Geometric Correction Research Articles

Geospatial Assessment of Oil Spill’s Impact in Obio/Akpor Local Government Area Rivers State, Nigeria

Environmental degradation resulting from oil spills has emerged as a critical issue requiring immediate attention. This study uses geospatial techniques to investigate the impact of oil spills in Obio/Akpor Local Government Area of Rivers State. Landsat images, administrative map of the study area and historical oil spill data were data used in carrying out this study. The images were pre-processed through atmospheric and geometric corrections. Spatial analysis of four remote sensing indices-Normalized Difference Vegetation Index (NDVI) for assessing vegetation health, Normalized Difference Water Index (NDWI) for detecting water bodies, Land Surface Temperature (LST) for measuring surface temperature, and Normalized Difference Built-Up Index (NDBI) for identifying built-up areas-was conducted using the raster calculator in ArcGIS. The study employed the Analytical Hierarchy Process (AHP) to assign weights to these indices based their relative significance regarding oil spill impact. Subsequently, reclassification and integration of this information were done to generate Oil Spill Index (OSI) maps for 2003, 2013, and 2023. Validation of the Oil Spill Index (OSI) of various points before and after an oil spill incident was performed, specifically contrasting 2003 with 2013 and 2023 OSI values. This was to affirm the accuracy and predictive capability of the OSI maps in identifying oil spill-prone areas. Findings revealed OSI values for 2003 and 2013, indicating a range of 1.04 to 3.48 for 2003, with a noticeable increase observed in 2013, expanding the range to 3.08 to 4.72. Similarly, a comparison of OSI values between 2003 and 2023 indicates OSI values, ranging from 1.64 to 3.92 in 2003, then in 2023 after the oil spill incidents, there were notable changes in the OSI values, with the range shifting to 3.2 to 4.84. This suggests a significant increase in the impact of oil spill on the affected areas over the two-decade. This comprehensive index map serves as a tool for monitoring and assessing the effects of oil spills on the study area, thereby achieving the research objectives effectively.

Detection of mainland Kedah’s shoreline changes (2013-2020); a case study

Abstract Shoreline erosion and accretion are natural processes that involve the gradual change in the shape and position of coastlines due to the movement of water, sediment, and geological factors. These processes have significant implications for coastal environments, ecosystems, and human activities. Therefore, it is essential to frequently assess the shoreline changes for effective coastal management, sustainable development, and safeguarding valuable ecosystems. In the present study, shoreline condition along mainland Kedah (109 km) was investigated through remote sensing and geographical information system (GIS) techniques. The assessment was performed over the period of 7 years (2013 and 2020) by analysing the satellite images captured by Landsat-8 satellite Operational Land Imager (OLI) at 15 m resolution. Preprocessing was established by performing image geometric correction and registration. Next, the Support Vector Machines (SVMs) toolbox was used for image classification to define the water and non-water fields. Later, the shoreline was extracted from the classified images and overlaid in a geodetic base in ArcGIS software to detect shoreline changes. The results showed that the majority of mainland Kedah’s shoreline did not experience extensive erosion or accretion at which 54% of the shoreline (58.8 km) was found to be stable. Erosion was observed at 6 locations with a total length of 10.1 km (9% of the total shoreline length) which was mainly concentrated in the non-protected areas. On the other hand, shoreline accretion was observed at 19 locations with a total length of 40.1 km (37% of the total length). It is worth highlighting that the erosion areas were concentrated in the southern part of the coastline, while the accretion areas were distributed between the middle part and the north side of mainland Kedah’s shoreline.

On the importance of the experimental setup design in hydrodynamic journal bearing friction measurement

Improving operating conditions and reducing the friction-related power losses of hydrodynamic journal bearings could lower emissions and contribute to efforts for a faster green transition. Experimental studies play an important role in evaluating and improving the performance of plain journal bearings. The concept of the experimental setup for friction measurements of journal bearings is well defined in the relevant standards, but the actual design is always left to the researchers conducting a particular study. Our study reveals that common practical assumptions about the frictional power losses caused by components of the experimental setup can lead to misleading conclusions about the measured coefficient of friction. The research premises were tested on the developed experimental setup using two methods for measuring the coefficient of friction and different load interfaces. In particular, the direct torque measurement using strain gauges on the shaft demonstrated the strongest correlation with theoretical values. However, the result was affected by the correct evaluation of the frictional losses of the support ball bearings. Conversely, the Stribeck curves showed that the combination of the indirect cantilever method and load interface with direct radial force application led to inferior results. The intricate interactions between setup components and unknown frictional conditions made straightforward geometric corrections challenging. The study encourages researchers in the field to critically assess their experimental setups and to rely on their own assessment of frictional losses.

