基于MODIS数据的典型草原非光合植被覆盖度估算
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摘要
非光合植被(non-photosynthetic vegetation,NPV)在草原生态系统中扮演了重要角色,影响着生态系统的碳、水和能量的流动与循环。定量掌握草原非光合植被覆盖度(fractional cover of non-photosynthetic vegetation,f NPV)信息对草地资源的科学有效利用以及生态环境保护具有重要意义。以内蒙古自治区锡林郭勒典型草原为研究区,运用线性回归分析方法,建立基于MODIS (MCD43A4)数据构建的多种非光合植被指数(non-photosynthetic vegetation indices,NPVIs)和野外实测f NPV数据的反演模型,并对模型的估算结果进行验证。研究结果表明,基于MODIS数据构建的NPVIs与f NPV的相关性较好,相关性依次为:DFI,SWIR32,NDTI,STI,NDI7,NDI5及NDSVI; DFI指数反演f NPV模型的估算精度较高,可用于典型草原地区大范围f NPV的快速监测。
Non-photosynthetic vegetation( NPV) is an important component of grassland ecosystem,which affects the flow and cycle of carbon,water and energy in the ecosystem. It is of great significance to quantitatively grasp the fractional cover of non-photosynthetic vegetation( f NPV) information for the scientific and effective utilization of grassland resources and the protection of the ecological environment. Taking the typical steppe of Xilingol in Inner Mongolia as the research area and using the regression analysis method,the authors used a variety of non-photosynthetic vegetation indices( NPVIs) based on MODIS( MCD43 A4) data and field measured f NPV data to invert the f NPV model and evaluated the estimation effect of the model. The results show that the NPVIs based on MODIS data have a good correlation with f NPV. The correlations are as follows: DFI,SWIR32,NDTI,STI,NDI7,NDI5 and NDSVI. The DFI index inversion f NPV model has higher estimation accuracy. It can be applied to the rapid monitoring of large scale f NPV in typical steppe.
Non-photosynthetic vegetation( NPV) is an important component of grassland ecosystem,which affects the flow and cycle of carbon,water and energy in the ecosystem. It is of great significance to quantitatively grasp the fractional cover of non-photosynthetic vegetation( f NPV) information for the scientific and effective utilization of grassland resources and the protection of the ecological environment. Taking the typical steppe of Xilingol in Inner Mongolia as the research area and using the regression analysis method,the authors used a variety of non-photosynthetic vegetation indices( NPVIs) based on MODIS( MCD43 A4) data and field measured f NPV data to invert the f NPV model and evaluated the estimation effect of the model. The results show that the NPVIs based on MODIS data have a good correlation with f NPV. The correlations are as follows: DFI,SWIR32,NDTI,STI,NDI7,NDI5 and NDSVI. The DFI index inversion f NPV model has higher estimation accuracy. It can be applied to the rapid monitoring of large scale f NPV in typical steppe.
引文
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[13]姬翠翠，贾永红，李晓松，等．线性/非线性光谱混合模型估算白刺灌丛植被覆盖度[J]．遥感学报，2016,20(6):1402-1412.Ji C C,Jia Y H,Li X S,et al. Research on linear and nonlinear spectral mixture models for estimating vegetation fractional cover of nitraria bushes[J]. Journal of Remote Sensing,2016,20(6):1402-1412.
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[18]张淼，李强子，蒙继华，等．作物残茬覆盖度遥感监测研究进展[J]．光谱学与光谱分析，2011,31(12):3200-3205.Zhang M,Li Q Z,Meng J H,et al. Review of crop residue fractional cover monitoring with remote sensing[J]. Spectroscopy and Spectral Analysis,2011,31(12):3200-3205.
[19] Jacques D C,Kergoat L,Hiernaux P,et al. Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands[J]. Remote Sensing of Environment,2014,153:40-49.
[20] van Deventer A P,Ward A D,Gowda P H,et al. Using thematic mapper data to identify contrasting soil plains and tillage practices[J]. Photogrammetric Engineering and Remote Sensing,1997,63(1):87-93.
[21] Qi J,Marsett R,Heilman P,et al. RANGES improves satellitebased information and land cover assessments in southwest United States[J]. EOS Transactions American Geophysical Union,2002,83(51):601-606.
