基于MODIS/NDVI的新疆伊犁河谷植被变化

利用2000-2010年16 d合成的MODIS/[NDVI]数据,结合植被异常指数、趋势线分析和Hurst指数等分析方法,对新疆伊犁河谷植被覆盖的时空变化特征进行了分析。结果表明：（1） 伊犁河谷内植被覆盖随海拔增高而先增加后减小,最高植被覆盖区位于2 000～2 500 m的高程带;2000-2010年伊犁河谷各高程带内植被覆盖整体下降趋势明显,但海拔低于1 000 m的高程带除外。（2）受干湿环境影响,伊犁河谷全区平均被植异常指数最高值出现在降水最多的2002年,最低值出现在降水最少的2008年,但不同区域植被异常指数的变化存在较大差异。（3）伊犁河谷内植被覆盖增加和减小的区域分别占总面积的4.09%和19.34%,增加区域主要位于伊犁河两岸的平原区,减小区域主要位于乌孙山两端以及伊犁河谷周围海拔2 000 m左右的低山区域;变标度极差分析结果表明,伊犁河谷内植被覆盖年际变化呈现很强的持续性,未来一定时间内将保持现有变化趋势不变。     

Spatial and temporal variations of vegetation in Qinghai Province based on satellite data

This paper used five years (2001-2006) time series of MODIS NDVI images with a 1-km spatial resolution to produce a land cover map of Qinghai Province in China. A classi-fication approach for different land cover types with special emphasis on vegetation, espe-cially on sparse vegetation, was developed which synthesized Decision Tree Classification, Supervised Classification and Unsupervised Classification. The spatial distribution and dy-namic change of vegetation cover in Qinghai from 2001 to 2006 were analyzed based on the land cover classification map and five grade elevation belts derived from Qinghai DEM. The result shows that vegetation cover in Qinghai in recent five years has been some improved and the area of vegetation was increased from 370,047 km2 in 2001 to 374,576 km2 in 2006. Meanwhile, vegetation cover ratio was increased by 0.63%. Vegetation cover ratio in high mountain belt is the largest (67.92%) among the five grade elevation belts in Qinghai Prov-ince. The second largest vegetation cover ratio is in middle mountain belt (61.80%). Next, in the order of the decreasing vegetation cover ratio, the remaining grades are extreme high mountain belt (38.98%), low mountain belt (25.55%) and flat region belt (15.46%). The area of middle density grassland in high mountain belt is the biggest (94,003 km2), and vegetation cover ratio of dense grassland in middle mountain belt is the highest (32.62%), and the in-creased area of dense grassland in high mountain belt is the greatest (1280 km2). In recent five years the conversion from sparse grass to middle density grass in high mountain belt has been the largest vegetation cover variation and the converted area is 15931 km2.     

城市植被覆盖变化可预测性及其空间格局的定量递归分析——以深圳市为例

由于人口的快速增加和经济要素的大量聚集,城市是下垫面性质改变最为显著的区域。本文应用定量递归分析技术对1999~2006年逐旬的1km SPOT-VEGETATION NDVI数据进行分析,以期揭示NDVI时间系列的非线性动态特征及其空间格局。研究结果表明,深圳市的NDVI时间系列的RQA特征值居于随机系列和确定性系列之间,属于含有随机成分的确定性系列。不同覆被类型下NDVI系列的特性不同,林地区的NDVI系列规则性最高,农地区次之,建设用地区最差,表明人类活动作为一种噪声对于NDVI系列性质的扰动。用二阶Rényi熵作为NDVI系列的可预测性指标,其空间格局表现为海拔高、坡度大、林地分布集中的西北和东南部可预测性高,而海拔相对低、建设用地相对集中的中南部地区可预测性低。     

