Acquisition Of Imagery Research Articles

Assessing intertidal sediment photopigment content from spectral reflectance with an UAV-mounted 10-band multispectral sensor

ABSTRACT Multispectral sensors mounted to unoccupied aerial vehicles (UAVs) can be leveraged to quantify microphytobenthos (MPB) biomass in intertidal mudflats, providing data products with cm-scale pixels. However, no standard protocol currently exists for calibrating UAV-acquired spectral information to sediment MPB content. Here, we present a new protocol for calibrating data from a UAV-mounted multispectral sensor to sediment MPB biomass as measured by photopigment content. To do so, we developed a methodology for acquiring and analyzing UAV imagery and sediment photopigment field data. We then implemented the protocol in the Fraser River Estuary, Canada to build a statistically valid calibration equation, testing the effectiveness of several spectral indices and photopigment measurements. Calibrated spectral index values can provide a very accurate measurement of MPB biomass, able to achieve 90 % correlation between the normalized difference vegetation index (NDVI) and sediment chlorophyl-a (chl-a) concentration. This high performance was achieved by closely pairing georeferenced sediment samples to corresponding multispectral imagery and minimizing the lag between sediment sample collection and UAV imagery acquisition. This protocol can facilitate the use of calibrated UAV-acquired multispectral imagery for investigating ecologically-important fine-scale spatial heterogeneity of MPB biomass.

A Comparative Study of Deep Learning Frameworks Applied to Coffee Plant Detection from Close-Range UAS-RGB Imagery in Costa Rica

Introducing artificial intelligence techniques in agriculture offers new opportunities for improving crop management, such as in coffee plantations, which constitute a complex agroforestry environment. This paper presents a comparative study of three deep learning frameworks: Deep Forest, RT-DETR, and Yolov9, customized for coffee plant detection and trained from images with a high spatial resolution (cm/pix). Each frame had dimensions of 640 × 640 pixels acquired from passive RGB sensors onboard a UAS (Unmanned Aerial Systems) system. The image set was structured and consolidated from UAS-RGB imagery acquisition in six locations along the Central Valley, Costa Rica, through automated photogrammetric missions. It was evidenced that the RT-DETR and Yolov9 frameworks allowed adequate generalization and detection with mAP50 values higher than 90% and mAP5095 higher than 54%, in scenarios of application with data augmentation techniques. Deep Forest also achieved good metrics, but noticeably lower when compared to the other frameworks. RT-DETR and Yolov9 were able to generalize and detect coffee plants in unseen scenarios that include complex forest structures within tropical agroforestry Systems (AFS).

A robust underwater image enhancement algorithm

Capturing clear images in underwater environments is a major challenge in marine engineering. There are many issues to consider in obtaining clear underwater images such as climate, environment, and human factors. The most important reasons are the atomization effect caused by dispersion and the color cast caused by inconsistent energy attenuation of each wavelength when light propagates in water. Recently, deep learning technology has shown impressive performance on underwater image enhancement. The deep learning-based methods apply to the underwater image enhancement tasks. We propose a deep learning model for inferring a degradation model to further improve image dynamic range through a network-guided underwater image enhancement network architecture with multicolor space embedding and convolutional media transfer, fixed an issue with limited dynamic range and brightness in underwater images. Quantitative and qualitative results show that our network performs relatively well in the Underwater Image Enhancement Benchmark (UIEB) [7] dataset compared to other recent methods, and is expected to be applied to different types of underwater work and environments in the future and reduce the degradation problems that often occur with underwater images. The acquisition of high-fidelity imagery in subaqueous environments presents significant technical challenges in marine engineering, encompassing a complex interplay of climatological variables, environmental parameters, and anthropogenic factors. Primary impediments to image clarity comprise the atomization phenomenon induced by optical scattering and chromatic distortion resulting from wavelength-dependent energy attenuation in aqueous media. The procurement of high-resolution underwater imagery is fundamental to numerous scientific applications, including marine biological research, autonomous underwater robotics, and environmental surveillance systems, where precise visual data acquisition substantially augments analytical efficacy. Contemporary developments in deep learning architectures have exhibited remarkable potential for enhancing underwater image quality. In response to these challenges, we present a novel deep learning framework that derives an empirical degradation model, utilizing a network-guided enhancement architecture incorporating multicolor space embedding and convolutional media transfer methodologies to optimize image dynamic range. This methodological approach specifically addresses the limitations in luminance distribution and dynamic range characteristics inherent in subsea imagery. Empirical evaluation of our architectural framework on the standardized Underwater Image Enhancement Benchmark (UIEB) [7] dataset demonstrates statistically significant performance improvements over contemporary methodologies, suggesting broad applicability across diverse submarine environments for mitigating common degradation phenomena.

