Maximum Likelihood Algorithm Research Articles

Spatial analysis of the effects of urban expansion on agricultural land use in sub-Saharan Africa: A case of Dar es Salaam region in Tanzania

This study aimed to analyze the effect of urban expansion on agricultural land use in Dar es Salaam. It involved Classification of Sentinel-2 Satellite Imagery to ascertain whether there was a change in land use/land cover for the time period 2016 until 2019 using maximum likelihood algorithm. The classification accuracy was 62% and Kappa Hat coefficient 0.48 for 2016 and 79.15% and Kappa Coefficient 0.75 for 2019 respectively. A total of 17,428.06 hectares of land were detected to have changed from other classes into Urban and built-up.

Delineation of Landfill Sites for Municipal Solid Waste Management using GIS

The use of landfills has been applauded in municipal solid waste management as a critical solution to open dumping and burning. Selection of suitable landfill sites presents a challenge as it involves integration of numerous criteria in waste management and sustainability template without prejudice to the environment. The aim of this paper is to delineate appropriate sites for landfills in the basement complex of Ado-Ekiti, South-western Nigeria. The need for landfill sites that meet scientific and environmental requirements is compelling. Landsat-8 OLI & TIRS images of 2016 and ASTER imagery (30m resolution) were acquired to generate land use/land cover maps and site selection criteria. The composite image was subjected to supervised classification using the maximum likelihood algorithm in ArcGIS 10.2.2. The classification permitted the extraction of the land use of urban areas (settlements), agricultural areas, and bare land. Geoelectrical characterization was deployed to produce in situdata. Integration of data using a weighted overlay yielded a landfill suitability index map that classified the area into four categories of unsuitable, partly suitable, moderately suitable, and very suitable locations of landfill sites. Sanitary landfills are desirable to mitigate the adverse effects of widespread open dumping. Doi: 10.28991/HEF-2022-03-03-05 Full Text: PDF

A knock-on deuteron imager for measurements of fuel and hotspot asymmetry in direct-drive inertial confinement fusion implosions (invited).

A knock-on deuteron imager (KoDI) has been implemented to measure the fuel and hotspot asymmetry of cryogenic inertial confinement fusion implosions on OMEGA. Energetic neutrons produced by D-T fusion elastically scatter ("knock on") deuterons from the fuel layer with a probability that depends on ρR. Deuterons above 10MeV are produced by near-forward scattering, and imaging them is equivalent to time-integrated neutron imaging of the hotspot. Deuterons below 6MeV are produced by a combination of side scattering and ranging in the fuel, and encode information about the spatial distribution of the dense fuel. The KoDI instrument consists of a multi-penumbral aperture positioned 10-20cm from the implosion using a ten-inch manipulator and a detector pack at 350cm from the implosion to record penumbral images with magnification of up to 35×. Range filters and the intrinsic properties of CR-39 are used to distinguish different charged-particle images by energy along the same line of sight. Image plates fielded behind the CR-39 record a 10keV x-ray image using the same aperture. A maximum-likelihood reconstruction algorithm has been implemented to infer the source from the projected penumbral images. The effects of scattering and aperture charging on the instrument point-spread function are assessed. Synthetic data are used to validate the reconstruction algorithm and assess an appropriate termination criterion. Significant aperture charging has been observed in the initial experimental dataset, and increases with aperture distance from the implosion, consistent with a simple model of charging by laser-driven EMP.

Evaluation of decadal land degradation dynamics in old coal mine areas of Central India

AbstractThe present study attempts to understand land use land cover change dynamics in an area subjected to opencast and underground coal mining for the last few decades in Kotma Coalmines of Anuppur District in Madhya Pradesh, India through geospatial techniques. Land use and land cover (LULC) change detection analysis was performed digitally classifying LANDSAT‐5 (2001) as well as LANDSAT‐8 (2020) satellite data using a maximum likelihood algorithm. Results revealed that area under dense native vegetation decreased drastically (by 13.74 km2) with the gradual and consistent expansion in the activities of coal mines which showed the highest increase in area over time (15.84 km2). Bivariate regression analysis showed the positive empirical relationships between vegetation indices and soil physico‐chemical parameters. Studies suggested that soil and vegetation is degraded over the large mining areas consistently over a long time period. Despite the continuous reforestation activities on mined areas, the decline area under dense vegetation and sparse vegetation over the twenty‐year time‐scale indicates that the reclamation activities are still in its infancy. A land degradation vulnerability Index (LDVI) map was generated to understand the extent of decadal land degradation trends and it shows that 8.60% of the area is highly vulnerable to degradation. The land degradation index (LDI) inputs will help the planners to develop alternate strategies to tackle vulnerability zones for safe mining. Monthly estimation of various meteorological parameters was also recorded to generate heat plots for the period 2001–2020. The study concludes that monitoring and assessment of fragile ecosystems are indispensable for holistic environmental management.

