Publications

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover: the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

The classification of remotely sensed images such as aerial photographs or satellite sensor images for deriving ecosystem-related maps (e.g., land cover, land use, vegetation, soil) is generally based on clustering of spatial entities within a spectral space. In most cases, Boolean logic is applied in order to map landscape patterns. One major concern is that this implies an ability to divide the gradual variability of the Earth's surface into a finite number of discrete non-overlapping classes, which are considered to be exhaustively defined and mutually exclusive. This type of approach is often inappropriate given the continuous nature of many ecosystem properties. Moreover, the standard data processing and image classification methods used will involve the loss of information as the continuous quantitative spectral information is degraded into a set of discrete classes. This leads to uncertainty in the products resulting from the use of remote sensing tools. It follows that any estimated ecosystem property has an associated error and/or uncertainty of unknown magnitude, and that the statistical quantification of uncertainty should be a core part of scientific research using remote sensing. In this paper we will review recent attempts to take explicitly into account uncertainty when mapping ecosystems.

The brick-making industry of the `Brick Belt' (spanning mostly parts of Bangladesh, Nepal, India and Pakistan) is a major employer, providing work for tens of millions people. Unfortunately, it is known via locally-based human rights groups that many of those working in the brick kilns are modern-day slaves. However, reliable and timely, spatially explicit and scalable data on the full extent of this slavery is currently unavailable. Since brick kilns leave a visible impact on the Earth's land cover that can be detected from space, and given that satellite sensor images are readily available globally for the past >40 years, using these data could afford a better understanding of the spatio-temporal dynamics of this slavery activity, affording optimal intervention. This research focuses on aging of brick kilns in the `Brick Belt' based on Landsat time series data, in order to reflect the evolution of brick kilns. Landsat 5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper (ETM+) and Landsat 8 Operational Land Imager (OLI) time series data from 1984 to 2018 were downloaded from Google Earth Engine according to the coordinates of a collection of kilns identified in the `Brick Belt'. A break detection method applied to the time series of data and random forest classifications were used to age the kilns. Results showed that the accuracy of kiln aging, determined using Google Earth as ground data, was approximately 83%, and the root-mean-square-error (RMSE) between the Landsat prediction date and Google Earth date was about 3.60 years, both highlighting the potential to age kilns across the broader region. The results also show that many kilns were younger than 10 years, indicating that the brick making industry has been highly active in recent years, being driven by the demand for bricks as the region's economy grows.

Abstract Satellite remote sensing offers the potential to accurately identify and monitor spectrally separable land‐cover classes at a range of spatial and temporal scales. This paper investigates the separability of despoiled land in imagery acquired by sensors carried by the Landsat satellites. These systems offer the opportunity to map areas at large to medium scales at a relatively high temporal frequency and so provide information on environmental quality necessary for many monitoring and planning activities. From an investigation using imagery of South Wales it was found that despoiled land was separable from other classes in the Landsat TM imagery, with an accuracy of over 90 per cent. Furthermore, with Landsat TM data three spectral wavebands were found to provide a level of separability similar to that based on all wavebands available, illustrating potential savings to the analyst. Despoiled land cover was classified from Landsat TM and MSS data and these classifications were evaluated against a manually produced map of despoiled land cover derived from the interpretation of aerial photographs. Estimates of the extent of despoiled land cover in administrative units derived from the Landsat TM data were significantly correlated ( r = 0.81) with the map based estimates, although a weaker correlation was observed with Landsat MSS data.

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Remote sensing classification has the potential to provide important information, such as tree species distribution maps, to ecologists, at a range of spatial and temporal scales. However, standard classification procedures often fail to provide the high accuracies required for many ecological applications. Previously, a modified remote sensing classification technique was used to provide very high classification accuracies for one or two classes (e.g. species) of interest. The aim of this paper was to demonstrate that the output from the method can be suitable for spatial ecological analyses, and to provide a generic simulation framework for assessing the adequacy of any given remote sensing classification for such analyses. Marked point pattern analysis (MPPA) was applied to tree species distribution data obtained for sycamore Acer pseudoplatanus and ash Fraxinus excelsior from a 400 ha ancient semi‐natural woodland in southern England using the modified remote sensing classification method to test several hypotheses of ecological interest relating to the spatial distribution and interaction of these species. Monte Carlo simulation methods were then used to evaluate the data and data quality requirements of the MPPA to check that the classified tree species maps for sycamore and ash were adequate. Using the combined method the spatial distributions for sycamore and ash were found to be aggregated and inter‐dependent at a range of spatial scales. Together, the remote sensing classification and simulation approaches provide the basis for exploiting more fully the potential of remote sensing to provide information of value to ecologists.

The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latter, the effects of reference data quality on land cover classifications from airborne thematic mapper data are explored. Variations in sampling intensity and effort are highlighted in a dataset that is widely used in mapping and modelling studies; these may need accounting for in analyses. The quality of the labelling in the reference dataset was also a key variable influencing mapping accuracy. Accuracy varied with the amount and nature of mislabelled training cases with the nature of the effects varying between classifiers. The largest impacts on accuracy occurred when mislabelling involved confusion between similar classes. Accuracy was also typically negatively related to the magnitude of mislabelled cases and the support vector machine (SVM), which has been claimed to be relatively insensitive to training data error, was the most sensitive of the set of classifiers investigated, with overall classification accuracy declining by 8% (significant at 95% level of confidence) with the use of a training set containing 20% mislabelled cases.

No abstract is provided for this article.

Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the accuracy with which they can label cases. Here, the potential to increase the accuracy of crowdsourced data on land cover identified from satellite remote sensor images through the use of weighted voting strategies is explored. Critically, the information used to weight contributions based on the accuracy with which a contributor labels cases of a class and the relative abundance of class are inferred entirely from the contributed data only via a latent class analysis. The results show that consensus approaches do yield a classification that is more accurate than that achieved by any individual contributor. Here, the most accurate individual could classify the data with an accuracy of 73.91% while a basic consensus label derived from the data provided by all seven volunteers contributing data was 76.58%. More importantly, the results show that weighting contributions can lead to a statistically significant increase in the overall accuracy to 80.60% by ignoring the contributions from the volunteer adjudged to be the least accurate in labelling.

This review examines high-quality research evidence that synthesises the effects of extreme heat on human health in tropical Africa. Web of Science (WoS) was used to identify research articles on the effects extreme heat, humidity, Wet-bulb Globe Temperature (WBGT), apparent temperature, wind, Heat Index, Humidex, Universal Thermal Climate Index (UTCI), heatwave, high temperature and hot climate on human health, human comfort, heat stress, heat rashes, and heat-related morbidity and mortality. A total of 5, 735 articles were initially identified, which were reduced to 100 based on a set of inclusion and exclusion criteria. The review discovered that temperatures up to 60°C have been recorded in the region and that extreme heat has many adverse effects on human health, such as worsening mental health in low-income adults, increasing the likelihood of miscarriage, and adverse effects on well-being and safety, psychological behaviour, efficiency, and social comfort of outdoor workers who spend long hours performing manual labour. Extreme heat raises the risk of death from heat-related disease, necessitating preventative measures such as adaptation methods to mitigate the adverse effects on vulnerable populations during hot weather. This study highlights the social inequalities in heat exposure and adverse health outcomes.

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition and thus violate the commonly made assumption of object purity that is implicit in a conventional object-based image analysis. Mixed objects can be a problem throughout a classification analysis, but are particularly challenging in the training stage as they can result in degraded training statistics and act to reduce mapping accuracy. In this paper the potential of using mixed objects in training object-based image classifications is evaluated. Remotely sensed data were submitted to a series of segmentation analyses from which a range of under- to over-segmented outputs were intentionally produced. Training objects were then selected from the segmentation outputs, resulting in training data sets that varied in terms of size (i.e. number of objects) and proportion of mixed objects. These training data sets were then used with an artificial neural network and a generalized linear model, which can accommodate objects of mixed composition, to produce a series of land cover maps. The use of training statistics estimated based on both pure and mixed objects often increased classification accuracy by around 25% when compared with accuracies obtained from the use of only pure objects in training. So rather than the mixed objects being a problem, they can be an asset in classification and facilitate land cover mapping from remote sensing. It is, therefore, desirable to recognize the nature of the objects and possibly accommodate mixed objects directly in training. The results obtained here may also have implications for the common practice of seeking an optimal segmentation output, and also act to challenge the widespread view that object-based classification is superior to pixel-based classification.

Accuracy assessment and land cover mapping have been inexorably linked throughout the first 50 years of publication of Remote Sensing of Environment. The earliest developers of land-cover maps recognized the importance of evaluating the quality of their maps, and the methods and reporting format of these early accuracy assessments included features that would be familiar to practitioners today. Specifically, practitioners have consistently recognized the importance of obtaining high quality reference data to which the map is compared, the need for sampling to collect these reference data, and the role of an error matrix and accuracy measures derived from the error matrix to summarize the accuracy information. Over the past half century these techniques have undergone refinements to place accuracy assessment on a more scientifically credible footing. We describe the current status of accuracy assessment that has emerged from nearly 50 years of practice and identify opportunities for future advances. The article is organized by the three major components of accuracy assessment, the sampling design, response design, and analysis, focusing on good practice methodology that contributes to a rigorous, informative, and honest assessment. The long history of research and applications underlying the current practice of accuracy assessment has advanced the field to a mature state. However, documentation of accuracy assessment methods needs to be improved to enhance reproducibility and transparency, and improved methods are required to address new challenges created by advanced technology that has expanded the capacity to map land cover extensively in space and intensively in time.

The ground reference dataset used in the assessment of classification accuracy is typically assumed implicitly to be perfect (i.e., 100% correct and representing ground truth). Rarely is this assumption valid, and errors in the ground dataset can cause the apparent accuracy of a classification to differ greatly from reality. The effect of variations in the quality in the ground dataset and of class abundance on accuracy assessment is explored. Using simulations of realistic scenarios encountered in remote sensing, it is shown that substantial bias can be introduced into a study through the use of an imperfect ground dataset. Specifically, estimates of accuracy on a per-class and overall basis, as well as of a derived variable, class areal extent, can be biased as a result of ground data error. The specific impacts of ground data error vary with the magnitude and nature of the errors, as well as the relative abundance of the classes. The community is urged to be wary of direct interpretation of accuracy assessments and to seek to address the problems that arise from the use of imperfect ground data.

Two classification approaches were investigated for the mapping of tropical forests from Landsat-TM data of a region north of Manaus in the Brazilian state of Amazonas. These incorporated textural information and made use of fuzzy approaches to classification. In eleven class classifications the texture-based classifiers (based on a Markov random field model) consistently provided higher classification accuracies than conventional per-pixel maximum likelihood and minimum distance classifications, indicating that they are more able to characterize accurately several regenerating forest classes. Measures of the strength of class memberships derived from three classification algorithms (based on the probability density function, a posteriori probability and the Mahalanobis distance) could be used to derive fuzzy image classifications and be used in post-classification processing. The latter, involving either the summation of class memberships over a local neighbourhood or the application of homogeneity measures, were found to increase classification accuracy by some 10 per cent in comparison with a conventional maximum likelihood classification, a result of comparable accuracy to that derived from the texture-based classifications.