Effect of deforestation on landslides in Nilgiris district — A case study

Landslides in the Nilgiris are mainly due to rainfall, but human intervention in the form of deforestation and plantation has turned this into a calamity. Our study in the Nilgiris district of Tamilnadu help of the Global Land Cover Facility data reveals that there is a drastic reduction in forest cover and human interventions in the form of unplanned tea estates has resulted in the loss of natural ecosystem of Nilgiris which is causing massive and frequent landslides.     

Hydrogeomorphological mapping in assessing ground water by using remote sensing data — A case study in lehra gaga block, sangrur district, Punjab

The paper presents the results of hydrogeomorphological mapping using IRS-IB LISS II data and evaluation of ground water prospects of each hydrogeomorphological unit in the Lehra Gaga block of Sangrur district, Punjab. The major geomorphic units identified in the area are, alluvial plain, sand dunes, palaeo channels and the Ghagar flood plain. The study area being part of alluvial plain has good to excellent ground water prospects. Field observations showed that ground water occurs under both confined and unconfined conditions with water table at shallow depth. The area on either side of the Ghagar river and along the major canals (about 46% of the total geographical area in the block) have good quality of ground water and is suitable for irrigation, whereas the water quality is marginal (sodic) in 52 per cent area of the block..     

Landuse/land cover mapping and change detection using satellite data — a case study of dehlon block, district Ludhiana, Punjab

The land use information collected for Dehlon block of Ludhiana district, Punjab from the analysis of the IRS-1B LISS-II data for the year 1993 and IRS PAN data for the year 1997 and SOI topographical maps for 1964 revealed a large change in the area of different land use categories during the period from 1964 to 1997. The agricultural land covering an area of about 94.14 per cent in 1964 reduced to 90.26 per cent in 1997. while the area under rural settlements increased from 312 ha in 1964 to 1162 ha in 1997. An extra area of about 169 ha under waste land was added during the period under study making total waste land area to about 400 ha in 1997. However, the block lacks the forest cover of the required limit. Considerable change in living environment was observed in the block. Number of persons per unit settlement area (ha) being 213.3 in 1964 reduced to 97.1 in 1991; it indicate that the living standard of the people of the block has improved with the changed cropping pattern and increased agricultural production during the period from 1964 to 1991.     

A terrain evaluation using remote sensing and gis—case study of neyyar wild life sanctuary, Kerala

A multi-thematic analysis based on different physical factors has been adopted to generate integrated maps on erosion proneness as well as on critical slope under a GIS platform for terrain evaluation. This spatial data on erosion proneness has demonstrated that 17.62 km² area, out of the 140 km² in the catchment zone, needs careful attention for ecorestoration. In the critical slope map four land stability classes have been demarcated. The area represented as Unstable and Moderately-stable is found to be important for slope stability problems. Temporal change in the drainage network over a period of four decades as well as the extent of loss in the perennial status of tributaries have been recorded to evaluate the landform changes. A catchment treatment plan has also been suggested.     

Monitoring of deforestation and land use changes (1925–2012) in Idukki district,Kerala, India using remote sensing and GIS

The studies on forest cover change can reveal the status of forests and facilitate for its conservation planning. Idukki is the largest district in the state of Kerala having a total geographical area of 5019 km². The objectives of the present study are to map forest cover in Idukki district using multi-temporal remote sensing data (1975, 1990, 2001 and 2012) and topographical maps (1925), to analyze the trends in deforestation and land use changes. Overall statistics for the period of 1925 indicate that about 4675.7 km² (93.2 %) of the landscape was under forest. The forest cover in 2012 was estimated as 2613.4 km² (52.1 %). Recently, due to the implementation of policies and protection efforts, the rate of deforestation was greatly reduced. The commencement of hydroelectric projects during 1925–1990 responsible for an increase of area under water bodies by inundating other land uses. The long term analysis shows agricultural area been decreasing and commercial plantations been increasing in the district. There has been a significant increase in the area of plantations from 1236.2 km² (1975) to 1317.3 km² (2012).     

