Trends of Land Use and Land Cover Change in the Savannah Ecological of the Protected Area Reserve Partielle de Dosso, Niger

Information on the dynamics of savannah is important to a country's plan to overcome the problems of uncontrolled development and environmental hazards. Taking the reserve partielle de Dosso, Niger as the case study area, this paper analyzed the long-term land use land cover change from 2002 to 2022. Satellite images were processed by using Google Earth Engine (GEE). Therefore, four major land cover classes were identified based on spectral characteristics of Land sat, namely, built-up, vegetation, cropland, bare land and water. The result revealed that barren and built-up areas increased at the expense of vegetation and water. From the four major land use land cover the large area is covered by vegetation which comprises about 192963.5 hectares followed by cropland and water consisting of 32506.43 and 1596.4 hectares respectively. The built-up area gained substantial area (most) during the study period. The reduction in some of the land cover/uses underlines the dangerous trend of the pressure poised by population growth and the changing functionality. Land cover change is influenced by a variety of societal factors operating on several spatial and temporal levels. The area estimates and spatial distributions of the LULC classes produced from the current study will assist local authorities, managers, and other stakeholders in decision-making and planning regarding forest land cover and uses.

Land Use and Land Cover Change Detection in the Saudi Arabian Desert Cities of Makkah and Al-Taif Using Satellite Data

Land use/land cover (LULC) changes have become a central issue in current global change and sustainability research. Saudi Arabia has undergone significant change in land use and land cover since the government embarked on a course of intense national development 30 years ago, as a result of huge national oil revenues. This study evaluates LULC change in Makkah and Al-Taif, Saudi Arabia from 1986 to 2013 using Landsat images. Maximum likelihood and object-oriented classification were used to develop LULC maps. The change detection was executed using post-classification comparison and GIS. The results indicated that urban areas have increased over the period by approximately 174% in Makkah and 113% in Al-Taif. Analysis of vegetation cover over the study area showed a variable distribution from year to year due to changing average precipitation in this environment. Object-based classification provided slightly greater accuracy than maximum likelihood classification. Information provided by satellite remote sensing can play an important role in quantifying and understanding the relationship between population growth and LULC changes, which can assist future planning and potential environmental impacts of expanding urban areas.

Change Detection of Land Use and Land Cover over a Period of 20 Years in Papua New Guinea

People have an inherent tenacity to throng coastal regions in pursuit of better living conditions. As such the brisk dynamism of land use/land cover activities in a coastal region becomes obvious. The former keeps changing rapidly due to burgeoning population. A digital change detection analysis is performed with the help of Geographic Information System (GIS) on the Remote Sensing data spanning over last 20 years, complemented by in-situ data and ground truth information. This current research briefly endeavours to find out the nature of change happening in the major three coastal cities of Papua New Guinea (PNG), namely Alotau, capital of Milnebay province;Lae, capital of Morobe province and Port Moresby, capital of Papua New Guinea. Changes in land use and land cover that took place over 20 years have been recorded using Landsat 5 thematic mapper (TM) data of 1992 and Landsat 8 operational land imager (OLI) data. Land use and land cover maps of 1992, and 2013/14, and change detection matrix of 1992-2013/14 are derived. Results show an immensely sprawling urban landscape, evincing about five times growth during 1992 to 2014. At the same time “natural forests” dwindled by 444.96 hectares in Alotau, 6977.25 hectares in Lae and “mangrove” and “grass/shrub land” decreased by 127.78 and 4859.39 hectares respectively around Port Moresby. The above changes owe to ever increasing population pressure, land tenure shift, agriculture and industrial development.

