A comparison of support vector machine and decision tree classifications using satellite data of Langkawi Island

This study investigates a new approach in image classification. Two classifiers were used to classify SPOT 5 satellite image; Decision Tree (DT) and Support Vector Machine (SVM). The Decision Tree rules were developed manually based on Normalized Difference Vegetation Index (NDVI) and Brightness Value (BV) variables. The classification using SVM method was implemented automatically by using four kernel types; linear, polynomial, radial basis function and sigmoid. The study indicates that the classification accuracy of SVM algorithm was better than DT algorithm. The overall accuracy of the SVM using four kernel types was above 73% and the overall accuracy of the DT method was 69%

Landslides and lineament mapping along the Simpang Pulai to Kg Raja highway, Malaysia

Geological structural features, such as the discontinuities that may be detected on satellite imagery as lineaments, in many cases control landslide occurrences. Lineament may represent the plane of weakness where the strength of the slope material has been reduced, eventually resulting in slope failure. The main objective of this study is to assess the relationship between lineament and landslide occurrences along the Simpang Pulai to Kg Raja highway, Malaysia. Lineament mapping was undertaken utilizing Landsat imagery and landslide distributions were identified based on field mapping and historical records. Lineament density maps of length, number and intersections were generated and compared with landslide distributions. The lineaments were also visually compared with the landslide occurrences. The results showed that there is an association between the lineaments and landslide distribution. Thus, lineament mapping is essential for the early stages of planning to prevent hazard potential from landslides

DISEASE DETECTION FROM FIELD SPECTROMETER DATA

ABSTRACT: Oil palm plants have been planted in large scale of areas. Ganoderma disease has been recognized and diagnosed in oil palm plants to infect almost half of the oil palm plants in Malaysia. To deal with this problem, the use of vegetation indices analysis on hyper spectral field data we will examine the ability of this data in discrimination between Ganoderma disease stages in oil palm plants which will be helpful in control the spread of the diseases. By using vegetation indices the oil palm plants could be classified into 1 (T1 healthy), 2 (T2 semi healthy) and 3 (T3 severe damage) plant classes accurately. The results showed that the best vegetation index is the Modified Red Edge Simple Ratio (MSR705) among the vegetation indices to discriminate between oil palm health stages. It was realized that the modification that was applied to the Modified Red Edge Simple Ratio (MSR705) index of Narrowband greenness VIs has been exhibited an acceptable results in differentiate between the oil palm plant stage 1 (T1 healthy) and stage 2 (T2 semi healthy).   ABSTRAK: Tanaman kelapa sawit ditanam secara meluas.  Penyakit ganoderma dikenali dan didiagnosikan menjangkiti hampir separuh tanaman kelapa sawit di Malaysia. Untuk mengawal penyakit ini daripada merebak, analisis indeks tanaman dijalankan ke atas data kawasan spektrum melampau di mana keupayaan data ini diuji dalam membezakan peringkat-peringkat penyakit Ganoderma terhadap tanaman kelapa sawit. Dengan menggunakan indeks tanaman, kelapa sawit dapat diklasifikasikan kepada 1 (T1 sihat), 2 (T2 separa sihat) dan 3 (T3 rosak); kelas tanaman dengan tepat. Keputusan menunjukkan indeks tanaman terbaik sebagai Modified Red Edge Simple Ratio (MSR705) yang merupakan indeks tanaman dalam membezakan peringkat kesihatan kelapa sawit. Adalah didapati pengubahsuaian terhadap indeks Modified Red Edge Simple Ratio (MSR705) yang juga indeks Jalur Sempit Hijau VI telah memberikan keputusan yang munasabah dalam membezakan peringkat tanaman kelapa sawit peringkat 1 (T1 sihat) dan peringkat 2 (T2 separa sihat)

Mapping the spatial distribution and changes of oil palm land cover using an open source cloud-based mapping platform

Oil palm has become well known for its oil palm yields that can be used to produce food, biodiesel and biogas. The rapid expansion of oil palm plantations over large areas has changed the land use and land cover of surroundings. Changes in land covers can be mapped and later used for further analysis. However, obtaining and classifying large coverages require massive amounts of data and computing resources and the skills and time of analysts. The Remote Ecosystem Monitoring Assessment Pipeline (REMAP) provides a cloud computing platform that hosts an open-source stacked Landsat data that allows land cover classification to be implemented using a built-in random forest supervised machine learning algorithm. Classifications were performed with the aid of predictor layers to discriminate the following land covers in Peninsular Malaysia: oil palm, built-up, bare soil, water, forest, other vegetation and paddy. The classification performed on period 1 (1999–2003) and period 2 (2014–2017) data produced an overall accuracy of 80.34% and 79.53% respectively. The analysis of the changes in oil palm distributions from period 1 to period 2 indicated an increment of 23.59%. Further analysis revealed that oil palm expansion in Peninsular Malaysia only minimally affected forested area and is mostly resulted from the conversion of less productive crops to oil palm. Results prove the land cover mapping and change detection capabilities of REMAP as a cloud computing platform for large areas. Despite its limitations, REMAP has the potential to achieve fast-paced mapping over large areas and monitor land changes in oil palm distributions

Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm

IMAGE CLASSIFICATION FOR MAPPING OIL PALM DISTRIBUTION VIA SUPPORT VECTOR MACHINE USING SCIKIT-LEARN MODULE

The world has been alarmed with the global warming effects. Global warming has been a distress towards the environment, thus shorten the Earth’s lifespan. It is a challenging task to reduce the global warming effects in a short period, knowing that the human population is increasing along with the electricity and energy demand. In order to reduce the effects, renewable energy is presented as an alternative method to produce energy in a way that will not harm the environment. Oil palm is one of the agricultural crops that produces huge amount of biomass which can be processed and used as a renewable energy source. In 2016, Malaysia has reported over 5 million hectares of land were covered by oil palm plantations. Placing Malaysia as the second largest country of oil palm producer in the world has given it an advantage to produce renewable energy source. However, there is a need to monitor the sustainability of oil palm plantations in Malaysia via effective mapping approaches. This study utilised two different platforms (open source and commercial) using a machine learning algorithm namely Support Vector Machine (SVM) to perform oil palm mapping. An open source Python programming-based technique utilising Scikit-learn module was performed to map the oil palm distribution and the result produced had an overall accuracy of 91.39%. To support and validate the efficiency of the Python programming-based image classification, a commercial remote sensing software (ENVI) was used and compared by implementing the same SVM algorithm and the result showed an overall accuracy of 98.21%

Oil Palm Mapping Over Peninsular Malaysia Using Google Earth Engine and Machine Learning Algorithms

Oil palm plays a pivotal role in the ecosystem, environment, economy and without proper monitoring, uncontrolled oil palm activities could contribute to deforestation that can cause high negative impacts on the environment and therefore, proper management and monitoring of the oil palm industry are necessary. Mapping the distribution of oil palm is crucial in order to manage and plan the sustainable operations of oil palm plantations. Remote sensing provides a means to detect and map oil palm from space effectively. Recent advances in cloud computing and big data allow rapid mapping to be performed over large a geographical scale. In this study, 30 m Landsat 8 data were processed using a cloud computing platform of Google Earth Engine (GEE) in order to classify oil palm land cover using non-parametric machine learning algorithms such as Support Vector Machine (SVM), Classification and Regression Tree (CART) and Random Forest (RF) for the first time over Peninsular Malaysia. The hyperparameters were tuned, and the overall accuracy produced by the SVM, CART and RF were 93.16%, 80.08% and 86.50% respectively. Overall, the SVM classified the 7 classes (water, built-up, bare soil, forest, oil palm, other vegetation and paddy) the best. However, RF extracted oil palm information better than the SVM. The algorithms were compared and the McNemar's test showed significant values for comparisons between SVM and CART and RF and CART. On the other hand, the performance of SVM and RF are considered equally effective. Despite the challenges in implementing machine learning optimisation using GEE over a large area, this paper shows the efficiency of GEE as a cloud-based free platform to perform bioresource distributions mapping such as oil palm over a large area in Peninsular Malaysia

Design of biomass value chains that are synergistic with the food-energy-water nexus: strategies and opportunities

Humanity’s future sustainable supply of energy, fuels and materials is aiming towards renewable sources such as biomass. Several studies on biomass value chains (BVCs) have demonstrated the feasibility of biomass in replacing fossil fuels. However, many of the activities along the chain can disrupt the food–energy–water (FEW) nexus given that these resource systems have been ever more interlinked due to increased global population and urbanisation. Essentially, the design of BVCs has to integrate the systems-thinking approach of the FEW nexus; such that, existing concerns on food, water and energy security, as well as the interactions of the BVCs with the nexus, can be incorporated in future policies. To date, there has been little to no literature that captures the synergistic opportunities between BVCs and the FEW nexus. This paper presents the first survey of process systems engineering approaches for the design of BVCs, focusing on whether and how these approaches considered synergies with the FEW nexus. Among the surveyed mathematical models, the approaches include multi-stage supply chain, temporal and spatial integration, multi-objective optimisation and uncertainty-based risk management. Although the majority of current studies are more focused on the economic impacts of BVCs, the mathematical tools can be remarkably useful in addressing critical sustainability issues in BVCs. Thus, future research directions must capture the details of food–energy–water interactions with the BVCs, together with the development of more insightful multi-scale, multi-stage, multi-objective and uncertainty-based approaches

ROOFING ASSESSMENT FOR ROOFTOP RAINWATER HARVESTING ADOPTION USING REMOTE SENSING AND GIS APPROACH

Rooftop rainwater harvesting refers to the collection and storage of water from rooftops whereby the quality of harvested rainwater depend on the types of roof and the environmental conditions. This system is capable to support the water supply in almost any place either as a sole source or by reducing stress on other sources through water savings. Remote sensing and GIS have been widely used in urban environmental analysis. Thus, this study aimed to develop the roofing layer in order to assess the potential area for rooftop rainwater harvesting adoption by integrating remote sensing and GIS approach. An urban area containing various urban roofing materials and characteristics was selected. High resolution satellite imagery acquired from WorldView-3 satellite systems with 0.3 m of spatial resolution was used in order to obtain spectral and spatial information of buildings and roofs. For quality assessment, the physical and chemical parameters of the rooftop harvested rainwater were performed according to the Standard Tests for Water and Wastewater. The potential area for rooftop rainwater harvesting adoption can be identified with the detail information of the rooftops and quality assessment in geospatial environment

A review of applying second-generation wavelets for noise removal from remote sensing data.

The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed based on a method called the lifting scheme, is almost a new version of wavelets, and its application in the remote sensing field is fresh. Although first-generation wavelets have been proven to offer effective techniques for processing remotely sensed data, second-generation wavelets are more efficient in some respects, as will be discussed later. The aim of this review paper is to examine all existing studies in the literature related to applying second-generation wavelets for denoising remote sensing data. However, to make a better understanding of the application of wavelet-based denoising methods for remote sensing data, some studies that apply first-generation wavelets are also presented. In the part of hyperspectral data, there is a focus on noise removal from vegetation spectrum