Search

The modern cities of the world are characterized by continuous warming status. Land surface temperature (LST) is a very important part of the environmental status of mixed urban land and it is intimately associated with normalized difference vegetation index (NDVI). The current study makes an attempt on the seasonal fluctuation of LST-NDVI relation on diversified land surface material in Raipur City of India by using a large Landsat dataset for different seasons from 1991–92 to 2018–19. The results present the considerable rising trend of mean LST (1.6 °C, 5.3 °C, 4.8 °C, and 1.1 °C in the pre-monsoon, monsoon, post-monsoon, and winter, respectively) during the study period. The LST and NDVI produce a strong negative correlation (− 0.74, − 0.54, − 0.63, and − 0.49 in these four seasons) on plants; a moderate negative correlation (− 0.42, − 0.34, − 0.42, and − 0.21 in the four aforesaid seasons) on the barren land and urban settlement; and an insignificant correlation (0.27, 0.08, 0.05, and 0.19 in the four abovementioned seasons) on water bodies. This study is helpful in future planning for the ecological development of a city in tropical environment.

Understanding land-use change is imperative in the management of Indonesian coastal ecosystems with numerous environmental impacts generated from land-use conversion. To assess these impacts in holistic perspective, we use three sources of data, land cover change, policy maps, and interviews, to triangulate the main issues. Combining these sources, we analyze land-use changes in the past based on perception interviews and secondary data from the government spatial plan policy map and observable land-cover. In Tanjung Batu and Derawan Island we found that local populations were more aware of the changes related to infrastructure development, but that there are discrepancies between assigned and actual land-use, illustrating the value of a triangulated framework.

Spatiotemporal changes in land use/land cover (LU/LC) have been relevant factors in causing the changes in Urban Heat Intensity (UHI) pattern across rural and urban areas of cities all over the world. Studies conducted have shown that this relation does not depend on extent meaning UHI that can be an important factor not only for a country but for environment of a city also. Over the years, it is reflected in local environment of cities. In this study, such relations are shown over Lucknow city of India for 4 decades from 1985 to 2015 using freely available 100 m resolution Decadal Land use map for India for year 1985, 1995 & 2005 and CGLS-100 product for year 2015. Land Surface Temperature (LST) is calculated from Landsat series data using NDVI-based emissivity method. LST is correlated with 5 LU classes Built-up, Agricultural, Vegetation, Water & Barren. LC indices Normalised Difference Built-up Index (NDBI), Normalised Difference Vegetation Index (NDVI), Normalised Difference Water Index (NDWI) & Normalised Difference Barren Index (NDBaI) are correlated with Normalised LST (NLST), respectively, and it is found that NLST is positively related with all indices except for NDVI with strong negative correlation and R2 of 0.27. Results show reduction of 22 km2 in area affected from UHI for Lucknow from 1985 to 2015 and expansion of UHI region for Lucknow towards its outer boundary. The study helps in understanding the locality of UHI effect in a city of a developing country with the help of open source data.

Acute Encephalitis Syndrome (AES) has been treated as a ‘mysterious disease’ because of the unknown aetiology in most of the reported cases. AES is assumed to have a linkage with meteorological variables and other environmental parameters in and around the Gorakhpur district of Uttar Pradesh, India. Hence, the current research is aimed to frame a model for AES association with meteorological variables. The Land Use Land Cover (LULC) changes are also considered in the analysis since 78% (approximately) of the land of Gorakhpur is under agriculture practices. The data for AES cases and meteorological variables rainfall (RF, mm), relative humidity (RH, %), and temperature (°C) are considered from Jan 2012 to Dec 2017. The Bayesian Generalized Additive Model (GAM) builds an association between AES and the meteorological variables and predicts AES cases over the study area. Various GAM models for different possible combinations of meteorological covariates are analysed. The least Deviance Information Criterion (DIC) (83.90) is found for the model with covariates-Relative Humidity (RH, %), Skin Temperature (SKT, °C), Two-meter Temperature (T2, °C), the Month of the Year (MoY), and lag-1 of AES cases. The impacts of covariates on AES cases are found non-linear and inconsistent. Results show the positive effect of the covariates in the following ranges; RH in the range of 55–63%, SKT greater than 30 °C, and 2-m temperature greater than 25 °C. Similarly, a positive impact of MoY is observed for July to September. The predictive ability of the model is assessed using an appropriate cross-validation technique. The absolute percentage error is found in the range of 0.5–47.6% monthly. The root mean square errors are also observed within the range of 0.05–2.0.

The present study uses Landsat satellite images of 1990, 2000 and 2018 to identify the land-use changes. Multilayer perceptron-neural network based land change modelling (LCM) has been applied to model future land-use/land cover (LULC). The prediction model has been validated using simulated and classified LULC maps of 2018 which resulted into an overall accuracy of 88%. The results indicate 389.27% increase in built-up area as the prominent land-use change during 1990–2018 and an increase of 56.25% in built-up area is forecasted during the year 2018–2040. Land absorption coefficient and land consumption rate indices, used to characterize urban expansion, indicate continued compact built-up structure during 1990–2018 due to population increase. The observations derived from this study would be useful as it will help the regional planners with forecasted land-use beforehand in planning the built-up and abundantly available natural resources in the area according to the increasing future demands.

The rapid urbanization and land-use/land-cover (LU/LC) changes have resulted in the unplanned and unsustainable growth of the Indian cities. This has resulted in a number of environmental issues such as escalating the urban heat island (UHI) intensity over the cities. Therefore, this study was designed to model and quantify the UHI dynamics of Mumbai city in response to the LU/LC change during 1991–2018 using temporal Landsat datasets. The result shows a significant decline in vegetation cover from 215.8 to 129.27 km2, while the built-up areas have almost doubled, i.e., from 173.09 to 346.02 km2 in the Mumbai city during 1991–2018. As a consequence of this, a significant increase in the LST has been noticed in both urban heat island (UHI) and non-UHI zones. Although the areas under UHI zones have not increased significantly, the land surface temperature (LST) gap (difference between minimum and maximum LST) has declined in the Mumbai city from 30.04 °C in 1991 to 20.7 °C in 2018. Further, the minimum and mean LST over each LU/LC classes have also shown a significant increase. On the other hand, the regression analysis shows that the association between UHI and normalized difference built-up index (NDBI) has increased in the city, while the association of vegetation density (NDVI) and normalized difference bareness index (NDBaI) has declined in the city. The study can provide useful insights into the process of urban planning and policy makings for urban spatial planning and UHI mitigation strategies.

