内蒙古河套平原耕地盐碱化时空演变及其对产能的影响
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摘要
以内蒙古自治区巴彦淖尔市河套平原为研究区,利用RS和GIS技术在野外样本采集的基础上,以第一次地理国情普查数据和LandsatTM/OLI、GF-1卫星影像为数据源,通过计算盐碱度与波段的相关性构建出反演模型,以专家决策树进行分类,对2006～2014年耕地盐碱化进行动态监测,并分析了不同盐碱化程度农田植被净初级生产力(NPP)时空变化特征。结论表明:2006～2014年研究区域非盐碱耕地和轻度盐碱耕地面积呈现持续减少的趋势,减少幅度为6.23%,占耕地总面积的3.55%,NPP先增后降,减少了2.06%;中度盐碱耕地面积呈先降后增的趋势,净增加幅度为22.40%,占耕地总面积的5.10%,NPP先不变后增加,增加了6.73%;而重度盐碱耕地面积呈现先增后降的趋势,净减少7.68%,占耕地总面积的1.55%,NPP则持续增加,增加了3.81%。总体上看,9 a间虽然因为自然、人为因素的影响,中度盐碱耕地面积增长,但可利用土地面积和有效耕地面积不断增加,且改良区域的NPP处于持续增长的趋势,在一定程度上说明盐碱地治理取得了成效。
This study takes Hetao Plain of Inner Mongolia as the research area, based on the RS and GIS technical support and the field sample collection, and uses the China first national geoinformation survey data as the data source, constructs the inversion model through calculating the correlation between the salinity degree and the band, classifies it with expert decision tree, and carries out dynamic monitoring of cultivated land salinization-alkalization in 2006-2014, and analyzes the spatio-temporal characteristics of NPP in different salinization-alkalization degree of cultivated land. The results shows that: Between 2006-2014, the non-saline-alkali cultivated land and the light-saline-alkali cultivated land in the study area showed a decreasing trend, with a reduction of 6.23%, that accounts for 3.55% of the total area of cultivated land. During this period, NPP increases first and then decreases, with a reduction of 2.06%. The medium-saline-alkali cultivated land showed a trend of first decline and then increase, with a net increase of 22.40%, that accounts for 5.10% of the total area of cultivated land. During this period, NPP changed none first and then increased, with a increase of 6.73%.The severe-saline-alkali cultivated land showed a trend of increase first and then decline, with a net decline of7.68%, that accounts for 1.55% of the total area of cultivated land. During this period, NPP continued to increase, with a increase of 3.81%. In general, although the area of medium-saline-alkali cultivated land increased in nine years because of natural and artificial factors, the available land area and effective cultivated land increased continuously, and the NPP in the improved area shows a trend of continuous growth. The results shows that the control of saline-alkali land has achieved success to a certain extent.
This study takes Hetao Plain of Inner Mongolia as the research area, based on the RS and GIS technical support and the field sample collection, and uses the China first national geoinformation survey data as the data source, constructs the inversion model through calculating the correlation between the salinity degree and the band, classifies it with expert decision tree, and carries out dynamic monitoring of cultivated land salinization-alkalization in 2006-2014, and analyzes the spatio-temporal characteristics of NPP in different salinization-alkalization degree of cultivated land. The results shows that: Between 2006-2014, the non-saline-alkali cultivated land and the light-saline-alkali cultivated land in the study area showed a decreasing trend, with a reduction of 6.23%, that accounts for 3.55% of the total area of cultivated land. During this period, NPP increases first and then decreases, with a reduction of 2.06%. The medium-saline-alkali cultivated land showed a trend of first decline and then increase, with a net increase of 22.40%, that accounts for 5.10% of the total area of cultivated land. During this period, NPP changed none first and then increased, with a increase of 6.73%.The severe-saline-alkali cultivated land showed a trend of increase first and then decline, with a net decline of7.68%, that accounts for 1.55% of the total area of cultivated land. During this period, NPP continued to increase, with a increase of 3.81%. In general, although the area of medium-saline-alkali cultivated land increased in nine years because of natural and artificial factors, the available land area and effective cultivated land increased continuously, and the NPP in the improved area shows a trend of continuous growth. The results shows that the control of saline-alkali land has achieved success to a certain extent.
