429 publications from this institution
The potential of super-resolution mapping (SRM) techniques for the representation of lakes was evaluated using both an established and a newly proposed method. Both super-resolution mapping techniques were typically able to provide representations that were visually and quantitatively more realistic than standard hard classifications. The new technique was able to represent more small lakes than the established technique. The results also demonstrate the value of using a time series of images as input to the super-resolution analysis, enabling researchers to usefully exploit the typically fine temporal resolution of coarse spatial resolution sensors for land cover mapping.
Aim Conservation activities have increasingly focused on issues at the level of the landscape but are constrained by limited data and knowledge relating to biodiversity at this scale. Satellite remote sensing has considerable, but under‐exploited, potential as a source of information on biodiversity at the landscape level. Remote sensing has generally been used to assess biodiversity indirectly, using approaches that often fail to fully exploit the information content of the imagery and typically only with regard to the species richness component of biodiversity. The aim of this paper was to assess the potential of remote sensing as a source of information on the richness, evenness and composition of tree species in a tropical rain forest. Location The test site was a c. 225 km 2 region centred on the Danum Valley Field Centre, Borneo. This test site contained regions of undisturbed and differentially logged rain forest. Methods Data on tree biodiversity had been acquired for fifty‐two sample plots by standard field survey methods and were used to derive summary indices of biodiversity for seedlings, saplings and mature trees. Differences between logged and unlogged sites were evaluated by comparison of the indices and species accumulation curves. A Landsat Thematic Mapper (TM) image of the site acquired close to the date of the field survey was obtained and rigorously pre‐processed. Feedforward neural networks were used to derive predictions of biodiversity indices from the imagery. A Kohonen self organizing map neural network was used to ordinate the field data to derive classes of forest defined by relative similarity in species composition. The separability of the defined classes in the Landsat TM image was evaluated with a discriminant analysis. Results Analyses of the field data revealed considerable variation in the biodiversity of seedlings, saplings and trees at the site, associated, in part, with differences in logging activities. This variation in biodiversity was manifest in the remotely sensed data. The analyses indicated an ability to (1) predict biodiversity indices, with the highest correlation between predicted and actual index observed for evenness described by Shannon entropy ( r = 0.546), but especially to (2) classify nine forest classes defined on the basis of similarity in tree species composition (accuracy 95.8%). Main conclusions Logging activities impacted on biodiversity and the resulting variation in biodiversity was reflected in the remotely sensed imagery. Using methods that exploit more fully the information content of the imagery than those used in other previous studies, a richer representation of biodiversity may be derived. This representation includes estimates of key summary indices of biodiversity, notably richness and evenness, as well as information on species composition. The results indicate that remotely sensed data may be used as a source of information on biodiversity at the landscape scale that may be used to inform conservation science and management.
This paper describes an attempt to characterize the flash flood potential in the wadi El-Alam, on the Red Sea coast of Egypt. Many important basin characteristics and morphometric parameters were defined from a digital elevation model (DEM). The range of hydrograph characteristics was estimated and the flood-vulnerable sites along the Idfu-Marsa Alam road identified.
There is much interest in being able to combine crowdsourced data. One of the critical issues in information sciences is how to combine data or information
This paper evaluates the potential of fine spatial resolution remotely sensed data for the identification and characterisation of windthrown gaps. The study site was Cwm Berwyn Forest, in central Wales, a planted forest of predominantly Sitka spruce (Picea sitchensis (Bong.) Carr.) containing windthrown gaps ranging in size from 50 to 3000m2. The remotely sensed data used were acquired by an 11 waveband airborne thematic mapper (ATM) sensor with a spatial resolution of ≈4m. This resolution is finer than the gaps on the site and comparable to that of future satellite sensors. A three class (gap, forest canopy and moorland) thematic land cover map was produced using a conventional maximum-likelihood classification of the data and provided an accurate representation of the classes (>90% of the pixels allocated correctly). This classification provided an accurate means of identifying windthrown gaps and was capable of identifying a greater number of gaps, than manual interpretation of temporally coincident aerial photographs. In addition, the magnitude and spatial distribution of class membership probabilities derived from the classification provided information on various gap properties, such as exposed soil and living, windthrown tree canopies. This may enable other properties such as windthrow direction to be inferred.
