Satellite Image Data Research Articles

Deformation Monitoring and Analysis of Baige Landslide (China) Based on the Fusion Monitoring of Multi-Orbit Time-Series InSAR Technology.

Due to the limitations inherent in SAR satellite imaging modes, utilizing time-series InSAR technology to process single-orbit satellite image data typically only yields one-dimensional deformation information along the LOS direction. This constraint impedes a comprehensive representation of the true surface deformation of landslides. Consequently, in this paper, after the SBAS-InSAR and PS-InSAR processing of the 30-view ascending and 30-view descending orbit images of the Sentinel-1A satellite, based on the imaging geometric relationship of the SAR satellite, we propose a novel computational method of fusing ascending and descending orbital LOS-direction time-series deformation to extract the landslide's downslope direction deformation of landslides. By applying this method to Baige landslide monitoring and integrating it with an improved tangential angle warning criterion, we classified the landslide's trailing edge into a high-speed, a uniform-speed, and a low-speed deformation region, with deformation magnitudes of 7~8 cm, 5~7 cm, and 3~4 cm, respectively. A comparative analysis with measured data for landslide deformation monitoring revealed that the average root mean square error between the fused landslide's downslope direction deformation and the measured data was a mere 3.62 mm. This represents a reduction of 56.9% and 57.5% in the average root mean square error compared to the single ascending and descending orbit LOS-direction time-series deformations, respectively, indicating higher monitoring accuracy. Finally, based on the analysis of landslide deformation and its inducing factors derived from the calculated time-series deformation results, it was determined that the precipitation, lithology of the strata, and ongoing geological activity are significant contributors to the sliding of the Baige land-slide. This method offers more comprehensive and accurate surface deformation information for dynamic landslide monitoring, aiding relevant departments in landslide surveillance and management, and providing technical recommendations for the fusion of multi-orbital satellite LOS-direction deformations to accurately reconstruct the true surface deformation of landslides.

Insights from a population grid of South Africa: An applied spatial satellite data analysis

The present study explores the reliability and accuracy of various spatial mapping methodologies in estimating and presenting the spatial characteristic and dynamics (location, distribution, density, and size) of the population in South Africa. As a basic underlying concept, the study first explores spatial heterogeneity, that is, that every location is related to every other location, and those nearby are related stronger. This study, therefore, illustrates the spatial relationships between locations and the spatial pattern of the population in South Africa. Analyzing the spatial images determines the extent of such influence and the nature of the spatial patterns. To this end, a granular gridded population dataset was derived using satellite image data, and the NASA’s Socioeconomic Data and Applications Center gridded population of the world version 4 population images and datasets were used. Several spatial data models and geostatistical applications were applied to study the spatial characteristics and dynamics of the population of South Africa from 2000 to 2020. Spatial analysis was performed using R-Studio, QGIS, and GeoDa. Among others, the results point to the fact that the South African population is very densely located that population density decreases marginally outward and suggests that the underlying process for the population distribution is stationary. This study proposes that it is indeed possible to reliably and accurately estimate and present gridded population images and datasets using spatial and geostatistical methodologies.

Identifying the Original Source of Megalithic Stones in Tambunan Using Spectral Signature Technique

Megalithic stones are a remnant of the ancient people. An intriguing megalithic stone legacy ought to emphasize the context in which it fits. Consequently, it is necessary to ascertain each megalithic stone’s historical context. This is significant for the archaeologist to obtain true and valid information. In Tambunan, there are numerous myths about megalithic stones. As a result, it is impossible to verify the story’s validity. These days, remote sensing has emerged as a crucial technical instrument for archaeologists to gather spatial data in both broad and localized locations. Prior research has demonstrated the efficacy of remote sensing in identifying archaeological site remnants. Nevertheless, there have never been any investigations utilizing remote sensing in relation to small-sized megalithic stones. Thus, this study employs spectral signature techniques and remote sensing to identify the original source of the megalithic stones discovered in Tambunan. This study carries out the spectral signature of megalithic stones in Tambunan using Sentinel-2A satellite image data with a 5-meter resolution using the SNAP application. Before that, the coordinates and measurements for each megalithic stone were obtained from the ground. The spectral signature of all the megalithic stones was then compared with the spectral signature of the stones near the river or near the hill. The study’s findings demonstrate that the spectral signature of the megalithic stones in question is identical to the stone found in the river near its location. As a result, this study was successful in providing scientific evidence that the river is the source of the megalithic stones in Tambunan, despite the stones’ considerable distance from their site.

