A review of methods for scaling remotely sensed data for spatial pattern analysis
A review of methods for scaling remotely sensed data for spatial pattern analysis
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DOI：
10.1007/s10980-022-01449-1
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发表时间：
2022-10
影响因子：
5.2
通讯作者：
Katherine Markham;Amy E. Frazier;Kunwar K. Singh;M. Madden
中科院分区：
文献类型：
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作者：
Katherine Markham;Amy E. Frazier;Kunwar K. Singh;M. Madden
景观生态学家早就意识到规模的重要性，在研究空间格局和需要一个科学的缩放。遥感数据，一个景观生态学家的工具箱，用于研究空间格局的一个关键组成部分，往往需要缩放，以满足study requirements.ObjectivesThis本文回顾方法缩放遥感为基础的数据，特别侧重于空间格局分析，并提取了众多的方法基于数据类型。它还讨论了知识差距和未来的方向。MethodsKey文件，确定通过系统的文献综述。趋势，发展，并从这些数据中得出的缩放遥感数据和空间产品的主要方法进行了识别和合成，详细介绍了景观ecology.ResultsUpscaling连续和分类数据的科学的一般进展可以oversimplify数据，空间格局分析的挑战。基于对象和邻域的方法可以提供帮助，由于斑块边界更可能与对象而不是像素对齐，因此这些可能是景观生态学家更好的选择。许多降尺度的方法存在，但这些方法并没有被广泛采用的空间格局analysis.ConclusionsA不同范围的缩放方法提供给景观生态学家，但工作仍然是将它们整合到空间格局分析。展望未来，应探索计算机科学和工程方面的进展，并鼓励跨学科研究，以促进遥感数据缩放科学。
ContextLandscape ecologists have long realized the importance of scale when studying spatial patterns and the need for a science of scaling. Remotely sensed data, a key component of a landscape ecologist’s toolbox used to study spatial patterns, often requires scaling to meet study requirements.ObjectivesThis paper reviews methods for scaling remote sensing-based data, with a specific focus on spatial pattern analysis, and distills the numerous approaches based on data type. It also discusses knowledge gaps and future directions.MethodsKey papers were identified through a systematic review of the literature. Trends, developments, and key methods for scaling remotely sensed data and spatial products derived from these data were identified and synthesized to detail the general progression of a science of scaling in landscape ecology.ResultsUpscaling both continuous and categorical data can oversimplify data, creating challenges for spatial pattern analysis. Object-based and neighborhood approaches can help, and since patch boundaries are more likely to align with objects than pixels, these may be better options for landscape ecologists. Many downscaling methods exist, but these approaches are not being widely employed for spatial pattern analysis.ConclusionsA diverse range of scaling methods are available to landscape ecologists, but work remains to integrate them into spatial pattern analysis. Moving forward, advances in computer science and engineering should be explored and cross-disciplinary research encouraged to further the science of scaling remotely sensed data.