基于MODIS数据的城市热岛动态监测及时空变化分析

以人口密集、经济快速发展的福建省沿海地区为研究区，在遥感和地理信息系统技术支持下，采用经优选的MODIS遥感数据反演的地表温度，提取城市热岛和植被覆盖信息，对研究区城市热岛进行动态监测和时空变化分析，并对地表温度与植被覆盖的互动关系进行定性与定量分析。结果表明：福建沿海城市热岛面积整体上呈逐年增长趋势，福州和莆田更为明显；城市热岛主要集中在人口密集、工商业发达的区域，同时沿海平原的热岛效应比内陆山区更为显著；地表温度与植被覆盖呈负相关，并且相关系数逐年增长。     

基于飞机和MODIS观测的华北地区气溶胶标高分析

为研究华北地区的气溶胶分布情况,利用2012—2014年飞机观测资料和MODIS卫星资料,对石家庄等地的气溶胶标高进行了分析。结果表明:(1)与飞机观测资料对比后认为可以利用MODIS光学厚度和常规能见度资料计算气溶胶标高;(2)华北地区春夏季气溶胶标高一般大于秋冬季;6个主要城市中,位于平原地区的城市气溶胶标高较低,西北山区城市标高较高;(3)在不进行飞行探测,仅使用地面资料和卫星资料,通过计算得到气溶胶垂直分布是可行的,尤其是在对流层中层的结果比底层更可信;(4)分别使用观测资料与拟合数据计算光学厚度,分析其误差后,确认在计算光学厚度的过程中可以使用气溶胶数密度指数递减假设。     

基于MODIS卫星资料研究三峡工程对库区地表温度的影响

利用2001-2012年MODIS地表温度资料,分析了三峡库区蓄水后水体对冬、夏两季白天和夜间地表温度的影响。由于下垫面水陆和地势的影响,白天地表温度高值区主要位于四川盆地东部,夜间则主要出现在长江江面;温度日较差在长江和海拔较高地区较小,且夏季水体日较差小于冬季。分别用水体和I～X缓冲区地表温度减去XI缓冲区去除气候背景场影响,发现冬季白天地表温度趋势在水体及I～VI缓冲区由下降转为上升,夜间地表温度在相同距离内显著升高。利用蓄水后（2003-2012年）地表温度或日较差分别减去蓄水前（2001-2002年）剔除地形影响,发现：冬季,三峡工程水库蓄水对局地地表温度具有增温效果,且强度和范围夜间大于白天;夏季,对地表温度有降温作用,白天大于夜间;同时,冬、夏季的温度日较差减小;且水体对局地地表温度和日较差的影响随距水体距离的增加而减小,其影响范围基本维持在0～8 km范围内。     

联合星载毫米波雷达和中分辨率光谱成像仪的云底高度反演

云底高度是云重要的宏观物理参数。本文基于MODIS和CPR探测得到的可见光、红外和毫米波数据,提出了用主成分分析(Principal Component Analysis,PCA)和BP神经网络反演云底高度的新方法,并以相对湿度阈值法处理探空资料所获取的云底高度为基准,对PCA-BP法和CloudSat产品获取的云底高度进行了对比分析。结果表明:对绝大部分类型的云,PCA-BP法的反演偏差小于CloudSat产品。PCA-BP法和CloudSat产品反演的云底高度在夏季偏低,在其他季节偏高,且PCA-BP法与探空仪的均方根误差在所有季节均小于CloudSat产品,两者反演的云底高度具有一致的季节特征,即夏高冬低。PCA-BP法和CloudSat产品所获取的云底高度随纬度升高有减小趋势,在不同地区,两者具有不同的反演效果,反演误差随纬度升高而逐渐减小,对比结果说明了PCA-BP法反演云底高度具有一定的可行性。     

Mapping rubber tree growth in mainland Southeast Asia using time-series MODIS 250 m NDVI and statistical data

Expanding global and regional markets are driving the conversion of traditional subsistence agricultural and occupied non-agricultural lands to commercial-agricultural purposes. In many parts of mainland Southeast Asia rubber plantations are expanding rapidly into areas where the crop was not historically found. Over the last several decades more than one million hectares of land have been converted to rubber trees in areas of China, Laos, Thailand, Vietnam, Cambodia and Myanmar, where rubber trees were not traditionally grown. This expansion of rubber plantations has replaced ecologically important secondary forests and traditionally managed swidden fields and influenced local energy, water and carbon fluxes. Accurate and up-to-date monitoring and mapping of rubber tree growth is critical to understanding the implications of this changing ecosystem. Discriminating rubber trees from second-growth forests and fallow land has proven challenging. Previous experiments using machine-learning approaches with hard classifications on remotely sensed data, when faced with the realities of a heterogeneous plant-life mixture and high intra-class variance, have tended to overestimate the areas of rubber tree growth. Our current research sought to: 1) to investigate the potential of using a Mahalanobis typicality model to deal with mixed pixels; and 2) to explore the potential for combining MOderate Resolution Imaging Spectroradiometer (MODIS) imagery with sub-national statistical data on rubber tree areas to map the distribution of rubber tree growth across this mainland Southeast Asia landscape. Our study used time-series MODIS Terra 16-day composite 250 m Normalized Difference Vegetation Index (NDVI) products (MOD13Q1) acquired between March 2009 and May 2010. We used the Mahalanobis typicality method to identify pixels where rubber tree growth had the highest probability of occurring and sub-national statistical data on rubber tree growth to quantify the number of pixels of rubber tree growth mapped per administrative unit. We used Relative Operating Characteristic (ROC) and error matrix analysis, respectively, to assess the viability of Mahalanobis typicalities and to validate classification accuracy. High ROC values, over 0.8, were achieved with the Mahalanobis typicality images of both mature and young rubber trees. The proposed method greatly reduced the commission errors for the two types of rubber tree growth to 1.9% and 2.8%, respectively (corresponding to user’s accuracies of 98.1% and 97.2%, respectively). Results indicate that integrating Mahalanobis typicalities with MODIS time-series NDVI data and sub-national statistics can successfully overcome the earlier overestimation problem.     

