A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets

Accurate and up-to-date global land cover data sets are necessary for various global change research studies including climate change, biodiversity conservation, ecosystem assessment, and environmental modeling. In recent years, substantial advancement has been achieved in generating such data products. Yet, we are far from producing geospatially consistent high-quality data at an operational level. We compared the recently available Global Land Cover 2000 (GLC-2000) and MODerate resolution Imaging Spectrometer (MODIS) global land cover data to evaluate the similarities and differences in methodologies and results, and to identify areas of spatial agreement and disagreement. These two global land cover data sets were prepared using different data sources, classification systems, and methodologies, but using the same spatial resolution (i.e., 1 km) satellite data. Our analysis shows a general agreement at the class aggregate level except for savannas/shrublands, and wetlands. The disagreement, however, increases when comparing detailed land cover classes. Similarly, percent agreement between the two data sets was found to be highly variable among biomes. The identified areas of spatial agreement and disagreement will be useful for both data producers and users. Data producers may use the areas of spatial agreement for training area selection and pay special attention to areas of disagreement for further improvement in future land cover characterization and mapping. Users can conveniently use the findings in the areas of agreement, whereas users might need to verify the informaiton in the areas of disagreement with the help of secondary information. Learning from past experience and building on the existing infrastructure (e.g., regional networks), further research is necessary to (1) reduce ambiguity in land cover definitions, (2) increase availability of improved spatial, spectral, radiometric, and geometric resolution satellite data, and (3) develop advanced classification algorithms.     

Seasonal dynamics and regional features of MODIS-derived land surface characteristics in dust source regions of East Asia

Atmospheric mineral dust is an important component of the climate system; however, representation of dust production in the climate models poses significant challenges. Satellite remote sensing has the potential to aid in determining the surface characteristics of active dust source regions that are of importance to dust emission modeling. This study uses data from the Moderate-resolution Imaging Spectroradiometer (MODIS) in conjunction with soil texture to investigate linkages of spatial distribution of surface characteristics related to dust emission, and their dynamics, at the seasonal time scale. In addition to standard MODIS land products such as surface albedo and NDVI which are strongly linked to dust emission, we introduce a roughness index (RI) and an arid soil surface index (ASSI) to aid in land surface characterization. Three regions of northwestern China known for dust emission, the Taklamakan, Badain Jaran, and Gurbantunggut Deserts, are examined for spatial and temporal changes of surface characteristics during winter-spring-summer transition February-July 2005. A new methodology is proposed by introducing regional masking derived from MODIS Band 10 surface albedo. The analysis demonstrates regional unique temporal and spatial characteristics in the 2005 seasonal transition for these areas. Seasonal modes of response are clearly present. The soil texture correlation results demonstrate that clay fraction has a consistently high negative correlation to albedo, as does vegetation. The analysis also demonstrates that RI is a dynamic characteristic changing both with season and on much shorter time scales.     

A model to predict land susceptibility to wind erosion in western Queensland,Australia

This paper describes the development and validation of the Australian Land Erodibility Model (AUSLEM), designed to predict land susceptibility to wind erosion in western Queensland, Australia. The model operates at a 5 × 5 km spatial resolution on a daily time-step with inputs of grass and tree cover, soil moisture, soil texture and surficial stone cover. The system was implemented to predict land erodibility, i.e. susceptibility to wind erosion, for the period 1980–1990. Model performance was evaluated using cross-correlation analyses to compare trajectories of mean annual land erodibility at selected locations with trends in wind speed and observational records of dust events and a Dust Storm Index (DSI). The validation was conducted at four spatial length scales from 25 to 150 km using windows to represent potential dust source areas centered on and positioned around eight meteorological stations within the study area. The predicted land erodibility had strong correlations with dust-event frequencies at half of the stations. Poor correlations at the other stations were linked to the inability of the model to account for temporal changes in soil erodibility, and comparing trends in the land erodibility of regions with dust events whose source areas lie outside the regions of interest. The model agreement with dust-event frequency trends was found to vary across spatial scales and was highly dependent on land type characteristics around the stations and on the types of dust events used for validation.     

