Estimating forest aboveground biomass using HJ-1 Satellite CCD and ICESat GLAS waveform data

The ecosystem in northeastern China and the Russian Far East is a hotspot of scientific research into the global carbon balance. Forest aboveground biomass (AGB) is an important component in the land surface carbon cycle. In this study, using forest inventory data and forest distribution data, the AGB was estimated for forest in Daxinganlin in northeastern China by combining charge-coupled device (CCD) data from the Small Satellite for Disaster and Environment Monitoring and Forecast (HJ-1) and Geoscience Laser Altimeter System (GLAS) waveform data from the Ice, Cloud and land Elevation Satellite (ICESat). The forest AGB prediction models were separately developed for different forest types in the research area at GLAS footprint level from GLAS waveform parameters and field survey plot biomass in the Changqing (CQ) Forest Center, which was calculated from forest inventory data. The resulted statistical regression models have a R ²=0.68 for conifer and R ²=0.71 for broadleaf forests. These models were used to estimate biomass for all GLAS footprints of forest located in the study area. All GLAS footprint biomass coupled with various spectral reflectivity parameters and vegetation indices derived from HJ-1 satellite CCD data were used in multiple regression analyses to establish biomass prediction models (R ²=0.55 and R ²=0.52 for needle and broadleaf respectively). Then the models were used to produce a forest AGB map for the whole study area using the HJ-1 data. Biomass data obtained from forest inventory data of the Zhuanglin (ZL) Forest Center were used as independent field measurements to validate the AGB estimated from HJ-1 CCD data (R ²=0.71). About 80% of biomass samples had an error less than 20 t ha⁻¹, and the mean error of all validation samples is 5.74 t ha⁻¹. The pixel-level biomass map was then stratified into different biomass levels to illustrate the AGB spatial distribution pattern in this area. It was found that HJ-1 wide-swath data and GLAS waveform data can be combined to estimate forest biomass with good precision, and the biomass data can be used as input data for future carbon budget analysis.

     

Aerosol optical depth retrieval by HJ-1/CCD supported by MODIS surface reflectance data

The high spatial resolution and temporal observation frequency of HJ-1/CCD make it suitable for aerosol monitoring. However, because of the lack of a shortwave infrared band, it is difficult to use HJ-1/CCD imagery to retrieve aerosol optical depth (AOD). We developed a new algorithm for HJ-1/CCD AOD retrieval by introducing MODIS surface reflectance outputs (MOD09) as support. In this algorithm HJ-1/CCD blue band surface reflectance was retrieved through MOD09 blue band surface reflectance by band matching of the two sensors. AOD at 550 nm was then generated through a pre-calculated look-up table for HJ-1/CCD. Eighteen HJ-1/CCD images covering the Jing-Jin-Tang (Beijing-Tianjin-Tangshan) region were used to retrieve AOD using the new algorithm, and the AODs were then validated using AERONET ground measurements in Beijing and Xianghe. The validation shows that compared with AERONET ground measurements, 27/29 AODs have error less than 0.1 in absolute value.

     

Aerosol optical depth retrieval by HJ-1/CCD supported by MODIS surface reflectance data

The high spatial resolution and temporal observation frequency of HJ-1/CCD make it suitable for aerosol monitoring. However, because of the lack of a shortwave infrared band, it is difficult to use HJ-1/CCD imagery to retrieve aerosol optical depth (AOD). We developed a new algorithm for HJ-1/CCD AOD retrieval by introducing MODIS surface reflectance outputs (MOD09) as support. In this algorithm HJ-1/CCD blue band surface reflectance was retrieved through MOD09 blue band surface reflectance by band matching of the two sensors. AOD at 550 nm was then generated through a pre-calculated look-up table for HJ-1/CCD. Eighteen HJ-1/CCD images covering the Jing-Jin-Tang (Beijing-Tianjin-Tangshan) region were used to retrieve AOD using the new algorithm, and the AODs were then validated using AERONET ground measurements in Beijing and Xianghe. The validation shows that compared with AERONET ground measurements, 27/29 AODs have error less than 0.1 in absolute value.     

