SPOT Images Research Articles

High spatial–temporal image fusion model for retrieving aerosol optical depth based on top-of-atmosphere reflectance

With the growth in industrialization and urban development, air pollution has become an increasing serious health concern. Although ground stations can effectively monitor air quality, they generally observe only located phenomena and limited in the spatial distribution. Remote-sensing approaches have thus been employed by many scholars for air quality monitoring in an entire region. However, no single satellite equips with sufficient spatial and temporal resolutions for detecting rapidly changing local phenomena, such as air quality variations. A top-of-atmosphere reflectance–based spatial–temporal image fusion model (TOA-STFM) is proposed in this paper to solve this problem. The proposed TOA-STFM is modified based on the spatial–temporal adaptive reflectance fusion model (STARFM) and yields fused images in which atmospheric properties are retained. A key process in the TOA-STFM is blurring effect adjustment (BEA), which is performed to match the atmospheric effects caused by aerosols in images with different spatial resolutions. The feasibility of fusing Himawari-8 images with SPOT-6 images was evaluated in this study. We used the proposed model to extract aerosol optical depths (AODs) from images produced by fusing Himawari-8 and SPOT-6 images and compared the extracted AODs with corresponding in-situ observations made by the AErosol RObotic NETwork (AERONET). The AOD relative errors of the proposed TOA-STFM were 2.3%–7.6%, which is a significant improvement comparing to a relative error of 8.4%–13.5% from Himawari-8 images and existing AOD products.

Exploring the Interplay of Wavelength, Quantum Yield, and Penetration Depth in In Vivo Fluorescence Imaging.

The intricate interplay between the irradiation wavelength, the fluorophore quantum yield (QY) and penetration depth profoundly influences the efficacy of in vivo fluorescence imaging in various applications. Understanding the complex behavior of fluorescence in vivo, specifically how variations in wavelength affect the QY of commonly used dyes and the depth of imaging is crucial for optimizing fluorescence imaging techniques, as it directly impacts the accuracy and efficiency of imaging in biological tissues. In our study, we explore these dynamics through Monte Carlo simulations conducted under conditions reflective of wide-field fluorescence imaging, examining how variations in wavelength impact the dye's QY and depth of imaging, and consequently, the fluorescence behavior. A transition in the exponential decay of the emission depth exponent is observed around the 500-600nm range, indicating varying degrees of influence of depth on the fluorescence emission. The analysis of the fluorophore's QY reveals wavelength-dependent variations, with the most significant impact observed in the 600-700nm range. Moreover, we continued our investigation to explore multiplexing, unveiling insights into the spacing between identical spots in multiplexing images across various depths and wavelengths.

Detection of β-ray from 3H with back illuminated sCMOS imager

Tritium occurs in nuclear power plants. The energy of beta-rays from tritium is so low that detecting the beta-rays is challenging. Therefore, method of measuring tritium surface contamination is required. Liquid scintillators are generally used to measure the beta-rays. However, this method is complicated to use on-site. To measure tritium surface contamination on-site, we attempted to detect tritium using the GSENSE2020BSI back-illuminated sCMOS imager.We used a tritium areal radiation source to validate the detection of beta rays from tritium. The beta-rays cause bright spots in images and spread to multipixel. The average number of the pixels by the beta-rays was 4.6 and covered within a 3x3 pixel region. The energy spectrum from 10,000 images with 3x3 binning patterns showed a continuous shape spectrum. The spectra shape was characteristic of beta-ray spectra, demonstrating that the energy spectrum could be obtained with low-energy beta rays from tritium using the back-illuminated sCMOS image sensor. The spectral shape was produced from a radiation simulation using Geant4. From the simulation, the sCMOS sensor was able to measure tritium contamination of 10 becquerels (Bq)/cm2 within 100 s and 4 Bq/cm2 within 480 s.

