429 publications from this institution
The ability of radar systems to record useful data independently of the prevailing weather conditions and the future increase in radar data availability is likely to result in their increased utilization for crop classification. However, a variety of factors relating to the quantity and quality of the data will influence the accuracy with which these data can be classified. This paper aims to illustrate the effect of some of these factors on crop classification accuracy from a multi-feature synthetic aperture radar data base. The results show that in addition to the number of data channels available for a classification, the method of radiometric correction applied to the data and the use of data from different look directions can have significant effects on the classification accuracy. The use of a multi-feature data base however, was found to enable accurate discrimination of a variety of crop types.
Studies of land-cover change using satellite remote sensing are often constrained to depict land-cover conversions only, with the equally important modifications undetected or misrepresented, resulting in significant error. Desert fluctuations within the Sahel were examined using an approach that indicated the magnitude of land-cover changes. This showed that the conventional post-classification comparison method of change detection appeared to underestimate the area of land-cover change and, where a change was detected, typically overestimate its magnitude. At the regional scale, the land-cover changes detected were strongly related to rainfall variability. This relationship did not, however, explain changes at a finer spatial scale and indicated that dryland degradation, and its causes, may remain far from understood.
Mixed pixels occur commonly in remotely-sensed imagery, especially those with a coarse spatial resolution. They are a problem in land-cover mapping applications since image classification routines assume 'pure' or homogeneous pixels. By unmixing a pixel into its component parts it is possible to enableinter alia more accurate estimation of the areal extent of different land cover classes. In this paper two approaches to estimating sub-pixel land cover composition are investigated. One is a linear mixture model the other is a regression model based on fuzzy membership functions. For both approaches significant correlation coefficients, all >0·7, between the actual and predicted proportion of a land cover type within a pixel were obtained. Additionally a case study is presented in which the accuracy of the estimation of tropical forest extent is increased significantly through the use of sub-pixel estimates of land-cover composition rather than a conventional image classification.
Remote sensing has great potential as a source of information on tree species. The classification approaches used commonly to extract species information from remotely sensed imagery typically aim to optimize the overall accuracy of species identification, a target which need not satisfy the requirements of a particular user. Often users are interested in a specific species or subset of species, and these may not be accurately identified in a conventional classification. Here, a two‐phase classification approach was used to map specific species from aerial sensor imagery of an ancient British woodland. Particular attention was focused on the identification of sycamore since this is displacing the native ash and information on its distribution would enhance basic understanding and management activities. The results show that the classification approach can be adapted to focus on a specific species of interest and used to increase classification accuracy significantly. For example, sycamore was classified to a low accuracy when a conventional approach to classification with a neural network was used (46.6–63.6%, depending on perspective), but the adoption of the two‐phase approach increased its accuracy significantly (82.3–93.3%). The results demonstrate the ability to map specific class(es) of interest accurately from remotely sensed imagery. The approach used also highlights the ability to tailor an analysis to the specific requirements of the ecological study in hand and is of broad applicability.
Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary information on soil type and prior knowledge of class occurrence the accuracy of an artificial neural network classification was increased by 29–93 to 77–37 per cent. An artificial neural network can therefore be used generate a fuzzy classification output that may be used with other data sets in a GIS, which may not have been available to the producer of the classification, to increase the accuracy with which land cover may be classified.
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.
No abstract is provided for this article.
Remote sensing is the only feasible means of mapping and monitoring land cover at regional to global scales. Unfortunately the maps are generally derived t
No abstract is provided for this article.
No abstract is provided for this article.
Fuzzy classification may be used to estimate the class composition of image pixels but it does not indicate how these classes are distributed spatially within the pixels. The potential to locate the distribution of classes more precisely through the use of a sharpening image was investigated. A sharpened fuzzy classification provided a visually improved representation of land cover and formed a more appropriate base for the derivation of quantitative measures of landscape mosaic than the fuzzy classification it was derived from.
Many factors influence the quality and value of a classification accuracy assessment and evaluation programme. This paper focuses on the size of the testing set(s) used with particular regard to the impacts on accuracy assessment and comparison. Testing set size is important as the use of an inappropriately large or small sample could lead to limited and sometimes erroneous assessments of accuracy and of differences in accuracy. Here, some of the basic statistical principles of sample size determination are outlined, including a discussion of Type II errors and their control. The paper provides a discussion on some of the basic issues of sample size determination for accuracy assessment and includes factors linked to accuracy comparison. With the latter, the researcher should specify the effect size (minimum meaningful difference in accuracy), significance level and power used in an analysis and ideally also fit confidence limits to derived estimates. This will help design a study and aid the use of appropriate sample sizes, as well as facilitate interpretation of results. In particular, it will help avoid problems, such as under-powered analyses, and provide a richer information base for classification evaluation. The paper includes equations that could be used to determine sample sizes for common applications in remote sensing, using both independent and related samples.
No abstract is provided for this article.
Thematic classification of remotely-sensed data assumes that class separability is aspatial. However, as classifiers discriminate between classes on the basis of their spectral responses this assumption is invalid since the latter vary spatially with the viewing geometry. Consequently classification accuracy is spatially variable. Classification accuracy statements can therefore over- and under-estimate inter-class discrimination at different locations within a scene and can therefore be misleading. This is illustrated by a classification of synthetic aperture radar data which had an aggregate accuracy of 75-7 per cent but also displayed a variation in accuracy between locations of up to 17-2 per cent.
