Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 92-101
Spectral differences in vegetation cover characteristics of tundra communities by Landsat sensors
1 Institute of Biology, Komi Science Centre UrB RAS, Syktyvkar, Russia
Accepted: 21.06.2021
DOI: 10.21046/2070-7401-2021-18-4-92-101
A comparison of the red and near-infrared spectral values and NDVI index of Landsat sensors (TM/ETM+/OLI) obtained on adjacent acquisition dates is performed. The TM and OLI images comparison of which was achieved via ETM+ had different overlapping periods. The imaging interval in the compiled pairs of sensors was 1 day. The image pairs were analyzed over two model areas localized in the eastern part of the shrub-dominated Bolshezemelskaya tundra. Significant changes in the NDVI index were not observed for the time interval considered 2009–2020. Phytocenoses classes with absence (65.1 %) or weak positive (28.5 %) changes prevailed in order of the average rate of NDVI index changes. Spectral brightness values for tundra phytocenoses shown insignificant deviations despite the significant difference in length of NIR bands for OLI (0.85–0.88 microns) and ETM+ (0.77–0.90 microns) sensors. The offsets of spectral values for different sites and survey years using the relative percentage difference (RPD, %) criterion had similar dependencies of the deviations for the vegetation cover classes of the analyzed pairs. Sensors ETM+ compared to TM systematically overestimated NIR (1.9 %) and NDVI (7.4 %) and underestimated RED (2.6 %). A slight overestimation for the OLI radiometer compared to the ETM+ for the NIR channel (1.7 %) and NDVI (4.5 %) and an underestimation for RED (2.3 %) was noted. The maximum differences in the NDVI index (averaging up to 11.9 % for a pair of images) were observed for the most time-dispersed OLI and TM sensor surveys. This has demonstrated a possible source of error in interannual comparisons, the measurement error could numerically exceed the variability of vegetation productivity indices caused by interannual weather patterns.
Keywords: tundra vegetation communities, comparison of Landsat, TM, ETM+ and OLI sensors, seasonal and inter-annual vegetation changes
Full textReferences:
- Aleksandrova V. D., Geobotanicheskoe raionirovanie Arktiki i Antarktiki (Geobotanical zoning of the Arctic and Antarctic), Leningrad: Nauka, 1977, 189 p. (in Russian).
- Andreev V. N., Galaktionova T. F., Govorov P. M., Sezonnaya i pogodovaya dinamika fitomassy v subarkticheskoi tundre (Seasonal and annual phytomass dynamic in the subarctic tundra), Novosibirsk: Nauka, 1978, pp. 50–52 (in Russian).
- Veselkin D. V., Morozova L. M., Gorbunova A. M., Decrease of NDVI values in the southern tundra of Yamal in 2001–2018 correlates with the size of domesticated reindeer population, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 2, pp. 143–155 (in Russian), DOI: 10.21046/2070-7401-2021-18-2-143-155.
- Gribova S. A., Tundra, In: Rastitel’nost’ evropeiskoi chasti SSSR (Vegetation of the European part of the USSR), Leningrad: Nauka, 1980, pp. 29–70 (in Russian).
- Elsakov V. V., Shchanov V. M., Current changes in vegetation cover of Timan tundra reindeer pastures from analysis of satellite data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 2, pp. 128–142 (in Russian), DOI: 10.21046/2070-7401-2019-16-2-128-142.
- Lavrinenko I. A., Vegetation dynamics of the Vaigach island under climate change impact, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2011, Vol. 8, No. 1, pp. 183–189 (in Russian).
- Matveeva N. V., Zonal’nost’ v rastitel’nom pokrove Arktiki (Zonality in Arctic vegetation cover), Saint Petersburg, 1998, 220 p. (in Russian).
- Nazemnoe geobotanicheskoe obsledovanie zemel’nykh uchastkov, arenduemykh pastbishchnykh ugodii SPK “Krasnyi Oktyabr’” dlya tselei severnogo olenevodstva (The ground geobotanical survey of reindeer farm land by SPK “Krasnyi Oktyabr’” for reindeer herding purposes), Contract report, 2013, 24 p. (in Russian).
- Polyakova E. V., Estimation of the vegetation cover for Vaigach isl. and by remote sensing data in a changing climate, Fundamental’nye issledovaniya, 2015, No. 2-22, pp. 4924–4929 (in Russian).
- Terekhin E. A., Spectral response dynamics of the reforestation sites in forests of the south of Central Russian Upland, Regional’nye geosistemy, 2020, Vol. 44, No. 2, pp. 210–220 (in Russian).
- Aubard V., Paulo J. A., Silva J. M. N., Long-Term Monitoring of Cork and Holm Oak Stands Productivity in Portugal with Landsat Imagery, Remote Sensing, 2019, No. 11, Art. No. 525.
- Goetz Sc., Bunn A. G., Fiske G. J., Houghton R. A., Satelite-observed photosynthetic trends across boreal North America associated with climate and fire disturbance, Proc. National Academy of Sciences of the United States of America (PNAS), 2005, Vol. 102, No. 38, pp. 13521–13525.
- Huang W., Huang J., Wang X., Wang F., Shi J., Comparability of Red/Near-Infrared Reflectance and NDVI Based on the Spectral Response Function between MODIS and 30 Other Satellite Sensors Using Rice Canopy Spectra, Sensors (Basel), 2013, Vol. 12, No. 13, pp. 16023–16050.
- Li P., Jiang L., Feng Z., Cross-Comparison of Vegetation Indices Derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 Operational Land Imager (OLI) Sensors, Remote Sensing, 2014, Vol. 1, No. 6, pp. 310–329.
- Mancino G., Ferrara A., Padula A., Nolè A., Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment, Remote Sensing, 2020, Vol. 2, No. 12, Art. No. 291.
- Markham B. L., Barker J. L., Landsat MSS and TM post-calibration dynamic ranges, exoatmospheric reflectances and at-satellite temperatures, EOSAT Landsat Tech. Notes, Lanham, 1986, No. 1, pp. 3–8.