Issue № 1 |
Methods of ecological investigations |
pdf-version |
Komarova Anna | Greenpeace Russia, Leningradsky pr-t 26/1, Moscow, anna.komarova@greenpeace.org |
Zhuravleva Ilona | Greenpeace Russia, Leningradsky pr-t 26/1, Moscow, ilona.zhuravleva@greenpeace.org |
Yablokov Vasily | Greenpeace Russia, Leningradsky pr-t 26/1, Moscow, vasily.yablokov@greenpeace.org |
Keywords: satellite images mapping plant communities methodology |
Summary: In the article the possibilities of the multispectral remote sensing data to study the plant communities are shown. The usage of open-source data in visual and infrared spectral bands is considered. The possible ways to receive free satellite images in different resolutions, the main methods of their analysis as well as the characteristics and the area of application of these images are presented. The information is specified with the examples of the projects realized at the GIS-Lab and Forest Unit of Greenpeace Russia. © Petrozavodsk State University |
Received on: 04 March 2016 Published on: 05 April 2016 |
Aksenov D. E. Dubinin M. Yu. Karpachevskiy M. L. Liksakova N. Skvorcov V. Smirnov D. Yanickaya T. Mapping high conservation value forests of Primorsky kray, Russian Far East. Categories important for preservation of flora and vegetation. Vladivostok; M., 2006. 186 p.
Antipin V. K. Tokarev P. N. Structural organization and mapping the mires of the national park “Vodlozersky”, Izvestiya Samarskogo nauchnogo centra RAN. 2012. No. 1–6. C. 1584–1586.
Arhipova M. V. The analysis of the actual state of deciduous forests in Central Russian upland using Landsat satellite images, Fundamental'nye issledovaniya. 2013. No. 11–6. C. 1181–1185.
Bartalev S. A. Ershov D. V. Isaev A. S. Potapov P. V. Turubanova S. A. Yaroshenko A. Yu. Russia's Forests: Dominating Forest Types and Their Canopy Density: a map. M., 2004. URL: http://forestforum.ru/info/pictures/rusmap.pdf (data obrascheniya: 27.03.2016).
Bartalev S. A. Egorov V. A. Ershov D. V. Isaev A. S. Lupyan E. A. Plotnikov D. E. Uvarov I. A. Mapping of Russia’s vegetation cover using MODIS satellite spectroradiometer data, Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2011. T. 8. No. 4. C. 285–302.
Bartalev S. A. Lupyan E. A. R&D on methods for satellite monitoring of vegetation by the Russian Academy of Science’ Space Research Institute, Issledovaniya Zemli iz kosmosa. 2013. T. 10. No. 1. C. 197–217.
Vladimirov I. N. Sofronov A. P. Sorokovoy A. A. Kobylkin D. V. Frolov A. A. Vegetation structure of the west part of the Verkhneangarskaya hollow, Geografiya i prirodnye resursy. 2014. No. 2. P. 44–53.
Gavrilyuk E. A. Ershov D. V. Methodology of processing of combined multiseasonal Landsat-TMs for mapping of Moscow region’s terrestrial ecosystems, Sovremennye problemy distancionnogo zondirovaniya Zemli iz Kosmosa. 2012. T. 9. No. 4. C. 15–23.
Dubovik D. S. Yakutin M. V. The ecosystems dynamics in the Ulug-Hem depression of the Republic of Tuva according to remote sounding, Interekspo Geo-Sibir'. 2012. No. 3. C. 56–60.
Elsakov V. V. Volodin V. V. Chadin I. F. Parshina E. I. Maruschak I. O. Satellite monitoring in the estimation of aconite high resources in subpolar Urals, Izvestiya Samarskogo nauchnogo centra RAN. 2010. No. 1–4. C. 1123–1129.
Ermakov N. B. Polyakova M. A. Chernikova T. C. Geobotanical mapping of forests in the Altai-Sayanian mountain region, Vestnik Novosibirskogo gosudarstvennogo universiteta. Ser.: Biologiya, klinicheskaya medicina. 2012. T. 10.2. C. 24–30.
Zharko V. O. Bartalev S. A. Forest tree species recognizability assessment based on satellite data on their spectral-reflective seasonal changes, Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2014. T. 11. No. 3. C. 159–170.
Zavadskaya A. V. Yablokov V. M. Ecological and geographical basis for recreation usage of thermal ecosystem (on the example of the Gejzernaya valley), Trudy Kronockogo gosudarstvennogo prirodnogo zapovednika. Voronezh, 2014. Vyp. 3. P. 190–208.
Isaev A. S. Chernen'kova T. V. Forest biodiversity monitoring: approaches and results, Lesnye resursy taezhnoy zony Rossii: problemy lesopol'zovaniya i lesovosstanovleniya: Materialy Vserop. nauch. konf. s mezhdunar. uchastiem. Petrozavodsk, 2009. P. 60–62.
Klimina E. M. Ostrouhov A. V. The analysis of dark-coniferous forests disturbance dynamics in Northern Sikhote-Alin on the basis of using the satellite data, Izvestiya Samarskogo nauchnogo centra Rossiyskoy akademii nauk. 2011. T. 13. No. 1–4. P. 996–1000.
Knizhnikov Yu. F. Kravcova V. I. Tutubalina O. N. Aerospace methods for geographical research. M., 2004. 336 p.
Kolesnikova O. N. Cherepanov A. S. PK ENVI’s opportunities for multispectral and hyperspectral data processing, Geomatika. 2009. No. 3. P. 24–27.
