Object- and pixel-based classifications of macroalgae farming area with high spatial resolution imagery

Macroalgae plays an important role in coastal ecosystems. The accurate delineation of macroalgae areas is important for environmental management. This study compared the pixel- and object-based methods using Gaofen satellite no. 2 image to explore an efficient classification approach. Expert system rules and nearest neighbour classifier were adopted for object-based classification, whereas maximum likelihood classifier was implemented in the pixel-based approach. Normalized difference vegetation index, normalized difference water index, mean value of the blue band and geometric characteristics were selected as features to distinguish macroalgae farms by considering the spectral and spatial characteristics. Results show that the object-based method achieved a higher overall accuracy and kappa coefficient than the pixel-based method. Moreover, the object-based approach displayed superiority in identifying Porphyra class. These findings suggest that the object-based method can delineate macroalgae farming areas efficiently and be applied in the future to monitor the macroalgae farms with high spatial resolution imagery.     

GPS动态定位数据处理模型的比较研究 (英文)

The characteristics of three GPS kinematic data processing models, Least Squares （LS）, Kalman filtering and filtering are discussed and their advantages and disadvantages are compared. With observational data and pertinent data processing software, the applicable condition, context and effect of the three models are experimented. Results show that when the mobile platform is in uniform motion, the accuracy of the three models are almost equal; when the mobile platform is in stochastic acceleration, the accuracy of H∞ filtering model is superior to that of LS, while that of Kalman filtering is the worst.     

A statistical significance of differences in classification accuracy of crop types using different classification algorithms

Crop classification is needed to understand the physiological and climatic requirement of different crops. Kernel-based support vector machines, maximum likelihood and normalised difference vegetation index classification schemes are attempted to evaluate their performances towards crop classification. The linear imaging self-scanning (LISS-IV) multi-spectral sensor data was evaluated for the classification of crop types such as barley, wheat, lentil, mustard, pigeon pea, linseed, corn, pea, sugarcane and other crops and non-crop such as water, sand, built up, fallow land, sparse vegetation and dense vegetation. To determine the spectral separability among crop types, the M-statistic and Jeffries–Matusita (J–M) distance methods have been utilised. The results were statistically analysed and compared using Z-test and χ²-test. Statistical analysis showed that the accuracy results using SVMs with polynomial of degrees 5 and 6 were not significantly different and found better than the other classification algorithms.     

线性/非线性光谱混合模型估算白刺灌丛植被覆盖度

及时监测干旱与半干旱区光合/非光合植被覆盖度时空变化,可以为指导荒漠化防治工程及植被衰退机制研究提供重要信息。本文以甘肃民勤典型植被白刺灌丛为研究对象,通过地面控制性光谱实验获取混合光谱、端元光谱与丰度信息,开展线性与非线性光谱混合模型(包括核函数非线性和双线性混合模型)估算光合和非光合植被覆盖度的对比研究,采用全限制最小二乘法进行模型解混,分别获取各样本数据中各类端元丰度及其精度信息,通过模型分解的均方根误差(RMSE)与地面验证精度确定用于光合和非光合植被覆盖度估算的最佳光谱混合模型,其中参考端元丰度采用神经网络(NNC)分类算法对数字影像进行分类获取。结果表明:(1)引入阴影端元的四端元模型相对于传统的三端元模型(光合/非光合植被与裸土)能有效提高光谱解混的精度,并提高光合和非光合植被覆盖度估算精度;(2)对白刺灌丛来说,光合植被、非光合植被、裸土及阴影间多重散射混合效应存在,但混合效应不够显著;考虑非线性参数的核函数非线性光谱混合模型表现略低于线性光谱混合模型,因此非线性光谱混合模型在估算白刺灌丛光合和非光合植被覆盖度时相对于线性光谱混合模型没有明显优势;(3)基于光合/非光合植被、裸土与阴影四端元的线性光谱混合模型可以实现白刺灌丛光合和非光合植被覆盖度的准确估算,光合植被覆盖度估算RMSE为0.11 77,非光合植被覆盖度估算RMSE为0.0835。     

