An approach for studying changes in ecology: A beech forest case study from Pernik Province, Western Bulgaria
Borislav Grigorov
, Adam Rusinko, Kiril Vassilev
Abstract: The present study deals with the investigation of the beech forests of Pernik Province. It aims to test a methodological approach for determination of the condition of beech forests. The area of research is located in the western parts of Bulgaria. Forests represent an essential natural resource that impacts the quality of the environment and the population living in its vicinity. The processes of mapping and monitoring of tree habitats has a long tradition in dendrology. Landsat 8 data has been used for the calculation of the Normalized Difference Vegetation (NDVI) for the study area. Four months (May, June, July and November) have been studied with the available satellite data. The tested methodological approach shows promise when it comes to June and July, yet data regarding May and November is controversial.
Keywords: Landsat; remote sensing; vegetation index
Citation: Grigorov, B., Rusinko, A. & Vassilev, K. (2024). An approach for studying changes in ecology: A beech forest case study from Pernik Province, Western Bulgaria. Bulg. J. Agric. Sci., 30 (Supplement 1), 90–96
| References: (click to open/close) | Alexander, S. & Palmer, C. (1999). Forest health monitoring in the United States: First four years. Environmental Monitoring and Assessment, 55(2), 267–277. https://doi.org/10.1023/A:1005905310299.
Alix-Garcia, J., Munteanu, C., Zhao, N., Potapov, P., Prishchepov, A., Radeloff, V., Krylov, A. & Bragina, E. (2016). Drivers of forest cover change in Eastern Europe and European Russia, 1985–2012. Land Use Policy, 59, 284–297. https://doi.org/10.1016/j.landusepol.2016.08.014
Ayeres, M. & Lombardero, M. (2000). Assessing the consequences of global change for forest disturbance from herbivores and pathogens. Science of The Total Environment, 262(3), 263-286. https://doi.org/10.1016/S0048-9697(00)00528-3.
Berveiller, D. & Damesin, C. (2008). Carbon assimilation by tree stems: potential involvement of posphoenolpyruvate carboxylase. Trees, 22(2), 149-157.
Bozhkov, P. (2017). Spatial and Temporal Analysis of Vegetation Canopy and their Relation with Slope Processes in Zemen Gorge (West Bulgaria). In: Proceedings of Seminar of Ecology - 2016 with international participation (21-22 April 2016), IBER–BAS, Sofia, Bulgaria, 223–229.
Brown, D., Pijanowski, B. & Duh, J. (2000). Modeling the relationships between land use and land cover on private lands in the Upper Midwest, USA. Journal of Environmental Management, 59(4), 247–263. https://doi.org/10.1006/jema.2000.0369.
Chu, H., Venevsky, S., Wu, C. & Wang, M. (2019). NDVI-based vegetation dynamics and its response to climate changes at Amur-Heilongjiang River Basin from 1982 to 2015. Science of the Total Environment, 650, 2051–2062. https://doi.org/10.1016/j.scitotenv.2018.09.115.
Eastman, J., Sangermano, F., Machado, E., Rogan, J. & Anyamba, A. (2013). Global trends in seasonality of Normalized Difference Vegetation Index (NDVI), 1982-2011. Remote Sensing, 5(10), 4799–4818. https://doi.org/10.3390/rs5104799.
García, C., Espelta, J. & Hampe, A. (2020). Managing forest regeneration and expansion at a time of unprecedented global change. J. Appl Ecol., 57, 2310–2315. DOI: 10.1111/1365-2664.13797.
Griffiths, P., Müller, D, Kuemmerle, T. & Hostert, P. (2013). Agricultural land change in the Carpathian ecoregion after the breakdown of socialism and expansion of the European Union. Environmental Research Letters, 8(4). https://doi.org/10.1088/1748-9326/8/4/045024.
Hemati, M., Hasanlou, M., Mahdianpari, M. & Mohammadimanesh, F. (2021). A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sensing, 13(15). https://doi.org/10.3390/rs13152869.
Hietel, E., Waldahrdt, R. & Otte, A. (2004). Analysing land-cover changes in relation to environmental variables in Hesse, Germany. Landscape Ecology, 19(5), 473–489. https://doi.org/10.1023/B:LAND.0000036138.82213.80.
Hoyos, L., Cabido, M. & Cingolani, A. (2018). A Multivariate Approach to Study Drivers of Land-Cover Changes through Remote Sensing in the Dry Chaco of Argentina. ISPRS International Journal of Geo-Information, 7(5). https://doi.org/10.3390/ijgi7050170.
Huang, S., Tang, L., Hupy, J., 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.
Ivanov, I. (1960). Géomorphologie du bassin de Dimitrovo. Ann. Univ. Sofia, 53(3) – Géog., 1-48 (Bg).
Ivanov, I. (1961). Géomorphologie du bassin de Radomir. Ann. Univ. Sofia, 54(3) – Géog., 1-44 (Bg).
