NDVI相似性分区下天山地区草地总产草量遥感估算
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摘要
山区草地总产草量遥感估算是定量评价区域牧业生产力的有效手段。常规总产草量地面观测数据准确性较高,但无法覆盖整个天山山区,且耗时耗力。针对此问题,以新疆天山山区为研究区,选取MODIS/MOD13Q1 250m植被指数(normalized difference vegetation index,NDVI)产品数据,以县(市)为单元,基于巴氏距离定量评价研究区植被指数分布区域相似性以得到有效遥感建模分区,在此分区基础上,结合草地总产草量实测数据,建立研究区植被指数-草地总产草量遥感估算模型。结果显示,1)基于各县(市)2009-2015年7月底至8月初植被生长期多年NDVI均值直方图计算巴氏距离,以巴氏距离d>0.5为阈值,研究区被划分为7个遥感建模区;2)各分区内NDVI-草地总产草量数据拟合方程形式不同,有线性、指数、幂指数和多项式回归方程几种形式。总体来看,各分区NDVI-草地总产草量拟合相关系数在0.784~0.836。交叉检验除天山北坡西段-伊犁河谷草原畜牧业区RMSE值在2 951kg·hm-2外,其他分区RMSE值均在266~928kg·hm-2,原因在于伊犁河谷草原畜牧业区实测草地总产量在10 000~30 000kg·hm-2的样点居多,区域草地总产量较其他区域多。
An estimation of the total production of herbage in Xinjiang using remote sensing technology is an effective method for quantitative evaluation of regional animal husbandry productivity.It is time-consuming and cannot cover the whole area of the Tianshan Mountains,although the measured accuracy of the total herbage yield using conventional means is very high.To address this problem,we used MODIS/MOD13 Q1 vegetation index products,with 250 mGSD as experimental data and city or county as a basic unit for analysis.The Bhattacharyya distance was used to quantitatively evaluate the distribution of the similarity of the vegetation index in the Tianshan Mountain Area as a study case.The purpose was to obtain an effective remote sensing modeling zoning,and then to construct an estimation model of the total production of herbage with respect to the vegetation index using remote sensing technology.Finally,the spatial distribution and feature analysis of the total herbage yield(fresh weight)in the Tianshan Mountains from the year 2009 to 2015 were determined based on the analysis of the spatial distribution and characteristics obtained under the GIS platform.The results showed the following:the seven modeling zones were derived from an analysis of the mean histogram of NDVI data collected during the optimal period(i.e.,July and August each year)of vegetation growth for each city or county in the study area via the Bhattacharyya distance with a threshold greater than 0.5.Secondly,the constructed estimation model of the total production of the herbage showed different fitting relationships to the vegetation index,and there were three forms,including the exponential,power index,and a unary regression equation with the second order.On the whole,the fitting correlation coefficient of the constructed estimation model could reach between 0.754 and 0.836 for each zones.The RMSE value of cross-validation in the northern slope of the Tianshan Mountains-Yili Valley was 2 951 kg·ha-1,and the RMSE value were between 266 and 928 kg·ha-1 in the other zones.This was because more measured samples which total production of the herbage was between 10 000 and 30 000 kg·ha-1 were collected in the zone.Also there was higher total production of the regional herbage in the zone than others.
An estimation of the total production of herbage in Xinjiang using remote sensing technology is an effective method for quantitative evaluation of regional animal husbandry productivity.It is time-consuming and cannot cover the whole area of the Tianshan Mountains,although the measured accuracy of the total herbage yield using conventional means is very high.To address this problem,we used MODIS/MOD13 Q1 vegetation index products,with 250 mGSD as experimental data and city or county as a basic unit for analysis.The Bhattacharyya distance was used to quantitatively evaluate the distribution of the similarity of the vegetation index in the Tianshan Mountain Area as a study case.The purpose was to obtain an effective remote sensing modeling zoning,and then to construct an estimation model of the total production of herbage with respect to the vegetation index using remote sensing technology.Finally,the spatial distribution and feature analysis of the total herbage yield(fresh weight)in the Tianshan Mountains from the year 2009 to 2015 were determined based on the analysis of the spatial distribution and characteristics obtained under the GIS platform.The results showed the following:the seven modeling zones were derived from an analysis of the mean histogram of NDVI data collected during the optimal period(i.e.,July and August each year)of vegetation growth for each city or county in the study area via the Bhattacharyya distance with a threshold greater than 0.5.Secondly,the constructed estimation model of the total production of the herbage showed different fitting relationships to the vegetation index,and there were three forms,including the exponential,power index,and a unary regression equation with the second order.On the whole,the fitting correlation coefficient of the constructed estimation model could reach between 0.754 and 0.836 for each zones.The RMSE value of cross-validation in the northern slope of the Tianshan Mountains-Yili Valley was 2 951 kg·ha-1,and the RMSE value were between 266 and 928 kg·ha-1 in the other zones.This was because more measured samples which total production of the herbage was between 10 000 and 30 000 kg·ha-1 were collected in the zone.Also there was higher total production of the regional herbage in the zone than others.
