A Modified Hopfield Neural Network Algorithm (MHNNA) Using ALOS Image for Water Quality Mapping

Decreasing water pollution is a big problem in coastal waters. Coastal health of ecosystems can be affected by high concentrations of suspended sediment. In this work, a Modified Hopfield Neural Network Algorithm (MHNNA) was used with remote sensing imagery to classify the total suspended solids (TSS) concentrations in the waters of coastal Langkawi Island, Malaysia. The adopted remote sensing image is the Advanced Land Observation Satellite (ALOS) image acquired on 18 January 2010. Our modification allows the Hopfield neural network to convert and classify color satellite images. The samples were collected from the study area simultaneously with the acquiring of satellite imagery. The sample locations were determined using a handheld global positioning system (GPS). The TSS concentration measurements were conducted in a lab and used for validation (real data), classification, and accuracy assessments. Mapping was achieved by using the MHNNA to classify the concentrations according to their reflectance values in band 1, band 2, and band 3. The TSS map was color-coded for visual interpretation. The efficiency of the proposed algorithm was investigated by dividing the validation data into two groups. The first group was used as source samples for supervisor classification via the MHNNA. The second group was used to test the MHNNA efficiency. After mapping, the locations of the second group in the produced classes were detected. Next, the correlation coefficient (R) and root mean square error (RMSE) were calculated between the two groups, according to their corresponding locations in the classes. The MHNNA exhibited a higher R (0.977) and lower RMSE (2.887). In addition, we test the MHNNA with noise, where it proves its accuracy with noisy images over a range of noise levels. All results have been compared with a minimum distance classifier (Min-Dis). Therefore, TSS mapping of polluted water in the coastal Langkawi Island, Malaysia can be performed using the adopted MHNNA with remote sensing techniques (as based on ALOS images)

Modified Hopfield Neural Network Classification Algorithm For Satellite Images

Air adalah bahan yang penting bagi kehidupan mahkluk di atas muka bumi ini. Aktiviti manusia dan pengaruh alam semula jadi memberi kesan terhadap kualiti air, dan ia dianggap satu daripada masalah terbesar yang membelenggui kehidupan. Water is an essential material for living creatures. Human activities and natural influences have an effecting on water quality, and this is considered one of the largest problems facing living forms

Mathematical Models Selection on the Total Suspended Solid Mapping using Reflective Satellite Image Data

Ujung Pangkah Gresik is a reasonably dynamic area in the growth of turbidity levels in coastal beaches. As an area that has the estuary of the River Bengawan Solo, then Ujung Pangkah each year will experience the sedimentation process, one of which is the result of the movement of the river flow. This study aims to find the best mathematical model as an illustration of the total dynamics of the dissolved solids occurring in the area. The method used is a linear regression analysis of several selected models such as linear model, exponent, logarithm, polynomial degree 2, polynomial degree 3 and power model. The independent variable used in this research is the reflectance value of the Aqua Modis Level 2 from satellite imagery at wavelength 412 nm, 531 nm, and 645 nm.  The results obtained from this study are the ability of Aqua Modis satellite imagery in mapping the total suspended solids, besides that it can also be used to predict changes in the total value of suspended solids by calculating remote sensing algorithms that produce optimal mathematical models, where the model used is the polynomial model degree 3 and the logarithmic model based on choosing a high correlation value of the model that is 0.75 obtained at a wavelength of 645 nanometer

Application Of Digital Camera Data For Air Quality Detection.

