星载激光雷达气溶胶数据辅助的近岸浑浊水体大气校正研究

提出了一种星载激光雷达CALIOP气溶胶数据辅助的MODIS/Aqua水色数据大气校正方法,并在长江口及其邻近浑浊水体区域进行了实验。通过与实测光谱数据的对比分析说明,本文方法能在一定程度上避免如近红外波段离水辐亮度为零的假设的不合理性,摆脱对实测数据的依赖,有效地反演离水辐亮度,修复SeaDAS软件中短波红外-近红外联合大气校正算法中短波红外波段引起的产品的条带问题。     

近海Ⅱ类水体的MODIS影像大气校正方法

利用光谱辐射传输理论对Gordon大气校正算法进行了修正,提出用解析的方法分离红外波段748nm和869 nm的离水辐射量与气溶胶的贡献,实现对近海Ⅱ类水体的大气校正,使之更适用于我国高悬浮泥沙和高气溶胶浓度海区。海上准同步实测数据表明,本文算法的校正精度比传统方法高,且能适用于影像中找不到理想Ⅰ类水体的情况,解决了近海水色数据大气校正问题。     

中国近岸浑浊水体大气修正的迭代与优化算法

中国近岸水体泥沙含量较高，且变化梯度大；泥沙浓度可从几千mg／l变化到1mg／l以下。针对上述浑浊二类水体的大气修正一直是水色遥感应用的难点之一。利用水体近红外固有光学特性的Arnone光谱迭代算法在泥沙含量较低时适用，其近红外光谱迭代关系与根据2003年春季黄东海水色试验数据得出的现场光谱关系大致相当。当泥沙含量达到某一程度时，该算法失效，导致蓝光等较短波段的离水辐亮度为负。根据现场数据分析结果，这一分界点大概在泥沙浓度10—20mg／l。因此，本文首次提出将中国近岸浑浊水体进一步区分为中低和中高浑浊水体，并给出初步的划分标准，采用光谱优化方法对中高浑浊水体进行水色大气修正。优化误差函数的选取以现场试验获取的可见光波段光谱关系式为基础。结果表明，优化算法在近岸高浑浊水体可给出满足光谱分布规律的反演结果。与其他大气校正方法一样，优化方法也需要进一步的微调。将Gordon标准算法、Arnone光谱迭代和优化方法结合，对SeaWiFS图像进行处理，分别得出归一化离水辐亮度和总悬浮物（TSM）浓度分布图像，结果令人满意。     

Phytoplankton Variability in the Bay of Bengal During Winter Monsoon Using Oceansat-1 Ocean Colour Monitor Data

Quantitative assessment of chlorophyll-a concentration and its variability is an important input for the oceanic primary productivity modeling and also a key parameter in the global carbon cycle studies. This present work is focused to understand the spatial and temporal variability of phytoplankton in the Bay of Bengal (BOB) during winter monsoon season of October 1999 to March 2000 using Ocean Colour Monitor (OCM) sensor onboard OCEANSAT-1 satellite. Daily chlorophyll-a images from OCM sensor were used in the study. Efforts were also put to study the correlation between chlorophyll-a concentrations; NOAA-AVHRR derived Sea Surface Temperature (SST) and QuickSCAT scatterometer derived wind stress data. Analysis of the chlorophyll-a images shows the presence of extensive phytoplankton blooms during mid December 1999 to early January 2000 in the western part of BOB. The bloom dominated regions also exhibit reduced SST (∼24–27°C) and enhanced wind stress indicating upwelling processes leading nutrient entrainment in the upper column of the sea surface. Apart from this, higher phytoplankton biomass associated with the fresh water reverine plumes has also been observed. During October 1999 a super cyclone was active in the BOB, as increase in the productivity was observed in the early November 1999 images of OCM data due to the cyclone induced churning of the water column.     

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.     