An evaluation of satellite dust-detection algorithms in the Middle East region

In the last 15 years, the frequency, spatial extent, and intensity of dust storms have increased and it is one of the main continuously occurring environmental hazard in the Middle East region. Since dust storms generally cover a large spatial extent and are highly dynamic, satellite Earth Observation (EO) is a key tool for detecting their occurrence, identifying their origin, and monitoring their transport and state. A variety of satellite dust detection algorithms have been developed to identify dust emissions sources and dust plumes once entrained in the atmosphere. This paper evaluates the performance of five widely applied dust detection algorithms: the Brightness Temperature Difference (BTD), D-parameter, Normalized Difference Dust Index (NDDI), Thermal-Infrared Dust Index (TDI) and the Middle East Dust Index (MEDI). These algorithms are applied to Moderate Resolution Imaging Spectroradiometer (MODIS) data to detect dust-contaminated pixels during three significant dust events in 2007 in the Middle East region that originated from sources in Iraq, Syria and Saudi Arabia. The results indicate that all methods have a comparable performance in detecting dust-contaminated pixels during the three dust events with an average detection rate (between all algorithms) of 85%. However, substantial differences exist in their ability to distinguish dust from clouds and the land surface, which resulted in large errors of commission. Direct validation of these algorithms with observations from seven Aerosol Robotic Network (AERONET) stations in the region found an average false detection rate (between all algorithms) of 89.6%. Although the algorithms performed well in detecting the dust-contaminated pixels their high false detection rate means it is challenging to apply these algorithms in operational context.     

Dust source identification using MODIS: A comparison of techniques applied to the Lake Eyre Basin, Australia

The impact of mineral aerosol (dust) in the Earth's system depends on particle characteristics which are initially determined by the terrestrial sources from which the sediments are entrained. Remote sensing is an established method for the detection and mapping of dust events, and has recently been used to identify dust source locations with varying degrees of success. This paper compares and evaluates five principal methods, using MODIS Level 1B and MODIS Level 2 aerosol data, to: (a) differentiate dust (mineral aerosol) from non-dust, and (2) determine the extent to which they enable the source of the dust to be discerned. The five MODIS L1B methods used here are: (1) un-processed false colour composite (FCC), (2) brightness temperature difference, (3) Ackerman's (1997: J.Geophys. Res., 102, 17069-17080) procedure, (4) Miller's (2003:Geophys. Res. Lett. 30, 20, art.no.2071) dust enhancement algorithm and (5) Roskovensky and Liou's (2005: Geophys. Res. Lett. 32, L12809) dust differentiation algorithm; the aerosol product is MODIS Deep Blue (Hsu et al., 2004: IEEE Trans. Geosci. Rem. Sensing, 42, 557-569), which is optimised for use over bright surfaces (i.e. deserts). These are applied to four significant dust events from the Lake Eyre Basin, Australia. OMI AI was also examined for each event to provide an independent assessment of dust presence and plume location. All of the techniques were successful in detecting dust when compared to FCCs, but the most effective technique for source determination varied from event to event depending on factors such as cloud cover, dust plume mineralogy and surface reflectance. Significantly, to optimise dust detection using the MODIS L1B approaches, the recommended dust/non-dust thresholds had to be considerably adjusted on an event by event basis. MODIS L2 aerosol data retrievals were also found to vary in quality significantly between events; being affected in particular by cloud masking difficulties. In general, we find that OMI AI and MODIS AQUA L1B and L2 data are complementary; the former are ideal for initial dust detection, the latter can be used to both identify plumes and sources at high spatial resolution. Overall, approaches using brightness temperature difference (BT10-11) are the most consistently reliable technique for dust source identification in the Lake Eyre Basin. One reason for this is that this enclosed basin contains multiple dust sources with contrasting geochemical signatures. In this instance, BTD data are not affected significantly by perturbations in dust mineralogy. However, the other algorithms tested (including MODIS Deep Blue) were all influenced by ground surface reflectance or dust mineralogy; making it impossible to use one single MODIS L1B or L2 data type for all events (or even for a single multiple-plume event). There is, however, considerable potential to exploit this anomaly, and to use dust detection algorithms to obtain information about dust mineralogy.     

