Use oe aerial photograph for study of soil salinity and landscape relationship in parts of Haryana

In order to know the development of soil salinity and alkalinity in relation to landform, a systematic approach has been adopted by using aerial photo-interpretation techniques followed by field checks. The major land form units identified in the area are (i) alluvial upland (ii) low land (iii) back-swamp areas (iv) remanent horstland (v) interfiuve (vi) abandoned channel (vii) plain undifferentiated and (viii) plain with aeolian activity. salt accumulation is pronounced in landform units (i), (ii), (iii) and (iv) due to configurating slopes and basement ridge control, whereas in landform units (v) and (vi), a patchy distribution of salinity has been found. Landform units (vii) and (viii) are free from this hazard The elements of landscape have controlled the distriburion of salts within each landscape.     

A new practical approach to GNSS high-dimensional ambiguity decorrelation

Based on both the lower and the upper triangular Cholesky decomposition algorithms, the (inverse) lower triangular Cholesky integer transformation and the (inverse) upper triangular Cholesky integer transformation are defined, and the (inverse) paired Cholesky integer transformation is proposed. Then, for the case of high-correlation ambiguity, a multi-time (inverse) paired Cholesky integer transformation is given. In addition, a simple and practical criterion is presented to solve the uniqueness problem of the integer transformation. It is verified by an example that (1) the (inverse) paired Cholesky integer transformation is very convenient and very efficient in practical computation; (2) the (inverse) paired Cholesky integer transformation is better than both the (inverse) lower triangular Cholesky integer transformation and the (inverse) upper triangular Cholesky integer transformation; and that (3) the inverse paired Cholesky integer transformation outperforms the paired Cholesky integer transformation slightly in the most cases.     

Evaluation of linear discriminant and support vector machine classifiers for classification of nitrogen status in mature oil palm from SPOT-6 satellite images: analysis of raw spectral bands and spectral indices

Nitrogen (N) management is important in sustaining oil palm production. Remote sensing-based approaches via spectral index have promise in assessing the N nutrition content. The objectives of this study are; (i) to examine the N classification capability of three spectral indices (SI) such as visible (Vis), near infrared (NIR) and a combination of visible and NIR (Vis + NIR) from the SPOT-6 satellite, and (ii) to compare the performance of linear discriminant analysis (LDA) and support vector machine (SVM) in discriminating foliar N content of mature oil palms. Nitrogen treatments varied from 0 to 2 kg per palm. The N-sensitive SIs tested in this study were age-dependent. The Vis index (BGRI₁) (CVA = 79.55%) and Vis + NIR index (NDVI, NG, IPVI and GNDVI) (CVA = 81.82%) were the best indices to assess N status of young and prime mature palms through the SVM classifier.     

Evaluation of multisensor data for delineating salt affected soils in arid rajasthan

The Landsat (MSS and TM), SPOT (PLA and MLA) and IRS (LISS-I and LISS-II) images of crop free period (April, May), rainfed crop (October) and rabi irrigated crop (January, February) have been evaluated for their capabilities of mapping (1) primary salt affected soils: (slightly, moderately and severely) (2) saline water irrigated saline soils, (3) sodic water irrigated sodic soils and (4) salt affected soils due to tank seepage in the arid region of Rajasthan. The moderately and severe salt affected soils could be mapped with Landsat, (IRS LISS-I) and SPOT, images of any season. However, the summer season imagery provided maximum extent of salt affected soils. The LISS-II imagery also provided delineation of slightly salt affected soils in addition to the moderate and severely salt affected soils. The delineation of saline and sodic water irrigated areas was possible by using Landsat False Colour Composite for the January month by their characteristic reflectance, existing cropping pattern and the quality of irrigation water being used in the area. The IRS (LISS-II) and SPOT PLA images for the May month were also used for mapping of saline and sodic water irrigated soils.     

