Skip to main content

Advertisement

Log in

Assessment of vegetation status of Sali River basin, a tributary of Damodar River in Bankura District, West Bengal, using satellite data

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Vegetation status of Sali River basin has been evaluated in this study applying the Landsat 8 data. Here, NDVI, EVI, GI, LAI, PVI, SI, BI and NDMI have been used to assess vegetation status (VS). Indices have been classified into five categories following natural breaks classification method. Apart from BI, all the indices represented higher value in forest cover area. Weights for all the themes and sub-themes were assigned following multi-criteria decision analysis with consistency ratio of 0.0685, and weighted overlay analysis technique had been employed for the assessment of the vegetation status. Very low, low and moderate VS was found mainly over the water body, urban and agricultural area, which is covering more than half of the basin. The rest of the area is covered with high and very high VS, representing fragmented and dense Sal forest and covering 15.81% and 22.88% basin area, respectively. Accuracy assessment and thorough field verification were done with 90.43% classification accuracy. Our result is quite similar to land use land cover map of Bhuvan, ISRO. So, keeping in the view of health of the river basin and vegetation, this area needs urgent attention to control the degradation of vegetation in a scientific way.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  • Aboelghar, M., Arafat, S., Saleh, A., Naeem, S., Shirbeny, M., & Belal, A. (2010). Retrieving leaf area index from SPOT4 satellite data. The Egyptian Journal of Remote Sensing and Space Science,13(2), 121–127.

    Google Scholar 

  • Amani, M., Salehi, B., Mahdavi, S., Masjedi, A., & Dehnavi, S. (2017). Temperature-vegetation-soil moisture dryness index (TVMDI). Remote Sensing of Environment,197, 1–14.

    Google Scholar 

  • Andersen, H. E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. Remote Sensing of Environment,94(4), 441–449.

    Google Scholar 

  • Anderson, G. L., Hanson, J. D., & Haas, R. H. (1993). Evaluating Landsat Thematic Mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sensing of Environment,45(2), 165–175.

    Google Scholar 

  • Anguela, T. P., Zribi, M., Baghdadi, N., & Loumagne, C. (2010). Analysis of local variation of soil surface parameters with TerraSAR-X radar data over bare agricultural fields. IEEE Transactions on Geoscience and Remote Sensing,48(2), 874–881.

    Google Scholar 

  • Anyamba, A., Tucker, C. J., & Eastman, J. R. (2001). NDVI anomaly patterns over Africa during the 1997/98 ENSO warm event. International Journal of Remote Sensing, 22(10), 1847–1860.

    Google Scholar 

  • Atzberger, C., Guérif, M., Baret, F., & Werner, W. (2010). Comparative analysis of three chemometric techniques for the spectro-radiometric assessment of canopy chlorophyll content in winter wheat. Computers and Electronics in Agriculture,73(2), 165–173.

    Google Scholar 

  • Avtar, R., Herath, S., Saito, O., Gera, W., Singh, G., Mishra, B., et al. (2014). Application of remote sensing techniques toward the role of traditional water bodies with respect to vegetation conditions. Environment, Development and Sustainability,16(5), 995–1011.

    Google Scholar 

  • Azizi, Z. (2008). Forest canopy density estimating using satellite images. Remote Sensing and Spatial Information Sciences: The International Archives of the Photogrammetry.

    Google Scholar 

  • Bajwa, S. G., Gowda, P. H., Howell, T. A., & Leh, M. (2008). Comparing artificial neural network with least square regression techniques for LAI retrieval from remote sensing data. Pecora. Retrieved from https://www.researchgate.net/profile/Prasanna_Gowda2/publication/256908657_Comparing_artificial_neural_network_and_least_square_regression_techniques_for_LAI_retrieval_from_remote_sensing_data/links/54cbf6b80cf29ca810f489a8.pdf. Accessed 18 Jan 2019.

  • Bankura District Forest Department. (2018). Govt. of West Bengal. http://bankuraforest.in/publication/. Accessed 18 Jan 2019.

  • Bannari, A., Morin, D., Bonn, F., & Huete, A. R. (1995). A review of vegetation indices. Remote Sensing Reviews,13(1–2), 95–120.

    Google Scholar 

  • Barati, S., Rayegani, B., Saati, M., Sharifi, A., & Nasri, M. (2011). Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Science,14(1), 49–56.

    Google Scholar 

  • Baret, F., & Guyot, G. (1991). Potentials and limits of vegetation indices for LAI and APAR assessment. Remote Sensing of Environment,35(2–3), 161–173. https://doi.org/10.1016/0034-4257(91)90009-U.

    Article  Google Scholar 

  • Baret, F., Hagolle, O., Geiger, B., Bicheron, P., Miras, B., Huc, M., et al. (2007). LAI, fAPAR and Cover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sensing of Environment,110(3), 275–286.

    Google Scholar 

  • Belward, A. S. (1999). The IGBP-DIS global 1-km land-cover data set DIS-Cover: A project overview. Photogrammetric Engineering and Remote Sensing, 65, 1013–1020. https://www.researchgate.net/profile/Prasanna_Gowda2/publication/256908657_Comparing_artificial_neural_network_and_least_square_regression_techniques_for_LAI_retrieval_from_remote_sensing_data/links/54cbf6b80cf29ca810f489a8.pdf. Accessed 18 Jan 2019.

  • Bertoldi, G., Della Chiesa, S., Notarnicola, C., Pasolli, L., Niedrist, G., & Tappeiner, U. (2014). Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling. Journal of Hydrology,516, 245–257. https://doi.org/10.1016/0034-4257(93)90031-R.

    Article  Google Scholar 

  • Bicheron, P., Leroy, M., & Hautecoeur, O. (1998). LAI and fAPAR mapping at global scale by model inversion against spaceborne POLDER data. In Geoscience and remote sensing symposium proceedings, 1998. IGARSS’98. 1998 IEEE international (Vol. 3, pp. 1228–1230). IEEE.

  • Bishop, C., & Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford: Oxford University Press.

    Google Scholar 

  • Blanco, L. J., Aguilera, M. O., Paruelo, J. M., & Biurrun, F. N. (2008). Grazing effect on NDVI across an aridity gradient in Argentina. Journal of Arid Environments,72(5), 764–776.

    Google Scholar 

  • Boles, S. H., Xiao, X., Liu, J., Zhang, Q., Munkhtuya, S., Chen, S., et al. (2004). Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sensing of Environment,90(4), 477–489.

    Google Scholar 

  • Bradley, B. A., Jacob, R. W., Hermance, J. F., & Mustard, J. F. (2007). A curve fitting procedure to derive inter-annual phenologies from time series of noisy satellite NDVI data. Remote Sensing of Environment,106(2), 137–145.

    Google Scholar 

  • Carlson, T. N., Gillies, R. R., & Perry, E. M. (1994). A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews,9(1–2), 161–173.

    Google Scholar 

  • Carlson, T. N., & Ripley, D. A. (1997). On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sensing of Environment,62(3), 241–252.

    Google Scholar 

  • Chakrabortty, R., Pal, S. C., Malik, S., & Das, B. (2018). Modeling and mapping of groundwater potentiality zones using AHP and GIS technique: a case study of Raniganj Block, PaschimBardhaman, West Bengal. Modeling Earth Systems and Environment,4, 1–26.