北京城市下垫面大气CO2反演算法

大气温室气体监测仪GMI（Greenhouse gases Monitor Instrument）是高分五号（GF-5）卫星载荷之一，主要用于全球温室气体含量监测和碳循环研究。高精度反演是卫星大气CO₂遥感的基本需求。地表反射率影响卫星遥感辐射量及辐射传输过程中的地气耦合过程，严重制约着CO₂的反演精度，针对GMI开发高精度的大气CO₂反演算法，地表反射是一个需要重点考虑的因素。城市是CO₂重要的发射源，且城市下垫面存在明显的二向反射特性，加上城市大气条件不良，复杂的地气耦合效应存在这都考验反演算法的准确性和鲁棒性。本文针对北京城市地区，利用2011年—2016年共5年的MODIS（MODerate-resolution Imaging Spectroradiometer）地表二向反射分布函数BRDF（Bidirectional Reflectance Distribution Function）数据，构建了适合利用单次观测数据反演的BRDF模型，并提出一种同时反演地表BRDF参数和大气CO₂含量的算法。结果表明在550 nm波长处气溶胶光学厚度AOD（Aerosol Optical Depth）小于0.4时，大部分GMI模拟数据的反演误差控制在0.5%（~2 ppm）内。利用GOSAT （Greenhouse gases Observing SATellite）实测数据的反演结果与修正后的日本国立环境研究所NIES（National Institute for Environmental Studies）反演结果进行对比，其平均误差为1.25 ppm，相关性达到0.85。本算法满足GMI数据在北京城市区域高精度CO₂反演的需求，并使得反演高值气溶胶区域数据成为可能，增加了GMI观测数据的利用率。     

Assessing the potential for satellite image monitoring of seagrass thermal dynamics: for inter- and shallow sub-tidal seagrasses in the inshore Great Barrier Reef World Heritage Area,Australia

Seagrass meadows are at increasing risk of thermal stress and recent work has shown that water temperature around seagrass meadows could be used as an indicator for seagrass condition. Satellite thermal data have not been linked to the thermal properties of seagrass meadows. This work assessed the covariation between 20 in situ average daily temperature logger measurement sites in tropical seagrass meadows and satellite derived daytime SST (sea surface temperature) from the daytime MODIS and Landsat sensors along the Great Barrier Reef coast in Australia. Statistically significant (R²?=?0.787–0.939) positive covariations were found between in situ seagrass logger temperatures and MODIS SST temperature and Landsat sensor temperatures at all sites along the reef. The MODIS SST were consistently higher than in situ temperature at the majority of the sites, possibly due to the sensor’s larger pixel size and location offset from field sites. Landsat thermal data were lower than field-measured SST, due to differences in measurement scales and times. When refined significantly and tested over larger areas, this approach could be used to monitor seagrass health over large (10⁶?km²) areas in a similar manner to using satellite SST for predicting thermal stress for corals.     

G0分布下基于白化滤波的极化SAR图像CFAR检测

在已有的极化合成孔径雷达(PolSAR)图像恒虚警(CFAR)检测方法中,存在着高分辨下杂波模型适用性差的难题。为解决此问题,提出了一种G_0分布下虚警概率具有闭合解析表达形式的CFAR检测方法,并定义虚警损失率(CFAR Loss, C_L)参数用以量化评估CFAR检测方法的恒虚警保持效果。首先,在乘积模型框架下,引入了逆Gamma纹理变量假设,推导出了多视极化白化滤波(MPWF)检测量的概率密度函数(PDF)。然后,对MPWF检测量的概率密度函数积分得到了虚警概率关于CFAR检测阈值的解析表达式,并设计了相应的CFAR检测流程。最后,采用仿真数据和AIRSAR实测数据对已有方法和新方法进行了算法运行时间、检测量拟合性能及目标检测性能对比。实验结果表明,方法运行时间比已有方法缩短3至30倍,具有良好的实时性;日本玉野地区的AIRSAR实测数据结果表明G_0分布对高分辨不均匀海区具有良好的拟合性能,且新方法在G_0分布和非G_0分布海区均能有效检测出目标,鲁棒性较强,相比其他检测方法品质因数(FoM)平均高出15.78%;C_L分析结果表明新方法具有良好的恒虚警保持性能,同时指出杂波对数累积量散点距离G_0分布曲线越近,新方法的恒虚警保持效果越好。     

