Change Detection Studies Research Articles

Atmospheric and Radiometric Normalization of Satellite Images for Landscape-Level Environmental Monitoring: The Case of The Mediterranean Region

Ensuring atmospheric and radiometric consistency among the frameworks of satellite data used in regional studies is a critical requirement for change detection studies employed in regional planning monitoring. The purpose of this article is to provide a guide for the necessary atmospheric correction and radiometric normalization processes required in generating environmental data at the landscape level for physical planning. In this context, adjustments were made to remove atmospheric effects before merging multiple ASTER satellite image frames used in a project supported by TÜBİTAK, covering landscape-level environmental inventory and monitoring. The Dark Object Subtraction method with the Cos(t) model was utilized in the atmospheric correction process. Subsequently, separate regression relationships were computed for each band by considering overlapping areas on adjacent tracks of ASTER data, and radiometric normalization was performed based on these regression equations. Thus, differences between satellite images used in monitoring land changes and affecting multiple frames were minimized.

Landscape metrics of the Brusy Commune before and after wind-storm: an assessment of the extent of changes based on Landsat-8 data

Monitoring the change in land cover in disaster-affected areas, such as forests, has become a conventional forest management practice, particularly in protected areas. Most change detection and fragmentation studies rely on single-dated satellite images even while investigating changes over a long temporal span. This study aims to move a step further to compare fragmentation before and after a derecho event that occurred in August 2017 using 23 Landsat-8 images of Brusy Commune within the Tuchola Forest Biosphere Reserve. The supervised classification was carried out in the Google Earth Engine using the machine learning algorithm of random forests within the summer months of 2017 and 2018. The high overall accuracy of 0.92 was obtained for the two images which were then analysed with landscape metrics such as mean patch size, number of patches, total edge and edge density using Patch Analyst. These landscape metrics facilitated the characterisation of landscape fragmentation at both the class and landscape levels. Shannon’s Diversity Index was employed to assess heterogeneity across the landscape. The findings indicate significant fragmentation, particularly in the forest and pasture classes, with overall low diversity. This study underscores the potential for future research to employ advanced machine learning techniques and non-parametric classifiers, such as neural networks, to enhance the prediction of fragmentation across various spatial scales.

Urban Green Spaces in Africa: A Bibliometric Analysis

Africa has a lower proportion of urban areas compared to other continents, yet it is experiencing rapid urbanization, which is posing a significant threat to the urban ecosystem. This study presents the results of a bibliometric analysis from publications on urban green spaces (UGS) research in African cities. From the Web of Science, Science Direct, Google Scholar, and PubMed databases, 264 articles on UGS research in Africa were included in this study. This research topic experienced rapid growth, as more than 68% of all the articles were produced in the past five years, where about 63% of the studies included in this analysis were carried out in only four countries. Most of the studies were carried out at the intracity scale, with the main focus being on the ecosystem services provided by UGS. Change detection and overall UGS mapping studies show that high-income cities have a high percentage of UGS, while many African cities exhibited lower green coverage. We commend the work from researchers; however, there is still a gap to fill both in terms of high-quality datasets and state-of-the-art technology usage, and there is also a need for more comparative studies among cities and countries at the continental scale.

Scenario-Based Land Use and Land Cover Change Detection and Prediction Using the Cellular Automata–Markov Model in the Gumara Watershed, Upper Blue Nile Basin, Ethiopia

Land use and land cover (LULC) change detection and prediction studies are crucial for supporting sustainable watershed planning and management. Hence, this study aimed to detect historical LULC changes from 1985 to 2019 and predict future changes for 2035 (near future) and 2065 (far future) in the Gumara watershed, Upper Blue Nile (UBN) Basin, Ethiopia. LULC classification for the years 1985, 2000, 2010, and 2019 was performed using Landsat images along with vegetation indices and topographic factors. The random forest (RF) machine learning algorithm built into the cloud-based platform Google Earth Engine (GEE) was used for classification. The results of the classification accuracy assessment indicated perfect agreement between the classified maps and the validation dataset, with kappa coefficients (K) of 0.92, 0.94, 0.90, and 0.88 for the LULC maps of 1985, 2000, 2010, and 2019, respectively. Based on the classified maps, cultivated land and settlement increased from 58.60 to 83.08% and 0.06 to 0.18%, respectively, from 1985 to 2019 at the expense of decreasing forest, shrubland and grassland. Future LULC prediction was performed using the cellular automata–Markov (CA–Markov) model under (1) the business-as-usual (BAU) scenario, which is based on the current trend of socioeconomic development, and (2) the governance (GOV) scenario, which is based on the Green Legacy Initiative (GLI) program of Ethiopia. Under the BAU scenario, significant expansions of cultivated land and settlement were predicted from 83.08 to 89.01% and 0.18 to 0.83%, respectively, from 2019 to 2065. Conversely, under the GOV scenario, the increase in forest area was predicted to increase from 2.59% (2019) to 4.71% (2065). For this reason, this study recommends following the GOV scenario to prevent flooding and soil degradation in the Gumara watershed. Finally, the results of this study provide information for government policymakers, land use planners, and watershed managers to develop sustainable land use management plans and policies.

