Linear Imaging Self-scanning Sensor Research Articles

Half-a-century (1971–2020) of glacier shrinkage and climatic variability in the Bhaga basin, western Himalaya

Glacier shrinkage is a globally occurring phenomena. High-resolution change detection based on frequent mapping and monitoring of high-altitude glaciers is necessary to precisely evaluate future water availability and to understand glacier evolution under different climatic scenarios in the Hindukush-Karakoram-Himalayan (HKH) region. This also holds true for the Bhaga basin of the western Himalaya. This study investigates glacier and glacier lake changes in the Bhaga basin, over the last five decades based on satellite imagery including Corona KH4 (1971), Landsat 7 Enhanced Thematic Mapper Plus (ETM+; 2000), Linear Imaging Self-Scanning Sensor (LISS IV; 2013), and Sentinel 2 (2020). Regional temperature and precipitation trends were evaluated from gridded climatic datasets (1900–2020). In the Bhaga basin 306 glaciers (>0.2 km2) were mapped with a total area of 360.3 ± 4.0 km2, of which 55.7 ± 0.6 km2 was covered with debris in 2013. The total glacier covered area decreased by ∼8.2 ± 1.5 % (0.16 ± 0.03 % yr−1) during the entire observation period 1971–2020, with noticeable heterogeneity between tributary watersheds. In the past two decades (2000–2020), the deglaciation rate has increased significantly (0.25 % yr−1) compared to the previous decades (1971–2000; 0.12 % yr−1). Glacier lake area increased by 0.6 ± 0.1 km2 (0.012 km2 yr−1) between 1971 and 2020. The NCEP/NCAR climatic data reveals an increase of 0.63°C in temperature and a decrease of 6.39 mm in precipitation for the period 1948–2018. In comparison, APHRODITE data shows an increasing trend in temperature of 1.14°C between 1961 and 2015 and decreasing trend in precipitation of 31 mm between 1951 and 2007. Both NCEP/NCAR and APHRODITE data reveal significant temperature increase and precipitation decrease since the 1990s, which have probably augmented ice loss in the Bhaga basin during the early 21st century.

Design of an optimized deep learning algorithm for automatic classification of high-resolution satellite dataset (LISS IV) for studying land-use patterns in a mining region

Accurate classification of the satellite image is always a challenging task, and hence uncertainty involves in the change detection analysis in the land use for mining regions. The present study attempts to demonstrate the development of an optimized deep convolutional neural network with stochastic gradient descent momentum (DCNN + SGDM) using Linear imaging self-scanning sensor- IV (LISS-IV) data for automatic classification of satellites image to assess the land-use pattern in a mining region. An image dataset was prepared by extracting the LISS-IV image samples of five common land-use types (coal mining region, built-up area, barren land, vegetation, and waterbody). A total of 6250 image samples (1250 for each class) of 6 × 6 patch size were generated for the model training and testing. The study area selected for analyzing the model performance and impact analysis is Jharia coalfield (JCF), Jharkhand. The study area consists of 5184 × 7794 pixels. The model performance results indicated that classification accuracies on the training and testing dataset are 99.04% and 83.50%, respectively. The model performance was also compared to the performance reported in the well-established deep learning algorithms, including Alex Net, Squeeze Net, Google Net, VGG Net 16, and VGG Net 19. It was observed that the DCNN + SGDM model performs better (99.04%) than the existing deep learning models (73.8% in Alex Net, 80.3 in Squeeze Net, 93.33 in Google Net, 93.20% in VGG Net 16, and 93.00 in VGG Net 19). Moreover, the DCNN + SGDM model's performance was compared with the two pixel-based classification algorithms (support vector machine (SVM) and deep neural network (DNN)) in terms of accuracy and processing time. The comparative values of accuracies are 70.40% for SVM, 69.10% for DNN, 99.04% for DCNN on the training dataset, and the respective elapsed time of the SVM, DNN, and DCNN models are 1.716 s, 3.59 s, and 1587 s. That is, the processing time of the DCNN model is high but offers higher accuracy in comparison to the pixel-based classification algorithms (SVM and DNN).