An Assessment of Land Use Land Cover Using Machine Learning Technique

This research paper presents a comprehensive assessment of the built-up area in Mysuru City over the decade spanning from 2010 to 2020, employing advanced geospatial techniques. The study aims to analyze the spatiotemporal patterns of urban expansion, land-use dynamics, and associated factors influencing the city’s built environment. Remote sensing imagery, Geographic Information System (GIS) tools, and machine learning algorithms are leveraged to process and interpret satellite data for accurate land-cover classification. The methodology involves the acquisition and preprocessing of multi-temporal satellite imagery to delineate and map the built-up areas at different time intervals. Land-use change detection techniques are employed to identify and quantify alterations in urban morphology over the specified period. Additionally, socio-economic and environmental variables are integrated into the analysis to discern the drivers of urban growth. The outcomes of this research contribute valuable insights into urbanization dynamics and land-use planning strategies, facilitating informed decision-making for sustainable urban development.

Near real‐time monitoring of wading birds using uncrewed aircraft systems and computer vision

Wildlife population monitoring over large geographic areas is increasingly feasible due to developments in aerial survey methods coupled with the use of computer vision models for identifying and classifying individual organisms. However, aerial surveys still occur infrequently, and there are often long delays between the acquisition of airborne imagery and its conversion into population monitoring data. Near real‐time monitoring is increasingly important for active management decisions and ecological forecasting. Accomplishing this over large scales requires a combination of airborne imagery, computer vision models to process imagery into information on individual organisms, and automated workflows to ensure that imagery is quickly processed into data following acquisition. Here we present our end‐to‐end workflow for conducting near real‐time monitoring of wading birds in the Everglades, Florida, USA. Imagery is acquired as frequently as weekly using uncrewed aircraft systems (aka drones), processed into orthomosaics (using Agisoft metashape), converted into individual‐level species data using a Retinanet‐50 object detector, post‐processed, archived, and presented on a web‐based visualization platform (using Shiny). The main components of the workflow are automated using Snakemake. The underlying computer vision model provides accurate object detection, species classification, and both total and species‐level counts for five out of six target species (White Ibis, Great Egret, Great Blue Heron, Wood Stork, and Roseate Spoonbill). The model performed poorly for Snowy Egrets due to the small number of labels and difficulty distinguishing them from White Ibis (the most abundant species). By automating the post‐survey processing, data on the populations of these species is available in near real‐time (<1 week from the date of the survey) providing information at the time scales needed for ecological forecasting and active management.

Deep-Sea Fauna Segmentation: A Comparative Analysis of Convolutional and Vision Transformer Architectures at Lucky Strike Vent Field

Abstract. Due to recent technological developments, the acquisition and availability of deep-sea imagery has increased exponentially in the last years, leading to an increasing backlog in image annotation and processing, attributable to limited specialized human resources. In this work, we investigate the performance of well-established convolutional neural networks and Vision Transformer (ViT) based architectures, namely, DeepLabv3+ and UNETR, for the segmentation of fauna in deep-sea images. The dataset consists of images captured at the Lucky Strike Vent field, located on the mid-Atlantic ridge, of three edifices named Montsegur, White Castle, and Eiffel Tower. Our experimental investigation reveals that the Vision Transformer consistently outperforms the fully convolutional deep learning architecture, by approximately 14% in terms of F1-Score, demonstrating the effectiveness of ViTs in capturing intricate patterns and long-range dependencies present in deep-sea imagery. Our findings highlight the potential of ViTs as a promising approach for accurate semantic segmentation in challenging environmental contexts, paving the way for improved understanding and analysis of deep-sea ecosystems.