Calibration of ASTER and ETM + imagery using empirical line method—A case study of north-east of Hajjah, Yemen

This study is aimed at using the Empirical Line Method (ELM) to eliminate atmospheric effects with respect to visible and near infrared bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) and enhanced thematic mapper plus (ETM+) data. Two targets (Amran limestone as light target and quartz-biotite-sericite-graphite schists as dark target), which were widely exposed and easy to identify in the imagery were selected. The accuracy of the atmospheric correction method was evaluated from three targets (vegetation cover, Amran limestone and Akbra shale) of the surface reflectance. Analytical spectral devices (ASD) FieldSpec3 was used to measure the spectra of target samples. ETM+ data were less influenced by the atmospheric effect when compared to ASTER data. Normalized differences vegetation indices (NDVI) displayed good results with reflectance data when compared with digital number (DN) data because it is highly sensitive to ground truth reflectance (GTR). Most of the differences observed before and after calibration of satellite images (ASTER and ETM+) were absorbed in the SWIR region.      

Selection of suitable sites for water harvesting structures in Soankhad watershed,Punjab using remote sensing and geographical information system (RS&;GIS) approach— A case study

A case study has been conducted to identify suitable sites for water harvesting structures in Soankhad watershed, Punjab using information technologies such as Remote Sensing and Geographical Information System (RS-GIS). The IRS-1C, P6 satellite imagery of the Soankhad watershed was used. The various Thematic maps such as land use map, hydrological soil group map, slope map and DEM map were prepared for selecting suitable site for construction of water harvesting structures. The suitable sites were not found for nala bunding and farm ponds due to steep slope, less soil thickness and high runoff velocity. Fourteen check dams and six percolation tanks were proposed for the construction as per Integrated Mission for Sustainable Development (IMSD) guidelines. The water balance study of the Soankhad watershed was also computed with monthly mean temperature and rainfall data using TM model. The average runoff for the wet season (July–September) 1996 was computed to be about 1543.82 mm and the total runoff volume from the Soankhad watershed was estimated to be about 143.52 Mm³.     

Temporal data mining using genetic algorithm and neural network —A case study of air pollutant forecasts

This paper integrates genetic algorithm and neural network techniques to build new temporal predicting analysis tools for geographic information system (GIS). These new GIS tools can be readily applied in a practical and appropriate manner in spatial and temporal research to patch the gaps in GIS data mining and knowledge discovery functions. The specific achievement here is the integration of related artificial intelligent technologies into GIS software to establish a conceptual spatial and temporal analysis framework. And, by using this framework to develop an artificial intelligent spatial and temporal information analyst (ASIA) system which then is fully utilized in the existing GIS package. This study of air pollutants forecasting provides a geographical practical case to prove the rationalization and justness of the conceptual temporal analysis framework.     

Monitoring of salt affected soils — A case study using aerial photographs,salyut‐7 space photographs,and landsat TM data

Soils of part of Ukai‐kakarapar Command area, Gujarat (India) have been mapped at 1:25, 000 scale using aerial photographs of December 1977. It was observed that about 36.3% of the area was affected by soil salinity/alkalinity. The test area has been remapped using Salyut‐7 space photographs taken during Indo‐Soviet joint flight in April, 1984. The area affected by soil salinity/alkalinity was found to be substantially higher (80.3%). The earlier mapping using aerial photographs was done when the soil surface was compartively moist (December 1977) as compared to date of Salyut‐7 photography (April 1984), when the soil surface was likely to be devoid of moisture and the salts moved to the surface. To have easy comparision with the map prepared by using aerial photographs, Landsat TM data of December, 1985 was used in which 45.7% of the total area was mapped as salt affected. The extent of area delineated using Landsat TM was higher than that of 1977 but much lesser than the area delineated using Salyut‐7 (MKF‐6M) photographs. This indicated that the increase in the extent of salt affected area in the map prepared using the MKF‐6M photographs might be partly due to actual increase in the salinity/alkalinity and partly due to the seasonal affects. Among the various bands of MKF‐6M, band ‐4 was found to be the best for delineating the salt affected soils. The boundaries were sharper in the FCC and band No.4 of MKF‐6M than in the aerial photographs.     

Integrated approach of using ASTER-derived emissivity and pixel temperature for delineating different granitoids – a case study in parts of Dharwar Craton,India

We have delineated different granitoids based on variation in emissivity and relative surface temperature recorded in thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor of EO-1 satellite. In this regard, we have used emissivity normalization algorithm to derive broadband emissivity from thermal bands of ASTER sensor to delineate different lithounits of the granitoid family. We have compared emissivity and radiance image composites in terms of delineation of different granitoids. We have also used false colour composite (FCC) image derived using two emissivity bands and temperature (derived using emissivity normalisation method) bands to delineate different granitoids. We could differentiate different granitoids in the three-dimensional (3D) data space of ASTER-derived emissivity bands (second and third bands) and temperature bands. Based on the analysis of 3D scatter plot, we also proposed a ternary diagram of emissivity and temperature, which can be used to delineate different granitoids.     