Validation of Chlorophyll-a Algorithms in the Coastal Waters of Bay of Bengal Initial Validation Results from OCM-2

In this paper we report chlorophyll measurements made during an ocean colour validation cruise in April 2011 of the research vessel, Sagar Paschimi in the coastal waters of Northern Bay of Bengal. The chlorophyll-a concentration in these waters range from 0.2 to 4.0 mg/m³. Chlorophyll-a concentration from OCM-2 was estimated using the global ocean colour algorithms namely, OC2, OC3, OC4 and Chl-a algorithms respectively. OCM data was processed using the global SeaWiFS Data Analysis System (SeaDAS) in which all the above mentioned algorithms are embedded for estimating the chlorophyll-a concentration. A comparative study was made between and in-situ and satellite derived chlorophyll-a concentration. Although the matchups between in-situ and satellite data from OCM-2 were sparse, it indicates that direct application of the standard SeaWiFS algorithm-the OC4-V4 algorithm—in the coastal waters of the Bay of Bengal will underestimate chlorophyll-a by up to 30%. The results show a good correlation with an R value of 0.61 using OC2 algorithm. However, all the other global algorithms over estimate the chlorophyll-a concentration even in low chlorophyll concentration range. The comparison between in-situ and all the existing chlorophyll algorithms shows the efficiency of these algorithms for quantification of chlorophyll in coastal waters and hence the need to develop regional algorithms and fluorescence based algorithms for better quantification.     

Comparison of Inherent Optical Properties (IOPs) in Open and Coastal Waters of Arabian Sea Using In-Situ Bio-Optical Data

The two main inherent optical properties (IOPs) namely absorption and back scattering coefficients were estimated using a quasi analytical algorithm (QAA) for open and coastal ocean waters of Arabian Sea. Absorption due to gelbstoff and back scattering due to the particulate matter were calculated using the quasi analytical algorithm for all the in-situ measured reflectance spectra collected in the Arabian Sea. A comparative study was made to study the spectral variability of reflectance spectra in open as well as coastal waters of Arabian Sea. Spectral analysis was made for the absorption and back scattering coefficients calculated using the QAA for both open and coastal waters. The absorption coefficient in the open ocean waters vary from a minimum value of 0.029 to a maximum value of 0.445 and it varies from a minimum value of 0.081 to a maximum value of 4.000 for the coastal waters of Arabian Sea. Absorption due to gelbstoff or the CDOM a_g(λ), calculated for the Arabian Sea waters show a variation of 0.000202 to 0.112437 for open ocean waters and it varies from 0.002848 to 2.8936 for coastal waters of Arabian Sea. Particulate back scattering coefficient for open ocean waters vary from 0.0000307 to 0.006575 whereas b_bp(λ) vary from 0.000167 to 0.026014 for coastal ocean waters. The minimum slope for the open ocean waters is 0.989 and maximum value of 2.147 (average value of 1.7) was observed; whereas a minimum value of 0.046 and a maximum value of 1.201 (average value of 0.6) were observed from the in-situ spectra for coastal waters of Veraval. The slope ‘Y’ estimated from the model is 1.957 for open ocean waters and 0.515 for coastal waters collected in the Arabian Sea.     

Variability of Total Suspended Matter in the Northern Coastal Bay of Bengal as Observed from Satellite Data

The coastal regions of northern Bay of Bengal (BoB) are one of the most turbid areas owing to the large freshwater discharge from the three major river systems. This study is conducted to investigate the seasonal and interannual variability of total suspended matter (TSM) in the northern BoB. TSM concentration derived from medium resolution imaging spectrometer for the period 2002–2011 is used for this study. TSM concentration is observed to be the highest during summer monsoon season along the north-eastern region, off the Ganges–Brahmaputra river mouth. The variability of the TSM concentration depicts the role of river runoff associated with the summer monsoon in influencing the same. It is observed that the sediment concentration tapers away towards the offshore regions with the maximum extent observed up to 21.5°N latitude. Interannual variability is also observed with highest TSM concentrations occurring in the years 2003, 2008 and 2011 and least during 2004. Time series analysis performed at three major river discharge regions illustrated the distinct and highly variable nature of TSM dynamics prevailing in the northern BoB.     