引文
[1]亢庆,于嵘,张增祥,等.土壤盐碱化遥感应用研究进展[J].遥感技术与应用,2005,20(4):447-454.[Kang Qing, Yu Rong, Zhang Zeng xiang et al. Advances in study on remote sensing application of soil salinization. Remote Sensing Technology and Application, 2005,20(4):447-454.]
[2]李建国,濮励杰,朱明,等.土壤盐渍化研究现状及未来研究热点[J].地理学报,2012,67(9):1233-1245.[Li Jianguo,Pu Lijie,Zhu Ming et al.The present situation and hot issues in the salt-affected soilresearch. Acta GeographicaSinica,2012,67(9):1233-1245.]
[3]王佳丽,黄贤金,钟大洋,等.盐碱地可持续利用研究综述[J].地理学报,2011,66(5):673-684.[Wang Jiali,Huang Xianjin,Zhong Dayang et al.Review on sustainable utilization of salt-affected land. Acta Geographica Sinica, 2011,66(5):673-684.]
[4]申文明,王文杰,罗海江,等.基于决策树分类技术的遥感影像分类方法研究[J].遥感技术与应用,2007,22(3):333-338.[Shen Wenming,Wang Wenjie,Luo Haijing et al.Classification methods of remote sensing image based on Decision Tree technologies. Remote Sensing Technology and Application, 2007,22(3):333-338.]
[5]李彤,吴骅.采用决策树分类技术对北京市土地覆盖现状进行研究[J].遥感技术与应用,2004,19(6):485-487.[Li Tong,Wu Ye.Application of Decision Tree Classification to Peking land cover. Remote Sensing Technology and Application,2004,19(6):485-487.]
[6]延森.遥感数字影像处理导论(原书第3版).Jensen, J R著.陈晓玲等译[M].北京:机械工业出版社, 2007.[Jensen J R.Introductory digital image processing Translated by Chen Xiaoling et al. Beijing:Machinery Industry Press,2007.]
[7]薄树奎,丁琳.训练样本数目选择对面向对象影像分类方法精度的影响[J].中国图象图形学报, 2010, 15(7):1106-1111.[Bo Shukui,Ding Lin.The effect of the size of training sampleon classification accuracy in object-oriented image analysis.Journal of Image and Graphics, 2010, 15(7):1106-1111.]
[8] Mathur A, Foody G M. Crop classification by support vector machine with intelligently selected training data for an operational application[J]. International Journal of Remote Sensing,2008, 29(8):2227-2233.
[9] Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS 250m NDVI data:An assessment for the U. S. Central Great Plains. Remote Sens Environ, 2008, 112:1096-1116.
[10] Matsushita B,Tamura M. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia[J].Remote Sensing of Environment,2002,81(1):58-66.
[11] McIver D K, Friedl M A. Estimating pixel-scale land coverclassification confidence using non-parametric machine learning methods[J]. IEEE Transactions on Geoscienceand Remote Sensing, 2001, 39:1959-1968.
[12]Hansen M, Dubayah R, DeFries R. Classification Trees,An alternative to traditional land cover classifiers[J].International Journal of Remote Sensing, 1996, 17:1075-1081.
[13] Metternicht G I,Zink J A.Modelling salinity-alkalinity classes for mapping salt-affected topsoilsin the semi-arid valleys of Cochabamba(Bolivia)[J]. ITC Journal, 1996(2):125-135.
[14] Taylor G, Dehaan R. Salinity mapping with hyperspectral imagery[R/OL]. www.geology. unsw.edu.au/research/Rsensing, 2000.
