Geoffrey J. Hay

A comparison of three image-object methods for the multiscale analysis of landscape structure

Within the conceptual framework of Complex Systems, we discuss the importance and challenges in extracting and linking multiscale objects from high-resolution remote sensing imagery to improve the monitoring, modeling and management of complex landscapes. In particular, we emphasize that remote sensing data are a particular case of the modifiable areal unit problem (MAUP) and describe how image-objects provide a way to reduce this problem. We then hypothesize that multiscale analysis should be guided by the intrinsic scale of the dominant landscape objects composing a scene and describe three different multiscale image-processing techniques with the potential to achieve this. Each of these techniques, i.e., Fractal Net Evolution Approach (FNEA), Linear Scale-Space and Blob-Feature Detection (SS), and Multiscale Object-Specific Analysis (MOSA), facilitates the multiscale pattern analysis, exploration and hierarchical linking of image-objects based on methods that derive spatially explicit multiscale contextual information from a single resolution of remote sensing imagery. We then outline the weaknesses and strengths of each technique and provide strategies for their improvement. D 2003 Elsevier Science B.V. All rights reserved.

Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline

What is Geographic Object-Based Image Analysis (GEOBIA)? To answer this we provide a formal definition of GEOBIA, present a brief account of its coining, and propose a key objective for this new discipline. We then, conduct a SWOT1 analysis of its potential, and discuss its main tenets and plausible future. Much still remains to be accomplished.

Object-based change detection

Characterizations of land-cover dynamics are among the most important applications of Earth observation data, providing insights into management, policy and science. Recent progress in remote sensing and associated digital image processing offers unprecedented opportunities to detect changes in land cover more accurately over increasingly large areas, with diminishing costs and processing time. The advent of high-spatial-resolution remote-sensing imagery further provides opportunities to apply change detection with object-based image analysis (OBIA), that is, object-based change detection (OBCD). When compared with the traditional pixel-based change paradigm, OBCD has the ability to improve the identification of changes for the geographic entities found over a given landscape. In this article, we present an overview of the main issues in change detection, followed by the motivations for using OBCD as compared to pixel-based approaches. We also discuss the challenges caused by the use of objects in change detection and provide a conceptual overview of solutions, which are followed by a detailed review of current OBCD algorithms. In particular, OBCD offers unique approaches and methods for exploiting high-spatial-resolution imagery, to capture meaningful detailed change information in a systematic and repeatable manner, corresponding to a wide range of information needs.

A multiscale framework for landscape analysis: object-specific analysis and upscaling

Landscapes are complex systems that require a multiscale approach to fully understand, manage, and predict their behavior. Remote sensing technologies represent the primary data source for landscape analysis, but suffer from the modifiable areal unit problem (MAUP). To reduce the effects of MAUP when using remote sensing data for multiscale analysis we present a novel analytical and upscaling framework based on the spatial influence of the dominant objects composing a scene. By considering landscapes as hierarchical in nature, we theorize how a multiscale extension of this object-specific framework may assist in automatically defining critical landscape thresholds, domains of scale, ecotone boundaries, and the grain and extent at which scale-dependent ecological models could be developed and applied through scale.

A Multiscale Object-Specific Approach to Digital Change Detection

Abstract Landscape spatial pattern is dependent not only on interacting physiographic and physiological processes, but also on the temporal and spatial scales at which the resulting patterns are assessed. To detect significant spatial changes occurring through space and time three fundamental components are required. First, a multiscale dataset must be generated. Second, a change detection framework must be applied to the multiscale dataset. Third, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce an object-specific multiscale digital change detection approach. This approach incorporates multitemporal SPOT Panchromatic (Pan) data, object-specific analysis (OSA), object-specific up-scaling (OSU), marker-controlled watershed segmentation (MCS) and image differencing change detection. By applying this framework to SPOT Pan data, image-objects that have changed between registration dates can be identified and delineated at their characteristic scale of expression. Results illustrate that this approach has the ability to automatically detect changes at multiple scales as well as suppress sensor related noise. This study was conducted in the forest region of the Orebro Administrative Province, Sweden.