Object-oriented land cover classification using HJ-1 remote sensing imagery

The object-oriented information extraction technique was used to improve classification accuracy, and addressed the problem that HJ-1 CCD remote sensing images have only four spectral bands with moderate spatial resolution. We used two key techniques: the selection of optimum image segmentation scale and the development of an appropriate object-oriented information extraction strategy. With the principle of minimizing merge cost of merging neighboring pixels/objects, we used spatial autocorrelation index Moran’s I and the variance index to select the optimum segmentation scale. The Nearest Neighborhood (NN) classifier based on sampling and a knowledge-based fuzzy classifier were used in the object-oriented information extraction strategy. In this classification step, feature optimization was used to improve information extraction accuracy using reduced data dimension. These two techniques were applied to land cover information extraction for Shanghai city using a HJ-1 CCD image. Results indicate that the information extraction accuracy of the object-oriented method was much higher than that of the pixel-based method.     

Chinese HJ-1A/B satellites and data characteristics

From the viewpoint of environmental remote-sensing applications, this article explains the overall technical characteristics of the Chinese HJ-1A and HJ-1B satellites. It also investigates the spectral characteristics and potential applications of charge-coupled devices, and the infrared and hyperspectral data obtained by the satellites. Examples of applications, such as the remote-sensing monitoring of algal bloom in Taihu Lake, straw burning in southern China, and aerosol optical depth in the area around Bohai sea are presented. These examples illustrate the application characteristics of the HJ-1A and HJ-1B satellite data.

     

Quantitative estimation of the shrub canopy LAI from atmosphere-corrected HJ-1 CCD data in Mu Us Sandland

The leaf area index (LAI) is an important ecological parameter that characterizes the interface between vegetation canopy and the atmosphere. In addition, it is used by most process-oriented ecosystem models. This paper investigates the potential of HJ-1 CCD data combined with linear spectral unmixing and an inverted geometric-optical model for the retrieval of the shrub LAI in Wushen Banner of Inner Mongolia in the Mu Us Sandland. MODTRAN (Moderate Resolution Atmospheric Radiance and Transmittance Model) was used for atmospheric correction. Shrubland was extracted using the threshold of the normalized difference vegetation index, with which water bodies and farmland were separated, in combination with a vegetation map of the People’s Republic of China (1:1000000). Using the geometric-optical model, we derive the per-pixel reflectance as a simple linear combination of two components, namely sunlit background and other. The fraction of sunlit background is related to the shrub LAI. With the support of HJ-1 CCD data, we employ linear spectral unmixing to obtain the fraction of sunlit background in an atmospherically corrected HJ image. In addition, we use the measured shrub canopy structural parameters for shrub communities to invert the geometric-optical model and retrieve the pixel-based shrub LAI. In total, 18 sample plots collected in Wushen Banner of Inner Mongolia are used for validation. The results of the shrub LAI show good agreement with R ² of 0.817 and a root-mean-squared error of 0.173.     

Photosynthetically active radiation retrieval based on HJ-1A/B satellite data

Photosynthetically active radiation (PAR) is essential for plant photosynthesis and carbon cycle, and is also important for meteorological and environmental monitoring. To advance China’s disaster and environmental monitoring capabilities, the HJ-1A/B satellites have been placed in Earth orbit. One of their environmental monitoring objectives is the study of PAR. We simulated direct solar, scattered and environment radiation between 400 and 700 nm under different atmospheric parameters (solar zenith angle, atmospheric water vapor, atmospheric ozone, aerosol optical thickness, surface elevation and surface albedo), and then established a look-up table between these input parameters and PAR. Based on the look-up table, we used HJ-1A/B aerosol and surface albedo outputs to derive the corresponding PAR. Validation of inversed instantaneous and observed PAR values using HJ-1 Heihe experimental data had a root mean square error of 25.2 W m^?2, with a relative error of 5.9%. The root mean square error for accumulated daily PAR and observed values was 0.49 MJ m^?2, with a relative error of 3.5%. Our approach improved significantly the computational efficiency, compared with using directly radiation transfer equations. We also studied the sensitivity of various input parameters to photosynthetically active radiation, and found that solar zenith angle and atmospheric aerosols were sensitive PAR parameters. Surface albedo had some effect on PAR, but water vapor and ozone had minimal impact on PAR.     

