Multiscale object-specific analysis: Scale problems and multiscale solutions (original) (raw)
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A multiscale framework for landscape analysis: object-specific analysis and upscaling
2001
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.
ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2001
Landscapes are Complex Systems, which by their very nature necessitate a multiscale approach in their monitoring, modeling and management. To assess such broad extents, remote sensing technology is the primary provider of landscape sized data sets, and while tremendous progress has been made over the last thirty years in terms of improved resolution, data availability, and public awareness, the vast majority of remote sensing analytical applications still rely on basic image processing concepts: in particular, per-pixel classification in multi-dimensional feature space. In this paper we describe and compare two technically and theoretically different image processing approaches, both of which facilitate multiscale pattern analysis, exploration, and the linking of landscape components based on methods that derive spatially-explicit multiscale contextual information from a single resolution of remote sensing imagery. Furthermore, we suggest how both methods may be integrated for impro...
2006
It is now widely recognized that landscapes are complex systems that are characterized by a large number of heterogeneous spatial components, nonlinear interactions, emergence, self-organization, adaptation through time, and scale multiplicity. The later property refers to the fact that landscapes exhibit distinctive spatial patterns associated to different processes at different scales. Since there is no way of defining a priori what are the appropriate scales associated to specific patterns, and because there is a need to derive adequate rules for transferring information through multiple scales, it is imperative to develop a multiscale approach that allows dominant patterns to emerge at their characteristic scales of expression. This paper describes Multiscale Object-Specific Analysis (MOSA) as a multiscale approach for landscape analysis that has been developed for the particular spatial sampling context of remote sensing data where each pixel is considered as part of an image-object. This approach reduces the effect of the modifiable areal unit problem (MAUP) and explicitly takes into account the hierarchical organization of the landscape. MOSA represents an integration of Object-Specific Analysis (OSA), Object-Specific Upscaling (OSU) and Marker-Controlled Segmentation (MCS) that allows for the generation of data at a range of scales from which objects can be detected, and for the delineation of individual objects as they emerge and evolve through scale. In this paper, we present a detailed description of MOSA, provide new information on the OSA kernel, and discuss improved methods for using MCS as a feature detector. This is followed by an application using an IKONOS-2 (Geo) dataset acquired over a highly fragmented agro-forested landscape in southwest Quebec, Canada.
A comparison of three image-object methods for the multiscale analysis of landscape structure
Isprs Journal of Photogrammetry and Remote Sensing, 2003
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
Multiscale image analysis for ecological monitoring of heterogeneous, small structured landscapes
2002
The main concept behind this paper is that pattern and processes are linked in a mutual way. In the last decades landscape ecology was dominated by quantitative descriptions ( landscape metrics ) of the landscape under concern and its components. Now there is a growing interest in the cause-effect -relationships between these environmental characteristics. Highresolution aerial photography hold an important amount of valuable information, but until recently only a little proportion of the entire information was usually used in scientific analyses due to conceptual and technical limitations. In this paper we present results derived with a multi-scale image segmentation approach and it is demonstrated how this approach allows for an identification of pattern at several scales simultaneously. First results testify that this is a suitable method for the delineation of meaningful landscape elements and subsequently for landscape monitoring, particularly if dealing with complex or small-scaled pattern. It is shown that hierarchically linked objects are more suitable for monitoring than pixels although the necessity for a comprehensive methodology for object-based change detection arises.
A multi-scale segmentation/object relationship modelling methodology for landscape analysis
Ecological Modelling, 2003
Natural complexity can best be explored using spatial analysis tools based on concepts of landscape as process continuums that can be partially decomposed into objects or patches. We introduce a five-step methodology based on multi-scale segmentation and object relationship modelling. Hierarchical patch dynamics (HPD) is adopted as the theoretical framework to address issues of heterogeneity, scale, connectivity and quasi-equilibriums in landscapes. Remote sensing has emerged as the most useful data source for characterizing land use/land cover but a vast majority of applications rely on basic image processing concepts developed in the 1970s: one spatial scale, per-pixel classification of a multi-scale spectral feature space. We argue that this methodology does not make sufficient use of spatial concepts of neighbourhood, proximity or homogeneity. In contrast, the authors demonstrate in this article the utility of the HPD framework as a theoretical basis for landscape analysis in two different projects using alternative image processing methodologies, which try to overcome the 'pixel-centred' view.
An automated object-based approach for the multiscale image segmentation of forest scenes
: International Journal of Applied Earth Observation and Geoinformation, 2005
Over the last decade the analysis of Earth observation data has evolved from what were predominantly per-pixel multispectralbased approaches, to the development and application of multiscale object-based methods. To empower users with these emerging object-based approaches, methods need to be intuitive, easy to use, require little user intervention, and provide results closely matching those generated by human interpreters. In an attempt to facilitate this, we present multiscale object-specific segmentation (MOSS) as an integrative object-based approach for automatically delineating image-objects (i.e., segments) at multiple scales from a high-spatial resolution remotely sensed forest scene. We further illustrate that these segments cognitively correspond to individual tree crowns, ranging up to forest stands, and describe how such a tool may be used in computer-assisted forest inventory mapping. MOSS is composed of three primary components: object-specific analysis (OSA), object-specific upscaling (OSU), and a new segmentation algorithm referred to as size constrained region merging (SCRM). The rationale for integrating these methods is that the first two provide the third with object-size parameters that otherwise would need to be specified by a user. Analysis is performed on an IKONOS-2 panchromatic image that represents a highly fragmented forested landscape in the Sooke Watershed on southern Vancouver Island, BC, Canada.
Spatial thresholds, image-objects, and upscaling: A multiscale evaluation
Remote Sensing of Environment, 1997
W hen examining a remotely sensed signal through various scale changes, what is the most appropriate upscaling technique to represent this signal at difierent scales? And how can this be validated? Solutions to thesg 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-specifk resampling technique. Analysis of the original and modeled datasets suggests that appropriately upscaled image y 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-speci$c approach for addressing this challenge. Upscaling evaluation is based on visual interpretation, an understanding of the applied resampling theories, and the root mean square error results of 6000 samples collected from a 10 m CASI scene, and from 1.5 m, 3 m, and 5 m same site CASI images upscaled to 10 m. Potential application of this object-specific approach in hierarchical ecosystem modeling is also brie$y described.