Forests represent a critical component of land-based ecosystems, making accurate forest cover assessment essential for sustainable landscape management. This study presents a two-part methodology combining Maximum Likelihood Estimation (MLE) and Sentinel-2 satellite imagery to support forest monitoring and spatial mapping. Unlike conventional forest mapping studies that primarily report land cover statistics, our work develops an integrated hybrid classification framework combining NDVI transformation and ISODATA spectral clustering with Maximum Likelihood Classification to quantify forest canopy transitions in a complex Himalayan landscape. Remote sensing and GIS techniques were applied using NDVI classification and pixel-based extraction, with unsupervised ISODATA clustering and ERDAS Imagine software employed in the classification workflow. Forest cover was categorized into five canopy density classes: Very Dense Forest (VDF), Moderately Dense Forest (MDF), Open Forest (OF), Scrub, and Non-Forest (NF). The study area encompassed Shimla Municipal Forests in the Western Himalayas, with change detection conducted over a four-year interval from 2015 to 2019. Results revealed a net loss of 113 ha in open forest and 48 ha in non-forest areas. The classification achieved an overall accuracy of 89.2% with a Kappa coefficient of 0.86, reflecting strong agreement between classified outputs and reference data. These findings demonstrate the effectiveness of MLE paired with Sentinel-2 imagery in accurately detecting forest cover change, while also offering a cost-effective solution and beyond estimating forest extent, the proposed framework provides a reproducible workflow for analyzing canopy density transitions that can support long-term environmental monitoring and evidence-based forest management in mountainous regions on a large scale.
FAO (2020) Global Forest Resource Assessment 2020. Food and Agriculture Organization of the United Nations. https://openknowledge.fao.org/items/d6f0df61-cb5d-4030-8814-0e466176d9a1
[3]
Gupta, H.K. (2007) Deforestation and Forest Cover Changes in the Himachal Himalaya, India. International Journal of Ecology and Environmental Sciences, 33, 207-218. https://hdl.handle.net/10535/3418
[4]
Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., et al. (2013) High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 342, 850-853. https://doi.org/10.1126/science.1244693
[5]
Sarre, A.D. and Davey, S.M. (2021) The Sustainable Development Goals, Forests, and the Role of Australian Forestry. Australian Forestry, 84, 41-49. https://doi.org/10.1080/00049158.2021.1920207
[6]
Murthy, M.S.R. and Jha, C.S. (2010) Forest and Vegetation. In: Roy, P.S., Dwivedi, R.S. and Vijayan, D., Eds., Remote Sensing Applications, National Remote Sensing Centre, 49-80.
[7]
Roy, P.S., Dutt, C.B.S., Joshi, P.K. and Nrsa, S. (2002) Tropical Forest Resource Assessment and Monitoring. Tropical Ecology, 43, 21-37.
