Abstract
Investigation of forest canopy density has become an important tool for proper management of natural resources. Vegetation cover density can identify the exact forest gaps within a particular area which in turn will provide the appropriate management strategies for future. Forest canopy density has become an essential tool for identifying the exact areas where the afforestation or reforestation programmes needs to be implemented. The aim and objective of this article is to show up the existing density of forest cover using remote sensing and geographic information system tools. Weighted overlay analysis method has been adopted for investigating forest canopy density of Sali river basin, Bankura district, West Bengal. Several indices likewise normalized difference vegetation index, bareness index, shadow index and perpendicular vegetation index etc. have been used for this study. Higher the weight was assigned for greater concentration of vegetation and lower the weight was assigned for lesser concentration of vegetation. Southern part of the region has very high density of forest coverage in comparison with the northern part of the region. It has been observed that 7.48% of the area is at very low density, 12.63% of low density, 24.84% of medium density, 23.92% of high density and 31.13% of very high forest canopy density.
Similar content being viewed by others
References
As-Syakur AR, Adnyana I, Arthana IW, Nuarsa IW (2012) Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sens 4(10):2957–2970
Azizi Z (2008) Forest canopy density estimating using satellite images. Int Arch Photogramm Remote Sens Spatial Inf Sci 8(11):1127–1130
Bayramov E, Buchroithner M, Bayramov R (2016) Quantitative assessment of 2014–2015 land-cover changes in Azerbaijan using object-based classification of LANDSAT-8 time series. Modeling Earth Syst Environ 2(1):35
Beaulieu E, Lucas Y, Viville D, Chabaux F, Ackerer P, Goddéris Y, Pierret MC (2016) Hydrological and vegetation response to climate change in a forested mountainous catchment. Modeling Earth Syst Environ 2(4):191
Belward AS, Estes JE, Kline KD (1999) The IGBP-DIS global 1-km land-cover data set DISCover: a project overview. Photogramm Eng Remote Sens 65(9):1013–1020
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press, Oxford
Boles SH, Xiao X, Liu J, Zhang Q, Munkhtuya S, Chen S, Ojima D (2004) Land cover characterization of Temperate East Asia using multi-temporal VEGETATION sensor data. Remote Sens Environ 90(4):477–489
Bradley AV, Rosa IM, Brandão A, Crema S, Dobler C, Moulds S, Ewers RM (2017) An ensemble of spatially explicit land-cover model projections: prospects and challenges to retrospectively evaluate deforestation policy. Model Earth Syst Environ 3(4):1–14
Carlson TN, Ripley DA (1997) On the relation between NDVI, fractional vegetation cover, and leaf area index. Remote Sens Environ 62(3):241–252
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, UK
Chuvieco E, Congalton RG (1989) Application of remote sensing and geographic information systems to forest fire hazard mapping. Remote Sens Environ 29(2):147–159
Cihlar J, Ly H, Xiao Q (1996) Land cover classification with AVHRR multichannel composites in northern environments. Remote Sens Environ 58(1):36–51
Coppin PR, Bauer ME (1994) Processing of multitemporal Landsat TM imagery to optimize extraction of forest cover change features. IEEE Trans Geosci Remote Sens 32(4):918–927
Crist EP, Cicone RC (1984) Application of the tasseled cap concept to simulated thematic mapper data. Ann Arbor 1001:48107
DeFries RS, Townshend JRG (1994) Global land cover: comparison of ground-based data sets to classifications with AVHRR data. In: Foody GM, Curran PJ (eds) Environmental remote sensing from regional to global scales. Wiley, Chichester, pp 84–110
FAO (2002) Food and agriculture organisation of the United Nations. Forests and the forestry sector: India. http://www.fao.org/forestry/country/57478/en/ind/. Accessed 23 Nov 2017
FAO (2010) Food and agriculture organization of the United Nations. Global forest resources assessment 2010—main report. http://www.fao.org/docrep/013/i1757e/i1757e.pdf. Accessed 5 Dec 2017
Forest Survey of India (2011) India state of forest report 2011. Ministry of Environment and Forests, Government of India. http://www.fsi.org.in/final_2011.pdf. Accessed 14 Dec 2017
Forestry in India (2017) https://en.wikipedia.org/wiki/Forestry_in_India
Giri C, Pengra B, Zhu Z, Singh A, Tieszen LL (2007) Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuar Coast Shelf Sci 73(1):91–100
Gottfried M, Pauli H, Grabherr G (1998) Prediction of vegetation patterns at the limits of plant life: a new view of the alpine-nival ecotone. Arct Alp Res 30(3):207–221
