黄淮海地区耕地复种指数的时空格局演变
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摘要
耕地复种指数是土地利用强度的重要表征,时空动态特征有助于理解人类活动与生态环境的耦合作用。以黄淮海地区2001—2015年MODIS(Moderate-resolution Imaging Spectroradiometer,中分辨率成像光谱仪)NDVI(Normalized Difference Vegetation Index,归一化植被指数)遥感影像为数据源,使用Savitzky-Golay滤波对时间序列曲线平滑重构后,结合研究区物候信息设置含有阈值的二次差分算法提取复种次数,最后在R环境下绘制复种指数空间分布图。结果表明:(1)河南省复种指数最高(169.3%),山东省次之,天津市最小;(2)各省市年际变化趋势大体一致,经历了升高-降低-升高的过程;从空间分布特征来看,耕地复种指数具有明显的地域性差异,二熟制主要集中于南部,东部和北部受地形和纬度影响,主要以一熟制为主。研究结果对于黄淮海农耕区的土地利用强度辨识、人类活动方式确定具有参考价值,同时也证明了该方法具有更大尺度推广的潜力。
The multiple cropping index is an important indicator of land utilization intensity,and the temporal-spatial dynamics can also help to understand the coupling effects of human activities and the ecological environment.In this study,MODIS(Moderate-resolution Imaging Spectroradiometer) NDVI(Normalized Difference Vegetation Index) remote sensing images in the Huang-Huai-Hai region from 2001 to 2015 were used as the source datasets.After a Savitzky-Golay filter was used to reconstruct the time series curve,the second-order difference method with threshold was used to extract the number of multiple cropping times,combined with the phenology of the study area.Finally,the spatial distribution map of the multiple cropping index was drawn using R language software.The result showed that:(1) the multiple cropping index of Henan Province was the highest(169.3%),followed by that of Shandong,and then Tianjin provinces;(2) during the study period,the inter-annual trends of the multiple cropping index showed a similar rising-decreasing-rising pattern in each province and city.From the spatial distribution characteristics,cultivation showed obvious regional differences.The two crops per year were mainly concentrated in the South.Owing to the terrain and latitude,the East and North showed mainlyone crop per year.The results are valuable to identify land utilization intensity and human activities in the Huang-Huai-Hai cropland region.They also proved that this method could potentially be used at a greater scale.
The multiple cropping index is an important indicator of land utilization intensity,and the temporal-spatial dynamics can also help to understand the coupling effects of human activities and the ecological environment.In this study,MODIS(Moderate-resolution Imaging Spectroradiometer) NDVI(Normalized Difference Vegetation Index) remote sensing images in the Huang-Huai-Hai region from 2001 to 2015 were used as the source datasets.After a Savitzky-Golay filter was used to reconstruct the time series curve,the second-order difference method with threshold was used to extract the number of multiple cropping times,combined with the phenology of the study area.Finally,the spatial distribution map of the multiple cropping index was drawn using R language software.The result showed that:(1) the multiple cropping index of Henan Province was the highest(169.3%),followed by that of Shandong,and then Tianjin provinces;(2) during the study period,the inter-annual trends of the multiple cropping index showed a similar rising-decreasing-rising pattern in each province and city.From the spatial distribution characteristics,cultivation showed obvious regional differences.The two crops per year were mainly concentrated in the South.Owing to the terrain and latitude,the East and North showed mainlyone crop per year.The results are valuable to identify land utilization intensity and human activities in the Huang-Huai-Hai cropland region.They also proved that this method could potentially be used at a greater scale.
引文
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[13]刘玉,高秉博,潘瑜春,任旭红.基于LMDI模型的黄淮海地区县域粮食生产影响因素分解.农业工程学报,2013,29(21):1-10.
[14]洪舒蔓,郝晋珉,周宁,陈丽,吕振宇.黄淮海平原耕地变化及对粮食生产格局变化的影响.农业工程学报,2014,30(21):268-277.
[15]陈丽,郝晋珉,艾东,朱传民,李牧,袁凌波.黄淮海平原粮食均衡增产潜力及空间分异.农业工程学报,2015,31(2):288-297.
[16]闫慧敏,肖向明,黄河清.黄淮海多熟种植农业区作物历遥感检测与时空特征.生态学报,2010,30(9):2416-2423.
[17]杨瑞珍,肖碧林,陈印军,卢布.黄淮海平原农业气候资源高效利用背景及主要农作技术.干旱区资源与环境,2010,24(9):88-93.
[18]李卓,孙然好,张继超,张翀.京津冀城市群地区植被覆盖动态变化时空分析.生态学报,2017,37(22):7418-7426.
[19]Hill M J,Donald G E.Estimating spatio-temporal patterns of Agricultural productivity in fragmented landscapes using AVHRR NDVI time series.Remote Sensing of Environment,2003,84(3):367-384.
[20]孙华生,徐爱功,林卉,张连蓬.基于不同算法的时间序列植被指数去噪效果分析.江苏农业科学,2012,40(5):375-379.
[21]Chen J,J9nsson P,Tamura M,Gu Z H,Matsushita B,Eklundh L.A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter.Remote sensing of Environment,2004,91(3/4):332-344.
[22]Boschetti M,Stroppiana D,Brivio P A,Bocchi S.Multi-year monitoring of rice crop phenology through time series analysis of MODIS images.International Journal of Remote Sensing,2009,30(18):4643-4662.
