CN103678914A - Alpine grassland soil respiration estimation method based on satellite remote sensing data - Google Patents
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Abstract
The invention relates to an alpine grassland soil respiration estimation method based on satellite remote sensing data. An MODIS and Landsat TM satellite remote sensing data are used for extracting spectrum vegetation indexes, establishing a fitting model and estimating the spatial pattern of alpine grassland soil respiration. An optimal index function relation is presented between the NDVI calculated through high spatial resolution TM images (30m) and the alpine grassland soil respiration, while area complete coverage is difficult to achieve due to the long revisiting period of the TM images and the influences of cloud, and the defect is well overcome by eight-day maximum resultant images (500m) of the low spatial resolution MODIS. Therefore, MODIS NDVI values are corrected by using TM NDVI values, and then acquisition of the spatial pattern of metaphase soil respiration in the grow seasons of alpine grassland of Tibet plateau is achieved based on the NDVI calculated through the surface reflectance data combining the eight-day maximum values of the MODIS.
Description
Technical field: ecologic environment remote sensing field
Background technology: soil respiration has been described soil to airborne release CO
2process, it is the second largest carbon flux component that is only second to gross primary productivity in Terrestrial Ecosystem Carbon Cycle, in Terrestrial Ecosystem Carbon Cycle and carbon revenue and expenditure, occupies critical role.Estimate that soil respiration the best way is exactly the soil respiration speed of directly measuring Different Soil surface, wherein, the dynamic case method of closed is that commonplace and soil respiration flux observation procedure that approved is (referring to document: Raich, J.W., Potter, C.S., Bhagawati, D.Interannual variability in global soil respiration, 1980-94.Global Change Biology, 2002,8 (8): 800-812.).This method can guarantee the accuracy that soil respiration is estimated, but is difficult to obtain by direct measurement the soil respiration of regional scale.The research that utilization can provide the satellite remote sensing technology of macro-scale terrestrial information to carry out regional soil breathing more and more receives people's concern.Chinese scholars utilizes remote sensing technology relatively many to the research of " on the ground " process in terrestrial ecosystems, as the gross primary productivity of land vegetation (GPP), net primary productivity (NPP) and global net ecosystem exchange (NEE) etc. are (referring to document: Running, S.W., Thornton, P.E., Nemani, R.R., Glassy, J.M.Global terrestrial gross and net primary productivity from the Earth observing system, in Methods in Ecosystem Science, edited by O.Sala et al., Springer, New York, 2000, pp.44-57.Xiao, X., Hollinger, D., Aber, J., Gholtz, M., Davidson, E.A., Zhang, Q., Moore, III B.Satellite-based modeling of gross primary production in an evergreen needleleaf forest, Remote Sensing of Environment, 2004, 89 (4): 519-534.Rahman, A.F., Sims, D.A., Cordova, V.D., El-Masri, B.Z.Potential of MODIS EVI and surface temperature for directly estimating per-pixel ecosystem C fluxes.Geophysical Research Letters, 2005, 32 (19), L19404, doi:10.1029/2005GL024127.Sims, D.A., Rahman, A.F., Cordova, V.D., El-Masri, B.Z., Baldocchi, D.D., Flanagan, L.B., Goldstein, A.H, Hollinger, D.Y., Misson, L., Monson, R.K., Oechel, W.C., Schmid, H.P., Wofsy, S.C., Xu, L.K.On the use of MODIS EVI to assess gross primary productivity of North American ecosystems.Journal of Geophysical Research, 2006, 111 (G4), G04015, doi:10.1029/2006JG000162.Peng, Y., Gitelson, A.A.2011.Application of chlorophyll-related vegetation indices for remote estimation of maize productivity.Agricultural and Forest Meteorology, 151 (9): 1267-1276.), but it is very few that remote sensing technology is applied to the research of " underground " soil respiration process.