Assessing Spatio-Temporal Land Cover Changes in Dhund River Basin, Eastern Rajasthan (India), Using Multi-Temporal Landsat Data

Land cover is an impression of natural cover on surface of earth such as bare soil, river, grass etc. and utilization of these natural covers for various human needs and purposes by mankind is defined as land use. Land cover identification, delineation and mapping is important for planning activities, resource management and global monitoring studies while baseline mapping and subsequent monitoring is done by application of land use to get timely information about quantity of land that has been used. The present study has been carried out in Dhund river watershed of Jaipur, Rajasthan which covers an area of about 1828 sq∙km. The minimum and maximum elevation of the area is found to be 214 m and 603 m respectively. Land use and land cover changes of three decades from 1991 to 2021 have been interpreted by using remotes sensing and GIS techniques. ArcGIS software (Arc map 10.2), SOI topographic map, Cartosat-1 DEM and satellite data of Landsat 5 and Landsat 8 have been used for interpretation of eleven classes. The study shows an increase in cultivated land, settlement, waterbody, open forest, plantation and mining due to urbanization because of increasing demands of food, shelter and water while a decrease in dense forest, river, open scrub, wasteland and uncultivated land has also been marked due to destruction of aforementioned by anthropogenic activities such as industrialization resulting in environmental degradation that leads to air, soil and water pollution.

Land Use Land Cover Analysis for Godavari Basin in Maharashtra Using Geographical Information System and Remote Sensing

The dynamic transformation of land use and land cover has emerged as a crucial aspect in the effective management of natural resources and the continual monitoring of environmental shifts. This study focused on the land use and land cover (LULC) changes within the catchment area of the Godavari River, assessing the repercussions of land and water resource exploitation. Utilizing LANDSAT satellite images from 2009, 2014, and 2019, this research employed supervised classification through the Quantum Geographic Information System (QGIS) software’s SCP plugin. Maximum likelihood classification algorithm was used for the assessment of supervised land use classification. Seven distinct LULC classes—forest, irrigated cropland, agricultural land (fallow), barren land, shrub land, water, and urban land—are delineated for classification purposes. The study revealed substantial changes in the Godavari basin’s land use patterns over the ten-year period from 2009 to 2019. Spatial and temporal dynamics of land use/cover changes (2009-2019) were quantified using three Satellite/Landsat images, a supervised classification algorithm and the post classification change detection technique in GIS. The total study area of the Godavari basin in Maharashtra encompasses 5138175.48 hectares. Notably, the built-up area increased from 0.14% in 2009 to 1.94% in 2019. The proportion of irrigated cropland, which was 62.32% in 2009, declined to 41.52% in 2019. Shrub land witnessed a noteworthy increase from 0.05% to 2.05% over the last decade. The key findings underscored significant declines in barren land, agricultural land, and irrigated cropland, juxtaposed with an expansion in forest land, shrub land, and urban land. The classification methodology achieved an overall accuracy of 80%, with a Kappa Statistic of 71.9% for the satellite images. The overall classification accuracy along with the Kappa value for 2009, 2014 and 2019 supervised land use land cover classification was good enough to detect the changing scenarios of Godavari River basin under study. These findings provide valuable insights for discerning land utilization across various categories, facilitating the adoption of appropriate strategies for sustainable land use in the region.

Urban Land Use Change Detection Using Multisensor Satellite Images

Due to inappropriate planning and management, accelerated urban growth and tremendous loss in land, especially cropland, have become a great challenge for sustainable urban development in China, especially in developed urban area in the coastal regions; therefore, there is an urgent need to effectively detect and monitor the land use changes and provide accurate and timely information for planning and management. In this study a method combining principal component analysis (PCA) of multisensor satellite images from SPOT (systeme pour l'observation de la terre or earth observation satellite)-5 multispectral (XS) and Landsat-7 enhanced thematic mapper (ETM) panchromatic (PAN) data, and supervised classification was used to detect and analyze the dynamics of land use changes in the city proper of Hangzhou. The overall accuracy of the land use change detection was 90.67% and Kappa index was 0.89. The results indicated that there was a considerable land use change (10.03% of the total area) in the study area from 2001 to 2003, with three major types of land use conversions: from cropland into built-up land, construction site, and water area (fish pond). Changes from orchard land into built-up land were also detected. The method described in this study is feasible and useful for detecting rapid land use change in the urban area.

