ID: 59013
Title: Fast-Responder: Mobile Access to Remote Sensing for Disaster Response.
Author: Bryan G Talbot, Lisa M Talbot.
Editor: Russell G Congalton.
Year: 2013
Publisher: James R Plasker, American Society for Photogrammetry and Remote Sensing.
Source: Centre for Ecological Sciences
Reference: Photogrammetic Engineering & Remote Sensing Vol 79(no.10), pp. 945-954 (2013)
Subject: PE & RS Mobile Mapping for Disaster Relief
Keywords: Mobile Access, Remote Sensing, Disaster Response.
Abstract: Fast-Responder is a data dissemination concept and research prototype designed to meet the needs of first responders to access recent remote sensing information for disaster response. It achieves fast performance by unique system trade-offs centered on a core concept called Fast-Earth, a geospatially-organised cache. Whereas traditional databases store images by collection time, requiring long searches and slow downloads, Fast-Earth subdivides the earth into small regions called plats of fixed size enabling rapid look-up and download. Recent data can be indexed rapidly in about 10 minutes/terabyte. With the mobile application, FastRspondr, Smartphone users can use a rapid Fast-Earth look-up to discover layers of data, download small chips, and flicker multiple image layers in less than a minute. Our fast rubble detection algorithm applied at the server enhances mono-temporal post-event optical imagery in less than one second/plat. We demonstrate the end-to-end system with remote sensing data from the 2010 Haiti earthquake.
Location: TE 12 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59012
Title: Web-service-based Monitoring and Analysis of Global Agricultural Drought.
Author: Meixia Deng, Liping Di, Weiguo Han, Ali L. Yagci, Chunming Peng, Gil Heo.
Editor: Russell G Congalton.
Year: 2013
Publisher: James R Plasker, American Society for Photogrammetry and Remote Sensing.
Source: Centre for Ecological Sciences
Reference: Photogrammetic Engineering & Remote Sensing Vol 79(no.10), pp. 929-943 (2013)
Subject: PE & RS Mobile Mapping for Disaster Relief
Keywords: Global Agricultural Drought, real-time monitoring, spatial and temporal resolutions.
Abstract: It is of great importance and an urgent demand to enable operational and near real-time monitoring and analysis of global agricultural drought at desirable spatial and temporal resolutions. Traditional approaches and existing systems are not able to meet the demand because of big-data and geoprocessing modelling challenges. The latest advances in Web service, geospatial interoperability and cyberinfrastructure technologies and the availability of near real-time global remote sensing data have shown potential to address the challenges and meet the demand. This paper presents a Web service approach to building the Global Agricultural Drought Monitoring and Forecasting System (GADMFS), an open, interoperable, and on-demand geospatial Web service system for meeting the demand. The big-data and geoprocessing-modelling issues in providing complete agricultural drought information systems in the world and better support decision making with improved global agricultural drought data and information dissemination and analysis services.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59011
Title: Geospatial Web-based Sensor Information Model for Integrating Satellite Observation: An Example in the Field of Flood Disaster Management.
Author: Chuli Hu, Nengcheng Chen, Jia Li.
Editor: Russell G Congalton.
Year: 2013
Publisher: James R Plasker, American Society for Photogrammetry and Remote Sensing.
Source: Centre for Ecological Sciences
Reference: Photogrammetic Engineering & Remote Sensing Vol 79(no.10), pp. 915-927 (2013)
Subject: PE & RS Mobile Mapping for Disaster Relief
Keywords: Geospatial Web-based Sensor Information, Integrating Satellite Observation, Flood Disaster Management.
Abstract: Flood observation-supporting satellite sensors (FO-SSs) are the important geospatial resources to flood disaster management. In the geospatial sensor Web environment, effective flood disaster management requires on-demand integration of instant observation information from the diverse web-ready FO-SSs. We propose a sharable and interoperable Earth Observation Satellite Sensor information (SSI) model for satellite observations integration. The SSI model reuses and extends the existing metadata standards to be a standard metadata-filled description framework. The SensorModel prototype serving here is to apply the SSI model into FO-SSs, retrieve and visualize the qualified Web-ready FO-SSs. An experiment on the flood emergency in the middle reaches of Yangtze River basin in China is conducted. The results show that, three kinds of users including FO-SSs providers, common emergency responders, and emergency managers can reliably and comprehensively integrate the satisfied Web-ready FO-SSs to guide the scheduling of the required observation activities.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59010
Title: Geospatial Web Services for Responding to Ecological Risks Posed by Oil Spills.