Urban heat island (UHI) is a phenomenon which may have adverse effects on our environment and is stimulated as a result of urbanisation or land cover changes. Thermal remote sensing has been found beneficial to study the effect of urbanisation on UHI intensity. This paper analyses the variation in Land surface temperature (LST) with land cover changes in Varanasi city of India from 1989 to 2018 using Landsat satellite images. A new index named Urban Heat Intensity Ratio Index (UHIRI) was proposed to quantify the urban heat intensity from 1989 to 2018 which was found to increase from 0.36 in year 1989 to 0.87 in year 2018. Further, contribution of each land cover towards UHI was determined using Land cover contribution index (LCCI). The negative value of LCCI for water and vegetation indicates its negative contribution towards UHI whereas positive value of LCCI for bare soil and built-ups depicted its positive contribution towards UHI. The LCCI value for urban land cover shows significant increase in 29 years i.e. 0.49, 1.43, 3.40, 4.37 for years 1989, 1997, 2008 and 2018 respectively. The change in normalized LST from years 1989 to 2018 for the conversion of bare land to built-ups and vegetation to built-ups were found to be as -0.11 and 0.42 respectively. This led to conclusion that the replacement of vegetation with urban land cover has severe impact on increasing UHI intensity.

This study is aimed to analyze the dynamics of land use/ land cover (LU/LC) change in a newly created special economic zone, Gautam Buddha Nagar district during 2003–2015. The Landsat satellite data has been used to map the LU/LC pattern of 2003 and 2015 of the study area, using indices overlay method. Consequently, the indices overlay have been created using three land-use indices, i.e. modified normalized difference water index (MNDWI), soil adjusted vegetation index (SAVI), and enhanced built-up and bareness index (EBBI), and then the maximum likelihood classifier (MLC) has been used for the LU/LC classification. The result illustrates that the built-up area (419.35%) and open land (388.36%) have increased during 2003–2015 while the cropland (− 34.38%), scrubland (− 73.25%), and water bodies (− 58.37%) have declined. Further, northern parts of the district have experienced maximum change in the LU/LC while the southern parts have experienced comparatively low change. The study also reveals that the increase in the built-up area occurred mostly at the cost of cropland and scrubland. The statistical analysis shows that the EBBI and SAVI have high relationships with LU/LC while the MNDWI has a comparatively low relationship. The study concludes that cropland and scrubland are the main LU/LC types that get transformed due into the built-up area the study area and the SAVI, MNDWI, and EBBI are the good indicators in the study of LU/LC classification and change analysis.

In recent years, rapid land use land cover (LULC) changes have continuously taken place in many regions all over the world as a result of human activities. In the present study, the changes in LULC were analyzed by means of multi-temporal remote sensing of Qena-Luxor Governorates in Egypt between 1984 and 2018. In order to map and monitor the land use land cover changes, several remotely sensed data were applied to create multi-maps using (1) the normalized difference vegetation index and (2) supervised classification of Landsat images using field chick and accuracy assessment, including field verification and Google Earth Professional. Therefore, the lands in the study area can be classified as follows: (1) agricultural lands, (2) built-up areas, (3) water bodies, (4) reclaimed lands, and (5) desert lands. The results indicate that agricultural lands grew from an average of 1238.7 km2 (9.8%) in 1984 to 1707.04 km2 (13.40%) in 2018 and urban lands increased from 345.2 km2 (2.7%) in 1984 to 445.28 km2 (3.5%) in 2019. Furthermore, the reclaimed lands increased approximately from 4379.7 km2 in 1984 (i.e., 34.4% of the total study area) to 4521.05 km2 in 2000 (35.507%). However, this class was followed by a marked decline to 4373.51 km2 (34.35%) between 2000 and 2010 and then increased to approximately 4442 km2 (34.89%) between 2010 and 2018. Desert lands (limestone plateau and some lowland desert fringes) decreased from 6635.4 km2 (52.2%) to 6003.5 km2 (47.15%). The results showed that the overall accuracy of the supervised classification of Landsat satellite images ranges from 87 to 92.5% while kappa statistics were from 0.83 to 90.

Research in the field of remote sensing attracts attention among researchers all over the world. From different remote sensing applications, the problem on Land Use/ Land Cover change analysis has been considered as the critical research for more than four decades. The researchers had discovered the new innovative ways of finding the solution to analyze the Land Use/ Land Cover change over a particular region. The multispectral and hyperspectral satellite images play a considerable part in analyzing environmental changes. Many algorithms developed and used by researchers for analyzing the Land Use/ Land Cover change are discussed in this paper. This review article aims to provide detailed analyses of performing Land Use/ Land Cover changes in the field of remote sensing. The main motive is to make the future researchers know about the flow of the Land Use/ Land Cover change analysis process and provide a clear presentation about every method. The results of this Land Use/ Land Cover problem mainly assist the land resource management, urban planners, and other government officials across the world in protecting the land resource and its nature for future needs.

Urbanization has evolved as one of the key factors responsible for the environmental health not only for the urban centres, but also for the suburban regions. In the recent years, most of the suburban regions in India are experiencing flash floods during the high intensity low duration rainfall condition. Such situations of flash floods in the urban and suburban areas are referred to as storm runoff. This paper deals with the computation of storm runoff in the suburban catchments of the Pune City. Five catchments, known for flash floods in the recent years, are identified for the present work. Storm runoff estimations in these catchments have been done using LandsatTM data of 1989 and 2011. Population-calibrated impervious surfaces (IS) were extracted for these catchments, and runoff has been calculated using the Soil Conservation Service-Curve Number method. Major changes in the land use land cover pattern in these 22 years have been detected, with a net growth in built-up area of almost 10 times from 1989 to 2011, leading to the increase in IS in the catchment. The impact of increase in built-up area and IS has augmented storm runoff in the catchment. Estimated runoff values increased from 461.8 m3 to 1068.52 m3 (Baner), 2026.4 m3 to 3638.73 m3 (Kharadi), 2947.5 m3 to 4736.46 m3 (Kondhwa Ghorpadi ), 1021.1 m3 to 2039.57 m3 (Wadgaon Sheri) and 1176.89 m3 to 3691.18 m3 (Wadgaon Budruk) from 1989 to 2011. Thus, it is quite evident that the growth in built-up area and impervious surfaces has enhanced the capacity of suburban basins to generate more runoff.

