Indian Remote Sensing Satellite Research Articles

Soil Erosion Hazard Zonation in Bandu Sub-Watershed, India

Abstract Soil hazard zonation in watershed is quite significant to take the necessary actions for soil conservation. The present study attempts to identify soil hazard zones for proper soil conservation and prepare a treatment plan for the Bandu sub-watershed of India through morphometric analysis techniques by giving rank based on priority. The result of the prioritization of eighteen micro-watersheds is entirely satisfactory due to the use of fourteen morphometric parameters. The study quantifies the potential soil loss and identifies the soil eroded zone of the sub-watershed using the universal soil loss equation method and the entire region is categorized into three soil hazard zones with varying degrees. Indian Remote Sensing Satellite data have been used to conduct the whole study. The micro-watershed prioritization has been estimated by applying the composite morphometric value. The micro-watershed having 5.625 composite value ranks first for prioritization (most vulnerable with maximum soil erosion) and having 15.875 composite value ranks last for prioritization (least vulnerable with minimum soil erosion). The result also shows that the soil loss ranges from 0-30 tonne/hectare/year with an average soil loss of 0-10 tonne/hectare/year in maximum areas of the sub-watershed. The soil loss map shows that along the Bandu and in some agricultural fields, the central part of the region is susceptible to soil erosion. The scientific approach of this research could be more effective in maintaining sustainable rural planning. The study can be used as a reference work for determining soil hazard zones in any tropical watershed with high soil loss risk.

Gangotri glacier dynamics from multi-sensor SAR and optical data

The present study has analyzed dynamics of Gangotri glacier using multiple remote sensing (RS) datasets and ground based observations. Interferometric Synthetic Aperture Radar (InSAR) data pairs from European Remote Sensing satellite (ERS 1/2) tandem pair for spring of 1996, Sentinel-1 SAR pairs and Japanese’s Advance Land Observation System (ALOS) PALSAR-2 SAR data for Spring of 2015 were used to derive glacier-surface velocity at seasonal time scale using Differential InSAR (DInSAR) techniques. Bi-static TanDEM-X (Experimental) data was used for the 1st time to estimate glacier surface elevation changes for a period of 22, 44, 88 days during summer of 2012 using InSAR techniques in this study. Annual glacier velocity was also estimated using temporal panchromatic data of LANDSAT-5 (30 m), LANDSAT-7/8 (15 m), Sentinel-2 (10 m) and Indian Remote Sensing Satellite IRS-1C/1D panchromatic (5 m) data during 1998–2019 with feature tracking approach. This study has estimated glacier surface velocity and surface elevation changes for the major parts of Gangotri glacier and its tributary glaciers using medium to high resolution optical and SAR datasets, at annual and seasonal time scale, which is an improvement over earlier studies, wherein snout based glacier recession or only main glacier velocities were reported. The velocity and slope were used to assess glacier-ice thickness distribution using Glabtop-2, slope dependent and laminar flow based methods over the Gangotri group of glaciers. The estimated ice thickness was estimated in the range of 58–550 m for the complete glacier while few small areas in middle & upper regions carry higher thickness of about 607 m. The estimated glacier-ice thickness was found in the range of 58–67 m at the snout region. The estimation was validated using 2014 field measurements from Terrestrial Laser Scanner (TLS) for the first time and correlation was found to be 0.799 at snout of the glacier.

Dynamics of the Maniyari River and its meandering nature in the Achanakmar Tiger Reserve Area, Chhattisgarh, India: A Geospatial analysis

