土地利用的空间动力学演化及其对地方传统生活变迁的研究

Q4 Social Sciences
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引用次数: 0

摘要

土地利用数据可用于了解经济行为模式,如土地利用与财产价值之间的关系,或土地利用对空气和水质等环境因素的影响。将土地利用数据与其他数据来源和分析方法相结合,可以对经济增长和行为产生重大见解。在这项研究中,通过谷歌地球引擎平台(GGE),使用多时相Sentinel-2图像(2019年和2021年)和随机森林对土地利用和土地覆盖(LULC)进行了分类,总体准确率超过89.79%。根据变化检测分析的结果,杂表面积增加16.96%,人工表面积增加15.50%。这些披露证实,作为传统经济功能的海盐养殖场正在损失37.40%。此外,通过对现有趋势的外推,利用CA马尔可夫模型预测了2023年土地利用模式的变化。预测的2023年LULC地图公布了海盐养殖场减少的趋势,而人工表面积却在增加。总之,本研究揭示了LULC与传统生活变化密切相关的证据,空间分析技术是合理和有价值的研究工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Spatial Dynamics Evolution of Land use for the Study of the Local Traditional Living Changes
Land use data can be used to understand patterns of economic behavior, such as the relationship between land use and property values or the impact of land use on environmental factors like air and water quality. The combination of land use data with other data sources and analysis methods can yield significant insights into economic growth and behavior. In this study, the land use and land cover (LULC) were classified using multi-temporal Sentinel-2 imagery (2019 and 2021) and random forest through the Google Earth Engine platform (GGE) with an overall accuracy of more than 89.79%. According to the results of the change detection analysis, there was a 16.96% increase in miscellaneous surface areas and a 15.50% increase in artificial surface areas. These disclose confirm that the sea salt farm, which are the traditional economic function, are losing 37.40%. Furthermore, the CA-Markov model was utilized to predict alterations in land use patterns in the year 2023 through the extrapolation of existing trends. The predicted LULC map of 2023 publicizes the trend of the sea salt farm decreasing, contrasty the artificial surface areas are increasing. In summary, this research reveals the evidence that LULC is strongly related to traditional living changes, and spatial analysis techniques are reasonable and committing tools for study.
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来源期刊
International Journal of Geoinformatics
International Journal of Geoinformatics Social Sciences-Geography, Planning and Development
CiteScore
1.00
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