引文
[1]刘新平,吕晓.新疆牧草地资源利用动态变化及其绩效分析.干旱区地理,2009,32(1):81-85.Liu X P,Lyu X.Dynamics and performance evaluation of grassland resources utilization in Xinjiang.Arid Land Geography,2009,32(1):81-85.(in Chinese)
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[3]黄敬峰,桑长青,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.自然资源学报,1993,8(1):10-17.Huang J F,Sang C Q,Feng Z W.The remote sensing dynamic monitoring model of the grass yield of natural grassland in the middle section of the Tianshan North slope.Journal of Natural Resources,1993,8(2):10-17.(in Chinese)
[4]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.山地学报,1999,17(2):119-124.Huang J F,Wang X Z.The characteristics of natural grassland spectral vegetation indices in eastern and middle part of Northern Tianshan Mountain.Journal of Mountain Science,1999,17(2):119-124.(in Chinese)
[5]黄敬峰,王秀珍,王人潮,蔡承侠,胡新博.天然草地牧草产量与气象卫星植被指数的相关分析.农业现代化研究,2000,21(1):33-35.Huang J F,Wang X Z,Wang R C,Cai C X,Hu X B.Relation analysis between the production of natural grassland and satellite vegetation indices.Research of Agricultural Modernization,2000,21(1):33-35.(in Chinese)
[6]黄敬峰,王秀珍,王人潮,胡新博.天然草地牧草产量遥感综合监测预测模型研究.遥感学报,2001,5(1):69-73.Huang J F,Wang X Z,Wang R C,Hu X B.A study on monitoring and predicting models of grass yield in natural grassland using remote sensing data and meteorological data.Journal of Remote Sensing,2001,5(1):69-73.(in Chinese)
[7]黄敬峰,王秀珍,蔡承侠,胡新博.利用NOAA/AVHRR资料监测北疆天然草地生产力.草业科学,1999,16(15):62-65.Huang J F,Wang X Z,Cai C X,Hu X B.Using NOAA/AVHRR data monitoring natural grassland productivity in the northern Xinjiang uygur autonomous region.Pratacultural Science,1999,16(15):62-65.(in Chinese)
[8]王新欣,朱进忠,范燕敏,武鹏飞.利用EOS/MODIS植被指数建立草地估产模型的研究.新疆农业科学,2008,45(5):843-846.Wang X X,Zhu J Z,Fan Y M,Wu P F.Estimation model of establishing grassland with EOS/MODIS vegetation Indexes.Xinjiang Agricultural Sciences,2008,45(5):843-846.(in Chinese)
[9]钱育蓉,杨峰,李建龙,于炯,贾振红.基于3S的新疆阜康典型草地产草量及草畜平衡分析.草业科学,2013,30(9):1330-1337.Qian Y R,Yang F,Li J L,Yu J,Jia Z H.Yield and animal-feed balance of typical grassland in Xinjiang Fukang using 3Stechniques.Pratacultural Science,2013,30(9):1330-1337.(in Chinese)
[10]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.生态学报,1998,18(5):504-510.Li J L,Jiang P,Dai R L.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.Acta Ecologica Sinica,1998,18(5):504-510.(in Chinese)
[11]严建武,李春娥,袁雷,陈全功.EOS-MODIS数据在草地资源监测中的应用进展综述.草业科学,2008,25(4):1-9.Yan J W,Li C E,Yuan L,Chen Q G.Application summary of EOS-MODIS data in the monitoring of grassland resources.Pratacultural Science,2008,25(4):1-9.(in Chinese)
[12]Jin Y X,Xu B,Yang X C,Li J Y,Wang D L,Ma H L.Remote sensing dynamic estimation of grass production in Xilinguole,Inner Mongolia.Scientia Sinica,2011,41(12):1185-1195.
[13]Leisher C,Hess S,Boucher T M,Beukering P V,Sanjayan M.Measuring the impacts of community-based grasslands management in Mongolia’s Gobi.PLoS One,2012,7(2):e30991.
[14]Ouyang W,Hao F H,Skidmore A K,Groen T A,Toxopeus A G,Wang T.Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed.Ecological Indicators,2012,18:163-170.