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

Crop change detection study is the foundation of agricultural sustainability. The inherent high spectral resolution of hyperspectral images, combined with multi-temporal datasets, facilitates the detection of subtle changes. To enhance the accuracy and applicability of hyperspectral change detection in agricultural scenes, this paper introduces a fast hyperspectral change detection approach for agricultural crops based on low-rank matrix and morphological feature extraction (FLRaMF). The goal is to improve detection precision and computational efficiency of the change detection process. The method initially employs rapid low-rank matrix extraction to separate changing and non-changing pixels in the spectral domain. Subsequently, spatial information is introduced using attribute profiles, restricting spectral anomalies through hyperspectral morphology, which ultimately improves the detection results. This study utilized four hyperspectral change detection datasets in agricultural crop scenarios, optimizing and analyzing parameters. Experimental results and analysis indicate that the FLRaMF method can achieve higher detection accuracy with reduced computation cost in unsupervised, default parameter scenarios when performing agricultural crop change detection tasks.

Between Anthropogenic Influence and Resource Management: Impacts of Risks and Preservation Strategies in the Ikopa Watershed, Madagascar

The impacts of anthropogenic risks on water resource management underscore the necessity for a careful and adaptable approach to ensure long-term sustainability. In the Ikopa Watershed in Madagascar, numerous human activities present significant challenges. Water quality is impacted by pollution resulting from agricultural practices. Uncontrolled deforestation intensifies watershed degradation, jeopardizing the availability of water resources. Rapid urbanization and escalating human activities further contribute to the deterioration of water quality. The hypothesis posits that responsible exploitation of natural resources contributes to the preservation and sustainability of water resources in the Ikopa Watershed. This study concentrates on investigating potential anthropogenic risks that influence resource degradation and the perception of mitigation measures. The main objective of this section is to assess the anthropogenic risk factors contributing to water resource degradation in the watershed. The study employs change detection studies through remote sensing and GIS approaches, complemented by social analysis. This methodology aims to identify potential anthropogenic risks affecting responsible water resource management, along with an exploration of local mitigation strategies. The results underscored potential risks faced by the local population in the watershed, including challenges associated with agriculture, urbanization, inadequate drainage network management, and high population density. Binary logistic regression analysis reveals significant connections between anthropogenic risks and the mitigation strategies adopted by local communities in the Ikopa Watershed. The findings indicate that specific characteristics of degradation or local interventions can either positively or negatively influence the adoption of these strategies.

The effect of urbanization on the shrinkage of wetlands in the Noida-Greater Noida region and its surrounding sub-urban areas

Increasing urbanization has an adverse effect on the wetlands of Gautam Buddh Nagar and its surrounding region, so the study aimed to understand the impact of urbanization on the wetlands and its surrounding region, Landsat satellite data was used for the change detection study. This study deals with the inventorisation of wetlands of size greater than 10 ha in Noida-Greater Noida region and its adjacent areas and assess the impact of urbanization on these wetlands. Land-Use/Land-Cover (LULC) maps were developed using GIS tools. Temporal data analysis detected a loss of 16.36% wetlands of size more than 10 ha since 2010 in the Gautam Buddha Nagar district. LULC analyses revealed the high impact of urbanization on 5 out of the 8 sites. LULC change also detected shrinkage of wetlands and alternations of land use in the catchment area of most of the wetlands. The findings of this study were then used to suggest management recommendations for the conservation of wetlands at a local scale.