Author: Anas Altartouri, Eva Ehrnsten, Inari Helle, Riikka, Venesjarvi, Ari Jolma.
Editor: Russell G Congalton.
Year: 2013
Publisher: James R Plasker, American Society for Photogrammetry and Remote Sensing.
Source: Centre for Ecological Sciences
Reference: Photogrammetic Engineering & Remote Sensing Vol 79(no.10), pp. 905-914 (2013)
Subject: PE & RS Mobile Mapping for Disaster Relief
Keywords: Geospatial Web Services, Ecological Risks, Oil Spills
Abstract: The increased maritime oil transportation raises the risk of marine oil spill accidents. An oil spill accident can cause severe harm to the ecosystem. Adequate contingency planning for oil spill and efficient combating operations require ecological data and knowledge to be integrated in tools that facilitate the decision making. A great deal of the decisions required during those operations require ecological data and knowledge to be integrated in tools that facilitate the decision making. A great deal of the decisions required during those operations is of spatial nature, such as defining areas with high priority for safeguarding. Also, real time decision making is required in the operations. All this calls for spatial and on-site accessible tools. We analyze and discuss in this paper geospatial Web services and an application developed for responding to the ecological risk posed by oil spills . The presented case study concerns the Gulf of Finland and the Finnish Archipelago Sea. The results indicate that geospatial services are an efficient method to deliver ecological knowledge and information for oil spill combating.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59009
Title: Stochastic gradient boosting classification trees for forest fuel types mapping through airborne laser scanning and IRS LISS-III imagery.
Author: G Chirici, R Scotti, A Montaghi, A Barbati, R Cartisano, G Lopez, M Marchetti, R E McRoberts, H Olsson, P Corona.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 87-97 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Airborne laser scanning, IRS LISS-III imagery, Forest fuel type mapping, Forest fuel type mapping, Forest fires, Mediterranean forests, Classification and regression trees.
Abstract: This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km2 and 487km2 in Sicily (Italy), 16,761 plots of size 30-m ? 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested : (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/ boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59008
Title: Characterization of the horizontal structure of the tropical forest canopy using object-based LiDAR and multispectral image analysis.
Author: Stephane Dupuy, Gerard Laine, Jacques Tassin, Jean-Michel Sarrailh.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 76-86 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: LiDAR, Multispectral, Tropical forest, OBIA, Canopy height, Horizontal structure.
Abstract: This article ' s goal is to explore the benefits of using Digital Surface Model (DSM) and Digital Terrain Model (DTM) derived from LiDAR acquisitions for characterizing horizontal structure of different facies in forested areas (primary forests vs secondary forests) within the framework of an object-oriented classification. The area under study is the island of Mayotte in the western Indian Ocean. The LiDAR data were the data originally acquired by an airborne small-footprint discrete-return LiDAR for the "Litto3D" coastline mapping project. They were used to create a Digital Elevation Model (DEM) at a spatial resolution of 1 m and a Digital Canopy Model (DCM) using median filtering. The use of two successive segmentation at different scales allowed us to adjust the segmentation parameters to the local structure of the landscape and of the cover. Working in object-oriented mode with LiDAR allowed us to discriminate six vegetation classes based on canopy height-transition co-occurrence matrix. Overall accuracy exceeds 90%. The resulting product is the first vegetation map of Mayotte which emphasizes the structure over the composition.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59007
Title: The assimilation of spectral sensing and the WOFOST model for the dynamics simulation of cadmium accumulation in rice tissues.
Author: Ling Wu, Xiangman Liu, Ping Wang, Botian Zhou, Meiling Liu, Xuqing Li.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 66-75 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Spectral information, Crop growth model, Assimilation, Cadmium accumulation, Dynamic simulation.
