Publications

1. Tackling large scale problems by scaling up J. Grace, P. R. van Gardingen and J. Luan 2. Scaling up and down: matching research with requirements in land management and policy through a system-based approach G. R. Squire and G. J. Gibson 3. Scale and spatial dependence P. M. Atkinson 4. Scaling remote sensing models C. E. Woodcock, J. B. Collins and D. L. B. Jupp 5. Scaling of PAR adsorption, photosynthesis and transpiration from leaves to canopy B. Kruijt and S. Ongeri, P. G. Jarvis 6. Scaling from plant to community and from plant to regional flora J. P. Grime, K. Thompson and C.W. MacGillivray 7. Variation in stomatal characteristics at the whole-leaf level J. D. B. Weyers, T. Lawson and Z. Y. Peng 8. Roots: measurement, function and dry matter budgets D. Atkinson and R. Fogel 9. Producing large-area land cover maps from satellite sensor images: scaling issues and generalization techniques M. J. Barnsley, S. L. Barr and T. Tsang 10. The carbon balance of tropical forests: from the local to the regional scale P. J. Curran, G. M. Foody, R. M. Lucas, M. Honzak and J. Grace 11. Issues in the aggregation of surface fluxes from a heterogeneous landscape: from sparse canopies up to the GCM grid scale R. J. Harding, E. M. Blyth and C. M. Taylor 12. Variability and scaling: matching methods and phenomena B. Marshall, J. W. Crawford and J. R. Porter 13. Problems with using models to predict regional crop production G. Russell and P. van Gardingen 14. Scaling behaviour of watershed processes R. B. Lammers, L. E. Band and C. L. Tague 15. Observation and simulation of energy budgets at the surface of a prairie grassland R. J. Gurney and I. J. Sewell 16. SiB2, a model for simulation of biological processes within a climate model J. A. Berry, G. J. Collatz, A. S. Denning, G. D. Colello, W. Fu, C. Grivet, D. A. Randall and P. J. Sellers Index.

Fully-fuzzy classification approaches have attracted increasing interest recently. These approaches allow for multiple and partial class memberships at the level of individual pixels and accommodate fuzziness in all three stages of a supervised classification of remotely sensed imagery. A fully-fuzzy classification strategy may be deemed more objective and correct than partially-fuzzy approaches where fuzziness is only accommodated in one or two of the three classification stages. This paper describes two approaches to the fully-fuzzy classification of remotely sensed imagery: a statistical approach based on a modified fuzzy c-means clustering algorithm performed in a supervised mode and an artificial neural network based approach. This is followed by the documentation of a case study using Landsat Thematic Mapper (TM) data of an Edinburgh suburb. Both approaches were applied to derive fully-fuzzy classifications of land cover, with fuzzy ground data, critical for training and testing the classifications, derived from indicator kriging. Results confirmed the superiority of fully-fuzzy over their respective partially-fuzzy classification counterparts, which is beneficial given their more relaxed requirements for training pixels (i.e. training pixels need not be pure). Similar accuracies were obtained with the artificial neural network and statistical approaches to classification. It is suggested that due emphasis must be placed on derivation and analysis of fuzzy ground data as well as fuzzy classified data in order to further improve fully-fuzzy classifications.

The area of land use or land cover change obtained directly from a map may differ greatly from the true area of change because of map classification error. An error-adjusted estimator of area can be easily produced once an accuracy assessment has been performed and an error matrix constructed. The estimator presented is a stratified estimator which is applicable to data acquired using popular sampling designs such as stratified random, simple random and systematic (the stratified estimator is often labeled a poststratified estimator for the latter two designs). A confidence interval for the area of land change should also be provided to quantify the uncertainty of the change area estimate. The uncertainty of the change area estimate, as expressed via the confidence interval, can then subsequently be incorporated into an uncertainty analysis for applications using land change area as an input (e.g., a carbon flux model). Accuracy assessments published for land change studies should report the information required to produce the stratified estimator of change area and to construct confidence intervals. However, an evaluation of land change articles published between 2005 and 2010 in two remote sensing journals revealed that accuracy assessments often fail to include this key information. We recommend that land change maps should be accompanied by an accuracy assessment that includes a clear description of the sampling design (including sample size and, if relevant, details of stratification), an error matrix, the area or proportion of area of each category according to the map, and descriptive accuracy measures such as user's, producer's and overall accuracy. Furthermore, mapped areas should be adjusted to eliminate bias attributable to map classification error and these error-adjusted area estimates should be accompanied by confidence intervals to quantify the sampling variability of the estimated area. Using data from the published literature, we illustrate how to produce error-adjusted point estimates and confidence intervals of land change areas. A simple analysis of uncertainty based on the confidence bounds for land change area is applied to a carbon flux model to illustrate numerically that variability in the land change area estimate can have a dramatic effect on model outputs.