CBAM UNet+++： Attention mechanism to guide change detection study of full-scale connected networks

CBAM UNet+++： Attention mechanism to guide change detection study of full-scale connected networks

ClimShift – A new toolbox for the detection of climate change

AbstractClimate change no longer involves and affects just a few people or communities. However, most of them need climate change detection studies to adapt to the current and future climate conditions efficiently. The present research aimed to detect climate changes by considering the shift in climate conditions from one region to another over different periods based on a similarity index in the Carpathians basin using the new ClimShift toolbox, specially created for this purpose. Developed in R, based on the cosine similarity index and using a set of 32 climate indices (temperature and precipitation), ClimShift uses NC raster format (NetCDF files) as input data. The application is compatible with Microsoft and Unix/Linux environments. The toolbox allows the detection of forward and backward climate shifts. The results can be employed as a Climate Service and are extremely helpful for an efficient process of adaption to climate changes at a local/regional scale. A user‐friendly interface and a tutorial on how to use the toolbox are also available. The toolbox was tested for four locations in the Carpathians Basin (Vienna, Bekes, Cluj‐Napoca and Kosice) using 1961–1990 as a base period and 1991–2021 as an analysis period for the forward climate shift analysis. For Cluj‐Napoca, the application was also tested for the backward climate shift, using 1991–2021 as the base period and 1961–1990 as the analysis period, identifying the region where present climate conditions were specific during the older period. The scientific results indicated a significant shift towards the east and northeast from the older period to the most recent one and a low percentage (6%–10%) in the overlapping area with highly similar conditions between the two periods.

RESEARCH ON SELF-CROSS-TRANSFORMER MODEL OF POINT CLOUD CHANGE DETECTION

Abstract. With the vigorous development of the urban construction industry, engineering deformation or changes often occur during the construction process. To combat this phenomenon, it is necessary to detect changes in order to detect construction loopholes in time, ensure the integrity of the project and reduce labor costs. Or the inconvenience and injuriousness of the road. In the study of change detection in 3D point clouds, researchers have published various research methods on 3D point clouds. Directly based on but mostly based on traditional threshold distance methods (C2C, M3C2, M3C2-EP), and some are to convert 3D point clouds into DSM, which loses a lot of original information. Although deep learning is used in remote sensing methods, in terms of change detection of 3D point clouds, it is more converted into two-dimensional patches, and neural networks are rarely applied directly. We prefer that the network is given at the level of pixels or points. Variety. Therefore, in this article, our network builds a network for 3D point cloud change detection, and proposes a new module Cross transformer suitable for change detection. Simultaneously simulate tunneling data for change detection, and do test experiments with our network.

5G Enabled Moving Robot Captured Image Encryption with Principal Component Analysis Method

Estimating the captured image of moving robots is very difficult. These images are vital in analyzing earth's surface objects for many applications like studying environmental conditions, Land use and Land Cover changes, and change detection studies of worldwide change. Multispectral robot-captured images have a massive amount of low-resolution data, which is lost due to a lack of capture efficiency due to artificial and atmospheric reasons. The image transformation is required in a 5G network with effective transmission by reducing noise, inconsistent lighting, and low resolution, degrading image quality. In this paper, the authors proposed the machine learning dimensionality reduction technique i.e. Principle Component Analysis (PCA) and which is used for metastasizing the 5 G-enabled moving robot captured image to enrich the image's visual perception to analyze the exact information of global or local data. The encryption algorithm implanted for data reduction and transmission over the 5G network gives sophisticated results compared with other standard methods. This proposed algorithm gives better performance in developing data reduction, network convergence speed, reduces the training time for object classification, and improves accuracy for multispectral moving robot-captured images by the support of 5G network.

When searching helps you see: Bridging the gap between incidental and intentional change detection.

