The perspective of European National Mapping Agencies (NMA) on the role of citizen sensing in map production was explored. The NMAs varied greatly in their engagement with the community generating volunteered geographic information (VGI) and in their future plans. From an assessment of NMA standard practices, it was evident that much VGI was acquired with a positional accuracy that, while less than that typically acquired by NMAs, actually exceeded the requirements of the nominal data capture scale used by most NMAs. Opportunities for VGI use in map revision and updating were evident, especially for agencies that use a continuous rather than cyclical updating policy. Some NMAs had also developed systems to engage with citizen sensors and examples are discussed. Only rarely was VGI used to collect data on features beyond the standard set used by the NMAs. The potential role of citizen sensing and so its current scale of use by NMAs is limited by a series of concerns, notably relating to issues of data quality, the nature and motivation of the contributors, legal issues, the sustainability of data source, and employment fears of NMA staff. Possible priorities for future research and development are identified to help ensure that the potential of VGI in mapping is realized.
Although flash floods are one of the major natural disasters that may hamper human development in arid areas, aspects of the process leading to their initi
No abstract is provided for this article.
Remotely sensed data are an attractive source of land cover data over a wide range of spatial and temporal scales. The realisation of the full potential of remote sensing as a source of land cover data is, however, restricted by numerous factors. One commonly encountered problem is the presence of mixed pixels, which cannot be appropriately accommodated in conventional image classification techniques used in thematic mapping from remotely sensed data. This problem has generally been resolved through the adoption of a soft or fuzzy classification from which the fractional coverage of classes in the image pixels may be mapped. In this type of approach, the strength of membership, a pixel displays to a class, is used as a surrogate for the fractional coverage of that class. The accuracy of the resulting land cover representation is, therefore, dependent on the relationships between class membership strength and associated class fractional coverage. Since class membership can only be measured in relation to the classes defined in the training stage of the classification, untrained classes may influence the accuracy of the class composition estimation. For example, a pixel representing an area of an untrained class can only display membership to the trained classes. The effect of an untrained class on the accuracy of sub-pixel class composition estimation will depend on how the class membership strength is calculated. Here, the effect of untrained classes on sub-pixel land cover composition estimation using algorithms that produce relative and absolute measures of class membership was assessed. The algorithms investigated were the widely used fuzzy c-means (FCM) and its possibilistic counterpart, the possibilistic c-means (PCM), algorithms which derive relative and absolute measures of class membership strength, respectively. Both algorithms were able to provide accurate estimates of sub-pixel land cover composition. When all classes had been defined in training a classification, the FCM generally provided the most accurate class composition estimates. The presence of an untrained class, however, could substantially degrade the accuracy of the sub-pixel land cover composition estimates derived from the FCM but had no effect on those from the PCM. Since untrained classes are commonly encountered it may be more appropriate to use approaches such as the PCM in addition to, or instead of, the FCM to enhance the extraction of land cover information from remotely sensed data.
Mapping surface water bodies from fine spatial resolution optical remote sensing imagery is essential for the understanding of the global hydrologic cycle. Although satellite data are useful for mapping, the limited spectral information captured by some satellite systems can be suboptimal for the task. For example, the very high-resolution images of Google Earth (GE) only contain RGB bands, which often means many water bodies and land objects are confused. Sentinel-2 (S2) imagery has a spectral resolution more suitable for mapping water bodies, but its medium spatial resolution limits the ability for detailed mapping of water-land boundaries. This letter proposes a deep feature and domain knowledge fusion network (DFDKFNet) for mapping surface water bodies by fusing GE and S2 images while incorporating domain knowledge. DFDKFNet uses the remote sensing indices of normalized difference water index (NDWI) and normalized difference vegetation index (NDVI) derived from the S2 image as the representative domain knowledge to better extract water bodies from terrestrial features. A similar pixel-based approach is used to downscale the NDWI and NDVI maps to match the spatial resolution between the GE and S2 images. The DFDKFNet uses the GE and downscaled NDWI and NDVI images to extract the deep semantic features of water bodies, which are fused with the domain knowledge extracted from the NDWI and NDVI images. DFDKFNet was compared with several state-of-the-art algorithms, and the results show that DFDKFNet can enhance water body mapping accuracy.
This paper describes the application of the geostastistical method to quantify noise from a compact airborne spectrograhic imager (CASI) data set. Estimation of noise contained within a remote sensing image is essential in order to quanitfy the effects of noise contamination. Noise was estimated from CASI imagery by calculation the noise as the square root of the nugget variance, a parameter of a fitte semivariogram model. The signal-to-noise ratio (SNR) can then be estimated by dividing the mean vaue by the square root of the nugget variance. Three wavebands 0.46-049μm (blue), 0-63-0.64μm (red) and 0.70-071μm (near-infrared) were used in the analysis. A total of five land covers were selected, each representing a common land cover type in the area which are i)bracken ii)conifer woodland iii)grassland iv)heathland and v)deciduous woodland. The results shows that the noise varies in different land cover types and wavelengths.
