Assessment of Forest Transitions and Regions of Conservation Importance in Udupi district, Karnataka
DOI:
https://doi.org/10.36808/if/2021/v147i9/164166Keywords:
Biodiversity, Ecosystems, Ecologically Fragile Regions, Udupi District.Abstract
The current study prioritizes regions of conservation importance at the disaggregated level in the Udupi district, Central Western Ghats, based on ecological, geo-climatic, land, and social aspects. Conservation importance regions (CIR) or Ecological Sensitive Regions (ESR) are the distinct ecological units with exceptional biotic and abiotic elements which need at most care and sustainable development. CIR prioritization at grid levels (5' × 5' grids or 9 × 9 km) acts as a spatial decision support system to better understand the forest landscape dynamics and planning. The analyses of forest landscape dynamics using the temporal remote sensing data in reveal an increase in built-up areas by 8.8% with a decline in forest cover, resulting in the rise in maximum temperature by 40C in Udupi district during 1990-2018. Multivariate statistical analysis is done to understand the role of landscape dynamics on the land surface temperature (LST). The correlation analysis shows an increasing trend of LST across the CIR region with r = 0.8 where CIR 1 indicates the lowest temperature and CIR 4 has the maximum temperature.References
Aronson J. and Alexander S. (2013). Ecosystem Restoration is Now a Global Priority: Time to Roll up our Sleeves. Restor Ecol., 21: 293–296. https://doi.org/10.1111/rec.12011.
Asgarian A., Amiri B.J. and Sakieh Y. (2015). Assessing the effect of green cover spatial patterns on urban land surface temperature using landscape metrics approach. Urban Ecosyst., 18: 209–222.
Bharath S., Rajan K.S. and Ramachandra T.V. (2013). Land Surface Temperature Responses to Land Use Land Cover Dynamics. Geo infor Geostat An Overv., 1:. https://doi.org/10.4172/2327-4581.1000112.
Bonan G.B. (2008). Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests. Science, (80-) 320: 1444–1449. https://doi.org/10.1126/science.1155121.
Chakraborty S.D., Kant Y. and Mitra D. (2015). Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data. J. Environ Manage., 148: 143–152.
Danodia A., Patel N.R. and Chol C.W. (2019). Application of SSEBI model for crop evapotranspiration using Landsat-8 data over parts of North India. Geocarto Int., 34: 114–131.
Estoque R.C., Murayama Y. and Myint S.W. (2017). Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Sci. Total Environ., 577: 349–359.
Gadgil M., Daniels R.J.R. and Ganeshaiah K.N. (2011). Mapping ecologically sensitive, significant and salient areas of Western Ghats: proposed protocols and methodology. Curr. Sci., 175–182.
Ganasri B.P. and Dwarakish G.S. (2015). Study of land use/land cover dynamics through classification algorithms for Harangi catchment area, Karnataka State, INDIA. Aquat Procedia, 4: 1413–1420.
Hansen M.C., DeFries R.S., Townshend J.R.G. and Sohlberg R. (2000). Global land cover classification at 1 km spatial resolution using a classification tree approach. Int. J. Remote Sens., 21: 1331–1364.
Hansen M.C., Stehman S.V. and Potapov P.V. (2010). Quantification of global gross forest cover loss. Proc. Natl. Acad. Sci., 107: 8650–8655.
Huang C., Song K. and Kim S. (2008). Use of a dark object concept and support vector machines to automate forest cover change analysis. Remote Sens. Environ., 112: 970–985.
Ibrahim I., Samah A.A., Fauzi R. and Noor N.M. (2016). The land surface temperature impact to land cover types. Int Arch Photogramm Remote Sens. Spat Inf. Sci., 41: 871.
Jackson R.B. and Baker J.S. (2010). Opportunities and Constraints for Forest Climate Mitigation. Bioscience, 60: 698–707. https://doi.org/10.1525/bio.2010.60.9.7.
Kayet N., Pathak K., Chakrabarty A. and Sahoo S. (2016). Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest, Jharkhand. Model Earth Syst Environ., 2: 1–10.
Kuemmerle T., Chaskovskyy O. and Knorn J. (2009). Forest cover change and illegal logging in the Ukrainian Carpathians in the transition period from 1988 to 2007. Remote Sens Environ., 113: 1194–1207.
Kumari B., Tayyab M. and Mallick J. (2018). Satellite-driven land surface temperature (LST) using Landsat 5, 7 (TM/ETM+ SLC) and Landsat 8 (OLI/TIRS) data and its association with built-up and green cover over urban Delhi, India. Remote Sens Earth Syst Sci., 1: 63–78.
Lawrence D. and Vandecar K. (2015). Effects of tropical deforestation on climate and agriculture. Nat Clim Chang., 5:27–36. https://doi.org/10.1038/NCLIMATE2430
Le-Xiang Q., Hai-Shan C.U.I. and Chang J. (2006). Impacts of land use and cover change on land surface temperature in the Zhujiang Delta. Pedosphere, 16:681–689.
