Geospatial variability in the egg raft distribution and abundance of Culex pipiens and Culex restuans in Urbana‐Champaign,Illinois

The aim of this study was to determine whether areas of high Culex pipiens and Culex restuans abundance in an urban environment, based on fixed oviposition surveillance sites, corresponded to remotely sensed data. A land use land cover (LULC) classification, based on Landsat‐7 Enhanced Thematic Mapper Plus (ETM+) data acquired in July 2003 and Landsat‐5 TM data from July 1991, was compared to the abundance of Culex egg rafts in Urbana‐Champaign, East‐Central Illinois. We performed a maximum likelihood, unsupervised classification and generated three land cover classifications: urban, non‐urban and water. Ground coordinates of the Culex surveillance sites were overlaid onto LULC maps with 10 m² grid cells. The grid was stratified based on levels of drainage: 0 = poorly drained and 1 = well‐drained. Total LULC change from 1991 to 2003 in the Urbana‐Champaign study site was relatively low, at 12.1%. The most frequent LULC category was maintained urban. The egg raft rate was significantly higher in urban LULC habitats. Remote stratification of the urban land cover using QuickBird visible and near‐infrared (NIR) data revealed that high‐density canopy coverage was most frequently associated with high Culex abundance in oviposition traps. We demonstrate that optical remote sensing can identify land use parameters associated with high Culex oviposition.     

The dynamics of urban expansion and land use/land cover changes using remote sensing and spatial metrics: the case of Mekelle City of northern Ethiopia

Information on the rate and pattern of urban expansion is required by urban planners to devise proper urban planning and management policy directions. This study evaluated the dynamics and spatial pattern of Mekelle City’s expansion in the past three decades (1984–2014). Multi-temporal Landsat images and Maximum Likelihood Classifier were used to produce decadal land use/land cover (LULC) maps. Changes in LULC and spatial pattern of urban expansion were analysed by post-classification change detection and spatial metrics, respectively. The results showed that in the periods 1984–1994, 1994–2004, and 2004–2014, the built-up area increased annually by 10%, 9%, and 8%, respectively; with an average annual increment of 19% (100 ha year^?1), from 531 ha in 1984 to 3524 ha in 2014. Between 1984 and 2014, about 88% of the gain in built-up area was from conversion of agricultural lands, which decreased by 39%. Extension of existing urban areas was the dominant growth type, which accounted for 54%, 75%, and 81% of the total new development during 1984–1994, 1994–2004, and 2004–2014, respectively. The spatial metrics analyses revealed urban sprawl, with increased heterogeneity and gradual dispersion in the outskirts of the city. The per capita land consumption rate (ha per person) increased from 0.009 in 1984 to 0.014 in 2014, indicating low density urban growth. Based on the prediction result, the current (2014) built-up area will double by 2035, and this is likely to have multiple socioeconomic and environmental consequences unless sustainable urban planning and development policies are devised.     

Evaluation of segment‐based gap‐filled Landsat ETM+ SLC‐off satellite data for land cover classification in southern Saskatchewan,Canada

This paper describes single‐date and multi‐date land‐cover classification accuracy results using segment‐based, gap‐filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single‐date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap‐filled Landsat data. Area estimates for the mid‐summer images differed, on average, from 0.6% to 1.9% for a four‐class and eight‐class classification, respectively. A multi‐date land‐cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%.     

The use of multi‐temporal Landsat images for the change detection of the coastal zone near Hurghada,Egypt

A Landsat 5 Thematic Mapper (TM) image of 1987 and a Landsat 7 Enhanced Thematic Mapper Plus (ETM+) image of 2000 were used to examine changes in land use/land cover (LULC) around Hurghada, Egypt, and changes in the composition of coral reefs offshore. Prior to coral reef bottom‐type classification, the radiance values were transformed to depth‐invariant bottom indices to reduce the effect of the water column. Subsequently, a multi‐component change detection procedure was applied to these indices to define changes. Preliminary results showed significant changes in LULC during the period 1987–2000 as well as changes in coral reef composition. Direct impacts along the coastline were clearly shown, but it was more difficult to link offshore changes in coral reef composition to indirect impacts of the changing LULC. Further research is needed to explore the effects of the different image‐processing steps, and to discover possible links between indirect impacts of LULC changes and changes in the coral reef composition.     