    Google Scholar 

  • Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006). Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes. Remote Sensing of Environment,104(2), 133–146.

    Google Scholar 

  • Cihlar, J., Ly, H., & Xiao, Q. (1996). Land cover classification with AVHRR multichannel composites in northern environments. Remote Sensing of Environment,58(1), 36–51.

    Google Scholar 

  • Clarke, T. R. (1997). An empirical approach for detecting crop water stress using multispectral airborne sensors. Hort Technology,7(1), 9–16.

    Google Scholar 

  • Cohen, W. B., Maiersperger, T. K., Gower, S. T., & Turner, D. P. (2003). An improved strategy for regression of biophysical variables and Landsat ETM+ data. Remote Sensing of Environment,84(4), 561–571.

    Google Scholar 

  • Contreras, F., Hanaki, K., Aramaki, T., & Connors, S. (2008). Application of analytical hierarchy process to analyze stakeholders preferences for municipal solid waste management plans, Boston, USA. Resources, Conservation and Recycling,52(7), 979–991.

    Google Scholar 

  • Crist, E. P. & Kauth, R. J. (1986b). The Tasseled Cap de-mystified.(transformations of MSS and TM data).

  • Crist, E. P., Laurin, R. & Cicone, R. C. (1986a). Vegetation and soils information contained in transformed Thematic Mapper data. In Proceedings of IGARSS’86 symposium (pp. 1465–1470). Paris: European Space Agency Publications Division.

  • Cristianini, N., & Shawe-Taylor, J. (2000). An introduction to support vector machines and other kernel-based learning methods. Cambridge: Cambridge University Press.

    Google Scholar 

  • Cyr, L. (1994). Apport des indices de vegetation pourl’evaluation de la couverture du sol envued’unemodelisationspatiale de l’erosion (French text).

  • Darvishzadeh, R., Skidmore, A., Atzberger, C., & van Wieren, S. (2008a). Estimation of vegetation LAI from hyperspectral reflectance data: Effects of soil type and plant architecture. International Journal of Applied Earth Observation and Geoinformation,10(3), 358–373. https://doi.org/10.1016/J.JAG.2008.02.005.

    Article  Google Scholar 

  • Darvishzadeh, R., Skidmore, A., Schlerf, M., & Atzberger, C. (2008b). Inversion of a radiative transfer model for estimating vegetation LAI and chlorophyll in a heterogeneous grassland. Remote Sensing of Environment,112(5), 2592–2604. https://doi.org/10.1016/J.RSE.2007.12.003.

    Article  Google Scholar 

  • Das, B., Pal, S. C., Malik, S., & Chakrabortty, R. (2018). Modeling groundwater potential zones of Puruliya district, West Bengal, India using remote sensing and GIS techniques. Geology, Ecology, and Landscapes, 3(3), 1–15.

    CAS  Google Scholar 

  • de Jong, P. (1984). A statistical approach to Saaty’s scaling method for priorities. Journal of Mathematical Psychology,28(4), 467–478.

    Google Scholar 

  • DeFries, R. S., & Townshend, J. R. G. (1994). NDVI-derived land cover classifications at a global scale. International Journal of Remote Sensing,15(17), 3567–3586.

    Google Scholar 

  • Demarez, V., Duthoit, S., Baret, F., Weiss, M., & Dedieu, G. (2008). Estimation of leaf area and clumping indexes of crops with hemispherical photographs. Agricultural and Forest Meteorology,148(4), 644–655.

    Google Scholar 

  • Deng, F., Chen, J. M., Plummer, S., Chen, M., & Pisek, J. (2006). Algorithm for global leaf area index retrieval using satellite imagery. IEEE Transactions on Geoscience and Remote Sensing,44(8), 2219–2229.

    Google Scholar 

  • Department of Land Revenue (2018). Land use and land cover map. Government of West Bengal. https://wb.gov.in/portal/web/guest/land-and-land-reforms. Accessed 4 Aug 2017.

  • Du Plessis, W. P. (1999). Linear regression relationships between NDVI, vegetation and rainfall in Etosha National Park, Namibia. Journal of Arid Environments,42(4), 235–260.

    Google Scholar 

  • Dunning, D. J., Ross, Q. E., & Merkhofer, M. W. (2000). Multiattribute utility analysis for addressing Section 316 (b) of the Clean Water Act. Environmental Science & Policy, 3, 7–14.

    Google Scholar 

  • Dutta, D., Kundu, A., Patel, N. R., Saha, S. K., & Siddiqui, A. R. (2015). Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Science,18(1), 53–63.

    Google Scholar 

  • Elhag, M. (2014). Sensitivity analysis assessment of remotely based vegetation indices to improve water resources management. Environment, Development and Sustainability,16(6), 1209–1222.

    Google Scholar 

  • Evans, J. P., & Geerken, R. (2006). Classifying rangeland vegetation type and coverage using a Fourier component based similarity measure. Remote Sensing of Environment,105(1), 1–8.

    Google Scholar 

  • Fang, B., & Lakshmi, V. (2014). Soil moisture at watershed scale: Remote sensing techniques. Journal of Hydrology,516, 258–272. https://doi.org/10.1016/J.JHYDROL.2013.12.008.

    Article  Google Scholar 

  • Fang, H., Liang, S., & Kuusk, A. (2003). Retrieving leaf area index using a genetic algorithm with a canopy radiative transfer model. Remote Sensing of Environment,85(3), 257–270.

    Google Scholar 

  • Fensholt, R. (2004). Earth observation of vegetation status in the Sahelian and Sudanian West Africa: comparison of Terra MODIS and NOAA AVHRR satellite data. International Journal of Remote Sensing,25(9), 1641–1659. https://doi.org/10.1080/01431160310001598999.

    Article  Google Scholar 

  • Fetriyuna, Helmi, & Fiantis, D. (2017). Impact of land-use changes on Kuranji River basin functions chapter 8. In G. Shivakoti, U. Pradhan, & H. Helmi (Eds.), Redefining diversity and dynamics of natural resources management in Asia (Vol. 4, pp. 105–114). Elsevier. https://doi.org/10.1016/B978-0-12-805451-2.00008-9. Accessed 18 Jan 2019.

  • Fiorella, M. & Ripple, W. J. (1995a). Analysis of conifer forest regeneration using landsat thematic mapper data. Retrieved from https://ntrs.nasa.gov/search.jsp?R=19950017681. Accessed 18 Jan 2019.

  • Fiorella, M., & Ripple, W. J. (1995b). Determining successional stage of temperate coniferous forests with Landsat satellite data. Geographic Information Analysis, 59(2), 239–246. Retrieved from https://ntrs.nasa.gov/search.jsp?R=19950017682. Accessed 18 Jan 2019.

  • Flug, M., Seitz, H. L., & Scott, J. F. (2000). Multicriteria decision analysis applied to Glen Canyon Dam. Journal of Water Resources Planning and Management, 126(5), 270–276.

    Google Scholar 

  • Forest Survey of India. (2018). http://fsi.nic.in/details.php?pgID=sb_64. Accessed 18 Jan 2019.

  • Fuller, D. O. (1998). Trends in NDVI time series and their relation to rangeland and crop production in Senegal, 1987–1993. International Journal of Remote Sensing,19(10), 2013–2018.

