Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 4, pp. 168-180
Optical-microwave diagnostics of agricultural land afforestation
A.V. Dmitriev
1 , T.N. Chimitdorzhiev
1 , S.I. Dobrynin
1 , O.A. Khudaiberdieva
1 , I.I. Kirbizhekova
1 1 Institute of Physical Materials Science SB RAS, Ulan-Ude, Russia
Accepted: 26.07.2022
DOI: 10.21046/2070-7401-2022-19-4-168-180
A method for comprehensive assessment of pine forest afforestation at abandoned agricultural fields is proposed in the context of clarifying carbon sequestration by Siberian boreal forests. The method is based on the correlation assessment between forest undergrowth biomass and the radar backscattering in L band as well as the analysis of long-term series of vegetation indices during the presence of snow cover. Data from synthetic aperture radars (SAR) ALOS 1, -2/PALSAR 1, -2, as well as 32 day composites of vegetation indices NDVI and EVI, obtained with the help of Google Earth Engine (GEE) cloud platform from multispectral optical images of Landsat-5, -7, -8 satellites were used for the research. Two areas of afforestation were considered for comparative assessment near Lake Baikal, the change of which was tracked using multi-temporal high-resolution data from the Google Earth service. A continuous increase of the radar backscattering from forest young growth for 14–15 years has been shown as a result of the conducted investigations. During this time period the total biomass of undergrowth (trunks and branches) reaches values at which further growth of trees does not affect the level of the radar backscattering, i.e. the «saturation» effect occurs. It is established that in the initial period of growth of young trees, the temporal dynamics of the backscattering intensity on cross-polarization can be described by a linear dependence (the coefficient of determination is greater than 0.9). A certain agreement was found between the dynamics of the EVI index and the radar backscattering intensity for one of the test sites, which is characterized by earlier and uniform afforestation. It is concluded that the proposed approach allows identifying the period of intense growth of forest undergrowth and can be used as a basis for forest classification in determining carbon sequestration.
Keywords: satellite radar, vegetation indices, time series analysis, afforestation
Full textReferences:
- 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.
- Dmitriev A. V., Chimitdorzhiev T. N., Dagurov P. N., 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.
- 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.
- 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).
- Chimitdorzhiev T. N., Dmitriev A. V., Kirbizhekova I. I., Sherhoeva A. A., Baltukhaev A. K., Dagurov P. N., 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.
- Banda F., Giudici D., Le Toan T., Mariotti d’Alessandro M., Papathanassiou K., Quegan S., Riembauer G., Scipal K., Soja M., Tebaldini S., Ulander L., Villard L., The BIOMASS Level 2 Prototype Processor: Design and Experimental Results of Above-Ground Biomass Estimation, Remote Sensing, 2020, Vol. 12, No. 6, Art. No. 985, 28 p., DOI: 10.3390/rs12060985.
- Bellassen V., Luyssaert S., Carbon sequestration: Managing forests in uncertain times, Nature, 2014, Vol. 506, No. 7487, pp. 153–155, DOI: 10.1038/506153a.
- 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, Art. No. 814, 10 p., DOI: 10.3390/f13050814.
- Chazdon R. L., Broadbent E. N., Rozendaal D. M. A., Bongers F., Zambrano A. M. A., Aide T. M., Balvanera P., Becknell J. M., Boukili V., Brancalion P. H. S., Craven D., Almeida-Cortez J. S., Cabral G. A. L., Jong Ben de, Denslow J. S., Dent D. H., DeWalt S. J., Dupuy J. M., Durán S. M., Espírito-Santo M. M., Fandino M. C., César R. G., Hall J. S., Hernández-Stefanoni J. L., Jakovac C. C., Junqueira A. B., Kennard D., Letcher S. G., Lohbeck M., Martínez-Ramos M., Massoca P., Meave J. A., Mesquita R., Mora F., Muñoz R., Muscarella R., Nunes Y. R. F., Ochoa-Gaona S., Orihuela-Belmonte E., Peña-Claros M., Pérez-García E. A., Piotto D., Powers J. S., Rodríguez-Velazquez J., Romero-Pérez I. E., Ruíz J., Saldarriaga J. G., Sanchez-Azofeifa A., Schwartz N. B., Steininger M. K., Swenson N. G., Uriarte M., Breugel M., Wal H., Veloso M. D. M., Vester H., Vieira I. C. G., Bentos T. V., Williamson G. B., Poorter L., Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics, Science Advances, 2016, Vol. 2, No. 5, Art. No. e1501639, 10 p., DOI: 10.1126/sciadv.1501639.