Long-term effects of Abay River flow regulation at Lake Tana on the geomorphic and ecological responses of the downstream river channels, Upper Blue Nile basin, Ethiopia

Alluvial rivers adjust their geometry in response to environmental and anthropogenic disturbances. Flow regulation results in a new geomorphic condition that affects the aquatic ecosystem and its nature. This paper examines the long-term effects of flow regulation on the geomorphic and ecological responses of the downstream river channels of the Abay River, which is the only natural outflow of Lake Tana, Ethiopia. Since 1996, the river’s discharge regime has been affected by constructing a head-rise weir (Chara Chara) at its natural outlet for hydropower production and dam construction on the Lake Tana tributary rivers. Hydrologic data collected at the outlet of the river, SPOT, and Google Earth images were used for the study. River banks and bed topography were extracted via ArcGIS for selected study periods. The study revealed that existing water resource development on the tributary rivers of Lake Tana modified (decreased) the outflow discharge on the Abay River, and resulted a changed morphological and moderate level ecological impact on the river system. Future ongoing and planned water resource developments will exacerbate the pressure on the lake. Without careful management, these changes are likely to have severe morphological, ecological and social consequences.

Miniaturized optical system of a laser seeker based on a dynamic and static performance co-design

The laser semi-active seeker, crucial for miniaturizing guided munitions, offers high precision, anti-jamming, and rapid response. Current research focuses on system response, spot parameters, and angular velocity accuracy. With small-caliber munitions on the rise, further miniaturization and weight reduction are essential. This paper proposes a design for an optical system that balances dynamic imaging quality and miniaturized static structure. Using quadrant detector principles, factors affecting detection performance are analyzed to determine the optimal spot size and imaging range. Based on imaging requirements and the optical properties of a dual-reflector structure, we determine the design parameters of the system to achieve a combination of dynamic and static performance. Evaluations using ZEMAX and MATLAB confirm that the system achieves ±4∘ linear and ±8∘ full fields of view, 0.12% distortion, 4.52 mrad angular accuracy, a 15.72 mm axial length, and has the possibility of actual production. The system is compact, with excellent spot roundness, stability, and uniform energy distribution, providing a reference for future guided weapon research.

An Analysis and Optimization of Distortion Effect Caused by Pupil Decentering in Optical Gun Scope

During the use of optical gun scopes, slight movements between the human eye and the instrument can cause the pupil to offset from the optical axis, resulting in a dynamic distortion effect. This affects the accuracy and stability of aiming. Based on the mechanism, this study established parameters of the centroid’s deviation of image spots for marginal field points under pupil decentering and centering conditions and their differences to quantitatively evaluate the distortion. These evaluation parameters were obtained by performing a double integral calculation of the ray aberration distribution function over the entire designed exit pupil. Based on this evaluation method, three optical design strategies for reducing the distortion were proposed: optimizing ray aberrations, optimizing centroid shift of image spots, and utilizing vignetting effects. An optimization process was established by combining increasing vignetting and suppressing centroid shift. For a gun scope with significant distortion, the distortion effect was significantly weakened by increasing the vignetting factor and optimizing the centroid shift of image spots. This proved the effectiveness of the proposed analysis, evaluation, and optimization design methods.

Image recognition based on THGS algorithm to optimize ResNet-18 model

In order to quickly and accurately identify brown spot images, an improved THGS-ResNet-18 recognition model is proposed in this paper. Firstly, the Hunger Game search algorithm is improved by using Tent chaos mapping to solve the problem of excessive randomness in the population initialization of the Hunger Game search algorithm. Secondly, the hyperparameters of the improved Hunger Game search algorithm are optimized for the ResNet-18 model. Finally, the improved model THGS-ResNet-18 is applied to identify 5064 rice leaf images, and compared with four other ResNet-18 models improved by swarm intelligence algorithm on seven evaluation indicators. Experiments show that the model proposed in this paper has improved accuracy 5.22%−6.09%, sensitivity 3.53%−5.31%, specificity 7.38%, precision 6.95%−7.13%, recall 3.53%−5.31%, f-measure 5.22%−6.20%and g-mean 5.24%−6.13%.

Image-Based Auto-Focus Microscope System with Visual Servo Control for Micro-Stereolithography.

Micro-stereolithography (μSL) is an advanced additive manufacturing technique that enables the fabrication of highly precise microstructures with fine feature resolution. One of the primary challenges in μSL is achieving and maintaining precise focus throughout the fabrication process. For the successful application of μSL, it is essential to maintain the sample surface within a focal depth of several microns. Despite the growing interest in auto-focus devices, limited attention has been directed towards auto-focus systems in image-based auto-focus microscope systems for precision μSL. To address this challenge, we propose an image-based auto-focus microscope system incorporating visual servo control. In the optical design, a transflective beam splitter is employed, allowing the laser beam to pass through for fabrication while reflecting the focused beam on the sample surface to the microscope and camera. Utilizing captured spot images and the Foucault knife-edge test, a deep learning-based laser spot image processing algorithm is developed to determine the focus position based on spot size and the number of spot pixels on both sides. Experimental results demonstrate that the proposed auto-focus system effectively determines the relative position of the focal point using the laser spot image and achieves auto-focusing through visual servo control.

