2,589 research outputs found
Geographical information science: GeoComputation and non-stationarity
No full textI IntroductionIn the previous report on geographical information science (GIS) I chose to concentrate on a single theme: uncertainty and geostatistics (Atkinson, 1999). In this report, I also focus on a single theme: nonstationary geostatistics. I have chosen to present this theme within the context of GeoComputation, which I describe first
Predicting missing field boundaries to increase per-field classification accuracy
No full textWith the emergence of very high spatial resolution satellite images, the spatial resolution gap which existed between satellite images and aerial photographs has decreased. A study of the potential of these images for tree species in" monoculture stands" identification was conducted. Two Ikonos images were acquired, one in June 2000 and the other in October 2000, for an 11- by 11-km area covering the Sonian Forest in the southeastern part of the Brussels-Capital region (Belgium). The two images were orthorectified using a digital elevation model and 1256 geodetic control points. The identification of the tree species was carried out utilizing a supervised maximum-likelihood classification on a pixel-by-pixel basis. Classifications were performed on the orthorectified data, NDVI transformed data, and principal components imagery. In order to decrease the intraclass variance, a mean filter was applied to all the spectral bands and neo-channels used in the classification process. Training and validation areas were selected and digitized using detailed geographical databases of the tree species. The selection of the relevant bands and neo-channels was carried out by successive addition of information in order to improve the classification results. Seven different tree species of one to two different age classes were identified with an overall accuracy of 86 percent. The seven identified tree species or species groups are Oaks (Quercus sp.), Beech (Fagus sylvatica L.), Purple Beech (Fagus sylvatica purpurea), Douglas Fir (Pseudotsuga menziesii (Mirb.) Franco), Scots Pine (Pinus sylvestris L.), Corsican Pine (Pinus nigra Arn. subsp. laricio (Poir.) Maire var. corsican), and Larch (Larix decidua Mill.)
Geographical information science: geostatistics and uncertainty
No full textThe previous progress report on geographical information science (GIS) (Atkinson, 1997) reviewed a wide range of developments in GIS that occurred in or were published in 1996. The coverage was relatively broad since the report prior to that appeared in 1993 and there was great diversity to include. However, given the limited space available for reports, wide coverage can be superficial. With this in mind, the present report focuses on one aspect of GIS of great importance: the assessment and modelling of uncertainty in spatial databases. In particular, the focus of this report is on the geostatistical assessment of uncertainty
Assessing uncertainty in fuzzy land cover maps obtained by remote sensing
No full textA common objective of remote sensing is the mapping of land cover. Traditionally, this has been achieved using hard classifiers such as the maximum likelihood classifier. In these circumstances, accuracy is readily assessed using the confusion matrix or contingency table. Based on this matrix the overall user’s and producer’s accuracies can be estimated as can the same accuracies per class. Further, Kappa statistics can be estimated that allow for randomness in the matrix. During the last 10 years or so, researchers have increasingly applied fuzzy classifiers, such as the fuzzy c-means classifier and the mixture model that estimates the membership of a pixel, to each class. Clearly, where a pixel represents a mixture of land cover classes (for example, 20 percent cereals, 50 percent heathland, and 30 percent forest) fuzzy classifiers provide the opportunity for greater accuracy, and for this reason, fuzzy classification has become a fundamentally important approach in remote sensing. There is no accepted standard method for assessing the accuracy of a fuzzy classification. A scatterplot between the observed values and the estimated values provides a useful graphical representation of the accuracy of the estimates; however, quantitative summary of the information in the scatterplot has proved elusive and researchers have often used ad-hoc combinations of statistics, such as the mean error, root mean square (RMS) error, and correlation coefficient. The RMS error is insufficient because it is insensitive to the variance per class and the overall number of classes. For example, a class that has small membership x in all pixels (say 0%stlgxstlg10%) may be estimated with small RMS error by setting all pixels to the mean membership for that class (say 5 percent) even though the correlation between observed and estimated memberships is zero. The likelihood of having small memberships per class increases with the number of classes c. The correlation coefficient is insufficient because it is insensitive to bias such that r may be large when the scatterplot lies away from the 1:1 line. A particular problem arises for classes whose memberships are bimodal (i.e., little mixing as is often the case for "water" classes where pixels are either close to 0 percent or 100 percent water but rarely lie between). Then r can be large when the correlation within each "cluster" is zero. Several statistics were evaluated that address these issues including schemes for standardising the RMS error and weighting schemes for estimating overall precision and bias. First, the above issues were explored using fuzzy maps of land cover obtained for four different sites representing different biomes (New Forest, U.K.; Cukurova Deltas, Turkey; BOREAS site, Canada; HAPEX-Sahel site, Niger). These fuzzy maps were obtained from the National Oceanic and Atmospheric Administration’s (NOAA) Advanced Very-High Resolution Radiometer (AVHRR) imagery (trained on classified fine spatial resolution imagery) using a standard feed-forward back-propagation artificial neural network. Second, the proposed statistics were evaluated using simulated data involving classes with different distributions of memberships and different numbers of these classes
Optimal ground-based sampling for remote sensing investigations: estimating the regional mean
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