Photosynthetically active radiation retrieval based on HJ-1A/B satellite data

Photosynthetically active radiation (PAR) is essential for plant photosynthesis and carbon cycle, and is also important for meteorological and environmental monitoring. To advance China’s disaster and environmental monitoring capabilities, the HJ-1A/B satellites have been placed in Earth orbit. One of their environmental monitoring objectives is the study of PAR. We simulated direct solar, scattered and environment radiation between 400 and 700 nm under different atmospheric parameters (solar zenith angle, atmospheric water vapor, atmospheric ozone, aerosol optical thickness, surface elevation and surface albedo), and then established a look-up table between these input parameters and PAR. Based on the look-up table, we used HJ-1A/B aerosol and surface albedo outputs to derive the corresponding PAR. Validation of inversed instantaneous and observed PAR values using HJ-1 Heihe experimental data had a root mean square error of 25.2 W m⁻², with a relative error of 5.9%. The root mean square error for accumulated daily PAR and observed values was 0.49 MJ m⁻², with a relative error of 3.5%. Our approach improved significantly the computational efficiency, compared with using directly radiation transfer equations. We also studied the sensitivity of various input parameters to photosynthetically active radiation, and found that solar zenith angle and atmospheric aerosols were sensitive PAR parameters. Surface albedo had some effect on PAR, but water vapor and ozone had minimal impact on PAR.

     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

基于太湖气溶胶类型分区的环境一号卫星CCD大气校正

由两颗卫星组成的环境卫星星座系统所提供的CCD数据具有较高的时间分辨率,使其在内陆湖泊水环境遥感监测中具有较大的应用潜力,对其有效的大气校正方法的研究则是其定量化参数反演的前提.基于准同步的MODIS数据辅助,根据气溶胶的差异性,将太湖划分为北部湖区、其他湖区两块区域,利用辐射传输模型,研究太湖环境一号CCD数据大气校正的方法,并对2009年4月17、21、25日数据进行大气校正.研究结果表明,该大气校正方法直接使用较为成熟的MODIS各类产品,克服了传统大气校正中依赖于现场同步测量大气参数的缺陷,能够快速、有效地完成环境一号CCD数据的大气校正.基于气溶胶类型对太湖进行分区后,所求算的遥感反射率精度高于6S模型和暗像元等大气校正方法得到的结果.     

Periodicity in time series of wind direction data

Summary The existence of periods of 24 and 92 hours has been proved on the basis of the analysis of time series of hourly mean wind direction data measured at the meteorological station in Praha-Ruzyn in the period from January to June, 1978. Besides, the closest mutual interdiurnal dependence of wind direction data observed at the climatological observation hour, at 7 a.m., has been proved. A method applicable to analysing time series of wind direction data by means of a computer is described.     