The Achanakmar Tiger Reserve (ATR) is an integral part of the Achanakmar Amarkantak Biosphere Reserve (AABR) with a total area of 914.017 sq. km. The Maniyari River, which flows through the reserve, is known as its life line. It originates from the core zone (Sihawal sagar) of ATR and merges in the Seonath River of the Mahanadi Basin. It flows about 105 km through Mungeli and Bilaspur districts. This river supplies its catchment area with water seasonally (6-8 months) and flows more than half of its length (about 60 km) within the protected area. Its catchment area has a rich biodiversity of tree species such as Shorea robusta, Terminalia tomentosa, Diospyros melanoxylon, Adina cordifolia, Pterocarpus marsupium, Madhuca indica, Anogeissus latifolia, Dendrocalamus strictus etc. A geospatial analysis was performed to understand the course of the Maniyari River within the ATR area over a fourteen year period between 2000 and 2013. Imagery from Indian Remote Sensing (IRS) satellite 1D and P6 for years 2000, 2004, 2008, 2010 and 2013 and Survey of India toposheets were analyzed to examine the changes in planform characteristics of the Maniyari River. The meander shift of the river was delineated considering the R1 (11.61 km), R2 (11.19 km), R3 (5.33 km) and R4 (5.02 km) reach’s respectively. Sinuosity Index (SI) for different reaches was calculated and evaluated. The SI ranged from 1.40 to 1.63, 1.20 to 1.29, 1.17 to 1.37, and 1.20 to 1.49 in R1, R2, R3 and R4 respectively and the magnitude of the shift was analyzed with the year 2000 as the base year. The overall SI for all the reach’s ranges between 1.30 and 1.36. The present study will be helpful in understanding the river dynamics as well as to maintaining the flow of the Maniyari River and preventing bank line erosion. The study will be helpful for the conservation of river flow and its tributaries in the ATR area in Central India.

Integrated use of regional weather forecasting and crop modeling for water stress assessment on rice yield

This study evaluated the effects of water stress on rice yield over Punjab and Haryana across North India by integrating Weather Research Forecasting (WRF) and Decision Support System for Agrotechnology Transfer (DSSAT) models. Indian Remote Sensing Satellite datasets were used to define land use/land cover in WRF. The accuracy of simulated rainfall and temperature over Punjab and Haryana was evaluated against Tropical Rainfall Measuring Mission and automated weather station data of Indian Space Research Organization, respectively. Data from WRF was used as weather input to DSSAT to simulate rice yield in Punjab and Haryana for 2009 and 2014. After simulated yield has been evaluated against district-level observed yield, the water balance components within the DSSAT model were used to analyze the impact of water stress on rice yield. The correlation (R2) between the crop water stress factor and the rice yield anomaly at the vegetative and reproductive stage was 0.64 and 0.52 for Haryana and 0.73 and 0.68 for Punjab, respectively. Severe water stress during the flowering to maturity stage inflicted devastating effects on yield. The study concludes that the regional climate simulations can be potentially used for early water stress prediction and its impact on rice yield.

A study on hydogeomorphological status of kadalundi river basin in kerala using geospatial technology

Detailed hydro geomorphologic study has been carried out for Kadalundi River Basin in Kerala to understand the drainage pattern, spatial and temporal water availability using secondary data collected from different Departments. Geomorphological parameters have been estimated as a part of this study and it is found that Kadalundi river basin is a sixth order stream. The drainage pattern is complex with considerable variation in spatial arrangements, which are controlled by topography, slope, rock type and structural deformations. The basin is characterized by dendritic type of drainage pattern with variable density. The densest dendritic pattern is developed on the hard Charnockite and Gneissic rocks. In some areas, the drainage pattern is sub dendritic reflecting structural control. The existing land use pattern is derived from Indian Remote Sensing Satellite (IRS – LISS II1, P6) using ERDAS software. Using hydrological toolset of Spatial Analyst, an extension of ArcGIS software, slope map, aspect map and Digital Elevation Model (DEM) have been derived. In addition, the basic thematic map on geology and soil also been derived.. For annual period, the rainfall varies from 1800- 4100 mm, 1400- 2600 mm for South –West monsoon period and 600-1100 mm for the North-East monsoon period.