Multiphase photoclinometry as applied to the lunar photometry with LROC NAC data

An original method for photometric and geometric correction of LROC NAC data with high resolution (up to 0.5 m/pix) has been developed. The technique is based on photogrammetry and multiphase photoclinometry and allows us to obtain a longitudinal slope map and digital elevation model (DEM) with the resolution of LROC NAC CDR input images as well as to map parameters of model phase function. Obtained DEMs, requiring only LROC NAC images and LRO SPICE-kernels, exhibit significantly fewer defects and artifacts compared to existing DEMs, because they are derived from the same images they correct (self-orthorectification).This method was used to study the area of the photometric anomaly, the Irregular Mare Patch (IMP) formation Ina. A zoning map of the correlation diagram of phase ratio vs. equigonal albedo shows significant differences in the optical (photometric) properties of the regolith for hummocky and blocky formations of the IMP Ina, compared to the surrounding areas, which indicates the different nature of their formation, age, and surface roughness, contradicting some models of the IMP formation. The analysis reveals the incompleteness or inconsistencies of previously proposed models of the IMP's formation mechanisms. The high-quality DEM of the Ina formation and surrounding area with a resolution of 0.5 m/pixel was constructed.

Efficient Geometric Correction Workflow for Airborne Hyperspectral Images through DEM-Driven Correction Techniques

Abstract. Geometric correction, a pivotal step in the preprocessing of airborne remote sensing imagery, is critical for ensuring the accuracy of subsequent quantitative analyses. Achieving precise and efficient geometric correction for airborne hyperspectral data remains a significant challenge in the field. This study presents a new method for system-level and fine-scale geometric correction of uncontrolled airborne images utilizing DEM data, which integrates forward and inverse transformation algorithms. Furthermore, an optimized workflow is proposed to facilitate the processing of large-scale hyperspectral datasets. The effectiveness of the proposed method is demonstrated through an application analysis using airborne HyMap imagery, with experimental outcomes indicating high application accuracy and enhanced processing efficiency.

Research on the Joint Construction of a National Multi-source and Multi-resolution image Checkpoint Database

Abstract. In the process of quality inspection of Remote sensing image data results, the reuse of spatial location information of multiple units, multiple projects and multiple sources can not only overcome the problems of long time to obtain control information, high cost and difficulty in obtaining some areas, but also the basis for achieving efficient and high-precision geometric correction. From the perspective of reusability of checkpoints and saving the cost of quality inspection of remote sensing images, this paper discusses the necessity of joint construction of multi-source and multi-resolution image checkpoint database. And put forward the construction principle and management objectives of checkpoint database. At last, this paper briefly introduces and prospects the application of the national multi-source and multi-resolution image checkpoint database.

Development of Multi-Strip Image Mosaicking for KOMPSAT-3A Images

Abstract. High-resolution satellite imagery has a limitation in terms of coverage area. This limitation presents challenges for extensive-scale analysis at regional or national levels. To maximize the utility of high-resolution satellite imagery, the implementation of image mosaicking techniques is essential. In this paper, we have developed seamline extraction techniques and relative geometric correction optimized for high-resolution satellite imagery. Ultimately, we proposed a multi-strip image mosaicking method for KOMPSAT-3A (Korea Multi-Purpose Satellite-3A) images. We applied the Dijkstra's shortest path algorithm to efficiently extract seamlines. we also performed image registration based on feature matching and homography transformation to correct the relative geometric errors between input images. We conducted experiments with our methods using 29 scenes from KOMPSAT-3A L1G data. The results indicated high relative geometric accuracy, with an average error of 1.63 pixels. Furthermore, we were able to obtain high-quality seamless mosaic images. Our proposed method is expected to enhance the utility of KOMPSAT-3A imagery for large-scale environmental and urban analysis and to provide more accurate and comprehensive data.

