地理学报 ›› 2016, Vol. 71 ›› Issue (11): 1948-1966.doi: 10.11821/dlxb201611007

• 生态与环境 • 上一篇    下一篇

新疆植被覆盖度趋势演变实验性分析

何宝忠1,2(), 丁建丽1,2(), 张喆1,2, 阿布都瓦斯提·吾拉木3   

  1. 1. 新疆大学资源与环境科学学院,乌鲁木齐 830046
    2. 新疆大学绿洲生态教育部重点实验室,乌鲁木齐 830046
    3.圣路易斯大学可持续性研究中心,圣路易斯 MO 63108
  • 收稿日期:2016-05-21 修回日期:2016-09-09 出版日期:2016-11-25 发布日期:2016-11-29
  • 作者简介:

    作者简介:何宝忠(1989-), 男, 重庆人, 博士生, 从事盐渍化监测与提取植被物候信息研究。E-mail: hbz108@163.com

  • 基金资助:
    新疆维吾尔自治区重点实验室课题(2016D03001);新疆大尺度土壤盐渍化监测与预警网络系统平台研发(201591101);国家自然科学基金项目(U1303381, 41261090, 41161063);教育部促进与美大地区科研合作与高层次人才培养项目

Experimental analysis of spatial and temporal dynamics of fractional vegetation cover in Xinjiang

Baozhong HE1,2(), Jianli DING1,2(), Zhe ZHANG1,2, Ghulam Abduwasit3   

  1. 1. College of Resource and Environmental Science, Xinjiang University, Urumqi 830046, China
    2. Key Laboratory for Oasis Ecology, Xinjiang University, Urumqi 830046, China
    3. Center for Sustainability, Saint Louis University, Saint Louis MO 63108, USA
  • Received:2016-05-21 Revised:2016-09-09 Online:2016-11-25 Published:2016-11-29
  • Supported by:
    Key Laboratory of Subject of the Xinjiang Uygur Autonomous Region, No.2016D03001;The Research and Development of the Network Platform for the Monitoring and Early Warning of the Large Scale Soil Salinization in Xinjiang, No.201591101;National Natural Science Foundation of China, No.U1303381, No.41261090, No.41161063;The Ministry of Education to Promote Cooperation with the Mei Da Area of Scientific Research and High-Level Personnel Training Project

摘要:

基于MODIS-NDVI数据,提取新疆2005-2015年植被覆盖度(FVC)。通过依据海拔和植被覆盖度的指标划分出山地、绿洲、平原、荒漠等11个子系统。通过斜率、变异系数、线性回归模型等方法来对全疆和不同生态分区的现状和未来发展趋势进行分析,并用BP人工神经网络来预测新疆2016-2020年的植被覆盖度的时空变化和分析2005-2020年时空动态变化趋势。主要结论为:① 新疆植被覆盖度总体为上升趋势,从西北向东南逐渐下降;山地呈逐年上升趋势,荒漠呈不显著退化趋势。植被覆盖度的变化主要是由降水量的变化引起;② 在整个新疆的荒漠和绿洲边缘构成了一个“绿洲—荒漠改善过渡带”,绿洲呈明显的改善趋势;③ 2009年是研究期内多数分区植被覆盖度的历史最低点;④ 在山脉的冰川积雪、湖泊周围的变异性很大,范围在150%~316%之间,这主要是由于气候变化、冰川消融和湖泊水位的波动变化所致;⑤ 北疆生态明显好于东疆与南疆,其绿洲区域呈现明显的改善趋势。伊犁地区的植被覆盖度相比于其他3个分区的变幅很大,山地区域呈明显的逐年退化趋势。伊犁地区植被覆盖度的局部最低点是在2008年,比其他分区的2009年提前了一年,相应的存在“实时”(伊犁)和“滞后”(东疆、南疆和北疆)的效应,主要是由于降水量和气温的变化所致。

关键词: 植被覆盖度, MODIS, BP-ANN, 气候变化, 新疆

Abstract:

This paper presents spatial and temporal dynamics of fraction of vegetation in Xinjiang Uygur Autonomous Region of China. Fractional vegetation cover (FVC) was estimated by using MODIS-NDVI data from 2005 to 2015. The study area was divided into 11 ecological and climate regions according to the altitude and land cover. Slope, variability and linear regression model were used to analyze the present situation and future tendency for FVC in Xinjiang and its sub-regions. The BP-ANN neural network analysis was used to predict FVC from 2016 to 2020, and the FVC trend over the entire study area during 2005-2020 was discussed. The results showed that: From 2005 to 2015, FVC increased in general over time, and spatially, decreased from northwest to southeast; In mountain areas, FVC increased in general; desert system showed no significant change, and multi-average FVC was about 0.10. The dynamic change of FVC was mainly caused by precipitation. We observed an improvement of vegetation cover over oasis and desert ecotone. FVC showed a significant increase over oasis. The year 2009 was the turning point with a historical low value. The variation near areas covered by ice and snow, river and lakes was remarkable, showing a change rate of 150%-316%. This change was probably responded by glacial depletion and fluctuation changes of lakes due to global climate change. The ecosystem in northern Xinjiang is obviously better than that in southern and eastern Xinjiang. In terms of oasis, the northern part is improved remarkably (P = 0.001). There was an obvious FVC fluctuation in Yili region compared to other regions. The mountain area showed an obvious degeneration tendency. The local minima point of FVC was observed in Yili in 2008, while it was in the other three regions in 2009. The lag of local minima occurring in the northern and southern parts of the study areas may have been caused by precipitation and temperature variation across the study area. Predicted average FVC from 2016-2020 demonstrated trends and patterns identical to 2005-2015 with some local differences. For example, FVC increases (P = 0.002) during 2005-2020 in general. In desert areas, the trend is from non-significant decrease during 2005-2015 to non-significant increase for 2005-2020. In oasis region, predicted FVC showed a slightly rising trend compared to the obviously rising trend in 2005-2015. The multi-average FVC is above 0.62 and it showed improvement during 2005-2020. For sub-regions and ecosystems, the trend differs significantly between 2005-2015 and 2005-2020. In northern part, the trend in 2005-2020 was almost the same with that of 2005-2015, while in 2016-2020 the tendency was opposite to 2005-2015, with oasis and mountain FVC showing a decreasing trend. In Yili, the general trend in 2005-2020 was almost the same with that of 2005-2015, but the amplitude of variation became smaller in 2016-2020 when compared to early stage and the mountain area showing a remarkably decreasing trend. Our results demonstrated that BP-ANN model can predict FVC in Xinjiang with statistical significance, the coefficient of determination (R2) of 0.95, root-mean-square error of 0.05, suggesting that this method gained a statisifactory result.

Key words: fractional vegetation cover (FVC), moderate resolution imaging spectroradiometer (MODIS), BP artificial neural network, climate change, Xinjiang