Satellite Imagery Data Research Articles

Inferring vegetation response to drought at multiscale from long-term satellite imagery and meteorological data in Afghanistan

Drought caused by climate change has significantly increased vegetation vulnerability in Afghanistan during the last decades. This paper investigates vegetation response to drought at multiple scales across the country based on historical data from 1980 to 2020. It explores the multiscale relationships between drought as indicated by the grid-based standardised precipitation evapotranspiration index (SPEI) and vegetation condition as represented by the satellite-derived vegetation anomaly index (VAI). It also examines the links of dominant land cover with drought and their implications for agriculture. We assess the spatiotemporal correlations by integrating TerraClimate grids with timeseries data sourced from NOAA AVHRR (National Oceanic and Atmospheric Administration Advanced Very High-Resolution Radiometer), MODIS (Moderate Resolution Imaging Spectroradiometer) images and ESA CCI (European Space Agency Climate Change Initiative) land cover maps. We evaluated the effect of cumulative drought on predominant land covers. Our results show years 2000–2001 and 2017–2018 as the driest in the last four decades, and vegetation substantially correlated to drought with spatial and temporal variations across Afghanistan. The most sensitive vegetation response months are June to July and the most significant cumulative drought impacts on vegetation are 6 to 8 months. The predominant land covers more prone to negative effects under severe drought are (in order) shrubland, rainfed cropland, grassland, trees, and irrigated cropland. These findings suggest the sensitivity of vegetation to drought that future national scale drought management should be focused on. This study also demonstrates the usefulness of open-access data to researchers and planners in data-poor countries.

Large-Scale Combined Adjustment of Optical Satellite Imagery and ICESat-2 Data through Terrain Profile Elevation Sequence Similarity Matching

Large-Scale Combined Adjustment of Optical Satellite Imagery and ICESat-2 Data through Terrain Profile Elevation Sequence Similarity Matching

Evaluation of U-Net transfer learning model for semantic segmentation of landslides in the Colombian tropical mountain region

Landslides in tropical regions, like the Colombian Andean region, pose unique challenges due to factors such as intense rainfall, steep slopes, and complex terrains. Mapping historical and current landslide activity through inventory maps is essential in tropical mountainous regions. While satellite data is commonly used for mapping, it can be time-consuming and manual-intensive, limiting inventory availability. Deep Learning (DL) models, especially Convolutional Neural Networks (CNNs), have shown promise in remote sensing applications with High Resolution (HR) imagery, including landslide detection. Despite advancements, their use in this field is still relatively limited. This study assesses the effectiveness of U-Net model, for automated landslide detection using spectral data from optical satellite imagery (RGB bands), two DEM-derived geo-indices (slope and curvature), and two Synthetic Aperture Radar (SAR) layers (VV amplitude pre- and post-landslide event in May 2015) across three image models (3, 5, and 7 bands). Initially, data is combined into multiband images, and the model is trained in the “La Argelia” river basin in Colombia’s Pacific region. Subsequently, the model is tested in the “La Liboriana” river basin in the western Andean region. The landslide detection results within the inference area are validated by comparing them with the landslide inventory and segmentation results. The U-Net model demonstrates good performance (F1-score around 0.70) for landslide detection, as confirmed in various geographical settings. By utilizing DL models and combining high-resolution satellite imagery, topographical, and SAR data, a comprehensive space-time mapping of landslides can be achieved. This approach has the potential to greatly improve the accuracy and effectiveness of landslide mapping, offering a more holistic view of the temporal dynamics related to these natural hazards.

Characterization of landuse and landcover dynamics and their impact on runoff generation patterns in dam catchments of Northern Ghana

