Danielle J. Marceau

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.

The Scale Issue in the Social and Natural Sciences

RESUMELechelle spatiale est un concept central dans les sciences qui sinteressent aux activites anthropiques et aux processus physiques qui se produisent a la surface de la terre. La litterature geographique est particulierement riche en discussions concernant limportance de lechelle dans les representations scientifique et familiere du monde (Meentemeyer, 1989; Ferras, 1992). Le concept dechelle est maintenant largement reconnu comme etant au cœur de la description de lorganisation hierarchique de lespace geographique. Plusieurs problemes environnementaux, tels que limpact des changements climatiques sur les ecosystemes, requierent une comprehension de la facon dont les processus operent a differentes echelles et comment ils peuvent etre lies a travers plusieurs echelles. La necessite de predire et de controler leffet dechelle et dagregation spatiale sur les resultats danalyses statistiques et de modelisation est aussi maintenant largement reconnue. Cet article presente une synthese des princ...

Remote Sensing Contributions to the Scale Issue

RESUMEAu cours de la derniere decennie, le probleme dechelle a attire lattention dun nombre croissant de chercheurs de diverses disciplines. Il a ete fortement suggere quune science de lechelle soit etablie. Alors quil est largement reconnu que la teledetection peut apporter une contribution significative au probleme dechelle, tres peu detudes ont ete menees dans le but de synthetiser le travail deja accompli et suggerer de futures directions de recherche. Cet article propose un cadre conceptuel a travers lequel les principales contributions de la teledetection au probleme dechelle sont revues. Les donnees de teledetection sont presentees comme un cas particulier du probleme des unites spatiales modifiables. Il est ensuite decrit comment les solutions proposees au probleme des unites spatiales modifiables ont ete et peuvent etre appliquees dans le contexte de la teledetection afin dinvestiguer les differentes composantes du probleme dechelle. Finalement, le role important de la teledetection da...

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.

Exploration of Spatial Scale Sensitivity in Geographic Cellular Automata

Cellular automata (CA) are individual-based models in which states, time, and space are discrete. Spatiotemporal dynamics emerge from the simple and local interactions of the cells. When using CA in a geographic context, nontrivial questions have to be answered about the choice of spatial scale, namely cell size and neighbourhood configuration. However, the spatial scale decisions involved in the elaboration of geographic cellular automata (GCA) are often made arbitrarily or in relation to data availability. The objective of this study is to evaluate the sensitivity of GCA to spatial scale. A stochastic GCA was built to model land-cover change in the Maskoutains region (Quebec, Canada). The transition rules were empirically derived from two Landsat-TM (30 m resolution) images taken in 1999 and 2002 that have been resampled to four resolutions (100, 200, 500, 1000 m). Six different neighbourhood configurations were considered (Moore, Von Neumann, and circular approximations of 2, 3, 4, and 5 cell radii). Simulations were performed for each of the thirty spatial scale scenarios. Results show that spatial scale has a considerable impact on simulation dynamics in terms of both land-cover area and spatial structure. The spatial scale domains present in the results reveal the nonlinear relationships that link the spatial scale components to the simulation results.

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.

Detecting dominant landscape objects through multiple scales: An integration of object-specific methods and watershed segmentation

Complex systems, such as landscapes, are composed of different critical levels of organization where interactions are stronger within levels than among levels, and where each level operates at relatively distinct time and spatial scales. To detect significant features occurring at specific levels of organization in a landscape, two steps are required. First, a multiscale dataset must be generated from which these features can emerge. Second, a procedure must be developed to delineate individual image-objects and identify them as they change through scale. In this paper, we introduce a framework for the automatic definition of multiscale landscape features using object-specific techniques and marker-controlled watershed segmentation. By applying this framework to a high-resolution satellite scene, image-objects of varying size and shape can be delineated and studied individually at their characteristic scale of expression. This framework involves three main steps: 1) multiscale dataset generation using an object-specific analysis and upscaling technique, 2) marker-controlled watershed transformation to automatically delineate individual image-objects as they evolve through scale, and 3) landscape feature identification to assess the significance of these image-objects in terms of meaningful landscape features. This study was conducted on an agro-forested region in southwest Quebec, Canada, using IKONOS satellite data. Results show that image-objects tend to persist within one or two scale domains, and then suddenly disappear at the next, while new image-objects emerge at coarser scale domains. We suggest that these patterns are associated to sudden shifts in the entire image structure at certain scale domains, which may correspond to critical landscape thresholds.

Spatio-Temporal Object-Oriented Data Model for Disaggregate Travel Behavior

The research field of transportation demand forecasting has started to focus on disaggregate travel behavior and micro-simulation models. To create data infrastructure, disaggregate trip surveys are conducted and large numbers of observations are collected. To efficiently exploit these surveys, the transfer of the individual trip data to a GIS must start with the development of a solid conceptual data model that fully captures the semantic richness of the application domain and emphasizes its spatio-temporal properties. This paper presents a data modeling process that is based on a combination of complex system theory and the object-oriented paradigm and produced an object-oriented spatio-temporal data model. Main domain entities are modeled as highly structured classes. They encapsulate a memory of their time bound connections and states. Observation data sets are sampled from the origin-destination survey conducted in the Quebec region in 1991. This survey incorporated street networks and activity places. The model was smoothly implemented into a proof-of-concept database prototype hosted by an object-oriented GIS shell. The prototype offers a means to navigate through a nested hierarchy of objects, providing a description of an individual’s travel behavior over space and time. The objects have a solid conceptual basis and can meet the needs of scientific research such as hypothesis formulation, simulation, forecasting and induction.

A fuzzy set approach for modelling time in GIS

Abstract This paper describes an application of fuzzy set theory to perform temporal interpolation in a raster GIS database. Specific fuzzy membership functions have been derived to simulate spatial changes between consecutive snapshots registered in the database. The proposed approach is tested on a very dynamic rural-urban environment of Montreal Metropolitan area in Quebec, Canada, covering the period from 1956 to 1986 with a temporal resolution of ten years. A user friendly software package named FUZZY_TEMP was developed and integrated in the GRASS4.1 environment in order to perform the implementation of the developed concepts.

Spatial pattern of coniferous and deciduous forest patches in an Eastern North America agricultural landscape: the influence of land use and physical attributes

In agricultural landscapes, most studies have investigated the influence of the spatial pattern of forest patches on other ecological phenomena and processes, such as animal movement and biodiversity. However, few have focused on explaining the spatial pattern of the forest patches themselves. Understanding how these patterns relate to the processes that generate them is fundamental in developing a sound theory of landscape ecology, and in devising rational management strategies. In this paper, the pattern of the overall forest patches, as well as the pattern of deciduous and coniferous patches in an agricultural landscape of Southern Quebec, Canada, were analyzed and related to landscape physical attributes and land use, using remote sensing, geographic information systems and statistical methods. Results show that the role of landscape physical attributes on forest patch pattern has been modified by land use. In the study area, coniferous or deciduous patches are not associated with a specific surface deposit. In addition, physical attributes explain only a small proportion of the abundance of conifers on past abandoned land compared with land-use factors. Physical attributes only indirectly influence the forest pattern because they strongly influence the land-use practices. Our results reveal a conifer recovery process with the abandonment of agricultural land. On past abandoned land, conifers expand with increasing stand age, mostly by invasion from neighboring coniferous patches. Spatially, coniferous patches are usually located on the margins of the overall forest patches, and they are connected to non-forest land-use types such as crop and pasture, the latter being the most important. By showing the importance of some coniferous forest types that did not exist in the precolonial forest, a new perspective emerges when landscape, especially, land-use dynamics are taken into account.