Detecting anomalous CO2 flux using space borne spectroscopy

Over the time-scale, earth's atmospheric CO₂ concentration has varied and that is mostly determined by balance among the geochemical processes including burial of organic carbon in sediments, silicate rock weathering and volcanic activity. The best recorded atmospheric CO₂ variability is derived from Vostok ice core that records last four glacial/interglacial cycles. The present CO₂ concentration of earth's atmosphere has exceeded far that it was predicted from the ice core data. Other than rapid industrialization and urbanization since last century, geo-natural hazards such as volcanic activity, leakage from hydrocarbon reservoirs and spontaneous combustion of coal contribute a considerable amount of CO₂ to the atmosphere. Spontaneous combustion of coal is common occurrence in most coal producing countries and sometimes it could be in an enormous scale. Remote sensing has already proved to be a significant tool in coalfire identification and monitoring studies. However, coalfire related CO₂ quantification from remote sensing data has not endeavoured yet by scientific communities because of low spectral resolution of commercially available remote sensing data and relatively sparse CO₂ plume than other geological hazards like volcanic activity. The present research has attempted two methods to identify the CO₂ flux emitted from coalfires in a coalmining region in north China. Firstly, a band rationing method was used for column atmospheric retrieval of CO₂ and secondly atmospheric models were simulated in fast atmospheric signature code (FASCOD) to understand the local radiation transport and then the model was implemented with the inputs from hyperspectral remote sensing data. It was observed that retrieval of columnar abundance of CO₂ with the band rationing method is faster as less simulation required in FASCOD. Alternatively, the inversion model could retrieve CO₂ concentration from a (certain) source because it excludes the uncertainties in the higher altitude.     

CO2 concentration and its spatiotemporal variation in the troposphere using multi-sensor satellite data,carbon tracker,and aircraft observations

Satellite-based atmospheric CO₂ observations have provided a great opportunity to improve our understanding of the global carbon cycle. However, thermal infrared (TIR)-based satellite observations, which are useful for the investigation of vertical distribution and the transport of CO₂, have not yet been studied as much as the column amount products derived from shortwave infrared data. In this study, TIR-based satellite CO₂ products – from Atmospheric Infrared Sounder, Tropospheric Emission Spectrometer (TES), and Thermal And Near infrared Sensor for carbon Observation – and carbon tracker mole fraction data were compared with in situ Comprehensive Observation Network for Trace gases by AIrLiner (CONTRAIL) data for different locations. The TES CO₂ product showed the best agreement with CONTRAIL CO₂ data resulting in R² ~ 0.87 and root-mean-square error ~0.9. The vertical distribution of CO₂ derived by TES strongly depends on the geophysical characteristics of an area. Two different climate regions (i.e., southeastern Japan and southeastern Australia) were examined in terms of the vertical distribution and transport of CO₂. Results show that while vertical distribution of CO₂ around southeastern Japan was mainly controlled by horizontal and vertical winds, horizontal wind might be a major factor to control the CO₂ transport around southeastern Australia. In addition, the vertical transport of CO₂ also varies by region, which is mainly controlled by anthropogenic CO₂, and horizontal and omega winds. This study improves our understanding of vertical distribution and the transport of CO₂, both of which vary by region, using TIR-based satellite CO₂ observations and meteorological variables.     

Use of earth observation data for applications in public health

The Earth Observation (EO) data with their advantages in spectral, spatial and temporal resolutions have demonstrated their great value in providing information about many of the components that comprise environmental systems and ecosystems for decades that are crucial to the understating of public health issues. This literature review shows that in conjunction with in situ data collection, EO data have been used to observe, monitor, measure and model many environmental variables that are associated with disease vectors. Furthermore, satellite derived aerosol optical depth has been increasingly employed to estimate ground-level PM_2.5 concentrations, which have been found to associate with various health outcomes such as cardiovascular and respiratory diseases. It is suggested that Landsat-like imagery data may provide important data sources to analyse and understand contagious and infectious diseases at the local and regional scales, which are tied to urbanisation and associated impacts on the environment. There is also a great need of data products from coarse resolution imagery, such as those from moderate resolution imaging spectrometer, multiangle imaging spectroradiometer and geostationary operational environmental satellite , to model and characterise infectious diseases at the continental and global scales. The infectious diseases at greater geographical scales have become unprecedentedly significant as global climate change and the process of globalisation intensify. The relationship between infectious diseases and environmental characteristic have been explored by using statistical, geostatistical and physical models, with recent emphasis on the use of machine-learning techniques such as artificial neural networks. Lastly, we suggest that the planned HyspIRI mission is crucial for observing, measuring and modelling environmental variables impacting various diseases as it will improve both spectral resolution and revisit time, thus contributing to better prediction of occurrence of infectious diseases, target intervention and tracking of epidemic events.     

