LULC Map Research Articles

CELLULAR AUTOMATA (CA) CONTIGUITY FILTERS IMPACTS ON CA MARKOV MODELING OF LAND USE LAND COVER CHANGE PREDICTIONS RESULTS

Abstract. In this study, attempts has been made to find out cellular automata (CA) contiguity filters impacts on Land use land cover change predictions results. Cellular Automata (CA) Markov chain model used to monitor and predict the future land use land cover pattern scenario in a part of Brahmaputra River Basin, India, using land use land cover map derived from multi-temporal satellite images. Land use land cover maps derived from satellite images of Landsat MSS image of 1987 and Landsat TM image of 1997 were used to predict future land use land cover of 2007 using Cellular Automata Markov model. The validity of the Cellular Automata Markov process for projecting future land use and cover changes calculates using various Kappa Indices of Agreement (Kstandard) predicted (results) maps with the reference map (land use land cover map derived from IRS-P6 LISS III image of 2007). The validation shows Kstandard is 0.7928. 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict LULC in 2007 using 1987 and 1997 LULC maps. Regression analysis have been carried out for both predicted quantity as well as prediction location to established the cellular automata (CA) contiguity filters impacts on predictions results. Correlation established that predicted LULC of 2007 and LULC derived from LISS III Image of 2007 are strongly correlated and they are slightly different to each-other but the quantitative prediction results are same for when 3x3, 5x5 and 7x7 CA contiguity filters are evaluated to predict land use land cover. When we look at the quantity of predicted land use land cover of 2007 area statistics are derived by using 3x3, 5x5 and 7x7 CA contiguity filters, the predicted area statistics are the same. Other hands, the spatial difference between predicted LULC of 2007 and LULC derived from LISS III images of 2007 is evaluated and they are found to be slightly different. Correlation coefficient (r) between predicted LULC classes and LULC derived from LISS III image of 2007 using 3x3, 5x5, 7x7 are 0.7906, 0.7929, 0.7927, respectively. Therefore, the correlation coefficient (r) for 5x5 contiguity filters is highest among 3x3, 5x5, and 7x7 filters and established/produced most geographically / spatially distributed effective results, although the differences between them are very small.

Modeling soil water balance of an agricultural watershed in the Guinea Savannah Agro-ecological Zone; a case of the Tono irrigation dam watershed

Water is the most significant of all the requirements for plant growth and development. Its availability or scarcity in the soil can denote a successful harvest, reduction in yield, or complete failure. Though Tono is a small community in the Kassena Nankana East Municipality in Upper East Region of Ghana, farmers in the area contribute significantly to the availability of food products in neighboring communities, and even in some parts of southern Ghana. However, changing climate and landscape dynamics are perceived to be the primary cause of decreasing water availability and responsible for the dwindling farming fortunes. The study therefore investigated the changes to soil water balance occurring within the Tono dam watershed, as well as changes to the landuse/landcover (LULC) of the area and its impacts on crop production using the Soil and Water Assessment Tool (SWAT). The LULC continues to change through anthropogenic activities thereby causing variations in the hydrological cycle over time. A 30-year period from 1984 to 2014 was used as the simulation period to run the model. Two (2) LULC maps and change scenarios for 1984 through to 2014 were assessed. The results revealed a relationship between landcover and the response to hydrology, in that, a decrease in landcover causes a decrease in surface water, soil moisture, and a corresponding infinitesimal decrease in evapotranspiration. The results showed a 17.6%, 9.6%, and 1.6% decrease in rainfall, soil moisture and evapotranspiration, respectively. The results showed the ability of the SWAT model to reveal spatio-temporal variation of the change in the landscape and the associated changes arising from climate change having significant effects on the Tono catchment.

