OLI Imagery Research Articles

Fire Severity Outperforms Remote Sensing Indices in Exploring Post-Fire Vegetation Recovery Dynamics in Complex Plateau Mountainous Regions

Understanding post-fire vegetation recovery dynamics is crucial for damage assessment and recovery planning, yet spatiotemporal patterns in complex plateau environments remain poorly understood. This study addresses this gap by focusing on Yunnan Province, a mountainous plateau region with high fire incidence. We developed an innovative approach combining differenced Normalized Burn Ratio (dNBR) and visual interpretation on Google Earth Engine (GEE) to generate high-quality training samples from Landsat 5 TM/7 ETM+/8 OLI imagery. Four supervised machine learning algorithms were evaluated, with Random Forest (RF) demonstrating superior accuracy (OA = 0.90) for fire severity classification compared to Support Vector Machine (SVM) OA of 0.88, Classification and Regression Tree(CART) OA o f0.85, and Naive Bayes(NB) OA of 0.78. Using RF, we generated annual fire severity maps alongside the Land Surface Water Index (LSWI), Normalized Difference Vegetation Index (NDVI), and Normalized Burn Ratio (NBR) from 2005 to 2020. Key findings include the following: (1) fire severity classification outperformed traditional remote sensing indices in characterizing vegetation recovery; (2) distinct recovery trajectories emerged across severity levels, with moderate areas recovering in 7 years, severe areas transitioning within 2 years, and low severity areas peaking at 2 years post-fire; (3) southern mountainous regions exhibited 1–2 years faster recovery than northern areas. These insights advance understanding of post-fire ecosystem dynamics in complex terrains and support more effective recovery strategies.

Spatial Soil Salinity Assessment by Using Principle Component Analysis and Geospatial Techniques in Central Punjab, Pakistan

Both natural and man-made soil salinity is a chief geological disaster in semi-arid and arid parts. In cultivated land, it has a negative impact on plant development and harvests, while in semi-arid and arid non-agricultural zones, due to subsidence, corrosion and groundwater quality, it affects urban structures, leading to additional soil erosion and land deprivation. The study was conducted at central Punjab, Pakistan with the aim to develop a baseline and to show the precision and accuracy of Geographic Information System (GIS) technology for delineating soil salinity in no data region. The samples of soil were gathered at deepness of 0-15 and 15-30 cm, and three factors (pH, Electrical Conductivity, and Sodium Adsorption Ratio) were analyzed in the laboratory. Landsat 8 OLI imagery were used for salinity indices development. A statistical index association was found between soil salinity noted in soil samples of field and 13 GIS-based salinity indices. The effect importance and model parameters for various soil salinity indices were assessed using regression model fitting. The data were divided into 3 categories: i) ground or field data, ii) brightness/intensity indicators, and iii) salinity indicators. Data were analyzed using principal component analysis (PCA). The results indicated that salinity indices were favorably related with ground data sets, but brightness/intensity indices had no significant relationship with ground or field data.

Analysis of land use using Landsat 8 OLI in coastal North Sumatra: a study from 2019 to 2024

Abstract Medan and Deli Serdang are recognized as the most developed regions in Northern Sumatra. However, there have been limited studies focusing on recent land use changes in their coastal zones. To address this gap, this study aims to analyze land use in Medan and Deli Serdang and examine the changes between 2019 and 2024. Using recent Landsat 8 OLI imagery, a supervised classification was conducted in Google Earth Engine. The CART classifier was used to divide the land use into water bodies, built-up, agricultural land, and mangroves. The Kappa index for 2019 and 2024 were 0.78 and 0.77, respectively, indicating reasonable classification accuracy. Agricultural land was found to be the dominant land use type, covering 52.78 % in 2019 and 51.76 % in 2024. A decrease was observed in agricultural land and water bodies, while mangrove coverage and built-up areas increased. The findings provide current conditions of land use in the coastal area of Medan and Deli Serdang, especially for planning and conservation in the coastal area.