利用遥感数据评价燃煤电厂空气质量

卫星观测数据可以评价燃煤电厂的空气质量等级。NO₂、SO₂ 和烟尘是燃煤电厂排放的主要污染物,本文利用卫星遥感观测的NO₂、SO₂和气溶胶光学厚度AOD(Aerosol Optical Depth)开展燃煤电厂空气质量评价。以中国华北地区为实验区,分析对比了3种污染物不同时间分辨率和空间分辨率的污染状况,确定了单因子的5级分级标准,根据燃煤电厂排放污染物的权重不同,提出了评价近地表空气质量状况的模型。本文综合考虑3种污染因子来反映电厂空气质量,有利于提高评价的准确性以及反应信息的全面性。结果表明,该模型能正确反映不同地区电厂的空气质量特点。     

Field spectroradiometer and simulated multispectral bands for discriminating invasive species from morphologically similar cohabitant plants

One of the challenges in fighting plant invasions is the inefficiency of identifying their distribution using field inventory techniques. Remote sensing has the potential to alleviate this problem effectively using spectral profiling for species discrimination. However, little is known about the capability of remote sensing in discriminating between shrubby invasive plants with narrow leaf structures and other cohabitants with similar ecological niche. The aims of this study were therefore to (1) assess the classification performance of field spectroradiometer data among three bushy and shruby plants (Artemesia afra, Asparagus laricinus, and Seriphium plumosum) from the coexistent plant species largely dominated by acacia and grass species, and (2) explore the performance of simulated spectral bands of five space-borne images (Landsat 8, Sentinel 2A, SPOT 6, Pleiades 1B, and WorldView-3). Two machine-learning classifiers (boosted trees classification and support vector machines) were used to classify raw hyperspectral (n = 688) and simulated multispectral wavelengths. Relatively high classification accuracies were obtained for the invasive species using the original hyperspectral bands for both classifiers (overall accuracy, OA = 83–97%). The simulated data resulted in higher accuracies for Landsat 8, Sentinel 2A, and WorldView-3 compared to those computed for bands simulated to SPOT 6 and Pleiades 1B data. These findings suggest the potential of remote-sensing techniques in the discrimination of different plant species with similar morphological characteristics occupying the same niche.     

New analytical and numerical approaches for geopotential modeling

 The standard analytical approach which is applied for constructing geopotential models OSU86 and earlier ones, is based on reducing the boundary value equation to a sphere enveloping the Earth and then solving it directly with respect to the potential coefficients _n,m . In an alternative procedure, developed by Jekeli and used for constructing the models OSU91 and EGM96, at first an ellipsoidal harmonic series is developed for the geopotential and then its coefficients _n,m ^e are transformed to the unknown _n,m . The second solution is more exact, but much more complicated. The standard procedure is modified and a new simple integral formula is derived for evaluating the potential coefficients. The efficiency of the standard and new procedures is studied numerically. In these solutions the same input data are used as for constructing high-degree parts of the EGM96 models. From two sets of _n,m (n≤360,|m|≤n), derived by the standard and new approaches, different spectral characteristics of the gravity anomaly and the geoid undulation are estimated and then compared with similar characteristics evaluated by Jekeli's approach (`etalon' solution). The new solution appears to be very close to Jekeli's, as opposed to the standard solution. The discrepancies between all the characteristics of the new and `etalon' solutions are smaller than the corresponding discrepancies between two versions of the final geopotential model EGM96, one of them (HDM190) constructed by the block-diagonal least squares (LS) adjustment and the other one (V068) by using Jekeli's approach. On the basis of the derived analytical solution a new simple mathematical model is developed to apply the LS technique for evaluating geopotential coefficients. Received: 12 December 2000 / Accepted: 21 June 2001     

