基于土壤湿度与植被覆盖变化的黄土高原生态恢复项目适宜性评价
|
摘要
基于2001—2014年的MODIS数据和Landsat数据,利用温度植被干旱指数对黄土高原土壤湿度进行了反演,并应用Theil-Sen趋势和Hurst指数,通过分析土壤湿度与植被覆盖的时空变化特征及其相互关系,得到了未来不同土壤湿度情境下植被覆盖的变化特征,经过筛选和分析划分出了黄土高原生态恢复项目适宜性区域。结果表明:(1)黄土高原TVDI的Hurst指数均值为0.49,其中持续性和反持续性面积分别占42.54%,57.46%。根据TVDI未来变化特征来看,未来土壤湿度减小的区域面积占54.08%且遍布整个研究区。(2) NDVI的Hurst均值为0.52,其中持续性面积占55.01%,表明黄土高原植被覆盖持续性强于反持续性。根据NDVI未来变化特征,植被覆盖持续改善面积达46.95%,退化转为改善占6.08%,呈良好趋势。(3)生态恢复项目弱适宜区面积最大,占总面积的59.90%,其次为不适宜区,面积占25.39%;适宜区面积仅占黄土高原的13.41%,较适宜区面积仅为1.30%。(4)未来的植被恢复工程主要针对坡度较大的耕地实施退耕还林,还需要考虑对土壤水分适宜的地区进行了退草还林,而且坡度较小的较适宜区应在粮食安全的基础上进行了退耕还林和退草还林。
Based on the MODIS data and Landsat data from 2001 to 2014, we have estimated the soil moisture in the Loess Plateau using temperature vegetation dryness index. The Theil-Sen method and Hurst index were used to analyze the spatiotemporal variation of soil moisture and vegetation cover as well as their interrelationship and get the different vegetation changes under different situations of soil moisture in the future. The suitability region of ecological restoration project was divided through filter and analysis in the Loess Plateau. The results showed that:(1) the average Hurst index of TVDI in the Loess Plateau was 0.49, of which the persistent and anti-persistent areas accounted for 42.54% and 57.46%, respectively; according to the characteristics of future changes of TVDI, the area of soil moisture will decrease in the future, accounting for 54.08% and distributing in the whole study area;(2) the average Hurst value of NDVI was 0.52, of which 55.01% was the sustained area, indicating that vegetation cover persistence in the Loess Plateau was stronger than that in reverse; according to the future variation characteristics of NDVI, area of vegetation cover continuing to improve accounted for 46.95%, and the area of conversion of degradation into improvement accounted for 6.08%, showing the good trend;(3) the ecological restoration project had carried out in the largest area with weak suitability, accounting for 59.9% of the total area, followed by unsuitable zones, accounting for 25.39% of the total area; the suitable area covered only 13.41% of the Loess Plateau, and the area of better suitable area was only 1.3% of the Loess Plateau;(4) the future vegetation restoration project should mainly focus on the larger sloping arable land to implement the conversion of sloping farmland into forestland, but also consider the soil water suitable areas where the grassland should be converted into the forestland, and the farmland should be converted into forestland, and the grassland should be converted into forestland in the more suitable areas with gentle slope on the basis of food security.
Based on the MODIS data and Landsat data from 2001 to 2014, we have estimated the soil moisture in the Loess Plateau using temperature vegetation dryness index. The Theil-Sen method and Hurst index were used to analyze the spatiotemporal variation of soil moisture and vegetation cover as well as their interrelationship and get the different vegetation changes under different situations of soil moisture in the future. The suitability region of ecological restoration project was divided through filter and analysis in the Loess Plateau. The results showed that:(1) the average Hurst index of TVDI in the Loess Plateau was 0.49, of which the persistent and anti-persistent areas accounted for 42.54% and 57.46%, respectively; according to the characteristics of future changes of TVDI, the area of soil moisture will decrease in the future, accounting for 54.08% and distributing in the whole study area;(2) the average Hurst value of NDVI was 0.52, of which 55.01% was the sustained area, indicating that vegetation cover persistence in the Loess Plateau was stronger than that in reverse; according to the future variation characteristics of NDVI, area of vegetation cover continuing to improve accounted for 46.95%, and the area of conversion of degradation into improvement accounted for 6.08%, showing the good trend;(3) the ecological restoration project had carried out in the largest area with weak suitability, accounting for 59.9% of the total area, followed by unsuitable zones, accounting for 25.39% of the total area; the suitable area covered only 13.41% of the Loess Plateau, and the area of better suitable area was only 1.3% of the Loess Plateau;(4) the future vegetation restoration project should mainly focus on the larger sloping arable land to implement the conversion of sloping farmland into forestland, but also consider the soil water suitable areas where the grassland should be converted into the forestland, and the farmland should be converted into forestland, and the grassland should be converted into forestland in the more suitable areas with gentle slope on the basis of food security.
