Super-resolution mapping (SRM) aims to determine the spatial distribution of the land cover classes contained in the area represented by mixed pixels to obtain a more appropriate and accurate map at a finer spatial resolution than the input remotely sensed image. The image-based SRM models directly use the observed images as input and can mitigate the uncertainty caused by class fraction errors. However, existing image-based SRM models always adopt a fixed set of endmembers used in the entire image, ignoring the spatial variability and spectral uncertainty of endmembers. To address this problem, this letter proposed an optimal endmember-based SRM (OESRM) model, which considers the spatial variations in endmembers, and determines the best-fit one for each coarse resolution pixel using the spectral angle and the spectral distance as the spectral similarity indexes. A Sentinel-2A and a Landsat-8 multispectral images were used to analyze the performance of OESRM, by comparing with three other SRM methods which adopt a fixed endmember set or multiple endmember sets. The results showed that OESRM generated resultant land cover maps with more spatial detail, and reduced the confusion between land cover classes with similar spectral features. The proposed OESRM model produced the results with the highest overall accuracy in both experiments, showing its effectiveness in reducing the effect of endmember uncertainty on SRM.
No abstract is provided for this article.
One of the major goals of remote sensing is to carry out monitoring programmes such as land‐cover change detection. However, the accuracy of such change detection activities can be limited by several factors. A key variable that can limit the accuracy of change detection is the misregistration error between the images used. Although the impacts of misregistration on change detection have been considered in various studies, a single global value for misregistration has typically been applied across the whole scene. The effect of misregistration, however, varies spatially and its effects on change detection could be more accurately predicted and ultimately removed if this spatial variation in error were modelled. The current study aimed to develop a model that described the spatial variation of misregistration for airborne image data. As misregistration is a function of geometric error, the geometric errors associated with the airborne data were modelled and this geometric error model was used to derive a model of misregistration. The impacts of various navigational variables on the accuracy of the automated geocorrection of compact airborne spectrographic imager (CASI) data were evaluated. A significant relationship was found between geometric error and angular acceleration (adjusted r 2 = 0.651; p = 0.017). The relationship between geometric error and angular acceleration together with a model of orthometric errors was used to derive an error model that described the spatial variation in geometric errors associated with the automated geocorrection of the CASI data. This model gave a probabilistic description of the spatial variation in geometric error. From the geometric error model, a model of misregistration between CASI images from two times was derived. This model was tested using data from an urban test site and a significant correlation, at 95% confidence, was found between predicted and measured misregistration. The models derived could be used in change detection, potentially reducing the impact of geometric errors and so misregistration in airborne sensor data, which is a major limitation in the use of remote sensing for environmental monitoring.
No abstract is provided for this article.
. This paper presents a framework for considering quality control of volunteered geographic information (VGI). Different issues need to be considered during the conception, acquisition and post-acquisition phases of VGI creation. This includes items such as collecting metadata on the volunteer, providing suitable training, giving corrective feedback during the mapping process and use of control data, among others. Two examples of VGI data collection are then considered with respect to this quality control framework, i.e. VGI data collection by National Mapping Agencies and by the most recent Geo-Wiki tool, a game called Cropland Capture. Although good practices are beginning to emerge, there is still the need for the development and sharing of best practice, especially if VGI is to be integrated with authoritative map products or used for calibration and/or validation of land cover in the future.
G. M. Foodya a School of Geography , University of Southampton , Highfield, Southampton SO17 1BJ, UK E-mail:
It has been postulated that tropical forests regenerating after deforestation constitute an unmeasured terrestrial sink of atmospheric carbon, and that the strength of this sink is a function of regeneration stage. Such regeneration stages can be characterized by biophysical properties, such as leaf and wood biomass, which influence the radiance emitted and/or reflected from the forest canopy. Remotely sensed data can therefore be used to estimate these biophysical properties and thereby determine the forest regenerative stage. Studies conducted on temperate forests have related biophysical properties successfully with red and near-infrared radiance, particularly within the Normalized Difference Vegetation Index (NDVI). However, only weak correlations have generally been observed for tropical forests and it is suggested here that the relationship between forest biophysical properties and middle and thermal infrared radiance may be stronger than that between those properties and visible and near-infrared radiance. An assessment of Landsat Thematic Mapper (TM) data revealed that radiance acquired in middle and thermal infrared wavebands contained significant information for the detection of regeneration stages in Amazonian tropical forests. It was demonstrated that tropical forest regeneration stages were most separable using middle infrared and thermal infrared wavebands and that the correlation with regeneration stage was stronger with middle infrared, thermal infrared or combinations of these wavebands than they were with visible, near infrared or combinations of these wavebands. For example, correlation coefficients increased from — 0·26 (insignificant at 95 per cent confidence level) when using the NDVI, to up to 0·93 (significant at 99 per cent confidence level) for a vegetation index containing data acquired in the middle and thermal infrared wavebands. These results point to the value of using data acquired in middle and thermal infrared wavebands for the study of tropical forests.