Landuse alteration is a critical factor that exerts influence over hydrological mechanisms and the oversight of water resources across the globe. Northern Ghana is characterized by erratic rainfall, leading to the intense construction of small dams and dugouts. This study investigated historical landuse/cover (LULC) changes and physical factors associated with surface runoff generation within eight (8) sub-catchments namely; Guno, Gbahali, Sambu, Duago, Denugo, Kwisini, Sandu, and Nyeko, with areas of 29.5, 110, 38.5, 106.04, 67.81, 2.59, 10.56 and 120.65 km2, respectively, located in four (4) regions of northern Ghana. For LULC change detection, historical satellite imagery and geospatial data were used with the aid of remote sensing and applying supervised maximum likelihood classification to Landsat images of 1995, 2005, 2015, and 2023. Runoff factors and depths were identified based on LULC classes, hydrologic soil groups, and precipitation conditions prevailing in the areas, based on the curve number (CN) method. With regards to LULC, the results from the overall analysis gave an insight into the rampant conversion, where grassland/mix vegetation witnessed significant declines, with decreases ranging from 150 to 395.25% in different catchments. Agricultural and arable lands emerged as the dominant landuse category, indicating a remarkable expansion of 11.61–51.17 km2, from 1995 to 2023 respectively, followed by built-up areas. While grassland decreased in all catchments over the same period, in some places, open tree (savannah woodland) increased, which confirmed the implementation of the afforestation policy. The CN values offered essential insights into the hydrological characteristics of each catchment. In addition to the impact of LULC and watershed characteristics on runoff, the observed variations in runoff patterns can be attributed to interannual variability in total annual precipitation which is critical driver of runoff. The findings hold significant importance for developing effective strategies and integrated watershed management policies to contribute to better water resource management and inform sustainable landuse planning in the face of changing environmental conditions.

Monitoring coastal water quality using trios ramses hyperspectral reflectance data

The coastal area is very strategic as the center of national economic activity. Coastal areas that have strategic value from the potential of aquatic resources and environmental services, among others, are located on the northern coast of the Java Sea, one of which is Muaragembong. The Muaragembong coast in general has a problem of decreasing water quality. One of the water quality parameters that is often used to measure conditions in the field is Total Suspended Solid (TSS). Monitoring the concentration of TSS in a waters can be done through an approach, namely the development of an empirical model using remote sensing data. In addition to satellite imagery data, remote sensing data can also be performed by measuring the spectral reflection of an aquatic object using a tool called a radiometer. This study aims to build an empirical model using TRIO RAMSES data which is simulated into a combination of wavelengths from the Sentinel 2 and Landsat 8 bands to be able to estimate TSS concentrations in waters.The results showed that Reflectance Remote Sensing (Rrs) from TRIOS RAMSES data which was simulated into the wavelength range of Sentinel 2A image data was able to build an empirical model using multivariate band combinations. The results obtained on Sentinel 2 imagery show an R2 value of 0.68 and RMSE 113.04 mg/L.

Improving the methodology for monitoring vegetation cover based on type segmentation

Monitoring is an important element for defining sustainable land cover management strategies. The monitoring technique proposed and developed in this work with integrated processing of remote sensing data has the potential to scale and optimize the interpretation of vegetation classes, making the analysis of the territory more detailed. This approach based on multiclass classification of composite images with data preprocessing based on Google Earth Engine (GEE) cloud platform. The article describes the characteristics and benefits of the uses cloud platform. Three regions, different in their characteristics and climatic features, were chosen as the study area: Voronezh Oblast, Republic of Tatarstan and Perm Krai. The objects of the study were the classes of forest vegetation, arable land and meadow vegetation. The article presents a step-by-step methodology for obtaining and processing Sentinel-2 satellite imagery data. The main steps of the methodology include obtaining a time series of satellite data and processing them, applying a vegetation index to satellite images, selecting reference data for validation, classifying objects using the random forests algorithm. An overview of WorldCover cartographic product from European Space Agency (ESA) and its advantages for use in working with geoinformation data is presented. The classification of target classes of vegetation in GEE cloud platform was carried out. The results of the study contain an analysis of a combination of normalized difference vegetation index (NDVI) for each studied class of vegetation. Implementation of the methodology is important for retrospective analysis and operational monitoring of vegetation cover classes. The method of multi-class segmentation of objects based on time series analysis will significantly improve the accuracy and speed of providing analytics to update information on land use.

GeoAI Dataset for Rural Hazardous Facilities Segmentation from KOMPSAT Ortho Mosaic Imagery

In South Korea, rural areas have been recognized for their potential as sustainable spaces for the future, but they are currently facing major problems. Unplanned construction of facilities such as factories, livestock facilities, and solar panels near residential areas is destroying the rural environment and deteriorating the quality of life of residents. Detection and monitoring of rural facilities are necessary to prevent disorderly development in rural areas and to manage rural space in a planned manner. In this study, satellite imagery data was utilized to obtain information on rural areas, which is useful for observing large areas and monitoring time series changes compared to field surveys. In this study, KOMPSAT ortho-mosaic optical imagery from 2019 and 2020 were utilized to construct AI training datasets for rural hazardous facilities segmentation for Seosan, Anseong, Naju, and Geochang areas. The dataset can be used in image segmentation models to classify rural facilities and can be used to monitor potentially hazardous facilities in rural areas. It is expected to contribute to solving rural problems by serving as the basis for rural planning.

