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Highlights • GlobeLand30 maps show gross land change from 2000 to 2010 on 9.8% of Asia. • Difference Components reveal gross change is 4.4 times larger than net change. • Grass has largest loss; Barren has largest gain; Shrub has most intense changes. • Artificial's gain targets Cultivated; Cultivated's gain targets Artificial. • Researchers need error assessment concerning temporal change for GlobeLand30. Abstract This article uses the GlobeLand30 maps of land cover to characterize the difference between years 2000 and 2010 in Asia. Methods of Intensity Analysis and Difference Components dissect the transition matrix for nine categories: Barren, Grass, Cultivated, Forest, Shrub, Water, Artificial, Wetland and Ice. Results show that Barren, Grass, Cultivated, and Forest each account for more than 21% of Asia at both 2000 and 2010, while transitions among those four categories account for more than half of the temporal difference. Nearly ten percent of Asia shows overall temporal difference, which is the sum of three components: quantity, exchange and shift. Quantity accounts for less than a quarter of the temporal difference, while exchange accounts for three quarters of the temporal difference. The largest quantity components at the category level are a net gain of Barren and net losses of Grass and Shrub. Shrub demonstrates the most intensive loss and gain relative to a category's size. The largest and most intensive transitions to Barren are from Grass and Shrub. The largest and most intensive transition to Artificial is from Cultivated. Error information is not available for GlobeLand30 concerning 2000 or temporal change, but a confusion matrix is available for the global extent at 2010. This article applies methods to interpret the difference between two time points when a confusion matrix is available for only the latter time point. If the 2010 global confusion matrix reflects errors in Asia, then such errors could help to explain some of the gross gain of Barren and the counter-intuitive loss of Artificial. If the GlobeLand30 data indicate true change, then gross change in Asia is 4.4 times larger than net change. [ABSTRACT FROM AUTHOR]

Abstract: This article presents a quantitative method to analyze maps of land categories from several points in time for a single site by considering cross-tabulation matrices, where one matrix summarizes the change in each time interval. There are three levels of analysis, starting from general to more detailed levels, where each level exposes different types of information given the previous level of analysis. First, the interval level examines how the size and speed of change vary across time intervals. Second, the category level examines how the size and intensity of gross losses and gross gains in each category vary across categories for each time interval. Third, the transition level examines how the size and intensity of a category''s transitions vary across the other categories that are available for that transition. At each level, the method tests for stationarity of patterns across time intervals. The unique contribution of this article is that it combines all three levels of analysis into one unified framework that we call intensity analysis, where the more detailed levels are conditional on the less detailed levels. The illustrative case study is for seven categories at the Plum Island Ecosystems site in northeastern Massachusetts, USA, where the largest transition is from Forest to Built during 1985, 1991, and 1999. We compare our approach to other established methods such as the Markov approach in order to show how our proposed intensity analysis gives more information concerning five possible reasons to explain why the transitions vary across time and space. [Copyright &y& Elsevier]

The family of Kappa indices of agreement claim to compare a map's observed classification accuracy relative to the expected accuracy of baseline maps that can have two types of randomness: (1) random distribution of the quantity of each category and (2) random spatial allocation of the categories. Use of the Kappa indices has become part of the culture in remote sensing and other fields. This article examines five different Kappa indices, some of which were derived by the first author in 2000. We expose the indices' properties mathematically and illustrate their limitations graphically, with emphasis on Kappa's use of randomness as a baseline, and the often-ignored conversion from an observed sample matrix to the estimated population matrix. This article concludes that these Kappa indices are useless, misleading and/or flawed for the practical applications in remote sensing that we have seen. After more than a decade of working with these indices, we recommend that the profession abandon the use of Kappa indices for purposes of accuracy assessment and map comparison, and instead summarize the cross-tabulation matrix with two much simpler summary parameters: quantity disagreement and allocation disagreement. This article shows how to compute these two parameters using examples taken from peer-reviewed literature. [ABSTRACT FROM AUTHOR]