Observing land-use/land-cover dynamics in semi-arid environment: Evidence from Damaturu Town and its surrounding lands, Yobe State, Nigeria

The fragile Sudano-Sahelian ecological zone of Nigeria has been classified as a hotspot of land cover change (LCC) that has been suffering from serious anthropogenic and biophysical stresses. Damaturu, being the fastest growing town situated in the region happened to be a victim of this negative development. The purpose of this study is to remotely observe and assess the prevailing land-use/land-cover (LULC) dynamics of Damaturu town and its delicate surrounding lands from the year 1987-2017 study periods. To achieve this, a supervised image classification technique with Maximum Likelihood Classifier (MLC) algorithm was used in ERDAS Imagine version 15 software to classify the three epochs multi-temporal and multi-spectral Landsat imageries (TM 1987, ETM+7 2000 and OLI 2017). The classified LULC maps and their resulting statistics were then used to assess the spatio-temporal aspects of the observed changes by placing the results within the wider context of previous related literature and evidences. Findings revealed that the built-up area has been expanding since 1987 with an annual change rate of 4.5% between 1987-2000, and 5.3% during 2000-2017 respectively. The growth of the town is being accompanied by massive farmlands expansion and vegetal cover (trees and shrubs) lost making the surrounding arable lands seriously disturbed. Thus, if the observed trends continue, the entire studied region will be subjected to severe environmental hazard such as desertification. Overall, the study provides valuable information required for sustainable environmental management.

Effects of land use and land cover change on soil erosion potential in Berhe district: a case study of Legedadi watershed, Ethiopia

ABSTRACT This research was done in Legedadi watershed since the area was highly susceptible to soil erosion problem that could be aggravated by heavy rainfall, and steep slope. The aim of the present study was to examine the impact of LULC change on soil erosion potential in Legedadi watershed, and its implication on sedimentation problems. Multi-temporal Landsat imagery of TM (1985), TM (1997) and ETM+ (2013) were deployed to categorize a LULC map of the study watershed. Soil erosion risk was calculated by using GIS and RUSLE. Digital image processing and classification technique have been done using ERDAS Imagine 9. 2. Results indicate that there was a continuous and active LULC change observed in the watershed for the last 28 years. The settlement area and cultivated land were increased by 14.34% and 18.3%, whereas grazing land and bare land classes were reduced continuously by 25.74% and 18.47% between (1985–2103) respectively. The estimated annual soil losses were 0.0-330 t ha−1year−1in 2013 whereas in 1997 its range was 0-257.1 t ha−1year−1. A mean annual soil loss in the watershed was 54.19 t ha−1year−1 in 1997and 66.21 t ha−1year−1 in 2013, respectively. The average annual soil loss from each sub-watershed ranges from 34.57–89.19 t ha−1year−1. This calls for the coordinated action of the local communities and government in ensuring sustainable natural resource conservation systems considering the Climate Resilient Green Economy strategy of Ethiopia.

LAND USE/ COVER (LULC) MAPPING IN BRAZILIAN CERRADO USING NEURAL NETWORK WITH SENTINEL-2 DATA

The Sentinel-2a and 2B satellites form a multispectral imaging mission for Earth observation. They have promising characteristics for the study of soils and vegetation cover, and their data can be applied for land use/cover (LULC) mapping. To this end, neural networks have shown good results in pattern recognition tasks in orbital images. In this sense, the study aimed to evaluate the use of Sentinel 2 (ESA) image for LULC mapping in the Cerrado Biome, through the application of artificial neural network methodology. Among the classes of use and occupation examined, 8 classes were selected, 4 of which were natural (water bodies, savanna, forest and field formation) and 4 anthropic (Pasture, Urban areas, Silviculture and Seasonal Crop). The classification system by artificial neural network (ANN) was considered successful, with thematic accuracy (Kappa coefficient) of 0.77. Although there are still some thematic confusions during the classification process, the classification results were considered superior when compared to the MaxVer classifier. The Sentinel-2 image, together with the use of a neural network, was shown a good input for carrying out this type of mapping. Key words: Orbital Remote Sensing System, Supervised Classification Techniques, LULC classes.

