近15 a陕西省植被时空变化与影响因素分析
|
摘要
利用2000—2014年MODIS/NDVI时间序列数据,采用栅格像元趋势分析、稳定性评价的方法,研究了陕西省近15 a植被的时空变化特征和规律;利用Hurst指数对陕西省植被未来变化趋势进行了预测;并利用相关性分析法分析了NDVI与年均温度和降雨量的关系。结果表明:2000年、2015年陕西省NDVI均值分别为0.427 3、0.494 2, 15 a来增加了0.067,增长了16.0%,其中陕北地区NDVI增加明显,关中部分地区出现负增长,陕南地区NDVI总体维持在较高水平。陕西省植被变化趋势具有明显的空间差异性,全省植被未变化的占52%,改善部分占44.27%,退化部分占3.73%,说明15 a间陕西省植被状况有所改善,植被覆盖改善面积大于退化面积,其中陕北地区植被改善区域面积较大,关中地区植被覆盖面积有所减少,陕南地区植被变化幅度较小。陕西省植被稳定区域占50%以上(00.2),说明15 a间陕西省植被较为稳定,变化程度不大;其中陕西省植被最稳定地区主要集中在陕南、延安南部,榆林部分、西安、渭南少部地区变化幅度较大。Hurst指数分析表明陕西省44.54%面积的植被未来有可能面临退化,主要分布在陕北和关中地区的北部,植被未来有可能退化也有可能改善的面积占49.78%,主要分布在延安和陕南地区。陕西省近15 a气温和降水量总体呈增加趋势,增加速率分别为0.48℃·(10 a)~(-1)和69.5 mm·a~(-1);相关性分析结果表明,年均降雨量是影响NDVI的主要气象因子,同时陕西省植被变化也受到了退耕还林还草、防沙治沙、生态政治等人为因素的影响。
Based on NDVI from MODIS during the time period from 2000 to 2014,the spatial and temporal variation of vegetation in Shaanxi Province,China was analyzed using the methods of raster pixel trend analysis and stability evaluation.The vegetation change trends in the future were forecasted by R/S(Rescaled range analysis) method in Shaanxi Province.The correlation analysis was applied between annual temperature,precipitation and NDVI in Shaanxi Province.The results showed that the mean values of NDVI in 2000 and 2014 were 0.427 3 and 0.494 2,respectively,which indicated an NDVI increase of 0.067,or 16% equivalently.The NDVI was increased significantly in northern region of Shaanxi and there was a negative growth in some parts of Guanzhong area,while the NDVI in southern region of Shaanxi was still maintained at a higher level and changed a little.The vegetation variation in Shaanxi Province has obvious spatial regularity.The areas with vegetation of little change accounted for 52.0% in the whole province,while the areas with improved vegetation coverage accounted for 44.27%,and the areas where the vegetation was degraded accounted for 3.73%.It explained that the overall vegetation coverage in Shaanxi Province was improved in the past 15 years.The stability region of vegetation in Shaanxi Province accounted for more than 50% and the Cv value was between 0 and 0.1.The moderate area of vegetation accounted for 28% and the Cv value was between 0.1 and 0.2.The unstable area of vegetation in Shaanxi Province was less than 2% and the Cv value was greater than 0.2.It shows that vegetation condition was relatively stable in Shanxi Province in the past 15 years.The vegetation status was most stable in southern area of Shaanxi Province,southern area of Yan'an City while vegetation status varied greatly in some part of Yulin City,Xi'an City and the southern part of Weinan City.The analysis of Hurst index showed that 44.54% area of vegetation in Shaanxi Province may face degradation of vegetation in the future.It is possibly distributed mainly in the northern area and the Guanzhong area.49.78% area of vegetation in Shaanxi Province may face improving or degrading in the future which is mainly distributed in Yan'an City and southern area of Shaanxi Province.The annual temperature and precipitation showed an increasing trend in Shaanxi Province in the past 15 years and the increasing rates were 0.48 ℃·(10 a)~(-1) and 69.5 mm·a~(-1) respectively.The correlation analysis results showed that the annual precipitation was the main meteorological factors affecting the NDVI.The changes of vegetation in Shanxi Province had also been influenced by artificial factors such as the project of returning farmland to forest and grass,sand prevention and ecological politics.
