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Estimation of forest canopy density through Geospatial Technology—a case study on Sathyamangalam Forest, Erode District, Tamil Nadu

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Abstract

The term forest canopy density (FCD) refers to one of the important criteria used to evaluate forest’s ecological health. It plays a significant role in assessing the health of the forest and serves as a key landmark for potential management actions. The canopy coverage or crown cover is referred to the percentage of the forest floor that is covered by the vertical projection of tree crowns and necessary for monitoring the condition of the forest. The present study aims to estimate the forest canopy density (FCD) through Geospatial Techniques for Sathyamangalam Forest for the period between 2016 and 2022 with SENTINEL 2A satellite data. The weighted overlay analysis method was implemented with biophysical parameters, namely, Normalize Difference Vegetation Index (NDVI), Advanced Vegetation Index (AVI), Shadow Index (SI), and Soil Bareness Index (SBI) to analyze the state of the forest and its activity. The results observed significantly that the forest canopy with 158.60 km2 in 2016 which is increased to 190.37 km2 in 2018 (1.14%) then suddenly decreased to 134.85 km2 in 2020 (2.47%). The forest canopy has recovered some of its original area with 168.83 km2 through better environmental conditions during 2021–2022 (1.52%). Therefore, Geospatial Technology plays a significant role in estimating recent changes in regional forest.

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References

  • Abdollahnejad, A., Panagiotidis, D., & Surový, P. (2017). Forest canopy density assessment using different approaches - Review. Journal of Forest Science, 63(3), 107–116. https://doi.org/10.17221/110/2016-JFS

    Article  Google Scholar 

  • Azizi, Z., Najafi, A., & Sohrabi, H. (2008). Forest canopy density estimating , using satellite images. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 4. https://doi.org/10.13140/2.1.2953.6967

  • Bera, B., Saha, S., & Bhattacharjee, S. (2020). Estimation of forest canopy cover and forest fragmentation mapping using Landsat satellite data of Silabati River Basin (India). KN - Journal of Cartography and Geographic Information, 70(4), 181–197. https://doi.org/10.1007/s42489-020-00060-1

    Article  Google Scholar 

  • Borowik, T., Pettorelli, N., Sönnichsen, L., & Jędrzejewska, B. (2013). Normalized difference vegetation index (NDVI) as a predictor of forage availability for ungulates in forest and field habitats. European Journal of Wildlife Research, 59(5), 675–682. https://doi.org/10.1007/s10344-013-0720-0

    Article  Google Scholar 

  • Chandrashekhar, M. B., Saran, S., Raju, P. L. N., & Roy, P. S. (2005). Forest canopy density stratification: How relevant is biophysical spectral response modelling approach? Geocarto International, 20(1), 15–21. https://doi.org/10.1080/10106040508542332

    Article  ADS  Google Scholar 

  • Deka, J., Tripathi, O. P., & Khan, M. L. (2013). Implementation of forest canopy density model to monitor tropical deforestation. Journal of the Indian Society of Remote Sensing, 41(2), 469–475. https://doi.org/10.1007/s12524-012-0224-5

    Article  Google Scholar 

  • Falensky, M. A., Sulti, A. L., Putra, R. D., & Marko, K. (2020). Application of forest canopy density (FCD) model for the hotspot monitoring of crown fire in Tebo, Jambi Province. Jurnal Geografi Lingkungan Tropik, 4(1). https://doi.org/10.7454/jglitrop.v4i1.76

  • Fasil, M., Surendran, U. P., Gopinath, G., Karimbanakkuzhi, A., & Sahadevan, A. S. (2022). Integration of GIS, remote sensing, and spectral indices for evaluation of forest canopy density model in drought and wet years in Western Ghats region of humid tropical Kerala, India. Arabian Journal of Geosciences, 15(12), 1143. https://doi.org/10.1007/s12517-022-10368-z

    Article  Google Scholar 

  • Gandhi, G. M., Parthiban, S., Thummalu, N., & Christy, A. (2015). Ndvi: vegetation change detection using remote sensing and gis - A case study of Vellore District. Procedia Computer Science, 57, 1199–1210. https://doi.org/10.1016/j.procs.2015.07.415

