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.     

利用MODIS图像反演海岸与海岛的地物光谱反射率

提出一种利用MODIS图像,用查找表反演海岸与海岛地物光谱反射率的方法。该方法首先借助AHMAD辐射传输模型,由MODIS图像的水体像元反演出气溶胶的光学特性;在所选影像为晴空无云条件下,假设一定范围内的海岛与海岸上空的大气和水体上空的大气一样,借助6S辐射传输模型计算基于地物光谱反射率的查找表,然后由MODIS图像的陆地像元的反射率和几何条件加上反演的气溶胶光学厚度,用插值法可求得地物光谱反射率。还给出了厦门地区实际卫星图像的反演结果,并就反演误差进行了分析。     

Coastal Validation of Ultra-high Resolution Wind Vector Retrieval From QuikSCAT in the Gulf of Maine

An experimental 2.5-km ultrahigh-resolution (UHR) wind product provided by NASA's QuikSCAT scatterometer offers the potential for new access to coastal surface wind dynamics at the mesoscale level and below. To give future users the best indication of the value of these data, the UHR wind retrievals must be fully validated in nearshore areas. Comparison with meteorological buoys and standard QuikSCAT products allows detailed investigation of UHR winds. Speed and direction residuals are calculated between all scatterometer products and collocated buoys. An ambiguity selection routine improves wind direction agreement between the UHR winds and the other products. Magnitude residuals follow the patterns of the standard QuikSCAT winds, with a 1–2 m/s positive bias in light winds (below 4 m/s) and high winds (above 16 m/s) and standard deviations consistently below 3 m/s. After application of a land contamination removal algorithm, the UHR product provides extended coverage near the coast. An example of a specific wind event illustrates the potential benefits of improved resolution measurements for examining ocean–atmosphere dynamics.      

Assessment of satellite ocean color products of MERIS,MODIS and SeaWiFS along the East China Coast (in the Yellow Sea and East China Sea)

The validation of satellite ocean-color products is an important task of ocean-color missions. The uncertainties of these products are poorly quantified in the Yellow Sea (YS) and East China Sea (ECS), which are well known for their optical complexity and turbidity in terms of both oceanic and atmospheric optical properties. The objective of this paper is to evaluate the primary ocean-color products from three major ocean-color satellites, namely the Moderate Resolution Imaging Spectroradiometer (MODIS), Medium Resolution Imaging Spectrometer (MERIS), and Sea-viewing Wide Field-of-view Sensor (SeaWiFS). Through match-up analysis with in situ data, it is found that satellite retrievals of the spectral remote sensing reflectance R_rs(λ) at the blue-green and green bands from MERIS, MODIS and SeaWiFS have the lowest uncertainties with a median of the absolute percentage of difference (APD_m) of 15–27% and root-mean-square-error (RMS) of 0.0021–0.0039 sr⁻¹, whereas the R_rs(λ) uncertainty at 412 nm is the highest (APD_m 47–62%, RMS 0.0027–0.0041 sr⁻¹). The uncertainties of the aerosol optical thickness (AOT) τ_a, diffuse attenuation coefficient for downward irradiance at 490 nm K_d(490), concentrations of suspended particulate sediment concentration (SPM) and Chlorophyll a (Chl-a) were also quantified. It is demonstrated that with appropriate in-water algorithms specifically developed for turbid waters rather than the standard ones adopted in the operational satellite data processing chain, the uncertainties of satellite-derived properties of K_d(490), SPM, and Chl-a may decrease significantly to the level of 20–30%, which is true for the majority of the study area. This validation activity advocates for (1) the improvement of the atmosphere correction algorithms with the regional aerosol optical model, (2) switching to regional in-water algorithms over turbid coastal waters, and (3) continuous support of the dedicated in situ data collection effort for the validation task.     

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.     