Estimation of clear-sky longwave downward radiation from HJ-1B thermal data

To satisfy the requirement of surface energy budget research on the meso-and micro-scale,a parameterization is developed to calculate high spatial resolution,clear-sky downward longwave radiation(DLR)from HJ-1B thermal data.The DLR algorithm is established based on extensive radiative transfer simulation and statistical analysis.To address the problem that HJ-1B has a single thermal channel and lacks atmospheric information,the brightness temperature of HJ-1B and water vapor content are used in the algorithm.An accuracy evaluation and error analysis for the algorithm is conducted using a simulated radiation dataset.The result shows that the algorithm performs well in most circumstances,but there is obvious underestimation when water vapor content is greater than 4g/cm2 .Error analysis indicates the accuracy of estimated DLRs is affected by uncertainties in input parameters,including water vapor content and top-of-atmosphere radiance.It is also affected by the difference between ground and near-surface air temperature.The algorithm is applied to actual HJ-1B data,and validated by ground data from six stations in the Heihe River and Haihe River basins.The estimated DLRs have good consistency with measured data except at Huazhaizi,and root mean square errors at most sites are around 20W/m2 ,which is slightly better than the result of MODIS. There is significant overestimation of DLR at Huazhaizi during summer,which is mainly produced by the large ground-air temperature difference.A correction process based on temperature difference is proposed and applied at Huazhaizi.The result shows that the positive bias is largely diminished after correction.     

Compositional time series analysis for Air Pollution Index data

The increasing importance of understanding the structure of Air Pollution Index (API) makes it necessary to come out with a compositional of API based on its pollutants. This will be more comprehensible for the public and easier to cooperate with authorities in reducing the causes of air pollution. Since five pollutants contribute in determining the API values, API can be shown as a compositional data. This study is conducted based on the data of API and its pollutants collected from Klang city in Malaysia for the period of January 2005 to December 2014. The proportion of each pollutant in API is considered as a component with five components in a compositional API. The existence of zero components in some pollutants, that have no effect on API, is a serious problem that prevents the application of log-ratio transformation. Thus, the approach of amalgamation has been used to combine the components with zero in order to reduce the number of zeros. Also, a multiplicative replacement has been utilized to eliminate the zero components and replace them with a small value that maintains the ratios of nonzero components. Transforming the compositional data to log-ratio coordinates has been done using the additive log ratio transformation, and the transformed series is then modeled by using a VAR model. Four criteria are used to determine the number of lags p of VAR(p) and these are: the Akaike Information, the Schwartz, the Hannan–Quinn and the Final Prediction Error criteria. Based on the results, A VAR (1) model with no constants or trend is considered as the best fitted model and it is used to forecast 12 months ahead. In addition, API values are mainly determined by PM₁₀ that has a proportion close to one most of the time during study period. Therefore, authorities and researchers need to study the sources of PM₁₀ and provide the public with useful information and alternatives in term of reducing the air pollution.     

Similarity search and pattern discovery in hydrological time series data mining

The rapid development of data mining provides a new method for water resource management, hydrology and hydroinformatics research. In the paper, based on data mining theory and technology, we analyse hydrological daily discharge time series of the Shaligunlanke Station in the Tarim River Basin in China from the year 1961 to 2000. Firstly, according to the four monthly statistics, namely mean monthly discharge, monthly maximum discharge, monthly amplitude and monthly standard deviation, K‐mean clustering was used to segment the annual process of the daily discharge. The clustering result showed that the annual process of the daily discharge can be divided into five segments: snowmelt period I (April), snowmelt period II (May), rainfall period I (June–August), rainfall period II (September) and dry period (October–December and January–March). Secondly, dynamic time warping (DTW), which is a different distance metric method from the traditional Euclidian distance metric, was used to look for similarities in the discharge process. On the basis of the similarity matrix, the similar discharge processes can be mined in each period. Thirdly, agglomerative hierarchical clustering was used to cluster and discover the discharge patterns in terms of the autoregressive model. It was found that the discharge had a close relationship with the temperature and the precipitation, and the discharge processes were more similar under the same climatic condition. Our study shows that data mining is a feasible and efficient approach to discover the hidden information in the historical hydrological data and mining the implicative laws under the hydrological process. Copyright © 2010 John Wiley & Sons, Ltd.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

