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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. 6, pp. 9-34

Application of remote sensing data for wetlands large-scale monitoring

S.S. Shinkarenko 1 , S.А. Bartalev 1 
1 Space Research Institute RAS, Moscow, Russia
Accepted: 16.10.2023
DOI: 10.21046/2070-7401-2023-20-6-9-34
The present review examines existing technologies for mapping wetland ecosystems (WE) based on remote sensing data. WEs are valuable ecosystems with significant conservation importance. There are numerous classifications of WEs, encompassing dozens of types. However, existing global or national-level WE maps typically do not consider their landscape specificity and are limited to only a few classes. Peatlands, swamp forests, high-productivity meadows, and riparian vegetation formations store substantial carbon reserves, which are released due to fires. The intensity of these fires has been increasing in recent years as a result of climate change. These issues necessitate the development of new large-scale monitoring methods for assessing the state of WEs, including mapping their types, determining biomass and carbon stocks, assessing the consequences of landscape fires, and evaluating greenhouse gas emissions and other combustion byproducts. First and foremost, it is necessary to develop a classification system for Russia’s WEs that considers their landscape diversity while being sufficiently generalized for satellite monitoring and annual updating of WE maps. Various remote sensing data types, including aerial imagery, lidar, and radar data, are used for WE mapping. The most promising direction for the development of WE monitoring technologies at the national level involves the use of long-term homogeneous time series data from MODIS (Moderate Resolution Imaging Spectroradiometer) and VIIRS (Visible Infrared Imaging Radiometer Suite), in combination with ground-based calibration measurements and high-resolution satellite optical and radar data.
Keywords: wetlands, remote sensing, landscape fires, biomass, mapping
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References:

  1. Barmin A. N., Golub V. B., Instructive lesson of results of reed thickets operation in the Volga river delta, Izvestiya Samarskogo nauchnogo tsentra RAN, 2000, Vol. 2, No. 2, pp. 295–299 (in Russian).
  2. Bartalev S. A., Egorov V. A., Zharko V. O., Loupian E. A., Plotnikov D. E., Khvostikov S. A., Shabanov N. V., Sputnikovoe kartografirovanie rastitel’nogo pokrova Rossii (Land cover mapping over Russia using Earth observation data), Moscow: IKI RAN, 2016, 208 p. (in Russian).
  3. Bartalev S. A., Stytsenko F. B., Khvostikov S. A., Loupian E. A., Methodology of post-fire tree mortality monitoring and prediction using remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2017, Vol. 14, No. 6, pp. 176–193 (in Russian), DOI: 10.21046/2070-7401-2017-14-6-176-193.
  4. Bartalev S. A., Bogodukhov M. A., Zharko V. O., Sidorenkov V. M., Investigation of ICESat-2 data capabilities for forest height estimation over Russia, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 5, pp. 195–206 (in Russian), DOI: 10.21046/2070-7401-2022-19-4-195-206.
  5. Berdengalieva A. N., Analysis of the lower Volga floodplain landscapes burning according to active fire and burnt areas satellite data, InterCarto. InterGIS, 2022, Vol. 28, No. 1, pp. 346–358 (in Russian), DOI: 10.35595/2414-9179-2022-1-28-346-358.
  6. Vomperskii S. E., Sirin A. A., Tsyganova O. P., Valyaeva N. A., Maikov D. A., Peatlands and paludified lands of Russia: attempt of analyses of spatial distribution and diversity, Izvestiya Rossiiskoi akademii nauk. Ser. Geograficheskaya, 2005, No. 5, pp. 39–50 (in Russian).
  7. Efremov S. P., Efremova T. T., Melent’eva N. V., Carbon stocks in wetland ecosystems, Uglerod v ekosistemakh lesov i bolot Rossii, Krasnoyarsk: VTs SO RAN, 1994, pp. 128–139 (in Russian).
  8. Krivenko V. G., Vinogradov V. G., Introduction, Wetlands in Russia, Volume 3. Wetlands on the Ramsar Shadow List, Wetlands International, Krivenko V. G. (ed.), Moscow: Domino, 2000, pp. 11–21 (in Russian).
  9. Kudeyarov V. N., Zavarzin G. A., Blagodatsky S. A., Borisov A. V., Voronin P. Yu., Demkin V. A., Demkina T. S., Evdokimov I. V., Zamolodchikov D. G., Karelin D. V., Komarov A. S., Kurganova I. N., Larionova A. A., Lopez de Gerenyu V. O., Utkin A. I., Chertov O. G., Puly i potoki ugleroda v nazemnykh ekosistemakh Rossii (Carbon Pools and Fluxes in Terrestrial Ecosystems of Russia), 2007, Moscow: Nauka, 315 p. (in Russian).
  10. Medvedev A. A., Tel’nova N. O., Kudikov A. V., Alekseenko N. A., Use of photogrammetric point clouds for the analysis and mapping of structural variables in sparse northern boreal forests, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 1, pp. 150–163 (in Russian), DOI: 10.21046/2070-7401-2020-17-1-150-163.