Monitoring land-use change-associated land development using multitemporal Landsat data and geoinformatics in Kom Ombo area,South Egypt

Due to the progressive increase in population, sustainable development of desert land in Egypt has become a strategic priority in order to meet the increasing demands of a growing population for food and housing. Such obligations require efficient compilation of accurate land-cover information in addition to detailed analysis of archival land-use changes over an extended time span. In this study, we applied a methodology for mapping land cover and monitoring change in patterns related to agricultural development and urban expansion in the desert of the Kom Ombo area. We utilized the available records of multitemporal Landsat Thematic Mapper and Enhanced Thematic Mapper Plus images to produce three land-use/land-cover maps for 1988, 1999 and 2008.

Post-classification change detection analysis shows that agricultural development increased by 39.2% through the study period with an average annual rate of land development of 8.7 km² year^?1. We report a total increase in urbanization over the selected time span of approximately 28.0 km² with most of this urban growth concentrated to the east of the Nile and occurring through encroachment on the former old cultivated lands. The archival record of the length of irrigation canals showed that their estimated length was 341.5, 461.8 and 580.1 km in the years 1988, 1999 and 2008, respectively, with a 70% increase in canal length from 1988 to 2008. Our results not only accurately quantified the land-cover changes but also delineated their spatial patterns, showing the efficiency of Landsat data in evaluating landscape dynamics over a particular time span. Such information is critical in making effective policies for efficient and sustainable natural resource management.     

Land‐cover classification in the Brazilian Amazon with the integration of Landsat ETM+ and Radarsat data

Land‐cover classification with remotely sensed data in moist tropical regions is a challenge due to the complex biophysical conditions. This paper explores techniques to improve land‐cover classification accuracy through a comparative analysis of different combinations of spectral signatures and textures from Landsat Enhanced Thematic Mapper Plus (ETM+) and Radarsat data. A wavelet‐merging technique was used to integrate Landsat ETM+ multispectral and panchromatic data or Radarsat data. Grey‐level co‐occurrence matrix (GLCM) textures based on Landsat ETM+ panchromatic or Radarsat data and different sizes of moving windows were examined. A maximum‐likelihood classifier was used to implement image classification for different combinations. This research indicates the important role of textures in improving land‐cover classification accuracies in Amazonian environments. The incorporation of data fusion and textures increases classification accuracy by approximately 5.8–6.9% compared to Landsat ETM+ data, but data fusion of Landsat ETM+ multispectral and panchromatic data or Radarsat data cannot effectively improve land‐cover classification accuracies.     

Historical and future land-cover changes in the Upper Ganges basin of India

The green revolution represents one of the greatest environmental changes in India over the last century. The Upper Ganges (UG) basin is experiencing rapid rates of change of land cover and irrigation practices. In this study, we investigated the historical rate of change and created future scenario projections by means of 30 m-resolution multi-temporal Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus data of the UG basin. Post-classification change analysis methods were applied to Landsat images in order to detect and quantify land-cover changes in the UG basin. Subsequently, Markov chain analysis was applied to project future scenarios of land-cover change. Fifteen different scenarios were generated based on historic land-cover change. These scenarios diverged in terms of future projections, highlighting the dynamic nature of the changes. This study has shown that between the years 1984 and 2010 the main land-cover change trends are conversion from shrubs to forest (+4.7%), urbanization (+5.8%), agricultural expansion (+1.3%), and loss of barren land (–9.5%). The land-cover change patterns in the UG basin were mapped and quantified, showing the capability of Landsat data in providing accurate land-cover maps. These results, in combination with those derived from the Markov model, provide the necessary evidence base to support regional land-use planning and develop future-proof water resource management strategies.     