A robust approach to satellite image encryption using chaotic map and circulant matrices

AbstractIn the modern era, where satellite imagery is vital for applications like ecological monitoring and national security, ensuring the safety and integrity of these data repositories is crucial. This study presents an improved satellite image encryption technique that combines the cryptographic strength of the circulant matrix in the Hill cipher with the dynamic characteristics of the hyperbolic tangent tent map, further enhanced by the Kronecker XOR product. The algorithm initiates with computing alterations by a shift amount. After preserving the leftmost pixel in each row, it executes XOR operations between alternating rows, combining the value of the current even or odd row with corresponding pixels in the adjacent rows followed by encryption using the Hill cipher. The resulting image undergoes a diffusion process utilizing a hyperbolic tangent tent map. The Kronecker XOR product operation is then applied to individual pixels to produce a secure image followed by additional diffusion with keys from the hyperbolic tangent tent map to achieve the final encrypted image. We conducted simulations using MATLAB to assess the efficiency of the proposed satellite image encryption from theoretical and statistical perspectives. The results exhibit robust encryption performance as demonstrated by metrics such as an entropy value of 7.9982, a UACI of 33.5333%, and an NPCR of 99.6038%. The experiment results demonstrate the proposed image encryption scheme's reliability, practicability, and efficiency in securing satellite images during data storage and transmission. Comprehensive testing against various attacks including correlation, histogram, chi‐square, NPCR, PSNR, UACI, SSIM, key space and key sensitivity analysis confirms the scheme's robustness, efficiency and speed. These findings verify the scheme's ability to come across the most stringent encryption and decryption standards, making it an effective solution for securing sensitive satellite image data.

Outdoor light at night, air pollution and risk of incident type 2 diabetes

BackgroundAir pollution and outdoor light at night (LAN) have been reported to be related to type 2 diabetes (T2D). However, their interaction with risk of T2D remains uncertain. Therefore, our study aimed to explore the relationship between outdoor LAN, air pollution and incident T2D. MethodsOur study included a cohort of 24,147 subjects recruited from 2015 to 2018 in Ningbo, China. Land use regression models were used to evaluate particulate matter with a diameter ≤2.5 μm (PM2.5), ≤10 μm (PM10) and nitrogen dioxide (NO2). Satellite images data with a spatial resolution of 500m was used to estimate outdoor LAN levels. T2D new cases were identified by medical records based on health information system. Cox proportional hazards models were used to estimate Hazard ratios (HRs) and 95% confidence intervals (CIs). Moreover, we investigated the multiplicative and additive interactions between air pollution and outdoor LAN. ResultsDuring 108,908 person-years of follow-up period, 1016 T2D incident cases were identified. The HRs (95% CIs) were 1.22 (1.15, 1.30) for outdoor LAN, 1.20 (1.00, 1.45) for PM2.5, 1.23 (1.11, 1.35) for PM10 and 1.19 (1.04, 1.37) for NO2 in every interquartile range increase, respectively. Furthermore, significant interactions were observed between outdoor LAN and NO2. ConclusionsOur findings indicated that air pollution and outdoor LAN were positively associated with T2D. Moreover, we observed an interaction between outdoor LAN and NO2 suggesting that stronger associations for outdoor LAN and T2D in areas with higher levels of NO2, and for NO2 and T2D in areas with higher levels of outdoor LAN.