Participants in incidental change detection studies often miss large changes to visually salient or conceptually relevant objects such as actor substitutions across video cuts, but there are competing explanations of why participants fail to detect these changes. According to an integrative processing account, object-based attention typically induces integrated representation and comparison processes sufficient to detect changes to that object. On this view, participants miss changes in incidental paradigms because those paradigms fail to elicit the level of attention necessary to trigger integrated representation and comparison processes. In contrast, a selective processing account posits that representation and comparison processes needed to detect changes do not occur by default, even for attended objects, but are only elicited in response to specific functional needs. In four experiments, we tested detection of actor substitutions when participants engaged in tasks that required actor identity processing but did not necessarily require the combination of processes necessary to detect changes. Change blindness for actor substitutions persisted when participants counted the number of actors in the video and sometimes persisted when participants were instructed to remember the substituted actor for later recall. Change blindness consistently diminished, however, when participants were shown the prechange actor before or during the video and instructed to search for that actor in the video. Our results refine the contrast between selective and integrative processing by specifying how task demands to create durable visual representations can remain independent of comparison processes, while search demands can induce integrative comparison processes in a naturalistic setting. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

LiDAR-change-based mapping of sediment movement from an extreme rainfall event

ABSTRACT Mapping landscape change at fine scales (e.g. <1.0 m resolution) using airborne LiDAR data from manned aircraft is a significant challenge. This challenge is magnified in disaster response contexts. A combination of collection and processing factors contributes to horizontal and vertical errors (and resulting uncertainty) in each pre- and post-LiDAR derived digital elevation model (DEM). Subsequently, the errors in the change surface from the two (or more) DEMs are an accumulation of the errors in the individual DEMs. Thus, reliable mapping erosion/deposition changes at sub-meter precision in change detection studies using LiDAR data is largely the domain of terrestrial LiDAR or sUAS with LiDAR scanners rather than manned aircraft. Unfortunately, terrestrial and sUAS LiDAR scanners are not well suited for mapping large areas and sUAS collections are subject to additional airspace constraints compared to manned aircraft. In this study, we probed one of the significant issues in airborne LiDAR change projects – vertical height errors from sequential flight lines. A simplified solution for determining flight line vertical biases in areas of low topographic relief with natural cover types was developed and tested for normalizing point clouds. The approach was tested in a fine-scale erosion/deposition study from an extreme rainfall event that eroded and deposited sand at depths of about 1.0 m. Airborne LiDAR had been collected prior to the rainfall event, and another airborne LiDAR collection was made 1 month after the event. Eleven field campaigns to collect reference data and visit anomalies in the change surface were conducted in a 15-month period after the event, beginning 25 February 2016 and ending 8 May 2017. The validation results indicate accuracies for the pre-event and post-event LiDAR derived DEMs were 7.8 cm and 13.0 cm RMSE, respectively. After modeling vertical errors and corrections applied to the post-event point clouds, the RMSE for the post-event DEM was 8.3 cm. In the depositional use case, 27 locations were sampled with auger boreholes/sand pits and compared with LiDAR-based change. The LiDAR-based change detection analysis resulted in predicted sand depth accuracies of 94% with a mean error of 4.7 cm.

Spatio-Temporal Change Detection Analysis of Land Use Land Cover of Bathinda District, Punjab, India

ABSTRACT: Due to rapid industrialization and urban sprawl in the last few decades, the land use pattern and its consumption takes place at a large scale that could lead to problems such as over-exploitation of land resources, food insecurity and pollution. It becomes imperative to carry out monitoring and subsequent modelling of land use land cover (LULC) changes. An attempt was made to study the changes in the LULC pattern of district of Bathinda, Punjab, India. Remote sensing (RS) and geographical information system (GIS) were used to perform the analysis of satellite data using image processing and classification procedures. For preparing LULC maps, supervised classification was carried out using maximum likelihood classification (MLC) algorithm, aided with Earth Resources Data Analysis System (ERDAS) Imagine 2014 and ArcGIS 10.3 software. Further, change detection study was done using multi-temporal Linear Imaging Self Scanning Sensor-III (LISS-III) data sets of the year 2006 and 2018 to analyze the temporal changes. It was observed that the region is occupied by various ground features such as water, built-up area, agricultural land, vegetation/trees and fallow land. The results revealed that the area under water bodies have increased by 0.413km2 in 2018. The built-up areas including human settlements, commercial infrastructures, roads and other pavements, have increased from 584.448km2 to 852.140km2 between 2006 and 2018, whereas the agricultural land has reduced from 2686.121km2 to 2398.384km2 during the period. The area under vegetation (trees) indicated that there was an increasing trend from 28.490km2 to 54.678km2 during 12years of time span whereas, the fallow land/barren land showed a decreasing trend from 26.361km2 to 18.367km2. It is suggested that the LULC change detection studies are very significant to conserve the land resources and to avoid further degradation.