Monitoring and modeling land use/cover changes in Arasbaran protected Area using and integrated Markov chain and artificial neural network

Land cover is rapidly changing across the globe. An understanding of how land use/cover changes (LUCCs) are made can lead to appropriate management measures to assess environmental changes and to develop policies and plans for sustainable land cover in the future. In this study, we used Land Change Modeler (LCM) to predict LULC by 2030. LUCC was detected using Landsat satellite images, TM, ETM+, and OLI sensors for the years 1987, 2002, and 2017. Transmission potential was modeled using an artificial neural network (multilayer perceptron) and seven variables. LUCCs were modeled for 2017 using the Markov chain with the calibration period 1987–2002, and then, we validated the LULC map that was predicted through LCM by comparing it with the ground reality map of 2017. Finally, LULCs were predicted for 2030 using the calibration period 2002–2017. The largest change in all periods is associated with the change of forest lands into other land uses. Prediction for 2030 suggest that approximately 6000 ha of forest land will change into pastures and 248 ha into agricultural lands.

Random Forest (RF) and Artificial Neural Network (ANN) Algorithms for LULC Mapping

In this paper to make use of complementary potential in the mapping of LULC spatial data is acquired from LandSat 8 OLI sensor images are taken in 2019. They have been rectified, enhanced and then classified according to Random forest (RF) and artificial neural network (ANN) methods. Optical remote sensing images have been used to get information on the status of LULC classification, and extraction details. The classification of both satellite image types is used to extract features and to analyse LULC of the study area. The results of the classification showed that the artificial neural network method outperforms the random forest method. The required image processing has been made for Optical Remote Sensing Data to be used in LULC mapping, include the geometric correction, Image Enhancements, The overall accuracy when using the ANN methods 0.91 and the kappa accuracy was found 0.89 for the training data set. While the overall accuracy and the kappa accuracy of the test dataset were found 0.89 and 0.87 respectively.

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

Mapping Land Use Land Cover Change in the Lower Mekong Basin from 1997 to 2010.

The Lower Mekong Basin (LMB) is biologically diverse, economically important, and home to about 65 million people. The region has undergone extensive environmental changes since the 1990s due to such factors as agricultural expansion and intensification, deforestation, more river damming, increased urbanization, growing human populations, expansion of industrial forest plantations, plus frequent natural disasters from flooding and drought. The Mekong river is also heavily used for human transportation, fishing, drinking water, and irrigation. This paper discusses use of pre-existing LULC maps from 1997 and 2010 to derive a LMB regional LULC change map for 9 classes per date using GIS overlay techniques. The change map was derived to aid SWAT hydrologic modeling applications in the LMB, given the 2010 map is currently used in multiple LMB SWAT models, whereas the 1997 map was previously used. The 2010 LULC map was constructed from Landsat and MODIS satellite data, while the 1997 map was from before the MODIS era and therefore based on available Landsat data. The 1997-2010 LULC change map showed multiple trends. Permanent agriculture had expanded in certain sub-basins into previously forested areas. Some agricultural areas were converted to industrial forest plantations. Extensive forest changes also occurred in some locations, such as areas changed to shifting cultivation or permanent crops. Also, the 1997 map under classified some urban areas, whereas the 2010 LULC map showed improved identification of such areas. LULC map accuracy were assessed for 213 randomly sampled locations. The 1997 and 2010 LULC maps showed high overall agreements with reference data exceeding 87%. The LULC change map yielded a moderately high level of overall agreement (78%) that improved to ~83% once LULC classification scheme specificity was reduced (forests and agriculture were each mapped as singular classes). The change map regionally showed a 4% decrease in agriculture and a 4 % increase in deciduous and evergreen forests combined, though deforestation hot spot areas also were evident. The project yielded LULC map data sets that are now available for aiding additional studies that assess LMB LULC change and the impacts such change may pose to water, agriculture, forestry, and disaster management efforts. More work is needed to map, quantify and assess LULC change since 2010 and to further update the 2010 LULC map currently used in the LMB SWAT models.