Analyzing the impacts of Discharges from coastal outfalls on water quality of Kuwait Bay via Landsat monitoring

Monitoring water resources plays an important role in water pollution control. Taking Samples of the whole water body is limited in practice for many reasons such as time-consuming, cost constraints, and the situation of topography. So, the remote sensing method has been used as it provides a reasonable and feasible approach to providing water quality data. In this study, Nitrates and phosphates were assessed along Kuwait's territorial water using data from Bands reflection that was taken from remote sensing images to improve the prediction of Nitrate and Phosphate concentrations in waterbodies. The predicting algorithms have been subjected to calibration and validation by using the in-site data measured in the field from the KEPA Monitor stations. All the algorithms produced a good performance in estimating the concentration of nitrate and phosphate at the outfall sites, but the nitrate algorithms had superior performance over the phosphate algorithms. The Relative Error of the Nitrate and Phosphate Algorithm was 16.851% and 37.793%, respectively. One of the accuracy requirements for estimating the quality of water for the satellite imagery method is that the Relative Error should not be more than 30%, and the accuracy of the nitrate prediction algorithms is more accepted than phosphate prediction algorithms. At last, Nitrate and phosphate were estimated at the outfall’s sites using Landsat 8 OLI imagery. The retrieval models result showed that all the Nitrate and Phosphate concentrations were above the KEPA ambient seawater quality criteria standard values which are for the Nitrate (NO3-) is equal to 0.095 milligrams per liter, and equal to 0.035 milligrams per liter for the Phosphate (PO43-). Although this case study is a particular study, the modeling procedures related to the Nitrate and Phosphate retrieval model can provide others the ability when they use the technology of satellite imagery to predict Nitrate and Phosphate for identical or different water bodies.

Earthquake risks assessment and urban vulnerability: Case of Nador city, northeast Morocco

This study introduces an innovative approach to assessing seismic risks and urban vulnerabilities in Nador, a coastal city in northeastern Morocco at the convergence of the African and Eurasian tectonic plates. By integrating advanced spatial datasets, including Landsat 8–9 OLI imagery, Digital Elevation Models (DEM), and seismic intensity metrics, the research develops a robust urban vulnerability index model. This model incorporates urban land cover dynamics, topography, and seismic activity to identify high-risk zones. The application of Landsat 8–9 OLI data enables precise monitoring of urban expansion and environmental changes, while DEM analysis reveals critical topographical factors, such as slope instability, contributing to landslide susceptibility. Seismic intensity metrics further enhance the model by quantifying earthquake risk based on historical event frequency and magnitude. The calculation based on higher density in urban areas, allowing for a more accurate representation of seismic vulnerability in densely populated areas. The modeling of seismic intensity reveals that the most susceptible impact area is located in the southern part of Nador, where approximately 50% of the urban surface covering 1780.5 hectares is at significant risk of earthquake disaster due to vulnerable geological formations, such as unconsolidated sediments. While the findings provide valuable insights into urban vulnerabilities, some uncertainties remain, particularly due to the reliance on historical seismic data and the resolution of spatial datasets, which may limit the precision of risk estimations in less densely populated areas. Additionally, future urban expansion and environmental changes could alter vulnerability patterns, underscoring the need for continuous monitoring and model refinement. Nonetheless, this research offers actionable recommendations for local policymakers to enhance urban planning, enforce earthquake-resistant building codes, and establish early warning systems. The methodology also contributes to the global discourse on urban resilience in seismically active regions, offering a transferable framework for assessing vulnerability in other coastal cities with similar tectonic risks.

Analisis Perubahan Penggunaan Lahan dan Daya Dukung Lahan Pertanian Sawah di Kecamatan X Koto Tahun 2005-2023

The objectives of this study are: (1) To analyze the extent of changes in the use of rice field agricultural land in X Koto District in the period of 2005-2014 and the period of 2014-2023, (2) To analyze the carrying capacity of rice field agricultural land in X Koto District for the period of 2005-2014 and the period of 2014-2023. This type of research uses a quantitative descriptive method with secondary analysis. The data used are secondary data in the form of Landsat 7 ETM Satellite Imagery in 2005, Landsat 8 OLI Imagery in 2014 and 2023, as well as data on the area of rice harvested land, the number of population, and the average rice production in X Koto District for the period of 2005-2014 and the period of 2014-2023. The data analysis technique uses Supervised Classification. The analysis of changes in rice field agricultural land in this study uses the overlay technique. The researcher found: (1) The area of rice field agricultural land in 2005 was 1973.40 ha, then in 2014 it decreased to 1513.15 ha with a reduction rate of 23.32%. In 2023, the area of paddy field agricultural land will decrease again to 1265.76 ha with a reduction rate of 16.34%. (2) In the period of 2005-2014, the carrying capacity of rice field agricultural land in X Koto District was 1.74 and the period of 2014-2023 was 1.89. X Koto District is an area with food self-sufficiency, where the area is able to meet the basic needs of the community, but has not been able to provide a decent life for its residents.