Total Suspended Sediments Mapping by Using ALOS Imagery Over the Coastal Waters of Langkawi Island, Malaysia

Remote sensing has been extensively used for water delineation and has played an important role in water quality evaluation and environmental management strategies. Suspended sediments are important determinants of water quality in coastal zones. Remote sensing enables the effective monitoring of total suspended sediments (TSS) and the detection of areas with critical water quality issues. This study aims to develop and implement regression models for estimating and mapping TSS concentrations from Advanced Land Observation Satellite (ALOS) images over the coastal waters of Langkawi Island, Malaysia. The algorithm was developed based on the water reflectance model, which is a function of the inherent optical properties of water. Such properties can then be linked to TSS concentration. In this study, an ALOS Advanced Visible and Near Infrared Radiometer type 2 device was used as the imaging sensor system. Concurrent complementary in-situ water samples were collected within the area coverage of the sensor, and digital numbers (DN) for each band corresponding to the sea-truth locations were determined. The extracted DN values were converted into reflectance values and then regressed with their respective sea-truth data. An algorithm was proposed to obtain the regression coefficient. This algorithm can estimate TSS concentrations with a high correlation coefficient (R²?=?0.96) and low root-mean-square error (RMSE?=?1.98 mg/l). Finally, a map of the TSS concentration was generated by using the proposed algorithm. This study found that TSS mapping can be conducted by using ALOS data over the coastal waters of Langkawi Island, Malaysia.     

Retrieval and Validation of Chlorophyll-a Concentrations in the Coastal Waters Off Yanam and Kakinada (Godavari) Basin Along East Coast of India

In this study chlorophyll measurements were made during March 2012 in the estuarine waters of Off Kakinada and Yanam coast, Bay of Bengal onboard a coastal vessel. In-situ water samples and optical data was collected at 21 stations (surface to 150 m depth) using Underwater radiometer (Hyperpro-II). In-vivo chlorophyll profiles were collected using wet labs fluorometer integrated with underwater Hyperspectral radiometer. Chlorophyll-a concentrations were estimated using HPLC by collecting the water samples at each sampling location. And also chlorophyll-a concentrations were retrieved from the OCM-2 data of OCEANSAT-2 satellite, processed using SeaDAS v.6.2 with the available global ocean colour algorithms namely, OC2 and OC4V4. A total of 33 samples used covering all the stations for chlorophyll-a estimation, and surface water samples of all the stations only being used for direct comparison among chlorophyll concentrations of HPLC, in-situ (fluorometrically integrated to Hyperpro-II) and retrieved from OCM-2. A good correlation found between the Fluorometer derived and HPLC measured chlorophyll-a concentration with an R² value of 0.78. The relation between Chlorophyll-a concentration measured from HPLC and retrieved from OCM-2 (OC2 and OC4V4 algorithms) using SeaDASv.6.2 for 10 samples has been compared for validation and obtained an R² value of 0.6. Also comparisons done with the in-situ measured (fluorometer) Chlorophyll-a concentration with OCM-2 chlorophyll data (OC4-V4 and OC2 algorithms) and validation with 10 concurrent in-situ surface measurements showed a significant overestimation by OCM-2 at low chlorophyll-a concentrations and underestimation at high chlorophyll-a concentrations.     

利用栅格扫描数据进行等高线的自动成组综合(化简)

本文着重讨论了栅格数据经自动预处理后,让电子计算机模拟人工制图综合,采用二维空间的曲率比较,寻找等高线上的特征点、筛选谷地点、跟踪谷底线、区分谷地重要性、量测谷间距、判断谷地的取舍,进而实现等高线谷地弯曲的成组概括等问题,同时给出了初步试验的结果。     

Spatial Analysis of Terrain-Impacted Photosynthetic Active Radiation (PAR) Using MODIS Data

This paper presents the spatial distribution of photosynthetic active radiation (PAR) using an atmospheric radiative transfer model under terrain impacts. The potential of the model for improved understanding of PAR impacts on ecosystem dynamics is demonstrated with the aid of MODIS (Moderate-resolution Imaging Spectroradiometer) data, using the Haihe Plain of China as a case study. Various types of MODIS data were aggregated in support of the modeling work at the regional scale. Findings clearly indicate that terrain factors do affect the estimation of instantaneous PAR significantly. Different relationships between PAR and elevation may be classified based on positive correlation, negative correlation, and no correlation. In addition, while there are different relations between PAR and slope, there is no significant correlation between PAR and aspect.     

Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model

The main objective of this paper is to analyze urban sprawl in the metropolitan city of Tripoli, Libya. Logistic regression model is used in modeling urban expansion patterns, and in investigating the relationship between urban sprawl and various driving forces. The 11 factors that influence urban sprawl occurrence used in this research are the distances to main active economic centers, to a central business district, to the nearest urbanized area, to educational area, to roads, and to urbanized areas; easting and northing coordinates; slope; restricted area; and population density. These factors were extracted from various existing maps and remotely sensed data. Subsequently, logistic regression coefficient of each factor is computed in the calibration phase using data from 1984 to 2002. Additionally, data from 2002 to 2010 were used in the validation. The validation of the logistic regression model was conducted using the relative operating characteristic (ROC) method. The validation result indicated 0.86 accuracy rate. Finally, the urban sprawl probability map was generated to estimate six scenarios of urban patterns for 2020 and 2025. The results indicated that the logistic regression model is effective in explaining urban expansion driving factors, their behaviors, and urban pattern formation. The logistic regression model has limitations in temporal dynamic analysis used in urban analysis studies. Thus, an integration of the logistic regression model with estimation and allocation techniques can be used to estimate and to locate urban land demands for a deeper understanding of future urban patterns.     

Support Vector Machine (SVM) Classification: Comparison of Linkage Techniques Using a Clustering-Based Method for Training Data Selection

Support vectors, which usually compose a subset of training sets, determine the decision function of support vector machine (SVM) classification. Selecting a subset including the support vectors through reducing a large training set is a challenge. This paper examines how different linkage techniques in a clustering-based reduction method affect classification accuracy for semiarid vegetation mapping. The investigated linkage techniques include single, complete, weighted pairgroup average, and unweighted pair-group average. Using a multiple-angle remote sensing data set, there is no loss of SVM accuracy when the original training set is reduced to 21%, 14%, 20%, and 20% for these four linkage techniques, respectively.     

利用车载动态PPP技术检核SRTM、ASTER GDEM和AW3D模型的高程精度

SRTM、ASTER GDEM和AW3D是比较有代表性的全球数字高程模型。本文探讨了采用车载动态PPP技术对上述3类模型的区域高程精度进行检核,首先沿广州至肇庆公路进行连续数据采集,采用动态精密单点定位（PPP）技术解算动态点的WGS-84坐标;然后利用EGM2008重力场模型和仪器高获得动态点的正常高;最后采用4种不同的插值方法对SRTM、ASTER GDEM和AW3D模型进行高程检验。检核结果显示：不同的插值方法具有较好的一致性,SRTM3 V4.1、ASTER GDEM V2、AW3D30的高程标准差分别优于3.4 m、4.1 m和3.3 m,均优于其全球标称高程精度;本文检核方法快速高效,有较好的适用性。     

Impact of Climate Change on Runoff of the Major River Basins of India Using Global Circulation Model (HadCM3) Projected Data

The effects of climate change on hydrological regimes have become a priority area for water and catchment management strategies. The terrestrial hydrology driven by monsoon rainfall plays a crucial role in shaping the agriculture, surface and ground water scenario in India. Thus, it is imperative to assess the impact of the changing climatic scenario projected under various climate change scenario towards the hydrological aspects for India. Runoff is one of the key parameters used as an indicator of hydrological process. A study was taken up to analyse the climate change impact on the runoff of river basins of India. The global circulation model output of Hadley centre (HADCM3) projected climate change data was used. Scenario for 2080 (A2 scenario indicating more industrial growth) was selected. The runoff was modeled using the curve number method in spatial domain using satellite derived current landuse/cover map. The derived runoff was compared with the runoff using normal climatic data (1951–1980). The results showed that there is a decline in the future climatic runoff in most of the river basins of India compared to normal climatic runoff. However, significant reduction was observed for the river basins in the eastern region viz: lower part of Ganga, Bahamani-Baitrani, Subarnrekha and upper parts of the Mahanadi. The mean projected runoff reduction during monsoon season (June–September) were 18 Billion Cubic Meter (BCM), 3.2 BCM, 3.5 BCM and 5.9 BCM for Brahmaputra-Barak Subarnrekha, Subarnarekha and Brahmini-Baitrani basin, respectively in comparison to normal climatic runoff. Overall reduction in seasonal runoff was high for Subarnrekha basin (54.1%). Rainfall to runoff conversion was high for Brahmaputra-Barak basin (72%), whereas coefficient of variation for runoff was more for Mahanadi basin (1.88) considering the monsoon season. Study indicates that eastern India agriculture may be affected due to shortage of surface water availability.     

