Журнал

About Editorial Board Information for authors and reviewers Publication ethics Contacts User Account: send / review a manuscript
Archive
Volume 21, 2024
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 20, 2023
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 19, 2022
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 18, 2021
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 17, 2020
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 16, 2019
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 15, 2018
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 14, 2017
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6 Number 7
Volume 13, 2016
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 12, 2015
Number 1 Number 2 Number 3 Number 4 Number 5 Number 6
Volume 11, 2014
Number 1 Number 2 Number 3 Number 4
Volume 10, 2013
Number 1 Number 2 Number 3 Number 4
Volume 9, 2012
Number 1 Number 2 Number 3 Number 4 Number 5
Volume 8, 2011
Number 1 Number 2 Number 3 Number 4
Volume 7, 2010
Number 1 Number 2 Number 3 Number 4
Issue 6, 2009
Volume 1 Volume 2
Issue 5, 2008
Volume 1 Volume 2
Issue 4, 2007
Volume 1 Volume 2
Issue 3, 2006
Volume 1 Volume 2
Issue 2, 2005
Volume 1 Volume 2
Issue 1, 2004
Volume 1
Search
Find:
русский
ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 4, pp. 165-174

A method for reforestation monitoring based on joint analysis of optical-microwave data on the NDVI – RVI plane

I.I. Kirbizhekova 1 , T.N. Chimitdorzhiev 1 , A.V. Dmitriev 1 
1 Institute of Physical Materials Science SB RAS, Ulan-Ude, Russia
Accepted: 04.07.2023
DOI: 10.21046/2070-7401-2023-20-4-165-174
This paper proposes a method for comprehensive assessing of the state and dynamics of young forest growth through joint analysis of multispectral optical images and satellite radar data. Previous research showed that separate analysis of vegetation (using the NDVI index) and radar (using the RVI index) data produced significantly different forecasts of reforestation levels after a fire. Therefore, we propose evaluating the temporal dynamics of reforestation through the NDVI – RVI plane, comparing against control areas of coniferous and mixed forests and also a treeless area. We demonstrate that these areas create a movable triangular zone, where changes in vegetation indices indicate an increase in forest projective cover, aboveground biomass growth, and replacement/restoration of species. To describe such dynamics, we introduce two quantitative indices: the degree of reforestation index (DRI) and the ratio of species index (RSI). To process the data and scale the results, we used the modern functionality of the Google Earth Engine (GEE) cloud platform. We obtained NDVI values from Landsat-5, -8 data during the 2007–2020 winter (snow) period, minimizing the influence of soil and grass on assessing deciduous and evergreen coniferous plantations’ crown dynamics. Additionally, we obtained the radar vegetation index RVI from synthetic aperture satellite radar (SAR) data of ALOS 1 PALSAR 1 (2006–2010) and ALOS 2 PALSAR 2 (2016–2020) with the help of the GEE cloud platform to study forest vegetation biomass changes.
Keywords: remote sensing, optical-microwave data, NDVI, RVI, time series, post-fire reforestation
Full text

References:

