Land-surface parameters for spatial predictive mapping and modeling
Land-surface parameters for spatial predictive mapping and modeling
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DOI：
10.1016/j.earscirev.2022.103944
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发表时间：
2022-01
影响因子：
12.1
通讯作者：
Aaron E. Maxwell;C. Shobe
中科院分区：
文献类型：
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作者：
Aaron E. Maxwell;C. Shobe
从数字陆面模型（DLSMs）导出的陆面参数（例如，坡度、表面曲率、地形位置、地形粗糙度、纵横比、热负荷指数和地形湿度指数）可以作为与地貌过程、地貌描绘、生态和栖息地表征以及地质灾害、土壤、湿地以及一般的专题制图和建模。然而，从大量潜在衍生物中选择可能预测特定特征或过程的特征可能是复杂的，并且现有文献可能提供矛盾或不完整的指导。多个数据源的可用性以及定义移动窗口形状、大小和单元权重的需要进一步使特征空间的选择和优化复杂化。本文重点介绍了经验空间预测建模应用中DLSM参数的计算和使用，这些参数依赖于训练数据和解释变量来预测特定地理范围内的景观特征和过程。本次审查的目标受众是研究人员和分析人员进行预测建模任务，利用最广泛使用的地形variables.To大纲最佳实践和突出未来的研究需求，我们回顾了一系列的陆面参数有关的陡峭度，局部起伏，粗糙度，坡度，太阳辐射，和湿度和表征它们的关系，地貌过程。然后，我们讨论了重要的考虑因素时，选择这些参数的预测映射和建模任务，以帮助分析师回答两个关键问题：什么样的景观条件或过程的一个给定的措施的特点？一个特定的度量标准与被映射、建模或研究的现象或特征有什么关系？我们建议使用景观和特定问题的试点研究，以回答，在可能的范围内，这些问题的潜在功能感兴趣的映射或建模任务。我们描述了现有的技术，以减少使用特征选择和特征减少方法的特征空间的大小，评估的重要性或贡献的具体指标，和参数化移动窗口或表征景观在不同的尺度上使用替代方法，同时突出的优势，缺点和知识差距的具体技术。最近的发展，如可解释的机器学习和基于卷积神经网络（CNN）的深度学习，可以指导和/或最大限度地减少对特征空间工程的需求，并简化DLSM在预测建模任务中的使用。
Land-surface parameters derived from digital land surface models (DLSMs) (for example, slope, surface curvature, topographic position, topographic roughness, aspect, heat load index, and topographic moisture index) can serve as key predictor variables in a wide variety of mapping and modeling tasks relating to geomorphic processes, landform delineation, ecological and habitat characterization, and geohazard, soil, wetland, and general thematic mapping and modeling. However, selecting features from the large number of potential derivatives that may be predictive for a specific feature or process can be complicated, and existing literature may offer contradictory or incomplete guidance. The availability of multiple data sources and the need to define moving window shapes, sizes, and cell weightings further complicate selecting and optimizing the feature space. This review focuses on the calculation and use of DLSM parameters for empirical spatial predictive modeling applications, which rely on training data and explanatory variables to make predictions of landscape features and processes over a defined geographic extent. The target audience for this review is researchers and analysts undertaking predictive modeling tasks that make use of the most widely used terrain variables.To outline best practices and highlight future research needs, we review a range of land-surface parameters relating to steepness, local relief, rugosity, slope orientation, solar insolation, and moisture and characterize their relationship to geomorphic processes. We then discuss important considerations when selecting such parameters for predictive mapping and modeling tasks to assist analysts in answering two critical questions: What landscape conditions or processes does a given measure characterize? How might a particular metric relate to the phenomenon or features being mapped, modeled, or studied? We recommend the use of landscape- and problem-specific pilot studies to answer, to the extent possible, these questions for potential features of interest in a mapping or modeling task. We describe existing techniques to reduce the size of the feature space using feature selection and feature reduction methods, assess the importance or contribution of specific metrics, and parameterize moving windows or characterize the landscape at varying scales using alternative methods while highlighting strengths, drawbacks, and knowledge gaps for specific techniques. Recent developments, such as explainable machine learning and convolutional neural network (CNN)-based deep learning, may guide and/or minimize the need for feature space engineering and ease the use of DLSMs in predictive modeling tasks.