Abstract
Forest ecosystems fulfill the entire ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. In the present study we focused to determine forest health pattern of Simlipal National Park (Odisha, India) based on Remote Sensing and GIS techniques. Multitemporal Landsat 8 operational Land Imager (OLI) data are derived from USGS Earth Explorer Community. Normalized difference vegetation index (NDVI), SARVI (Soil and Atmospherically Resistant Vegetation Index), Modified Chlorophyll Absorption Ratio (MCARI), and Moisture Stress Index (MSI) have been used to create different vegetation indices to estimate forest health. Finally, Weighted overlay analysis is performed on GIS platform to identify the forest health pattern in the national park. NDVI index showed the maximum accuracy for identifying vegetation classes. Results showed in the eastern and central part of the study area having excellent vegetation cover. Good to moderate vegetation cover areas are observed in the south and small pockets in north of the study area. The excellent vegetation coverage area also increases day by day. To exclude the agricultural lands and cloud cover from forest area images from the month of January are selected.
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References
Brockerhoff EG, Barbaro L, Castagneyrol B et al (2017) Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodivers Conserv 26:3005–3035. https://doi.org/10.1007/s10531-017-1453-2
Bruelheide H, Nadrowski K, Assmann T, Bauhus J, Both S, Buscot F, Chen XY, Ding B, Durka W, Erfmeier A et al (2014) Designing forest biodiversity experiments: general considerations illustrated by a new large experiment in subtropical China. Methods Ecol Evol 5:74–89
Coleman TL, Gudapati L, Derrington J (1990) Monitoring forest plantations using landsat thematic mapper data. Remote Sens Environ 33:211–221
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens Environ 37(1):35–46
Fredric County, Maryland (2009) Forest resource ordinance, pp 1–14. https://www.stormwatercenter.net/Model%20Odinances/misc-forest-conservation
Das S, Das BP (2008) Similipal biosphere: genesis of historicity. Orissa review
Dash M, Behera B (2012) Management of similipal biosphere reserve forest. Adv. Forest Lett (AFL) 1(1):7–15
Dash M, Behera B (2013) Biodiversity conservation and local livelihoods: a study on similipal biosphere reserve in India. J Rural Dev 32(4):409–426
Daughtry CST, Walthall CL, Kim MS, Colstoun EBD, Mcmurtrey JEI (2000) Estimating corn leaf chlorophyll concentration from leaf and canopy reflectance. Remote Sens Environ 74:229–239
Eden MJ (1996) Forest degradation in the tropics: environmental and management issues. In: Eden JM, John TP (eds) Land degradation in the tropics. Printer, A Cassell Imprint, New York, USA, pp 41–47
Frolking S, Palace MW, Clark DB, Chambers JQ, Shugart HH, Hurtt GC (2009) Forest disturbance and recovery: a general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure. J Geophys Res 114:3–27
Goutam NC (1983) Satellite remote sensing technique for natural resource survey. In: Singh RL et al (eds) Environmental management. Allahabad Geographical Society, pp 177–181
Gross D (2005) Monitoring agricultural biomass using NDVI time series, food and agriculture organization of the United Nations (FAO). Italy, Rome
Guerra C, Navarro LM, Kissling WD, London MC, Turak E, Balvanera P, Costello MJ, Delavaud A, El Serafy GY, Ferrier S et al (2017) Monitoring biodiversity change through effective global coordination. Curr Opin Environ Sustain 29:158–169
Jena ML (2005) Similipal’s scenic splendor. Women’s Era 32(752):110–112
Karjalainen E, Sarjala T, Raitio H (2010) Promoting human health through forests: overview and major challenges. Environ Health Prev Med 15(1):1–8
Kaufman YJ, Tanré D (1992) Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remote Sens 30:261–270
Krug CB, Schaepman ME, Shannon LJ, Cavender-bares J, Cheung W, Mcintyre PB, Metzger JP, Obura DO, Schmid B, Strassburg BBN et al (2017) Observations, indicators and scenarios of biodiversity and ecosystem services change—a framework to support policy and decision-making. Curr Opin Environ Sustain 29:198–206
Kumari R, Asok S (2017) Remote sensing based forest health analysis using gis along fringe forests of Kollam district, Kerala. Int J Res Appl Sci Eng Technol (IJRASET) 5(x):740–751
Lausch A, Borg E, Bumberger J, Dietrich P, Heurich M, Huth A, Jung A, Klenke R, Knapp S, Mollenhauer H, Paasche H, Paulheim H, Pause M, Schweitzer C, Schmulius C, Settele I, Skidmore AK, Wegmann M, Zacharias S, Kirsten T, Schaepman ME (2018) Understanding Forest health with remote sensing, part III: requirements for a scalable multi-source forest health monitoring network based on data science approaches. Remote Sens 10(7):1120. https://doi.org/10.3390/rs10071120
Lindner M, Fitzgerald JB, Zimmermann NE, Reyer C, Delzon S, van der Maaten E et al (2014) Climate change and European forests: What do we know, what are the uncertainties, and what are the implications for forest management? J Environ Manage 146:69–83
Forest Management (2008) www.manage.gov.in
MEA (2005) Ecosystems and human wellbeing. Island Press, Covelo, CA, Millennium Ecosystem Assessment
Millar CI, Stephenson NL (2015) Temperate forest health in an era of emerging mega disturbance. Science 349:823–826
Philip H (1990) Forest, forest reserve and forest land in Thailand. Geograph J 156(2) Blackwell Publishing on behalf of The Royal Geographical Society (with the Institution of British Geographers), pp 166–174
Roy DP, Wulder MA, Loveland TR, Woodcock CE, Allen RG, Anderson MC et al (2014) Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ 145:154–172
Traub B, Meile R, Speich S, Rösler E (2017) The data storage and analysis system of the Swiss national forest inventory. Comput Electron Agric 132:97–107
Trumbore S, Brando P, Hartmann H (2015) Forest health and global change. Science 349:814–818
Turkmen, N, Duzenli A, Cakan H, Ozturk M (1996) Impact of environmental problems on the natural vegetation in the east mediterranean part of Turkey. In: Kapur AS, Eswaran HA, Kelling G, Vita-Finzi C, Mermut AR, Ocal AD (eds) Proceedings of 1st international conference on land degradation, Cukurova University Press, pp 245–253, ISBN 975-4870519
White JC, Coops NC, Wulder MA, Vastaranta M, Hilker T, Tompalski P (2016) Remote sensing technologies for enhancing forest inventories: a review. Can J Remote Sens 8992:1–23
Wulder MA, Masek JG, Cohen WB, Loveland TR, Woodcock CE (2012) Opening the archive: how free data has enabled the science and monitoring promise of landsat. Remote Sens Environ 122:2–10
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Mahato, P.S., Bandhopadhyay, K., Bhunia, G.S. (2021). Assessment of Forest Health using Remote Sensing—A Case Study of Simlipal National Park, Odisha (India). In: Shit, P.K., Pourghasemi, H.R., Das, P., Bhunia, G.S. (eds) Spatial Modeling in Forest Resources Management . Environmental Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-56542-8_9
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