Cropland inventory in the command area of Krishnarajasagar project using satellite data

Inter-state disputes on water utilization from the Cauvery river have necessitated an accurate and timely estimate of the irrigated crop area in each season to optimise the water supply through the canal system and to suggest a departure from the traditional system of protective kharif irrigation to productive rabi irrigation. An inventory of the cropland in the command area of Krishnarajasagar Project was made by visually interpreting satellite imagery. Satellite data showed a reduction of about 6 per cent in the irrigated area from 1973 to 1986. Effect of scale of imagery and percentage of boundary pixels on accuracy of estimated irrigated area was studied. 1:250000 scale was found to be adequate for this large command area. The accuracy of estimated irrigated crop area was within 5 per cent of the DES estimate for the entire command area. At taluk-level, total enumeration gave an overestimate of irrigated area by 13 per cent compared to an overestimation of about 18 per cent using multistage-proportionate-probability sampling with a grid size of 2-5 km by 2-5 km. The per hectare cost of estimating the crop land using satellite remote sensing was found to be two to five times cheaper than the conventional system. An attempt was also made to evaluate various vegetation indices derived from Landsat-TM for identifying irrigated croplands and IRS-1A/LISS-II data for identification of crop types. It was concluded, from the results of this study, that satellite remote sensing is an effective time saving techqniue for providing seasonal assessment of irrigated cropland in the command areas.     

The impact of seasonal rainfall variability on NDVI in the Tunga and Bhadra river basins,Karnataka, India

Rainfall is the major climatic factor that affects the growth and distribution of natural vegetation at a regional scale. The high space–time variability of rainfall in the Tunga and Bhadra river basins caused by the high-elevation Western Ghats mountains forces changes in the seasonal distribution of local vegetation. Understanding the relationship between vegetation greenness and rainfall is a key feature in managing the vegetation of the river basin. For this, we have analysed a 7-year (2005–2011) time series of the Moderate Resolution Imaging Spectroradiometer normalized difference vegetation index (NDVI) and Tropical Rainfall Measuring Mission 3B42 rainfall data. The results show that rainfall exerts seasonal control on vegetation greenness. A significant negative correlation was observed for the monsoon season and a favourable positive association for the rest of the seasons. We found a maximum amount of vegetation greenness in the post-monsoon season (October–December). The availability of enough soil moisture from the southwest monsoon season along with suitable climatic conditions triggers an increased greenness amount during the post-monsoon months. We also investigated seasonal and monthly correlations of monsoon rainfall with the NDVI of its subsequent months. The results suggest that monsoon rainfall is a key factor that sustains the long-term greenness in the river basins.     

Water balance study and conjunctive water use planning in an irrigation canal command area: A remote sensing perspective

Water budgeting of the D-36 and D-36 A distributaries confined between Pedda Vagu, Korutla Vagu and Kakatiya main canal of the Sri RamSagar Project (SRSP) Command area was conducted using remote sensing derived crop areas, land cover information, irrigation tank inventory and source-wise distribution of irrigated areas, together with conventional meteorological, canal flows and well inventory data. A semi-empirical water balance model was developed and validated using remote sensing derived objective information of the command area and the validated model used for predicting the groundwater table under normal rainfall conditions. Recharge and water balance in the study area indicated that the net recharge to the aquifer is negative to the tune of 2.54 Mm³ resulting in a fall of the groundwater table by 0.79 m during 1992-93. However, normalized groundwater recharge and water balance estimates indicate an impending waterlogging problem with an annual groundwater table rise of 0.35 m. In view of existing water management practices, a conjunctive water use plan of rotational operation of aquifers and canals is suggested.     

Improving schedule stability in single-machine rescheduling for new operation insertion

The problem studied here entails inserting a new operation into an existing predictive schedule (preschedule) on a (non-preemptive) single machine by rescheduling its operations, so that the resultant schedule is the most stable one among schedules with minimal maximum tardiness. Stability is measured by the sum of absolute deviations of post-rescheduling start times from the pre-rescheduling start times. In addition to several simple heuristics, this study investigates a hybrid branch-and-bound/local-search algorithm. A large set of instances that include cases with inserted idle times allows for tests of the performance of the heuristics for preschedules with varying degrees of robustness. The results show that algorithms can be developed that significantly improve the stability of schedules with no degradation in T_max. In addition, new insights emerge into the robustness characteristics of a preschedule. Specifically, the number of gaps in the schedule, equal distribution of total slack among these gaps, and the slack introduced beyond the amount enforced by release times all have effects on schedule robustness and stability.     

