Refine your search
Collections
Co-Authors
Journals
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Jain, Gaurav
- Characterization and Retrieval of Snow and Urban Land Cover Parameters using Hyperspectral Imaging
Abstract Views :207 |
PDF Views:91
Authors
S. K. Singh
1,
Gaurav Jain
1,
Asfa Siddiqui
2,
Smruti Naik
1,
B. P. Rathore
1,
Vaibhav Garg
2,
Snehmani
3,
Vinay Kumar
2,
I. M. Bahuguna
1,
S. A. Sharma
1,
Chander Shekhar
3,
Praveen K. Thakur
2,
Kavach Mishra
2,
Pramod Kumar
2,
T. H. Painter
4,
J. Dozier
5
Affiliations
1 Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, IN
2 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
3 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
4 University of California, Los Angeles, CA, US
5 University of California, Santa Barbara, CA, US
1 Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, IN
2 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
3 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
4 University of California, Los Angeles, CA, US
5 University of California, Santa Barbara, CA, US
Source
Current Science, Vol 116, No 7 (2019), Pagination: 1182-1195Abstract
Snow and urban land cover are important due to their role in hydrological management and utility, climate response, social aspects and economic viability, along with influencing the Earth’s environment at local, regional and global scale. Hyperspectral data enable identification, characterization and retrieval of these land-cover features based on physical and chemical properties of compositional materials. AVIRISNG hyperspectral airborne data, with synchronous ground observations using field spectroradiometer and collateral instruments, were collected over two widely varied land-cover types, viz. a relatively homogenous area covered by snow in the extreme cold environment of the Himalaya (Bhaga sub-basin, Himachal Pradesh), and a completely heterogeneous urban area of a metropolitan city (Ahmedabad, Gujarat).
AVIRIS-NG airborne data were analysed to understand the effect of terrain parameters such as slope and aspect on snow reflectance. Snow grain index using visible and near-infrared (VNIR) bands and absorption peak in the near-infrared (NIR) were used to retrieve grain size in parts of the Himalayan region. A radiative transfer model was used to understand the grain size variability and its effect on absorption peak in NIR. Continuum removal was performed for snow spectral observations obtained from airborne, modelled and field platforms to estimate band depth at 1030 nm. Grain size was observed to vary with altitude from 100 to 500 μm using AVIRIS-NG image. In the urban area, the data also separated pervious and impervious surface cover using spectral unmixing technique, identified several urban features over multispectral data such as buildings with red tiled roofs, metallic surfaces and tarpaulin sheets using the material spectral profiles. Two single-frame superresolution methods namely sparse regression and natural prior (SRP), and gradient profile prior (GPP) were applied on AVIRIS-NG data for the mixed environment around Kankaria Lake in the city of Ahmedabad, which revealed that SRP method was better than GPP, and affirmed by eight indices. Preliminary analysis of AVIRIS-NG imaging over snow-covered areas and densely populated cities indicated utility of future spaceborne hyperspectral missions, particularly for hydrological and climatological applications in such diverse environments.
Keywords
AVIRIS-NG, Hyperspectral Imaging, Snow Reflectance, Super-Resolution Method, Terrain Parameters, Urban Land Cover.Full Text
References
- Kulkarni, A. V., Singh, S. K., Mathur, P. and Mishra, V. D., Algorithm to monitor snow cover using AWiFS data of RESOURCESAT-1 for the Himalayan region. Int. J. Remote Sensing, 2006, 27, 2449–2457.
- Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. and Bayr, K. J., MODIS snow cover products. Remote Sensing Environ., 2002, 83, 181–194.
- Singh, S. K., Rathore, B. P., Bahuguna, I. M. and Ajai, Snow cover variability in the Himalayan–Tibetan region. Int. J. Climatol., 2014, 34, 446–452; doi:10.1002/joc.3687.
- Rathore, B. P. et al., Trends of snow cover in Western and West– Central Himalayas during 2004–2014. Curr. Sci., 2018, 114(25), 800–807.
- O’Brian, H. W. and Munis, H., Red and near-infrared spectral reflectance of snow. U.S. Army Cold Regions Research & Engineering Laboratory, Res. Rep., 1975, No. 332, p. 18.
- Warren, S. G. and Wiscombe, W. J., A model for the spectral albedo of snow – II: snow containing aerosols. J. Atmos. Sci., 1980, 37, 2734–2745.
- Wiscombe, W. J. and Warren S. G., A model for the spectral albedo of snow I: pure snow. J. Atmos. Sci., 1980, 37, 2712–2733.
- Colbeck, S. C., Grain clusters in wet snow. J. Colloid Interface Sci., 1979, 72(3), 371–384.
