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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, 2018, Vol. 15, No. 4, pp. 9-24

Remote optical-microwave measurements of forest parameters: modern state of research and experimental assessment of potentials

T. N. Chimitdorzhiev 1 , A. V. Dmitriev 1 , I. I. Kirbizhekova 1 , A. A. Sherhoeva 1 , A. K. Baltukhaev 1 , P. N. Dagurov 1 
1 Institute of Physical Materials Science SB RAS, Ulan-Ude, Russia
Accepted: 10.07.2018
DOI: 10.21046/2070-7401-2018-15-4-9-24
The paper presents an overview of modern trends in remote sensing (RS) of forest with the help of fusion of multispectral images, radar interferometry and partially, polarimetry data. Basing on the analysis of publications of recent years, we show that the considered complex approach allows to expand the capabilities of RS to assess the forest’s taxonomic parameters in comparison with technologies which involve the analysis of characteristics only by radar or only by optical multispectral methods. The experimental part briefly describes the algorithms of optical and polarimetric radar data processing which serve to determine the predominant species, canopy closeness, aboveground biomass. For one of the forest taxonomic key parameters ― average height, the calculation method is described in more detail. Analysis of the accuracy of radar interferometric measurements is carried out basing on the results obtained by the authors. The systematic underestimation of the actual forest height was established: the discrepancy between the results of radar interferometry and field measurements reached 5.5 m at the values of stand fullness equaled to 0.5, 0.9 and 1, and varies in the range from 2 to 4 m at fullness spanned from 0.6 to 0.8. The conclusion is made about necessity of updating results of radar interferometry by means of appropriate corrections obtained for different values of the forest fullness. The results of remote optical-microwave measurements of forest parameters are available on the Internet in accordance with the modern trends of free distribution of scientific data.
Keywords: radar interferometry, radar polarimetry, spectral analysis, image texture, data fusion, forest inventory
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References:

  1. 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 (Satellite mapping of vegetation cover in Russia), Moscow: IKI RAN, 2016, 208 p.
  2. Bondur V. G., Sovremennye podkhody k obrabotke bol’shikh potokov giperspektral’noi i mnogospektral’noi aerokosmicheskoi informatsii (Modern approaches to processing large flows of hyperspectral and multispectral aerospace information), Issledovaniye Zemli iz kosmosa, 2014, No. 1, pp. 3–17.
  3. Gavrilyuk E. A., Ershov D. V., Tematicheskoe kartografirovanie porodnoi struktury lesov na osnove sputnikovykh izobrazhenii Landsat-TM/ETM+ (Thematic mapping of forest species structure based on Landsat-TM\ETM+ satellite images), Aerokosmicheskie metody i geoinformatsionnye tekhnologii v lesovedenii i lesnom khozyaistve: doklady V Vserossiiskoi konferentsii (Aerospace Method and GIS-Technologies in Forestry and Forest Management: Proc. 5th All-Russian Conf.), Moscow, April 22–24, 2013, Moscow: CEPL RAS, 2013, pp. 112–115.
  4. Dmitriev A. V., Chimitdorzhiev T. N., Gusev M. A., Dagurov P. N., Emel’yanov K. S., Zakharov A. I., Kirbizhekova I. I., Bazovye produkty zondirovaniya Zemli kosmicheskimi radiolokatorami s sintezirovannoi aperturoi (Basic products of earth sensing by space radars with synthetic aperture), Issledovanie Zemli iz kosmosa, 2014, No. 5, pp. 83−83.
  5. Dmitriev A. V., Chimitdorzhiev T. N., Kirbizhekova I. I., Dagurov P. N., Bazarov A. V., Garmaev A. M., Emel’yanov K. S., Gusev M. A., Tekhnologiya sozdaniya i primeneniya bazovykh produktov sputnikovoi radiolokatsii (Technology of creation and application of satellite radar basic products), Vychislitel’nye tekhnologii, 2014, Vol. 19, No. 3, pp. 5−13.
  6. Efremenko V. V., Moshkov A. V., Semenov A. A., Chimitdorzhiev T. N., Metod vyyavleniya ugnetennoi rastitel’nosti po dannym spektrozonal’nogo skanera (Method for detection of the oppressed vegetation with help of multispectral scanner), Issledovanie Zemli iz kosmosa, 1997, No. 6, pp. 3−10.
