, Volume 25, Issue 7, pp 985-998
Date: 13 Apr 2010

Quantifying historic landscape heterogeneity from aerial photographs using object-based analysis

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Abstract

Spatial landscape heterogeneity is routinely used to characterize ecological processes, particularly over time. Critical to the use of landscape heterogeneity as an ecological indicator, is a consistent and quantitative definition, especially in terms of a baseline description. As the oldest and most frequently used form of remotely sensed data, aerial photographs are a unique source of detailed, historic landscape information with the potential to provide this baseline data. Using aerial photographs, texture information, and terrain data of an unharvested watershed in 1937/1938, we quantify baseline heterogeneity. To do this, we explore the use of a relatively new spatial method which utilizes an object-based approach to quantify landscape pattern over multiple spatial scales. Based on quantitative metrics derived from our object-based analysis, the primary dimensions of landscape heterogeneity were first identified using factor analysis, and subsequently summarized with cluster analysis. Sixteen distinct elements of heterogeneity were identified which explained over 76% of the overall variance within the original factors. Several elements of heterogeneity extracted using this approach are common in landscape ecology, including patch compaction, shape, size, texture, and neighboring characteristics (context). However, new elements of spatial heterogeneity were also identified, representing tonal, textural, topographic, and positional variability over multiple spatial scales. We also explored differences in heterogeneity between landscape types of contrasting structure and ecology (riparian versus upland). Few quantitative differences were identified between landscapes, despite obvious ecological and biophysical differences. The results of this analysis provide an alternative description of baseline landscape heterogeneity, which recognizes elements not previously identified.