  11. Medvedeva M. A., Vozbrannaya A. E., Sirin A. A., Maslov A. A., Potential of different multispectral satellite data for monitoring abandoned fire hazardous peatlands and rewetting effectiveness, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 150–159 (in Russian), DOI: 10.21046/2070-7401-2019-16-2-150-159.
  12. Medvedeva M. A., Makarov D. A., Sirin A. A., Applicability of different spectral indexes based on satellite data for peat fire area estimation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 5, pp. 157–166 (in Russian), DOI: 10.21046/2070-7401-2020-17-5-157-166.
  13. Miklashevich T. S., Bartalev S. A., Plotnikov D. E., Interpolation algorithm for the recovery of long satellite data time series of vegetation cover observation, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 6, pp. 143–154 (in Russian), DOI: 10.21046/2070-7401-2019-16-6-143-154.
  14. Romanova E. A., Bybina R. T., Golitsina E. F., Ivanova G. M., Usova L. I., Trushnikova L. G., Tipologicheskaya karta bolot Zapadno-Sibirskoi ravniny (Typological map of swamps of the West Siberian Plain), Leningrad: GUGK, 1977, 500 p. (in Russian).
  15. Sabrekov A. F., Filippov I. V., Glagolev M. V. et al., Methane emission from West Siberian forest-steppe and subtaiga reed fens, Russian Meteorology and Hydrology, 2016, Vol. 41, No. 1, pp. 37–42, DOI: 10.3103/S1068373916010052.
  16. Sirin A. A., Maslov A. A., Valyaeva N. A., Tsyganova O. P., Glukhova T. V., Mapping of peatlands in the Moscow Oblast based on high-resolution remote sensing data, Contemporary Problems of Ecology, 2014, No. 5, pp. 65–71, DOI: 10.1134/S1995425514070117.
  17. Terent’eva I. E., Filippov I. V., Sabrekov A. F. et al., Western Siberia’s taiga wetlands mapping based on remote sensing data, Izvestiya Rossiiskoi akademii nauk, Seriya geograficheskaya, 2020, Vol. 84, No. 6, pp. 920–930 (in Russian), DOI: 10.31857/S2587556620060102.
  18. Shinkarenko S. S., Vasil’chenko A. A., Actual state of the lower don spawning grounds according to remote sensing data, Vestnik Moskovskogo universiteta. Seriya 5: Geografiya, 2023, No. 1, pp. 16–27 (in Russian), DOI: 10.55959/MSU0579-9414-5-2023-1-16-27.
  19. Shinkarenko S. S., Bartalev S. A., Berdengalieva A. N., Vypritskii A. A., Dynamics of water bodies areas in the Western Ilmen Lake Region of the Volga Delta, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 285–290 (in Russian), DOI: 10.21046/2070-7401-2021-18-4-285-290.
  20. Shinkarenko S. S., Bartalev S. A., Berdengalieva A. N., Ivanov N. M., Spatio-temporal analysis of burnt area in The Lower Volga floodplain, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 1, pp. 143–157 (in Russian), DOI: 10.21046/2070-7401-2022-19-1-143-157.
  21. Shinkarenko S. S., Bartalev S. A., Bogodukhov M. A. et al., The Lower Volga floodplain classification based on long-term hydrological and remote sensing data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2023, Vol. 20, No. 3, pp. 119–135 (in Russian), DOI: 10.21046/2070-7401-2023-20-3-119-135
  22. Adeli S., Salegi B., Mahdianpari M. et al., Wetland Monitoring Using SAR Data: A Meta-Analysis and Comprehensive Review, Remote Sensing, 2020, Vol. 12, Article 2190, DOI: 10.3390/rs12142190.
  23. Allen T. R., Wang Y., Gore B., Coastal wetland mapping combining multi-date SAR and LiDAR, Geocarto Intern., 2013, Vol. 28, pp. 616–631, DOI: 10.1080/10106049.2013.768297.
  24. Arino O., Ramos P., Jose J. et al., Global Land Cover Map for 2009 (GlobCover 2009), European Space Agency, Université Catholique de Louvain (UCL), PANGAEA, 2012, DOI: 10.1594/PANGAEA.787668.
  25. Aslan A. M., Rahman A. F., Warren M. W., Robenson S. M., Mapping spatial distribution and biomass of coastal wetland vegetation in Indonesian Papua by combining active and passive remotely sensed data, Remote Sensing of Environment, 2016, Vol. 183, pp. 65–81, DOI: 10.1016/j.rse.2016.04.026.
  26. Baier S., Corti Meneses N., Geist J., Schneider T., Assessment of Aquatic Reed Stands from Airborne Photogrammetric 3K Data, Remote Sensing, 2022, Vol. 14, Article 337, DOI: 10.3390/rs14020337.
