Mapping impervious surface expansion using medium-resolution satellite image time series: a case study in the Yangtze River Delta,China

Cities have been expanding rapidly worldwide, especially over the past few decades. Mapping the dynamic expansion of impervious surface in both space and time is essential for an improved understanding of the urbanization process, land-cover and land-use change, and their impacts on the environment. Landsat and other medium-resolution satellites provide the necessary spatial details and temporal frequency for mapping impervious surface expansion over the past four decades. Since the US Geological Survey opened the historical record of the Landsat image archive for free access in 2008, the decades-old bottleneck of data limitation has gone. Remote-sensing scientists are now rich with data, and the challenge is how to make best use of this precious resource. In this article, we develop an efficient algorithm to map the continuous expansion of impervious surface using a time series of four decades of medium-resolution satellite images. The algorithm is based on a supervised classification of the time-series image stack using a decision tree. Each imerpervious class represents urbanization starting in a different image. The algorithm also allows us to remove inconsistent training samples because impervious expansion is not reversible during the study period. The objective is to extract a time series of complete and consistent impervious surface maps from a corresponding times series of images collected from multiple sensors, and with a minimal amount of image preprocessing effort. The approach was tested in the lower Yangtze River Delta region, one of the fastest urban growth areas in China. Results from nearly four decades of medium-resolution satellite data from the Landsat Multispectral Scanner (MSS), Thematic Mapper (TM), Enhanced Thematic Mapper plus (ETM+) and China–Brazil Earth Resources Satellite (CBERS) show a consistent urbanization process that is consistent with economic development plans and policies. The time-series impervious spatial extent maps derived from this study agree well with an existing urban extent polygon data set that was previously developed independently. The overall mapping accuracy was estimated at about 92.5% with 3% commission error and 12% omission error for the impervious type from all images regardless of image quality and initial spatial resolution.     

Monitoring and modelling of chlorophyll-a concentrations in rivers using a high-resolution satellite image: a case study in the Nakdong river,Korea

The feasibility of using remote-sensing data with high spatial resolution was assessed for monitoring and modelling of chlorophyll-a (chl-a) in river waters. Two-band and three-band reflectance models including the red-edge band were examined as spectral coefficients using a RapidEye image for river waters, where the scale is smaller and narrower than for ocean waters. A red?red-edge?NIR three-band model calculated by a cubic function explained 73% of variance in the estimated data using the relationship between spectral indices such as absorption coefficients obtained using the model and chl-a concentrations and performed better than the red?red-edge two-band. Chl-a concentrations were simulated by a one-dimensional water quality model, QUALKO2, and image-derived and measured chl-a concentrations were applied in the calibration step of simulation. The image-derived chl-a dataset showed more stable calibration throughout the study area and enhanced the results rather than measured data. It is expected that chl-a estimation techniques using high resolution satellite data, RapidEye, have the capability to support rapid and widespread water quality monitoring and modelling, when a field dataset is not large or precise enough to do it, but still requires the improvement of estimation accuracy.     

Characterization and transport of aerosols over the Bay of Bengal during the winter monsoon: a comparative study using in-situ and satellite measurements

Natural processes, such as dust storms and sea salt spray, and anthropogenic activities, such as the burning of fossil fuels and biomass, introduce aerosols into the atmosphere. Their concentration, geographic distribution and particle size promote significant climatic consequences. Aerosol transport processes, from landmasses to oceans, are scarcely understood because of inadequate in-situ observations. This study reports the results of spectral aerosol optical depth (AOD) measurements using a five-channel (380, 440, 500, 675 and 870 nm) handheld MICROTOPS Sun-photometer used during a sea-truth data collection campaign conducted in the central Bay of Bengal (BOB) during the northeastern monsoon period (10 November to 13 December 2007). For the entire cruise period, the mean values of the daily average of the AODs at 500 nm and 870 nm were 0.39 ± 0.065 and 0.22 ± 0.047, respectively, the mean value of the Angstrom exponent (α) was 1.23 ± 0.2 and the turbidity parameter (β) was 0.183 ± 0.044. A smaller α value together with a larger β value suggests the presence of an abundance of smaller aerosol particles near the coast. An air mass back-trajectory analysis was undertaken to identify the potential source regions of the aerosols. Analysis of the results demonstrated the effect of the aerosol transport and source regions on the spectral behaviour of the AODs. In-situ measured AOD (550 nm) and α (550 nm, 865 nm) values were further compared with Moderate Resolution Imaging Spectroradiometer (MODIS)-derived parameters. The in-situ and MODIS-derived AOD values were found to be in good agreement, with a coefficient of determination (R ²) of 0.78 and a standard error of 0.05, while the R ² for α was 0.68 with a standard error of 0.14.     

