Spatiotemporal analysis of land use and land cover change in the Brazilian Amazon

Spatial transformation of land use and land cover and identification of hotspots using geospatial technology: a case of major industrial zone of eastern India

Technology-driven population expansion is closely linked to land use change. Unregulated mining, urbanization, industrialization, and forest clearing threaten land use and cover. This study used GIS and statistical methods to examine land use and cover changes in eastern India's Asansol-Durgapur Development Authority (ADDA). The Kappa coefficient was used to validate each year's LULC map accuracy. This region is changing rapidly due to industrial and urban development, which might cause environmental issues. Thus, this area is ideal for a scientific land-use change study. The central hypothesis of this study is that the LULC of an industrial area is spatially heterogeneous and that the number of hotspots is gradually increasing in response to the dynamicity of land use change over time and space. Three years (1992, 2007, and 2022) were used to determine the estimated transition rate. Hotspots of land use change were identified using autocorrelation statistics for LULC clustering using Moron's I and Gi Z statistics. The proportion of land encompassed by natural vegetation experienced a decline from 12% in 1992 to 4% in 2022. Similarly, the extent of land occupied by agricultural activities decreased from 47 to 38% during the period spanning from 1992 to 2022. The industrial and coal mining sectors experienced a modest growth rate of 1% during the period spanning from 1992 to 2022. If the current rate of land use change persists, it will gradually and consistently alter the existing landscape. This study's findings can potentially inform strategies to mitigate the adverse impacts of industrialization and urbanization on the region's natural resources.

Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation

Timely and accurate estimation of rice-growing areas and forecasting of production can provide crucial information for governments, planners, and decision-makers in formulating policies. While there exists studies focusing on paddy rice mapping, only few have compared multi-scale datasets performance in rice classification. Furthermore, rice mapping of large geographical areas with sufficient accuracy for planning purposes has been a challenge in Pakistan, but recent advancements in Google Earth Engine make it possible to analyze spatial and temporal variations within these areas. The study was carried out over southern Punjab (Pakistan)-a region with 380,400 hectares devoted to rice production in year 2020. Previous studies support the individual capabilities of Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) for paddy rice classification. However, to our knowledge, no study has compared the efficiencies of these three datasets in rice crop classification. Thus, this study primarily focuses on comparing these satellites’ data by estimating their potential in rice crop classification using accuracy assessment methods and area estimation. The overall accuracies were found to be 96% for Sentinel-2, 91.7% for Landsat-8, and 82.6% for MODIS. The F1-Scores for derived rice class were 83.8%, 75.5%, and 65.5% for Sentinel-2, Landsat-8, and MODIS, respectively. The rice estimated area corresponded relatively well with the crop statistics report provided by the Department of Agriculture, Punjab, with a mean percentage difference of less than 20% for Sentinel-2 and MODIS and 33% for Landsat-8. The outcomes of this study highlight three points; (a) Rice mapping accuracy improves with increase in spatial resolution, (b) Sentinel-2 efficiently differentiated individual farm level paddy fields while Landsat-8 was not able to do so, and lastly (c) Increase in rice cultivated area was observed using satellite images compared to the government provided statistics.

Spatio-temporal analysis of land use/land cover change dynamics in Paraguai/Jauquara Basin, Brazil

Although global climate change is receiving considerable attention, the loss of biodiversity worldwide continues. In this study, dynamics of land use/land cover (LULC) change in the Paraguai/Jauquara Basin, Mato Grosso, Brazil, were investigated. Two analyses were performed using R software. The first was a comparative study of LULC among the LULC classes at the polygon scale, and the second was a spatio-temporal analysis of moving polygons restricted to the agricultural regions in terms of topology, size, distance, and direction of change. The data consisted of Landsat images captured in 1993, 1997, 2001, 2005, 2009, 2013, and 2016 and processed using ArcGIS software. The proposed analytical approach handled complex data structures and allowed for a deeper understanding of LULC change over time. The results showed that there was a statistically significant change from regions of natural vegetation to pastures, agricultural regions, and land for other uses, accompanied by a significant trend of expansion of agricultural regions, appearing to stabilize from 2005. Furthermore, different patterns of LULC change were found according to soil type and elevation. In particular, the purple latosol soil type presented the highest expansion indexes since 2001, and the elevated agricultural areas have been expanding and/or stabilizing since 1997.