Developing a GIS-based multicriteria evaluation system for selecting suitable sites for solar energy power plants in Anambra state Nigeria

The increasing demand for clean energy in society raises concerns about alternative energy sources, making it difficult to rely solely on one or two sources of electricity generation. This study aims to develop a GIS-based multicriteria evaluation system for identifying suitable solar energy power plants in Anambra State, Nigeria. Key criteria include solar irradiation, land use, proximity to transmission lines, accessibility, slope, and environmental constraints. GIS tools and satellite imagery data are used to analyze and visualize these criteria, enabling an efficient decision-making process. The methodology includes data collection, selecting and determining criteria for suitable site selection, constructing a hierarchical structure of site selection procedure, data acquisition, classification of acceptable criteria and factors into rank of suitability, determining the relative importance of each criterion using AHP (Analytical Hierarchy Process) based on pair-wise comparisons, assessing the consistency of judgments through a pair-wise comparisons method, and producing the final evaluation map. The results provide valuable insights into identifying suitable sites for solar power plants in Anambra State and are advised for use in other parts of the country. The GIS-based multicriteria evaluation system facilitates location decisions and increases efficiency in dealing with solar energy power plant siting. The study workflow model is suggested for smooth and quick appropriateness analysis and easy export as a python script.

Sustainable Planning for Investing in Natural Resources in the Jazeera Region in Al-Anbar Governorate

Objectives: This study aims to explore and identify the natural resources existing in the Jazeera region in the Anbar Governorate and analyze their spatial distribution. In that region, there are numerous natural resources, including renewable energy, raw materials, water, and biodiversity, all of which play a crucial role in achieving economic growth. The study also aims to determine how the distribution of natural resources affects the nature of development investments and show the impact of that distribution on identifying feasible development investments, so that sustainable development can be achieved in the region.
 Methods: The study adopts an inductive approach to collect and analyze information accurately and comprehensively. As a result, data were gathered from multiple sources, such as previous studies and research, government reports, field surveys, and a comprehensive review of the existing resources. Quantitative methods were used to analyze the data to ensure the accuracy and objectivity of the results. ArcGIS software was utilized for visual analysis, satellite imagery interpretation, mapping, and data visualization.
 Results: It has been found that the studied area possesses immense and diverse natural resources that form an important basis for achieving strong economic development. These resources can be sustainably exploited to support local development, enhance investment opportunities, and promote economic growth in the region.
 Conclusions: The economic feasibility of each natural resource should be determined, especially in the field of alternative energy source investments, and the focus should be on adopting sustainable planning for strategic investments in any natural resource, especially in agricultural production.

Analysis of Chlorophyll-A Distribution And Sea Surface Temperature for Estimation of Skipjack Fishing Grounds (Katsuwonus Pelamis) Based on Different Seasons in South Buton Waters

The success of fishing activities is certainly greatly influenced by the conditions of the Fishing Ground (FG). Therefore, information about potential fishing areas is needed by fishermen in fishing activities. Chlorophyll-a is the primary productivity in water. The development of aquatic chlorophyll is influenced by Sea Surface Temperature (SST). The purpose of this study was to analyze the distribution of chlorophyll-a and sea surface temperature (SST) for estimating fish fishing areas based on different seasons in South Buton Waters. This type of research is exploratory research. This study used remote sensing method. The direct interview method with respondents aims to validate data (fishermen) from remote sensing processing. The study used Aqua Modis satellite imagery data to derive parameter values of chlorophyll-a and sea surface temperature. The results of the analysis of the distribution of chlorophyll-a in South Buton waters are the highest with chlorophyll-a concentrations ranging from 0.3-3.0 mg / m³ spread in the waters of Sampolawa and Batauga Districts with surface temperatures ranging from 26 °C and 33 °C. Eastern and Transitional Season II have high levels of chlorophyll-a concentration. This season will be a potential skipjack fishing area in South Buton Waters.