Synergistic use of orbital satellite image and ground-based information in environmental monitoring of railway alignment construction - a case study

Environmental monitoring of road/railway/highway alignment construction activity is necessary to assess the probable negative impacts on the physical, biological and hydrological environment. This would aid in assessing the effectiveness of mitigation measures proposed and executed measures. Multitemporal orbital remote sensing data along with site-based information on water and air quality and noise level were used in the environmental impact assessment (EIA) and auditing of Konkan railway alignment in India. This method of integrated data collection and analysis could be used effectively for monitoring similar projects elsewhere.     

Reconstruction of satellite chlorophyll-a data using a modified DINEOF method: a case study in the Bohai and Yellow seas,China

A data-interpolating empirical orthogonal function (DINEOF) method was applied to 8 day composited satellite-derived chlorophyll-a (chl-a) images to produce a long-term, cloud-free chl-a data set over the Bohai Sea and Yellow Sea from 1997 to 2010. In this study, two additional procedures, a depth subdivision scheme and a new process of outlier detection and removal, improved the overall performance of this interpolating technique. The whole chl-a data set was divided into three subsets according to 20 and 50 m isobaths and the DINEOF reconstruction was performed on each subset. This subdivision scheme can significantly improve the accuracy of reconstruction, but is achieved with loss of computational efficiency due to the increased number of iterations required for reconstruction of the three subsets. A simple and new outlier detection method based on standardized residuals theory was developed to eliminate the spurious values (outliers) from the chl-a data set. The accuracy of the DINEOF reconstruction was significantly improved by the application of the outlier detection and removal process.     

Mapping of coral reefs using hyperspectral CASI data; a case study: Fordata,Tanimbar, Indonesia

Airborne remote sensing with a CASI‐550 sensor has been used to map the benthic coverage and the bottom topography of the Pulau Nukaha coral reef located in the Tanimbar Archipelago (Southeast Moluccas, Eastern Indonesia). The image classification method adopted was performed in three steps. Firstly, five geomorphological reef components were identified using a supervised spectral angle mapping algorithm in combination with data collected during the field survey, i.e. benthic cover type, percentage cover and depth. Secondly, benthic cover mapping was performed for each of the five geomorphological components separately using an unsupervised hierarchical clustering algorithm followed by class aggregation using both spectral and spatial information. Finally, 16 benthic cover classes could be labelled using the benthic cover data collected during the field survey. The overall classification accuracy, calculated on the biological diverse fore reef, was 73% with a kappa coefficient of 0.63. A reliable bathymetric model (up to a depth of 15 m) of the Pulau Nukaha reef was also obtained using a semi‐analytical radiative transfer model. When compared with independent in‐situ depth measurements, the result proved relatively accurate (mean residual error: ?0.9 m) and was consistent with the seabed topography (Pearson correlation coefficient: 86%).     

Assessing tropical cyclone impacts using object-based moderate spatial resolution image analysis: a case study in Bangladesh

The environmental and societal impacts of tropical cyclones could be reduced using a range of management initiatives. Remote sensing can be a cost effective, accurate, and potential tool for mapping the multiple impacts caused by tropical cyclones using high-to-moderate spatial resolution (5–30 m) satellite imagery to provide data on the following essential parameters – evacuation, relief, and management of natural resources. This study developed and evaluated an approach for assessing the impacts of tropical cyclones through object-based image analysis and moderate spatial resolution imagery. Pre- and post-cyclone maps of artificial and natural features are required for assessing the overall impacts in the landscape that could be acquired by mapping specific land cover types. We used the object-based approach to map land-cover types in pre- and post-cyclone Satellite Pour l’Observation de la Terre (SPOT) 5 image data and the post-classification comparison technique to identify changes in the particular features in the landscape. Cyclone Sidr (2007) was used to test the applicability of this approach in Sarankhola Upazila in Bangladesh. The object-based approach provided accurate results for classifying features from pre- and post-cyclone satellite images with an overall accuracy of 95.43% and 93.27%, respectively. Mapped changes identified the extent, type, and form of cyclone induced impacts. Our results indicate that 63.15% of the study area was significantly affected by cyclone Sidr. The majority of mapped damage was found in vegetation, cropped lands, settlements, and infrastructure. The damage results were verified through the high spatial resolution satellite imagery, reports and pictures that were taken after the cyclone. The methods developed may be used in future to assess the multiple impacts caused by tropical cyclones in Bangladesh and other similar environments for the purposes of tropical cyclone disaster management.     