Landslide susceptibility assessment in the Himalayan range based along Kasauli – Parwanoo road corridor using weight of evidence, information value, and frequency ratio

The Himalayan region is prone to landslides. Rainfall-induced slope failure activities in the Indian Himalaya cause considerable damage, posing a serious risk to life and property. This study attributes information regarding landslide triggering parameters to further delineate landslide susceptibility maps of Himachal Pradesh in the Himalayan region. The landslide inventory map was created using information from field visits, Linear Imaging Self-Scanning Sensor (LISS III), and Google Earth. Thereafter, eight landslide causative factors, viz. slope, aspect, curvature, elevation, Landuse Landcover (LULC), soil, lithology, and drainage density were performed by employing the weight of evidence (WOE), information value method (IVM) and the frequency ratio (FR) methods. Using the ArcGIS reclassification tool, the final landslide susceptibility zonation (LSZ) maps were categorized into five susceptibility zones: “very low, low, medium, high, and very high.” The success rate for WOE, FR, and IVM models was determined as 76.27%, 78.20%, and 76.09% respectively, depicting that the FR model based LSZ map is more accurate. According to the FR model based LSZ map, the highly susceptible classes in the case of aspect, curvature, and lithology are southeast, concave, and TBS, respectively. The sparsely vegetated areas are more prone to landslides than the other LULC areas. The higher elevations, ranging from 1191 to 1434, 1434–1655, and 1655–1876 m, are more vulnerable to landslide activities as compared to low elevations. The slope classes 30–45 and 45–60, medium drainage density class and fine loamy class of soils are more likely to landslides. The prepared susceptibility zone map can be used for future mitigation planning in the high and very high susceptibility zones in order to reduce landslide-related human and economic losses.

Curvelet Transform based Denoising of Multispectral Remote Sensing Images

With the advent of sensor technology, the exertion of multispectral image (MSI) is comely omnipresent. Denoising is an essential quest in multispectral image processing which further improves recital of unmixing, classification and supplementary ensuing praxis. Explication and ocular analysis are essential to extricate data from remote sensing images for broad realm of supplications. This paper describes curvelet transform based denoising of multispectral remote sensing images. The implementation of curvelet transform is done by using both wrapping function and unequally spaced fast Fourier transform (USFFT) and they diverge in selection of spatial grid which is used to construe curvelets at every orientation and scale. The coefficients of curvelets are docket by a scaling factor, angle and spatial location criterion. This paper crisps on denoising of Linear Imaging Self Scanning Sensor (LISS) III images. The proposed denoising approach has also been collated with some existing schemes for assessment. The efficacy of proposed approach is analyzed with calculation of facet matrices such as Peak signal to noise ratio and Structural similarity at distinct variance of noise..

Synthesized pansharpening using curvelet transform and adaptive neuro-fuzzy inference system

Image fusion is an important technique in remote sensing to improve visual interpretation and classification. Pansharpening is the procedure of fusing panchromatic (PAN) and multispectral images to produce high spatial and spectral resolution images. Synthesized pansharpening is performed on Linear Imaging Self-Scanning Sensor III and Advanced Wide Field Sensor data, which are freely available and provided by the National Remote Sensing Center. The Adaptive Neuro-Fuzzy Inference System (ANFIS) in multiscale transform domain for multisensor image fusion application is evaluated. The state-of-the-art method has been evaluated by various quality metrics. The computational cost of ANFIS with wavelet, contourlet, shearlet, and curvelet transform is investigated. This study proves that curvelet with ANFIS-based fusion technique outperformed state-of-the-art techniques. The application will be used to incorporate the missing spectral information in the high spatial resolution PAN image to identify objects, highlighting the regions clearly.