Based on NDVI from MODIS during the time period from 2000 to 2014,the spatial and temporal variation of vegetation in Shaanxi Province,China was analyzed using the methods of raster pixel trend analysis and stability evaluation.The vegetation change trends in the future were forecasted by R/S(Rescaled range analysis) method in Shaanxi Province.The correlation analysis was applied between annual temperature,precipitation and NDVI in Shaanxi Province.The results showed that the mean values of NDVI in 2000 and 2014 were 0.427 3 and 0.494 2,respectively,which indicated an NDVI increase of 0.067,or 16% equivalently.The NDVI was increased significantly in northern region of Shaanxi and there was a negative growth in some parts of Guanzhong area,while the NDVI in southern region of Shaanxi was still maintained at a higher level and changed a little.The vegetation variation in Shaanxi Province has obvious spatial regularity.The areas with vegetation of little change accounted for 52.0% in the whole province,while the areas with improved vegetation coverage accounted for 44.27%,and the areas where the vegetation was degraded accounted for 3.73%.It explained that the overall vegetation coverage in Shaanxi Province was improved in the past 15 years.The stability region of vegetation in Shaanxi Province accounted for more than 50% and the Cv value was between 0 and 0.1.The moderate area of vegetation accounted for 28% and the Cv value was between 0.1 and 0.2.The unstable area of vegetation in Shaanxi Province was less than 2% and the Cv value was greater than 0.2.It shows that vegetation condition was relatively stable in Shanxi Province in the past 15 years.The vegetation status was most stable in southern area of Shaanxi Province,southern area of Yan'an City while vegetation status varied greatly in some part of Yulin City,Xi'an City and the southern part of Weinan City.The analysis of Hurst index showed that 44.54% area of vegetation in Shaanxi Province may face degradation of vegetation in the future.It is possibly distributed mainly in the northern area and the Guanzhong area.49.78% area of vegetation in Shaanxi Province may face improving or degrading in the future which is mainly distributed in Yan'an City and southern area of Shaanxi Province.The annual temperature and precipitation showed an increasing trend in Shaanxi Province in the past 15 years and the increasing rates were 0.48 ℃·(10 a)~(-1) and 69.5 mm·a~(-1) respectively.The correlation analysis results showed that the annual precipitation was the main meteorological factors affecting the NDVI.The changes of vegetation in Shanxi Province had also been influenced by artificial factors such as the project of returning farmland to forest and grass,sand prevention and ecological politics.
引文
1] WANG Q,ZHANG B,DAI S P,et al.Dynamic changes in vegetation coverage in the Three-North Shelter Forest Program based on GIMMS AVHRR NDVI[J].Resources Science,2011,33(8):1613-1620.
[2] 刘爽,宫鹏.2000—2010年中国地表植被绿度变化[J].科学通报,2012,57(16):1423-1434.[LIU Shuang,GONG Peng.Change of surface cover greenness in China between 2000 to 2010[J].Chinese Science Bulletin,2012,57(16):1423-1434.]
[3] AN Y Z,LIU C S,SHI R H,et al.Spatio-temporal analysis of the vegetation changes based on MODIS time-series data in the Yangtze River Delta region[J].Ecology and Environment Sciences,2012,21(12):1923-1927.
[4] 张华,张改改,吴睿.基于GF-1卫星数据的面向对象的民勤绿洲植被分类研究[J].干旱区地理,2017,40(4):831-838.[ZHANG Hua,ZHANG Gaigai,WU Rui.Object-based vegetable classification based on GF-1 imagery in Minqin Oasis[J].Arid Land Geography,2017,40(4):831-838.]
[5] 李净,刘红兵,李龙,等.基于多源遥感数据集的近30 a西北地区植被动态变化研究[J].干旱区地理,2016,39(2):387-394.[LI Jing,LIU Hongbing,LI Long,et al.Vegetation dynamic changes in northwest China based on multi-source remote sensing datasets in recent 30 years[J].Arid Land Geography,2016,39(2):387-394]
[6] 宋开山,张柏,王宗明,等.大豆叶绿素含量高光谱反演模型研究[J].农业工程学报,2006,22(8):16-21.[SONG Kaishan,ZHANG Bai,WANG Zongming,et al.Inverse model for estimating soybean chlorophyll concentration using in-situ collected canopy hyperspectral data[J].Transactions of the Chinese Society of Agricultural Engineering,2006,22(8):16-21.]
[7] 徐新刚,王纪华,黄文江,等.基于权重最优组合和多时相遥感的作物估产[J].农业工程学报,2009,25(9):137-142.[XU Xingang,WANG Jihua,HUANG Wenjiang,et al.Estimation of crop yield based on weight optimization combination and multi-temporal remote sensing data[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(9):137-142.]
