Remote sensing imagery needs to be converted into tangible information which can be utilised in conjunction with other data sets, often within widely used Geographic Information Systems (GIS). As long as pixel sizes remained typically coarser than, or at the best, similar in size to the objects of interest, emphasis was placed on per-pixel analysis, or even sub-pixel analysis for this conversion, but with increasing spatial resolutions alternative paths have been followed, aimed at deriving objects that are made up of several pixels. This paper gives an overview of the development of object based methods, which aim to delineate readily usable objects from imagery while at the same time combining image processing and GIS functionalities in order to utilize spectral and contextual information in an integrative way. The most common approach used for building objects is image segmentation, which dates back to the 1970s. Around the year 2000 GIS and image processing started to grow together rapidly through object based image analysis (OBIA - or GEOBIA for geospatial object based image analysis). In contrast to typical Landsat resolutions, high resolution images support several scales within their images. Through a comprehensive literature review several thousand abstracts have been screened, and more than 820 OBIA-related articles comprising 145 journal papers, 84 book chapters and nearly 600 conference papers, are analysed in detail. It becomes evident that the first years of the OBIA/GEOBIA developments were characterised by the dominance of ‘grey’ literature, but that the number of peer-reviewed journal articles has increased sharply over the last four to five years. The pixel paradigm is beginning to show cracks and the OBIA methods are making considerable progress towards a spatially explicit information extraction workflow, such as is required for spatial planning as well as for many monitoring programmes.

Object based image analysis for remote sensing

A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.

Random forest in remote sensing: A review of applications and future directions

An Introduction to MultiAgent Systems.

https://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf_files/0_Task1_Cover/Cover_TOC.pdf

Good Practice Guidance for Land Use, Land-Use Change and Forestry

The interactions between groundwater and surface water are complex. To understand these interactions in relation to climate, landform, geology, and biotic factors, a sound hydrogeoecological framework is needed. All these aspects are synthesized and exemplified in this overview. In addition, the mechanisms of interactions between groundwater and surface water (GW–SW) as they affect recharge–discharge processes are comprehensively outlined, and the ecological significance and the human impacts of such interactions are emphasized. Surface-water and groundwater ecosystems are viewed as linked components of a hydrologic continuum leading to related sustainability issues. This overview concludes with a discussion of research needs and challenges facing this evolving field. The biogeochemical processes within the upper few centimeters of sediments beneath nearly all surface-water bodies (hyporheic zone) have a profound effect on the chemistry of the water interchange, and here is where most of the recent research has been focusing. However, to advance conceptual and other modeling of GW–SW systems, a broader perspective of such interactions across and between surface-water bodies is needed, including multidimensional analyses, interface hydraulic characterization and spatial variability, site-to-region regionalization approaches, as well as cross-disciplinary collaborations.

/pdf/interactions-between-groundwater-and-surface-water-the-state-521l9polte.pdf

Interactions between groundwater and surface water: the state of the science

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.

https://www.ucalgary.ca/~gjhay/PDFs/Peer_reviewed/2003_ghay_ISPRS_Full_PDF.pdf

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

RESUMEL'echelle spatiale est un concept central dans les sciences qui s'interessent 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 l'importance de l'echelle dans les representations scientifique et familiere du monde (Meentemeyer, 1989; Ferras, 1992). Le concept d'echelle est maintenant largement reconnu comme etant au cœur de la description de l'organisation hierarchique de l'espace geographique. Plusieurs problemes environnementaux, tels que l'impact 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 l'effet d'echelle et d'agregation spatiale sur les resultats d'analyses statistiques et de modelisation est aussi maintenant largement reconnue. Cet article presente une synthese des princ...

The Scale Issue in the Social and Natural Sciences

RESUMEAu cours de la derniere decennie, le probleme d'echelle a attire l'attention d'un nombre croissant de chercheurs de diverses disciplines. Il a ete fortement suggere qu'une science de l'echelle soit etablie. Alors qu'il est largement reconnu que la teledetection peut apporter une contribution significative au probleme d'echelle, tres peu d'etudes 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 d'echelle 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 d'investiguer les differentes composantes du probleme d'echelle. Finalement, le role important de la teledetection da...

https://people.ucalgary.ca/~gjhay/PDFs/Peer_reviewed/1999_Marceau_Hay_CJRS_RS_Scale.pdf

Remote Sensing Contributions to the Scale Issue

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.

https://people.ucalgary.ca/~gjhay/PDFs/Peer_reviewed/2002%20Hay_LSE_Full.pdf

Danielle J. Marceau

Papers

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

The Scale Issue in the Social and Natural Sciences

Remote Sensing Contributions to the Scale Issue

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

Remote sensing and the measurement of geographical entities in a forested environment. 1. The scale and spatial aggregation problem