Assessment of the alpine plant species biodiversity in the western Himalaya using Resourcesat-2 imagery and field survey

In the alpine ecosystem of the Himalaya, an observation of species diversity with habitat heterogeneity predicts some important factors that govern them. Information theory-based species biodiversity at the community level and habitat heterogeneity at the landscape level were studied. Resourcesat-2 linear imaging self-scanning sensor (LISS-III and LISS-IV)-based spectral diversity indices and species diversity indices of four summits with increasing elevation gradients were estimated. The species richness decreased with an increase in elevation. The southern aspect of the sub-alpine zone has the highest biodiversity having a 3.5 Shannon’ entropy (H). Despite receiving higher insolation, the increase in elevation leading to coldness and dominance of a few species make the southern aspects less diverse at the higher elevation. Both elevational gradients and microclimatic conditions define biodiversity in the Himalaya. Resolution from coarser (LISS-III) to finer (LISS-IV) to micro (field) scale showed an increasing range of values, H = 0.1, 0.2 and 2.1, respectively. There is significantly less correlation between field and satellite measured biodiversity indices (r, −0.5 to 0.3). To go closer to the field level of biodiversity assessment, there is a need to use satellite data having a higher spatial resolution. Spectral variation hypothesis does not hold good in the alpine ecosystem of the Himalaya.

Neural network-based modelling for forest biomass assessment

ABSTRACTForest biomass is an important parameter for assessing the status of forest ecosystems. In the present study, forest biomass was assessed by integrating remotely-sensed satellite data and field inventory data using an artificial neural network (ANN) technique in Barkot forest, Uttarakhand, India. Spectral and texture variables were derived from Resourcesat-1 (RS1) LISS-III (Linear Imaging Self-Scanning Sensor) data of April 24, 2013. ANN was used for finding the relation of spectral and texture variables to field-measured biomass. The top 10 variables, namely shortwave infrared (SWIR) band reflectance, near infrared (NIR) band reflectance, normalized difference vegetation index (NDVI), difference vegetation index (DVI), green band contrast, green band variance, SWIR band contrast, NIR band dissimilarity, SWIR band second angular moment, and red band mean, were selected for generating a multiple linear regression model to predict the biomass. The predicted biomass showed a good relationship (R2 = 0.75 and root mean square error (RMSE) = 85.32 Mg ha−1) with field-measured biomass. The model was validated yielding R2 = 0.74 and RMSE = 93.41 Mg ha−1. The results showed that RS1 LISS-III satellite data have good capability to estimate forest biomass, and the ANN technique can be used to enhance the scope of biomass estimation with a minimum number of spectral and texture variables.

Spatial Distribution of Phlebotomus argentipes (Diptera: Psychodidae) in Eastern India, a Case Study Evaluating Multispatial Resolution Remotely Sensed Environmental Evidence and Microclimatic Data.

Remote sensing, a powerful tool for analyzing landscape factors, is being used to explore the spatial ecology of vectors of several diseases. This study aims to explore the role of buffer size in identification and quantification of geo-environmental factors from multispatial resolution satellite data and its application along with microclimatic data to kala-azar vector abundance modeling.Sand fly abundance and microclimatic data were collected from 210 sample sites during the premonsoon and postmonsoon season of 2014 from Muzaffarpur district of Bihar (India). Linear imaging self-scanning sensor (LISS-III; 23.5 m) and advanced wide field sensor (AWiFS; 56 m) imageries were used for generating environmental variables at 300- and 500-m buffer zones. Four analytical models of sand fly density were developed and evaluated for predictive accuracy.A total of 33 geo-environmental and four microclimatic variables were tested for the prediction of sand fly density, of which the best four were maximum temperature, relative humidity, Euclidean nearest-neighbor distance of settlement area to mixed bush-grass land, and surface water body. Predictive accuracy of the LISS-III models was found to be higher than AWiFS models at all buffer sizes.The results show that geo-environmental parameters and microclimatic data are the best predictors for sand fly density modeling. Buffer sizes play an important role in identifying the explanatory variables. Model parameters may be useful in identifying predisposing factors of sand fly habitat suitability at the micro level.

