Mapping of Krau Wildlife Reserve (KWR) protected area using Landsat 8 and supervised classification algorithms

Human-dominated ecosystems speed up the loss of habitats, populations, and species. Thus, monitoring and managing the Earth’s heritage of biodiversity is a challenge in natural resource management. Mapping protected areas (PAs) is essential in understanding the disturbance that can affect biodiversity and conservation management. Land use-land cover (LULC) maps can be used as a decision making tool by policy makers to ensure sustainable development and understanding of the effect of human activities within and around PAs. However, in Malaysia, the limited updated maps of PAs make the effective management of PAs problematic. Therefore, this study aimed to produce an updated Land LULC map for the PA Krau Wildlife Reserve (KWR) and its surroundings using remote sensing and related geospatial technologies. Three supervised classification algorithms were used and compared. Multidated images from Landsat 8 were utilized, and spectral angle mapper (SAM), support vector machine (SVM), and artificial neural network (ANN) classifiers were applied and evaluated. The approaches of pan-sharpening and cloud patching were used to enhance the accuracy of LULC classification. The images were classified into five classes: dense forest, less dense forest or agriculture, built-up area, bare soil, and water. The overall accuracies of SAM, ANN, and SVM for the 15 m spatial resolution images were 81.96%, 98.22% and 97.40%, respectively. The ANN map produced the highest overall accuracy and was consequently utilized to extract additional information related to disturbance and encroachment within and around the PA. Findings indicated that socioeconomic activities played a major role in altering the environment of KWR

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

Mapping the distribution of oil palm using Landsat 8 data by comparing machine learning and non-machine learning algorithms

Oil palm is one of the major crops in Malaysia; it accounts for 47% of the global palm oil supply. Equatorial climate has provided Malaysia with the potential to produce oil palm biomass, which is one of the major contributors to the local economy. The utilisation of oil palm biomass as a source of renewable energy is one of the effective methods to promote green energy. Therefore, there is a need to have sufficient data related to oil palm biomass such as yield estimation, oil palm distributions, and locations. The aim of this study was to produce a land cover map on the distribution of oil palm plantations on three districts located in Selangor. Landsat 8 images of resolutions 15 x 15 m were used and classified via machine learning and non-machine learning algorithms. In this study, three different classifier algorithms were compared using support vector machines, artificial neural networks, and maximum likelihood classifications in which the values obtained for overall accuracy were 98.96%, 99.39%, and 15.30% respectively. The output showed that machine learning algorithms, support vector machines and artificial neural networks gave rise to high accuracies. Hence, the mapping of oil palm distributions via machine learning algorithm was better than that via non-machine learning algorithm

Mapping of oil palm land cover using integration of cloud computing, machine learning and big data

Oil palm is one of the agricultural crops that produces high amount of biomass, in which contributes to support the Sustainable Development Goals (SDGs). Furthermore, oil palm is a climate-friendly product that can generate energy in a more efficient way than using harmful element such as fossil fuel. However, the expansion of oil palm plantation has been recognised as a threat to the wildlife species and had caused massive amount of deforestations. A tropical country with humid weather, Malaysia has been listed as one of the top three countries with the highest rate of deforestation in the world. Obtaining information of oil palm plantation over a large area will be very intensive, costly and time consuming. Thus, this study had utilised cloud-based platforms, namely Google Earth Engine (GEE) and Remote Ecosystem Monitoring Assessment Pipeline (REMAP) to map the oil palm areas. Random Forest (RF) machine learning algorithm was utilised to produce and classify the land cover maps covering the Peninsular Malaysia. By using Landsat data obtained in Period 1 (1999 – 2003) and Period 2 (2014 – 2017), both cloud-based platforms were able to produce the land cover maps of Peninsular Malaysia. The overall accuracies produced by the GEE and REMAP for Period 1 data were 78.60% and 79.52% respectively. Meanwhile, the overall accuracies produced for Period 2 data were 79.77% and 80.00% for GEE and REMAP respectively. The changes of oil palm plantations noted from Period 1 to Period 2 using both cloud-based platforms were assessed, and the results showed oil palm plantation in Peninsular Malaysia is at sustainable state. For the first time, cloud-based platforms such as REMAP and GEE were being assessed for monitoring the changes to oil palm land cover in Peninsular Malaysia. Furthermore, the utilisation of REMAP and GEE were implemented to validate each other and to see the consistency of the results produced. This is a new paradigm shift from the normal approach utilising desktop-based big archives data analysis over huge areas that consumes tremendous amount of time and computing resources. In conclusion, both GEE and REMAP were able to produce the oil palm land cover maps and the changes of the oil palm can be analysed. In the future, the produced oil palm land cover maps of Peninsular Malaysia can be integrated with other ancillary data in a Geographic Information System (GIS) which later can assist the authorities in producing better decision-making and action plans

Performance of Sentinel-2A remote sensing system for urban area mapping in Malaysia via pixel-based and OBIA methods

Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications

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. <br/