Automatic Detection and Counting of Urban Housing and Settlement in Depok City, Indonesia: An Object-Based Deep Learning Model on Optical Satellite Imageries and Points of Interests

Detecting urban housing and settlements has a substantial position in decision-making problems such as monitoring housing and development, not to mention the widely required urban mapping application. One of the most important goals in the United Nations Sustainable Development Goals (SDGs) is to improve urban living conditions globally by 2030. We propose an automatic detection of urban housing and settlements on remote sensing satellite imagery data using object detection-based deep learning using semantic segmentation and the potential availability of remote sensing datasets at high spatial resolutions, Open Street Map (OSM) geolocation point of interest dataset, and Sentinel-2 optical satellite imagery data. The detection model using Mask Region-based Convolutional Neural Networks (Mask R-CNN) is implemented in Depok City, Indonesia. These regions were chosen because it is the second most populous suburb in Indonesia and the tenth most populous globally and, making it challenging to extract building features from satellite imagery. This model categorizes dense, moderate, and sparse conditions and has a promising result of an average precision of 100% and an F1-score of 67% with evaluation performance metrics only considering points associated with buildings, not building boundaries or the intersection over union (IoU). The model performance has been compared to ground check results of field surveys, and it performs best in sparse conditions. Our findings offer the potential implementation of the model for fast and accurate monitoring of housing, settlement, and regional planning in urban areas.

Land cover change analysis of buffer areas in New Capital City of Nusantara, Indonesia: A cellular automata approach on satellite imageries data

The proposed plan to move Indonesia's capital city to the New Capital City of Nusantara in East Kalimantan Province undoubtedly requires careful efforts to ensure food supply for the population. Population migration to the new capital may pose a food security challenge. To address this fundamental issue, one of the most crucial approaches is to establish buffer areas that can support the food needs of the new capital. The currently existing official Area Sampling Frame survey conducted by the government to assess food vulnerability faced several limitations, including weather conditions, field terrain variations, and high cost. In this study, we propose the utilization of remote sensing satellite imagery data in buffer areas to analyze changes and predict future land cover, which can provide valuable data for assessing food availability. We investigate the integration of a Cellular Automata method with the two most popular analytical methods of classical Logistic Regression and data-driven Artificial Neural Networks, known as CA-LR and CA-ANN, to identify and map land cover changes in the new capital buffer zones. Our findings reveal that both combined methods, CA-LR and CA-ANN, yield fairly promising results, with correctness and kappa statistic values exceeding 80%. Prediction results indicate that buffer areas are predominantly covered by trees, while built-up areas are still limited. The flooded vegetation cover, including rice fields, is predicted to decrease by 2024. This should be a matter of concern for stakeholders, considering the construction of the new capital city is still ongoing and the number of migrants is expected to keep rising.

A Geovisualization Dashboard of Granular Food Security Index Map using GIS for Monitoring the Provincial Level Food Security Status

This study aims to build a web-based interactive geovisualization dashboard from a granular food security index map using satellite imagery and other geospatial big data. The map dashboard is built using a two-dimensional (2D) data visualization approach. Making a two-dimensional map using QuantumGIS (QGIS) tools, displayed in the form of WebGIS with the plugin used "Qgis2web" based on javascript leaflets. Once included in WebGIS, interactive visualizations are displayed on websites with interfaces based on hypertext markup language (HTML), cascading style sheets (CSS), and JavaScript (JS). The dashboard map is equipped with interactive features such as legend, click grid, zoom, show me where I am, measure distance, and search. Therefore, the dashboard map can be used to monitor the food security index, search for food security index areas, as well as geographical identification of food security index areas which are useful for supporting the analysis of decision-making or policies by the government regarding food security strategies.