引文
[1] 易浪,任志远,张翀,等.黄土高原植被覆盖变化与气候和人类活动的关系[J].资源科学,2014,36(1):166-174.
[2] Chen Y,Wang K,Lin Y,et al.Balancing green and grain trade[J].Nature Geoscience,2015,10(8):739-741.
[3] 刘宇,傅伯杰.黄土高原植被覆盖度变化的地形分异及土地利用/覆被变化的影响[J].干旱区地理,2013,36(6):1097-1102.
[4] 李双双,延军平,万佳.近10年陕甘宁黄土高原区植被覆盖时空变化特征[J].地理学报,2012,67(7):960-970.
[5] 刘宪锋,杨勇,任志远,等.2000—2009年黄土高原地区植被覆盖度时空变化[J].中国沙漠,2013,33(4):1244-1249.
[6] 王朗,傅伯杰,吕一河,等.生态恢复背景下陕北地区植被覆盖的时空变化[J].应用生态学报,2010,21(8):2109-2116.
[7] 张宝庆,吴普特,赵西宁.近30 a黄土高原植被覆盖时空演变监测与分析[J].农业工程学报,2011,27(4):287-293.
[8] 郭忠升.土壤水分植被承载力的理论与实践[M].北京:科学出版社,2014.
[9] 李玉山.黄土高原森林植被对陆地水循环影响的研究[J].自然资源学报,2001,16(5):427-432.
[10] 田均良.黄土高原生态建设环境效应研究[M].北京:气象出版社,2010.
[11] Noy-Meir I.Desert ecosystems:environment and producers[J].Annual Review of Ecology and Systematics,1973,4(1):25-51.
[12] 张静静,郑辉,朱连奇,等.豫西山地植被NDVI及其气候响应的多维变化[J].地理研究,2017,36(4):765-778.
[13] 赵舒怡,宫兆宁,刘旭颖.2001—2013年华北地区植被覆盖度与干旱条件的相关分析[J].地理学报,2015,70(5):717-729.
[14] 刘宪锋,潘耀忠,朱秀芳,等.2000—2014年秦巴山区植被覆盖时空变化特征及其归因[J].地理学报,2015,70(5):705-716.
[15] 方利,王文杰,蒋卫国,等.2000—2014年黑龙江流域(中国)植被覆盖时空变化及其对气候变化的响应[J].地理科学,2017,37(11):1745-1754.
[16] 王子玉,许端阳,杨华,等.1981—2010年气候变化和人类活动对内蒙古地区植被动态影响的定量研究[J].地理科学进展,2017,36(8):1025-1032.
[17] 白天路.基于遥感和地面实测水分数据的小流域土壤水分模拟[D].西安:西北大学,2010.
[18] Kogan F,Adamenko T,Guo W.Global and regional drought dynamics in the climate warming era[J].Remote Sensing Letters,2013,4(4):364-372.
[19] Karnieli A,Agam N,Pinker R T.Use of NDVI and land surface temperature for drought assessment:Merits and limitations [J].Journal of Climate,2010,23(3):618-633.
[20] 陈书林,刘元波.卫星遥感反演土壤水分研究综述[J].地球科学进展,2012,27(11):1192-1203.
[21] 王鹏新,龚健雅,李小文.基于植被指数和土地表面温度的干旱监测模型[J].地球科学进展,2003,18(4):527-533.
[22] 段春青,刘昌明,陈晓楠.区域水资源承载力概念及研究方法的探讨[J].地理学报,2010,65(1):82-90.
[23] 李新荣,何明珠,贾荣亮.黑河中下游荒漠区植物多样性分布对土壤水分变化的响应[J].地球科学进展,2008,23(7):685-691.
[24] 刘立文,张吴平,段永红.模型的农业旱情时空变化遥感应用[J].生态学报,2014,34(13):3704-3711.
[25] Patel N R,Anapashsha R,Kumar S,et al.Assessing potential of MODIS derived temperature/vegetation condition index(TVDI) to infer soil moisture status[J].International Journal of Remote Sensing,2009,30(1):23-39.
