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Steven E. Franklin

Bio: Steven E. Franklin is an academic researcher from Trent University. The author has contributed to research in topics: Thematic Mapper & Digital elevation model. The author has an hindex of 53, co-authored 165 publications receiving 9471 citations. Previous affiliations of Steven E. Franklin include University of Calgary & University of Saskatchewan.


Papers
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Journal ArticleDOI
TL;DR: In this article, a simple conceptual framework for refugia is presented, and the authors examine the factors that describe them and demonstrate how different disciplines are contributing to their understanding and the tools that they provide for identifying and quantifying refugias.
Abstract: Aim Identifying and protecting refugia is a priority for conservation under projected anthropogenic climate change, because of their demonstrated ability to facilitate the survival of biota under adverse conditions. Refugia are habitats that components of biodiversity retreat to, persist in and can potentially expand from under changing environmental conditions. However, the study and discussion of refugia has often been ad hoc and descriptive in nature. We therefore: (1) provide a habitat-based concept of refugia, and (2) evaluate methods for the identification of refugia. Location Global. Methods We present a simple conceptual framework for refugia and examine the factors that describe them. We then demonstrate how different disciplines are contributing to our understanding of refugia, and the tools that they provide for identifying and quantifying refugia. Results Current understanding of refugia is largely based on Quaternary phylogeographic studies on organisms in North America and Europe during significant temperature fluctuations. This has resulted in gaps in our understanding of refugia, particularly when attempting to apply current theory to forecast anthropogenic climate change. Refugia are environmental habitats with space and time dimensions that operate on evolutionary time-scales and have facilitated the survival of biota under changing environmental conditions for millennia. Therefore, they offer the best chances for survival under climate change for many taxa, making their identification important for conservation under anthropogenic climate change. Several methods from various disciplines provide viable options for achieving this goal. Main conclusions The framework developed for refugia allows the identification and description of refugia in any environment. Various methods provide important contributions but each is limited in scope; urging a more integrated approach to identify, define and conserve refugia. Such an approach will facilitate better understanding of refugia and their capacity to act as safe havens under projected anthropogenic climate change.

835 citations

Journal ArticleDOI
TL;DR: In this paper, pixel-based and object-based image analysis approaches for classifying broad land cover classes over agricultural landscapes are compared using three supervised machine learning algorithms: decision tree (DT), random forest (RF), and the support vector machine (SVM).

785 citations

Book
13 Jun 2001
TL;DR: In this article, the authors define sustainable forest management and ecosystem management criteria and indicators of sustainable forest Management Information Needs of Forest Managers Role of Remote Sensing ACQUISITION of IMAGERY Field, Aerial, and Satellite Imagery Data Characteristics Resolution and Scale Aerial Platforms and Sensors Satellite Platforms And Sensors General Limits of Airborne and Satellite Remote Sensensing Data IMAGE CALIBRATION and PROCESSING Georadiometric Effects and Spectral Response Image Processing Systems and Functionality Image Analysis Support Functions Image Information Extraction Image Understanding FOREST MODEL
Abstract: INTRODUCTION Forest Management Questions Remote Sensing Data and Methods Categories of Applications of Remote Sensing Organization of the Book SUSTAINABLE FOREST MANAGEMENT Definition of Sustainable Forest Management Ecosystem Management Criteria and Indicators of Sustainable Forest Management Information Needs of Forest Managers Role of Remote Sensing ACQUISITION OF IMAGERY Field, Aerial, and Satellite Imagery Data Characteristics Resolution and Scale Aerial Platforms and Sensors Satellite Platforms and Sensors General Limits of Airborne and Satellite Remote Sensing Data IMAGE CALIBRATION AND PROCESSING Georadiometric Effects and Spectral Response Image Processing Systems and Functionality Image Analysis Support Functions Image Information Extraction Image Understanding FOREST MODELING AND GIS Geographical Information Science Ecosystem Process Models Spatial Pattern Modeling FOREST CLASSIFICATION Information on Forest Classes Classification Systems for Use with Remote Sensing Data Level I Classes Level II Classes Level III Classes FOREST STRUCTURE ESTIMATION Information on Forest Structure Forest Inventory Variables Biomass Volume and Growth Assessment FOREST CHANGE DETECTION Information on Forest Change Harvesting and Silviculture Activity Natural Disturbances Change in Spatial Structure CONCLUSION The Technological Approach - Revisited References