[22]刘春静，张丽，周宇，等．中国新疆及中亚五国干旱区草地覆盖度反演与分析[J]．草业科学，2016,33(5):861-870.Liu C J,Zhang L,Zhou Y,et al. Retrieval and analysis of grassland coverage in arid Xinjiang,China and five countries of Central Asia[J]. Pratacultural Science,2016,33(5):861-870.
[23]吴林，张元明．以数码照相法估算生物土壤结皮盖度[J]．中国沙漠，2013,33(6):1810-1815.Wu L,Zhang Y M. Coverage estimation on biological soil crusts based on digital photos[J]. Journal of Desert Research,2013,33(6):1810-1815.
[24]张浩彬，李俊生，向南平，等．基于MODIS地表反射率数据的水体自动提取研究[J]．遥感技术与应用，2015,30(6):1160-1167.Zhang H B,Li J S,Xiang N P,et al. A study of extracting water bodies automatically based on the MODIS surface reflectance data[J]. Remote Sensing Technology and Application,2015,30(6):1160-1167.
[25] Serbin G,Daughtry C S T,Hunt E R,et al. Effects of soil composition and mineralogy on remote sensing of crop residue cover[J].Remote Sensing of Environment,2009,113(1):224-238.
[26]唐世浩，朱启疆，孙睿．基于方向反射率的大尺度叶面积指数反演算法及其验证[J]．自然科学进展，2006,16(3):331-337.Tang S H,Zhu Q J,Sun R. The inversion algorithm and its validation of coarse scale leaf area index based on the direction of the reflectivity[J]. Progress in Natural Science,2006,16(3):331-337.
[27]孙晨曦，刘良云，关琳琳，等．锡林浩特草原区域MODIS LAI产品真实性检验与误差分析[J]．遥感学报，2014,18(3):518-536.Sun C X,Liu L Y,Guan L L,et al. Validation and error analysis of the MODIS LAI product in Xilinhot grassland[J]. Journal of Remote Sensing,2014,18(3):518-536.
[28]于惠，吴玉锋，金毅，等．基于MODIS SWIR数据的干旱区草地地上生物量反演及时空变化研究[J]．遥感技术与应用，2017,32(3):524-530.Yu H,Wu Y F,Jin Y,et al. An approach for monitoring the aboveground biomass of grassland in arid areas based on MODIS SWIR bands[J]. Remote Sensing Technology and Application,2017,32(3):524-530.
[29] Gelder B K,Kaleita A L,Cruse R M. Estimating mean field residue cover on midwestern soils using satellite imagery[J]. Agronomy Journal,2009,101(3):635-643.
[30] Cao X,Cui X,Yue M,et al. Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression[J]. International Journal of Remote Sensing,2013,34(19):6679-6700.
[31] Ren H,Zhou G. Estimating senesced biomass of desert steppe in Inner Mongolia using field spectrometric data[J]. Agricultural and Forest Meteorology,2012,161(4):66-71.
[32]谢小燕，刘咏梅，李京忠，等．黄土丘陵区林草地枯落层盖度遥感估算研究[J]．光谱学与光谱分析，2016,36(7):2217-2223.Xie X Y,Liu Y M,Li J Z,et al. Remote sensing estimation of plant litter cover based on the spectra of plant litter-soil mixed scenes[J]. Spectroscopy and Spectral Analysis,2016,36(7):2217-2223.
[2] Guerschman J P,Hill M J,Renzullo L J,et al. Estimating fractional cover of photosynthetic vegetation,non-photosynthetic vegetation and bare soil in the Australian tropical savanna region upscaling the EO-1 Hyperion and MODIS sensors[J]. Remote Sensing of Environment,2009,113(5):928-945.
[3] Daughtry C S T,Hunt E R,Doraiswamy P C,et al. Remote sensing the spatial distribution of crop residues[J]. Agronomy Journal,2005,97(3):864-874.
[4] Daughtry C S T,Doraiswamy P C,Hunt E R,et al. Remote sensing of crop residue cover and soil tillage intensity[J]. Soil and Tillage Research,2006,91(1-2):101-108.
