Mapping seagrass from satellite remote sensing data

This paper reviews some early results on a method adopted in mapping seagrass using Landsat-5 Thematic Mapper data. Seagrass information was extracted from satellite remotely sensed data using depth invariant index (DII) where the sea bottom features were expressed as index (i.e. each bottom type was represented by one index). DII was determined from radiance values recorded in band 1, 2 and 3 which taking into account the effect of water attenuation. Sea truth samples collected during the satellites overpass were used in calibrating DII and an independent accuracy assessment of information extracted

Simulation of shoreline change using AIRSAR and POLSAR C-band data

This paper presents a new approach for modeling shoreline change due to wave energy effects from remotely sensed data. The airborne AIRSAR and POLSAR data were employed to extract wave spectra information and integrate them with historical remotely sensed data such as aerial photography data to model the rate of change of the shoreline. A partial differential equation (PDE) of the wave conversion model was applied to investigate the wave refraction patterns. The volume of sediment transport at several locations was estimated based on the wave refraction patterns. The shoreline change model developed was designed to cover a 14-km stretch of shoreline of Kuala Terengganu in Peninsular Malaysia. The model utilized data from aerial photographs, AIRSAR, POLSAR, ERS-2, and in situ wave data. The results show that the shoreline rate of change modeled from the quasi-linear wave spectra algorithm has a significant relationship with one estimated from historical vector layers of aerial photography, AIRSAR, and POLSAR data. With the quasi-linear algorithm, an error of ±0.18 m/year in shoreline rate of change determination was obtained with Cvv band

Integration of Remote Sensing-GIS Techniques for Mapping and Monitoring Seagrass and Ocean Colour off Malaysian Coasts

This paper describes seagrass and ocean colour mapping off Peninsular Malaysia. The seagrass were extracted from visible bands of Landsat TM using the depth invariant index of the scabottom type. The ocean colour which much referred to plankton concentration is derived by regressing samples from known site collected at time of satellite overpass. Out these information were then input into GIS database which were also being established to assist the Marine Fisheries Management and Development Centre in managing and monitoring coastal areas This paper also addresses the experience gained in building spatial database for coastal areas various dala collected from various mapping environments were carried out

Spawning induction and larval rearing of the sea cucumber Holothuria scabra in Malaysia

The sea cucumber Holothuria scabra was induced to spawn by the methods of thermal and algal stimulation. Thermal stimulation proved to be the better method. The larvae were given a mix of microalgal diet, the concentration of which was based on the larval growth. Doliolaria larvae appeared 11 days after fertilisation, and then became pentactula 18 days after fertilisation. A survival rate of 4.2% was recorded from three successful spawning

Mapping of urban above-ground biomass with high resolution remote sensing data

This paper reports on study carried out to determine and map the distributions and density of the urban total above-ground biomass (TAGB) content using high resolution satellite data of the SPOT-4 and Quickbird, with respective 10 and 4 meter spatial resolution for mapping two levels of urban biomass, Level I biomass derived at selected residential areas in Johor Bahru city urban landscape, and Level II biomass derived from SPOT-4 data in for the entire urban district (including the suburbs). The results of this study indicated that, Level I and Level II of biomass were derived at respective accuracy of ±0.3kgm-2 and ±0.4kgm-2, validated with in-situ verification

Assessment of Impact on Landscape Development to Ecological Service Values and Goods Using Integrated Remote Sensing and GIS Techniques

Amongst the impacts of converting forest to agricultural activities is the degradation of ecology service values and goods (ESVG). Impacts on ESVG can be devastating in environmental, biological, and socio-economics manners. This paper highlights the study undertaken on the impacts of agricultural development in 0.8x106 ha of forest dominated landscape in Pasoh Forest Region (PFR), Malaysia, within period of 8 years from 1995 to 2003. Three folds of impacts on agricultural development examined and analysed are: (i) relationship of total soil loss and changes in land use pattern, (ii) mapping trends of ESVG for PFR in 1995 and 2003, and (iii) risk assessment of ESVG based on simulation of converting 339x103ha of primary forest into mass-scale oil palm plantation. Results of this study indicated that although only minor changes of about 1464ha (~0.2% of PFR) of primary forest was converted to agricultural activities, it have significantly increased the total soil loss from 59x106 to 69x106 t/ha/yr. The mean rate of soil loss within PFR is 0.8x106 t/yr, and if translated into ESVG term, costing US$4.8x106/yr. However, majority of the soil loss within all land use classes are within range of very low - low risk categories (<10 t/ha/yr). Estimated cost of ESVG for PFR was US$179x106 in 1995, declined to US$114x106 in 2003 due to 0.2$963x106 and US$575x106, respectively. This difference, however, is only marginal when full 17 attributes of ESVG were considered

Factors influencing antenatal mothers' choice of hospital for delivery at Hospiatl Universiti Sains Malaysia (HUSM) and Hospital Kota Bharu(HKB)