Air pollution problem becomes increasingly critical in this present-day, whether in the developed or developing countries. Air management is one of the important issues in this 21st century. Malaysia is also affected by this problem

NPS MODELING OF SUNGAI PINANG CATCHMENT AREA & WATER QUALITY IMPROVEMENT BY USING BIORETENTION

One of the major problems facing the Department of Environment nowadays is the pollution of rivers with suspended solid coming from non-point source pollution in urban areas. This project is related to the improvement of water quality inside the river by applying the bioretention before the runoff from construction areas enters the river. It will involve the quality modeling by using specific software that was developed in Australia called the Model of Urban Stormwater Improvement Conceptualization (MUSIC) in order to predict the percentage removal ofTSS (Total Suspended Solid), TN (Total Nitrogen) and TP (Total Phosphorus). The other methods involved in this project which are data gathering, construct the physical model ofbioretention, laboratory and data analysis. From the predicted and laboratory analysis, bioretention can removes about 80-90% of TN, 80-85% ofTSS, and 70%-83% ofTP. The laboratory model was tested under Malaysia condition using soil in Seri Iskandar area The testing was based on column studies representing bioretention to improve the water quality of a stream in UTP campus. The predicted model represents the water quality of Sungai Pi nang in Penang. The performance of the laboratory model was comparable to the predicted model but the performance of laboratory model was slightly lower probably due to acclimatization period. The bioretention should be acclimatizing at least two months in order to achieve the good results

Spatial variability assessment of local chlorophyll-A estimation using satellite data

The estimation of Chlorophyll-a (Chl-a) for optically complex water from satellite is challenging. Moderate Resolution Imaging Spectroradiometer (MODIS) is an ocean colour satellite which has low spatial resolution and this has led to bias estimate and scale effect that eventually induced errors in Chl-a retrieval using local ocean colour algorithm. Studies on Chl-a variation, assessment of MODIS data and development of local ocean colour algorithm are less for Malacca Straits water. The aim of this study is to locally calibrate and validate the Chl-a derived from MODIS standard Chl-a algorithm (OC3M) on the latest R2013 data within the acceptable error tolerance at the Absolute Percentage Difference (APD) below 35% and to test the algorithm’s applicability. Iterative regression method with weighted function (WFd) namely Iterative Conditional Regression Model (ICRM) is introduced to reduce the spatial bias in the Chl-a estimate. Locally calibrated OC3M algorithm with in-situ data taken at two static stations and kernel 7×7 size named as OCms1 (calibrated with in-situ Case-1 water) and OCms2 (calibrated with in-situ Case-2 water) remarkably reduced the Chl-a bias with APD of 37% and 30% from 54% and 116% respectively. Then, using the ICRM, the APD of OCms1 WFd and OCms2 WFd is 26% and 29% respectively. Results of OCms WFd and OCms (with and without weighted function respectively) are combined for mapping the Chl-a in Case-1 and Case-2 waters. Result of applicability test and statistical analysis shows that OCms WFd ocean colour algorithm provides statistically highest accuracy for Chl-a estimation. The development of local Chl-a algorithm is essential for accurate Chl-a retrieval and it is significant to other marine studies such as in primary production and algal bloom in Malacca Strait water

NPS MODELING OF SUNGAI PINANG CATCHMENT AREA & WATER QUALITY IMPROVEMENT BY USING BIORETENTION

One of the major problems facing the Department of Environment nowadays is the pollution of rivers with suspended solid coming from non-point source pollution in urban areas. This project is related to the improvement of water quality inside the river by applying the bioretention before the runoff from construction areas enters the river. It will involve the quality modeling by using specific software that was developed in Australia called the Model of Urban Stormwater Improvement Conceptualization (MUSIC) in order to predict the percentage removal ofTSS (Total Suspended Solid), TN (Total Nitrogen) and TP (Total Phosphorus). The other methods involved in this project which are data gathering, construct the physical model ofbioretention, laboratory and data analysis. From the predicted and laboratory analysis, bioretention can removes about 80-90% of TN, 80-85% ofTSS, and 70%-83% ofTP. The laboratory model was tested under Malaysia condition using soil in Seri Iskandar area The testing was based on column studies representing bioretention to improve the water quality of a stream in UTP campus. The predicted model represents the water quality of Sungai Pi nang in Penang. The performance of the laboratory model was comparable to the predicted model but the performance of laboratory model was slightly lower probably due to acclimatization period. The bioretention should be acclimatizing at least two months in order to achieve the good results