Comparing Four Machine Learning Algorithms for Land Cover Classification in Gold Mining: A Case Study of Kyaukpahto Gold Mine, Northern Myanmar

Numerous studies have been undertaken to determine the optimal land use/cover classification algorithm. However, there have not been many studies that have compared and evaluated the performance of maximum likelihood (ML), random forest (RF), support vector machine (SVM), and classification and regression trees (CART) using ASTER imagery, especially in a mining district. Therefore, this study aims to investigate land use/cover (LULC) change over three decades (1990–2020), comparing the performance of the ML, RF, SVM, and CART machine learning algorithms. The Landsat and ASTER data were retrieved using Google Earth Engine (GEE). Traditional ML classification was performed on ArcGIS 10.2 software while RF, SVM, and CART classification were undertaken on GEE. Then, thematic accuracy assessments were conducted for the four algorithms and their performances were compared. The results showed that the largest changes in area occurred in forest cover that decreased from 37.8 to 27.3 km2 during the three decades. The remarkable expansion of gold mining occurred during 2005–2010 with the increases of 1.6%. The mining land rose by 2.9% during the study period whereas agricultural land increased significantly by 10.7% between 1990 and 2020. When comparing the four algorithms, the RF algorithm gives the highest accuracy with an overall accuracy of 95.85% while SVM follows RF with 91.69%. This study proved that RF is the best choice for optimal land use/cover classification, particularly in the mining district.

Dynamics and Prediction of Land Use and Land Cover Changes Using Geospatial Techniques in Abelti Watershed, Omo Gibe River Basin, Ethiopia

Ethiopia is a growing country which is in need of scientific ground for land use planning and agricultural-based economy. Evaluation of land use/land cover (LULC) changes helps for proper scheduling and use of natural resources with safe administration in accordance with time and dynamic population growth of the country, specifically in the study area. One of the detailed and useful ways to develop land use evaluation and classification maps is the use of geospatial techniques such as remote sensing and geographic information systems (GIS). The main focus of this study is to evaluate the dynamics of land use and land cover (LULC) changes in the Abelti Watershed, Omo-Gibe River basin, Ethiopia. Maximum likelihood algorithm approach supervised classification method was used for identifying the LULC changes using satellite data to know LULC changes in the watershed. Quantifications of spatial and temporal dynamics of land use/cover changes were accomplished by using three satellite images of 2000, 2010, and 2017 and classifying them via a supervised classification algorithm by using Earth Resources and Development System (ERDAS) software and finally applying the postclassification change detection technique was performed by using ArcGIS 10.3. From the LULC analysis, the increase was observed in the agricultural area and settlement area from 2000 to 2017. On the other hand, shrub land followed a declining trend during the study period. However, forest and bare land followed variable trends during the study period in which forest declined from 2000 to 2010 but increased from 2010 to 2017 and bare land increased from 2000 to 2010 and declined from 2010 to 2017. Generally, the driving force behind this change was population growth, rapid urbanization, and deforestation which resulted in a wide range of environmental impacts, including degraded habitat quality in the watershed.

Implications of Spatio-Temporal Land Use/Cover Changes for Ecosystem Services Supply in the Coastal Landscapes of Southwestern Ghana, West Africa

Land use/land cover change (LULCC) is an important driver of ecosystem changes in coastal areas. Despite being pervasive in coastal Ghana, LULCC has not been investigated to understand its effects on the potential for coastal landscapes to supply ecosystem services (ES). In this study, the impacts of LULCC on the potential supply of ES by coastal landscapes in Southwestern Ghana was assessed for the years 2008 and 2018 by using remote sensing and benefit transfer approaches. Based on available data, relevant provisioning and regulating ES were selected for the assessment while indicators to aid the quantification of the ES were obtained from literature. Supervised classification methods and maximum likelihood algorithms were used to prepare land use/land cover (LULC) maps and the derived LULC categories were assigned according to the descriptions of the Land Cover Classification System (LCCS). Potential supply of provisioning (food, fuelwood) and regulating (carbon storage) services was quantified and the spatial and temporal distributions of these ES illustrated using maps. The results show variations in food and fuelwood supply and carbon storage potentials over the study period and across different locations on the landscape. Potentials for fuelwood supply and carbon storage in mangrove forests indicated declining trends between 2008 and 2018. On the other hand, food-crop supply and carbon storage potential in rubber plantations depicted increasing patterns over the same period. Population, slope and elevation exhibited strong effects on LULC conversions to food crop and rubber plantations whereas these factors were less important determinants of mangrove forest conversions. The findings of the study have implications for identifying and addressing tradeoffs between land uses for agriculture, industrial development and conservation of critical coastal ES within the context of rapid land system transformations in the study region.

