Sentinel-2A Satellite Imagery Research Articles

Geoinformatics and Modeling Approaches for Estimating Above-Ground Biomass in the Moist Deciduous Forests of Uttara Kannada, Karnataka, India

The present study aims to estimate Above-Ground Biomass (AGB) in the moist deciduous forests of Mundgod Taluk, Uttara Kannada district, Karnataka, India using a combination of field sampling and remote sensing techniques. AGB was assessed using Sentinel-2A satellite imagery with a spatial resolution of 10 meters. Field data were collected using the Point-Centered Quarter (PCQ) method, it includes measuring tree girth at breast height (GBH), tree height, and canopy density. AGB was calculated using artificial form factor and specific gravity of tree species and further modelled using the Normalized Difference Vegetation Index (NDVI) derived from satellite data. The results showed a significant correlation between canopy density and biomass, with very dense forests exhibiting the highest AGB values. The field-measured AGB was 436.96 t/ha for very dense forests, 259.89 t/ha for moderately dense forests, and 161.67 t/ha for open forests. A linear regression model developed between AGB and NDVI showed a high R² value of 0.91 for open forests, 0.87 for moderately dense forests, and 0.85 for very dense forests. The total area-weighted biomass for the study area was estimated at 8.87 million tons, with 4.79 million tons in very dense forests, 3.44 million tons in moderately dense forests, and 0.76 million tons in open forests. The regression model validation indicated low Root Mean Square Error (RMSE) values, with the moderately dense forests 8.81 t/ha and the in very dense forests 7.54 t/ha. This study highlights the effectiveness of integrating field measurements with remote sensing for rapid, reliable AGB estimation. The approach offers valuable insights into biomass distribution and carbon sequestration potential, providing a scalable method for carbon inventories at state and national levels, contributing to forest management and climate change mitigation efforts.

Aboveground biomass inversion of forestland in a Jinsha River dry-hot valley by integrating high and medium spatial resolution optical images: A case study on Yuanmou County of Southwest China

It is crucial to develop a comprehensive method for estimating the aboveground biomass (AGB) of trees, shrubs, grasslands, and sparse tree areas in ecologically fragile dry, hot valley regions with vertical zonation. Multi-source remote-sensing data can fulfill this requirement, providing help in monitoring the health of ecosystems and providing a basis for regional biodiversity conservation and restoration. Sentinel-2A satellite imagery was used to classify the forests, shrubs, and grasslands in Yuanmou County, Chuxiong Yi Autonomous Prefecture, Yunnan Province, China. The Gaofen-2 satellite (GF-2) was used to extract the canopy width and calculate tree biomass in the valley-type savanna region. These data were combined with remote-sensing factors and measured survey data, and random forest (RF) and extreme gradient boosting (XGBoost) models were used to estimate the biomass. Using GF-2 images to segment sparse tree areas effectively reduced the overestimation of low-resolution remote-sensing images, enabling the AGB of sparse trees to be accurately estimated. The biomass estimations based on the Sentinel-2A images attained coefficient of determination (R2) values of 0.45 and 0.47 for the forest, 0.55 and 0.61 for the shrubs, and 0.32 and 0.37 for the grasslands using RF and XGBoost models, respectively, demonstrating variable effectiveness across vegetation types. In addition, the XGBoost model was more robust than the RF model for all three vegetation types. Our methodology provides scientific support for the sustainable development of ecologically fragile dry, hot valleys and savanna areas.

Leveraging deep learning for coastal monitoring: A VGG16-based approach to spectral and textural classification of coastal areas with sentinel-2A data

Coastal ecosystems are vital for the planet's health, providing essential habitats for diverse species and supporting human communities. However, these complex environments face increasing threats from climate change and human activities. Effective monitoring of these areas requires large-scale, efficient, and accurate methods. This study explores the potential of deep learning for automated coastal land cover classification using Sentinel-2A satellite imagery on the Google Earth Engine (GEE) platform. We investigate the impact of transfer learning and spectral band combinations on classification accuracy for five coastal types: artificial, bedrock, sandy, muddy, and vegetation-covered. Our findings demonstrate that a pre-trained VGG16 Convolutional Neural Network (CNN) with transfer learning significantly improves classification accuracy (average 19.3% increase) compared to using default weights. Notably, including the near-infrared (NIR) band in training data leads to superior results, particularly for artificial and bedrock coastlines, where the NIR band's effectiveness in separating land-water boundaries enhances classification accuracy. These results highlight the potential of deep learning for large-scale, automated coastal monitoring, informing applications in sustainable fisheries management, coastal vulnerability assessment, and marine ecosystem conservation.