[23]朱孝林,李强,沈妙根,陈晋,吴锦.基于多时相NDVI数据的复种指数提取方法研究.自然资源学报,2008,23(3):534-544.
[24]Helming K,Pérez-Soba M,Tabbush P.Sustainability Impact Assessment of Land Use Changes.Berlin,Heidelberg:Springer,2008.
[25]梁守真,马万栋,施平,陈劲松.基于MODIS NDVI数据的复种指数监测——以环渤海地区为例.中国生态农业学报,2012,20(12):1657-1663.
[26]左丽君,董婷婷,汪潇,赵小丽,易玲.基于MODIS/EVI的中国北方地区耕地复种指数提取.农业工程学报,2009,25(8):141-146.
[27]唐鹏钦,吴文斌,姚艳敏,杨鹏.基于小波变换的华北平原耕地复种指数提取.农业工程学报,2011,27(7):220-225.
[2]王宏广.中国耕作制度70年.北京:中国农业出版社,2005:101-112.
[3]申健,常庆瑞,李粉玲,秦占飞,谢宝妮.2000—2013年关中地区耕地复种指数遥感动态监测.农业机械学报,2016,47(8):280-287.
[4]张伟,李爱农,雷光斌.复种指数遥感监测研究进展.遥感技术与应用,2015,30(2):199-208.
[5]Kühling I,Broll G,Trautz D.Spatio-temporal analysis of agricultural land-use intensity across the Western Siberian grain belt.Science of the Total Environment,2016,544:271-280.
[6]戈大专,龙花楼,屠爽爽,张英男.黄淮海地区土地利用转型与粮食产量耦合关系研究.农业资源与环境学报,2017,34(04):319-327.
[7]闫慧敏,刘纪远,曹明奎.近20年中国耕地复种指数的时空变化.地理学报,2005,60(4):559-566.
[8]边金虎,李爱农,宋孟强,马利群,蒋锦刚.MODIS植被指数时间序列Savitzky-Golay滤波算法重构.遥感学报,2010,14(4):725-741.
[9]关兴良,方创琳,鲁莎莎.中国耕地变化的空间格局与重心曲线动态分析.自然资源学报,2010,25(12):1997-2006.
[10]Devendra C,Thomas D.Smallholder farming systems in Asia.Agricultural Systems,2002,71(1/2):17-25.
[11]Verburg P H,Chen Y Q,Veldkamp T A.Spatial explorations of land use change and grain production in China.Agriculture,Ecosystems&Environment,2000,82(1/3):333-354.
[12]丁明军,陈倩,辛良杰,李兰晖,李秀彬.1999—2013年中国耕地复种指数的时空演变格局.地理学报,2015,70(7):1080-1090.
[13]刘玉,高秉博,潘瑜春,任旭红.基于LMDI模型的黄淮海地区县域粮食生产影响因素分解.农业工程学报,2013,29(21):1-10.
[14]洪舒蔓,郝晋珉,周宁,陈丽,吕振宇.黄淮海平原耕地变化及对粮食生产格局变化的影响.农业工程学报,2014,30(21):268-277.
[15]陈丽,郝晋珉,艾东,朱传民,李牧,袁凌波.黄淮海平原粮食均衡增产潜力及空间分异.农业工程学报,2015,31(2):288-297.
[16]闫慧敏,肖向明,黄河清.黄淮海多熟种植农业区作物历遥感检测与时空特征.生态学报,2010,30(9):2416-2423.
[17]杨瑞珍,肖碧林,陈印军,卢布.黄淮海平原农业气候资源高效利用背景及主要农作技术.干旱区资源与环境,2010,24(9):88-93.
[18]李卓,孙然好,张继超,张翀.京津冀城市群地区植被覆盖动态变化时空分析.生态学报,2017,37(22):7418-7426.
[19]Hill M J,Donald G E.Estimating spatio-temporal patterns of Agricultural productivity in fragmented landscapes using AVHRR NDVI time series.Remote Sensing of Environment,2003,84(3):367-384.
[20]孙华生,徐爱功,林卉,张连蓬.基于不同算法的时间序列植被指数去噪效果分析.江苏农业科学,2012,40(5):375-379.
[21]Chen J,J9nsson P,Tamura M,Gu Z H,Matsushita B,Eklundh L.A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter.Remote sensing of Environment,2004,91(3/4):332-344.
[22]Boschetti M,Stroppiana D,Brivio P A,Bocchi S.Multi-year monitoring of rice crop phenology through time series analysis of MODIS images.International Journal of Remote Sensing,2009,30(18):4643-4662.
[23]朱孝林,李强,沈妙根,陈晋,吴锦.基于多时相NDVI数据的复种指数提取方法研究.自然资源学报,2008,23(3):534-544.
[24]Helming K,Pérez-Soba M,Tabbush P.Sustainability Impact Assessment of Land Use Changes.Berlin,Heidelberg:Springer,2008.
[25]梁守真,马万栋,施平,陈劲松.基于MODIS NDVI数据的复种指数监测——以环渤海地区为例.中国生态农业学报,2012,20(12):1657-1663.
[26]左丽君,董婷婷,汪潇,赵小丽,易玲.基于MODIS/EVI的中国北方地区耕地复种指数提取.农业工程学报,2009,25(8):141-146.
[27]唐鹏钦,吴文斌,姚艳敏,杨鹏.基于小波变换的华北平原耕地复种指数提取.农业工程学报,2011,27(7):220-225.