Leilei L., Jianrong F. and Yang C. (2014). The relationship analysis of vegetation cover, rainfall and land surface temperature based on remote sensing in Tibet, China. In: IOP conference series: earth and environmental science, p 12034.
Liang S., Wang D., He T. and Yu Y. (2019). Remote sensing of earth's energy budget: synthesis and review. Int. J. Digit Earth, 12:737–780.
Liu L., Jing X., Wang J. and Zhao C. (2009). Analysis of the changes of vegetation coverage of western Beijing mountainous areas using remote sensing and GIS. Environ Monit Assess., 153:339–349.
Mohamed A.A., Odindi J. and Mutanga O. (2017). Land surface temperature and emissivity estimation for Urban Heat Island assessment using medium-and low-resolution space-borne sensors: A review. Geocarto Int., 32:455–470.
Mujabar S. and Rao V. (2018). Estimation and analysis of land surface temperature of Jubail Industrial City, Saudi Arabia, by using remote sensing and GIS technologies. Arab J., Geosci 11:1–13.
Ramachandra T.V. and Aithal B.H. (2012). Land use dynamics at Padubidri, Udupi District with the implementation of large scale thermal power project. Int J Earth Sci Eng., 5:409–417.
Ramachandra T.V. and Bharath S. (2019a). Global Warming Mitigation Through Carbon Sequestrations in the Central Western Ghats. Remote Sens Earth Syst Sci., 2: 39–63. https://doi.org/10.1007/s41976-019-0010-z.
Ramachandra T.V. and Bharath S. (2019b) Carbon Sequestration Potential of the Forest Ecosystems in the Western Ghats, a Global Biodiversity Hotspot. Nat Resour Res., 29:2753–2771. https://doi.org/10.1007/ s11053-019-09588-0.
Ramachandra T.V., Bharath S. and Bharath A.H. (2020). Insights of Forest Dynamics for the Regional Ecological Fragility Assessment. J Indian Soc Remote Sens., 48:1169–1189. https://doi.org/10.1007/s12524-020-01146-z.
Ramachandra T.V., Bharath S. and Chandran M.D.S. (2016). Geospatial analysis of forest fragmentation in Uttara Kannada District, India. For Ecosyst., 3:10. https://doi.org/10.1186/ s40663-016-00 69-4.
Ramachandra T.V., Bharath S., Chandran M.D.S. and Joshi N. V. (2018). Salient Ecological Sensitive Regions of Central Western Ghats, India. Earth Syst Environ., 2:15–34.
Ramachandra T.V. and Bharath S. (2019c). Sustainable Management of Bannerghatta National Park, India, with the Insights in Land Cover Dynamics. FIIB Bus Rev., 8:118–131. https://doi.org/10.1177/2319714519828462.
Ramachandra T. V. and Kumar U. (2010). Greater Bangalore: Emerging urban heat island. GIS Dev, 14:86–104.
Ramachandra T.V., Tara N.M. and Bharath S. (2017). Web based spatial decision support system for sustenance of western ghats biodiversity, ecology and hydrology. Creat Congition Art Des Ed by Aneesha Sharma Jamuna Rajeswaran, 58–70.
Ramachandra T.V., Vinay S. and Bharath S. (2021) Visualisation of landscape alterations with the proposed linear projects and their impacts on the ecology. Model Earth Syst Environ., 1–13.
Reddy M.B. (2009). Land cover classification using IRS LISS III satellite image and Digital Elevation Model in hilly environmenta case study in Nongkhyllem Wildlife Sanctuary, Meghalaya. Indian Forester, 135:487–499.
Sahana M., Ahmed R. and Sajjad H. (2016). Analyzing land surface temperature distribution in response to land use/land cover change using split window algorithm and spectral radiance model in Sundarban Biosphere Reserve, India. Model Earth Syst Environ., 2: 81.
Shwetha H.R. and Kumar D.N. (2016). Prediction of high spatio-temporal resolution land surface temperature under cloudy conditions using microwave vegetation index and ANN. ISPRS J. Photogramm Remote Sens., 117: 40–55.
Xianhong Q. (2015). Eco-Sensitivity assessment and protection policy in a complex geomorphic region in China. J. Resour Ecol., 6: 44–51.
Zhu W., Jia S., Lü A. and Yan T. (2012). Analyzing and modeling the coverage of vegetation in the Qaidam Basin of China: The role of spatial autocorrelation. J. Geogr Sci., 22: 346–358.
Downloads
Downloads
Published
How to Cite
Issue
Section
License
Unless otherwise stated, copyright or similar rights in all materials presented on the site, including graphical images, are owned by Indian Forester.