Investigating the Use of Remote Sensing and GIS Techniques to Detect Land Use and Land Cover Change: A Review

The accuracy of change detection on the earth’s surface is important for understanding the relationships and interactions between human and natural phenomena. Remote Sensing and Geographic Information Systems (GIS) have the potential to provide accurate information regarding land use and land cover changes. In this paper, we investigate the major techniques that are utilized to detect land use and land cover changes. Eleven change detection techniques are reviewed. An analysis of the related literature shows that the most used techniques are post-classification comparison and principle component analysis. Post-classification comparison can minimize the impacts of atmospheric and sensor differences between two dates. Image differencing and image ratioing are easy to implement, but at times they do not provide accurate results. Hybrid change detection is a useful technique that makes full use of the benefits of many techniques, but it is complex and depends on the characteristics of the other techniques such as supervised and unsupervised classifications. Change vector analysis is complicated to implement, but it is useful for providing the direction and magnitude of change. Recently, artificial neural networks, chi-square, decision tree and image fusion have been frequently used in change detection. Research on integrating remote sensing data and GIS into change detection has also increased.

Monitoring of Temporal and Spatial Changes of Land Use and Land Cover in Metropolitan Regions through Remote Sensing and GIS

The use of remote sensing techniques and subsequent analysis by means of geographical information system (GIS) offers an effective method for monitoring temporal and spatial changes of landscapes. This work studies the urbanization processes and associated threats to natural ecosystems and resources in the metropolitan areas of Berlin and Erlangen-Fürth-Nürnber?Schwabach (EFNS). To compute the land use/cover (LULC) of the study areas, a supervised classification of “maximum likelihood” using Landsat data for the years of 1972, 1985, 1998, 2003, and 2015 is used. Results show that the built-up area is the dominant land use in both regions throughout the study period. This land use has increased at the expense of green and open areas in EFNS and at the expense of agricultural land in Berlin. Likewise, 5% of forest in EFNS is replaced with urban infrastructure. However, the amount of forest in Berlin increased by 3%. While EFNS experienced relatively big changes in its water bodies from 1972 to 1985, changes in water bodies in Berlin were rather slight during the last 40 years. The overall accuracy of our remotely sensed LULC maps was between 88% and 94% in Berlin and between 85.87% and 87.4% for EFNS. The combination of remote sensing and GIS appears to be an indispensable tool for monitoring changes in LULC in urban areas and help improving LU planning to avoid environmental and ecological problems.

Change Analysis of Land Use and Urban Growth in the Municipalities of Culiacan and Navolato, Sinaloa, Mexico Using Statistical Techniques and GIS

Previous analysis of land use and land cover changes help us to understand the range, importance and effects that this dynamic has in the environment and its relation with the human’s activities. This work consists in analyzing the land use/cover for the Municipalities of Culiacan and Navolato, Mexico, through statistical techniques and Geographic Information Systems. The methodology is allowed to determine the changes, gains, losses and transitions in the different categories in the period studied. The results show significant changes in the denominate categories, agriculture and forest. However, the greatest change is the increase of the urban areas. The knowledge in the studied area and its dynamics are carried out and this work serves as a reference to study, manage and plan for our territory.

Evaluation of Land Use &Land Cover Change Using Multi-Temporal Landsat Imagery: A Case Study Sulaimaniyah Governorate, Iraq

Land use & land cover change detection in rapid growth urbanized area have been studied by many researchers and there are many works on this topic. Commonly, settlement sprawl in area depends on many factors such as eco-nomic prosperity and population growth. Iraq is one of the countries which witnessed rapid development in the settlement area. Remote sensing and geographic information system (GIS) are analytical software technologies to evaluate this familiar worldwide phenomenon. This study illustrates settlement development in Sulaimaniyah Governorate from 2001 to 2017 using Landsat satellite imageries of different periods. All images had been classified using remote sensing software in order to proceed powerful mapping of land use classification. Maximum likelihood method is used in the accurately extracted solution information from geospatial imagery. Landsat images from the study area were categorized into four different classes. These are: forest, vegetation, soil, and settlement. Change detection analysis results illustrate that in the face of an explosive demographic shift in the settlement area where the record + 8.99 percent which is equivalent to 51.80 Km2 over a 16-year period and settlement area increasing from 3.87 percent in 2001 to 12.86 percent in 2017. Accuracy assessment model was used to evaluate (LULC) classified images. Accuracy results show an overall accuracy of 78.83% to 90.09% from 2001 to 2017 respectively while convincing results of Kappa coefficient given between substantial and almost perfect agreements. This study will help decision-makers in urban plan for future city development.