  7. Zharko V. O., Bartalev S. A., Sidorenkov V. M., Issledovanie vozmozhnostei ispol’zovaniya dannykh Sentinel 2, poluchennykh v usloviyakh nalichiya snezhnogo pokrova, dlya otsenki zapasa stvolovoi drevesiny v lesakh (Study of the feasibility of using Sentinel 2 data obtained under snow cover to estimate the stock of stem wood in forests.), XV Vserossiyskaya otkrytaya konferentsiya “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (XV All-Russia Open Conf. “Current Problems in Remote Sensing of the Earth from Space”‘), Book of Abstracts, Moscow, 2017, p. 360, available at: http://smiswww.iki.rssi.ru/d33_conf/thesisshow.aspx?page=144&thesis=6501.
  8. Loupian E. A., Savorskii V. P., Bazovye produkty obrabotki dannykh distantsionnogo zondirovaniya Zemli (Basic products of Earth remote sensing data processing), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 2, pp. 87–97.
  9. Loupian E. A., Mazurov A. A., Nazirov R. R., Proshin A. A., Flitman E. V., Krashenninikova Yu. S., Tekhnologii postroeniya informatsionnykh sistem distantsionnogo monitoringa (Technologies for building information systems for remote monitoring), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp. 26–43.
  10. Loupian E. A., Savorskii V. P., Shokin Yu. I., Aleksanin A. I., Nazirov R. R., Nedoluzhko I. V., Panova O. Yu., Sovremennye podkhody i tekhnologii organizatsii raboty s dannymi distantsionnogo zondirovaniya Zemli dlya resheniya nauchnykh zadach (Modern approaches and technologies of organization of work with data of the Earth remote sensing for the solution of scientific problems), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2012, Vol. 9, No. 2, pp. 21–44.
  11. Miller S. A., Arkhitektura infrastruktury prostranstvennykh dannykh Germanii (The architecture of the infrastructure of Germany’s spatial data), Prostranstvennye dannye, 2010, No. 2, pp. 7–15, available at: http://www.gisa.ru/70146.html.
  12. Chimitdorzhiev T. N., Bykov M. E., Kantemirov Yu. I., Kholets F., Barbieri M., Tekhnologiya kolichestvennoi otsenki vysoty lesa po dannym kosmicheskikh radarnykh tandemnykh interferometricheskikh s″emok so sputnikov TerraSAR-X/TanDEM-X (Technology of quantitative estimation of forest heights on the basis of data of space tandem radar interferometric surveys by the TerraSAR-X/TanDEM-X satellites), Geomatika, 2014, No. 1, pp. 72−79.
  13. Chimitdorzhiev T. N., Bykov M. E., Kantemirov Yu. I., Kirbizhekova I. I., Labarov B. B., Baltukhaev A. K., K voprosu o tochnosti opredeleniya vysoty lesa po dannym radiolokatsionnoi interferometrii TanDEM-X (On the accuracy of determining the forest height from the data of TanDEM-X radar interferometry), Sibirskii lesnoi zhurnal, 2016, No. 4, pp. 128−133.
  14. Askne J. I., Dammert P. B., Ulander L. M., Smith G., C-band repeat-pass interferometric SAR observations of the forest, IEEE Transactions on Geoscience and Remote Sensing, 1997, Vol. 35, No. 1, pp. 25−35, DOI: 10.1109/36.551931.
  15. Beaudoin A., Bernier P. Y., Guindon L., Villemaire P., Guo X. J., Stinson G., Bergeron T., Magnussen S., Hall R. J., Mapping attributes of Canada’s forests at moderate resolution through kNN and MODIS imagery, Canadian J. Forest Research, 2014, Vol. 44, No. 5, pp. 521–532, DOI: 10.1139/cjfr-2013-0401.
  16. Colgan M. S., Baldeck C. A., Féret J.-B., Asner G. P., Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data, Remote Sensing, 2012, Vol. 4, No. 11, pp. 3462–3480, DOI:10.3390/rs4113462
  17. Deutscher J., Gutjahr K., Perko R., Raggam H., Hirschmugl M., Schardt M., Humid tropical forest monitoring with multi-temporal L-, C- and X-band SAR data, Proc. IEEE 9th Intern. Workshop on the Analysis of Multitemporal Remote Sensing Images (Multi-Temp), Bruges, Belgium, 2017, pp. 1–4.
  18. Dobson M. C., Ulaby F. T., LeToan T., Beaudoin A., Kasischke E. S., Christensen N., Dependence of radar backscatter on coniferous forest biomass, IEEE Transactions on Geoscience and Remote Sensing, 1992, Vol. 30, No. 2, pp. 412−415, DOI: 10.1109/36.134090.