Technical Note: The use of mirrors during an assembly task: a study of ergonomics and productivity

Industrial assembly tasks often require awkward, sustained neck and/or shoulder postures that can lead to increased musculoskeletal discomfort and reduced productivity. The aim of this study was to investigate the effects of mirror and periscope visual aids as ergonomic interventions designed to eliminate awkward postures of the cervicobrachial region during assembly tasks. Participants simulated a simple assembly task by using a cordless screwdriver to drive screws into a pre-tapped aluminium block. Trials of 15 min were run for each of four distinct assembly workstation configurations: industry standard (in-line screwdriver, work at elbow height, no visual aid); pistol grip (pistol grip screwdriver, work at shoulder height, no visual aid); mirror (in-line screwdriver, work at elbow height, single mirror visual aid); and periscope (in-line screwdriver, work at elbow height, two-mirror visual aid system). Muscular activity, discomfort, body posture, productivity and operator subjective assessment were recorded to determine the effects of the visual aid interventions. The results show that when comparing the interventions to the industry standard condition, there was a 45% reduction in average cervical erector spinae activity, a 90% reduction in average neck flexion angle and a 72% reduction in neck discomfort with the interventions. When comparing these interventions to the pistol grip condition there was an 80% reduction in activity of the dominant side deltoid, a 92% reduction in shoulder flexion angle and an 81% decrease in shoulder discomfort with the interventions. Productivity was greatest in the industry standard configuration followed by the pistol grip (9% lower), the periscope (13% lower) and the mirror (23% lower) configurations. A follow-up study that compared the productivity of the periscope configuration with that of the industry standard configuration showed that within a 4-h work period this productivity differential decreased by over 33%.     

Reconstruction of sand wave bathymetry using both satellite imagery and multi-beam bathymetric data: a case study of the Taiwan Banks

Sand waves are a widespread bed-form in the tidal environment. Their formation, migration, and other properties have great significance in the fields of geology and oceanography. Currently, research on sand waves is mainly based on multi-beam bathymetric (MB) data. For large sand wave regions, however, the acquisition ability of MB data becomes limited; instead, interval-line measurement is adopted but with the inherent problem that it cannot achieve the full spatial coverage required. Reconstructed sand wave bathymetry (SWB) using interpolation algorithms cannot reflect the real SWB either. In this article, we propose a new approach in using both satellite imagery and MB data to reconstruct SWB, which takes advantage of characteristic information (ripple and crest) of SWB rendered in satellite imagery and MB data. The new approach is exemplified by a case study of the Taiwan Banks. We use the imagery obtained by the Charge-Coupled Device from the Small Satellite Constellation for Environment and Disaster Monitoring and Forecasting, and the MB data from R2Sonic to reconstruct the digital elevation model. The results show that the root mean square error of the reconstructed water depth is 1.47 m (compared to the MB data not using this approach), suggesting that the new approach is effective in reconstructing SWB. This approach allows a reduction in MB data track density, which may lead to an improvement in efficiency.     

The identification of pseudo-invariant targets using ground field spectroscopy measurements intended for the removal of atmospheric effects from satellite imagery: a case study of the Limassol area in Cyprus

This study proposes five types of commonly located surfaces that were evaluated to be determined for use as pseudo-invariant targets (PITs) in order to apply the so-called ‘empirical-line’ atmospheric correction method. Spectroradiometric measurements were taken over wet and dry conditions to obtain the spectral signatures of the targets. From the acquired in situ spectroradiometric campaign, it was found that the proposed commonly found PITs (sandy, concrete and asphalt) are suitable non-variant targets. An accuracy assessment of the empirical-line atmospheric correction method using the five PITs was performed using other calibration targets and aerosol optical thickness (AOT) values from MICROTOPS hand-held sun photometers acquired simultaneously with the satellite overpass. The radiative transfer equation was used to determine AOT levels, using the reflectance values derived from the empirical-line method, in order to conduct an accuracy assessment with in situ AOT measurements. It was also shown that precipitation conditions should be considered when using non-variant targets in atmospheric correction methods.     

Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: a case study for South and Southeast Asian countries

A geospatial database on the spatial distribution of rice areas and rice cultural types of major rice-producing countries of South and Southeast Asia has been developed in this study using remote-sensing and ancillary data sets. Multitemporal SPOT VGT normalized difference vegetation index (NDVI) data for the period 2009–2010 were used for the analysis. The classification was performed adopting ISODATA clustering to build a non-agricultural area mask followed by rice area mapping. The derived rice area was stratified by logical modelling of ancillary data sets into five rice cultural types: irrigated wet, upland, flood-prone, drought-prone, and deep-water. The uniqueness of this study is a synergistic approach based solely on single-source, high-temporal remote-sensing data coupled with ancillary data, which demonstrate the application of SPOT VGT NDVI data in building a geospatial database for rice crops over a wide spatial extent. This approach was adopted for cost effectivity as the study extent was vast and thus lacking ground truth information. Comparison of the derived rice area against the reported literature values for validation yielded a good correlation (linear coefficient of determination, R² = 0.95–0.99). The high-temporal resolution NDVI data enabled effective characterization of vegetation phenology. The derived spatial outputs can be used in various studies associated with the assessment of greenhouse gas emissions from paddy fields, change detection, and inputs to crop simulation models, which are significantly related to different rice cultural types.     

Field-based crop classification using SPOT4, SPOT5, IKONOS and QuickBird imagery for agricultural areas: a comparison study

A comparison of agricultural crop maps from independent field-based classifications of the Satellite Pour l'Observation de la Terre (SPOT) 4 multispectral (XS), SPOT5 XS, IKONOS XS, QuickBird XS and QuickBird pan-sharpened (PS) images is presented. An agricultural area within the north-west section of Turkey was analysed for field-based crop identification. The SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird images were collected in similar climatic conditions during July and August 2004. The classification of each image was carried out separately on a per-field basis on all bands and the coincident bands that are green, red and near-infrared (NIR). To examine the effect of filtering on field-based classification, the images were each filtered using the 3?×?3, 5?×?5, 7?×?7 and 9?×?9 mean filter and the filtered bands were also classified on per-field basis. For the unfiltered images, IKONOS XS provided the highest overall accuracies of 88.9% and 88.1% for the all-bands and the coincident bands classifications, respectively. On average, IKONOS XS performed slightly better than QuickBird XS and QuickBird PS, while it outperformed SPOT4 XS and SPOT5 XS. The use of filtered images in field-based classification reduced the accuracies for SPOT4 XS, SPOT5 XS, IKONOS XS and QuickBird XS. The results of this study indicate that smoothing images prior to classification does not improve the accuracies for the field-based classification. On the contrary, the accuracies for the filtered QuickBird PS images indicated a slight improvement. On the whole, both IKONOS and QuickBird images produced quite promising results for field-based crop mapping, yielding overall accuracies above 83%.     

Retrieval of land surface albedo and temperature using data from the Indian geostationary satellite: a case study for the winter months

The shortwave and longwave radiation budget at land surfaces is largely dependent on two fundamental quantities, the albedo and the land surface temperature (LST). A time series (November 2005 to March 2006) of daily data from the Indian geostationary satellite Kalpana‐1 Very High Resolution Radiometer (K1VHRR) sensor in the visible (VIS), water vapour (WV) and thermal infrared (TIR) bands from noontime (0900 GMT) observations were processed to retrieve these quantities in clear skies for five winter months. Cloud detection was carried out using bispectral threshold tests (in both VIS and TIR bands) in a dekadal time series. Surface albedo was retrieved using a simple atmospheric transmission model. K1VHRR albedo was compared with Moderate Resolution Imaging Spectroradiometer (MODIS) AQUA noontime albedo over different land targets (agriculture, forest, desert, scrub and snow) that showed minimum differences over agriculture and forest. The comparison of spatial albedo over different landscapes yielded a root mean square deviation (RMSD) of 0.021 in VHRR albedo (9% of MODIS albedo). A mono‐window algorithm was implemented with a single TIR band to retrieve the LST. Its accuracy was also verified over different land targets by comparison with aggregated MODIS AQUA LST. The maximum RMSD was obtained over agriculture. Spatial comparison of VHRR and AQUA LSTs over homogeneous and heterogeneous landscape cutouts revealed an overall RMSD of 2.3 K. An improvement in the retrieval accuracy is expected to be achieved with atmospheric products from the sounder and split thermal bands in the imager of future INSAT 3D missions.     

