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MODIS land surface temperature data (MODIS Ts) products are quantified from the earth surface’s reflected thermal infrared signal via sensors onboard the Terra and Aqua satellites. MODIS Ts products are a great value to many environmental applications but often subject to discrepancies when compared to the air temperature (Ta) data that represent the temperature measured at 2 m above the ground surface. Although they are different in their nature, the relationship between Ts and Ta has been established by many researchers. Further validation and correction on the relationship between these two has enabled the estimation of Ta from MODIS Ts products in order to overcome the limitation of Ta that can only provide data in a point form with a very limited area coverage. Therefore, this study was conducted with the objective to assess the accuracy of MODIS Ts products, i.e., MOD11A1, MOD11A2, MYD11A1, and MYD11A2 against Ta and to identify the performance of a modified Linear Scaling using a constant and monthly correction factor (LS-MBC), and Quantile Mapping Mean Bias Correction (QM-MBC) methods for lowland area of Peninsular Malaysia. Furthermore, the correction factor (CF) values for each MBC were adjusted according to the condition set depending on the different bias levels. Then, the performance of the pre- and post-MBC correction for by stations and regions analysis were evaluated through root mean square error (RMSE), percentage bias (PBIAS), mean absolute error (MAE), and correlation coefficient (r). The region dataset is obtained by stacking the air temperature (Ta_r) and surface temperature (Ts_r) data corresponding to the number of stations within the identified regions. The assessment of pre-MBC data for both 36 stations and 5 regions demonstrated poor correspondence with high average errors and percentage biases, i.e., RMSE = 3.33–5.42 °C, PBIAS = 1.36–12.07%, MAE = 2.88–4.89 °C, and r = 0.16–0.29. The application of the MBCs has successfully reduced the errors and bias percentages, and slightly increased the r values for all MODIS Ts products. All post-MBC depicted good average accuracies (RMSE and MAE < 3 °C and PBIAS between ±5%) and r between 0.18 and 0.31. In detail, for the station analysis, the LS-MBC using monthly CF recorded better performance than the LS-MBC using constant CF or the QM-MBC. For the regional study, the QM-MBC outperformed the others. This study illustrated that the proposed LS-MBC, in spite of its simplicity, managed to perform well in reducing the error and bias terms of MODIS Ts as much as the performance of the more complex QM-MBC method.
Nurul Bahari; Farrah Muharam; Zed Zulkafli; Norida Mazlan; Nor Husin. Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia. Remote Sensing 2021, 13, 2589 .
AMA StyleNurul Bahari, Farrah Muharam, Zed Zulkafli, Norida Mazlan, Nor Husin. Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia. Remote Sensing. 2021; 13 (13):2589.
Chicago/Turabian StyleNurul Bahari; Farrah Muharam; Zed Zulkafli; Norida Mazlan; Nor Husin. 2021. "Modified Linear Scaling and Quantile Mapping Mean Bias Correction of MODIS Land Surface Temperature for Surface Air Temperature Estimation for the Lowland Areas of Peninsular Malaysia." Remote Sensing 13, no. 13: 2589.
Tuberculosis (TB) cases have increased drastically over the last two decades and it remains as one of the deadliest infectious diseases in Malaysia. This cross-sectional study aimed to establish the spatial distribution of TB cases and its association with the sociodemographic and environmental factors in the Gombak district. The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of origin, educational level, employment status, health care worker status, income status, residency, and smoking status from 1st January 2013 to 31st December 2017 in Gombak district were collected from the MyTB web and Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic. Environmental data consisting of air pollution such as air quality index (AQI), carbon monoxide (CO), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter 10 (PM10,) were obtained from the Department of Environment Malaysia from 1st July 2012 to 31st December 2017; whereas weather data such as rainfall were obtained from the Department of Irrigation and Drainage Malaysia and relative humidity, temperature, wind speed, and atmospheric pressure were obtained from the Malaysia Meteorological Department in the same period. Global Moran’s I, kernel density estimation, Getis-Ord Gi* statistics, and heat maps were applied to identify the spatial pattern of TB cases. Ordinary least squares (OLS) and geographically weighted regression (GWR) models were used to determine the spatial association of sociodemographic and environmental factors with the TB cases. Spatial autocorrelation analysis indicated that the cases was clustered (p<0.05) over the five-year period and year 2016 and 2017 while random pattern (p>0.05) was observed from year 2013 to 2015. Kernel density estimation identified the high-density regions while Getis-Ord Gi* statistics observed hotspot locations, whereby consistently located in the southwestern part of the study area. This could be attributed to the overcrowding of inmates in the Sungai Buloh prison located there. Sociodemographic factors such as gender, nationality, employment status, health care worker status, income status, residency, and smoking status as well as; environmental factors such as AQI (lag 1), CO (lag 2), NO2 (lag 2), SO2 (lag 1), PM10 (lag 5), rainfall (lag 2), relative humidity (lag 4), temperature (lag 2), wind speed (lag 4), and atmospheric pressure (lag 6) were associated with TB cases (p<0.05). The GWR model based on the environmental factors i.e. GWR2 was the best model to determine the spatial distribution of TB cases based on the highest R2 value i.e. 0.98. The maps of estimated local coefficients in GWR models confirmed that the effects of sociodemographic and environmental factors on TB cases spatially varied. This study highlighted the importance of spatial analysis to identify areas with a high TB burden based on its associated factors, which further helps in improving targeted surveillance.
