The study on biomass estimation in Mongolian Grassland
using satellite data and field measurement data
3.4 Analysis of Meteorological Station data
15 meteorological stations are located at flat and homogeneous grassland in eastern part of Mongolia (Figure.2). Acquisition date is from the beginning of June to the end of September every 10 days. Observation items are ground image by photographs, wet and dry grass weight, and grass height. Cutting area is 1m ×1m, and cutting method is almost same as field biomass measurement. Dry weight is the grass weight dried by sun for a few days. In this study, meteorological station data was used as evaluation of estimated biomass in the wide area and construction of VCR-Biomass model.
In model construction, using 4 corners of the clipping area as control point, affine transformation method was conducted to the non-vertical digitized photograph. After this process, the RGB Pattern method and Hue and Intensity method were conducted to the image. 270 good points of 420 measurement points were used as VCR data. And then VCR-Biomass model was constructed by simple linear regression between this VCR data and biomass data of meteorological station.
In evaluation of estimated biomass, as above-mentioned, corrected reflectance of satellite sensor is calculated each channels, therefore NDVI is derived from these reflectance data. Moreover, biomass map in the wide area can be calculated by using biomass estimation model derived from field experiment data. The following equation was used as accuracy of the estimated biomass:
e = (T-M)/T×100
e: Relative Error
M: Estimated Biomass
T: Meteorological Station Data (15 Stations) … (1)
Moreover, the estimation biomass was evaluated every composite period. The influence of the plant growth for composite period was also evaluated.
Figure.2 The Distribution of 15 Meteorological Stations in Mongolia
| No. | Station Name | Latitude
(N, deg) | Longitude
(E,deg) |
| 1 | Baruunurt | 46.68 | 113.28 |
| 2 | Bayanchadmani | 48.23 | 106.28 |
| 3 | Bayandelger | 45.73 | 112.37 |
| 4 | Bayanuul(0 nou) | 49.12 | 112.68 |
| 5 | Bulgan(0 rkhon) | 48.80 | 103.55 |
| 6 | Dalanzadgad | 43.58 | 104.42 |
| 7 | Dashbalbar | 49.55 | 114.40 |
| 8 | Khalkhgol | 47.62 | 118.62 |
| 9 | Khustai | 47.80 | 105.92 |
| 10 | Maanit | 47.30 | 107.48 |
| 11 | Mandalgobi | 45.77 | 106.28 |
| 12 | Matad | 47.15 | 115.67 |
| 13 | Sainhand | 44.90 | 110.12 |
| 14 | Tumenthogt | 45.60 | 112.20 |
| 15 | Yamag (U laanbartar) | 47.79 | 106.63 |
4. Result
4.1 Biomass Estimation Model Using Field data and Meteorological Station Data
Figure.3 depicts example of averaged spectral reflectance obtained by mobile measurement. It is clearly that enough amount of data was collected in the wide area, and it can be said the representative reflectance on a satellite scale. NDVI was calculated from convolved reflectance to NOAA AVHRR spectral resolution. The left side of Figure.4 shows relationship between NDVI and vegetation cover ratio (VCR). It is good correlation between VCR and NDVI. Therefore they are able to be constructed NDVI-VCR model on a satellite scale. This model can be obtained by simple regression analysis, and it expressed as the following formula:
VCR = 0.9375 NDVI-0.0830 (R = 0.847)
VCR : Vegetation Cover Ratio …(2)
Figure.3 Example of Averaged Spectral Reflectance obtained by Mobile Measurement
