dominant land use types (Bao and Gao, 2016;Saunders, 2012;Yang et al, 2016). Construction land affects the global and regional climate by changing the nature of the underlying surface and intensifying the combustion of fossil fuels (Gu et al, 2011;Yan et al, 2016).…”

Reconstruction of cropland area in the European part of Tsarist Russia from 1696 to 1914 based on historical documents

“…For example, the global cropland area of 1992 in PJ dataset is approximately 24% more than that in HYDE dataset (Klein Goldewijk et al, 2011); it has been evinced that large uncertainties of the global datasets existed on regional scales by many regional scale reconstructions which are more likely to be the real historical LUCC (Ye and Fang, 2011;Li et al, 2013;Ye et al, 2015;Kaplan et al, 2017). Thus, more and more reconstructions have been carried out on regional or local scales (Li et al, 2016;Wei et al, 2016;Yang et al, 2016;He et al, 2017;Li et al, 2017;Kukushkina et al, 2018). Such regions are generally the major agricultural regions of the world and have abundant historical materials to provide enough data of cropland, population, food consumption, and so on for historical cropland cover reconstruction.…”

Spatiotemporal LUCC Simulation under Different RCP Scenarios Based on the BPNN_CA_Markov Model: A Case Study of Bamboo Forest in Anji County

“…Tian et al [12] projected China's land-use demand under different scenarios from 2010 to 2050 based on the system dynamic model. Spatial models that mainly include the cellular automata (CA) model, the conversion of land use and its effects at small regional extent (CLUE-S) model and Geomod model, combine remote sensing and geographic information system technology to overcome the shortcomings of quantitative models regarding the spatial scale, and could be used to reveal the LUCC and its interrelationships on different temporal and spatial scales [13][14][15][16]. However, the efficiency of some spatial models, such as the CLUE-S model, is not satisfactory because the model must rely on relevant results provided by quantitative models to make reasonable predictions for various land-use types [17,18].…”