- Dobson M. C., Ulaby F. T., LeToan T., Beaudoin A., Kasischke E. S., Christensen N., 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.
- Gorelick N., Hancher M., Dixon M., Ilyushchenko S., Thau D., Moore R., 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.
- Huffman G. J., Stocker E. F., Bolvin D. T., Nelkin E. J., Tan J., GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree×0.1 degree V06, Greenbelt, MD: Goddard Earth Sciences Data and Information Services Center (GES DISC), 2020, DOI: 10.5067/Gpm/Imerg/3b-Hh/06 (accessed 24.05. 2022).
- Jiang M., Medlyn B. E., Drake J. E., Duursma R. A., Anderson I. C., Barton C. V.M., Boer M. M., Carrillo Y., Castañeda-Gómez L., Collins L., Crou K. Y., De Kauwe M. G., dos Santos B. M., Emmerson K. M., Facey S. L., Gherlenda A. N., Gimeno T. E., Hasegawa S., Johnson S. N., Kännaste A., Macdonald C. A., Mahmud K., Moore B. D., Nazaries L., Neilson E. H. J., Nielsen U. N., Niinemets U., Noh N. J., Ochoa-Hueso R., Pathare V. S., Pendall E., Pihlblad J., Piñeiro J., Powell J. R., Power S. A., Reich P. B., Renchon A. A., Riegler M., Rinnan R., Rymer P. D., Salomón R. L., Singh B. K., Smith B., Tjoelker M. G., Walker J. K. M., Wujeska-Klause A., Yang J., Zaehle S., Ellsworth D. S., 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.
- Kobayashi S., Omura Y., Sanga-Ngoie K., Widyorini R., Kawai S., Supriadi B., Yamaguchi Y., Characteristics of Decomposition Powers of L Band Multi-Polarimetric SAR in Assessing Tree Growth of Industrial Plantation Forests in the Tropics, Remote Sensing, 2012, Vol. 4, No. 10, pp. 3058–3077, DOI: 10.3390/rs4103058.
- 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.
- Lehmann E. A., Caccetta P., Lowell K., Mitchell A., Zhou Z.-S., Held A., Milne T., Tapley I., 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.
- Pinnington E. M., Casella E., Dance S. L., Lawless A. S., Morison J. I., Nichols N. K., Wilkinson M., Quaife T. L., 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.
- Pugh T. A. M., Lindeskog M., Smith B., Poulter B., Arneth A., Haverd V., Calle L., Role of forest regrowth in global carbon sink dynamics, Proceedings of the National Academy of Sciences, 2019, Vol. 116, No. 10, pp. 4382–4387, DOI: 10.1073/pnas.1810512116.
- Rees W. G., Tomaney J., Tutubalina O., Zharko V., Bartalev S., Estimation of Boreal Forest Growing Stock Volume in Russia from Sentinel-2 MSI and Land Cover Classification, Remote Sensing, 2021, Vol. 13, No. 21, Art. No. 4483, DOI: 10.3390/rs13214483.
- Santoro M., Cartus O., Mermoz S., Bouvet A., Le Toan T., Carvalhais N., Rozendaal D., Herold M., Avitabile V., Quegan S., Carreiras J., Rauste Y., Balzter H., Schmullius C., Seifert F. M., GlobBiomass — global above-ground biomass and growing stock volume datasets, 2018, available at http://globbiomass.org/products/global-mapping/ (accessed 24.05. 2022).
- Small D., Flattening Gamma: Radiometric Terrain Correction for SAR Imagery, IEEE Trans. Geoscience and Remote Sensing, 2011, Vol. 49, No. 8, pp. 3081–3093, DOI: 10.1109/TGRS.2011.2120616.
- Yu Y., Saatchi S., Sensitivity of L Band SAR Backscatter to Aboveground Biomass of Global Forests, Remote Sensing, 2016, Vol. 8, No. 6, Art. No. 522, DOI: 10.3390/rs8060522.