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These works are licensed under a Creative Commons Attribution 4.0 International License. A. Karpov, B. Waske Complete text of the article:Download article (pdf, 1.7MB )UDС630*1DOI:10.37482/0536-1036-2020-3-83-92AbstractSatellite data becomes an important tool for monitoring global change in forest cover. Further development of remote sensing technologies creates opportunities for solving more complex problems requiring multi-time analysis of satellite data. Assessment of success reforestation after a disturbance in forest cover is such an important task. The traditional method of an assessment of successful reforestation is laying out the ground plots, which task requires significant time and resources. Fieldworks and transfer of land to forest cover land is carried out according to the method, which is developed by the Federal Agency for Forestry of Russia. This method has various criteria of success reforestation for every region. Arkhangelsk region, Vologda region and Republic of Karelia became the territories for research. Forest vegetation of this region belongs to the taiga zone and is divided into five groups: the area of pre-tundra forests and sparse taiga, northern taiga, middle taiga and south taiga. International forest classification relates this area to boreal forest. The task of transfer land to forest cover land can be optimized by using remote sensing data. This research shows analysis of recovery of the normalized difference vegetation index, the shortwave vegetation index and the normalized burn ratio in the framework of reforestation objects. Filed data was collected for every object and this data includes a number of young trees, average height and species composition. Processing of a considerable number of satellite imageries requires significant computing power because of the Google Earth Engine platform using for analysis data. The most suitable index was chosen in the analysis of the obtained data for the development of an automatic method for transfer land to forest cover land. The most suitable index for dividing lands on forest cover and nonforest cover lands is the shortwave vegetation index. Optimal threshold for transferring land is achievement of recovery index of 80 % from initial values before disturbance. The automatic method was developed using unsupervised classification and threshold values of recovery index.AuthorsA. Karpov, PhD Intern; ResearcherID: H-1915-2019,ORCID: https://orcid.org/0000-0002-9087-8399 B. Waske, Doctor of Geography, Prof. AffiliationOsnabrück University, Wachsbleiche 27, Osnabruck, 49090, Germany; e-mail: lesnoy.monitoring@gmail.comKeywordsland management, forest management, reforestation, LandsatFundingThis research was undertaken as part of the project “Forest Monitoring in the Arkhangelsk Region, Using Multisensory Remote Sensing Data” funded by the Russian Ministry of Education and Science and the German Academic Exchange Service in the framework of the Michail‐Lomonosov‐Programme (project no. АААА-А19-119020590052-2).For citationKarpov A., Waske B. Method for Transferring Non-Forest Cover to Forest Cover Land Using Landsat Imageries. Lesnoy Zhurnal [Russian Forestry Journal], 2020, no. 3, pp. 83–92. DOI: 10.37482/0536-1036-2020-3-83-92References1. Aleshko R.A., Alekseeva A.A., Shoshina K.V., Bogdanov A.P., Guriev A.T. Development of the Methodology to Update the Information on a Forest Area Using Satellite Imagery and Small UAVs. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current problems in remote sensing of the Earth from space], 2017, vol. 14, no. 5, pp. 87–99. DOI: 10.21046/2070-7401-2017-14-5-87-992. Aleshko R.A., Guriev A.T., Shoshina K.V., Schenikov V.S. Development of Methodology for Visualization and Processing of Geospatial Data. Nauchnaya vizualizatsiya [Scientific Visualization], 2015, no. 1, pp. 20–29. 3. Bogdanov A.P., Karpov A.A., Demina N.A., Aleshko R.A. Improving Forest Monitoring by Using Cloud Technologies as an Element of Sustainable Forest Management. Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current problems in remote sensing of the Earth from space], 2018, vol. 15, no. 1, pp. 89–100. DOI: 10.21046/2070-7401-2018-15-1-89-100 4. Order of the Ministry of Natural Recourses and Ecology of the Russian Federation “On the Approval of the Rules of Reforestation” on June 29, 2016 no. 375. 5. Aicardi I., Garbarino M., Lingua A., Lingua E., Marzano R., Piras M. Monitoring Post-Fire Forest Recovery Using Multi-Temporal Digital Surface Models Generated from Different Platforms. EARSeL eProceedings, 2016, vol. 15, no. 1, pp. 1–8. 6. Chu T., Guo X., Takeda K. Remote Sensing Approach to Detect Post-Fire Vegetation Regrowth in Siberian Boreal Larch Forest. Ecological Indicators, 2016, vol. 62, pp. 32–46. DOI: 10.1016/j.ecolind.2015.11.026 7. Frazier R.J., Coops N.C., Wulder M.A. Boreal Shield Forest Disturbance and Recovery Trends Using Landsat Time Series. Remote Sensing of Environment, 2015, vol. 170, pp. 317–327. DOI: 10.1016/j.rse.2015.09.015 8. Gauthier M.-M., Barrette M., Tremblay S. Commercial Thinning to Meet Wood Production Objectives and Develop Structural Heterogeneity: A Case Study in the Spruce-Fir Forest, Quebec, Canada. Forests, 2015, vol. 6(2), pp. 510–532. DOI: 10.3390/f6020510 9. Gradel A., Ammer C., Ganbaatar B., Nadaldorj O., Dovdondemberel B., Wagner S. On the Effect of Thinning on Tree Growth and Stand Structure of White Birch (Betula platyphylla Sukaczev) and Siberian Larch (Larix sibirica Ledeb.) in Mongolia. Forests, 2017, vol. 8(4), art. 105. DOI: 10.3390/f8040105 10. Hansen M.C., Potapov P.V., Moore R., Hancher M., Turubanova S.A., Tyukavina A. et al. High-Resolution Global Maps of 21st-Century Forest Cover Change. Science, 2013, vol. 342, iss. 6160, pp. 850–853. DOI: 10.1126/science.1244693 11. Keshava N., Mustard J.F. Spectral Unmixing. IEEE Signal Processing Magazine, 2002, vol. 19, iss. 1, pp. 44–57. DOI: 10.1109/79.974727 12. Pickell P.D., Andison D.W., Coops N.C. Characterizations of Anthropogenic Disturbance Patterns in the Mixedwood Boreal Forest of Alberta, Canada. Forest Ecology and Management, 2013, vol. 304, pp. 243–253. DOI: 10.1016/j.foreco.2013.04.031 13. Pickell P.D., Hermosilla T., Frazier R.J., Coops N.C., Wulder M.A. Forest Recovery Trends Derived from Landsat Time Series for North American Boreal Forests. International Journal of Remote Sensing, 2015, vol. 37, iss. 1, pp. 138–149. DOI: 10.1080/2150704X.2015.1126375 14. Potapov P., Turubanova S., Hansen M.C. Regional-Scale Boreal Forest Cover and Change Mapping Using Landsat Data Composites for European Russia. Remote Sensing of Environment, 2011, vol. 115, iss. 2, pp. 548–561. DOI: 10.1016/j.rse.2010.10.001 15. Turner B.L., Lambin E.F., Reenberg A. The Emergence of Land Change Science for Global Environmental Change and Sustainability. PNAS, 2007, vol. 104(52), pp. 20666–20671. DOI: 10.1073/pnas.0704119104 16. Vila J.P.S., Barbosa P. Post-Fire Vegetation Regrowth Detection in the Deiva Marina Region (Liguria-Italy) Using Landsat TM and ETM+ Data. Ecological Modelling, 2010, vol. 221, iss. 1, pp. 75–84. DOI: 10.1016/j.ecolmodel.2009.03.011 17. White J.C., Saarinen N., Kankare V., Wulder M.A., Hermosilla T., Coops N.C. et al. Confirmation of Post-Harvest Spectral Recovery from Landsat Time Series Using Measures of Forest Cover and Height Derived from Airborne Laser Scanning Data. Remote Sensing of Environment, 2013, vol. 216, pp. 262–275. DOI: 10.1016/j.rse.2018.07.004 18. Yi K., Tani H., Zhang J., Guo M., Wang X., Zhong G. Long-Term Satellite Detection of Post-Fire Vegetation Trends in Boreal Forests of China. Remote Sensing, 2013, vol. 5, iss. 12, pp. 6938–6957. DOI: 10.3390/rs5126938 19. Zhao F.R., Meng R., Huang C., Zhao M., Zhao F.A., Gong P., Yu L., Zhu Z. LongTerm Post-Disturbance Forest Recovery in the Greater Yellowstone Ecosystem Analyzed Using Landsat Time Series Stack. Remote Sensing, 2016, vol. 8(11), art. 898. DOI: 10.3390/rs8110898 |
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