Financing and reliability analysis for schedules in the project calendar planning problem

We continue our earlier studies of the profit maximization problem in calendar planning of investment projects taking into account reinvesting of the obtained revenue and possible credit financing. We construct the corresponding model and describe a situation when only part of the jobs is financed with own money and credits, and the rest is raised by reinvesting the revenue. We study the calendar planning problem where incomes from various jobs are random values. We study the risks related both to getting a smaller income than expected and to failing a project. We propose an approach to estimate the reliability of job schedules.     

Satellite imagery and household survey for tracking chickpea adoption in Andhra Pradesh,India

The objective of this study was to map the temporal changes in chickpea cropped area over the last decade in Andhra Pradesh using remote-sensing imagery. Moderate Resolution Imaging Spectroradiometer (MODIS) data composited for every 16 days were used to map the spatial distribution of seasonal crop extent in Andhra Pradesh. MODIS derived 16 day normalized difference vegetation index (NDVI) and maximum value composite (MVC) with seasonal ground survey information for the years 2005–2006 and 2012–2013 were used. A subset of ground survey information was also used to assess the pixel-based accuracies of the MODIS-derived major cropland extent. Chickpea-growing areas were identified and mapped based on their characteristic growing periods during the post-rainy season. Significant growth in the chickpea-growing areas was observed in the four districts of Andhra Pradesh between 2001 and 2012. The area cropped to chickpea almost tripled from 0.22 million ha during 2000–2001 to 0.6 million ha by 2012–2013. Furthermore, survey data were also used to assess the accuracy of the MODIS estimates of chickpea-growing areas. When compared with ground survey, the 10 land-use and land-cover classes derived from the MODIS temporal imagery resulted in overall accuracies of 86% of actual. The accuracy of areas identified as cropped to chickpea was 94%. To complement this remote-sensing study, a state-level representative primary household survey was conducted to elicit information on the socio-economic characteristics of chickpea-growing farmers, the extent of adoption of improved cultivars, costs and returns from chickpea cultivation, competitiveness of chickpea with other post-rainy crops, etc. during 2012–13. The findings revealed that nearly 98% of the chickpea cropped area is now under improved cultivars, with an average increase in yield of 37% over yields achieved with unimproved varieties. The average annual per capita incomes have increased to US$ 1.89 day^?1 with this silent chickpea revolution across the rain-fed areas of Andhra Pradesh.     

Application of Meteosat derived meteorological information for crop yield predictions in Europe

This paper presents the utilization of surface fluxes and relative evapotranspiration derived from satellites for crop yield prediction using a dedicated crop growth simulation algorithm, the Environmental Analysis and Remote Sensing (EARS) Crop Growth Simulation algorithm (EARS-CGS). The objective was to test the EARS-CGS algorithm independent of ground data for crop yield prediction at national level in Europe. The algorithm is based on existing crop yield models but has been modified to assimilate satellite derived global solar radiation and actual evaporation information. The algorithm simulates crop biomass. A statistical method is utilized to relate crop biomass to crop yield and to correct for regional differences in yields that are not the result of radiation or water limitation. Six years of Meteosat data were processed to predict winter wheat and spring barley yields for Spain and the UK. The predicted yields were compared to the national reported yields and to forecasts of the European Statistical Office (EUROSTAT) and the Monitoring Agriculture by Remote Sensing-Crop Growth Monitoring System (MARS-CGMS). To evaluate the timeliness of the predictions the reported yields were compared to yield predictions made at different stages of the growing season. The results presented in this paper demonstrate that crop yields predicted from meteorological satellites can be applied to provide timely and reliable crop yield forecasts.     

Testing a high-resolution satellite interpretation technique for crop area monitoring in developing countries

District-level crop area (CA) is a highly uncertain term in food production equations, which are used to allocate food aid and implement appropriate food security initiatives. Remote sensing studies typically overestimate CA and production, as subsistence plots are exaggerated at coarser resolution, which leads to overoptimistic food reports. In this study, medium-resolution (MR) Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images were manually classified for Niger and corrected using CA estimates derived from high-resolution (HR) sample image, topographic and socioeconomic data. A logistic model with smoothing splines was used to compute the block-average (0.1°) probability of an area being cropped. Livelihood zones and elevation explained 75% of the deviance in CA, while MR did not add explanatory power. The model overestimates CA when compared to the national inventory, possibly because of temporal changes in intercropping and the exclusion of some staple crops in the national inventory.     