Development of relationship between crop coefficient and NDVI using geospatial technology

In the present study, potential evapotranspiration (PET) is estimated using geographic information system (GIS) and remote sensing (RS) data. Linear imaging self-scanning sensor (LISS-III) satellite images of Tapi river basin area were used in the present study. The study area is classified into five different zones, namely forest land, agricultural land, barren land, urban land and water bodies bysupervised classification. NDVI values for the respective zones (class) were used to estimate crop coefficient (Kc) using linear regression model. Reference evapotranspiration (ETo) was calculated from Penman–Monteith model and PET was calculated by multiplying the Kc and ETo. The results obtained by this methodology were validated using actual evapotranspiration. A new linear model was developed for the study area between NDVI and Kc. Developed methodology for the determination of Kc derived fromRS data and NDVI-Kc model may be useful for irrigation scheduling and estimating water use efficiency.

Management of Coastal Erosion Using Remote Sensing and GIS Techniques (SE India)

World wide, coastal erosion is recognized as a great threat for beach environment. Total control of coastal erosion is not feasible but it should not be ignored and needs timely management. Erosional activities have been significantly noticed along the coastal tract of Vembar and Kallar (Kallurani), South India. An attempt has been made here to delineate different zones based on their sand budget and erosion rate. Linear Imaging Self Scanning Sensor (LISS) III 2001 and Linear Imaging Self Scanning Sensor III and PAN merged data of the year 2001 have been utilized to identify the coastal geomorphological features, shoreline changes and river course changes. A Geographic Information System (GIS) software namely ArcGIS (9.1) has been used as a tool to delineate the coastal erosion hazard for proper planning and management of coastal developments. Beach profile studies have shown significant variation in the beach morphology. The study area has been categorized into five different zones in the GIS analysis based on the degree of coastal erosion and sediment dynamics namely (i) very high - Kalaignanapuram, (ii) high - Sippikulam (iii) medium - Periyasamypuram (iv) low - Vembar and Kallar (Kallurani) (v) very low - Pachayapuram.

Upscaling of leaf area index in Terai forest plantations using fine- and moderate-resolution satellite data

Ecophysiological variables, such as leaf area index (LAI), play a key role in the functioning of ecosystem processes and are thus a useful determinant of primary production, evapotranspiration, and biogeochemical cycling. In the present study, upscaling of LAI was carried out by applying a transfer function from field LAI measurements to fine-resolution LISS III images and subsequently to coarse resolution Moderate Resolution Imaging Spectroradiometer (MODIS) data using a photosynthetically active radiation (PAR)/LAI ceptometer (AccuPAR model LP-80). Field data were collected from differently aged forest plantation types in the Terai Central Forest Division of Nainital district in Uttarakhand, India. The upscaling was done by establishing an empirical exponential relationship between normalized difference vegetation index (NDVI) and LAI. Results reveal a significant relationship (p < 0.01) between NDVI and LAI for each of the studied plantation types, i.e. teak, poplar, eucalyptus, and mixed plantation. The LAI was mapped at 23.5 m resolution by applying a plantation-specific LAI versus NDVI relationship derived from IRS Linear Imaging Self-Scanning Sensor images. The LAI maps were upscaled by using a simple linear averaging within nonoverlapping windows to match with Terra–MODIS 250 m resolution NDVI images. The upscaled time series of LAI was compared with representative field-measured LAI measurements and also with MODIS LAI at a resolution of 1000 m. Upscaled LAI was found to be significantly related to the field-measured LAI with a value of 0.69 for coefficient of determination and with a root mean square error of 0.77. On the other hand, upscaled LAI has less agreement with the MODIS LAI product (R2 = 0.5 and root mean square error = 0.70).

