Skip to main content

Advertisement

SpringerLink
Log in

Modeling the effectiveness of natural and anthropogenic disturbances on forest health in Buxa Tiger Reserve, India, using fuzzy logic and AHP approach

  • Original Article
  • Published:
Modeling Earth Systems and Environment Aims and scope Submit manuscript

Abstract

Forests are the most valuable natural resource to protect organisms as well as ecosystem at a different level. With the rising change of land use and land cover pattern due to anthropogenic and natural disturbances, this resource is now subjected to experience constant exploitation and degradation. This paper explored the level of disturbances on forest health in Buxa Tiger Reserve (BTR), a foothill ecosystem of Himalaya. Sentinel-2 data (2019) and fuzzy logic models were executed to understand the forest health status by using different vegetation indices. GIS-based Analytical Hierarchy Process (AHP) was applied to know the beat-wise spatial disturbances of natural and anthropogenic factors in the study area. Then, disturbance maps were categorized into five zones from very high to very low. The result reveal that overall imprint of natural disturbance in BTR was a little bit high (very high = 13.76%, high = 31.58%, moderate = 15.91%, low = 27.03%, very low = 11.72%) in comparison to anthropogenic disturbance (very high = 11.09%, high = 19.07%, moderate = 24.47%, low = 20.01%, very low = 25.36%), but beat wise it varies significantly. Finally, the effectiveness of both disturbances on forest health was judged through correlation statistics. The forest beats (ID: 2, 4, 6, 7) which cover the core area of BTR have experienced less natural and anthropogenic disturbances with healthy and dense forest cover. On the other hand, less disturbance with poor forest health was found in hilly areas of buxa road and chunabhati beats (ID: 9, 15). Moreover, the effective natural and anthropogenic disturbances were mainly responsible to deteriorate the forest health adequately in most of the areas of BTR.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

Similar content being viewed by others

References

  • Alexander DE (2008) A brief survey of GIS in mass-movement studies, with reflections on theory and methods. Geomorphology 94:261–267

    Google Scholar 

  • An P, Moon WM, Rencz A (1991) Application of fuzzy set theory to integrated mineral exploration. Can J Explor Geophys 27(1):1–11

    Google Scholar 

  • Anderson GL, Hanson JD, Haas RH (1993) Evaluating landsat thematic mapper derived vegetation indices for estimating above-ground biomass on semiarid rangelands. Remote Sens Environ 45:165–175

    Google Scholar 

  • Avetisyan D (2015) Assessment of vegetation cover degradation and soil erosion in chuprene reserve (northwestern bulgaria) using remote sensing and geographical information systems. Ecol Eng Environ Prot 1:47–56

    Google Scholar 

  • Bannari A, Morin D, Bonn F, Huete AR (1995) A review of vegetation indices. Remote Sens Rev 13:95–120

    Google Scholar 

  • Beniston M (2003) Climate change in Mountain region: a review of possible impacts. Clim Change 59:5–31

    Google Scholar 

  • Bonham-Carter GF (1994) Geographic information systems for geoscientists, modelling with GIS. Pergamon, Oxford, p 398

    Google Scholar 

  • Bui DT, Tsangaratos P, Ngo PTT, Pham TD, Pham BT (2019) Flash flood susceptibility modeling using an optimized fuzzy rule based feature selection technique and tree based ensemble methods. Sci Total Environ 668:1038–1054

    Google Scholar 

  • Byers A (1985) A geomorphic study of man-induced soil-erosion Sagarmatha (MT. Everest) National Park Khumbu Nepal. Mt Res Dev 6(1):83–87

    Google Scholar 

  • Cao Q, Miao Y, Shen J, Yu W, Yuan F, Cheng S, Huang S, Wang H, Yang W, Liu F (2016) Improving in-season estimation of rice yield potential and responsiveness to topdressing nitrogen application with Crop Circle active crop canopy sensor. Precis Agric 17:136–154. https://doi.org/10.1007/s11119-015-9412-y