Predicting species potential and future distribution has become a relevant tool in biodiversity monitoring and conservation. In this data article we present the suitability map of a virtual species generated based on two bioclimatic variables, and a dataset containing more than 700,000 random observations at the extent of Europe. The dataset includes spatial attributes such as: distance to roads, protected areas, country codes, and the habitat suitability of two spatially clustered species (grassland and forest species) and a wide-spread species.
Terrestrial chlorophyll content is a key environmental variable that is difficult to estimate accurately using remotely sensed data. Some of the pioneering studies in this field were undertaken by Kirill Kondratyev and co-workers in the 1970s and early 1980s. These...
Remotely sensed data have considerable potential for mapping and monitoring tropical forests. For the production of regional scale maps which may be up-dated periodically, relatively coarse spatial resolution remotely sensed data, such as those from the NOAA AVHRR, are an appropriate source of data for such mapping applications. These maps, however, typically depict land cover at the nominal level only and may be unsuitable for the estimation of forest extent and dynamics. In this paper, results of an investigation into the estimation of sub-pixel forest cover and classification at the ordinal level are presented. Based on an analysis of Landsat MSS data that had been degraded spatially to a 1.2-km resolution, a strong correlation, r = 0.94, was observed between predicted and actual sub-pixel forest cover (...)
Superresolution mapping (SRM) is a method to produce a fine-spatial-resolution land cover map from coarse-spatial-resolution remotely sensed imagery. A popular approach for SRM is a two-step algorithm, which first increases the spatial resolution of coarse fraction images by interpolation and then determines class labels of fine-resolution pixels using the maximum a posteriori (MAP) principle. By constructing a new image formation process that establishes the relationship between the observed coarse-resolution fraction images and the latent fine-resolution land cover map, it is found that the MAP principle only matches with area-to-point interpolation algorithms and should be replaced by deconvolution if an area-to-area interpolation algorithm is to be applied. A novel iterative interpolation deconvolution (IID) SRM algorithm is proposed. The IID algorithm first interpolates coarse-resolution fraction images with an area-to-area interpolation algorithm and produces an initial fine-resolution land cover map by deconvolution. The fine-spatial-resolution land cover map is then updated by reconvolution, back-projection, and deconvolution iteratively until the final result is produced. The IID algorithm was evaluated with simulated shapes, simulated multispectral images, and degraded Landsat images, including comparison against three widely used SRM algorithms: pixel swapping, bilinear interpolation, and Hopfield neural network. Results show that the IID algorithm can reduce the impact of fraction errors and can preserve the patch continuity and the patch boundary smoothness simultaneously. Moreover, the IID algorithm produced fine-resolution land cover maps with higher accuracies than those produced by other SRM algorithms.
Despite a growing interest in species distribution modelling, relatively little attention has been paid to spatial autocorrelation and non‐stationarity. Both spatial autocorrelation (the tendency for adjacent locations to be more similar than distant ones) and non‐stationarity (the variation in modelled relationships over space) are likely to be common properties of ecological systems. This paper focuses on non‐stationarity and uses two local techniques, geographically weighted regression (GWR) and varying coefficient modelling (VCM), to assess its impact on model predictions. We extend two published studies, one on the presence–absence of calandra larks in Spain and the other on bird species richness in Britain, to compare GWR and VCM with the more usual global generalized linear modelling (GLM) and generalized additive modelling (GAM). For the calandra lark data, GWR and VCM produced better‐fitting models than GLM or GAM. VCM in particular gave significantly reduced spatial autocorrelation in the model residuals. GWR showed that individual predictors became stationary at different spatial scales, indicating that distributions are influenced by ecological processes operating over multiple scales. VCM was able to predict occurrence accurately on independent data from the same geographical area as the training data but not beyond, whereas the GAM produced good results on all areas. Individual predictions from the local methods often differed substantially from the global models. For the species richness data, VCM and GWR produced far better predictions than ordinary regression. Our analyses suggest that modellers interpolating data to produce maps for practical actions (e.g. conservation) should consider local methods, whereas they should not be used for extrapolation to new areas. We argue that local methods are complementary to global methods, revealing details of habitat associations and data properties which global methods average out and miss.
Commonly the assessment of the quality of image segmentations used in object-based land cover classification uses the geometric match between the derived segmentation and a reference dataset. This paper argues that a more appropriate assessment of a segmentation is to also consider the thematic content of the objects generated. This allows the assessment to be tailored to the needs of the specific user. A new method for image segmentation quality assessment is described, which combines a traditional geometric-only method with the thematic similarity index (TSI), a metric that expresses the degree of thematic quality of objects from a user’s perspective. The perspectives of two users (a wolf researcher and a general user of land cover information) were adopted in a case study to demonstrate the new method. The results show that the new method allowed the production of more accurate land cover classifications for the two users than the use of the geometric-only approach.