Application of Satellite Data on Water Pollution: An Intensive Review

Evaluation of water pollution is a priority work nowadays. The signature of the waterbody reveals its excellence or mediocrity and reflectance that can measure by a sensor used to analyse the health status of the waterbody. The remote sensing analysis has become the latest state of art technologies for monitoring large-scale waterbodies. High-resolution satellite data are now available to estimate water pollution through various water quality parameters like clarity, chlorophyll, suspended solids, turbidity, temperature, salinity, organic matter, etc. In this review study, a special emphasise has been given to the various satellites like Landsat, sentinel, satellite pourl’Observation de la terre (SPOT), moderate resolution imaging spectroradiometer (MODIS), medium resolution imaging spectrometer (MERIS), Indian remote sensing satellites (IRS) and its application on water pollution. Availability of satellite data, algorithms, and models to assess water quality has also been reviewed in detailed. The review suggests development and innovation in satellites, sensors and techniques to assess the non-optically active constituents of water quality for better understanding and management of water pollution.

Updated Glacial Lake Inventory of Indus River Basin based on High-Resolution Indian Remote Sensing Satellite Data

Updated Glacial Lake Inventory of Indus River Basin based on High-Resolution Indian Remote Sensing Satellite Data

Rural Land Transformation along Chandigarh-Kharar Road

Chandigarh being one of the well-planned cities in the country have attracted the folks of the nearby states to come to the city for better standards of living and improved sources of livelihood. The city in recent past have been developing as an educational and commercial hub. Although the city centre or one can say the planned portion of the city is unchanged and untouched from the rapid transformation with the time but the periphery is not so lucky in this manner. People coming from near and far places get settle in the periphery and they commute on daily basis from the surrounding regions to the city and its periphery. This have resulted in the large-scale development of educational and commercial hubs growing day by day along the major roads connecting the city with its neighbouring towns and villages. Majority of the transformation of agricultural land have occurred along the major roads. Here in the study an attempt has been maid to assess the intensity and direction of the rural land transformation happening in the periphery. For the purposes of the study satellite data is used acquired from Landsat, Indian Remote Sensing Satellite and Google Earth Pro along with other secondary data sources. One-kilometre buffer zone along the Chandigarh – Kharar road falling in Chandigarh periphery is selected. Data for the year 2003 and 2017 is analysed and compared. The results are presented in the forms of maps and figures.

Monitoring the quality of life in urban area using TDVI- Case study of Kalaburagi city

Many vegetation indices have been proposed over last decades made specialists search for the most suitable vegetation index for a given remote sensing application. Measuring the Quality of Place (QOP) is a hard task since it involves both physical and socio-economic dimensions. Being one of the major land use categories, urban vegetation plays a significant role in one‟s judgment for QOP in a neighborhood. Both quantity and quality of the community parks and recreation areas are major determinants of neighborhood attraction. For these reasons, detection of urban vegetation cover has been one of the important implication areas of urban image classification techniques. “Transformed Difference Vegetation Index (TDVI) developed by Bannari et al. (2002), is tested in a previous work where the index has performed better than NDVI and SAVI. In that work, a comparative study between TDVI, SAVI and NDVI for estimating vegetation cover in urban environment from the Indian Remote Sensing Satellite (IRS-1D) imagery has been conducted. The validation of the obtained results according to the ground truth showed that the TDVI is an excellent tool for vegetation cover monitoring in urban environment. It does not saturate like NDVI or SAVI, it shows an excellent linearity as a function of the rate of vegetation cover. This paper adds on the previous work by analyzing the performance of TDVI in urban image classification. Results indicate that, the performance of TDVI in urban image classification is better than NDVI and SAVI. The new index not only differentiates the urban vegetation cover better but also helps to minimize the error in classifying other unclassified pixels of urban categories.

