GIS and Remote Sensing for Mangroves Mapping and Monitoring

Malaysia is one of the few South East Asian counties with large tracts of mangroves. They provide ecosystem goods and services to the environment and the surroundings regarding shoreline stabilization, storm protection, water quality maintenance, micro-climate stabilization, recreation, tourism, fishing and supply of various forest products. Despite extensive distribution of the mangroves, threats posed by different land use activities are inevitable. Therefore, knowledge on mangroves distribution and change is importance for effective management and making protection policies. Although remote sensing (RS) and geographic information system (GIS) has been widely used to characterize and monitor mangroves change over a range of spatial and temporal scales, studies on mangroves change in Malaysia is lacking. Effective mangrove management is vital via acquiring knowledge on forest distribution and changes to establish protection policies. This chapter will elaborate technically how GIS and RS were utilized to identify, map, and monitor changes of mangroves ecosystem in Malaysia. It also highlights how GIS can enhance the current governance and regulations related to forestry in Malaysia

Effects of hyperspectral data transformations on urban inter-class separations using a support vector machine

This study investigated the performance of different data types used in a hyperspectral data classification process. Data in the form of spectral reflectance, first derivative spectra and wavelet coefficients were used as inputs for the Support Vector Machine (SVM) algorithm used to classify five different classes. The first derivative spectra gave a lower classification accuracy (35.6%) than the spectral reflectance (82%) and the use of wavelet coefficients further improved the classification accuracy to 100%. Proper selection of the wavelet transformation method, the mother wavelet, the number of vanishing moments and the decomposition level could improve classification accuracy. In summary, wavelet coefficients could maximise discrimination capability as compared to the spectral reflectance and first derivative spectra

Comparison between topographic expression of RADARSAT and DEM in Simpang Pulai to Pos Selim, Malaysia.

Radar and digital elevation model had been utilised in many structural studies. The main objective of this study is to compare the RADARSAT and digital elevation model for lineament interpretation which probably represent the main joints or faults along the Simpang Pulai to Pos Selim highway, Malaysia. These joints and faults may influence the instability along the highway. Manual comparison in terms of topographical aspect was undertaken between RADARSAT with 25 m spatial resolution and digital elevation model derived from 20 m contour interval of the topographical map. The previously interpreted lineaments of more than 2 km in the study area was draped over the RADARSAT and digital elevation model to compared whether the lineament concurred with the topographical representation. The interpreted lineaments were derived from Landsat TM of 1990 and 2002, where the DEM had been utilised in the negative lineament determination. It is concluded that the application RADARSAT is not very useful in terms of topographical expression in the structural geological interpretation for the study area compared to DEM derived from contour data. Further work is suggested before any conclusion can be confidently derived

Utilization of Remote Sensing Technology for Carbon Offset Identification in Malaysian Forests

Rapid growth of Malaysia’s economy recently is often associated with various environmental disturbances, which have been contributing to depletion of forest resources and thus climate change. The need for more spaces for numerous land developments has made the existing forests suffer from deforestation. This chapter presents an overview and demonstrates how remote sensing data is used to map and quantify changes of tropical forests in Malaysia. The analysis dealt with image processing that produce seamless mosaics of optical satellite data over Malaysia, within 15 years period, with 5-year intervals. The challenges were about the production of cloud-free images over a tropical country that always covered by clouds. These datasets were used to identify eligible areas for carbon offset in land use, land use change and forestry (LULUCF) sector in Malaysia. Altogether 580 scenes of Landsat imagery were processed to complete the observation period and came out with a seamless, wall to wall images over Malaysia from year 2005 to 2020. Forests have been identified from the image classification and then classified into three major types, which are dry-inland forest, peat swamp and mangroves. Post-classification change detection technique was used to determine areas that have been undergoing conversions from forests to other land uses. Forest areas were found to have declined from about 19.3 Mil. ha (in 2005) to 18.2 Mil. ha in year 2020. Causes of deforestation have been identified and the amount of carbon dioxide (CO2) that has been emitted due to the deforestation activity has been determined in this study. The total deforested area between years 2005 and 2020 was at 1,087,030 ha with rate of deforestation of about 72,469 ha yr.−1 (or 0.37% yr.−1). This has contributed to the total CO2 emission of 689.26 Mil. Mg CO2, with an annual rate of 45.95 Mil. Mg CO2 yr.−1. The study found that the use of a series satellite images from optical sensors are the most appropriate sensors to be used for monitoring of deforestation over the Malaysia region, although cloud covers are the major issue for optical imagery datasets