  1. Bartalev S. A., Vorushilov I. I., Egorov V. A., Creation and radiometric normalisation of cloud-free composite satellite images of snow-covered terrestrial surface for forest monitoring, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 2, pp. 57–69 (in Russian), DOI: 10.21046/2070-7401-2022-19-2-57-69.
  2. Dmitriev A. V., Chimitdorzhiev T. N., Dagurov P. N. (2022a), Optics and microwave detection of forest restoration after fires, Computational technologies, 2022, Vol. 27, No. 2, pp. 105–121 (in Russian), DOI: 10.25743/ICT.2022.27.2.009.
  3. Dmitriev A. V., Chimitdorzhiev T. N., Dobrynin S. I., Khudaiberdieva O. A., Kirbizhekova I. I. (2022b), Optical-microwave diagnostics of agricultural land afforestation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 168–180 (in Russian), DOI: 10.21046/2070-7401-2022-19-4-168-180.
  4. Rodionova N. V., Vakhnina I. L., Zhelibo T. V., Assessment of the Dynamics of Postfire State of Vegetation in Territory Ivan-Arakhley Natural Park (Zabaikalsky Krai) Using Radar and Optical Sentinel 1/2 Data, Issledovanie Zemli iz kosmosa, 2020, No. 3, pp. 14–25 (in Russian), DOI: 10.31857/S0205961420030045.
  5. Khovratovich T. S., Bartalev S. A., Kashnitskii A. V., Forest change detection based on sub-pixel estimation of crown cover density using bitemporal satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 4, pp. 102–110 (in Russian), DOI: 10.21046/2070-7401-2019-16-4-102-110.
  6. Chimitdorzhiev T. N., Efremenko V., On the use of various vegetation indices in remote sensing of ecosystems, Issledovanie Zemli iz kosmosa, 1998, No. 3, pp. 49–56 (in Russian).
  7. Chimitdorzhiev T. N., Dmitriev A. V., Kirbizhekova I. I. et al., Remote optical-microwave measurements of forest parameters: modern state of research and experimental assessment of potentials, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 9–24 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-9-24.
  8. Banda F., Giudici D., Le Toan T. et al., The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation, Remote Sensing, 2020, Vol. 12, No. 6, Article 985, DOI: 10.3390/rs12060985.
  9. Bellassen V., Luyssaert S., Carbon sequestration: Managing forests in uncertain times, Nature, 2014, Vol. 506, No. 7487, pp. 153–155, DOI: 10.1038/506153a.
  10. Bondur V. G., Chimitdorzhiev T. N., Kirbizhekova I. I., Dmitriev A. V., Estimation of Postfire Reforestation with SAR Polarimetry and NDVI Time Series, Forests, 2022, Vol. 13, No. 5, Article 814, DOI: 10.3390/f13050814.
  11. Chazdon R. L., Broadbent E. N., Rozendaal D. M. A. et al., Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics, Science Advances, 2016, Vol. 2, No. 5, p. e1501639, DOI: 10.1126/sciadv.1501639.
  12. Dobson M. C., Ulaby F. T., Le Toan T. et al., Dependence of Radar Backscatter on Coniferous Forest Biomass, IEEE Trans. Geoscience and Remote Sensing, 1992, Vol. 30, No. 2, pp. 412–415, DOI: 10.1109/36.134090.
  13. Gorelick N., Hancher M., Dixon M. et al., Google Earth Engine: Planetary-scale geospatial analysis for everyone, Remote Sensing of Environment, 2017, Vol. 202, pp. 18–27, DOI: 10.1016/j.rse.2017.06.031.
  14. Jiang M., Medlyn B. E., Drake J. E. et al., The fate of carbon in a mature forest under carbon dioxide enrichment, Nature, 2020, Vol. 580, No. 7802, pp. 227–231, DOI: 10.1038/s41586-020-2128-9.
  15. Le Toan T., Beaudoin A., Riom J., Guyon D., Relating Forest Biomass to SAR Data, IEEE Trans. Geoscience and Remote Sensing, 1992, Vol. 30, No. 2, pp. 403–411, DOI: 10.1109/36.134089.
  16. Lehmann E. A., Caccetta P., Lowell K. et al., SAR and optical remote sensing: Assessment of complementarity and interoperability in the context of a large-scale operational forest monitoring system, Remote Sensing of Environment, 2015, Vol. 156, pp. 335–348, DOI: 10.1016/j.rse.2014.09.034.
  17. Pinnington E. M., Casella E., Dance S. L. et al., Understanding the effect of disturbance from selective felling on the carbon dynamics of a managed woodland by combining observations with model predictions, J. Geophysical Research: Biogeosciences, 2017, Vol. 122, No. 4, pp. 886–902, DOI: 10.1002/2017JG003760.
  18. Pugh T. A. M., Lindeskog M., Smith B. et al., Role of forest regrowth in global carbon sink dynamics, Proc. National Academy of Sciences, 2019, Vol. 116, No. 10, pp. 4382–4387, DOI: 10.1073/pnas.1810512116.
  19. Santoro M., Cartus O., Mermoz S. et al., GlobBiomass — global above-ground biomass and growing stock volume datasets, 2018, https://doi.org/10.1594/PANGAEA.894711.
  20. Yadav V. P., Prasad R., Bala R. et al., Appraisal of Dual Polarimetric Radar Vegetation Index in First Order Microwave Scattering Algorithm Using Sentinel-1A (C-Band) and ALOS 2 (L-Band) SAR Data, Geocarto Intern., 2022, Vol. 37, pp. 6232–6250, DOI: 10.1080/10106049.2021.1933209.
  21. Yu Y., Saatchi S., Sensitivity of L-Band SAR Backscatter to Aboveground Biomass of Global Forests, Remote Sensing, 2016, Vol. 8, No. 6, Article 522, DOI: 10.3390/rs8060522.