The research of Geng et al (2012) finds that the underground biomass that Alpine Grasslands is grown mid-term in season is to determine that the major control factor of its soil respiration spatial variations is (referring to document: Geng, Y., Wang, Y.H., Yang, K., Wang, S.P., Zeng, H., Baumann, F., Kuehn, P., Scholten, T., He, J.S.Soil respiration in Tibetan alpine grasslands:Belowground biomass and soil moisture, but not soil temperature, best explain the large-scale patterns.PLoS ONE, 2012, 7 (4): e34968.).Therefore, monitoring the variation of Alpine Grasslands biomass by remote sensing technology, is remote sensing technology to be applied to a bridge of Alpine Grasslands soil respiration spatial spread.The Landsat thematic mapper of high spatial resolution (Landsat TM) image (spatial resolution is 30m), aspect the variation of monitoring top, bringing into play great role (referring to document: Vogelman, J.E., Howard, S.M., Yang, L.M., Larson, C.R., Wylie, B.K., Van Driel, N.Completion of the1990s National Land Cover Data set for the conterminous United States for Landsat Thematic Mapper data and ancillary data sources.Photogrammetric Engineering and Remote Sensing, 2001, 67:650-662.), but, its cycle of heavily visiting of 16 days and frequently cloud pollute may limit the application of Landsat data on larger space yardstick, especially in the very unsettled region of these atmospheric conditions, Qinghai-Tibet Platean.The Moderate Imaging Spectroradiomete (MODIS) of lift-launch on Terra and Aqua satellite provides the low spatial resolution remotely-sensed data (spatial resolution is 500m) of frequent observation, aspect monitored area variation in time, bringing into play advantage very greatly.Therefore, comprehensive utilization Landsat and MODIS data are monitored the spatial framework that large regional soil breathes and may Billy be produced better effect by single data.Based on this imagination, we have proposed the method that comprehensive utilization Landsat TM image and MODIS image are estimated Qinghai-Tibet Alpine Grasslands growth soil respiration in mid-term in season spatial framework.
Summary of the invention: the Research foundation of this method is that the underground biomass that Qinghai-Tibet Alpine Grasslands is grown mid-term in season is a very important driving factor that drives its soil respiration spatial variations.Therefore, utilize the index that can reflect vegetation growth state (that is, vegetation index) that satellite different-waveband detection data combines can be for the estimation of Alpine Grasslands soil respiration.Three vegetation indexs (quadrature difference vegetation index (NDVI), the soil adjusting vegetation index (MSAVI) that strengthens vegetation index (EVI) and revise) that can explain preferably Alpine Grasslands biomass (comprising ground biomass and underground biomass) spatial variations that calculate based on MODIS and Landsat TM image are for estimating the spatial framework of soil respiration.Wherein, between the NDVI that TM image calculates and soil respiration, presented best exponential function relation, but LandsatTM image is due to the covering completely of the very difficult feasible region of impact of the long cycle of heavily visiting and cloud, within MODIS8 days, the synthetic image of maximal value has well overcome this shortcoming.Therefore, in conjunction with Landsat TM image and MODIS image advantage separately, utilize TM NDVI value (30m spatial resolution) to proofread and correct corresponding MODIS NDVI value (500m spatial resolution), then the NDVI of synthetic Reflectivity for Growing Season data calculating in the MODIS8 based on 500m resolution days, realizes obtaining of Qinghai-Tibet Alpine Grasslands growth soil respiration in mid-term in season spatial framework.
Accompanying drawing explanation:
Fig. 1 obtains Qinghai-Tibet Alpine Grasslands growth 500m spatial resolution soil respiration in mid-term in season Spatial Distribution Pattern process flow diagram.Data Layer and model represent by grey box; Operation steps represents by white box.
