Assessment of Afforestation and Reforestation Suitability Using Remote Sensing and GIS: A Case Study of the Sisian Forestry Unit, Syunik Province, Armenia
DOI:
https://doi.org/10.11137/1982-3908_2026_49_72776Keywords:
Forest restoration, Mountain ecosystems, Geospatial analysisAbstract
This study aims to identify suitable areas for afforestation in the Sisian Forestry Unit of Syunik Province (Armenia) using remote sensing data and GIS-based spatial analysis. Sentinel-2 multispectral imagery was utilized to derive vegetation and moisture-related indicators, namely the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Moisture Index (NDMI), which provide information on vegetation condition and vegetation water content. In addition, a 30 m resolution Digital Elevation Model (DEM) was employed to extract key topographic parameters, including elevation, slope, and aspect, which are critical for forest growth in mountainous environments. NDVI and NDMI were calculated using appropriate Sentinel-2 spectral bands to identify degraded or sparsely vegetated areas with favorable vegetation moisture conditions for potential afforestation. Topographic suitability was assessed based on regional forest management guidelines, considering slope gradients of 10–30°, north-, northwest-, and east-facing aspects, and elevations ranging from 1200 to 2400 m. These criteria were integrated within a GIS environment to generate a composite afforestation suitability map. The results reveal spatially distinct zones that provide optimal ecological conditions for forest restoration and the establishment of new forest plantations. The study demonstrates the effectiveness of integrating remote sensing and GIS techniques to support afforestation planning and sustainable forest management in mountainous regions.
Downloads
References
Berdyyev, A., Al-Masnay, Y.A., Juliev, M. & Abuduwaili, J. 2024, ‘Desertification monitoring using machine learning techniques with multiple indicators derived from Sentinel-2 in Turkmenistan’, Remote Sensing, vol. 16, no. 23, p. 4525, DOI: 10.3390/rs16234525
Brouwer, R., Bongers, F., Peña-Claros, M., Zuidema, P.A., Brancalion, P., Lohbeck, M., Hernández-Guzmán, A., Heinze, A., Guillemot, J., Kramer, K. & Sheil, D. 2024, ‘Forest restoration, biodiversity, and ecosystem services’, in P. Katila et al. (eds), Restoring Forests and Trees for Sustainable Development: Policies, Practices, Impacts, and Ways Forward, Oxford University Press, pp. 160–198, DOI: 10.1093/9780197683958.003.0007
Cheng, Z., Aakala, T. & Larjavaara, M. 2023, ‘Elevation, aspect, and slope influence woody vegetation structure and composition but not species richness in a human-influenced landscape in northwestern Yunnan, China’, Frontiers in Forests and Global Change, vol. 6, art. 1187724, DOI: 10.3389/ffgc.2023.1187724
DeWitt, J.D., Warner, T.A., Chirico, P.G. & Bergstresser, S.E. 2017, ‘Creating high resolution bare earth digital elevation models (DEMs) from stereo imagery in an area of densely vegetated deciduous forest using combinations of procedures designed for lidar point cloud filtering’, GIScience & Remote Sensing, vol. 54, no. 4, pp. 552–577, DOI: 10.1080/15481603.2017.1303299
EOS (Earth Observing System) 2026, LandViewer, viewed 13 Feb. 2025, http://eos.com/landviewer/
Guth, P.L., Müller, J.P., Hawker, L., Florinsky, I.V., Gesch, D., Reuter, H.I., Herrera-Cruz, V., Riazanoff, S., López-Vázquez, C., Carabajal, C.C., Albinet, C. & Strobl, P. 2021, ‘Digital elevation models: Terminology and definitions’, Remote Sensing, vol. 13, no. 18, p. 3581, DOI: 10.3390/rs13183581
HAYANTAR SNPO 2020, Republic of Armenia Ministry of Environment, viewed 16 Feb. 2025, http://env.am/storage/files/sisian-23-05.pdf
Li, M., Cao, S., Zhu, Z., Wang, Z., Myneni, R.B. & Piao, S. 2023, ‘Spatiotemporally consistent global dataset of the GIMMS normalized difference vegetation index (PKU GIMMS NDVI) from 1982 to 2022’, Earth System Science Data, vol. 15, pp. 4181–4207, DOI: 10.5194/essd-15-4181-2023
Mohammed, K., Kpienbaareh, D., Bezner Kerr, R., Wang, J., Luginaah, I., Lupafya, E., Dakishoni, L. & Mkandawire, M. 2025, ‘Integrating participatory GIS, remote sensing, and explainable machine learning to assess forest provisioning services’, Environmental Impact Assessment Review, vol. 112, art. 108245, DOI: 10.1016/j.eiar.2025.108245
Morell Monzó, S., Sebastiá-Frasquet, M.T., Estornell, J. & Moltó, E. 2023, ‘Detecting abandoned citrus crops using Sentinel-2 time series: A case study in the Comunitat Valenciana region (Spain)’, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 201, pp. 54–66, DOI: 10.1016/j.isprsjprs.2023.05.003
Sargsyan, S.G. & Efendyan, P.S. 2025, ‘Analysis of changes in forest areas based on remote sensing data (on the example of Syunik Region)’, Proceedings of Yerevan State University: Chemistry and Biology Series, vol. 59, no. 2, p. 511, DOI: 10.46991/PYSUC.2025.59.2.511
Spadoni, G., Cavalli, A., Congedo, L. & Munafò, M. 2020, ‘Analysis of Normalized Difference Vegetation Index (NDVI) multi-temporal series for the production of forest cartography’, Remote Sensing Applications: Society and Environment, vol. 18, art. 100419, DOI: 10.1016/j.rsase.2020.100419
Stage, A.R. & Salas, C. 2007, ‘Interactions of elevation, aspect, and slope in models of forest species composition and productivity’, Forest Science, vol. 53, no. 4, pp. 486–492, DOI: 10.1093/forestscience/53.4.486
U.S. Geological Survey (USGS) 2025, EarthExplorer, viewed 27 Sep. 2025, http://earthexplorer.usgs.gov/.
Varouchakis, E.A., Komnitsas, K. & Galetakis, M. 2025, ‘Spatiotemporal analysis of vegetation health and moisture dynamics in rehabilitated mining quarries using satellite imagery’, Mining, Metallurgy & Exploration, DOI: 10.1007/s40710-025-00781-3
Wu, M., He, H.S., Zong, S., Tan, X., Du, H., Zhao, D., Liu, K. & Liang, Y. 2018, ‘Topographic controls on vegetation changes in alpine tundra of the Changbai Mountains’, Forests, vol. 9, no. 12, p. 756, DOI: 10.3390/f9120756
Downloads
Published
How to Cite
Issue
Section
License

This work is licensed under a Creative Commons Attribution 4.0 International License.
This journal is licensed under a Creative Commons — Attribution 4.0 International — CC BY 4.0, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.