Identifying factors and predicting the future land-use change of protected area in the agricultural landscape of Malaysian peninsula for conservation planning

Given that surrounding land-use change affects the function of protected areas, it is crucial to identify the driving forces that influence them and modelling future changes to prevent further decline of natural resources. Thus far, most of the studies predicting future land-use in the protected area are not considering to measure the driving force of land-use change inside and outside protected areas separately. As a result, it is not clear for protected area managers and policymakers to focus on the relevant underlying factors of land-use change inside and outside the protected areas. This study aims to identify the significant driving forces of land-use change inside and outside the Tasek Bera Ramsar Site and how it determines the future land-use inside and outside this protected area separately. To achieve these objectives, past LULC maps of 1988, 2000, and 2014 were developed using ERDAS and ArcGIS software package. The LCM module was used to identify the appropriate drivers of land-use change and predicting the LULC scenario of 2028, based on Multi-Layer Perceptron (MLP) and Markov Chain analysis. The MLP was trained with six variables, including; elevation, slope, soil type, distance from the forest edge, distance from the road, and distance from the river. The result revealed that the most significant driving forces were the distance from forest edge followed by distance from road and soil type. The model validation showed an accuracy more than 75%, and this indicates that the selected variable is acceptable and reliable. The simulated LULC maps of 2028 showed the forest areas inside and outside the Tasek Bera Ramsar Site continue to decline primarily converted to commercial agriculture. The future rate of forest loss will increase and put biodiversity at risk if proper action is not taken. Thus, information obtained from this study can be utilised in decision-making tools to safeguard, conserve, and implement law enforcement in the protected area.

Prediction of future situation of land use/cover change and modeling sensitivity to pollution in Edku Lake, Egypt based on geospatial analyses

Assessing land use/cover (LULC) change in coastal lakes is an essential process for sustainable development. Edku Lake is one of the most important lakes in the northern part of Nile Delta in Egypt. It is directly subject to severe anthropogenic activities. The main objectives of this work were to predict LULC changes within Edku Lake area and to develop a pollution sensitivity model using remote sensing data and modern geographical information system (GIS) techniques. Three Landsat images were acquired in 2000, 2011 and 2018. These images were preprocessed, geo-referenced and prepared for further supervised classification using the maximum likelihood classifier. Five LULC classes were found in the area (water, vegetation, urban, bare and fish farm). CA–Markov model was applied to predict LULC changes using IDRISI software between each two consequent years. A predicted LULC map was obtained for 2025. This map can provide suggestions for future urban and vegetative planning in the studied area. This area was subject to severe changes from 2000 to 2018. Fish farms were significantly increased during this period. The predicted LULC map for 2025 indicates that large vegetative areas will be converted into urban and fish farms. The pollution sensitivity model showed that the eastern parts of the lake are more sensitive to pollution compared with the rest of the lake. Accordingly, protection of biodiversity in Edku Lake can be achieved based on future scenarios and measuring sensitivity of environmental factors and surrounding activities.

Land Use and Land Cover Change Modeling and Future Potential Landscape Risk Assessment Using Markov-CA Model and Analytical Hierarchy Process

Land use and land cover change (LULCC) has directly played an important role in the observed climate change. In this paper, we considered Dujiangyan City and its environs (DCEN) to study the future scenario in the years 2025, 2030, and 2040 based on the 2018 simulation results from 2007 and 2018 LULC maps. This study evaluates the spatial and temporal variations of future LULCC, including the future potential landscape risk (FPLR) area of the 2008 great (8.0 Mw) earthquake of south-west China. The Cellular automata–Markov chain (CA-Markov) model and multicriteria based analytical hierarchy process (MC-AHP) approach have been considered using the integration of remote sensing and GIS techniques. The analysis shows future LULC scenario in the years 2025, 2030, and 2040 along with the FPLR pattern. Based on the results of the future LULCC and FPLR scenarios, we have provided suggestions for the development in the close proximity of the fault lines for the future strong magnitude earthquakes. Our results suggest a better and safe planning approach in the Belt and Road Corridor (BRC) of China to control future Silk-Road Disaster, which will also be useful to urban planners for urban development in a safe and sustainable manner.

