Search

Surface water plays an important role in understanding the hydrological behaviour of a wetland and is crucial for the sustainability of wetland ecosystems. Remote sensing increasingly is used for the estimation of surface water levels in larger inland waterbodies. However, there are few investigations that have employed multi-sourced remote sensing data for water level predictions in wetlands, which was the motivation for undertaking this study. Sentinel-2 and Landsat-8 are among the latest satellites providing optical imagery with high spatial resolution and coverage that are available in the public domain. Different water indices have been applied to estimate surface water levels using these satellite image sources; however, what index to use for a particular application requires thorough, site-specific analysis. In this study, the Normalized Difference Water Index (NDWI), two versions of the Modified Normalized Difference Water Index (MNDWI), and the Water Ratio Index (WRI) were used to estimate water levels in a constructed wetland, as part of an effort to better guide regulation and decision-making for a local management agency. The satellite data were complemented with high resolution aerial photogrammetric images and LiDAR data to assess the accuracy of water level predictions provided by the satellite images. The photogrammetric images were used as reference datasets while the LiDAR data supported the development of area-elevation curves for the wetland. Accuracy assessment between the satellite and reference images was performed using the Kappa co-efficient (K). MNDWI performed better than the other water indices for both satellite data sources; however, the optimum threshold was different for each satellite (− 0.35 for Sentinel-2 and − 0.25 for Landsat-8). K values for the optimum threshold ranged between 0.72 and 0.77 for Sentinel-2 and 0.73 and 0.87 for Landsat-8. The water levels estimated using the remotely sensed data were assessed against in situ, continuously measured water levels using multiple efficiency evaluation metrics including R2, RMSE, and SSE. Estimated water levels with Sentinel-2 and Landsat-8 resulted in an R2 of 0.86 and 0.88, RMSE of 0.04 m and 0.06 m, and an SSE of 0.02 m and 0.06 m, respectively. These results show that even for a small wetland, it is possible to use satellite imagery to estimate water levels with high accuracy. [ABSTRACT FROM AUTHOR]

Accurate and near-real-time flood monitoring is crucial for effective post-disaster relief efforts. Although extensive research has been conducted on flood classification, efficiently and automatically processing multi-source imagery to generate reliable flood inundation maps remains challenging. In this study, a new automatic flood monitoring method, utilizing optical and Synthetic Aperture Radar (SAR) imagery, was developed based on the Google Earth Engine (GEE) cloud platform. The Normalized Difference Flood Vegetation Index (NDFVI) was innovatively combined with the Edge Otsu segmentation method, utilizing SAR imagery, to enhance the initial accuracy of flood area mapping. To more effectively distinguish flood areas from non-seasonal water bodies, such as lakes, rivers, and reservoirs, pre-flood Landsat-8 imagery was analyzed. Non-seasonal water bodies were classified using multi-index methods and water body probability distributions, thereby further enhancing the accuracy of flood mapping. The method was applied to the catastrophic floods in Poyang Lake, Jiangxi Province, in 2020, and East Dongting Lake, Hunan Province, China, in 2024. The results demonstrated classification accuracies of 92.6% and 97.2% for flood inundation mapping during the Poyang Lake and East Dongting Lake events, respectively. This method offers efficient and precise information support to decision-makers and emergency responders, thereby fully demonstrating its substantial potential for practical applications. [ABSTRACT FROM AUTHOR]

The Urban Heat Island (UHI) effect refers to the phenomenon where urban areas experience significantly higher temperatures than their rural surroundings due to human activities and modifications, such as replacing natural land cover with impervious surfaces, reducing vegetation, and increasing heat-absorbing materials. Kütahya central district has a dense population and building layout, which contributes to the intense UHI. Geographic Information System (GIS) and its tools are widely used in assessing UHI in urban areas, providing insights for both researchers and local authorities. The study aims to spatially evaluate the urban heat island in Kütahya using Landsat-8 satellite data. Landsat-8 images, operating in the 10.60 - 11.19 µm range acquired on August 8, 2023 (Path: 179, Row: 33, Cloud Cover: 0%) were utilized to create the Urban Heat Island (UHI) map for the study area. Through spatial analysis using Landsat-8 data for Kütahya city center, six different classes were defined, with the UHI map created using land surface temperature values. The highest and lowest UHI values vary between 4.245°C and - 3.457°C. It has been observed that the average UHI increases by 12% in areas lacking dense building structures, characterized by rock surfaces and limited green areas. Conversely, in areas with parks, forests, and sparse settlements, the UHI decreases by 10%. With this study, a comprehensive evaluation of UHI was conducted for Kütahya city center. While the results obtained are expected to be an important resource for researchers and local authorities, they are also anticipated to be beneficial in the fields of engineering geology, urban geology, and urban planning. [ABSTRACT FROM AUTHOR]