Automatic registration of multi-temporal remote sensing images based on nature-inspired techniques

In this article, we present nature-inspired techniques for automatic image registration of multi-temporal satellite images. Multi-temporal satellite image registration is becoming increasingly important to aid in flood damage assessment. We consider two images in the registration process: one before-flood image and another during-flood image. The objective is to maximise the similarity metric (of these two images) using information theoretic measures such as mutual information (MI). The maximum MI would imply that the images are better registered. The function of these metrics for transformation parameters is generally non-convex and irregular and, therefore, makes it difficult to use standard optimisation methods for the global solution. In this study, nature-inspired techniques – genetic algorithm (GA), particle swarm optimisation (PSO) and firefly algorithm (FA) are used to search for the maximum MI. The multi-temporal images – Linear Imaging Self-Scanning Sensor III (LISS III) image (before flood) and Synthetic Aperture Radar (SAR) image (during flood) and Moderate Resolution Imaging Spectroradiometer (MODIS) images (before and during flood) are used to demonstrate the performance of the proposed image registration approach. From the results obtained, we compare performance evaluations and conclude that nature-inspired techniques are accurate and reliable in solving satellite image registration.

Rule-based semi-automated approach for the detection of landslides induced by 18 September 2011 Sikkim, Himalaya, earthquake using IRS LISS3 satellite images

Landslide is considered as one of the most devastating and most costly natural hazards in highlands, which is triggered mainly by rainfalls or earthquakes. In comparison with other methods, landslide mapping and monitoring via remote sensing data products are considered as the least expensive method of data collection. The current research attempts to detect landslides which occurred due to a 6.9 magnitude earthquake in Sikkim Himalaya, India, on 18 September 2011 and also to establish the spatial relationship between landslides and the slope of the terrain. To detect the landslides, decision tree method was applied on two Indian remote sensing satellites linear imaging self-scanning sensor (LISS III) images acquired from 2007 and 2011 which were taken before and after the earthquake. As the study area was relatively huge for identifying the landslides, the region was separated into two parts: “tested study area” and “real study area”. The overall accuracy of landslide detection was 76%, and 75% for tested and real study area, respectively. Then, the spatial relationship between the landslides and the slope of the terrain was conducted using the digital elevation model. The results revealed that most of the landslides occurred between the slope of 25° and 45° covering 2.3 km2 and no landslide recorded in the slope of 65°–90° in the real study area. The results obtained in this study may be useful for decision-making and policy support towards reconstruction effort after the landslide occurrence. In addition, the information can be useful for reducing the risk of potential damages to substructures and properties by developing new and efficient strategies.

Shoreline dynamics and littoral transport around the tidal inlet at Pulicat, southeast coast of India

Pulicat is India׳s second largest brackish water lagoon with its mouth opening in the Bay of Bengal. The dynamics of the tidal inlet at Pulicat has been reported in this study on the basis of long term analysis using satellite remote sensing aided with Geographical Information System (GIS) and berm crest data. In addition, the short term analysis was carried out using monthly in situ Global Positioning System (GPS) profiling and sediment grain size analysis. The study of the shoreline dynamics along the inlet using the Linear Imaging Self – Scanning Sensor (LISS) – III along with the Survey of India toposheet of 1954 reveals that the inlet once with a single mouth is divided into two, over the period of time with the southern mouth migrating towards north with an inconsistent migration pattern of the northern mouth. The overall extent of migration is found to be within 3km. The outcome of the 25 year berm crest data starting from 1978 revealed the coastline around Pulicat is eroding upto 1990 and thereafter it is accreting with the net shoreline erosion rate of 20.34m/yr. The monthly in situ beach profiling studies from August 2004 to July 2005 revealed that the Pulicat inlet follows a variable migratory pattern with a shift of upto 914m North during August to December with a reversal migration of 600m thus leading to a net migration towards the north. The grain size analysis has unravelled the sediment transport around Pulicat inlet. The study of the inlet using satellite imagery during monsoon has clearly justified the dominant fresh water inflow regulatory force over the tidal forces and the positive influence of the Tsunami on opening the inlet during monsoon failure.