    Article  Google Scholar 

  • Giridharan, N., & Sivakumar, R. (2022). NDVI based image processing for forest change detection in Sathyamangalam reserve forest. Proceedings of International Conference on Technological Advancements in Computational Sciences, ICTACS, 2022, 731–734. https://doi.org/10.1109/ICTACS56270.2022.9988184

    Article  Google Scholar 

  • Godinho, S., Gil, A., Guiomar, N., Neves, N., & Pinto-Correia, T. (2016). A remote sensing-based approach to estimating montado canopy density using the FCD model: A contribution to identifying HNV farmlands in southern Portugal. Agroforestry Systems, 90(1), 23–34. https://doi.org/10.1007/s10457-014-9769-3

    Article  Google Scholar 

  • Greene, C. S., & Millward, A. A. (2017). Getting closure: The role of urban forest canopy density in moderating summer surface temperatures in a large city. Urban Ecosystems, 20(1), 141–156. https://doi.org/10.1007/s11252-016-0586-5

    Article  Google Scholar 

  • Gulhane, V. A., Rode, S. V., & Pande, C. B. (2023). Correlation analysis of soil nutrients and prediction model through ISO cluster unsupervised classification with multispectral data. Multimedia Tools and Applications, 82(2), 2165–2184. https://doi.org/10.1007/s11042-022-13276-2

    Article  Google Scholar 

  • Hasegawa, S. (1976). Monitoring vegetation systems in the great plains with ERTS. Journal of Agricultural and Food Chemistry, 24(1), 309–317. https://doi.org/10.1021/jf60203a024

    Article  Google Scholar 

  • Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1

    Article  Google Scholar 

  • Huemmrich, K. F. (1996). Effects of shadows on vegetation indices. International Geoscience and Remote Sensing Symposium (IGARSS), 4, 2372–2374. https://doi.org/10.1109/igarss.1996.516990

    Article  Google Scholar 

  • 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. Modeling Earth Systems and Environment, 6(3), 1373–1385. https://doi.org/10.1007/s40808-020-00755-4

    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), 1–17. https://doi.org/10.1007/s40808-016-0243-2

    Article  Google Scholar 

  • Kumar, J., Talwar, P., & A.P., K. (2015). Forest canopy density and ASTER DEM based study for dense forest investigation using remote sensing and GIS techniques around East Singhbhum in Jharkhand, India. International Journal of Advanced Remote Sensing and GIS, 4(1), 1026–1032. https://doi.org/10.23953/cloud.ijarsg.96

    Article  Google Scholar 

  • Kumar, J., Talwar, P., & Krishna, A. P. (2017). Assessment of forest cover conditions and canopy density using remote sensing and GIS techniques in parts of Jharkhand State. International Journal of Research in Agriculture and Forestry, 4(4). https://doi.org/10.22259/ijraf.0404003

  • Kumar, A., Singh, A., Kumar, S., & Kanga, S. (2018). Estimating the change in forest cover density and predicting NDVI for west Singhbhum using linear regression. ESSENCE International Journal for Environmental Rehabilitation and Conservation, 9(1), 193–203. https://doi.org/10.31786/09756272.18.9.1.125

    Article  Google Scholar 

  • Lei, C. L., Ju, C. Y., Cai, T. J., Jing, X., Wei, X. H., & Di, X. Y. (2012). Estimating canopy closure density and above-ground tree biomass using partial least square methods in Chinese boreal forests. Journal of Forestry Research, 23(2), 191–196. https://doi.org/10.1007/s11676-012-0232-x

    Article  Google Scholar 

  • Li, Y., Kang, W., Han, Y., & Song, Y. (2018). Spatial and temporal patterns of microclimates at an urban forest edge and their management implications. Environmental Monitoring and Assessment, 190(2), 1–13. https://doi.org/10.1007/s10661-017-6430-4

    Article  CAS  Google Scholar 

  • Liu, F., Yang, Z., & gao, & Zhang, G. (2020). Canopy gap characteristics and spatial patterns in a subtropical forest of South China after ice storm damage. Journal of Mountain Science, 17(8), 1942–1958. https://doi.org/10.1007/s11629-020-6020-8

    Article  Google Scholar 

  • Masood, M. U., Haider, S., Rashid, M., Aldlemy, M. S., Pande, C. B., Ðurin, B., Homod, R. Z., Alshehri, F., & Elkhrachy, I. (2023). Quantifying the impacts of climate and land cover changes on the hydrological regime of a complex dam catchment area. Sustainability, 15, 15223.