The selection of a hospital for delivery does not become an issue for women until they become pregnant. The main aim for promoting hospital delivery is to ensure safety to the mother and the newborn child. The main objective in this study is to determine the factors that influence antenatal mothers choice of hospitals for delivery at Hospital Universiti Sains Malaysia (HUSM) and Hospital Kota Bharu (HKB). The study was carried out in two phases. In phase one, a cross sectional study was conducted on 344 Malays, multiparty antenatal mothers who attended selected Maternal and Child Health Clinics (MCHC) in Kota Bharu district, from November 2003 to February 2004. Subjects were selected using two-stage sampling. Data were obtained using an interviewer guided, validated and piloted questionnaire. In order to ensure high quality of the interview, only one dedicated interviewer was involved. The questionnaire consists of a few domains namely socio-economic, accessibility, convenience, previous delivery experience, and interpersonal relationship with doctors and nurses, comfort of the patients and their relatives. The data were analyzed using logistic regression. Focus Group Discussions (FGD) were carried out in phase two in March 2004. FGD was carried out purposely to explore in depth the influencing factors, which cannot be explored through questionnaire. To fulfill this objective, 24 volunteered antenatal mothers were recruited in this phase after being consented and agreed to involve in this study. Four FGD sessions, each group consisted of six participants were conducted. Their responses were transcribed and analyzed based on the framework questions directed to them. The prevalence for choosing HUSM for delivery center was 38.0% and HKB 62.0°/o respectively. Based on the simple logistic regression, ten predictors variables namely health center, previous delivery hospital, distance to hospital, accessibility to hospital, good nursing care, short waiting hours, clean wards, children friendly (accept visitor under twelve) and fast admission to wads were significantly associated with the outcomes,. Among these only three factors remained significantly influenced when analyzed through multiple logistic regression. The final model was tested and it was found fit. The factors derived from the final model were previous delivery hospital, accessibility and children-friendly hospital. The findings in FGD support the model above and were able to extract the underlying facts. This study concludes previous delivery hospital; accessibility and children friendly hospital (hospital allows children under 12 years to visit their mothers in the wards) significantly influences the choice of hospital for delivery among antenatal mothers in Kota Bhe-ru distric

Advanced of Mathematics-Statistics Methods to Radar Calibration for Rainfall Estimation; A Review

Ground-based radar is known as one of the most important systems for precipitation measurement at high spatial and temporal resolutions. Radar data are recorded in digital manner and readily ingested to any statistical analyses. These measurements are subjected to specific calibration to eliminate systematic errors as well as minimizing the random errors, respectively. Since statistical methods are based on mathematics, they offer more precise results and easy interpretation with lower data detail. Although they have challenge to interpret due to their mathematical structure, but the accuracy of the conclusions and the interpretation of the output are appropriate. This article reviews the advanced methods in using the calibration of ground-based radar for forecasting meteorological events include two aspects: statistical techniques and data mining. Statistical techniques refer to empirical analyses such as regression, while data mining includes the Artificial Neural Network (ANN), data Kriging, Nearest Neighbour (NN), Decision Tree (DT) and fuzzy logic. The results show that Kriging is more applicable for interpolation. Regression methods are simple to use and data mining based on Artificial Intelligence is very precise. Thus, this review explores the characteristics of the statistical parameters in the field of radar applications and shows which parameters give the best results for undefined cases. DOI: 10.17762/ijritcc2321-8169.15012

Volterra algorithm for modelling sea surface current circulation from satellite altimetry data

This paper was utilized a new approach for modelling sea surface current from JASON-1 satellite altimetry data. This was based on utilizing of the Volterra series expansion in order to transform the time series satellite altimetry data into a real ocean surface current. Thus,the basic equation of hydrodynamic has been solved by second order Volterra model. Then, the Volterra kernel inversion used to obtain the sea surface current velocity. The finite element model of Lax-Wendorff schemes used which was based on triangular space-time elements to map the spatial current variation in the South China Sea over different monsoon periods. In situ sea surface current measurements were collected along the east coast of peninsular Malaysia by using electromagnetic current meters. The study shows that the maximum current magnitude of 1.2 m/s was occurred during the northeast monsoon period as compared to other monsoon periods. The main noticeable feature was an existence of anticlockwise eddy in the Gulf of Thailand. The results also shows a good correlation between in situ current measurements and the Volterra-Lax-Wendrof scheme with high R2 of 0.91. It can be said that Volterra-Lax-Wendrof scheme can be used as numerical scheme for modelling sea surface current from altimetry data

Hydrothermal alteration mapping of mineralogical imprints associated with subtle geothermal system using mixture tuned matched filtering approach on ASTER VNIR And SWIR data

The purpose of this study is to evaluate the applicability of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Visible near infrared (VNIR) and Shortwave infrared (SWIR) bands in discriminating hydrothermal alteration mineralogy related to thermal springs as proxy for identifying subtle Geothermal (GT) systems at Yankari Park in north eastern Nigeria. The area is characterized by a number of thermal springs including, Gwana, Dimmil, Mawulgo and Wikki which is used directly for recreation and tourism. A Decorrelation Stretch (DCS) transform was initially used on ASTER to highlight alteration zones and generate regions of interest (ROIs) which guided field validation and identification of associated exposed alteration zones. GPS field survey and sampling of hydrothermally altered rocks and laboratory analysis using Analytical Spectral Device (ASD) and X-Ray Diffraction (XRD) is conducted for verification. Observed and validated alteration sites (ROIs) are subsequently used to extract mean image spectra from the ASTER data. We then explored the utility of mean image spectra for mapping subtle mineralogical imprints associated to geothermal systems as proxy for identifying targets in unexplored regions by using the Mixture Tuned Match Filtering (MTMF) algorithm on ASTER VNIR to SWIR spectral subsets. The results indicate that ASTER data could reliably be used for prefeasibility stage narrowing of targets and mapping of subtle alterations using image derived spectra. These could have significant implications especially for mapping unconventional GT systems in unexplored regions