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12. Trends, Drivers, and Land Use Strategies for Facility Agricultural Land during the Agricultural Modernization Process: Evidence from Huzhou City, China.

Author

Chen, Yun, Wang, Zhifeng, You, Kaijiang, Zhu, Congmou, Wang, Ke, Gan, Muye, and Zhang, Jing

Subjects
FARMS, AGRICULTURAL processing, LAND use, FARM management, AGRICULTURAL resources, DRIVERS' licenses
Abstract

Facility agriculture is an important initiative to adopt an all-encompassing approach to food and build a diversified food supply system. Understanding the evolution of facility agricultural land and the factors that drive it can contribute to the development of scientifically strategic agricultural planning and agricultural modernization. Therefore, this paper constructs a "situation-structure-behavior-value" theoretical framework; quantifies the relevant driving factors (physical, proximal, and socioeconomic) and their impacts on the development and layout of facility agriculture land by using a multivariate logistic regression model; and provides a strategy for optimizing land use. The results showed that the area of facility agriculture in Huzhou is rapidly expanding. Regarding drivers, facility agricultural land tends to be located in areas with higher slopes according to plot selection. Facility agriculture is more likely to develop in plots with convenient transportation and closer proximity to markets. At the economic level, economic efficiency, agricultural resource superiority, and policies significantly impact facility agriculture expansion. Finally, we propose three land use policy options to facilitate the sustainable development of facility agriculture. This study elucidates the underlying factors driving different types of facility agricultural land and offers methodological guidance for policy support, planning, control, and optimization strategies for facility agriculture. [ABSTRACT FROM AUTHOR]

Published
2024
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43. Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA

Author

Shahtahmassebi, Amir Reza, Lin, Yue, Lin, Lin, Atkinson, Peter Michael, Moore, Nathan, Wang, Ke, He, Shan, Huang, Lingyan, Wu, Jiexia, Shen, Zhangquan, Gan, Muye, Zheng, Xinyu, Su, Yue, Teng, Hongfen, Li, Xiaoyan, Deng, Jinsong, Sun, Yuanyuan, Zhao, Mengzhu, Shahtahmassebi, Amir Reza, Lin, Yue, Lin, Lin, Atkinson, Peter Michael, Moore, Nathan, Wang, Ke, He, Shan, Huang, Lingyan, Wu, Jiexia, Shen, Zhangquan, Gan, Muye, Zheng, Xinyu, Su, Yue, Teng, Hongfen, Li, Xiaoyan, Deng, Jinsong, Sun, Yuanyuan, and Zhao, Mengzhu

Abstract

Survey data describing land cover information such as type and diversity over several decades are scarce. Therefore, our capacity to reconstruct historical land cover using field data and archived remotely sensed data over large areas and long periods of time is somewhat limited. This study explores the relationship between CORONA texture—a surrogate for actual land cover type and complexity—with spectral vegetation indices and texture variables derived from Landsat MSS under the Spectral Variation Hypothesis (SVH) such as to reconstruct historical continuous land cover type and complexity. Image texture of CORONA was calculated using a mean occurrence measure while image textures of Landsat MSS were calculated by occurrence and co-occurrence measures. The relationship between these variables was evaluated using correlation and regression techniques. The reconstruction procedure was undertaken through regression kriging. The results showed that, as expected, texture based on the visible bands and corresponding indices indicated larger correlation with CORONA texture, a surrogate of land cover (correlation >0.65). In terms of prediction, the combination of the first-order mean of band green, second-order measure of tasseled cap brightness, second-order mean of Normalized Visible Index (NVI) and second-order entropy of NIR yielded the best model with respect to Akaike’s Information Criterion (AIC), r-square, and variance inflation factors (VIF). The regression model was then used in regression kriging to map historical continuous land cover. The resultant maps indicated the type and degree of complexity in land cover. Moreover, the proposed methodology minimized the impacts of topographic shadow in the region. The performance of this approach was compared with two conventional classification methods: hard classifiers and continuous classifiers. In contrast to conventional techniques, the technique could clearly quantify land cover complexity and type. Future applicati

Published
2017

45. Reconstructing Historical Land Cover Type and Complexity by Synergistic Use of Landsat Multispectral Scanner and CORONA

Author

Shahtahmassebi, Amir Reza, primary, Lin, Yue, additional, Lin, Lin, additional, Atkinson, Peter M., additional, Moore, Nathan, additional, Wang, Ke, additional, He, Shan, additional, Huang, Lingyan, additional, Wu, Jiexia, additional, Shen, Zhangquan, additional, Gan, Muye, additional, Zheng, Xinyu, additional, Su, Yue, additional, Teng, Hongfen, additional, Li, Xiaoyan, additional, Deng, Jinsong, additional, Sun, Yuanyuan, additional, and Zhao, Mengzhu, additional

Published
2017
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