Remote sensing analysis of base cover of the water in Bontosua Island, Pangkajene and Islands Regency

Background: Among the Spermonde Archipelago's strategically important and predominantly medieval islands is Bontosua Island. In order to preserve the health and vitality of the ecosystems, the local communities on this island actively participate in environmental conservation. With the help of this study, the bottom cover of the waters surrounding Bontosua Island, Pangkajene Regency, and the archipelago will be mapped, and the relationships between various types of bottom cover will be ascertained. Methods: From March to September of 2022, the study was carried out. Images from the Sentinel-2A satellite were obtained on July 29, 2021. Using the Lyzenga procedure and the values ki/kj = 0.876875437 and a = -0.280796034, water column adjustment was carried out. The Unsupervised Classification approach was used to classify the images, and the Rapid Reef Assessment (RRA) method was used to verify the bottom water cover in the field. Findings: With a total area of 69.42 hectares, the results revealed seven dominating objects: deep sea, sand, dead coral, dead coral with algae, and coral debris (11.17 ha), living coral (17.32 ha), broken coral (12.73 ha), sand (4.43 ha), seagrass (12.61 ha), and dead coral with algae (11.16 ha). In the waters surrounding Bontosua Island, live coral constituted the greatest portion of the bottom cover, accounting for 25% of the total, while sand made up the least amount, just 6%. Novelty/Originality of this Study: The study on Bontosua Island's water bottom cover is novel due to its use of Sentinel-2A satellite imagery combined with the Lyzenga procedure and Unsupervised Classification approach, offering high-resolution mapping of underwater ecosystems. Additionally, the study's comprehensive classification and verification process through RRA provides detailed insights into the distribution and health of various bottom cover types, contributing to enhanced environmental conservation efforts in the Spermonde Archipelago.

Analysis of Devanur and Manamedu Ophiolite Complexes in SGT, India: A detailed examination employing remote sensing techniques and Laboratory Spectral Signature investigations

This study employs advanced satellite imagery from ASTER and Sentinel-2A to conduct detailed lithological mapping of the Devanur and Manamedu ophiolite complexes in the southern Central Shear Zone (CSZ). The primary focus is on the Manamedu Ophiolite Complex (MOC) and the Devanur Ophiolitic Complex (DOC). Image enhancement techniques such as Color composites, Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) were utilized to differentiate various rock types. RGB band combinations derived from PCA and MNF outputs demonstrated effective discrimination of rock units. Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classification methods were employed on ASTER and Sentinel-2A images, yielding classified lithologies that closely matched existing maps from the Geological Survey of India (GSI) and other studies, validating the accuracy of the findings. Additionally, Laboratory Spectral Signature Studies were conducted on 10 rock samples using an ASD FieldSpec Pro® spectroradiometer, providing reflectance spectra from 350 nm to 2500 nm. These spectra, particularly the continuum-removed reflectance, revealed diagnostic absorption features that were corroborated by geochemical analyses. A detailed analysis investigated how elemental compositions and key minerals influenced absorption bands. Major oxide geochemical compositions of DOC and MOC samples were identified using XRF methods. The aim of this research is to characterize DOC and MOC through remote sensing and spectral signature analysis. Sentinel-2A data proved more effective in lithological discrimination compared to ASTER, with spectral signatures indicating the presence of iron (Fe) and magnesium (Mg) contents. Notably, SVM classification of Sentinel-2A MNF + DEM data achieved an overall accuracy of more than 90% when compared with field investigations. This study underscores the efficacy of processing VNIR and SWIR bands from ASTER and Sentinel-2A satellite imagery alongside DEM data and ground surveys for mapping mafic-ultramafic rocks in the DOC and MOC regions of the CSZ.