- Hyvärinen, T. and Lammasniemi, J., Infrared measurement of free-water content and grain size of snow. Opt. Eng., 1987, 26(4), 342–348.
- Nolin, A. W. and Dozier, J., Estimating snow grain size using AVIRIS data. Remote Sensing Environ., 1993, 44, 231–238.
- Nolin, A. W. and Dozier, J., A hyperspectral method for remotely sensing the grain size of snow. Remote Sensing Environ., 2000, 74, 207–216.
- Nolin, A. W., Recent advances in remote sensing of seasonal snow. J. Glaciol., 2010, 56(200), 1141–1150.
- Dozier, J. and Painter, T. H., Multispectral and hyperspectral remote sensing of alpine snow properties. Annu. Rev. Earth Planet. Sci., 2004, 32, 465–494; doi 10.1146/annurev.earth.32.101802.120404.
- Singh, S. K., Kulkarni, A. V. and Chaudhary, B. S., Hyperspectral analysis of snow reflectance to understand the effects of contamination and grain size. Ann. Glaciol., 2010, 54(44), 83–88.
- Singh, S. K., Kulkarni, A. V. and Chaudhary, B. S., Spectral characterization of soil and coal contamination on snow reflectance using hyperspectral analysis. J. Earth Syst. Sci., 2011, 120(2), 321–328.
- Negi, H. S., Singh, S. K., Kulkarni, A. V. and Semwal, B. S., Field based spectral reflectance measurements of seasonal snow cover in Indian Himalaya. Int. J. Remote Sensing, 2010, 31(9), 2393–2417.
- Singh, S. K., Applications of hyperspectral data for snowpack characterization in the Himalayan region. Ph D thesis, University, 2013, p. 132.
- Negi, H. S. and Kokhanovsky, A., Retrieval of snow grain size and albedo of western Himalayan snow cover using satellite data. Cryosphere, 2011, 5, 831–847.
- Zhao, S., Jiang, T. and Wang, Z., Snow grain-size estimation using Hyperion imagery in a typical area of the Heihe river basin, China. Remote Sensing, 2013, 5, 238–253; doi:10.3390/ rs5010238.
- Green, R. O., Painter, T. H., Roberts, D. A. and Dozier, J., Measuring the expressed abundance of the three phases of water with an imaging spectroradiometer over melting snow. Water Resour. Res., 2006, 42, W10402, 1–12; doi:10.1029/2005WR004509.
- Painter, T. H., Dozier J., Roberts, D. A., Davis, R. E. and Green, R. O., Retrieval of subpixel snow-covered area and grain size from imaging spectrometer data. Remote Sensing Environ., 2003, 83, 64–77.
- Painter, T. H., Seidel, F. C., Bryant, A. C., Skiles, S. Mc. and Rittger, K., Imaging spectroscopy of albedo and radiative forcing by light-absorbing impurities in mountain snow. J. Geophys. Res.: Atmos., 2013, 118, 1–13; doi:10.1002/jgrd.50520.
- Negi, H. S., Chander, S. and Singh, S. K., Snow and glacier investigations using hyperspectral data in the Himalaya. Curr. Sci., 2015, 108(5), 892–902.
- Slonecker, E. T., Jennings, D. B. and Garofalo, D., Remote sensing of impervious surfaces: a review. Remote Sensing Rev., 2001, 20(3), 227–255.
- Yuan, F. and Bauer, M. E., Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery. Remote Sensing Environ., 2007, 106, 375–386.
- Weng, Q., Remote sensing of impervious surfaces in the urban areas: requirements, methods, and trends. Remote Sensing Environ., 2012, 117, 34–49.
- Phinn, S., Stanford, M., Scarth, P., Murray, A. T. and Shyy, P. T., Monitoring the composition of urban environments based on the vegetation–impervious surface–soil (VIS) model by subpixel analysis techniques. Int. J. Remote Sensing, 2002, 23(20), 4131–4153.
- Deng, C. and Wu, C., A spatially adaptive spectral mixture analysis for mapping subpixel urban impervious surface distribution. Remote Sensing Environ., 2013, 133, 62–70.
- Sun, G., Chen, X., Ren, J., Zhang, A. and Jia, X., Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping. Int. J. Appl. Earth Obs. Geoinform., 2017, 60, 38–48.
- Small, C., Estimation of urban vegetation abundance by spectral mixture analysis. Int. J. Remote Sensing, 2001, 22(7), 1305–1334.
- Wu, C. and Murray, A. T., Estimating impervious surface distribution by spectral mixture analysis. Remote Sensing Environ., 2003, 84, 493–505.