[5] Daughtry C S T,Hunt E R,Mc Murtrey J E. Assessing crop residue cover using shortwave infrared reflectance[J]. Remote Sensing of Environment,2004,90(1):126-134.
[6] Daughtry C S T. Discriminating crop residues from soil by shortwave infrared reflectance[J]. Agronomy Journal,2001,93(1):125-131.
[7] Henry H A L,Brizgys K,Field C B. Litter decomposition in a California annual grassland:Interactions between photodegradation and litter layer thickness[J]. Ecosystems,2008,11(4):545-554.
[8] Nagler P L,Inoue Y,Glenn E P,et al. Cellulose absorption index(CAI)to quantify mixed soil-plant litter scenes[J]. Remote Sensing of Environment,2003,87(2-3):310-325.
[9] Elmore A J,Asner G P,Hughes R F. Satellite monitoring of vegetation phenology and fire fuel conditions in Hawaiian drylands[J].Earth Interactions,2005,9(21):1-21.
[10] Mcnairn H,Protz R. Mapping corn residue cover on agricultural fields in Oxford County,Ontario,using thematic mapper[J]. Canadian Journal of Remote Sensing,1993,19(2):152-159.
[11] Cao X,Chen J,Matsushita B,et al. Developing a MODIS-based index to discriminate dead fuel from photosynthetic vegetation and soil background in the Asian steppe area[J]. International Journal of Remote Sensing,2010,31(6):1589-1604.
[12]李涛，李晓松，李飞．基于Hyperion的锡林郭勒草原光合植被、非光合植被覆盖度估算[J]．生态学报，2015,35(11):3643-3652.Li T,Li X S,Li F. Estimating fractional cover of photosynthetic vegetation and non-photosynthetic vegetation in the Xilingol steppe region with EO-1 Hyperion data[J]. Acta Ecologica Sinica,2015,35(11):3643-3652.
[13]姬翠翠，贾永红，李晓松，等．线性/非线性光谱混合模型估算白刺灌丛植被覆盖度[J]．遥感学报，2016,20(6):1402-1412.Ji C C,Jia Y H,Li X S,et al. Research on linear and nonlinear spectral mixture models for estimating vegetation fractional cover of nitraria bushes[J]. Journal of Remote Sensing,2016,20(6):1402-1412.
[14]李志婷，王昌昆，潘贤章，等．基于模拟Landsat-8 OLI数据的小麦秸秆覆盖度估算[J]．农业工程学报，2016,32(s1):145-152.Li Z T,Wang C K,Pan X Z,et al. Estimation of wheat residue cover using simulated Landsat-8 OLI datas[J]. Transactions of the Chinese Society of Agricultural Engineering,2016,32(s1):145-152.
[15]王光镇，王静璞，邹学勇，等．基于像元三分模型的锡林郭勒草原光合植被和非光合植被覆盖度估算[J]．生态学报，2017,37(17):5722-5731.Wang G Z,Wang J P,Zou X Y,et al. Estimation of fractional cover of photosynthetic and non-photosynthetic vegetation in the Xilingol steppe region using the NDVI-DFI model[J]. Acta Ecologica Sinica,2017,37(17):5722-5731.
[16] Li X,Zheng G,Wang J,et al. Comparison of methods for estimating fractional cover of photosynthetic and non-photosynthetic vegetation in the Otindag Sandy Land using GF-1 wide-field view data[J]. Remote Sensing,2016,8(10):800.
[17]王光镇，王静璞，邹学勇，等．遥感技术估算非光合植被覆盖度研究综述[J]．遥感技术与应用，2018,33(1):1-9.Wang G Z,Wang J P,Zou X Y,et al. A review on estimating fractional cover of non-photosynthetic vegetation by using remote sensing[J]. Remote Sensing Technology and Application,2018,33(1):1-9.
[18]张淼，李强子，蒙继华，等．作物残茬覆盖度遥感监测研究进展[J]．光谱学与光谱分析，2011,31(12):3200-3205.Zhang M,Li Q Z,Meng J H,et al. Review of crop residue fractional cover monitoring with remote sensing[J]. Spectroscopy and Spectral Analysis,2011,31(12):3200-3205.