Spatial Distribution of Mangrove Density in Gili Gede, West Lombok Regency

Gili Gede hosts a vital mangrove ecosystem, essential for the community and protecting coastal areas from large waves. However, increasing development and human activities in and around the mangrove areas threaten their distribution and extent. This study aims to map the spatial distribution, density, and area of mangroves on Gili Gede. Sentinel-2A satellite imagery was utilized with the Normalized Difference Vegetation Index (NDVI) method, and the image data were validated using hemispherical photography. Environmental parameters measured included sediment salinity, temperature, pH, humidity, NPK nutrients, and substrate type. Additional data were gathered on slope, buffer distances, and through semi-structured interviews using purposive sampling. The findings revealed that the mangrove density on Gili Gede is predominantly sparse, covering 15.56 hectares or 77.07% of the total mangrove area. Medium-density mangroves span 3.45 hectares or 17.09%, while dense mangroves occupy the smallest area, 1.18 hectares or 5.84%. Mangroves are distributed across the northern, western, and eastern sides of Gili Gede. The study identified 22 species of mangroves, both true and associated, across 14 families. Environmental factors influencing the distribution, density, and extent of mangroves across three locations include sediment salinity, temperature, pH, humidity, and nutrient content (NPK), as well as substrate type, slope, and anthropogenic influences such as human activities and the proximity of settlements to the mangrove ecosystem.

Impact of Urbanization Processes on Vegetation Cover in the Cities Over the Last Two Decades

Background: The process of urbanization serves as a prominent factor leading to the vegetation cover decline in major cities worldwide. This issue has significant consequences for the living environment on a global scale. Methods: Leveraging the latest advancements in remote sensing techniques (RSTs) and utilizing high-resolution satellite imagery, this study aims to assess the vegetation cover alteration (VCA) in correlation with the urbanization process in Thai Nguyen City, Vietnam. Landsat 5 TM and Sentinel 2A satellite imagery were used to analyze the VCA in Thai Nguyen City during the period from 2001 to 2023. Thai Nguyen city is recognized as the third largest in Northern Vietnam, following the capital city of Hanoi and Hai Phong City and ranks as the tenth most populous city in Vietnam. Result: The results obtained from this study demonstrate a substantial overall expansion, with the total urban land increasing from 386 hectares in 2001 to 1045 hectares in 2010 and experiencing a rapid surge in 2023, reaching a remarkable 4409 hectares. In general, analyzed results based on VCA maps indicated an overall vegetation cover reduction across the study area. This indicates that the study area has reached a critically high level of degradation in terms of vegetation cover. These findings emphasize the escalating risks associated with the diminishing vegetation cover detected across the study area.

Computer model for detecting tsunami wave hazard on built-up land using machine learning and sentinel 2A satellite imagery

<p>The aim of this research is to compile a tsunami wave hazard scale based on built-up land density extracted and classified by machine learning from Sentinel 2A satellite and digital elevation model (DEM) imageries. This research was carried out in 5 stages, namely: (i) pre-processing of Sentinel 2A and DEM images, (ii) Classification of VI data using the machine learning algorithms, (iii) Spatial prediction using the ordinary kriging method, (iv) Field testing using the confusion matrix method, (v) Preparation of decision matrix for tsunami wave hazard. The results of the study show that the most accurate classification algorithm for classifying built-up indices data is the k-nearest neighbor (k-NN) algorithm. The results of the statistical accuracy test show that the most accurate is normalized difference built-up index (NDBI) with a mean of square error (MSE) value of 0.073 and a mean of absolute error (MAE) of 0.003. DEM analysis shows that the research area is at an altitude of 0–15 meters above sea level so it is in the high vulnerability to medium vulnerability category. Field testing showed user accuracy of 91.11%, manufacturer accuracy of 92.16%, and overall average accuracy of 91%.</p>

Changes In Greenness Level In PT Arumin Indonesia's RPT NPLCT Area With Normalized Difference Vegetation Index (NDVI)