Detecting and Monitoring Artisanal Mining Operations in Semi-Arid Terrain Using Multitemporal SAR Data for InSAR Coherence Estimation and Unsupervised Classification

Abstract. The impact of artisanal mining in sub-Saharan Africa and other developing countries are often huge as they leave trails of environmental and socio-economic effects on the people and the environment. 123 Multi-temporal SAR data acquired between 2019 and 2023 was used. SARPROZ was used to co-register the images and other preprocessing steps like radiometric and geometric correction to transform the images from SAR coordinates to geographic coordinates after generating all the interferograms. A stack of the 122 coherence map was created. Unsupervised classification was implemented on the stack. Principal component analysis of dimensionality reductions method yielded a far better result than the other unsupervised cluster methods attempted, it showed a very high classification accuracy of the terrain. The principal component analysis worked by computing the covariance matrix of the stacked coherence map then performed eigen-decomposition on it to yield eigenvectors and eigenvalues, The eigenvectors corresponding to the largest eigenvalues represent the principal components. These principal components capture the directions of maximum variance, and the eigenvectors provides a reduced-dimensional representation of the image stack which is then used to reconstruct an approximation of the original image that captures the essential features of the original SAR data. Backscatter intensity of the SAR images processed for this study period for unsupervised change detection and land cover classification, delineated the different features and classes based on long term coherence values.

Use of Google Earth Engine for the estimation and variation of the vegetable cover of the amazon forest generated by anthropic activity in Huepetuhe period 2016 to 2022

The estimation and variation of the vegetation cover of the Amazon forest generated by anthropic activity in Huepetuhe had the purpose of estimating the loss of vegetation and the deforestation rates for an Amazon forest, the study was carried out using Landsat 8 satellite images, the treatment of the raster images was made using the ERDAS software, the images were subjected to geometric and radiometric corrections, for this purpose control points were taken on the ground, for the analysis four categories of forest degradation were established: Forest with null vegetation with presence of bare soil, forest with sparse vegetation, forest with moderate vegetation and total area of deterioration. The results from the analysis of the multispectral images and the NDVI show that compared to 2016, 30.1 ha increased in the forest category with no vegetation, 28.8 ha in forests with sparse vegetation, 236.9 ha for forests with moderate vegetation and the area of global deterioration increased by 295.8 ha. The annual deforestation rate was 5% (6.02 ha/year) for forests with no vegetation and presence of bare soil and 9% (59.16 ha/year) for global degradation, the coefficient of determination for forests with no vegetation and area naked was R2=0.9704, and for global deterioration of the forest R2=0.957.

Integrating Tehran metropolitan air pollution into the current transport system and sprawl growth: an emphasis on urban performance and accessibility

Air pollution is one of the most challenging issues for urban environment and environmental management. The goal of this study was to determine the impact of Tehran metropolitan's performance and accessibility on urban transportation and air pollution as sprawl grows. Tehran, with a population of 15.98 million people, has many environmental issues, including air pollution. Secondary data were collected from the Tehran Air Quality Control Company as well as Landsat satellite imagery (OLI). The raw data of intra-city and suburban traffic counts for spatial analysis of movements, combined with the raw data of measuring stations, were then used as a sample in the ArcGIS software environment for three selected days in 2013, 2014, and 2016. Following geometric and radiometric correction, programming methods and a multivariate regression algorithm were applied to the images, yielding results in the form of additional stations. According to the results obtained (about 3.29 m) root-mean-square error (RMSE), the Inverse Distance Weighting (IDW) model was used in air pollution maps for better assessment. The findings suggest that Tehran is not the only source of air pollution and that TMA performance and accessibility play a significant role in the amount of air pollution. Furthermore, there is a strong correlation (more than 75%) between air pollution maps and transportation flow maps on specific days (February 23, 2013, February 26, 2015, and December 28, 2016). Finally, the analysis of this situation in three circles showed that the highest volume of traffic was done in the city of Tehran (CBD), suburban area (Suburban), and suburban area (Exurban), respectively, and the pattern of distribution and spatial accumulation of pollution has also been a function of this situation.