Abstract: The accurate detection of heavy metal-induced stress on crop growth is important for food security and agricultural, ecological and environmental protection. Spectral sensing offers an efficient and undamaged observation tool to monitor soil and vegetation contamination. This study proposed a methodology for dynamically estimating the total cadmium (Cd) accumulation in rice tissues by assimilating spectral information into WOFOST (World Food Study) model. Based on the differences among ground hyperspectral data of rice in three experiments fields under different Cd concentration levels, the spectral indicies MCARI1, NREP and RH were selected to reflect the rice stress condition and dry matter production of rice. With assimilating these sensitive spectral indices into the WOFOST + PROSPECT+ SAIL model to optimize the Cd pollution stress factor fwi, the dynamic dry matter production processes of rice were adjusted. Based on the relation between dry matter production and Cd accumulation, we dynamically simulating the Cd accumulation in rice tissues. The results showed that the method performed well in dynamically estimating the total amount of Cd accumulation in rice tissue with R2 over 85%. This study suggests that the proposed method of integrating the spectral information and the crop growth model could successfully dynamically simulate the Cd accumulation in rice tissues.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59006
Title: Remote sensing as a tool for monitoring plant invasions: Testing the effects of data resolution and image classification approach on the detection of a model plant species Heracleum mantegazzianum (gaint hogweed)
Author: Jana Mullerova, Jan Pergl, Petr Pysek.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 55-65 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Accuracy assessment, Historical aerial VHR photography, Invasion progress, Object based, Pixel-based classification, Rapid Eye.
Abstract: Plant invasions represent a threat not only to biodiversity and ecosystem functioning but also to the character of traditional landscapes. Despite the worldwide efforts to control and eradicate invasive species, their menace grows. New techniques enabling fast and precise monitoring and providing information on spatial structure of invasions are needed for efficient management strategies to be implemented. We present remote sensing assessment of noxious invasive species Heracleum mantegazzianum (gaint hogweed) that integrates different data sources, spatial and spectral resolution, and image processing techniques. Panchromatic, multispectral and color very high spatial resolution (VHR) aerial photography (1947-2006, resolution 0.5m) and medium spatial resolution satellilte data (Rapid Eye 2010, resolution 5 m) were analysed to assess their potential for hogweed monitoring by udsing pixel- (both supervised and unsupervised) and object-based image analysis (OBIA, automated hierarchical, iterative, and rule-based). Both point and grid based accuracy assessment was carried out. Described methods of object-based image analysis of VHR data enabled monitoring of hogweed at high classification accuracies measured by various means, regardless of the spectral resolution of the data provided that the data came from the species flowering period. Although the processed automated processing of VHR data is relatively time-effective and standardized, application over large areas would be rather demanding due to the size of datasets, and multispectral satellite data of medium spatial resolution (lower than the size of individuals) was therefore tested. On such imagery, only larger stands could be identified but still the pixel-based supervised classification achieved moderate accuracy. Depending on the size of the area of interest and the detail needed the very high or medium spatial resolution data (acquired at the species flowering period) are to be used. High accuracies achieved for VHR data indicate the possible application of described methodology for monitoring invasions and their long-term dynamics elsewhere, making management measures comparable precise, fast and efficient.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59005
Title: Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels.
Author: M Schlemmer, A Gitelson, J Schepers, R Ferguson, Y Peng, J Shanahan, D Rundquist.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 47-54 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Chlorophyll, Nitrogen, Reflectance, Remote sensing, Vegetation index.
Abstract: Leaf and canopy nitrogen (N) status relates strongly to leaf and canopy chlorophyll (Chl) content. Remote sensing is a tool that has the potential to assess N content at leaf, plant, field, regional and global scales. In this study, remote sensing techniques were applied to estimate N and Chl contents of irrigated maize (Zea mays L) fertilized at five N rates. Leaf N and Chl contents were determined using the red-edge chlorophyll index with R2 of 0.74 and 0.94, respectively. Results showed that the canopy level, Chl and N contents can be accurately retrieved using green and red-edge Chl indices using near infrared (780-800nm) and either green (540-560nm) or red-edge (730-750 nm) spectral brands. Spectral bands that were found optimal for Chl and N estimations coincide well with the red-edge band of the MSI sensor onboard the near future Sentinel-2 satellite. The coefficient of determination for the relationships between the red-edge chlorophyll index, simulated in Sentinel-2 bands, and Chl and N content was 0.90 and 0.87, respectively.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59004
Title: Retrieval of remotely sensed LAI using Landsat ETM+data and ground measurements of solar radiation and vegetation structure: Implication of leaf inclination angle.