Mapping stem volume in fast-growing eucalypt plantations: integrating spectral, textural, and temporal remote sensing information with forest inventories and spatial models

Key messageAccurate volume estimation in Eucalyptus plantation stands was achieved by a linear model using SPOT and Landsat multispectral imagery, specifically texture indices and pixel-scale NDVI time integrals, which reflect the local plantation growth history. Spatial modelling techniques such as Kriging with External Drift and Generalized Additive Model slightly improved predictions by accounting for spatial correlation of volume between sample points.ContextForest inventories are widely used to estimate stand production. To capture the inherent spatial variability within stands, spatial modelling techniques such as Kriging with External Drift (KED) and the generalized additive model (GAM) have emerged. These models incorporate information on spatial correlation and auxiliary variables that can be obtained from satellite imagery.AimsOur study explored the use of reflectance data from SPOT and Landsat multispectral imagery. We focused on texture indices and temporal integration of vegetation indices as auxiliary variables in KED and GAM to predict stem volume of fast-growing Eucalyptus sp. plantations in Brazil.MethodsThe components extracted from the high-resolution SPOT-6 image included spectral band values, band ratio metrics, key vegetation indices (NDVI, SAVI, and ARVI), texture measurements, and indices derived from texture analysis. Additionally, we included the accumulated NDVI time series acquired from Landsat 5, 7, and 8 satellites between the planting date and the forest inventory measurement date.ResultsThe best linear model of stem volume using remotely sensed predictors gave an R-squared value of 0.95 and a Root Mean Square Error (RMSE) of 12.44 m3/ha. The R-squared increased to 0.96 and the RMSE decreased to 10.6 m3/ha when the same predictors were included as auxiliary variables in the KED and GAM spatial models.ConclusionThe linear model using remotely sensed predictors contributed most to volume prediction, but the addition of spatial coordinates in the KED and GAM spatial models improved local volume predictions.

Expo2020 Dubai and Tourism Marketing: An Online User-Generated Content Analysis

Dubai is currently one of the world’s leading cities in the tourism field. Expo2020 has placed it in a remarkable position in sustainable tourism development amongst other tourist destinations. Information communication technologies (ICTs) have become essential for connecting tourism destinations with their visitors and other market partners. X (Twitter) facilitates online communication; embracing it have proved to be critical for boosting the brand image and reputation of tourism spots and augmenting their competitive advantage in the market. User-generated content (UGC) and electronic word of mouth (eWOM) have become important key players in this field. The purpose of this research was to deepen the study of the Expo2020 event in relation to tourism within the framework of ICTs. To do so, 3811 tweets posted from April 2021 to March 2022 using the hashtags #expo2020 and #visitdubai were analyzed. Users generated more than 10 tweets daily on this topic. Dubai’s institutional accounts were highly involved in creating content, leading personal accounts to follow the eWOM and UGC. Destination marketing organization (DMO) related to the Indian segment account stood out as one of the main players in the analyzed sample.

Detection of artificial spots in fundus images using modified U-Net based semantic segmentation

Today, deep learning algorithms are playing a very crucial role in the initial stage diagnosis of several fundus diseases like glaucoma, hypertension, and diabetic retinopathy. Lots of research is going on in this area now-a-days. During the acquisition of fundus images some artificial spots (e.g. because of the device itself, dust particles in the surroundings) have been added in captured images. In this paper, artificial spots in the fundus images that are generated due to the non-standardized conditions of scanning devices, are detected with the help of a newly proposed modified UNet (mU-Net) semantic segmentation model. Initially, preprocessing methods such as Gaussian blur, thresholding, and Hough transform have been used to create artificial spots. Now these preprocessed images have been used for training the proposed model. To make the proposed model more effective, the following modifications like, Regularization techniques (early stopping, greater weight decay, and Adam optimizer), decay learning rate scheduler, categorical cross-entropy loss function, and a significant number of filters have been modified in simple U-Net model. Apart from these mentioned modifications in the base U-Net model, a feature injecting module (FIM) has been added between the expansion and the contraction section of the simple U-Net model. FIM adds the features of the input image at the time of up-sampling. The addition of FIM to a simple U-Net model improves the detection of artificial spots and enhances the performance of the model. The mU-Net has been compared with other models, namely simple U-Net, V-Net, UNet++, ResUnet-a, WideU-Net, and Swin-Unet. The Friedman test that has been conducted on IOU, DICE, MAE, PSNR, and SSIM scores, found that mU-Net balances evaluation metrics well. It appears that the nonparametric Friedman test will improve reproducibility by demonstrating statistical significance. The IOU, DICE, MAE, PSNR, and SSIM scores of the proposed model indicate superior performance as compared to other models.

Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study.

The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource-heavy challenge that could potentially benefit from AI-tool assistance, particularly if provided in widely available AI solutions. A novel smartphone application(app)-based AI system, "SCAI," was developed and trained to recognize spots in paired images of skin, pursuing identification of new skin lesions. This pilot study aimed to investigate the feasibility of the SCAI-app to identify simulated skin changes in vivo. The study was conducted in a controlled setting with healthy volunteers and standardized, simulated skin changes (test spots), consisting of customized 3-mm adhesive spots in three colors (black, brown, and red). Each volunteer had a total of eight test spots adhered to four areas on back and legs. The SCAI-app collected smartphone- and template-guided standardized images before and after test spot application, using its backend AI algorithms to identify changes between the paired images. Twenty-four volunteers were included, amounting to a total of 192 test spots. Overall, the detection algorithms identified test spots with a sensitivity of 92.0% (CI: 88.1-95.9) and a specificity of 95.5% (CI: 95.0-96.0). The SCAI-app's positive predictive value was 38.0% (CI: 31.0-44.9), while the negative predictive value was 99.7% (CI: 99.0-100). This pilot study showed that SCAI-app could detect simulated skin changes in a controlled in vivo setting. The app's feasibility in a clinical setting with real-life skin lesions remains to be investigated, where the challenge with false positives in particular needs to be addressed.

Size photometry and fluorescence imaging of immobilized immersed extracellular vesicles.

Immunofluorescence analysis of individual extracellular vesicles (EVs) in common fluorescence microscopes is gaining popularity due to its accessibility and high fluorescence sensitivity; however, EV number and size are only measurable using fluorescent stains requiring extensive sample manipulations. Here we introduce highly sensitive label-free EV size photometry (SP) based on interferometric scattering (iSCAT) imaging of immersed EVs immobilized on a glass coverslip. We implement SP on a common inverted epifluorescence microscope with LED illumination and a simple 50:50 beamsplitter, permitting seamless integration of SP with fluorescence imaging (SPFI). We present a high-throughput SPFI workflow recording >10,000 EVs in 7min over ten 88×88µm2 fields of view, pre- and post-incubation imaging to suppress background, along with automated image alignment, aberration correction, spot detection and EV sizing. We achieve an EV sizing range from 37 to ∼220nm in diameter with a dual 440 and 740nm SP illumination scheme, and suggest that this range can be extended by more advanced image analysis or additional hardware customization. We benchmark SP to flow cytometry using calibrated silica nanoparticles and demonstrate superior, label-free sensitivity. We showcase SPFI's potential for EV analysis by experimentally distinguishing surface and volumetric EV dyes, observing the deformation of EVs adsorbed to a surface, and by uncovering distinct subpopulations in <100nm-in-diameter EVs with fluorescently tagged membrane proteins.

NEUROBIOLOGICAL PROPERTIES OF THE STRUCTURE OF THE PARALLEL-HIERARCHICAL NETWORK AND ITS USAGE FOR PATTERN RECOGNITION

The paper presents the analysis of neurobiological data on the existence of the structure of a parallel-hierarchical network. Discussed method of parallel-hierarchical transformation based on population coding and its application for the pattern recognition task. Based on the analysis, we can conclude that using the methods proposed, it is possible to measure the geometric parameters and properties of images, which can significantly increase the efficiency of processing, in particular estimating the center of mass based on moment characteristics. Experimental results demonstrate that due to various destabilizing factors, accurately measuring the energy center coordinates of laser beam spot images is challenging. However, training the PI network and classifying the fragments into "good" and "bad" can considerably enhance the accuracy of these measurements.