Implementation of Rainfall Monitoring through an Information System Based on Radar and Satellite Image Data using the Kalman Filter Method

Implementation of Rainfall Monitoring through an Information System Based on Radar and Satellite Image Data using the Kalman Filter Method

Quantifying the dynamic of cereals and broadleaf plants in semi-arid grasslands using a high-spatial-resolution satellite imaging

The vegetation phenology in pasture grasslands temporarily changes during the growing season due to the different physiological properties of broadleaf and cereal species. Determining their dynamics is required for maximal pasture utilization and sustainability. Widely used low-spatial resolution satellites are suitable for plains but are less effective in patchy landscapes. Therefore, the study's objectives are: (i) to determine the dynamic of cereals and broadleaf vegetation in arid grasslands using high-spatial resolution satellite imaging data, and (ii) to determine the influence of different grazing intensities on grassland vegetation phenology. The study site is in Migda LTER farm, the northern Negev, Israel. Fifty-three grassland plots, sized 10x10m, were located. Seventeen plots were grazed: six heavily (90 min. per plot), five moderately (60 min.), and six lightly (30 min.). A sheep and goats' herd of 120–130 animals grazed on these plots once a year between April to May, from 2019 to 2023. As a reference, fenced, ungrazed plots were located, mainly covered by cereals (Conserved). Imaging data from PlanetScope® was acquired (Spatial resolution: 3 m pixel−1, Temporal: Once in three days, Spectral: RGB and NIR). Using a novel timeline analysis, based on normalizing the different treatments' NDVI by the Conserved values, yielded a three-phased result: a parabolic increase in fresh vegetation reflectance (FVR) from the treatment in the first and third phases, and a parabolic increase of FVR of the Conserved in the second phase. In most grazing treatments, phase duration and maximal NDVI were positively correlated to the rainfall amounts. Rehabilitation trends were calculated for the Lightly grazed, which was correlated to increased broadleaf biomass, while a degradation trend was found for the Heavily grazed. The findings indicate that a parabolic change takes place in vegetation composition in arid grasslands. The novel methodology we used can be adapted and utilized in other ecosystems.

Research on ship navigation strategy in dynamic sea ice environments based on flexibility velocity obstacles algorithm

Sea ice constitutes one of the primary risks in Arctic navigation. Currently, how to maneuver a ship within a sea ice environment relies predominantly on the experience of the pilot. In this paper, based on satellite image data and an improved sea ice target matching approach, a dynamic ship navigation environment within the sea ice zone is established. Subsequently, we developed a sea ice collision risk index (ICRI), which takes into account the relative motion relationship between the sea ice and the ship as well as the sea ice area parameter. Finally, a flexible velocity obstacle (FVO) algorithm is proposed by the Arctic ice navigation proposal, enabling the optimization of ship routes and preventing ships from passing through the central region of large floating ice. The results show that the method proposed in this paper successfully constructs the navigation environment model in the drifting ice region, and can provide real-time maneuvering guidance for ships, effectively reduce the risk of sea ice collision, and optimize the navigation path planning in the ice region. It can improve the safety and pre-planning efficiency of ship navigation in the ice region.

Research and Application of Destriping Algorithm based on Weighted Block Sparse Representation to FengYun Satellite Imager Data

Abstract In remote sensing imaging systems, stripe noise is a pervasive issue primarily caused by the inconsistent response of multiple detectors. Stripe noise not only affects image quality but also severely hinders subsequent quantitative derived products and applications. Therefore, it is crucial to eliminate stripe noise while preserving detailed structure information in order to enhance image quality. Although existing destriping methods have achieved certain effects to some extent, they still face problems such as loss of image details, image blur, and ringing artifacts. To address these issues, this study proposes an image stripe correction algorithm based on weighted block sparse representation. This research applies techniques such as differential low-rank constraint and edge weight factor to remove stripe noise while retaining image detail information. The algorithm also uses the alternating direction method of multipliers (ADMM) to solve the MCP regularized least squares optimization problem model, improving the processing efficiency of the model. The results of this study have been applied and validated in imager data from the Medium Resolution Spectral Imager (MERSI-II) onboard FengYun-3D satellite, the Multi-channel Scanning Radiometer (AGRI) onboard FengYun-4A satellite and Precipitation Microwave Radiometer (MWRI–RM) onboard FengYun-3G. Compared to typical stripe correction methods, the proposed method achieves better stripe removal while preserving image detail information. The destriped image data can be used to generate high-quality quantitative products for various applications. Overall, by combining insights from prior research and innovative techniques, this study provides a more effective and robust solution to the stripe noise problem in remote sensing and weather forecast.