[8]
Trotter, C.M. (1991) Remotely-Sensed Data as an Information Source for Geographical Information Systems in Natural Resource Management a Review. International Journal of Geographical Information Systems, 5, 225-239. https://doi.org/10.1080/02693799108927845
[9]
Nguyen, H.T.T., Doan, T.M., Tomppo, E. and McRoberts, R.E. (2020) Land Use/Land Cover Mapping Using Multitemporal Sentinel-2 Imagery and Four Classification Methods—A Case Study from Dak Nong, Vietnam. Remote Sensing, 12, Article 1367. https://doi.org/10.3390/rs12091367
[10]
Singh, A. (1989) Review Article Digital Change Detection Techniques Using Remotely-Sensed Data. International Journal of Remote Sensing, 10, 989-1003. https://doi.org/10.1080/01431168908903939
[11]
Mani, J.K. and Varghese, A.O. (2018) Remote Sensing and GIS in Agriculture and Forest Resource Monitoring. In: Reddy, G. and Singh, S., Eds., Geospatial Technologies in Land Resources Mapping, Monitoring and Management, Springer, 377-400. https://doi.org/10.1007/978-3-319-78711-4_19
[12]
Addabbo, P., Focareta, M., Marcuccio, S., Votto, C. and Ullo, S.L. (2016) Contribution of Sentinel-2 Data for Applications in Vegetation Monitoring. Acta Imeko, 5, 44-54. https://doi.org/10.21014/acta_imeko.v5i2.352
[13]
Topalo？lu, R.H., Sertel, E. and Musao？lu, N. (2016) Assessment of Classification Accuracies of Sentinel-2 and Landsat-8 Data for Land Cover/USE Mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 8, 1055-1059. https://doi.org/10.5194/isprs-archives-xli-b8-1055-2016
[14]
Immitzer, M., Vuolo, F. and Atzberger, C. (2016) First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe. Remote Sensing, 8, Article 166. https://doi.org/10.3390/rs8030166
[15]
Puletti, N., Chianucci, F. and Castaldi, C. (2018) Use of Sentinel-2 for Forest Classification in Mediterranean Environments. Annals of Silvicultural Research, 42, 32-38. https://doi.org/10.12899/asr-1463
[16]
Szostak, M., Hawry？o, P. and Piela, D. (2017) Using of Sentinel-2 Images for Automation of the Forest Succession Detection. European Journal of Remote Sensing, 51, 142-149. https://doi.org/10.1080/22797254.2017.1412272
[17]
Phiri, D., Simwanda, M., Salekin, S., Nyirenda, V., Murayama, Y. and Ranagalage, M. (2020) Sentinel-2 Data for Land Cover/Use Mapping: A Review. Remote Sensing, 12, Article 2291. https://doi.org/10.3390/rs12142291
[18]
Lechner, A.M., Foody, G.M. and Boyd, D.S. (2020) Applications in Remote Sensing to Forest Ecology and Management. One Earth, 2, 405-412. https://doi.org/10.1016/j.oneear.2020.05.001
[19]
Jensen, J.R. (1985) Introductory Digital Image Processing: A Remote Sensing Perspective. Pearson.
[20]
Erbek, F.S., ？zkan, C. and Taberner, M. (2004) Comparison of Maximum Likelihood Classification Method with Supervised Artificial Neural Network Algorithms for Land Use Activities. International Journal of Remote Sensing, 25, 1733-1748. https://doi.org/10.1080/0143116031000150077
[21]
Patil, M.B., Desai, C.G. and Umrikar, B.N. (2012) Image Classification Tool for Land Use/Land Cover Analysis: A Comparative Study of Maximum Likelihood and Minimum Distance Method. International Journal of Geology, Earth and Environmental Sciences, 2, 189-196.
[22]
Kumar, A., Uniyal, S.K. and Lal, B. (2007) Stratification of Forest Density and Its Validation by NDVI Analysis in a Part of Western Himalaya, India Using Remote Sensing and GIS Techniques. International Journal of Remote Sensing, 28, 2485-2495. https://doi.org/10.1080/01431160600693583
[23]
Haboudane, D. and El Mustapha Bahri, (2007) Deforestation Detection and Monitoring in Cedar Forests of the Moroccan Middle-Atlas Mountains. 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, 23-28 July 2007, 4327-4330. https://doi.org/10.1109/igarss.2007.4423809