This study is aimed to analyse the impacts of Land Use/Land Cover (LULC) to Land Surface Temperature (LST) and water level fluctuation in Raipur city, for which pre- & post-monsoon data for four years 2000, 2008, 2014 and 2018 have been analysed. By this study, it has been observed that changing LULC has its direct impact on surface temperature as it is increasing with rapid urbanization. Also due to the modern agricultural practices and increasing urbanization as the major factor responsible for changing LULC, and the declining water level.

The profound increase in human activities and the degrading scenario in fragile ecosystems of the western Himalayas like that of Wular Lake have highlighted the need to analyze the impact of increasing human population on such environments. Wular Lake, the largest freshwater lake, has under gone/witnessed degradation from the last few decades because of tremendous increase in human population in its catchment area. The present work deals with the land transformation in the Wular lake catchment over a period of 28 years (1991–2018) which represents the specific changes in the classes, majorly like waterbody (− 51.60%), swamps/marshy (− 30.92%), plantation (− 6.34%), dense forest (− 10.56%) and snow cover (− 3.66%). To carry out this study, Landsat-5 (TM) images of 1991, 2001, 2011 and Landsat-8 image of 2018 have been used. The population data for the same time period has also been used to coincide with the study dates. Thereafter, the correlation between the changing LULC and human population has been calculated using Pearson’s correlation (rho ρ). LULC categories like barren/rocky, agriculture, horticulture and settlement showed a positive correlation while the categories like snow cover, waterlogged/swampy, water body, dense forest and plantation showed negative correlation with the growing population.

The interaction of land use/land cover (LULC) and climate change, to a large extent, involves anthropogenic activities. This study was carried out in the derived savannah of Nigeria, a delicate, transition ecological zone between the rainforest and savanna zones where the interaction of LULC and climate change could be well appreciated. Using the remote sensing and GIS, Land Change Modeler (LCM), and multivariate geostatistics tools, the study evaluated coupled interaction between LULC and climate change and assessed the changes in the land use/land cover patterns for the periods 1972, 1986, 2002, and 2019. It also evaluated the present (1941-2019) and future (2020-2050) variability in rainfall patterns and made an attempt to predict the interaction between LULC and climate change in future climate. The results suggest that the urban (built-up) area, waterbody, woodland, and farmland experienced a rapid increase of about 2,400%, 583%, 277%, and 32%, respectively, while the forest cover lost about 39% between 1972 and 2019. Furthermore, the study predicted 46% and 29% reduction in the forested area between 2002 and 2050 and 2019 and 2050, respectively. The study concludes that rainfall will be the major driver of LULC change within the study area under a future climate.

Urbanization is proving stressful for the supply-demand budget of ecosystem services (ESS) and a real challenge of the present times. The process is accelerating in Pakistan and demands enhanced focus on ESS research. Keeping in view the contextual constraints, the land cover–based Matrix Model was relied upon for assessments regarding ESS in the present study. The model links the land use/land cover (LULC) types of a contextual setting for the assessments of ESS. The model amalgamates local ecological knowledge (LEK) and expert-based judgments for considerations. The findings of the study divulge that ecological resources are the most proficient contributors to the ESS; their main beneficiaries are the built-up areas. The degeneration in the performances of the ecological infrastructure is more acute in Rawalpindi as compared to Islamabad. The observed variations are rooted in the diverse sociodemographic aspects, planning, and management paradigms. The findings confirmed that the Matrix Model is a pragmatic option for assessing the supply-demand potentials of ESS in the developing regions.

Deep learning convolutional neural network (CNN) is popular as being widely used for classification of unstructured data. Land use land cover (LULC) classification using remote sensing data can be used for crop identification also. Present study aims to examine the use of deep learning CNN for LULC classification on Indian Pines dataset and for crop identification on our study area dataset. In the present work, AVIRIS sensor’s Indian Pines standard dataset has been used for LULC classification. Study area from Phulambri, Aurangabad, MH, India, has been used for crop classification. Data have been gathered from EO-1 Hyperion sensor. The accuracy of CNN model depends on optimizer, activation function, filter size, learning rate and batch size. Deep learning CNN is evaluated by changing these parameters. It has been observed that deep learning CNN using optimized combination of parameters has provided 97.58% accuracy for the Indian Pines dataset, while 79.43% accuracy for our study area dataset. The empirical results demonstrate that CNN works well in practice for unstructured data as well as for small size dataset.

Preprint version In this article, we present an approach to land-use and land-cover (LULC) mapping from multispectral satellite images using deep learning methods. The terms satellite image classification and map production, although used interchangeably have specific meanings in the field of remote sensing. Satellite image classification describes assignment of global labels to entire scenes, whereas LULC map production involves producing maps by assigning a class to each pixel. We show that by classifying each pixel in a satellite image into a number of LULC categories we are able to successfully produce LULC maps. This process of LULC mapping is achieved using deep neural networks pre-trained on the ImageNet large-scale visual recognition competition datasets and fine-tuned on our target dataset, which consists of Landsat 5/7 multispectral satellite images taken of the Province of Manitoba in Canada. This approach resulted in 88% global accuracy. Performance was further improved by considering the state-of-the-art generative adversarial architecture and context module integrated with the original networks. The result is an automated deep learning framework that can produce highly accurate LULC maps images significantly faster than current semi-automated methods. The contribution of this article includes extensive experimentation of different FCN architectures with extensions on a unique dataset, high classification accuracy of 90.46%, and a thorough analysis and accuracy assessment of our results. "This research has been supported by through a Queen Elizabeth II Diamond Jubilee scholarship and by the Natural Sciences and Engineering Research Council of Canada (NSERC) discovery Grants 194376 and 418413. Moreover, this work would not have been possible without the support and cooperation of GeoManitoba." https://link.springer.com/article/10.1007/s00521-019-04349-9

Nowadays, the rate of urban expansion is quite alarming in developing countries, especially in India. This hasty rate of urban expansion has significantly transformed the natural landscape and creates negative impact on environment. For planned development, we need to understand the urbanization process clearly, and also need to understand the changes in land use/land cover (LULC). Currently, remote-sensing data and GIS technique have been used extensively to understand the urbanization processes and its impact on land use. The aim of the current study is to analyze the expansion of Bhopal city and its impact on land use, using remotely sensed data along with several statistical techniques such as Shannon’s entropy and Chi square statistic. The analysis shows that the city has experienced a rapid rate of horizontal expansion; city area increased from 15.8 km2 in 1991 to 184.5 km2 in 2016 at an average change of 7 km2 year−1 with spatial disparity. LULC classes of the city area were altered by this expansion process. Expansions of built-up area mainly come from the agricultural and forest land. These are the two primary classes which were affected badly by this expansion process. The present study suggests to make cities more compact, and the need to take proper land and housing policy by the local governing authority and policy makers.