GIS-based hazard and vulnerability assessment of a torrential watershed

Torrents are ephemeral streams which usually carry heavy bedload and flash flows. The frequent changes in its course caused by meandering and migration lead to extensive damage to environment in valley and alluvial land. In the present study, application programming interface (API) was developed to analyze temporal Landsat data in Google Earth Engine(GEE) to understand land use/land cover and vegetation characteristics in the Tangri (Dangri) River, India, and Indian Remote Sensing Satellites data were used for hazard and vulnerability assessment. It is observed that because of various conservation activities as well as due to watershed inhabitants’ intervention, the areas under cropland, orchard/plantation and grass/scrub have increased. The monthly mean enhanced vegetation index (EVI) has also increased in the watershed. Although, the average annual soil loss is high, i.e., 40.4 t/ha/yr. It is highest for mountain physiographic unit (67.6 t/ha/yr.) and scrub forest (61.5 t/ha/yr.). Based on flood inundation modeling, it is estimated that for two-, five-, ten-, twenty-five-, fifty- and hundred-year return periods, 1.42–16.14% areas of watershed are likely to get inundated. Torrents’ vulnerability analysis was carried out using analytical hierarchy process (AHP) to derive composite vulnerability index by aggregating weights of various parameters and sub-parameters. The alternatives for the treatment of vulnerable sections in torrential regime were evaluated using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and ELimination Et Choice Translating REality (ELECTRE) models. The methodology demonstrated has potential to analyze torrential systems and also for their proficient planning, monitoring and management.

On-Board Ship Detection for Medium Resolution Optical Sensors.

In recent years there has been an increased interest in ocean surveillance. The activity includes control and monitoring of illegal fisheries, manmade ocean pollution and illegal sea traffic surveillance, etc. The key problem is how to identify ships and ship-like objects accurately and in a timely manner. In this context, currently, many solutions have been proposed based on high resolution optical and radar remote sensing systems. Most often, these systems suffer from two major limitations viz., limited swath, thereby requiring multiple satellites to cover the region of interest and huge volumes of data being transmitted to ground, even though effective per-pixel information content is minimal. Another limitation is that the existing systems are either simulated on ground or built using the non-space qualified/Commercial Of-The-Shelf (COTS) components. This paper proposes an efficient on-board ship detection system/package connected with medium resolution wide swath optical camera. The methodology adopted has three major components, viz., onboard data processing for improving the radiometric fidelity, followed by a ship detection using modified Constant False Alarm Rate algorithm (CFAR) and a false alarm suppression module to mask false identifications. Finally, the package outputs only the locations of the ships, which is transmitted to the ground. The proposed system reduces the effective volume of data to be transmitted and processed on ground and also significantly cuts down the turnaround time for achieving the end objective. The system is built on radiation hardened Field Programmable Gate Array (FPGA) devices to meet the various engineering constraints such as real-time performance, limited onboard power, radiation hardness, handling of multiple custom interfaces etc. The system is tested with one of the medium resolution Multispectral Visual and Near Infra-Red (MX-VNIR) sensor having a spatial resolution of around 50 m and swath of around 500 Kms, which would be flown with one of the upcoming satellites. The systems performance is also verified on ground with Indian Remote Sensing (IRS) Satellite’s Resourcesat’s Advanced Wide Field Sensor (AWiFS) data and the results are found to be quite encouraging as well as meeting the mission objectives.

A geo‐spatial approach to assess Trees outside Forest (ToF) in Haryana State, India

AbstractMapping and monitoring the Trees outside Forest (ToF) is gaining significance in the scientific community as they provide critical ecosystem services such as protecting soil and water resources, wildlife habitat, and aesthetics including food, fuel, and fibre. Quantifying ToF can also provide useful information for emission estimation relating to the agriculture, forests, and other land use (AFOLU) category of the Intergovernmental Panel for Climate Change (IPCC). Despite the importance of quantifying ToF, very few studies have attempted to quantify them in India's natural resource inventory programs. In this study, we focused on Haryana State, India, to inventory ToF using very high‐resolution (VHR) Indian Remote Sensing (IRS) satellite data. Haryana's landscape is interspersed with croplands and ToF, thus providing a challenging environment to test VHR satellite data's ability to quantify the diversified landscape structure. We specifically used CARTOSAT‐1 panchromatic (2.5 m) and multispectral LISS‐IV (5.8 m) datasets to quantify the vegetation and build a much‐needed database for ToF. We used a novel classification scheme based on the geometry, that is, point, line, or polygon formations, to quantify ToF at a scale of 1:10,000. The obtained results suggest ToF with linear and block formations extended to 128.83 and 20.51 km2, respectively, accounting for ~3.38% of the Total Georgraphical Area of Haryana State while point formations established 2,774,531 in numbers. This study highlights the usefulness of VHR satellite data and fused imagery to quantify ToF in highly diverse landscape of Haryana. The results will help address vital ecosystem services from ToF, including greenhouse gas emissions quantification from the Agriculture, Forests and Other LandUse (AFOLU) category.