    Google Scholar 

  • Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Joel, G., et al. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications,5(1), 28–41.

    Google Scholar 

  • Ganguly, S., Samanta, A., Schull, M. A., Shabanov, N. V., Milesi, C., Nemani, R. R., et al. (2008). Generating vegetation leaf area index Earth system data record from multiple sensors. Part 2: Implementation, analysis and validation. Remote Sensing of Environment,112(12), 4318–4332.

    Google Scholar 

  • Gao, X., Huete, A. R., Ni, W., & Miura, T. (2000). Optical–biophysical relationships of vegetation spectra without background contamination. Remote Sensing of Environment,74(3), 609–620.

    Google Scholar 

  • Geerken, R., & Ilaiwi, M. (2004). Assessment of rangeland degradation and development of a strategy for rehabilitation. Remote Sensing of Environment,90(4), 490–504.

    Google Scholar 

  • Geerken, R., Zaitchik, B., & Evans, J. P. (2005). Classifying rangeland vegetation type and coverage from NDVI time series using Fourier filtered cycle similarity. International Journal of Remote Sensing,26(24), 5535–5554.

    Google Scholar 

  • Geological Survey of India. (2003). https://www.gsi.gov.in/webcenter/portal/OCBIS/page1478/page1872?_adf.ctrl-state=10i7wmzvd4_5&_afrLoop=1920464058426572#!. Accessed 10 Jan 2019.

  • Ghebrezgabher, M. G., Yang, T., Yang, X., Wang, X., & Khan, M. (2016). Extracting and analyzing forest and woodland cover change in Eritrea based on landsat data using supervised classification. The Egyptian Journal of Remote Sensing and Space Science,19(1), 37–47. https://doi.org/10.1016/J.EJRS.2015.09.002.

    Article  Google Scholar 

  • Gillies, R. R., Kustas, W. P., & Humes, K. S. (1997). A verification of the ‘triangle’ method for obtaining surface soil water content and energy fluxes from remote measurements of the Normalized Difference Vegetation Index (NDVI) and surface e. International Journal of Remote Sensing,18(15), 3145–3166.

    Google Scholar 

  • Gilmanov, T. G., Tieszen, L. L., Wylie, B. K., Flanagan, L. B., Frank, A. B., Haferkamp, M. R., et al. (2005). Integration of CO2 flux and remotely-sensed data for primary production and ecosystem respiration analyses in the Northern Great Plains: Potential for quantitative spatial extrapolation. Global Ecology and Biogeography,14(3), 271–292.

    Google Scholar 

  • Goetz, S. J. (1997). Multi-sensor analysis of NDVI, surface temperature and biophysical variables at a mixed grassland site. International Journal of Remote Sensing,18(1), 71–94.

    Google Scholar 

  • González-Sanpedro, M. C., Le Toan, T., Moreno, J., Kergoat, L., & Rubio, E. (2008). Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data. Remote Sensing of Environment,112(3), 810–824. https://doi.org/10.1016/J.RSE.2007.06.018.

    Article  Google Scholar 

  • Goodwin, N. R., Coops, N. C., Wulder, M. A., Gillanders, S., Schroeder, T. A., & Nelson, T. (2008). Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote Sensing of Environment,112(9), 3680–3689. https://doi.org/10.1016/J.RSE.2008.05.005.

    Article  Google Scholar 

  • Green, R. O., Eastwood, M. L., Sarture, C. M., Chrien, T. G., Aronsson, M., Chippendale, B. J., et al. (1998). Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment,65(3), 227–248.

    Google Scholar 

  • Gu, Y., Brown, J. F., Verdin, J. P., & Wardlow, B. (2007). A five-year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central great plains of United States. Geophysical Research Letters,34(L06407), 1–6.

    Google Scholar 

  • Gurung, R. B., Breidt, F. J., Dutin, A., & Ogle, S. M. (2009). Predicting Enhanced Vegetation Index (EVI) curves for ecosystem modeling applications. Remote Sensing of Environment,113(10), 2186–2193.

    Google Scholar 

  • Hansen, M. C., DeFries, R. S., Townshend, J. R., & Sohlberg, R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. International Journal of Remote Sensing,21(6–7), 1331–1364.

    Google Scholar 

  • Hardisky, M. A., Michael Smart, R., & Klemas, V. (1983). Growth response and spectral characteristics of a short Spartina alterniflora salt marsh irrigated with freshwater and sewage effluent. Remote Sensing of Environment,13(1), 57–67. https://doi.org/10.1016/0034-4257(83)90027-5.

    Article  Google Scholar 

  • Harris, A. T., & Asner, G. P. (2003). Grazing gradient detection with airborne imaging spectroscopy on a semi-arid rangeland. Journal of Arid Environments,55(3), 391–404.

    Google Scholar 

  • He, Y., Guo, X., & Wilmshurst, J. (2006). Studying mixed grassland ecosystems I: Suitable hyperspectral vegetation indices. Canadian Journal of Remote Sensing,32(2), 98–107. https://doi.org/10.5589/m06-009.

    Article  Google Scholar 

  • Hobbs, T. J. (1995). The use of NOAA-AVHRR NDVI data to assess herbage production in the arid rangelands of Central Australia. International Journal of Remote Sensing,16(7), 1289–1302.

    Google Scholar 

  • Horler, D. N. H., & Ahern, F. J. (1986). Forestry information content of Thematic Mapper data. International Journal of Remote Sensing,7(3), 405–428. https://doi.org/10.1080/01431168608954695.

    Article  Google Scholar 

  • Hu, J., Su, Y., Tan, B., Huang, D., Yang, W., Schull, M., et al. (2007). Analysis of the MISR LAI/FPAR product for spatial and temporal coverage, accuracy and consistency. Remote Sensing of Environment,107(1–2), 334–347.

    Google Scholar 

  • Huang, C., Wylie, B., Yang, L., Homer, C., & Zylstra, G. (2002). Derivation of a tasselled cap transformation based on Landsat 7 at-satellite reflectance. International Journal of Remote Sensing,23(8), 1741–1748.

    Google Scholar 

  • Huang, N., He, J. S., et al. (2013). Estimating the spatial pattern of soil respiration in Tibetan alpine grasslands using Landsat TM images and MODIS data. Ecological Indicators,26, 117–125.

    Google Scholar 

  • Huemmrich, K. F. (1996). Effects of shadows on vegetation indices. In Geoscience and Remote Sensing Symposium, 1996. IGARSS’96. Remote Sensing for a Sustainable Future, International (Vol. 4, pp. 2372–2374). IEEE.

  • Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment,83(1–2), 195–213.

    Google Scholar 

  • Huete, A., Didan, K., van Leeuwen, W., Miura, T., & Glenn, E. (2008). MODIS vegetation indices. In Land remote sensing and global environmental change: NASA’s earth observing system and the science of ASTER and MODIS 2008, (pp. 125–146).

  • Huete, A. R., Jackson, R. D., & Post, D. F. (1985). Spectral response of a plant canopy with different soil backgrounds. Remote Sensing of Environment,17(1), 37–53.

