基于MODIS数据的锡林郭勒草原植被覆盖变化及驱动因子分析

防治荒漠化管理往往需要掌握人类因素对植被生长动态影响的关键数据,气候变化背景下的干旱半干旱地区草原植被覆盖多属于缓慢量变,这种变化很难通过土地利用/覆盖类型的转换来分析.旨在掌握2001—2014年内蒙古锡林郭勒草原植被覆盖变化时空特征,定量掌握关键区域气候因子与植被的动态关联关系,进而分离和评估气候和人类因素对区域植被覆盖变化的贡献.结果表明:研究区植被覆盖2001—2014年总体呈增加趋势,年内最大的归一化植被指数(normalized difference vegetation index,NDVI)每年增幅为0.001 8;气温和降水对锡林郭勒草原植被变化均有显著作用,两者共同驱动的NDVI预测模型决定系数(R2=0.65)大于气温(R2=0.46)或降水(R2=0.54)单要素驱动的结果;气候和人类因素对区域植被覆盖增长总体均呈现积极的作用,人类因素特别是生态治理保护措施对草原植被覆盖的贡献远大于气候因素变化.耦合遥感数据的植被变化驱动力分析方法,有助于提高干旱半干旱地区人地相互作用的定量评估能力,为防治荒漠化工作提供重要科学数据支撑.

2000年～2013年甘南州植被覆盖变化的驱动力研究

针对RESTREND方法在植被覆盖变化研究中应用的局限性,使用孙建国等提出的改进残差趋势法分析了2000年~2013年甘南州植被覆盖变化的驱动力。同时,通过实验对该方法进行验证。方法包括两步：1使用去趋势回归模型分离气候和非气候的作用;2通过外业调查获取一定数量的人类活动强度稳定的像元,基于这些像元上的非气候作用估算所有像元上的大气施肥效应,进而得到人类活动的作用。结果表明,在约占全区面积45%的植被显著增强区,气候变化、大气施肥和人类活动的贡献率约为54%、101%和-55%;在约占全区面积22%的植被显著减弱区,气候变化、大气施肥和人类活动的贡献率约为-21%、-130%和251%。

基于MODIS NDVI数据的陕西省植被绿度时空变化及人类活动影响

气候和人类活动是控制和影响植被空间分布及其变化的基本驱动力。利用2001—2018年的MODIS NDVI和1999—2018年的降雨时间序列数据,分析了陕西省NDVI的时空变化规律。采用TSS-RESTREND(Time Series Segmentation and Residual Trend)算法剥离了气候要素(降雨)对植被NDVI的影响,分析了人类活动对植被变化的影响程度和区域。(1)2001—2018年间,陕西省NDVI呈显著增加,全省平均增加速率为0.006/a;(2)相比18年来的平均值,77.29%的区域大于均值。其中,陕北的榆林市、延安市大于均值的区域较大,分别为97.52%和89.03%,秦巴山区次之,为73.91%。2012年之后,NDVI高值向北逐年推进趋势明显。(3)全省NDVI增加的区域达71.77%,而陕北地区的增加量明显大于关中平原区和陕南秦巴山地,其中陕北的榆林NDVI增加区域为72.11%,延安为86.44%,均超过了全省平均水平。(4)总体上陕西全省呈变绿趋势。榆林市和延安市的变绿区域明显多于关中平原和秦巴山地,延安和榆林的剧烈增长区域分别为55.46%和34.34%,而陕南为41.03%,说明处于湿润气候区的陕南地区也有显著变绿趋势。

Did Ecological Engineering Projects Have a Significant Effect on Large-scale Vegetation Restoration in Beijing-Tianjin Sand Source Region, China？ A Remote Sensing Approach

Aiming for the restoration of degraded ecosystems, many ecological engineering projects have been implemented around the world. This study investigates the ecological engineering project effectiveness on vegetation restoration in the Beijing-Tianjin Sand Source Region(BTSSR) from 2000 to 2010 based on the rain use efficiency(RUE) trend in relation to the land cover. More than half of the BTSSR experienced a vegetation productivity increase from 2000 to 2010, with the increasing intensity being sensitive to the indicators chosen. A clear tendency towards smaller increasing areas was shown when using the net primary productivity(NPP, 51.30%) instead of the accumulated normalized difference vegetation index(59.30%). The short-term variation in the precipitation and intra-seasonal precipitation distribution had a great impact on the remote sensing-based vegetation productivity. However, the residual trends method(RESTREND) effectively eliminated this correlation, while incorporating the variance and skewness of the precipitation distribution increased the models′ ability to explain the vegetation productivity variation. The RUE combined with land cover dynamics was valid for the effectiveness assessment of the ecological engineering projects on vegetation restoration. Particularly, the result based on growing season accumulated normalized difference vegetation index(ΣNDVI) residuals was the most effective, showing that 47.39% of the BTSSR experienced vegetation restoration from 2000 to 2010. The effectiveness of the ecological engineering projects differed for each subarea and was proportional to the strength of ecological engineering. The water erosion region dominated by woodland showed the best restoration, followed by the wind-water erosion crisscross regions, while the wind erosion regions dominated by grassland showed the worst effect. Seriously degraded regions still cover more area in the BTSSR than restored regions. Therefore, more future effort should be put in restoring degraded land.