An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm

An evaluation of methods to handle missing data in the context of latent variable interaction analysis: multiple imputation, maximum likelihood, and random forest algorithm

Identifying Coffee Agroforestry System Types Using Multitemporal Sentinel-2 Data and Auxiliary Information

Coffee is one of the most important agricultural commodities of Mexico. Mapping coffee land cover is still a challenge because it is grown mainly on small areas in agroforestry systems (AFS), which are located in hard-to-access mountainous regions. The objective of this research was to map coffee AFS types in a mountainous region using the changing spectral response patterns over the dry season as well as supplementary data. We employed Sentinel-1, Sentinel-2 and ALOS-Palsar images, a digital elevation model, soil moisture layers, and 150 field plots. First, we defined three coffee AFS types based on their structural and spectral characteristics. Then, we performed a recursive feature elimination analysis to identify the most relevant predictor variables for each land use/cover class in the region. Next, we constructed a predictor variable dataset for each AFS type and one for the remaining land use/cover classes. Afterward, four maps were generated using a random forest (RF) classifier. Finally, we combined the four maps into a unique land-cover map through a maximum likelihood algorithm. Using a validation sample of 932 sites derived from Planet images (4.5 m pixel size), we estimated a 95% map overall accuracy. Two AFS types were classified as having low error; the third, with the highest tree density, had the lowest accuracy. The results obtained show that the infrared and near-infrared bands from the Sentinel-2 scenes are particularly useful for coffee AFS discrimination. However, supplementary data are required to improve the performance of the classifier. Our findings also highlight the importance of the multi-temporal and multi-dataset approach for identifying complex production systems in areas of high topographic heterogeneity.

Influence of Electrode Configuration on Muscle-Fiber-Conduction-Velocity Estimation Using Surface Electromyography.

Muscle fiber conduction velocity (MFCV) is an important myoelectric parameter and can be estimated by analysing surface electromyography (EMG). Among many factors, electrode configuration plays a key role on MFCV estimation. Most studies adopt bipolar configuration (BC) for CV estimation. However, a thorough understanding of the underlying mechanism is lacking, confusing the design of the most appropriate EMG measurement setup for CV estimation. The aim of this study is therefore to systematically investigate the influence of electrode configuration on MFCV estimation. Four possible configurations are considered, including BC, monopolar configuration (MC), common average reference (CAR), and a special monopolar configuration (SMC) using a fixed channel on the active muscle as reference. For each configuration, mathematical models computing the time delay between adjacent channels are derived and evaluated by dedicated simulation as well as real EMG measurements. MFCV was calculated using the maximum likelihood algorithm with and without channel normalization. The simulation results are in line with the mathematical models. The CVs estimated from the real EMG with and without normalization are 4.3 ±0.7 and 7.2 ±3.7 m/s, 5.7 ±1.3 and 20.4 ±4.7 m/s, 9.0 ±3.4 and 20.6 ±9.8 m/s, and 5.5 ±2.5 and 5.5 ±2.4 m/s for BC, MC, SMC, and CAR, respectively. Our results show normalized BC to produce the most accurate CV estimation, in line with the mathematical models and the simulation results. These findings enable a better understanding of the influence of electrode configuration on MFCV estimation, providing useful information for EMG measurement setup design aiming at MFCV studies.