The observation focused on PT Arutmin Indonesia's NPLCT RPT area of 98 hectares. These observations are viewed with the help of remote sensing technology and geographic information systems (GIS). This observation aims to analyze changes in greenness levels that occur from 2019 to 2023 using the normalized difference vegetation index (NDVI) method and the materials used sentinel-2A imagery. The goal is to evaluate changes in greenness levels and their interpolation from 2019 to 2023. An area of 98 hectares was monitored using sentinel-2A satellite imagery with a pixel sharpness of 10 meters x 10 meters. The results show sentinel imagery from 2019 to 2020, 38.41 ha experienced reforestation, 1.65 ha experienced deforestation and 58.14 ha did not change. Sentinel-2A image from 2020 to 2021, 79.09 ha experienced reforestation, 1.35 ha experienced deforestation and 17.68 ha did not change. Sentinel-2A image from 2021 to 2022, 1.08 ha experienced reforestation, 79.48 ha experienced deforestation and 17.68 ha did not change. Sentinel-2A imagery from 2022 to 2023, 0.47 ha experienced reforestation, 40.65 ha experienced deforestation and 57.17 ha did not change. The sentinel-2A image changes from 2019 to 2023, 2.27 ha reforested, 20.35 ha deforested and 75.64. These observations highlight the importance of remote sensing technology and geographic information systems (GIS) in monitoring and analyzing land cover change to support conservation, rehabilitation and natural resource management policies in Kotabaru District, South Kalimantan.

Analyzing the impact of Jakarta’s reclamation on the distribution of TSS using remote sensing technology

Jakarta Bay is a bay supporting the economy of thousands of nearby fishermen. Therefore, the reclamation in Jakarta Bay raises many environmental, economic, and social problems. During this reclamation, dredging and stockpiling activities increase the turbidity of the waters. This research aims to determine the changes in the distribution of total suspended solids (TSS) after reclamation using Sentinel 2A satellite imagery from 2016 to 2022. This research implemented the previous algorithm to produce the distribution of TSS in Jakarta Bay for attaining TSS values from Sentinel 2A satellite imagery processed by Google Earth Engine with overlay analysis method in a geographic information system. Spatial data were obtained by extracting TSS values using the previous estimation algorithm, namely the Budhiman algorithm, with a correlation coefficient of -0.745 and NMAE of 0.167. Based on the data processing results, it was found that the distribution of TSS from 2016 to 2022 was at a moderate level, which had a relatively significant influence on fisheries, requiring the fishermen to go to the open sea farther from the coast. This shows that reclamation carries no impact on the life of marine biota in Jakarta Bay.

Measuring the Urban Metropolitanization of Kedungsepur Based on Polycentricity and Spatial Transformation

Kedungsepur, situated in Central Java Province, Indonesia, encompasses the regions of Kendal, Demak, Ungaran, Semarang, Salatiga, and Purwodadi, making it a strategically significant area. Its designation as a metropolitan area was established by Presidential Decree Number 78 of 2017, with one of its primary objectives being to promote equity by reducing urban primacy. This study seeks to assess the metropolitanization of Kedungsepur from 2016 to 2020, focusing on two key indicators: polycentricity and spatial transformation. Polycentricity is evaluated based on concentration, accessibility, and connectivity, while spatial transformation is measured by analyzing the percentage of built-up land and urban level. Polycentricity is assessed using secondary data from BPS-Statistics Indonesia, while the percentage of built-up land and urban level are determined through the interpretation of Sentinel 2A satellite imagery from 2016 and 2020. The findings indicate a moderate improvement in polycentricity during the aforementioned period, with partial spatial transformation occurring, particularly along highway corridors between cities. In conclusion, the process of metropolitanization demonstrates positive progress toward its objectives, albeit requiring more balanced control over spatial transformation processes.

Vegetation indices monitoring by using copernicus data in the old-growth forests of the Republic of Srpska/Bosnia and Herzegovina

