基于植被指数估算天山牧区不同利用类型草地总产草量
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摘要
针对天山牧区草地面积广、生长环境差异大等特点,选用MODIS/MOD13Q1数据,生成归一化植被指数、增强植被指数、土壤调节植被指数和差值植被指数4种从不同角度反映牧草长势的植被指数,对4个不同牧草利用类型分区分别建立了4种指数及其组合与总产草量的4类回归方程。利用留一交叉检验法评价各模型精度,最终获得适合不同草地利用类型的产草量遥感估算模型。结果表明:4种植被指数都可用于产草量估算,不同植被指数估算模型的拟合精度有区域差异性。当采用SAVI和二次多项式拟合时,RMSE最大值出现在I区,为5 857 kg/hm2,当采用NDVI和二次多项式拟合时,最小值出现在III区,仅为616.487 kg/hm2。值得注意的是采用差值植被指数、土壤调节植被指数估算产草量需考虑植被覆盖状况。其次,多个植被指数组合有信息互补的优势,采用线性回归模型估产时,多个植被指数组合精度高于单一植被指数。该研究可为天山地区草地总产草量遥感估算提供有意义的借鉴。
The rapid and precise estimation model of herbage yield using remote sensing technology is of great significance to maintain grassland ecological balance, reasonably manage the yield of animal husbandry and determine carrying capacity. Taking the Tianshan pastoral area of Xinjiang into account, the vegetation index NDVI(normalized difference vegetation index), the vegetation index EVI(enhanced vegetation index), the soil conditioning index SAVI(soil adjusted vegetation index) and the difference index DVI(soil adjusted vegetation index) were calculated using the MODIS/MOD13 Q1 data with 250 m spatial resolution. And the 4 indices and their different combinations were used to establish a regression model related to total herbage yield(fresh weight) of grassland for each subregion divided by herbage use type. Then the leave-one-out cross validation method was used to evaluate the precision of the model and the difference in simulation of different vegetation indices in the same pastoral area so as to obtain the pasture herbage yield-optimal vegetation index estimation model in the cases of different pasture use types. So, spatial distribution and difference of the herbage yield(fresh weight) in Tianshan mountainous area could be estimated. The results showed that estimation precision using nonlinear regression equation with respect to single vegetation index is higher than that of the linear regression equation. There is a nonlinear relationship between the herbage yield and the vegetation index derived from remote sensing image. Secondly, the precision of grass yield estimation model based on the combination of multiple vegetation indices is higher than that of the model based on one vegetation index in that the use of the combination of complementary information from different vegetation indices is preferred to improve the estimation precision of grass yield with the remote sensing technology. Additionally, the 4 indices including NDVI, EVI, DVI and SAVI were correlated to the herbage yield of pasture and could be used to estimate the herbage yield. However, there is a difference in estimation precision for different vegetation indices, the maximum of RMSE(root mean square error) when fitting with quadratic polynomial and SAVI occurs in the Subregion I, which is 5 857 kg/hm2, and the minimum of RMSE when fitting with quadratic polynomial and NDVI occurs in the Subregion III, which is 616.487 kg/hm2. It is noteworthy that both DVI and SAVI considering environment factors especially like vegetation cover circumstance in the study area are most suitable for herbage yield monitoring. The work can provide significant suggestion for the estimation of total production of herbage in Tianshan Mountains.
The rapid and precise estimation model of herbage yield using remote sensing technology is of great significance to maintain grassland ecological balance, reasonably manage the yield of animal husbandry and determine carrying capacity. Taking the Tianshan pastoral area of Xinjiang into account, the vegetation index NDVI(normalized difference vegetation index), the vegetation index EVI(enhanced vegetation index), the soil conditioning index SAVI(soil adjusted vegetation index) and the difference index DVI(soil adjusted vegetation index) were calculated using the MODIS/MOD13 Q1 data with 250 m spatial resolution. And the 4 indices and their different combinations were used to establish a regression model related to total herbage yield(fresh weight) of grassland for each subregion divided by herbage use type. Then the leave-one-out cross validation method was used to evaluate the precision of the model and the difference in simulation of different vegetation indices in the same pastoral area so as to obtain the pasture herbage yield-optimal vegetation index estimation model in the cases of different pasture use types. So, spatial distribution and difference of the herbage yield(fresh weight) in Tianshan mountainous area could be estimated. The results showed that estimation precision using nonlinear regression equation with respect to single vegetation index is higher than that of the linear regression equation. There is a nonlinear relationship between the herbage yield and the vegetation index derived from remote sensing image. Secondly, the precision of grass yield estimation model based on the combination of multiple vegetation indices is higher than that of the model based on one vegetation index in that the use of the combination of complementary information from different vegetation indices is preferred to improve the estimation precision of grass yield with the remote sensing technology. Additionally, the 4 indices including NDVI, EVI, DVI and SAVI were correlated to the herbage yield of pasture and could be used to estimate the herbage yield. However, there is a difference in estimation precision for different vegetation indices, the maximum of RMSE(root mean square error) when fitting with quadratic polynomial and SAVI occurs in the Subregion I, which is 5 857 kg/hm2, and the minimum of RMSE when fitting with quadratic polynomial and NDVI occurs in the Subregion III, which is 616.487 kg/hm2. It is noteworthy that both DVI and SAVI considering environment factors especially like vegetation cover circumstance in the study area are most suitable for herbage yield monitoring. The work can provide significant suggestion for the estimation of total production of herbage in Tianshan Mountains.