    Article  Google Scholar 

  • Chen H, Lee CF (2003) A dynamic model for rainfall-induced landslides on natural slopes. Geomorphology 51:269–288

    Google Scholar 

  • Dale VH, Joyce LA, McNulty S, Neilson RP, Ayres MP, Flannigan MD, Simberlof D (2001) Climate change and forest disturbances: climate change can affect forests by altering the frequency, intensity, duration, and timing of fire, drought, introduced species, insect and pathogen outbreaks, hurricanes, windstorms, ice storms, or landslides. Biosci 51(9):723–734

    Google Scholar 

  • Das BK (2012) Losing biodiversity, improvershing forest villagers: analysing forest polices in the context of flood disaster in a National Park of Sub Himalayan Bengal, India. Occasional Paper35, Institute of Development Studies, Kolkata

  • Deleo JM (1993) Receiver operating characteristic laboratory (ROCLAB): software for developing decision strategies that account for uncertainty. In: Proc. 2nd Int. Sym. uncertainty modelling and analysis. Computer Society Press, College Park, 318–325

  • Dimitrov P, Kamenova I, Roumenina E, Filchev L, Ilieva I, Jelev G, Gikov A, Banov M, Krasteva V, Kolchakov V, Kercheva M, Dimitrov E, Miteva N (2019) Estimation of biophysical and biochemical variables of winter wheat through Sentinel-2 vegetation indices. Bulg J Agric Sci 25(5):819–832

    Google Scholar 

  • Dimobe K, Ouédraogo A, Soma S, Goetze D, Porembski S, Thiombiano A (2015) Identification of driving factors of land degradation and deforestation in the Wildlife Reserve of Bontioli (Burkina Faso, West Africa). Global Ecol Conserv 4:559–571

    Google Scholar 

  • Donevska KR, Gorsevski PV, Jovanovski M, Peševski I (2012) Regional non-hazardous landfill site selection by integrating fuzzy logic, AHP and geographic information systems. Environ Earth Sci 67:121–131

    Google Scholar 

  • Eken G, Bennun L, Brooks TM, Darwall W, Fishpool LDC, Foster M, Knox D, Langhammer P, Matiku P, Radford E (2004) Key biodiversity areas as site conservation targets. Bioscience 54:1110–1118. https://doi.org/10.1641/0006-3568(2004)054[1110:KBAASC]2.0.CO;2

    Article  Google Scholar 

  • Eriksson M (2006) Climate change and its implication for human health in the Himalaya. Sustainable mountain development in the Greater Himalayan region. ICIMOD Summer News Letter No 50:11–13

    Google Scholar 

  • Fritz S, Linda S, Christoph P, Ian M, Christian S, Dmitry S, Martina D, Mathias K, Christopher D, Juan-Carlos LB, Myroslava L, Inian M, Carl FS, Olha D, Tobias S, Franziska A, Liangzhi Y, Florian K, Michael O (2017) Data Descriptor: A global dataset of crowdsourced land cover and land use reference data. Nat Sci Data. https://doi.org/10.1038/sdata.2017.7

    Article  Google Scholar 

  • Gao Z, Xu X, Wang J, Hao Y, Huang W, Feng H (2013) A method of estimating soil moisture based on the linear decomposition of mixture pixels. Math Comput Modell 58:606–613

    Google Scholar 

  • Gray RE, Ewers RM, Boyle MJ, Chung AY, Gill RJ (2018) Efect of tropical forest disturbance on the competitive interactions within a diverse ant community. Sci Rep 8(1):5131

    Google Scholar 

  • Gwal S, Singh S, Gupta S, Anand S (2020) Understanding forest biomass and net primary productivity in Himalayan ecosystem using geospatial approach. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-00844-4

    Article  Google Scholar 

  • Healey SP, Cohen WB, Zhiqiang Y, Krankina ON (2005) Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sens Environ 97(3):301–310

    Google Scholar 

  • Hérault B, Piponiot C (2018) Key drivers of ecosystem recovery after disturbance in a neotropical forest. For Ecosyst 5(1):2

