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The goal of change detection is to identify the cause of change in the landscape.
False
Visual inspection is suitable for detecting subtle changes in land cover.
False
Post-classification comparison is a method used in multi-temporal image analysis for change detection.
True
Change detection is only possible using high-resolution satellite images.
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The accuracy of change detection results can be assessed using metrics such as the Kappa Coefficient.
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A Kappa value of 0.70 indicates a very good agreement between the two observers.
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Change detection methods can only be used to detect long-term changes such as urban growth or desertification.
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When applying Temporal Image Differencing, the values of the change image are typically between 0 and 255
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Radiometric and atmospheric corrections are not necessary for accurate change detection analysis.
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A Kappa value of -0.5 implies that the two observers have a perfect agreement.
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Temporal Image Differencing using a single band can detect subtle changes in land cover
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Spatial registration of images with an accuracy of 1-2 pixels is sufficient for change detection analysis.
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Areas of no change in the Temporal Image Differencing method have a value of 0 in the change image
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The NDVI method of Temporal Image Differencing can be used to detect changes in vegetation health
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Temporal Image Differencing using NDVI can be used to classify land cover changes into more than two categories
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The Kappa Coefficient is a measure of the accuracy of change detection results, with values ranging from $0$ to $1$, where $1$ indicates perfect agreement and $0$ indicates no agreement.
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The Maximum Likelihood Classifier is a type of unsupervised classification method.
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Temporal image differencing is a method of change detection that involves comparing two or more images of the same area taken at different times.
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The confusion matrix is a metric used to assess the accuracy of change detection results, and it provides a summary of the number of true positives, false positives, true negatives, and false negatives.
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The k-Nearest Neighbour Classifier is a type of unsupervised classification method that assigns each pixel to the class of its k-nearest neighbours.
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