[26] 宋春桥,游松财,刘高焕.基于TVDI的藏北地区土壤湿度空间格局[J].地理科学进展,2011,30(5):570-576.
[27] 邸兰杰,王卫,成贺玺.基于ATI和TVDI模型的河北平原土壤湿度遥感反演[J].中国生态农业学报,2014,22(6):737-743.
[28] 李天祺,朱秀芳,潘耀忠,等.MODIS陆地表面温度数据重构方法研究[J].北京师范大学学报:自然科学版,2015,51(S1):70-76.
[29] 徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,2002.
[30] Kendall M G.Rank Correlation Methods,3rd edition [M].New York:Hafner Publishing Company,1962.
[31] Sandholt I,Rasmussen K,Andersen J.A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J].Remote Sensing of Environment,2002,79(2/3):213-224.
[32] John R,Chen Jiquan,Lu Nan,et al.Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia[J].Remote Sensing of Environment,2008,112(5):2018-2032.
[33] 张蕾,吕厚荃,王良宇.中国土壤湿度的时空变化特征[J].地理学报,2016,71(9):1494-1508.
[34] 韦振锋,王德光,张翀.近12年陕甘宁黄土高原区植被物候时空变化特征[J].生态与农村环境学报,2014,30(4):423-429.
[35] 李强,张翀.近15年黄土高原植被物候时空变化特征分析[J].中国农业科学,2016,49(22):4352-4365.
[36] 谢宝妮,秦占飞,王洋.基于遥感的黄土高原植被物候监测及其对气候变化的响应[J].农业工程学报,2015,31(15):153-160.
[37] 张翀,雷田旺,宋佃星.黄土高原植被覆盖与土壤湿度的时滞互相关时空特征分析[J].生态学报,2018,38(6):1-11.
[38] 杨涛,宫辉力,李小娟.土壤水分遥感监测研究进展[J].生态学报,2010,30(22):6264-6277.
[39] Zhao W,Du S.Spectral-spatial feature extraction for hyper-spectral image classification:A dimension reduction and deep learning approach[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4544-4554.
[40] 刘大伟,韩玲,韩晓勇.基于深度学习的高分辨率遥感影像分类研究[J].光学学报,2016,36(4):298-306.
[41] Jean N,Burke M,Xie M,et al.Combining satellite imagery and machine learning to predict poverty[J].Science,2016,353(6301):790-794.
[2] Chen Y,Wang K,Lin Y,et al.Balancing green and grain trade[J].Nature Geoscience,2015,10(8):739-741.
[3] 刘宇,傅伯杰.黄土高原植被覆盖度变化的地形分异及土地利用/覆被变化的影响[J].干旱区地理,2013,36(6):1097-1102.
[4] 李双双,延军平,万佳.近10年陕甘宁黄土高原区植被覆盖时空变化特征[J].地理学报,2012,67(7):960-970.
[5] 刘宪锋,杨勇,任志远,等.2000—2009年黄土高原地区植被覆盖度时空变化[J].中国沙漠,2013,33(4):1244-1249.
[6] 王朗,傅伯杰,吕一河,等.生态恢复背景下陕北地区植被覆盖的时空变化[J].应用生态学报,2010,21(8):2109-2116.
[7] 张宝庆,吴普特,赵西宁.近30 a黄土高原植被覆盖时空演变监测与分析[J].农业工程学报,2011,27(4):287-293.
[8] 郭忠升.土壤水分植被承载力的理论与实践[M].北京:科学出版社,2014.
[9] 李玉山.黄土高原森林植被对陆地水循环影响的研究[J].自然资源学报,2001,16(5):427-432.
[10] 田均良.黄土高原生态建设环境效应研究[M].北京:气象出版社,2010.
[11] Noy-Meir I.Desert ecosystems:environment and producers[J].Annual Review of Ecology and Systematics,1973,4(1):25-51.
[12] 张静静,郑辉,朱连奇,等.豫西山地植被NDVI及其气候响应的多维变化[J].地理研究,2017,36(4):765-778.
[13] 赵舒怡,宫兆宁,刘旭颖.2001—2013年华北地区植被覆盖度与干旱条件的相关分析[J].地理学报,2015,70(5):717-729.
[14] 刘宪锋,潘耀忠,朱秀芳,等.2000—2014年秦巴山区植被覆盖时空变化特征及其归因[J].地理学报,2015,70(5):705-716.