[15]关元秀,刘高焕,刘庆生,等.黄河三角洲盐碱地遥感调查研究[J].遥感学报,2001,5(1):46-52.[Guan Yuanxiu,Liu Gaohuan,Liu Qingsheng et al.The study of salt-affected soils in the Yellow River Delta based on remote sensing. Journal of Remote Sensing, 2001,5(1):46-52.]
[16]邓小炼,于嵘,亢庆等.基于遥感的黄河三角洲地区盐碱地分布监测[J].遥感信息,2006,5:34-36.[Deng Xiaolian,Yu Rong,Kang Qing et al. Monitoring of the saline-alkali land distribution in Yellow River Delta based on remote sensing. Remote Sensing Information, 2006,5:34-36.]
[17]骆玉霞,陈焕伟. GIS支持下的TM图像土壤盐碱化分级[J].遥感信息,2001(4):12-15.[Luo Yuxia,Chen Huanwei.Soil salinization classification based on TM Image supported by GIS. Remote Sensing Information,2001(4):12-15.]
[18]陈静清,闫慧敏,王绍强,等.中国陆地生态系统总初级生产力VPM遥感模型估算[J].第四纪研究, 2014, 34(4):732-742.[Chen Jingqing,Yan Huimin,Wang Shaoqiang et al.Estimation of total primary productivity of terrestrial ecosystem in China by VPM remote sensing model.Quaternary Studies, 2014, 34(4):732-742.]
[19]匡文慧,闫慧敏,张树文,等.呼伦贝尔农垦集团草畜平衡状况与粮经饲配置模式[J].科学通报,2018,17:1711-1721.[Kuang Wenhui,Yan Huimin,Zhang Shuwen et al.Forage-livestock status in farms and ranches of ecological grass-animal husbandry construction and allocation model of grain-warp-feed in Hulunbuir Agricultural Reclamation Group. Chinese Science Bulletin,2018,17:1711-1721.]
[20]苏日古嘎,苏根成,闫慧敏,等.21世纪以来呼伦贝尔市植被净初级生产力时空变化特征分析[J].干旱区资源与环境,2017,31(10):150-155.[Su Riguga,Su Gencheng,Yan Huimin et al.Temporal-spatial characteristics of vegetation NPP in HulunBuir since the 21st century. Journal of Arid Land Resources and Environment,2017,31(10):150-155.]
[21]巩国丽,刘纪远,邵全琴.草地覆盖度变化对生态系统防风固沙服务的影响分析—以内蒙古典型草原为例[J].地球信息科学,2014,16(3):426-434.[Gong Guoli,Liu Jiyuan,Shao Quanqin.Effects of vegetation coverage change on soil conservation service of typical steppe in Inner Mongolia.Journal of Geo-information Science,2014,16(3):426-434.]
[22]牛忠恩,闫慧敏,黄玫,等.基于MODIS-OLI遥感数据融合技术的农田生产力估算[J].自然资源学报,2016,31(5):875-884.[Niu Zhongen,Yan Huimin,Huang Mei et al.Agricultural productivity estimation with MODIS-OLI Fusion Data. Journal of Naturnal Resources, 2016,31(5):875-884.
[23]冀咏赞,闫慧敏,刘纪远,等.基于MODIS数据的中国耕地高中低产田空间分布格局[J].地理学报,2015,70(5):766-778.[Ji Yongzan,Yan Huimin,Liu Jiyuan et al.A MODIS data derived spatial distribution of high-, mediumand low-yield cropland in China. Acta Geographica Sinica, 2015,70(5):766-778.]
[24]吴成东,许可,韩中华,等.基于粗糙集和决策树的数据挖掘方法[J].东北大学学报,2006, 27(5):481-484.[Wu Chengdong,Xu Ke,Han Zhonghua et al.Approach to data mining based on Rough Sets and Decision Tree. Journal of Northeastern University,2006, 27(5):481-484.]