Spatio-temporal variation in land use/land cover pattern and channel migration in Majuli River Island, India

Precise land use and land cover (LULC) change information of a land surface is significant for sustainable development programs as the earth's surface is undergoing rapid changes. Majuli Island is located in the upper reach of the Brahmaputra River in India. It is continuously changing its shape by the action of erosion of the Brahmaputra River, incurring both tangible and intangible losses. This study aims to find out the changes that occurred in the island by analyzing the land use/land cover along with channel migration in the Brahmaputra River that occurred in that area over the period 1973 to 2019. This paper assesses the changes and present status of Majuli River Island from 1973 to 2019 using Landsat MSS (1973), TM (1985, 1995), ETM + (2009), and OLI (2019) satellite imageries. Here, the maximum likelihood classification (MLC) technique for LULC analysis and their temporal changes and normalized difference vegetation index (NDVI) technique for the vegetation characteristics have been processed and analyzed with the help of the geospatial information system (GIS). From the results, it is found that area of vegetation has gradually decreased from 365.59 (26.85%) in 1973 to 262.79 km² (19.29%) in 2019. In contrast, the barren land had increased from 4.82 (0.35%) in 1973 to 31.88 km² (2.34%) in 2019. Other LULC categories like agricultural lands, built-up areas, water bodies, and sand deposition also have changed significantly. The NDVI values are also changed due to channel shifting, soil erosion, and deforestation. The accuracy assessment for the supervised classification of LULC classes for all years showed excellent results in all six classes.

Land Use and Land Cover Changes in the Diversity and Life Zone for Uncontacted Indigenous People: Deforestation Hotspots in the Yasuní Biosphere Reserve, Ecuadorian Amazon

Land use and land cover change (LULC) is an essential component for the monitoring environmental change and managing natural resources in areas of high natural and cultural biodiversity, such as the Amazon biome. This study was conducted in in the northern Amazon of Ecuador, specifically in the Diversity and Life Zone (DLZ) of the Yasuní Biosphere Reserve (YBR). The general aim was to investigate the territorial dynamics of land use/land cover changes to support policies for environmental and sociocultural protection in the DLZ. Specific objectives included (i) mapping LULC spatial and temporal dynamics in the DLZ in the period from 1999 to 2018, (ii) identifying sensitive LULC hotspots within the DLZ, and (iii) defining the possible policy implications for sustainable land use in the DLZ. Multitemporal satellite imagery from the Landsat series was used to map changes in LULC, which were divided into three-time stages (1999–2009, 2009–2018, 1999–2018). We adopted open-access Landsat images downloaded from the United States Geological Survey (USGS). The processes for assessing LULC in the DLZ included (1) data collection and analysis, (2) data processing for remote sensing, (3) thematic land cover, and (4) homogenization and vectorization of images. The results showed that in the period 1999–2018, most of the uses and land cover were transformed into pastures in the DLZ. Therefore, it is important to improve territorial planning, to avoid conflicts between indigenous populations, migrant settlers, and uncontacted indigenous populations that live in the DLZ, within the YBR.

Monitoring of Land Use–Land Cover Change and Potential Causal Factors of Climate Change in Jhelum District, Punjab, Pakistan, through GIS and Multi-Temporal Satellite Data

Land use–land cover (LULC) alteration is primarily associated with land degradation, especially in recent decades, and has resulted in various harmful changes in the landscape. The normalized difference vegetation index (NDVI) has the prospective capacity to classify the vegetative characteristics of many ecological areas and has proven itself useful as a remote sensing (RS) tool in recording vegetative phenological aspects. Likewise, the normalized difference built-up index (NDBI) is used for quoting built-up areas. The current research objectives include identification of LULC, NDVI, and NDBI changes in Jhelum District, Punjab, Pakistan, during the last 30 years (1990–2020). This study targeted five major LULC classes: water channels, built-up area, barren land, forest, and cultivated land. Satellite imagery classification tools were used to identify LULC changes in Jhelum District, northern Punjab, Pakistan. The perception data about the environmental variations as conveyed by the 500 participants (mainly farmers) were also recorded and analyzed. The results depict that the majority of farmers (54%) believe in the appearance of more drastic changes such as less rainfall, drought, and decreased water availability for irrigation during 2020 compared to 30 years prior. Overall accuracy assessment of imagery classification was 83.2% and 88.8% for 1990, 88.1% and 85.7% for 2000, 86.5% and 86.7% for 2010, and 85.6% and 87.3% for 2020. The NDVI for Jhelum District was the highest in 1990 at +0.86 and the lowest in 2020 at +0.32; similarly, NDBI values were the highest in 2020 at +0.72 and the lowest in 1990 at −0.36. LULC change showed a clear association with temperature, NDBI, and NDVI in the study area. At the same time, variations in the land area of barren soil, vegetation, and built-up from 1990 to 2020 were quite prominent, possibly resulting in temperature increases, reduction in water for irrigation, and changing rainfall patterns. Farmers were found to be quite responsive to such climatic variations, diverting to framing possible mitigation approaches, but they need government assistance. The findings of this study, especially the causes and impacts of rapid LULC variations in the study area, need immediate attention from related government departments and policy makers.