Spatiotemporal Analysis of Land Use/Cover Patterns and Their Relationship with Land Surface Temperature in Nanjing, China

Rapid urbanization is one of the most concerning issues in the 21st century because of its significant impacts on various fields, including agriculture, forestry, ecology, and climate. The urban heat island (UHI) phenomenon, highly related to the rapid urbanization, has attracted considerable attention from both academic scholars and governmental policymakers because of its direct influence on citizens’ daily life. Land surface temperature (LST) is a widely used indicator to assess the intensity of UHI significantly affected by the local land use/cover (LULC). In this study, we used the Landsat time-series data to derive the LULC composition and LST distribution maps of Nanjing in 2000, 2014, and 2018. A correlation analysis was carried out to check the relationship between LST and the density of each class of LULC. We found out that cropland and forest in Nanjing are helping to cool the city with different degrees of cooling effects depending on the location and LULC composition. Then, a Cellar Automata (CA)-Markov model was applied to predict the LULC conditions of Nanjing in 2030 and 2050. Based on the simulated LULC maps and the relationship between LST and LULC, we delineated high- and moderate-LST related risk areas in the city of Nanjing. Our findings are valuable for the local government to reorganize the future development zones in a way to control the urban climate environment and to keep a healthy social life within the city.

Employing Neural Networks Algorithm for LULC Mapping

Employing Neural Networks Algorithm for LULC Mapping

Land mapping and its change detection analysis in Chitradurga taluk of Karnataka state, india using geospatial technology

Land is one of the non-renewable resources and mapping of Land Use/ Land Cover (LU/LC) is essential for planning and development of land, water resources of a region with suitable technology. There is a significant change in LC/LU across the globe due to the climatic changes, rapid increase in population and over demand of the growing economic resources. The present aim is to detect the changes in LU/LC patterns and its aerial extent due to different socio-economic factors in the study area. Efforts have been made to evaluate the LU/LC patterns using SoI topomap (1:50,000) with limited field visits; geo-coded FCC of IRS-1D, PAN+LISS-III multispectral, multi-temporal satellite images. Spatial, spectral and temporal analysis type of approach has greater importance in mapping of specific LU/LC patterns and its change detection over time and coverage areas calculated using ArcGIS v10. Supervised classification using Maximum Likelihood Classifier (MLC) is applied to prepare LULC maps using NRSC guidelines (1995) through GIS software’s. Different classes of vegetation activities affect the surface flow of run-off water leading to maximize infiltration. The final results highlight the capability of geospatial techniques to bring out the real changes observed in LU/LC patterns and provide meaningful information in proper planning and developmental strategies for land sustainability.

A time-series classification approach based on change detection for rapid land cover mapping

Land-Use/Land-Cover Time-Series Classification (LULC-TSC) is an important and challenging problem in terrestrial remote sensing. Detecting change-points, dividing the entire time series into multiple invariant subsequences, and classifying the subsequences can improve LULC classification efficiency. Therefore, we have proposed a Time-Series Classification approach based on Change Detection (TSCCD) for rapid LULC mapping that uses the Prophet algorithm to detect the ground-cover change-points and perform time-series segmentation in a time dimension and the DTW (Dynamic Time Warping) algorithm to classify the sub-time series. Since we can assume that the ground cover remains unchanged in each subsequence, only one time-training sample selection and one LULC classification are needed, which greatly improves the work efficiency. Prophet can accurately detect large and subtle changes, capture change direction and change rate, and is strongly robust for handling noise and missing data. DTW is mainly used to improve the accuracy of time-series classification and to resolve the time misalignment problems of ground-cover series data caused by irregular observations or missing values. The results of comparative experiments with BFAST, LandTrendR, and CCDC using simulated time-series showed that TSCCD can detect large and subtle changes and capture change direction and change rate, performing substantially better than the other three contrasting algorithms overall in time-series change detection. Finally, the MODIS (Moderate Resolution Imaging Spectroradiometer) time-series images of Wuhan City from 2000 to 2018 were selected for TSCCD, and the results of China’s national land-use surveys in 2000, 2005, 2008, 2010, 2013, and 2015 were used for cross-validation. The results showed that the classification accuracy of each tested subsequence was higher than 90% and that most Kappa coefficients were greater than 0.9. This means that the proposed TSCCD approach can effectively solve real LULC-TSC problems and has high application value. It can be used for large-area, long time-series LULC classification, which is of great guiding significance for studying global environmental changes, forest-cover changes, and conducting land-use surveys.