This study investigates the uncertainty of satellite-derived bathymetry (SDB) in turbid port environments by integrating multi-temporal composites of Sentinel-2 and Landsat 8 satellite imagery with in situ bathymetry and turbidity data. The research aims to evaluate the effectiveness of SDB and its spatiotemporal correlation with satellite-based turbidity indicators across seven Chinese port areas. Results indicate that both Sentinel-2 and Landsat 8, using a three-band combination, achieved comparable performance in SDB estimation, with R2 values exceeding 0.85. However, turbidity showed a negative correlation with SDB accuracy, and higher turbidity levels limited the maximum retrievable water depth, resulting in SDB variances ranging from 0 to 15 m. Landsat 8 was more accurate in low to moderate turbidity environments (12–15), where SDB variance was lower, while higher turbidity (above 15) led to greater SDB variance and reduced accuracy. Sentinel-2 outperformed Landsat 8 in moderate to high turbidity environments (36–203), delivering higher R2 values and more consistent SDB estimates, making it a more reliable tool for areas with variable turbidity. These findings suggest that SDB is a viable method for bathymetric and turbidity mapping in diverse port settings, with the potential for broader application in coastal monitoring and marine management. [ABSTRACT FROM AUTHOR]

Fires threaten life all over the world and damage millions of hectares of area every year. Remote sensing provides advantages for damage detection in terms of time and cost. By using satellite imagery, burned areas can be detected without the need to visit the area. Since factors such as image band configuration, optimisation algorithms, and thresholds affect the results, this study aims to observe their impact on burned area detection. Thus, by using Landsat-8 images and U-Net architecture through the Python programming language, various combinations were created and different thresholds were used. According to the results, the combination of 7, 5, 4 bands and the AdaMax algorithm were selected for the final model, and the results were improved by data augmentation. Consequently, accuracy obtained in the final model was 97.76%, which was the highest for a threshold of 0.5. The F1 score obtained for the same threshold was 79.38%. [ABSTRACT FROM AUTHOR]

Copyright of Geomatik is the property of Murat Yakar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, and the atmosphere can be controlled. Routine estimations of ET on a daily, monthly, and seasonal basis can give relevant information on small-scale agricultural practices, such as the Ribb watershed in Ethiopia. However, MODIS sensors have recently given high temporal resolution ET products across large areas, but their low spatial resolution limits its application on a local scale. The primary goal of the study was to downscale the MODIS ET (1 km) product to a finer spatial resolution at the watershed level. The model's 12 predictor variables (NDVI, EVI, LAI, FVC, SAVI, NDMI, NDWI, Albedo, emissivity, LST, and DEM: slope and elevation) were produced using the random forest (RF) algorithm using Sentinel-2 (S-2) 20 m and Landsat-8 (L-8) 30 m. The RF regression model was used to assess the relationship between predicted variables and downscaled MODIS ET. The FAO-PM ET model, developed from meteorological stations, was validated by R 2 and RMSE for three seasons (rainy, post-rainy, and dry) in 2022. The results were in good agreement with MODIS ET, with an RMSE of 0.22 for S-2 and 0.28 for L-8. In the FAO-PM ET model, the downscaled result showed greater spatial details and better agreement with gage station readings ( R 2 ≈ 0.88 and 0.82 ). Thus, considering the effectiveness and simplicity of machine learning techniques, our study demonstrated the potential for ET downscaling. Furthermore, the study suggests integrating spatiotemporal time series data to reach higher resolution. [ABSTRACT FROM AUTHOR]