A new differential code bias (C1–P1) estimation method and its performance evaluation

The current satellite clock products are computed using the ionosphere-free phase (L1/L2) and code (P1/P2) observations. Thus, if users conduct undifferenced positioning using these clock products together with C1 and P2 observations, the differential code bias (DCB) (C1–P1) should be properly compensated. The influence of DCB (C1–P1) on the undifferenced ambiguity solutions is investigated. Based on the investigation, we propose a new DCB (C1–P1) estimation method. Using it, the satellite DCB (C1–P1) can be computed. A 30-day (DOY 205–234, 2012) dual-frequency GPS data set is processed to estimate the DCB (C1–P1). Comparing the estimated results with that of IGS DCB products, the accuracy is better than 0.13 m. The performances of DCB (C1–P1) in the code-based single-point positioning, precise point positioning (PPP) convergence and wide-lane uncalibrated phase delay (UPD) estimation are investigated using the estimated DCB (C1–P1). The results of the code-based single-point positioning show that the influence of DCB (C1–P1) on the up direction is more evident than on the horizontal directions. The accuracy is improved by 50 % and reaches to decimeter level with DCB (C1–P1) application. The performance of DCB (C1–P1) in PPP shows that it can accelerate PPP convergence through improving the accuracy of the code observation. The computed UPD values show that influence of DCB (C1–P1) on UPD of each satellite is different, and some values are larger than 0.3 cycles.     

Generation of Vegetation Fraction and Surface Albedo Products Over India from Ocean Colour Monitor (OCM) Data Onboard Oceansat-2

The use of Local Area Coverage (LAC) data from Ocean Color Monitor (OCM) sensor of Oceansat-2 with its high radiometric resolution (12 bits/pixel) and 2-day repeat cycle for rapid monitoring of vegetation growth and estimating surface albedo for the Indian region is demonstrated in this study. For the vegetation monitoring, normalized difference vegetation index (NDVI) and vegetation fraction (VF) products were estimated by maximum value composite approach fortnightly and were resampled to 1 km. The surface albedo products were realized by converting narrow-band eight-band spectral reflectance OCM data to a) visible (300–700 nm) and b) broad band (300–3,000 nm) data. For validation, the derived products were compared with respective MODIS global products and found to be in good agreement.     

基于等式的等间隔GM(1,1)模型比较研究

本文归纳了基于等式的2种等间隔GM(1,1)模型,比较了这2种模型与常规模型的精度,算例显示:当发展系数a较小时,3种模型的后验差比值C和小误差概率P的精度一致;当发展系数较大时,基于等式的模型比常规模型的后验差比值C和小误差概率P的精度高,且基于等式的这2种模型的后验差比值C和小误差概率P精度基本相当.     

CBERS02B卫星CCD传感器数据反演陆地气溶胶

研究利用CBERS02B卫星的CCD传感器数据反演陆地气溶胶的方法。采用的方法是暗像元法。具体步骤为: 根据地面采集的植被光谱数据, 结合CCD传感器特点, 建立浓密植被(暗像元)红蓝波段(CCD传感器的第三和第一波段)反射率与地表反射率之间的关系, 确定了暗像元识别的阈值, 讨论气溶胶光学厚度对暗像元识别的影响以及消除这种影响的方法; 利用6S进行辐射传输运算, 构建查找表; 根据CBERS02B卫星的CCD传感器数据, 从查找表插值得到气溶胶光学厚度, 并进行了算法的误差分析。用广西南宁市及北京地区附近的两景数据进行了实际的反演试验, 使用MODIS的气溶胶产品与反演结果进行比对。结果显示, CBERS02B卫星的CCD传感器数据能够较好的反演陆地气溶胶。     

Pre-harvest wheat yield prediction using agromet-spectral-trend-yield models for hoshiarpur and rupnagar districts of punjab

Wheat yield prediction using different agrometeorological indices, spectral index (NDVI, Normalized Difference Vegetation Index) and trend predicted yield (TPY) were developed in Hoshiarpur and Rupnagar districts of Punjab. On the basis of examination of Correlation Coefficients (R), Standard Error of Estimate (SEOE) and Relative Deviation (RD) values resulted from different agromet models, the best agromet subset were selected as Minimum Temperature (T_min), Maximum Temperature (T_max) and accumulated Heliothermal Units (HTU) in case of Hoshiarpur district and Minimum Temperature (T--_min), accumulated Temperature Difference (TD) and accumulated Pan Evaporation (E) for Rupnagar district at reproductive stage (2^nd week of March) of wheat. It was found that Agromet-Spectral-Trend-Yield model could explain 96 % (SEOE = 87 kg/ha) and 91 % (SEOE = 146 kg/ha) of wheat yield variations for Hoshiarpur and Rupnagar districts, respectively.     