[25]乔梅,韩文秀.基于Rough集的决策树算法[J].天津大学学报,2005, 38(9):842-846.[Qiao Mei,Han Wenxiu.Decision Tree algorithm baesd on Rough Set.Journal of Tianjin University,2005, 38(9):842-846.]
[26]周兴东,于胜文,赵长胜,等.利用遥感图像进行土地利用分类方法的研究[J].煤炭学报,2007,32(5):481-484.[Zhou Xingdong,Yu Shengwen,Zhao Changsheng et al.Study on land use classification using remote sensing images.Journal of Coal Science,2007,32(5):481-484.]
[27]杨静,张楠男,李建.决策树算法的研究与应用[J].计算机技术与发展,2010(2):114-116.[Yang Jing,Zhang Nannan,Li Jian.Research and application of Decision Tree Algorithms.Computer Technology and Development,2010(2):114-116.]
[28]柯新利,边馥苓.基于C5.0决策树算法的元胞自动机土地利用变化模拟模型[J].长江流域资源与环境,2010,19(4):403-408.[Ke Xinli,Bian Fuling.Land-use change CA Model based on C5.0 Decision Tree.Resources and Environment in the Yangtze Basin,2010,19(4):403-408.]
[29]汪海锐,李伟.基于关联规则的决策树算法[J].计算机工程,2011,37(9):104-106.[Wang Hairui,Li Wei.Decision Tree Algorithm based on association rules.Computer Engineering,2011,37(9):104-106.]
[30]孙娟,王熙照.自适应模糊决策树算法[J].计算机工程与设计,2013,34(2):649-653.[Sun Juan,Wang Xizhao.Adaptive fuzzy decision tree algorithms.Computer Engineering and Design,2013,34(2):649-653.]
[31] Fry J A,Xian G,Jin S et al.Completion of the 2006 national land cover database for the conterminous United States[J].Photogrammetric Engineering and Remote Sensing,2011,77(9):858-864.
[32]汪炜,汪云甲,连达军.基于决策树分类的某市土地利用变化研究[J].测绘标准化,2011,27(1):4-7.[Wang Wei,Wang Yunjia,Lian Dajun.Study on land use change in a city based on Decision Tree classification. Standardization of Surveying and Mapping,2011,27(1):4-7.]
[2]李建国,濮励杰,朱明,等.土壤盐渍化研究现状及未来研究热点[J].地理学报,2012,67(9):1233-1245.[Li Jianguo,Pu Lijie,Zhu Ming et al.The present situation and hot issues in the salt-affected soilresearch. Acta GeographicaSinica,2012,67(9):1233-1245.]
[3]王佳丽,黄贤金,钟大洋,等.盐碱地可持续利用研究综述[J].地理学报,2011,66(5):673-684.[Wang Jiali,Huang Xianjin,Zhong Dayang et al.Review on sustainable utilization of salt-affected land. Acta Geographica Sinica, 2011,66(5):673-684.]
[4]申文明,王文杰,罗海江,等.基于决策树分类技术的遥感影像分类方法研究[J].遥感技术与应用,2007,22(3):333-338.[Shen Wenming,Wang Wenjie,Luo Haijing et al.Classification methods of remote sensing image based on Decision Tree technologies. Remote Sensing Technology and Application, 2007,22(3):333-338.]
[5]李彤,吴骅.采用决策树分类技术对北京市土地覆盖现状进行研究[J].遥感技术与应用,2004,19(6):485-487.[Li Tong,Wu Ye.Application of Decision Tree Classification to Peking land cover. Remote Sensing Technology and Application,2004,19(6):485-487.]
[6]延森.遥感数字影像处理导论(原书第3版).Jensen, J R著.陈晓玲等译[M].北京:机械工业出版社, 2007.[Jensen J R.Introductory digital image processing Translated by Chen Xiaoling et al. Beijing:Machinery Industry Press,2007.]