Applicability of low-cost cameras for monitoring suspended sediment in rivers through close-range remote sensing

Suspended sediment in rivers is a major problem globally. Monitoring of water turbidity and suspended sediment concentration (SSC) using satellites and in-situ sampling has been used widely to assess fine sediment pollution. However, due to low image resolution, application of satellite remote sensing is limited to only large water bodies, while in-situ sampling does not provide the continuous spatial data that are needed to address certain scientific questions or management problems. This research aimed to understand the potential of using low-cost cameras to estimate SSC in smaller rivers and streams and produce reach scale ‘maps’ of SSC. The study consists of development and testing of statistical models to predict SSC from pixel information contained in digital images, and validation of these models through field tests. An overarching goal was to assess the transferability of models between rivers and the effects of different camera sensors on SSC predictions. Laboratory experiments developed predictive models for two cameras (Vivo V9 smartphone and DJI Mavic Pro drone). Experiments involved manipulation of SSC in a water filled tank, with images taken with each camera and over a different coloured bed at each controlled sediment concentration. Digital Number (DN) values for each bed colour, camera and colour channel combination was extracted, with Generalised Additive Models fitted to Red, Blue and Green (R, G, B) colour bands. In general, there were significant relations between SSC and the mean DN values, with G and B most frequently providing the best fits. Relations differed appreciably depending on bed characteristics, as a function of the relative colour of the bed and the material in suspension; some relations were direct (positive) and some indirect (negative). Thus, laboratory tests indicated that predictive relations need to be developed on a river-by-river basis due to differences in bed characteristics. There were some subtle differences between the two cameras, but in general both yielded images from which SSC could be predicted reliably in laboratory conditions. However, almost all relations broke down at very high SSCs depending on the bed colour, camera and colour channel combination; once the amount of fine material in suspension exceeded a certain threshold, SSC could not be predicted reliably from DN values. The field tests demonstrated that it is possible to produce accurate maps of SSC using an orthomosaic developed directly using DN values. These involved developing a calibration relationship for SSC v DN from images collected from drone flights at 30 m height above a reach of the Semenyih River, Malaysia. This relationship successfully predicted SSC, with the B colour band providing the best fit (R2 >0.86 for the observed v predicted). The SSC map was able to shed light on the influence of a tributary on main stem SSCs and patterns of mixing of the fine sediment delivered by the tributary. Such fine scale spatial patterns (1cm2/pixel) are evident neither from satellite data nor in-situ monitoring. The methods presented here are applicable to a variety of questions and contexts, from understanding downstream changes in SSC in glacial rivers to assessing effects of forest loss on SSC in tropical systems

Remotely sensed imagery data application in mangrove forest: a review

The mangrove forest ecosystem acts as a shield against the destructive tidal waves, preventing the coastal areas and other properties nearby from severe damages; this protective function certainly deserves attention from researchers to undertake further investigation and exploration. Mangrove forest provides different goods and services. The unique environmental factors affecting the growth of mangrove forest are as follows: distance from the sea or the estuary bank, frequency and duration of tidal inundation, salinity, and composition of the soil. These crucial factors may under certain circumstances turn into obstacles in accessing and managing the mangrove forest. One effective method to circumvent this shortcoming is by using remotely sensed imagery data, which offers a more accurate way of measuring the ecosystem and a more efficient tool of managing the mangrove forest. This paper attempts to review and discuss the usage of remotely sensed imagery data in mangrove forest management, and how they will improve the accuracy and precision in measuring the mangrove forest ecosystem. All types of measurements related to the mangrove forest ecosystem, such as detection of land cover changes, species distribution mapping and disaster observation should take advantage of the advanced technology; for example, adopting the digital image processing algorithm coupled with high-resolution image available nowadays. Thus, remote sensing is a highly efficient, low-cost and time-saving technique for mangrove forest measurement. The application of this technique will further add value to the mangrove forest and enhance its in-situ conservation and protection programmes in combating the effects of the rising sea level due to climate change