The feasibility of landscape pattern analysis within the alpine steppe of the Yellow River source based on historical CORONA panchromatic imagery

Quanjun Jiao; Bing Zhang; Liangyun Liu

doi:10.1117/12.974622

25 October 2012 The feasibility of landscape pattern analysis within the alpine steppe of the Yellow River source based on historical CORONA panchromatic imagery

Quanjun Jiao, Bing Zhang, Liangyun Liu

Author Affiliations +

Proceedings Volume 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III; 85381N (2012) https://doi.org/10.1117/12.974622
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

Abstract

Multi-scale analysis of landscape statistics is very important to research ecological processes. However, it was lack of high-resolution data to extract the landscape pattern features of suspected ecological degradation region a few decades ago. CORONA panchromatic film photos acquired between 1960 and 1972 provide the historical high-resolution image resource in small-scale ecological pattern research. The purpose of this research was to assess the feasibility of landscape pattern analysis within the alpine steppe area of the Yellow River source through historical CORONA panchromatic imagery. Histogram analysis result shows that the swamp meadow is difficult to be distinguished from water, but there is a potential of mapping four patch types in alpine steppe in gray imagery. Through segmentation image of alpine steppe area, different landscape pattern metrics were calculated in several selected areas. The result also shows that landscape pattern metrics are closely related to NDVI level of alpine grassland. It can be concluded that historical CORONA panchromatic imagery is a potentially valuable high-resolution remote sensing source for long-time ecological monitoring.

Citation Download Citation

Quanjun Jiao, Bing Zhang, and Liangyun Liu "The feasibility of landscape pattern analysis within the alpine steppe of the Yellow River source based on historical CORONA panchromatic imagery", Proc. SPIE 8538, Earth Resources and Environmental Remote Sensing/GIS Applications III, 85381N (25 October 2012); https://doi.org/10.1117/12.974622

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