The extraordinary rate of urban growth in developing countries causes expansion of cities towards their outskirts resulting in work space insufficiency and unsuitableness to microenterprises. However, this situation has received insignificant emphases/coverage within latest interdisciplinary literatures surveyed. So, the main objective of the current study was to investigate the effect of urban flat housing expansion on the user-friendliness of premises to selected micro businesses in Shashemene City, West Arsi Zone, Oromia Regional State of Ethiopia. Simple random sampling was employed to select a total of 240 businesses. The obtained data were analyzed quantitatively and qualitatively/description and revealed that businesses are suffering from paucity, unsuitableness, high- priced premises and frequent location shift for the work places, lack of expediency and infrastructure. Furthermore, the findings of this study revealed that permanency of selected business in familiarized location depends on their financial strength and experiences on the same business. Thus, it could be suggested that authorities need to practice measures like; business clusters development, creating coalitions among chambers of commerce for construction of common use multiplex business premises, and also involve share companies or private developers; revisit urban land leasehold proclamation performance and construction works progress in the City.

Providing the information about land use/cover is crucial for many purposes, especially that concern with planning, determination, sprawl and primary human requirements, and health. The extracted information additionally supports in controlling the dynamics of land production from a developing population. In this study, the impact of speckle filtering was examined on the classification of LULC (land use/land cover) by combined data of SAR Sentinel-1A (VH, VV) and multispectral Sentinel-2B data in Kirkuk city, Iraq. Various speckle methods such as Boxcar, Frost, Gamma, and Lee filters were applied on the Sentinel-1A dataset with a different window size and then quantitatively evaluated using various filter performance indices. The Gram–Schmidt (GS) fusion process was chosen to combine the multispectral Sentinel data and VH, VV bands of Sentinel-1 data with different window size. The random forest classifier was applied to classify LULCon, the original data and fused data. The acquired outcomes showed that the Frost filter presented the highest performance of filter using different types of evaluation indices on sentinel-1A (VH, VV) data. The result also demonstrates that the Frost filter is the best filter to enhance the classification precision using the combination data between Sentinal-1(VH) and Sentinel-2B, with an overall accuracy of 97.211% using a 9 × 9 window size and total accuracy of 95.9362% compared with the other filters. Boxcar filter showed the highest classification accuracy indicated in window size 11 × 11, with an accuracy of 95.62%, while the lowest accuracy recorded in 3 × 3 window size of 93.63% with a reduction of efficiency 0.4 % compared with the (VH) data. We obtain good results with increasing of efficiency as 3.191%, 0.192%, 5.582%, 5.58% using windows size of 5 × 5, 7 × 7, 9 × 9, and 11 × 11 respectively, and total enhancement noted was 2.829% compared with the combination with (VV) data.

Background The impact of watershed land-use on surface water quality is one of the under researched areas in mega cities of the developing countries like Addis Ababa. The study examined the impact of watershed land uses on the Kebena river water quality within its seasonal and spatial variation and assessed the relationship between river water pollution and dominant land-use types in the sub-watersheds. Method The main land use types in the sub-watersheds were digitized from aerial photograph of 2016, and quantified for water quality impact analysis. Water samples were collected from the main Kebena river and the three sub-watersheds source and outlet points. A total of 128 samples were collected during the dry and wet seasons of 2016 and 2017 and analyzed for various water quality parameters. The study employed ANOVA, independent t-tests and multiple regression analysis to examine variations in water quality and assess the influence of the different land uses on water quality. Results Forest, built-up area and cultivated lands are the three major land use types in the Kebena watershed accounting for 39.14, 32.51 and 27.25% of the total area, respectively. Kebena catchment is drained by three sub-watersheds namely, Denkaka (44.9% cultivated land), Little-Kebena (60.87% forested) and Ginfle (90.44% urban land). The concentration of pollutants in the Kebena river was significantly higher (P P Conclusion Integrating watershed planning with land use planning is of paramount importance to address water quality problems in urban areas. Thus, in the urban dominated sub-catchment, land-use planning should aim to relocating river front communities, providing sufficient river buffer-zones and forwarding appropriate storm water management schemes. In the forested sub-catchment, planning should protect, retain and enhance the existing natural green spaces through open space planning, and management schemes while providing wide river-buffer with natural vegetation cover to minimize pollution load to urban rivers from agriculture dominated sub-watershed.

Kashmir Valley has witnessed a catastrophic flood in 2014, which led thousands of people homeless and devastated the agricultural lands. In the present study, the impact of 2014 flood occurred during the month of September was analysed in the vicinity of Dal lake using the pre- and post-flood periods satellite observations. The study exhibited an inundation of 42.50 km2 area (52.47% of total area) during September 2014 flood period, which is primarily topographical low-lying area (i.e. below 1530 m). The land use/land cover (LULC) analysis during pre- and post-flood periods exhibited significant impact over the built-up land with 20.4% decrease in the built-up (from 25.44 to 20.25 km2), which was mostly evident in the western, southern and eastern parts of Dal lake. It has also severely affected the road network with inundation of 220.84 kms (55.62% of total road network). The road network in the lower elevation (

Rapid urban expansion and associated land-use land-cover (LULC) change in India have emerged as a serious environmental threat that accelerates the impacts of urban heat island intensity (UHII). Three independent investigations have been conducted in this study using a series of Landsat data. The objectives of this work are: (1) To predict the near-future LULC scenario using an integrated model; (2) To understand the connection between band mean for particular LULC class with LST; (3) To analyze the temporal relationship between different types of built-up clusters and LST. The LULC and LST maps reveal that LST increases from 27.01° to 33.86°C, whereas built-up areas rise from 6.93% to 27.10% during 1988–2018, respectively. We observed that the near-future LULC scenario of KMA shows a huge expansion of built-up areas paid by decreased vegetation and open spaces. A clear significant correlation has been found between band mean and LST in all three Landsat sensors with the R2 = 0.84; p