  27. Baird A., Belyea L., Comas X., Reeve A. S., Slater L. D., Carbon Cycling in Northern Peatlands, Geophysical Monograph Ser., AGU, 2013, Vol. 184, 297 p.
  28. Bartalev S. A., Belward A. S., Erchov D. V., Isaev A. S., A new SPOT4-VEGETATION derived land cover map of Northern Eurasia, Intern. J. Remote Sensing, 2003, Vol. 24, No. 9, pp. 1977–1982.
  29. Bartholome E., Belward A., Frederic A., Bartalev S., Carmona-Moreno C., Eva H., Fritz S., Gregoire J. M., Mayaux P., Stibig H.-J. E. E., GLC 2000: Global Land Cover Mapping for the Year 2000: Project Status 2002, 2002, 67 p.
  30. Bontemps S., Boettcher M., Brockmann C., Kirches G., Lamarche C., Radoux J., Santoro M., Vanbogaert E., Wegmuller U., Herold M., Achard F., Ramoino F., Arino O., Defourny P., Multi-year global land cover mapping at 300 m and characterization for climate modelling: achievements of the Land Cover component of the ESA Climate Change Initiative, Intern. Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015, Vol. XL-7/W3, pp. 323–328.
  31. Chasmer L., Cobbaert D., Mahoney C. et al. (2020a), Remote Sensing of Boreal Wetlands 1: Data Use for Policy and Management, Remote Sensing, 2020, Vol. 12, No. 8, Article 1320, DOI: 10.3390/rs12081320.
  32. Chasmer L., Mahoney C., Millard K. et al. (2020b), Remote Sensing of Boreal Wetlands 2: Methods for Evaluating Boreal Wetland Ecosystem State and Drivers of Change, Remote Sensing, 2020, Vol. 12, No. 8, Article 1321, DOI: 10.3390/rs12081321.
  33. Chen C., Ren G., Wang J., Aboveground biomass of salt-marsh vegetation in coastal wetlands: Sample expansion of in situ hyperspectral and Sentinel-2 data using a generative adversarial network, Remote Sensing of Environment, 2022, Vol. 270, Article 112885, DOI: 10.1016/j.rse.2021.112885.
  34. Chen J., Ban Y., Li S., China: Open access to Earth land-cover map,  Nature, 2014, Vol. 514, No. 7523, Article 434, DOI: 10.1038/514434c.
  35. Chen Y., Huang C., Ticehurst C. et al., An evaluation of MODIS daily and 8-day composite products for floodplain and wetland inundation mapping, Wetlands, 2013, Vol. 33, pp. 823–835, DOI: 10.1007/s13157-013-0439-4.
  36. Chuvieco E., Pettinari M. L., Lizundia-Loiola J., Storm T., Padilla Parellada M., ESA Fire Climate Change Initiative (Fire_cci): MODIS Fire_cci Burned Area Pixel product, version 5.1, Centre for Environmental Data Analysis, 2018, DOI: 10.5285/58f00d8814064b79a0c49662ad3af537.
  37. Corti Meneses N., Brunner F., Baier S. et al., Quantification of Extent, Density, and Status of Aquatic Reed Beds Using Point Clouds Derived from UAV–RGB Imagery, Remote Sensing, 2018, Vol. 10, No. 12, Article 1869, DOI: 10.3390/rs10121869.
  38. Ding Y., Yang X., Wang Z. et al., A Field-Data-Aided Comparison of Three 10 m Land Cover Products in Southeast Asia, Remote Sensing, 2022, Vol. 14, No. 19, Article 5053, DOI: 10.3390/rs14195053.
  39. Doughty C. L., Cavanaugh K. C., Mapping Coastal Wetland Biomass from High Resolution Unmanned Aerial Vehicle (UAV) Imagery, Remote Sensing, 2019, Vol. 11, Article 540, DOI: 10.3390/rs11050540.
  40. Doughty C. L., Ambrose R. F., Okin G. S., Cavanaugh, K. C., Characterizing spatial variability in coastal wetland biomass across multiple scales using UAV and satellite imagery, Remote Sensing in Ecology and Conservation, 2021, Vol. 7, pp. 411–429, DOI: 10.1002/rse2.198.
  41. Dronova I., Object-Based Image Analysis in Wetland Research: A Review, Remote Sensing, 2015, Vol. 7, No. 5, pp. 6380–6413, DOI: 10.3390/rs70506380.
  42. Dronova I., Kislik C., Dinh Z., Kelly M., A Review of Unoccupied Aerial Vehicle Use in Wetland Applications: Emerging Opportunities in Approach, Technology, and Data, Drones, 2021, Vol. 5, No. 2, Article 45, DOI: 10.3390/drones5020045.
  43. Dugan P., Wetlands in Danger: A World Conservation Atlas, N. Y., USA: Oxford University Press, 1993, 192 p.
  44. Dugdale S. J., Malcolm I. A., Hannah D. M., Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models, Science of the Total Environment, 2019, Vol. 678, pp. 326–340, DOI: 10.1016/j.scitotenv.2019.04.229.