非球形冰晶的毫米波k-Ze关系研究

针对毫米波雷达处理数据的实际需要,应用离散偶极子近似法DDA,获得了非球形冰晶的后向散射及衰减截面并进行了参数化,并主要基于细化的冰云模型,假设冰晶粒子谱为Γ分布,通过模拟取样各1330次（代表1330种粒子分布）,分别计算得到了W波段（94 GHz）与Ka波段（35 GHz）毫米波雷达探测的冰云衰减系数k及雷达反射率因子Z_e,而且利用数值模拟的方法,建立了k-Z_e关系的具体表达式。计算表明,非球形和非瑞利散射对W波段毫米波雷达衰减的影响较大,而且在同样滴谱分布条件下,W波段毫米波雷达的衰减比Ka波段毫米波雷达的大几倍,此外细化的冰云模型对k-Z_e关系具有影响。本研究对中纬度非降水性冰云的毫米波雷达的衰减订正具有参考价值,并对中国的毫米波雷达应用具有借鉴作用。     

Wheat crop production estimation using satellite data

This paper reports acreage, yield and production forecasting of wheat crop using remote sensing and agrometeorological data for the 1998–99 rabi season. Wheat crop identification and discrimination using Indian Remote Sensing (IRS) ID LISS III satellite data was carried out by supervised maximum likelihood classification. Three types of wheat crop viz. wheat-1 (high vigour-normal sown), wheat-2 (moderate vigour-late sown) and wheat-3 (low vigour-very late sown) have been identified and discriminated from each other. Before final classification of satellite data spectral separability between classes were evaluated. For yield prediction of wheat crop spectral vegetation indices (RVI and NDVI), agrometeorological parameters (ET_max and TD) and historical crop yield (actual yield) trend analysis based linear and multiple linear regression models were developed. The estimated wheat crop area was 75928.0 ha. for the year 1998–99, which sowed ?2.59% underestimation with land record commissioners estimates. The yield prediction through vegetation index based and vegetation index with agrometeorological indices based models were 1753 kg/ha and 1754 kg/ha, respectively and have shown relative deviation of 0.17% and 0.22%, the production estimates from above models when compared with observed production show relative deviation of ?2.4% and ?2.3% underestimations, respectively.     

Implications of ocean color in the upper water thermal structure at NINO3.4 region: a sensitivity study for optical algorithms and ocean color variabilities

Chlorophyll a (Chl-a) has been the most commonly used biomass metric in biological oceanographic processes. Although limited to two-dimensional surfaces, remote-sensing tools have been successfully providing the most recent state of marine phytoplankton biomass to better understand bottom-up processes initiating daily marine material cycles. In this exercise, ocean color products with various time-scales, derived from Sea-Viewing Wide Field-of-View Sensor (SeaWiFS), were used to investigate how their bio-optical properties affect the upper-ocean thermal structure in a global ocean modeling framework. This study used a ¼-degree Hybrid Coordinate Ocean Model forced by hourly atmospheric fluxes from the Climate Forecast System Reanalysis at National Oceanic Atmospheric Administration. Three numerical experiments were prepared by combining two ocean color products – downwelling diffuse attenuation coefficients (K_dPAR) and chlorophyll a (Chl-a) – and two shortwave radiant flux algorithms. These three runs are: (1) KparCLM, based on a 13-year long-term climatological K_dPAR derived from SeaWiFS; (2) ChlaCLM, based on a 13-year long-term Chl-a derived from SeaWiFS; and (3) ChlaID, which uses the inter-annual time-series of monthly-mean SeaWiFS Chl-a product. The KparCLM experiment uses a Jerlov-like two-band scheme; whereas, both ChlaCLM and ChlaID use a two-band scheme that considers inherent (absorption (a) and backscattering (b_b) coefficients) and apparent optical properties (downwelling attenuation coefficient (K_d) and solar zenith angle (θ, varying 0–60°)). It is found that algorithmic differences in optical parameterizations have a bigger impact on the simulated temperatures in the upper-100 m of the eastern equatorial Pacific, NINO3.4 region, than other parts of the ocean. Overall, the K_dPAR-based approach estimated relatively low surface temperatures compared to those estimated from the chlorophyll-based method. In specific, this cold bias, pronounced in the upper 20–30 m, is speculated to be due to optical characteristics of the algorithm and K_dPAR products, or due to nonlinear hydrodynamical processes involving displacement of mixed-layer depth. Comparisons between each experiment against Global Ocean Data Assimilation System (GODAS; Behringer and Xue 2004) analyses find that KparCLM-based simulations have lower mean differences and variabilities with higher cross-correlation coefficients compared to ChlaCLM- and ChlaID-based experiments.     