[7]薄树奎,丁琳.训练样本数目选择对面向对象影像分类方法精度的影响[J].中国图象图形学报, 2010, 15(7):1106-1111.[Bo Shukui,Ding Lin.The effect of the size of training sampleon classification accuracy in object-oriented image analysis.Journal of Image and Graphics, 2010, 15(7):1106-1111.]
[8] Mathur A, Foody G M. Crop classification by support vector machine with intelligently selected training data for an operational application[J]. International Journal of Remote Sensing,2008, 29(8):2227-2233.
[9] Wardlow B D, Egbert S L. Large-area crop mapping using time-series MODIS 250m NDVI data:An assessment for the U. S. Central Great Plains. Remote Sens Environ, 2008, 112:1096-1116.
[10] Matsushita B,Tamura M. Integrating remotely sensed data with an ecosystem model to estimate net primary productivity in East Asia[J].Remote Sensing of Environment,2002,81(1):58-66.
[11] McIver D K, Friedl M A. Estimating pixel-scale land coverclassification confidence using non-parametric machine learning methods[J]. IEEE Transactions on Geoscienceand Remote Sensing, 2001, 39:1959-1968.
[12]Hansen M, Dubayah R, DeFries R. Classification Trees,An alternative to traditional land cover classifiers[J].International Journal of Remote Sensing, 1996, 17:1075-1081.
[13] Metternicht G I,Zink J A.Modelling salinity-alkalinity classes for mapping salt-affected topsoilsin the semi-arid valleys of Cochabamba(Bolivia)[J]. ITC Journal, 1996(2):125-135.
[14] Taylor G, Dehaan R. Salinity mapping with hyperspectral imagery[R/OL]. www.geology. unsw.edu.au/research/Rsensing, 2000.
[15]关元秀,刘高焕,刘庆生,等.黄河三角洲盐碱地遥感调查研究[J].遥感学报,2001,5(1):46-52.[Guan Yuanxiu,Liu Gaohuan,Liu Qingsheng et al.The study of salt-affected soils in the Yellow River Delta based on remote sensing. Journal of Remote Sensing, 2001,5(1):46-52.]
[16]邓小炼,于嵘,亢庆等.基于遥感的黄河三角洲地区盐碱地分布监测[J].遥感信息,2006,5:34-36.[Deng Xiaolian,Yu Rong,Kang Qing et al. Monitoring of the saline-alkali land distribution in Yellow River Delta based on remote sensing. Remote Sensing Information, 2006,5:34-36.]
[17]骆玉霞,陈焕伟. GIS支持下的TM图像土壤盐碱化分级[J].遥感信息,2001(4):12-15.[Luo Yuxia,Chen Huanwei.Soil salinization classification based on TM Image supported by GIS. Remote Sensing Information,2001(4):12-15.]
[18]陈静清,闫慧敏,王绍强,等.中国陆地生态系统总初级生产力VPM遥感模型估算[J].第四纪研究, 2014, 34(4):732-742.[Chen Jingqing,Yan Huimin,Wang Shaoqiang et al.Estimation of total primary productivity of terrestrial ecosystem in China by VPM remote sensing model.Quaternary Studies, 2014, 34(4):732-742.]
[19]匡文慧,闫慧敏,张树文,等.呼伦贝尔农垦集团草畜平衡状况与粮经饲配置模式[J].科学通报,2018,17:1711-1721.[Kuang Wenhui,Yan Huimin,Zhang Shuwen et al.Forage-livestock status in farms and ranches of ecological grass-animal husbandry construction and allocation model of grain-warp-feed in Hulunbuir Agricultural Reclamation Group. Chinese Science Bulletin,2018,17:1711-1721.]
[20]苏日古嘎,苏根成,闫慧敏,等.21世纪以来呼伦贝尔市植被净初级生产力时空变化特征分析[J].干旱区资源与环境,2017,31(10):150-155.[Su Riguga,Su Gencheng,Yan Huimin et al.Temporal-spatial characteristics of vegetation NPP in HulunBuir since the 21st century. Journal of Arid Land Resources and Environment,2017,31(10):150-155.]