The successful launch of Landsat-9 in 2020 ensures the continuity of Landsat Earth observation data. However, before Landsat-9 data can be effectively used in conjunction with Landsat-8, it is necessary to consider the differences between data from different sensors. This study utilized the Reduced Major Axis (RMA) regression to compare simulated reflectance data derived from the spectral response functions and spectral library data, showing minor spectral response differences between the two sensors (RMA slope close to 1, RMSD ≤ 0.0005); The consistency of more than 100 million pairs of top-of-atmosphere (TOA) and surface reflectance (SR) data from two sensors extracted based on Google Earth Engine was then evaluated using RMA and ordinary least squares (OLS) regression, and a transfer function developed using OLS regression was provided (R2 > 0.84); The results showed that the atmospheric state has a significant effect on the continuity of the sensors, especially in the shorter wavelength bands; there are differences in the TOA and SR data, with the absolute mean difference |MD| ≤ 0. 0011, root mean square difference |RMSD| ≤ 0.0278 and mean relative difference |MRD| ≤ 0.47%; In addition, the influence of seasonal factors on the consistency of the two sensors was investigated, and the corresponding transformation functions were provided; The OLS transformation functions developed in this paper were validated on over 10 million samples in Africa, with R2 > 0.86 for TOA, R2 > 0.93 for SR, and R2 > 0.91 for the SR seasonal transformations.

Urbanization requires human comfort that leads to industrial growth that alters existing physical and atmospheric conditions and pollutes the environment. This study area covers 200 km2 located in the northeast of the Greater Visakhapatnam Municipal Corporation (GVMC). The study area is famous for industries as well as tourism due to its bowl-shape topography and rich wild flora and fauna. Study was carried out using Landsat-8 and Landsat-9 satellite data for the years 2018, 2021, and 2022 to assess land surface temperature (LST) and the impact of air pollutants to identify human health hotspots. Industrial and vehicular emissions release harmful gases such as CO2, CO, oxides of nitrogen, sulphur, and suspended particulate matter (SPM) into the atmosphere. The LST for 2018 and 2021 results are nearly the same while the LST for 2022 decreased relatively by 3°C. Data from the monitoring stations located at the Police Barracks, Gnanapuram, and Visakhapatnam Port-Trust show that the ambient air quality with its corresponding LST in the industrial area has been identified with high LST with a 0.67°C increase from 2018 to 2021. There is a decrease of nearly 2°C from 2021 to 2022 at the monitoring stations of the Police Barracks and Gnanapuram located in urban areas, whereas Visakhapatnam Port-Trust is located in the vicinity of industries. The poor Air Quality Index (AQI) is creating discomfort and reduction of visibility in the vicinity. The relative humidity for 2018 is 72%, for 2021 is 64%, and for 2022 is 76%, which causes feelings of stickiness and sweat, and in rare cases skin irritation was reported. The regression analysis of AQI with LST shows a negative correlation in April 2018 and a positive correlation in March 2021 and April 2022. This indicates that the prominent parameter PM10 in AQI contains both cooling and warming properties. Practical Applications: The study was carried out on land surface temperature (LST) and the influence of air pollution and its impact on human health. Practical applications of LST are to identify the urban heat island (UHI) in order to predict micro- to macroclimate zones for urban human comfort and assessment of energy demand, and so on. The impact of air pollutants on LST affects human health. The research focuses on identifying human health hotspots. The LST and air pollutants are the major driving factors. The LST for 2018 and 2021 were comparatively same, while 2022 shows a decrease of LST 3°C. The ambient air quality data when overlaid on the resultant LST shows that Gnanapuram and the Police Barracks areas identified as human health hotspots. The wind speed levels are low, supporting heat retention on the surface. The relative humidity results in feelings of stickiness and sweat and some rare cases of skin irritation. In general, in 2021, the poor Air Quality Index (AQI) was creating discomfort and reduction of visibility in the vicinity. The relative humidity for 2018 was 72%, and for 2021 AQI values were high, thus creating discomfort. The prominent parameter in AQI, PM10, contains both cooling properties of sulphates and the warming nature of black carbon. Therefore, the LST with AQI shows a negative correlation in April 2018 and positive correlation in March 2021 and April 2022. [ABSTRACT FROM AUTHOR]