一种适用于电离层层析成像的TV-MART算法

针对电离层电子密度重构问题,提出了一种综合利用总变差最小化(toalvariation,TV)与乘法代数重构算法(multiplicativealgebraicreconstructiontechnique,MART)的电离层层析成像算法。该算法对反演模型的参数施加总变差约束,以提高反演过程的稳定性和结果的精确性。通过模拟数据和实测数据对模型的重构结果进行验证,结果表明,相对于乘法代数重构算法,该算法能够有效地提高电离层电子密度的重构精度。     

Three‐dimensional terrain modeling with multiple‐source illumination

Three‐dimensional (3D) terrain modeling based on digital elevation models (DEMs) with the use of orthographic and perspective projections is a standard procedure implemented in many commercial and open‐source geoinformation systems. However, standard tools may be insufficient for 3D scientific visualization. In particular, single‐source illumination of 3D models may be deficient for topographically complex terrains. We present an approach for 3D terrain modeling with multiple‐source illumination in the virtual environment of the Blender free and open‐source software. The approach includes the following key stages: (1) automatic creation of a polygonal object; (2) selecting an algorithm to model the 3D geometry; (3) selecting a vertical exaggeration scale; (4) selecting types, parameters, a number, and positions of light sources; (5) selecting methods for generating shadows; (6) selecting a shading method for the 3D model; (7) selecting a material for the 3D model surface; (8) overlaying a texture on the 3D model; (9) setting a virtual camera; and (10) rendering the 3D model. To illustrate the approach, we processed a test DEM extracted from the International Bathymetric Chart of the Arctic Ocean version 3.0 (IBCAO 3.0). The approach is currently being used to develop a system for geomorphometric modeling of the Arctic Ocean floor.     

Geomorphological map of Bist Doab Tract,Punjab from Landsat imagery analysis

The Bist Doab Tract of Punjab is an agriculturaly important region lying between the two rivers viz: Beas and Satluj on two sides and the Siwaliks range on the third side. Landsat imagery of bands 5,7 and false colour composite covering this tract were analysed for the texture, shape, size and pattern to study the different geomorphic units developed under different geological periods and environments. The study has enabled to identify the following land form zones (i) Structural hill-Siwaliks-Z1 (ii) Table land-Z2 (iii) Upper piedmont-Z3 (iv) Lower piedmont-Z4 (v) Alluvial plain-Z5 (vi) Sandy and Saline tract-Z6 (vii) Older flood plain-Z7 (viii) Active flood plain-Z8. The resultant geomorphological map of the area has been prepared on 1: 1M scale and as such the applicability of the remotely sensed data has been found to be of immense use in the quick identification of regional geomorphic setting of the area.     

Multivariate Analysis and Geovisualization with an Integrated Geographic Knowledge Discovery Approach

The discovery, interpretation, and presentation of multivariate spatial patterns are important for scientific understanding of complex geographic problems. This research integrates computational, visual, and cartographic methods together to detect and visualize multivariate spatial patterns. The integrated approach is able to: (1) perform multivariate analysis, dimensional reduction, and data reduction (summarizing a large number of input data items in a moderate number of clusters) with the Self-Organizing Map (SOM); (2) encode the SOM result with a systematically designed color scheme; (3) visualize the multivariate patterns with a modified Parallel Coordinate Plot (PCP) display and a geographic map (GeoMap); and (4) support human interactions to explore and examine patterns. The research shows that such "mixed initiative" methods (computational and visual) can mitigate each other's weakness and collaboratively discover complex patterns in large geographic datasets, in an effective and efficient way.     