    Google Scholar 

  • Huang C, Goward SN, Masek JG, Thomas N, Zhu Z, Vogelmann JE (2010) An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks. Remote Sens Environ 114(1):183–198

    Google Scholar 

  • Huanga Y, Lanb Y, Thomsona SJ, Fangc A, Hoffmannb WC, Laceyd RE (2010) Development of soft computing and applications in agricultural and biological engineering. Comput Electron Agric 71:107–127

    Google Scholar 

  • Huete AR, Jackson RD (1988) Soil and atmosphere influences on the spectra of partial canopies. Remote Sens Environ 25:89–105

    Google Scholar 

  • Huo LZ, Boschetti L, Sparks AM (2019) Object-based classifcation of forest disturbance types in the conterminous United States. Remote Sens 11(5):477

    Google Scholar 

  • Ikpa TF, Dera BA, Jande JA (2009) Biodiversity conservation: Why local inhabitants destroy habitat in protected areas. Sci World J 4(4):22–27

    Google Scholar 

  • IPCC (2001) Climate Change 2001: the scientific basis. Contribution of working group I to the Third assessment report of the intergovernmental panel on climate change. In: Houghton JTY, Ding DJ, Griggs M, Noguer PJ, van der Linden XD, Maskell K, Johnson CA (eds) Cambridge University Press, Cambridge, New York, pp 881

  • Jain P, Ahmed R, Rehman S, Sajjad H (2020) Detecting disturbed forest tracts in the Sariska Tiger Reserve, India, using forest canopy density and fragmentation models. Model Earth Syst Environ. https://doi.org/10.1007/s40808-020-00755-4

    Article  Google Scholar 

  • Janizadeh S, Avand M, Jaafari A, Phong TV, Bayat M, Ahmadisharaf E, Prakash I, Pham BT, Lee S (2019) Prediction success of machine learning methods for flash flood susceptibility mapping in the tafresh watershed, Iran. Sustainability 11:5426

    Google Scholar 

  • Jiang Z, Huete AR, Didan K, Miura T (2008) Development of a two-band enhanced vegetation index without a blue band. Remote Sens Environ 112(10):3833–3845

    Google Scholar 

  • Joiner J, Yoshida Y, Zhang Y, Duveiller G, Jung M, Lyapustin A, Wang Y, Tucker C (2018) Estimation of terrestrial global gross primary production (GPP) with satellite data-driven models and eddy covariance flux data. Remote Sens 10:1346

    Google Scholar 

  • Joshi PK, Kumar M, Vijayanand NM, Paliwal A (2006) Assessing areas deforested by coal mining activities through satellite remote sensing images and GIS in parts of korba, chattisgarh Photonirvachak. J Indian Soc Remote Sens 34(4):415–421

    Google Scholar 

  • Karanth KK, Curran LM, Reuning-Scherer JD (2006) Village size and forest disturbance in Bhadra wildlife sanctuary, Western Ghats. India Biol Conserv 128(2):147–157

    Google Scholar 

  • Karlsson BG (2013) Contested belonging; an indigenous people’s struggle for forest and identity in sub-himalayan bengal. Routledge, London and New York.

  • Kelatwang S, Garzuglia M (2006) Changes in forest area in Africa 1990–2005. Int for Rev 8:21–30. https://doi.org/10.1505/ifor.8.1.21

    Article  Google Scholar 

  • Kennedy RE, Cohen WB, Schroeder TA (2007) Trajectory-based change detection for automated characterization of forest disturbance dynamics. Remote Sens Environ 110(3):370–386

    Google Scholar 

  • Kienholz H, SchneIder G, Bischel M, Grunder M, Mool P (1984) Mapping of mountain hazards and slope stability. Mt Res Dev 4(3):247–266

    Google Scholar 

  • Li X, Mao F, Du H, Zhou G, Xu X, Han N, Sun S, Gao G, Chen L (2017) Assimilating leaf area index of three typical types of subtropical forest in China from MODIS time series data based on the integrated ensemble Kalman filter and PROSAIL model. ISPRS J Photogramm Remote Sens 126:68–78. https://doi.org/10.1016/j.isprsjprs.2017.02.002