Nur Adibah Mohidem; Malina Osman; Zailina Hashim; Farrah Melissa Muharam; Saliza Mohd Elias; Rafiza Shaharudin. Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia. PLOS ONE 2021, 16, e0252146 .
AMA StyleNur Adibah Mohidem, Malina Osman, Zailina Hashim, Farrah Melissa Muharam, Saliza Mohd Elias, Rafiza Shaharudin. Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia. PLOS ONE. 2021; 16 (6):e0252146.
Chicago/Turabian StyleNur Adibah Mohidem; Malina Osman; Zailina Hashim; Farrah Melissa Muharam; Saliza Mohd Elias; Rafiza Shaharudin. 2021. "Association of sociodemographic and environmental factors with spatial distribution of tuberculosis cases in Gombak, Selangor, Malaysia." PLOS ONE 16, no. 6: e0252146.
In the past decade, the inevitable increase in temperature has caused Malaysia to experience more extreme heat events, and yet very little research has been dedicated in exploring the heat-related vulnerability of exposed population. In this study, the extreme heat vulnerability index (EHVI) has been evaluated to identify the most vulnerable districts to extreme heat events. We evaluated exposure, population sensitivity and adaptive capacity from sociodemographic and remote sensing data. We have applied multivariate analysis on 13 indicators for every 87 districts to elucidate the extreme heat vulnerability in Peninsular Malaysia. The EHVI was generated by summing up the normalized extreme heat exposure scores and factor scores from the multivariate analysis. Our findings clarify that the most vulnerable populations are confined in the urban and northern region of Peninsular Malaysia. The source of vulnerability varied between both regions, with urbanization and population density increase the vulnerability in urban areas, high heat exposure and sensitive population are the dominant factors of vulnerability in the northern region. These findings are valuable in identifying districts vulnerable to extreme heat and help regulatory body; in designing effective adaptation and preparedness strategies to increase the population resilience towards extreme heat.
Nurfatin Izzati Ahmad Kamal; Zulfa Hanan Ash'Aari; Ahmad Makmom Abdullah; Faradiella Mohd Kusin; Ferdaus Mohamat Yusuff; Amir Hamzah Sharaai; Farrah Melissa Muharam; Noor Afiza Mohd Ariffin. Extreme heat vulnerability assessment in tropical region: a case study in Malaysia. Climate and Development 2021, 1 -15.
AMA StyleNurfatin Izzati Ahmad Kamal, Zulfa Hanan Ash'Aari, Ahmad Makmom Abdullah, Faradiella Mohd Kusin, Ferdaus Mohamat Yusuff, Amir Hamzah Sharaai, Farrah Melissa Muharam, Noor Afiza Mohd Ariffin. Extreme heat vulnerability assessment in tropical region: a case study in Malaysia. Climate and Development. 2021; ():1-15.
Chicago/Turabian StyleNurfatin Izzati Ahmad Kamal; Zulfa Hanan Ash'Aari; Ahmad Makmom Abdullah; Faradiella Mohd Kusin; Ferdaus Mohamat Yusuff; Amir Hamzah Sharaai; Farrah Melissa Muharam; Noor Afiza Mohd Ariffin. 2021. "Extreme heat vulnerability assessment in tropical region: a case study in Malaysia." Climate and Development , no. : 1-15.
Good index selection is key to minimising basis risk in weather index insurance design. However, interannual, seasonal, and intra-seasonal hydroclimatic variabilities pose challenges in identifying robust proxies for crop losses. In this study, we systematically investigated 574 hydroclimatic indices for their relationships with yield in Malaysia’s irrigated double planting system, using the Muda rice granary as a case study. The responses of seasonal rice yields to seasonal and monthly averages and to extreme rainfall, temperature, and streamflow statistics from 16 years’ observations were examined by using correlation analysis and linear regression. We found that the minimum temperature during the crop flowering to the maturity phase governed yield in the drier off-season (season 1, March to July, Pearson correlation, r = +0.87; coefficient of determination, R2 = 74%). In contrast, the average streamflow during the crop maturity phase regulated yield in the main planting season (season 2, September to January, r = +0.82, R2 = 67%). During the respective periods, these indices were at their lowest in the seasons. Based on these findings, we recommend temperature- and water-supply-based indices as the foundations for developing insurance contracts for the rice system in northern Peninsular Malaysia.
Zed Zulkafli; Farrah Muharam; Nurfarhana Raffar; Amirparsa Jajarmizadeh; Mukhtar Abdi; Balqis Rehan; Khairudin Nurulhuda. Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design. Sustainability 2021, 13, 5207 .
AMA StyleZed Zulkafli, Farrah Muharam, Nurfarhana Raffar, Amirparsa Jajarmizadeh, Mukhtar Abdi, Balqis Rehan, Khairudin Nurulhuda. Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design. Sustainability. 2021; 13 (9):5207.