地理遥感信息模型与地理数学

地理图像信息模型是在地形模型、物理模型、数学模型的基础上提出的一种新型模型。概述了地理图像信息模型的基本概念、技术与应用。提出了符合地理科学自身规律的非线性数学方法 ,解决了确定性问题与不确定问题的结合 ,从形式逻辑计算发展到辩证逻辑的计算 ,从抽象公式的计算发展到抽象思维与形象思维结合的公式与图像合为一体的计算 ,从而解决了地理数学问题 ,为发展地理数学奠定了基础。     

Monitoring Urban Landscape Dynamics Over Delhi (India) Using Remote Sensing (1998–2011) Inputs

Urbanization is increasingly becoming a widespread phenomenon at all scales of development around the globe. Be it developing or developed nations, all are witnessing urbanization at very high pace. In order to study its impacts, various methodologies and techniques are being implemented to measure growth of urban extents over spatial and temporal domains. But urbanization being a very dynamic phenomenon has been facing ambiguities regarding methods to study its dynamism. This paper aims at quantifying urban expansion in Delhi, the capital city of India. The process has been studied using urban land cover pattern derived from Landsat TM/ETM satellite data for two decades (1998–2011). These maps show that built-up increased by 417 ha in first time period (1998–2003) and 6,633 ha during next period (2003–2011) of study. For quantification of metrics for urban expansion, the Urban Landscape Analysis Tool (ULAT) was employed. Land cover mapping was done with accuracy of 92.67 %, 93.3 % and 96 % respectively for years 1998, 2003 and 2011. Three major land covers classes mapped are; (i) built-up, (ii) water and (iii) other or non-built-up. The maps were then utilized to extract degree of urbanization based on spatial density of built-up area consisting of seven classes, (i) Urban built-up, (ii) Suburban built-up,(iii) Rural built-up, (iv) Urbanized open land, (v) Captured open land, (vi) Rural open land and (vii) Water. These classes were demarcated based on the urbanness of cells. Similarly urban footprint maps were generated. The two time maps were compared to qualitatively and quantitatively capture the dynamics of urban expansion in the city. Along with urbanized area and urban footprint maps, the new development areas during the study time periods were also identified. The new development areas consisted of three major categories of developments, (i) infill, (ii) extension and (iii) leapfrog.     

Environmental public health applications using remotely sensed data

We describe a remote sensing and geographic information system (GIS)-based study that has three objectives: (1) characterize fine particulate matter (PM_2.5), insolation and land surface temperature (LST) using NASA satellite observations, Environmental Protection Agency (EPA) ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.     

A hyperspectral experiment over tropical forests based on the EO-1 orbit change and PROSAIL simulation

ABSTRACT

We designed a unique hyperspectral experiment from the Earth Observing One (EO-1) orbit change to evaluate solar illumination effects over tropical forests in Brazil. Ten nadir-viewing Hyperion images collected over a fixed site and period of the year (July to August) were selected for analysis. We evaluated variations in reflectance and in 16 narrowband vegetation indices (VIs) with increasing solar zenith angle (SZA) from the pre-drift (2004–2008) to the EO-1 drift period (2011–2016). To detect changes in reflectance and shadows, we applied spectral mixture analysis (SMA) and principal component analysis (PCA) and calculated the similarity spectral angle (θ) between the vegetation spectra measured with variable SZA. The magnitude of the illumination effects was also evaluated from change-point analysis and nonparametric Mann-Whitney U tests applied over the time series. Finally, we complemented our experiment using the PROSAIL model to simulate the VIs variation with increasing SZA resultant from satellite drift. The results showed significant changes in Hyperion reflectance and VIs, especially when the EO-1 crossed the study area at earlier times and larger SZA in 2015 (9:05 a.m.; SZA = 59°) and 2016 (8:30 a.m.; SZA = 67°). Compared to the pre-drift period (10:30 a.m.; SZA = 45°), the SZA differences of 14° (2015) and 22° (2016) increased the shade fractions and decreased the vegetation brightness. PCA separated the pre-drift and drift reflectance datasets, showing shifts in scores due to changes in brightness. θ increased with SZA, indicating changes in the shape of the vegetation spectra with drift. For most VIs, the change-point analysis indicated 2015 (SZA = 59°) as the predominant year of detected changes. Compared to the EO-1 original orbit, the Plant Senescence Reflectance Index (PSRI), Anthocyanin Reflectance Index (ARI) and Structure Insensitive Pigment Index (SIPI) presented the largest positive changes during drift, while the Photochemical Reflectance Index (PRI), Visible Atmospherically Resistant Index (VARI) and Enhanced Vegetation Index (EVI) had the largest negative changes. The effect size of the illumination geometry on these VIs was large, as indicated by increasing values of the Cohen’s r metric toward 2016. The anisotropy of the Hyperion VIs was generally consistent with that from PROSAIL in the simulated pre-drift and drift periods. Focusing on structural indices, it affected the relationships between VIs and simulated leaf area index (LAI) at large SZA.     