The ground biomass (AGB) of Figure 22 006 Tibetan Plateau growth Alpine Grasslands in mid-term in season and the exponential fitting relation between spectrum vegetation index (VIs).These vegetation indexs are: quadrature difference vegetation index (NDVI), the soil adjusting vegetation index (MSAVI) that strengthens vegetation index (EVI) and revise
The underground biomass (BGB) of Figure 32 006 Tibetan Plateau growth Alpine Grasslands in mid-term in season and the exponential fitting relation between spectrum vegetation index (VIs).These vegetation indexs are: quadrature difference vegetation index (NDVI), the soil adjusting vegetation index (MSAVI) that strengthens vegetation index (EVI) and revise
Day soil respiration (the R of Figure 42 006 Tibetan Plateau growth Alpine Grasslands in mid-term in season
s) and spectrum vegetation index (VIs) between exponential fitting relation.These vegetation indexs are: quadrature difference vegetation index (NDVI), the soil adjusting vegetation index (MSAVI) that strengthens vegetation index (EVI) and revise
Transformational relation between Figure 52 006 Tibetan Plateau growth Alpine Grasslands NDVI_MODIS in mid-term in season (NDVI calculating by MODIS image) and NDVI_TM (NDVI calculating by TM image)
The spatial variations general layout of Figure 62 006 Tibetan Plateau growth Alpine Grasslands soil respiration in mid-term in season
Fig. 7 utilizes the relation between the soil respiration that 2006 Tibetan Plateaus growth Alpine Grasslands soil respiration speed in mid-term in season and the ground of the NDVI_MODIS estimation of TM adjustment of image surveys
Embodiment:
The method comprises 3 committed steps: the relation of 1. analyzing three vegetation indexs (quadrature difference vegetation index (NDVI), the soil adjusting vegetation index (MSAVI) that strengthens vegetation index (EVI) and revise) and Alpine Grasslands biomass (comprising ground biomass and underground biomass); 2. analyze the relation between three vegetation indexs and the Alpine Grasslands soil respiration of field survey, and determine optimum soil respiration forecast model; 3. utilize the vegetation index calculating based on Landsat TM image to proofread and correct MODIS vegetation index, and the MODIS image of use 500m spatial resolution carry out the space scale expansion of soil respiration.Specific implementation process is as follows:
Step 1: by the method for random packet, all sampled datas are divided into two data sets.A data set has the sampling number of 3/4ths sampled point quantity according to being used for modeling, and another data set has the sampling number of 1/4th sampling number amounts according to the checking for model.At Qinghai-Tibet Alpine Grasslands, grow the most vigorous season, underground biomass is found to be and drives the most important factor of influence of large scale soil respiration spatial variations (referring to document: Geng, Y., Wang, Y.H., Yang, K., Wang, S.P., Zeng, H., Baumann, F., Kuehn, P., Scholten, T., He, J.S.Soil respiration in Tibetan alpine grasslands:Belowground biomass and soil moisture, but not soil temperature, best explain the large-scale patterns.PLoS ONE, 2012, 7 (4): e34968.).Therefore, use respectively linear, logarithm and exponential function to analyze the relation between Alpine Grasslands soil respiration or biomass and the vegetation index calculating based on MODIS or Landsat TM image, and selection have the model of fit of the high coefficient of determination and predict soil respiration.
The vegetation index (VIs_TM) calculating based on Landsat TM image and the relation between Alpine Grasslands ground biomass and vegetation index (VIs_MODIS) based on the calculating of MODIS image and the relation object between ground biomass seemingly, the spatial variations of Alpine Grasslands ground biomass that has been all the best explanations of the exponential function of NDVI.And VIs_TM and ground biomass exponential fitting relation are wanted the conforming matching relation (Fig. 2) being better than between VIs_MODIS and ground biomass.
The relation (Fig. 3) of its ground biomass and VIs_MODIS or VIs_TM has been reacted in the Alpine Grasslands growth underground biomass in vigorous season with the relation between VIs_MODIS or VIs_TM.But the coefficient of determination of exponential fitting will be apparently higher than the coefficient of determination (Fig. 2 and Fig. 3) of matching relation between each vegetation index and ground biomass between each vegetation index and underground biomass.
And the relation object between each vegetation index and biomass is seemingly, the description that exponential fitting function is best relation between each vegetation index and Alpine Grasslands soil respiration.No matter be based on MODIS or Landsat TM image, NDVI has better explained the spatial variations (Fig. 4) of Alpine Grasslands soil respiration than EVI and MSAVI.But, with the exponential model (R of NDVI_MODIS
2=0.56) compare, high a lot (R wanted in the coefficient of determination of NDVI_TM exponential model
2=0.71) (Fig. 4).In addition, NDVI_TM is also the evaluation studies region Alpine Grasslands biomass best predictor of (comprising ground biomass and underground biomass) (Fig. 2 and Fig. 3).Therefore, we think that NDVI_TM is than NDVI_MODIS, can better assess the spatial framework of Alpine Grasslands soil respiration.