Texture analysis of remote sensing images has been received a substantial amount of attention as it plays a vital role in improving the classification accuracy of heterogeneous landscape. However, it is inadequately studied that how the images from different sensors with varying spatial resolutions influence the choice of textural features. This study endeavors to examine the textural features from the Landsat 8-OLI, RISAT-1, Resourcesat 2-LISS III, Sentinel-1A and Resourcesat 2-LISS IV satellite images with spatial resolution of 30, 25, 23.5, 5×20 and 5.8 m respectively, for improving land use/land cover (LULC) classification accuracy. The textural features were extracted from the aforesaid sensor data with the assistance of gray-level co-occurrence matrix (GLCM) with different moving window sizes. The best combination of textural features was recognized using standard deviations and correlation coefficients following separability analysis of LULC categories based on training samples. A supervised support vector machine (SVM) classifier was employed to perform LULC classification and the results were evaluated using ground truth information. This work demonstrates the significance of textural features in improving the classification accuracy of heterogeneous landscape and it becomes more significant as the spatial resolution improved. It is also revealed that textures are vital especially in the case of SAR data.

Land use/land cover (LULC) changes have emerged as a major concern on global as well as on the local stage because of its considerable impact on climate and environment, especially in rapidly developing areas. Therefore, accurate mapping of LULC and ongoing changes over a time period have drawn a lot of attention in recent years. Remote sensing images from Landsat series satellites are a major information source for LULC change analysis. The present study mainly focuses on the evaluation of three classification techniques, namely maximum likelihood classifier (MLC), artificial neural network (ANN) and support vector machine (SVM) using multi-temporal Landsat images in order to choose the best method among them. The overall analysis based on accuracy measures indicates that the SVM is superior to ANN and MLC. The classification results achieved by the best recognized technique (SVM) were applied to assess the spatio-temporal changes in LULC that occurred in a fast growing Varanasi district of India over a period of 14 years (2001–2014). A paired samples t test was also carried out to determine the statistical significance of changes in LULC between different studied time periods. The results reveal the rapid expansion in built-up area resulted in substantial decrease in agricultural land and other LULC classes. This study also highlights the importance of Landsat images to provide accurate and timely LULC maps that can be used as inputs in a number of land management and planning activities.

Urban settlements are the socio-economic drivers of human community. However, rapid urban expansion during recent times has a profound effect on regional as well as global rise in temperatures. Remote sensing is a powerful tool to monitor and quantify the expansion of urban built-up area and assess its effects on regional heating. Landsat-derived Land Use Land Cover (LULC) maps have shown a very rapid urbanization in Bhubaneswar, a tier-2 city and the capital of Odisha in the eastern part of India, almost doubling over the last 15 years. Our study on the effects of urbanization reveals that during the period of 2003-2017, the built-up area in the urban district of Bhubaneswar has increased by about 77% at the cost of natural vegetative cover and agricultural/fallow land. This affects the behavior of several climate variables over the urban centers with a clear distinction in these variables observed between the urban and surrounding rural settlements. Land surface temperature (LST) derived from the thermal bands of Landsat satellites at a spatial resolution of 30 m is used for the analysis over the area of interest. The Bhubaneswar airport (urban reference) has shown an increase in mean summer LST by 8% over the study period of 15 years, and 9.84% in LST in the last 5 years from 2013 to 2017 alone. From the present study, it is inferred that it is essential to adopt a sustainable approach for any urban growth to restrain its effect on urban temperature rise.

Land use changes are vital to the food security challenge. Food security has determined the history of humanity. The global population will increase to about 9 billion in 2050–2060. Food and feed demands have been projected to double in the 21st century, which will further increase the pressure on the use of land, water, and nutrients. The study of the relationship between food security and land use is of paramount importance for policy formulation. During the past one decade, the study area has undergone many LULC changes due to rapid urban growth, poorly planned infrastructural development and expansion of horticulture which adversely affected the food security. For LULC change detection analysis temporal Landsat satellite data sets captured by thematic mapper were employed. Maximum likelihood supervised classification algorithm was applied to classify the study area, whereas, post classification comparison approach was adopted to analyze the LULC changes. The results have revealed that the area under horticulture and forests has declined by 3.7 and 2.26% respectively while the area under horticulture and built-up has increased by 2.17 and 1.13% respectively.

Land use/land cover changes (LULCC) are one of the foremost aspects of environmental changes caused by human-induced activities mainly in rapidly developing areas. This study endeavors to evaluate and compare three hybrid models: stochastic Markov chain (ST-MC), cellular automata-Markov chain (CA-MC), and multi-layer perceptron-Markov chain (MLP-MC) to predict future land use/land cover (LULC) scenario in Varanasi district. LULC information extracted for years 1988 and 2001 was first employed to predict LULC scenario for 2015 using three hybrid models. The predicted results were compared with the observed LULC information for the year 2015 to appraise the validity of models through kappa index statistics. The MLP-MC model yielded reliable and best results. Finally, based on this consequence, the prediction of future LULC scenarios for years 2030 and 2050 was performed. The findings of this study exhibited the constant but overall increase of built up area and a considerable reduction in agricultural land. The results also demonstrate the potentiality of MLP-MC hybrid model for better understanding of spatio-temporal dynamics and predicting future landsacpe scenario in Varanasi district of Uttar Pradesh, India.

Land use/land cover change (LULCC) exerts significant influence on regional climate extremes, but its relative importance compared with other anthropogenic climate forcings has not been thoroughly investigated. This study compares land use forcing with other forcing agents in explaining the simulated historical temperature extreme changes since preindustrial times in the CESM-Last Millennium Ensemble (LME) project. CESM-LME suggests that the land use forcing has caused an overall cooling in both warm and cold extremes, and has significantly decreased diurnal temperature range (DTR). Due to the competing effects of the GHG and aerosol forcings, the spatial pattern of changes in 1850–2005 climatology of temperature extremes in CESM-LME can be largely explained by the land use forcing, especially for hot extremes and DTR. The dominance of land use forcing is particularly evident over Europe, eastern China, and the central and eastern US. Temporally, the land-use cooling is relatively stable throughout the historical period, while the warming of temperature extremes is mainly influenced by the enhanced GHG forcing, which has gradually dampened the local dominance of the land use effects. Results from the suite of CMIP5 experiments partially agree with the local dominance of the land use forcing in CESM-LME, but inter-model discrepancies exist in the distribution and sign of the LULCC-induced temperature changes. Our results underline the overall importance of LULCC in historical temperature extreme changes, implying land use forcing should be highlighted in future climate projections.