Komarova A. F. Fir (Abies nordmanniana (Stev.) Spach) share of forest crown layer: mapping with neural network method, Lomonosov-2012: Tez. dokl. M., 2012. P. 294–295.
Komarova A. F. Kuksina N. V. Bobrovskiy M. V. Plotnikov M. P. An example of fir High Conservation Value Forests identification in Krasnodar and Adygeya regions, Vestnik Rossiyskogo universiteta druzhby narodov. 2010. No. 5. P. 56–61.
Kravcova V. I. Procedural approaches for space imagery-based research of nothern timber-line dynamics, Geografiya i prirodnye resursy. 2012. No. 3. P. 133–139.
Kurbanov E. A. Vorob'ev O. N. Gubaev A. V. Lezhnin S. A. Polevschikova Yu. A. Demisheva E. N. Four decades of forest research with the use of Landsat images, Vestnik Povolzhskogo gosudarstvennogo tehnologicheskogo universiteta. Ser.: Lep. Ekologiya. Prirodopol'zovanie. 2014. No. 1. P. 18–32.
Kurbanov E. A. Vorob'ev O. N. Nezamaev S. A. Gubaev A. V. Lezhnin S. A. Polevschikova Yu. A. Thematic mapping and stratification of forests in Middle Zavolsgie by Landsat satellite images, Vestnik Povolzhskogo gosudarstvennogo tehnologicheskogo universiteta. Ser.: Lep. Ekologiya. Prirodopol'zovanie. 2013. No. 3 (19). P. 72–82.
Labutina I. A. Baldina E. A. Remote sensing data for the monitoring of protected areas’ ecosystems. M., 2011. 88 p.
Lipshic S. Yu. On the flora and vegetation of Kamchatka hot springs, Byull. MOIP. Otd. Biol. 1936. T. 45. No. 2. P. 143–158.
Lur'e I. K. Geoinformational mapping. Methods of geoinformatics and digital processing of satellite images. M., 2010. 424 c.
Malysheva N. V. Automated decoding of aerospace images of forests. M., 2012. 154 p.
Myachina K. V. Remote monitoring of vegetation on the plot of natural steppe in the Orenburg region, Izvestiya Samarskogo nauchnogo centra RAN. 2014. No. 5. P. 178–181.
Neshataeva V. Yu. Vegetation of Kamchatka peninsula. M., 2009. 537 p.
Ismailova D. M. Nazimova D. I. Satellite monitoring of Sayanian mountain forests, Zhurnal Sibirskogo federal'nogo universiteta. Biologiya. 2011. No. 4(1). P. 75–85.
Popova T. A. Bychkova I. A. Remote sensing methods for vegetation research, Otechestvennaya geobotanika: osnovnye vehi i perspektivy: Materialy Vserop. nauch. konf. s mezhdunar. uchastiem. SPb., 2011. T. 1. P. 404–408.
Popova T. A. Bychkova I. A. Overgrowing the water reservoires in the North-West of Russia in various ecological conditions, Izvestiya Samarskogo nauchnogo centra Rossiyskoy akademii nauk. 2012. T. 14. No. 1(6). P. 1515–1518.
Potapov P. V. Yaroshenko A. Yu. Turubanova S. A. Intact forest areas in the North of European Russia, Vostochnoevropeyskie lesa: istoriya v golocene i sovremennost': V 2 kn. M.: Nauka, 2004. P. 146–153.
Puzachenko M. Yu. Kotlov I. P. Chernen'kova T. The technological sсheme of monitoring natural objects using GIS and RS methods, Monitoring biologicheskogo raznoobraziya lesov Rossii: metodologiya i metody. M., 2008. P. 347–355.
Puzachenko M. Yu. Chernen'kova T. V. Basova E. V. Mapping of nature-anthropogenic variability of vegetation at the central part of Murmansk region, Otechestvennaya geobotanika: osnovnye vehi i perspektivy: Materialy Vserop. nauch. konf. s mezhdunar. uchastiem. SPb., 2011. T. 1. P. 408–411.
Rasskazov A. A. Galaganova L. A. Landsat data for the assessment of dynamics and change in Meshchera vegetation, Nauchnye trudy Instituta nepreryvnogo professional'nogo obrazovaniya. 2014. No. 3. P. 236–239.
Savel'ev A. A. Biochorological diversity and spatial modelling of vegetation (geoinformation approach). Kazan', 2004. 244 c.
Slabuhina S. V. Studying the microlandscape of morphological structure of the Vasyugan peat by satellite images decoding, Vestnik Tomskogo gosudarstvennogo universiteta. 2014. No. 388. P. 253–256.
Tkachuk T. E. Multi-year vegetation dynamics of Daursky nature reserve according to remote-sensing data, Izvestiya Samarskogo nauchnogo centra Rossiyskoy akademii nauk. 2012. T. 14. No. 1(5). P. 1391–1394.
Trass H. H. On the vegetation of hot springs’ surroundings in the Geyzernaya valley, Kamchatka peninsula, Issledovanie prirody Dal'nego Vostoka. Tallin, 1963. P. 112–146.
Finichenko E. N. Dmitriev V. V. Simulation of vegetation parameters of wetlands vegetation of the West Siberian region by using satellite and ground data, Sovremennye problemy distancionnogo zondirovaniya Zemli iz kosmosa. 2011. T. 8. No. 4. P. 239–245.