    Google Scholar 

  • Huete, A. R., Liu, H., & van Leeuwen, W. J. (1997). The use of vegetation indices in forested regions: issues of linearity and saturation. In Geoscience and remote sensing, 1997. IGARSS’97. Remote sensing—a scientific vision for sustainable development, 1997 IEEE international (Vol. 4, pp. 1966–1968). IEEE.

  • Hunt, E. R., Jr., & Miyake, B. A. (2006). Comparison of stocking rates from remote sensing and geospatial data. Rangeland Ecology & Management,59(1), 11–18.

    Google Scholar 

  • Hunt, E. R., & Rock, B. N. (1989). Detection of changes in leaf water content using near- and middle-infrared reflectances. Remote Sensing of Environment,30(1), 43–54. https://doi.org/10.1016/0034-4257(89)90046-1.

    Article  Google Scholar 

  • Indian Meteorological Department. (2015). Govt. of India. http://hydro.imd.gov.in/hydrometweb/(S(hmm3t555tdwjidawji21ts55))/DistrictRaifall.aspx. Accessed 18 Jan 2019.

  • Jackson, R. D., Pinter Jr, P. J., Reginato, R. J., & Idso, S. B. (1980). Hand-held radiometry. Agricultural Reviews and Manuals W-19. US Dept. of Agriculture. Science and Education Admin., Oakland, CA.

  • Jackson, R. D., Idso, S. B., Reginato, R. J., & Pinter, P. J. (1981). Canopy temperature as a crop water stress indicator. Water Resources Research,17(4), 1133–1138. https://doi.org/10.1029/WR017i004p01133.

    Article  Google Scholar 

  • Jackson, T. J., Chen, D., Cosh, M., Li, F., Anderson, M., Walthall, C., et al. (2004). Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans. Remote Sensing of Environment,92(4), 475–482.

    Google Scholar 

  • Jacquemoud, S., & Baret, F. (1990). PROSPECT: A model of leaf optical properties spectra. Remote Sensing of Environment,34(2), 75–91. https://doi.org/10.1016/0034-4257(90)90100-Z.

    Article  Google Scholar 

  • Jamal, M., & Mandal, S. (2016). Monitoring forest dynamics and landslide susceptibility in Mechi–Balason interfluves of Darjiling Himalaya, West Bengal using forest canopy density model (FCDM) and landslide susceptibility index model (LSIM). Modeling Earth Systems and Environment,2(4), 184.

    Google Scholar 

  • Jenks, G. F. (1967). The data model concept in statistical mapping. International yearbook of cartography,7, 186–190.

    Google Scholar 

  • Jensen, J. L., Humes, K. S., Vierling, L. A., & Hudak, A. T. (2008). Discrete return lidar-based prediction of leaf area index in two conifer forests. Remote Sensing of Environment,112(10), 3947–3957.

    Google Scholar 

  • Jensen, J. R., Lin, H., Yang, X., Ramsey, E., III, Davis, B. A., & Thoemke, C. W. (1991). The measurement of mangrove characteristics in southwest Florida using SPOT multispectral data. Geocarto International,6(2), 13–21.

    Google Scholar 

  • Jensen, J. R., & Lulla, K. (1987). Introductory digital image processing: A remote sensing perspective. Geocarto International, 2(1), 65. https://doi.org/10.1080/10106048709354084.

    Article  Google Scholar 

  • Jha, C. S., Dutt, C. B. S., & Bawa, K. S. (2000). Deforestation and land use changes in Western Ghats, India. Current Science,79, 231–238.

    Google Scholar 

  • Ji, L., & Peters, A. J. (2003). Assessing vegetation response to drought in the northern Great Plains using vegetation and drought indices. Remote Sensing of Environment, 87(1), 85–98.

    Google Scholar 

  • Jiang, Z., Huete, A. R., Didan, K., & Miura, T. (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment,112(10), 3833–3845.

    Google Scholar 

  • Joubert, A., Stewart, T. J., & Eberhard, R. (2003). Evaluation of water supply augmentation and water demand management options for the City of Cape Town. Journal of Multi-Criteria Decision Analysis, 12(1), 17–25.

    Google Scholar 

  • Jordan, C. F. (1969). Derivation of leaf-area index from quality of light on the forest floor. Ecology,50(4), 663–666. https://doi.org/10.2307/1936256.

    Article  Google Scholar 

  • Joshi, P. K. (2002). Geospatial analysis of central India for conservation and planning using remote sensing and geographical information system (Doctoral dissertation, Ph.D. Thesis, GurukulaKangri University, Hariwar).

  • Joshi, P. K., Roy, P. S., Singh, S., Agrawal, S., & Yadav, D. (2006). Vegetation cover mapping in India using multi-temporal IRS Wide Field Sensor (WiFS) data. Remote Sensing of Environment,103(2), 190–202.

    Google Scholar 

  • Joshi, P. K., Singh, S., Agarwal, S., & Roy, P. S. (2001). Forest cover assessment in western Himalayas, Himachal Pradesh using IRS 1C/1D WiFS data. Current Science,80, 941–947.

    Google Scholar 

  • Joshi, P. K., Singh, S., Agarwal, S., Roy, P. S., & Joshi, P. C. (2004). Aerospace technology for forest vegetation characterization and mapping in central India. Asian Journal of Geoinformatics,4(3), 19–26.

    Google Scholar 

  • Karmaker, S. (2006). Study of mangrove biomass, net primary production & species distribution using optical & microwave remote sensing data. Dissertion Indian Institute of Remote Sensing. Retrieved from http://www.iirs.gov.in/iirs/sites/default/files/StudentThesis/thesis_sandipan_iirs.pdf.

  • Kauth, R.J. & Thomas, G.S. (1976). The tasselled cap–a graphic description of the spectral-temporal development of agricultural crops as seen by Landsat. In LARS symposia (p. 159).

  • Kerr, J. T., & Ostrovsky, M. (2003). From space to species: Ecological applications for remote sensing. Trends in Ecology & Evolution,18(6), 299–305.

    Google Scholar 

  • Kilpeläinen, P., & Tokola, T. (1999). Gain to be achieved from stand delineation in LANDSAT TM image-based estimates of stand volume. Forest Ecology and Management,124(2–3), 105–111.

    Google Scholar 

  • Knight, J. F., Lunetta, R. S., Ediriwickrema, J., & Khorram, S. (2006). Regional scale land cover characterization using MODIS-NDVI 250 m multi-temporal imagery: A phenology-based approach. GIScience & Remote Sensing,43(1), 1–23.

    Google Scholar 

  • Kogan, F. N. (1995). Application of vegetation index and brightness temperature for drought detection. Advances in Space Research,15(11), 91–100.

    Google Scholar 

  • Kogan, F. N. (1997). Global drought watch from space. Bulletin of the American Meteorological Society,78(4), 621–636.

    Google Scholar 

  • Kornelsen, K. C., & Coulibaly, P. (2013). Advances in soil moisture retrieval from synthetic aperture radar and hydrological applications. Journal of Hydrology,476, 460–489. https://doi.org/10.1016/J.JHYDROL.2012.10.044.

    Article  Google Scholar 

  • Kundu, A., & Dutta, D. (2011). Monitoring desertification risk through climate change and human interference using remote sensing and GIS techniques. International Journal of Geomatics and GeoSciences,2(1), 21.

