Forest Type Mapping of Mandi District of Himachal Pradesh, India: Using Sentinel-2B Multispectral Satellite Data with Supervised Classification Techniques

Manuscript ID: IJECOL-26-0152
Section: Online First
Article Type: Research Article
DOI: https://doi.org/10.55362/IJECOL/2026/0152
Citations: Crossref

Authors & Affiliations

Manoj Thakur Corresponding Author
Vallabh Government College, Mandi, Himachal Pradesh, India
ORCID: 0000-0001-9061-1910
Email: manojthakur0083@gmail.com
Krishan Thakur
Consultant, HP State Disaster Management Society, Shimla, Himachal Pradesh, India

Article Dates

RECEIVED March 16, 2026
REVISED July 06, 2026
ACCEPTED July 07, 2026
AVAILABLE ONLINE July 16, 2026

Abstract

Accurate mapping of forest types is essential for their sustainable management, ecological studies, and climate change mitigation. Remote sensing techniques using high-resolution satellite imagery have emerged as a cost-effective and reliable tool for vegetation classification and biomass estimation studies. This study focuses on forest type mapping of Mandi district of Himachal Pradesh using Sentinel-2B multispectral satellite data having 10m resolution. In this supervised classification method with Maximum Likelihood Classifier (MLC) was adopted, which was further supported by ground truth data and ancillary resources like SOI toposheets. A total forest area of 1603.19 km2(40.50% of the geographical area of the district) was classified into 13 major forest types. The most dominant forest types included Moist Deodar forest (24.17%) followed by Chir Pine forest (21.10%), Western Mixed Coniferous Forest (18.04%), and Ban Oak Forest (11.19%), while the remaining forest types occupied comparatively smaller proportions. The overall classification accuracy was 84.67% with a Kappa coefficient of 0.83 which reflects the strong reliability of the results. These findings highlight the dominance of temperate coniferous and broad leaved forests in the district due to its climatic and altitudinal variations. The study demonstrates the potential of Sentinel-2 data to produce detailed, accurate, and up-to-date forest type maps that can serve as critical inputs for biomass estimation, resource management, and climate change studies in Himalayan ecosystems.

Keywords

Forest type mapping Sentinel-2B Remote sensing Supervised classification Mandi district Himachal Pradesh

Classifications

Forest ecology

Reference List

Bijalwan, A., Sharma, C.M., & Kediyal, V.K. (2010). Remote sensing applications in forestry: A Himalayan perspective. Indian Forester, 136(9), 1172-1181.
Bisht, N.S. (2015). Satellite remote sensing applications in the Himalayan forest ecosystems. Journal of Applied and Natural Science, 7(1), 390-395.
Champion, H.G., & Seth, S.K. (1968). A Revised Survey of the Forest Types of India. Government of India Press, New Delhi, India
Congalton, R.G. (1991). A review of assessing the accuracy of classifications of remotely sensed data.Remote Sensing of Environment, 37(1), 35-46.
Forest Survey of India. (2019). India state of forest report 2019. Ministry of Environment, Forest and Climate Change, Government of India.
Franklin, S.E., & Wulder, M.A. (2002). Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas. Progress in Physical Geography, 26(2), 173-205.
Immitzer, M., Atzberger, C., & Koukal, T. (2016). Tree species classification with random forest using Sentinel-2 data. Remote Sensing, 8(3), 166.
Jain, A., Sharma, R., & Singh, P. (2021). Forest type classification in the Indian Himalaya using Sentinel-2 imagery. Journal of Forestry Research, 32, 1281-1294.
Lu, D., Mausel, P., Brondizio, E., & Moran, E. (2005). Change detection techniques. International Journal of Remote Sensing, 25(12), 2365-2401.
Pandey, R., Kumar, A., & Singh, V. (2022).Assessment of forest carbon stock using Sentinel-2 data.Carbon Management, 13(3), 287-300.
Reddy, C.S., Jha, C.S., Dadhwal, V.K., & Diwakar, P.G. (2015). Quantification and monitoring of deforestation in India using remote sensing and GIS.Tropical Ecology, 56(1), 1-15.
Roy, P.S. (1987). Forest vegetation types of India using remote sensing.International Journal of Remote Sensing, 8(8), 1235-1247.
Roy, P.S., Joshi, P.K., & Singh, S. (2006). Geoinformatics for tropical forest cover monitoring. Current Science, 90(6), 782-788.
Singh, G., Ghosh, S.K., & Khare, S. (2023). Phenological analysis of the Western Himalayan forest using temporal remote sensing data.International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLVIII-1/W2, 1553-1559.
Singh, S., Kumar, A., & Joshi, P.K. (2023). Seasonal vegetation dynamics and altitudinal influence on forest spectral responses in the Western Himalaya using Sentinel-2 imagery. Ecological Informatics, 74, 101980.
Srivastava, A., Bharadwaj, S., Dubey, R., Sharma, V.B., & Biswas, S. (2022). Mapping vegetation and measuring the performance of machine learning algorithm in LULC classification in the large area using Sentinel-2 and Landsat-8 datasets of Dehradun as a test case. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLIII-B3, 529-536.
Srivastava, P.K., Pandey, P.C., & Gupta, M. (2022). Machine learning approaches for land use and vegetation mapping in mountainous ecosystems using Sentinel-2 imagery. Environmental Monitoring and Assessment, 194(6), 421.
Vanguri, R., Laneve, G., &Hościło, A. (2024).Mapping forest tree species and its biodiversity using EnMAP hyperspectral data along with Sentinel-2 temporal data: An approach of tree species classification and diversity indices.Ecological Indicators, 167, 112671.
Waser, L.T., Ginzler, C., Rehush, N., & Hurni, L. (2019).Mapping dominant tree species and tree species diversity across Switzerland using Sentinel-2.Remote Sensing of Environment, 229, 77-91.

Cite As

Thakur, M. & Thakur, K. (2026). Forest Type Mapping of Mandi District, Himachal Pradesh (India) Using Sentinel-2B Multispectral Satellite Data using Supervised Classification Techniques. Indian Journal of Ecology, Online first publication. https://doi.org/10.55362/IJECOL/2026/0152

Download Full Article

Access the complete article in PDF format

22 downloads

Download PDF