  19. Engler R., Waser L. T., Zimmermann N. E., Schaub M., Berdos S., Ginzler C., Psomas A., Combining ensemble modeling and remote sensing for mapping individual tree species at high spatial resolution, Forest ecology and management, 2013, Vol. 310, pp. 64–73, DOI: 10.1016/j.foreco.2013.07.059.
  20. Englhart S., Keuck V., Siegert F., Aboveground biomass retrieval in tropical forests — The potential of combined X- and L-band SAR data use, Remote Sensing of Environment, 2011, Vol. 115, No. 5, pp. 1260–1271, DOI: 10.1016/j.rse.2011.01.008.
  21. Feng Q., Chen E., Li Z., Li L., Zhao L., Forest vertical structure parameters extraction from airborne X-band InSAR data, 2016 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), IEEE, 2016, pp. 155–158, DOI: 10.1109/IGARSS.2016.7729031.
  22. Fransson J. E. S., Smith G., Askne J., Olsson H., Stem volume estimation in boreal forests using ERS-1/2 coherence and SPOT XS optical data, Intern. J. Remote Sensing, 2001, Vol. 22, No. 14, pp. 2777−2791, DOI: 10.1080/01431160010006872.
  23. Gama F. F., Dos Santos J. R., Mura J. C., Eucalyptus Biomass and Volume Estimation Using Interferometric and Polarimetric SAR Data, Remote Sensing, 2010, Vol. 2, No. 4, pp. 939–956, DOI: 10.3390/rs2040939.
  24. Gonçalves F. G., Santos J. R., Treuhaft R. N., Stem volume of tropical forests from polarimetric radar, Intern. J. Remote Sensing, 2011, Vol. 32, No. 2, pp. 503−522, DOI: 10.1080/01431160903475217.
  25. Goodenough D. G., Chen H., Cloude S. R., Gordon P., Multisource aboveground carbon estimation of forests, 2016 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), IEEE, 2016, pp. 147–150, DOI: 10.1109/IGARSS.2016.7729029.
  26. Haack B., Mahabir R., Separability Analysis of Integrated Spaceborne Radar and Optical Data: Sudan Case Study, J. Remote Sensing, 2017, Vol. 5, No. 1, pp. 10–21, DOI: 10.18005/JRST0501002.
  27. Imhoff M. L., Radar backscatter and biomass saturation-Ramifications for global biomass inventory, IEEE Transactions on Geoscience and Remote Sensing, 1995, Vol. 33, No. 2, pp. 511–518, DOI: 10.1109/36.377953.
  28. Jaaskelainen T., Silvennoinen R., Hiltunen J., Parkkinen J. P. S., Classification of the reflectance spectra of pine, spruce, and birch, Applied Optics, 1994, Vol. 33, No. 12, pp. 2356–2362, DOI: 10.1364/AO.33.002356.
  29. Joshi N., Baumann M., Ehammer A., Fensholt R., Grogan K., Hostert P., Jepsen M. R., Kuemmerle T., Meyfroidt P., Mitchard E. T. A., Reiche J., Ryan C. M., Waske B., A review of the application of optical and radar remote sensing data fusion to land use mapping and monitoring, Remote Sensing, 2016, Vol. 8, No. 1, p. 70, DOI: 10.3390/rs8010070.
  30. Le Maire G., François C., Soudani K., Berveiller D., Pontailler J.-Y., Bréda N., Genet H., Davi H., Dufrêne E., Calibration and validation of hyperspectral indices for the estimation of broadleaved forest leaf chlorophyll content, leaf mass per area, leaf area index and leaf canopy biomass, Remote Sensing of Environment, 2008, Vol. 112, No. 10, pp. 3846–3864, DOI: 10.1016/j.rse.2008.06.005.
  31. Li W., Chen E., Li Z., Ke Y., Zhan W., Forest aboveground biomass estimation using polarization coherence tomography and PolSAR segmentation, Intern. J. Remote Sensing, 2015, Vol. 36, No. 2, pp. 530−550, DOI: 10.1080/01431161.2014.999383.
  32. Neumann M., Ferro-Famil L., Reigber A., Estimation of forest structure, ground, and canopy layer characteristics from multibaseline polarimetric interferometric SAR data, IEEE Transactions on Geoscience and Remote Sensing, 2010, Vol. 48, No. 3, pp. 1086−1104, DOI: 10.1109/TGRS.2009.2031101.
