ABSTRACT

Categorical maps of landscape types (LTs) are useful abstractions that simplify spatial and thematic complexity of natural landscapes, thus facilitating land resources management. A local landscape arises from a fusion of patterns of natural themes (such as land cover, landforms, etc.), which makes an unsupervised identification and mapping of LTs difficult. This paper introduces the integrated co-occurrence matrix (INCOMA) – a signature for numerical representation of multi-thematic categorical patterns. INCOMA enables an unsupervised identification and mapping of LTs. The region is tessellated into a large number of local landscapes – patterns of themes over small square-shaped neighborhoods. With local landscapes represented by INCOMA signatures and with dissimilarities between local landscapes calculated using the Jensen-Shannon Divergence ($JSD$), LTs can be identified and mapped using standard clustering or segmentation techniques. Resultant LTs are typically heterogeneous with respect to categories of contributing themes reflecting the human perception of a landscape. LTs calculated by INCOMA are more faithful abstractions of actual landscapes than LTs obtained by the current method of choice – the map overlay. The concept of INCOMA is described, and its application is demonstrated by an unsupervised mapping of LT zones in Europe based on combined patterns of land cover and landforms.

摘要

景观类型分类图 (LT) 是有用的抽象，可简化自然景观的空间和主题复杂性，从而促进土地资源管理。局部景观源于自然主题模式（例如土地覆盖、地貌等）的融合，这使得 LT 的无监督识别和制图变得困难。本文介绍了综合共生矩阵 (INCOMA)——一种用于多主题分类模式的数字表示的签名。INCOMA 支持无监督地识别和映射 LT。该地区被镶嵌成大量的当地景观——小方形社区上的主题图案。当地景观由 INCOMA 签名代表，当地景观之间的差异使用 Jensen-Shannon Divergence ($J秒D$)，可以使用标准聚类或分割技术识别和映射 LT。由此产生的 LT 通常在反映人类对景观感知的贡献主题类别方面是异质的。INCOMA 计算的 LT 比当前选择的方法（地图叠加）获得的 LT 更真实地抽象了实际景观。描述了 INCOMA 的概念，并通过基于土地覆盖和地形的组合模式对欧洲 LT 区域进行无监督制图来证明其应用。