The transformation of land cover, in particular coniferous forest, constitutes one of the most notable agents of regional-to-global-scale environmental change. Remote sensing provides an excellent opportunity for providing forest cover information at appropriate spatial and temporal scales. The optimal exploitation of remote sensing relies on the link between known forest cover and the remotely sensed dataset. This paper explores the accuracy of three methods – vegetation indices, regression analysis and neural networks – for estimating coniferous forest cover across the United States Pacific Northwest. All methods achieved a similar accuracy of forest cover estimation. However, in view of the benefits and limitations of each, the neural network approach is recommended for future consideration.
The accuracy of thematic maps derived by image classification analyses is often compared in remote sensing studies. This comparison is typically achieved by a basic subjective assessment of the observed difference in accuracy but should be undertaken in a statistically rigorous fashion. One approach for the evaluation of the statistical significance of a difference in map accuracy that has been widely used in remote sensing research is based on the comparison of the kappa coefficient of agreement derived for each map. The conventional approach to the comparison of kappa coefficients assumes that the samples used in their calculation are independent, an assumption that is commonly unsatisfied because the same sample of ground data sites is often used for each map. Alternative methods to evaluate the statistical significance of differences in accuracy are available for both related and independent samples. Approaches for map comparison based on the kappa coefficient and proportion of correctly allocated cases, the two most widely used metrics of thematic map accuracy in remote sensing, are discussed. An example illustrates how classifications based on the same sample of ground data sites may be compared rigorously and highlights the importance of distinguishing between one- and two-sided statistical tests in the comparison of classification accuracy statements.
Thematic maps have a central and often unquestioned role in geographical information systems (GIS) (Woodcock and Gopal, 2000). They are one of the most common ways of representing spatial data and are unsurprisingly, therefore, a common input and output of analyses undertaken in GIS. They are, however, only a model or simplification and hence a flawed representation of reality. Consequently, it is important that the quality of thematic maps is evaluated and expressed in a meaningful way so that their suitability for use may be assessed. This is important not only in providing a guide to the quality of a map and its fitness for a particular purpose but also in understanding error and its likely implications, especially if allowed to propagate through analyses linking the thematic map to other data sets. Unfortunately, for many geospatial data sets there is commonly a lack of information on data quality and what exists is often poorly communicated (Johnston and Timlin, 2000). The quality of spatial data sets is a very broad issue that may relate to a variety of properties (Worboys, 1998) but with thematic maps the property of interest is typically map accuracy. The accuracy of spatial data sets has long been an important issue in GIS and has been the focus of considerable research, particularly since the influential book by Goodchild and Gopal (1989). As accuracy remains a major research topic and issue of concern to many researchers, it seems pertinent to revisit the topic of accuracy, especially that of thematic maps. For instance, it is apparent that considerable development is required in the methods of accuracy assessment (Scepan, 1999) particularly as the accuracy of thematic maps is one of the greatest limitations to many users (Guisan and Zimmermann, 2000).
Aim This article aims to test for and explore spatial nonstationarity in the relationship between avian species richness and a set of explanatory variables to further the understanding of species diversity variation. Location Sub‐Saharan Africa. Methods Geographically weighted regression was used to study the relationship between species richness of the endemic avifauna of sub‐Saharan Africa and a set of perceived environmental determinants, comprising the variables of temperature, precipitation and normalized difference vegetation index. Results The relationships between species richness and the explanatory variables were found to be significantly spatially variable and scale‐dependent. At local scales > 90% of the variation was explained, but this declined at coarser scales, with the greatest sensitivity to scale variation evident for narrow ranging species. The complex spatial pattern in regression model parameter estimates also gave rise to a spatial variation in scale effects. Main conclusions Relationships between environmental variables are generally assumed to be spatially stationary and conventional, global, regression techniques are therefore used in their modelling. This assumption was not satisfied in this study, with the relationships varying significantly in space. In such circumstances the average impression provided by a global model may not accurately represent conditions locally. Spatial nonstationarity in the relationship has important implications, especially for studies of species diversity patterns and their scaling.
Standard methodologies for estimating the thematic accuracy of hard classifications, such as those using the confusion matrix, do not provide indications of where thematic errors occur. However, spatial variation in thematic error can be a key variable affecting output errors when operations such as change detection are applied. One method of assessing thematic error on a per‐pixel basis is to use the outputs of a classifier to estimate thematic uncertainty. Previous studies that have used this approach have generally used a single classifier and so comparisons of the relative accuracy of classifiers for predicting per‐pixel thematic uncertainty have not been made. This paper compared three classification methods for predicting thematic uncertainty: the maximum likelihood, the multi‐layer perceptron and the probabilistic neural network. The results of the study are discussed in terms of selecting the most suitable classifier for mapping land cover or predicting thematic uncertainty.