Mildly explosive eruptions at Martian low-shield volcanoes

AbstractOngoing acquisition of Martian surface imagery constantly provides new opportunities to reveal previously undiscovered small-scale volcanic landforms, yielding critical insights into volcanic processes, and challenging existing inferences. Here, using the most recent, high-resolution topographical data, we mapped the accumulation of pyroclastic deposits occurring along the margins of several volcanic vents. They share morphological similarities with terrestrial volcanic deposits attributed to low-intensity lava fountaining occurring during mild explosive activity. Our identified, explosive volcanic deposits are associated with late Amazonian volcanic activity in Tharsis. The identification of these very recent (<100 Ma) deposits across the entire Tharsis volcanic province needs reconciling with our current view of the evolution of explosive volcanism on Mars. We contend that these small volume landforms, produced by mildly explosive volcanic activity, need to be considered in models surrounding planet-scale magmatic evolution and atmospheric volatile budgets.

Coarse-to-Fine Structure and Semantic Learning for Single-Sample SAR Image Generation

Synthetic Aperture Radar (SAR) enables the acquisition of high-resolution imagery even under severe meteorological and illumination conditions. Its utility is evident across a spectrum of applications, particularly in automatic target recognition (ATR). Since SAR samples are often scarce in practical ATR applications, there is an urgent need to develop sample-efficient augmentation techniques to augment the SAR images. However, most of the existing generative approaches require an excessive amount of training samples for effective modeling of the SAR imaging characteristics. Additionally, they show limitations in augmenting the interesting target samples while maintaining image recognizability. In this study, we introduce an innovative single-sample image generation approach tailored to SAR data augmentation. To closely approximate the target distribution across both the spatial layout and local texture, a multi-level Generative Adversarial Network (GAN) architecture is constructed. It comprises three distinct GANs that independently model the structural, semantic, and texture patterns. Furthermore, we introduce multiple constraints including prior-regularized noise sampling and perceptual loss optimization to enhance the fidelity and stability of the generation process. Comparative evaluations against the state-of-the-art generative methods demonstrate the superior performance of the proposed method in terms of generation diversity, recognizability, and stability. In particular, its advantages over the baseline method are up to 0.2 and 0.22 in the SIFID and SSIM, respectively. It also exhibits stronger robustness in the generation of images across varying spatial sizes.

Integrating drones into disaster town watching: Participatory action data collection of local knowledge in Tuguegarao City, Philippines

Disaster town watching comprises a participatory technique by a community to observe, identify, understand, and build resilience to natural or human-induced hazards. Participants walk through their communities, taking notes and imagery for subsequent participatory risk mapping exercises. The goal is to develop a more nuanced and contextualized understanding of the community's state of disaster risk management. This article explores the integration of drones in the acquisition of imagery for disaster town watching. It followed standard practices for conducting disaster town watching research in a community in the Philippines. However, in addition to standard walking teams, one team learned to pilot drone missions, take aerial images, and then interpret them. The imagery they produced elicited more spatially significant relationships between and across features and locations in the community. The types of local knowledge identified differed significantly from that taken from images produced with cellular phones or Google Earth. This study revealed that drone imagery can provide new insights into local knowledge on hazards, vulnerability, and resources and the enhancement of social, economic, and environmental resilience it engenders. Participants build greater confidence in their ability to determine self-help solutions and mutual help countermeasures for enhancing disaster preparedness and community resilience against disaster.

Status and Prospects for the Use of Remote Sensing Data for the Detection of Wildfires

The article discusses the problem of fires as a serious threat to life, economy and ecosystems, highlighting the need for early detection and suppression of fires. The potential of the combination of Earth remote sensing and neural networks for rapid and accurate detection of natural fires is studied. The significance of applying artificial intelligence, the development of deep learning methods for neural network models, to analyze space images and detect early signs of fires is emphasized. The article also provides examples of successful projects and research in the field of wildfire detection. The final part of the paper emphasizes the need for further research and development of neural network training methods, expansion of training datasets and improvement of space imagery acquisition technologies for effective control and prevention of fires, in order to protect the environment and minimize damage to people.