Land Use and Land Cover Changes of West Tahta Region,Sohag Governorate,Upper Egypt

Due to the growing of population in Egypt during the last few decades, great changes in agricultural area, and urbanization of Egypt art occurred. In this study we utilized the available record of multitemporal Land sat Thematic Mapper (Tm) and Egyptsat image to produce land cover/land use map of the area between 1987 and 2009. Also we used the post-classification change detection analysis to detect the change in the agriculture, urban areas and the change in the River Nile during the period between 1987 and 2009. The post-classification change detection analysis shows that agriculture development increased by 1785.96 Hectare through the study period with average annual rate of land reclamation 81.18 Hectare/year. While the urban area increased by 2231.24 Hectare with average annual rate 101.42 Hectare/ year the increase of the urbanization and the growth occurring through encroachment into the farmer old cultivated lands. The change in the River Nile is not so much is reached to 138.32 hectare in the study period with average annual rate 6.29 Hectare/year. The result of this study show that, the accuracy are quantify with the land cover changes and also delineate their spatial patterns, which display the efficiency of land sat in evaluating landscape dynamics over a particular time span. This data are very useful for natural resources management.

Land Use Land Cover Changes and Encroachment Issues in Kapkatet Wetland, Kenya

Kapkatet Wetland is a vital ecosystem in Kenya that supports rural livelihoods through the provision of various ecological goods and services. However, this ecosystem has been undergoing rapid degradation arising from competing land uses. It’s important to document these changes to obtain insights that can aid decision-making for effective restoration and conservation. This study, therefore, sought to assess the extent and patterns of land use and land cover changes in Kapkatet Wetland between 1986 and 2019, and their driving forces. The study followed a mixed-method research approach involving a combination of remote sensing and descriptive surveys. To quantify the wetland changes, remotely sensed imageries for 1986, 2000, and 2019 were utilized in classifying land use and land cover maps through the Maximum Likelihood algorithm. Household questionnaires and focus group discussions were used to obtain information about peoples’ perceptions of the driving forces of landscape change within the wetland. Results generally showed that Kapkatet wetland declined by 24.77% over the past years (1986-2019). Wetland vegetation declined drastically as open grounds increased while tree cover and disturbed reeds showed a fluctuating trend. These changes were majorly driven by land conversion activities within the wetland. The study recommends a community-based enforcement approach to existing laws and policies by both National and Local governments to curb the continuous loss of this wetland.

Assessing Urban Land Use Change in New Braunfels, Texas from 2013 to 2020

Urban cities are the major drivers of economic growth and development. Economic growth and development however results in considerable land cover land use dynamics. This study assessed the dynamics in land cover land use that have occurred in New Braunfels, Texas in the last 7 years (2013-2020) to observe areas in the city that had experienced considerable shifts in land cover and land use. 30-meter resolution Landsat images were used to examine possible changes in land cover land use. New Braunfels was observed to have experienced significant changes in land use especially in developed areas. This change can be attributed to the influx of people into the city, contributing to the need for increased urban development. Analysis of this study shows that about 16% (about 553 hectares) of forest land cover class and 28% (about 1139 hectares) of grassland class in time 1 (August 31, 2013) changed to built-up land use class in time 2 (November 5, 2020). A limitation to this study was that of the spatial resolution of images used. Higher spatial resolution images could impact the producers, users, and overall accuracy assessment. Results from this study can aid in supporting better decision-making for sustainable urban development and climate change mitigation.

Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

The rate of forest degradation and deforestation in Nigeria has been increasing over the years and is prominent in the southwestern parts. Despite the significant change and degradation observed in a lowland rainforest in the region—Ogbese Forest Reserve, there is a great dearth of information about the level of forest cover change. Therefore, this study determined the cover dynamics of the rainforest reserve over the epoch of 20 years using Geographic Information System and remote sensing techniques. Coordinates of the boundary and some other benchmark places within the forest reserve were obtained. Secondary data collection included: Landsat imageries of 1998, 2002 and 2018. An interview guide was used to obtain information from forest officials and locals of the surrounding communities to complement the spatial data obtained. Image classification was done using the maximum likelihood algorithm. The rate of change across the epochs was determined using the area of the land cover classes. The level of vegetation disturbance in the reserve was determined through Normalized Difference Vegetation Index. Five different forest cover classes were identified in the study area: forest, plantation, farmland, grassland, and bare land. The natural forest reduced significantly from 34.43 km2 (48%) in 1998 to 8.73 km2 (12%) in 2002 and was depleted further by 2018, while other cover classes increased. NDVI value also reduced from 0.25 to 0.13. Agriculture, among others, was observed as the main driver of forest degradation and deforestation in Ogbese Forest Reserve. The study concluded that the remaining forest (i.e. plantation) could also be depleted by 2025, as it decreases by −0.94 km2 per year if proper reforestation and management practices are not introduced.

Land Use/Land Cover (LULC) Dynamics in a Semi-Arid Watershed in Eastern Rajasthan, India Using Geospatial Tools

Land use/land cover (LULC) change analysis has become a unique approach in determining the extent of degradation of natural resources within a given period of time. Remote sensing and GIS techniques have proved to be efficient tools for mapping and analyzing LULC changes over the last few decades. LULC change analysis has been carried out in Ruparel watershed which is situated in Alwar district, Eastern Rajasthan, India, based on visual image interpretation and change detection analysis of multi-temporal satellite data pertaining to IRS-P6 LISS III data of 2004 (Path-Row 95:52), IRS-P6 LISS III of 2014 (Path-Row 95:52) and IRS-R2A LISS III data of 2021. Visual image interpretation led to the delineation of 13 LULC classes using ArcGIS 10.5 software and include categories such as cultivated land, fallow land dense forest, open forest, degraded forest, open scrub, gullied/ravenous land, settlement/built-up land, River/waterbody, dry waterbody/dry river, plantation, barren/rocky/stony waste, and stone quarry. Results of the analysis depict significant LULC changes that have taken place in the area from 2004 to 2021. LULC categories such as cultivated land and settlement/built-up land have reported major changes in terms of their increase with 56.42 km2 (4.63%) and 31.9 km2 (2.63%) respectively primarily because of an increase in population. Likewise, the dense forest has reported a decrease of 33.78 km2 (2.78%) in its area and has been converted into degraded forest i.e., 32.04 km2 (2.64%) and open forest 2.85 km2 (0.24%) due to increased human exploitation of forest resources and mining activities taking place within the forested area. The study area needs the immediate attention of policymakers and stakeholders as the study area being part of the National Capital Region (NCR) will see excessive in-migration of the population in coming years which will further deplete the precious resources in the area.

Change Detection of Land Use and Riverbank in Mekong Delta, Vietnam Using Time Series Remotely Sensed Data

By taking the advantages of remote sensing technology, changes of land cover in Ca Mau Peninsular （the Southern part of the Mekong delta） and erosion of riverbank of the Mekong River have been defined. Change detection using time-series optical and radar satellite images （Landsat, SPOT and Radarsat） is the main approach in this study. Typical land cover in Ca Mau Peninsular is mangrove; its changes are observable by analysing imageries from 1973 to 2008. The results showed that more than half of the mangrove area was converted into shrimp farms. This made forest cover severely declined. Riverbank of the Mekong River also was detected to be eroded and accreted in a complicated form. Locations along the left and right banks of Tien and Hau river are defined as hot spots that are seriously suffered erosions. Topographic maps in 1966-1968 were taken into account as base line data, which was analyzed along with satellite images from 1989 to 2009. In addition, the Digital Shoreline Analysis System （DSAS） package was taken to analyze trend of changes in terms of changing prediction.

Detection of short-term urban land use changes by combining SAR time series images and spectral angle mapping

Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.