Changes in the glaciers of Chandra–Bhaga basin, Himachal Himalaya, India, between 1980 and 2010 measured using remote sensing

This study reports the glacier changes of Chandra–Bhaga basin, northwest Himalaya, India, from 1980 to 2010. Satellite remote-sensing data from the Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM), the Linear Imaging Self Scanning Sensor (LISS) and Advanced Wide Field Sensor (AWiFS) of the Indian Remote Sensing (IRS) series, and the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) were used to study the changes in glacier parameters such as glacier area, length, snout elevation, and the impact of glacier topographical parameters (glacier slope, aspect, and altitude range) on the glacier changes. It was found that the total glaciated area had shrunk to 368.2 km2 in 2010 from 377.6 km2 in 1980, a loss of 2.5%. The average position of glacier terminuses retreated by 465.5 ± 169.1 m from 1980 to 2010 with an average rate of 15.5 ± 5.6 m year−1. The decadal scale analysis showed that the average rate of retreat had increased the most in the recent decade. A moraine-dammed lake located in the study region was found to have expanded in area from (0.65 ± 0.01) km2 in 1980 to (1.26 ± 0.03) km2 in 2010. Glaciers with steep slope and less altitude range have lost more area than the glaciers having gentle slope and greater altitude range.

Metric Based on Morphological Dilation for the Detection of Spatially Significant Zones

The ability to derive spatially significant zones (e.g., water bodies and zones of influence) within a cluster of zones has interesting applications in understanding commonly sharing physical mechanisms. Using a morphological dilation distance technique, we introduce geometric-based criteria that serve as indicator of the spatial significance of zones within a cluster of zones. This letter focuses on the problem of identifying zones that are “strategic” in the sense that they are the most central or important based on their proximity to other zones. We have applied this technique to a task aiming at detecting a spatially significant water body from a cluster of water bodies retrieved from Indian Remote Sensing Satellite Linear Imaging Self-scanning Sensor (IRS LISS-III) multispectral satellite data.

Assessment of digital image classification algorithms for forest and land-use classification in the eastern Himalayas using the IRS LISS III sensor

The article describes the assessment and discusses the potential of different methods of classifying Linear Imaging Self-Scanning Sensor (LISS) III sensor data for vegetation-cover type and land-use mapping in the hilly terrain of the eastern Arunachal Pradesh, in the northeastern part of India. The forest cover types and their distribution in the study area are governed by climatic conditions and topographical features, which change along the gradient from tropical in the south to alpine in the Greater Himalayas in the north. Arunachal Pradesh is part of the Himalayan Biodiversity Hotspot and is at the tri-junction of Indian, Sino-Japanese, and Indo-Malayan floristic regions. The evergreen forests in the area represent climatic climaxes, which are partly original virgin and partly affected by anthropogenic pressures. Due to phenological and physiognomic similarities in ecotone regions, the differences in the spectral reflectance between adjacent forest types are not well pronounced. The age of the forests, terrain characteristics, and the nature of the vegetation itself could be other reasons for the near-similar reflectance. It is for these reasons that conventional classification algorithms for supervised and unsupervised classification do not perform well. Therefore, there is a need to find a suitable method for vegetation and land-use mapping with high mapping accuracy in this region, which is a biodiversity hotspot. A suitable classification technique is important to characterize vegetation-cover type in this complex terrain. We tested unsupervised, supervised, hybrid, object-oriented, and expert classification techniques for vegetation and land-use mapping on Indian Remote Sensing (IRS)-1C LISS III data. The expert classifier produced the highest accuracy (93.39%) followed by object-oriented, hybrid, and supervised and unsupervised classification techniques.