Hanov Z. M. Pshegusov R. H. Spatial analysis and distribution modelling of some lichens in Central Caucasus, Lihenologiya v Rossii: aktual'nye problemy i perspektivy issledovaniy. SPb., 2014. P. 221–230.
Hvorostuhin D. P. Klikunov A. A. Application of GIS, remote sensing and tasseled cap transformation for the study of modern landscape plants region, Izvestiya Saratovskogo universiteta. Nov. ser. Ser.: Nauki o Zemle. 2013. T. 13. Vyp. 2. P. 40–42.
Chandra A. M. Gosh S. K. Remote Sensing and GIS. M., 2008. 312 c.
Chernen'kova T. V. Levickaya N. N. Kozlov D. N. Tihonova E. V. Ogureeva G. N. Pesterova O. A. Boidiversity assessment of Moscow region’s forests by field and remote sensing methods, Raznoobrazie i dinamika lesnyh ekosistem Rossii: V 2 kn. M., 2012. Kn. 1. P. 316–370.
Cherosov M. M. Ammosova E. V. Troeva E. I. On the correction of small-scale map contours of the vegetation in the north-eastern Yakutia (based on GIS methods and map analysis), Izvestiya Samarskogo nauchnogo centra Rossiyskoy akademii nauk. 2012. T. 14. No. 1–6. P. 1656–1659.
Shabanov D. I. Iolin M. M. Borzova A. S. Agoshkova E. V. Use of GIS and remote sensing for estimation of the desertification of Northern Caspian region, Vestnik Volgogradskogo gosudarstvennogo universiteta. Ser. 11. Estestv. nauki. 2014. No. 4 (10). P. 48–56.
Sharikalov A. G. Yakutin M. V. The Analysis of Taiga Ecosystems Condition Applying Automatic Decoding Method, Izvestiya Altayskogo gosudarstvennogo universiteta. 2014. No. 3. P. 123–127.
Shipunov A. B. Baldin E. M. Volkova P. A. Korobeynikov A. I. Nazarova S. A. Petrov S. V. Sufiyanov V. G. Pictorial statistic. Using R!. M., 2012. 298 c.
Shovengerdt R. Remote sensing. Models and methods of image processing. M., 2010. 560 p.
Yablokov V. M. Zavadskaya A. V. Geoinformation modelling of temperature within geothermal systems (case study from the Geizernaya valley), Geodeziya i kartografiya. 2013. No. 3. P. 24–31.
Yaroshenko A. Yu. Dobrynin D. A. Egorov A. V. Zhuravleva I. V. Manisha A. E. Potapov P. V. Turubanova S. A. Hakimulin E. V. Forests of the Center and North of European Russia: a map. M., 2008. URL: http://forestforum.ru/info/map_for_print.pdf (data obrascheniya: 27.03.2016).
Bannari A., Morin D., Bonn F., Huete A. R. A review of vegetation indices, Rem. Sens. Reviews. 1995. Vol. 13: 1–2. P. 95–120.
Banskota A., Kayastha N., Falkowski M., Wulder M. A., Froese R. E., White J. C. Forest monitoring using Landsat time-series data: A review, Canadian Journal of Rem. Sens. 2014. Vol. 40. No. 5. P. 362–384. DOI:10.1080/07038992.2014.987376.
Barrachinaa M., Cristóbalb J., Tulla A. F. Estimating above-ground biomass on mountain meadows and pastures through remote sensing, Int. J. of Appl. Earth Observ. and Geoinf. 2015. Vol. 38. P. 184–192. DOI: 10.1016/j.jag.2014.12.002.
Bartalev S. A., Belward A. S., Erchov D. V., Isaev A. S. A new SPOT4-VEGETATION derived land cover map of Northern Eurasia, Int. J. of Rem. Sens. 2003. Vol. 24(9). P. 1977–1982.
Beck P. S. A., Jönsson P., Høgda K, A., Karlsen S. R., Eklundh L., Skidmore A. K. A ground‐validated NDVI dataset for monitoring vegetation dynamics and mapping phenology in Fennoscandia and the Kola peninsula, Int. J. of Rem. Sens. 2007. Vol. 28:19. P. 4311–4330. DOI: 10.1080/01431160701241936.
Bradley B. A. Mustard J. F. 1132:CTLDOA2.0.CO;2.
Bradley B. A. Remote detection of invasive plants: a review of spectral, textural and phenological approaches, Biological invasions. 2014. Vol. 16.7. P. 1411–1425. DOI: 10.1007/s10530-013-0578-9.
Buchanan G. M., Brink A. B., Leidner A. K., Rose R., Wegmann M. Advancing terrestrial conservation through remote sensing, Ecological Informatics. 2015 (in press).
Buck O., Millán V. E. G., Klink A., Pakzad K. Using information layers for mapping grassland habitat distribution at local to regional scales, Int. J. of Appl. Earth Observ. and Geoinf. 2015. Vol. 37. P. 83–89. DOI: 10.1016/j.jag.2014.10.012.
Camathias L., Bergamini A., Küchler M., Stofer S., Baltensweiler A. High‐resolution remote sensing data improves models of species richness, Applied Vegetation Science. 2013. Vol. 16.4. P. 539–551. DOI: 10.1111/avsc.12028.
Chen Y., Dengsheng L., Geping L., Jingfeng H. Detection of vegetation abundance change in the alpine tree line using multitemporal Landsat Thematic Mapper imagery, Int. J. of Rem. Sens. 2015. Vol. 36:18. P. 4683–4701. DOI: 10.1080/01431161.2015.1088675.