Application of a regionalized knowledge-based model for classifying the impacts of nitrogen,sulfur, and organic acids on lakewater chemistry

To maintain healthy ecosystems, it is increasingly imperative that federal land managers be prepared to monitor and assess levels of atmospheric pollutants and ecological effects in national parks, wildlife refuges, and wilderness areas. Atmospheric deposition of sulfur and/or nitrogen has the potential to damage sensitive terrestrial, and especially aquatic, ecosystems and can affect the survival of in-lake and in-stream biota. Federal land managers have a need to assess, at the individual park or wilderness area level, whether surface water resources are sensitive to air pollution degradation and the extent to which they have been impacted by atmospheric deposition of sulfur or nitrogen or influenced by other complicating factors. The latter can include geologic sources of sulfur, natural organic acidity, and the influence of disturbance and land use on water quality. This paper describes a knowledge-based decision support system (DSS) network for classifying lakewater resources in five acid-sensitive regions of the United States. The DSS allows federal land managers to conduct a preliminary assessment of the status of individual lakes prior to consulting an acid–base chemistry expert. The DSS accurately portrays the decision structure and assessment outcomes of domain experts while capturing interregional differences in acidification sensitivity and historic acid deposition loadings. It is internally consistent and robust with respect to missing water chemistry input data.     

Urban growth and change analysis using remote sensing and spatial metrics from 1975 to 2003 for Hanoi,Vietnam

Since the beginning of the ‘Doi Moi’ policy in 1986, Hanoi has witnessed significant changes in its urban areas. Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) scenes were used to identify built-up areas in Hanoi, and spatial metrics were used to characterize urban change patterns from 1975 to 2003. Firstly, a spatial metric called the ‘percentage of like adjacency’ was used to discern urban growth patterns, which were classified into three sub-patterns: expansion growth, infill growth and outlying growth. Secondly, the driving force underlying the urbanization of the city for the 1975–1984, 1984–1992, 1992–2001, 2001–2003 periods was investigated using a spatial metric analysis programme (FRAGSTATS). The expansion of urban areas along major transportation routes in the latter 1980s was identified as the main form of urbanization in Hanoi. This paper shows the potential application of spatial metrics as secondary sources of information for supporting remotely sensed data and their use to characterize urban growth patterns.     

Characterizing urban sprawl using multi-stage remote sensing images and landscape metrics

This study intends to explore the spatial analytical methods to identify both general trends and more subtle patterns of urban land changes. Landsat imagery of metropolitan Kansas City, USA was used to generate time series of land cover data over the past three decades. Based on remotely sensed land cover data, landscape metrics were calculated. Both the remotely sensed data and landscape metrics were used to characterize long-term trends and patterns of urban sprawl. Land cover change analyses at the metropolitan, county, and city levels reveal that over the past three decades the significant increase of built-up land in the study area was mainly at the expense of non-forest vegetation cover. The spatial and temporal heterogeneity of the land cover changes allowed the identification of fast and slow sprawling areas. The landscape metrics were analyzed across jurisdictional levels to understand the effects of the built-up expansion on the forestland and non-forest vegetation cover. The results of the analysis suggest that at the metropolitan level both the areas of non-forest vegetation and the forestland became more fragmented due to development while large forest patches were less affected. Metrics statistics show that this landscape effect occurred moderately at the county level, while it could be only weakly identified at the city level, suggesting a scale effect that the landscape response of urbanization can be better revealed within larger spatial units (e.g., a metropolitan area or a county as compared to a city). The interpretation of the built-up patch density metrics helped identify different stages of urbanization in two major urban sprawl directions of the metropolitan area. Land consumption indices (LCI) were devised to relate the remotely sensed built-up growth to changes in housing and commercial constructions as major driving factors, providing an effective measure to compare and characterize urban sprawl across jurisdictional boundaries and time periods.     

Comparison of pixel‐based and object‐oriented image classification approaches—a case study in a coal fire area,Wuda, Inner Mongolia,China

Pixel‐based and object‐oriented classifications were tested for land‐cover mapping in a coal fire area. In pixel‐based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object‐oriented classification, a region‐growing multi‐resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land‐cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object‐oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel‐based classification by 36.77%, and the user’s and producer’s accuracy of almost all the classes were also improved. In particular, the accuracy of (potential) surface coal fire areas mapping showed a marked increase. The potential surface coal fire areas were defined as areas covered by coal piles and coal wastes (dust), which are prone to be on fire, and in this context, indicated by the two land‐cover types ‘coal’ and ‘coal dust’. Taking into account the same test sites utilized, McNemar’s test was used to evaluate the statistical significance of the difference between the two methods. The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object‐oriented image analysis approach gave a much higher accuracy than that obtained using the pixel‐based approach..     