The accuracy of a supervised image classification is a function of the training data used in its generation. It is, therefore, critical that the training stage of a supervised classification is designed to provide the necessary information. Guidance on the design of the training stage of a classification typically calls for the use of a large sample of randomly selected pure pixels in order to characterise the classes. Such guidance is generally made without regard to the specific nature of the application in-hand, including the classifier to be used. The design of the training stage should really be based on the classifier to be used since individual training cases can vary in value as can any one training set to a range of classifiers. It is argued here that the training stage can be designed on the basis of the way the classifier operates and with emphasis on the desire to separate the classes rather than describe them. An approach to the training of a support vector machine (SVM) classifier that is the opposite of that generally promoted for training set design is suggested. This approach uses a small sample of mixed spectral responses drawn from purposefully selected locations (geographical boundaries) in training. The approach is based on mixed pixels which are normally masked-out of analyses as undesirable and problematic. A sample of such data should, however, be easier and cheaper to acquire than that suggested by conventional approaches. This new approach to training set design was evaluated against conventional approaches with a set of classifications of agricultural crops from satellite sensor data. The main result was that classifications derived from the use of the mixed spectral responses and the conventional approach did not differ significantly, with the overall accuracy of classifications generally ∼92%.
This article introduces a strategic initiative, COST Action TD1202, focused on the role of citizen sensors in mapping. It outlines the Action's scope, aims and current status. In particular, the article outlines the potential of citizen science in mapping activities and indicates the scope of current work undertaken by the Action's four working groups. It is stressed that the Action is at an early stage and that it is open to new members.
The comparison of classification accuracy statements has generally been based upon tests of difference or inequality when other scenarios and approaches may be more appropriate. Procedures for evaluating two scenarios with interest focused on the similarity in accuracy values, non-inferiority and equivalence, are outlined following a discussion of tests of difference (inequality). It is also suggested that the confidence interval of the difference in classification accuracy may be used as well as or instead of conventional hypothesis testing to reveal more information about the disparity in the classification accuracy values compared.
No abstract is provided for this article.
Land surface temperature (LST) is an important environmental variable for urban studies such as those focused on the urban heat island (UHI). Though satellite-derived LST could be a useful complement to traditional LST data sources, the spatial resolution of the thermal sensors limits the utility of remotely sensed thermal data. Here, a thermal sharpening technique is proposed which could enhance the spatial resolution of satellite-derived LST based on super-resolution mapping (SRM) and super-resolution reconstruction (SRR). This method overcomes the limitation of traditional thermal image sharpeners that require fine spatial resolution images for resolution enhancement. Furthermore, environmental studies such as UHI modelling typically use statistical methods which require the input variables to be independent, which means the input LST and other indices should be uncorrelated. The proposed Super-Resolution Thermal Sharpener (SRTS) does not rely on any surface index, ensuring the independence of the derived LST to be as independent as possible from the other variables that UHI modelling often requires. To validate the SRTS, its performance is compared against that of four popular thermal sharpeners: the thermal sharpening algorithm (TsHARP), adjusted stratified stepwise regression method (Stepwise), pixel block intensity modulation (PBIM), and emissivity modulation (EM). The privilege of using the combination of SRR and SRM was also verified by comparing the accuracy of SRTS with sharpening process only based on SRM or SRR. The results show that the SRTS can enhance the spatial resolution of LST with a magnitude of accuracy that is equal or even superior to other thermal sharpeners, even without requiring fine spatial resolution input. This shows the potential of SRTS for application in conditions where only limited meteorological data sources are available yet where fine spatial resolution LST is desirable.
Mis-registration of data sets is one of the largest sources of error in many remote sensing studies. An initial contribution to this error arises through the mis-location of ground control points (GCPs) used to derive geometrical transformation equations. Here, it is proposed that a soft classification of land cover may be used to direct the estimation of GCP location. The soft classification provides an estimate of the class composition of each image pixel. The spatial distribution of a pixel's component land covers may then be modeled over the area it represents and used to reduce the error in estimating the location of a GCP that lies within this area. An example is provided in which the error in locating a set of GCPs was reduced by up to 35.7 percent when information from a soft classification was available to aid the estimation of their location at a sub-pixel scale.
In this final chapter, we speculate on future developments in the field of Volunteered Geographic Information (VGI); we focus on how VGI will be affected by future technological developments, but we also consider issues such as VGI
No abstract is provided for this article.
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21215.1 PROGRESS IN CA DYNAMIC MODELLINGIt has been over 10 years now since we saw initial diffusion of interests in using cellular automata (CA) to model human and environmental changes (in particular urban land development). The special sessions of the Annual Meeting of the Association of American Geographers in 1994 marked the development of the paradigm of highly disaggregated CA modelling, which is very different from "traditional" land use and transport modelling. The sessions subsequently resulted in a special issue in Environment and Planning B (Batty et al., 1997). Advances in GIS visualization and data processing capacities have further added momentum to CA applications. Since then, there have been widespread applications of various sorts of CA.