Step 2: because Landsat TM image has the long heavily visit cycle, add the impact of weather condition, be difficult to the covering completely of feasible region.Within MODIS8 days, the synthetic image of maximal value can provide the image that quality is higher.Therefore, we convert MODIS vegetation index to its corresponding TM vegetation index, and the soil respiration forecast model based on selecting then utilizes 8 days synthetic MODIS images to estimate the Spatial Distribution Pattern of whole survey region soil respiration.
By linear regression, NDVI_TM corresponding to NDVI_MODIS convert to, to realize the soil respiration of measuring in the level of plot to the scaling up of whole plateau region.NDVI_MODIS and NDVI_TM have presented good linear regression relation (R
2=0.83) (Fig. 5).According to the regression relation between NDVI_MODIS and NDVI_TM, we change into its corresponding NDVI_TM value by NDVI_MODIS value.Last as follows for assessment of the forecast model of Alpine Grasslands soil respiration:
R
s＝0.9805×e
2.5763×(0.9655×NDVI_MODIS+0.0166 (1)
R
2＝0.71，p＜0.0001
Wherein, R
sa day soil respiration speed (gC m
-2d
-1), NDVI_MODIS is the NDVI calculating based on MODIS image.
Step 3: the spatial distribution map of the soil respiration spatial distribution map calculating based on MODIS image by stack and the Alpine Grasslands obtaining based on soil cover data, obtains the spatial framework (Fig. 6) that Qinghai-Tibetan plateau soil is breathed.Then, utilize independently verification msg collection, evaluate the precision of prediction (Fig. 7) of soil respiration model.The soil respiration that field survey obtains relatively approaches with the soil respiration of the NDVI_MODIS assessment that utilizes TM adjustment of image to cross.Based on verification msg collection independently, the spatial variations of ground survey soil respiration 78% has been explained in the soil respiration of the corrected NDVI_MODIS assessment of TM, and RMSE is 1.45gC m
-2d
-1.
Because Alpine Grasslands has higher biomass density in growth vigorous season, so its autotrophic respiration accounts for a big chunk of total soil respiration, and then underground biomass has been explained the spatial variations of soil respiration preferably.Therefore the spectrum vegetation index, being associated with Alpine Grasslands underground biomass can be for assessment of the space distribution of soil respiration.This research method can be applied to other and have the vegetation distribution district of similar physiological characteristic to Alpine Grasslands, and accurate estimation area carbon source is converged to important role.
Claims (3)
1. the Alpine Grasslands soil respiration evaluation method based on satellite remote sensing date, it is characterized in that: utilize Moderate Imaging Spectroradiomete (MODIS) and Landsat thematic mapper (Landsat TM) satellite remote sensing date to extract spectrum vegetation index and set up the spatial framework that model of fit is estimated Qinghai-Tibet Alpine Grasslands soil respiration, concrete steps are as follows:
(1) Alpine Grasslands is grown and is had very high root biomass density mid-term in season, and then root system respiration is the factor of determination that drives Alpine Grasslands soil respiration spatial variations, therefore, utilize the grow spatial framework of soil respiration in mid-term in season of the vegetation index that can reflect vegetation growth state that satellite different-waveband detection data combines (quadrature difference vegetation index (NDVI), strengthen vegetation index (EVI) and the soil revised regulates vegetation index (MSAVI)) estimation Alpine Grasslands;
(2) utilize respectively linear, logarithm and exponential function to analyze the relation between Alpine Grasslands soil respiration and the vegetation index calculating based on MODIS or Landsat TM image, select optimum vegetation index and there is the grow spatial framework of soil respiration in mid-term in season of the model of fit estimation Alpine Grasslands of the high coefficient of determination.
2. method according to claim 1, it is characterized in that: described optimum vegetation index is quadrature difference vegetation index (NDVI), first utilize high spatial resolution Landsat TM image to calculate NDVI (spatial resolution is 30m) and proofread and correct the NDVI (spatial resolution is 500m) that low spatial resolution MODIS Reflectivity for Growing Season data are calculated, then utilize the spatial framework of the MODIS NDVI data estimation Alpine Grasslands growth soil respiration in mid-term in season after proofreading and correct.
3. according to the method described in claim 1 and 2, it is characterized in that: claimed in claim 1 to have the model of fit of the high coefficient of determination be exponential Function Model, and the driving data of this exponential Function Model is the MODIS NDVI data of the 500m spatial resolution of utilizing TM NDVI Data correction described in claim 2.
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