    Google Scholar 

  • Kundu, A., Dutta, D., Patel, N. R., Saha, S. K., & Siddiqui, A. R. (2014). Identifying the process of environmental changes of Churu district Rajasthan (India) using remote sensing indices. Asian Journal of Geoinformatics,14(3), 14–22.

    Google Scholar 

  • Kundu, A., Patel, N. R., Saha, S. K., & Dutta, D. (2015). Monitoring the extent of desertification processes in western Rajasthan (India) using geo-information science. Arabian Journal of Geosciences,8(8), 5727–5737.

    CAS  Google Scholar 

  • Kunwar, R. M., Evans, A., Mainali, J., Ansari, A. S., Rimal, B., & Bussmann, R. W. (2018). Change in forest and vegetation cover influencing distribution and uses of plants in the Kailash Sacred Landscape (pp. 1–16). Development and Sustainability: Nepal. Environment.

    Google Scholar 

  • Kurtz, D. B., Asch, F., Giese, M., Hülsebusch, C., Goldfarb, M. C., & Casco, J. F. (2016). High impact grazing as a management tool to optimize biomass growth in northern Argentinean grassland. Ecological Indicators,63, 100–109.

    Google Scholar 

  • Laman, T. (2011). State of the world’s forests. Retrieved from http://www.fao.org/docrep/013/i2000e/i2000e.pdf.

  • Lambin, E. F., & Ehrlich, D. (1996). The surface temperature-vegetation index space for land cover and land-cover change analysis. International Journal of Remote Sensing, 17(3), 463–487.

    Google Scholar 

  • Landscape Tool Box. (2019). https://wiki.landscapetoolbox.org/doku.php/remote_sensing_methods:enhanced_vegetation_index. Accessed 27 Jan 2019.

  • Lee, K. S., Cohen, W. B., Kennedy, R. E., Maiersperger, T. K., & Gower, S. T. (2004). Hyperspectral versus multispectral data for estimating leaf area index in four different biomes. Remote Sensing of Environment,91(3–4), 508–520. https://doi.org/10.1016/J.RSE.2004.04.010.

    Article  Google Scholar 

  • Li, S., & Chen, X. (2014). A new bare-soil index for rapid mapping developing areas using LANDSAT 8 data. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences,40(4), 139.

    Google Scholar 

  • Lillesand, T., Kiefer, R. W., & Chipman, J. (2007). Remote sensing and image interpretation (5th ed.). John Wiley & Sons.

  • Liang, S., Wang, D., Tao, X., Cheng, J., Yao, Y., Zhang, X., et al. (2018). Methodologies for integrating multiple high-level remotely sensed land products. Comprehensive Remote Sensing. https://doi.org/10.1016/b978-0-12-409548-9.10342-2.

    Article  Google Scholar 

  • Liu, W., & Yamazaki, F. (2012). Object-based shadow extraction and correction of high-resolution optical satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,5(4), 1296–1302.

    Google Scholar 

  • Liu, X., Hou, Z., Shi, Z., Bo, Y., & Cheng, J. (2017). A shadow identification method using vegetation indices derived from hyperspectral data. International Journal of Remote Sensing,38(19), 5357–5373.

    Google Scholar 

  • Major, D. J., Baret, F., & Guyot, G. (1990). A ratio vegetation index adjusted for soil brightness. International Journal of Remote Sensing,11(5), 727–740.

    Google Scholar 

  • Malmström, C. M., Thompson, M. V., Juday, G. P., Los, S. O., Randerson, J. T., & Field, C. B. (1997). Inter-annual variation in global-scale net primary production: Testing model estimates. Global Biogeochemical Cycles,11(3), 367–392.

    Google Scholar 

  • Maselli, F., Conese, C., De Filippis, T., & Norcini, S. (1995). Estimation of forest parameters through fuzzy classification of TM data. IEEE Transactions on Geoscience and Remote Sensing,33(1), 77–84.

    Google Scholar 

  • Masemola, C., Cho, M. A., & Ramoelo, A. (2016). Comparison of Landsat 8 OLI and Landsat 7 ETM+ for estimating grassland LAI using model inversion and spectral indices: case study of Mpumalanga, South Africa. International Journal of Remote Sensing,37(18), 4401–4419. https://doi.org/10.1080/01431161.2016.1212421.

    Article  Google Scholar 

  • Matsushita, B., Yang, W., Chen, J., Onda, Y., & Qiu, G. (2007). Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: a case study in high-density cypress forest. Sensors,7(11), 2636–2651.

    Google Scholar 

  • McMaster, R., & McMaster, S. (2002). A history of twentieth-century American academic cartography. Cartography and Geographic Information Science,29(3), 305–321.

    Google Scholar 

  • Miura, T., Huete, A. R., Yoshioka, H., & Holben, B. N. (2001). An error and sensitivity analysis of atmospheric resistant vegetation indices derived from dark target-based atmospheric correction. Remote Sensing of Environment,78(3), 284–298.

    Google Scholar 

  • Moleele, N., Ringrose, S., Arnberg, W., Lunden, B., & Vanderpost, C. (2001). Assessment of vegetation indexes useful for browse (forage) prediction in semi-arid rangelands. International Journal of Remote Sensing,22(5), 741–756.

    Google Scholar 

  • Mondal, I., Bandyopadhyay, J., & Kumar J. M. (2013). Mangrove zonation and succession pattern of Fazergange and Bakkhali area at Sundarban, W. B., India using remote sensing & GIS techniques. Indian Cartographer, 33, 311–315. Retrieved from https://www.researchgate.net/profile/Ismail_Mondal/publication/271710940_Mangrove_Zonation_and_Succession_Pattern_of_Fazergange_and_Bakkhali_area_at_Sundarban_WB_India_Using_Remote_Sensing_GIS_Techniques/links/5a4f599d0f7e9bbfacfcfd5a/Mangrove-Zonation-an. Accessed 11 Jan 2019.

  • Moran, M. S., Clarke, T. R., Inoue, Y., & Vidal, A. (1994a). Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index. Remote Sensing of Environment,49(3), 246–263. https://doi.org/10.1016/0034-4257(94)90020-5.

    Article  Google Scholar 

  • Moran, M. S., Clarke, T. R., Kustas, W. P., Weltz, M., & Amer, S. A. (1994b). Evaluation of hydrologic parameters in a semiarid rangeland using remotely sensed spectral data. Water Resources Research,30(5), 1287–1297.

    Google Scholar 

  • Morrissey, A. J., & Browne, J. (2004). Waste management models and their application to sustainable waste management. Waste Management, 24(3), 297–308.

    CAS  Google Scholar 

  • Myneni, R. B., Hoffman, S., Knyazikhin, Y., Privette, J. L., Glassy, J., Tian, Y., et al. (2002). Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sensing of Environment,83(1–2), 214–231. https://doi.org/10.1016/S0034-4257(02)00074-3.

    Article  Google Scholar 

  • Myneni, R. B., & Williams, D. L. (1994). On the relationship between FAPAR and NDVI. Remote Sensing of Environment,49, 200–211.

    Google Scholar 

  • Nagler, P. L., Cleverly, J., Glenn, E., Lampkin, D., Huete, A., & Wan, Z. (2005). Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data. Remote Sensing of Environment,94(1), 17–30.