  45. Dutta D., Das P. K., Paul S., Sharma J. R., Dadhwal V. K., Assessment of ecological disturbance in the mangrove forest of Sundarbans caused by cyclones using MODIS time-series data (2001–2011), Natural Hazards, 2015, Vol. 79, pp. 775–790, DOI: 10.1007/s11069-015-1872-x.
  46. Dyukarev E. A., Alekseeva M. N., Golovatskaya E. A., Study of Wetland Ecosystem Vegetation Using Satellite Data, Izvestiya, Atmospheric and Oceanic Physics, 2017, Vol. 53, pp. 1029–1041, DOI: 10.1134/S0001433817090092.
  47. Fatoyinbo T. E., Marc S.,Washington-Allen R. A., Shugart H. H., Landscape-scale extent, height, biomass, and carbon estimation of Mozambique’s mangrove forests with Landsat ETM+ and Shuttle Radar Topography Mission elevation data, J. Geophysical Research: Biogeosciences, 2008, Vol. 113, pp. 1–13, DOI: 10.1029/2007JG000551.
  48. Fernandes M. R., Aguiar F. C., Martins M. J. et al., Carbon Stock Estimations in a Mediterranean Riparian Forest: A Case Study Combining Field Data and UAV Imagery, Forests, 2020, Vol. 11, Article 376, DOI: 10.3390/f11040376.
  49. Finlayson C. M., Spiers N. C., Global Review of Wetland Resources and Priorities for Wetland Inventory, Supervising Scientist Report 144, Canberra, Australia: Supervising Scientist, 1999, 524 p.
  50. Flores-de-Santiago F., Valderrama-Landeros L., Rodríguez-Sobreyra R., Flores-Verdugo F., Assessing the Effect of Flight Altitude and Overlap on Orthoimage Generation for UAV Estimates of Coastal Wetlands, J. Coastal Conservation, 2020, Vol. 24, Article 35, DOI: 10.1007/s11852-020-00753-9.
  51. Fournier R. A., Grenier M., Lavoie A., Hélie R., Towards a strategy to implement the Canadian Wetland Inventory using satellite remote sensing, Canadian J. Remote Sensing, 2007, Vol. 33, Article 16, DOI: 10.5589/m07-051.
  52. Friedl M. A., McIver D. K., Hodges J. C. F. et al., Global land cover mapping from MODIS: Algorithms and early results, Remote Sensing of Environment, 2002, Vol. 83, pp. 287–302, DOI: 10.1016/S0034-4257(02)00078-0.
  53. Giglio L., Justice C., Boschetti L., Roy D., MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC, 2015, DOI: 10.5067/MODIS/MCD64A1.006.
  54. Giglio L., Boschetti L., David P. R., Humber M. L. Justice C. O., The Collection 6 MODIS burned area mapping algorithm and product, Remote Sensing of Environment, 2018, Vol. 217, pp. 72–85, DOI: 10.1016/j.rse.2018.08.005.
  55. Gong P., Liu H., Zhang M. et al., Stable classification with limited sample: transferring a 30-m resolution sample set collected in 2015 to mapping 10-m resolution global land cover in 2017, Science Bull., 2019, Vol. 64, Issue 6, pp. 370–373, DOI: 10.1016/j.scib.2019.03.002.
  56. Gray P., Ridge J., Poulin S. et al., Integrating drone imagery into high resolution satellite remote sensing assessments of estuarine environments, Remote Sensing, 2018, Vol. 10, Article 1257, DOI: 10.3390/rs10081257.
  57. Guo M., Li J., Wen L., Huang S., Estimation of CO2 Emissions from Wildfires Using OCO-2 Data, Atmosphere, 2019, Vol. 10, Article 581, DOI: 10.3390/atmos10100581.
  58. Hayasaka H., Sokolova G. V., Ostroukhov A., Naito D., Classification of Active Fires and Weather Conditions in the Lower Amur River Basin, Remote Sensing, 2020, Vol. 12, Article 3204, DOI: 10.3390/rs12193204.
  59. Henderson F. M., Lewis A. J., Radar detection of wetland ecosystems: a review, Intern. J. Remote Sensing, 2015, Vol. 29, No. 20, pp. 5809–5835, DOI: 10.1080/01431160801958405.
  60. Hong S.-H., Wdowinski S., Multitemporal Multitrack Monitoring of Wetland Water Levels in the Florida Everglades Using ALOS PALSAR Data With Interferometric Processing, IEEE Geoscience and Remote Sensing Letters, 2014, Vol. 11, pp. 1355–1359, DOI: 10.1109/LGRS.2013.2293492.