Calibration system correction factor for measuring the reference air kerma rate (RAKR) applied to high dose rate 192Ir sources (HDR)

The aim of this study was to use computer simulation to analyze the impact of the aluminum fixing support on the Reference Air Kerma (RAK), a physical quantity obtained in a calibration system that was experimentally developed in the Laboratory of Radiological Sciences of the University of the State of Rio de Janeiro (LCR-UERJ). Correction factors due to scattered radiation and the geometry of the 192Ir sources were also sought to be determined. The computational simulation was validated by comparing some parameters of the experimental results with the computational results. These parameters were: verification of the inverse square law of distance, determination of (RAKR), analysis of the source spectrum with and without encapsulation, and the sensitivity curve of the Sourcecheck 4PI ionization chamber response, as a function of the distance from the source along the axial axis, using the microSelectron-v2 (mSv2) and GammaMedplus (GMp) sources. Kerma was determined by activity in the Reference air, with calculated values of 1.725 × 10−3 U. Bq−1 and 1.710 × 10−3 U. Bq−1 for the ionization chamber NE 2571 and TN 30001, respectively. The expanded uncertainty for these values was 0.932% and 0.919%, respectively, for a coverage factor (k = 2). The correction factor due to the influence of the aluminum fixing support for measurements at 1 cm and 10 cm from the source was 0.978 and 0.969, respectively. The geometric correction factor of the sources was ksg = 1.005 with an expanded uncertainty of 0.7% for a coverage factor (k = 2). This value has a difference of approximately 0.2% compared to the experimental values.

Enhancing Flood Impact Analysis through the Integration of Landsat and MODIS Imagery

This article explores the efficacy of integrating Landsat and MODIS satellite imagery for comprehensive flood impact analysis. By employing advanced remote sensing technologies and sophisticated data processing techniques, this study offers a methodological framework that enhances the precision and depth of environmental analysis. The core methodology involves the systematic processing of satellite data, including radiometric and geometric corrections, combined with the use of analytical indices such as the Normalized Difference Water Index (NDWI) and the Enhanced Vegetation Index (EVI). These indices play a crucial role in accurately delineating water bodies and assessing the extent of flooding. The approach not only improves the reliability of flood mapping but also contributes to the broader understanding of environmental changes and aids in effective disaster management. Through this study, we demonstrate how strategic data integration can provide valuable insights for policymakers, enhancing responses to environmental crises.

A Method of Image Restoration for Distortion of Object in Water-Air Cross-Media

Uneven water-air media distribution or irregular liquid flow can cause changes in light propagation, leading to blurring and distortion of the extracted image, which presents a challenge for object recognition accuracy. To address these issues, this paper proposes a repair network to correct object image distortion in water-air cross-media. Firstly, convolutional combination performs feature extraction on water-air cross-media images, which retains the same features at the same scale and marks feature points with large differences. Then, an attention correction module for geometric lines is proposed to correct geometric lines in water-air cross-media images by comparing and sensing the marked feature points with large differences and utilizing the line similarity in positive and negative samples. Finally, the blurring artifact elimination module eliminates artifacts caused by image blurring and geometric line correction by using multiscale fusion of individual U-Net information streams. This completes the image restoration of object distortion under water-air cross-media. The proposed method is feasible and effective for restoring aberrated objects in water-air cross-media environments, with numerous experiments conducted on water-air cross-media image datasets.

Utilization of Satellite Imagery for Mapping the Distribution of Seagrass on Buhung Pitue Island

Buhung Pitue Island has seagrass beds those which are spread almost evenly along its coast. Research using remote sensing technology in an effort to support seagrass conservation in Indonesia needs to be carried out. Spatial data is relatively easy to obtain because there are many types of images with various spatial resolutions. The image can be obtained on google earth. Analysis of the distribution of seagrass areas was obtained by digitizing on screen in ArcGIS software, namely in seagrass areas where the boundaries are known. Digitizing is conducted by enlarging the seagrass area in the downloaded image, performing radiometric and geometric corrections, and digitizing to create a shapefile (shp) storing the location, shape, and attributes of geographic features. The seagrass distribution area of Buhung Pitue Island was of 36.5 Ha in 2014 and was of 39.6 in 2021. The rate of change in area from 2014 to 2021 was of 0.085% (an increase of 3.1 ha). The distribution area of seagrass has increased due to natural factors and restrictions on human activities during the COVID-19 pandemic. In addition, another factor supporting the increase in seagrass distribution is the abundance of Enhalus acoroides seagrass species growing and spreading over long distances. The sea surface temperature was high, which was 30.37 °C, while the current speed was categorized as slow because it was around 0.01 m/s. Although the results are obtained from high-resolution imagery, an accuracy test still needs to be conducted.

