Indian Remote Sensing Research Articles

Assessing the significance of leaf chlorophyll content for measuring the vegetation health in mangrove species along the Kerala Coast, India – A multi-proxy approach

This study aimed to estimate the chlorophyll content of mangrove species distributed along the Kerala coast of India, and also to address the critical issue of assessing the health of these ecosystems. We identified 16 mangrove species and estimated their chlorophyll concentration levels through a combination of field reconnaissance, in situ radiometric measurements at 218 locations, and UV spectrophotometric (ultraviolet–visible) analysis in the laboratory, and estimated the vegetation health from spectral indices using IRS (Indian Remote Sensing) Resourcesat LISS-IV (Linear Imaging and Self-Scanning Sensor) satellite imagery. This study revealed that the dominant species in Kerala are Avicennia officinalis, A. marina, Rhizophora mucronata and Excoecaria agallocha, whereas, Bruguiera sexangula is sparsely spread. Approximately 14 % of the mangrove cover along the Kerala coast is under stress due to various factors, including climate change and anthropogenic influences. Significantly, two of the 16 species, Avicennia marina and Rhizophora appiculata appeared relatively healthy, with average chlorophyll concentration index (CCI) values of 311.89±40.13 µmol/m2 and 553.42±23.24 µmol/m2, respectively. However, other dominant species Avicennia officinalis, Rhizophora mucronata, and Excoecaria agallocha exhibited average CCI values of 582.35±147.61, 611.7±104.58, and 559.04±141.13 respectively, and seemed to be under stress, calling for serious conservation measures. Our study also revealed the potential of cost-effective IRS LISS-IV imagery for identifying mangrove species and for estimating the chlorophyll concentration and plant vigour together with field and laboratory observations and measurements.

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.

Spatial assessment and chemical characterization of degraded (salt-affected) soils at post-reclamation stage of the Indo-Gangetic Plain in Haryana State.

Dynamics of soil salinity and sodicity is a common feature driven by anthropogenic causes such as soil reclamation, the effect of extreme climate events, disturbed salt, and water balance in irrigated areas that are devoid of any good quality groundwater source and adequate natural surface drainage condition in a semiarid climatic region. Periodic soil salinity assessment is therefore vital to know the current soil salinity status, plan reclamation, and/or management strategies for sustained agricultural growth and livelihood security. Temporal studies using Indian Remote Sensing (IRS) LISS III data, at pre- (1997) and post-reclamation (2017) stages have indicated spatial changes as reclaimed areas (~ 35%) and dynamics of soil salinity as increased areas (~ 61%) under irrigation across the Gangetic plain of Haryana State. The prominent areas of reclaimed sodic soil soils were located in the old alluvial plain which covered Panipat (12.32%), Karnal (6.01%), and Jind (5.9%) districts. Based on pH, ECe, and ESP values, these were classified as slight (Sso1, 8.75%), moderate (Sso2, 24.73%), and strong (Sso3, 18.20%) sodic soils, respectively. Significant salinity-inflictions (emerging areas) were identified at low-lying, poorly drained, irrigated soils in south and central Haryana that cover Jhajjar (13.99%), Sirsa (11.06%), Hisar (10.15%), Rohtak (8.73%), Bhiwani (6.43%), Palwal (4.31%), and Rewari (3.01%) districts. Slight (Ssa1, 16.82%), and moderate (Ssa2, 22.13%), categories are dominant soils, respectively. Among the landforms, significant areas (28.24%) were identified in the old alluvial plain with sand dunes (OAPSD), aeo-fluvial plain (AFP, 8.6%), and fluvio-aeolian plain (FAP, 6.0%), respectively. Dominant areas of reclaimed soils (14.4%) were identified in OAPSD. The soil analysis data indicated that these soil are moderate to strongly sodic (pH 8.7-11.0) and saline (ECe 4-26 dS m-1). The reclaimed sodic soils showed prominent improvement in soil pH and sodicity levels (pH 8.3-9.2) at 0-15cm depth and are commonly located in the Ghaggar and Yamuna river plains. Poor quality groundwater with high Residual Sodium Carbonate (RSC) was dominant at selected locations under the arid and semiarid climate. The database can also be used as a reference database for further monitoring of soil salinity status particularly in the irrigated regions. Currently, it is also used as a primary database for harmonization, monitoring, and reconciling of similar soils of the world under the Global Soil Partnership projects.

Fusion of Satellite Images in Transform Domain

Unique—Image fusion in light of the wavelet and fourier trans-form comes about rich multispectral points of interest yet gives less spatial subtle elements from source images. Wavelet transform performs well at straight highlights yet not at non-direct discontinuities since Wavelets don't utilize the geometric properties of structures. Curvelet transforms defeat such troubles in include rep-resentation. A novel Image fusion rule by means of high pass balance utilizing Local Magnitude Ratio (LMR) in Fast Discrete Curvelet Transforms domain (FDCT) and Discrete wavelet transform (DWT) is characterized. Indian Remote Sensing Geo satellite images are utilized for MS and Pan images. This fusion rule creates HR multispectral image with high spatial resolution. This technique is contrasted and wavelet, Principal Component Analysis (PCA), Fast Discrete Curvelet Transforms domain fusion strategies. Master postured technique spatially performs alternate strategies and results rich multispectral information.