Quantifying the impacts of landscape heterogeneity and model resolution on dust emissions in the Arabian Peninsula

This study evaluates the spatiotemporal variability of dust emission in the Arabian Peninsula and quantifies the emission sensitivity to the land-cover heterogeneity by using the Community Land Model version 4 (CLM4³) at three different spatial resolutions. The land-cover heterogeneity is represented by the CLM4-default plant function types (PFTs) and the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover types, respectively, at different grids. We area-average surface vegetation data and use the default nearest neighbor method to interpolate meteorological variables. We find that using MODIS data leads to a slightly higher coverage of vegetated land than the default PFT data; the former also gives more dust emission than the latter at 25- and 50-km grids as the default PFT data have more gridcells favoring less dust emission. The research highlights the importance of using proper data-processing methods or dust emission thresholds to preserve the dust emission accuracy in land models.     

Landsat continuity: Issues and opportunities for land cover monitoring

Initiated in 1972, the Landsat program has provided a continuous record of earth observation for 35 years. The assemblage of Landsat spatial, spectral, and temporal resolutions, over a reasonably sized image extent, results in imagery that can be processed to represent land cover over large areas with an amount of spatial detail that is absolutely unique and indispensable for monitoring, management, and scientific activities. Recent technical problems with the two existing Landsat satellites, and delays in the development and launch of a successor, increase the likelihood that a gap in Landsat continuity may occur. In this communication, we identify the key features of the Landsat program that have resulted in the extensive use of Landsat data for large area land cover mapping and monitoring. We then augment this list of key features by examining the data needs of existing large area land cover monitoring programs. Subsequently, we use this list as a basis for reviewing the current constellation of earth observation satellites to identify potential alternative data sources for large area land cover applications. Notions of a virtual constellation of satellites to meet large area land cover mapping and monitoring needs are also presented. Finally, research priorities that would facilitate the integration of these alternative data sources into existing large area land cover monitoring programs are identified. Continuity of the Landsat program and the measurements provided are critical for scientific, environmental, economic, and social purposes. It is difficult to overstate the importance of Landsat; there are no other systems in orbit, or planned for launch in the short-term, that can duplicate or approach replication, of the measurements and information conferred by Landsat. While technical and political options are being pursued, there is no satellite image data stream poised to enter the National Satellite Land Remote Sensing Data Archive should system failures occur to Landsat-5 and -7.     

Cropping pattern of Uttar Pradesh using IRS-P6 (AWiFS) data

The cropping pattern (rotation) of a region depends on the soil, water availability, economic conditions and climatic factors. Remote sensing is one of the effective tools that can provide precise and up-to-date information on the performance of agricultural systems. Four seasons data from the Indian Remote Sensing Satellite (IRS)-P6 Advanced Wide Field Sensor (AWiFS) were used for the generation of the cropping pattern of Uttar Pradesh by geographic information system (GIS)-aided integration of digitally classified crop and land use inventories of the kharif, rabi and zaid crop seasons. Twelve different cropping patterns were delineated and mapped in the Indo-Gangetic plain of Uttar Pradesh. The forests covered about 6.32% of the total geographical area. The net cropped area was 20 282 159.46 ha (84.18% of the total geographical area) and the non-agricultural area observed was 3 437 376.00 ha (14.26% of the total geographical area). Rice was the single most dominant crop of the state, occupying about 32.94% of the total geographical area during the kharif season. Maize/jowar was the second major cereal crop, accounting for 13.77% of the total geographical area of the state. The major crops grown during the rabi season were wheat and pulses/oilseed, covering areas of 7 979 267.71 ha (33.12%) and 5 974 742.58 ha (24.80%), respectively. Rice-wheat, sugarcane and rice-pulses were the major cropping patterns, occupying about 3 958 739.85 ha (16.43%), 3 609 939.74 ha (14.98%) and 2 511 298.24 ha (10.42%), respectively. The areas under pulses/oilseed were significantly higher in the rabi season. Sugarcane-wheat and pulses shared an almost equal area (6.49%). The maize/jowar-wheat cropping pattern occupied 6.14% of the total geographical area of the state. Single cropping patterns (i.e. rice-fallow, fallow-pulses, fallow-wheat, maize-fallow and sugarcane-fallow) were minor, occupying 6.08, 2.94, 4.06, 2.69 and 2.51%, respectively. Waste land, including gulley, salt-affected, waterlogged and rock land, accounted for 3.80% of the total geographical area. The results of this study indicate that temporal IRS-P6 (AWiFS) data are very useful for studying spatial cropping patterns. The values of the Multiple Cropping Index (MCI) and the Cultivated Land Utilization Index (CLUI) show that the study area has a high cropping intensity.     

