SUPPORTING INFORMATION Ecological restoration and its effects on the regional climate: the case in the source region of the Yellow River, China Zhouyuan Li, Xuehua Liu,* Tianlin Niu, De Kejia, Qingping Zhou, Tianxiao Ma, and Yunyang Gao The number of pages: 8 The number of figures: 4 The number of tables: 4
Classification and Accuracy Assessment of Land Cover Changes To learn the land cover in the study area, we took the field work in the summers of 2012 when we collected 230 GPS-sampled points in total. For these points of the ground information, 83 of them were used to train the classifier and guide the interpretation with the polygon tool of AOI (Area Of Interest) in ERDAS IMAGINE, and the left 147 points were used to validate and assess the result of the assessment (Table S-1). The sampling size (the area for certain types around the sampled points) was approximately over 200 200 m 2, and the training polygons drawn during the interpretation covered the areas of approximately 60 60 m 2 ~120 120 m 2 (2 2 to 4 4 pixels). The latest cloud-free satellite image we could find during the project was the scene of the summer in 2009 which was supposed to be little different from the landscape of the sampled points during the field work. We made trials on the scene of 2009 at first and then established the interpretation criterion based on the corresponding features between the satellite image and the natural scenery to classify the images. The maps of census, the regional land survey, and Google Earth, were also used as the reference to assist the validation for the classification. 1 We applied the method of maximum likelihood in ERDAS IMAGINE 2011 to classify the land cover into 5 types, including bare soils, low coverage grassland, middle coverage grassland, high coverage grassland, and water body. To set the interpretation criterion, we defined the land with no vegetation, and construction areas and sands as the bare soil; the grass coverage less than 30% as the low coverage grassland; the grass coverage between 30% -70% as the middle coverage grassland; the grass coverage more than 70% as high coverage grassland; and the lakes and rivers as the water body respectively (Table S-2). S1
Table S-1. The training points and validating points for the land cover classification land cover types training points training polygons validating points bare soils 16 832 22 low coverage grassland 12 1220 49 middle coverage grassland 25 2309 38 high coverage grassland 28 2493 29 water body 2 18341 9 sum 83 25195 147 Table S-2. Classification criterions of the study area in the source region of the Yellow River land cover types Interpretation criterion image photograph bare soils no vegetation lands, construction areas and sands Low coverage grassland the grass coverage less than 30% Middle coverage grassland the grass coverage between 30% - 70% High coverage grassland the grass coverage more than 70% Water body Lakes and rivers Table S-3. The accuracy assessment for the land covers classification. years 1990 1994 2000 2006 2009 overall accuracy% 65.22 82.61 78.26 69.75 65.22 kappa statistics 0.5365 0.7547 0.7028 0.5774 0.5365 S2
Retrieval on Remote Sensing Data We used the value adding products (VAP) of ATCOR2 module in ERDAS IMAGINE 2011 to retrieve the surface energy balance and the heat fluxes. ATCOR2 serves as an atmospheric correction tool for the flat terrain to calculate the ground reflectance and the surface temperature with satellite data. 2 In the In the packaged modules of VAP, we calculated a series of key variables, including normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), leaf area index (LAI), surface temperature (T s ), and net radiation (R n ).The heat fluxes including ground heat flux (G), sensible heat flux, and latent heat flux (LE) were also obtained. Finally evapotranspiration (ET) was transformed from the LE (Table S-4). Table S-4. The key variables and formulas of ATCOR model. Key variables Formulas Basic variables Normalized difference NDVI=(ρ 3 -ρ 4 )/( ρ 3 +ρ 4 ) ρ 3 and ρ 4, reflectance for band 3 and 4 vegetation index Surface temperature (K) T s = K 2 /ln(k 1 ε/l 6 +1) K 1, constant for Landsat images, 607.76 mw/cm 2 /sr/µm K 2, constant for Landsat images, 1260.56mW/cm 2 /sr/µm L 6, the spectral radiance of band 6 Net radiation flux (W/m 2 ) R n = R solar +R atm +R surface R solar, the absorbed short-wave solar radiation(w/m 2 ) R atm, the long-wave radiation emitted from the atmosphere toward the surface(w/m 2 ) R surface, the longwave radiation emitted from the surface into the atmosphere (W/m 2 ) Ground heat flux(w/m 2 ) G=R n *0.4*(SAVI m SAVI ) / SAVI m S3 SAVI, soil-adjusted vegetation index SAVI m, index of full vegetation cover, 0.814 Latentheatflux (W/m 2 ) LE = R n -G- B(T s -T a ) n B, coefficient, B = 286*(0.0109+0.051*NDVI/0.75) n, coefficient, n = 1.067-0.372*NDVI/0.75 T a, the air temperature (K) Sensible heat flux(w/m 2 ) H=R n G LE - Evapotranspiration(cm/day) ET =LE /286 -
The retrieval models in the calculation are based on the regional energy budget equation and require the visible, near-infrared and thermal infrared data together with the meteorological data (Fig S-1). The local meteorological data were collected from China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/) to set the initial conditions for modeling. Figure S-1. Flow chart of the key variables computation in the value adding products of ATCOR2 module in ERDAS IMAGINE 2011. Validation of the Estimated Results with Meteorological Data To validate T s and ET values estimated from remote sensing data, the interpolation on meteorological observation was performed to obtain spatially continuous data. We collected the average values of air temperature (T a ) and observed ET (ET obs ) in summers (June-September) from 13 stations around the study area from 1990-2010 to apply inverse distance weighted (IDW) approach using ArcGIS 9.0 extension tool. 3,4 The spatially continuous data of T a and ET obs from 1990-2009 were mapped and the data of S4
1990, 1994, 2000, 2006, and 2009 were coordinated with the raster data of remotesensing estimated T s and ET (Fig S-2). After overlaying the images of the corresponding raster data, the values of estimated and observed were extracted within the study area which were taken with regression analysis. Figure S-2. Overlaying the spatial interpolation of meteorological data, T a and ET obs, to compare the remote-sensing estimated values of T s and ET respectively From spatial pattern changes of T a, it was found that the trend of warming for the study area was obvious [Fig S-3 (a) (b)]. For the dynamic of ET obs, the interpolation results demonstrated the level climbed up since around 2000 [Fig S-4 (a) (b)]. The regression results of T a -T s and ET obs -ET were shown that the estimation was generally accordant with the observation [Fig S-3 (c), Fig S-4 (c)]. The discreteness and the mismatched scale of the meteorological stations could be attributed to the dispersion in the regression results at first. The second reason is that the S5
processing ways of the two methods for the spatially continuous data, the IDW interpolation and remote sensing estimation, is totally different. The interpolation processing, IDW, mapped T a and ET obs based on the values of the singular points observation and output only through the numerical calculation. 5 In remote sensing estimation model, T s and ET was calculated by the physical mechanism for each pixel. More detailed explanation and discussion on validation could be referred in the previous relevant studies. 6 S6
Figure S-3 The interpolation pattern dynamics of T a (a), trends of T a (b), and regression of T a -T s S7
Figure S-4 The interpolation pattern dynamics of ET obs (a), trends of ET obs (b), and regression of ET obs -ET S8
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