Automatic optimization design of laser triangulation ranging sensors using an improved genetic algorithm

Optical system parameter design is of great importance to ensure the accuracy of asymmetry systems such as laser triangulation ranging systems. However, the system parameter determination often depends on the experience and manual attempts of designers, which is not only time-consuming but also inevitable to introduce human errors. Therefore, in this paper an automatic optimization design method based on nonlinear programming genetic algorithm with elitism strategy (E-NPGA) is proposed, to accurately and fast determine the optimal system parameters of laser triangulation ranging systems assisting in improving the measurement accuracy. Firstly, an optimization model of system parameters is developed under the Scheimpflug rule establishing the constraints for various measurement resolutions and ranges. An image size constraint is constructed for the first time to improve and evaluate the parameter optimization. Secondly, the E-NPGA is proposed with nonlinear optimization and elitism strategy, which can determine the optimal system parameters in 15 iterations avoiding local extremum. In design examples, using the E-NPGA determined system parameters ZEMAX simulation and experimental results of the parameters depended image spot size show a slight relative difference below 0.6%. Moreover, the experiment results demonstrate the sensor system designed by using the E-NPGA enables a distance measurement with submicron absolute error and 10−4 relative uncertainty. The automatic optimization method proposed in this paper is compared with the conventional GA method and PSO method, and it is validated that the convergence accuracy of the proposed method is much higher than the conventional ones.

Research on Point Cloud Acquisition and Calibration of Deep Hole Inner Surfaces Based on Collimated Ring Laser Beams.

In this study, a ring light point cloud calibration technique based on collimated laser beams is developed, aiming to reduce errors caused by the position and attitude changes of traditional ring light measurement devices. This article details the generation mechanism of the ring beam and the principle of deep hole measurement. It introduces the collimated beam as a reference, building on traditional ring light measurement devices, to achieve the synchronous acquisition of the ring beam and collimated spot images by an industrial camera. The Steger algorithm is employed to accurately extract the coordinates of the point cloud contours of both the ring beam and the collimated spot. By analyzing the shape and position changes of the collimated spot contour, the spatial position and attitude of the measuring device are precisely determined. This technique is applied to the 3D reconstruction of the inner surface of deep holes, ensuring the accurate restoration of the spatial positional attitude of the ring beam by incorporating the spatial positional attitude parameters of the measuring device to precisely calibrate the cross-sectional point cloud coordinates. Experimental results with ring gauges and deep hole workpieces demonstrate that this technique effectively reduces the percentage of point cloud data outside the tolerance range, and improves the accuracy of the 3D reconstruction model by 6.287%, thereby verifying the accuracy and practicality of this technique.

Portable, smartphone-linked, and miniaturized photonic resonator absorption microscope (PRAM Mini) for point-of-care diagnostics.

We report the design, development, and characterization of a miniaturized version of the photonic resonator absorption microscope (PRAM Mini), whose cost, size, and functionality are compatible with point-of-care (POC) diagnostic assay applications. Compared to previously reported versions of the PRAM instrument, the PRAM Mini components are integrated within an optical framework comprised of an acrylic breadboard and plastic alignment fixtures. The instrument incorporates a Raspberry Pi microprocessor and Bluetooth communication circuit board for wireless control and data connection to a linked smartphone. PRAM takes advantage of enhanced optical absorption of ∼80 nm diameter gold nanoparticles (AuNP) whose localized surface plasmon resonance overlaps with the ∼625 nm resonant reflection wavelength of a photonic crystal (PC) surface. When illuminated with wide-field low-intensity collimated light from a ∼617 nm wavelength red LED, each AuNP linked to the PC surface results in locally reduced reflection intensity, which is visualized by observing dark spots in the PC-reflected image with an inexpensive CMOS image sensor. Each AuNP in the image field of view can be easily counted with digital resolution. We report upon the selection of optical/electronic components, image processing algorithm, and contrast achieved for single AuNP detection. The instrument is operated via a wireless connection to a linked mobile device using a custom-developed software application that runs on an Android smartphone. As a representative POC application, we used the PRAM Mini as the detection instrument for an assay that measures the presence of antibodies against SARS-CoV-2 infection in cat serum samples, where each dark spot in the image represents a complex between one immobilized viral antigen, one antibody molecule, and one AuNP tag. With dimensions of 23 × 21 × 10 cm3, the PRAM Mini offers a compact detection instrument for POC diagnostics.