引文
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[24]Ouyang W,Hao F H,Skidmore A K,et al.Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed[J].Ecological Indicators,2012,18:163-170.
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[29]陈彦光.基于Matlab的地理数据分析[M].北京:高等教育出版社,2012.
[30]Olden J D,Jackson D A.Torturing data for the sake of generality:How valid are our regression models?[J].Ecoscience,2000,7(4):501-510.
[31]屈莎,李振海,邱春霞,等.基于开花期氮素营养指标的冬小麦籽粒蛋白质含量遥感预测[J].农业工程学报,2017,33(12):186-193.Qu Sha,Li Zhenhai,Qiu Chunxia,et al.Remote sensing prediction of winter wheat grain protein content based on nitrogen nutrition index at anthesis stage[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(12):186-193.(in Chinese with English abstract)
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[33]Huete A R,Jackson R D,Post D F.Spectral response of a plant canopy with different soil backgrounds.[J].Remote Sensing of Environment,1985,17(1):37-53.
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[2]黄敬峰,胡新博.新疆北部天然草地产草量遥感监测预测模型研究[J].浙江大学学报(农业与生命科学版),1999,25(2):125-129.Huang Jinfeng,Hu Xinbo.Studies on grass yield monitoring and predicting models of natural grassland using remote sensing data in Northern Xinjiang[J].Journal of Zhejiang Agricultural University,1999,25(2):125-129.(in Chinese with English abstract)
[3]Zhang L,Wylie B,Loveland T,et al.Evaluation and comparison of gross primary production estimates for the Northern Great Plains grasslands[J].Remote Sensing of Environment,2007,106(2):173-189.
[4]Zhao M,Heinsch F A,Nemani R R,et al.Improvements of the MODIS terrestrial gross and net primary production global data set[J].Remote Sensing of Environment,2005,95(2):164-176.
[5]张连义,张静祥,赛音吉亚,等.典型草原植被生物量遥感监测模型——以锡林郭勒盟为例[J].草业科学,2008,25(4):31-36.Zhang Lianyi,Zhang Jingxiang,Sai Yinjiya,et al.Remote sensing monitoring model I for grassland vegetation biomass monitoring in typical steppe–a case study from Xilinguole[J].Pratacultural Science,2008,25(4):31-36.(in Chinese with English abstract)
[6]Todd S W,Hoffer R M,Milchunas D G.Biomass estimation on grazed and ungrazed rangelands using spectral indices[J].International Journal of Remote Sensing,1998,19(3):427-438.
[7]Foereid B,H?gh-Jensen H.Carbon sequestration potential of organic agriculture in northern Europe:A modelling approach[J].Nutrient Cycling in Agroecosystems,2004,68(1):13-24.
[8]吕新苗,郑度.气候变化对长江源地区高寒草甸生态系统的影响[J].长江流域资源与环境,2006,15(5):603-607.Lv Xinmiao,Zheng Du.Impactsof global change on the alpine meadow ecosystem in the sourceregion of the Yangtze River[J].Resources and Environment in the Yangtze Basin,2006,15(5):603-607.(in Chinese with English abstract)
[9]陈辰,王靖,潘学标,等.气候变化对内蒙古草地生产力影响的模拟研究[J].草地学报,2013,21(5):850-860.Chen Chen,Wang Jing,Pan Xuebiao,et al.Simulation study regarding the impact of climate change on grass productivity in Inner Mongolia[J].Acta Agrectir Sinica,2013,21(5):850-860.(in Chinese with English abstract)
[10]黄敬峰,冯振武.天山北坡中段天然草场牧草产量遥感动态监测模式[J].干旱区资源与环境,1993,8(2):10-17.