A Light-Weight Neural Network Using Multiscale Hybrid Attention for Building Change Detection

The study of high-precision building change detection is essential for the sustainable development of land resources. However, remote sensing imaging illumination variation and alignment errors have a large impact on the accuracy of building change detection. A novel lightweight Siamese neural network building change detection model is proposed for the error detection problem caused by non-real changes in high-resolution remote sensing images. The lightweight feature extraction module in the model acquires local contextual information at different scales, allowing it to fully learn local and global features. The hybrid attention module consisting of the channel and spatial attention can make full use of the rich spatiotemporal semantic information around the building to achieve accurate extraction of changing buildings. For the problems of large span of changing building scales, which easily lead to rough extraction of building edge details and missed detection of small-scale buildings, the multi-scale concept is introduced to divide the extracted feature maps into multiple sub-regions and introduce the hybrid attention module separately, and finally, the output features of different scales are weighted and fused to enhance the edge detail extraction capability. The model was experimented on the WHU-CD and LEVIR-CD public data sets and achieved F1 scores of 87.8% and 88.1%, respectively, which have higher change detection accuracy than the six comparison models, and only cost 9.15 G MACs and 3.20 M parameters. The results show that our model can achieve higher accuracy while significantly reducing the number of model parameters.

Is climate really changing? Insights from analysis of 30-year daily CHIRPS and station rainfall data in Zimbabwe

Climate change debate has been ongoing for at least two decades world over. However, an understanding of how much change has occurred in developing countries remains limited. Using a combination of daily Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and gauge rainfall data, this study analysed whether rainfall characteristics over 30 years (1989 to 2018) in Zimbabwe exhibited any significant trends. Firstly, Kappa statistic and a confusion matrix were used to test the appropriateness of CHIRPS satellite derived rainfall estimates for carrying out trend analysis. Thereafter, the Mann Kendall test, Sen's slope and kriging were used, to detect trends, ascertain magnitude of change and map the spatial variation in rainfall characteristics, respectively. The rainfall characteristics included: Total Number of Wet Days (TNWD), Maximum Number of Consecutive Wet Days (MNCWD), Maximum Number of Consecutive Dry Days (MNCDD) and Maximum Rainfall per Day (MRD). Validation results showed that overall accuracy ranged from 0.862 to 0.897 while kappa statistic ranged from 0.739 to 0.81, suggesting a substantial agreement between satellite derived- and gauge rainfall. No statistically significant trends were detected for the four rainfall characteristics analysed at 95% confidence interval. Although no significant trends were detected at 95% confidence interval to signal climate change it was observed that MNCDD, MRD and TNWD were increasing while MNCWD was generally decreasing. This key finding opens new opportunities for geographically targeted and integrated intervention strategies for agricultural and water management in Zimbabwe. This is particularly important considering that Zimbabwe mainly depends on rain fed agriculture and understanding of the key rainfall characteristics can enable proper planning and decision making in terms of adaptation that which can ensure water availability and food security. Food security is fundamental in achieving national security. In addition, this study opens opportunities for further studies on the factors that influence the trends through using sub-daily and daily satellite data for a relatively longer period i.e., >30 years. It is therefore our contention that fine spatial and temporal scales adopted in this study yield better characterisation of changes in rainfall regimes, hence brings out better insights on understanding intra-seasonal fluctuations for climate change detection studies.

Evaluation of Anantapuramu District Watershed Projects Using Remote Sensing &amp; GIS Technology

Change detection studies have been carried out for evaluation of fifteen watersheds in Anantapuramu District of Andhra Pradesh. The study was carried out using high resolution Resourcesat-2, Linear Imaging and Self Scanning-IV (LISS-IV) data of 2011 (pre-treatment) and 2016 (post-treatment) to assess the changes in land use/land cover and biomass over a period of five years (2011-16). With the implementation of the watershed developmental activities, an additional area of 14440 ha has been brought under cultivation. The output of Normalized Difference Vegetation Index classification indicates the increase in dense vegetation from 5842 ha to 13160 ha. An additional area of 396 ha increased under water bodies and 1101 ha waste land converted to cultivable land due to construction of farm ponds, percolation tanks and check dams.