Geospatial Big Data Approaches to Estimate Granular Level Poverty Distribution in East Java, Indonesia using Machine Learning and Deep Learning Regressions

One of the economic development the focus of the Indonesian government's efforts is for reducing poverty. In Indonesia, collecting poverty data uses the conventional method, the name is National Socio-Economic Survey (SUSENAS) which takes a large cost, time, and effort. To overcome these limitations, there is a need for additional data to provide more detailed poverty data. Recent studies show that the use of geospatial big data could identify poverty at a granular level, with a lower cost and faster update because of their unique and unbiased capacity to identify physical and socioeconomic phenomena. The integrated multi-source satellite imagery data such as the normalized difference vegetation index (NDVI) for detecting rural areas based on vegetation, built-up index (BUI) for identifying urban areas through building distribution, normalized difference water index (NDWI) for land cover detection, day time land surface temperature (LST) for identifying urban regions based on surface temperature, and pollutants such as carbon monoxide (CO), nitrogen dioxide (NO2), and sulfur dioxide (SO2) to evaluate economic activities based on pollution. Additionally, point of interest (POI) density and minimum POI distance are used to measure area accessibility. Therefore, the contribution of this research is to implement the utilization of geospatial big data to estimate the numbers of poverties at a granular level to the 666 sub-districts in East Java Province using machine learning and deep learning regression models. The evaluation results to estimate sub-district level poverty shows that the best model development using Support Vector Regression (SVR) in machine learning was the best root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) values of 0.365, 0.293, and 0.032 with R-squared of 0.59 and MLP in deep learning algorithm with 0.444, 0.345, and 0.039 values of RMSE, MAE, and MAPE with R2 0.52. In addition, the results of visual identification revealed that high estimates of lower poverty are typically found in urban areas with high accessibility, and these areas are not spatially deprived areas with limited accessibility.

Integrating Satellite Imageries and Multiple Geospatial Big Data for Granular Mapping of Spatial Distribution of Human Development Index in East Java, Indonesia

The availability of data on the Human Development Index (HDI) is crucial as a gauge of regional performance, particularly in terms of assessing the development of human resources. In Indonesia, the collecting of HDI data usesthe conventional method, such as undirect estimation, National Socio-Economic Survey (SUSENAS), The Ministry of Religion, or inventory of sectoral data that used the large cost, time, and effort. Additional data are required to provide more detailed poverty data at a lower cost and with more recent information to overcome these limitations. According to recent studies, the quality of life for measuring HDI can be identified down to the granular level using geospatial big data. Therefore, the contribution of this research is to implement the use of geospatial big data, such as integrated multi-source satellite imagery data and Point of Interest (POI). Besides that, this study develops the relative spatial human development index in 11 km x 11 km resolution for the granular mapping of the quality of life to measure the HDI in East Java, Indonesia. The kinds of weighted sum models used in this study such as equal weight (EWS), Pearson (PCCWS), Spearman (SCCWS) correlation-based weight, and Principal Component Analysis (PCA)-based weight (PCAWS). The best RSHDI PCCWS for representing the human development index in East Java in 2022, which was determined using a weight-sum model based on Pearson correlation, has a correlation coefficient of 0.7858 (p-value = 5.078 x 10-9) and is highly correlated with official HDI data. The use of this RSHDI as a predictor variable in the estimation of HDI data shows the ideal model had an RMSE of 3.098% and an R2 of up to 61.75% using RSHDI PCCWS. According to the findings of the descriptive analysis of this map, areas with low RSHDI scores typically in some regencies areas in Madura Island and the east area of East Java with geographically depressed, while areas with high RSHDI scores typically have dense populations and have better accessibility such as urban area in Surabaya and Kota Malang. As a result, the official human development index data can be supported by the RSHDI's ability to map spatially deprive areas

SAR Bathymetry Review and Its Possibility Implementation in Indonesia

Indonesia needs bathymetry information for diverse applications as a maritime country. There are various methods of determining the water depth for bathymetry. The advancement of satellite imagery data has led to the increasing use of remote sensing data for depth measurements. With satellite imaging, wide area coverage can be achieved in a relatively short time, making depth data acquisition more cost-effective. SAR (Synthetic Aperture Radar) imagery is an active remote sensing technology developed to estimate depth data known as the SAR Bathymetry method. This method is still not widely applied, especially in Indonesia, even though it has considerable potential with cloud-free imageries, where it becomes a severe problem in tropical countries when using optical imagery. Therefore, this paper will discuss algorithms and techniques for depth data estimation using SAR Bathymetry and their possible implementation in Indonesia. The optimum depth, SAR image recommendation, and conditions required to apply this method will also be discussed.