Fuzzy methods in remote sensing have received growing interest for their particular value in situations where the geographical phenomena are inherently fuzzy. A fuzzy approach is investigated for the classification of sub-urban land cover from remote sensing imagery and the evaluation of classification accuracy. Under the fuzzy strategy, fuzziness, intrinsic to both remotely sensed data and ground data, is accommodated and usefully explored. For comparative purposes, hard and fuzzy classifications were produced and tested using hard and fuzzy evaluation techniques. The results show that the fuzzy approach holds advantages over both conventional hard methods and partially fuzzy approaches, in which fuzziness in only the remotely sensed imagery is accommodated. It was found that Kappa coefficients were more than doubled when applying the fuzzy evaluation technique as opposed to the hard evaluation technique. Furthermore, the fuzzy approach paves the way towards an integrated handling of remotely sensed data and other spatial data.

The dynamic nature of geographical phenomena is widely recognised. Changes may be perceived as good or bad but are often of considerable importance. Environmental changes, for example, may have a marked impact on human health and well-being which, in addition to normal scientific curiosity, call for a greater understanding of phenomena undergoing change and of the effects of their dynamism. To support studies of dynamic geographical phenomena there is a need for information on environmental properties at a range of spatial and temporal scales. Various geospatial technologies have developed to provide the data needed to further the understanding of the environment (Konecny, 2003; Thurston et al., 2003). These geospatial technologies include the use of global position systems (GPS) for accurate information on location; geographical information systems (GIS) for data integration and analysis; and geostatistical tools for quantitative analyses, which recognise the spatial dependence that exists with most geographical data. In Section I of the book, however, the major focus is upon another major technology used in the study of GeoDynamics, remote sensing.

The spectral, spatial, and temporal resolutions of Envisat's Medium Resolution Imaging Spectrometer (MERIS) data are attractive for regional‐ to global‐scale land cover mapping. Moreover, two novel and operational vegetation indices derived from MERIS data have considerable potential as discriminating variables in land cover classification. Here, the potential of these two vegetation indices (the MERIS global vegetation index (MGVI), MERIS terrestrial chlorophyll index (MTCI)) was evaluated for mapping eleven broad land cover classes in Wisconsin. Data acquired in the high and low chlorophyll seasons were used to increase inter‐class separability. The two vegetation indices provided a higher degree of inter‐class separability than data acquired in many of the individual MERIS spectral wavebands. The most accurate landcover map (73.2%) was derived from a classification of vegetation index‐derived data with a support vector machine (SVM), and was more accurate than the corresponding map derived from a classification using the data acquired in the original spectral wavebands.

Ground reference data are typically required to evaluate the quality of a supervised image classification analysis used to produce a thematic map from remotely sensed data. Acquiring a suitable ground data set for a rigorous assessment of classification quality can be a major challenge. An alternative approach to quality assessment is to use a model-based method such as can be achieved with a latent class analysis. Previous research has shown that the latter can provide estimates of class areal extent for a non-site specific accuracy assessment and yield estimates of producer’s accuracy which are commonly used in site-specific accuracy assessment. Here, the potential for quality assessment via a latent class analysis is extended to show that an estimate of a complete confusion matrix can be predicted which allows a suite of standard accuracy measures to be generated to indicate global quality on an overall and per-class basis. In addition, information on classification uncertainty may be used to illustrate classification quality on a per-pixel basis and hence provide local information to highlight spatial variations in classification quality. Classifications of imagery from airborne and satellite-borne sensors were used to illustrate the potential of the latent class analysis with results compared against those arising from the use of a conventional ground data set.