Chicago/Turabian StyleZed Zulkafli; Farrah Muharam; Nurfarhana Raffar; Amirparsa Jajarmizadeh; Mukhtar Abdi; Balqis Rehan; Khairudin Nurulhuda. 2021. "Contrasting Influences of Seasonal and Intra-Seasonal Hydroclimatic Variabilities on the Irrigated Rice Paddies of Northern Peninsular Malaysia for Weather Index Insurance Design." Sustainability 13, no. 9: 5207.
Rapid, accurate and inexpensive methods are required to analyze plant traits throughout all crop growth stages for plant phenotyping. Few studies have comprehensively evaluated plant traits from multispectral cameras onboard UAV platforms. Additionally, machine learning algorithms tend to over- or underfit data and limited attention has been paid to optimizing their performance through an ensemble learning approach. This study aims to (1) comprehensively evaluate twelve rice plant traits estimated from aerial unmanned vehicle (UAV)-based multispectral images and (2) introduce Random Forest AdaBoost (RFA) algorithms as an optimization approach for estimating plant traits. The approach was tested based on a farmer’s field in Terengganu, Malaysia, for the off-season from February to June 2018, involving five rice cultivars and three nitrogen (N) rates. Four bands, thirteen indices and Random Forest-AdaBoost (RFA) regression models were evaluated against the twelve plant traits according to the growth stages. Among the plant traits, plant height, green leaf and storage organ biomass, and foliar nitrogen (N) content were estimated well, with a coefficient of determination (R2) above 0.80. In comparing the bands and indices, red, Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), Red-Edge Wide Dynamic Range Vegetation Index (REWDRVI) and Red-Edge Soil Adjusted Vegetation Index (RESAVI) were remarkable in estimating all plant traits at tillering, booting and milking stages with R2 values ranging from 0.80–0.99 and root mean square error (RMSE) values ranging from 0.04–0.22. Milking was found to be the best growth stage to conduct estimations of plant traits. In summary, our findings demonstrate that an ensemble learning approach can improve the accuracy as well as reduce under/overfitting in plant phenotyping algorithms.
Farrah Muharam; Khairudin Nurulhuda; Zed Zulkafli; Mohamad Tarmizi; Asniyani Abdullah; Muhamad Che Hashim; Siti Mohd Zad; Derraz Radhwane; Mohd Ismail. UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits. Agronomy 2021, 11, 915 .
AMA StyleFarrah Muharam, Khairudin Nurulhuda, Zed Zulkafli, Mohamad Tarmizi, Asniyani Abdullah, Muhamad Che Hashim, Siti Mohd Zad, Derraz Radhwane, Mohd Ismail. UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits. Agronomy. 2021; 11 (5):915.
Chicago/Turabian StyleFarrah Muharam; Khairudin Nurulhuda; Zed Zulkafli; Mohamad Tarmizi; Asniyani Abdullah; Muhamad Che Hashim; Siti Mohd Zad; Derraz Radhwane; Mohd Ismail. 2021. "UAV- and Random-Forest-AdaBoost (RFA)-Based Estimation of Rice Plant Traits." Agronomy 11, no. 5: 915.
Basal stem rot disease (BSR) in oil palm plants is caused by the Ganoderma boninense (G. boninense) fungus. BSR is a major disease that affects oil palm plantations in Malaysia and Indonesia. As of now, the only available sustaining measure is to prolong the life of oil palm trees since there has been no effective treatment for the BSR disease. This project used an ALOS PALSAR-2 image with dual polarization, Horizontal transmit and Horizontal receive (HH) and Horizontal transmit and Vertical receive (HV). The aims of this study were to (1) identify the potential backscatter variables; and (2) examine the performance of machine learning (ML) classifiers (Multilayer Perceptron (MLP) and Random Forest (RF) to classify oil palm trees that are non-infected and infected by G. boninense. The sample size consisted of 55 uninfected trees and 37 infected trees. We used the imbalance data approach (Synthetic Minority Over-Sampling Technique (SMOTE) in these classifications due to the differing sample sizes. The result showed backscatter variable HV had a higher correct classification for the G. boninense non-infected and infected oil palm trees for both classifiers; the MLP classifier model had a robust success rate, which correctly classified 100% for non-infected and 91.30% for infected G. boninense, and RF had a robust success rate, which correctly classified 94.11% for non-infected and 91.30% for infected G. boninense. In terms of model performance using the most significant variables, HV, the MLP model had a balanced accuracy (BCR) of 95.65% compared to 92.70% for the RF model. Comparison between the MLP model and RF model for the receiver operating characteristics (ROC) curve region, (AUC) gave a value of 0.92 and 0.95, respectively, for the MLP and RF models. Therefore, it can be concluded by using only the HV polarization, that both the MLP and RF can be used to predict BSR disease with a relatively high accuracy.
Izrahayu Hashim; Abdul Shariff; Siti Bejo; Farrah Muharam; Khairulmazmi Ahmad. Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease. Agronomy 2021, 11, 532 .
AMA StyleIzrahayu Hashim, Abdul Shariff, Siti Bejo, Farrah Muharam, Khairulmazmi Ahmad. Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease. Agronomy. 2021; 11 (3):532.
Chicago/Turabian StyleIzrahayu Hashim; Abdul Shariff; Siti Bejo; Farrah Muharam; Khairulmazmi Ahmad. 2021. "Machine-Learning Approach Using SAR Data for the Classification of Oil Palm Trees That Are Non-Infected and Infected with the Basal Stem Rot Disease." Agronomy 11, no. 3: 532.