[11]黄敬峰,王秀珍.天山北坡中东段天然草地光谱植被指数特征[J].山地学报,1999,17(2):119-124.Huang Jingfeng,Wang Xiuzheng.The Characteristics of natural grassland spectral vegetation indices in eastern and middle part of Northern Tianshan Mountain[J].Journal of Mountain Science,1999,17(2):119-124.(in Chinese with English abstract)
[12]黄敬峰,王秀珍,王人潮,等.天然草地牧草产量与气象卫星植被指数的相关分析[J].农业现代化研究,2000,21(1):33-36.Huang Jingfeng,Wang Xiuzhen,Wang Renchao,et al.Relation analysis beween the production of natural grassland and satallite vegetation indices[J].Research of Agricultural Modernization,2000,21(1):33-36.(in Chinese with English abstract)
[13]黄敬峰,王秀珍,王人潮,等.天然草地牧草产量遥感综合监测预测模型研究[J].遥感学报,2001,5(1):69-74.Huang Jingfeng,Wang Xiuzhen,Wang Renchao,et al.A study on monitoring and predicting models of grass yield in natural grassland[J].Journal of Remote Sensing,2001,5(1):69-74.(in Chinese with English abstract)
[14]黄敬峰,蔡承侠.利用NOAA/AVHRR资料监测北疆天然草地生产力[J].草业科学,1999(5):62-69.Huang Jingfeng,Cai chengxia.Using NOAA/AVHRR data monitoring natural grassland productivity in the northern Xinjiang Uygur autonomous region[J].Pratacultural Science,1999(5):62-69.(in Chinese with English abstract)
[15]李建龙,蒋平,戴若兰.RS,GPS和GIS集成系统在新疆北部天然草地估产技术中的应用进展[J].生态学报,1998,18(5):504-510.Li Jianlong,Jiang Ping,Dai Ruolan.Advances in study on the remote sensing technology and gps and gis integration systems in estimating grassland yield applications in the north of Xinjiang,China[J].Acta Ecologica Sinica,1998,18(5):504-510.(in Chinese with English abstract)
[16]王新欣,朱进忠,范燕敏,等.利用EOS/MODIS植被指数建立草地估产模型的研究[J].新疆农业科学,2008,45(5):843-846.Wang Xinxin,Zhu Jinzhong,Fan Yanming,et al.Estimation model of establishing grassland with EOS/MODIS vegetation indexes[J].Xinjiang Agricultural Sciences,2008,45(5):843-846.(in Chinese with English abstract)
[17]钱育蓉,杨峰,李建龙,等.基于3S的新疆阜康典型草地产草量及草畜平衡分析[J].草业科学,2013,30(9):1330-1337.Qian Yurong,Yang Feng,Li Jianlong,et al.Yield and animal-feed balance of typical grassland in Xinjiang Fukang using 3S techniques[J].Pratacultural Science,2013,30(9):1330-1337.(in Chinese with English abstract)
[18]Jin Y,Yang X,Qiu J,et al.remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland,Northern China[J].Remote Sensing,2014,6(2):1496-1513.
[19]Gao T,Xu B,Yang X C,et al.Using MODIS time series data to estimate aboveground biomass and its spatiotemporal variation in Inner Mongolia's grassland between2001 and 2011[J].Int.J.Remote Sens.2013,34:7796-7810.
[20]Xu B,Yang X C,Tao W G,et al.MODIS-based remote sensing monitoring of grass production in China[J].Int.J.Remote Sens,2008,29:5313-5327.
[21]Piao S L,Fang J Y,He J S,et al.Spatial distribution of grassland biomass in China[J].Acta Phytoecol.Sin.2004,28:491-498.
[22]Jin Y X,Bin X U,Yang X C,et al.Remote sensing dynamic estimation of grass production in Xilinguole,Inner Mongolia[J].Scientia Sinica,2011,41(12):1185-1195.
[23]Leisher C,Hess S,Boucher T M,et al.Measuring the impacts of community-based grasslands management in Mongolia's Gobi[J].Plos One,2012,7(2):e30991.
[24]Ouyang W,Hao F H,Skidmore A K,et al.Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed[J].Ecological Indicators,2012,18:163-170.
[25]Muukkonen P,Heiskanen J.Biomass estimation over a large area based on stand wise forest inventory data and ASTER and MODIS satellite data:A possibility to verify carbon inventories[J].Remote Sensing of Environment,2007,107(4):617-624.
[26]杨秀春,徐斌,朱晓华,等.北方农牧交错带草原产草量遥感监测模型[J].地理研究,2007,26(2):213-221.Yang Xiuchun,Xu Bin,Zhu Xiaohua,et al.Models of grass production based on remote sensing monitoring in northern agro-grazing ecotone[J].Geographical Research,2007,26(2):213-221.(in Chinese with English abstract)
[27]Yang Y H,Fang J Y,Pan Y D,et al.Aboveground biomass in Tibetan grasslands[J].Journal of Arid Environments,2009,73(1):91-95.
[28]Zeng H,Jia G,Forbes B C.Shifts in Arctic phenology in response to climate and anthropogenic factors as detected from multiple satellite time series[J].Environmental Research Letters,2013,8(3):035036.
[29]陈彦光.基于Matlab的地理数据分析[M].北京:高等教育出版社,2012.
[30]Olden J D,Jackson D A.Torturing data for the sake of generality:How valid are our regression models?[J].Ecoscience,2000,7(4):501-510.
[31]屈莎,李振海,邱春霞,等.基于开花期氮素营养指标的冬小麦籽粒蛋白质含量遥感预测[J].农业工程学报,2017,33(12):186-193.Qu Sha,Li Zhenhai,Qiu Chunxia,et al.Remote sensing prediction of winter wheat grain protein content based on nitrogen nutrition index at anthesis stage[J].Transactions of the Chinese Society of Agricultural Engineering(Transactions of the CSAE),2017,33(12):186-193.(in Chinese with English abstract)
[32]Huete A R.A soil-adjusted vegetation index(SAVI)[J].Remote Sensing of Environment,1988,25(3):295-309.
[33]Huete A R,Jackson R D,Post D F.Spectral response of a plant canopy with different soil backgrounds.[J].Remote Sensing of Environment,1985,17(1):37-53.
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