Land use/land cover classification and its change detection using multi-temporal MODIS NDVI data

Detailed analysis of Land Use/Land Cover （LULC） using remote sensing data in complex irrigated basins provides complete profile for better water resource management and planning. Using remote sensing data, this study provides detailed land use maps of the Lower Chenab Canal irrigated region of Pakistan from 2005 to 2012 for LULC change detection. Major crop types are demarcated by identifying temporal profiles of NDVI using MODIS 250 m × 250 m spatial resolution data. Wheat and rice are found to be major crops in rabi and kharif seasons, respectively. Accuracy assessment of prepared maps is performed using three dif- ferent techniques： error matrix approach, comparison with ancillary data and with previous study. Producer and user accuracies for each class are calculated along with kappa coeffi- cients （K）. The average overall accuracies for rabi and kharif are 82.83% and 78.21%, re- spectively. Producer and user accuracies for individual class range respectively between 72.5% to 77% and 70.1% to 84.3% for rabi and 76.6% to 90.2% and 72% to 84.7% for kharif. The K values range between 0.66 to 0.77 for rabi with average of 0.73, and from 0.69 to 0.74 with average of 0.71 for kharif. LULC change detection indicates that wheat and rice have less volatility of change in comparison with both rabi and kharif fodders. Transformation be- tween cotton and rice is less common due to their completely different cropping conditions. Results of spatial and temporal LULC distributions and their seasonal variations provide useful insights for establishing realistic LULC scenarios for hydrological studies.

Annual 30-m land use/land cover maps of China for 1980–2015 from the integration of AVHRR, MODIS and Landsat data using the BFAST algorithm

Annual land use land cover(LULC)change information at medium spatial resolution(i.e.at 30 m)is required in numerous subjects,such as biophysical modelling,land management and global change studies.Annual LULC information,however,is usually not available at continental or national scale due to reasons such as insufficient remote sensing data coverage or lack of computational capabilities.Here we integrate high temporal resolution and coarse spatial resolution satellite images(i.e.,Moderate Resolution Imaging Spectroradiometer(MODIS)and Global Inventory Modelling and Mapping Studies(GIMMS)normalized difference vegetation index(NDVI))with high spatial resolution datasets(China’s Land-Use/cover Datasets(CLUDs)derived from 30-meter Landsat TM/ETM+/OLI)to generate reliable annual nominal 30 m LULC maps for the whole of China between 1980 and 2015.We also test the performance of a statistical based change detection algorithm(Breaks for Additive Seasonal and Trend),originally designed for tracking forest change,in classifying all-type LULC change.As a result,a nominal 30 m annual land use/land cover datasets(CLUD-A)from 1980 to 2015 was developed for the whole China.The mapping results were assessed with a change sample dataset,a regional annual validation sample set and a three-year China sample set.Of the detected change years,75.61%matched the exact time of conversion within±1 year.Annual mapping results provided a detail process of urbanization,deforestation,afforestation,water and cropland dynamics over the past 36 years.The consistent characterization of land change dynamics for China can be further used in scientific research and to support land management for policy-makers.

Object-oriented Urban Dynamic Monitoring——A Case Study of Haidian District of Beijing

It is crucial to conduct the land use/cover research to obtain the global change information.Urban area is one of the most sensitive areas in land use/cover change.Therefore land use/cover change in urban areas is very im-portant in global change.It is vital to incorporate the information of urban land use/cover change into the process of decision-making about urban area development.In this paper,a new urban change detection approach,urban dynamic monitoring based on objects,is introduced.This approach includes four steps:1)producing multi-scale objects from multi-temporal remotely sensed images with spectrum,texture and context information;2)extracting possible changed objects adopting object-oriented classification;3)obtaining shared objects as the basic units for urban change detection;4)determining the threshold to segment the changed objects from the possible changed objects using Otsu method.In this paper,the object-based approach was applied to detecting the urban expansion in Haidian District,Beijing,China with two Landsat Thematic Mapper(TM)data in 1997 and 2004.The results indicated that the overall accuracy was about 84.83%,and Kappa about 0.785.Compared with other conventional approaches,the object-based approach was advantageous in reducing the error accumulation of image classification of each datum and in independence to the radiometric correction and image registration accuracy.