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

On possible traversable wormhole solutions supported by Karmarkar condition in [formula omitted]gravity within the [formula omitted]formalism

In their seminal work, Morris and Thorne (1988) introduced the concept of traversable wormholes (WHs), which are geometric structures serving as bridges to connect two distinct spacetimes or different points within the same spacetime. The properties of these WHs are determined by the choice of the shape function. Extensive research has been conducted in the literature on WHs within modified theories of gravity, considering various types of shape functions. In this paper, we focus on deriving the simplest solutions for traversable WHs within the framework of f(R,T) gravity. Our specific focus lies on the functional form f(R,T)=R+αR2+λT, where R represents the Ricci scalar and T denotes the trace of the energy–momentum tensor. By incorporating quadratic geometric and linear material corrections, we demonstrate that the matter content of WHs can obediently conform to the energy conditions (ECs). This study significantly advances our understanding of WHs within the context of modified gravity. Our findings shed new light on the behavior and properties of WHs, highlighting their compatibility with the f(R,T) gravity framework. By unveiling the ability of WHs to adhere to ECs through the inclusion of geometric and material corrections, we provide groundbreaking insights into these intriguing objects. Overall, our research contributes to the broader field of gravitational physics by presenting a unique perspective on WH modeling. By exploring WHs within the f(R,T) gravity framework and demonstrating their conformity to ECs, we expand our knowledge of these fascinating structures and pave the way for further investigations in this area.

Accurate error compensation method for multi-axis parallel machine via singularized jacobi geometric parameter correction and coupling error evaluation

The Jacobian model is a prevalent tool for error compensation in multi-axis parallel mechanisms. However, discrepancies between the model's nominal and actual geometrical parameters, combined with equivalent replacements and high-order rounding in the modeling process, lead to equation solving challenges and modeling errors. These inaccuracies result in residual errors in the Jacobian model compensation. To address these problems, this paper proposes an optimal Jacobian correction approach. This is based on a geometrical parameter singularized Jacobian correction model, and a module for the evaluation of coupling errors for multi-axis parallel mechanisms was incorporated. Instead of relying on iterative processes, a singularized geometrical error solution method (SESM) was developed. Through this method, precise derivation of the Jacobian correction parameters is ensured, effectively addressing the indefinite equation challenge and partial posture non-solution problem. Moreover, modeling errors resulting from equivalent infinitesimal replacements and the overlooking of high-order minor values are compensated for by the SESM. It was observed that varying singularized geometrical parameters in the Jacobian model can produce different coupling effects and compensation outcomes. Therefore, a sensitivity-based error predictive evaluation method (EPEM) was introduced. By this method, the optimal correction parameter of the Jacobian model across the entire workspace is identified, ensuring precise pose error compensation. The proposed method was validated using a three-axis parallel mechanism. Through these tests, its superior efficacy was revealed. In comparison to the traditional uncorrected Jacobian compensation, reductions in position and orientation errors by 64.93% and 55.29%, respectively, were achieved. This method provides a new approach for error modeling, equation solving, and parameter correction for multi-axis mechanism error compensation and precision equipment development.

QR Code Recognition Based on Image Processing

To solve the QR code recognition problem caused by ordinary camera collection, the recognition algorithm based on image processing is put forward in this paper. The whole process including image binarization, image tilt correction, image orientation, image geometric correction and image normalization allows images collected on different illumination conditions. Experiments show that the improved method can enhance the recognition speed of two-dimensional code and accuracy. QR i.e. “Quick Response” code is a 2D matrix code that is designed by keeping two points under consideration, i.e. it must store large amount of data as compared to 1D barcodes and it must be decoded at high speed using any handheld device like phones. QR code provides high data storage capacity, fast scanning, omnidirectional readability, and many other advantages including, error-correction (so that damaged code can also be read successfully) and different type of versions. Different varieties of QR code symbols like logo QR code, encrypted QR code, QR Code are also available so that user can choose among them according to their need. Now these days, a QR code is applied in different application streams related to marketing, security, academics etc. and gain popularity at a really high pace. Day by day more people are getting aware of this technology and use it accordingly. The popularity of QR code grows rapidly with the growth of smartphone users and thus the QR code is rapidly arriving at high levels of acceptance worldwide.