IntroductionOld-growth forests (OGFs) are vital for global biodiversity, acting as irreplaceable carbon sinks, fostering ecosystem stability, and preserving unique ecological and cultural heritage. There are three old-growth forests in the Republic of Srpska/Bosnia and Herzegovina (Lom, Janj, and Perućica). They are dominated by mixed forests of beech, fir, and spruce (Piceo-Abieti-Fagetim illyricum). The satellite imagery results confirm that the vegetational indices are crucial for monitoring old-growth forests. Vegetation indices provide significant information for monitoring forest ecosystems. Based on Copernicus Sentinel-2 images, remote sensing is enabled, providing data in various spatial and spectral resolutions. The analysis of images related to old-growth forests allows for monitoring the spatial dynamics of changes in vitality and the health status of forests. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests.MethodsThe research methodology includes the application of Sentinel 2A satellite imagery, which is taken from the European Space Agency (ESA) database. The Sentinel 2 mission consists of two identical satellites (Sentinel 2A and Sentinel 2B) that have multispectral sensors with 13 spectral channels (bands) at spatial resolutions of 10, 20, and 60 m. The analysis included the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), and visible atmospheric resistance index (VARI). For determining the significance of differences in vegetation index magnitudes between old-growth forests, a one-way analysis of variance with equal repetitions was employed.Results and discussionThe parts of old-growth forests are located in different stages of development, and the trees in them are characterized by different degrees of vitality and have different values of vegetation indices. It is assumed that parts of old-growth forests in the terminal phase of development with a large number of diseased and rotting trees have the smallest sizes of vegetation indices. This research aims to characterize old-growth forests from space through vegetation indices to obtain a more in-depth analysis of the ecological condition of these forests. Using satellite imagery and their analysis of the obtained sizes of vegetation indices, it is observed that there is a significant difference in their sizes between old-growth forests. The Janj old-growth forest stands out as the area with the highest NDVI values, followed by the Lom old-growth forest, while the Perućica old-growth forest has the lowest NDVI values. However, the Perućica old-growth forest has significantly higher values of EVI and SAVI vegetation indices, while the other two old-growth forests have approximately similar values. The VARI index values are highest in the Lom old-growth forest, slightly lower in the Perućica old-growth forest, and lowest in the Janj old-growth forest. The results of these studies indicate statistically significant differences in vegetation index magnitudes between old-growth forests. The application of Copernicus satellite data enables the monitoring of changes in vegetation index values, which is of great assistance in spatially defining the developmental stages in old-growth forests (OGFs). Considering the relatively small number of sample areas covered by these studies, similar research needs to be conducted on a larger number of sample areas (replications), especially during the vegetation period.

Using remote sensing data to derive built-form indexes to analyze the geography of residential burglary and street thefts

ABSTRACT By deploying remotely sensed data together with spatial statistical modeling, we use regression modeling to investigate the relationship between the density of the built environment and two types of crime. We show how the Global Human Settlement Layer (GHSL) data set, which is a measure of building density generated from Sentinel 2A satellite imagery, can be used to create different indexes to describe the built environment for the purpose of analyzing crime patterns for indoor crimes (residential burglary) and open space crimes (street theft). Analysis is at neighborhood level for Stockholm, Sweden. Modeling is then extended to incorporate six planning areas which represent different neighborhood types within the city. Modeling is further extended by adding selected social, economic, demographic and land use variables that have been found to be significant in explaining spatial variation in the two crime categories in Stockholm. Significant associations between the GHSL-based indexes and the two crime rates are observed but results indicate that allowance for differences in neighborhood type should be recognized. Average income and transport hubs were also significant variables in the investigated crime categories. The article provides a practical demonstration and assessment of the use of high-resolution satellite data to examine the association between urban density and two common types of crime and offers reflections about the use of satellite image data in crime analysis.

Model Penduga Biomassa Hutan Mangrove Menggunakan Citra Satelit Sentinel -2A di Kabupaten Rembang Jawa Tengah

Mangrove forest is one of the forests that has the highest carbon storage in the tropics and is very high compared to the average carbon storage in various other forest types in the world, which contains around 1.023 Mg of carbon per hectare. This study aims to obtain a model for estimating the biomass potential of mangrove forests in Rembang Regency by utilizing Sentinel-2A Satellite imagery and making a map of the distribution of mangrove forest biomass potential through the best equation model. Sampling was done by purposive systematic sampling method. The correlation test results obtained a value of 0.640 which states that there is a strong relationship between biomass and NDVI. Biomass estimation modeling uses equation models namely Linear, Quadratic, Power, and Exponential. Model selection is done by considering several parameters such as R², RMSE, Bias, χ²-hit, SA, and SR. The selected model for estimating mangrove forest biomass potential is the quadratic model ???? = 17.4 -110.2 ???????????????? + 220 ????????????????² with an R² value of 0.474, RMSE value of 51.55, Chi-square test result of 3.46 and has the highest score value. The potential biomass distribution map generated from the selected model consists of 3 classes, namely classes < 3.7 (tons/ha), 3.7-10.5 (tons/ha), 10.5-35.7 (tons/ha). Mangrove forests in Rembang Regency have dominant biomass potential in the 10.5-35.7 (tons/ha) class.