Mapping functional vegetation abundance in a coastal dune environment using a combination of LSMA and MLC: a case study at Kenfig NNR,Wales

The interactions between wind-blown sand transport, pioneer vegetation and succession vegetation in coastal dune fields play an important role in landform development and determine the balance between stabilization and re-activation of these aeolian landscapes. High-resolution mapping of vegetation communities across a dune field – in particular, the mixture of different functional plant types such as pioneer versus succession species – is critical for the calculation of landscape metrics that enable a rigorous and quantitative testing of numerical simulation models, as well as for informing targeted land management actions that maintain biodiversity and ecological functions. This article presents a method of using maximum likelihood classification (MLC) to inform linear spectral mixture analysis (LSMA) for quantifying sub-pixel abundance of sand, pioneer and succession vegetation in a coastal dune area in Wales, from archived imagery obtained from the Compact Airborne Spectral Imager (CASI) in 1997. LSMA is first applied to derive sub-pixel fractional abundances of soil, green vegetation (GV) and non-GV elements. An MLC is developed separately for automatically identifying pixels believed to contain a mixture of the two functional vegetation types, and this then serves as a basis for applying a transform that interprets the LSMA results in terms of sand and pioneer and succession vegetation communities. Very high resolution (0.1 m pixels) colour aerial photography, taken simultaneously with the CASI data, and field survey data from both 1997 and 2009 were used to aid the MLC and the transform algorithm and were also used for a limited validation exercise. The LSMA abundance maps achieved an overall accuracy of 82.7% (kappa coefficient κ?=?0.78). The reduced MLC vegetation maps (four classes) achieved an overall accuracy of 98.2% (kappa coefficient κ?=?0.96). Although it was not possible to validate the final pioneer and succession vegetation abundance maps quantitatively, a qualitative review of the results for selected locations within the dune field indicates the viability of applying MLC to help direct a transformation of LSMA abundance maps into functional vegetation abundance data.     

Mapping tree distribution and LAI in peatlands using field methods and imaging spectroscopy: a case study for the Haaksbergerveen,the Netherlands

Leaf area index (LAI) is among the vegetation parameters that play an important role in climate, hydrological and ecological studies, and is used for assessing growth and expansion of vegetation. The main objective of this study was to develop a methodology to map the LAI distribution of birch trees (Betula pendula) in peatland ecosystems using field-based instruments and airborne-based remote-sensing techniques. The developed mapping method was validated using field-based LAI measurements using the LAI-2000 instrument. First vegetation indices, including simple ratio (SR), normalized difference vegetation index (NDVI), and reduced simple ratio (RSR), were derived from HyMap data and related to ground-based measurements of LAI. LAI related better with RSR (R² = 0.68), followed by NDVI (R² = 0.63) and SR (R² = 0.58), respectively. Areas with birch were identified using Spectral Angle Mapper (SAM) to classify the image into 11 end members of dominant species including bare soil and open water. Next, the relationship between LAI and RSR was applied to areas with birch, yielding a birch LAI map. Comparison of the map of the birch trees and field-based LAI data was done using linear regression, yielding an R² = 0.38 and an RMSE = 0.25, which is fairly accurate for a structurally highly diverse field situation. The method may prove an invaluable tool to monitor tree encroachment and assess tree LAI in these remote and poorly accessible areas.     

A multi-scale approach for modeling fire occurrence probability using satellite data and classification trees: A case study in a mountainous Mediterranean region

Fires constitute one major ecological disturbance which influences the natural cycle of vegetation succession and the structure and function of ecosystems. There is no single natural scale at which ecological phenomena are completely understood and thus the capacity to handle scale is beneficial to methodological frameworks for analyzing and monitoring ecosystems. Although satellite imagery has been widely applied for the assessment of fire related topics, there are few studies that consider fire at several spatial scales simultaneously. This research explores the relationships between fire occurrence and several families of environmental factors at different spatial observation scales by means of classification and regression tree models. Predictors accounting for vegetation status (estimated by spectral indices derived from Landsat imagery), fire history, topography, accessibility and vegetation types were included in the models of fire occurrence probability. We defined four scales of analysis by identifying four meaningful thresholds related to fire sizes in the study site. Sampling methodology was based on random points and the power-law distribution describing the local fire regime. The observation scale drastically affected tree size, and therefore the achieved level of detail, and the most explanatory variables in the trees. As a general trend, trees considering all the variables showed a spectral index ruling the most explicative split. According to the comparison of the four pre-determined analysis scales, we propose the existence of three eventual organization levels: landscape patch or ecosystem level, local level and the basic level, the most heterogeneous and complex scale. Rules with three levels of complexity and applicability for management were defined in the tree models: (i) the repeated critical thresholds (predictor values across which fire characteristics change rapidly), (ii) the meaningful final probability classes and (iii) the trees themselves.     

