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Land Suitability Modeling using a Geographic Socio-Environmental Niche-Based Approach: A Case Study from Northeastern Thailand

机译：利用基于地理社会环境的地理环境的土地适用性建模：泰国东北部的案例研究

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Understanding the pattern-process relations of land use/land cover change is an important area of research that provides key insights into human-environment interactions. The suitability or likelihood of occurrence of land use such as agricultural crop types across a human-managed landscape is a central consideration. Recent advances in niche-based, geographic species distribution modeling (SDM) offer a novel approach to understanding land suitability and land use decisions. SDM links species presence-location data with geospatial information and uses machine learning algorithms to develop non-linear and discontinuous species-environment relationships. Here, we apply the MaxEnt (Maximum Entropy) model for land suitability modeling by adapting niche theory to a human-managed landscape. In this article, we use data from an agricultural district in Northeastern Thailand as a case study for examining the relationships between the natural, built, and social environments and the likelihood of crop choice for the commonly grown crops that occur in the Nang Rong District – cassava, heavy rice, and jasmine rice, as well as an emerging crop, fruit trees. Our results indicate that while the natural environment (e.g., elevation and soils) is often the dominant factor in crop likelihood, the likelihood is also influenced by household characteristics, such as household assets and conditions of the neighborhood or built environment. Furthermore, the shape of the land use-environment curves illustrates the non-continuous and non-linear nature of these relationships. This approach demonstrates a novel method of understanding non-linear relationships between land and people. The article concludes with a proposed method for integrating the niche-based rules of land use allocation into a dynamic land use model that can address both allocation and quantity of agricultural crops.
机译：了解土地利用/土地覆被变化的模式-过程关系是一个重要的研究领域，可为人类与环境之间的相互作用提供重要见解。整个人类管理景观中土地使用（如农作物类型）的适宜性或可能性是主要考虑因素。基于生态位的地理物种分布模型（SDM）的最新进展提供了一种新颖的方法来理解土地的适宜性和土地使用的决策。 SDM将物种的存在位置数据与地理空间信息联系起来，并使用机器学习算法来建立非线性和不连续的物种-环境关系。在这里，我们通过将生态位理论适应人类管理的景观，将MaxEnt（最大熵）模型应用于土地适宜性建模。在本文中，我们以泰国东北部一个农业区的数据为例，研究了自然，建筑和社会环境与南荣区常见种植作物的作物选择可能性之间的关系–木薯，重米和茉莉香米，以及新兴作物，果树。我们的结果表明，虽然自然环境（例如海拔和土壤）通常是农作物可能性的主要因素，但可能性还受到家庭特征的影响，例如家庭资产和邻里条件或建筑环境。此外，土地利用-环境曲线的形状说明了这些关系的非连续和非线性性质。这种方法展示了一种理解土地与人之间非线性关系的新颖方法。本文最后提出了一种将基于生态位的土地利用分配规则整合到可以解决农业作物分配和数量的动态土地利用模型中的方法。

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