Effects of data transformation and classifier selections on urban feature discrimination using hyperspectral imagery

Hyperspectral remote sensing has been used in various applications which include urban applications. Classifying hyperspectral remote sensing data from urban environments is challenging due to spectrally heterogeneous materials with similar spectral properties. There is a lack of studies on the use of hyperspectral technology in urban mapping in Malaysia although it has widely been used in other countries. The selection of mapping techniques in classification which is the selection of data transformation and classifier selections are very essential to acquire maximum mapping accuracy. This research was conducted to study the effects of data transformation and classifier selections in urban feature discrimination using hyperspectral imagery. Two techniques of data transformation are tested in this study which are the spectral derivative and wavelet transformations. Various wavelet parameters which are the selection of wavelet transformation techniques, mother wavelets, number of vanishing moments and scale or level decompositions have been tested in this study. The selection of classifiers such as Minimum Distance to Mean, Spectral Angle Mapper and Support Vector Machine are also tested in this study. The performance of each parameter tested in this study is assessed through their classification accuracy. McNemar statistical test is used to test the significance difference between two classification results. Three hyperspectral images from two different sensors are tested in this study which are two images came from AisaEAGLE sensor while the other image acquired by AISA CLASSIC sensor. The results show that each transformation parameter and classifier selected gave different results. The classification accuracy derived from derivative transformation is lower than the classification accuracy of reflectance. The right selection of wavelet transformation parameters can give maximum classification accuracy. There is no best wavelet transformation parameters can be determined since the best wavelet transformation parameters of all images are different. Classification using Support Vector Machine gave better accuracy than other classifiers for all images and more robust as it is not affected by the types of data used. The results clearly show the advantages of the Support Vector Machine and wavelet-based data in terms of accuracy. They significantly outperform the other method and achieve overall higher accuracies. Thus, both methods can be considered attractive and useful for the classification of urban hyperspectral data. Wavelet based images of rbio2.2 (Scale-8CWT) of first data set and bior2.8 (Scale-16 CWT) of second data set, and reflectance image of third data set gave highest classification accuracies using SVM classifier which are 96%, 98.4% and 98.4%

Time-series maps of aboveground biomass in dipterocarps forests of Malaysia from PALSAR and PALSAR-2 polarimetric data

Abstract Background Malaysia typically suffers from frequent cloud cover, hindering spatially consistent reporting of deforestation and forest degradation, which limits the accurate reporting of carbon loss and CO2 emissions for reducing emission from deforestation and forest degradation (REDD+) intervention. This study proposed an approach for accurate and consistent measurements of biomass carbon and CO2 emissions using a single L-band synthetic aperture radar (SAR) sensor system. A time-series analysis of aboveground biomass (AGB) using the PALSAR and PALSAR-2 systems addressed a number of critical questions that have not been previously answered. A series of PALSAR and PALSAR-2 mosaics over the years 2007, 2008, 2009, 2010, 2015 and 2016 were used to (i) map the forest cover, (ii) quantify the rate of forest loss, (iii) establish prediction equations for AGB, (iv) quantify the changes of carbon stocks and (v) estimate CO2 emissions (and removal) in the dipterocarps forests of Peninsular Malaysia. Results This study found that the annual rate of deforestation within inland forests in Peninsular Malaysia was 0.38% year−1 and subsequently caused a carbon loss of approximately 9 million Mg C year−1, which is equal to emissions of 33 million Mg CO2 year−1, within the ten-year observation period. Spatially explicit maps of AGB over the dipterocarps forests in the entire Peninsular Malaysia were produced. The RMSE associated with the AGB estimation was approximately 117 Mg ha−1, which is equal to an error of 29.3% and thus an accuracy of approximately 70.7%. Conclusion The PALSAR and PALSAR-2 systems offer a great opportunity for providing consistent data acquisition, cloud-free images and wall-to-wall coverage for monitoring since at least the past decade. We recommend the proposed method and findings of this study be considered for MRV in REDD+ implementation in Malaysia