Flooded forests are rare and highly dynamic ecosystems, yet they can store a significant amount of carbon because of their ability to produce biomass rapidly. Estimation and mapping of the carbon that is stored in flooded forests are challenging tasks through the use of optical remote sensing because these ecosystems are often located in moist regions where clouds can interfere with data acquisition and image interpretation. This study models the aboveground carbon (AGC) stocks of a flooded forest in Turkish Thrace with synthetic aperture radar (SAR) data, which are less affected by weather and illumination conditions compared to optical imagery. Forest management plan data, including inventory records of 229 sample plots, a detailed forest cover map, and stand tables of the 2.119-ha Igneada Longoz Forest, were used to calculate AGC and to develop spatially explicit models based on ALOS/PALSAR-2 (Advanced Land Observing Satellite/Phased Array L-band Synthetic Aperture Radar) and Landsat-8 images. The results indicated that the horizontally transmitted and horizontally received (HH) and cross-polarization ratio (CPR) bands of ALOS/PALSAR were the most influential variables in the linear and nonlinear regression models. The models did not include any variables from either radar- or optical-based vegetation indices. While the estimation accuracies of the two models were similar (root mean square percentage error ≈ 26%), the linear model yielded negative estimations in several land cover classes (e.g., dune, forest opening, degraded forest). AGC stock was estimated and mapped using the nonlinear model in these cases. The density map revealed that Igneada Longoz Forest stored 279,258.9 t AGC, with a mean and standard deviation of 124 ± 115.4 t C ha-1. AGC density varied significantly depending on stand types and management units across the forest, and carbon hotspots accumulated in the northern and southern sites of the study area, primarily composed of ash and alder seed stands. The models and maps that this study developed are expected to help in the rapid and cost-effective assessment of AGC stored in flooded forest ecosystems across the temperate climate zone.

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam. [ABSTRACT FROM AUTHOR]

Understanding water quality is crucial for environmental management and policy formulation. However, existing methods for assessing water quality are often unable to fully integrate with multi-source remote sensing data. This study introduces a method that employs a stacking algorithm within the Google Earth Engine (GEE) for classifying water quality grades in the Songhua River Basin (SHRB). By leveraging the strengths of multiple machine learning models, the Stacked Generalization (SG) model achieved an accuracy of 91.67%, significantly enhancing classification performance compared to traditional approaches. Additionally, the analysis revealed substantial correlations between the normalized difference vegetation index (NDVI) and precipitation with water quality grades. These findings underscore the efficacy of this method for effective water quality monitoring and its implications for understanding the influence of natural factors on water pollution. [ABSTRACT FROM AUTHOR]

The Nakdong River is a crucial water resource in South Korea, supplying water for various purposes such as potable water, irrigation, and recreation. However, the river is vulnerable to algal blooms due to the inflow of pollutants from multiple points and non-point sources. Monitoring chlorophyll-a (Chl-a) concentrations, a proxy for algal biomass is essential for assessing the trophic status of the river and managing its ecological health. This study aimed to improve the accuracy and reliability of Chl-a estimation in the Nakdong River using machine learning models (MLMs) and simultaneous use of multiple remotely sensed datasets. This study compared the performances of four MLMs: multi-layer perceptron (MLP), support vector machine (SVM), random forest (RF), and eXetreme Gradient Boosting (XGB) using three different input datasets: (1) two remotely sensed datasets (Sentinel-2 and Landsat-8), (2) standalone Sentinel-2, and (3) standalone Landsat-8. The results showed that the MLP model with multiple remotely sensed datasets outperformed other MLMs with 0.43 – 0.86 greater in R2 and 0.36 – 5.88 lower in RMSE. The MLP model demonstrated the highest performance across the range of Chl-a concentrations and predicted peaks above 20 mg/m3 relatively well compared to other models. This was likely due to the capacity of MLP to handle imbalanced datasets. The predictive map of the spatial distribution of Chl-a generated by MLP well captured the areas with high and low Chl-a concentrations. This study pointed out the impacts of imbalanced Chl-a concentration observations (dominated by low Chl-a concentrations) on the performance of MLMs. The data imbalance likely led to MLMs poorly trained for high Chl-a values, producing low prediction accuracy. In conclusion, this study demonstrated the value of multiple remotely sensed datasets in enhancing the accuracy and reliability of Chl-a estimation, mainly when using the MLP model. These findings would provide valuable insights into utilizing MLMs effectively for Chl-a monitoring. [ABSTRACT FROM AUTHOR]