Vegetation monitoring in mining area using canopy reflectance and landsat8 oli image

The aim of this study is to find out the vegetation monitoring in mining areas which is of great significance in grasping the ecological environment status of mining areas. As the first target of spectral reflection, the vegetation canopy directly affects the results of spectral reception. Leaf Angle Distribution (LAD) is a key canopy structural parameter, which directly determines the amount of solar radiation intercepted by the canopy. Taking the relationship between the effect of leaf inclination angle on vegetation canopy reflectance and NDVI as an entry point, the study monitored and analyzed the vegetation in Dexing mining area, China, from 2013 to 2018 by NDVI, based on which the Copper Stress Vegetation Index (CSVI), which is applicable to the narrow wavelength band of remote sensing imagery, was evaluated whether it could be applied to the wide wavelength band. The results show that the NDVI value of the horizontal distribution is significantly higher than that of other leaf inclination distributions, and the NDVI in the direction of the horizontal distribution is 0.82, while the NDVI in the direction of the hi-straight distribution is 0.75; the NDVI of the mining area decreases gradually with the increase of the area of the mining area, and the correlation coefficients of the two groups of CSVI based on the wide and narrow bands are not large, which is not suitable for the vegetation monitoring by using the Landsat8 OLI imagery. Wide band is less applicable for vegetation monitoring. Bangladesh J. Bot. 53(3): 835-847, 2024 (September) Special

Multi-spatial Resolution Imagery to Estimate Above-Ground Carbon Stocks in Mangrove Forests

Mangroves are a type of vegetation that can absorb carbon and have an essential role in controlling CO2 levels in the atmosphere. Mangroves can absorb carbon better than terrestrial ecosystems because of their ability to bury carbon in sediment. This research aims to compare and measure the carbon stock content above the surface of mangroves in the field using multi-spatial resolution imagery, namely, Landsat 8 OLI, Sentinel 2A, and Planetscope. Field carbon calculations were carried out using the allometric method based on mangrove species. The calculation results are then linked through regression analysis with the vegetation index Difference Vegetation Index (DVI) results. The total carbon obtained from PlanetScope imagery was 535.27 tons, Sentinel 2A imagery was 549.23 tons, and Landsat 8 OLI imagery was 533.57 tons. Among the three images used, based on Sentinel 2A statistical analysis reflects the possibility of overfitting or the best with higher r and R2 values in the calculations. However, based on SE accuracy tests, PlanetScope has better accuracy than the other two images. Apart from that, the accuracy test results using a 1:1 goodness of fit plot for each image, the distribution pattern of mangrove carbon stock estimates shows that the entire model in mapping mangrove carbon stocks is over-estimated. The overestimated results are possible because more objects around the mangrove, especially canopy density, are recorded by remote sensing sensors compared to tree diameter as input for field carbon results.

Response of Hydrothermal Conditions to the Saturation Values of Forest Aboveground Biomass Estimation by Remote Sensing in Yunnan Province, China

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research.

Advancing non-optical water quality monitoring in Lake Tana, Ethiopia: insights from machine learning and remote sensing techniques

Water quality is deteriorating in the world's freshwater bodies, and Lake Tana in Ethiopia is becoming unpleasant to biodiversity. The objective of this study is to retrieve non-optical water quality data, specifically total nitrogen (TN) and total phosphorus (TP) concentrations, in Lake Tana using Machine Learning (ML) techniques applied to Landsat 8 OLI imagery. The ML methods employed include Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RF), XGBoost Regression (XGB), AdaBoost Regression (AB), and Gradient Boosting Regression (GB). The XGB algorithm provided the best result for TN retrieval, with determination coefficient (R2), mean absolute error (MARE), relative mean square error (RMSE) and Nash Sutcliff (NS) values of 0.80, 0.043, 0.52, and 0.81 mg/L, respectively. The RF algorithm was most effective for TP retrieval, with R2 of 0.73, MARE of 0.076, RMSE of 0.17 mg/L, and NS index of 0.74. These methods accurately predicted TN and TP spatial concentrations, identifying hotspots along river inlets and northeasters. The temporal patterns of TN, TP, and their ratios were also accurately represented by combining in-situ, RS and ML-based models. Our findings suggest that this approach can significantly improve the accuracy of water quality retrieval in large inland lakes and lead to the development of potential water quality digital services.