Land Use Land Cover Changes (LULCC) has impacts on a wide range of environmental and landscape attributes including the quality of land, water and air. The main objective of this study was to analysis the impact of anthropogenic activities and LULCC on weather (rainfall and temperature) and vegetation in the study area over a period of 28 years. The study has employed temporal LANDSAT sensors data to identify the changes in LULC and Vegetation Indices through anthropogenic influence. Moreover, statistical analysis of temperature and rainfall data (1985–2015) has also been done of Bambasi station. It has been found that the average temperature has been increased approximately 2.2 °C and the average rainfall amount was declined throughout the period. The drastic changes have been noticed in LULC, vegetation health and its area through NDVI during the first interval (1987–2001) but the land change growth was less in second interval (2001–2015). The increasing population, urbanization and resettlement scheme for refugees were the responsible factors for changes. This case study indicates that the impact of anthropogenic activities leads the change in LULC and the climate has been influenced or vice versa over the time period in this semi-arid region of Ethiopia.

Land use land cover (LU/LC) changes over the period are essential to understand the development of human activities within a region to define the impact of anthropogenic and natural activities. The virtual assessment of LU/LC changes between 2000 and 2016 is carried out in Pearl City (Thoothukudi) using Remote-Sensing data and GIS tool. This change detection is done by 15 m resolution cloud-free Advanced Spaceborne Thermal Emission and Reflection Radiometer data captured during the year 2000 and 2016. The dynamic changes depict the major land use features like settlements, saltpan, crop land, vegetation, barren and water body from satellite data through supervised classifications of a maximum likelihood algorithm. The classified images highlight a series of constructive results in settlement (4.30%), vegetation (4.00%), saltpan (0.24%) and pessimistic results in barren land (−7.68%), crop land (−0.64%) and water body (−0.23%) respectively. The study recommends that urbanization of settlement land usage is highly mobilized in Thoothukudi coastal areas.

Akure is one of the cities undergoing rapid growth in Nigeria, which has resulted to remarkable Urban Heat Island (UHI) effect. To examine the impact of this development in the city and its environs, this study derived the brightness temperatures and land use land cover (LULC) types using Landsat images between 1984 and 2016, of 190/55 path and row. Normalized Difference Built up Index (NDBI) and Normalized Difference Vegetation Index (NDVI), were used as LULC indicators for sub-pixel analysis. The results of this study revealed a remarkable change in built-up area at the expense of vegetation (forest) and bare lands. The sub-pixels analysis between land use/cover indicators and temperature revealed a strong correlation between LULC and temperature in the area. Findings revealed a 4.63 °C temperature increase in vegetated areas between 1984 and 2016; and an overall temperature values ranged from about 14 to 36 °C, which have been categorized into six classes (

In most of the developing countries, man-made developments in the environment have led to the growing demand to contextualize the land use land cover (LULC) changes and land surface temperature (LST) variations. Due to the modification in the surface properties of the cities, a difference in energy balance between the cities and its nonurban surroundings is observed. The aim of this study is to analyze the spatial and temporal patterns of LULC and LST and its interrelationship in Bengaluru urban district, India, during the period from 1989 to 2017 using remote sensing data. Intensity analysis was performed for the interval to analyze the LULC change and identify the driving forces. The impact of LULC change on LST was assessed using hot spot analysis (Getis-Ord Gi* statistics). The results of this study show that (a) dominant LULC change experienced is the increase in urban area (approximately 40%) and the rate of land use change was faster in the time period 1989-2001 than 2001-2017; (b) the major transition witnessed is from barren and agricultural land to urban; (c) over the period of 28 years, LST patterns for different land use classes exhibit an increasing trend with an overall increase of approximately 6 °C and the mean LST of urban area increased by about 8 °C; (d) LST pattern change can be effectively analyzed using hot spot analysis; and (e) as the urban expansion occurs, the cold spots have increased, and it is mainly clustered in the urban area. It confirms the presence of an urban cool island effect in Bengaluru urban district. The findings of this work can be used as a scientific basis for the sustainable development and land use planning of the region in the future.

In this study, the effect of land use–land cover change (LULCC) on surface (direct) runoff was estimated for Lake Basaka catchment using the soil conservation services—curve number model in the geospatial information system (ArcInfo), assisted by remote sensing. The result indicated that Lake Basaka catchment experienced a significant LULCC. About 86% of forest coverage and 46% of grasslands were lost over the study period (1973–2015), which were shifted to open bushy woodlands, farms, lake water and wetlands. The runoff responses were observed to be increasing since 1970s, especially after the inception of large-scale irrigation schemes to the region. The highest increase of surface runoff was observed to occur after mid-1980s, which is in line with the significant LULCC and the corresponding increment of lake level in that period. The reduction in vegetation cover has resulted in an increase of runoff coefficient (rc) from 0.07 in the 1960s to about 0.23 in 2000s. The sensitivity analysis result indicated that about 70% of the increase runoff rate in the lake catchment is attributed to LULCC, and the remaining proportion is due to rainfall. However, the effect of extreme rainfall on runoff process could not be underemphasized since it has significant impact especially during extreme events (observed rc of 0.33 in 2008). Overall, when predicting the runoff response of the lake catchment, it is importance to take into account possible future LULCC and evolution.

Human‐induced land use/land cover (LULC) changes are among the most important processes that shape the dynamics of the earth’s surface. This phenomenon, which is occurring at an astonishing rate, and its consequential environmental impacts have become an important area of research for scientists.Therefore, a wide range of methods and models have been developed to detect and predict these alterations, among which cellular automata (CA) models such as the CA‐Markov model, due to their affinity to geographic information system (GIS) and remote sensing (RS), are appropriate for detailed resolution modelling and simulating dynamic spatial processes. In Iran, the district of Ravansar has undergone severe LULC changes recently, thus to take the necessary precautions, decision‐makers need to predict and determine the extent of these changes. In this study, using spatial analysis methods the LULC changes in Ravansar were investigated from 1992 to 2015. Subsequently, the CA‐Markov model was applied to simulate the spatial pattern changes of LULC until 2030. Our results indicated that from 1992 to 2015, this region has witnessed a noticeable increase in the areas of the built‐up and agricultural lands (both aquatic and non‐aquatic), resulting in the decrease of the gardens, range, and bare lands. The simulated LULC map showed that this trend will continue due to more urbanization and development of agricultural areas.