Landscape Dynamics in North East Region of India (Meghalaya State) Using Spatial Decision Tree Model

Abstract

India has figured with two hotspots ‐ the Western Ghats and the Eastern Himalayas ‐ in an identification of 8 ‘hottest’ biodiversity hotspots (Myers et. al. 2000). The Meghalaya state (study area) in North Eastern India lies within the “Indo‐Burma” area, which is one of the 8 ‘hottest’ biodiversity hotspots. Timber extraction, the age‐old practice of shifting cultivation and mining has been a major cause of extensive changes in this landscape. Human induced disturbance differs from natural disturbance especially in extension, severity and frequency. Spatial presentation of landscape dynamics can be used to infer disturbance regimes horizontally. Disturbance regimes are mostly dominated by landuse practices in Meghalaya and these landuse practices are important contributors for overall interpretation of ecological processes operating within the landscape. In this scenario, the development of models to study landscape dynamics using remote sensing and GIS would be of great importance to ecologists. In this paper, we make an attempt to characterize landscape dynamics using a decision tree based approach. The varying impacts of human interventions reflected in three zones at landscape level have been brought out in the present study. The physiographic zones of Meghalaya (viz. Garo hills, Khasi hills and Jaintia hills) manifest different landscape characteristics and present varying degree of degradation status. Garo hills, experiences maximum shifting cultivation and has shown highest dynamism in the study area followed by Jaintia and Khasi hills. Characterization of landscape dynamics is important in the perspective of decision makers and policy makers in order to prioritize conservation strategies, so that urgent and necessary action can be taken.     

FVCOM与ROMS在美国东海岸洋流模拟的对比研究

ROMS与FVCOM是在美国东海地区应用较为普遍的两种海洋模型,常采用模拟粒子轨迹的手段解决沿海海洋问题。在实际应用前,对模型在洋流模拟上精度的验证是至关重要的。本文使用2004至2010年在美国东海岸部署的drifter,通过与模型模拟轨迹对比的方法对第三代FVCOM海洋模型(GOM3)与ROMS(GOMOFS)模型的表层洋流模拟能力进行评定,采用分离率与距离比两种精度指标,得出ROMS模型的表层洋流模拟能力优于FVCOM模型。     

海岛(礁)DEM成果质量检验关键技术研究

在人机交互质量检验技术的基础上,提出了解决海岛(礁)DEM成果质量检验问题的3个关键技术。首先,提出了目录组织模型用于质量检验的自动化检查方法,该模型支持对各个单位汇交的成果数据组织、数据命名和数据格式等方面进行检查,实现了对空间参照系、逻辑一致性等质量元素较为全面的检查,提高了大规模批量海岛(礁)DEM成果的检验效率;其次,提出了桩点法统计高程中误差,以取代外业现场打点检查DEM高程精度的检测方法,解决了海岛(礁)无法布设足够外业检查点的技术难题;最后,开发了质量等级评定工具,实现了DEM成果质量检验评价结果的电子化统一管理。通过对3000多幅海岛(礁)DEM成果质量检验的工作证实,该技术提升了质量检验能力和效率,保证了测绘成果检验的正确性和科学性。     

Application of Landsat data in determining the range biomass in Jodhpur district,Westren Rajasthan (India)

The range biomass in three different soil types of Jodhpur district has been estimated from the computer print out of Landsat imagery. The total biomass in the younger alluvial soil varies from 36.1 to 35910.0 kg; in pipar soils it ranges between 23.2 to 21541.2 kg, and in Chirai soils total biomass varies from 26.6 to 6852.7 kg.     

Artificial Neural Network (ANN) Based Inversion of Benthic Substrate Bottom Type and Bathymetry in Optically Shallow Waters - Initial Model Results

Ocean-colour remote sensing in optically shallow waters is influenced by contribution from the water column depth as well as by the substrate type. Therefore, it is required to include the contribution from the water column and substrate bottom type for bathymetry estimation. In this report we demonstrate the use of Artificial Neural Network (ANN) based approach to spectrally distinguish various benthic bottom types and estimate depth of substrate bottom simultaneously in optically shallow waters. We have used in-water radiative transfer simulation modeling to generate simulated top-of-the-water column reflectance the four major benthic bottom types viz. sea grass, coral sand, green algae and red algae using Hydrolight simulation model. The simulated remote sensing reflectance, for the four benthic bottom types having benthic bottom depth up to 30 m were generated for moderately clear waters. A multi-layer perceptron (MLP) type neural network was trained using the simulated data. ANN based approach was used for classification of the benthic bottom type and simultaneous inversion of bathymetry. Simulated data was inverted to yield benthic bottom type classification with an accuracy of ~98% for the four benthic substrate types and the substrate depth were estimated with an error of 0% for sea grass, 1% for coral sand and 1–3% for green and red algae up to 25 m, whereas for substrate bottom deeper than 25 m depth the classification errors increased by 2–5% for three substrate bottom types except sea grass bottom type. The initial results are promising which needs validation using the in-situ measured remote sensing reflectance spectra for implementing further on satellite data.     