Coral reef condition Kepala Jeri Island, Batam, Riau Islands, Indonesia

Abstract. Aunurrahman, Anggoro S, Saputra SW, Muskananfola MR. 2024. Coral reef condition Kepala Jeri Island, Batam, Riau Islands, Indonesia. Biodiversitas 25: 2820-2827. Understanding the actual condition of vulnerable coral reef ecosystems such as Kepala Jeri Island (Indonesia) is critical to enabling appropriate management planning. This research aimed to identify current coverage, observe the condition of the coral ecosystem, and identify the species composition of coral reefs in Kepala Jeri Waters. Data collection includes the spatial distribution, actual condition, and species composition of the coral ecosystem on Kepala Jeri Island, Riau Islands, Indonesia. Using an algorithm-based classification, we analyzed the spatial distribution of coral reefs from Landsat 8 OLI imagery. The study used Landsat OLI 8 images from the US Geological Survey (USGS) and observed the data using a line intercept transect. We obtained the actual coral conditions and species composition through field observation. The results revealed a total distribution of 239.67 ha of coral reef ecosystem in the surrounding area of Kepala Jeri Island. Unfortunately, the coral reef on Kepala Jeri Island was in poor condition, with an approximate living coral coverage of only 5-29.33%. Compared to other locations, we found the sand cover to be the highest, ranging from 28.33 to 51.67%. We also found that the seagrass bed had invaded the coral ecosystem, suggesting a reduced or absent population of halos grazers. Only the western and northern sides of Kepala Jeri Island exhibit adequate coral diversity despite discovering 26 coral species. Even though 26 coral species were found on Kepala Jeri Island, only the western and northern sides have adequate coral diversity. The findings suggest that the coral reef ecosystem on Kepala Jeri Island is under severe degradation, while the risk of further degradation remains. Therefore, human intervention is absolutely important to improve the coral reef ecosystem on Kepala Jeri Island, ensuring its sustainability.

Satellite-based rainwater harvesting sites assessment for Dera Ghazi Khan, Punjab, Pakistan.

Water scarcity in arid regions poses significant livelihood challenges and necessitates proactive measures such as rainwater harvesting (RWH) systems. This study focuses on identifying RWH sites in Dera Ghazi Khan (DG Khan) district, which recently experienced severe water shortages. Given the difficulty of large-scale ground surveys, satellite remote sensing data and Geographic Information System (GIS) techniques were utilized. The Analytic Hierarchy Process (AHP) approach was employed for site selection, considering various criteria, including land use/land cover, precipitation, geological features, slope, and drainage. Landsat 8 OLI imagery, GPM satellite precipitation data, soil maps, and SRTM DEM were key inputs. Integrating these data layers in GIS facilitated the production of an RWH potential map for the region. The study identified 9 RWH check dams, 12 farm ponds, and 17 percolation tanks as suitable for mitigating water scarcity, particularly for irrigation and livestock consumption during dry periods. The research region was classified into four RWH zones based on suitability, with 9% deemed Very Good, 33% Good, 53% Poor, and 5% Very Poor for RWH projects. The generated suitability map is a valuable tool for hydrologists, decision-makers, and stakeholders in identifying RWH potential in arid regions, thereby ensuring water reliability, efficiency, and socio-economic considerations.