Techniques of classification for landuse/landcover with special reference to forest type mapping in Jaldapara Wildlife Sanctuary, Jalpaiguri District, West Bengal—a case study

The accuracy of three classification techniques namely Maximum likelihood, contextual and neural network for landuse/landcover with special emphasis on forest type mapping was evaluated in Jaldapara Wildlife Sanctuary area using IRS-1B LISS II data of Dec. 1994. The area was segregated into ten categories by using all the three classification techniques taking same set of training areas. The classification accuracy was evaluated from the error matrix of same set of training and validating pixels. The analysis showed that the neural net work achieved maximum accuracy of 95 percent, maximum likelihood algorithm with 91.06 percent and contextual classifier with 87.42 percent. It is concluded that the neural network classifier works better in heterogeneous and contextual in homogenous forestlands whereas the maximum likelihood is the best in both the conditions.     

Modelling for forest growing stock assessment using satellite data — A case study

The satellite digital vegetation index data has been correlated with the forest growing stock by fitting linear regression models. The goodness of fit was tested. The analysis showed that the vegetation index which is the ratio of reflectance of vegetation in near infrared band to red wave band of electromagnetic spectrum is highly correlated to forest growing stock and the same can be used to predict the volume in remote forest areas for quick assessment purpose. Implications for future forest inventory are discussed.     

Stratification approach for forest cover type and landuse mapping using IRS-1A LISS-II data — A case study

Indian Remote Sensing Satellite-1A (IRS-1A) LISS-II data of 24th Nov., 1988 was analysed digitally to differentiate three density classes viz. dense/closed forest, open forest and degraded forest within each vegetation type in the district, Jalpaiguri, West Bengal. Stratification approach was used to classify separately forest cover into pure sal forests, mixed forests, riverine forests along with man-made sal/teak plantations. In this approach the forested and non-forested areas were classified separately through supervised classification techniques using maximum likelihood algorithm using VAX 11/780 based VIPS-32 Image Processing software. Later the two classified outputs were composited to provide entire area of the district. The forest cover of the district was 1420.89 sq. km, (22.82 percent). Other broad landuse/landcover dominant in the district include agricultural areas.(45.20 percent) and tea gardens (10.49 percent). The accuracy of the classified output was estimated to be 90 percent for forested areas and 85 percent in case of other landuse/landcover classes.     

Spatial–temporal variations of water vapor content over Ethiopia: a study using GPS observations and the ECMWF model

GPS Solutions - We characterize the spatial–temporal variability of integrated water vapor (IWV) in Ethiopia from a network of global positioning system (GPS) stations and the European Center...     

Spatial pattern analysis of fire events in Central India – A case study

The impact of fires on environment can have adverse effects. To fully understand the synoptic behaviour of fire events, information on the spatial distributions and their pattern are highly important. In this study, we used 9-year (1997–2005) integrated fire count datasets derived from Along Track Scanning Radiometer (ATSR) satellite to geographically map the distribution of fire events in the Madhya Pradesh state, central India. We then used robust spatial metrics to test the spatial pattern of fire events against the hypothesis of complete spatial randomness (CSR). Specifically, we used the index of dispersion, Green's index, in addition to nearest neighbour statistic for testing CSR. Also, quantification of clustering is carried out using Ripley's K-function. To spatially map the fire events, we used Kernel density estimation that relies on bi-variate probability density functions. Results from using different spatial pattern metrics and nearest neighbour statistics suggested relatively high clustering of fire events in the study area. In addition, results from Ripley's K-function suggested the fire events to be clustered at a lag-distance of ～60 mile radius. By converting original fire ignition locations that are based on historical records to continuous density surfaces, the probability of fire events could be mapped effectively using kernel density estimation. As each fire event is the result of certain spatial process including biophysical and anthropogenic attributes, results from this study can provide useful information on fire management at a local district level. Also, the analysis presented in this study illustrates how spatial patterns in the point datasets can be quantified using different dispersion indices, clustering and density estimation techniques.     