Guhathakurta P, Roy S (2000) Joint forest management in West Bengal: a critique. World Wide Fund for Nature, India
Guisan A, Weiss SB, Weiss AD (1999) GLM versus CCA spatial modeling of plant species distribution. Plant Ecol 143(1):107–122
Hansen MC, Defries RS, Townshend JRG, Sohlberg R (2000) Global land cover classification at 1 km spatial resolution using a classification tree approach. Int J Remote Sens 21(6–7):1331–1364
He C, Shi P, Xie D, Zhao Y (2010) Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens Lett 1(4):213–221
Huang X, Zhang L (2012) Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE J Sel Topics Appl Earth Obs Remote Sens 5(1):161–172
Huemmrich KF (1996) Effects of shadows on vegetation indices. In Geoscience and Remote Sensing Symposium, 1996. IGARSS’96.’Remote Sensing for a Sustainable Future.’, International 4: 2372–2374
Huete AR (1988) A soil-adjusted vegetation index (SAVI). Remote Sens Environ 25(3):295–309
Jaiswal RK, Mukherjee S, Raju KD, Saxena R (2002) Forest fire risk zone mapping from satellite imagery and GIS. Int J Appl Earth Obs Geoinf 4(1):1–10
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 Syst Environ 2(4):184
Jelaska SD (2009) Vegetation mapping applications. Dev Soil Sci 33:481–496
Jelaska SD, Antonić O, Božić M, Križan J, Kušan V (2006) Responses of forest herbs to available understory light measured with hemispherical photographs in silver fir–beech forest in Croatia. Ecol Modeling 194(1):209–218
Jha CS, Dutt CBS, Bawa KS (2000) Deforestation and land use changes in Western Ghats, India. Curr Sci 79(2):231–238
Jin X, Davis CH (2005) Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information. EURASIP J Adv Signal Process 2005(14):745309
Joshi PK (2002) Geospatial analysis of central India for conservation and planning using remote sensing and geographical information system. Ph.D. Thesis. Gurukula Kangri University Hariwar
Joshi PK, Singh S, Agarwal S, Roy PS (2001) Land cover assessment in Jammu and Kashmir using phenology as discriminant—an approach using Wide swath satellite (IRS—WiFS). Curr Sci 81(4):392–398
Joshi PK, Joshi PC, Singh S, Agarwal S, Roy PS (2004) Tropical forest covers type characterization in central highlands of India, using multi-temporal IRS-1C WiFS data. Indian J For 27(2):157–168
Joshi PK, Roy PS, Singh S, Agrawal S, Yadav D (2006) Vegetation cover mapping in India using multi-temporal IRS Wide Field Sensor (WiFS) data. Remote Sens Environ 103(2):190–202
Kayet N, Pathak K, Chakrabarty A, Sahoo S (2016) Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Modeling Earth Syst Environ 2(3):127
Kilpelainen P, Tokola T (1999) Gain to be achieved from stand delineation in LANDSAT TM image-based estimates of stand volume. For Ecol Manage 124(2):105–111
Kumar KV, Nair RR, Lakhera RC (1993) Digital image enhancement for delineating active landslide areas. Asia-Pac Remote Sens J 6(1):63–66
Lambin EF (1999) Monitoring forest degradation in tropical regions by remote sensing: some methodological issues. Glob Ecol Biogeogr 8(3-4):191–198
Lesaignoux A, Fabre S, Briottet X, Olioso A, Belin E, Cedex T (2009) Influence of surface soil moisture on spectral reflectance of bare soil in the 0.4–15 µm domain. In proceedings of the 6th EARSeL SIG IS workshop, pp 6
Liu X, Hou Z, Shi Z, Bo Y, Cheng J (2017) A shadow identification method using vegetation indices derived from hyperspectral data. Int J Remote Sens 38(19):5357–5373
Maiti KK, Mondal S, Chakravarty D, Bandyopadhyay J (2015) Assessment of vegetation canopy using geo-spatial techniques over mining areas of Pandabeswar in Barddhaman district, West Bengal, India. Int J Remote Sens Geosci 4(4):18–22
Maselli F, Conese C, De Filippis T, Norcini S (1995) Estimation of forest parameters through fuzzy classification of TM data. IEEE Trans Geosci Remote Sens 33(1):77–84
Pfeffer K, Pebesma EJ, Burrough PA (2003) Mapping alpine vegetation using vegetation observations and topographic attributes. Landsc Ecol 18(8):759–776
Pôças I, Rodrigues A, Gonçalves S, Costa PM, Gonçalves I, Pereira LS, Cunha M (2015) Predicting grapevine water status based on hyperspectral reflectance vegetation indices. Remote Sens 7(12):16460–16479
Polidorio AM, Flores FC, Imai NN, Tommaselli AM, Franco C (2003) Automatic shadow segmentation in aerial color images. In computer graphics and image processing, 2003. SIBGRAPI 2003. XVI Brazilian symposium on, pp 270–277
Price JC (2003) Comparing MODIS and ETM + data for regional and global land classification. Remote Sens Environ 86(4):491–499