The landscape patches that are fundamental to landscape ecology may be considered as objects to be extracted from remotely sensed imagery. The accuracy with which objects may be characterised varies as a function of the spatial resolution of the imagery used. In general terms, a coarsening of the spatial resolution degrades the characterization of objects, notably through an increase in the proportion of mixed pixels which cannot be appropriately represented by conventional hard classification techniques. Accurate landscape mapping may often require either the adoption of fine spatial resolution imagery or use of sub-pixel scale analyses of coarse spatial resolution imagery. As the former is often impractical, the full realization of the potential of remote sensing as a source of information on landscape objects requires developments in sub-pixel scale techniques. In this paper, a new method of superresolution mapping based on a unifying framework of image halftoning, inverse halftoning and Hopfield neural network techniques is proposed as a means of gaining accurate information on landscape patches from coarse spatial resolution images. Fine temporal resolution of coarse spatial resolution remote sensing systems is exploited by fusing the time-series data as an input for the superresolution mapping. The accuracy of the analyses is evaluated relative to conventional a hard classification technique using object characterization. The results show that the proposed hybrid method is considerably more accurate than standard hard analyses in estimating the shape of the objects. The results also demonstrate that objects that are smaller than a pixel, which are missed using the hard classification techniques, can be detected using the super-resolution mapping. * Corresponding author.
The Earth is undergoing an accelerated rate of native ecosystem conversion and degradation and there is increased interest in measuring and modelling biodiversity from space. Biogeographers have a long-standing interest in measuring patterns of species occurrence and distributional movements and an interest in modelling species distributions and patterns of diversity. Much progress has been made in identifying plant species from space using high-resolution satellites (QuickBird, IKONOS), while the measurement of species movements has become commonplace with the ARGOS satellite tracking system which has been used to track the movements of thousands of individual animals. There have been signifi cant advances in land-cover classifi cations by combining data from multi-passive and active sensors, and new classifi cation techniques. Species distribution modelling has been growing at a striking rate and the incorporation of spaceborne data on climate, topography, land cover, and vegetation structure has great potential to improve models. There have been signifi cant advances in modelling species richness, alpha diversity, and beta diversity using multisensors to quantify land-cover classifi cations and landscape metrics, measures of productivity, and measures of heterogeneity. Remote sensing of nature reserves can provide natural resources managers with near real-time data within and around reserves that can be used to support conservation efforts anywhere in the world. Future research should focus on incorporating recent spaceborne sensors, more extensive integration of available spaceborne imagery, and the collection and dissemination of high-quality fi eld data. This will improve our understanding of the distribution of life on earth.
Mixed pixels are one of the largest sources of error and uncertainty in mapping from remotely sensed data. A Hopfield neural network based approach to super-resolution mapping has become popular for mapping at a sub-pixel scale, partly because it seeks to maintain the class proportional information indicated by a soft classification analysis. The use of the approach is, however, handicapped by a lack of guidance on the parameter setting values and of the impacts of different landscape patterns on the analysis. Here, the sensitivity of the Hopfield neural network for super-resolution mapping is investigated with a focus on the effect of different landscape types and parameter settings using simulated and real data sets. It is shown that the method's suitability varies between landscapes, being most suited to situations in which landscape patches are large (>; 1 pixel) . Additionally, for such landscapes the widely used scenario in which the weighting parameters are set at equal values is successful but the approach is less effective for the mapping of small isolated land cover patches. With the latter, it is shown to be important to weight the area constraint highly and undertake a large number of iterations. Critically, it is shown that equal weighted parameter settings and imbalanced settings to emphasize the area constraint are most suitable for landscapes comprising large and small patches respectively. Moreover, the positive attributes of these two sets of parameter settings may be combined to yield an enhanced mapping method for landscapes that comprise a mixture of patch sizes.
The Malaysian shoreline is dynamic and constantly changing in location. Although the shoreline may be mapped accurately from fine spatial resolution imagery, this is an impractical approach for use over large areas. An alternative approach using coarse spatial resolution satellite sensor imagery is to fit a shoreline boundary at sub‐pixel scale. This paper evaluates the use of soft classification and super‐resolution mapping techniques to accurately map the shoreline. A localized soft classification approach was used to provide an accurate prediction of the thematic composition of each image pixel. This involves the use of training statistics derived locally rather than globally in the classification. Using the derived class proportion information the shoreline boundary was determined within the pixels using super‐resolution techniques. Results show that by using a localized approach in the prediction of the pixel's thematic class composition, the accuracy of shoreline prediction was increased. Notably, the use of the localized approach resulted in the shoreline with an rms error of <1.51 m, smaller than the rms error of 2.13 m derived from the use of the global approach.
Classification accuracy statements derived from remote sensing are typically global measures. These provide a summary measure of the quality of the entire classification and are typically assumed to apply uniformly over the region represented. Classification accuracy may, however, vary across the region. A simple means of measuring and characterizing accuracy locally, which also facilitates the representation of the spatial variation in classification accuracy, is to constrain geographically the data used for accuracy assessment. The use of this approach is illustrated with a crop classification from Satellite pour l'Observation de la Terre (SPOT) High Resolution Visible (HRV) data. The global accuracy of the classification was estimated to be 84.0% but accuracy was found to vary locally from 53.33% to 100%. Moreover, accuracy varied from 0–100% over the region on a per‐class basis. These variations in accuracy arose mainly as functions of the geographical distribution of the classes and highlight dangers in using a global measure of accuracy that masks spatial variation as a tool in classification evaluation. Local accuracy assessment can, therefore, be a useful analysis and, as the locational information is known, may be achieved at no substantial extra cost to the analysis.