Performance of Deep Learning in Land Use Land Cover Classification of Indian Remote Sensing (IRS) LISS – III Multispectral Data

Identification of land use land cover is a very important task. However, methods existing for the above mention purpose are labor incentives, time-consuming, and costly. Remote sensing plays very important role in the mappings. classification of land cover features and offers very noteworthy and sensed information. The present study shows the semantic segmentation of Indian remote sensing (IRS) LISS-III multispectral image and the comparison of three algorithms U-Net, Deeplabv3+and Tiramisu. The deep neural network was used to perform the study. We present total 3 innovative datasets, built on these LISS-III images that has 4 different spectral bands (Band – 2 (Blue), Band-3 (Green), Band-4(Red), and Band-5 (Nearly Infrared), FCC (false color composite) images and the ground truth mask images. Dataset has 13500 labelled images. A fully-convolutional network (FCN) with skip connections is trained to take an input image of size 128 X 128 X 3 and outputs a matrix of shape 128 X 128 X 4 i.e., a one-hot encoded version of the mask. The experiment identifies 4 classes successfully (Water Bodies, Vegetation, Uncultivated Land, and Residential areas). The experiment showed that the U-Net algorithm has a very good capability for the classification of LISS -III images for land use land cover class detection then Tiramisu and Deeplabv3+. U-Net achieved accuracy 84%, Deelabv3+ achieved 29% whereas Tiramisu achieved accuracy 33%.

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.

Glacier retreat in Himachal from 1994 to 2021 using deep learning

Traditional glacier identification using satellite data involves making basic band combinations, image processing, and calculations based on the spectral properties of glacier bodies. The results from computer processing of satellite data are sometimes affected by segmentation and thresholding techniques, due to which water bodies, rock surfaces, and other features are classified as glaciers. Deep learning (DL) based methods for image classification, object detection, and segmentation have proven to be very efficient and accurate in recent years. Deep learning-based simple semantic network U-Net has been utilized in various disciplines for image processing and efficient image segmentation. This paper emphasizes the application of this simple semantic segmentation network, i.e., U-net, for glacier identification using Indian Remote sensing (IRS) and Landsat satellite data with some improvement in segmentation and glacier identification. The glaciers in the Himachal Pradesh province of India, located in the Western Indian Himalayas are identified, segmented, and mapped using the U-net. The glaciers for the past three decades, i.e. from 1994 to 2021 mapped in the study area to assess the impact of climate change on glacial retreat. The suggested technique can automate the entire glacial mapping process, has a greater identification accuracy of 95 percent, and is less time-consuming. The glaciated area in Himachal Pradesh decreased at a rate of 67.84 km2 per annum from 4020.6 km2 in 1994–2198.5 km2 in 2021. It is observed that from 1994 to 2021, the glacier area decreased approximately by a percentage loss of 1.678 per annum, with decreasing decadal trend from 2.31% in 1994–2001 to 1.398 in 2011–2021.

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.

Soil degradations in the IGP of central Haryana, India-a spatial assessment for reclamation and management.

Natural and anthropogenic processes are primary drivers for soil degradations in the Gangetic plain of India. Indian Remote Sensing (IRS) data was used to assess soil degradations and study spatial changes comparing with the legacy datasets. Based on the nature, extent, and degrees of limitations, these soils were evaluated to suggest suitable reclamation and management options. Typical strong signature (white tones) from a barren highly salinized soil (P3, P6, P9, P11, and P15) and higher energy absorption (blue-black tones) from a permanent waterlogged surface (P13, P14, P10) in the irrigated areas have favored delineation of salt-affected, strong, and permanent waterlogged areas. Moderate and slightly salt-affected soils (P1, P2, P4, P5, P7, P8, and P12) and the areas with high water table depths (P13 and P14) have shown mixed spectral signatures for associations of soil, water, and crops and were mapped using ground truth and soil chemical analysis data. Irrigation with poor quality ground water (RSC: 1.2 to 12.3 me L-1) has prompted developing salinity and sodicity in P2, P5, and P11, while irrigation in poorly drained areas caused appearance of waterlogging (WTD < 1.5m) and salinization (P3, P5, P7, P9, P13, P14). The gypsum application and sub-surface drainage (SSD) techniques have enhanced soil reclamation by (i) neutralizing CO32 - and HCO3 - in P12 (pH 9.2, ESP 24) and (ii) controlling soil salinity (EC 3.0 dS m-1) and waterlogging (WTD > 1.5m) in P14. Soils with CaCO3 layers at shallow depth in P3 (0.7-4.2%), P6 (1.2-7.1%), P9 (8-16%), P11 (4-15%), and P13 (0.9-9.4%) were managed using forest plantations. Ground water with high SAR in T1 to T7 can be applied for agriculture using mixing or cyclic mode. Water with high RSC in T4, T5, T7, T8, T9, and T10 needs treatment with gypsum. A significant area (~ 24.87%) of salt-affected soils was reclaimed since 1971. Prominent changes (3.27 to 17.71%) were shown in Panipat and Karnal districts. Small areas of brick kiln (P11), industrial effluent (P10), riverine sand, partially stabilized dunes, and mining have appeared due to anthropogenic activities.

