Uncertainty in ecosystem mapping by remote sensing

Rocchini, Duccio; Foody, Giles M.; Nagendra, Harini; Ricotta, Carlo; Anand, Madhur; He, Kate S.; Amici, Valerio; Kleinschmit, Birgit; Förster, Michael; Schmidtlein, Sebastian; Feilhauer, Hannes; Ghisla, Anne; Metz, Markus; Neteler, Markus

doi:10.1016/j.cageo.2012.05.022

Cited by 117 publications

(76 citation statements)

References 115 publications

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“…A fuzzy classifier produces a measure of the degree of similarity for every class, which is also called a class membership. The similarity measure or the membership indicates the uncertainty between the classes and provides more details about the urban fringe land cover classification [32]. Fuzzy rough theory, which is an extension of classical rough set theory, provides a solid foundation for handling a fuzzified rough set [33,34].…”

Section: Introductionmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

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Urban fringe is the transition zone fine grained with urban and non-urban land cover types. The complex landscape mosaic in this area challenges the land cover classification based on the remote-sensing data. Spectral signatures are not efficient to discriminate all pixels into classes. To improve the recognition and handle the uncertainty, this paper provides a novel integrated approach, based on a fuzzy rough set and evidential reasoning (FRSER), for land cover classification in an urban fringe area. The approach is implemented on Landsat Operation Land Imager data covering the urban fringe area of Wuhan city, China. A fuzzy rough set is first used to define a decision table from multispectral imagery and ground reference data. Then the fuzzy rough information system is interpreted using the Dempster-Shafer theory, based on an evidential reasoning system. A final land cover classification with uncertainty is achieved by evidential reasoning. The results are compared with the traditional maximum likelihood classifier (MLC) and some rough set-based classifiers including classical rough set classifier (RS), fuzzy rough set classifier (FRS), and variable precision fuzzy rough set classifier (VPFRS). The better overall accuracy, user's and producer's accuracies, and the kappa coefficient, in comparison with the other classifiers, suggest that the proposed approach can effectively discriminate land cover types in urban fringe areas with high inter-class similarities and intra-class heterogeneity. It is also capable of handling the uncertainty in data processing, and the final land cover map comes with a degree of uncertainty. The proposed approach that can efficiently integrate the merits of both the fuzzy rough set and DS theory provides an efficient method for urban fringe land cover classification.

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Section: Introductionmentioning

confidence: 99%

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Yang

et al. 2016

Remote Sensing

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“…Another main critical and still disregarded topic is how to interpret "no data values" (i.e., lack of data in some geographic areas) and bias due to both volunteer attitudes and field logistics (e.g., shots of slow-moving animals are more likely to be contributed, because they are easy to catch, while fast-running animal shots are harder; more observations are provided in easily-accessible locations than in remote ones). Nevertheless, strategies and technological means have been proposed to manage such biases, incompleteness, and uncertainty [37][38][39][40].…”

Section: Discussionmentioning

confidence: 99%

CS Projects Involving Geoinformatics: A Survey of Implementation Approaches

Criscuolo¹,

Bordogna²,

Carrara³

et al. 2018

IJGI

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Abstract:In the last decade, citizen science (CS) has seen a renewed interest from both traditional science and the lay public as testified by a wide number of initiatives, projects, and dedicated technological applications. One of the main reasons for this renewed interest lies in the fact that the ways in which citizen science projects are designed and managed have been significantly improved by the recent advancements in information and communications technologies (ICT), especially in the field of geoinformatics. In this research work, we investigate currently active citizen science projects that involve geoinformation to understand how geoinformatics is actually employed. To achieve this, we define eight activities typically carried out during the implementation of a CS initiative as well as a series of approaches for each activity, in order to pinpoint distinct strategies within the different projects. To this end, a representative set of ongoing CS initiatives is selected and surveyed. The results show how CS projects address the various activities, and report which strategies and technologies from geoinformatics are massively or marginally used. The quantitative results are presented, supported by examples and descriptions. Finally, cues and critical issues coming from the research are discussed.

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“…Supervised classification's binning of a pixel into one of several user-defined land classes based on overall spectral profile may fail to capture the more nuanced biophysical meaning behind a pixel, and such classification schemes may also substantially bypass land-change processes such as forest degradation that tend to be heterogeneous on finer, sub-pixel scales [58,59]. Moreover, dividing continuous quantitative information, such as those found in satellite images, into a finite number of discrete land classes that are considered at the outset to be exhaustively defined and mutually exclusive may lend itself to the further loss of information [60]. Such techniques may fail to accurately detect and separate "edge pixels," for example, that exist near the spectral boundaries of different classes [16,61] as well as pixels that exhibit high reflectance variability [62].…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

Lui

Coomes

2015

Remote Sensing

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Abstract:Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application. OPEN ACCESSRemote Sens. 2015, 7 2782

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Uncertainty in ecosystem mapping by remote sensing

Cited by 117 publications

References 115 publications

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

Classification of Complex Urban Fringe Land Cover Using Evidential Reasoning Based on Fuzzy Rough Set: A Case Study of Wuhan City

CS Projects Involving Geoinformatics: A Survey of Implementation Approaches

A Comparison of Novel Optical Remote Sensing-Based Technologies for Forest-Cover/Change Monitoring

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