Background: In the last few decades, public health surveillance has been conducted using various programming languages implementing statistical methods to analyze the spatial distribution of a disease. Nevertheless, contact tracing and follow up control measures for tuberculosis (TB) patients remain challenging because many public health officers lack the appropriate programming skills to use the related software. Therefore, this study aimed to develop a TB mapping application associated with sociodemographic factor in Gombak. Methods: The sociodemographic data of 3325 TB cases such as age, gender, race, nationality, country of birth, educational level, employment status, healthcare worker, income status, residency, and smoking status between January 2013 and December 2017 in Gombak district were collected from the Tuberculosis Information System (TBIS) database at the Gombak District Health Office and Rawang Health Clinic and myTB website. Apart from that, the sociodemographic data of TB cases were extracted from the Aeronautical Reconnaissance Coverage Geographic Information System (ArcGIS) version 10.7 and subsequently uploaded into the Portal TB Gombak. The application was set up in the Python Shapefile (PHP) CodeIgniter framework with ArcGIS JavaScript API 3.7 and HyperText Markup Language (HTML), Cascading Style Sheet (CSS), JavaScript, and PHP as programming languages to build the system. Additionally, the ESRI map was used as the base map and combined with the web GIS technology via ArcGIS Application Programming Interface (API). Results: The application displays the location of TB cases on an interactive map based on sociodemographic factor. Conclusion: Portal TB Gombak allows public health officers to visualize the potential risk areas of TB cases without a trained programmer and geospatial statistician. This application will help healthcare personnel better understand TB transmission, thus improving case detection and minimize the public health impact of the disease.
Nur Adibah Mohidem; Malina Osman; Farrah Melissa Muharam; Saliza Mohd Elias; Rafiza Shaharudin; Zailina Hashim. Portal TB Gombak: Web-Based Application for Plotting of Tuberculosis Cases in Gombak, Selangor, Malaysia. 2021, 1 .
AMA StyleNur Adibah Mohidem, Malina Osman, Farrah Melissa Muharam, Saliza Mohd Elias, Rafiza Shaharudin, Zailina Hashim. Portal TB Gombak: Web-Based Application for Plotting of Tuberculosis Cases in Gombak, Selangor, Malaysia. . 2021; ():1.
Chicago/Turabian StyleNur Adibah Mohidem; Malina Osman; Farrah Melissa Muharam; Saliza Mohd Elias; Rafiza Shaharudin; Zailina Hashim. 2021. "Portal TB Gombak: Web-Based Application for Plotting of Tuberculosis Cases in Gombak, Selangor, Malaysia." , no. : 1.
Ganodermaboninense (G. boninense) is a fungus that causes one of the most destructive diseases in oil palm plantations in Southeast Asia called basal stem rot (BSR), resulting in annual losses of up to USD 500 million. The G. boninense infects both mature trees and seedlings. The current practice of detection still depends on manual inspection by a human expert every two weeks. This study aimed to detect early G. boninense infections using visible-near infrared (VIS-NIR) hyperspectral images where there are no BSR symptoms present. Twenty-eight samples of oil palm seedlings at five months old were used whereby 15 of them were inoculated with the G. boninense pathogen. Five months later, spectral reflectance oil palm leaflets taken from fronds 1 (F1) and 2 (F2) were obtained from the VIS-NIR hyperspectral images. The significant bands were identified based on the high separation between uninoculated (U) and inoculated (I) seedlings. The results indicate that the differences were evidently seen in the NIR spectrum. The bands were later used as input parameters for the development of Support Vector Machine (SVM) classification models, and these bands were optimized according to the classification accuracy achieved by the classifiers. It was observed that the U and I seedlings were excellently classified with 100% accuracy using 35 bands and 18 bands of F1. However, the combination of F1 and F2 (F12) gave better accuracy than F2 and almost similar to F1 for specific classifiers. This finding will provide an advantage when using aerial images where there is no need to separate F1 and F2 during the data pre-processing stage.
Aiman Noor Azmi; Siti Bejo; MahiraH Jahari; Farrah Muharam; Ian Yule; Nur Husin. Early Detection of Ganoderma boninense in Oil Palm Seedlings Using Support Vector Machines. Remote Sensing 2020, 12, 3920 .
AMA StyleAiman Noor Azmi, Siti Bejo, MahiraH Jahari, Farrah Muharam, Ian Yule, Nur Husin. Early Detection of Ganoderma boninense in Oil Palm Seedlings Using Support Vector Machines. Remote Sensing. 2020; 12 (23):3920.
Chicago/Turabian StyleAiman Noor Azmi; Siti Bejo; MahiraH Jahari; Farrah Muharam; Ian Yule; Nur Husin. 2020. "Early Detection of Ganoderma boninense in Oil Palm Seedlings Using Support Vector Machines." Remote Sensing 12, no. 23: 3920.