<span>Assessing the impact of land-use/land-cover change on provisioning ecosystem services in the Long Xuyen Quadrangle from 2000 to 2021</span>

Changes in land use/land cover (LULC) have a significant impact on the changes in ecosystem service values. This study aims to assess the impacts of LULC changes on provisioning ecosystem services (pES) in the Long Xuyen Quadrangle region (LXQ) from 2000 to 2021. Using Landsat 5 TM and Landsat 8 OLI imagery, this study analyzes the normalized difference vegetation index (NDVI) time series using MODIS imagery to monitor and evaluate the impacts of LULC changes in the LXQ region. Additionally, interviews with farmers were conducted to estimate the ecosystem service values of different rice cultivation models. The research results in the creation of LULC maps for the LXQ in 2000 and 2021, with relatively high reliability, including overall accuracy (OA) of 83.18% (K = 0.772) and 94.26% (K = 0.9284), respectively. The main LULC change observed in the LXQ region from 2000 to 2021 is the conversion from wetlands to double-cropped rice and triple-cropped rice systems. The study identified an area of 54,667.722 ha (26.24%) where wetlands were converted to double-cropped rice, and an area of 36,779.633 ha (17.65%) where wetlands were converted to triple-cropped rice. Furthermore, the estimated ecosystem service values after the conversion from wetlands to triple-cropped rice were approximately 122,504,931 VND/ha/year, and for double-cropped rice, it was approximately 87,772,604.88 VND/ha/year. The research findings indicate that wetland agriculturalization, including double-cropped rice and triple-cropped rice, has affected the ecosystem service provision in the LXQ region. These research results provide significant considerations for future comprehensive valuation of ecosystem service values in the LXQ, Vietnamese MD.

Mapping a tropical regolith architecture: A comparative LANDSAT-8 and SENTINEL-2 analysis

Understanding of the lateritic regolith units is important for regional geological and pedological mapping and provides valuable insights into landscape evolution and mineral exploration, especially in tropical environments. To contribute to these issues, this study used Landsat-8 OLI and Sentinel-2 MSI imagery to map the different lateritic regolith units in an area of 133 thousand km2 in the Midwest of Brazil. The approach used the Directed Principal Component Analysis (DPCA) technique and SRTM data as predictor variables for the Random Forest (RF) classification model, with the aim of comparing the efficiency of both sensors following identical classification methods. The results showed a similar overall accuracy of 73% for Landsat-8 and 74% for Sentinel-2, however the Sentinel-2-based maps are the most accurate with a kappa of 0.63 and a tau of 0.68. The significant levels of accuracy highlight the efficiency and feasibility of the multispectral imagery and machine learning approach to lateritic regolith mapping. It also emphasizes the importance of considering the spectral and spatial characteristics of sensors when selecting the most appropriate one for geological, pedological and geomorphological studies in tropical regions.

Multitemporal Analysis of Seagrass Dynamics on Derawan Island (2003–2021) Using Remote Sensing Techniques

The shallow waters around Derawan Island are renowned for their beauty, attracting a significant number of tourists. Since the 2008 National Sports Week (PON) in East Kalimantan, the construction of inns and jetties has enhanced both accommodation and accessibility on the island. However, this development has also impacted the seagrass beds in the surrounding shallow waters. This study examines the changes in the area and density of seagrass beds from 2003 (prior to the PON activities) through to 2011 (a few years post-PON) and in 2021 (the most recent conditions), assessing the effects of lodging and jetty construction on these beds. Data were collected via field surveys using the photo transect method, and the benthic habitat map was created using Landsat 8 OLI Imagery, applying the Lyzenga water column correction algorithm and unsupervised classification method. The Normalized Difference Building Index (NDBI) algorithm and land digitization were utilized to track the development of the inns and jetties, revealing a rapid, widespread increase in construction throughout the island's southern region (R-square = 0.59). The study findings indicate a significant degradation of seagrass meadows between 2003 and 2021, particularly near populated areas on the southern coast, resulting in decreased density levels.

Spatio-Temporal Analysis of Land Use/Land Cover Change and the Implications on Sustainable Development Goals in the Vea Catchment of Ghana

Globally, land use and land cover change (LULCC) is recognized as posing a substantial environmental challenge with lasting and severe consequences. This study quantified LULCC within the Vea catchment area spanning 24 years, from 1998 to 2022. Landsat TM imagery from 1998, ETM+ imagery from 2006, and Landsat 8 OLI imagery from 2014 to 2022 were used. A supervised classification algorithm was employed to classify the LULC classes. The findings reveal a notable transformation in the Vea catchment area from 1998 to 2022, primarily by converting grassland to cropland. Agricultural activities emerged as a significant contributor to the observed LULCC trend. Notably, cropland expanded from 10.9% to 51.98% between 1998 and 2022, while grassland and mixed vegetation/forest areas decreased from 54.8% to 18.14% and 31.7% to 22.73%, respectively. These results, it is argued, underscore the potential implications for achieving the targets set by the UN's Sustainable Development Goals.

