## 1 Introduction
At current sub-meter spatial resolutions, penguin colonies are detected by the guano stain they create. Because chinstrap and Adelie penguins nest at a relatively fixed density, the area of the guano stain can be used to estimate the size of the penguin colony [22], though weathering of the guano and the timing of the image relative to the colony's breeding phenology can affect the size of the guano stain as measured by satellites. While these guano stains are spectrally distinct from bare rock, there are spectrally similar features in the Antarctic created by certain clay soils (e.g., kaolinite and illite) and by the guano of other, non-target, bird species. Manual interpretation of guano hinges on auxiliary information such as the size and shape of the guano stain, texture within the guano stain, and the spatial context of the stain, such as its proximity to the coastline and relationship to surrounding land cover classes. Creating the image-to-assessment pipeline will require automated algorithms and workflows for classification such as have been successfully developed for medium-resolution imagery [21; 30]. While the encoded high-resolution information in VHSR imagery is an indispensable resource for fine scale human-augmented wildlife census, it is problematic with respect to automated image interpretation because traditional pixel-based image classification methods fail to extract meaningful information on target classes. The GEographic Object-Based Image Analysis (GEODIA) framework, a complementary approach to per-pixel based classification methods, attempts to mimic the innate cognitive processes that humans use in image interpretation [33; 34; 35; 36; 37]. GEOBIA has evolved in response to the proliferation of very high resolution satellite imagery and is now widely acknowledged as an important component of multi-faceted RS applications [38; 39; 40; 41]. GEOBIA involves more than sequential feature extraction [42]. It provides a cohesive methodological framework for machine-based characterization and classification of spatially-relevant, real-world entities by using multiscale regionalization techniques augmented with nested representations and rule-based classifiers [42; 43]. Image segmentation, a process of partitioning a complex image-scene into non-overlapping homogeneous regions with nested and scaled representations in scene space [44], is the fundamental step within the geographic object-based information retrieval chain [45; 46; 47; 48; 40]. This step is critical because the resulting image segments, which are candidates for well-defined objects on the landscape [36; 49], form the basis for the subsequent classification (unsupervised/supervised methods or knowledge-based methods) based on spectral, spatial, topological, and semantic features [34; 35; 39; 44; 47; 50; 51; 52; 53]. The use of VHSR imagery for penguin survey is rapidly evolving to the point at which spatially-comprehensive EO-based population estimates can be seamlessly integrated into continent-scale models linking spatiotemporal changes in penguin populations to climate change or competition with Southern Ocean fisheries. To reach this objective, however, it is critical to move beyond manual image interpretation by exploiting sophisticated image processing techniques. The study described below reports the first attempt to use a geographic object-based image analysis approach to detect and delineate penguin guano stains. The central objective of this research is to develop a GEOBIA-driven methodological framework that integrates spectral, spatial, textural, and semantic information to extract the fine-scale boundaries of penguin colonies from VHSR images and to closely examine the degree of transferability of knowledge-based rulesets across different study sites and across different sensors focusing on the same semantic class. The remainder of this paper is structured as follows. Section 2 describes the study area, data, and conceptual framework and implementation.