Studying the temporal and spatial changes of vegetation coverage in different climatic regions and the relation between climatic factors and vegetation growth is of great significance to the construction and governance of ecological environment. Based on the GIMMS NDVI 3g dataset from 1982 to 2015, we examined the temporal and spatial changes of vegetation coverage in growth season in different climatic regions of the Yellow River Basin using the mean method, Sen+Mann Kendall trend analysis, partial correlation coefficient, multiple linear regression model plus residual method. We also analyzed the impacts of climatic factors and human activities on vegetation changes. Results demonstrated that: 1) The interannual change of NDVI in the Yellow River Basin and different climatic regions showed a slow upward trend from 1982 to 2015. The changes in arid region were steady, while the changes in semi-humid areas were more obvious. 2) In the past 34 years, vegetation increased remarkably in most of the climatic regions, of which the semi-arid region accounted for the largest proportion, whereas the southwest and south part of the semi-humid region mainly subjected to slight reduce. 3) Precipitation, temperature, and sunshine time in various climatic regions had positive impacts on NDVI, among which sunshine time had the greatest impact; in semi-arid region precipitation had the greatest impact on NDVI, whereas in semi-humid region the least impact; in semi-humid region temperature had the greatest impact on NDVI, while in arid region the least impact. 4) In the past 34 years, human activities exerted far more positive impact on vegetation than negative impact.
Key words
Normal Difference Vegetation Index(NDVI) /
spatiotemporal changes /
different climate zones /
climate factors /
human activities /
Yellow River Basin
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
[1] 孙进瑜. 1982—2006年全球植被生长时空变化及其与气候变化和城市化过程的关系[D]. 北京:北京大学,2010.
[2] 徐浩杰, 杨太保. 柴达木盆地植被生长时空变化特征及其对气候要素的响应[J]. 自然资源学报, 2014, 29(3):398-409.
[3] 何远梅, 姚文俊, 张 岩, 等. 黄土高原区植被恢复的空间差异性分析[J]. 中国水土保持科学, 2015,13(2): 67-73.
[4] XIN Z B,XU J X,ZHENG W. Spatiotemporal Variations of Vegetation Cover on the Chinese Loess Plateau(1981―2006): Impacts of Climate Changes and Human Activities[J]. Science in China Series D: Earth Sciences, 2008, 51(1): 67-78.
[5] 杨尚武. 黄河流域不同时间尺度植被覆盖变化及与气候因子的关系[D]. 兰州:西北师范大学, 2015.
[6] 贺 振, 贺俊平. 近32年黄河流域植被覆盖时空演化遥感监测[J]. 农业机械学报,2017,48(2):179-185.
[7] 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.
[8] 李生勇, 王晓卿, 李 彪. 基于MODIS数据的科尔沁区植被覆盖时空变化分析[J].raybet体育在线
院报, 2016, 33(2): 118-122,127.
[9] CUO L, ZHANG Y, GAO Y, et al. The Impacts of Climate Change and Land Cover/Use Transition on the Hydrology in the Upper Yellow River Basin, China[J]. Journal of Hydrology, 2013, 502(Complete): 37-52.
[10]朱莹莹. 1992—2015年黄河流域植被净初级生产力遥感估算及其对气候变化的响应[D]. 西安:长安大学, 2019.
[11]郑景云,尹云鹤,李炳元.中国气候区划新方案[J]. 地理学报, 2010, 65(1): 3-12.
[12]邓兴耀, 姚俊强, 刘志辉. 基于GIMMS NDVI的中亚干旱区植被覆盖时空变化[J]. 干旱区研究, 2017(1):10-19.
[13]杜加强, 舒俭民, 赵晨曦, 等. 两代AVHRR GIMMS NDVI数据集的对比分析:以新疆地区为例[J]. 生态学报, 2016,36(21): 6738-6749.
[14]HOLBEN B N. Characteristics of Maximum-Value Composite Images from Temporal AVHRR Data[J]. International Journal of Remote Sensing, 1986, 7(11): 1417-1434.
[15]张月丛, 赵志强, 李双成, 等. 基于SPOT NDVI的华北北部地表植被覆盖变化趋势[J]. 地理研究, 2008, 27(4): 745-754.
[16]毛德华, 王宗明, 松开山, 等. 东北多年冻土区植被NDVI变化及其对气候变化和土地覆被变响应[J]. 中国环境科学,2011, 31(2): 283-292.
[17]徐泽华. 山东省植被时空变化特征及其对气象干旱指数的响应[D]. 济南:山东师范大学, 2019.
[18]刘顺忠. 数理统计理论、方法、应用和软件计算[M]. 武汉: 华中科技大学出版社, 2005.
[19]李辉霞, 刘国华, 傅伯杰. 基于NDVI的三江源地区植被生长对气候变化和人类活动的响应研究[J]. 生态学报, 2011, 31(19): 5495-5504.
[20]孙建国, 张 卓, 韩 惠, 等. 气候和人类因素在黄土高原西北部植被变化中的贡献率研究[J]. 遥感信息, 2014, 29(2): 83-88.
[21]杨根生,拓万全,戴丰年,等. 风沙对黄河内蒙古河段河道泥沙淤积的影响[J].中国沙漠,2003(2):54-61.
[22]范微维, 易桂花, 张廷斌, 等. 黄河源区青海省玛多县2000—2014年NDVI变化及气候驱动因子[J]. 水土保持通报, 2017, 37(1): 335-340.
[23]AUSTIN A T, VITOUSEK P M. Nutrient Dynamics on a Precipitation Gradient in Hawaii[J]. Oecologia, 1998, 113(4): 519-529.
PDF(10289 KB)