人类活动和气候变化对红碱淖植被覆盖变化的影响

利用GIMMS NDVI、SPOT VGT两种归一化植被指数(NDVI)数据和气候资料对红碱淖地区植被覆盖变化、气候变化进行了研究, 从气候变化和人类活动的角度分析了植被覆盖变化的原因。结果表明,1957—2007年期间红碱淖地区温度上升趋势显著,降水经历了由少许的增加转向减少的过程,进入了一个相对干旱气候态。1982—2007年期间红碱淖地区植被覆盖变化趋势是在波动中逐渐增加,大致经历了4个阶段：①1982—1988年植被覆盖持续增加;②1989—1998年小幅波动,相对稳定;③1999年植被覆盖迅速下降,1999—2001年维持较低值;④2002年快速增加到较高水平,2007年达最大值。气候变暖使春季生长季节提前、秋季生长期延长。春季的降水量对春季的植被覆盖影响明显,春夏之交降水量对NDVI的影响存在一个月的滞后现象。生长季的降水量变化趋势与植被覆盖的变化趋势相一致。夏季温度上升加速了地表蒸散发过程,同时降水具有减少的趋势,干旱对植被生长有抑制作用,夏季的植被覆盖却在显著增加。     

Vegetation change of ecotone in west of Northeast China plain using time-series remote sensing data

Multi-temporal series of satellite SPOT-VEGETATION normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data from 1998 to 2007 were used for analyzing vegetation change of the ecotone in the west of the Northeast China Plain. The yearly and monthly maximal values,anomalies and change rates of NDVI and NDWI were calculated to reveal the interannual and seasonal changes in vegetation cover and vegetation water content. Linear regression method was adopted to characterize the trends in vegetation change. The yearly maximal NDVI decreased from 0.41 in 1998 to 0.37 in 2007,implying the decreasing trend of vegetation activity. There was a significant decrease of maximal NDVI in spring and summer over the study period,while an increase trend was observed in autumn. The vegetation-improved regions and vegetation-degraded regions occupied 17.03% and 20.30% of the study area,respectively. The maximal NDWI over growing season dropped by 0.027 in 1998–2007,and about 15.15% of the study area showed a decreasing trend of water content. Vegetation water stress in autumn was better than that in spring. Vegetation cover and water content variations were sensitive to annual precipitation,autumn precipitation and summer temperature. The vegetation degradation trend in this ecotone might be induced by the warm-drying climate especially continuous spring and summer drought in the recent ten years.     

中国西北地区植被NDVI的时空变化及其影响因子分析

利用GIMMS/NDVI数据分析了中国西北地区1982-2006年植被NDVI时空变化特征及其影响因子。近25年来,中国西北地区年均植被NDVI增速为0.5%/10a,并存在明显的空间差异。天山、阿尔泰山、祁连山、青海的中东部等地区植被NDVI显著增加;青海南部地区、陕西和宁夏交界地区、甘肃部分地区,以及新疆部分地区的植被NDVI下降。从不同植被类型看:林地、草地和耕地的年均NDVI都在提高。研究表明:中国西北地区植被NDVI变化是各种自然和人为因素综合作用的结果。植被NDVI与气温、降水的年际变化整体上都呈弱的正相关。但与其年内变化则都呈显著的线性关系,当月均温量超过20℃时,植被NDVI呈下降趋势;当月降水量在0¹00mm期间,植被NDVI随降水线性增长,当月降水量超过100mm之后,不再有明显的增长趋势。农业生产水平提高和植被生态建设等人类活动对西北地区植被NDVI增加有重要影响。     

黄土高原地区NDVI与气候因子空间尺度依存性及非平稳性研究

基于MODIS传感器的植被指数产品(MOD13Q1)及50年气候数据,通过地理加权回归与普通最小二乘回归模型对比,对中国黄土高原地区NDVI与气候因子间的空间尺度依存性及非平稳性进行研究,以期准确建立二者间关系.结果表明:① 研究区域内,NDVI与气候因子间存在很强的空间尺度依存关系,相同空间尺度下,年均降水较年均温对NDVI影响的波动性更大;② 与普通最小二乘回归模型相比,地理加权回归模型能够更准确地展现二者间关系;③气候因子对该地区NDVI的影响差异明显,降水存在直接正向影响,而温度的影响则较复杂;④ NDVI与气候因子间沿东北--西南的分布格局体现出区域内不同植被--气候区差异特征.二者间的异质情况还反映出除气候外,人类活动,地形等其他因素对NDVI的影响.     

青藏高原植被覆盖变化及其与气候变化的关系

近几十年来,全球气候变化对青藏高原植被覆盖产生了重要影响。基于青藏高原1981—2005年遥感影像及同期气象数据,结合生态学模型,分析了青藏高原植被覆盖度变化趋势及其与气候变化的关系。结果显示,25 a间,青藏高原温度升高、降水量增加,植被覆盖度呈"整体升高、局部退化"趋势;地表植被改善区主要位于植被低覆盖区,退化区主要位于高覆盖区;从不同植被类型看,除针叶林、阔叶林受采伐影响覆盖度下降外,其他植被覆盖度均不同程度的上升;植被覆盖度变化与同期降水量变化、温度变化均呈正相关,且具有明显的区域差异。     

3个时期骆马湖大型水生植物的分布及变化

大型水生植物对湖泊的生物地球化学循环具有重要影响.一方面,大型水生植物在生长过程吸收营养;另一方面,其通过向水体释放氧气而影响磷元素以及其他相关因子,进而影响磷元素的生物地球化学循环.为了从宏观上了解骆马湖生态系统变化,以1990年9月20日、2000年5月2日和2008年10月15日Landsat TM/ETM+影像为主要数据源,以大型水生植物的归一化植被指数(NDVI)为测试变量,运用分类回归树(classifica-tion and regression tree,CART)方法确定分割阈值,通过构建知识决策树的方法识别骆马湖大型水生植物动态变化特征.3个时期的遥感分类的总体精度与kappa系数分别为92.28％和0.87、91.73％和0.86、93.38％和0.88,因此,该方法的分类精度完全满足骆马湖水生植物分布及变化的研究.研究结果表明,骆马湖大型水生植物分布面积由1990年的55.461 6 km2,减少到2000年的41.801 4 km2,2008年又增加到79.065 km2;大型水生植物主要分布在骆马湖北部河湖交汇区;人类活动干扰是造成骆马湖大型水生植物分布面积发生变化的主要原因.     

The aboveground biomass of desert steppe and its spatiotemporal variation in western Inner Mongolia

A precise understanding of the aboveground biomass of desert steppe and its spatio-temporal variation is important to understand how arid ecosystems respond to climate change and to ensure that scarce grassland resources are used rationally. On the basis of 756 ground survey quadrats sampled in western Inner Mongolia steppe in 2005–2011 and remote sensing data from the Moderate Resolution Imaging Spectroradiometer (MODIS)—the normalized difference vegetation index (NDVI) dataset for the period of 2001–2011—we developed a statistical model to estimate the aboveground biomass of the desert steppe and further explored the relationships between aboveground biomass and climate factors. The conclusions are as follows: (1) the aboveground biomass of the steppe in the research area was 5.27 Tg (1 Tg=10¹² g) on average over 11 years; between 2001 and 2011, the aboveground biomass of the western Inner Mongolia steppe exhibited fluctuations, with no significant trend over time; (2) the aboveground biomass of the steppe in the research area exhibits distinct spatial variation and generally decreases gradually from southeast to northwest; and (3) the important factor causing interannual variations in aboveground biomass is precipitation during the period from January to July, but we did not find a significant relationship between the aboveground biomass and the corresponding temperature changes. The precipitation in this period is also an important factor influencing the spatial distribution of aboveground biomass (R²=0.39, P<0.001), while the temperature might be a minor factor (R²=0.12, P<0.01). The uncertainties in our estimate are primarily due to uncertainty in converting the fresh grass yield estimates to dry weight, underestimates of the biomass of shrubs, and error in remote sensing dataset.