Estimation of The Potential of Large Pelagic Fish Fisheries Using Satellite Data in The Banda and Arafura Seas

The Banda and Arafura Sea possesses significant potential for the fisheries and fishing industry in the country. The large pelagic fish production data studied were derived from satellite imagery data in the Banda and Arafura Seas (2002-2022). The data of chlorophyll-a content, sea surface temperature and Photosynthetically Available Radiation (PAR) derived from AQUA MODIS satellite images were used to estimate the value of primary productivity with the Vertically Generalised Production Model (VGPM). The results showed that the biomass of large pelagic fish fluctuated seasonally, with the Banda Sea and Arafura Sea having high average fish biomass values in the eastern season. With the highest fish biomass peak in August and the lowest in December. The high value of fish biomass in the Banda Sea and Arafura Sea is caused by upwelling events where sea surface temperatures are low but chlorophyll is high. in the Arafura Sea the value of fish biomass is higher than the Banda Sea, this is due to the large amount of nutrient input from large rivers in the area.

Experimental Analysis on Hanging Dam Formation and Evolution

Hanging dams are thick accumulations of frazil ice beneath an existing ice cover that are formed during the freeze-up period at locations where a fast-flowing river section enters a section with relatively low velocity. Hanging dams can have a substantial impact on the hydraulics of an ice-covered river. This paper presents an experimental study on hanging dam formation and evolution conducted using a laboratory physical model of a river issuing water into a relatively large reservoir using simulated frazil ice and a simulated ice cover. The incoming ice supply rate and the approach Froude number of the river are the two parameters that have an impact on the hanging dam formation with respect to several physical characteristics of the hanging dam. Hanging dam erosion was observed by increasing the approach Froude number of the river after a hanging dam had already formed. Both the formation and erosion of the hanging dam were qualitatively compared with field observations of hanging dam occurrences using satellite imagery and hydrometric data to support the applicability of the experimental results to a field scenario. The results presented in this paper comprise the first published qualitative laboratory data on hanging dam formation, helping to improve our understanding of the fundamental mechanisms of hanging dam formation and evolution.

Can the government environmental vertical reform reduce air pollution? A quasi-natural experiment in China

Addressing environmental challenges is crucial for mitigating and adapting to the consequences of climate change. The existing literature investigates whether government environmental reform can address environmental problems but yields conflicting findings. This study examines the effect of government environmental vertical reform (GEVR) on air pollution. Using city-county mergers in China as a quasi-natural experiment, satellite imagery, and socio-economic data from 2000 to 2016, we observe that GEVR reduces a noteworthy 4% air pollution in reformed counties based on a staggered Difference-in-Differences (DID) design. Mechanism analysis reveals that GEVR cuts air pollutant emissions by optimizing resource inputs and mitigating the impact of polluting firms. Heterogeneity analysis demonstrates whether the reformed counties located at administrative boundaries and the reform strategy influence the effectiveness of GEVR. In the future, policymakers should contemplate suitable government environmental reforms to tackle forthcoming climate change challenges.

Evolution of Increased Volcanic Activity in Arjuno-Welirang Based on LST Analysis of Landsat 8 Satellite Imagery Using GEE Cloud Computing

Typically, monitoring the volcanic activity of a volcano is carried out using volcanic seismic methods. However, this method is technically less flexible. Volcano seismic data is not freely available. Access to these data centers must be authorized by the data authority. Therefore, it is necessary to use other methods as an alternative. The alternative method used in this study is remote sensing using the Landsat 8 satellite sensors. Landsat 8 satellite imagery data can be freely accessed and easily downloaded. Landsat 8 image analysis is implemented with Google Earth Engine (GEE). GEE is a remote sensing image analysis programming tool with a cloud computing platform. The GEE programming implementation is open source. With GEE, the evolution of Arjuno-Welirang volcanic activity can be monitored accurately. The use of GEE with a cloud computing platform also makes it easier to process large remote sensing data because the downloaded file's size is unlimited. GEE has successfully conducted an LST analysis on Landsat 8 satellite imagery of the Arjuno-Welirang complex area in the 2016-2021 range. The LST calculation is performed by adding the surface emissivity correction obtained based on the NDVI value. According to the results of the LST calculations that have been obtained, the surface temperature in the Arjuna Welirang Crater area experienced the highest increase in 2018, reaching 33.94 oC, with a larger contour size of thermal distribution image than the others. This increase in thermal based volcanic activity is in accordance with the increase in seismic activity monitored by the VSI (Volcanological Survey of Indonesia).