Groundwater Potential Zoning in Thirumanimuttar Sub-Basin Tamilnadu, India—A GIS and Remote Sensing Approach

The main objective of the study is to identify groundwater potential zones in Thirumanimuttar basin with an integrated approach using Remote Sensing and geographical information system(GIS).FCC Image of Landsat TM 30 m resolution data and topographic maps has been used to generate thematic maps like geology,geomorphology,lineament and lineament density,drain-age,drainage density,and slope map of the study area.A number of geomorphic units such as Denudational hills,structural hills,Bajadas,Colluvial plain,Pediplain,Deep Pediment and Alluvial plains have been observed.A composite groundwater potential map has been generated as very high,high,medium,low and very low based on the groundwater availability area.The upper,mid-dle and downstream of the basins have been identified as potential zones for groundwater exploration.The regions of lineaments and intersecting lineaments proved for groundwater potential zones.The data generated was validated with field checks and ob-served to be in conformity with the same.     

Inventory of degraded lands of Palamau district, Bihar — A remote sensing approach

In this study, an attempt has keen made for the identification, categorisation and mapping of degraded lands in Palamau district of Bihar using remotely sensed data (IRS-1A, raise colour composite, on 1:50,000 scale), It was observed that water erosion is the main cause of land degradation affecting 6.27 per cent area of the district, of which 5.22 per cent is subjected to severe to very severe erosion and remaining 1.05 per cent is moderately to severely gulleid land Degradation due to rock quarries, mine spoils and dumps accounted for only 0.01 per cent of the district area. Degraded lands were mostly confined to agricultural areas (4.76%) follwed by forest lands (1.10%) and opert-scrub/pastures (0.42%), respectively, Subdivision-wise categorisation of degraded lands was also attempted for efficient location-speeific reclamation planning and easy monitoring.     

Semantic similarity measure of natural language text through machine learning and a keyword-aware cross-encoder-ranking summarizer—A case study using UCGIS GIS&T body of knowledge

Initiated by the University Consortium of Geographic Information Science (UCGIS), the GIS&T Body of Knowledge (BoK) is a community-driven endeavor to define, develop, and document geospatial topics related to geographic information science and technologies (GIS&T). In recent years, GIS&T BoK has undergone rigorous development in terms of its topic re-organization and content updating, resulting in a new digital version of the project. While the BoK topics provide useful materials for researchers and students to learn about GIS, the semantic relationships among the topics, such as semantic similarity, should also be identified so that a better and automated topic navigation can be achieved. Currently, the related topics are either defined manually by editors or authors, which may result in an incomplete assessment of topic relationships. To address this challenge, our research evaluates the effectiveness of multiple natural language processing (NLP) techniques in extracting semantics from text, including both deep neural networks and traditional machine learning approaches. Besides, a novel text summarization—KACERS (Keyword-Aware Cross-Encoder-Ranking Summarizer)—is proposed to generate a semantic summary of scientific publications. By identifying the semantic linkages among key topics, this work guides the future development and content organization of the GIS&T BoK project. It also offers a new perspective on the use of machine learning techniques for analyzing scientific publications and demonstrates the potential of the KACERS summarizer in semantic understanding of long text documents.     

Groundwater assessment through hydrogeomorphological and geophysical survey — A case study in Godavari sub-watershed, Giridih, Bihar

Groundwater assessment has been carried out through hydrogeomorphological and geophysical survey. Remotely sensed data has been used to delineate the hydrogeomorphological units. Each hydrogeomorphological unit has been further sub-divided based on the tone, texture and pattern. These units its have been categorised based on depth of weathering, local geomorphological association, recharge characteristics etc. The top layer resistivity of hydrogeomorphological units has been analysed by electrical resistivity method. Correlation between different sub-units of same hydrogeomorphic unit and top soil resistivity has been attempted. The correlation and integration of resistivity map with geomorphological map were carried out through. ‘GRAM’ Geographic information System, This has helped in better understanding the surface resistivity pattern. These sub divisions have different geohydrological and recharge characteristics. Resistivity zonation map prepared on the basis of limited field data anfd through interpolation/extrapolation can be modified/corrected with the help of information derived from remotely sensed data to obtain more realistic picture.     

Remote sensing techniques to locate ground water potential zones in upper Urmil River Basin, district Chhatarpur — Central India

In the present study efforts have been made to locate favourable zones for ground water targeting using IRS-USS-II data, Hydromorphogeological lineament, lineament density and ground water prospect maps have been prepared through visual interpretation of geocoded images on 1:50,000 scale and Survey of India topographical maps of the same scale. The resulting base line information has been integrated for evaluating ground water potential of mapping units. The alluvial plain, flood plain, infilled valley and deeply buried pediplain ace the prospective zones of ground water exploration and development. Fractures and faults parallel to drainage courses constitute priority zones for ground water targetting.