Land cover classification with coarse spatial resolution data to derive continuous and discrete maps for complex regions

Regularly updated land cover information at continental or national scales is a requirement for various land management applications as well as biogeochemical and climate modeling exercises. However, monitoring or updating of map products with sufficient spatial detail is currently not widely practiced due to inadequate time-series coverage for most regions of the Earth. Classifications of coarser spatial resolution data can be automatically generated on an annual or finer time scale. However, discrete land cover classifications of such data cannot sufficiently quantify land surface heterogeneity or change. This study presents a methodology for continuous and discrete land cover mapping using moderate spatial resolution time series data sets. The method automatically selects sample data from higher spatial resolution maps and generates multiple decision trees. The leaves of decision trees are interpreted considering the sample distribution of all classes yielding class membership maps, which can be used as estimates for the diversity of classes in a coarse resolution cell. Results are demonstrated for the heterogeneous, small-patch landscape of Germany and the bio-climatically varying landscape of South Africa. Results have overall classification accuracies of 80%. A sensitivity analysis of individual modules of the classification process indicates the importance of appropriately chosen features, sample data balanced among classes, and an appropriate method to combine individual classifications. The comparison of classification results over several years not only indicates the method's consistency, but also its potential to detect land cover changes.     

Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures

The term urban heat island describes the phenomenon of altered temperatures in urban areas compared to their rural hinterlands. A surface urban heat island encompasses the patterns of land surface temperatures in urban areas. The classical indicator to describe a surface urban heat island is the difference between urban and rural surface temperatures. However, several other indicators for this purpose have been suggested in the literature. In this study, we compared the eleven different indicators for quantifying surface urban heat islands that were most frequently used in recent publications on remote sensing-based urban heat island assessments. The dataset used here consists of 263 European cities with monthly mean temperatures from MODIS data products for July 2002, January 2003 and July 2003. We found that (i) the indicators individually reveal diurnal and seasonal patterns but show rather low correlations over time, and (ii) for single points in time, the different indicators show only weak correlations, although they are supposed to quantify the same phenomenon. Differentiating cities according to thermal climate zones increased the relationships between the indicators. Thus, we can identify temporal aspects and indicator selection as important factors determining the estimation of urban heat islands. We conclude that research should take into account the differences and instabilities of the indicators chosen for quantifying surface urban heat islands and should use several indicators in parallel for describing the surface urban heat island of a city.     

Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Land surface and climate modelling requires continuous and consistent Leaf Area Index (LAI). High spatiotemporal resolution and long-time record data are more in demand nowadays and will continue to be in the future. MODIS LAI products meet these requirements to some degree. However, due to the presence of cloud and seasonal snow cover, the instrument problems and the uncertainties of retrieval algorithm, the current MODIS LAI products are spatially and temporally discontinuous and inconsistent, which limits their application in land surface and climate modelling. To improve the MODIS LAI products on a global scale, we considered the characteristics of the MODIS LAI data and made the best use of quality control (QC) information, and developed an integrated two-step method to derive the improved MODIS LAI products effectively and efficiently on a global scale. First, we used the modified temporal spatial filter (mTSF) method taking advantage of background values and QC information at each pixel to do a simple data assimilation for relatively low quality data. Then we applied the post processing-TIMESAT (A software package to analyze time-series of satellite sensor data) Savitzky-Golay (SG) filter to get the final result. We implemented the method to 10 years of the MODIS Collection 5 LAI data. In comparison with the LAI reference maps and the MODIS LAI data, our results showed that the improved MODIS LAI data are closer to the LAI reference maps in magnitude and also more continuous and consistent in both time-series and spatial domains. In addition, simple statistics were used to evaluate the differences between the MODIS LAI and the improved MODIS LAI.     

Large area forest stem volume mapping in the boreal zone using synergy of ERS-1/2 tandem coherence and MODIS vegetation continuous fields

ERS-1/2 tandem coherence was reported to have high potential for the mapping of boreal forest stem volume (e.g. Santoro et al., 2002, 2007a; Wagner et al., 2003; Askne & Santoro, 2005). Large-scale application of the data for forest stem volume mapping, however, is hindered by the variability of coherence with meteorological and environmental acquisition conditions. The traditional way of stem volume retrieval is based on the training of models, relating coherence to stem volume, with the aid of forest inventory data which is generally available for a few small test sites but not for large areas. In this paper a new approach is presented that allows model training using the MODIS Vegetation Continuous Fields canopy cover product (Hansen et al., 2003) without further need for ground data. A comparison of the new approach with the traditional regression-based and ground-data dependent model training is presented in this paper for a multi-seasonal ERS-1/2 tandem dataset covering several well known Central Siberian forest sites. As a test scenario for large-area application, the approach was applied to a multi-seasonal ERS-1/2 tandem dataset of 223 ERS-1 and ERS-2 image pairs covering Northeast China (~ 1.5 million km²) to map four stem volume classes (0-20, 20-50, 50-80, and > 80 m³/ha).     