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.

High resolution inventory and hazard assessment of potentially dangerous glacial lakes in upper Jhelum basin, Kashmir Himalaya, India

In the Himalayan Mountain region, a large number of glacial lakes have developed in the recent past due to glacier recession under the influence of climate change. In this study, we used high resolution satellite data such as Indian Remote Sensing (IRS) LISS-IV (5.8 m) and Google earth images supplemented with field survey to generate an updated glacial lake inventory of Upper Jhelum Basin (UJB) of Kashmir Himalaya. The Sentinel-2A (10 m), Landsat-OLI (30 m) and MSS (60 m), and Cartosat-DEM (30 m) were additional data sources used for glacial lake mapping and change detection analysis. A total of 393 glacial lakes covering an area of 21.55 ± 3.8 km2 were identified, mapped and inventoried. The lake inventory includes 102 proglacial lakes, 13 supraglacial lakes and 278 unconnected glacial lakes. Using the weighted index-based method, 21 glacial lakes were found as Potentially Dangerous Glacial Lakes (PDGLs). Out of these, 7 lakes were classified as High, 9 as Medium and 5 as Low hazard glacial lakes as per the hazard assessment. Change detection analysis of PDGLs from 1980 to 2020 revealed an increase in area from 5.92 km2 to 8.46 km2 thereby, indicating a growth of 2.51 ±0.9 km2(30%) at a rate of 0.063 km2/year. The formation and growth of glacial lakes in this area is attributed to continuous glacier recession under the warming trend of temperature and declining nature of precipitation. In this study, the findings showed that Tavg and Tmin are rising significantly at a rate of 0.004ºC/year and 0.013ºC/year respectively. This study provides an important database for future GLOF studies in the region.

Analysis of urban development on land cover changes of three cities of Gujarat state, India

Urbanization generally serves as a key navigator of the economic growth and development of the country. There is a need for fast and accurate urban planning to accommodate more and more people in the city area. Remote sensing technology has been used for planning the expansion and design of city areas. A novel machine learning (ML) classifier formed by combining AdaBoost and extra trees algorithm have been investigated for change detection in the urban area of three cities in the Gujarat region of India. Using Indian Remote Sensing (IRS) Resourcesat-2 LISS IV satellite images, the performance of the object-based AdaBoosted extra trees classifier (ABETC) in terms of overall accuracy (OA) and kappa coefficient (KC) for urban area change detection was compared to benchmarked object-based algorithms. As the first step in object-based classification (OBC), the Shepherd segmentation algorithm was used to segment satellite images. For all three cities, the object-based ABETC demonstrated the highest efficiency when compared to conventional classifiers. The rise in the built-up area of Ahmedabad city has been noted by 87.39 sq km from the year 2011 to 2020 showing the urban development of the city. This increase in the built-up area of Ahmedabad was compensated by the depletion of 30.26 sq. km. vegetation area, and 57.13 sq. km. of open land class. The built-up area of Vadodara and Rajkot city has been enlarged by 17.24 sq km and 6.79 sq km respectively. The highest OA of 96.04% and KC of 0.94 has been noted for a satellite image of Vadodara city with a novel object based ABETC algorithm.

EPOCH: enhanced procedure for operational change detection using historical invariant features and PCA guided multivariate statistical technique

In this article, we have presented a methodology developed for automatic historical change detection using multi-decadal time-lapse remote sensing images, which we call as EPOCH. The unpaired bi-temporal images are spatially aligned using Mode Improved Scale Invariant Feature Transform (M-SIFT) to achieve sub-pixel co-registration accuracy. The surface changes are detected using Guided Image Filter Enhanced Multivariate Alteration Detection (GIF-MAD). The guidance image is extracted using Principal Component Analysis (PCA), and an operational processing framework is devised to generate change detection map. EPOCH is evaluated with Indian Remote Sensing (IRS) images and Landsat multi-temporal images that observe Earth for more than three decades. The procedure is generalized to detect changes using different satellite images over one of our neighboring planet Mars. EPOCH is compared with state-of-the-art techniques, and found to have closest consensus with ground truth data. The proposed approach achieved an overall accuracy of 90.9% with kappa value of 0.81.

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.