  61. Huang C., Peng Y., Lang M., Yeo I. Y., Mccarty G., Wetland inundation mapping and change monitoring using Landsat and airborne LiDAR data, Remote Sensing of Environment, 2014, Vol. 141, pp. 231–242, DOI: 10.1016/j.rse.2013.10.020.
  62. Karra K., Kontgis C, Statman-Weil Z. et al., Global land use/land cover with Sentinel 2 and deep learning, 2021 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), 2021, pp. 4704–4707, DOI: 10.1109/IGARSS47720.2021.9553499.
  63. Kuzmina Zh. V., Treshkin S. E., Shinkarenko S. S., Effects of River Control and Climate Changes on the Dynamics of the Terrestrial Ecosystems of the Lower Volga Region,  Arid Ecosystems, 2018, Vol. 8, No. 4, pp. 231–244, DOI: 10.1134/S2079096118040066.
  64. Kuzmina Zh. V., Shinkarenko S. S., Solodovnikov D. A., Main Tendencies in the Dynamics of Floodplain Ecosystems and Landscapes of the Lower Reaches of the Syr Darya River under Modern Changing Conditions, Arid Ecosystems, 2019, Vol. 9, No. 4, pp. 226–236, DOI: 10.1134/S207909611904005X.
  65. Kuzmina Zh. V., Shinkarenko S. S., Solodovnikov D. A., Markov M. L., The Effects of River Control and Climatic and Hydrological Changes on the State of Floodplain and Delta Ecosystems of the Lower Don, Arid Ecosystems, 2022, Vol. 12, No. 4, pp. 361–373, DOI: 10.1134/S2079096122040126.
  66. Lee H., Yuan T., Jung H. C., Beighley E., Mapping wetland water depths over the central Congo Basin using PALSAR ScanSAR, Envisat altimetry, and MODIS VCF data, Remote Sensing of Environment, 2014, Vol. 159, pp. 70–79, DOI: 10.1016/j.rse.2014.11.030.
  67. Lee S.-K., Fatoyinbo T. E., TanDEM-X Pol-InSAR Inversion for Mangrove Canopy Height Estimation, IEEE J. Selected Topics in Applied Earth Observations and Remote Sensing, 2015, Vol. 8, pp. 3608–3618, DOI: 10.1109/JSTARS.2015.2431646.
  68. Lee Y.-K., Park J.-W., Choi J.-K. et al., Potential uses of TerraSAR-X for mapping herbaceous halophytes over salt marsh and tidal flats, Estuarine, Coastal and Shelf Science, 2012, Vol. 115, pp. 366–376, DOI: 10.1016/j.ecss.2012.10.003.
  69. Lehner B., Doll P., Development and validation of a global database of lakes, reservoirs and wetlands, J. Hydrology, 2004, Vol. 296, Issue 1, No. 4, pp. 1–22, DOI: 10.1016/j.jhydrol.2004.03.028.
  70. Li C., Zhou L., Xu W., Estimating Aboveground Biomass Using Sentinel-2 MSI Data and Ensemble Algorithms for Grassland in the Shengjin Lake Wetland, China, Remote Sensing, 2021, Vol. 13, Article 1595, DOI: 10.3390/rs13081595.
  71. Li X., Xiao J., A Global 0.05-Degree Product of Solar-Induced Chlorophyll Fluorescence Derived from OCO-2, MODIS, and Reanalysis Data, Remote Sensing, 2019, Vol. 11, Article 517.
  72. Liu T., Abd-Elrahman A., Dewitt B. et al., Evaluating the Potential of Multi-View Data Extraction from Small Unmanned Aerial Systems (UASs) for Object-Based Classification for Wetland Land Covers, GIScience and Remote Sensing, 2019, Vol. 56, pp. 130–159, DOI: 10.1080/15481603.2018.1495395.
  73. Liu Y., Gong W., Xing Y. et al., Estimation of the forest stand mean height and aboveground biomass in Northeast China using SAR Sentinel-1B, multispectral Sentinel-2A, and DEM imagery, ISPRS J. Photogrammetry and Remote Sensing, 2019, Vol. 151, pp. 277–289, DOI: 10.1016/j.isprsjprs.2019.03.016.
  74. Long T., Zhang Z., He G. et al., 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine, Remote Sensing, 2019, No. 11, Article 489, DOI: 10.3390/rs11050489.
  75. Loveland T. R., Belward A. S., The IGBP-DIS global 1km land cover data set, DISCover: First results, Intern. J. Remote Sensing, 1997, Vol. 18, No. 15, pp. 3289–3295, DOI: 10.1080/014311697217099.
  76. Loveland T. R., Merchant J. W., Ohlen D. O., Brown J. F., Development of a Land-Cover Characteristics Database for the Conterminous U. S., Photogrammetric Engineering Remote Sensing, 1991, Vol. 57, pp. 1453–1463.
  77. Lu L., Luo J., Xin Y. et al., How can UAV contribute in satellite-based Phragmites australis aboveground biomass estimating? Intern. J. Applied Earth Observations and Geoinformation, 2022, Vol. 114, Article 103024, DOI: 10.1016/j.jag.2022.103024.