HJ-1B/IRS热红外数据反演太湖水温的方法比较

太湖是我国典型的富营养化湖泊,水温是影响太湖藻类生长的重要环境因子之一,我国环境减灾卫星HJ-1B搭载的红外多光谱相机IRS对太湖水温动态遥感监测具有较大的性能优势.利用6景过境太湖的IRS热红外遥感影像,分别采用单通道普适性算法、辐射传输模型法和单窗算法反演太湖水温,并与实测水温和同期的TERRA/MODIS温度产品进行对比.结果表明,普适性单通道算法反演水温偏高,而辐射传输模型法和单窗算法则偏低;3种算法反演水温的均方根误差在1.001 K以内,单窗算法反演精度最高,其次是辐射传输模型法,再次为普适性单通道算法,而同期MODIS温度产品的均方根误差为1.507 K.3种算法从IRS热红外数据反演的水温直方图均呈正峰态、尖峰状态分布,反演结果能真实地反映太湖水温的空间分布特征.本研究对只有单个热红外通道的卫星传感器开展内陆水体水温遥感监测具有一定的参考意义.     

基于长时序T/P和Jason系列卫星高度计数据的中国南海潮汐信息提取研究

本文利用j、v模型对26年长时间序列的多个卫星高度计(TOPEX/Poseidon和Jason-1、Jason-2、Jason-3)变轨前后两种轨道的数据,使用调和分析法提取南海M₂、S₂、K₁和O₁分潮的调和常数.首次将Jason-3高度计数据用于潮汐信息提取研究,证明了Jason-3高度计与Jason-2高度计数据具有较好的一致性和连续性,且数据可用性强.提出并使用回归诊断法分析卫星轨道的交叉点处上升轨和下降轨之间调和分析结果的误差和相关系数,同时检验与评估潮汐信息提取方法的稳定性;选取南海47个验潮站,将验潮站实测数据与得到的卫星高度计数据提取的调和分析结果进行比较,进而检验潮汐提取结果的精度.最后,绘制同潮图验证提取潮汐调和常数的准确性.结果表明:M₂、S₂、K₁和O₁分潮的振幅均方根分别为4.28 cm、2.24 cm、2.92 cm、3.10 cm,迟角均方根分别为10.88°、11.10°、7.95...     

CDP mapping and image reconstruction by using offset VSP data

Because zero-offset VSP (Vertical Seismic Profile) data can only provide the information of rock properties and structure in the area around the Fresnel zone within the well, the scheme of VSP with offset was developed to acquire the reflection information away from the borehole in order to widen the range of VSP survey and to improve the precision of imaging.In this paper, we present a new CDP (Common Depth Point) mapping approach to image the reflecting structure by using offset VSP data. For the processing of offset VSP data, we firstly separated the up-going and down-going wave-fields from VSP data by means of F-K filtering technique, and we can calculate the mapping conditions (position and reflecting traveltime for CDP point) in homogeneous media, and then reconstruct the inner structure of the earth.This method is tested by using the offset VSP data which are used to simulate the case of super-deep borehole by means of finite-difference method. The imaged structure matches the real model very well. The results show that the method present here could accurately image the inner structure of the earth if the deviation of initial velocity model from the true model is less than 10%. Finally, we presented the imaged results for the real offset data by using this method.     

Detection of the urban heat island in Beijing using HJ-1B satellite imagery

Satellite images are used extensively in studying the urban heat island (UHI) phenomenon. We evaluated the suitability of thermal infrared (TIR) data from the HJ-1B satellite for detecting UHI using a case study in Beijing. Two modified algorithms for retrieving the land surface temperature (LST) from HJ-1B data were tested. The results were compared with LST images derived from a Landsat TM thermal band and the MODIS LST output. The spatial pattern of UHI generated using HJ-1B data matched well with that produced using TM and MODIS data. Of the two algorithms, the mono-window algorithm performed better but further tests are necessary. With more frequent coverage than TM and higher spatial resolution than MODIS, the HJ-1B TIR data present a unique opportunity to study thermal environments in cities in China and neighboring countries.