The maximum-likelihood classification of remotely sensed data involves considerable computational effort, in the process calculating a large amount of information on the class membership characteristics for each case (e.g., pixel). Little of this information, however, is made available in the conventional output, which consists simply of the most likely class of membership for each case. More of the information generated in the classification can be output, specifically the a posteriori probabilities and typicalities of class membership.

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The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.

No abstract is provided for this article.

Over the last decade, the beaching of massive quantities of sargassum in the Caribbean has become a major problem. Relatively few studies have focused on beached sargassum which has numerous negative environmental, social and economic impacts. To aid understanding of the problem and develop approaches to effectively manage it, a satellite-based mapping and monitoring service is being developed on the basis of information generated in a set of user needs workshops. Here, we report on the foundations of this mapping and monitoring service. In particular, user needs obtained from a large and diverse set of stakeholders are used to identify appropriate satellite remote sensing systems to use as data sources. The system will use mainly PlanetScope and Sentinel-2 data to meet the spatio-temporal demands of users. It is planned to develop an operational system that may if desired be extended to the Caribbean as a whole and beyond.

Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce annual, global 25 m forest maps between 2007 and 2010, and the latest global forest maps of 2015 and 2016 were produced by using the ALOS-2 PALSAR-2 data. However, annual 25 m spatial resolution forest maps during 2011–2014 are missing because of the gap in operation between ALOS and ALOS-2, preventing the construction of a continuous, fine resolution time-series dataset on the world's forests. In contrast, the MODerate Resolution Imaging Spectroradiometer (MODIS) NDVI images were available globally since 2000. This research developed a novel method to produce annual 25 m forest maps during 2007–2016 by fusing the fine spatial resolution, but asynchronous PALSAR/PALSAR-2 with coarse spatial resolution, but synchronous MODIS NDVI data, thus, filling the four-year gap in the ALOS and ALOS-2 time-series, as well as enhancing the existing mapping activity. The method was developed concentrating on two key objectives: 1) producing more accurate 25 m forest maps by integrating PALSAR/PALSAR-2 and MODIS NDVI data during 2007–2010 and 2015–2016; 2) reconstructing annual 25 m forest maps from time-series MODIS NDVI images during 2011–2014. Specifically, a decision tree classification was developed for forest mapping based on both the PALSAR/PALSAR-2 and MODIS NDVI data, and a new spatial-temporal super-resolution mapping was proposed to reconstruct the 25 m forest maps from time-series MODIS NDVI images. Three study sites including Paraguay, the USA and Russia were chosen, as they represent the world's three main forest types: tropical forest, temperate broadleaf and mixed forest, and boreal conifer forest, respectively. Compared with traditional methods, the proposed approach produced the most accurate continuous time-series of fine spatial resolution forest maps both visually and quantitatively. For the forest maps during 2007–2010 and 2015–2016, the results had greater overall accuracy values (>98%) than those of the original JAXA forest product. For the reconstructed 25 m forest maps during 2011–2014, the increases in classifications accuracy relative to three benchmark methods were statistically significant, and the overall accuracy values of the three study sites were almost universally >92%. The proposed approach, therefore, has great potential to support the production of annual 25 m forest maps by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016.

No abstract is provided for this article.

The spectral separability of thirteen topical vegetation classes, including twelve forest types, was assessed. Although the thirteen classes could not be classified to a high accuracy the results of a set of supervised and unsupervised classifications revealed that three groups of classes were highly separable; a classification of the three groups by a discriminant analysis had an accuracy of 92·20 per cent. These three spectrally separable groups also corresponded closely to ecological groups identified from an ordination of data on tree species contained within a detailed ground data set. On the basis of the class separability analyses the three spectrally separable groups were mapped, with an accuracy of 94·84 per cent, from Landsat TM data by a maximum likelihood classification. It was apparent that some of the errors in this classification could be resolved through the use of contextual information and ancillary information, particularly on topography.