Quantification and comparison of spectral characteristics of sensors on board Resourcesat-1 and Resourcesat-2 satellites

This letter presents quantification and comparison of spectral characteristics of three similar sensors, namely Linear Imaging Self-Scanning Sensor (LISS)-III, LISS-IV and Advanced Wide Field Sensor (AWiFS) on board Indian Remote Sensing (IRS) satellites Resourcesat-1 (RS1) and Resourcesat-2 (RS2). Theoretical estimates of three critical spectral characteristics, effective bandwidth (Δλ), central wavelength (λc) and bandpass solar exoatmospheric irradiance (E 0), have been computed based on the laboratory measurements of the relative spectral response (RSR) for each spectral band. A comparative analysis between two estimates computed through the moment method and the full width at half maximum (FWHM) method showed noticeable differences of the order of 4.4–14.1 nm in Δλ and λc for the corresponding band of Resourcesat sensors. A coefficient of variation (CV) of the order of 0.1–1.3% was noticed in E 0 among various Resourcesat sensors, which could induce a difference of 0.26–3.27% in the estimation of top-of-atmosphere (TOA) reflectance. The variations in spectral characteristics among the Resourcesat sensors should be taken into account when comparisons are made between measurements from sensors on board RS1 and RS2 satellites, for example, for long-term monitoring of the Earth's environment.

Soil loss estimation through MUSLE using Kirpich and Williams times of concentration using RS and GIS techniques: a case study

Water is one of the most vital requirements for sustenance of life. Water, along with soil, is the most essential natural resource for economic and social development. A study of soil and water dynamics at a watershed level can facilitate a scientific approach toward their conservation and management. The present study is an attempt to apply the Modified Universal Soil Loss Equation (MUSLE) along with the Soil Conservation Service Curve Number (SCS-CN) method for runoff estimation and comparison of soil loss estimates for the catchments of Baindur Hole and Yedamavina Hole in the Udupi District of Karnataka State, India, obtained by times of concentration calculated by the Kirpich equation and the Williams equation. The base map and thematic maps were prepared using Indian Remote Sensing satellite 1C (IRS-1C) LISS-III (Linear Imaging Self-Scanning Sensor) image for land use/land cover and from Survey of India topo sheet in a GIS environment for overlaying and extraction of results. The time of concentration estimated by the Kirpich formula is lower in all cases; hence, the corresponding soil loss is 1.4 times higher than that determined with Williams formula. Catchments 23 and 17 with a lower drainage length show comparatively higher values of soil loss in case of the Williams equation, which can be attributed to the greater importance of drainage length in the equation.

Recent coal-fire and land-use status of Jharia Coalfield, India from satellite data

The Jharia Coalfield (JCF) in India is known for its high grade coal and associated coal fires. Before it can be exploited, valuable coal reserves are destroyed in the sub-surface due to fire. The combined act of fire and subsidence has endangered the environmental safety of the JCF, although several methods have been adopted to control the coal fires. Coal fire is a dynamic phenomenon, hence, its location and extent changes with time. To control the coal fires effectively, the status of the fires and the landscape must be assessed periodically. In this study, the thermal band in Landsat-7 Enhanced Thematic Mapper Plus (ETM+) data (daytime) of 29 March 2003 and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (night-time) of 9 October 2006 are used to delineate the coal-fire areas. The kinetic temperature of the coal fire-affected areas is calculated from Landsat-7 ETM+ data using a Normalized Difference Vegetation Index (NDVI)-derived emissivity model, and from band 13 of ASTER data with a fixed emissivity value. The study showed that the eastern part of the JCF is more affected by coal fires than the western part. The affected collieries in the eastern part are Kusunda, Lodna, Bararee, Gonudih and Ena. Among all collieries, Kusunda is the most affected by coal fires (29% of the area) and showed a 0.56 km2 increase in fire area from the year 2003 to 2006. During this period, a total increase in coal-fire area of 0.51 km2 occurs in the JCF. The land-use map prepared from Indian Remote Sensing (IRS) Satellite-P6 Linear Imaging Self-scanning Sensor (LISS)-III data showed that 6.9% of the area in the JCF is occupied by mining-related activities, which indicates its vulnerability to environmental degradation.