    Article  Google Scholar 

  • Mishra, A. P., Singh, S., Jani, M., Singh, K. A., Pande, C. B., & Varade, A. M. (2022). Assessment of water quality index using Analytic Hierarchy Process (AHP) and GIS: A case study of a struggling Asan River. International Journal of Environmental Analytical Chemistry, 00(00), 1–13. https://doi.org/10.1080/03067319.2022.2032015

    Article  CAS  Google Scholar 

  • Mohanasundaram, S., Baghel, T., Thakur, V., Udmale, P., & Shrestha, S. (2023). Reconstructing NDVI and land surface temperature for cloud cover pixels of Landsat-8 images for assessing vegetation health index in the Northeast region of Thailand. In Environmental Monitoring and Assessment (Vol. 195, 1). Springer International Publishing. https://doi.org/10.1007/s10661-022-10802-5

    Chapter  Google Scholar 

  • Moharir, K. N., Pande, C. B., Gautam, V. K., Singh, S. K., & Rane, N. L. (2023). Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India. Environmental Research, 228, 115832. https://doi.org/10.1016/j.envres.2023.115832

    Article  ADS  CAS  PubMed  Google Scholar 

  • Mon, M. S., 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. https://doi.org/10.1080/01431161.2010.549851

    Article  ADS  Google Scholar 

  • Mondal, I., Thakur, S., Juliev, M., & Kumar De, T. (2021). Comparative analysis of forest canopy mapping methods for the Sundarban biosphere reserve, West Bengal, India. Environment, Development and Sustainability, 23(10), 15157–15182. https://doi.org/10.1007/s10668-021-01291-6

    Article  Google Scholar 

  • Nandy, S., Joshi, P. K., & Das, K. K. (2003). Forest canopy density stratification using biophysical modeling. Journal of the Indian Society of Remote Sensing, 31(4), 291–297. https://doi.org/10.1007/BF03007349

    Article  Google Scholar 

  • Nguyen, C. T., Chidthaisong, A., Diem, P. K., & Huo, L. Z. (2021). A modified bare soil index to identify bare land features during agricultural fallow-period in southeast asia using landsat 8. Land, 10(3), 1–18. https://doi.org/10.3390/land10030231

    Article  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. https://doi.org/10.1007/s40808-018-0445-x

    Article  Google Scholar 

  • Pande, C. B., & Moharir, K. N. (2023). Application of hyperspectral remote sensing role in precision farming and sustainable agriculture under climate change: A review (pp. 503–520). Springer Climate. https://doi.org/10.1007/978-3-031-19059-9_21

    Book  Google Scholar 

  • Pande, C. B., Moharir, K. N., Kumar Singh, S., Varade, A. M., Elbeltagi, A., Khadri, S. F. R., & Choudhari, P. (2021). Estimation of crop and forest biomass resources in a semi-arid region using satellite data and GIS. Journal of the Saudi Society of Agricultural Sciences, 20(5), 302–311. https://doi.org/10.1016/j.jssas.2021.03.002

    Article  Google Scholar 

  • Pande, C. B., Moharir, K. N., Varade, A. M., Abdo, H. G., Mulla, S., & Yaseen, Z. M. (2023). Intertwined impacts of urbanization and land cover change on urban climate and agriculture in Aurangabad city (MS), India using google earth engine platform. Journal of Cleaner Production, 422, 138541. https://doi.org/10.1016/j.jclepro.2023.138541

    Article  Google Scholar 

  • Prasad, P. R. C., Nagabhatla, N., Reddy, C. S., Gupta, S., Rajan, K. S., Raza, S. H., & Dutt, C. B. S. (2010). Assessing forest canopy closure in a geospatial medium to address management concerns for tropical islands-Southeast Asia. Environmental Monitoring and Assessment, 160(1–4), 541–553. https://doi.org/10.1007/s10661-008-0717-4