  78. Lucas R. M., Mitchell A. L., Rosenqvist A. et al., The potential of L-band SAR for quantifying mangrove characteristics and change: Case studies from the tropics, Aquatic Conservation Marine and Freshwater Ecosystems, 2007, Vol. 17, pp. 245–264, DOI: 10.1002/aqc.833.
  79. Lumbierres M., Méndez P. F., Bustamante J. et al., Modeling Biomass Production in Seasonal Wetlands Using MODIS NDVI Land Surface Phenology, Remote Sensing, 2017, Vol. 9, Article 392, DOI: 10.3390/rs9040392.
  80. Magagi R., Bernier M., Ung C. H., Quantitative analysis of RADARSAT SAR data over a sparse forest canopy, IEEE Trans. Geoscience and Remote Sensing, 2002, Vol. 40, pp. 1301–1313, DOI: 10.1109/TGRS.2002.800235.
  81. Mahdavi S., Salehi B., Granger J. et al., Remote sensing for wetland classification: a comprehensive review, GIScience and Remote Sensing, 2018, Vol. 55, No. 5, pp. 623–658, DOI: 10.1080/15481603.2017.1419602.
  82. Mahdianpari M., Granger J. E., Mohammadimanesh F. et al., Meta-Analysis of Wetland Classification Using Remote Sensing: A Systematic Review of a 40-Year Trend in North America, Remote Sensing, 2020, Vol. 12, No. 11, Article 1882, DOI: 10.3390/rs12111882.
  83. Mayaux P., Bartholome E., Massart M., van Cutsem C., Cabral A., Nonguierma A., Diallo O., Pretorius C., Thompson M., Cherlet M., A Land Cover Map of Africa, European Communities: Luxembourg, 2003.
  84. Mirmazloumi S. M., Moghimi A., Ranjgar B. et al., Status and Trends of Wetland Studies in Canada Using Remote Sensing Technology with a Focus on Wetland Classification: A Bibliographic Analysis, Remote Sensing, 2021, Vol. 13, No. 20, Article 4025, DOI: 10.3390/rs13204025.
  85. Mitsch W. J., Gosselink J. G., Wetlands, 4th ed., N. Y., USA: Wiley, 2007, 600 p.
  86. Mizuochi H., Hiyama T., Ohta T., Nasahara K., Evaluation of the surface water distribution in North-Central Namibia based on MODIS and AMSR series, Remote Sensing, 2014, Vol. 6, pp. 7660–7682, DOI: 10.3390/rs6087660.
  87. Moreau S., Bosseno R., Gu X. F., Baret F., Assessing the biomass dynamics of Andean bofedal and totora high-protein wetland grasses from NOAA/AVHRR, Remote Sensing of Environment, 2003, Vol. 85, pp. 516–529, DOI: 10.1016/S0034-4257(03)00053-1.
  88. Morgan G. R., Wang C., Morris J. T., RGB Indices and Canopy Height Modelling for Mapping Tidal Marsh Biomass from a Small Unmanned Aerial System, Remote Sensing, 2021, Vol. 13, Article 3406, DOI: 10.3390/rs13173406.
  89. Navarro A., Young M., Allan B. et al., The application of Unmanned Aerial Vehicles (UAVs) to estimate aboveground biomass of mangrove ecosystems, Remote Sensing of Environment, 2020, Vol. 242, Article 111747, DOI: 10.1016/j.rse.2020.111747.
  90. Niu B., He Y., Zhang X. et al., Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau, Remote Sensing, 2016, Vol. 8, Article 592, DOI: 10.3390/rs8070592.
  91. O’Connell J. L., Byrd K. B., Kelly M., A Hybrid Model for Mapping Relative Differences in Belowground Biomass and Root: Shoot Ratios Using Spectral Reflectance, Foliar N and Plant Biophysical Data within Coastal Marsh, Remote Sensing, 2015, Vol. 7, pp. 16480–16503, DOI: 10.3390/rs71215837.
  92. Ordoyne C., Friedl M. A., Using MODIS data to characterize seasonal inundation patterns in the Florida Everglades, Remote Sensing of Environment, 2008, Vol. 112, pp. 4107–4119, DOI: 10.1016/j.rse.2007.08.027.
  93. Ostroukhov A., Klimina E., Kuptsova V., Naito D., Estimating Long-Term Average Carbon Emissions from Fires in Non-Forest Ecosystems in the Temperate Belt, Remote Sensing, 2022, Vol. 14, No. 5, Article 1197, DOI: 10.3390/rs14051197.
  94. Page S. E., Siegert F., Rieley J. O. et al., The amount of carbon released from peat and forest fires in Indonesia during 1997, Nature, 2002, Vol. 420, pp. 61–65, DOI: 10.1038/nature01131.
