Current Science

P. Jayaprasad ¹, Touseef Ahmad ¹, Saroj Maity ¹, Arundhati Misra ¹

Affiliations
1 Advanced Microwave and Hyperspectral Technique Development Group, Earth, Ocean, Atmosphere, Planetary Sciences and Applications Area, Space Applications Centre, ISRO, Ahmedabad 380 015, IN

Abstract

A huge portion of the Larsen C ice shelf (~50,000 km²) in Antarctic Peninsula calved away to an iceberg of area ~6,200 km² between 10 and 12 July 2017. Larsen C is the fourth largest ice shelf in Antarctica, after Filchner-Ronne, Ross and Amery ice shelves. Unusual rift propagation at Larsen C ice shelf has excited the scientific community during the last six months. The calved area is ~1.6 times the area of Goa and ~4 times the area of Delhi.

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Nilima R. Chaube ¹, Sasmita Chaurasia ¹, Rojalin Tripathy ¹, Dharmendra Kumar Pandey ¹, Arundhati Misra ¹, B. K. Bhattacharya ¹, Prakash Chauhan ², Kiran Yarakulla ³, G. D. Bairagi ⁴, Prashant Kumar Srivastava ⁵, Preeti Teheliani ⁶, S. S. Ray ⁶

Affiliations
1 Space Applications Centre, ISRO, Ahmedabad 380 015, IN
2 Indian Institute of Remote Sensing, Dehradun 248 001, IN
3 Vellore Institute of Technology, Vellore 632 014, IN
4 M.P. Council of Science and Technology, Bhopal 462 003, IN
5 Banaras Hindu University, Varanasi 221 005, IN
6 Mahalanobis National Crop Forecast Centre, Delhi 110 012, IN

Abstract

SCATSAT-1 measures the backscattering coefficient over land surfaces, which is a function of vegetation structure, surface roughness, soil moisture content, incidence angle and dielectric properties of vegetation. Scatterometer image reconstruction techniques provide fine resolution data to explore the emerging applications over land by using ambiguous backscatter from land. In this paper, 2 km resolution products of ISRO’s scatterometer SCATSAT-1 are exploited for land target detection, rice crop phenology stages detection for kharif and rabi seasons and estimation of relative soil moisture over parts of India. Temporal and spatial backscatter changes are due to seasonal and changes in Land Use Land Cover. Crop phenology stages such as transplanting, maximum tillering, panicle emergence and physiological maturity stages are identified by analysing SCATSAT-1 time series along with NDVI and findings are supported by appropriate ground truth observations and crop cutting experiment (CCE) data. The relative soil moisture change detection is validated with in situ ground truth measurements using Hydraprobe, carried for SCATSAT-1 ascending and descending passes.

Keywords

Crop Phenology, Gamma-0, Rice, Sigma-0, Soil Moisture, Vegetation Dynamics.

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Manoj K. Mishra ¹, Anurag Gupta ¹, Jinya John ¹, Bipasha P. Shukla ¹, Philip Dennison ², S. S. Srivastava ¹, Nitesh K. Kaushik ¹, Arundhati Misra ¹, D. Dhar ¹

Affiliations
1 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
2 Department of Geography, University of Utah, Salt Lake City, UT, US

Abstract

Applications of high-spatial resolution imaging spectrometer data acquired from the Airborne Visible/ Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) under India campaign 2015–16, require a thorough compensation for atmospheric absorption and scattering. The data-processing algorithms used for retrieving critically important atmospheric parameters, namely ‘water vapour and aerosol optical depth (AOD)’ over land and water surfaces are presented. Over land surfaces, the dark dense vegetation method and radiative transfer modelling are used for deriving spectral AOD for boxes of 20 × 20 pixels. For AOD retrieval over water surfaces, dark-target approximation is used with near-infrared and shortwave infrared measurements. Estimation of precipitable water vapour is carried out using short-wave hyperspectral measurements for each pixel. A differential absorption technique (continuum interpolated band ratio) has been used for this purpose. The retrieved AOD and water vapour values were compared with in situ sun-photometer and radiosonde data respectively, indicating good matches. Further, these parameters were used to derive ‘atmospherically corrected surface reflectance and remote sensing reflectance’, for land and water surface respectively, assuming horizontal surfaces having Lambertian reflectance.

Keywords

Aerosol, Atmospheric Correction, Hyperspectral Imaging, Surface Reflectance, Water Vapour.

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C. S. Jha ¹, Rakesh ¹, J. Singhal ¹, C. S. Reddy ¹, G. Rajashekar ¹, S. Maity ², C. Patnaik ², Anup Das ², Arundhati Misra ², C. P. Singh ², Jakesh Mohapatra ², N. S. R. Krishnayya ³, Sandhya Kiran ³, Phil Townsend ⁴, Margarita Huesca Martinez ⁵

Affiliations
1 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
2 Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, IN
3 MS University of Baroda, Vadodara 390 002, IN
4 University of Wisconsin, Madison 53706, US
5 University of California, Davis 95616, US