GPS民用广播星历中ISC参数精度分析及其对导航定位的影响

2005年起发射的GPS Block IIR-M卫星在L2频率上新增了L2C民用信号,2010年起发射的Block IIF卫星新增了L5频率及L5I、L5Q两种民用信号。为此,GPS民用广播星历在原有时间群延迟(TGD)参数的基础上,新增了ISC_(C/A)、ISC_(L2C)、ISC_(L5I)及ISC_(L5Q)4种ISC(inter-signal correction)参数,以服务GPS实时导航定位用户。本文给出了ISC参数在GPS单/双频定位中的改正方法、利用不同机构提供的后处理差分码偏差(DCB)产品评估了ISC参数的实际精度、研究了ISC参数对GPS民用导航定位精度的影响。在明确TGD、ISC和DCB 3类参数之间区别与联系的基础上,本文研究表明:GPS广播的ISC_(C/A)参数精度可达0.2ns,TGD、ISCL2C及ISCL5Q参数精度可达0.5ns;以2014年连续11d全球12个MGEX(multi-GNSS experiment)站为例,经由ISC参数改正后,GPS L2C单频标准单点定位(SPP)的位置解精度提高了30.6%,L1C/A+L2C双频SPP的位置解精度提高了12.2%,该精度与L1P(Y)+L2P(Y)消电离层组合SPP的位置解精度相当。     

The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery

In geological imaging spectrometry (i.e., hyperspectral remote sensing), surface compositional information (e.g., mineralogy and subsequently chemistry) is obtained by statistical comparison (by means of spectral matching algorithms) of known field- or library spectra to unknown image spectra. Though these algorithms are readily used, little emphasis has been given to comparison of the performance of the various spectral matching algorithms. Four spectral measures are presented: three that calculate the angle (spectral angle measure, SAM), the vector distance (Euclidean distance measure, ED) or the vector cross-correlation (spectral correlation measure, SCM), between a known reference and unknown target spectrum and a fourth measure that measures the discrepancy of probability distributions between two pixel vectors (the spectral information divergence, SID). The performance of these spectral similarity measures is compared using synthetic hyperspectral and real (i.e., Airborne Visible Infrared Imaging Spectrometer, AVIRIS) hyperspectral data of a (artificial or real) hydrothermal alteration system characterised by the minerals alunite, kaolinite, montmorillonite and quartz. Two statistics are used to assess the performance of the spectral similarity measures: the probability of spectral discrimination (PSD) and the power of spectral discrimination (PWSD). The first relates to the ability of the selected set of spectral endmembers to map a target spectrum, whereas the second expresses the capability of a spectral measure to separate two classes relative to a reference class. Analysis of the synthetic data set (i.e., simulated alteration zones with crisp boundaries at 1–2 nm spectral resolution) shows that (1) the SID outperforms the classical empirical spectral matching techniques (SAM, SCM and ED), (2) that SCM (SID, SAM and ED do not) exploits the overall shape of the reflectance curve and hence its outcomes are (positively and negatively) affected by the spectral range selected, (3) SAM and ED give nearly similar results and (4) for the same reason as in (2), the SCM is also more sensitive (again in positive and negative sense) to the spectral noise added. Results from the study of AVIRIS data show that SAM yields more spectral confusion (i.e., class overlap) than SID and SCM. In turn, SID is more effective in mapping the four target minerals than SCM as it clearly outperforms SCM when the target mineral coincides with the mineral phase on the ground.