  95. Pashaei M., Kamangir H., Starek M. J., Tissot P., Review and Evaluation of Deep Learning Architectures for Efficient Land Cover Mapping with UAS Hyper-Spatial Imagery: A Case Study Over a Wetland, Remote Sensing, 2020, Vol. 12, Article 959, DOI: 10.3390/rs12060959.
  96. Peregon A., Maksyutov S., Kosykh N. P., Mironycheva-Tokareva N. P., Map-based inventory of wetland biomass and Net Primary Production in Western Siberia, J. Geophysical Research: Biogeosciences, 2008, Vol. 113, pp. 168–182, DOI: 10.3390/rs12060959.
  97. Pereira L. O., Furtado L. F. A., Novo E. M. L. M. et al., Multifrequency and Full-Polarimetric SAR Assessment for Estimating Above Ground Biomass and Leaf Area Index in the Amazon Várzea Wetlands, Remote Sensing, 2018, Vol. 10, Article 1355, DOI: 10.3390/rs10091355.
  98. Plank S., Jüssi M., Martinis S., Twele A., Mapping of flooded vegetation by means of polarimetric Sentinel-1 and ALOS-2/PALSAR-2 imagery, Intern. J. Remote Sensing, 2017, Vol. 38, pp. 3831–3850, DOI: 10.1080/01431161.2017.1306143.
  99. Poulin B., Davranche A., Lefebvre G., Ecological assessment of Phragmites australis wetlands using multi-season SPOT-5 scenes, Remote Sensing of Environment, 2010, Vol. 114, pp. 1602–1609, DOI: 10.1016/j.rse.2010.02.014.
  100. Poulter B., Christensen N. L., Halpin P. N., Carbon emissions from a temperate peat fire and its relevance to interannual variability of trace atmospheric greenhouse gases, J. Geophysical Research: Atmospheres, 2006, Vol. 111, pp. 907–923.
  101. Qiu R., Han G., Ma X. et al., A Comparison of OCO-2 SIF, MODIS GPP, and GOSIF Data from Gross Primary Production (GPP) Estimation and Seasonal Cycles in North America, Remote Sensing, 2020, Vol. 12, Article 258, DOI: 10.3390/rs12020258.
  102. Rapinel S., Hubert-Moy L., Clement B., Combined use of LiDAR data and multispectral earth observation imagery for wetland habitat mapping, Intern. J. Applied Earth Observation and Geoinformation, 2015, Vol. 37, pp. 56–64, DOI: 10.1016/j.jag.2014.09.002.
  103. Rappold A. G., Stone S. L., Cascio W. E. et al., Peat bog wildfire smoke exposure in rural north Carolina is associated with cardiopulmonary emergency department visits assessed through syndromic surveillance, Environmental Health Perspectives, 2011, Vol. 119, pp. 1415–1420, DOI: 10.1289/ehp.1003206.
  104. Rocha A. V., Goulden M. L., Why Is Marsh Productivity so High? New Insights from Eddy Covariance and Biomass Measurements in a Typha Marsh, Agricultural and Forest Meteorolog, 2009, Vol. 149, pp. 159–168, DOI: 10.1016/j.agrformet.2008.07.010.
  105. Santoro M., Beer C., Cartus O., Schmullius C., Shvidenko A., McCallum I., Wegmueller U., Wiesmann  A., The BIOMASAR algorithm: An approach for retrieval of forest growing stock volume using stacks of multi-temporal SAR data, Proc. ESA Living Planet Symp., 28 June – 2 July 2010, 2010, Vol. 28.
  106. Shang W., Gao Z., Jiang X., Chen M., The Extraction of Wetland Vegetation Information Based on UAV Remote Sensing Images, Proc. Remote Sensing and Modeling of Ecosystems for Sustainability XV, Gao W., Chang N.-B., Wang J. (eds.), San Diego, CA, USA: SPIE, 2018, 38 p.
  107. Sheng Y., Smith L. C., MacDonald G. M., A high-resolution GIS-based inventory of the west Siberian peat carbonpool, Global Biogeochemical. Cycles, 2004, Vol. 18, Article GB3004, DOI: 10.1029/2003GB002190.
  108. Shvidenko A., Schepaschenko D., Vaganov E. et al., Impacts of vegetation fire in Russian territories on ecosystems and global carbon budget in 1998–2010, Doklady Earth Science, 2011, Vol. 441, pp. 1678–1682, DOI: 10.1134/S1028334X11120075.
  109. Sirin A., Medvedeva M., Remote Sensing Mapping of Peat-Fire-Burnt Areas: Identification among Other Wildfires, Remote Sensing, 2022, Vol. 14, Article 194, DOI: 10.3390/rs14010194.
  110. Solodovnikov D. A., Shinkarenko S. S., Present-Day Hydrological and Hydrogeological Regularities in the Formation of River Floodplains in the Middle Don Basin,  Water Resources, 2020, Vol. 47, No. 6, pp. 719–728, DOI: 10.1134/S0097807820060135.