DOA estimation of incoherently distributed sources using importance sampling maximum likelihood

In this paper, an importance sampling maximum likelihood (ISML) estimator for direction-of-arrival (DOA) of incoherently distributed (ID) sources is proposed. Starting from the maximum likelihood estimation description of the uniform linear array (ULA), a decoupled concentrated likelihood function (CLF) is presented. A new objective function based on CLF which can obtain a closed-form solution of global maximum is constructed according to Pincus theorem. To obtain the optimal value of the objective function which is a complex high-dimensional integral, we propose an importance sampling approach based on Monte Carlo random calculation. Next, an importance function is derived, which can simplify the problem of generating random vector from a high-dimensional probability density function (PDF) to generate random variable from a one-dimensional PDF. Compared with the existing maximum likelihood (ML) algorithms for DOA estimation of ID sources, the proposed algorithm does not require initial estimates, and its performance is closer to Cramer-Rao lower bound (CRLB). The proposed algorithm performs better than the existing methods when the interval between sources to be estimated is small and in low signal to noise ratio (SNR) scenarios.

Sequencing and phylogenomics of the complete mitochondrial genome of Allodiplogaster sp. (Rhabditida: Diplogasteridae): A new gene order and its phylogenetic implications

Gene order has been utilized as a phylogenetic signal for many taxa. However, its phylogenetic performance has not been evaluated in Nematoda. As there is only one nematode mitogenome available to date, in the Diplogasteridae family, we sequenced the mitogenome of Allodiplogaster sp. and constructed a phylogeny for Nematoda using this updated mitogenome dataset. We then compared this phylogeny to one constructed using gene order. The complete mitochondrial genome of Allodiplogaster sp. was 13,953 bp in size and included 22 tRNAs, two rRNAs, and 12 protein-coding genes. To assess how Allodiplogaster sp. is related to other nematode species, we used Bayesian inference and maximum likelihood algorithms to construct phylogenetic trees of the Nematoda. We found that: 1) The target species Allodiplogaster sp. is closely related to Allodiplogaster sudhausi. The topology of the mitogenome based phylogeny was nearly identical to previous phylogenies created using 18S rRNA data, except for the placement of the Strongyloididae family. 2) The maximum likelihood tree constructed using gene order was roughly consistent with the mitogenome-based tree at the family level, but not at the species level. 3) Protein-coding genes were ordered differently in Allodiplogaster sp. versus Allodiplogaster sudhausi; this represents the first report of such a reordering in the class Chromadorea in our study. Our study confirms that gene order represents useful phylogenetic information for the Nematoda: the maximum likelihood tree based on gene order provided additional support for the nematode phylogeny constructed using molecular data.

Climate Change and Land Use Dynamics in Djirnda Commune (Fatick Region - Senegal): Remote Sensing Approach

Climate change is one of the greatest challenges of our time. It is a real and rapid phenomenon which, perhaps, does not dramatically affect people today but will certainly have harmful effects on future generations. It has led to the weakening of ecosystems and accelerated the degradation of natural resources. This situation has prompted the scientific world to sound the alarm to warn of its environmental and socio-economic impacts. This study seeks to analyze the dynamics of land use in Djirnda municipality in climate change. A pixel-based classification approach and the maximum likelihood algorithm were retained. The data used concerns Landsat images from 1986, 2000 and 2015. The post-classification comparison method is used to detect changes in the classified images. The overall accuracy of the classifications gives satisfactory results with 90 % for 1986, 89 % for 2000 and 91 % for 2015 and a respective Kappa coefficient of 89 %, 85 % and 90 %. The results of land use dynamics revealed an increase in mangroves (1553.13 ha) and salt lands (957.15 ha), and a regression in water surface (- 458.64 ha) and mudflats (- 2051.64 ha).

Single-qubit reaped quantum state tomography

Quantum state tomography is the experimental procedure of determining an unknown state. It is not only essential for the verification of resources and processors of quantum information but is also important in its own right with regard to the foundation of quantum mechanics. Standard methods have been elusive for large systems because of the enormous number of observables to be measured and the exponential complexity of data post-processing. Here, we propose a new scheme of quantum state tomography that requires the measurement of only three observables (acting jointly on the system and pointer) regardless of the size of the system. The system is coupled to a “pointer” of single qubit, and the wavefunction of the system is “reaped” onto the pointer upon the measurement of the system. Subsequently, standard two-state tomography on the pointer and classical post-processing are used to reconstruct the quantum state of the system. We also developed an efficient and scalable iterative maximum likelihood algorithm to estimate states from statistically incomplete data.