A Method to Upscale the Leaf Area Index (LAI) Using GF-1 Data with the Assistance of MODIS Products in the Poyang Lake Watershed

A leaf area index is a key parameter reflecting the growth changes of vegetation and one of the most important canopy structural parameters for performing quantitative analyses of many ecological and climate models. Although using high-resolution satellite data and the radiative transfer model (RTM) can be used to generate high resolution LAI products, the RTM method has some problems because its temporal resolution is low, the input parameters are more appropriate for a physics model, and some parameters are difficult to obtain. Problems that urgently need to be solved include improving the temporal-spatial resolution for LAI products and localizing LAI products. To explore an applicable method for the high-resolution LAI products in a small basin and to improve the inversion accuracy, we propose an approach for GF-1 WFV LAI retrieval using MOD15A2 data and the measured LAI of the Poyang Lake watershed. Empirical models were used to retrieve high resolution LAI values, and the results show that these models are well designed for analyzing time-series satellite data. Good correlations were obtained between the NDVI of the GF-1 WFV data, the retrieved LAI values and the MODIS LAI data from samples acquired in both summer and winter. The exponential NDVI model obtained the best LAI value estimation results from the GF-1 WFV data (R2 = 0.697, RMSE = 1.100); the best synthetic validation of the RMSE is 0.883, close to the optimum model. Therefore, the retrieval results more fully reflect the growth process of the different features. This study proposed an upscale method for developing a high spatial resolution GF-1 satellite standard LAI products retrieval model using MODIS data. The proposed method will be helpful for efficiently improving the temporal-spatial resolution of LAI products to benefit the extraction of vegetation parameter information and dynamic land use monitoring.     

Identification of Loriya Fault,Its Reactivation Due to 2001 Bhuj Earthquake, (Using Remote Sensing Data) and Its Bearing on the Kachchh Mainland Fault,Kachchh District,Gujarat

The 2001 Bhuj earthquake (M_w 7.7), one of the most severe earthquakes in the recent history of India, reactivated various existing active faults. It is manifested in the form of coseismic ground fissures/cracks and upheaval of land in the form of bumps. Identification and reactivation of Loriya Fault is established by 1—Geomorphic changes with the help of digital imagery (LISS III images). 2—Coseismic changes through ground checks and 3—Geophysical signatures through magnetic and gravity survey. A lineament cutting the north-western part of the Pur River alluvial fan has been revealed by satellite imagery. The streams flowing along the lineament add to the evidences of a weak plane, while the occurrence of coseismic ground fissures confirms the existence of an active fault. No dip slip movement is recorded in the trenches made across the Loriya active fault while the en-echelon pattern of ground fissures suggest strike slip movement along the fault due to 2001 earthquake.     

Landslide Hazard Assessment Using Random SubSpace Fuzzy Rules Based Classifier Ensemble and Probability Analysis of Rainfall Data: A Case Study at Mu Cang Chai District,Yen Bai Province (Viet Nam)

Landslide hazard assessment at the Mu Cang Chai district; Yen Bai province (Viet Nam) has been done using Random SubSpace fuzzy rules based Classifier Ensemble (RSSCE) method and probability analysis of rainfall data. RSSCE which is a novel classifier ensemble method has been applied to predict spatially landslide occurrences in the area. Prediction of temporally landslide occurrences in the present study has been done using rainfall data for the period 2008–2013. A total of fifteen landslide influencing factors namely slope, aspect, curvature, plan curvature, profile curvature, elevation, land use, lithology, rainfall, distance to faults, fault density, distance to roads, road density, distance to rivers, and river density have been utilized. The result of the analysis shows that RSSCE and probability analysis of rainfall data are promising methods for landslide hazard assessment. Finally, landslide hazard map has been generated by integrating spatial prediction and temporal probability analysis of landslides for the land use planning and landslide hazard management.