The use of temporal metrics for land cover change detection at coarse spatial scales

Successful land cover change analysis requires selection of an appropriate set of variables for measuring and characterizing change. Coarse spatial resolution satellite sensors offer the advantage of frequent coverage of large areas and this facilitates the monitoring of surface processes. Fine spatial resolution satellite sensors provide reliable land cover information on a local basis. This work examines the ability of several temporal change metrics to detect land cover change in sub-Saharan Africa using remote sensing data collected at a coarse spatial resolution over 16 test sites for which fine spatial resolution data are available. We model change in the fine-resolution data as a function of the coarse spatial resolution metrics without regard to the type of change. Results indicate that coarse spatial resolution temporal metrics (i) relate in a statistically significant way to aggregate changes in land cover, (ii) relate more strongly to fine spatial resolution change metrics when including a measure of surface temperature instead of a vegetation index alone, and (iii) are most effective as land cover change indicators when various metrics are combined in multivariate models.     

Land cover mapping of large areas from satellites: Status and research priorities

Although land cover mapping is one of the earliest applications of remote sensing technology, routine mapping over large areas has only relatively recently come under consideration. This change has resulted from new information requirements as well as from new developments in remote sensing science and technology. In the near future, new data types will become available that will enable marked progress to be made in land cover mapping over large areas at a range of spatial resolutions. This paper is concerned with mapping strategies based on 'coarse' and 'fine' resolution satellite data as well as their combinations. The status of land cover mapping is discussed in relation to requirements, data sources and analysis methodologies - including pixel or scene compositing, radiometric corrections, classification and accuracy assessment. The overview sets the stage for identifying research priorities in data pre-processing and classification in relation to forthcoming improvements in data sources as well as new requirements for land cover information.     

Remote-sensing image-based analysis of the patterns of urban heat islands in rapidly urbanizing Jinan,China

According to the UN Population Reference Bureau, 1.4 billion more people will have settled in urban areas by 2030. One of the key environmental effects of rapid urbanization is the urban heat island (UHI) effect. Understanding the mechanism of surface UHIs associated with land-use/land-cover (LULC) change patterns is important for improving the ecology and sustainability of cities. In this article, time series Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+) data were used to extract LULC data and land surface temperature (LST) data for the city of Jinan, China, from 1987 to 2011, a period during which the city experienced rapid urbanization. With the aid of a geographical information system (GIS) and remote sensing (RS) approach, the changes in this urban area’s LULC were explored, and the impact of these changes on the spatiotemporal patterns and underlying driving forces of the surface UHI effect were further quantitatively characterized. The results show that significant changes in land use and land cover occurred over the study period, with loss of farmland, forest, and shrub vegetation to urban use, leading to spatial growth of impervious surfaces. Consequently, the land surface characteristics and spatiotemporal patterns of the UHI have changed drastically. According to the seasonal and inter-annual variations in intensity of UHIs, mean differences in UHI intensity between city centre, peri-urban, and nearby rural areas were stronger during summer and spring and weaker during winter and autumn. Spatially, there were significant LST gradients from the city centre to surrounding rural areas. The city centre exhibited higher LSTs and remarkable variation in LSTs, while the surrounding rural areas exhibited lower LSTs and lower variation in LSTs. Moreover, the analysis of LSTs and indices showed that great differences of temperature even existed in a LULC type except for variations between different LULC types. In addition, a local-level analysis revealed that the intensity of the UHI effect is proportional to the size of the urban area, the population density, and the frequent occurrence of certain activities.     

