CARBON STOCK ESTIMATION USING REMOTE SENSING DATA AND FIELD MEASUREMENT IN HALOXYLON AMMODENDRON DOMINANT WINTER COLD DESERT REGION OF MONGOLIA
- 1Institute of Geography and Geo-ecology, Mongolian Academy of Sciences, Ulaanbaatar-15170, Mongolia
- 2Mongolian Geo-spatial Association, Ulaanbaatar-15141, Mongolia
- 3Mongolian University of Life Sciences Sciences, Zaisan-17024, Ulaanbaatar, Mongolia
Keywords: Carbon stock, Haloxylon ammodendron, Saxaul vegetation, Winter cold desert, Western Mongolia, Gobi-Altai
Abstract. The UN-REDD Mongolia National Programme has studied about forest carbon emissions, and enhance and sustainably manage its carbon stocks, through the implementation of REDD+ activities since 2011. However, the current assessments seem to remain uncertain, the study for estimating carbon storage based on field survey are still rare. Because the Haloxylon ammodendron, where Gobi desert ecosystems are covering large areas, it is necessary to develop a modelling approach applying remote sensing. The study area is locating in Gobi-Altai province, Trans-Altai area as the south-western part of Mongolia. A total of 32 plots were established on eighth different land cover types to represent the range of variability. The study was used high spatial resolution imagery of Pleiades-1 and both of active and passive data from Sentinel-1 and Sentinel-2. The growing height in 32 plots is ranging from 20 to 460 cm with between 0.002 and 544.9 cm2 for basal area and between 526.5 and 166106.0 cm2 for canopy area, respectively. Shrub density is very high in plot 4 (n=135) and plot 5 (n=117) with low above-ground biomass 12 kg and 10.9 kg. The backscatter (dB) values of vegetated area and non-vegetated were comparable, −27.86 and −17.36 in VH polarisation and −22.72 and −10.61 in VV polarisation, respectively. Model-M1 was best demonstrated when a combination of vegetation coverage area was used as Pleiades-1 and Sentinel-2 derived vegetation cover data. For model-M9, the results were comparable to model-M1 but with lower the coefficient of determination. In this work, NDVI and MSAVI appear as a good indicator of biomass mainly because it does not saturate in sparse shrubs and is more sensitive to canopy parameters.