[15] 方利,王文杰,蒋卫国,等.2000—2014年黑龙江流域(中国)植被覆盖时空变化及其对气候变化的响应[J].地理科学,2017,37(11):1745-1754.
[16] 王子玉,许端阳,杨华,等.1981—2010年气候变化和人类活动对内蒙古地区植被动态影响的定量研究[J].地理科学进展,2017,36(8):1025-1032.
[17] 白天路.基于遥感和地面实测水分数据的小流域土壤水分模拟[D].西安:西北大学,2010.
[18] Kogan F,Adamenko T,Guo W.Global and regional drought dynamics in the climate warming era[J].Remote Sensing Letters,2013,4(4):364-372.
[19] Karnieli A,Agam N,Pinker R T.Use of NDVI and land surface temperature for drought assessment:Merits and limitations [J].Journal of Climate,2010,23(3):618-633.
[20] 陈书林,刘元波.卫星遥感反演土壤水分研究综述[J].地球科学进展,2012,27(11):1192-1203.
[21] 王鹏新,龚健雅,李小文.基于植被指数和土地表面温度的干旱监测模型[J].地球科学进展,2003,18(4):527-533.
[22] 段春青,刘昌明,陈晓楠.区域水资源承载力概念及研究方法的探讨[J].地理学报,2010,65(1):82-90.
[23] 李新荣,何明珠,贾荣亮.黑河中下游荒漠区植物多样性分布对土壤水分变化的响应[J].地球科学进展,2008,23(7):685-691.
[24] 刘立文,张吴平,段永红.模型的农业旱情时空变化遥感应用[J].生态学报,2014,34(13):3704-3711.
[25] Patel N R,Anapashsha R,Kumar S,et al.Assessing potential of MODIS derived temperature/vegetation condition index(TVDI) to infer soil moisture status[J].International Journal of Remote Sensing,2009,30(1):23-39.
[26] 宋春桥,游松财,刘高焕.基于TVDI的藏北地区土壤湿度空间格局[J].地理科学进展,2011,30(5):570-576.
[27] 邸兰杰,王卫,成贺玺.基于ATI和TVDI模型的河北平原土壤湿度遥感反演[J].中国生态农业学报,2014,22(6):737-743.
[28] 李天祺,朱秀芳,潘耀忠,等.MODIS陆地表面温度数据重构方法研究[J].北京师范大学学报:自然科学版,2015,51(S1):70-76.
[29] 徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,2002.
[30] Kendall M G.Rank Correlation Methods,3rd edition [M].New York:Hafner Publishing Company,1962.
[31] Sandholt I,Rasmussen K,Andersen J.A simple interpretation of the surface temperature/vegetation index space for assessment of surface moisture status[J].Remote Sensing of Environment,2002,79(2/3):213-224.
[32] John R,Chen Jiquan,Lu Nan,et al.Predicting plant diversity based on remote sensing products in the semi-arid region of Inner Mongolia[J].Remote Sensing of Environment,2008,112(5):2018-2032.
[33] 张蕾,吕厚荃,王良宇.中国土壤湿度的时空变化特征[J].地理学报,2016,71(9):1494-1508.
[34] 韦振锋,王德光,张翀.近12年陕甘宁黄土高原区植被物候时空变化特征[J].生态与农村环境学报,2014,30(4):423-429.
[35] 李强,张翀.近15年黄土高原植被物候时空变化特征分析[J].中国农业科学,2016,49(22):4352-4365.
[36] 谢宝妮,秦占飞,王洋.基于遥感的黄土高原植被物候监测及其对气候变化的响应[J].农业工程学报,2015,31(15):153-160.
[37] 张翀,雷田旺,宋佃星.黄土高原植被覆盖与土壤湿度的时滞互相关时空特征分析[J].生态学报,2018,38(6):1-11.
[38] 杨涛,宫辉力,李小娟.土壤水分遥感监测研究进展[J].生态学报,2010,30(22):6264-6277.
[39] Zhao W,Du S.Spectral-spatial feature extraction for hyper-spectral image classification:A dimension reduction and deep learning approach[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4544-4554.
[40] 刘大伟,韩玲,韩晓勇.基于深度学习的高分辨率遥感影像分类研究[J].光学学报,2016,36(4):298-306.
[41] Jean N,Burke M,Xie M,et al.Combining satellite imagery and machine learning to predict poverty[J].Science,2016,353(6301):790-794.
© 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 |