[24]
Uddin, K., Gilani, H., Murthy, M.S.R., Kotru, R. and Qamer, F.M. (2015) Forest Condition Monitoring Using Very-High-Resolution Satellite Imagery in a Remote Mountain Watershed in Nepal. Mountain Research and Development, 35, 264-277. https://doi.org/10.1659/mrd-journal-d-14-00074.1
[25]
Sothe, C., Almeida, C., Liesenberg, V. and Schimalski, M. (2017) Evaluating Sentinel-2 and Landsat-8 Data to Map Sucessional Forest Stages in a Subtropical Forest in Southern Brazil. Remote Sensing, 9, Article 838. https://doi.org/10.3390/rs9080838
[26]
Mohajane, M., Essahlaoui, A., Oudija, F., El Hafyani, M. and Cláudia Teodoro, A. (2017) Mapping Forest Species in the Central Middle Atlas of Morocco (Azrou Forest) through Remote Sensing Techniques. ISPRS International Journal of Geo-Information, 6, Article 275. https://doi.org/10.3390/ijgi6090275
[27]
Recanatesi, F., Giuliani, C. and Ripa, M.N. (2018) Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve. Sustainability, 10, Article 3308. https://doi.org/10.3390/su10093308
[28]
Astola, H., H？me, T., Sirro, L., Molinier, M. and Kilpi, J. (2019) Comparison of Sentinel-2 and Landsat 8 Imagery for Forest Variable Prediction in Boreal Region. Remote Sensing of Environment, 223, 257-273. https://doi.org/10.1016/j.rse.2019.01.019
[29]
Miranda, E., Mutiara, A.B. and Wibowo, W.C. (2019) Forest Classification Method Based on Convolutional Neural Networks and Sentinel-2 Satellite Imagery. International Journal of Fuzzy Logic and Intelligent Systems, 19, 272-282. https://doi.org/10.5391/ijfis.2019.19.4.272
[30]
Campos-Taberner, M., García-Haro, F.J., Martínez, B., Izquierdo-Verdiguier, E., Atzberger, C., Camps-Valls, G., et al. (2020) Understanding Deep Learning in Land Use Classification Based on Sentinel-2 Time Series. Scientific Reports, 10, Article No. 17188. https://doi.org/10.1038/s41598-020-74215-5
[31]
Rajani, A. and Varadarajan, S. (2020) LU/LC Change Detection Using NDVI & MLC through Remote Sensing and GIS for Kadapa Region. In: Mallick, P., Balas, V., Bhoi, A. and Chae, G.S., Eds., Cognitive Informatics and Soft Computing, Springer, 215-223. https://doi.org/10.1007/978-981-15-1451-7_24
[32]
Nelson, S.A. and Khorram, S. (2018) Image Processing and Data Analysis with ER-DAS IMAGINE？. CRC Press.
[33]
Reddy, R.S., Babu, G.A. and Reddy, A.R.M. (2020) Geospatial Approach for the Analysis of Forest Cover Change Detection Using Machine Learning. Geosfera Indonesia, 5, 335-351. https://doi.org/10.19184/geosi.v5i3.20157
[34]
Elhag, M., Boteva, S. and Al-Amri, N. (2021) Forest Cover Assessment Using Remote-Sensing Techniques in Crete Island, Greece. Open Geosciences, 13, 345-358. https://doi.org/10.1515/geo-2020-0235
[35]
Hagner, O. and Reese, H. (2007) A Method for Calibrated Maximum Likelihood Classification of Forest Types. Remote Sensing of Environment, 110, 438-444. https://doi.org/10.1016/j.rse.2006.08.017
[36]
Rawat, J.S. and Kumar, M. (2015) Monitoring Land Use/Cover Change Using Remote Sensing and GIS Techniques: A Case Study of Hawalbagh Block, District Almora, Uttarakhand, India. The Egyptian Journal of Remote Sensing and Space Science, 18, 77-84. https://doi.org/10.1016/j.ejrs.2015.02.002
[37]
Alam, A., Bhat, M.S. and Maheen, M. (2020) Using Landsat Satellite Data for Assessing the Land Use and Land Cover Change in Kashmir Valley. GeoJournal, 85, 1529-1543. https://doi.org/10.1007/s10708-019-10037-x
[38]
Roy, P.S. and Giriraj, A. (2008) Land Use and Land Cover Analysis in Indian Context. Journal of Applied Sciences, 8, 1346-1353. https://doi.org/10.3923/jas.2008.1346.1353