Author: Pavel Propastin, Oleg Panferov.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 38-46 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Birch, Landsat ETM+, LAI, Radiative transfer model, Germany.
Abstract: A time series of leaf area index (LAI) of a managed birch forest in Germany (near Dresden) has been developed based on 16-day normalized difference vegetation index (NDVI) data from the Landsat ETM+ sensor at 30 m resolution. The Landsat ETM+ LAI was retrieved using a modified physical radiative transfer (RTM) model which establishes a relationship between LAI, fractional vegetation cover (fC), and gives patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. In situ measurements of photosynthetically active radiation (PAR) and vegetation structure parameters using hemispherical photography (HSP) served for calibration of model parameters, while data from litter collection at the study site provided the ground-based estimates of LAI for validation of modelling results. Influence of view- illumination conditions on optical properties of canopy was simulated by a view angle geometry model incorporating the solar zenith angle and the sensor viewing angle. Effects of intra- annual and inter-annual variabililty of structural properties of the canopy on the light extinction coefficients were simulated by implementing variability of the leaf inclination angle (LIA), which was confirmed in the study site. The results revealed good compatibility of the produced Landsat ETM+LAI data set with the litter-estimated LAI. The results also showed high sensitivity of the LAI retrieval algorithm to variability of structural properties of the canopy: the implementation of LIA dynamics into the LAI retrieval algorithm significantly improved the model accuracy.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59003
Title: Next generation of global land cover characterization, mapping, and monitoring.
Author: C Giri, B Pengra, J Long, T R Loveland.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 30-37 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Global land cover, Landsat, Image processing, Validation.
Abstract: Land cover change is increasingly affecting the biophysics, biogeochemistry, and biogeography of the earth ' s surface and the atmosphere, with far-reaching cosequences to human well being. However, our scientific understanding of the distribution and dynamics of land cover and land cover change (LCLCC) is limited. Previous global land cover assessments performed using coarse spatial resolution (300 m- 1 km) satellite data did not provide enough thematic detail or change information for global change studies and for management. High resolution (~30 m) land cover characterization and monitoring is needed that permits detection of land change at the scale of most human activity and offers the increased flexiblity of environmental model parameterization needed for global change studies. However, there are a number of challenges to overcome before producing such data sets including unavailability of consistent global coverage of satellite data, sheer volume of data, unavailability of timely and accurate training and validation data, difficulties in preparing image mosiacs, and high performance computing requirements. Integration of remote sensing and information technology is needed for process automation and high-performance computing needs. Recent developments in these areas have created on opportunity for operational high resolution land cover mapping and monitoring of the world. Here, we report and discuss these advancements and opportunities in producing the next generations of global land cover characterization, mapping and monitoring at 30-m spatial resolution primarily in the context of United States, Group on Earth Observations Global 30m land cover initiative (UGLC).
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59002
Title: Multi range spectral feature fitting for hyperspectral imagery in extracting oilseed rape planting area.
Author: Zhuokun Pan, Jingfeng Huang, Fumin Wang.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 21-29 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Hyperspectral imagery, Multi range spectral feature fitting, Oilseed rape, Field-measured spectra, Endmember.
Abstract: Spectral feature fitting (SFF) is a commonly used strategy for hyperspectral imagery analysis to discriminate ground targets. Compared to other image analysis techniques, SFF does not secure higher accuracy in extracting image information in all circumstances. Multi range spectral feature fitting (MRSFF) from ENVI software allows user to focus on those interesting spectral features to yield better performance. Thus spectral wavelength ranges and their corresponding weights must be determined. The purpose of this article is to demonstrate the performance of MRSFF in oilseed rape planting area extraction. A practical method for defining the weighted values, the variance coefficient weight method was proposed to set up criterion. Oilseed rape field canopy spectra from the whole growth stage were collected prior to investigating its phenological varieties; oilseed rape endmember spectra were extracted from the Hyperion image as identifying samples to be used in analyzing the oilseed rape field. Wavelength range divisions were determined by the difference between field-measured spectra and image spectra, and image spectral variance coefficient weights for each wavelength range were calculated corresponding to field-measured spectra from the closest date. By using MRSFF, wavelength ranges were classified to characterize the target ' s spectral features without compromising spectral profile ' s entirety. The analysis was substantially successful in extracting oilseed rape planting areas (RMSE ? 0.06), and the RMSE histogram indicated a superior result compared to a conventional SFF. Accuracy assessment web based on the mapping result compared with spectral angle mapping (SAM) and the normalized difference vegetation index (NDVI). The MRSF yielded a robust, convincible result and, therefore, may further the use of hyperspectral imagery in precision agriculture.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59001
Title: Developing a gross primary production model for coniferous forests of northeastern USA from MODIS data.