Cohen W. B., Spies T. A. Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and Spot imagery, Rem. Sens. of Env. 1992. No. 41(1). P. 1–17. DOI: 10.1016/0034-4257(92)90056-P.
Cord A., Rödder D. Inclusion of habitat availability in species distribution models through multi-temporal remote sensing data?, Ecological Applications. 2011. Vol. 21(8). P. 3285–3298. DOI: 10.1890/11-0114.1.
Cord A. F., Klein D., Mora F., Dech S. Comparing the suitability of classified land cover data and remote sensing variables for modeling distribution patterns of plants, Ecological Modelling. 2014. Vol. 272. P. 129–140. DOI: 10.1016/j.ecolmodel.2013.09.011.
Dalmayne J., Möckel T., Prentice H. C., Schmid B. C., Hall K. Assessment of fine-scale plant species beta diversity using WorldView-2 satellite spectral dissimilarity, Ecological Informatics. 2013. Vol. 18. P. 1–9. DOI: 10.1016/j.ecoinf.2013.05.004.
Davranchea A., Lefebvreb G., Poulinb B. Wetland monitoring using classification trees and SPOT-5 seasonal time series, Rem. Sens. of Env. 2010. Vol. 114. Issue 3. P. 552–562. DOI: 10.1016/j.rse.2009.10.009.
DeFries R. S., Townshend J. R. G. NDVI-derived land cover classifications at a global scale, Int. J. of Rem. Sens. 1994. Vol. 15.17. P. 3567–3586. DOI: 10.1080/01431169408954345.
Drusch M., Del Bello U., Carlier S., Colin O., Fernandez V., Gascon F., Hoersch B. et al. Sentinel-2: ESA's optical high-resolution mission for GMES operational services, Rem. Sens. of Env. 2012. Vol. 120. P. 25–36. DOI: 10.1016/j.rse.2011.11.026.
Fassnacht F. E., Li L., Fritz A. Mapping degraded grassland on the Eastern Tibetan Plateau with multi-temporal Landsat 8 data—where do the severely degraded areas occur?, Int. J. of Appl. Earth Observ. and Geoinf. 2015. Vol. 42. P. 115–127. DOI: 10.1016/j.jag.2015.06.005.
Fassnacht K. S., Cohen W. B., Spies T. A. Key issues in making and using satellite-based maps in ecology: A primer, Forest Ecology and Management. 2006. Vol. 222. No. 1. P. 167–181. DOI: 10.1016/j.foreco.2005.09.026.
Foody G. M. Status of land cover classification accuracy assessment, Rem. Sens. of Env. 2002. Vol. 80.1. P. 185–201. DOI: 10.1016/S0034-4257(01)00295-4.
Fox L. Essential Earth imaging for GIS. California, 2015. 115 p.
Fuller D. O. Remote detection of invasive Melaleuca trees (Melaleuca quinquenervia) in South Florida with multispectral IKONOS imagery, Int. J. of Rem. Sens. 2005. Vol. 26:5. P. 1057–1063. DOI: 10.1080/01430060512331314119.
Gallant A. L. The Challenges of Remote Monitoring of Wetlands, Rem. Sens. 2015. Vol. 7(8). P. 10938–10950. DOI: 10.3390/rs70810938.
Gavier-Pizarro G. I., Kuemmerle T., Hoyo L. E., Stewart S. I., Huebner C. D., Keuler N. S., Radeloff V. C. Monitoring the invasion of an exotic tree (Ligustrum lucidum) from 1983 to 2006 with Landsat TM/ETM+ satellite data and Support Vector Machines in Córdoba, Argentina, Rem. Sens. of Env. 2012. Vol. 122. P. 134–145. DOI: 10.1016/j.rse.2011.09.023.
Gillespie T. W., Foody G. M., Rocchini D., Giorgi A. P., Saatchi S. Measuring and modelling biodiversity from space, Progress in Physical Geography. 2008. Vol. 32(2). P. 203–221. DOI: 10.1177/0309133308093606.
Gould W. Remote sensing of vegetation, plant species richness, and regional biodiversity hotspots, Ecological applications. 2000. Vol. 10. No. 6. P. 1861–1870. DOI: 10.1890/1051-0761(2000)010%5B1861:RSOVPS%5D2.0.CO%3B2.
Hansen M. C., Townshend J. R. G., DeFries R. S., Carroll M. Estimation of tree cover using MODIS data at global, continental and regional/local scales, Int. J. of Rem. Sens. 2005. Vol. 26. No. 19. P. 4359–4380. DOI:10.1080/01431160500113435.
Hojas-Gascón L., Belward A., Eva H., Ceccherini G., Hagolle O., Garcia J., Ceruttid P. Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program, Int. Archives of the Photogram., Rem. Sens & Spatial Inf. Sciences. 2015. P. 417–423.
Homolova L., Malenovský Z., Clevers J. G., Garcia-Santos G., Schaepman M. E. Review of optical-based remote sensing for plant trait mapping, Ecological Complexity. 2013. Vol. 15. P. 1–16. DOI: 10.1016/j.ecocom.2013.06.003.
Hou X., Gao S., Niu Z., Xu Z. Extracting grassland vegetation phenology in North China based on cumulative SPOT-VEGETATION NDVI data, Int. J. of Rem. Sens. 2014. Vol. 35:9. P. 3316–3330. DOI: 10.1080/01431161.2014.903437.
Huang C., Asner G. P. Applications of Remote Sensing to Alien Invasive Plant Studies – Review, Sensors. 2009. Vol. 9(6). P. 4869–4889. DOI:10.3390/s90604869.