On March 2019 a severe flood episode inundated large agricultural areas of Golestan province, in northern Iran. In this study, we assessed the impacts of the flooding episode on cotton bollworm, Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae). To do so, a network of pheromone-bait traps distributed throughout Golestan was installed and used to weekly record moth captures from May to September. The interpolated spatial distribution of cotton bollworm infestation by the Inverse Distance Weighted (IDW) interpolation method was overlaid by satellite-imagery flood maps. Flood extent maps were provided by seven mosaicked images of Sentinel-1. In addition to SAR products, a 15 m-NDWI of Landsat-8 was used. The flood extent based on NDWI (pan-sharped 15 m) was 52752 ha (47423 ha based on slope mask). Using ground control points of flooded and non-flooded areas within the supervised SVM classification procedure, the kappa coefficient (κ) was as 0.89 and 0.91 for VH polarization and NDWI, respectively. Our results showed a reduction in moth populations in the areas that were flooded in spring for the earlier generations of H. armigera. Highlights: The impact of a flood episode on moth populations of H. armigera was investigated Change detection analysis from satellite images at two times allowed flood mapping Moth populations were drastically reduced in flooded areas Flood mainly affected the populations in the first and second generations Our results suggest that intentional flooding might be used for controlling H. armigera [ABSTRACT FROM AUTHOR]

The Taihu Lake basin is one of the fastest-growing regions in China, where the natural environment has been seriously affected by humans. The plant area index (PAI) is an important parameter reflecting the change in vegetation growth, which plays a crucial role in studying vegetation growth and protecting the ecological environment. Advancements in remote sensing technology, complemented by machine learning techniques, have facilitated the accurate and efficient acquisition of PAI over large areas. In this study, the Taihu Lake Basin vegetation area was taken as the research object. Global Ecosystem Dynamics Investigation (GEDI) point cloud data and Landsat-8 remote sensing images were the primary information sources. MODIS land cover types were utilized to classify the vegetation into six categories. Three classical machine learning models, namely, Random Forest (RF), Support Vector Regression (SVR), and Back Propagation Neural Network (BPNN), were used to estimate the PAI in the Taihu Lake Basin. It was found that the RF model showed the best performance. The determination coefficients (R2) for grassland, evergreen forest, mixed forest, deciduous forest, farmland, and wetland were 0.71, 0.67, 0.69, 0.66, 0.65, and 0.69, respectively. Over 2000-2022, the PAI exhibited an absolute change rate of 0.035, with an overall increasing trend. The area of improved and degraded vegetation accounted for 58.33% and 41.67% of the total area, respectively. The study also revealed that PAI was positively correlated with precipitation (R = 0.64, P < 0.05) and negatively correlated with temperature (R = -0.58, P < 0.05). Different land types' effects on PAI were also analyzed, with wetland PAI having the smallest mean value and evergreen forest PAI having the most considerable mean value. This research underscores the effectiveness of integrating GEDI data and Landsat-8 imagery in PAI assessment, providing valuable insights for environmental monitoring and analysis in the Taihu Lake Basin. [ABSTRACT FROM AUTHOR]

Accurate river ice mapping is crucial for predicting and managing floods caused by ice jams and for the safe operation of hydropower and water resource facilities. Although satellite multispectral images are widely used for river ice mapping, atmospheric contamination limits their effectiveness. This study developed river ice mapping models for the Han River in South Korea using atmospherically uncorrected Landsat-8 Operational Land Imager (OLI) multispectral reflectance data, addressing atmospheric influences with a Random Forest (RF) classification approach. The RF-based river ice mapping models were developed by implementing various combinations of input variables, incorporating the Landsat-8 multispectral top-of-atmosphere (TOA) reflectance, normalized difference indices for snow, water, and bare ice, and atmospheric factors such as aerosol optical depth, water vapor content, and ozone concentration from the Moderate Resolution Imaging Spectroradiometer observations, as well as surface elevation from the GLO-30 digital elevation model. The RF model developed using all variables achieved excellent performance in the classification of snow-covered ice, snow-free ice, and water, with an overall accuracy and kappa coefficient exceeding 98.4% and 0.98 for test samples, and higher than 83.7% and 0.75 when compared against reference river ice maps generated by manually interpreting the Landsat-8 images under various atmospheric conditions. The RF-based river ice mapping model for the atmospherically corrected Landsat-8 multispectral surface reflectance was also developed, but it showed very low performance under atmospheric conditions heavily contaminated by aerosol and water vapor. Aerosol optical depth and water vapor content were identified as the most important variables. This study demonstrates that multispectral reflectance data, despite atmospheric contamination, can be effectively used for river ice monitoring by applying machine learning with atmospheric auxiliary data to mitigate atmospheric effects. [ABSTRACT FROM AUTHOR]