A new supervised classifier exploiting spectral-spatial information in the Bayesian framework

Conventional machine learning methods are often unable to achieve high degrees of accuracy when only spectral data are involved in the classification process. The main reason of that inaccuracy can be brought back to the omission of the spatial information in the classification. The present paper suggests a way to combine effectively the spectral and the spatial information and improve the classification’s accuracy. In practice, a Bayesian two-stage methodology is proposed embodying two enhancements: i) a geostatistical non-parametric classification approach, the universal indicator kriging and ii) the smooth multivariate kernel method. The former provides an informative prior, while the latter overcomes the assumption (often not true) of independence of the spectral data. The case study reports an application to land-cover classification in a study area located in the Apulia region (Southern Italy). The methodology performance in terms of overall accuracy was compared with five state-of-the-art methods, i.e. naïve Bayes, Random Forest, artificial neural networks, support vector machines and decision trees. It is shown that the proposed methodology outperforms all the compared methods and that even a severe reduction of the training set does not affect seriously the average accuracy of the presented method.     

结合特征分量构建和面向对象方法提取高分辨率卫星影像阴影

针对高分辨率卫星影像,提出一种特征分量构建与面向对象结合的阴影提取方法。分析遥感阴影光谱特性,构建彩色不变特征C3、亮度特征I、主成分第一特征量PC1以及蓝色波段和近红外波段归一化比率特征RATIOb_nir,增强阴影信息。采用线性变换将几个特征分量Digital Number(DN)值归一化到相同范围,对这几个分量进行综合分析。以I和PC1分量为输入对影像进行多尺度分割,建立包括波段均值、标准差、最大差异等特征的规则集,实现面向对象的阴影信息提取。选取20幅QuickBird影像为例进行阴影提取实验,平均总体精度为97%,平均用户精度为96%,平均Kappa系数为0.94。实验结果表明,相对传统基于像素信息提取方法,本文方法提取阴影斑块完整,无破碎图斑;相对基于原始光谱的面向对象方法,本文方法提取精度更高。     

Application of geographical information system for mapping/monitoring seismological hazards in Pakistan

This study attempts to use the geographic information system (GIS) technique to map and understand the tectonics and crustal structures of Pakistan. Maps of surficial tectonic features and seismological parameters including Moho depth, P_n velocity and P_g velocity are complied. Based on the seismological data-set of the country the earthquake hazard map of Pakistan is also presented by applying regression technique on seismological, geological and topographical parameters. A case study of 8 October 2005 earthquake is used to validate the hazard map. It is envisaged that the developed GIS database would help policy-makers and scientists in natural hazard evaluation, seismic risk assessment and understanding of earthquake occurrences in Pakistan.     

粗糙集理论进行遥感图像监督分类的样本质量评价的研究(英文)

In the supervised classification process of remotely sensed imagery, the quantity of samples is one of the important factors affecting the accuracy of the image classification as well as the keys used to evaluate the image classification. In general, the samples are acquired on the basis of prior knowledge, experience and higher resolution images. With the same size of samples and the same sampling model, several sets of training sample data can be obtained. In such sets, which set reflects perfect spectral characteristics and ensure the accuracy of the classification can be known only after the accuracy of the classification has been assessed. So, before classification, it would be a meaningful research to measure and assess the quality of samples for guiding and optimizing the consequent classification process. Then, based on the rough set, a new measuring index for the sample quality is proposed. The experiment data is the Landsat TM imagery of the Chinese Yellow River Delta on August 8th, 1999. The experiment compares the Bhattacharrya distance matrices and purity index zl and △x based on rough set theory of 5 sample data and also analyzes its effect on sample quality.