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

Assessing lake shoreline change and prediction for 2030 by physical drivers: A Case Study from Lake Batur, Batur UNESCO Global Geopark, Bali

Over ten years, the water level of Lake Batur has increased. Agriculture area and settlements around Lake Batur are threatened by rising water level. This study aims to analyze the shoreline change of Lake Batur, located in the Batur UNESCO Global Geopark (BUGG), during the period of 2007 – 2018 and design a prediction for 2030. Understanding the shoreline change is very important for lake management and planning. Shoreline changes were analyzed in Geographic Information System (GIS) application. The data were obtained based on Remote Sensing (RS) data, Landsat ETM+ imagery on September 24, 2007, and Landsat OLI imagery on October 24th, 2018. Predictions of lake shoreline in 2030 result from modelling by integrating ASTER-GDEM V2 data, lake water level data for 2007–2018, annual average rainfall data for 2007–2018, and bathymetry data for 2013 and 2015. The results of the satellite imagery analysis show that there has been a change in the length of the shoreline, which has increased from 20.47 km in 2007 to 21.28 km (3.96%) in 2018. The lake surface area changed from 15.34 km2 in 2007 to 16.16 km2 in 2018 (5.35%). The prediction of lake shoreline changes in 2030 showed that Lake Batur will increase to 26.90 km (26.41%), and lake surface area is predicted to increase by 17.67 km2 (9.34%) from 2018. This is because of the morphological change of Lake Bottom.

Soil moisture mapping for drought monitoring in urban areas

Soil moisture is an important indicator for drought monitoring. Mapping of soil moisture in this study uses remote sensing, namely Landsat 9 OLI imagery, because it can be relied upon as a cheap source of information, and its good temporal resolution or revisit period. Two parameters indirectly related to soil moisture, namely vegetation were analyzed using the Normalized Difference Vegetation Index (NDVI) approach, and land surface temperature (LST). Drought analysis was verified using the Normalized Difference Drought Index (NDDI). The remote sensing imagery used in this study is Landsat 9 OLI imagery by selecting images with 30% cloud cover from 1 January 2022 to 31 December 2022 with the support of the cloud-based Google Earth Engine computing platform. The results of the analysis indicate high LST values in the southern part of the study area whose dominant land use is built-up areas, namely in the sub-districts of Depok, Gamping, Ngaglik, and Mlati. The effect of vegetation on soil moisture is indicated by the NDVI value, which has a relatively strong positive correlation with SMI (R= 0.46). The SMI value is in contrast to LST, where the spatial distribution of high SMI is spread in the northern part, namely Pakem, Cangkringan, and Turi districts. On the other hand, a low SMI is spread across the central and southern parts of the study area, which have a high drought index (extreme moderate). Overall, it is concluded that the SMI has the potential to map drought and is a reliable index for initial analysis of drought risk management.

Identification of lava presence on Anak Krakatau volcano using normalized hotspot index before a flank collapse in 2018

On December 22, 2018, a catastrophic tsunami struck Anyer Beach in Banten, Indonesia, resulting in numerous casualties and extensive damage to the region. A flank collapse in the southwest sector of Anak Krakatau primarily triggered this tsunami. Intense eruptions and subsequent lava discharges precipitated this collapse. Indonesia’s Vulcanology Geological Hazard Mitigation Center has reported heightened activity at this location since June 2018. Comprehensive field studies, bolstered by remote sensing data, have been pivotal in enhancing our understanding of the behavior of Anak Krakatau over recent years. This research strives to pinpoint lava’s presence in Anak Krakatau during its eruptions. The methodologies employed include the Normalized Hotspot Index (NHI) transformation index combined with the SWIR-1 and SWIR-2 channels of the Landsat 8 OLI imagery. The goal is to elucidate the intense lava flow patterns associated with the flank collapse. Our findings indicate that the NHI can detect elevated lava flow activity in Anak Krakatau. Within the 13 datasets from 2018, lava flow was discerned on seven occasions between July 29 and December 20, predominantly directed towards the southwest sector of the volcano.