Qi J, Huete AR, Moran MS, Chehbouni A, Jackson RD (1993) Interpretation of vegetation indices derived from multi-temporal SPOT images. Remote Sens Environ 44(1):89–101
Raha AK (2007) Real time forest cover mapping using IRS—P6 data. Paper presented in second ESRI Asia—pacific user conference, 18–19 Jan 2007, New Delhi, India
Raha AK, Sudhakar S, Prithviraj M (1997) Forest change detection studies and wetland mapping through digital image processing of indian remote sensing satellite data. In at Rahaa. A.K. 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
Raha AK, Mishra AV, Das S, Zaman S, Ghatak S, Bhattacharjee S, Mitra A (2014) Time series analysis of forest and tree cover of West Bengal from 1988 to 2010, using RS/GIS, for monitoring afforestation programmes. J Ecol (Photon) 108:255–265
Richardson AJ, Wiegand CL (1977) Distinguishing vegetation from soil background information. Photogramm Eng Remote Sens 43(12):1541–1552
Rikimaru A (1997) Development of forest canopy density mapping and monitoring model using indices of vegetation, bare soil and shadow. In 18th Asian conference on remote sensing, October 20–24, Malaysia, 1997
Robbins PF, Chhangani AK, Rice J, Trigosa E, Mohnot SM (2007) Enforcement authority and vegetation change at Kumbhalgarh wildlife sanctuary, Rajasthan, India. Environ Manag 40(3):365–378
Rouse J Jr, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS symposium, vol I. NASA SP-351, pp 309–317
Roy PS, Ranganath BK, Diwakar PG, Vohra TPS, Bhan SK, Singh IJ, Pandian VC (1991) Tropical forest typo mapping and monitoring using remote sensing. Int J Remote Sens 12(11):2205–2225
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 Syst Environ 1(4):49
Schmidt H, Karnieli A (2001) Sensitivity of vegetation indices to substrate brightness in hyper-arid environment: the Makhtesh Ramon Crater (Israel) case study. Int J Remote Sens 22(17):3503–3520
Singh S, Agarwal S, Joshi PK, Roy PS (1999a) Vegetation mapping through phenological variability—an application of multidate IRS 1C/1D WiFS data. In proceedings of the XIX INCA international congress Vasco-Da-Gama, Goa, India, pp 26–28
Singh S, Agarwal S, Joshi PK, Roy PS (1999b) Biome level classification of vegetation in western India—an application of wide field view sensor (WiFS). In proceedings of the joint workshop of ISPRS working groups I/1, I/3 and IV/4: Sensors and mapping from space, Hanover, Germany, pp 27–30
Sudhakar S, Sengupta S, Venkata Ramana I, Raha AK, Bardhan Roy BK (1996) Forest cover mapping of west Bengal with special reference to north Bengal using IRS-1B satellite LISS II data. Int J Remote Sens 17(1):29–42
Sukristiyanti R (2007) Suharyadi; Jatmiko, RH Evaluasi Indeks Urban pada citra Landsat Multitemporal dalam ekstraksi kepadatan bangunan. Jurnal Riset Geologi dan Pertambangan 17:1–10
Tieszen LL, Reed BC, Bliss NB, Wylie BK, DeJong DD (1997) NDVI, C3 and C4 production, and distributions in Great Plains grassland land cover classes. Ecol Appl 7(1):59–78
Townshend J, Justice C, Li W, Gurney C, McManus J (1991) Global land cover classification by remote sensing: present capabilities and future possibilities. Remote Sens Environ 35(2–3):243–255
Tucker CJ, Sellers PJ (1986) Satellite remote sensing of primary production. Int J Remote Sens 7(11):1395–1416
Van Leeuwen WJ, Huete AR, Laing TW (1999) MODIS vegetation index compositing approach: a prototype with AVHRR data. Remote Sens Environ 69(3):264–280
Weng Q (2008) Remote sensing of impervious surfaces: an overview. In: Weng Q (ed) Remote sensing of impervious surfaces. CRC Press, Taylor & Francis Group, Boca Raton, FL, USA
Woodcock CE, Collins JB, Gopal S, Jakabhaz VD, Li X, Macomber S, Warbington R (1994) Mapping forest vegetation using Landsat TM imagery and a canopy reflectance model. Remote Sens Environ 50(3):240–254
Wulder MA, Dechka JA, Gillis MA, Luther JE, Hall RJ, Beaudoin A, Franklin SE (2003) Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program. For Chron 79(6):1075–1083
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 Sens Environ 82(2):335–348
Zha Y, Gao J, Ni S (2003) Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens 24(3):583–594
Zhang X, Friedl MA, Schaaf CB, Strahler AH, Hodges JC, Gao F, Huete A (2003) Monitoring vegetation phenology using MODIS. Remote Sens Environ 84(3):471–475
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pal, S.C., Chakrabortty, R., Malik, S. et al. Application of forest canopy density model for forest cover mapping using LISS-IV satellite data: a case study of Sali watershed, West Bengal. Model. Earth Syst. Environ. 4, 853–865 (2018). https://doi.org/10.1007/s40808-018-0445-x
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40808-018-0445-x