Multi-temporal and multi-source cartography of the glacial cover of Nevado Coropuna (Arequipa,Peru) between 1955 and 2003

This study reports on the glacial cover evolution of the Nevado Coropuna between 1955 and 2003, based on Peruvian topographic maps and satellite images taken from the Landsat 2 and 5 multispectral scanner (MSS), Landsat 5 Thematic Mapper (TM) and Landsat 7 (ETM+). The normalized difference snow index has been applied to these images to estimate the glacierized area of Coropuna. The satellite-based results show that the glacier area was 105 ± 16 km² in 1975 (Landsat 2 MSS), which then reduced to 96 ± 15 km² in 1985 (Landsat 5 MSS), 64 ± 8 km² in 1996 (Landsat 5 TM) and 56 ± 6 km² in 2003 (Landsat 5 TM). Altogether, between 1955 and 2003, Coropuna lost 66 km² of its glacial cover, which represents a mean retreat of 1.4 km² year^?1, that is, a loss of 54% in 48 years (11% loss per decade). The maximum rate of retreat occurred during the 1980s and 1990s, a phenomenon probably linked with the pluviometric deficit of El Niño events of 1983 and 1992.     

Changes in snow and glacier cover in an arid watershed of the western Kunlun Mountains using multisource remote-sensing data

Snow and glaciers in the mountain watersheds of the Tarim River basin in western China provide the primary water resources to cover the needs of downstream oases. Remote sensing provides a practical approach to monitoring the change in snow and glacier cover in those mountain watersheds. This study investigated the change in snow and glacier cover in one such mountain watershed using multisource remote-sensing data, including the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat (Multispectral Scanner (MSS), Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+)), Corona, and Google Earth^TM imagery. With 10 years’ daily MODIS snow-cover data from 2002 to 2012, we used two de-cloud methods before calculating daily snow-cover percentage (SCP), annual snow-cover frequency (SCF), and annual minimum snow-cover percentage (AMSCP) for the watershed. Mann–Kendall analysis showed no significant trend in any of those snow-cover characterizations. With a total of 22 Landsat images from 1967 to 2011, we used band ratio and supervised classification methods for snow classification for Landsat TM/ETM+ images and MSS images, respectively. The Landsat snow-cover data were divided into two periods (1976–2002 and 2004–2011). Statistical tests indicated no significant difference in either the variance or mean of SCPs between the two periods. Three glaciers were identified from Landsat images of 1998 and 2011, and their total area increased by 12.6%. In addition, three rock glaciers were also identified on both the Corona image of 1968 and the Google high-resolution image of 2007, and their area increased by 2.5%. Overall, based on multisource remote-sensing data sets, our study found no evidence of significant changes in snow and glacier cover in the watershed.     

Analysis of land use/cover changes and urban expansion of Nairobi city using remote sensing and GIS

We used three Landsat images together with socio‐economic data in a post‐classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Nairobi city. Land use/cover statistics, extracted from Landsat Multi‐spectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM+) images for 1976, 1988 and 2000 respectively, revealed that the built‐up area has expanded by about 47?km². The road network has influenced the spatial patterns and structure of urban development, so that the expansion of the built‐up areas has assumed an accretive as well as linear growth along the major roads. The urban expansion has been accompanied by loss of forests and urban sprawl. Integration of demographic and socio‐economic data with land use/cover change revealed that economic growth and proximity to transportation routes have been the major factors promoting urban expansion. Topography, geology and soils were also analysed as possible factors influencing expansion. The integration of remote sensing and Geographical Information System (GIS) was found to be effective in monitoring land use/cover changes and providing valuable information necessary for planning and research. A better understanding of the spatial and temporal dynamics of the city's growth, provided by this study, forms a basis for better planning and effective spatial organization of urban activities for future development of Nairobi city.     

One century of land use changes in the metropolitan area of Milan (Italy)

Abstract

This paper presents the possible contribution of multi-temporal Landsat Thematic Mapper (TM) data to the assessment of land-use modifications in the most important portion of the metropolitan area of Milan where rapid transformations, starting from urban areas and then gradually extending to rural areas, took place. The study area corresponds to the so-called ‘Great Milan’ which includes a protected area, the ‘South Milan Agricultural Park’, where a widespread conflict between agricultural and-urban land use has arisen. Park realisation will contribute improving agricultural activities and creating a belt for environment protection around the city. Digital thematic maps, digitizing Istituto Geografico Militare dTtalia cartography of 1888–90 and 1945–50, were extracted. Normalized Difference Vegetation Indices (NDVI) were produced from three Landsat-5 Thematic Mapper images of 1984, January, June and August, and a Multi-temporal Colour NDVI Composition (MCNC) output was produced. Maximum Likelihood Classification for land use mapping was applied both on MCNC data jointly with band 5 of June, and on 12 April 1990 Landsat TM image. Classification accuracy was assessed and results summarized. An historical analysis of land-use changes from XIX century up to today was performed by comparison of different surface classes from historical (1888–90 and 1945–50) and satellite (1984 and 1990) thematic maps. Results confirm the useful contribution of satellite remote sensing studying land-use/land-cover modifications in areas affected by phenomena of agriculture rapid transformation and residential or industrial development.     

