Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2024, Vol. 21, No. 5, pp. 161-172
Patterns of influence of disturbance types in forest-steppe broad-leaved forests on spectral reflectance
1 Belgorod State National Research University, Belgorod, Russia
Accepted: 01.07.2024
DOI: 10.21046/2070-7401-2024-21-5-161-172
Disturbance is key indicator of forest condition. The article explores the influence of disturbances, such as tree diseases, insect pests, clear-cutting, in broad-leaved forests of Central Russian forest-steppe on their satellite-derived spectral reflectance. The sequence “clear cuttings – influence of insect pests – influence of diseases – undisturbed forests” shows a consistent decrease in the near and short-wave infrared reflectance. It is due to a decrease in the magnitude of the impact of the negative factor. No similar patterns have been identified for visible range reflectance. In the near and short-wave infrared ranges, statistically significant differences are observed between all forest disturbance types. At the same time, significant differences between areas of disturbance due to tree diseases from undisturbed forests were not established in any range. The influence of insect pests and tree diseases forms a positive trend in the short-wave infrared reflectance. The presence of the trend is a criterion of the corresponding forest disturbance types. Clear cuttings result in a 50–60 % increase in short-wave infrared reflectance. The absence of disturbances in forests forms a negative reflectance trend or absence of a pronounced trend, depending on forest age.
Keywords: forest disturbance, broad-leaved forests, Landsat, remote sensing data, time series
Full textReferences:
- Bartalev S. A., Kuryatnikova T. S., Stibig H.-J., Methods for the analysis of time-series of high-resolution satellite images for the assessment of logging in the taiga, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2005, Vol. 2, No. 2, pp. 217–227 (in Russian).
- Bugaev V. A., Musievsky A. L., Tsaralunga V. V. Oak forests in the European part of Russia, Russian Forestry J., 2004, No. 2, pp. 7–13 (in Russian).
- Bugaev V. A., Musievsky A. L., Tsaralunga V. V., Dubravy lesostepi (Oak forests of the forest-steppe), Voronezh: Voronezhskaya gosudarstvennaya lesotekhnicheskaya akademiya, 2013, 247 p. (in Russian).
- Vorobiev O. N., Kurbanov E. A., Polevshchikova Y. A., Lezhnin S. A., Assessment of dynamics and disturbance of forest cover in the Middle Povolzhje by Landsat images, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2016, Vol. 13, No. 4, pp. 124–134 (in Russian), DOI: 10.21046/2070-7401-2016-13-3-124-134.
- Degtyar A. V., Grigoreva O. I., Development of land forests of the Belgorod region for the 400-year period, Nauchnye vedomosti Belgorodskogo gosudarstvennogo universiteta. Seriya: Estestvennye nauki, 2018, Vol. 42, No. 4, pp. 574–586 (in Russian), DOI: 10.18413/2075-4671-2018-42-4-574-586.
- Zamolodchikov D. G., Grabovsky V. I., Shulyak P. P., Chestnykh O. V., The impacts of fires and clear-cuts on the carbon balance of Russian forests, Contemporary Problems of Ecology, 2013, Vol. 6, No. 3, pp. 714–726, DOI: 10.1134/S1995425513070123.
- Kamyshev N. S., Khmelev K. F., Rastitel’nyi pokrov Voronezhskoi oblasti i ego okhrana (Vegetation cover of the Voronezh region and its protection), Voronezh: Izd. Voronezhskogo universiteta, 1976, 181 p. (in Russian).
- Rubtsov V. I., Forests of the Central Chernozem region, Lesa SSSR. T. 3: Lesa Evropeiskoi chasti SSSR i Zakavkaz’ya (Forests of the USSR. Vol. 3 : Forests of the European part of the USSR and Transcaucasia), Moscow: Nauka, 1966, pp. 107–140 (in Russian).
- Terekhin E. A., Detection of disturbed forest ecosystems in the forest-steppe zone using reflectance values, Computer Optics, 2019, Vol. 43, No. 3, pp. 412–418 (in Russian), DOI: 10.18287/0134-2452-2019-43-3-412-418.
- Terekhin E. A., Estimation of forest disturbance in the forest-steppe zone at the beginning of the XXI century using satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 2, pp. 134–146 (in Russian), DOI: 10.21046/2070-7401-2020-17-2-134-146.
- Harchenko N. A., Harchenko N. N., To question about degrade sprouts oak wood Central Chernozem, Vestnik Moskovskogo gosudarstvennogo universiteta lesa — Lesnoi vestnik, 2007, No. 4, pp. 29–31 (in Russian).
