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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]

Time-series maps have become more detailed in terms of numbers of categories and time points. Our paper proposes methods for raster datasets where detailed analysis of all categorical transitions would be initially overwhelming. We create two measurements: Incidents and States. The former is the number of times a pixel’s category changes across time intervals; the latter is the number of categories that a pixel represents across time points. The combinations of Incidents and States summarize change trajectories. We also describe categorical transitions in terms of annual flow matrices, which quantify the additional information generated by intermediate time points within the temporal extent. Our approach summarizes change at the pixel and landscape levels in ways that communicate where and how categories transition over time. These methods are useful to detect hotspots of change and to consider whether the apparent changes are real or due to map error. [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]

This article presents the clearest description to date concerning how to compute the hypothetical classification errors that could explain deviations from uniform land changes in the context of Intensity Analysis. Intensity Analysis is an accounting framework that analyses a square contingency table to measure how the sizes of the changes compare to the sizes of the categories. Our case study analyses maps of a portion of southern Nigeria at 1987 and 2002 that show the categories: cultivation, forest, settlement, and water. The data reveal that the changes are not uniformly proportional to the sizes of the categories in terms of categorical gains, categorical losses, and transitions. The methods in this article show that hypothetical error in 9% of the 2002 map could explain all the deviations from uniform categorical gains, hypothetical error in 11% of the 1987 map could explain all the deviations from uniform categorical losses, and hypothetical error in 6% of the 1987 map could explain all deviations from uniform transitions. Larger hypothetical error indicates stronger evidence for a particular deviation from the relevant hypothesized uniform intensity. It is helpful to know the strength of evidence, even when the actual errors in the maps are unknown, which is frequently the case for historical time points. [ABSTRACT FROM PUBLISHER]

A common task is to measure the difference between two maps that show the same spatial extent for the same categorical variable, such as land-cover type. One popular technique is to express the overall difference as the sum of two components called quantity and allocation. This article shows how to take an additional step to express allocation difference as the sum of two components called exchange and shift. Exchange exists for a pair of pixels when one pixel is classified as category A in the first map and as category B in the second map, while simultaneously the paired pixel is classified as category B in the first map and as category A in the second map. If there are more than two categories, then it is possible to have a component called shift, which is allocation difference that is not exchange. Our article shows how to compute all three components of overall difference: quantity, exchange, and shift. We show also how to compute the three components for each category and to reveal the category pairs that account for the largest exchanges. Our article applies the principles to characterize both temporal changes and classification errors using land-cover maps from suburban Massachusetts, USA. [ABSTRACT FROM PUBLISHER]

The implementation of an international programme for reducing carbon emissions from deforestation and degradation (REDD) can help to mitigate climate change and bring numerous benefits to environmental conservation. Information on land change modelling and carbon mapping can contribute to quantify future carbon emissions from deforestation. However limitations in data availability and technical capabilities may constitute an obstacle for countries interested in participating in the REDD programme. This paper evaluates the influence of quantity and allocation of mapped carbon stocks and expected deforestation on the prediction of carbon emissions from deforestation. The paper introduces the conceptual space where quantity and allocation are involved in predicting carbon emissions, and then uses the concepts to predict carbon emissions in the Brazilian Amazon, using previously published information about carbon mapping and deforestation modelling. Results showed that variation in quantity of carbon among carbon maps was the most influential component of uncertainty, followed by quantity of predicted deforestation. Spatial allocation of carbon within carbon maps was less influential than quantity of carbon in the maps. For most of the carbon maps, spatial allocation of deforestation had a minor but variable effect on the prediction of carbon emissions relative to the other components. The influence of spatial carbon allocation reaches its maximum when 50% of the initial forest area is deforested. The method can be applied to other case studies to evaluate the interacting effects of quantity and allocation of carbon with future deforestation on the prediction of carbon emissions from deforestation. [ABSTRACT FROM PUBLISHER]

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]

If a scientist overlays two perfectly accurate maps of land categories of the same place from two points in time, then the differences indicate land change. A land transition matrix summarizes the amount of land that changes from each category at the initial time to each category at the subsequent time. This article proposes methods to compute the land transition matrix in a manner that accounts for errors in the maps, where confusion matrices estimate those errors. If empirical confusion matrices are not available, sensitivity analysis can show the effect of possible errors. The proposed methods produce maps that show the probability of any land transition, given the maps and their confusion matrices. Additional techniques show how possible errors in the maps influence the total land change in terms of two components of quantity and allocation. This article illustrates the methods using data from 1971 to 1999 in Massachusetts, USA. [ABSTRACT FROM AUTHOR]

This article describes a quantitative assessment of the output from the Behavioral Landscape Model (BLM), which has been developed to simulate the spatial pattern of deforestation (i.e. forest fragmentation) in the Amazon basin in a manner consistent with human behavior. The assessment consists of eighteen runs for a section of the Transamazon Highway in the lower basin, where the BLM's simulated deforestation map for each run is compared to a reference map of 1999. The BLM simulates the transition from forest to non-forest in a spatially explicit manner in 20-m × 20-m pixels. The pixels are nested within a hierarchical stratification structure of household lots within larger development rectangles that emanate from the Transamazon Highway. Each of the eighteen runs derives from a unique combination of three model parameters. We have derived novel methods of assessment to consider (1) the nested stratification structure, (2) multiple resolutions, (3) a simpler model that predicts deforestation near the highway, (4) a null model that predicts forest persistence, and (5) a uniform model that has accuracy equal to the expected accuracy of a random spatial allocation. Results show that the model's specification of the overall quantity of non-forest is the most important factor that constrains and correlates with accuracy. A large source of location agreement is the BLM's assumption that deforestation within household lots occurs near roads. A large source of location disagreement is the BLM's less than perfect ability to simulate the proportion of deforestation by household lot. This article discusses implications of these results in the context of land change science and dynamic simulation modeling. [ABSTRACT FROM AUTHOR]

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience. [ABSTRACT FROM AUTHOR]

A correction to the article "Object-Based Classification With Features Extracted by a Semi-Automatic Feature Extraction Algorithm--SEaTH," by Yan Gao and colleagues, published in previous issue.