The study examines long-term land use/land cover change (LUCC) at a finer scale in a semi-arid region of India. The objectives were to study and quantify the spatiotemporal LUCC and uncover the major drivers causing the change in the Mula Pravara river basin, which is located in a semi-arid region of Maharashtra state, India. Advanced very high-resolution radiometer (AVHRR)-Normalized Difference Vegetation Index (NDVI) 3g data for the years 1982-2015 were used to identify the 'hotspot' with significant positive and negative LUCC. Multi-temporal Landsat imagery was used to produce finer scale land use maps. From 1991 to 2016, the agricultural land area increased by approximately 98% due to the conversion of uncultivable and fallow lands to agriculture. The built-up area increased by 195%, and in recent years, an urban expansion has occurred in agricultural lands close to the urban fringe areas. There has been a shift from food crops to commercial crops, as observed from the steep increase in the amount of land under horticultural plantations, by 1601% from 2001 to 2016. In addition, the area under forest canopy was reduced in the protected regions. Institutional factors that improved access to water resources were the major drivers of change in hotspots, especially in the context of agriculture. Technological and economic factors were the other supporting factors that contributed to the change. This study demonstrates the advantages of using satellite remote sensing techniques to monitor the LUCC, which is useful for predicting future land changes and aids in planning adaptation strategies.

Groundwater is the most important source of water in meeting irrigation, drinking, and other needs in India. The assessment of the potential zone for its recharge is critical for sustainable usage, quality management, and food security. This study reports alternative mapping of the groundwater recharge potential of a selected block by including large-scale soil data. Thematic layers of soil, geomorphology, slope, land use land cover, topographical wetness index, and drainage density of Darwha block (District Yavatmal, Maharashtra, India) were generated and integrated in a geographic information system environment. The topographic maps, thematic maps, field data, and satellite image were processed, classified, and weighted using analytical hierarchical process for their contribution to groundwater recharge. The layers were integrated by weighted linear combination method in the GIS environment to generate four groundwater potential zones viz., “poor,” “poor to moderate,” “moderate to high,” and “high.” Based on the generated groundwater potential map, about 9830 ha (12%) of the study area was categorized as high potential for recharge, 25,558 ha (31%) as poor to moderate, 33,398 ha (40%) as moderate to high, and 12,565 ha (15%) as poor potential zone. The zonation corresponds well with the field data on greater well density (0.22/ha) and irrigated crop area (27%) in the high potential zone as against 0.02 wells/ha and only 6% irrigated area in the poor zone. The map is recommended for use in regulating groundwater development decisions and judicious expenditure on drilling new wells by farmers and the state authorities.

Understanding land-use/cover change (LUCC) and groundwater resources is important for land-use planning and natural resource management. The development of agriculture has encouraged farmers in the Khanmirza plain in Iran to increase the number of agricultural wells and irrigated land over the past three decades. This has caused a continual decrease in groundwater level which threatens life in the groundwater aquifer reservoir. The present study used integrated spatiotemporal analysis to monitor changes in groundwater quantity and LUCC for sustainable resource management in Khanmirza plain. LUCCs were extracted using Landsat TM data, and groundwater quantity changes were generated for the piezometric and agricultural wells. The periods of analysis were 1987–2000, 2000–2010, and 1987–2010. Decision tree classification, ancillary data, and post-classification comparison processing was used to classify the images and LUCCs. Results showed that irrigated land increased by approximately 1350 ha and agricultural landscapes trended toward integrated land between 1987 and 2000. The most remarkable change occurred between 2000 and 2010, when the total irrigated land decreased gradually to about 1126 ha and rain-fed agriculture increased. The sharp drop in groundwater of from 7.17 to 39.1 m in this region decreased available meadow land to 1000 ha between 1987 and 2010, which decreased the area of potential agricultural land. Consequently, fragmentation of land use occurred throughout the period. These changes have produced devastating effects on different parts of the study area.

This study was aimed at analyzing land-use/land-cover changes (LULCCs) in a protected area, the Presidential estate of Castelporziano, a nature reserve located 20 km southwest of Rome in a peri-urban area. In this territory, there are two SCIs (sites of community importance) and several environments of naturalistic value. This research can be seen as a study case in landscape planning and transformative processes in terms of governance, in consideration of the natural and cultural heritage of the area and its location. In fact, Castelporziano, located in the proximity of two urban centers, Rome and Ostia, is a peri-urban territory that has undergone a significant transformation in recent decades as a result of anthropic pressure in terms of land-use change. Photo-interpretation of aerial photographs taken in 1930 and 2010 was carried out to perform an LULCCs and land-use change detection analysis. To this aim Patch Analyst 3.1 software was also used to calculate landscape indices in order to characterize the landscape structure.

Background Land use/land cover (LU/LC) change is the challenging and continuous drivers of environment change. Understanding the rate and process of change is, therefore, basic for managing the environment. This study was intended to analyze the LU/LC changes from 1985 to 2015 periods, and predict the situation to 2030 and 2045 in the Andassa watershed of Blue Nile basin, Ethiopia. The hybrid classification technique for extracting thematic information from satellite images and CA-Markov model for prediction of LU/LC were employed. Results Cultivated land was expanding from 62.7% in 1985 to 73.1% in 2000 and to 76.8% in 2015. The area of built-up also slightly increased (0.1–1.1%) between 1985 and 2015 periods. In contrast, forest, shrubland and grassland were reduced from 3.5 to 1.9%, 26.2 to 15.3% and 7.6 to 4.9% in 1985 and 2015 periods, respectively. The increase of cultivated land and built-up area, and the withdrawing of forest, shrubland and grassland were further continued in 2030 and 2045 periods. Conclusion Significant amount of LU/LC conversions had occurred in the watershed from 1985 to 2015 periods, and expected to continue in 2030 and 2045 periods. Thus, appropriate interventions to revert the trends are very much critical.