    Article  Google Scholar 

  • Lillisend TM, Keifer R (2004) Remote sensing Image interpretation. John Wiley, New York

    Google Scholar 

  • Lin S, Li J, Liu Q, Longhui L, Zhao J, Yu W (2019) Evaluating the effectiveness of using vegetation indices based on red-edge reflectance from sentinel-2 to estimate gross primary productivity. Remote Sens 11:1303. https://doi.org/10.3390/rs11111303

    Article  Google Scholar 

  • Liu JG, Mason PJ, Clerici N, Chen S, Davis A, Miao F, Deng H, Liang L (2004) Landslide hazard assessment in the three gorges area of the Yangtze River ASTER imagery: Zigui–Badong. Geomorphology 61:171–187

    Google Scholar 

  • Liu J, Pattey E, Jégo G (2012) Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons. Remote Sens Environ 123:347–358

    Google Scholar 

  • McDaniel KC, Haas RH (1982) Assessing mesquite-grass vegetation condition from Landsat. Photogramm Eng Remote Sens 48:441–450

    Google Scholar 

  • Mitchell AL, Rosenqvist A, Mora B (2017) Current remote sensing approaches to monitoring forest degradation in support of countries measurement, reporting and verifcation (MRV) systems for REDD+. Carbon Bal Manag 12(1):9

    Google Scholar 

  • Ouédraogo I, Tigabu M, Savadogo P, Compaoré H, Odén P, Ouadba J (2010) Land cover change and its relation with population dynamics in Burkina Faso, West Africa. Land Degrad Dev 21:453–462

    Google Scholar 

  • Ozdogan M (2014) A practical and automated approach to large area forest disturbance mapping with remote sensing. PLoS One 9(4):1

    Google Scholar 

  • Padalia H, Sinha SK, Bhave V, Trivedi NK, Senthil AK (2020) Estimating canopy LAI and chlorophyll of tropical forest plantation (North India) using Sentinel-2 data. Adv Space Res 65:458–469

    Google Scholar 

  • Pande A (2016) Assessment of slope instability and its impact on land status: a case study from Central Himalaya India. Landform Anal 32:27–43. https://doi.org/10.12657/landfana.032.003

    Article  Google Scholar 

  • Pearson RL, Miller LD (1972) Remote mapping of standing crop biomass and estimation of the productivity of the short grass prairie, Pawnee National Grasslands, Colorado. In: Proceedings of the 8th international symposium on remote sensing of the environment, Ann Arbor, MI, USA, 2–6 October, pp 1357–1381

  • Pearson RL, Miller LD, Tucker CJ (1976) Hand-held spectral radiometer to estimate gramineous biomass. Appl Opt 15(2):416–418

    Google Scholar 

  • Pham BT, Prakash I, Chen W, Ly HB, Ho LS, Omidvar E, Tran VP, Bui DT (2019) A novel intelligence approach of a sequential minimal optimization-based support vector machine for landslide susceptibility mapping. Sustainability 11:6323

    Google Scholar 

  • Pokhriyal P, Rehman S, Areendran G, Raj K, Pandey R, Kumar M, Sahana M, Sajjad H (2019) Assessing forest cover vulnerability in Uttarakhand, India using analytical hierarchy process. Model Earth Syst Environ. https://doi.org/10.1007/s40808-019-00710-y

    Article  Google Scholar 

  • Qi J, Chehbouni A, Huete AR, Kerr YH, Sorooshian S (1994) A modified soil adjusted vegetation index (MSAVI). Remote Sens Environ 48:119–126

    Google Scholar 

  • Rouse JW, Haas RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the great plains with Erts Freden SC, ed. Third Earth Resources Technology Satellite-1 Symposium. Technical Presentations, vol. 1. NASA, Washington, DC, pp 309–317

  • Saaty TL (1980) The analytic hierarchy process: planning, priority setting, resource allocation. McGraw Hill International, New York