Predicting gross primary production from the enhanced vegetation index and photosynthetically active radiation: Evaluation and calibration

The approach of using primarily satellite observations to estimate ecosystem gross primary production (GPP) without resorting to interpolation of many surface observations has recently shown promising results. Previous work has shown that the remote sensing based greenness and radiation (GR) model can give accurate GPP estimates in crops. However, the feasibility of its application and the model calibration to other ecosystems remain unknown. With the enhanced vegetation index (EVI) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) images and the surface based estimates of photosynthetically active radiation (PAR), we provide an analysis of the GR model for estimating monthly GPP using flux measurements at fifteen sites, representing a wide range of ecosystems with various canopy structures and climate characteristics. Results demonstrate that the GR model can provide better estimates of GPP than that of the temperature and greenness (TG) model for the overall data classified as non-forest (NF), deciduous forest (DF) and evergreen forest (EF) sites. Calibration of the GR model is also conducted and has shown reasonable results for all sites with a root mean square error of 47.18 g C/m²/month. Different coefficients acquired for the three plant functional types indicate that there are shifts of importance among various factors that determine the monthly vegetation GPP. The analysis firstly shows the potential use of the GR model in estimating GPP across biomes while it also points to the needs of further considerations in future operational applications.     

应用MODIS资料对城乡地表热通量的估算

为了揭示城市热岛形成机制,基于MODIS资料,结合自动气象站实测的气象资料,利用地表能量参数化方法估算了地表热通量,分析了城乡地表热通量的空间分布及变化特征.结果表明城乡地气热交换差异明显,与相关文献对比证明该方法是可行、有效的.     

基于MODIS的春、夏季中国近海气溶胶对于云参数的影响差异研究

通过分析中国近海MODIS数据中气溶胶参数与云参数的相互关系,讨论了该区域气溶胶的间接效应及其对于云的可能影响.结果表明,在中国近海,气溶胶具有明显的间接效应,而且由于气溶胶种类和水汽的季节变化,使得气溶胶的间接作用具有很强的时间变化特征.在夏季,由于人为气溶胶占主导,它作为有效的云凝结核,使得气溶胶光学厚度(AOT)分别与云凝结核数(CCN)有正相关、与云滴有效半径(CER)有负相关性、与云光学厚度(COT)也存在着正相关,气溶胶的间接效应明显;在春季,由于沙尘气溶胶盛行,同时沙尘并不是很好的云凝结核,使得气溶胶光学厚度(AOT)分别与云凝结核数(CCN)的正相关减弱、与云滴有效半径(CER)则由夏季的负相关变为正相关、与云光学厚度(COT)存在弱的负相关,气溶胶的间接效应不明显.     

时间序列MODIS数据赤潮信息提取研究

MODIS数据在赤潮遥感监测中发挥着重要作用,其较高的光谱分辨率和时间分辨率在赤潮监测中有较大优势。本文利用2008年5月份多个时相MODIS数据对东海赤潮进行了研究,分别采用叶绿素浓度固定阈值法、月平均值差值法和海温及多光谱综合法对水体异常信息进行提取分析。通过对提取图像及其统计数据比较分析发现,固定阈值法所提取的信息会随阈值设置不同而变化,海温及多光谱综合法在有赤潮发生时提取信息与其他方法较一致,在未发生赤潮时则会发生多提误判。叶绿素浓度月平均值差值法所提取的时间序列赤潮信息则最客观地反映了赤潮变化趋势,能够很好地用于东海赤潮遥感监测。     

Atmospheric correction of CBERS CCD images with MODIS data

China Brazil Earth Resource Satellite (CBERS) CCD images have much potential for inland water environmental monitoring. However, their atmospheric accuracy correction can affect their quantitative applications. This paper contains an atmospheric correction algorithm for CBERS CCD images with MODIS data from the same day, the use of which improves the atmospheric correction algorithm of ocean color remote sensing developed by Gordon (1993, 1994) and makes it applicable to inland waters. The improved algorithm retrieves atmospheric parameters from MODIS data and uses them to perform the atmospheric correction of CBERS CCD images. Experimental results show that the atmospheric correction algorithm of CBERS CCD images assisted by MODIS data is reliable. Furthermore, MODIS data can be freely obtained on a daily basis, making the algorithm developed in this paper useful for environmental monitoring of inland waters.     

EOS/MODIS 遥感资料探测海洋赤潮信息方法

近几年来, 我国沿海赤潮的发生越来越频繁, 已经成为一种常见海洋灾害。它的发生给沿海经济、居民生活和生态系统造成了很大影响。通过分析赤潮水体及其周边水体的光谱特性, 以及赤潮发生期间海水叶绿素a 浓度的变化特点, 提出了利用EOS/ MODIS 通道4 与通道3 的反射率比和通道11 与通道9 的离水辐射率比再结合相关的悬浮泥沙信息提取海水中赤潮信息的方法。利用此方法, 对2002 年6 月15 日和2004 年5 月31 日发生在我国渤海的赤潮进行了信息提取。结果表明此方法可以有效地提取海水中的赤潮信息。