Mountainous regions are more sensitive to climatic condition changes and are susceptible to recent increases in temperature. Due to urbanization and land use/land cover (LULC) issues, Cameron Highlands has been impacted by rising land surface temperature (LST) variation. Thus, this study was carried out to explore the impact of the LULC change on LST in the Cameron Highlands from 2009 to 2019 using remote sensing images acquired from Landsat 7 ETM+, Landsat 8 Operational Land Imager (OLI/TIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS) 11A Thermal sensors. A split-window algorithm was applied to Landsat 8 images (2013–2019) to derive the LST. Air temperature data of the study area were also obtained to cross-validate data sources. Based on the validation results, the accuracy of LULC and LST outputs were more than 94.6% and 80.0%, respectively. The results show that the current trend of urban growth continues at a rate of 0.16% per year, and the area experienced an LST increase of 2 °C between 2009 and 2019. This study is crucial for land planners and environmentalists to understand the impacts of LULC change on LST and to propose appropriate policy measures to control development in Cameron Highlands.
Darren How Jin Aik; Mohd Hasmadi Ismail; Farrah Melissa Muharam. Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia. Land 2020, 9, 372 .
AMA StyleDarren How Jin Aik, Mohd Hasmadi Ismail, Farrah Melissa Muharam. Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia. Land. 2020; 9 (10):372.
Chicago/Turabian StyleDarren How Jin Aik; Mohd Hasmadi Ismail; Farrah Melissa Muharam. 2020. "Land Use/Land Cover Changes and the Relationship with Land Surface Temperature Using Landsat and MODIS Imageries in Cameron Highlands, Malaysia." Land 9, no. 10: 372.
This paper highlights the application of hyperspectral sensing in conjunction with imbalance approaches and machine learning (ML) algorithms to monitor the nutrients status of mature oil palm. As an alternative to the traditional foliar analysis, hyperspectral spectroscopy have portrayed a promising direction in appraising nutrients status of oil palm since the former approach is expensive, time-consuming and labour-intensive for the vast area of oil palm plantations. The aims of this study were to i) identify the spectral features that characterized leaf calcium (Ca), potassium (K), magnesium (Mg), nitrogen (N) and phosphorus (P) sufficiency levels of mature oil palm as affected by N fertilizer and ii) examine the performance of ML classifiers (Logistic Model Tree (LMT) and Naïve Bayes (NB)), as well as imbalance approaches (Synthetic Minority Over-Sampling TEchnique (SMOTE), Adaptive Boosting (AdaBoost) and combination of SMOTE and Ada-Boost (SMOTE+AdaBoost)) in classifying the Ca, K, Mg, N and P sufficiency levels from different frond numbers using the spectral features obtained in objective i. N fertilizers ranging from 0 to 6 kg N palm−1 were applied to the mature Tenera palm stands (12 and 15 years old) for three consecutive years. Spectral regions relevant to the classification of Ca, Mg and N status were the visible (Vis), near-infrared (NIR) and shortwave infrared (SWIR) while NIR and SWIR and Vis and SWIR were essential for P and K. The best discrimination of Ca, K, Mg, N and P sufficiency levels was via the LMT-SMOTE+AdaBoost model with balance accuracies (AccBalance) ranging from 76.13 to 100.00%. In general, the AccBalance of the nutrients tended to decrease as frond gets older. In summary, for assessment of oil palm nutrient status via remote sensing platforms, frond 9 was more appropriate than frond 17.
Amiratul Diyana Amirruddin; Farrah Melissa Muharam; Mohd Hasmadi Ismail; Ngai Paing Tan. Hyperspectral spectroscopy and imbalance data approaches for classification of oil palm's macronutrients observed from frond 9 and 17. Computers and Electronics in Agriculture 2020, 178, 105768 .
AMA StyleAmiratul Diyana Amirruddin, Farrah Melissa Muharam, Mohd Hasmadi Ismail, Ngai Paing Tan. Hyperspectral spectroscopy and imbalance data approaches for classification of oil palm's macronutrients observed from frond 9 and 17. Computers and Electronics in Agriculture. 2020; 178 ():105768.
Chicago/Turabian StyleAmiratul Diyana Amirruddin; Farrah Melissa Muharam; Mohd Hasmadi Ismail; Ngai Paing Tan. 2020. "Hyperspectral spectroscopy and imbalance data approaches for classification of oil palm's macronutrients observed from frond 9 and 17." Computers and Electronics in Agriculture 178, no. : 105768.
Metisa plana (Walker) is a leaf defoliating pest that is able to cause staggering economical losses to oil palm cultivation. Considering the economic devastation that the pest could bring, an early warning system to predict its outbreak is crucial. The state of art of satellite technologies are now able to derive environmental factors such as relative humidity (RH) that may influence pest population's fluctuations in rapid, harmless, and cost-effective manners. This study examined the relationship between the presence of Metisa plana at different time lags and remote sensing (RS) derived RH by using statistical and machine learning approaches. Metisa plana census data of cumulated larvae instar 1, 2, 3, and 4 were collected biweekly in 2014 and 2015 in an oil palm plantation in Muadzam Shah, Pahang, Malaysia. Relative humidity values derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite images were apportioned to 6 time lags; 1 week (T1), 2 weeks (T2), 3 week (T3), 4 weeks (T4), 5 week (T5) and 6 weeks (T6) and paired with the respective census data. Pearson's correlation was carried out to analyse the relationship between Metisa plana and RH at different time lags. Regression analyses and artificial neural network (ANN) were also conducted to develop the best prediction model of Metisa plana's outbreak. The results showed relatively high correlations, positively or negatively, between the presences of Metisa plana with RH ranging from 0.46 to 0.99. ANN was found to be superior to regression models with the adjusted coefficient of determination (R2) between the actual and predicted Metisa plana values ranging from 0.06 to 0.57 versus 0.00 to 0.05. The analysis on the best time lags illustrated that the multiple time lags were more influential on the Metisa plana population than the individual time lags. The best Metisa plana prediction model was derived from T1, T2 and T3 multiple time lags modelled using the ANN algorithm with R2 value of 0.57, errors below 1.14 and accuracies above 93%. Based on the result of this study, the elucidation of Metisa plana's landscape ecology was possible with the utilization of RH as the predictor variable in consideration of the time lag effects of RH on the pest's population.