Use of vegetation index and meteorological parameters for the prediction of crop yield in India

Monsoon rainfall distribution over the Indian sub‐continent is inconsistent every year. Due to uncertainty and dependence on the monsoon onset and weather conditions, estimation of crop yield in India is difficult. In this paper, analyses of the crop yield, normalized difference vegetation index, soil moisture, surface temperature and rainfall data for 16 years (from 1984 to 1999) have been carried out. A non‐linear iterative multivariate optimization approach (quasi‐Newton method with least square loss function) has been used to derive an empirical piecewise linear crop yield prediction equation (with a break point). The derived empirical equation (based on 1984 to 1998 data) has been used to predict 1999 crop yield with R²>0.90. The model has been validated for the three years 1997, 1998 and 1999. A crop yield prediction equation has been obtained for each province in India (for wheat and rice) that accounts for>90% of the variance in the dataset.     

Application of remote sensing techniques for nuclear power project site selection in India

Abstract

A successful engineering project has to be meticulously planned so that it causes least disturbance to the natural environment around the project. This consideration becomes very critical during selection of a site for locating a Nuclear Power Project. The task warrants the involvement of a number of experts from several disciplines, like civil engineering, geology, seismology, health-physics and others. Most of the real-time reliable thematic information for the project can be generated by scale-elastic aerospace remote sensing data. This data is to be supplemented with the available data-base covering geology, geomorphology, seismotectonics, ground water, population centres, rail-road network, other strategic installations, forest-mineral resource information and others. Systematic evaluation of these data-base aids regional analysis and screening of the potential and candidate sites, thus leading to the final selection of the preferred candidate site as well as arriving at safety related aspects of the detailed stage of design and construction.

This article is an attempt to provide an insight into the applicability of remote sensing in site selection procedures. Application of these techniques to seismotec-tonic aspects around the Kakrapar Atomic Power Project (KAPP) site in Gujarat are dealt with in detail.     

Remote sensing and GIS for locating favourable zones of lead-zinc-copper mineralization in Rajpura-Dariba area,Rajasthan, India

This paper reports the results of a pilot study carried out in RajpuraDariba area, Rajasthan, for locating favourable zones of lead-zinc-copper (Pb-Zn-Cu) mineralization using remote sensing, Geographical Information System (GIS) and geostatistical modelling techniques. Remotely sensed data, both aerial and satellite, were used to update the existing geological map. ATLAS GIS software and multivariate geostatistical techniques were used to analyse and integrate different types of geological and geophysical datasets. The Favourability Index (FI) maps prepared during this study show the occurrence of three favourable zones for Pb-Zn-Cu mineralization. They are: (i) around and north of Rawan ka Khera; (ii) isolated spots between Ruppura and Bhupalsagar; and (iii) north of Dhani. Selective geochemical sampling and resistivity profiling carried out in these favourable zones indicated the presence of geochemical anomalies (anomalous concentrations of Zn and Cu) and low/moderate resistivity zones, respectively. Recent drilling carried out by the Department of Mines and Geology (DMG), Rajasthan, at about 2.5 km north of Rawan ka Khera (one of the predicted favourable zones) indicated evidence of Cu mineralization at a depth of about 70 m.     

Colour additive viewing techniques for small-scale soil mapping in an area of Karimnagar district,Andhra Pradesh,India

Multi-spectral imagery from Landsat relating to the Karimnagar district of Andhra Pradesh, in South India was analysed using colour additive viewing techniques. The results were correlated with an existing soil association map prepared from aerial photo-interpretation with adequate ground truth. The four landscapes and their subdivisions that were identified on the soil map also could be identified on the Landsat map. An overall classification performance of 83·9, 82·4, 80·9 and 95·7 percent respectively has been achieved for rugged (R), undulating hummocky (U), very gently sloping (V) and river alluvium (A) landscapes. The best class performance was in the order of 95·7, 97·6, 81.8 and 95·7 percent and that of the lowest class performance of 50·5, 57·3, 70·1 and 95·7 percent for R, U, V and A landscapes, respectively. The overall combined performance of 85·7 percent has been achieved with respect to soilscape boundary delineations for all the four landscapes. The Student's t-test of significance revealed that for units R₄, U₄ and V₂ the differences were significant and the rest were found to be nonsignificant. This study has demonstrated the usefulness of colour additive viewing techniques in the analysis of Landsat-MSS data for small-scale soil mapping and the same could be used for the preparation of small-scale soil maps of the States and the country.     