    Google Scholar 

  • Nagler, P. L., Glenn, E. P., Kim, H., Emmerich, W., Scott, R. L., Huxman, T. E., et al. (2007). Relationship between evapotranspiration and precipitation pulses in a semiarid rangeland estimated by moisture flux towers and MODIS vegetation indices. Journal of Arid Environments,70(3), 443–462.

    Google Scholar 

  • Nakajima, T., Tao, G., & Yasuoka, Y. (2002). Simulated recovery of information in shadow areas on IKONOS image by combing ALS data. In Proceeding of Asian conference on remote sensing (ACRS).

  • Nemani, R., Pierce, L., Running, S., & Goward, S. (1993). Developing satellite-derived estimates of surface moisture status. Journal of Applied Meteorology,32(3), 548–557.

    Google Scholar 

  • Nemani, R., & Running, S. (1997). Land cover characterization using multitemporal red, near-IR, and thermal-IR data from NOAA/AVHRR. Ecological Applications,7(1), 79–90.

    Google Scholar 

  • Nemani, R. R., & Running, S. W. (1989). Estimation of regional surface resistance to evapotranspiration from NDVI and thermal-IR AVHRR data. Journal of Applied Meteorology,28(4), 276–284.

    Google Scholar 

  • Nicholson, S. E., & Farrar, T. J. (1994). The influence of soil type on the relationships between NDVI, rainfall, and soil moisture in semiarid Botswana. I. NDVI response to rainfall. Remote Sensing of Environment,50(2), 107–120.

    Google Scholar 

  • NRSC Bhuvan. (2018). http://bhuvan.nrsc.gov.in/gis/thematic/index.php. Accessed 18 Jan 2019.

  • O’Malley, L. S. (1908). Bengal District Gazeteers Bankura. India: Government of West Bengal.

    Google Scholar 

  • Ochege, F. U., George, R. T., Dike, E. C., & Okpala-Okaka, C. (2017). Geospatial assessment of vegetation status in Sagbama oilfield environment in the Niger Delta region, Nigeria. The Egyptian Journal of Remote Sensing and Space Science,20(2), 211–221. https://doi.org/10.1016/J.EJRS.2017.05.001.

    Article  Google Scholar 

  • Ochege, F. U., & Okpala-Okaka, C. (2017). Remote sensing of vegetation cover changes in the humid tropical rainforests of South eastern Nigeria (1984–2014). Cogent Geoscience,3(1), 1307566. https://doi.org/10.1080/23312041.2017.1307566.

    Article  Google Scholar 

  • Ono, A., Kajiwara, K., & Honda, Y. (2010). Development of new vegetation indexes, shadow index (SI) and water stress trend (WST). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science,38, 710–714.

    Google Scholar 

  • Pal, S. C., Chakrabortty, R., Malik, S., & Das, B. (2018). Application of forest canopy density model for forest cover mapping using LISS-IV satellite data: A case study of Sali watershed, West Bengal. Modeling Earth Systems and Environment,4(2), 853–865.

    Google Scholar 

  • Palmer, A. R., & Fortescue, A. (2004). Remote sensing and change detection in rangelands. African Journal of Range and Forage Science,21(2), 123–128.

    Google Scholar 

  • Paruelo, J. M., Epstein, H. E., Lauenroth, W. K., & Burke, I. C. (1997). ANPP estimates from NDVI for the central grassland region of the United States. Ecology,78(3), 953–958.

    Google Scholar 

  • Paruelo, J. M., Oesterheld, M., Di Bella, C. M., Arzadum, M., Lafontaine, J., Cahuepé, M., et al. (2000). Estimation of primary production of subhumid rangelands from remote sensing data. Applied Vegetation Science, 3(2), 189–195.

    Google Scholar 

  • Peñuelas, J., Filella, I., Biel, C., Serrano, L., & Save, R. (1993). The reflectance at the 950–970 nm region as an indicator of plant water status. International Journal of Remote Sensing,14(10), 1887–1905.

    Google Scholar 

  • Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology & Evolution,20(9), 503–510.

    Google Scholar 

  • Podvezko, V. (2009). Application of AHP technique. Journal of Business Economics and Management, 10(2), 181–189.

    Google Scholar 

  • Poggi, D., Porporato, A., Ridolfi, L., Albertson, J. D., & Katul, G. G. (2004). The effect of vegetation density on canopy sub-layer turbulence. Boundary-Layer Meteorology,111(3), 565–587.

    Google Scholar 

  • Polidorio, A. M., Flores, F. C., Imai, N. N., Tommaselli, A. M., & Franco, C. (2003). Automatic shadow segmentation in aerial color images. In XVI Brazilian symposium on computer graphics and image processing, 2003. SIBGRAPI 2003, (pp. 270–277). IEEE.

  • Pourghasemi, H. R., Pradhan, B., & Gokceoglu, C. (2012). Application of fuzzy logic and analytical hierarchy process (AHP) to landslide susceptibility mapping at Haraz watershed, Iran. Natural hazards,63(2), 965–996.

    Google Scholar 

  • Price, J. C. (2003). Comparing MODIS and ETM+ data for regional and global land classification. Remote Sensing of Environment,86(4), 491–499.

    Google Scholar 

  • Propastin, P., & Panferov, O. (2013). Retrieval of remotely sensed LAI using Landsat ETM+ data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle. International Journal of Applied Earth Observation and Geoinformation,25, 38–46. https://doi.org/10.1016/J.JAG.2013.02.006.

    Article  Google Scholar 

  • Qi, J., Chehbouni, A., Huete, A. R., Kerr, Y. H., & Sorooshian, S. (1994). A modified soil adjusted vegetation index. Remote Sensing of Environment,48(2), 119–126. https://doi.org/10.1016/0034-4257(94)90134-1.

    Article  Google Scholar 

  • Qureshi, M. E., & Harrison, S. R. (2003). Application of the analytic hierarchy process to riparian revegetation policy options. Small-Scale Forest Economics, Management and Policy,2(3), 441.

    Google Scholar 

  • Raha, A. K., Mishra, A. V., Das, S., Zaman, S., Ghatak, S., Bhattacharjee, S., et al. (2014). Time Series Analysis of forest and tree cover of West Bengal from 1988 to 2010, using RS/GIS, for monitoring afforestation programmes. The Journal of Ecology (Photon),108, 255–265.

    Google Scholar 

  • Rao, B. V., & Briz-Kishore, B. H. (1991). A methodology for locating potential aquifers in a typical semi-arid region in India using resistivity and hydrogeological parameters. Geoexploration,27(1–2), 55–64.

    Google Scholar 

  • Reeves, M. C., Winslow, J. C., & Running, S. W. (2001). Mapping weekly rangeland vegetation productivity using MODIS algorithms. Journal of Range Management,54, A90.

    Google Scholar 

  • Riaño, D., Valladares, F., Condés, S., & Chuvieco, E. (2004). Estimation of leaf area index and covered ground from airborne laser scanner (Lidar) in two contrasting forests. Agricultural and Forest Meteorology,124(3–4), 269–275.

    Google Scholar 

  • Richardson, A. J., & Wiegand, C. L. (1977). Distinguishing vegetation from soil background information. Photogrammetric Engineering and Remote Sensing, 43(12), 1541–1552. Retrieved from https://www.asprs.org/wp-content/uploads/pers/1977journal/dec/1977_dec_1541-1552.pdf. Accessed 18 Jan 2019.