Isaacson B. N., Serbin S. P., Townsend P. A. Detection of relative differences in phenology of forest species using Landsat and MODIS, Landscape ecology. 2012. Vol. 27. No. 4. P. 529–543. DOI: 10.1007/s10980-012-9703-x.
Johnston S. E., Henry M. C., Gorchov D. L. Using Advanced Land Imager (ALI) and Landsat Thematic Mapper (TM) for the Detection of the Invasive Shrub Lonicera maackii in Southwestern Ohio Forests, GIScience & Rem. Sens. 2012. Vol. 49:3. P. 450–462. DOI: 10.2747/1548-1603.49.3.450.
Joshi C., De Leeuw J., Skidmore A. K., Van Duren I. C., Van Oosten H. Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods, Int. J. of Appl. Earth Observ. and Geoinf. 2006. Vol. 8(2). P. 84–95. DOI: 10.1016/j.jag.2005.08.004.
Karlson M., Ostwald M., Reese H., Sanou J., Tankoano B., Mattsson E. Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest, Rem. Sens. 2015. Vol. 7(8). P. 10017–10041. DOI: 10.3390/rs70810017.
Klemas V. Remote sensing of wetlands: case studies comparing practical techniques, Journal of Coastal Research. 2011. Vol. 27. No. 3. P. 418–427. DOI: 10.2112/JCOASTRES-D-10-00174.1.
Krankina O. N., Pflugmacher D., Friedl M., Cohen W. B., Nelson P., Baccini A. Meeting the challenge of mapping peatlands with remotely sensed data, Biogeosciences. 2008. Vol. 5(6). P. 1809–1820. DOI: 10.5194/bg-5-1809-2008.
Kuenzer C., Ottinger M., Wegmann M., Guo H., Wang C., Zhang J., Dech S., Wikelski M. Earth observation satellite sensors for biodiversity monitoring: potentials and bottlenecks, Int. J. of Rem. Sens. 2014. Vol. 35:18. P. 6599–6647. DOI: 10.1080/01431161.2014.964349.
Laba M., Downs R., Smith S., Welsh S., Neider C., White S., Richmond M., Philpot W., Baveye, P. Mapping invasive wetland plants in the Hudson River National Estuarine Research Reserve using Quickbird satellite imagery, Rem. Sens. of Env. 2008. Vol. 112(1). P. 286–300. DOI: 10.1016/j.rse.2007.05.003.
Laurin G. V., Frate F. D., Pasolli L., Notarnicola C., Guerriero L., Valentini R. Discrimination of vegetation types in alpine sites with ALOS PALSAR-, RADARSAT-2-, and lidar-derived information, Int. J. of Rem. Sens. 2013. Vol. 34:19. P. 6898–6913. DOI: 10.1080/01431161.2013.810823.
Lehnert L. W., Meyer H., Wang Y., Miehe G., Thies B., Reudenbach C., Bendix, J. Retrieval of grassland plant coverage on the Tibetan Plateau based on a multi-scale, multi-sensor and multi-method approach, Rem. Sens. of Env. 2015. Vol. 164. P. 197–207. DOI: 10.1016/j.rse.2015.04.020.
Leitão P. J., Schwieder M., Suess S., Catry I., Milton E. J., Moreira F., Osborne P. E., Pinto M.J ., van der Linden S., Hostert P. Mapping beta diversity from space: Sparse generalised dissimilarity modelling (SGDM) for analysing high-dimensional data, Methods Ecol. Evol. 2015. Vol. 6. P. 764–771. DOI: 10.1111/2041-210X.12378.
Levin N., Shmida A., Levanoni O., Tamari H., Kark S. Predicting mountain plant richness and rarity from space using satellite‐derived vegetation indices, Diversity and Distributions. 2007. Vol. 13(6). P. 692–703. DOI: 10.1111/j.1472-4642.2007.00372.x.
Liu W., Song C., Schroeder T. A., Cohen W. B. Predicting forest successional stages using multitemporal Landsat imagery with forest inventory and analysis data, Int. J. of Rem. Sens. 2008. Vol. 29:13. P. 3855–3872. DOI: 10.1080/01431160701840166.
Margono B. A., Bwangoy J, R. B., Potapov P. V., Hansen M. C. Mapping wetlands in Indonesia using Landsat and PALSAR data-sets and derived topographical indices, Geo-spatial Information Science. 2014. Vol. 17:1. P. 60–71. DOI: 10.1080/10095020.2014.898560.
McRoberts R. E., Cohen W. B., Næsset E., Stehman S. V., Tomppo E.O. Using remotely sensed data to construct and assess forest attribute maps and related spatial products, Scandinavian Journal of Forest Research. 2010. Vol. 25:4. P. 340–367. DOI: 10.1080/02827581.2010.497496.
Möckel T. Hyperspectral and multispectral remote sensing for mapping grassland vegetation: PhD diss. Lund University, 2015. 41 p.
Mozumder C., Tripathi N. K., Tipdecho T. Ecosystem evaluation (1989–2012) of Ramsar wetland Deepor Beel using satellite-derived indices, Environmental monitoring and assessment. 2014. Vol. 186. No. 11. P. 7909–7927. DOI: 10.1007/s10661-014-3976-2.
Mui A., He Y., Weng Q. An object-based approach to delineate wetlands across landscapes of varied disturbance with high spatial resolution satellite imagery, ISPRS Journal of Photogrammetry and Rem. Sens. 2015. Vol. 109. P. 30–46. DOI: 10.1016/j.isprsjprs.2015.08.005.