Siti Aisyah Ruslan; Farrah Melissa Muharam; Zed Zulkafli; Dzolkhifli Omar; Muhammad Pilus Zambri. Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models. PLOS ONE 2019, 14, e0223968 .
AMA StyleSiti Aisyah Ruslan, Farrah Melissa Muharam, Zed Zulkafli, Dzolkhifli Omar, Muhammad Pilus Zambri. Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models. PLOS ONE. 2019; 14 (10):e0223968.
Chicago/Turabian StyleSiti Aisyah Ruslan; Farrah Melissa Muharam; Zed Zulkafli; Dzolkhifli Omar; Muhammad Pilus Zambri. 2019. "Using satellite-measured relative humidity for prediction of Metisa plana’s population in oil palm plantations: A comparative assessment of regression and artificial neural network models." PLOS ONE 14, no. 10: e0223968.
In oil palm management, age is one of the yield determinant factors. The conventional field investigations are often exhaustive and costly methods when implemented on a large scale. Despite much attention to classify individual oil palm ages by using various remote sensing images, none of the studies depicted satisfying overall accuracies. The overall aim of this study was to optimize window size and number of texture measurements for oil palm ages classification. The study was conducted in a commercial oil palm plantation comprised of palms of multiple ages planted from 1991 to 2008. Three Satellite Pour l’Observation de la Terre (SPOT)-5 multispectral images, acquired on 12 April 2012, 4 April 2013, and 14 April 2014, were evaluated. The individual ages were successfully classified with accuracy ranging from 59% to 97%, with an average overall accuracy of 84%. The results illustrated that the largest window size related to the smallest oil palm planting block in the study area, 390 m × 390 m on-the-ground window size, and seven combination of texture measurements, resulted in the highest classification overall accuracy. The utilization of texture measurements produced synergistic effects able to discriminate the oil palm age, with mean, entropy, homogeneity, and angular second moment as among the significant textures.
Camalia Saini Hamsa; Kasturi Devi Kanniah; Farrah Melissa Muharam; Nurul Hazrina Idris; Zainuriah Abdullah; Luqman Mohamed. Textural measures for estimating oil palm age. International Journal of Remote Sensing 2018, 40, 7516 -7537.
AMA StyleCamalia Saini Hamsa, Kasturi Devi Kanniah, Farrah Melissa Muharam, Nurul Hazrina Idris, Zainuriah Abdullah, Luqman Mohamed. Textural measures for estimating oil palm age. International Journal of Remote Sensing. 2018; 40 (19):7516-7537.
Chicago/Turabian StyleCamalia Saini Hamsa; Kasturi Devi Kanniah; Farrah Melissa Muharam; Nurul Hazrina Idris; Zainuriah Abdullah; Luqman Mohamed. 2018. "Textural measures for estimating oil palm age." International Journal of Remote Sensing 40, no. 19: 7516-7537.
Farrah Melissa Muharam; Tina Delahunty; Stephen J. Maas. Evaluation of nitrogen treatment effects on the reflectance of cotton at different spatial scales. International Journal of Remote Sensing 2018, 39, 8482 -8504.
AMA StyleFarrah Melissa Muharam, Tina Delahunty, Stephen J. Maas. Evaluation of nitrogen treatment effects on the reflectance of cotton at different spatial scales. International Journal of Remote Sensing. 2018; 39 (23):8482-8504.
Chicago/Turabian StyleFarrah Melissa Muharam; Tina Delahunty; Stephen J. Maas. 2018. "Evaluation of nitrogen treatment effects on the reflectance of cotton at different spatial scales." International Journal of Remote Sensing 39, no. 23: 8482-8504.
The betaine aldehyde dehydrogenase (BADH) gene plays a multifunctional role in plants. It is an important factor in fragrance production, abiotic stresses and antibiotic-free selection of transgenic plants. Molecular studies have presented a new picture of this critical factor involved in abiotic stress responses via the MAPK (mitogen-activated protein kinase) signalling pathway in numerous plants. Besides BADH, glycine betaine performs an important function in plant tolerance to environmental stresses. The presence of glycine betaine can help maintain the integrity of cell membranes against unexpected environmental stresses. BADH leads to production of glycine betaine through the oxidation of betaine aldehyde. Hence, BADH is considered a key regulator for glycine betaine formation. Consequently, by providing glycine betaine as a chemical interface, there is a critical role of BADH in enhancing the tolerance in an extensive range of plants subjected to different destructive abiotic stresses. The present article reviews the significant multifunctional role of the BADH gene in various plants, and also particularly argues how this important gene plays a responsive function to different destructive abiotic stresses, and its potential use in crop improvement using advanced technologies. Consequently, cloning of more BADH genes, specially from stress-tolerant plants, discovering their responsive signalling roles under environmental stresses, and validating such candidates for their potential are very helpful, and can open new windows to generate new stress-resistant crop cultivars.