A GIS tool for historical instability processes data entry: An approach to hazard management in two Italian Alpine river basins

Analysis of instability processes requires historical data over a range of temporal and spatial scales. While historical data offer a wealth of information about when, where and how a flood or a landslide happened or may recur, managing the data remains problematic. Before the data can be entered into historical and geographical databases, they need to be extracted from a vast variety of paper documents and transformed into a standard format. To do this, we developed a Geographical Information System (GIS)-based tool that permits easy data entry for comparing information on different temporal and spatial scales. The GIS tool was combined with a methodology for spatial data analysis to identify main hazardous areas. The historical and geographical databases were then queried with this tool to obtain the frequency of catastrophic events and their spatial recurrence. The GIS tool allowed accurate and rapid data management for establishing a connection between textual and spatial information for new data generation.This paper illustrates a methodology that utilizes the GIS tool for analyzing instability processes in two Italian river basins in the Western Alps.     

The identification of tropical deforestation fronts at broad spatial scales

Land cover changes are measured at increasingly broader spatial scales. Yet understanding and modelling change processes with a satisfactory accuracy require fine scale observations. The objective of this study is to design and test a methodology to detect tropical deforestation 'hot spots' at broad spatial scales. This methodology is designed to allow concentration of the data collection and modelling of change processes only on the areas characterized by a high rate of change. The procedure is based on a hierarchical set of decision rules with selection criteria being first measured on an exhaustive basis at a global scale and then only for the areas retained in the first sorting, with increasingly selective constraints. The first set of criteria, i.e., proportions in key land cover, landscape fragmentation, and fire activities, were derived from subcontinental scale remote sensing data. Socio-economic variables were also measured at that scale. These different variables were combined over West Africa and the northern boundary of the Central African evergreen forest to identify potential tropical deforestation fronts. Different models were used to generate maps of deforestation hot spots. These were validated with data from the literature.     

Evaluating Hyperion capability for land cover mapping in a fragmented ecosystem: Pollino National Park, Italy

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy).Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution).Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification).The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor.     

Exploiting temporal information in parcel data to refine small area population estimates

Temporal analysis of small-area demographic data commonly relies on areal interpolation methods to create temporally consistent and compatible areal units. In this study, cadastral (parcel) data are used to identify residential land and to dasymetrically refine census tracts, with the goal of achieving more accurate small-area estimates. The built date recorded for residential parcel units is used to create residential land layers for two different time points used in the areal interpolation. Three different areal interpolation methods are employed with and without dasymetric refinement, including areal weighting (AW), target density weighting (TDW) and pycnophylactic modeling (PM). The methods interpolate tract-level population counts in Hennepin County, Minnesota, in 2000 into census tract boundaries from the year 2010. The mean absolute error, median absolute error, root mean square error and the 90th percentile of absolute error are calculated for each of the methods, and spatial variation in the interpolations are displayed in maps. Parcel-based refinements are also compared with refinements using the National Land Cover Dataset (NLCD).Results show that spatial refinement using residential parcels has the potential to improve the accuracy of areal interpolation for temporal analysis. Parcel-refined TDW out-performs the other tested methods, as well as the NLCD-refined TDW in this example. Parcel data identify residential land more reliably in rural areas. However, parcel units can have very large extents potentially biasing residential area delineation and population counts. Parcel-based refinement has the potential to further advance demographic change analysis over long time periods and large areas where the built date attribute is included in the dataset.     

Localization of aerosol sources in East-European region by back-trajectory statistics

A concentration-weighted trajectory method for aerosol source localization based on joint statistical analysis of aerosol column volume concentrations and back-trajectory data was used to estimate the spatial distribution of aerosol sources in the East-European region. The aerosol column volume concentration data measured at five AERONET network sites, Belsk, Minsk, Kyiv, Moldova/Kishinev, and Sevastopol, were used. The geographical areas responsible for increased aerosol content at the monitoring sites were mapped separately for coarse-mode and fine-mode aerosol fractions. The investigated area is located between 42° and 62° N in latitude and between 12° and 50° E in longitude.

It was shown that the northeastern territories (in relation to the monitoring stations) give a small contribution to the coarse-mode aerosol content. The events of increased coarse-mode aerosol concentration have been caused by sources in the southeastern regions. On average, the air masses with a large content of coarse-mode aerosol particles were delivered to all stations from regions around Donetsk, Rostov-on-Don, and Kharkiv cities. The fine-mode aerosol fraction originated from areas of Tambov, Voronezh, and Kharkiv cities. The calculated aerosol source regions partly correspond to European Monitoring and Evaluation Programme data for eastern Europe. The cause of difference between calculated regions responsible for increased aerosol content at the monitoring sites and the sources of particle emission according to European Monitoring and Evaluation Programme data are discussed.