Three Decades of Indian Remote Sensing in Coastal Research

The launch of the first Indian Remote Sensing Satellite in 1988 ushered in a new era of utilization of satellite data and technology in natural resources monitoring, coastal area management, disaster mitigation, weather and climate change studies. India with its very long coast line, areal extent, resources and population would have been a huge challenge to the coastal researchers and administrators if not for the Indian Remote Sensing satellite data that provided the much needed temporal, thematic information for effective mapping, monitoring and management of the coastal areas. The country’s large and diverse coastal regions are economically important and ecologically rich but also extremely vulnerable and highly prone to natural and anthropogenic hazards. Through the last three decades, the Indian remote sensing programme has contributed immensely to our understanding of the coast and ocean processes, coastal ecosystems, habitats, marine pollution and the vulnerability of the coast to natural hazards. IRS-1C with its improved spatial resolutions, additional spectral band, improved repetitively ushered in a new era of research specifically in the management of coastal areas and resources. Availability of spatial data at various resolutions, low cost, advances in technology resulted in new applications for management coastal areas. IRS-1C also can be aptly termed as the precursor for thematic satellite series such as OCEANSAT, RESOURCESAT and CARTOSAT, thereby setting the environs for thematic research in coastal areas. Integrated utilization of remote sensing data, numerical models, GIS and GPS has paved the way for the development of operation systems for management of coastal areas, thereby realizing the vision of Dr. Vikram Sarabhai, the father of Indian Space Programme, of building a self-reliant and indigenous Space Programme for the betterment of quality of life of the common people.

Using Satellite Image Fusion to Evaluate the Impact of Land Use Changes on Ecosystem Services and Their Economic Values

Accelerated land use change is a current challenge for environmental management worldwide. Given the urgent need to incorporate economic and ecological goals in landscape planning, cost-effective conservation strategies are required. In this study, we validated the benefit of fusing imagery from multiple sensors to assess the impact of landscape changes on ecosystem services (ES) and their economic values in the Long County, Shaanxi Province, China. We applied several landscape metrics to assess the local spatial configuration over 15 years (2004–2019) from fused imageries. Using Landsat-7 Enhanced Thematic Mapper Plus (ETM+), Landsat-8 Operational Land Imager (OLI) and Indian Remote Sensing Satellite System Linear Imaging Self Scanning Sensor 3 (IRS LISS 3) imageries fused for 2004, 2009, 2014 and 2019, we reclassified land use/land cover (LULC) changes, through the rotation forest (RF) machine-learning algorithm. We proposed an equivalent monetary metric for estimating the ES values, which also could be used in the whole China. Results showed that agriculture farmland and unused land decreased their spatial distribution over time, with an observed increase on woodland, grassland, water bodies and built-up area. Our findings suggested that the patterns of landscape uniformity and connectivity improved, while the distribution of landscape types stabilized, while the landscape diversity had a slight improvement. The overall ES values increased (4.34%) under a benefit transfer approach, mainly concerning woodland and grassland. A sensitivity analysis showed the selected economic value (EV) was relevant and suitable for the study area associated with our ES for LULC changes. We suggested that changes in landscape patterns affected the ESV trends, while the increases on some LULC classes slightly improved the landscape diversity. Using an interdisciplinary approach, we recommend that local authorities and environmental practitioners should balance the economic benefits and ecological gains in different landscapes to achieve a sustainable development from local to regional scales.