Spatiotemporal detection of land use/land cover changes and land surface temperature using Landsat and MODIS data across the coastal Kanyakumari district, India

This study assesses the changes in land use/land cover (LULC) and land surface temperature (LST) to identify their impacts from 2000 to 2020 along the coast of Kanyakumari district, India using remote sensing techniques. Landsat images are used to estimate the LULC changes and the MODIS data for LST. The Maximum Likelihood Classification (MLC) method is used, and the LULC is classified into six categories: Agriculture Land, Barren Land, Salt Pan, Sandy Beach, Settlement, and Waterbody. Within the two decades of the present change detection study, upheave in the Settlement area of 49.89% is noticed, and the Agriculture Land is exploited by 20.09%. Salt Pan emits a high LST of 31.57 °C, and the Waterbodies are noticed with a low LST of 28.9 °C. However, the overall rate of LST decreased by 0.56 °C during this period. This study will help policymakers make appropriate planning and management to overcome the impact of LULC and LST in the forthcoming years.

Assessment of land use, land cover change in the mangrove forest of Ghogha area, Gulf of Khambhat, Gujarat

The most important biological ecosystems on the earth are mangroves, palms, shrubs, and trees that have adjusted to the challenging environments of high salinity, warm air and temperatures, severe tides, murky, sediment-hampered waterways, and oxygen-depleted soils make up mangroves that are mostly found in the intertidal zone. It is a sanctuary for a variety of aquatic animals and provide protection to coastal land. It acts as “Kalpvruksh” for human being and aquatic culture. Despite the importance of the mangrove ecosystem, the area of mangroves has been declining for several decades. The multispectral data and image processing techniques are frequently used to map and monitor mangrove changes. In this study, the changes of mangroves were accomplished along the Ghogha coast, Bhavnagar district of Gujarat coast, India using the Landsat 8 OLI and Sentinel–2A satellite data for 2016 and 2018, respectively. The image analysis and change detection studies were carried out using QGIS (3.10.14) and ArcMap (10.7.1) software. Using high-resolution satellite data and the Normalized Difference Vegetation Index (NDVI), this research attempts to determine the geographical extent of mangroves. The result shows that the total mangrove cover decreased from 2016 to 2018 and Sentinel-2A images shows a better performance in comparison to Landsat 8 OLI, because of its higher spatial resolution.

Detection of land use / cover changes of the KOSH region over a period of 14 years using the South African National Land Cover datasets for 2000 and 2014

Simple algebraic change detection techniques viz. image difference and image ratio were applied to the South African national land use / cover (NLC) datasets of years 2000 and 2014, prepared in grid format covering the Klerksdorp–Orkney–Stilfontein–Hartebeestfontein (KOSH) region in order to assess land use/land cover changes. Both the 2000 and 2014 NLC datasets were generated from Landsat images using different classification schemes and the code values &amp; attributes of the land cover classes of the two datasets were different/not comparable. In order to make these datasets comparable for change detection, the NLC2000 dataset was examined in ArcView GIS by superimposing it onto the NLC2014 dataset and similarities and differences were identified. For each cover type of the NLC2000 dataset, comparable cover type of the 2014 dataset was identified by making a query to the NLC2000 dataset and after viewing the spatial distributions of selected units in respect of the NLC2014 dataset. Suitable code values of NLC2014 dataset were identified for the NLC2000 dataset and it was later reclassified. The land use / cover change detection study reveals that increase in areas were observed for the cover types: Cultivated common fields (low), Cultivated common fields (med), Mines 2 semi-bare, Wetlands, Urban commercial and Plantations/woodlots mature. The Grassland, Thicket/dense bush, Urban residential (dense trees/bush), Mines 1 bare, and Cultivated common pivots (high) showed a decrease in places. During the 14 years, Grassland had decreased from 2,132.47 km2 (77.35% of the total area) to 1,629.78 km2 (59.11% of the total area) owing to landscape transformation to other land covers (e.g. Cultivated common fields and Urban residential) due to human activities. The percentage increase in areas observed for the Cultivated common fields (low and medium) were 8.21% and 2.96% while the Mines 2 semi-bare, Wetlands, Urban commercial, Plantations/woodlots mature showed increases of 0.67%, 0.32%, 0.28% and 0.23% respectively. The area of Thicket/dense bush decreased from 108.15 km2 to 56.71 km2 (change of 1.87%). Maps of land use/land cover changes and statistics obtained for the changed areas are very useful for identifying various changes occurring in different classes and for monitoring land use dynamics.