Mapping fire regimes in China using MODIS active fire and burned area data

Relatively little is known about vegetation fire regimes in China. In this study, we investigated fire regime characteristics and their potential drivers, utilizing information extracted from the MODerate resolution Imaging Spectrometer (MODIS) satellite. Twelve fire regime variables were selected and computed on a regular grid over all of China, using MODIS burned area and active fire data during the period 2001 to 2016, to identify fire incidence and its inter-annual variability, seasonality, intensity, fire size distribution and vegetation types affected by fire. The variables were normalized and clustered to define six fire regimes with distinctive fire attributes. Results show that 78.6% of the land in China was affected by fire during the study period. The barren or sparsely vegetated lands of western China are nearly fire-free. Active fires were observed in Central China, but area burned was not detectable from MODIS. Forest fires in northeastern China are relatively large, infrequent, with a short fire season that peaks in non-winter seasons and higher inter-annual variability, implying a high likelihood of accidental causes. In contrast, forest fires in southern China are relatively small, frequent, with a long fire season that peaks in non-summer seasons, and lower inter-annual variability, suggesting regular use as a land management tool. Low inter-annual variability and low fire intensity were associated with cropland fires, whereas grassland fires generally exhibit the opposite traits. We have also discussed the potential drivers of each fire regime characteristics.     

Monitoring deforestation with MODIS Active Fires in Neotropical dry forests: An analysis of local-scale assessments in Mexico,Brazil and Bolivia

The detection of vegetation fires using remote sensing has proven useful for highlighting areas undergoing rapid conversion in humid forests, but not in tropical dry forest (TDF). To further understand this relationship, we explored the correlation between MODIS Active Fires and forest cover change at local scales using 3 × 3 km sampling grids in three TDF landscapes in Bolivia; Mexico, and Brazil. Our analysis showed no single overall correlation among sites between the frequency of fires reported by the MODIS Active Fire Mapping product and forest cover change. Also, aggregated patterns of fire occurrence in Brazil and Bolivia did not correspond to areas with high percentage of forest loss, which indicates that the fire/deforestation relationship in TDF is not apparent in a simple fire frequency map. However, statistically significant correlations were found in sampling boxes with 50–60%, 50–70%, 50–95% forest cover at “initial state” of the time series in the Mexican site, Bolivian site and Brazilian site, respectively. Our findings suggest that complex interactions between anthropogenic fire-use, satellite-detected fires, and deforestation in highly fragmented TDF landscapes are difficult to describe at regional scales and might only be possible to analyze using finer resolution sampling grids.     

Analyzing land-cover change and corresponding impacts on carbon budget in a fast developing sub-tropical region by integrating MODIS and Landsat TM/ETM+ images

Land-cover change has significant impacts on regional carbon dynamics. Understanding the carbon consequences of land-cover change is necessary for decision makers to address the issues of carbon reduction and climate change mitigation. Optical remote sensing images have been widely used for detecting regional land-cover change. However, it is difficult to acquire desirable images for regions that are frequently affected by cloudy and rainy weather. In this study, we proposed an approach to deal with this problem by integrating moderate-resolution imaging spectroradiometer (MODIS) and Landsat images based on the mixed-label analysis (MLA) model. We tested this model in Guangdong Province, a fast developing sub-tropical region in China, to derive the provincial land-cover data for the analysis of land-cover change between 2000 and 2009 and its impacts on regional carbon dynamics. Results show that forest land decreased by 3.03%, while built-up area rapidly expanded by 73.01% from 2000 to 2009. The regional vegetation carbon sink declined by 2.6%, whereas the regional carbon emissions increased by more than 100% due to the fast urbanization and economic development. The regional vegetation carbon sink can only offset 4.1% of total carbon emissions in 2009, far below the national level (about 7.0–7.7%) at the same period. Future efforts to improve the regional carbon budget should focus more on the control of land development and the advance of energy efficiency.     

基于MODIS数据的城市光化学污染预警系统及预警等级研究

通过分析MODIS遥感数据反演的大气气溶胶光学厚度(AOD)与大气环境污染的关系,结果表明当空气清洁没有污染时,AOD小于0.3,当空气轻度污染时,AOD在0.3~1.0之间,当空气污染严重时,AOD大于1.0。在分析AOD与地面大气污染的关系的基础上,结合地面空气污染监测数据的空气污染指数,将城市光化学污染的预警等级分为无、微弱、较弱、较强和强五级,并对城市光化学污染预警系统进行了框架分析,为进一步建设城市光化学污染预警系统提供基础。     

MODIS在三江平原湿地分布研究中的应用

湿地研究的关键在于定量化获取和分析湿地信息,特别是湿地分布信息。以三江平原为例,结合湿地的光谱特征和时相特征,利用多时相MODIS数据,采用MNF(最小噪音分离变化)技术和非监督分类获取了研究区湿地分布,其精度达79%。研究表明,利用MODIS数据可以有效提取湿地分布。