  33. Perko R., Raggam H., Deutscher J., Gutjahr K., Schardt M., Forest assessment using high resolution SAR data in X-band, Remote sensing, 2011, Vol. 3, No. 4, pp. 792−815, DOI:10.3390/rs3040792.
  34. Puliti S., Solberg S., Næsset E., Gobakken T., Zahabu E., Mauya E., Malimbwi R. E., Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data, Remote Sensing, 2017, Vol. 9, No. 10, p. 984, DOI: 10.3390/rs9100984.
  35. Pulliainen J., Engdahl M., Hallikainen M., Feasibility of multi-temporal interferometric SAR data for stand-level estimation of boreal forest stem volume, Remote Sensing of Environment, 2003, Vol. 85, No. 4, pp. 39−409, DOI: 10.1016/S0034-4257(03)00016-6.
  36. Reiche J., Hamunyela E., Verbesselt J., Hoekman D., Herold M., Improving near-real time deforestation monitoring in tropical dry forests by combining dense Sentinel-1 time series with Landsat and ALOS-2 PALSAR-2, Remote Sensing of Environment, 2018, Vol. 204, pp. 147−161, DOI: 10.1016/j.rse.2017.10.034.
  37. Remote Sensing of Forest Environments: Concepts and Case Studies, Wulder M. A., Franklin S. E. (eds.), Springer Science & Business Media, 2003, 519 p., DOI: 10.1007/978-1-4615-0306-4.
  38. Sadeghi Y., St-Onge B,, Leblon B., Simard M., Papathanassiou K., Role of Vegetation Phenology (Leaf-on, Leaf-off) in the Accuracy of Forest Height Maps Derived from TanDEM-X Interferograms, Conf. POLinSAR 2015, Frascati, Italy, available at: https://goo.gl/aPNMsr.
  39. Santoro M., Eriksson L. E. B., Fransson J. E. S., Reviewing ALOS PALSAR Backscatter Observations for Stem Volume Retrieval in Swedish Forest, Remote Sensing, 2015, Vol. 7, No. 4, pp. 4290–4317, DOI: 10.3390/rs70404290.
  40. Sauer S., Kugler F., Lee S. K., Papathanassiou K., Polarimetric decomposition for forest biomass retrieval, 2010 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), IEEE, 2010, pp. 4780−4783, DOI: 10.1109/IGARSS.2010.5653894.
  41. Schepaschenko D., McCallum I., Shvidenko A., Fritz S., Kraxner F., Obersteiner M., A new hybrid land cover dataset for Russia: a methodology for integrating statistics, remote sensing and in situ information, J. Land Use Science, 2011, Vol. 6, No. 4, pp. 245–259, DOI: 10.1080/1747423X.2010.511681.
  42. Schmitt M., Shahzad M., Zhu X., Forest remote sensing on the individual tree level by airborne millimeterwave SAR, 2016 IEEE Intern. Geoscience and Remote Sensing Symp. (IGARSS), IEEE, 2016, pp. 151–154, DOI: 10.1109/IGARSS.2016.7729030.
  43. Soudani K., François C., Le Maire G., Le Dantec V., Dufrêne E., Comparative analysis of IKONOS, SPOT, and ETM+ data for leaf area index estimation in temperate coniferous and deciduous forest stands, Remote Sensing of Environment, 2006, Vol. 102, No. 1, pp. 161−175, DOI: 10.1016/j.rse.2006.02.004.
  44. Tian J., Schneider T., Straub C., Kugler F., Reinartz P., Exploring Digital Surface Models from Nine Different Sensors for Forest Monitoring and Change Detection, Remote Sensing, 2017, Vol. 9, No. 3, p. 287, DOI: 10.3390/rs9030287.
  45. Wilson B. T., Lister A. J., Riemann R. I., A nearest-neighbor imputation approach to mapping tree species over large areas using forest inventory plots and moderate resolution raster data, Forest Ecology and Management, 2012, Vol. 271, pp. 182–198, DOI: 10.1016/j.foreco.2012.02.002.
  46. Zhang Z., Ni W., Sun G., Huang W., Ranson K. J., Cook B. D., Guo Z., Biomass retrieval from L-band polarimetric UAVSAR backscatter and PRISM stereo imagery, Remote Sensing of Environment, 2017, Vol. 194, pp. 331–346, DOI: 10.1016/j.rse.2017.03.034.