Analysis of Land Use and Land Cover Change Using Time-Series Data and Random Forest in North Korea

North Korea being one of the most degraded forests globally has recently been emphasizing in forest restoration. Monitoring the trend of forest restoration in North Korea has important reference significance for regional environmental management and ecological security. Thus, this study constructed and analyzed a time-series land use land cover (LULC) map to identify the LULC changes (LULCCs) over extensive periods across North Korea and understand the forest change trends. The analysis of LULC used Landsat multi-temporal image and Random Forest algorithm on Google Earth Engine(GEE) from 2001 to 2018 in North Korea. Through the LULCC detection technique and consideration of the cropland change relation with elevation, the forest change in North Korea for 2001–2018 was evaluated. We extended the existing sampling methodology and obtained a higher overall accuracy (98.2% ± 1.6%), with corresponding kappa coefficients (0.959 ± 0.037), and improved the classification accuracy in cropland and forest cover. Through the change detection and spatial analysis, our research shows that the forests in the southern and central regions of North Korea are undergoing restoration. The sampling method we extended in this study can effectively and reliably monitoring the change trend of North Korea forests. It also provides an important reference for the regional environmental management and ecological security in North Korea.

Agricultural Land Use Change in Chongqing and the Policy Rationale behind It: A Multiscale Perspective

Agricultural land resources have been the central issue for the Chinese government in its attempts to secure food and agricultural sustainability. Yet strict land use control does not protect the agricultural land from erosion by urban expansion. Identifying the specific patterns and mechanisms of the agricultural land conversion, thus, is critical for land management and related decision making. Based on the annual nominal 30 m land use/land cover datasets (called CLUD-A), this study goes below the national/regional level to examine agricultural land conversion in Chongqing from a multiscale perspective. At the metropolis and its subdivision’s scales, the volume of the conversion area has been generally increasing, from 122.40 km2 in 1980–1990, 162.26 km2 in 1990–2000, and 706.14 km2 in 2000–2010, to 684.83 km2 in 2010–2015. Such a conversion in the main city area and its surroundings far outweighed that in the rural outskirts, as 68.9% (1990–2000), 92.2% (2000–2010), and 82.7% (2010–2015) of the conversion happened in the former. Moreover, values of Gini coefficients and coefficient of variation (CV) based on the county/district scale (Gini [0.46, 0.64], CV [0.69, 0.99] throughout the four periods) are much lower than those based on the town/village scale (Gini [0.88, 0.94], CV [3.18, 4.47] throughout the four periods), suggesting the uneven extent of spatial distribution of the agricultural land conversion trickles down along with the downscale of administration: the lower the administrative level, the more severe the unbalance. The policy rationale behind this transition is also discussed. This research argues for tangible approaches to a sustainable rural-urban transformation.

Landscape service flow dynamics in the metropolitan area of Córdoba (Argentina)

Human decisions, policies, and management strategies play an important role in structuring landscape patterns in a metropolitan area. Land-use/land-cover (LULC) changes can be considered probably the most important factor affecting the environment and the maintenance of landscape service flow. In particular, processes such as agricultural intensification, deforestation, urbanization and industrialization affect landscape heterogeneity in terms of composition and configuration. However, the multifunctional aspect of LULC as well as traditional agricultural practices can contribute to the maintenance of landscape service flow. This research aims to analyze and assess: (1) LULC dynamics and change from 1988 to 2019 within the metropolitan area of Córdoba (Argentina); (2) the effect of this change on landscape composition and configuration; (3) the flow of landscape services from 1988 to 2019, with the identification of hot-spots of landscape service provision. To analyze LULC dynamics and change within the study area, three Landsat images were utilized, while change detection analysis has been performed to identify the areas most affected by changes, the spatial distribution of change and the change trajectories of LULC classes in terms of landscape composition and configuration. Finally, the valuation of landscape service flow has been carried out by placing an economic value on the LULC classes, through the use of proxies. LULC pattern change has resulted in the expansion of extensive agriculture. The total variation from 1988 to 2019 has highlighted a significant reduction of Horticulture, Forests, and Grasslands, which have been converted into other classes (Urban and Extensive Agriculture). This conversion of LULC classes has had profound effects on landscape service flow, which guarantees the well-being of local communities. This research has contributed to the knowledge of where the hot-spots of landscape service' provision are located by helping landscape managers to identify suitable local policies able to preserve them, thus avoiding their loss, and enhancing landscape integrity, functionality, and resilience.