Author: Nasreen Jahan, Thian Yew Gan.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 11-20 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Coniferous forest, Gross primary production, MODIS, Land surface temperature, NIR albedo, Enhanced vegetation index, Global vegetation moisture index.
Abstract: Accurate estimation of ecosystem carbon fluxes is crucial for understanding the feedbacks between the terrestrial biosphere and the atmosphere and for making climate-policy decisions. A statistical model is developed to estimate the gross primary production (GPP) of coniferous forests of northeastern USA using remotely sensed (RS) radiation (land surface temperature and near-infrared albedo) and ecosystem variables (enhanced vegetation index and global vegetation moisture index) acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. This GPP model (called R-GPP-Coni), based only on remotely sensed data, was first calibrated with GPP estimates derived from the eddy covariance flux tower of the Howland forest west tower site, Duke pine forest and North Carolina loblolly pine site, which demonstrate its transferability to other coniferous ecoregions of northeastern USA. The proposed model captured the seasonal dynamics of the observed 8-day GPP successfully by explaining 84-94% of the observed variations with a root mean squared error (RMSE) ranging from 1.10 to 1.64g C/m2/day over the 4 study sites and outperformed the primary RS-based GPP algorithm of MODIS.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 59000
Title: Rectilinear building roof contour extraction basee on snakes and dynamic programming.
Author: Antonio Juliano Fazan, Aluir Porfirio Dal Poz.
Editor: F. van der Meer.
Year: 2013
Publisher: Elsevier.
Source: Centre for Ecological Sciences
Reference: International Journal of Applied Earth Observation and Geoinformation Vol 25, pp. 1-10 (2013)
Subject: International Journal of Applied Earth Observation and Geoinformation.
Keywords: Snakes, Dynamic programming, Building extraction, Image analysis.
Abstract: This paper presents a method for extracting building roof contours from digital images collected over urban landscapes. The proposed method utilizes an energy function based on snakes that represents building roof contours in digital images and is optimized with a dynamic programming (DP) algorithm. Because most building roof contours are characterised by rectilinear sides that intercept at right angles, appropriate geometric constraints are enforced in the previously reported snake-based energy function. The main advantage of using the DP algorithm for optimizing the proposed snake-based energy function is its better radius of convergence compared to that typically obtained in the original solution based on variational approaches. Experimental evaluation, which included visual inspections and numerical analyses, was performed using real data, and the obtained results demonstrated that the proposed method has significant potential for successfully extracting building roof contours from digital images.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None
ID: 58999
Title: Analysis of Fluoride Ion Concentration from Salt Pans to Marakkanam, Villupuram District, Tamil Nadu.
Author: R Sundarakumar, S Ponnusamy.
Editor: K P Sharma, P K Goel.
Year: 2013
Publisher: Tara P Goel, Technoscience Publications.
Source: Centre for Ecological Sciences
Reference: Nature Environment and Pollution Technology Vol 12(no 3), pp. 551-553 (2013)
Subject: Nature Environment and Pollution Technology
Keywords: Fluoride, Salt pans, Fluorosis, Marakkanam.
Abstract: Fluorine is and important trace element that forms fluoride compounds. Fluoride as salt is known for both positive and negative effects on human health. Deficiency of fluoride leads to dental caries and excess causes a severe disease called fluorosis. Intake of fluoride rich food is also responsible for fluorosis. WHO recommendation for the permissible limit of fluoride is 1.5 mg/L. The saline water and salt samples collected from Marakkanam, Villupuram District were studied with special reference to the fluoride content. The samples were drawn in the pre-monsoon period comprising eight days during May 2011. The findings revealed that the concentration of fluoride was between 0.9 mg/L and 1.4 mg/L, which is within the permissible limits. The concentration of fluoride ion increased in the salt water till the start of crystallization, which subsequently decreased with an increase in salt formation.
Location: TE 15 New Biology Building
Literature cited 1: None
Literature cited 2: None