Murray H., Lucieer A., Williams R. Texture-based classification of sub-Antarctic vegetation communities on Heard Island, Int. J. of Appl. Earth Observ. and Geoinf. 2010. Vol. 12. No. 3. P. 138–149. DOI: 10.1016/j.jag.2010.01.006.
Nagendra H., Lucas R., Honrado J. P., Jongman R. H., Tarantino C., Adamo M., Mairota P. Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats, Ecological Indicators. 2013. Vol. 33. P. 45–59. DOI: 10.1016/j.ecolind.2012.09.014.
Nilsen L., Arnesen G., Joly D., Malnes E. Spatial modelling of Arctic plant diversity, Biodiversity. 2013. Vol. 14:1. P. 67–78. DOI: 10.1080/14888386.2012.717008.
Odindi J., Adam E., Ngubane Z., Mutanga O., Slotow R. Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm, Journal of Applied Rem. Sens. 2014. Vol. 8(1). P. 083527-1 – 083527-16. DOI: 10.1117/1.JRS.8.083527.
Onojeghuo A. O., Blackburn G. A. Mapping reedbed habitats using texture-based classification of QuickBird imagery, Int. J. of Rem. Sens. 2011. Vol. 32. No. 23. P. 8121–8138. DOI: 10.1080/01431161.2010.532822.
Ozesmi S. L., Bauer M. E. Satellite remote sensing of wetlands, Wetlands ecology and management. 2002. Vol. 10. No. 5. P. 381–402. DOI: 10.1023/A:1020908432489.
Parviainen M., Luoto M., Heikkinen R. K. The role of local and landscape level measures of greenness in modelling boreal plant species richness, Ecological Modelling. 2009. Vol. 220. No. 20. P. 2690–2701. DOI: 10.1016/j.ecolmodel.2009.07.017.
Parviainen M., Zimmermann N. E., Heikkinen R. K., Luoto M. Using unclassified continuous remote sensing data to improve distribution models of red-listed plant species, Biodiversity and Conservation. 2013. Vol. 22. P. 1731–1754. DOI: 10.1007/s10531-013-0509-1.
Peterson E. B. Estimating cover of an invasive grass (Bromus tectorum) using tobit regression and phenology derived from two dates of Landsat ETM+ data, Int. J. of Rem. Sens. 2005. Vol. 26:12. P. 2491–2507. DOI: 10.1080/01431160500127815.
Petrou Z. I., Manakos I., Stathaki T. Remote sensing for biodiversity monitoring: a review of methods for biodiversity indicator extraction and assessment of progress towards international targets, Biodiversity and Conservation. 2015. Vol. 24. No. 10. P. 2333–2363. DOI: 10.1007/s10531-015-0947-z.
Pettorelli N., Vik J. O., Mysterud A., Gaillard J, M., Tucker C. J., Stenseth N. C. Using the satellite-derived NDVI to assess ecological responses to environmental change, Trends in Ecology and Evolution. 2005. Vol. 20. P. 503–510. DOI: 10.1016/j.tree.2005.05.011.
Petus C., Lewis M., White D. Monitoring temporal dynamics of Great Artesian Basin wetland vegetation, Australia, using MODIS NDVI, Ecological Indicators. 2013. Vol. 34. P. 41–52. DOI: 10.1016/j.ecolind.2013.04.009.
Pflugmacher D., Cohen W. B., Kennedy R. E. Using Landsat-derived disturbance history (1972–2010) to predict current forest structure, Rem. Sens. of Env. 2012. Vol. 122. P. 146–165. DOI: 10.1016/j.rse.2011.09.025.
Polychronaki A., Spindler N., Schmidt A., Stoinschek B., Zebisch M., Renner K., Sonnenschein R., Notarnicola C. Integrating RapidEye and ancillary data to map alpine habitatsin South Tyrol, Italy Int. J. of Appl. Earth Observ. and Geoinf. 2015. Vol. 37. P. 65–71. DOI: 10.1016/j.jag.2014.11.008.
Potapov P., Turubanova S., Hansen M. C. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia, Rem. Sens. of Env. 2011. Vol. 115. No. 2. P. 548–561.
Potapov P. V., Turubanova S. A., Tyukavina A., Krylov A. M., McCarty J. L., Radeloff V. C., Hansen M. C. Eastern Europe's forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive, Rem. Sens. of Env. 2015. Vol. 159. P. 28–43. DOI: 10.1016/j.rse.2014.11.027.
Pouteau R., Meyer J. Y., Taputuarai R., Stoll B. Support vector machines to map rare and endangered native plants in Pacific islands forests, Ecological Informatics. 2012. Vol. 9. P. 37–46. DOI: 10.1016/j.ecoinf.2012.03.003.
Price K. P., Guo X., Stiles J. M. Optimal Landsat TM band combinations and vegetation indices for discrimination of six grassland types in eastern Kansas, Int. J. of Rem. Sens. 2002. Vol. 23:23. P. 5031–5042. DOI: 10.1080/01431160210121764.
Rapinel S., Clément B., Magnanon S., Sellin V., Hubert-Moy L. Identification and mapping of natural vegetation on a coastal site using a Worldview-2 satellite image, Journal of environmental management. 2014. Vol. 144. P. 236–246. DOI: 10.1016/j.jenvman.2014.05.027.
