Spectral response dynamics of the reforestation sites in forests of the south of Central Russian Upland
The reported study was funded by RFBR according to the research project № 18-35-20018
DOI:
https://doi.org/10.18413/2712-7443-2020-44-2-210-220Keywords:
forest stands, reforestation, Central Russian Upland, forest-steppe, spectral response, LandsatAbstract
The study of reforestation processes on clear cutting is one of the key tasks in monitoring forest lands. It is especially relevant for territories in which significant areas of forests have been covered by felling in the past or present. The results of spectral response dynamics analysis of the former clean felling due to reforestation are presented. Spectral response was estimated based on Landsat data from 1985–2015 in various spectral ranges. The study was conducted for broad-leaved forests typical of the south of the Central Russian Upland. The spectral response of forest areas that differ in the main forest-forming species is compared. For forests with a predominance of oak or ash in the upper tiers, no statistically significant differences were found in the infrared spectral ranges. In the infrared ranges (SWIR1 and SWIR2), a tendency toward a decrease in reflection due to the formation of forest stands was revealed. The formation of stands 23–25 years old leads to a 15 % decrease in reflectance in the range of 1,55–1,75 μm and by 30 % in the range of 2,09–2,35 μm. An increase in the age of forest stands on clear-cutting leads to a decrease in the coefficients of variation of reflective features. The greatest decrease in reflectances is observed in the first 3-4 years after the start of the reforestation process.
Downloads
References
Белова Е.И., Ершов Д.В. 2015. Опыт оценки естественного лесовосстановления на сплошных вырубках по временным рядам Landsat. Лесоведение, 5: 339–345.
Жирин В.М., Князева С.В., Эйдлина С.П. 2011. Дистанционное сопровождение лесообразовательного процесса в послерубочных таежных лесах Русской равнины. Лесоведение, 6: 29–38.
Жирин В.М., Сухих В.И., Шаталов А.В., Бутусов О.Б., Эйдлина С.П. 2004. Использование космических снимков для изучения динамики зарастания гарей. Исследование Земли из космоса, 5: 69–76.
Кардаков А.А., Кивисте А.К., Петерсон У.К. 2013. Формирование яркостных значений восстанавливающихся сплошных рубок на зимних изображениях среднего пространственного разрешения. Исследование Земли из космоса, 1: 48–59.
Курбанов Э.А., Воробьев О.Н., Лежнин С.А. 2013. Оценка лесных гарей Чувашии методами дистанционного зондирования. Вестник Иркутской государственной сельскохозяйственной академии, 54: 80–87.
Терехин Э.А. 2012. Анализ каналов спутниковых данных LANDSAT TM для оценки характеристик лесных насаждений Лесостепной провинции Среднерусской возвышенности. Исследование Земли из космоса, 2: 53–61.
Харин Н.Г., Татеиши Р. 2003. Применение снимков NOAA/AVHRR для изучения фенологии лесов России. Лесоведение, 2: 10–17.
Baumann M., Ozdogan M., Wolter P.T., Krylov A., Vladimirova N., Radeloff V.C. 2014. Landsat remote sensing of forest windfall disturbance. Remote Sensing of Environment, 143: 171–179.
DeVries B., Decuyper M., Verbesselt J., Zeileis A., Herold M., Joseph S. 2015. Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series. Remote Sensing of Environment, 169: 320–334.
Earthexplorer. 2019. URL: http://earthexplorer.usgs.gov/ (accessed 03/06/2020).
Eklundh L., Jin H., Schubert P., Heliasz M., Guzinski R. 2011. An optical sensor network for vegetation phenology monitoring and satellite data calibration. Sensors, 11 (8): 7678–7709.
Franks S., Masek J.G., Turner M.G. 2013. Monitoring forest regrowth following large scale fire using satellite data - A case study of Yellowstone National Park, USA. European Journal of Remote Sensing, 46: 561–569.
Healey S., Cohen W. B., Zhiqiang Y., Krankin O.N. 2005. Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection. Remote Sensing of Environment, 97: 301–310.
Li P., Jiang L., Feng Z. 2014. Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors. Remote Sensing. 6 (1): 310–329.
Mancino G., Ferrara A., Padula A., Nolè A. 2020. Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment. Remote Sensing, 12 (2): 291.
Potapov P.V., Turubanova S.A., Tyukavina A., Krylov A.M., McCarty J.L., Radeloff V.C., Hansen M.C. 2015. Eastern Europe’s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive. Remote Sensing of Environment, 159: 28–43.
Schmidt M., Lucas R., Bunting P., Verbesselt J., Armston J. 2015. Multi-resolution time series imagery for forest disturbance and regrowth monitoring in Queensland, Australia. Remote Sensing of Environment, 158: 156–168.
Schroeder T.A., Cohen W.B., Yang Z. 2007. Patterns of forest regrowth following clearcutting in western Oregon as determined from a Landsat time-series. Forest Ecology and Management, 243: 259–273.
Abstract views: 435
Share
Published
How to Cite
Issue
Section
Copyright (c) 2020 REGIONAL GEOSYSTEMS
This work is licensed under a Creative Commons Attribution 4.0 International License.