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Highlights • Scientists should use better methods to assess models that simulate change. • We give novel metrics to reveal differences among three time intervals. • Intensity Analysis reveals various types of quantity disagreements through time. • FOM's components distinguish quantity from allocation disagreement during validation. • The CA part caused more error than the Markov part in a CA-Markov simulation. Abstract Some popular metrics to evaluate land change simulation models are misleading. Therefore, land change scientists have called for the development of methods to evaluate various aspects of modelling applications. This article answers the call by giving novel methods to compare three types of land change: 1) reference change during the calibration time interval, 2) simulation change during the validation time interval, and 3) reference change during the validation time interval. We compare these changes by using Intensity Analysis' three levels and the Figure of Merit's four components: Misses, Hits, Wrong Hits and False Alarms. We illustrate the concepts by applying a Cellular Automata – Markov land change model to a case study in northeast Hungary. We used reference maps of five land categories to calibrate the model during 2000–2006, then to validate the simulation during 2006–2012. Intensity Analysis' time interval level shows that the simulation change and the reference change decelerated from 2000–2006 to 2006–2012. Intensity Analysis' category level shows that the simulation losses were less than what a pure Markov chain would have dictated. Intensity Analysis' transition level shows that the model's Markov algorithm simulated correctly that the gain of Forest targeted Agriculture and Wetland. The Figure of Merit's components reveals more allocation error than quantity error. Our collection of metrics show that more error derived from the Cellular Automata algorithm than from the Markov algorithm. We recommend that scientists use Intensity Analysis and the Figure of Merit's components to reveal various fundamental aspects of modelling applications. [ABSTRACT FROM AUTHOR]

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]

Changsha has undergone speedy socio-economic development, rapid modification of industrial structure, and acceleration of urbanization, which has influenced land cover change during the most recent three decades. Policies have aimed to conserve total agricultural area, but it is not clear how successful these policies have been. Our purpose is to characterize and interpret spatiotemporal patterns of land change with respect to the policy to maintain agricultural area in Changsha, China. Maps at 1990, 2000, and 2010 show four land categories: Built, Forest, Crop and Other. We compute change components and apply Intensity Analysis to compare the land changes during two time intervals: 1990-2000 and 2000-2010. We also compare the central region to the peripheral region during 1990-2010. The maps show that Changsha’s land change accelerated from 1990-2000 to 2000-2010. Change was more intensive in the central region than in the peripheral region. Crop and Forest experienced net decreases while Built experienced net increase during both time intervals and in both regions. Built’s gain targeted Crop and avoided Forest during both time intervals and in both regions. The central region’s largest change component is quantity change, due to Built’s net gain. The peripheral region’s largest change component is exchange, due to simultaneous transitions from Forest to Crop and from Crop to Forest. According to these data, policies have not maintained the quantity of Crop, as the peripheral region has not gained Crop sufficiently to compensate for Crop’s loss from the central region. [ABSTRACT FROM AUTHOR]

Land change in Kigali, Rwanda, is examined using Intensity Analysis, which measures the temporal stationarity of changes among categories. Maps for 1981, 2002 and 2014 were produced that show the land categories Built, Vegetated and Other, which is composed mainly of croplands and bare surfaces. Land change accelerated from the first time interval (1981–2002) to the second time interval (2002–2014), as increased human and economic activities drove land transformation. During the first interval, Vegetated showed net loss whereas Built showed net gain, in spite of a small transition directly from Vegetated to Built. During the second interval, Vegetated showed net gain whereas Built showed nearly equal amounts of gross loss and gross gain. The gain of Built targeted Other during both time intervals. A substantial portion of overall change during both time intervals consisted of simultaneous transitions from Vegetated to Other in some locations and from Other to Vegetated in other locations. [ABSTRACT FROM PUBLISHER]

Abstract: This paper characterizes transitions among three land categories during three consecutive time intervals in a subtropical coastal watershed of southeast China that has enormous influence on the region’s economic and ecological health. Landsat Thematic Mapper satellite imagery of 1986, 1996, 2002, and 2007 were used to create maps of each time point for three categories: Agriculture, Natural, and Built. Intensity analysis was applied to quantify the annual intensity of the changes at three levels: time interval, category, and transition. The results show that overall land transformation is accelerating across the three time intervals. Agriculture shows net gain in the first time interval, then net losses in the subsequent two time intervals. The swap change is larger than the net change for both Agriculture and Natural during all three time intervals. Nearly all of the changes are exchanges between Agriculture and Natural areas. Agriculture and Built are active categories, whereas Natural is a dormant category during all three time intervals. The changes are stationary for all three time intervals in terms of the intensities of categories and transitions for Natural to Agriculture, Agriculture to Natural, and Agriculture to Built. The transition from Agriculture to Built is intensively systematic. The land-cover change is associated with the overall economic growth and recent agricultural decline of the area. The intensity analysis in the Jiulong River watershed reveals information that links patterns to processes of land-use and land-cover changes that are common in many other urbanizing places in China. [Copyright &y& Elsevier]