Geostatistical Analysis of Groundwater Data

Water resources are stressed because of the country's increasing population and increased water requirements. Even though a good understanding of both surface and groundwater hydrological systems make it possible to manage these resources properly. To study the main characteristics of formation of clusters of groundwater levels, statistical analysis has been used. Geostatistics is a class of statistics used to analyze and predict the values associated with spatial or spatiotemporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. The Statistical analysis is applied to monthly groundwater levels fluctuation data over a period of 2004-2017 in Mysuru, Mandya, Chamarajanagara and Hassan districts of Southern Karnataka in India. The groundwater levels data is collected from 197 Observation Wells from the districts. The Statistical methods like K-Means Clustering and Agglomerative Hierarchical Cluster Analysis is used to perform the datasets. Grouping is made using AHC method, during this process results are obtained by graph called Dendrogram. The obtained results are compared with the LULC maps of all 4 districts. Different grouping (cluster) is made for groundwater level fluctuations for proper conclusion to arrive.

Spatial Resolution Effect of Remotely Sensed Data on Flood Hydrograph Simulation

The objective of this study is to compare the effect of different spatial resolution of satellite images (Landsat-8, Sentinel-2 and Gaofen-1) for deriving LULC map and its effects on the performance of HEC-HMS lumped and Flood Hydro distributed models in flood hydrograph simulation in the Ammameh Watershed, Iran. The Soil Conservation Service Curve Number method was used for considering the rainfall loss rate in both models. The value of curve number was determined based on hydrologic soil groups and produced LULC maps from the satellite images with respect to soil moisture conditions. The performance of HEC-HMS lumped and Flood Hydro distributed models in flood hydrograph simulation was evaluated for 15 and five rainfall-runoff events, respectively. The simulation results of flood hydrographs in validation phase showed that both models provided acceptable results, and there were no significant differences among the results of three remotely sensed images for simulating the flood hydrographs.

Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment

Local climate zone (LCZ) maps are increasingly being used to help understand and model the urban microclimate, but traditional land use/land cover map (LULC) accuracy assessment approaches do not convey the accuracy at which LCZ maps depict the local thermal environment. 17 types of LCZs exist, each having unique physical characteristics that affect the local microclimate. Many studies have focused on generating LCZ maps using remote sensing data, but nearly all have used traditional LULC map accuracy metrics, which penalize all map classification errors equally, to evaluate the accuracy of these maps. Here, we proposed a new accuracy assessment approach that better explains the accuracy of the physical properties (i.e., surface structure, land cover, and anthropogenic heat emissions) depicted in an LCZ map, which allows for a better understanding of the accuracy at which the map portrays the local thermal environment.

Prediction of Surface Runoff Changes with Landuse-Land Cover Impact Using Remote Sensing Data and GIS Based ARcSWAT Model of Indrayani Watershed, Maharashtra, India

Modern Mathematical Models have been developed for studying the complex hydrological processes of a watershed and their direct relation to weather, topography, geology and land use. In this study the hydrology of Indrayani watershed located in Indrayani River basin at the North-East of Pune is modelled, using the Soil and water Assessment Tool (SWAT). It aims to simulate the surface runoff using a Temporal resolution of 10 years LandUse-LandCover (Lu-Lc) maps of 2003-04 and 2013-14. The ArcSWAT interface implemented in the ArcGIS software was used to delineate the study area and its sub-components, combine the data layers and edit the model database. The ArcSWAT model predicted Indrayani watershed hydrologic component of Surface Runoff with weather components of precipitation and temperature of 25 years along with Food and Agriculture Organisation soil layer. The SWAT model run in 2 Phases with Lu-Lc of 2003-04 as Phase-I and also model run using 2013-14 Lu-Lc as phase-II. The runoff predicted within a 10 year temporal changes of Lu-Lc, 919 mm of Surface Runoff obtained with Lu-Lc of 2003-04 and 767 mm of Surface Runoff predicted from 2013-14 Lu-Lc map layer respectively. The higher runoff predicted with Lu-Lc 2003-04, as such it has higher area of Agricultural land with lower urban area covered and less water bodies than 2013-14 Landuse-Lancover classes.