  111. Stone T. A., Schlesinger P., Houghton R. A., Woodwell G. M., A Map of the Vegetation of South America Based on Satellite Imagery, Photogrammetric Engineering and Remote Sensing, 1994, Vol. 60, pp. 541–551.
  112. Tan Q., Shao Y., Yang S., Wei Q., Wetland vegetation biomass estimation using Landsat-7 ETM+data, Proc. 2003 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS’03), Toulouse, France, 21–25 July 2003, 2003, pp. 2629–2631.
  113. Tao J., Mishra D. R., Cotten D. L. et al., A Comparison between the MODIS Product (MOD17A2) and a Tide-Robust Empirical GPP Model Evaluated in a Georgia Wetland, Remote Sensing, 2018, Vol. 10, Article 1831, DOI: 10.3390/rs10111831.
  114. Terent’eva I. E., Sabrekov A. F., Glagolev M. V. et al., A new map of wetlands in the southern taiga of the West Siberia for assessing the emission of methane and carbon dioxide, Water Resources, 2017, Vol. 44, No. 2, pp. 297–307, DOI: 10.1134/S0097807817020154.
  115. Toriyama J., Takahashi T., Nishimura S. et al., Estimation of fuel mass and its loss during a forest fire in peat swamp forests of central Kalimantan, Indonesia, Forest Ecology and Management, 2014, Vol. 314, pp. 1–8, DOI: 10.1016/j.foreco.2013.11.034.
  116. Townsend P. A., Estimating forest structure in wetlands using multitemporal SAR, Remote Sensing of Environment, 2002, Vol. 79, pp. 288–304, DOI: 10.1016/S0034-4257(01)00280-2.
  117. Tuanmu M. N., Jetz W., A global 1-km consensus land-cover product for biodiversity and ecosystem modeling, Global Ecology and Biogeography, 2014, Vol. 23, No. 9, pp. 1031–1045, DOI: 10.1111/geb.12182/abstract.
  118. Wang D., Wan B., Liu J. et al., Estimating aboveground biomass of the mangrove forests on northeast Hainan Island in China using an upscaling method from field plots, UAV-LiDAR data and Sentinel-2 imagery, Intern. J. Applied Earth Observation and Geoinformation, 2020, Vol. 85, Article 101986, DOI: 10.1016/j.jag.2019.101986.
  119. White L., Brisco B., Dabboor M. et al., A Collection of SAR Methodologies for Monitoring Wetlands, Remote Sensing, 2015, Vol. 7, No. 6, pp. 7615–7645, DOI: 10.3390/rs70607615.
  120. White M. A., Thornton P. E., Running S. W., Nemani R. R., Parameterization and sensitivity analysis of the BIOME-BGC terrestrial ecosystem model: net primary production controls, Earth Interactions, 2000, Vol. 4, No. 3, pp. 1–85, DOI: 10.1175/1087-3562(2000)004<0003:PASAOT>2.0.CO;2.
  121. Wohlfart C., Winkler K., Wendleder A., Roth A., TerraSAR-X and Wetlands: A Review, Remote Sensing, 2018, Vol. 10, Article 916, DOI: 10.3390/rs10060916.
  122. Xu T., Weng B., Yan D. et al., Wetlands of International Importance: Status, Threats, and Future Protection, Intern. J. Environmental Research and Public Health, 2019, Vol. 16, Article 1818, DOI: 10.3390/ijerph16101818.
  123. Zanaga D., Van De Kerchove R., De Keersmaecker W. et al., ESA WorldCover 10 m 2020 v100, 2021, DOI: 10.5281/zenodo.5571936.
  124. Zhang W., Gao F., Jiang N. et al., High-Temporal-Resolution Forest Growth Monitoring Based on Segmented 3D Canopy Surface from UAV Aerial Photogrammetry, Drones, 2022, Vol. 6, No. 7, Article 158, DOI: 10.3390/drones6070158.
  125. Zhang W., Liu L., Zhao T. et al., GWL_FCS30: a global 30 m wetland map with a fine classification system using multi-sourced and time-series remote sensing imagery in 2020, Earth System Science Data, 2023, Vol. 15, Issue 1, pp. 265–293, DOI: 10.5194/essd-15-265-2023.
  126. Zhao Y., Mao D., Zhang D. et al., Mapping Phragmites australis Aboveground Biomass in the Momoge Wetland Ramsar Site Based on Sentinel-1/2 Images, Remote Sensing, 2022, Vol. 14, Article 694, DOI: 10.3390/rs14030694.
  127. Zharko V. O., Bartalev S. A., Sidorenkov V. M., Forest growing stock volume estimation using optical remote sensing over snow-covered ground: a case study for Sentinel-2 data and the Russian Southern Taiga region, Remote Sensing Letters, 2020, Vol. 11, Issue 7, pp. 677–686, DOI: 10.1080/2150704X.2020.1755473.