Carbon Stock Estimation in Small-Scale Peat Ecosystems Based on the NDVI Vegetation Index on the Google Earth Engine Cloud Computing Platform

Peatlands are wetland ecosystems that originate from piles of rotting organic material on the surface of the soil. Peatlands have unique conditions that are not found in other land system units, these conditions include having a high biomass value and carbon storage capacity. Jambu Baru Village is one of the villages located on a peatland unit with ecosystem conditions that have not experienced major changes or conversion of function. Field measurements aim to test and validate the value of soil biomass and carbon content in peatlands. The greenness index model from Sentinel 2A satellite imagery is used as the main basis for determining sampling plots for biomass measurements in each class of greenness index for each land cover in the field. The research results showed that the greater the diameter and height of the plant, the higher the biomass content and carbon stock, high stands obtained a biomass value of 14.95 kg with a carbon stock of 20.36 kg; medium stands obtained a biomass value of 9.08 kg with a carbon stock of 7.33 kg and low stands obtained a biomass value of 0.89 kg with a carbon stock of 0.42 kg. In this research, results were also obtained which showed that the tree and pole sources had the highest biomass among the other sources, 24.04 kg with 27.68 kg of carbon. The results of calculating biomass and carbon stock values for the wetland ecosystem in Jambu Baru Village are presented in spatial modeling in the form of a map of estimated biomass and carbon stock values.

Benthic Habitat Classification Using Sentinel-2a Image With And Without Water Column Correction In Pengudang Village, Bintan Regency

Research on the mapping of benthic habitats in the waters of Pengudang Village, Bintan Regency, uses Sentinel-2A imagery. This mapping process has the potential to display the results of classification of benthic habitats in shallow waters. The purpose of this study was to assess the Maximum Likelihood Classification (MLH) algorithm model for mapping benthic habitat cover through Sentinel-2A satellite imagery using the lyzenga water column correction and without water column correction and to calculate the accuracy of the MLH classification results for benthic habitats in waters Pengudang Village. This classification uses a pixel-based image classification which is grouped into a cover class for each pixel. This classification groups into 4 benthic habitat classes, namely dead coral sand (KMP), sea grass (LM), live coral sand (PKH) and sand (PS). The results showed that the application of the lyzenga correction using the MLH algorithm in mapping benthic habitats resulted in a significant accuracy rate (80,00%), while the application without lyzenga correction using the MLH algorithm resulted in an accuracy rate (76,00%). These findings support the use of the lyzenga correction method which can increase the level of accuracy by (4,00%) with the MLH algorithm as a guide in better mapping of benthic habitat cover in Pengudang Village.

Burned area assessment using Sentinel-2A satellite imagery and DNBR spectral index (Case study: forest areas on the anticline in the Khaiz region, Iran)

The main advantage of incorporating remote sensing techniques into wildfire management is their ability to provide real-time data. This study aimed to investigate the extent of forest fires in southwestern Iran using remote sensing data. Sentinel-2A data with a resolution of 20 meters were used to conduct this study. It is worth noting that the spectral bands selected in this study, namely spectral band 8A (red edge 4) and band 12 (SWIR 2), have proved their suitability for fire intensity classification. In this study, 1NBR (Normalized Burn Ratio) values within the study area ranged from −0.096 to 0.81. These values were categorized based on the United States Geological Survey classification table. The study area covered 4,758.915 hectares, with approximately 32.41% (1,542.284 hectares) having calculated 1NBR values. Of the total area, 60.97% (2,901.675 hectares) was burned at low intensity, while approximately 6.62% (314.956 hectares) was burned at medium intensity. Unfortunately, due to the limited extent of the study area, regions with moderate to high fire intensity and high intensity were not included in the classification. The research results indicate that the studied index has satisfactory efficiency. The application of this index to regions with characteristics similar to those of the Khaiz anticline is likely to provide valuable and reliable results.