Classification accuracy and trend assessments of land cover- land use changes from principal components of land satellite images

The paper evaluated the accuracy of classifying Land Cover-Land Use (LCLU) types and assessed the trends of their changes from Principal Components (PC) of Land satellite (Landsat) images. The accuracy of the image classification of LCLU was evaluated using the confusion matrices and assessed with cross-referencing of samples of LCLU types interpreted and classified from System Pour l’Observation de la Terre (SPOT) images and topographical map. LCLU changes were detected, quantified, and statistically analysed. The interpretation error of the composite image of Landsat Enhanced Thematic Mapper Plus (Landsat ETM⁺) (2006) was high compared with that from the PC image of Landsat ETM⁺ (2006). From 1986 – 2006 the area covered by settlements increased by 0.8% (230,380.00 km²), agricultural land decreased by 7.5% (1009.40 km²), vegetation cover decreased by 0.9% (114.00 km²) while waterbody increased by 0.2% (25.91 km²). Also, from 1986 – 2006 the average annual rates of change in the area of settlements was 6.7%. Agricultural land and bare land showed fluctuations of change rates from 6.7% and 5.0% annually in 1986 and 2006 respectively. The quantitative evidences of LCLU changes revealed the growth of settlements. The conversions of land from agriculture to urban land represent the most significant land cover changes. The rate of change was as high as 4.8% for settlements while agricultural lands were converted at 5.0% per year. The Principal Component Analysis (PCA) of the Landsat images and supervised classification method used made it possible to classify and determine the area of LCLU classes from the set of Landsat images without prior depiction and delimitation of individual LCLU type. It permitted the measurement of area of each LCLU class at a high accuracy level and kept the level of error relatively constant. The PCA analysis in this study affirms the previous research findings. Future research works should focus on the use of remotely sensed images with high temporal and spatial resolutions such as Quick Bird and SPOT 6 to develop effective and accurate LCLU change mapping and monitoring at the local scale.

The PCA technique has been used quite widely to study changes in land cover and land use in many ‘developed’ countries but much still needs to be done in developing and undeveloped countries where land cover and land use change is poorly mapped and knowledge of such changes is very important for planning development of the country.     

Optimizing unsupervised classifications of remotely sensed imagery with a data-assisted labeling approach

The quality of remotely sensed land use and land cover (LULC) maps is affected by the accuracy of image data classifications. Various efforts have been made in advancing supervised or unsupervised classification methods to increase the repeatability and accuracy of LULC mapping. This study incorporates a data-assisted labeling approach (DALA) into the unsupervised classification of remotely sensed imagery. The DALA-unsupervised classification algorithm consists of three steps: (1) creation of N spectral-class maps using Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA); (2) development of LULC maps with assistance of reference data; and (3) accuracy assessments of all the LULC maps using independent reference data and selection of one LULC map with the highest accuracy. Classification experiments with a composite image of a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image suggest that DALA was effective in making unsupervised classification process more objective, automatic, and accurate. A comparison between the DALA-unsupervised classifications and some conventional classifications suggests that the DALA-unsupervised classification algorithm yielded better classification accuracies compared to these conventional approaches. Such a simple, effective approach has not been systematically examined before but has great potential for many applications in the geosciences.     