  • Richter, K., Atzberger, C., Vuolo, F., Weihs, P., & D’Urso, G. (2009). Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize. Canadian Journal of Remote Sensing,35(3), 230–247. https://doi.org/10.5589/m09-010.

    Article  Google Scholar 

  • Rikimaru, A., & Miyatake, S. (1997). Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow, presented paper for the 18th ACRS. Malaysia: Kuala Lumpur.

    Google Scholar 

  • Robbins, P. F., Chhangani, A. K., Rice, J., Trigosa, E., & Mohnot, S. M. (2007). Enforcement authority and vegetation change at Kumbhalgarh wildlife sanctuary, Rajasthan, India. Environmental Management,40(3), 365–378.

    Google Scholar 

  • Rouse Jr, J., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS (pp. 309–317). Washington: NASA. https://ntrs.nasa.gov/search.jsp?R=19740022614. Accessed 05 Jan 2019.

  • Roy, P. S., Miyatake, S., & Rikimaru, A. (1997). Biophysical spectral response modelling approach for forest density stratification. In Proceedings of the 18th Asian conference on remote sensing.

  • Roy, P. S., Ranganath, B. K., Diwakar, P. G., Vohra, T. P. S., Bhan, S. K., Singh, I. J., et al. (1991). Tropical forest typo mapping and monitoring using remote sensing. Remote Sensing,12(11), 2205–2225.

    Google Scholar 

  • Saaty, T. (1980). The analytic process: Planning, priority setting, resources allocation. New York: McGraw.

    Google Scholar 

  • Saaty, T. L. (2013). Analytic hierarchy process. In Dresbach, S., Encyclopedia of operations research and management science (pp. 52–64). Boston, MA: Springer US. http://iors.ir/journal/files/site1/user_files_ba3acb/mehdi_ghotboddini-A-10-6-2-f082faa.pdf. Accessed 18 Jan 2019.

  • Saaty, T. L., & Decision, H. T. M. A. (1990). The analytic hierarchy process. European Journal of Operational Research,48, 9–26.

    Google Scholar 

  • Sader, S. A. (1989). Multispectral and seasonal characteristics of northern hardwood and boreal forest types in Maine. Image Processing,89, 109–116.

    Google Scholar 

  • Sahana, M., Sajjad, H., & Ahmed, R. (2015). Assessing spatio-temporal health of forest cover using forest canopy density model and forest fragmentation approach in Sundarban reserve forest, India. Modeling Earth Systems and Environment,1(4), 49.

    Google Scholar 

  • Sandholt, I., Rasmussen, K., & Andersen, J. (2001). Derivation of a dryness index from NOAA-AVHRR data for use in large-scale hydrological modelling (pp. 212–216), IAHS Publication.

  • Sandholt, I., Rasmussen, K., & Andersen, J. (2002). A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status. Remote Sensing of Environment,79(2–3), 213–224.

    Google Scholar 

  • Seiler, R. A., Kogan, F., & Wei, G. (2000). Monitoring weather impact and crop yield from NOAA AVHRR data in Argentina. Advances in Space Research, 26(7), 1177–1185.

    Google Scholar 

  • Sellers, P. J., Meeson, B. W., Hall, F. G., Asrar, G., Murphy, R. E., Schiffer, R. A., et al. (1995). Remote sensing of the land surface for studies of global change: Models—algorithms—experiments. Remote Sensing of Environment,51(1), 3–26. https://doi.org/10.1016/0034-4257(94)00061-Q.

    Article  Google Scholar 

  • Singh, G., Wasson, R. J., & Agrawal, D. P. (1990). Vegetational and seasonal climatic changes since the last full glacial in the Thar Desert, northwestern India. Review of Palaeobotany and Palynology,64(1–4), 351–358.

    Google Scholar 

  • Smith, R. C. G., & Choudhury, B. J. (1991). Analysis of normalized difference and surface temperature observations over southeastern Australia. Remote Sensing,12(10), 2021–2044.

    Google Scholar 

  • SOI. (1978). Topographical Maps. Government of India: Survey of India.

    Google Scholar 

  • Su Mon, M., Mizoue, N., Htun, N. Z., Kajisa, T., & Yoshida, S. (2012). Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: A comparison of three classification approaches. International Journal of Remote Sensing,33(4), 1042–1057.

    Google Scholar 

  • Tang, H., Brolly, M., Zhao, F., Strahler, A. H., Schaaf, C. L., Ganguly, S., et al. (2014). Deriving and validating Leaf Area Index (LAI) at multiple spatial scales through lidar remote sensing: A case study in Sierra National Forest, CA. Remote Sensing of Environment,143, 131–141.

    Google Scholar 

  • Taugourdeau, S., Le Maire, G., Avelino, J., Jones, J. R., Ramirez, L. G., Quesada, M. J., et al. (2014). Leaf area index as an indicator of ecosystem services and management practices: An application for coffee agroforestry. Agriculture, Ecosystems & Environment,192, 19–37.

    Google Scholar 

  • Todd, S. W., Hoffer, R. M., & Milchunas, D. G. (1998). Biomass estimation on grazed and ungrazed rangelands using spectral indices. International Journal of Remote Sensing,19(3), 427–438.

    Google Scholar 

  • Townshend, J., Justice, C., Li, W., Gurney, C., & McManus, J. (1991). Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sensing of Environment,35(2–3), 243–255.

    Google Scholar 

  • Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment,8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0.

    Article  Google Scholar 

  • Tucker, C. J. (1980). Remote sensing of leaf water content in the near infrared. Remote Sensing of Environment,10(1), 23–32.

    Google Scholar 

  • Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., et al. (2005). An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data. International Journal of Remote Sensing,26(20), 4485–4498.

    Google Scholar 

  • Tucker, C. J., & Sellers, P. J. (1986). Satellite remote sensing of primary production. International Journal of Remote Sensing,7(11), 1395–1416.

    Google Scholar 

  • Turner, D. P., Cohen, W. B., Kennedy, R. E., Fassnacht, K. S., & Briggs, J. M. (1999). Relationships between Leaf Area Index and Landsat TM spectral vegetation indices across three temperate zone sites. Remote Sensing of Environment,70(1), 52–68. https://doi.org/10.1016/S0034-4257(99)00057-7.

    Article  Google Scholar 

  • Van Leeuwen, W. J., Huete, A. R., & Laing, T. W. (1999). MODIS vegetation index compositing approach: A prototype with AVHRR data. Remote Sensing of Environment,69(3), 264–280.

    Google Scholar 

  • Verbesselt, J., Jonsson, P., Lhermitte, S., Van Aardt, J., & Coppin, P. (2006). Evaluating satellite and climate data-derived indices as fire risk indicators in savanna ecosystems. IEEE Transactions on Geoscience and Remote Sensing,44(6), 1622–1632.

    Google Scholar 

  • Vidal, A., Pinglo, F., Durand, H., Devaux-Ros, C., & Maillet, A. (1994). Evaluation of a temporal fire risk index in mediterranean forests from NOAA thermal IR. Remote Sensing of Environment,49(3), 296–303. https://doi.org/10.1016/0034-4257(94)90024-8.