Flood risk assessment of slums in Dhaka city

Slums are characterized as some of the most impoverished and vulnerable communities within urban areas. They often face a multitude of challenges, including extreme poverty, social marginalization, and inadequate infrastructure. The poor quality of buildings and the lack of adequate infrastructure further exacerbate the vulnerability of slum dwellers to the impacts of flooding. This study focuses on accurate assessment of flood risk in slum area of the Dhaka metropolitan area, the capital of Bangladesh. The man-machine interactive method was utilized to extract slum information from Sentinel-2A satellite imagery. Thirteen evaluation indicators were selected from the aspects of hazard, sensitivity, exposure, and prevention and mitigation capacity. The fuzzy analytic hierarchy process (FAHP) was employed to determine the relative weights of these indicators. A comprehensive evaluation model for flood disaster risk was established, enabling the determination of the spatial distribution of flood risk in slums of Dhaka city. The results indicate that the flood risk distribution in the slums of Dhaka city exhibits an overall high flood risk. The slums with very high flood risk constitute about 22.035% of the total area and are concentrated in Badda, Khilkhet, and Dakshinkhan districts. Slums with high-risk constitute 41.600% of the total area and are located mainly in Turag, Pallabi, Badda, and Gulshan districts. Those areas with abundant rainfall, dense river networks, and flat topography have higher flood risk in slums compared to other regions. These research findings can be a reference for regional flood risk zoning, prevention, and sustainable development planning.

Estimasi Simpanan Karbon Tegakan Menggunakan Citra Sentinel-2A Pada Kawasan Mangrove Labuan Tereng Kabupaten Lombok Barat

The primary worry in addressing climate change problems is the elevation in global temperatures resulting from the growing levels of CO2 emissions in the atmosphere. Mangrove ecosystems contribute to the "blue carbon" plan which is capable of storing carbon well, this research was conducted to assess carbon storage within the mangrove forest ecosystem by combining Sentinel-2A satellite imagery with on-site field measurements. The data analysis findings indicate the presence of six distinct mangrove varieties, namely R. mucronata, A. marina, R. apiculata, S. alba, E. agallocha, and C. decandra. The R. mucronata type is the type that dominates the mangrove area with an average carbon amount of 122.1 tonnes/ha. Correlation analysis shows a strong relationship between IKVm and mangrove forest carbon stocks, with a correlation coefficient value of 80%. In the regression model, the power model provides the best equation for estimating carbon stocks with a coefficient of determination value of 64.4% giving a model equation of y = 109.51x1.2381. Analysis of image carbon reserves obtained the lowest value, namely 0.02-10.46 tonnes/ha which was in the very rare vegetation density type and the highest carbon reserve value was 58.30-59.02 tonnes/ha in the very high density class.

Estimating Fine Fuel Load Using Sentinel-2A Imagery and Machine Learning: A Case Study in the Mountainous Forests of Changsha, China

Fine fuel load (FFL) is a crucial variable influencing the occurrence of wildfire. Accurate knowledge of the distribution of FFL in mountainous forests is essential for ongoing wildfire risk management and the stability of mountain ecosystems. Traditional methods of estimating forest fuel load typically involve ground surveys combined with remote sensing, which can be costly and inefficient. Therefore, low-cost, large-scale FFL estimation remains challenging. In this study, Sentinel-2A satellite imagery from the Changsha forest region was used as the data source. Firstly, different feature variables were constructed based on false-color (B843), true-color (B432), four-band (B8432) combinations, and the Normalized Difference Water Index (NDWI). Subsequently, a machine learning approach based on random convolution was employed to estimate FFL. This study also included accuracy assessments of the estimation results and the creation of FFL maps for the study area. The results showed that the FFL estimation based on the B8432 band combination achieved the highest accuracy, with RMSE and R2 values of 5.847 t·hm−2 and 0.656, respectively. FFL estimation results based on false-color imagery followed, with true-color imagery and NDWI index-based estimation results exhibiting lower accuracy. This study offers critical FFL insights using random convolution techniques applied to Sentinel-2A imagery, enhancing the ability to monitor and manage forest fuel conditions effectively, thereby facilitating more informed regional wildfire risk management strategies.