基于ASAR和MODIS图像的藻华范围提取

本文将同期的EnviSat-ASAR图像和MODIS图像进行配准,利用经过大气校正后的MODIS光学图像,根据经验模型反演得到叶绿素a的浓度,借此对太湖的藻华和非藻华区域进行划分,并以此为据在ASAR图像上研究两者的V/V后向散射特性,使用阈值法对藻华范围进行提取,并将结果与MODIS图像上得到的藻华范围进行对比,结果较好的吻合.分析二者结果表明,风速会造成的藻华漂移和沉浮并且影响湖面粗糙度,是决定提取结果好坏的关键因素.另外从成像机理上,太湖粗糙度不规则性和介电特性上的误差使得藻华提取阈值不具有适用性,也是此次提取藻华结果不完全一致的原因,恰恰也成为了利用微波数据监测藻华的局限所在.     

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针对国内外尚无对霾的遥感监测,霾在NASA的MODIS产品中作为薄云处理的情况,基于MODIS数据,采用.NET分布式架构,结合MatlabCOM, ArcEngine等组件,建立一个区域霾监测系统,论述系统的设计思想及改进的霾反演算法,对华北地区部分霾天进行监测,结果显示该系统能够准确地模拟大气污染状况。     

CropStraw Fire Remote Sensing Monitoring and Its Algorithm Implementation

in order to obtain the information and achieve the effective control of crop straw fire spatial distribution in Central China Region.The MODIS L1B remote sensing datasets during 2014 for the main data source in this article,and combined with land use data,the farmland of Central China Region was taken as study region.Based on the enhanced contextual fire remote sensing detection algorithm,and make full use of the theoretical knowledge of quantitative remote sensing and Geospatial Data Abstraction Library (GDAL)and other technical means,to achieve the crop straw fire recognition in Central China Region.Using Ministry of Environmental Protection of the People’s Republic of China release the daily newspaper of crop straw fire in China and the standard fire products (MYD14)of MODIS for the comparative analysis of the quantitative and spatial.The results indicate that the algorithmof this paper can achieve crop straw fire remote sensing monitoring of this study region effectively,and the parameters can be adjusted in real time based on the characteristic of the study region,and improve the automation and working efficiency of crop straw fire monitoring.     

Temporal and Spatial Distribution of Ulva.prolifera in the Yellow Sea and Influence of Typhoon “CHAN-HOM” in 2015

A recurrent floating green algae bloom was detected in the Yellow Sea since 2007.The Ulva.prolifera is non\|toxic,but the massive accumulations can result in significant environmental damage and cause economic loss to marine industries.In this study,the spatial and temporal patterns of Ulva.prolifera green tides were investigated in the Yellow Sea during 2015 using HJ\|1A/1B and MODIS satellite images by means of NDVI (normalized difference vegetation index)and artificial interpretation.The results showed：(1)A little Ulva.prolifera was discovered firstly in adjacent sea of Yancheng,Jiangsu province in early May with distribution area 0.831 km².Under the action of the southeast monsoon,Ulva.prolifera was gradually drifted to Shandong peninsula waters from south to north.The influential area and range reached a peak value with 1 752.756 km² in late June,and gradually subsided from July to August.And Ulva.prolifera about 38.791 km² was monitored in the South Bay of North Korea.In conclusion,Ulva.prolifera in the Yellow Sea in 2015 has experienced five major processes including “Occur\|Development\|Outbreak\|Recession\|Disappeared”.(2)Typhoon "CHAN\|HOM" certainly influenced the northward pathway of Ulva.prolifera and shifted towards the southwest,resulting in most of Ulva.prolifera moved to the east coast of Lianyungang,and speculated that minority Ulva.prolifera drifted to the South Bay of North Korea.(3)From the monitoring data,the spatial resolution between MODIS and ENVISAT (HJ\|1A / 1B)is difference significantly,250 m and 30 m respectively.A functional relation of the two data with monitoring area difference about 2.26 times was established to make up for the shortage of the environmental satellite (HJ\|1A/1B)images.      

Hierarchical Land Cover Classification based on MODIS NDVI Time-series

In this paper,we mainly used MODIS NDVI time-series dataset at 16-days temporal resolution and 250-meters spatial resolution to analyze land cover mapping of northeastern China.We used two different filter methods to fit NDVI time-series dataset,and compared their average classes’ separability based on Jeffries-Matusita distance index.In addition,we made use of hierarchical classification method to complete classification,combined with short-wave infrared spectral reflectance data and DEM.We conformed to the principle that separate area hierarchically into several parts first and then classify each part further,and use a single characteristic band first and then multiple feature bands.In the process of classification,we adopted threshold value method,support vector machine,artificial net neural and C5.0 decision tree classification to distinguish each land-cover type hierarchically.Finally,we evaluated the accuracy of the final classification of study area using known land-cover classification data and high-resolution remote sensing imagery,overall accuracy is 84.61%,Kappa coefficient is 0.8262.     

Integration of geostationary FRP and polar-orbiter burned area datasets for an enhanced biomass burning inventory

Earth Observation (EO) sensors play an important role in quantifying biomass burning related fuel consumption and carbon emissions, and capturing their spatial and temporal dynamics. Typically, biomass burning emissions inventories are developed by exploiting either burned area (BA) or active fire (AF) measures of fire radiative energy (FRE). These approaches have both advantages and limitations. For example, methods based on burned area data typically require hard-to-obtain estimates of fuel load and combustion completeness, and the accuracy of the BA algorithm may deteriorate for small fires or those in densely forested terrain. Conversely, ‘raw’ FRE-based methods are typically low-biassed due to the non-detection of low intensity fires, and are also hindered by cloud cover. Here we develop a methodology integrating these two EO data types to deliver a high temporal resolution emissions inventory, maximising the benefit of each data type without requiring additional information. We focus on Africa, the most fire affected continent, and combine daily FRE observations provided by Meteosat Spinning Enhanced Visible and Infrared Imager (SEVIRI) with BA measures delivered by Moderate Resolution Imaging Spectroradiometer (MODIS). For individual fires detected by both types of data, we estimate fuel consumption per unit area (FC_A: g·m^− 2) via the ratio of FRE-derived total fuel consumption (FC_T) to BA. These values are then extrapolated to fires that were mapped using the BA data but which remained undetected in the SEVIRI AF product, thus correcting for the ‘low spatial resolution bias’ inherent in geostationary AF datasets. Calculated daily continental scale FC_T for Africa varies between 0.3 and 20 Tg for the period February 2004-January 2005. We estimate annual continental FC_T to be 1418 Tg, far closer to the 2272 Tg provided by the widely used Global Fire Emissions Database (version 3; GFEDv3) than is obtained when using ‘raw’ FRE data alone. This synergistic approach has substantially narrowed the gap between GFEDv3 and FRE-derived emissions inventories, whilst the geostationary FRP observations offer the advantage that the daily emissions estimates can be distributed more accurately over the diurnal fire cycle if required for linking to atmospheric transport models.     

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).