A Review of IRS-1C Applications in Urban and Regional Studies, and Infrastructure Planning

The multifaceted data from Indian remote sensing satellite (IRS)-1C with Panchromatic (PAN, 5.8 m, 0.50–0.75 μm), Linear Imaging Self-Scanning Sensor-III (LISS-III, 23.5 m, four multi-spectral bands) and Wide Field Sensor (WiFS, 188 m, red and near-infrared bands) sensors onboard, along with stereo imaging and 5-day revisit capability have been effectively utilised for mapping, monitoring, planning and development of urban and regional areas. It made possible to explore urban and regional areas either independently or in combination with other remote sensing data for base map preparation, land use survey and planning, growth modelling, environment and hazards analysis, utilities and infrastructure planning, etc. This review article articulates the scale and mapping potential of IRS-1C data for urban and regional areas, data fusion methods and information retrieval based on visual or digital image processing techniques, including advanced classifiers. It recapitulates the application potential demonstrated in last 25 years in urban and regional studies, and for infrastructure planning.

Himalayan Cryospheric Science: A Paradigm Shift By IRS 1C/1D DATA

Himalayan region is a water tower (W.T.) of Asia due to a large concentration of cryosphere. The area generates a large amount of water and used by the millions of people living in the Indo-Gangetic plains. The W.T. provides a sustainable source of water for Indus, Ganga, and Brahmaputra river basins. By considering the importance of the Himalayan cryosphere, the field-based glaciological programme started in the mid-1970s. It provided limited information about the Himalayan cryosphere. Therefore, based upon minimal observations, numerous conclusions were made about glacier mass, length, and area loss, leading to the erroneous conclusion by the Intergovernmental Panel on Climate Change (IPCC) in 2007. At the same time, the results from a cryosphere programme of the Indian Space Research Organization (ISRO) started to arrive, where satellite data of Indian Remote Sensing Satellite (IRS) 1C and 1D were analysed. The information helped to resolve IPCC controversy and provided invaluable insight into Himalayan cryosphere. New models, techniques, and methods were developed in the ISRO programme, providing information on the distribution of snow and glaciers, the formation of glacier lakes, glacier retreat, and mass loss. The information was unique and otherwise not available to the scientific community. The data were also extensively used for developmental activities and to assess national security. These include monitoring of snow in Ladakh, hydropower development in Himachal Pradesh, and risk assessment from glacier lakes in Uttarakhand. The methods and techniques are still being used or improved upon, making remote sensing as an indispensable tool to study the Himalayan cryosphere.

Use of IRS-1C and Its Follow-On Missions for Geological Applications: A Review

Satellite-based remote sensing is being used by geologists for the last five decades for conducting geological surveys and applications. The launch of Indian Remote Sensing Satellite (IRS)-1C in December 1995 opened new opportunities to study and characterise Earth’s surface materials, landforms and surface as well as subsurface processes. The availability of panchromatic (PAN) camera and Wide Field Sensor (WiFS) in addition to multispectral Linear Imaging Self-Scanning sensor-III (LISS-III) made the IRS-1C unique among its contemporary satellites. While wide coverage provided by the WiFS images could be used for studying regional features like geological and geomorphic provinces and mega lineaments/fractures, the PAN-sharpened LISS-III images facilitated improved interpretability and made large-scale (up to 1:10,000) mapping possible. In this article, we review the geological applications of the IRS-1C and its follow-on missions carrying similar sensors, with an aim to present the status of the achievements made with this unique mission. From the basic geological and geomorphological mapping to different applied geological investigations in the fields of hydrogeology, mineral and hydrocarbon exploration, geoengineering surveys, geoenvironmental appraisal of engineering, mining and infrastructural projects, and geological hazards are covered in this review. While reviewing different applications, we highlight major projects executed, new initiatives undertaken, and scientific findings through the use of IRS-1C data and its successor missions. We also provide some recommendations on the future requirements of remote sensing systems and processes from the geological perspective.

Three Decades of Nationwide Forest Cover Mapping Using Indian Remote Sensing Satellite Data: A Success Story of Monitoring Forests for Conservation in India