    Article  PubMed  Google Scholar 

  • Rahimizadeh, N., Babaie Kafaky, S., Sahebi, M. R., & Mataji, A. (2020). Forest structure parameter extraction using SPOT-7 satellite data by object- and pixel-based classification methods. Environmental Monitoring and Assessment, 192(1). https://doi.org/10.1007/s10661-019-8015-x

  • Rane, N. L., Achari, A., Choudhary, S. P., Mallick, S. K., Pande, C. B., Srivastava, A., & Moharir, K. N. (2023). A decision framework for potential dam site selection using GIS, MIF and TOPSIS in Ulhas river basin, India. Journal of Cleaner Production, 423, 138890. https://doi.org/10.1016/j.jclepro.2023.138890

    Article  Google Scholar 

  • Roy, P. S., Sharma, K. P., & Jain, A. (1996). Stratification of density in dry deciduous forest using satellite remote sensing digital data - An approach based on spectral indices. Journal of Biosciences, 21(5), 723–734. https://doi.org/10.1007/BF02703148

    Article  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), 1–10. https://doi.org/10.1007/s40808-015-0043-0

    Article  Google Scholar 

  • Samson, A., & Ramakrishnan, B. (2020). Distribution pattern and habitat utilization of vultures in Sathyamangalam Tiger Reserve, Tamil Nadu, Southern India. International Journal of Pure and Applied Zoology, 8(4), 24–29. https://doi.org/10.35841/2320-9585.8.24-29

    Article  Google Scholar 

  • Sathya, M., & Jayakumar, S. (2017). A contemporary assessment of tree species in Sathyamangalam Tiger Reserve, Southern India. Proceedings of the International Academy of Ecology and Environmental Sciences, 7(2), 30–46. http://www.iaees.org/publications/journals/piaees/articles/2017-7(2)/assessment-of-tree-species.pdf

    Google Scholar 

  • Senthilkumar, N., Prakash, S., Kannan, C. R., Prasath, A. A., Krishnakumar, N., & Senthilkumar, N. (2014). Revisiting forest types of India (Champion and Seth, 1968): A case study on Myristica swamp forest in Kerala. International Journal of Advanced Research, 2(2), 492–501 http://www.journalijar.com

    Google Scholar 

  • Sudhakar, S., Sridevi, G., Ramana, I. V., Venkateswara Rao, V., & Raha, A. K. (1999). Techniques of classification for landuse/landcover with special reference to forest type mapping in Jaldapara Wildlife Sanctuary, Jalpaiguri district, West Bengal - A case study. Journal of the Indian Society of Remote Sensing, 27(4), 217–224. https://doi.org/10.1007/BF02990834

    Article  Google Scholar 

  • Tsai, Y. H., Stow, D. A., López-Carr, D., Weeks, J. R., Clarke, K. C., & Mensah, F. (2019). Monitoring forest cover change within different reserve types in southern Ghana. Environmental Monitoring and Assessment, 191(5). https://doi.org/10.1007/s10661-019-7450-z

  • Xie, B., Cao, C., Xu, M., Yang, X., Duerler, R. S., Bashir, B., Huang, Z., Wang, K., Chen, Y., & Guo, H. (2022). Improved forest canopy closure estimation using multispectral satellite imagery within Google Earth Engine. Remote Sensing, 14(9). https://doi.org/10.3390/rs14092051

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Acknowledgements

The authors are grateful to the SRM Institute of Science and Technology for offering all necessary amenities and continuous support for doing this research work.

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  1. Department of Civil Engineering, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, Tamil Nadu, 603203, India

    Giridharan Namasivayam & Sivakumar Ramamoorthy

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  1. Giridharan Namasivayam
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G.N. contributed Data collection, conceptualization, methodology, investigation, Analysis, writing draft; S.R. contributed conceptualization, methodology, investigation, Analysis, review and editing. Both the authors read and approved the final manuscript.

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Correspondence to Sivakumar Ramamoorthy.

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Namasivayam, G., Ramamoorthy, S. Estimation of forest canopy density through Geospatial Technology—a case study on Sathyamangalam Forest, Erode District, Tamil Nadu. Environ Monit Assess 196, 209 (2024). https://doi.org/10.1007/s10661-024-12356-0

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