A multitemporal multiple density slice method for wetland mapping across the state of Queensland,Australia

The Australian and Queensland Governments are developing comprehensive wetland maps at a scale of 1:100 000 for the state of Queensland, Australia. Spectral classifications for water features were developed using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) imagery acquired over a 16‐year period. A multiple density slice/supervised classification method, the Standing Water Body (SWB) method, was developed to separate the main spectral and land cover elements of wetlands (vegetation, water and shadow cast by vegetation and topographic relief) and used rules to combine spectral classes to provide multitemporal (MT) information on wetland extent and water inundation regimes for features of at least 0.25 ha. Accuracy assessment in four trial areas compared the SWB method to the Normalized Difference Water Index (NDWI). The assessments of classified features were scale adjusted to maximum class‐area proportions to enable statistical comparison and to account for the large area of non‐wetland in the four trial areas. The average overall accuracy for wetland classification was 95.9% for the SWB method and 95.3% for the NDWI. The average unadjusted KHAT statistic for the wetland classification was 0.84 and 0.90 for the SWB and NDWI, respectively. The scale‐adjusted KHAT statistic was much lower for both methods, averaging 0.45 for the SWB and 0.39 for the NDWI, mainly due to large omission errors. A method for the implementation of the SWB method for systematic and repeatable mapping of wetland areas is presented. The study recommends enhancement of the SWB classification through the inclusion of the NDWI classification and ancillary data such as vegetation mapping and drainage networks.     

Detecting war‐induced abandoned agricultural land in northeast Bosnia using multispectral,multitemporal Landsat TM imagery

The use of satellite technology by military planners has a relatively long history as a tool of warfare, but little research has used satellite technology to study the effects of war. This research addresses this gap by applying satellite remote sensing imagery to study the effects of war on land‐use/land‐cover change in northeast Bosnia. Although the most severe war impacts are visible at local scales (e.g. destroyed buildings), this study focuses on impacts to agricultural land. Four change detection methods were evaluated for their effectiveness in detecting abandoned agricultural land using Landsat Thematic Mapper (TM) data from before, during and after the 1992–95 war. Ground reference data were collected in May 2006 at survey sites selected using a stratified random sampling approach based on the derived map of abandoned agricultural land. Fine‐resolution Quickbird imagery was also used to verify the accuracy of the classification. Results from these analyses show that a supervised classification of the Landsat TM data identified abandoned agricultural land with an overall accuracy of 82.5%. The careful use of freely available Quickbird imagery, both as training data for the supervised classifier and as supplementary ground reference data, suggests that these methods are applicable to other civil wars too dangerous for researchers' fieldwork.     

Detection of deforestation and land conversion in Rondônia,Brazil using change detection techniques

Fires associated with tropical deforestation, land conversion and land use greatly contribute to emissions as well as the depletion of carbon and nutrient pools. The objective of this research was to compare change detection techniques for identifying deforestation and cattle pasture formation during a period of early colonization and agricultural expansion in the vicinity of Jamari, Rondônia. Multi-date Landsat Thematic Mapper (TM) data between 1984 and 1992 were examined in a 94?370?ha area of active deforestation to map land cover change. The tasselled cap (TC) transformation was used to enhance the contrast between forest, cleared areas and regrowth. TC images were stacked into a composite multi-date TC and used in a principal components (PC) transformation to identify change components. In addition, consecutive TC image pairs were differenced and stacked into a composite multi-date differenced image. A maximum likelihood classification of each image composite was compared for identification of land cover change. The multi-date TC composite classification had the best accuracy of 0.78 (kappa). By 1984, only 5% of the study area had been cleared, but by 1992, 11% of the area had been deforested, primarily for pasture, and 7% lost due to hydroelectric dam flooding. Finally, discrimination of pasture versus cultivation was improved due to the ability to detect land under sustained clearing opposed to land exhibiting regrowth with infrequent clearing.     

Dynamic monitoring of land-use/land-cover change and urban expansion in Shenzhen using Landsat imagery from 1988 to 2015