    Article  Google Scholar 

  • Villamuelas, M., Fernández, N., Albanell, E., Gálvez-Cerón, A., Bartolomé, J., Mentaberre, G., et al. (2016). The Enhanced Vegetation Index (EVI) as a proxy for diet quality and composition in a mountain ungulate. Ecological Indicators,61, 658–666.

    Google Scholar 

  • Vogelmann, J. E., & Rock, B. N. (1988). Assessing forest damage in high-elevation coniferous forests in Vermont and New Hampshire using thematic mapper data. Remote Sensing of Environment,24(2), 227–246. https://doi.org/10.1016/0034-4257(88)90027-2.

    Article  Google Scholar 

  • Vohland, M., & Jarmer, T. (2008). Estimating structural and biochemical parameters for grassland from spectro-radiometer data by radiative transfer modelling (PROSPECT + SAIL). International Journal of Remote Sensing,29(1), 191–209. https://doi.org/10.1080/01431160701268947.

    Article  Google Scholar 

  • Wang, H., Chen, F., Zhang, R., & Qin, L. (2017). Seasonal dynamics of vegetation of the central Loess Plateau (China) based on tree rings and their relationship to climatic warming. Environment, Development and Sustainability,19(6), 2535–2546.

    Google Scholar 

  • Wang, J., Price, K. P., & Rich, P. M. (2001). Spatial patterns of NDVI in response to precipitation and temperature in the central Great Plains. International Journal of Remote Sensing, 22(18), 3827–3844.

    Google Scholar 

  • Wang, J., Rich, P. M., Price, K. P., & Kettle, W. D. (2004). Relations between NDVI and tree productivity in the central Great Plains. International Journal of Remote Sensing,25(16), 3127–3138.

    Google Scholar 

  • Waring, R. H., & Running, S. W. (2007). Forest ecosystems: Analysis at multiple scales (3rd ed.). Amsterdam: Elsevier.

    Google Scholar 

  • Wellens, J. (1997). Rangeland vegetation dynamics and moisture availability in Tunisia: An investigation using satellite and meteorological data. Journal of Biogeography,24(6), 845–855.

    Google Scholar 

  • Westergaard-Nielsen, A., Lund, M., Hansen, B. U., & Tamstorf, M. P. (2013). Camera derived vegetation greenness index as proxy for gross primary production in a low Arctic wetland area. ISPRS Journal of Photogrammetry and Remote Sensing,86, 89–99.

    Google Scholar 

  • Wiegand, C. L., Richardson, A. J., Escobar, D. E., & Gerbermann, A. H. (1991). Vegetation indices in crop assessments. Remote Sensing of Environment,35, 105–119.

    Google Scholar 

  • Wilkie, D. S., Bennett, E. L., Peres, C. A., & Cunningham, A. A. (2011). The empty forest revisited. Annals of the New York Academy of Sciences,1223(1), 120–128.

    Google Scholar 

  • Wilson, E. H., & Sader, S. A. (2002). Detection of forest harvest type using multiple dates of Landsat TM imagery. Remote Sensing of Environment,80(3), 385–396. https://doi.org/10.1016/S0034-4257(01)00318-2.

    Article  Google Scholar 

  • Woodcock, C. E., Collins, J. B., Gopal, S., Jakabhazy, V. D., Li, X., Macomber, S., et al. (1994). Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model. Remote Sensing of Environment,50(3), 240–254.

    Google Scholar 

  • Wulder, M. A., Dechka, J. A., Gillis, M. A., Luther, J. E., Hall, R. J., Beaudoin, A., et al. (2003). Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program. The Forestry Chronicle,79(6), 1075–1083.

    Google Scholar 

  • Wylie, B. K., Johnson, D. A., Laca, E., Saliendra, N. Z., Gilmanov, T. G., Reed, B. C., et al. (2003). Calibration of remotely sensed, coarse resolution NDVI to CO2 fluxes in a sagebrush–steppe ecosystem. Remote Sensing of Environment,85(2), 243–255.

    Google Scholar 

  • Xiao, X., Boles, S., Liu, J., Zhuang, D., & Liu, M. (2002). Characterization of forest types in Northeastern China, using multi-temporal SPOT-4 VEGETATION sensor data. Remote Sensing of Environment,82(2–3), 335–348.

    Google Scholar 

  • Xiao, Z., Liang, S., Wang, J., Xiang, Y., Zhao, X., & Song, J. (2016a). Long-time-series global land surface satellite leaf area index product derived From MODIS and AVHRR surface reflectance. IEEE Transaction on Geoscience and Remote Sensing,54(9), 5301–5318.

    Google Scholar 

  • Xiao, Z., Liang, S., Wang, T., & Jiang, B. (2016b). Retrieval of leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR) from VIIRS time-series data. Remote Sensing,8(4), 351.

    Google Scholar 

  • Yamamoto, Y., Oberthür, T., & Lefroy, R. (2009). Spatial identification by satellite imagery of the crop–fallow rotation cycle in northern Laos. Environment, Development and Sustainability,11(3), 639–654.

    Google Scholar 

  • Yang, Y., Fang, J., Smith, P., Tang, Y., Chen, A., Ji, C., et al. (2009). Changes in topsoil carbon stock in the Tibetan grasslands between the 1980s and 2004. Global Change Biology,15(11), 2723–2729.

    Google Scholar 

  • Zhan, Q., Shi, W., & Xiao, Y. (2005). Quantitative analysis of shadow effects in high-resolution images of urban areas. International Archives of Photogrammetry and Remote Sensing, 36(8/W27).

  • Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H., Hodges, J. C. F., Gao, F., et al. (2003). Monitoring vegetation phenology using MODIS. Remote Sensing of Environment,84(3), 471–475. https://doi.org/10.1016/S0034-4257(02)00135-9.

    Article  Google Scholar 

  • Zhao, H., & Chen, X. (2005). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. In Geoscience and Remote sensing symposium, 2005. IGARSS’05. Proceedings. 2005 IEEE international (Vol. 3, pp. 1666–1668). IEEE.

  • Zhou, L., Tucker, C. J., Kaufmann, R. K., Slayback, D., Shabanov, N. V., & Myneni, R. B. (2001). Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. Journal of Geophysical Research: Atmospheres,106(D17), 20069–20083.

    Google Scholar 

  • Zhou, Y., Yang, G., Wang, S., Wang, L., & Wang, F. (2014). A new index for mapping built-up and bare land areas from Landsat-8 OLI data. Remote Sensing. Retrieved from http://www.tandfonline.com/doi/abs/10.1080/2150704X.2014.973996. Accessed 05 Jan 2019.

Download references

Acknowledgements

We are thankful to The University of Burdwan, for providing us infrastructural support such as using of diverse types of GIS and remote sensing software to prepare this research work. The authors are also thankful to Luc Hens (editor, Environment, Development and Sustainability) and the anonymous reviewers for their appreciation and suggestions in regard with the improvement in this paper.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Subodh Chandra Pal.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author declares that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Malik, S., Pal, S.C., Das, B. et al. Assessment of vegetation status of Sali River basin, a tributary of Damodar River in Bankura District, West Bengal, using satellite data. Environ Dev Sustain 22, 5651–5685 (2020). https://doi.org/10.1007/s10668-019-00444-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-019-00444-y

Keywords

Navigation