Ivanov, M. & Tyufekchiev, K. (2019). Remote Sensing Based Vegetation Analysis in Parangalitsa Reserved Area. Ecologia Balkanica, Special Edition, 2, 187-197.
Kramer, M., Hansen, A., Taper, M. & Kissinger, E. (2001). Abiotic Controls on Long-Term Windthrow Disturbance and Temperate Rain Forest Dynamics in Southeast Alaska. Ecology 82(10), 2749–2768. https://doi.org/http://dx.doi.org/10.2307/2679958.
Nedkov, S., Borissova, B., Nikolova, M., Zhiyanski, M., Dimitrov, S., Mitova, R., Koulov, B., Hristova, D., Prodanova, H., Semerdzhieva, L., Dodev, Y., Ihtimanski, I. & Stoyanova, V. (2021). A methodological framework for mapping and assessment of ecosystem services provided by the natural heritage in Bulgaria. Journal of the Bulgarian Geographical Society, 45, 7-18. https://doi.org/10.3897/jbgs.e78680.
Pazúr, R., Lieskovsky, J., Burgi, M., Müller, D., Lieskovsky, T., Zhang, Z. & Prischepov, A. (2020). Abandonment and recultivation of agricultural lands in Slovakia-patterns and determinants from the past to the future. Land, 9(9). https://doi.org/10.3390/LAND9090316.
Potapov, P., Turubanova, S., Tyukavina, A., Krylov, A., Mccarty, J., Radeloff, V. & Hansen, M. (2015). Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment, 159, 28–43. https://doi.org/10.1016/j.rse.2014.11.027.
Pravalie, R., Sîrodoev, I. & Peptenatu, D. (2014). Detecting climate change effects on forest ecosystems in Southwestern Romania using Landsat TM NDVI data. J. Geogr. Sci., 24, 815–832. https://doi.org/10.1007/s11442-014-1122-2.
Purnamasayangsukasih, P., Norizah, K., Ismail, A. & Shamsudin, I. (2016). A review of uses of satellite imagery in monitoring mangrove forests. In: IOP Conference Series: Earth and Environmental Science, 37(1). https://doi.org/10.1088/1755-1315/37/1/012034.
Rogan, J. & Chen, D. (2004). Remote sensing technology for mapping and monitoring land-cover and land-use change. Progress in Planning, 61(4), 301–325. https://doi.org/10.1016/S0305-9006(03)00066-7.
Stoyanova, M., Kandilarov, A., Koutev, V., Nitcheva, O. & Dobreva, P. (2018). Potential of multispectral imaging technology for assessment of coniferous forests bitten by bark beetle in Central Bulgaria. MATEC Web of Conferences, 145, 01005. https://doi.org/10.1051/matecconf/201814501005.
Toplyiski, D. (2006). Climate of Bulgaria. Foundation AMSTELS (Bg).
Touminen, J., Lipping, T., Kuosmanen, V. & Haapanen, R. (2009). Remote Sensing of Forest Health. In: Geoscience and Remote Sensing INTECH, Gee, P. & Ho, P. (Eds.), 29–52. https://doi.org/10.5772/8283.
Trumbore, S., Brando, P. & Hartmann, H. (2015). Forest Health and Global Change. Science, 349, 6250, 814-818. https://doi.org/10.1126/science.aac6759.
Tucker, C. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0.
Uvsh, D., Gehlbach, S., Potapov, P., Munteanu, C., Bragina, E. & Radeloff, V. (2020). Correlates of forest-cover change in European Russia, 1989–2012. Land Use Policy, 96, 104648. https://doi.org/10.1016/j.landusepol.2020.104648.
Wilford, D., Cherubini, P. & Sakals, M. (2005). Dendroecology: A Guide for Using Trees to Date Geomorphic and Hydrologic Events. B.C. Min. For., Res. Br., Victoria B.C. Land Manage. Handb. No. 58. https://www.for.gov.bc.ca/hfd/pubs/Docs/Lmh/Lmh58.htm.
Xiao, Q. & Mcpherson, E. (2005). Tree health mapping with multispectral remote sensing data at UC Davis, California. Urban Ecosystems, 8(3–4), 349–361. https://doi.org/10.1007/s11252-005-4867-7.
Xue, J. & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors. https://doi.org/10.1155/2017/1353691.
Zaitunah, A., Samsuri, S., Ahmad, A. & Safitri, R. (2018). Normalized difference vegetation index (ndvi) analysis for land cover types using landsat 8 oli in besitang watershed, Indonesia. In: IOP Conference Series: Earth and Environmental Science, 126(1). https://doi.org/10.1088/1755-1315/126/1/012112.
https://eos.com/blog/ndvi-faq-all-you-need-to-know-about-ndvi/ |
|
| Date published: 2024-12-13
Download full text 