[21]巩国丽,刘纪远,邵全琴.草地覆盖度变化对生态系统防风固沙服务的影响分析—以内蒙古典型草原为例[J].地球信息科学,2014,16(3):426-434.[Gong Guoli,Liu Jiyuan,Shao Quanqin.Effects of vegetation coverage change on soil conservation service of typical steppe in Inner Mongolia.Journal of Geo-information Science,2014,16(3):426-434.]
[22]牛忠恩,闫慧敏,黄玫,等.基于MODIS-OLI遥感数据融合技术的农田生产力估算[J].自然资源学报,2016,31(5):875-884.[Niu Zhongen,Yan Huimin,Huang Mei et al.Agricultural productivity estimation with MODIS-OLI Fusion Data. Journal of Naturnal Resources, 2016,31(5):875-884.
[23]冀咏赞,闫慧敏,刘纪远,等.基于MODIS数据的中国耕地高中低产田空间分布格局[J].地理学报,2015,70(5):766-778.[Ji Yongzan,Yan Huimin,Liu Jiyuan et al.A MODIS data derived spatial distribution of high-, mediumand low-yield cropland in China. Acta Geographica Sinica, 2015,70(5):766-778.]
[24]吴成东,许可,韩中华,等.基于粗糙集和决策树的数据挖掘方法[J].东北大学学报,2006, 27(5):481-484.[Wu Chengdong,Xu Ke,Han Zhonghua et al.Approach to data mining based on Rough Sets and Decision Tree. Journal of Northeastern University,2006, 27(5):481-484.]
[25]乔梅,韩文秀.基于Rough集的决策树算法[J].天津大学学报,2005, 38(9):842-846.[Qiao Mei,Han Wenxiu.Decision Tree algorithm baesd on Rough Set.Journal of Tianjin University,2005, 38(9):842-846.]
[26]周兴东,于胜文,赵长胜,等.利用遥感图像进行土地利用分类方法的研究[J].煤炭学报,2007,32(5):481-484.[Zhou Xingdong,Yu Shengwen,Zhao Changsheng et al.Study on land use classification using remote sensing images.Journal of Coal Science,2007,32(5):481-484.]
[27]杨静,张楠男,李建.决策树算法的研究与应用[J].计算机技术与发展,2010(2):114-116.[Yang Jing,Zhang Nannan,Li Jian.Research and application of Decision Tree Algorithms.Computer Technology and Development,2010(2):114-116.]
[28]柯新利,边馥苓.基于C5.0决策树算法的元胞自动机土地利用变化模拟模型[J].长江流域资源与环境,2010,19(4):403-408.[Ke Xinli,Bian Fuling.Land-use change CA Model based on C5.0 Decision Tree.Resources and Environment in the Yangtze Basin,2010,19(4):403-408.]
[29]汪海锐,李伟.基于关联规则的决策树算法[J].计算机工程,2011,37(9):104-106.[Wang Hairui,Li Wei.Decision Tree Algorithm based on association rules.Computer Engineering,2011,37(9):104-106.]
[30]孙娟,王熙照.自适应模糊决策树算法[J].计算机工程与设计,2013,34(2):649-653.[Sun Juan,Wang Xizhao.Adaptive fuzzy decision tree algorithms.Computer Engineering and Design,2013,34(2):649-653.]
[31] Fry J A,Xian G,Jin S et al.Completion of the 2006 national land cover database for the conterminous United States[J].Photogrammetric Engineering and Remote Sensing,2011,77(9):858-864.
[32]汪炜,汪云甲,连达军.基于决策树分类的某市土地利用变化研究[J].测绘标准化,2011,27(1):4-7.[Wang Wei,Wang Yunjia,Lian Dajun.Study on land use change in a city based on Decision Tree classification. Standardization of Surveying and Mapping,2011,27(1):4-7.]