    Google Scholar 

  • Saaty TL (1990) The analytic hierarchy process. RWS Publications

    Google Scholar 

  • Saaty TL, Vargas LG (2000) Models, methods, concepts, and applications of the analytic hierarchy process. Kluwer Academic Publishers, Boston

    Google Scholar 

  • Sabins FF (1987) Remote sensing: principles and interpretation, 2nd edn. Freeman and Company, New York

    Google Scholar 

  • Scharlemann JPW, Kapos V, Campbell A, Lysenko I, Burgess ND, Hansen MC, Gibbs HK, Dickson B, Miles L (2010) Securing tropical forest carbon, the contribution of protected areas to REDD. Oryx 44(3):352–357. https://doi.org/10.1017/S0030605310000542

    Article  Google Scholar 

  • Shrestha DP (1997) Assessment of soil erosion in the nepalese himalaya, a case study in likhu khola valley, middle mountain region. Land Husbandry 2(1): Oxford & IBH Publishing Co. Pvt. Ltd, 59–80.

  • Silleos NG, Alexandridis TK, Gitas IZ, Perakis K (2008) Vegetation indices: advances made in biomass estimation and vegetation monitoring in the last 30 years. Geocarto Int 21(4):21–28

    Google Scholar 

  • Sun Y, Qin Q, Ren H, Zhang T, Chen S (2019) Red-edge band vegetation indices for leaf area index estimation from sentinel-2/MSI imagery. IEEE Tran Geosci Remote Sens 58(2):826–840

    Google Scholar 

  • Talukdar NR, Ahmed R, Choudhury P, Barbhuiya NA (2019) Assessment of forest health status using a forest fragmentation approach: a study in Patharia Hills Reserve Forest, northeast India. Model Earth Syst Environ. https://doi.org/10.1007/s40808-019-00652-5

    Article  Google Scholar 

  • Tesfaye AA, Awoke BG (2019) Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem. Spatial Inf Res. https://doi.org/10.1007/s41324-020-00339-5

    Article  Google Scholar 

  • Tiwari PC (2000) Land use changes in Himalaya and their impact on the plains ecosystem: need for sustainable land use. Land Use Policy 17:101–111

    Google Scholar 

  • Tucker CJ (1979) Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ 8:127–150

    Google Scholar 

  • Van Westen CJ, Rengers N, Soeters R (2003) Use of geomorphological information in indirect landslide susceptibility assessment. Nat Hazards 30:399–419

    Google Scholar 

  • Van Westen CJ, Castellanos E, Kuriakose SL (2008) Spatial data for landslide susceptibility, hazard, and vulnerability assessment: an overview. Eng Geol 102:112–131

    Google Scholar 

  • Xiao X, Zhang Q, Braswell B, Urbanski S, Boles S, Wofsy S, Iii BM, Ojima D (2004) Modeling gross primary production of temperate deciduous broadleaf forest using satellite images and climate data. Remote Sens Environ 91:256–270

    Google Scholar 

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Google Scholar 

  • Zimmerman HJ (1996) Fuzzy set theory and it applications. Kluwer Academic Publishers, Norwell, p 435

    Google Scholar 

  • Zimmerman M, Bischel M, Kienholz H (1986) Mountain hazards mapping in the Khumbu Himal, Nepal with prototype map, scale: 1: 50,000. Mt Res Dev 6(1):29–40

    Google Scholar 

  • Zipperer WC, Burgess RL, Nyland RD (1990) Patterns of deforestation and reforestation in different landscape types in central New York. For Ecol Manage 36:103–117. https://doi.org/10.1016/0378-1127(90)90066-K

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Geography, Dr. B.N.D.S. Mahavidyalaya, West Bengal, India

    Koyel Sam

Authors
  1. Koyel Sam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Koyel Sam.

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

Sam, K. Modeling the effectiveness of natural and anthropogenic disturbances on forest health in Buxa Tiger Reserve, India, using fuzzy logic and AHP approach. Model. Earth Syst. Environ. 8, 2261–2276 (2022). https://doi.org/10.1007/s40808-021-01227-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40808-021-01227-z

Keywords

Search

Navigation