Fluviomorphological investigations in the flood plain area of river satluj in the North East of Ludhiana (Punjab State) using photo-interpretation techniques

Various geomorph.ological features associated with fluvial processes of the river Satluj are mapped using photo-interpretation techniques with a view to assess the environmental status of the area in the north-east of Ludhiana (Punjab State). Features mapped include braided river channels, oxbow lakes and meander scrolls. The younger alluvium supports good vegetation and cultivation because of -shallow depth of water level; though prone to annual floods in the lower levels and major floods in the higher levels. It is saved from these hazards by providing bunding on both sides of river isolated sand deposit patches have been demarcated in the older alluvium. Shifting of the main river channel towards north is noticed as it has gradually shifted from its earlier course which lies towards south.     

Delineation of groundwater potential zone in hard rock terrain of India using remote sensing,geographical information system (GIS) and analytic hierarchy process (AHP) techniques

The present study has been undertaken to delineate the groundwater potential zones in the hard rock terrain of Palamu district, Jharkhand using the advanced applications of remote sensing, geographical information systems and analytic hierarchy process techniques. The integration and analyses of various thematic databases viz., geomorphology, lithology, soil, slope, lineament density, weathered zone thickness, drainage density and rainfall proved useful in the delineation of GWP zones. The study indicates that only 136?km² of the study area exhibit excellent groundwater potential, 248?km² has very good groundwater potential, whereas 36.89 and 38.23% are under poor and very poor groundwater potential zones, respectively. Hence, only a total of 11.6% of the area (490?km²) is classified as high to excellent groundwater potential. The final groundwater prospect map obtained was classified as excellent potential, very good potential, good potential, moderate potential, poor potential and very poor potential zone.     

Run-off potential indices of watersheds in Tilaiya Catchment,Bihar (India) through use of remote sensing and implementation of GIS

The present study was aimed at assessing the potential of remote sensing data in providing input to the SCS model developed by USDA and exploring the possibilities of improving the model through implementation of a GIS package. Subsequently, prioritization of watersheds in Tilaiya Catchment area (Bihar, India) based on their run-off potential indices was envisaged. The results demonstrated the capability of Landsat data in providing multithematic maps which could be used to provide input to the run-off model. Implementation of the GIS package enabled computation of run-off potential indices on pixed-by-pixel basis imparting physically distributed approach to the model.     

Integrating Remote Sensing Techniques,Geographical Information Systems (GIS), and Stochastic Models for Monitoring Land Use and Land Cover (LULC) Changes in the Northern Coastal Region of Nayarit,Mexico

Land use and land cover (LULC) changes in northern Nayarit, Mexico were estimated using post-classification change detection methods and a Markov chain model. Three thematic maps were generated by classifying Landsat images from 1973, 1900, and 2000, which were then overlaid to generate three change-detection matrices to assess the intensity and direction of changes. Between 25% and 30% of the region displayed LULC changes, attributable to a stochastic behavior that can be modeled with a first-order Markov chain. The steady-state distribution estimates indicate that the LULC patterns in the region have not yet reached equilibrium and predict the expansion of the agricultural boundaries.     

Assessment of groundwater potential zones in coal mining impacted hard-rock terrain of India by integrating geospatial and analytic hierarchy process (AHP) approach

The study aims at delineating groundwater potential zones using geospatial technology and analytical hierarchy process (AHP) techniques in mining impacted hard rock terrain of Ramgarh and part of Hazaribagh districts, Jharkhand, India. Relevant thematic layers were prepared and assigned weight based on Saaty’s 9-point scale and normalized by eigenvector technique of AHP to identify groundwater prospect in the study area. The weighted linear combination method was applied to prepare the groundwater potential index in geographic information system. Final groundwater prospects were classified as excellent, very good, good, moderate, poor and very poor groundwater potential zones. Study thus revealed that the excellent, very good and good groundwater potential zones, respectively, cover 148.3, 373.66 and 438.86 km² of the study area, whereas the poor groundwater potential zone covers 180.05 km². Validation was done through a receiver operating characteristic curve, which indicated that AHP had good prediction accuracy (AUC = 75.45%).     