To analyse changes in human settlement in Shenzhen City during the past three decades, changes in land use/land cover (LULC) and urban expansion were investigated based on multi-temporal Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images. Using C4.5-based AdaBoost, a hierarchical classification method was developed to extract specific classes with high accuracy by combining a specific number of base-classifier decisions. Along with a classification post-processing approach, the classification accuracy was greatly improved. The statistical analysis of LULC changes from 1988 to 2015 shows that built-up areas have increased 6.4-fold, whereas cultivated land and forest continually decreased because of rapid urbanization. Urban expansion driven by human activities has considerably affected the landscape change of Shenzhen. The urban-expansion pattern of Shenzhen is a mixture of three urban-expansion patterns. Among these patterns, traffic-driven urban expansion has been the main form of urban expansion for some time, especially in the Non-Special Economic Zone. In addition, by taking 8 to 10 year periods as time intervals, urban expansion in Shenzhen was divided into three stages: the early-age urbanization stage (1988–1996), the rapid urbanization stage (1996–2005), and the intensive urbanization stage (2005–2015). For different stages, the state of urban expansion is different. In long-term LULC dynamic monitoring and urban-expansion detection, it was possible to obtain 11 LULC maps, which took 2 to 4 years as a research interval. With regard to the short research periods, LULC changes and urban expansion were investigated in detail.     

Mapping and monitoring riparian vegetation distribution,structure and composition with regression tree models and post-classification change metrics

Riparian systems have become increasingly susceptible to both natural and human disturbances as cumulative pressures from changing land use and climate alter the hydrological regimes. This article introduces a landscape dynamics monitoring protocol that incorporates riparian structural classes into the land-cover classification scheme and examines riparian change within the context of surrounding land-cover change. We tested whether Landsat Thematic Mapper (TM) imagery could be used to document a riparian tree die-off through the classification of multi-date Landsat images using classification and regression tree (CART) models trained with physiognomic vegetation data. We developed a post-classification change map and used patch metrics to examine the magnitude and trajectories of riparian class change relative to mapped disturbance parameters. Results show that catchments where riparian change occurred can be identified from land-cover change maps; however, the main change resulting from the die-off disturbance was compositional rather than structural, making accurate post-classification change detection difficult.     

Mapping land-use and land-cover change along Bolivia's Corredor Bioceánico with CBERS and the Landsat series: 1975–2008

This study uses a combination of Landsat series data (Multispectral Scanner or MSS, Thematic Mapper or TM and Enhanced Thematic Mapper or ETM+) to map land-use and land-cover change (LULCC) from 1975 to 2001. It extends the land change record to 2008 using Chinese–Brazil Earth Resources Satellite (CBERS)-2 and CBERS-2B data on a multi-scene level. It also establishes a methodology to correct for systematic distortion inherent in CBERS imagery without the loss of information present in Landsat 7 ETM+ imagery post-2003. Image analysis focuses on a 63 000 km² strip of land along a main highway and railroad in southeastern Bolivia named the Corredor Bioceánico. This strip of land is one of the most important agriculturally driven deforestation hotspots in Latin America. It is also located in one of the most poorly understood forest biomes in the world in terms of LULCC – Southern Hemisphere seasonally dry tropical forests – which have very high conservation values globally. Over the 33-year study period, approximately 12 000 km² of forest was lost among the three sub-regions – which is an area nearly the size of Connecticut. Evidence suggests that agriculture-driven deforestation is pushing into sensitive areas threatening globally important ecosystems such as those in the Chaco, Chiquitano and Pantanal as well as noteworthy protected areas. The results also show that imagery of CBERS-2 and CBERS-2B can help to fill the imagery gap created by Landsat ETM's Scan Line Corrector (SLC) failure in 2003. They can help to extend the land change record forward in time.     

Use of cartographic data and Landsat TM images to determine land use change in the vicinity of Mexico City

Evaluation of change in land use is important for planning further development in populated areas. Here we attempt to determine the growth of urban areas in the vicinity of Mexico City, using a 1993 Landsat Thematic Mapper (TM) image and cartographic data contained in maps published by the Instituto Nacional de Estadistica Geografia e Informatica (INEGI 1975, 1983). The area occupied by urban areas in 1975 and 1983 was quantified using raster images generated by scanning the maps. Supervised classification processes were applied to a 1993 Landsat TM image in bands 1, 2, 3, 4, 5 and 7, of the area of Chalco. The image was pre-processed and then processed to enhance the spectral response of the surface materials. The different land cover types that characterise distinct land uses in the study area were identified in the image and an overall classification accuracy of 82% was estimated using aerial photographs from the Chalco area. The resulting evaluation of the land use changes in the Chalco urban area was plotted, and a growth greater than 14% per year was estimated.