Examination of Drone Usage in Estimating Hardwood Plantations Structural Metrics

Planting hardwood trees on retired marginal agricultural land is one of the main strategies used to restore forested wetlands. Evaluating effectiveness of wetland restoration requires efficient monitoring to evaluate recovery trajectories and desired conditions. Recent advancements in unmanned aerial system (UAS) technologies have prompted wide-scale adoption of UAS platforms in providing a range of ecological data. In this study, we examined the use of UAS Structure from Motion (SfM) derived point clouds in estimating tree density, canopy height, and percent canopy cover for bottomland hardwood plantations within four wetland reserve easements. Using a local maxima approach for individual tree detection produced plantation level estimates with mean absolute errors of 150 trees per hectare, 0.5 m, and 18.4% for tree density, canopy height, and percent canopy cover, respectively. At the plot level, UAS-derived tree counts (r = 0.53, p < 0.01) and canopy height (r = 0.57, p < 0.01) were significantly correlated with ground-based estimates. We demonstrate that UAS-SfM is a viable method of assessing bottomland hardwood plantations for applications that require precision levels congruent with the mean absolute errors reported here. The accuracy of tree density estimates was reliant upon specific local maxima window parameters relative to stand conditions. Therefore, acquisition of leaf-off and leaf-on imagery may allow for better individual tree detection and subsequently more accurate tree density and other structural attributes.

Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

This study aimed to examine the efficiency of the vegetation index (VI) thresholding approach for mapping deadwood caused by spruce bark beetle outbreak. For this, the study used upscaling from individual dead spruce detection by unmanned aerial (UAS) imagery as reference data for continuous spruce deadwood mapping at a stand/landscape level by VI thresholding binary masks calculated from satellite Sentinel-2 imagery. The study found that the Normalized Difference Vegetation Index (NDVI) was most effective for distinguishing dead spruce from healthy trees, with an accuracy of 97% using UAS imagery. The study results showed that the NDVI minimises cloud and dominant tree shadows and illumination differences during UAS imagery acquisition, keeping the NDVI relatively stable over sunny and cloudy weather conditions. Like the UAS case, the NDVI calculated from Sentinel-2 (S2) imagery was the most reliable index for spruce deadwood cover mapping using a binary threshold mask at a landscape scale. Based on accuracy assessment, the summer leaf-on period (June–July) was found to be the most appropriate for spruce deadwood mapping by S2 imagery with an accuracy of 85% and a deadwood detection rate of 83% in dense, close-canopy mixed conifer forests. The study found that the spruce deadwood was successfully classified by S2 imagery when the spatial extent of the isolated dead tree cluster allocated at least 5–7 Sentinel-2 pixels.

Street view imagery: AI-based analysis method and application

Street view imagery is an emerging form of geographic big data. It presents urban visual environments from the perspective of urban residents and also contains non-visual environment of cities, such as urban human activities and socio-economic development. However, traditional digital image processing has its limitations, and the continuous development of artificial intelligence, especially computer vision and deep learning, provides strong technical support for exploring the rich semantic information in street view imagery. This paper reviews the related research on street view imagery and its artificial intelligence analysis methods and applications. It outlines the acquisition, storage, and common data sources of street view imagery. Then it introduces computer vision, deep learning, and commonly used open-source datasets in street view imagery analysis. It also detailed three aspects of AI-based street view imagery applications, namely quantification of the physical space, urban perception, and spatial semantic speculation. Finally, issues like data acquisition, domain adaption and deep learning black box are discussed. The hotspots and prospects for the development of this research topic are also prospected.

Automatic Detection of Phytophthora pluvialis Outbreaks in Radiata Pine Plantations Using Multi-Scene, Multi-Temporal Satellite Imagery

This study demonstrates a framework for using high-resolution satellite imagery to automatically map and monitor outbreaks of red needle cast (Phytophthora pluvialis) in planted pine forests. This methodology was tested on five WorldView satellite scenes collected over two sites in the Gisborne Region of New Zealand’s North Island. All scenes were acquired in September: four scenes were acquired yearly (2018–2020 and 2022) for Wharerata, while one more was obtained in 2019 for Tauwhareparae. Training areas were selected for each scene using manual delineation combined with pixel-level thresholding rules based on band reflectance values and vegetation indices (selected empirically) to produce ‘pure’ training pixels for the different classes. A leave-one-scene-out, pixel-based random forest classification approach was then used to classify all images into (i) healthy pine forest, (ii) unhealthy pine forest or (iii) background. The overall accuracy of the models on the internal validation dataset ranged between 92.1% and 93.6%. Overall accuracies calculated for the left-out scenes ranged between 76.3% and 91.1% (mean overall accuracy of 83.8%), while user’s and producer’s accuracies across the three classes were 60.2–99.0% (71.4–91.8% for unhealthy pine forest) and 54.4–100% (71.9–97.2% for unhealthy pine forest), respectively. This work demonstrates the possibility of using a random forest classifier trained on a set of satellite scenes for the classification of healthy and unhealthy pine forest in new and completely independent scenes. This paves the way for a scalable and largely autonomous forest health monitoring system based on annual acquisitions of high-resolution satellite imagery at the time of peak disease expression, while greatly reducing the need for manual interpretation and delineation.