485 citations

Journal ArticleDOI
TL;DR: The pertinent technical aspects of remote sensing for images at high spatial resolution, existing and future options for the processing and analysis of remotely sensed data, and attributes that can be estimated with these data for forest ecosystems are presented.
Abstract: Characterization of ecosystem structure, diversity, and function is increasingly desired at finer spatial and temporal scales than have been derived in the past. Many ecological applications require detailed data representing large spatial extents, but these data are often unavailable or are impractical to gather using field-based techniques. Remote sensing offers an option for collecting data that can represent broad spatial extents with detailed attribute characterizations. Remotely sensed data are also appropriate for use in studies across spatial scales, in conjunction with field-collected data. This article presents the pertinent technical aspects of remote sensing for images at high spatial resolution (i.e., with a pixel size of 16 square meters or less), existing and future options for the processing and analysis of remotely sensed data, and attributes that can be estimated with these data for forest ecosystems.

366 citations

Journal ArticleDOI
TL;DR: The objective of this paper is to review and assess general medium spatial resolution satellite remote sensing land cover classification approaches with the goal of identifying the outstanding issues that must be overcome in order to implement a large-area, land cover classified protocol.
Abstract: Numerous large-area, multiple image-based, multiple sensor land cover mapping programs exist or have been proposed, often within the context of national forest monitoring, mapping and modelling initiatives, worldwide. Common methodological steps have been identified that include data acquisition and preprocessing, map legend development, classification approach, stratification, incorporation of ancillary data and accuracy assessment. In general, procedures used in any large-area land cover classification must be robust and repeatable; because of data acquisition parameters, it is likely that compilation of the maps based on the classification will occur with original image acquisitions of different seasonality and perhaps acquired in different years and by different sensors. This situation poses some new challenges beyond those encountered in large-area single image classifications. The objective of this paper is to review and assess general medium spatial resolution satellite remote sensing land cover cl...

311 citations


Cited by
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Journal ArticleDOI
TL;DR: 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.
Abstract: 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.

3,809 citations

Journal ArticleDOI
TL;DR: This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting.
Abstract: 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.

3,244 citations

Journal ArticleDOI
TL;DR: This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature and summarizes and reviews these techniques.
Abstract: Timely and accurate change detection of Earth's surface features is extremely important for understanding relationships and interactions between human and natural phenomena in order to promote better decision making. Remote sensing data are primary sources extensively used for change detection in recent decades. Many change detection techniques have been developed. This paper summarizes and reviews these techniques. Previous literature has shown that image differencing, principal component analysis and post-classification comparison are the most common methods used for change detection. In recent years, spectral mixture analysis, artificial neural networks and integration of geographical information system and remote sensing data have become important techniques for change detection applications. Different change detection algorithms have their own merits and no single approach is optimal and applicable to all cases. In practice, different algorithms are often compared to find the best change detection results for a specific application. Research of change detection techniques is still an active topic and new techniques are needed to effectively use the increasingly diverse and complex remotely sensed data available or projected to be soon available from satellite and airborne sensors. This paper is a comprehensive exploration of all the major change detection approaches implemented as found in the literature.

2,785 citations

Journal ArticleDOI
TL;DR: It is suggested that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map and the selection of a suitable classification method is especially significant for improving classification accuracy.
Abstract: Image classification is a complex process that may be affected by many factors. This paper examines current practices, problems, and prospects of image classification. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition, some important issues affecting classification performance are discussed. This literature review suggests that designing a suitable image-processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. Non-parametric classifiers such as neural network, decision tree classifier, and knowledge-based classification have increasingly become important approaches for multisource data classification. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. More research, however, is needed to identify and reduce uncertainties in the image-processing chain to improve classification accuracy.

2,741 citations

Journal ArticleDOI
TL;DR: An overview of the GMES Sentinel-2 mission including a technical system concept overview, image quality, Level 1 data processing and operational applications is provided.

2,517 citations