[8] 马磊,闫浩文,何毅,等.2001—2015年喜马拉雅南麓地区植被变化遥感监测[J].干旱区地理,2017,40(2):405-414.[MA Lei,YAN Haowen,HE Yi,et al.Vegetation changes in south Himalayas areas based on remote sensing monitoring during 2001-2015[J].Arid Land Geography,2017,40(2):405-414.]
[9] 方秀琴,张万昌.叶面积指数(LAI)的遥感定量方法综述[J].国土资源遥感,2003,15(3):58-62.[FANG Xiuqin,ZHANG Wanchang.The application of remotely sensed data to the estimation of the leaf area index[J].Remote Sensing for Land and Recourses,2003,15(3):58-62.]
[10] 秦超,李君轶,陈宏飞,等.陕西省植被覆盖时空演变特征及其与气候因子的关系[J].中国农业气象,2015,36(1):108-114.[QIN Chao,LI Junyi,CHEN Hongfei,et al.Temporal and spatial evolution of vegetation cover and its relationship with climate factors in Shaanxi Province[J].Chinese Journal of Agrometeorology,2015,36(1):108-114.]
[11] 李登科,范建忠,权文婷.陕西省植被退化及其驱动因素分析[J].生态学杂志,2015,34(10):2907-2913.[LI Dengke,FAN Jianzhong,QUAN Wenting.Analysis of vegetation degradation and its driving factors in Shaanxi Province[J].Chinese Journal of Ecology,2015,34(10):2907-2913.]
[12] 刘英,侯恩科,岳辉.基于MODIS的陕西省植被变化遥感监测分析[J].人民黄河,2017,39(3):90-94.[LIU Ying,HOU Enke,YUE Hui.Remote sensing monitoring of vegetation changes in Shaanxi based on MODIS[J].Yellow River,2017,39(3):90-94.]
[13] 张君,延军平.1982—2013年陕西不同植被类型NDVI变化特征分析[J].干旱区资源与环境,2017,31(4):86-92.[ZHANG Jun,YAN Junping.Characteristics of NDVI changes under the different vegetation types in Shaanxi Province from 1982 to 2013[J].Journal of Arid Land Resources and Environment,2017,31(4):86-92.]
[14] STOW D,PETERSEN A,HOPE A,et al.Greenness trends of Arctic tundra vegetation in the 1990s:Comparison of two NDVI datasets from NOAA AVHRR system[J].International Journal of Remote Sensing,2007,28(21):4807-4822.
[15] 侯美亭,赵海燕,王筝,等.基于卫星遥感的植被NDVI对气候变化响应的研究进展[J].气候与环境研究,2013,18(3):353-364.[HOU Meiting,ZHAO Haiyang,WANG Zheng,et al.Vegetation responses to climate change by using the satellite-derived normalized difference vegetation index: A review[J].Climatic and Environmental Research,2013,18(3):353-364.]
[16] 王桂钢,周可法,孙莉,等.近10 a新疆地区植被动态与R/S分析[J].遥感技术与应用,2010,25(1):84-90.[WANG Guigang,ZHOU Kefa,SUN Li,et al.Study on the vegetation dynamic change and R/S analysis in the past ten years in Xinjiang[J].Remote Sensing Technology and Application,2010,25(1):84-90.]
[2] 刘爽,宫鹏.2000—2010年中国地表植被绿度变化[J].科学通报,2012,57(16):1423-1434.[LIU Shuang,GONG Peng.Change of surface cover greenness in China between 2000 to 2010[J].Chinese Science Bulletin,2012,57(16):1423-1434.]
[3] AN Y Z,LIU C S,SHI R H,et al.Spatio-temporal analysis of the vegetation changes based on MODIS time-series data in the Yangtze River Delta region[J].Ecology and Environment Sciences,2012,21(12):1923-1927.
[4] 张华,张改改,吴睿.基于GF-1卫星数据的面向对象的民勤绿洲植被分类研究[J].干旱区地理,2017,40(4):831-838.[ZHANG Hua,ZHANG Gaigai,WU Rui.Object-based vegetable classification based on GF-1 imagery in Minqin Oasis[J].Arid Land Geography,2017,40(4):831-838.]