Rapinel S., Bouzillé J. B., Oszwald J., Bonis A. Use of bi-Seasonal Landsat-8 Imagery for Mapping Marshland Plant Community Combinations at the Regional Scale, Wetlands. 2015. Vol. 35. Issue 6. P. 1043–1054. DOI: 10.1007/s13157-015-0693-8.
Red Data Book of Plant Communities in the former USSR. Birmingham, 1997. 69 p.
Reese H., Nyström M., Nordkvist K., Olsson H. Combining airborne laser scanning data and optical satellite data for classification of alpine vegetation, Int. J. of Appl. Earth Observ. and Geoinf. 2014. Vol. 27. P. 81–90. DOI: 10.1016/j.jag.2013.05.003.
Resasco J., Hale A. N., Henry M. C., Gorchov D. L. Detecting an invasive shrub in a deciduous forest understory using late-fall Landsat sensor imagery, Int. J. Remote Sens. 2007. Vol. 29. P. 3739–3745. DOI: 10.1080/01431160701373721.
Richards J. A., Jia X. Remote sensing digital image analysis. An introduction. Berlin, 2006. 439 p.
Rocchini D. Effects of spatial and spectral resolution in estimating ecosystem α-diversity by satellite imagery, Rem. Sens. of Environ. 2007. Vol. 111. P. 423–434. DOI: 10.1016/j.rse.2007.03.018.
Rocchini D., Balkenhol N., Carter G. A., Foody G. M., Gillespie T. W., He K. S., Kark S., Levin N., Lucas K., Luoto M., Nagendra H., Oldeland J., Ricotta C., Southworth J., Neteler M. Remotely sensed spectral heterogeneity as a proxy of species diversity: recent advances and open challenges, Ecol. Inform. 2010. Vol. 5. P. 318–329. DOI: 10.1016/j.ecoinf.2010.06.001.
Rocchini D., Andreo V., Förster M., Garzon-Lopez C. X., Gutierrez A. P., Gillespie T. H., Hauffe H. C., He K. S., Kleinschmit B., Mairota P., Marcantonio M., Metz M., Nagendra N., Pareeth S., Ponti L., Ricotta C., Rizzoli A., Schaab G., Zebisch M., Zorer R., Neteler M. Potential of remote sensing to predict species invasions. A modelling perspective, Prog. Phys. Geogr. 2015a. Vol. 39. P. 283–309.
Rocchini D., Hernández-Stefanoni J. L., He K. S. Advancing species diversity estimate by remotely sensed proxies: a conceptual review, Ecol. Inform. 2015b. Vol. 25. P. 22–28. DOI: 10.1016/j.ecoinf.2014.10.006.
Rodriguez-Galiano V. F., Chica-Rivas M. Evaluation of different machine learning methods for land cover mapping of a Mediterranean area using multi-seasonal Landsat images and Digital Terrain Models, Int. J. of Digital Earth. 2014. Vol. 7. No. 6. P. 492–509. DOI: 10.1080/17538947.2012.748848.
Schaffrath D., Barthold F. K., Bernhofer C. Spatiotemporal variability of grassland vegetation cover in a catchment in Inner Mongolia, China, derived from MODIS data products, Plant and soil. 2011. Vol. 340. No.1–2. P. 181–198. DOI: 10.1007/s11104-010-0465-4.
Schmidt M., Konig K., Muller J. V. Modelling species richness and life form composition in Sahelian Burkina Faso with remote sensing data, Journal of Arid Environments. 2008. Vol. 72. P. 1506–1517. DOI: 10.1016/j.jaridenv.2008.02.015.
Schuster C., Schmidt T., Conrad C., Kleinschmit B., Förster, M. Grassland habitat mapping by intra-annual time series analysis–Comparison of RapidEye and TerraSAR-X satellite data, Int. J. of Appl. Earth Observ. and Geoinf. 2015. Vol. 34. P. 25–34. DOI: 10.1016/j.jag.2014.06.004.
Sexton J. O., Song X, P., Feng M., Noojipady P., Anand A., Huang C., Kim D, H., Collins K. M., Channan S., DiMiceli C., Townshend J. R. Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error, Int. J. of Digital Earth. 2013. Vol. 6:5. P. 427–448. DOI: 10.1080/17538947.2013.786146.
Shmitt U., Ruppert G. S. Forest classification of multitemporal mosaicked satellite images, Int. Arch. of Photogrammetry and Remote Sensing. 1996. Vol. XXXI. Part B7. P. 602–605.
Short N. M. The LANDSAT Tutorial Workbook: Basics of Satellite Remote Sensing. NASA Reference Publication 1078. NASA. 2011. URL: http://pdf20.termsbooks.org/pdf/the-landsat-tutorial-workbook-basics-of-satellite-remote-sensing-nasa-re_d7mnv.pdf (data obrascheniya: 05.12.2015).
Shouse M., Liang L., Fei S. Identification of understory invasive exotic plants with remote sensing in urban forests, Int. J. of Appl. Earth Observ. and Geoinf. 2013. Vol. 21. P. 525–534. DOI: 10.1016/j.jag.2012.07.010.
Silva T. S., Costa M. P., Melack J. M., Novo E. M. Remote sensing of aquatic vegetation: theory and applications, Env. Monit. and Assessment. 2008. Vol. 140(1–3). P. 131–145. DOI: 10.1007/s10661-007-9855-3.
Somodi I., Čarni A., Ribeiro D., Podobnikar T. Recognition of the invasive species Robinia pseudacacia from combined remote sensing and GIS sources, Biological conservation. 2012. Vol. 150(1). P. 59–67. DOI: 10.1016/j.biocon.2012.02.014.