Farahnaz Sadat Golestan Hashemi; Mohd Razi Ismail; Mohd Y. Rafii; Farzad Aslani; Gous Miah; Farah Melissa Muharam. Critical multifunctional role of the betaine aldehyde dehydrogenase gene in plants. Biotechnology & Biotechnological Equipment 2018, 32, 815 -829.
AMA StyleFarahnaz Sadat Golestan Hashemi, Mohd Razi Ismail, Mohd Y. Rafii, Farzad Aslani, Gous Miah, Farah Melissa Muharam. Critical multifunctional role of the betaine aldehyde dehydrogenase gene in plants. Biotechnology & Biotechnological Equipment. 2018; 32 (4):815-829.
Chicago/Turabian StyleFarahnaz Sadat Golestan Hashemi; Mohd Razi Ismail; Mohd Y. Rafii; Farzad Aslani; Gous Miah; Farah Melissa Muharam. 2018. "Critical multifunctional role of the betaine aldehyde dehydrogenase gene in plants." Biotechnology & Biotechnological Equipment 32, no. 4: 815-829.
The Tropical Rainfall Measuring Mission (TRMM) was the first Earth Science mission dedicated to studying tropical and subtropical rainfall. Up until now, there is still limited knowledge on the accuracy of the version 7 research product TRMM 3B42-V7 despite having the advantage of a high temporal resolution and large spatial coverage over oceans and land. This is particularly the case in tropical regions in Asia. The objective of this study is therefore to analyze the performance of rainfall estimation from TRMM 3B42-V7 (henceforth TRMM) using rain gauge data in Malaysia, specifically from the Pahang river basin as a case study, and using a set of performance indicators/scores. The results suggest that the altitude of the region affects the performances of the scores. Root Mean Squared Error (RMSE) is lower mostly at a higher altitude and mid-altitude. The correlation coefficient (CC) generally shows a positive but weak relationship between the rain gauge measurements and TRMM (0 < CC < 0.4), while the Nash-Sutcliffe Efficiency (NSE) scores are low (NSE < 0.1). The Percent Bias (PBIAS) shows that TRMM tends to overestimate the rainfall measurement by 26.95% on average. The Probability of Detection (POD) and Threat Score (TS) demonstrate that more than half of the pixel-point pairs have values smaller than 0.7. However, the Probability of False Detection (POFD) and False Alarm Rate (FAR) show that most of the pixel-point gauges have values lower than 0.55. The seasonal analysis shows that TRMM overestimates during the wet season and underestimates during the dry season. The bias adjustment shows that Mean Bias Correction (MBC) improved the scores better than Double-Kernel Residual Smoothing (DS) and Residual Inverse Distance Weighting (RIDW). The large errors imply that TRMM may not be suitable for applications in environmental, water resources, and ecological studies without prior correction.
Siti Najja Mohd Zad; Zed Zulkafli; Farrah Melissa Muharram. Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia. Remote Sensing 2018, 10, 388 .
AMA StyleSiti Najja Mohd Zad, Zed Zulkafli, Farrah Melissa Muharram. Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia. Remote Sensing. 2018; 10 (3):388.
Chicago/Turabian StyleSiti Najja Mohd Zad; Zed Zulkafli; Farrah Melissa Muharram. 2018. "Satellite Rainfall (TRMM 3B42-V7) Performance Assessment and Adjustment over Pahang River Basin, Malaysia." Remote Sensing 10, no. 3: 388.
Nitrogen (N) management is important in sustaining oil palm production. Remote sensing-based approaches via spectral index has promise in assessing the N nutrition content. The objectives of this study are; (i) to examine the N classification capability of three spectral indices (SI) such as visible (Vis), near infrared (NIR) and a combination of visible and NIR (Vis+NIR) from the SPOT-6 satellite, and (ii) to compare the performance of linear discriminant analysis (LDA) and support vector machine (SVM) in discriminating foliar N content of mature oil palms. Nitrogen treatments varied from 0 to 2 kg per palm. The N-sensitive SIs tested in this study were age-dependent. The Vis index (BGRI1) (CVA= 79.55%) and Vis+NIR index (NDVI, NG, IPVI and GNDVI) (CVA= 81.82%) were the best indices to assess N status of young and prime mature palms through the SVM classifier.
Amiratul Diyana Amirruddin; Farrah Melissa Muharam. Evaluation of linear discriminant and support vector machine classifiers for classification of nitrogen status in mature oil palm from SPOT-6 satellite images: analysis of raw spectral bands and spectral indices. Geocarto International 2018, 34, 735 -749.
AMA StyleAmiratul Diyana Amirruddin, Farrah Melissa Muharam. Evaluation of linear discriminant and support vector machine classifiers for classification of nitrogen status in mature oil palm from SPOT-6 satellite images: analysis of raw spectral bands and spectral indices. Geocarto International. 2018; 34 (7):735-749.