Using GIS tools to detect the land use/land cover changes during forty years in Lodhran District of Pakistan

Land use/land cover (LULC) change has serious implications for environment as LULC is directly related to land degradation over a period of time and results in many changes in the environment. Monitoring the locations and distributions of LULC changes is important for establishing links between regulatory actions, policy decisions, and subsequent LULC activities. The normalized difference vegetation index (NDVI) has the potential ability to identify the vegetation features of various eco-regions and provides valuable information as a remote sensing tool in studying vegetation phenology cycles. Similarly, the normalized difference built-up index (NDBI) may be used for quoting built-up land. This study aims to detect the pattern of LULC, NDBI, and NDVI change in Lodhran district, Pakistan, from the Landsat images taken over 40 years, considering four major LULC types as follows: water bodies, built-up area, bare soil, and vegetation. Supervised classification was applied to detect LULC changes observed over Lodhran district as it explains the maximum likelihood algorithm in software ERDAS imagine 15. Most farmers (46.6%) perceived that there have been extreme changes of onset of temperature, planting season, and less precipitation amount in Lodhran district in the last few years. In 2017, building areas increased (4.3%) as compared to 1977. NDVI values for Lodhran district were highest in 1977 (up to + 0.86) and lowest in 1997 (up to - 0.33). Overall accuracy for classification was 86% for 1977, 85% for 1987, 86% for 1997, 88% for 2007, and 95% for 2017. LULC change with soil types, temperature, and NDVI, NDBI, and slope classes was common in the study area, and the conversions of bare soil into vegetation area and built-up area were major changes in the past 40 years in Lodhran district. Lodhran district faces rising temperatures, less irrigation water, and low rainfall. Farmers are aware of these climatic changes and are adapting strategies to cope with the effects but require support from government.

Deforestation dynamics in Brazil's Amazonian settlements: Effects of land-tenure concentration

Brazil's Amazon deforestation is a major global and national environmental concern, and the ability to model and project both its course and the effect of different policy options depends on understanding how this process occurs at present and how it might change in the future. The present paper addresses one key factor in Amazon deforestation: land-tenure concentration in settlements. Brazil's policies for establishing and regulating settlement projects represent critical government decisions shaping the landscape in the 5 × 10⁶ km² Legal Amazonia region. We used remote-sensing data and information provided by the National Institute for Colonization and Agrarian Reform (INCRA) to evaluate the effect of land-tenure concentration in a settlement project (Projeto de Assentamento) located in a frontier area where cattle-ranching is expanding. We identified the actors and their deforestation patterns in the Matupi settlement in the southern part of Brazil's state of Amazonas. We spatially identified actors who concentrated "lots" (the parcels of land distributed to individual settlers) in 2011 and assessed whether the concentration was done by individual landholders or by "families" (where members merged their lots and the clearing was done together). Deforestation rates (1995-2011) were estimated for each type of actor and the trajectory of deforestation in the settlement (cumulative deforestation to 1994 and annual deforestation 1995-2016) was also analyzed. Concentrators occupied 28% (9653 ha) of the settlement and 29% of the lots (152 lots) analyzed; the numbers of lots concentrated ranged from two to ten. Concentrators of two lots and non-concentrators were the predominant actor types in the settlement. The mean annual clearing per landholding for concentrators of two lots (families: 4.1 ± 2.8 ha (mean ± SD); individuals: 5.1 ± 4.6 ha) was greater than for non-concentrators (1.7 ± 1.2 ha), despite their having similar patterns of small clearings. Concentrators of three or more lots had mean annual clearing per landholding between 6.2 ± 12.2 ha and 23.9 ± 38.7 ha and, the pattern of patches cleared per year >34 ha in area was predominant. The deforestation rate per lot was higher among concentrators as compared to non-concentrators, showing that lot concentration speeds deforestation. Analysis of deforestation patterns helps to better understand the process of lot concentration by spatially identifying the predominant patterns of each type of actor. The approach used in our study could assist authorities in identifying and monitoring land-tenure concentration in settlements. Agrarian-reform policymakers need to monitor this process, since it speeds deforestation in Amazonian settlement projects, as well as undermining the social objectives of the agrarian-reform program.