[15]Zhao F,Xu B,Yang X,Jin Y,Li J,Xia L,Chen S,Ma H L.Remote Sensing estimates of grassland aboveground biomass based on MODIS Net Primary Productivity(NPP):A case study in the Xilingol grassland of northern China.Remote Sensing,2014,6(6):5368-5386.
[16]Jin Y X,Yang X C,Qiu J J,Li J Y,Gao T,Wu Q,Zhao F,Ma H L,Yu H D,Xu B.Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland,Northern China.Remote Sensing,2014,6(2):1496-1513.
[17]贺俊杰.锡林郭勒草地NDVI和牧草估产产量的变化特征.中国农学通报,2015,31(17):1-5.He J J.Variation characteristics of the NDVI and grass estimation yield in Xilingol grassland.Chinese Agricultural Science Bulletin,2015,31(17):1-5.(in Chinese)
[18]姚兴成,曲恬甜,常文静,尹俊,李永进,孙振中,曾辉.基于MODIS数据和植被特征估算草地生物量.中国生态农业学报,2017,25(4):530-541.Yao X C,Qu T T,Chang W J,Yin J,Li Y J,Sun Z Z,Zeng H.Estimation of grassland biomass using MODIS data and plant community characteristics.Chinese Journal of Eco-Agriculture,2017,25(4):530-541.(in Chinese)
[19]徐佳,陈媛媛,黄其欢,何秀凤.综合灰度与纹理特征的高分辨率星载SAR图像建筑区提取方法研究.遥感技术与应用,2012,27(5):692-698.Xu J,Chen Y Y,Huang Q H,He X F.Built-up areas extraction in high resolution space borne SAR image based on the integration of grey and texture features.Remote Sensing Technology and Application,2012,27(5):692-698.(in Chinese)
[20]李侃,平西建.基于图像内容和特征融合的隐写盲检测.应用科学学报,2013,31(1):97-103.Li K,Ping X J.Blind steg analysis based on image content and feature fusion.Journal of Applied Sciences,2013,31(1):97-103.(in Chinese)
[21]赵小敏,孙志刚,夏明.基于局部学习的车辆图像识别方法.浙江工业大学学报,2017,45(4):439-444.Zhao X M,Sun Z G,Xia M.A vehicle image recognition method based on local learning.Journal of Zhejiang University of Zhejiang University of Technology,2017,45(4):439-444.(in Chinese)
[22]Olden J D,Jackson D A.Torturing data for the sake of generality:How valid are our regression models.Ecoscience,2000,7(4):501-510.
[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.浙江农业大学学报,1999,25(2):125-129.Huang J F,Hu X B.Studies on grass yield monitoring and predicting models of natural grassland using remote sensing data in Northern Xinjiang.Journal of Zhejiang Agricultural University,1999,25(2):125-129.(in Chinese)
[3]黄敬峰,桑长青,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.自然资源学报,1993,8(1):10-17.Huang J F,Sang C Q,Feng Z W.The remote sensing dynamic monitoring model of the grass yield of natural grassland in the middle section of the Tianshan North slope.Journal of Natural Resources,1993,8(2):10-17.(in Chinese)
[4]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.山地学报,1999,17(2):119-124.Huang J F,Wang X Z.The characteristics of natural grassland spectral vegetation indices in eastern and middle part of Northern Tianshan Mountain.Journal of Mountain Science,1999,17(2):119-124.(in Chinese)
[5]黄敬峰,王秀珍,王人潮,蔡承侠,胡新博.天然草地牧草产量与气象卫星植被指数的相关分析.农业现代化研究,2000,21(1):33-35.Huang J F,Wang X Z,Wang R C,Cai C X,Hu X B.Relation analysis between the production of natural grassland and satellite vegetation indices.Research of Agricultural Modernization,2000,21(1):33-35.(in Chinese)
[6]黄敬峰,王秀珍,王人潮,胡新博.天然草地牧草产量遥感综合监测预测模型研究.遥感学报,2001,5(1):69-73.Huang J F,Wang X Z,Wang R C,Hu X B.A study on monitoring and predicting models of grass yield in natural grassland using remote sensing data and meteorological data.Journal of Remote Sensing,2001,5(1):69-73.(in Chinese)
[7]黄敬峰,王秀珍,蔡承侠,胡新博.利用NOAA/AVHRR资料监测北疆天然草地生产力.草业科学,1999,16(15):62-65.Huang J F,Wang X Z,Cai C X,Hu X B.Using NOAA/AVHRR data monitoring natural grassland productivity in the northern Xinjiang uygur autonomous region.Pratacultural Science,1999,16(15):62-65.(in Chinese)