- Yang, J. and He, Y., Automated mapping of impervious surfaces in urban and suburban areas: linear spectral unmixing of high spatial resolution imagery. Int. J. Appl. Earth Observ. Geoinform., 2017, 54, 53–64.
- Herold, M., Roberts, D. A., Gardner, M. E. and Dennison, P. E., Spectrometry for urban area remote sensing – development and analysis of a spectral library from 350 nm to 2400 nm. Remote Sensing Environ., 2004, 91, 304–319.
- Heiden, U., Segl, K., Roessner, S. and Kaufmann, H., Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data. Remote Sensing Environ., 2007, 111, 537–552.
- Nasarudin, N. E. M. and Shafri, H. Z. M., Development and utilization of urban spectral library for remote sensing of urban environment. J. Urban Environ. Eng., 2011, 5(1), 44–56.
- Kotthaus, S., Smith, T. E. L., Wooster, M. J. and Grimmond, C. S. B., Derivation of an urban materials spectral library through emittance and reflectance spectroscopy. ISPRS J. Photogramm. Remote Sensing, 2014, 94, 194–212.
- Weng, Q., Hu, X. and Lu, D., Extracting impervious surface from hyperspectral imagery with linear spectral mixture analysis. In Remote Sensing of Impervious Surfaces (ed. Weng, Q.), Taylor and Francis Group, LLC, Florida, USA, 2008, pp. 93–118.
- Roessner, S., Segl, K., Heiden, U. and Kaufmann, H., Automated differentiation of urban surfaces based on airborne hyperspectral imagery. IEEE Trans. Geosci. Remote Sensing, 2001, 39(7), 1525– 1532.
- Herold, M., Gardner, M. E. and Roberts, D. A., Spectral resolution requirements for mapping urban areas. IEEE Trans. Geosci. Remote Sensing, 2003, 41(9), 1907–1919.
- Sugumaran, R., Gerjevic, J. and Voss, M., Transportation infrastructure extraction using hyperspectral remote sensing. In Remote Sensing of Impervious Surfaces (ed. Weng, Q.), Taylor and Francis Group, LLC, Florida, USA, 2009, pp. 163–178.
- Franke, J., Roberts, D. A., Halligan, K. and Menz, G., Hierarchical multiple endmember spectral mixture analysis (MESMA) of hyperspectral imagery for urban environments. Remote Sensing Environ., 2009, 113, 1712–1723.
- Axelsson, M., Friman, O., Haavardsholm, T. V. and Renhorn, I., Target detection in hyperspectral imagery using forward modeling and in-scene information. ISPRS J. Photogramm. Remote Sensing, 2016, 119, 124–134.
- Chen, F., Wang, K., Voorde, T. V. and Tang, T. F., Mapping urban land cover from high spatial resolution hyperspectral data: an approach based on simultaneously unmixing similar pixels with jointly sparse spectral mixture analysis. Remote Sensing Environ., 2017, 196, 324–342.
- Chen, F., Jiang, H., Voorde, T. V., Lu, S., Xu, W. and Zhou, Y., Land cover mapping in urban environments using hyperspectral APEX data: a study case in Baden, Switzerland. Int. J. Appl. Earth Observ. Geoinform., 2018, 71, 70–82.
- Census of India, Primary Census Abstracts, Office of the Registrar General, Government of India, 2011.
- Flanner, M. G., Zender, C. S., Randerson, J. T. and Rasch, P. J., Present day climate forcing and response from black carbon in snow. J. Geophys. Res., 2007, 112, D11202; doi:10.1029/ 2006JD008003.
- Flanner, M. G., Zender, C. S., Hess, P. G., Mahowald, N. M., Painter, T. H., Ramanathan, V. and Rasch, P. J., Springtime warming and reduced snow cover from carbonaceous particles. Atmos. Chem. Phys., 2009, 9, 2481–2497.
- Toon, O. B., McKay, C. P., Ackerman, T. P. and Santhanam, K., Rapid calculation of radiative heating rates and photo dissociation rates in inhomogeneous multiple scattering atmospheres. J. Geophys. Res., 1989, 94(D13), 16287–16301.
- Bonifazi, G., Capobianco, G. and Serranti, S., Hyperspectral imaging applied to the identification and classification of asbestos fibers. IEEE Sensors, Busan, 2015, pp. 1–4.
- Gaffey, S. J., Spectral reflectance of carbonate minerals in the visible and near infrared (0.35–2.55 μm): calcite, aragonite, and dolomite. Am. Mineral., 1986, 71, 151–162.
- Gaffey, S. J., Spectral reflectance of carbonate minerals in the visible and near infrared (0.35–2.55 μm): anhydrous carbonate minerals. J. Geophys. Res., 1987, 92(2), 1429–1440.