Landform analysis and ground water potential in the Bist Doab area,Punjab, India

Abstract

In the Bist Doab tract of the Punjab, occurrence of ground water is controlled by geological and geomorphological features. In this study an attempt has been made to analyse different landforms and geomorphological features and to evaluate their ground water potential. The geomorphological units identified include linear ridge, structural hills, alluvial fans, piedmont plain, alluvial plain, sand dunes, flood plain, seasonal rivulets and braided river channels. The palaeochannels, ox-bows and meander scars have prominent shallow aquifers of good quality with excellent yield. The low lying alluvial plain is cropped extensively due to more moisture and/or shallow aquifers. Flood plains are potential sites for artificial recharge. Tapping off the flood plain for ground water can be easy and cheap. The run-off and recharge zones have been identified from satellite data.     

Truck schedule recovery for solid waste collection in Porto Alegre, Brazil

This paper considers a truck schedule recovery problem in the context of solid waste collection in the city of Porto Alegre, Brazil. When a truck on a scheduled trip breaks down, a backup truck needs to be selected to serve the cargo on that trip and other trucks might be rescheduled in order to gain the minimum operating and delay costs. The problem consists of designing, in the case of a severe disruption in a trip, new schedules taking into account the existing trucks in the system and a set of unfinished and not initiated collection trips, on which the trucks collect the solid waste in fixed routes and empty the loads in one of the several operational recycling facilities. The main objective is to minimize the total distances traveled and delay costs, as well as to obtain balanced assignments of truck unloads into the recycling facilities, due to the social benefits of the solid waste program. We modeled the problem as a mixed-integer linear problem and used CPLEX to solve it. Finally, computational experiments are conducted on real-world data. The results show that our approach successfully reduces the distances traveled and delays, simultaneously balancing the number of trucks unloading at each recycling facility, in comparison with the current manual strategy.     

Assessing the efficacy of Landsat-8 OLI imagery derived models for remotely estimating chlorophyll-a concentration in the Upper Ganga River,India

ABSTRACT

Chlorophyll-a (chl-a) serves as an indicator of productivity in surface water. Estimating chl-a concentration is pivotal for monitoring and subsequent conservation of surface water quality. Artificial neural network (ANN) based models were validated and tested for their efficacy against various regression models to determine the chl-a concentration in the Upper Ganga river. Landsat-8 Operational Land Imager (OLI) surface reflectance (SR) imagery for May and October along with in-situ data over a period of 2 years (2016–2017) was used to develop and validated models. Regression model performance was acceptable with a coefficient of determination (R²) of 0.57, 0.63, 0.66 and 0.68 for linear, exponential, logarithmic and power model, respectively. However, there was a significant improvement in the efficacy of chl-a determination using ANN model performance having a root mean square error (RMSE) of 1.52 µg l^–1 and R² = 0.97 in comparison to the best-performing regression model (power) with RMSE = 9.86 µg l^–1 and R² = 0.68. ANN exhibited comparatively more precise spatial and seasonal variability with mean absolute error (MAE) of 1.26 µg l^–1 as compared to the best regression model (power) MAE = 7.98 µg l^–1 suggesting the applicability of ANN for large-scale spatial and temporal monitoring river stretches using Landsat-8 OLI SR images.     

Remote sensing of coastal geomorphology to understand river migration in the Thengapatnam area,southern India

The aim of this work is to study the geomorphic processes that control river migration in the Thengapatnam coastal tract bordering the Arabian Sea in the Kanyakumari District, Tamil Nadu, southern India. Satellite image data were used to identify the geomorphic units. An attempt was made to interpret geological evidence indicative of migration of the Kuzhithura river channel. In addition, study was made of the geological evidence from the field indicative of entrenching of the channel, possibly as a result of relative uplift of the land or lowering of the mean sea level. Satellite images and information gathered from the field reveal that the river has migrated 1.5 km towards the south-east. Satellite images and Digital Elevation Models (DEMs) provide more information about the landforms that were missed out during the field survey.     