[8]王新欣,朱进忠,范燕敏,武鹏飞.利用EOS/MODIS植被指数建立草地估产模型的研究.新疆农业科学,2008,45(5):843-846.Wang X X,Zhu J Z,Fan Y M,Wu P F.Estimation model of establishing grassland with EOS/MODIS vegetation Indexes.Xinjiang Agricultural Sciences,2008,45(5):843-846.(in Chinese)
[9]钱育蓉,杨峰,李建龙,于炯,贾振红.基于3S的新疆阜康典型草地产草量及草畜平衡分析.草业科学,2013,30(9):1330-1337.Qian Y R,Yang F,Li J L,Yu J,Jia Z H.Yield and animal-feed balance of typical grassland in Xinjiang Fukang using 3Stechniques.Pratacultural Science,2013,30(9):1330-1337.(in Chinese)
[10]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.生态学报,1998,18(5):504-510.Li J L,Jiang P,Dai R L.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.Acta Ecologica Sinica,1998,18(5):504-510.(in Chinese)
[11]严建武,李春娥,袁雷,陈全功.EOS-MODIS数据在草地资源监测中的应用进展综述.草业科学,2008,25(4):1-9.Yan J W,Li C E,Yuan L,Chen Q G.Application summary of EOS-MODIS data in the monitoring of grassland resources.Pratacultural Science,2008,25(4):1-9.(in Chinese)
[12]Jin Y X,Xu B,Yang X C,Li J Y,Wang D L,Ma H L.Remote sensing dynamic estimation of grass production in Xilinguole,Inner Mongolia.Scientia Sinica,2011,41(12):1185-1195.
[13]Leisher C,Hess S,Boucher T M,Beukering P V,Sanjayan M.Measuring the impacts of community-based grasslands management in Mongolia’s Gobi.PLoS One,2012,7(2):e30991.
[14]Ouyang W,Hao F H,Skidmore A K,Groen T A,Toxopeus A G,Wang T.Integration of multi-sensor data to assess grassland dynamics in a Yellow River sub-watershed.Ecological Indicators,2012,18:163-170.
[15]Zhao F,Xu B,Yang X,Jin Y,Li J,Xia L,Chen S,Ma H L.Remote Sensing estimates of grassland aboveground biomass based on MODIS Net Primary Productivity(NPP):A case study in the Xilingol grassland of northern China.Remote Sensing,2014,6(6):5368-5386.
[16]Jin Y X,Yang X C,Qiu J J,Li J Y,Gao T,Wu Q,Zhao F,Ma H L,Yu H D,Xu B.Remote sensing-based biomass estimation and its spatio-temporal variations in temperate grassland,Northern China.Remote Sensing,2014,6(2):1496-1513.
[17]贺俊杰.锡林郭勒草地NDVI和牧草估产产量的变化特征.中国农学通报,2015,31(17):1-5.He J J.Variation characteristics of the NDVI and grass estimation yield in Xilingol grassland.Chinese Agricultural Science Bulletin,2015,31(17):1-5.(in Chinese)
[18]姚兴成,曲恬甜,常文静,尹俊,李永进,孙振中,曾辉.基于MODIS数据和植被特征估算草地生物量.中国生态农业学报,2017,25(4):530-541.Yao X C,Qu T T,Chang W J,Yin J,Li Y J,Sun Z Z,Zeng H.Estimation of grassland biomass using MODIS data and plant community characteristics.Chinese Journal of Eco-Agriculture,2017,25(4):530-541.(in Chinese)
[19]徐佳,陈媛媛,黄其欢,何秀凤.综合灰度与纹理特征的高分辨率星载SAR图像建筑区提取方法研究.遥感技术与应用,2012,27(5):692-698.Xu J,Chen Y Y,Huang Q H,He X F.Built-up areas extraction in high resolution space borne SAR image based on the integration of grey and texture features.Remote Sensing Technology and Application,2012,27(5):692-698.(in Chinese)
[20]李侃,平西建.基于图像内容和特征融合的隐写盲检测.应用科学学报,2013,31(1):97-103.Li K,Ping X J.Blind steg analysis based on image content and feature fusion.Journal of Applied Sciences,2013,31(1):97-103.(in Chinese)
[21]赵小敏,孙志刚,夏明.基于局部学习的车辆图像识别方法.浙江工业大学学报,2017,45(4):439-444.Zhao X M,Sun Z G,Xia M.A vehicle image recognition method based on local learning.Journal of Zhejiang University of Zhejiang University of Technology,2017,45(4):439-444.(in Chinese)
[22]Olden J D,Jackson D A.Torturing data for the sake of generality:How valid are our regression models.Ecoscience,2000,7(4):501-510.