Spatial quantitative analysis of the potential driving factors of land surface temperature in different "Centers" of polycentric cities: A case study in Tianjin, China

Revealing the dominant driving factors of land surface temperature (LST) plays an important role in mitigating the urban heat island (UHI) effect. Numerous international metropolises are developing polycentric forms under the process of suburbanization in conjunction with rapid urbanization, generating new UHI spatial patterns in internal urban areas. To comprehensively understand the effects of multi-factors on the thermal environment, our study examined a typical polycentric city, Tianjin. According to the concept of polycentrism, this study focused on three types of city "centers": major city core, new district core and industrial park. Eleven potential driving factors of LST were explored from four layers, and the geo-detector model was applied to rank the explanatory degree of these factors on LST. Three different city centers of the polycentric city showed varied UHI spatial pattern characteristics, and their response to the effect of natural factors and social factors on LST were quite diverse. Heat island areas were distributed homogeneously in the major city core; the UHI pattern on the east-west axis was unbalanced in the new district core due to the unsaturated urban space and dynamic planning policies; in industrial park, production areas were segregated by green belts with clear boundaries. For the whole city and the major city core, the imperviousness factor had the highest explanatory rate for LST, followed by the greenness factor. In contrast to the results of previous studies, the wetness factors had a greater impact on LST in the new district core and industrial park, second only to the greenness factor. Furthermore, selected factors exhibited bilinear or nonlinear enhanced relationships in their interactions. The driving laws of LST in different city centers were summarized with an explorative case study, aimed at providing theoretical basis and practical guidance for optimizing urban thermal environment planning, especially for highly urbanized polycentric cities.

Study of land cover/land use changes using RS and GIS: a case study of Multan district, Pakistan

Water and land both are limited resources. Current management strategies are facing multiple challenges to meet food security of an increasing population in numerous South Asian countries, including Pakistan. The study of land cover/land use changes (LCLUC) and land surface temperature (LST) is important as both provide critical information for policymaking of natural resources. We spatially examined LCLU and LST changes in district Multan, Pakistan, and its impacts on vegetation cover and water during 1988 to 2017. The LCLUC indicate that rice and sugarcane had less volatility of change in comparison with both cotton and wheat. Producer's accuracy (PA) is the map accuracy (the producer of map), but user's accuracy (UA) is the accuracy from the point of view of a map user, not the map maker. Average overall producer's and user's accuracy for the region was 85.7% and 87.7% for Rabi (winter) and Kharif (summer) seasons, respectively. The results of this study showed that 'built-up area' increased with 7.2% of all the classes during 1988 to 2017 in the Multan district. Anthropogenic activities decreased the vegetation, leading to an increase in LST in study area. Changes on LCLU and LST during the last 30 years have shown that vegetation pattern has changed and temperature has increased in the Multan district.

Exploring the Relationship between Urbanization and the Eco-Environment: A Case Study of Beijing

Beijing is one of the most developed cities in China and has experienced a series of environmental problems. In accordance with the Major Function Zone planning, Beijing is divided into four zones in an attempt to coordinate development between urban areas and the eco-environment. Classic coupling model uses statistical data to evaluate the interactions of these two subsystems; however, it lacks the capability to express dynamic changes to land cover. Thus, we extracted land cover data from Landsat images and examined the urbanization and eco-environment level as well as the coupling coordination in Beijing and its functional zones. The main conclusions are as follows. (1) Between 2001 and 2011, both urbanization and the eco-environment level in Beijing and its functional zones grew steadily. Different zones coordinated together according to their own characteristics, and the overall coupling coordination of the city transformed from the “basically balanced” to the “superiorly balanced” stage of development. (2) After 2011, the condition of the eco-environment worsened in Beijing and in most of the function zones, while the coordination between increased urbanization and the worsened eco-environment may be a result of environmental lag. This study integrated land cover data into the coupling mode and fully utilized the advantages of spatiotemporal analysis and the coupling model. In other words, the spatiotemporal analysis explains the land cover changes visually over the research period, while the coupling model explores the interaction mechanisms between urbanization and the eco-environment. The land cover data enriches the coupling theory and provides a reference for evaluating the effectiveness of local development policy.