Specification and testing using generalized machines: a presentation and a case study

Although testing is a major part of software development, it rarely gets the attention it deserves from researchers, partly because its foundations are weak and ill-understood. The principal purpose of testing is to detect (and then remove) faults in a software system. However, very few of the existing methods allow the tester to make any precise statement about the type or number of faults that remain undetected after testing is completed. In particular, none of the main techniques used by the software industry can give serious guarantees that a system is fault-free after testing has been completed. This paper advocates the use of a formal method both as a specification language and as the basis of a test data selection strategy. It presents a new method for generating test cases from this type of formal specification that provides a more convincing answer to the problem of detecting all faults in a software system. The method is reductionist in the sense that it guarantees that a system is fault-free provided that its components are fault-free; in turn, the same method could be used to test the resulting sub-systems, so the reduction will continue until the components considered are either known to be correct or are fairly simple pieces of code that can be successfully tested using traditional methods. The formal method used, X-machines, is a blend of finite state machines, data structures and processing functions and provides a simple and intuitive way of specifying computer systems. The use of X-machines as a specification tool and the testing method are illustrated with a case study. © 1998 John Wiley & Sons, Ltd.     

Within country accuracy of tree crown transparency estimates using the image analysis system CROCO: a case study from Switzerland

We evaluated the accuracy of tree crown transparency estimates made with the image analysis system CROCO compared to visual estimates by field observers in Switzerland. Data from Swiss field training courses in 1998 and 1999 each comprising four sessions were used and the median values of the different observers’ scores were assumed as the true values. First, Swiss reference photographs alone were used to obtain the calibration curves needed to predict crown transparency from the image analysis measure DSO. In this case, the CROCO estimates were biased for many species. Second, data from the first three field training sessions were added to the reference photos to derive the calibration curves. In this second case, CROCO provided unbiased estimates of both years, while the estimates by some of the field teams were significantly different from the true values. The result suggests that CROCO can be used as a more reliable reference to detect observer bias within a country than control observers. We conclude that few reference photographs alone are not sufficient, so additional field data should also be used to estimate crown transparency more accurately using CROCO.     

Land-cover change dynamics and coastal aquaculture development: a case study in the East Godavari delta,Andhra Pradesh,India using multi-temporal satellite data

The present study focuses on the identification and quantification of land-cover changes occurring in the coastal stretches of the East Godavari delta, Andhra Pradesh, India. The analysis of series of multi-temporal satellite data provides an accurate quantification and therefore a better understanding of the process of land-cover changes during 1990–2005. Land-cover changes were quantified based on normalized difference vegetation index (NDVI) image differencing and a post-classification comparison approach. The change detection results were examined in terms of the proportion of land-cover classes and change trajectories with particular emphasis on coastal aquaculture development within the study area. The study shows that the total area under aquacultural ponds increased from 2985 ha in 1990 to 7067 ha in 2005. The major changes in the study area occurred during 1990–1994, when 2873 ha of agricultural land and 762 ha of degraded mangroves were converted into aquacultural ponds. The prediction of land-cover distribution in 2010 on the basis of a Markov chain shows a continuing upward trend of the aquaculture area (8267 ha) with less impact on the mangrove area. The analysis predicts that the agricultural land area will continue to decrease from 50 122 to 46 978 ha during 2005–2010.     

nSpace and GeoTime: a VAST 2006 case study.

The nSpace and GeoTime visual analytic tools enable investigation and understanding of complex, ambiguous situations. nSpace is a system of systems supporting the whole analytic workflow. GeoTime provides insight into events and behaviors in time and space. Junior analysts used the analytic capabilities that these tools support to investigate the 2006 Visual Analytics Science and Technology contest data set and to produce an intelligence assessment report. Their work won first place in the corporate category. This article is a detailed case study describing how the nSpace and GeoTime tools created an analytical environment that enabled two novice analysts to examine a scenario, discover patterns, trace connections, assess evidence, visually represent meaningful hypotheses with associated evidence, track progress, collaborate with others, and then produce a final report. nSpace and GeoTime are developed in collaboration with analysts to support the investigation of large and complex multidimensional data sets.