SPATIO-TEMPORAL ANALYSIS OF CHANGES IN ECOSYSTEMS OF MUNICIPAL WASTE LOCALISATION AREAS USING GIS AND REMOTE SENSING DATA

The Zdovbytsia municipal solid waste (MSW) dump (the Rivne region, Ukraine) as a case study allows demonstrating informativeness of identifying waste dump sites and the adjusted areas using the spectral characteristics and texture of earth’s covers obtained from the satellite images for the different years. To identify the MSW dump in the surveyed area, direct coding features were considered, which characterized the properties of the object and are directly represented in the images. These are such features as geometric (shape, shadow, size), brightness (phototone, brightness level, color, spectral image), structural (texture, structure) ones. In addition, satellite images reliably identified the presence of discharging from the dump area, their directions, areas of unloading, the damage to herbaceous cover and other vegetation, etc. The informativeness of the study of dynamics for dump geometrical parameters, the change of its internal “structure,” the functional changes in the surrounding ecosystem elements (first, the changes in the forest stands and agricultural lands) is demonstrated. In particular, satellite image data for the period of 2003 to 2021 allow identifying the increase in a number of the Zdovbytsia MSW dump sites at the expense of adjacent forest stands. Thus, in 2003 one site of the dump is operated, whereas in 2019—already three ones, that integrated in 2021. The study of dynamics for the change of the total area of dump has revealed the increase of this area by approximately 6.25 times (from 6 521 m2 in 2003 to 20 438 m2 in 2021). In addition, the total perimeter along the contours of its stages has enlarged more than 3.35 times (from 438 to 1 466 m, respectively). The use of satellite images of higher spatial resolution in the area of about 0.6 m/pixel will specify the cases of unsatisfactory condition of the dump.

SPATIAL TEMPORAL ANALYSIS OF LAND SURFACE TEMPERATURE CHANGES IN AMBON ISLAND FROM LANDSAT 8 IMAGE DATA USING GEOGLE EARTH ENGINE

This study aims to analyze land surface temperature changes on Ambon Island using Landsat 8 imagery data and Google Earth Engine. The research method involves the use of Ambon Island boundary data from the Geospatial Information Agency (BIG) as well as Landsat 8 Collection 2 Tier 1 and Real-Time satellite image data that have been calibrated to reflect the reflectance of the Earth's surface to the atmosphere. The analysis steps include temperature conversion from Kelvin to Celsius, land surface temperature classification, and data export to Arc GIS software. The results showed an increase in land surface temperature associated with urban development in Ambon City, highlighting the importance of sustainable urban planning and good resource management to mitigate the negative impacts of land surface temperature increase and support adaptation to climate change. Keywords: Ambon Island, Geogle Earth Engine, Land Surface Temperature

Spatial Analysis Model For Landslide Detection Using Relative Different NDVI (rdNDVI) Method Thought The Google Earth Engine Platform (Case Study: Sukajaya District, Bogor Regency)

This research utilizes the Google Earth Engine platform to detect landslides with relatively different NDVI (rdNDVI) methods. The purpose of the research is to improve our understanding of landslide analysis and detection, particularly those that occurred in Sukajaya district, Bogor Regency, Indonesia, on January 1, 2020. This research identifies vegetation changes associated with landslide likelihood using Sentinel-2A satellite image data available on Google Earth Engine. The results show that the rdNDVI method is effective in detecting landslides and can be used to determine areas that may be affected by landslides. This research also evaluates the accuracy of landslide detection by determining the threshold value to determine which areas are affected by landslides, by applying different slope values, the slopes used are slope 10, 15, 20, and 25. Comparing each slope results in a slope of 10 percent and a slope of 15 percent with 90% accuracy making the best accuracy compared to other slopes. The results of this research are expected to help the Regional Disaster Management Agency (BPBD) of Bogor Regency in managing landslides by conducting a careful and accurate analysis of areas that may be affected by landslides.

Characteristics of Changes in Typical Mountain Wetlands in the Middle and High Latitudes of China over the Past 30 Years

Analysis of the driving mechanisms of wetland change can help identify spatial differences in the mechanisms affecting various elements, enabling a more scientific approach to the conservation and utilization of wetlands. This study investigated the impacts of natural and anthropogenic factors on the spatiotemporal evolution of the Altay and Greater and Lesser Khingan Mountains areas using Landsat satellite image data from 1980 to 2018 and fieldwork data from 2019 to 2020. A transfer matrix, correlation analysis, and dynamic characteristics were applied to calculate and analyze the transformation types and areas of wetland resources across all consecutive periods. Finally, the dominant factors influencing the spatiotemporal evolution of the wetland were explored and revealed using the drought index (Standardized Precipitation Index, SPEI) and statistical almanacs. The results showed: (1) From 1980 to 2018, the wetlands area in the Altay Mountains exhibited a decreasing trend, whereas the wetlands area in the Greater and Lesser Khingan Mountains showed an increasing trend. The primary type of wetland transfer in the Altay Mountains was grassland, whereas in the Greater and Lesser Khingan Mountains regions, the primary types of wetland transfer were grassland and forestland. The wetlands area transferred out of the Altay Mountain region was larger than the area of wetland types transferred into during 2010–2018, whereas the wetland areas of the Greater and Lesser Khingan Mountain areas showed the opposite trend. (2) From 1980 to 2018, the wetland ecosystem types in the Altay Mountains exhibited the highest dynamic and conversion degrees of the channels. Similarly, the mountain areas of the Greater Khingan Mountains showed the highest dynamic and conversion degrees of marshes and channels among the wetland types. In addition, the mountainous areas of the Lesser Khingan Mountains showed the highest dynamic and conversion degrees for reservoirs and rivers. (3) Natural driving factor analysis revealed that the SPEI values in the Altay Mountains and the Greater and Lesser Khingan Mountains areas exhibited an increasing trend, indicating that the climate has been warm and humid over the past 30 years and that the expansion of cropland and human-made wetland areas has been significantly influenced by human activities. Therefore, the wetland areas of the Greater and Lesser Khingan Mountains in the northeast are strongly influenced by human activities, whereas the wetland in the Altay Mountains in the northwest is strongly influenced by the climate.

Analysis of Agricultural Land Classification in East Kotawaringin District, Central Kalimantan Province using Landsat 8-OLI Image

Purpose:This research aims to map agricultural land in East Kotawaringin Regency, Kalimantan Province, and analyze it using Landsat 8 OLI satellite imagery in 2023.Methodology:The method used in this research is supervised classification, which will then carry out a data analysis process related to natural conditions in the field. Based on the results of satellite image data processing, there are five categories of land cover classification in the East Kotawaringin Regency area: built-up land, empty land, fields/moors, gardens and forests.Findings:According to the accuracy test results, the overall accuracy value of satellite imagery in East Kotawaringin Regency is 87%. Community activities, such as economic activities and meeting needs for public facilities and housing, influence the distribution of existing land cover areas.Implication:The classification results obtained later through maps of agricultural land in East Kotawaringin Regency can be used to find various studies, especially on expanding the agricultural sector.

Solving satellite image data downlink scheduling problem with family attribute via a bi-stage differential evolutionary algorithm

Due to the asynchronous development of observation and transition capabilities, the original image data (OID) obtained by a one-time observation cannot be completely transmitted in one transmission cycle between the EOS and GS, which is named a visible time window (VTW). First, it is needed to segment the OID into several segmented image datasets (SIDs), which are then transmitted in several VTWs. Furthermore, this can cause the satellite image data downlink scheduling problem (SIDSP). This study defines an innovative SIDSP as a satellite image data downlink scheduling problem with the family attribute (SIDSPFA), where massive OID are segmented using a fast segmentation operator, and all SIDs and other no-segmented OID are transmitted in the second step. In addition, two optimization objectives, namely the failure rate (FR) and Segmentation Time (ST) of the image data transmission, are defined to formalize the SIDSPFA as a bi-objective discrete optimization model. Further, a bi-stage differential evolutionary algorithm (DE-CD) is developed. The results of the extensive simulation tests verify the efficiency of the models, strategies, algorithms, and operators used in this study.