Maravanthe Beach has been witnessing erosion due to waves caused by tidal forces, destroying vegetation, and causing enormous damage to people's lives, livestock, and property. Hence, an attempt has been made to understand the shoreline change analysis using the automatic shoreline extraction method and determine the rate of change in terms of erosion and accretion rate before and after the construction of coastal structures during the period from 2014 to 2023. The SWAT model was applied to analyze the influence of surface runoff on shoreline changes near Maravanthe Beach for the periods 2004–2014 and 2012–2023. Also, runoff was chosen as one of the parameters for validation of the analysis. It was noticed that the shoreline stretch had experienced significant erosion prior to the construction of shore protection infrastructure. Soon after the deployment of certain shore structures, the shoreline progressed from a high erosion stretch to a low erosion stretch. This study reveals that Maravanthe Beach witnessed greater erosion during the period 2014–2018 than it did from 2019–2023 and also observed a decrease in erosion from 11% to 3% and an increase in the accretion zones from 2 to 9%. [ABSTRACT FROM AUTHOR]

Accurate forest tree-species classification not only provides data support for forest resource management but also serves as a crucial parameter for simulating various ecological processes. However, the results of forest tree-species classification have been affected by multiple factors, such as the spectral resolution, spatial resolution, and radiometric resolution of imagery, the classification algorithms used, the sample size, and the timing of image acquisition phases. Although there are many studies on the impact of individual factors on tree-species classification, there is a lack of systematic studies quantifying the magnitude of these factors' influences, leading to uncertainties about the relative importance of different factors. In this study, Landsat-8, Landsat-9, and Sentinel-2 imagery was used as the foundational data, and random forest (RF), gradient tree boosting (GTB), and support vector machine (SVM) algorithms were employed to classify forest tree species. High-accuracy regional forest tree-species classification was achieved by exploring the impacts of spectral resolution, spatial resolution, radiometric resolution, classification algorithms, sample size, and image time phases. The results show that, for the commonly used Landsat-8, Landsat-9, and Sentinel-2 imagery, the tree-species classification results from Landsat-9 are the best, with an overall accuracy of 74.21% and a kappa of 0.71. Among the various influencing factors, the classification algorithm, image time phases, and sample size have relatively larger impacts on tree-species classification results, each exceeding 10%, while the positive impact of radiometric resolution is the smallest, at only 3.15%. Conversely, spectral and spatial resolutions had negative effects on tree-species classification results, at −4.09% and −1.4%, respectively. Based on the 30-m spring Landsat-9 and Sentinel-2 imagery, with 300 samples for each tree-species category, the classification results using the RF algorithm were the best, with an overall accuracy of 87.07% and a kappa coefficient of 0.85. The results indicate that different factors have different impacts on forest tree-species classification results, with classification algorithms, image time phases, and sample size having the largest impacts. Higher spatial and spectral resolutions do not improve the classification accuracy. Therefore, future studies should focus on selecting appropriate classification algorithms, sample sizes, and images from seasons with greater tree differences to improve tree-species classification results. [ABSTRACT FROM AUTHOR]