Stehman S. V., Czaplewski R. L. Design and analysis for thematic map accuracy assessment: fundamental principles, Rem. Sens. of Env. 1998. Vol. 64. P. 331–344. DOI: 10.1016/S0034-4257(98)00010-8.
Stow D. A., Hope A., McGuire D., Verbyla D., Gamon J., Huemmrich F., Houston S., Racine C., Sturm M., Tape K., Hinzman L., Yoshikawa K., Tweedie C., Noyle B., Silapaswan C., Douglas D., Griffith B., Jia G., Epstein H., Walker D., Daeschner S., Petersen A., Zhou L., Myneni R. Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems, Rem. Sens. of Env. 2004. Vol. 89(3). P. 281–308. DOI: 10.1016/j.rse.2003.10.018.
Townsend P. A., Walsh S. J. Remote sensing of forested wetlands: application of multitemporal and multispectral satellite imagery to determine plant community composition and structure in southeastern USA, Plant Ecology. 2001. Vol. 157. No. 2. P. 129–149. DOI: 10.1023/A:1013999513172.
Tuanmu M. N., Viña A., Bearer S., Xu W., Ouyang Z., Zhang H., Liu, J. Mapping understory vegetation using phenological characteristics derived from remotely sensed data, Rem. Sens. of Env. 2010. Vol. 114(8). P. 1833–1844. DOI: 10.1016/j.rse.2010.03.008.
Tucker C. J. Red and photographic infrared linear combinations for monitoring vegetation, Rem. Sens. of Env. 1979. Vol. 8. P. 127–150. DOI: 10.1016/0034-4257(79)90013-0.
Turner W., Spector S., Gardiner N., Fladerland M., Sterling E., Steininger M. Remote sensing for biodiversity science and conservation, Trends Ecol. Evol. 2003. Vol. 18. P. 306–314. DOI: 10.1016/S0169-5347(03)00070-3.
Turner W., Rondinini C., Pettorelli N., Mora B., Leidner A. K., Szantoi Z., Buchanan G., Dech S., Dwyer J., Herold M., Koh L. P., Leimgruber P., Taubenboeck H., Wegmann M., Wikelski M., Woodcock, C. Free and open-access satellite data are key to biodiversity conservation, Biological Conservation. 2015. Vol. 182. P. 173–176. DOI: 10.1016/j.biocon.2014.11.048.
Vanselow K. A., Samimi C. Predictive mapping of dwarf shrub vegetation in an arid high mountain ecosystem using remote sensing and random forests, Rem. Sens. 2014. Vol. 6(7). P. 6709–6726. DOI: 10.3390/rs6076709.
Viedma O., Torres I., Pérez B., Moreno J. M. Modeling plant species richness using reflectance and texture data derived from QuickBird in a recently burned area of Central Spain, Rem. Sens. of Env. 2012. Vol. 119. P. 208–221. DOI: 10.1016/j.rse.2011.12.024.
Wang C., Guo H., Zhang L., Qiu Y., Sun Z., Liao J., Liu G., Zhang Y. Improved alpine grassland mapping in the Tibetan Plateau with MODIS time series: a phenology perspective, Int. J. of Digital Earth. 2015. Vol. 8. No.2. P. 133–152. DOI: 10.1080/17538947.2013.860198.
Wang Z. J., Jiao J. Y., Lei B., Su Y. An approach for detecting five typical vegetation types on the Chinese Loess Plateau using Landsat TM data, Environmental monitoring and assessment. 2015. Vol. 187(9). P. 1–16. DOI: 10.1007/s10661-015-4799-5.
Whiteside T. G., Bartolo R. E. Mapping Aquatic Vegetation in a Tropical Wetland Using High Spatial Resolution Multispectral Satellite Imagery, Remote Sens. 2015. Vol. 7(9). P. 11664–11694. DOI: 10.3390/rs70911664.
Wilfong B. N., Gorchov D. L., Henry M. C. Detecting an invasive shrub in deciduous forest understories using remote sensing, Weed Science. 2009. Vol. 57. No. 5. P. 512–520. DOI: 10.1614/WS-09-012.1.
Wright C., Gallant A. Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data, Rem. Sens. of Env. 2007. Vol. 107. No. 4. P. 582–605. DOI: 10.1016/j.rse.2006.10.019.
Xie Y., Sha Z., Yu M. Remote sensing imagery in vegetation mapping: a review, Journal of plant ecology. 2008. Vol. 1. No. 1. P. 9–23. DOI: 10.1093/jpe/rtm005.
Xie Y., Zhang A., Welsh W. Mapping Wetlands and Phragmites Using Publically Available Remotely Sensed Images, Photogram. Engineering & Rem. Sens. 2015. Vol. 81. No.. 1. P. 69–78. DOI: http://dx.doi.org/10.14358/PERS.81.1.69.
Xu M., Watanachaturaporn P., Varshney P. K., Arora M. K. Decision tree regression for soft classification of remote sensing data, Rem. Sens. of Env. 2005. Vol. 97(3). P. 322–336. DOI: 10.1016/j.rse.2005.05.008.
Zhang Y., Lu D., Yang B., Sun C., Sun M. Coastal wetland vegetation classification with a Landsat Thematic Mapper image, Int. J. of Rem. Sens. 2011. Vol. 32:2. P. 545–561. DOI: 10.1080/01431160903475241.