- Tsaralunga V. V., Damage caused to Voronezh oak forests by the construction of the Belgorod Line, Vestnik Tsentral’no-Chernozemnogo regional’nogo otdeleniya nauk o lese Rossiiskoi akademii estestvennykh nauk Voronezhskoi gosudarstvennoi lesotekhnicheskoi akademii, 2002, Vol. 4, No. 1, pp. 132–137 (in Russian).
- Tsvetkov M. A., Izmenenie lesistosti evropeiskoi Rossii s kontsa XVII stoletiya po 1914 god (Changes in forest cover in European Russia from the end of the 17th century to 1914), Moscow: Izd. AN SSSR, 1957, 213 p.
- Shorokhova E. V., Korepin A. A., Kapitsa E. A. et al., Cenotic diversity and the long-term dynamics of the primeval middle boreal forests, Lesovedenie, 2022, No. 6, pp. 643–657 (in Russian), DOI: 10.31857/S0024114822060109.
- Bullock E. L., Woodcock C. E., Olofsso P., Monitoring tropical forest degradation using spectral unmixing and Landsat time series analysis, Remote Sensing of Environment, 2020, Vol. 238, Article 110968, DOI: 10.1016/j.rse.2018.11.011.
- DeVries B., Decuyper M., Verbesselt J. et al., Tracking disturbance-regrowth dynamics in tropical forests using structural change detection and Landsat time series, Remote Sensing of Environment, 2015, Vol. 169, pp. 320–334, DOI: 10.1016/j.rse.2015.02.012.
- Frantz D., Röder A., Udelhoven T. et al., Forest disturbance mapping using dense synthetic Landsat/MODIS time-series and permutation-based disturbance index detection, Remote Sensing, 2016, Vol. 8, No. 4, Article 277, DOI: 10.3390/rs8040277.
- Frolking S., Palace M. W., Clark D. B. et al., Forest disturbance and recovery: A general review in the context of spaceborne remote sensing of impacts on aboveground biomass and canopy structure, J. Geophysical Research, 2009, Vol. 114, Issue G2, 27 p., DOI: 10.1029/2008JG000911.
- Huang C., Goward S. N., Masek J. G. et al., An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks, Remote Sensing of Environment, 2010, Vol. 114, No. 1, pp. 183–198, DOI: 10.1016/j.rse.2009.08.017.
- Kennedy R. E., Yang Z., Cohen W. B., Detecting trends in forest disturbance and recovery using yearly Landsat time series: 1. LandTrendr — Temporal segmentation algorithms, Remote Sensing of Environment, 2010, Vol. 114, No. 12, pp. 2897–2910, DOI: 10.1016/j.rse.2010.07.008.
- Lisetskii F. N., Buryak Z. A., Runoff of water and its quality under the combined impact of agricultural activities and urban development in a small river basin, Water, 2023, Vol. 13, No. 15, Article 2443, DOI: 10.3390/w15132443.
- Matasci G., Hermosilla T., Wulder M. A. et al., Three decades of forest structural dynamics over Canada’s forested ecosystems using Landsat time-series and lidar plots, Remote Sensing of Environment, 2018, Vol. 216, pp. 697–714, DOI: 10.1016/j.rse.2018.07.024.
- Slagter B., Reiche J., Marcos D. et al., Monitoring direct drivers of small-scale tropical forest disturbance in near real-time with Sentinel 1 and -2 data, Remote Sensing of Environment, 2023, Vol. 295, Article 113655, DOI: 10.1016/j.rse.2023.113655.
- Stahl A. T., Andrus R., Hicke J. A. et al., Automated attribution of forest disturbance types from remote sensing data: A synthesis, Remote Sensing of Environment, 2023, Vol. 285, Article 113416, DOI: 10.1016/j.rse.2022.113416.
- Yuan Q., Shen H., Li T. et al., Deep learning in environmental remote sensing: Achievements and challenges, Remote Sensing of Environment, 2020, Vol. 241, Article 111716, DOI: 10.1016/j.rse.2020.111716.
- Zhao F., Huang C., Goward S. N. et al., Development of Landsat-based annual US forest disturbance history maps (1986–2010) in support of the North American Carbon Program (NACP), Remote Sensing of Environment, 2018, Vol. 209, pp. 312–326, DOI: 10.1016/j.rse.2018.02.035.