Free and Open Source Geographic Information Tools for Landscape Ecology

Abstract Geographic Information tools (GI tools) have become an essential component of research in landscape ecology. In this article we review the use of GIS (Geographic Information Systems) and GI tools in landscape ecology, with an emphasis on free and open source software (FOSS) projects. Specifically, we introduce the background and terms related to the free and open source software movement, then compare eight FOSS desktop GIS with proprietary GIS to analyse their utility for landscape ecology research. We also provide a summary of related landscape analysis FOSS applications, and extensions. Our results indicate that (i) all eight GIS provide the basic GIS functionality needed in landscape ecology, (ii) they all facilitate customisation, and (iii) they all provide good support via forums and email lists. Drawbacks that have been identified are related to the fact that most projects are relatively young. This currently affects the size of their user and developer communities, and their ability to include advanced spatial analysis functions and up-to-date documentation. However, we expect these drawbacks to be addressed over time, as systems mature. In general, we see great potential for the use of free and open source desktop GIS in landscape ecology research and advocate concentrated efforts by the landscape ecology community towards a common, customisable and free research platform.

An object-specific image-texture analysis of H-resolution forest imagery☆

A new structural image-texture technique, termed the triangulated primitive neighborhood method (TPN), is employed to investigate the variable spatial characteristics of high-resolution forest objects, as modeled by a Compact Airborne Spectrographic Imager data set. Based on current psychophysical texture theory, this technique incorporates location-specific primitives and a variable-sized and shaped moving kernel to automatically provide object- and area-specific regularized images. These object-rich, but variance-reduced images allow a traditional classifier to be used on a complex high-resolution forest data set with improved accuracy. The robustness of this technique is evaluated by comparing the maximum likelihood classification accuracy of nine forest classes generated from a combination of the grey level cooccurrence matrix method, semivariance, and customized filters, against those derived from the TPN method. By including into the classification scheme an object-specific channel that models crown density, the highest overall classification accuracy (78%)from all techniques is achieved with the TPN method.

Spatial thresholds, image-objects, and upscaling: A multiscale evaluation

Abstract When examining a remotely sensed signal through various scale changes, what is the most appropriate upsealing technique to represent this signal at different scales? And how can this be validated? Solutions to these questions were approached by examining how the 660 nm signal of six forest stands vary through four different scales of same-.sensor imagery, four traditional resampling techniques, and a new object-specific resampling technique. Analysis of the original and modeled datasets suggests that appropriately upscaled imagery represents a more accurate scene-model than an image obtained at the upscaled resolution. Results further indicate the need for a multiscale approach to feature extraction and upscaling, as no single spatial resolution of imagery appears optimal for detecting or Upscaling the varying sized, shaped, and spatially distributed objects within a scene. By employing the human eye as a model, we describe a novel object-specific approach for addressing this challenge. Upscaling evaluation is based on visual interpretation, an understanding of the applied resampling theories, and the root mean square error results of 6000 samples collected from a 10 in CASI scene, and from 1.5 m, 3 in, and 5 m same site CASI images upscaled to 10 m. Potential application of this object-specific approach in hierarchical ecosystem modeling is also briefly described.

A scale-space primer for exploring and quantifying complex landscapes

Over the last two decades, the scale-space community has developed into a reputable field in computer vision, yet its nontrivial mathematics (i.e. group invariance, differential geometry and tensor analysis) limit its adoption by a larger body of researchers and scientists, whose interests in multiscale analysis range from biomedical imaging to landscape ecology. In an effort to disseminate the ideas of this community to a wider audience we present this non-mathematical primer, which introduces the theory, methods, and utility of scale-space for exploring and quantifying multi-scale landscape patterns within the context of Complex Systems theory. In addition, we suggest that Scale-Space theory, combined with remote sensing imagery and blob-feature detection techniques, satisfy many of the requirements of an idealized multiscale framework for landscape analysis.

Image objects and geographic objects

Object-Based Image Analysis (OBIA) has gained considerable impetus over the last decade. However, despite the many newly developed methods and the numerous successful case studies, little effort has been directed towards building the conceptual foundations underlying it. In particular, there are at least two questions that need a clear answer before OBIA can be considered a discipline: i) What is the definition and ontological status of both image objects and geographic objects? And ii) How do they relate to each other? This chapter provides the authors’ tentative response to these questions.