COMPARATIVE ANALYSES OF SECONDARY ECOLOGICAL SUCCESSION FOLLOWING WILDFIRES IN THREE DISTINCT FOREST TYPES. A STUDY CASE FROM MOGUER, SPAIN

Wildfires have become increasingly prevalent and destructive in forest ecosystems worldwide, necessitating a comprehensive understanding of post-fire recovery dynamics for effective conservation and management. Remote sensing technology, coupled with vegetation indices such as Normalized Burn Ratio (NBR), Normalized Difference Vegetation Index (NDVI), Green Red Vegetation Index (GRVI), and Red Vegetation Index (RVI), offers a powerful means to investigate these processes. In this study, we utilize remote sensing techniques to conduct a comparative analysis of secondary ecological succession following wildfires in three distinct forest types (Coniferous, Sclerophyll and Mixed) of a forest affected by fire near Moguer, Spain. Through the acquisition and analysis of multispectral satellite imagery, we monitored changes in vegetation health and recovery across the region of interest. The NBR index allowed us to assess the severity and extent of wildfire damage, while NDVI quantified vegetation greenness and regrowth. GRVI and RVI provided insights into subtle variations in vegetation composition and health. We identified distinct temporal and spatial patterns in post-fire recovery among the different forest types by applying these indices for the period between 2017 and 2021. Our findings underscore the significance of understanding the diverse responses of these ecosystems to wildfires. While common recovery patterns emerged, such as an initial decrease in NDVI followed by regeneration, variations were observed in the timing and magnitude of recovery. These distinctions are attributed to differences in species composition, fire adaptations, and ecological processes specific to each forest type. In conclusion, the utilization of NBR, NDVI, GRVI, and RVI indices allows for a more nuanced evaluation of post-fire recovery dynamics.

Performance Assessment of Machine Learning Techniques for Gully Erosion Mapping in South-East Nigeria

Soil erosion is a serious environmental hazard affecting southeast Nigeria. The rate of erosion has continued to increase with devastating impact and has led to land degradation (gullies). To assess the socio-economic and environmental implications of gully erosion and to develop management plans to deal with them, quantitative and qualitative data on soil erosion rates at regional scales are needed. Hence, this study aimed at investigating the spatial and temporal variations of gully erosion impacted areas of South East Nigeria using machine learning techniques to develop a suitable approach for accurately modelling gully erosion impact in South East, Nigeria. Its objectives are to: acquire, process, and perform object-based gully mapping analysis with Kompsat-3 imagery of South East Nigeria, Nigeria using three machine learning algorithms (Linear discriminant analysis, Support Vector Machine, and Artificial Neural Network); compare the performance of the three selected machine learning algorithms using statistical techniques and determine the best suited for gully mapping within the study area; determine the pattern and trend of Gully Erosion in South East, Nigeria between 2010 and 2020, using the best-suited machine learning algorithm; and predict the future gully development pattern for the next 10 years. The methodology involved the acquisition and use of KOMPSAT-3 imagery, identification of gully erosion impacted areas, and topographic maps. Image preprocessing, development of classification scheme, object-based image analysis, trend analysis, and pattern of gully development and expansion. Accuracy assessment and gully development prediction to 2030 as data analysis. The results showed consistent identification of eight class features, including waterbody, dense vegetation, agricultural land, bare ground, built-up area, sand, savannah, and gully erosion. The performance evaluation of the three techniques revealed promising outcomes. LDA achieved an accuracy of 0.759, while SVM demonstrated a higher accuracy of 0.853 and balanced sensitivity and specificity. ANN showed good performance with an accuracy of 0.790. Based on its superior accuracy and balanced performance, SVM was selected for mapping the spatial trend of gully erosion in the study area.