Habitat Suitability Assessment of <Emphasis Type="Italic">Ardeotis nigriceps</Emphasis> (Vigors) in Great Indian Bustard Sanctuary,Maharashtra (India) Using Remote Sensing and GIS

Ardeotis nigriceps, commonly known as Great Indian Bustard (GIB), is a Critically Endangered, Evolutionary Distinct and Globally Threatened (EDGE) and endemic species to the Indian subcontinent. GIB is under tremendous threat in its last strongholds and sliding inextricably towards extinction. The GIB sanctuary in Maharashtra (India) is one of the last refuges of the bird constituting an area of 8496 km² spread over in seven talukas of Solapur and Ahemednagar districts. Major portion of the sanctuary (94.3 %) consists of privately owned lands under a variety of economic vocations and large number of villages and townships. In view of the legal restrictions relating to Protected Area under the Wildlife (Protection) Act of India 1972, the inhabitants of villages and townships faced a very difficult situation regarding use of their lands, development of properties and deriving benefits from planned local and regional development. This created conflict between local people and the forest department over the use of land, which necessitated the rationalization of the sanctuary. The objective of the present study was to map the suitable habitat of GIB in GIB Wildlife Sanctuary as an input for the realignment of the GIB Sanctuary by identifying areas that are important for the GIB. Main parameters considered for the habitat suitability assessments are, habit and habitat of GIB, slope, minimum patch size and disturbance sources. Based on the criteria derived for the ecological and biological requirements of GIB, binary deductive habitat suitability modeling has been done using remote sensing and GIS and prioritized the potential habitats of GIB. The net area of important suitable habitat of GIB in GIB sanctuary is 2304.99 km² out of 8496.44 km². The output of the present study has been used as an input by the committee (set by Honorable Supreme court of India) on rationalization of the GIB Sanctuary and the sanctuary has been rationalized with an area of 1222 km².     

Excel、Word与CASS(AutoCAD)在测量绘图中的应用

将常用的办公软件:Excel、Word等与CASS绘图软件相结合,提供了解决绘图中常见的批量标注、插值和参数计算等问题的方法,改变了传统的纯手式输入的方式,较好的提高了数据处理的效率和准确性。     

Using Advanced Land Imager (ALI) and Landsat Thematic Mapper (TM) for the Detection of the Invasive Shrub Lonicera maackii in Southwestern Ohio Forests

We tested how accurately image data from the Advanced Land Imager (ALI) sensor vs. the Landsat Thematic Mapper (TM) predict the land cover of Lonicera maackii in the forest understory, taking advantage of this invasive shrub's extended leaf retention in the fall when the canopy is leafless. Percent cover of L. maackii in 20 woodlots in southwestern Ohio was regressed on values for spectral vegetation indices (SVIs) derived for each image. The land cover of L. maackii was best explained by the Simple Ratio (SR) using TM data (R ² = 0.537). The regression results for SVIs from TM vs. ALI suggest that the ALI image was acquired too late in the fall to accurately detect this invasive shrub.     

Deformation analysis of the Lake Urmia causeway (LUC) embankments in northwest Iran: insights from multi-sensor interferometry synthetic aperture radar (InSAR) data and finite element modeling (FEM)

This paper presents deformation analysis of Lake Urmia causeway (LUC) embankments in northwest Iran using observations from interferometry synthetic aperture radar (InSAR) and finite element model (FEM) simulation. 58 SAR images including 10 ALOS, 30 Envisat and 18 TerraSAR-X are used to assess settlement of the embankments during 2003–2013. The interferometric dataset includes 140 differential interferograms which are processed using InSAR time series technique of small baseline subset approach. The results show a clear indication of large deformation on the embankments with peak amplitude of \(>\) 50 mm/year in 2003–2010, increasing to \(>\!\!80\)  mm/year in 2012–2013 in the line of sight (LOS) direction from ground to the satellite. 2D decomposition of InSAR observations from Envisat and ALOS satellites that overlap in the years 2007–2010 shows that the rate of the vertical settlement and horizontal motion is not uniform along the embankments; Both eastern and western embankments show significant vertical motion, while horizontal motion plays a more significant role in eastern embankment than western embankment. The InSAR results are then used to simulate deformation using FEM at two cross-sections at the distance of 4 and 9 km from the most western edge of the LUC for which detailed stratigraphy data are available. Results suggest that consolidation due to dissipation of excess pore pressure in embankments can satisfactory predict settlement of the LUC embankments. Our numerical modeling indicates that nearly half of the consolidation since the construction time of the causeway 30 years ago has been done.