[19] Jacques D C,Kergoat L,Hiernaux P,et al. Monitoring dry vegetation masses in semi-arid areas with MODIS SWIR bands[J]. Remote Sensing of Environment,2014,153:40-49.
[20] van Deventer A P,Ward A D,Gowda P H,et al. Using thematic mapper data to identify contrasting soil plains and tillage practices[J]. Photogrammetric Engineering and Remote Sensing,1997,63(1):87-93.
[21] Qi J,Marsett R,Heilman P,et al. RANGES improves satellitebased information and land cover assessments in southwest United States[J]. EOS Transactions American Geophysical Union,2002,83(51):601-606.
[22]刘春静，张丽，周宇，等．中国新疆及中亚五国干旱区草地覆盖度反演与分析[J]．草业科学，2016,33(5):861-870.Liu C J,Zhang L,Zhou Y,et al. Retrieval and analysis of grassland coverage in arid Xinjiang,China and five countries of Central Asia[J]. Pratacultural Science,2016,33(5):861-870.
[23]吴林，张元明．以数码照相法估算生物土壤结皮盖度[J]．中国沙漠，2013,33(6):1810-1815.Wu L,Zhang Y M. Coverage estimation on biological soil crusts based on digital photos[J]. Journal of Desert Research,2013,33(6):1810-1815.
[24]张浩彬，李俊生，向南平，等．基于MODIS地表反射率数据的水体自动提取研究[J]．遥感技术与应用，2015,30(6):1160-1167.Zhang H B,Li J S,Xiang N P,et al. A study of extracting water bodies automatically based on the MODIS surface reflectance data[J]. Remote Sensing Technology and Application,2015,30(6):1160-1167.
[25] Serbin G,Daughtry C S T,Hunt E R,et al. Effects of soil composition and mineralogy on remote sensing of crop residue cover[J].Remote Sensing of Environment,2009,113(1):224-238.
[26]唐世浩，朱启疆，孙睿．基于方向反射率的大尺度叶面积指数反演算法及其验证[J]．自然科学进展，2006,16(3):331-337.Tang S H,Zhu Q J,Sun R. The inversion algorithm and its validation of coarse scale leaf area index based on the direction of the reflectivity[J]. Progress in Natural Science,2006,16(3):331-337.
[27]孙晨曦，刘良云，关琳琳，等．锡林浩特草原区域MODIS LAI产品真实性检验与误差分析[J]．遥感学报，2014,18(3):518-536.Sun C X,Liu L Y,Guan L L,et al. Validation and error analysis of the MODIS LAI product in Xilinhot grassland[J]. Journal of Remote Sensing,2014,18(3):518-536.
[28]于惠，吴玉锋，金毅，等．基于MODIS SWIR数据的干旱区草地地上生物量反演及时空变化研究[J]．遥感技术与应用，2017,32(3):524-530.Yu H,Wu Y F,Jin Y,et al. An approach for monitoring the aboveground biomass of grassland in arid areas based on MODIS SWIR bands[J]. Remote Sensing Technology and Application,2017,32(3):524-530.
[29] Gelder B K,Kaleita A L,Cruse R M. Estimating mean field residue cover on midwestern soils using satellite imagery[J]. Agronomy Journal,2009,101(3):635-643.
[30] Cao X,Cui X,Yue M,et al. Evaluation of wildfire propagation susceptibility in grasslands using burned areas and multivariate logistic regression[J]. International Journal of Remote Sensing,2013,34(19):6679-6700.
[31] Ren H,Zhou G. Estimating senesced biomass of desert steppe in Inner Mongolia using field spectrometric data[J]. Agricultural and Forest Meteorology,2012,161(4):66-71.
[32]谢小燕，刘咏梅，李京忠，等．黄土丘陵区林草地枯落层盖度遥感估算研究[J]．光谱学与光谱分析，2016,36(7):2217-2223.Xie X Y,Liu Y M,Li J Z,et al. Remote sensing estimation of plant litter cover based on the spectra of plant litter-soil mixed scenes[J]. Spectroscopy and Spectral Analysis,2016,36(7):2217-2223.