Spinach Yield Mapping Using Multispectral UAV Imagery

Crop yield prediction and mapping are essential for crop management and decision-making. The development of unmanned aerial vehicles (UAVs) accelerates the acquisition of high-resolution imagery for the evaluation and monitoring of crop growth and development. In this study, a cost-effective yield mapping workflow was developed in a commercial setting. Two UAVs were deployed to rapidly obtain RGB and multispectral images of spinach at different growth stages in multiple spinach fields to assess crop physiological attributes in the field environment. Based on the imagery, an image processing workflow for farm field orthoimage rotation and segmentation was proposed. Then, twelve vegetation indices (VIs) were extracted from the processed images. The most robust VIs were selected by comparing the correlations among the VIs and yields and regression tree and stepwise multiple linear regression. Excess Green Index (ExG) and Normalized Difference Vegetation Index (NDVI) were identified as two of the most robust VIs for predicting spinach yield at various stages of growth. A linear regression model was developed using 45 manually harvested calibration locations and a coefficient of determination (R2) of 0.98. The root mean squared error (RMSE) was 0.19 kg/m with a mean absolute percentage error (MAPE) of 9.0 % when using a validation dataset. The produced yield maps provided the basis for a farm harvesting decision-making process and for a general crop management strategy.

ESTIMATION OF COASTAL WATERS TURBIDITY USING SENTINEL-2 IMAGERY

Turbidity is an important water quality parameter and an indicator of water pollution. Marine remote sensing techniques has become a useful tool for mapping of turbidity at coastal waters. The advantage of using remote sensing for water quality analysis is its ability to obtain synoptic data from the entire study area to produce continuous surface data, can shows detailed spatial variability and periodically. The empirical modeling has been applied in this study to formulate the mathematical relationship between coastal waters turbidity with Sentinel-2 reflectance. This study integrated field survey and image processing. Measurement of in-situ turbidity was done in accordance with imagery acquisition time. Imageries used for this study were Sentinel-2 level-2A. The mathematical relationship was obtained by multiple linear regression model between turbidity and Sentinel-2 reflectance. A mathematical model has been developed in Sentinel-2 imagery and successfully applied to obtain surface turbidity. Estimated turbidity derived from Sentinel-2 imagery is very close to observed turbidity so the proposed model can be used to retrieve turbidity of coastal waters.

Evaluation of Multi-Spectral Band Efficacy for Mapping Wildland Fire Burn Severity from PlanetScope Imagery

Increased spatial resolution has been shown to be an important factor in enabling machine learning to map burn extent and severity with extremely high accuracy. Unfortunately, the acquisition of drone imagery is a labor-intensive endeavor, making the capture of drone imagery impractical for large catastrophic fires, which account for the majority of the area burned each year in the western US. To overcome this difficulty, satellites, such as PlanetScope, are now available which can produce imagery with remarkably high spatial resolution (approximately three meters). In addition to having higher spatial resolution, PlanetScope imagery contains up to eight bands in the visible and near-infrared spectra. This study examines the efficacy of each of the eight bands observed in PlanetScope imagery using a variety of feature selection methods, then uses these bands to map the burn extent and biomass consumption of three wildland fires. Several classifications are produced and compared based on the available bands, resulting in highly accurate maps with slight improvements as additional bands are utilized. The near-infrared band proved contribute most to increased mapping accuracy, while the green 1 and yellow bands contributed the least.

Bathymetric DEM derived from Lyzenga’s algorithm (multispectral Bathymetry) using Landsat 8 images: a case study of Fort Lauderdale coast

Optical bathymetry can be applied to multispectral images of coastal regions with low spectral and spatial resolutions, and the results obtained are satisfactory in waters up to 15 m. However, the limitations and effectiveness may vary as they are related to physical factors such as water turbidity, specular reflection, and the presence of clouds and shadows during imagery acquisition. Some of these factors also affect the surveys conducted using green Light Detection and Ranging (LiDAR) technology. This study aimed to show that topographic representation by multispectral images is directly related to the physical factors of the water body and the atmospheric conditions during the image acquisition process, good atmospheric conditions and shallow water can generate accurate topographic representation. The experiments carried out in this study using optical bathymetry showed that Landsat 8 satellite images could be used in multispectral bathymetry with a linear regression model by using depth samples obtained from shallow water. Images taken at three different time points were used, with a certain time interval between the data collection performed via the Green LiDAR technology. The depths estimated by this model showed some differences that were possibly caused by water turbidity and temporally-occurring activities. Despite such constraints, the depths obtained by the linear regression model showed a good adaptation to the model derived from the Green LiDAR for depths up to 22 m, with an average error of -0.271 m and a root mean square error of 0.936 m.