Estimating the effect of crop classification error on evapotranspiration derived from remote sensing in the lower Colorado River basin, USA

In the U.S. Bureau of Reclamation's Lower Colorado River Accounting System (LCRAS), crop classifications derived from remote sensing are used to calculate regional estimates of crop evapotranspiration for water monitoring and management activities on the lower Colorado River basin. The LCRAS accuracy assessment was designed to quantify the impact of crop classification error on annual total crop evapotranspiration (ETc), as calculated from the Penman-Monteith method using the map crop classification as input. The accuracy assessment data were also used to generate a sample-based estimate of total ETc using the crop type identified by direct ground observation of each sample field. A stratified random sampling design was implemented using field size as the stratification variable. The stratified design did not markedly improve precision for the accuracy assessment objective, but it was highly effective for the objective of estimating ETc derived from the ground-observed crop types. The sampling design and analysis methodology developed for LCRAS demonstrates the utility of a multi-purpose approach that satisfies the accuracy assessment objectives, but also allows for rigorous, sample-based estimates of other collective properties of a region (e.g., total ETc in this study). We discuss key elements of this multi-purpose sampling strategy and the planning process used to implement such a strategy.     

Irrigated area mapping in heterogeneous landscapes with MODIS time series,ground truth and census data,Krishna Basin,India

Diverse irrigated areas were mapped in the Krishna River Basin (258,912 km²), southern India, using an irrigated fraction approach and multiple ancillary data sources. Unsupervised classification of a monthly time series of net difference vegetation index (NDVI) images from the Moderate Resolution Imaging Spectrometer (MODIS) over January–December 2002 generated 40 classes. Nine generalized classes included five irrigated classes with distinct NDVI time signatures: continuous irrigation, double‐cropped, irrigated with low biomass, minor irrigation, and groundwater irrigation. Areas irrigated by surface water began greening 45 days after groundwater‐irrigated areas, which allowed separation of surface and groundwater irrigation in the classification. The fraction of each class area irrigated was determined using three different methods: ground truth data, a linear regression model calibrated to agricultural census data, and visual interpretation of Landsat TM imagery. Irrigated fractions determined by the three methods varied least for the double‐cropped irrigated class (0.62–0.79) and rangeland (0.00–0.02), and most for the minor irrigated class (0.06–0.43). Small irrigated patches (<0.1 km²) accounted for more irrigated area than all major surface water irrigated areas combined. The irrigated fractions of the minor and groundwater‐irrigated classes differed widely by method, suggesting that mapping patchy and small irrigated areas remains challenging, but comparison of multiple data sources improves confidence in the classification and highlights areas requiring more intensive fieldwork.     

Global irrigated area map (GIAM), derived from remote sensing,for the end of the last millennium

A Global Irrigated Area Map (GIAM) has been produced for the end of the last millennium using multiple satellite sensor, secondary, Google Earth and groundtruth data. The data included: (a) Advanced Very High Resolution Radiometer (AVHRR) 3‐band and Normalized Difference Vegetation Index (NDVI) 10 km monthly time‐series for 1997–1999, (b) Système pour l'Observation de la Terre Vegetation (SPOT VGT) NDVI 1 km monthly time series for 1999, (c) East Anglia University Climate Research Unit (CRU) rainfall 50 km monthly time series for 1961–2000, (d) Global 30 Arc‐Second Elevation Data Set (GTOPO30) 1 km digital elevation data of the World, (e) Japanese Earth Resources Satellite‐1 Synthetic Aperture Radar (JERS‐1 SAR) data for the rain forests during two seasons in 1996 and (f) University of Maryland Global Tree Cover 1 km data for 1992–1993. A single mega‐file data‐cube (MFDC) of the World with 159 layers, akin to hyperspectral data, was composed by re‐sampling different data types into a common 1 km resolution. The MFDC was segmented based on elevation, temperature and precipitation zones. Classification was performed on the segments.

