Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction

Flanders, David; Hall-Beyer, Mryka; Pereverzoff, Joan

doi:10.5589/m03-006

Cited by 221 publications

(164 citation statements)

References 5 publications

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“…Some multispectral sensors, such as Ikonos, have both high spatial resolution (small pixel size) and high temporal resolution (the repeat time for taking images over a certain plot of land is short) and may have great utility in terms of observing small lakes (Folgo a Batista et al 2003). Recent advances in object-oriented classification software allow for the identification of waterbodies based on both spectral signature and geometry without the need for time-consuming manual digitizing (Flanders et al 2003). These advances should provide nearly complete enumeration of lakes in a region, as opposed to most current estimates, which are potentially inaccurate as a result of mapping omission and the types of statistical uncertainties we have presented here.…”

Section: Discussionmentioning

confidence: 99%

Does the Pareto distribution adequately describe the size‐distribution of lakes?

Seekell

Pace

2011

Limnology & Oceanography

110

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When it comes to evaluating lakes at regional and global scales, a key need is accurate estimates of the abundance and size-distribution of lakes, which are usually described with the Pareto distribution. We demonstrate the considerable uncertainty that truncation in the lower tail of the Pareto distribution introduces into lake abundance estimates and the selection of the lake size-distribution. Truncation in the lower tail eliminates lakes below a certain size and is generally performed because small lakes are not accurately represented on maps. When simulated data are truncated to mimic available lake size data, non-Pareto distributions are visually and statistically indistinguishable from the Pareto distribution. The Pareto distribution may be one of many possible forms that mimic the global lake size-distribution in the upper tail, but the fit of the Pareto to the lower tail is uncertain, largely because the abundance of small lakes is uncertain. Some other potential sizedistributions, such as the lognormal distribution, predict abundances of small lakes to be orders of magnitude lower than do the Pareto distribution predictions. Highly resolved regional lake size data for the Adirondack Mountains of New York and the Northern Highland Lake District of Wisconsin do not conform to the Pareto distribution. Lake sizes on Mars also do not conform to the Pareto. Uncertainty in the lake size-distribution seriously limits understanding of the significance of lakes as repositories of organic carbon as well as the calculation of global greenhouse gas emissions from these systems.

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Section: Discussionmentioning

confidence: 99%

Does the Pareto distribution adequately describe the size‐distribution of lakes?

Seekell

Pace

2011

Limnology & Oceanography

110

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“…However, object-based classification has proven more effective than pixel based also in some cases where coarser resolution data were used, e.g. the delineation of forest clear cuts with Landsat data, using spectral features, polygon shape parameters, and context with other classes (Flanders et al 2003). In general, multiple reasons favour the use of object-based analysis over pixel-based approaches, particularly for mapping applications.…”

Section: Terrestrial Mappingmentioning

confidence: 99%

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

2015

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Recognizing the imperative need for biodiversity protection, the Convention on Biological Diversity (CBD) has recently established new targets towards 2020, the so-called Aichi targets, and updated proposed sets of indicators to quantitatively monitor the progress towards these targets. Remote sensing has been increasingly contributing to timely, accurate, and cost-effective assessment of biodiversity-related characteristics and functions during the last years. However, most relevant studies constitute individual research efforts, rarely related with the extraction of widely adopted CBD biodiversity indicators. Furthermore, systematic operational use of remote sensing data by managing authorities has still been limited. In this study, the Aichi targets and the related CBD indicators whose monitoring can be facilitated by remote sensing are identified. For each headline indicator a number of recent remote sensing approaches able for the extraction of related properties are reviewed. Methods cover a wide range of fields, including: habitat extent and condition monitoring; species distribution; pressures from unsustainable management, pollution and climate change; ecosystem service monitoring; and conservation status assessment of protected areas. The advantages and limitations of different remote sensing data and algorithms are discussed. Sorting of the methods based on their reported accuracies is attempted, when possible. The extensive literature survey aims at reviewing highly performing methods that can be used for large-area, effective, and timely biodiversity assessment, to encourage the more systematic use of remote sensing solutions in monitoring progress towards the Aichi targets, and to decrease the gaps between the remote sensing and management communities.

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“…The authors similarly observed relatively high overall accuracies, with only one or two land cover types being poorly classified. While the focus of this analysis was not to compare Random Forest to object-based classification, it is worth noting that the latter approach has greater flexibility in terms of being able to make site-specific adjustments to the segmentation approach, as well as to the threshold values being used [73,74]. Demers et al [13] theorized that this could improve results on a site-by-site basis, though this would require more user interference developing the model.…”

Section: Potential For Remote Predictive Mappingmentioning

confidence: 99%

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

et al. 2015

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Abstract:The Random Forest algorithm was used to classify 86 Wide Fine Quadrature Polarized RADARSAT-2 scenes, five Landsat 5 scenes, and a Digital Elevation Model covering an area approximately 81,000 km 2 in size, and representing the entirety of Dease Strait, Coronation Gulf and Bathurst Inlet, Nunavut. The focus of this research was to assess the potential to operationalize shoreline sensitivity mapping to inform oil spill response and contingency planning. The impact of varying the training sample size and reducing model data load were evaluated. Results showed that acceptable accuracies could be achieved with relatively few training samples, but that higher accuracies and greater probabilities of correct class assignment were observed with larger sample sizes. Additionally, the number of inputs to the model could be greatly reduced without impacting overall performance. Optimized models reached independent accuracies of 91% for seven land cover types, and classification probabilities between 0.77 and 0.98 (values for latter represent per-class averages generated from independent validation sites). Mixed OPEN ACCESSRemote Sens. 2015, 7 13529 results were observed when assessing the potential for remote predictive mapping by simulating transferability of the model to scenes without training data.

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Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction

Cited by 221 publications

References 5 publications

Does the Pareto distribution adequately describe the size‐distribution of lakes?

Does the Pareto distribution adequately describe the size‐distribution of lakes?

Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets

Assessing the Potential to Operationalize Shoreline Sensitivity Mapping: Classifying Multiple Wide Fine Quadrature Polarized RADARSAT-2 and Landsat 5 Scenes with a Single Random Forest Model

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