Multiscale Modeling of Flow in Rod Bundles: From Direct Numerical Simulation and Subchannel to Coarse-Mesh CFD

Fast and accurate evaluation of flow and heat transfer phenomena in rod bundles is a problem of long-standing interest in nuclear engineering. Computational fluid dynamics (CFD) can provide accurate but relatively time-intensive estimates, such that simulations of very long transients with high spatial detail are infeasible. On the other hand, subchannel codes require relatively low computational time and can provide pin-level estimates, but have substantial empiricism in evaluating aspects such as crossflows and turbulent mixing coefficients. A multiscale method (SC+) for bridging this accuracy/speed gap, based on the Subchannel CFD (SubChCFD) method of Liu et al. [Nucl. Eng. Design, Vol. 355, paper 110318 (2019)], is demonstrated and developed here with a focus on a 5 × 5 square rod bundle geometry. The method has been newly implemented into the commercial code STAR-CCM+ and benchmarked against Liu et al.’s data. The 5 × 5 geometry was chosen in part due to the availability of direct numerical simulation (DNS) data at a relevant Reynolds number of a similar configuration for comparison. The SC+ results are variously compared against results from DNS, large eddy simulation, wall-resolved Reynolds-averaged Navier-Stokes, and coarse-mesh CFD methods. As an expansion to the original SubChCFD approach, a simple Hi2Lo approach is demonstrated using the DNS data as a correction to the original friction factor correlations employed. This is verified to improve the predictions. Additional test cases with geometric perturbations are pursued, illustrating the flexibility of SC+. The potential of this method for modeling the narrow gap vortex instability, which would represent an advancement over standard subchannel approaches, is also assessed. The method is expanded to include transverse flow losses, which was found to improve the results for modeling the gap instability. Initial extensions of SC+ for hexagonal rod bundles are also presented; some inaccuracies for the coarsest meshes prompted a detailed investigation of the mesh convergence behavior of the method. Geometric correction factors were devised that provided substantial improvement on these very coarse meshes, improving the prospects of SC+ for wider usage. Future work plans are to expand the methodology to wire-wrapped rod bundles and to implement the method into Pronghorn, with a unified pipeline via the Cardinal wrapper between the codes NekRS, Pronghorn, and BISON, to solve fuel performance problems of direct interest to industry.

Fine-resolution land cover mapping over large and mountainous areas for Lāna‘i, Hawaii using posterior probabilities, and expert knowledge

ABSTRACT The task of accurately mapping species-specific vegetation cover in remote and topographically complex regions like those found in Hawaiʻi presents unique challenges. This study leverages a machine learning approach to accurately classify vegetation into fine species-specific classes across the island of Lāna‘i, Hawaii, offering a novel methodology for tackling such challenges. Utilizing high-resolution WordView-2 satellite imagery, a neural network classifier and a custom lidar-based geometric correction, we introduced two new approaches to refine our high-resolution land cover classifications. This included the implementation of prior-based adjustments to class posterior probabilities to enhance land cover classification accuracy. Moreover, we developed mixed hierarchical classification maps that use class posterior probabilities to identify, at the pixel level, the finest land cover class that meets a user-defined confidence threshold. The resulting high-resolution land cover map for Lāna‘i captures the rich diversity and distribution of native and invasive plant species with high overall accuracy, generally exceeding 95%, based on independent ground control data. The capacity to produce wall-to-wall species-level vegetation maps provides a new window into monitoring vegetation dynamics on Lāna‘i and similarly remote and topographically complex regions, and contributes to our broader understanding of ecosystem responses to invasive species, climatic changes, and land management practices such as erosion and sediment control planning. Our approach offers a blueprint for similar efforts in other complex and remote ecosystems.