Quantitative spectral matching techniques (SMTs) used in hyperspectral data analysis were adopted to group class spectra derived from unsupervised classification and match them with ideal or target spectra. A rigorous class identification and labelling process involved the use of: (a) space–time spiral curve (ST‐SC) plots, (b) brightness–greenness–wetness (BGW) plots, (c) time series NDVI plots, (d) Google Earth very‐high‐resolution imagery (VHRI) ‘zoom‐in views’ in over 11 000 locations, (e) groundtruth data broadly sourced from the degree confluence project (3 864 sample locations) and from the GIAM project (1 790 sample locations), (f) high‐resolution Landsat‐ETM+ Geocover 150 m mosaic of the World and (g) secondary data (e.g. national and global land use and land cover data). Mixed classes were resolved based on decision tree algorithms and spatial modelling, and when that did not work, the problem class was used to mask and re‐classify the MDFC, and the class identification and labelling protocol repeated. The sub‐pixel area (SPA) calculations were performed by multiplying full‐pixel areas (FPAs) with irrigated area fractions (IAFs) for every class.

A 28 class GIAM was produced and the area statistics reported as: (a) annualized irrigated areas (AIAs), which consider intensity of irrigation (i.e. sum of irrigated areas from different seasons in a year plus continuous year‐round irrigation or gross irrigated areas), and (b) total area available for irrigation (TAAI), which does not consider intensity of irrigation (i.e. irrigated areas at any given point of time plus the areas left fallow but ‘equipped for irrigation’ at the same point of time or net irrigated areas). The AIA of the World at the end of the last millennium was 467 million hectares (Mha), which is sum of the non‐overlapping areas of: (a) 252 Mha from season one, (b) 174 Mha from season two and (c) 41 Mha from continuous year‐round crops. The TAAI at the end of the last millennium was 399 Mha. The distribution of irrigated areas is highly skewed amongst continents and countries. Asia accounts for 79% (370 Mha) of all AIAs, followed by Europe (7%) and North America (7%). Three continents, South America (4%), Africa (2%) and Australia (1%), have a very low proportion of the global irrigation. The GIAM had an accuracy of 79–91%, with errors of omission not exceeding 21%, and the errors of commission not exceeding 23%. The GIAM statistics were also compared with: (a) the United Nations Food and Agricultural Organization (FAO) and University of Frankfurt (UF) derived irrigated areas and (b) national census data for India. The relationships and causes of differences are discussed in detail. The GIAM products are made available through a web portal (http://www.iwmigiam.org).     

Mapping hydrothermal alteration minerals using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski gold deposit area,India

ABSTRACT

The present study exploits high-resolution hyperspectral imagery acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor from the Hutti-Maski gold deposit area, India, to map hydrothermal alteration minerals. The study area is a volcanic-dominated late Archean greenstone belt that hosts major gold mineralization in the Eastern Dharwar Craton of southern India. The study encompasses pre-processing, spectral and spatial image reduction using Minimum Noise Fraction (MNF) and Fast Pixel Purity Index (FPPI), followed by endmember extraction using n-dimensional visualizer and the United States Geological Survey (USGS) mineral spectral library. Image derived endmembers such as goethite, chlorite, chlorite at the mine site (chlorite mixed with mined materials), kaolinite, and muscovite were subsequently used in spectral mapping methods such as Spectral Angle Mapper (SAM), Spectral Information Divergence (SID) and its hybrid, i.e. SIDSAM_tan. Spectral similarity matrix of the target and non-target-based method has been proposed to find the possible optimum threshold needed to obtain mineral map using spectral mapping methods. Relative Spectral Discrimination Power (RSDPW) and Confusion Matrix (CM) have been used to evaluate the performance of SAM, SID, and SIDSAM_tan. The RSDPW and CM illustrate that the SIDSAM_tan benefits from the unique characteristics of SAM and SID to achieve better discrimination capability. The Overall Accuracy (OA) and kappa coefficient (?) of SAM, SID, and SIDSAM_tan were computed using 900 random validation points and obtained 90% (OA) and 0.88 (?), 91.4% and 0.90, and 94.4% and 0.93, respectively. Obtained mineral map demonstrates that the northern portion of the area mainly consists of muscovite whereas the southern part is marked by chlorite, goethite, muscovite and kaolinite, indicating the propylitic alteration. Most of these minerals are associated with altered metavolcanic rocks and migmatite.