Abstract

Species diversity and vegetation health are two critical components to be monitored for sustainable forest management and conservation of biodiversity. The present study characterizes species dominance and α -diversity of a forest for the selected region in Mudumalai Wildlife Sanctuary (MWS), Western Ghats, which represents one of the most economically important forest types in India – the tropical dry deciduous forest. NASA’s Next-Generation Airborne Visible and Infrared Imaging Spectrometer (AVIRIS-NG) data at spectral resolution of 5 nm and spatial resolution of 5 m were used to analyse the forest matrix. Biodiversity (α -diversity) map thus generated from airborne platform over 14.5 sq. km area mostly represents the forest tree species diversity. Dominant tree species in the study area were also mapped using AVIRIS data for 21.7 sq. km. Canopy emergent dominant species, viz. Anogeissus latifolia, Tectona grandis, Terminalia alata, Grewia tiliifolia, Syzygium cumini and Shorea roxburghii were classified using spectral angle mapper technique and image-based spectra in the MWS study site. The study shows that nearly 40% area is dominated by A. latifolia and 27.5% by T. grandis in the study site. This study concludes that AVIRIS data can be used in the delineation of species and α -diversity mapping at community level; however, the accuracy achieved for species classification is moderate (60%) due to intermixing of species in the study area. For the Shimoga study site in Karnataka, the field spectra were collected using a spectroradiometer and used for the classification for the three dominant tree species using absorption peak decomposition technique. Fieldcollected pure spectra were analysed and species-wise absorption peaks (Gaussian) with central wavelength, peak amplitude and dispersion were used as the endmembers for classification. AVIRIS-NG data over Shoolpaneshwar Wildlife Sanctuary (SWS) study site used for fuel load estimation with narrow band indices calculated from AVIRIS-NG datasets. AVIRIS-NG data for MWS and Shimoga study site were collected during 2 and 5 January 2016, while for SWS site data were collected on 8 February 2016.

Keywords

Airborne Sensors, Forest Health, Hyperspectral Imaging, Species Diversity.

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Nilima R. Chaube ¹, Nikhil Lele ¹, Arundhati Misra ¹, T. V. R. Murthy ¹, Sudip Manna ², Sugata Hazra ³, Muktipada Panda ⁴, R. N. Samal ⁴

Affiliations
1 Space Applications Centre, Ahmedabad 380 015, IN
2 Department of Physics, Presidency University, Kolkata 700 073, IN
3 School of Oceanographic Studies, Jadavpur University, Kolkata 700 032, IN
4 Chilika Development Authority, Bhubaneshwar 751 014, IN

Abstract

Mangroves play a major role in supporting biodiversity, providing economic and ecological security to the coastal communities, mitigating the effects of climate change and global warming. Species level classification of mangrove forest, understanding physical as well as chemical properties of mangrove vegetation, mangrove health, pigments, and levels of stress are some of the key issues for making scientific and management decisions. Hyperspectral remote sensing owing to its narrow bands, yield information on structural details and canopy parameters. Hyperspectral data over Sundarban and Bhitarkanika mangrove forests are analyzed for species discrimination and forest health assessment. In all, 15 mangrove species in Sundarban and 7 mangrove species in Bhitarkanika have been identified and classified using Spectral Angle Mapper technique. In-situ spectro-radiometer data has been used along with AVIRIS-NG hyperspectral data. Based on response of vegetation in blue, red and near-infrared regions, combination of vegetation indices are used to assess mangrove forest’s health. Reduction in NIR reflectance with shift towards lower wavelength has been observed in less healthy groups.

Keywords

Coastal Forest Management, Health Assessment, Hyperspectral Data, Mangrove Species.

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Esha Shah ¹, P. Jayaprasad ², M. E. James ¹, Deepak Putrevu ², Arundhati Misra ²

Affiliations
1 Physics Department, Gujarat University, Ahmedabad 380 009, IN
2 Space Applications Centre (ISRO), Ahmedabad 380 015, IN

Abstract

We have monitored the changes that have occurred over nine Antarctic ice shelves between 1997 and 2019 using Sentinel-1 and RADARSAT-1 images of Antarctica using change detection technique. The net loss of Antarctic ice shelves during the period was about 14,723 sq. km in surface area, corresponding to 1.21% area of ice shelves. The Ross and Filchner–Ronne ice shelves retreated significantly in terms of total area, while shelves in that Antarctic Peninsula, namely Wilkins and Larsen C retreated drastically in terms of percentage change.

Keywords

Climate Change, Change Detection, Satellite, Ice-shelf Calving, Remote Sensing.

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Esha Shah ¹, P. Jayaprasad ², Raghav Mehra ², Deepak Putrevu ², Arundhati Misra ², M. E. James ¹

Affiliations
1 Physics Department, Gujarat University, Ahmedabad 380 009, IN
2 Space Applications Centre, Ahmedabad 380 015, IN

Abstract

Compact polarimetry has gained popularity due to its advantages, such as larger swath, simple architecture and low power consumption. The backscattered signal and scattering decomposition vary for different targets based on their electrical, geometrical and structural properties. As of now, the potential of hybrid polarimetric synthetic aperture radar (SAR) data for exploring Antarctic ice features is not fully explored. Here, we present a comprehensive polarimetric feature analysis and classification results of the hybrid polarimetric dataset acquired by RISAT-1 near the Indian Antarctic research station Maitri. The single-look complex images have been subjected to polarimetric data processing for extracting Antarctic ice features using POLSARPRO software. The polarimetric coherence matrix is generated and then filtered to eliminate speckles. Raney m–χ decomposition technique has been utilized to understand the scattering mechanism of the targets. The decomposed RGB image is classified using Wishart-supervised classification, and classification accuracy is assessed using a confusion matrix. It is found that the comparatively simple hybrid polarimetric SAR provides sufficient information to detect and discriminate various Antarctic ice features. Features such as rifts, ice–rises, ice shelves and icebergs are clearly discriminated using Wishart-supervised classification. It is also found that the overall accuracy of the classification of study areas varies between 80% and 97%, suggesting a good classification outcome.

Keywords

Classification Accuracy, Confusion Matrix, Hybrid Polarimetry, Ice Features, m–χ Decomposition, Synthetic Aperture Radar Data.

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