429 publications from this institution
Thematic mapping via a classification analysis is one of the most common applications of remote sensing. The accuracy of image classifications is, however, often viewed negatively. Here, it is suggested that the approach to the evaluation of image classification accuracy typically adopted in remote sensing may often be unfair, commonly being rather harsh and misleading. It is stressed that the widely used target accuracy of 85% can be inappropriate and that the approach to accuracy assessment adopted commonly in remote sensing is pessimistically biased. Moreover, the maps produced by other communities, which are often used unquestioningly, may have a low accuracy if evaluated from the standard perspective adopted in remote sensing. A greater awareness of the problems encountered in accuracy assessment may help ensure that perceptions of classification accuracy are realistic and reduce unfair criticism of thematic maps derived from remote sensing.
Neural networks are powerful general purpose computing tools. They have become popular in the analysis of remotely sensed data, particularly for classification and regression-type problems in which they have often been demonstrated to extract information more accurately than conventional methods. Although not free from problems, it seems likely that neural networks will be used increasingly in ecological research using remote sensing. Moreover, as some of the problems encountered in use of neural networks arise from a tendency to focus upon the MLP only it is likely that there will be a greater use of other network types. In addition, it is expected that the range of applications of neural networks in remote sensing will broaden. Applications in which neural networks have already been used and increased usage may be expected include: image preprocessing (e.g. geometric, atmospheric and radiometric correction), stereo-matching imagery, image compression, feature extraction, map generalisation, multi-source data analysis, data fusion and image sharpening (e.g. Day, 1997; Foody, 1999a). Thus while neural networks have rapidly become established in remote sensing it is likely that they will be used increasingly and in a broader range of activities that will help exploit more fully the potential of remote sensing as a useful tool in ecological research.
The signal-to-noise ratio (SNR) has been estimated for remotely sensed imagery using several image-based methods such as the homogeneous area (HA) and geostatistical (GS) methods. For certain procedures such as regression, an alternative SNR (SNRvar), the ratio of the variance in the signal to the variance in the noise, is potentially more informative and useful. In this paper, the GS method was modified to estimate the SNRvar, referred to as the SNRvar(GS). Specifically, the sill variance c of the fitted variogram model was used to estimate the variance of the signal component and the nugget variance c0 of the fitted model was used to estimate the variance of the noise. The assumptions required in this estimation are presented. The SNRvar(GS) was estimated using the modified GS method for six different land-covers and a range of wavelengths to explore its properties. The SNR*var(GS) was found to vary as a function of both wavelength and land-cover. The SNR*var(GS) represents a useful statistic that should be estimated and presented for different land-cover types and even per-pixel using a local moving window kernel.
No abstract is provided for this article.
The main objective of this research is to assess the impact of intra-class spectral variation on the accuracy of soft classification and super-resolution mapping. The accuracy of both analyses was negatively related to the degree of intra-class spectral variation, but the effect could be reduced through the use of spectral sub-classes. The latter is illustrated in mapping the shoreline at a sub-pixel scale from Landsat ETM+ data. Reducing the degree of intra-class spectral variation increased the accuracy of soft classification, with the correlation between predicted and actual class coverage rising from 0.87 to 0.94, and super-resolution mapping, with the RMSE in shoreline location decreasing from 41.13 m to 35.22 m.
Information on tropical forest types and their biophysical properties is needed as they underpin our understanding and prediction of Earth system processes. This paper explores the potential use of reflected MIR, derived from channel 3 of the NOAA AVHRR sensor, for estimating the biomass of Cameroonian tropical forests. Results indicate significant inverse relationships between biomass and reflected MIR.
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.
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.
Although soft classification analyses can reduce problems such as those associated with mixed pixels that impact negatively on conventional hard classifications their accuracy is often low. One approach to increasing the accuracy of soft classifications is the use of an ensemble of classifiers, an approach which has been successful for hard classifications but rarely applied for soft classifications. Four methods for combining soft classifications to increase soft classification accuracy were assessed. These methods were based on (i) the selection of the most accurate predictions on a class‐specific basis, (ii) the average of the outputs of the individual classifications for each case, (iii) the direct combination of classifications using evidential reasoning and (iv) the adaptation of the outputs to enable the use of a conventional (hard classification) ensemble approach. These four approaches were assessed with classifications of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High‐Resolution Radiometer (AVHRR) imagery of Australia. The data were classified using two neural networks and a probabilistic classifier. All four ensemble approaches applied to the outputs of these three classifiers were found to increase classification accuracy. Relative to the most accurate individual classification, the increases in overall accuracy derived ranged from 2.20% to 4.45%, increases that were statistically significant at 95% level of confidence. The results highlight that ensemble approaches may be used to significantly increase soft classification accuracy.
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 significant advances in land-cover classifications by combining data from multi-passive and active sensors, and new classification 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 significant advances in modelling species richness, alpha diversity, and beta diversity using multisensors to quantify land-cover classifications 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 field data. This will improve our understanding of the distribution of life on earth.
Remote sensing is an attractive source of data for land cover mapping applications. Mapping is generally achieved through the application of a conventional statistical classification, which allocates each image pixel to a land cover class. Such approaches are inappropriate for mixed pixels, which contain two or more land cover classes, and a fuzzy classification approach is required. When pixels may have multiple and partial class membership measures of the strength of class membership may be output and, if strongly related to the land cover composition, mapped to represent such fuzzy land cover. This type of representation can be derived by softening the output of a conventional 'hard' classification or using a fuzzy classification. The accuracy of the representation provided by a fuzzy classification is, however, difficult to evaluate. Conventional measures of classification accuracy cannot be used as they are appropriate only for 'hard' classifications. The accuracy of a classification may, however, be indicated by the way in which the strength of class membership is partitioned between the classes and how closely this represents the partitioning of class membership on the ground. In this paper two measures of the closeness of the land cover representation derived from a classification to that on the ground were used to evaluate a set of fuzzy classifications. The latter were based on measures of the strength of class membership output from classifications by a discriminant analysis, artificial neural network and fuzzy c-means classifiers. The results show the importance of recognising and accommodating for the fuzziness of the land cover on the ground. The accuracy assessment methods used were applicable to pure and mixed pixels and enabled the identification of the most accurate land cover representation derived. The results showed that the fuzzy representations were more accurate than the 'hard' classifications. Moreover, the outputs derived from the artificial neural network and the fuzzy c-means algorithm in particular were strongly related to the land cover on the ground and provided the most accurate land cover representations. The ability to appropriately represent fuzzy land cover and evaluate the accuracy of the representation should facilitate the use of remote sensing as a source of land cover data.
Synthetic aperture radar data possess many different characteristics to conventional remotely sensed data. They therefore should not be analysed in the same fashion. However, since there is no standard technique for the classification of radar data techniques formulated for use on other data sets are often used. Since these make no allowance for the unusual properties of radar data they will not exploit fully the data's information content. A method which compensates for some of the main characteristics of the radar data and significantly increases classification accuracy is proposed in this article.
No abstract is provided for this article.
No abstract is provided for this article.