Phylogenetic placement of the enigmatic Floodplain water snake, Lycodonomorphus obscuriventris FitzSimons, 1964

Lycodonomorphus is a genus of lamprophiid water snake endemic in Africa. Although widespread, abundant and presumably an important component of many aquatic and semi-aquatic food webs, these snakes are poorly understood taxonomically, particularly from a phylogenetic perspective. With only four of the nine species currently sequenced, this study attempts to improve our understanding of the evolutionary relationships within the genus through the phylogenetic placement of one of the most elusive species, Lycodonomorphus obscuriventris. Collected in the Ramsar declared Makuleke Wetlands in northern Kruger National Park (South Africa), the sample used in this study not only yielded the first DNA sequences for the taxon but also represented the most northerly South African record, bridging the gap between the southern and northern populations. The snake was sequenced for three partial mitochondrial genes (16S, Cyt-b, ND4) and one partial nuclear gene (c-mos) and phylogenetically placed, relative to the rest of the genus, using maximum likelihood (ML) and Bayesian inference (BI). Sequence divergences between sister taxa were also estimated using pairwise distance analysis. The concatenated phylogenetic reconstruction yielded similar topological structuring when compared to phylogenies from past articles, with both the ML and BI algorithms recovering strong support for L. obscuriventris as sister to a clade comprising of L. whytii + L. laevissimus + L. rufulus. The phylogenetic placement, albeit based on a single sample, challenges the original placement (morphological) of L. obscuriventris as sub-specific within L. whytii, suggesting that multiple species concepts should be considered when delineating species within this group.Conservation implications: Prior to the discovery of the new record, the global distribution of L. obscuriventris was characterised by two disjunct populations. The new record bridges the distribution gap between these two populations, rendering the distribution continuous. This bodes well for the species as there is likely no barrier to gene flow, thereby buffering the species from localised threats given the more expansive distribution. Furthermore, given that the specimen was sampled from the Kruger National Park, the species is likely to be well-protected as much of its distribution within South Africa seems to fall within protected areas.

Investigation of Near-Field Source Localization Using Uniform Rectangular Array

In fifth-generation (5G) wireless communications, large-scale arrays pose a challenge to the accuracy of signal models based on the plane wavefront. In this paper, a novel method for 3D near-field direction of arrival (DOA) estimation is proposed based on large-scale uniform rectangular array (URA). First, the near-field signal model based on the vertical rectangular array and the delay phase shift of the received array is presented. Afterwards, the proposed method divides the complete parameters set into multiple-parameters subsets, and only estimates one of them in each iteration, leaving the others in the fixed subset. As a result, we can obtain the maximum convergence rate of the deterministic maximum likelihood (DML) algorithm. Finally, the simulation results demonstrate that the root mean square errors (RMSEs) of the proposed algorithm are closer to the Cramer-Rao lower bounds and converge faster than those of the DML algorithm, confirming its effectiveness and superiority.

Ant Colony Pheromone Mechanism-Based Passive Localization Using UAV Swarm

The problem of passive localization using an unmanned aerial vehicle (UAV) swarm is studied. For multi-UAV localization systems with limited communication and observation range, the challenge is how to obtain accurate target state consistency estimates through local UAV communication. In this paper, an ant colony pheromone mechanism-based passive localization method using a UAV swarm is proposed. Different from traditional distributed fusion localization algorithms, the proposed method makes use of local interactions among individuals to process the observation data with UAVs, which greatly reduces the cost of the system. First, the UAVs that have detected the radiation source target estimate the rough target position based on the pseudo-linear estimation (PLE). Then, the ant colony pheromone mechanism is introduced to further improve localization accuracy. The ant colony pheromone mechanism consists of two stages: pheromone injection and pheromone transmission. In the pheromone injection mechanism, each UAV uses the maximum likelihood (ML) algorithm with the current observed target bearing information to correct the initial target position estimate. Then, the UAV swarm weights and fuses the target position information between individuals based on the pheromone transmission mechanism. Numerical results demonstrate that the accuracy of the proposed method is better than that of traditional localization algorithms and close to the Cramer–Rao lower bound (CRLB) for small measurement noise.

Tracking the molecular evolution and transmission patterns of SARS-CoV-2 lineage B.1.466.2 in Indonesia based on genomic surveillance data