Performance evaluation of YOLOv5 and YOLOv8 models in car detection

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

ANALISIS PERKEMBANGAN LAHAN TERBANGUN BERDASARKAN METODE SUPERVISED CLASSIFICATION MENGGUNAKAN GOOGLE EARTH ENGINE (STUDI KASUS: DESA CIPUTI, KECAMATAN PACET, KAB.CIANJUR)

Monitoring the development of built-up areas can be done by observing remote sensing time series data such as Satellite Imagery. Google Earth Engine (GEE) makes it easy for users to access satellite image data, data processing and data analysis. GEE provides various machine learning algorithms to extract land cover data. This research aims to analyze the development of built-up areas using time series of remote sensing data, namely Sentinel 2A images recorded in 2019 and 2023 and comparing Random Forest (RF), Classification and Regression Tree (CART), Support Vector Machine (SVM) and Gradient Tree Boost (GTB) algorithms and predicts built-up areas in 2027. Based on the results of this research, RF is the algorithm with the highest accuracy in mapping land cover in Ciputri Village with an Overall Accuracy (OA) of 92% and a Kappa Coefficent (KC) of 0.89 in both the 2019 and 2023 classification results, while the lowest accuracy is the SVM algorithm. A comparison of the built-up land area between the 2019 and 2023 classification results shows a decrease in the built-up land area of 3.08 ha. Meanwhile, the prediction results for 2027 show an increase in built-up areas to 114.72 ha.

Interpretasi Fase Awan dengan Metode CCO Dan RGB pada Data Satelit Himawari-9 di DKI Jakarta (Studi Kasus: 1 Januari 2023)

Indonesia is the largest archipelagic country in the world so it receives large amounts of energy from solar radiation which makes it the most active convective region in the world. In December and January, DKI Jakarta tends to experience the rainy season. On January 1 2023, BMKG gave an early warning that it would rain in Jakarta. Reporting from several news reports, in the new year 2023, Jakarta was hit by heavy rain. Cloud phase analysis is needed to determine the potential for rain. This research identifies cloud phases using Himawari-9 satellite image data which is then processed using the Cloud Convective Overlays (CCO) method and the Red Green Blue (RGB) method. The CCO method can identify the distribution of cumulonimbus clouds and the RGB method can determine the flow of air masses, the type of air mass (Airmass), identify cloud phase, and the presence of high clouds containing ice (Day Convective Storm, Day Natural Color, and Cloud Phase Distinction ). The data used are bands 3 (0.6 µm), 5 (1.6 µm), 7 (3.7 µm), 8 (6.2 µm), 10 (7.3 µm), 12 (9.6 µm), 13 (10 .4 µm), and 15 (12.4 µm) at 7.00, 8.00, 9.00, 10.00, 11.00, 12.00, 13.00, and 14.00 WIB (GMT+7). The data was processed and visualized using Grid Analysis and Display System (GrADS) and GMSLPD SATAID software.

Monitoring of coastline change using Sentinel-2 MSI data. A case study in Thanh Hoa Province, Vietnam

Vietnam is a coastal country with a coastline of more than 3,260 km, stretching from north to south. Coastal change in Vietnam is complicated further by the effects of climate change, including erosion and accretion, causing great impacts on infrastructure and the environment. This article presents the results of assessing coastline changes in Thanh Hoa Province (North Central region of Vietnam) from Sentinel-2 MSI satellite image data for the period 2015–23. Three Sentinel-2 MSI images taken in December 2015, December 2020 and December 2023 were used to calculate the water indices, including Normalised Difference Water Index (NDWI), Modified Normalised Difference Water Index (MNDWI) Argumented Normalised Difference Water Index (ANDWI) and Automated Water Extraction Index (AWEIsh), then extract the shoreline using the thresholding method and select the water index with the highest accuracy through comparing the overall accuracy and Kappa coefficient. The coastlines of 2015, 2020 and 2023 years are overlaid to evaluate the coastal changes in the study area. The results received in the study provide objective and timely information, helping managers effectively monitor and respond to coastline changes.

Klasifikasi Tutupan Lahan di Kabupaten Trenggalek Menggunakan Google Earth Engine

Land cover is defined as the physical and biological cover of the earth's surface, both those formed naturally such as swamps, hills and rivers and those formed by man-made means such as rice fields, gardens, forests and buildings. As technology develops, conventional methods of satellite image processing are starting to be abandoned. This is because conventional methods require quite a long time to process satellite image data. The presence of Google Earth Engine (GEE), which is a cloud computing-based platform, makes it easier for users to process satellite image data boldly and for free. This research aims to classify satellite image land cover in the Trenggalek Regency area, East Java. The level of accuracy in this study uses a confusion matrix. The accuracy test results show a value of 90.23%.