India is a biodiversity-rich country. The panorama of Indian forests ranges from tropical evergreen rain forests in the Andaman and Nicobar Islands, the Western Ghats, and the north-eastern States, to Dry Alpine Scrub high in the Himalayas in the north. On the other extreme, tropical dry deciduous and thorn forest is found in the deserts and dry areas of Rajasthan and Gujarat. The variety and distribution of forest vegetation are large, and India is one of the 17 mega biodiversity regions of the world. Forestry in India supports a sizeable non-wood forest product-based economy apart from timber and fuelwood. In forested landscapes people are integrated with forest and make living out of it. On the other hand urbanization and infrastructure development are putting considerable pressure on forests. These issues need effective and efficient forest monitoring system. Hence in late 1980s, India entrusted Forest Survey of India to evolve robust and scientific method of forest monitoring. Thus, India has taken an early lead in establishing a forest monitoring system. The history of forest cover mapping using satellite images in the country goes back to 1983 when National Remote Sensing Agency (NRSA—now known as NRSC-ISRO) prepared the first-ever forest cover map of India. Later, Forest Survey of India has been mandated with the forest cover mapping of the country in the biennial cycle since 1987. Three decades of forest cover mapping on a regular basis have been a remarkable achievement in the field of natural resource monitoring. India’s remote sensing-based periodic monitoring of forest cover, which is unique in the world, has played a major role in conservation and enhancement of forest cover of the country. Evolving with satellite data improvement and technological advancements, the methodology of forest cover mapping by FSI has seen significant changes over the years, wherein Indian remote sensing data, particularly the IRS 1C/1D LISS III data, have been the primary input in the time series mapping. This paper reviews the chronology and significance of this important forest monitoring programme of India.

A novel method of satellite based river discharge estimation using river hydraulic geometry through genetic algorithm technique

Many countries experiences floods due to heavy flows carried by trans-boundary Rivers. Hydrological modelling in un-gauged catchments and trans-boundary Rivers has become a challenging task for hydrological modellers. This research aims at estimating river discharges computed solely using satellite images, with no ground-based or a prior information at all. It is also aimed at testing the applicability of approach in very well defined channel to braided channels and in very wide range of discharge conditions. The approach works owing to principle of hydraulic geometry of natural rivers. Monitoring these discharges in upstream areas will help in forecasting flows in the downstream areas. In this paper, an attempt is made to estimate the river discharge using river flow width information derived from temporal satellite remote sensing data alone. The approach is based on Many Stations Hydraulic Geometry (AMHG) principle through Genetic Algorithm Technique. The technique is demonstrated in four major rivers in India for various discharge values using temporal remote sensing data of Indian Remote Sensing Satellites (IRS) and LANDSAT. Computed discharges are validated with observed discharges in all the identified river reaches using statistical method and found to have very good match. This novel approach is useful globally for quantifying river discharges with continuous imaging of the rivers and has strong advantage in flood early warning and water resources assessment.

Increasing heavy rainfall events in south India due to changing land use and land cover

AbstractThrough an analysis of land use and land cover (LULC) data for the years 2005 and 2017 from the Advanced Wide Field Sensor onboard the Indian Remote Sensing satellite, we find considerable changes in the LULC in three major states of south India, namely, Tamil Nadu, Telangana and Kerala. This change is mainly due to increasing urbanization, in addition to the change of prevalent mixed forest into deciduous needle/leaf forest in Kerala. Motivated by this finding, we study the impact of these LULC changes over a decade on the extremes of twelve heavy rainfall events in these states through several sensitivity experiments with a convection‐permitting Weather Research and Forecasting model, by changing the LULC boundary conditions. We particularly focus on three representative heavy rainfall events, specifically, over (a) Chennai (1 December 2015), (b) Telangana (24 September 2016), and (c) Kerala (15 August 2018). The simulated rainfall patterns of the three heavy rainfall events are found to be relatively better with the use of the 2017 LULC boundary conditions. The improvement is statistically significant in the case of the Chennai and Kerala events. On analysis of these simulations, and output from additional simulations we have conducted for nine other heavy rainfall events, we suggest that the recent LULC changes result in higher surface temperatures and sensible heat fluxes, and a deeper and moist boundary layer. This causes a relatively higher convective available potential energy and, consequently, heavier rainfall. We find the LULC changes in the three states, mainly dominated by the increasing urbanization in Telangana and Tamil Nadu, enhance the rainfall during the heavy rainfall events by 20–25%. This is the first extensive investigation of multiple and multi‐regional cases over the Indian region.