[5] 李净,刘红兵,李龙,等.基于多源遥感数据集的近30 a西北地区植被动态变化研究[J].干旱区地理,2016,39(2):387-394.[LI Jing,LIU Hongbing,LI Long,et al.Vegetation dynamic changes in northwest China based on multi-source remote sensing datasets in recent 30 years[J].Arid Land Geography,2016,39(2):387-394]
[6] 宋开山,张柏,王宗明,等.大豆叶绿素含量高光谱反演模型研究[J].农业工程学报,2006,22(8):16-21.[SONG Kaishan,ZHANG Bai,WANG Zongming,et al.Inverse model for estimating soybean chlorophyll concentration using in-situ collected canopy hyperspectral data[J].Transactions of the Chinese Society of Agricultural Engineering,2006,22(8):16-21.]
[7] 徐新刚,王纪华,黄文江,等.基于权重最优组合和多时相遥感的作物估产[J].农业工程学报,2009,25(9):137-142.[XU Xingang,WANG Jihua,HUANG Wenjiang,et al.Estimation of crop yield based on weight optimization combination and multi-temporal remote sensing data[J].Transactions of the Chinese Society of Agricultural Engineering,2009,25(9):137-142.]
[8] 马磊,闫浩文,何毅,等.2001—2015年喜马拉雅南麓地区植被变化遥感监测[J].干旱区地理,2017,40(2):405-414.[MA Lei,YAN Haowen,HE Yi,et al.Vegetation changes in south Himalayas areas based on remote sensing monitoring during 2001-2015[J].Arid Land Geography,2017,40(2):405-414.]
[9] 方秀琴,张万昌.叶面积指数(LAI)的遥感定量方法综述[J].国土资源遥感,2003,15(3):58-62.[FANG Xiuqin,ZHANG Wanchang.The application of remotely sensed data to the estimation of the leaf area index[J].Remote Sensing for Land and Recourses,2003,15(3):58-62.]
[10] 秦超,李君轶,陈宏飞,等.陕西省植被覆盖时空演变特征及其与气候因子的关系[J].中国农业气象,2015,36(1):108-114.[QIN Chao,LI Junyi,CHEN Hongfei,et al.Temporal and spatial evolution of vegetation cover and its relationship with climate factors in Shaanxi Province[J].Chinese Journal of Agrometeorology,2015,36(1):108-114.]
[11] 李登科,范建忠,权文婷.陕西省植被退化及其驱动因素分析[J].生态学杂志,2015,34(10):2907-2913.[LI Dengke,FAN Jianzhong,QUAN Wenting.Analysis of vegetation degradation and its driving factors in Shaanxi Province[J].Chinese Journal of Ecology,2015,34(10):2907-2913.]
[12] 刘英,侯恩科,岳辉.基于MODIS的陕西省植被变化遥感监测分析[J].人民黄河,2017,39(3):90-94.[LIU Ying,HOU Enke,YUE Hui.Remote sensing monitoring of vegetation changes in Shaanxi based on MODIS[J].Yellow River,2017,39(3):90-94.]
[13] 张君,延军平.1982—2013年陕西不同植被类型NDVI变化特征分析[J].干旱区资源与环境,2017,31(4):86-92.[ZHANG Jun,YAN Junping.Characteristics of NDVI changes under the different vegetation types in Shaanxi Province from 1982 to 2013[J].Journal of Arid Land Resources and Environment,2017,31(4):86-92.]
[14] STOW D,PETERSEN A,HOPE A,et al.Greenness trends of Arctic tundra vegetation in the 1990s:Comparison of two NDVI datasets from NOAA AVHRR system[J].International Journal of Remote Sensing,2007,28(21):4807-4822.
[15] 侯美亭,赵海燕,王筝,等.基于卫星遥感的植被NDVI对气候变化响应的研究进展[J].气候与环境研究,2013,18(3):353-364.[HOU Meiting,ZHAO Haiyang,WANG Zheng,et al.Vegetation responses to climate change by using the satellite-derived normalized difference vegetation index: A review[J].Climatic and Environmental Research,2013,18(3):353-364.]
[16] 王桂钢,周可法,孙莉,等.近10 a新疆地区植被动态与R/S分析[J].遥感技术与应用,2010,25(1):84-90.[WANG Guigang,ZHOU Kefa,SUN Li,et al.Study on the vegetation dynamic change and R/S analysis in the past ten years in Xinjiang[J].Remote Sensing Technology and Application,2010,25(1):84-90.]
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |