人口与经济发展对植被的影响研究——以重庆市为例

植被是陆地表面主要的覆盖物,也是许多地球系统过程的重要变量.人类活动对植被的影响在区域尺度上是显著的.以重庆市为例,使用滑动平均和SG滤波对SPOT/VGT-NDVI时间序列数据进行了平滑处理,结合GDP和人口栅格化数据,从时间序列和空间相关场两个方面分析了植被与人类活动因子之间的时空相关性.结果表明,从1998到2005年,植被的分布和生长状况并没有随GDP和人口的持续增加而增加.在时间序列上,植被覆盖与GDP和人口之间总体上呈显著的负相关关系;在空间上,这种相关关系表现出了较强的异质性.空间相关场表明:较强的负相关主要发生在相对发达的主城区周围,反映了快速的经济发展和城市化使得植被面积和生产力降低;然而在偏远的山区,由于经济落后同时植被覆盖较少,出现了正相关.     

MODIS气溶胶光学厚度对城市空气质量的指示

空气污染指数API(Air Pollution Index)是关系大众生产生活的一个重要指标,论证了一种由MODIS(中分辨率成像光谱仪)反演的气溶胶光学厚度产品AOT(Aerosol Optics Thickness)预报空气质量的方法,即依据API的分级标准,将API用特定的步长分组,再把各组的AOT和API均值建立相关模型,并用该模型来预测空气质量指数,经验证,预测值和实际值之间的精度为77.59%,基本达到业务运行的要求。     

Preliminary Evidence of a Barrier Effect of a Railroad on the Migration of Mongolian Gazelles

Abstract:  Much remains to be learned about how anthropogenic structures affect migration in large mammals. We examined the potential influence of the international railroad in Mongolia on migration in Mongolian gazelles (Procapra gutturosa). We examined gazelle movements and the normalized difference vegetation index (NDVI) on both sides of the railroad. The tracked gazelles never crossed the railroad, despite movements that mainly followed the railroad in winter and higher NDVI values on the opposite side. It is likely that the railroad had a barrier effect on gazelle migration because it split the gazelles' habitat. Our results, although based on an extremely small sample, have utility in indicating approaches that might be useful for examining migration in ungulates.     

Using NOAA AVHRR data to assess flood damage in China

The article used two NOAA-14 Advanced Very High ResolutionRadiometer (AVHRR) datasets to assess flood damage in the middleand lower reaches of China's Changjiang River (Yangtze River) in 1998. As the AVHRR is an optical sensor, it cannot penetratethe clouds that frequently cover the land during the flood season, and this technology is greatly limited in flood monitoring. However the widely used normalized difference vegetation index (NDVI) can be used to monitor flooding, sincewater has a much lower NDVI value than other surface features.Though many factors other than flooding (e.g. atmospheric conditions, different sun-target-satellite angles, and cloud) can change NDVI values, inundated areas can be distinguished fromother types of ground cover by changes in the NDVI value beforeand after the flood after eliminating the effects of other factors on NDVI. AVHRR data from 26 May and 22 August, 1998 wereselected to represent the ground conditions before and after flooding. After accurate geometric correction by collecting GCPs,and atmospheric and angular corrections by using the 6S code, NDVI values for both days and their differences were calculatedfor cloud-free pixels. The difference in the NDVI values betweenthese two times, together with the NDVI values and a land-use map, were used to identify inundated areas and to assess the arealost to the flood. The results show a total of 358 867 ha, with 207 556 ha of cultivated fields (paddy and non-irrigated field) inundated during the flood of 1998 in the middle and lower reaches of the Changjiang River Catchment; comparing with the reported total of 321 000 and 197 000 ha, respectively. The discrimination accuracy of this method was tested by comparing the results from two nearly simultaneous sets of remote-sensingdata (NOAA's AVHRR data from 10 September, 1998, and JERS-1 synthetic aperture radar (SAR) data from 11 September, 1998, with a lag of about 18.5 hr) over a representative flooded regionin the study area. The results showed that 67.26% of the total area identified as inundated using the NOAA data was also identified as inundated using the SAR data.     

MODIS遥感气溶胶光学厚度产品在地面能见距中的应用

利用气象站点能见距的历史资料和NASA的MOD1S卫星遥感手段获取10km＊10km分辨率的气溶胶光学厚度资料，建立二者之间的季节平均关系。得到了上海地区季节变化的气溶胶标高，并利用标高数据和气溶胶光学厚度的季节分布。反演出上海地区季节变化的区域能见距分布，研究近地层大气气溶胶与地面能见距的关系，分析上海市能见度时空分布特征，结果显示：上海城区在冬春季平均能见距较差，市区中心能见距在lOkm以下；低能见距中心分布明显。且主要分布在杨浦、桃浦、吴淞等工业区范围。     

2001—2017年中国北方省份气溶胶光学厚度的时空特征

利用2001年3月—2017年2月MODIS MOD08M3遥感反演气溶胶光学厚度产品数据,结合GIS统计分析方法和集合经验模态分解方法（EEMD）,对中国北方京津冀鲁豫晋陕7个省市的气溶胶光学厚度时空特征进行分析.结果表明：①从空间分布特征来看,人口密度与AOD空间分布有着直接的联系.一方面,地形因素主要通过影响人口分布来间接的影响AOD空间分布,另一方面地形因素还可以影响气溶胶的水平扩散,这使得AOD的空间变化梯度在平原和山脉交界处急剧增大,形成了AOD空间分布与地形高度契合的分布特征,因此地形因素是中国北方AOD空间分布重要的影响因素之一.②从时变特征来看,将EEMD应用于AOD时间序列变化研究,可以从频率域的角度提取不同尺度变化的规律和特征,AOD时间序列变化是由多个波动规律叠加在一起的最终结果,具体包括：年际间变化情况（IMF5）、季节性波动（IMF3）、时间间隔为12个月的人为事件波动（IMF2）、突发事件和情况（IMF1）.这些结果有助于研究AOD时空变化原因和北方地区的气候环境.     

基于MODIS数据的中国气溶胶光学厚度时空分布特征

利用Aqua/MODIS C006大气气溶胶光学厚度(AOD)遥感数据,统计了中国2007—2017年的AOD值,对11年间AOD的空间分布特征及年际和四季变化特征进行分析,并采用Theil-Sen Median趋势分析、标准偏差和Hurst指数3种方法,分析了基于像元的中国大陆地区AOD时空变化特征.结果表明:①空间特征:11年间AOD分布具有东部高、西部低,东部减少、西部基本不变的特征;②时间特征:AOD变化在年际间呈余弦曲线式波动下降的特征,最高值出现在2007年,为0.34,最低值出现在2016年,为0.22,11年间AOD平均值下降了35.29%;年内AOD值表现出春夏高(0.33/0.32),秋季次之(0.26),冬季最小(0.15)的季节变化特征;③稳定性与持续性:东部稳定性差但集聚、西部稳定性好但分散,东西差异显著;中国AOD持续性特征以弱反持续性为主,弱持续和弱反持续镶嵌分布,西北干旱区表现为强反持续性的特点.     

基于混合效应模型的京津冀地区PM2.5日浓度估算

利用2016年182d的MODIS 3km AOD数据与地面监测数据,评估了混合效应模型不同参数组合的模拟性能,得出模型在解释AOD-PM_2.5关系时,对时间序列变异的解释能力要比空间差异更佳.在此基础上,利用混合效应模型建立京津冀地区每日的AOD-PM_2.5关系,模型拟合R²为0.92,交叉验证调整R²为0.85,均方根误差（RMSE）为12.30 μg/m³,平均绝对误差（MAE）为9.73 μg/m³,说明模型拟合精度较高.基于此模型估算的2016年京津冀地区年均PM_2.5浓度为42.98 μg/m³,暖季（4月1日~10月31日）为43.35 μg/m³,冷季（11月1日~3月31日）为38.52 μg/m³,与同时期的地面监测数据差值分别为0.59,0.7,5.29 μg/m³.空间上,京津冀地区的PM_2.5浓度呈现南高北低的特征,有一条明显的西南-东北走向的高值区.研究结果表明,基于每日混合效应模型可以准确评估京津冀地区的地面PM_2.5浓度,且模型估算的PM_2.5浓度分布状况为区域大气污染防治提供了基础的数据支撑.     

高分辨率气溶胶光学厚度在珠三角及香港地区区域颗粒物监测中的应用研究

利用最新的MODlS(中分辨率成像光谱仪)气溶胶光学厚度(AOD)反演算法,反演珠江三角洲及香港地区2008年高分辨率(1 km x1 km)AOD分布,并与AERONET观测数据进行了验证(r=0.917).分析了2008年珠江三角洲及香港地区5个站点地面监测PM10质量浓度与反演的AOD数据相关性.结果表明:两者直...     

Estimation of Southeast Asian rice paddy areas with different ecosystems from moderate-resolution satellite imagery

Multi-temporal satellite imagery from the Moderate Resolution Imaging Spectrometer (MODIS) was used to map the different ecosystems of Southeast Asian (SEA) rice paddies. The algorithm was based on temporal profiles of vegetation strength and/or water content, using MODIS surface reflectance in visible to near-IR range. The results obtained from the analysis were compared to national statistics. Estimated SEA regional rice area was 42 × 10⁶ ha, which agrees with published values. The model performance was dependent on rice ecosystems. Good linear relationships between the model results and the national statistics were observed for rainfed rice. High linear coefficients of determination, R², were also found for irrigated rice and upland rice, but the model tended to underestimate irrigated rice and overestimate upland rice. However, these high R² values indicated that the model effectively simulated spatial distribution of these rice areas. These R² values were either of similar magnitude or larger than those reported in literature, regardless of the rice ecosystem. Poor correlation was observed for deepwater rice.