Chicago/Turabian StyleAmiratul Diyana Amirruddin; Farrah Melissa Muharam. 2018. "Evaluation of linear discriminant and support vector machine classifiers for classification of nitrogen status in mature oil palm from SPOT-6 satellite images: analysis of raw spectral bands and spectral indices." Geocarto International 34, no. 7: 735-749.
Monitoring nitrogen (N) in oil palm is crucial for the production sustainability. The objective of this study is to examine the capability of visible (Vis), near infrared (NIR) and a combination of Vis and NIR (Vis + NIR) spectral indices acquired from different sensors for predicting foliar N content of different palm age groups. The N treatments varied from 0 to 2 kg per palm, subjected according to immature, young mature and prime mature classes. The Vis + NIR indices from the ground level-sensor that is green + red + NIR (G + R + NIR) was the best index for predicting N for immature palms (R2 = 0.91), while Vis indices blue + red (B + R) and Green Red Index from the space-borne sensor were significantly useful for N assessment of young and prime mature palms (R2 = 0.70 and 0.50), respectively. The application of vegetation indices for monitoring N status of oil palm is beneficial to examine extensive plantation areas.
Amiratul Diyana Amirruddin; Farrah Melissa Muharam; Daljit Singh Karam. Evaluation of ground-level and space-borne sensor as tools in monitoring nitrogen nutrition status in immature and mature oil palm. Journal of Plant Nutrition 2017, 41, 371 -383.
AMA StyleAmiratul Diyana Amirruddin, Farrah Melissa Muharam, Daljit Singh Karam. Evaluation of ground-level and space-borne sensor as tools in monitoring nitrogen nutrition status in immature and mature oil palm. Journal of Plant Nutrition. 2017; 41 (3):371-383.
Chicago/Turabian StyleAmiratul Diyana Amirruddin; Farrah Melissa Muharam; Daljit Singh Karam. 2017. "Evaluation of ground-level and space-borne sensor as tools in monitoring nitrogen nutrition status in immature and mature oil palm." Journal of Plant Nutrition 41, no. 3: 371-383.
Farrah Melissa Muharam; Siti Aisyah Ruslan; Siti Liyana Zulkafli; Norida Mazlan; Nur Azura Adam; Nor Azura Husin. Remote Sensing Derivation of Land Surface Temperature for Insect Pest Monitoring. Asian Journal of Plant Sciences 2017, 16, 160 -171.
AMA StyleFarrah Melissa Muharam, Siti Aisyah Ruslan, Siti Liyana Zulkafli, Norida Mazlan, Nur Azura Adam, Nor Azura Husin. Remote Sensing Derivation of Land Surface Temperature for Insect Pest Monitoring. Asian Journal of Plant Sciences. 2017; 16 (4):160-171.
Chicago/Turabian StyleFarrah Melissa Muharam; Siti Aisyah Ruslan; Siti Liyana Zulkafli; Norida Mazlan; Nur Azura Adam; Nor Azura Husin. 2017. "Remote Sensing Derivation of Land Surface Temperature for Insect Pest Monitoring." Asian Journal of Plant Sciences 16, no. 4: 160-171.
Lai Lai; Mohd Razi Ismail; Farrah Melissa Muharam; Martini Mohammad Yusof; Roslan Ismail; Noraini Md Jaafar. Effects of Rice Straw Biochar and Nitrogen Fertilizer on Rice Growth and Yield. Asian Journal of Crop Science 2017, 9, 159 -166.
AMA StyleLai Lai, Mohd Razi Ismail, Farrah Melissa Muharam, Martini Mohammad Yusof, Roslan Ismail, Noraini Md Jaafar. Effects of Rice Straw Biochar and Nitrogen Fertilizer on Rice Growth and Yield. Asian Journal of Crop Science. 2017; 9 (4):159-166.
Chicago/Turabian StyleLai Lai; Mohd Razi Ismail; Farrah Melissa Muharam; Martini Mohammad Yusof; Roslan Ismail; Noraini Md Jaafar. 2017. "Effects of Rice Straw Biochar and Nitrogen Fertilizer on Rice Growth and Yield." Asian Journal of Crop Science 9, no. 4: 159-166.
Amiratul Diyana Amirruddin; Farrah Melissa Muharam; Tan Ngai Paing; Daljit Singh Karam Singh; Martini Mohammad Yusoff. Nitrogen Effects on Growth and Spectral Characteristics of Immature and Mature Oil Palms. Asian Journal of Plant Sciences 2017, 16, 200 -210.
AMA StyleAmiratul Diyana Amirruddin, Farrah Melissa Muharam, Tan Ngai Paing, Daljit Singh Karam Singh, Martini Mohammad Yusoff. Nitrogen Effects on Growth and Spectral Characteristics of Immature and Mature Oil Palms. Asian Journal of Plant Sciences. 2017; 16 (4):200-210.
Chicago/Turabian StyleAmiratul Diyana Amirruddin; Farrah Melissa Muharam; Tan Ngai Paing; Daljit Singh Karam Singh; Martini Mohammad Yusoff. 2017. "Nitrogen Effects on Growth and Spectral Characteristics of Immature and Mature Oil Palms." Asian Journal of Plant Sciences 16, no. 4: 200-210.