Mapping and quantification of ferruginous outcrop savannas in the Brazilian Amazon: A challenge for biodiversity conservation

The eastern Brazilian Amazon contains many isolated ferruginous savanna ecosystem patches (locally known as ‘canga vegetation’) located on ironstone rocky outcrops on the top of plateaus and ridges, surrounded by tropical rainforests. In the Carajás Mineral Province (CMP), these outcrops contain large iron ore reserves that have been exploited by opencast mining since the 1980s. The canga vegetation is particularly impacted by mining, since the iron ores that occur are associated with this type of vegetation and currently, little is known regarding the extent of canga vegetation patches before mining activities began. This information is important for quantifying the impact of mining, in addition to helping plan conservation programmes. Here, land cover changes of the Canga area in the CMP are evaluated by estimating the pre-mining area of canga patches and comparing it to the actual extent of canga patches. We mapped canga vegetation using geographic object-based image analysis (GEOBIA) from 1973 Landsat-1 MSS, 1984 and 2001 Landsat-5 TM, and 2016 Landsat-8 OLI images, and found that canga vegetation originally occupied an area of 144.2 km² before mining exploitation. By 2016, 19.6% of the canga area was lost in the CMP due to conversion to other land-use types (mining areas, pasturelands). In the Carajás National Forest (CNF), located within the CMP, the original canga vegetation covered 105.2 km² (2.55% of the CNF total area), and in 2016, canga vegetation occupied an area of 77.2 km² (1.87%). Therefore, after more than three decades of mineral exploitation, less than 20% of the total canga area was lost. Currently, 21% of the canga area in the CMP is protected by the Campos Ferruginosos National Park. By documenting the initial extent of canga vegetation in the eastern Amazon and the extent to which it has been lost due to mining operations, the results of this work are the first step towards conserving this ecosystem.

A GEOBIA Approach for Multitemporal Land-Cover and Land-Use Change Analysis in a Tropical Watershed in the Southeastern Amazon

The southeastern Amazon region has been intensively occupied by human settlements over the past three decades. To evaluate the effects of human settlements on land-cover and land-use (LCLU) changes over time in the study site, we evaluated multitemporal Landsat images from the years 1984, 1994, 2004, 2013 and Sentinel to the year 2017. Then, we defined the LCLU classes, and a detailed “from-to” change detection approach based on a geographic object-based image analysis (GEOBIA) was employed to determine the trajectories of the LCLU changes. Three land-cover (forest, montane savanna and water bodies) and three land-use types (pasturelands, mining and urban areas) were mapped. The overall accuracies and kappa values of the classification were higher than 0.91 for each of the classified images. Throughout the change detection period, ~47% (19,320 km2) of the forest was preserved mainly within protected areas, while almost 42% (17,398 km2) of the area was converted from forests to pasturelands. An intrinsic connection between the increase in mining activity and the expansion of urban areas also exists. The direct impacts of mining activities were more significant throughout the montane savanna areas. We concluded that the GEOBIA approach adopted in this study combines the advantages of quality human interpretation and the capacities of quantitative computing.

Assessment of land use and land cover change using spatiotemporal analysis of landscape: case study in south of Tehran

In the recent years, dust storms originating from local abandoned agricultural lands have increasingly impacted Tehran and Karaj air quality. Designing and implementing mitigation plans are necessary to study land use/land cover change (LUCC). Land use/cover classification is particularly relevant in arid areas. This study aimed to map land use/cover by pixel- and object-based image classification methods, analyse landscape fragmentation and determine the effects of two different classification methods on landscape metrics. The same sets of ground data were used for both classification methods. Because accuracy of classification plays a key role in better understanding LUCC, both methods were employed. Land use/cover maps of the southwest area of Tehran city for the years 1985, 2000 and 2014 were obtained from Landsat digital images and classified into three categories: built-up, agricultural and barren lands. The results of our LUCC analysis showed that the most important changes in built-up agricultural land categories were observed in zone B (Shahriar, Robat Karim and Eslamshahr) between 1985 and 2014. The landscape metrics obtained for all categories pictured high landscape fragmentation in the study area. Despite no significant difference was evidenced between the two classification methods, the object-based classification led to an overall higher accuracy than using the pixel-based classification. In particular, the accuracy of the built-up category showed a marked increase. In addition, both methods showed similar trends in fragmentation metrics. One of the reasons is that the object-based classification is able to identify buildings, impervious surface and roads in dense urban areas, which produced more accurate maps.