Land use/land cover change dynamics along the estuarine complex of Mandovi–Zuari in the coastal state of Goa, India is studied by the application of multi-temporal remote sensing data. This tropical estuarine region has faced significant changes in the last few decades due to diverse pressures from various sectors, mainly of anthropogenic origin. Spatial and temporal changes in the LU/LC classes have been evaluated using satellite datasets from Landsat MSS, TM and IRS-LISS III for the years 1973, 1989, 2001 and 2011. Considering a spectrally heterogeneous landscape of the region, the LU/LC changes are identified by using principal component analysis (PCA) as a pre-classification step, followed by the implementation of a combination of supervised and unsupervised classification called hybrid classification. The application of PCA has proved to be suitable to successfully identify discrete land surface classes for this study area. An overall accuracy of 95.56 % (kappa = 0.9556), 92.93 % (kappa = 0.92), 84.64 % (kappa = 0.85) and 86.36 % (kappa = 0.85) has been obtained for the year 2011, 2001, 1989 and 1973, respectively. The study reveals a gradual increase in built-up area due to urbanization corresponding to a decrease in agricultural land and a considerable loss in beach area. Changes in the mangroves are also assessed within this extent as Goa has significant pockets of mangroves which are located mainly along the estuarine complexes of Mandovi and Zuari (along with the Cumbarjua canal complex). The results highlight an increase in mangrove coverage within the study extent by 1,585.8 ha from 1973 to 2011. The mangrove areal extent along Mandovi has increased from 149.45 to 1,059.77 ha and from 142.95 to 676.12 ha along Zuari (including Cumbarjua canal). Increasing awareness and the steps taken by the forest department to provide suitable environment for the growth of mangroves are the main reasons for this increment.

The Markov Chain and Cellular Automata (Markov-CA) approach have been applied to create the dynamics of land use/land cover (LULC) change modeling in the Tondano watershed, North Sulawesi, Indonesia. The multi temporal of remotely sensed data, Landsat 5 TM in 1997, Landsat 7 TM in 2002 and Landsat 8 LDCM in 2015 were used to produce the LULC maps. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Map (GDEM) data were used as input for the flood modeling created by the Monte Carlo algorithm. The LULC maps in 1997 and 2002 were used to create predictions and modeling LULC map with the Markov-CA approach in the next few years (for the year 2015, 2025, 2035 and 2050). Meanwhile, the LULC map in 2015 with an accuracy of 80.11 % based on the calculation of the Kappa index has been used as a reference map to determine the accuracy of the Markov-CA approach to produce a model of the LULC map in 2015. The result of the accuracy by using cross-correlation matrix between the LULC model in 2015 with the LULC reference in 2015 is 75.88 %. The dynamics of LULC changes showed that area-class forest, dry land, paddy fields and shrubbery would be expected to experience an area decreases in the extent from the year 2015 to 2050, with the rate of change in average: 10.52, 13.22, 14.49 and 1.15 ha/year, respectively. Meanwhile, the area-class bare soil, plantation, settlement and water body would be expected to experience an area increases, with the rate of change in average: 6.79, 11.14, 11.49 and 9.7 ha/year, respectively. Furthermore, flood damage assessment can be calculated by estimating LULC area affected by the flood, which is determined based on the overlay between LULC maps from the result of Markov-CA with flood maps from the result of Monte Carlo algorithm. Under current conditions, estimated flood damage exposure to extreme flood events with return periods of 100 years for the water level scenario Hc = 3 m and Hc = 5 m is more than €520 and €958 million, respectively.

In this paper, we have studied the comparative hydrogeochemistry of the Ken and Betwa Rivers of Bundelkhand area, considering the importance of the Ken–Betwa River linking project (KBLP) in India. Factor analysis and principal component analysis (PCA) has been done to identify the highly correlated and interrelated water-quality parameters. All the physico-chemical parameters for both rivers are within the highest desirable or maximum permissible limit set by WHO (World Health Organization) except some anions viz. $\mathrm{NO}_{3}^{-}$ , Cl−, $\mathrm{SO}_{4}^{2-}$ and F− at some sampling points. The Ken River showed a high spatial variability and significant ionic concentration due to the higher geological and pedological watershed richness as well as absence of pollution from anthropogenic point sources. The Betwa River showed a low spatial variability and higher mineralization due to the anthropogenic point sources that exist downstream. This preliminary study shows the spatio-temporal variability of the hydrogeochemical parameters of the Ken–Betwa River basin.

The coastal zone along the districts of Surat, Navsari, and Valsad in southern Gujarat, India, is reported to be facing serious environmental challenges in the form of shoreline erosion, wetland loss, and man-made encroachments. This study assesses the decadal land use/ land cover (LULC) changes in these three districts for the years 1990, 2001, and 2014 using satellite datasets of Landsat TM, ETM, and OLI. The LULC changes are identified by using band ratios as a pre-classification step, followed by implementation of hybrid classification (a combination of supervised and unsupervised classification). An accuracy assessment is carried out for each dataset, and the overall accuracy ranges from 90 to 95 %. It is observed that the spatial extents of aquaculture, urban built-up, and barren classes have appreciated over time, whereas the coverage of mudflats has depreciated due to rapid urbanization. The changes in the shoreline of these districts have also been analyzed for the same years, and significant changes are found in the form of shoreline erosion. The LULC maps prepared as well as the shoreline change analysis done for this study area will enable the local decision makers to adopt better land-use planning and shoreline protection measures, which will further aid in sustainable future developments in this region.

The temporal changes (1988-89 to 1997) in land-use/land cover were studied using multi-temporal satellite data in Mahi Right Bank Canal (MRBC) command area in Kheda district of Gujarat state. The canal command area is affected by waterlogging and salinity. The land-use/land cover change is maximum in a distributary (Lambhvel) situated in highly urbanised zone around Anand city, where built-up area increased from 205 ha to 868 ha. In Nadiad command area also there is an increase in urban area from 281 ha to 460 ha, causing a decrease in agricultural area. Waterlogging is significant in Pansora command area with 586 ha of waterlogged area in 1997. Water logging has also increased in commands of other distributaries. The salt affected area has increased in Chikhaliya command whereas it has decreased in Manej command.

Land use/land cover changes over a period of 30 years were studied using remote sensing technology in a part of Gohparu block, Shahdol district of Madhya Pradesh. Land use/ land cover maps were prepared by visual interpretation of two period remotely sensed data. Post-classification comparison technique was adopted for this purpose. The loss of vegetation cover was estimated to be 22 percent and 14 percent of the land was found to have been tranformed into wasteland between 1967 and 1996. Overall rate of change was found to be 1.8 percent per year during this period.

Land use/land cover investigation have been carried out in Varaha river basia (VRB) using remote sensing data of 1986 and 1992 and the changes in land use/land cover during the period are estimated Visual interpretation techniques have been followed to delineate various land use/laad cover categories It is inferred that the land use/land cover pattern of the area is controlled by local topography, climate and resourees, The tesults revealed significant changes in the area of built-up land irrigated land, plantation degraded forest and upland over a period of six years.