A Study on the Application of Electronic Nose Coupled with DFA and Statistical Analysis for Evaluating the Relationship between Sample Volumes versus Sensor Intensity of Agarwood Essential Oils Blending Ratio

The exquisite agarwood oils are primary used for perfumery industries either as pure essential oils or in a perfume base. Commonly, Agarwood oils are extracted from low grade 100% agarwood chips via distillation processes and the extracted oil is called as pure agarwood essential oil which containing 100% of extracted material. In perfumery industry, the agarwood pure oils are often blend with other essential oils such as geranium, sandalwood, gurjum balsam, jasmine and Ylang ylang to create rich, complex and pleasant oils compared to pure Agarwood oils smell alone that may not suit all users preferences. To dates, agarwood oil quality assessment is typically carried out manually via human olfactory system which produces different results and inconsistency from traders and buyers. From the results, multiple linear regression analysis used to run the multiple regression prediction models using combination of 11 sensors shown better results by increasing the R2 value from 0.674 to 0.915 and the RMSE value from 14.65% to 6.80% compared to single regression prediction models using sensor LY2/G. The sensors intensity values from multiple sensors are showing a strong correlation to the volume of the B1 in the blended samples (M11~M20) as the ratio of B1 is increased

A Study on the Application of Electronic Nose Coupled with DFA and Statistical Analysis for Evaluating the Relationship between Sample Volumes versus Sensor Intensity of Agarwood Essential Oils Blending Ratio

The exquisite agarwood oils are primary used for perfumery industries either as pure essential oils or in a perfume base. Commonly, Agarwood oils are extracted from low grade 100% agarwood chips via distillation processes and the extracted oil is called as pure agarwood essential oil which containing 100% of extracted material. In perfumery industry, the agarwood pure oils are often blend with other essential oils such as geranium, sandalwood, gurjum balsam, jasmine and Ylang ylang to create rich, complex and pleasant oils compared to pure Agarwood oils smell alone that may not suit all users preferences. To dates, agarwood oil quality assessment is typically carried out manually via human olfactory system which produces different results and inconsistency from traders and buyers. From the results, multiple linear regression analysis used to run the multiple regression prediction models using combination of 11 sensors shown better results by increasing the R2 value from 0.674 to 0.915 and the RMSE value from 14.65% to 6.80% compared to single regression prediction models using sensor LY2/G. The sensors intensity values from multiple sensors are showing a strong correlation to the volume of the B1 in the blended samples (M11~M20) as the ratio of B1 is increased

Structural geological mapping in Simpang Pulai to Pos Selim, Malaysia utilising satellite imagery

Remote sensing data have becoming an improtant source of structural geological information. Integration of multispectral and radar imagery, with the digital elevation model may improve the structural interpretation. The main objective of this study is to utilise the radar imagery for lineament interpretation which probably represent the main joints or faults along the presently Simpang Pulai to Pos Selim highway. The Landsat was chosen because during the image capture, the highway stretch is still not been build, thus the interpretation of the lineamnet will not be influenced by the highway alignment. The resultant lineament will be compared to the previously interpreted lineaments in the study area using Landsat TM