BackgroundAs a new epi-center of COVID-19 in Asia and a densely populated developing country, Indonesia is facing unprecedented challenges in public health. SARS-CoV-2 lineage B.1.466.2 was reported to be an indigenous dominant strain in Indonesia (once second only to the Delta variant). However, it remains unclear how this variant evolved and spread within such an archipelagic nation.MethodsFor statistical description, the spatiotemporal distributions of the B.1.466.2 variant were plotted using the publicly accessible metadata in GISAID. A total of 1302 complete genome sequences of Indonesian B.1.466.2 strains with high coverage were downloaded from the GISAID’s EpiCoV database on 28 August 2021. To determine the molecular evolutionary characteristics, we performed a time-scaled phylogenetic analysis using the maximum likelihood algorithm and called the single nucleotide variants taking the Wuhan-Hu-1 sequence as reference. To investigate the spatiotemporal transmission patterns, we estimated two dynamic parameters (effective population size and effective reproduction number) and reconstructed the phylogeography among different islands.ResultsAs of the end of August 2021, nearly 85% of the global SARS-CoV-2 lineage B.1.466.2 sequences (including the first one) were obtained from Indonesia. This variant was estimated to account for over 50% of Indonesia’s daily infections during the period of March–May 2021. The time-scaled phylogeny suggested that SARS-CoV-2 lineage B.1.466.2 circulating in Indonesia might have originated from Java Island in mid-June 2020 and had evolved into two disproportional and distinct sub-lineages. High-frequency non-synonymous mutations were mostly found in the spike and NSP3; the S-D614G/N439K/P681R co-mutations were identified in its larger sub-lineage. The demographic history was inferred to have experienced four phases, with an exponential growth from October 2020 to February 2021. The effective reproduction number was estimated to have reached its peak (11.18) in late December 2020 and dropped to be less than one after early May 2021. The relevant phylogeography showed that Java and Sumatra might successively act as epi-centers and form a stable transmission loop. Additionally, several long-distance transmission links across seas were revealed.ConclusionsSARS-CoV-2 variants circulating in the tropical archipelago may follow unique patterns of evolution and transmission. Continuous, extensive and targeted genomic surveillance is essential.

Characterization of the complete mitochondrial genome of Ixodes granulatus (Ixodidae) and its phylogenetic implications.

Ticks are deemed to be second only to mosquitoes as the most common vector of human infectious diseases worldwide that give rise to human and animal diseases and economic losses to livestock production. Our understanding of the phylogenetic analysis between tick lineages has been restricted by the phylogenetic markers of individual genes. Genomic data research could help advance our understanding of phylogenetic analysis and molecular evolution. Mitochondrial genomic DNA facilitated the phylogenetic analysis of eukaryotes containing ticks. In this study, we sequenced and assembled the circular complete mitogenome information of Ixodes granulatus. The 14,540-bp mitogenome consists of 37 genes, including 13 protein-coding genes (PCGs), two genes for ribosomal RNA (rRNAs), and 22 genes for transfer RNA (tRNAs), and the origin of the L-strand replication region. The directions of the coding strand and component genes in the non-Australasian Ixodes mitochondrial genome were similar to those found in most other Australasian Ixodes, except for the loss of a lengthy control region. The phylogenetic tree based on maximum likelihood (ML) and Bayesian inference (BI) computational algorithms showed that I. granulatus exhibits a close relationship with I. hexagonus and I. ricinus. To our knowledge, this is the first study exploring the complete mitogenome for the species I. granulatus. Our results provide new insights for further research on the evolution, population genetics, systematics, and molecular ecology of ticks.

Mapping pre and post earthquake land cover change in Melangkap, Kota Belud Sabah using multi-temporal satellite Landsat 8/OLI and Sentinel 2 Imagery

Earthquakes in mountainous belt can trigger major disturbance to the landscape impound a large amount of debris exported largely to the streams and forming landslide. During the 5th of June 2015, an earthquake of 5.9 Richter scale hit the heart of Mount Kinabalu, Sabah. The tremors caused the surrounding area to be affected largely to the population that is located near the river stream site. This study maps the land cover in Kota Belud following the earthquake that occurred in 2015 using Landsat 8 Operational Land Imagery (year 2014) and Sentinel-2 Multispectral Imagery data (2020). It was reported as one of the locations that severely impacted by the earthquake. Maximum likelihood algorithm was used to generate the map of land cover change of Kota Belud year 2014 and year 2020. Nine (9) classes of land cover were identified in the study area consisting of primary forest, secondary forest, shrubland, paddy, rubber plantation, bare rock, bare soil, built up, and river. The result obtained shows the most affected area is the river area, Sungai Panataran, that flow through Melangkap from Mount Kinabalu. Other land cover type also shows changes but mostly due to deforestation and agricultural activities. In conclusion, mapping the Kota Belud area using remote sensing and geographical information system (GIS) is useful for monitoring the landscape change of the site due to the vulnerability to natural disaster such as flood and earthquake.