Investigating Blind Spot Design Effects on Drivers’ Cognitive Load with Lane Changing: A Comparative Experiment with Multiple Types of Intelligent Vehicles

Lane changing is a frequent traffic accident scenario. To improve the driving safety in lane changing scenarios, the blind spot display of lane changing is increased through human–machine interaction (HMI) interfaces in intelligent vehicles to improve the driver’s rate of risk perception with regard to the driving environment. However, blind spot information will increase the cognitive load of drivers and lead to driving distraction. To quantify the coupling relationship between blind spot display and drivers’ cognitive load, we proposed a method to quantify the cognitive load of the driver’s interaction by improving the AttenD algorithm, collecting feature data by carrying out a variety of real-vehicle road-testing experiments on three kinds of intelligent vehicles, and then establishing a model blind spot design and driver cognitive load correlation model using Bayesian Logistic Ordinal Regression (BLOR) and Categorical Boosting (CatBoost). The results show that the blind spot image display can reduce the driver’s cognitive load more effectively as it is closer to the driver, has a larger area, and occupies a higher proportion of the center control screen, especially when it is located in the middle and upper regions of the center control screen. The improved AttenD algorithm is able to quantify the cognitive load of the driver, which can be widely used in vehicle testing, HMI interface development and evaluation. In addition, the analytical framework constructed in this paper can help us to understand the complex impact of HMI in intelligent vehicles and provide optimization criteria for lane change blind spot design.

Characterization and Properties of ZnO:Nd&lt;sup&gt;3+&lt;/sup&gt; Nanomaterial Synthesized by Chemical Synthesis Method

Nd3+ ion-doped ZnO nanomaterial was prepared using chemical synthesis method and its fluorescence spectra have been investigated at room temperature. From SEM images of the synthesized ZnO: Nd3+ nanoparticles it is observed that an increase in concentration of Nd3+ ions leading to the decrease in the particle size. Nearly hexagonal shapes for the dark spots in the SAED images indicate that the ZnO nanoparticles are almost hexagonal. The oscillator strengths leading to 4f ↔ 4f transitions are characterized by different Judd-Ofelt intensity parameters Ωλ (λ = 2, 4 and 6). These Ωλ parameters along with the fluorescence data and various radiative properties viz., spontaneous emission probability (A), radiative life time (t), fluorescence branching ratio (b) and stimulated emission cross-section (sp) were evaluated and compared with the reported values. The values of these parameters indicate that the observed transitions 4F3/2 → 4I11/2, 4F3/2 → 4I13/2 and 4F3/2 → 4I15/2 can be considered to be good laser transitions in the near infrared region for different optoelectronic and spintronic uses.

Evaluating pasture cover density mapping: a comparative analysis of Sentinel-2 and Spot-5 multispectral sensor images

Vegetation density extraction is influenced by the characteristics of satellite images, vegetation type, classification algorithm, and region, but there is little information about multispectral imaging (MSI). Studying the compatibility of the information content of sensors to replace sensors in areas where there is no easy access to their data is necessary for remote sensing (RS) studies. This study aims to assess the suitability of MSI from Sentinel-2 and Spot-5 satellites for generating pasture density maps. The Middle Kashkan watershed in the Lorestan Province of Iran was the study area. Geometric correction of the images was performed using ground control points (GCP) and the area's digital elevation model, achieving an accuracy of 0.21 pixels or better. Supervised classification techniques including parallelogram, minimal distance, maximum likelihood, and artificial neural network (ANN) algorithms were applied to the primary MSI of each satellite, as well as the integrated image of Spot-5 and the resulting pasture density map. Three density categories were considered: 5–25%, 25–50%, and over 50%. To validate the accuracy of the classification, a ground truth map of the region was created by interpreting a referenced official digital orthophotomosaic image at a scale of 1:40,000. Comparative analysis of the classified images revealed that the Sentinel-2 image with PCA-2-8 band composition and ANN classification algorithm yielded superior results, with an overall accuracy of 65.72% and a kappa coefficient of 0.08, compared to the Spot-5 image with PCA-3-1 band composition and the ANN classification algorithm. Spot-5 satellite images demonstrated greater effectiveness in generating pasture cover maps across the three density categories. These findings suggest that satellite images with suitable spatial and spectral resolution can be effectively utilized for generating accurate pasture density maps and monitoring long-term pasture preservation, particularly in regions characterized by high aerial photography altitudes in pasture areas. This approach holds the potential for effective pasture management and conservation efforts on a global scale.