Non-photosynthetic vegetation (NPV) plays a crucial role in vegetation–soil ecosystems by influencing the dynamic uptake of carbon, water, and nutrients. The accurate estimation of the fractional cover of NPV (FNPV) is vital for managing natural resources, modeling carbon dynamics, and monitoring vegetation systems. NPV indices (NPVIs) are widely utilized for estimating FNPV over large areas. However, distinguishing NPV from bare soil (BS) remains challenging owing to subtle differences in their multispectral reflectance and the interference of the soil background. To address this, we propose a novel NPV spectral slope difference index (NSSDI) based on the distinct spectral curve shapes of NPV and BS. The NSSDI incorporates three spectral slopes: two between the visible and near-infrared bands and one between the shortwave infrared 1 (SWIR1) and SWIR2 bands from Sentinel-2 (S2) and Landsat-8 (L8). Additionally, we investigate the integration of soil information and radar data to improve the FNPV estimation using three machine learning algorithms. Validation against in situ measurements reveals that the NSSDI from S2 is more sensitive to FNPV than the recently published NPV–soil separation index (NSSI), whereas the NSSDI from L8 outperforms traditional NPVIs. Incorporating soil properties such as soil organic matter and soil moisture improves the FNPV estimation accuracy compared with using S2 or L8 NPVIs alone. The combination of Sentinel-1 (S1) synthetic aperture radar (SAR) data with either optical satellite also enhances the retrieval accuracy. The Gaussian process regression (GPR) model, which integrates S2 NSSDI, soil data, and S1 SAR data, achieves an R2 of 0.78 and the lowest RMSE of 0.109. Similarly, the GPR model based L8 NPVIs, soil data, and SAR data attains an R2 of 0.71 and an RMSE of 0.128. Both GPR models outperform the random forest and XGBoost models. Monthly FNPV estimates from April to August 2019 in the Northern Territory, Australia demonstrates strong spatial consistency across both satellites using the GPR models in the Google Earth Engine. These results suggest that combining NPVIs with soil and SAR data can facilitate accurate large-scale FNPV estimations.

The recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites’ SR data, with an average absolute percent change (PC) of 1.88% and an average R2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24°C lower than Landsat-8 LST across the five test areas but can be 0.58°C lower in built-up land-dominated areas and 0.42°C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

Cloud cover is a significant factor affecting the effectiveness of satellite-based Earth observations. Existing cloud detection algorithms primarily rely on imaging data from satellite sensors in the visible to near-infrared spectral range, making it challenging to achieve day-and-night cloud monitoring. Convolutional neural networks have shown outstanding performance in previous cloud detection algorithms due to their robust ability to extract local information. However, their inherent inductive bias limits their capacity to learn long-range semantic information. To address these challenges, we proposed SwinCloud, a U-shaped semantic segmentation network based on an enhanced Swin Transformer for cloud detection in the thermal infrared spectral range. Specifically, we augment the Swin Transformer's window attention module with a CNN-based parallel pathway to effectively model global-local information. We employ a feature fusion module before the final upsampling module in the decoder to better integrate low-level spatial information and high-level semantic information. On the Landsat-8 cloud detection dataset, our model outperforms state-of-the-art methods. When transferred to the SDGSAT-TIS cloud detection dataset, the mIOU of experiment results reaches 69.9%, demonstrating the strong transferability of SwinCloud across different sensors. We also applied SwinCloud to cloud detection in the visible bands of Landsat-8. The results demonstrated SwinCloud's generalization capability across different bands.

Changes in land use and cover play an important role in sustainable natural resource management. Muaro Jambi Regency, which is rich in biodiversity, has ecosystem services for residents in the area, especially plantations which are one of the leading commodities. Forests as the largest land cover in Muaro Jambi Regency have experienced quite rapid changes, especially after forest and land fires occur almost every year. Real-time monitoring and assessment on a spatio-temporal basis is important to determine changes in land cover. This research examines changes in land cover in Sarolangun Regency from Landsat-8 time series images from 2018 to 2023. Land cover is classified into 6 classes using the supervised classification method. The data findings show that forests have experienced a significant decline, while settlements have experienced a very substantial increase. Comprehensive research regarding the vulnerability of periodic land cover changes at local and regional levels must be carried out to maintain sustainable environmental ecosystems.

Monitoring oil drift by integrating multi-source satellite imagery has been a relatively underexplored practice due to the limited time-sampling of datasets. However, this limitation has been mitigated by the emergence of new satellite constellations equipped with both Synthetic Aperture Radar (SAR) and optical sensors. In this manuscript, we take advantage of multi-temporal and multi-source satellite imagery, incorporating SAR (Sentinel-1 and ICEYE-X) and optical data (Sentinel-2/3 and Landsat-8/9), to provide insights into the spatio-temporal variations of oil spills. We also analyze the impact of met–ocean conditions on oil drift, focusing on two specific scenarios: marine floating oil slicks off the coast of Qatar and oil spills resulting from a shipwreck off the coast of Mauritius. By overlaying oils detected from various sources, we observe their short-term and long-term evolution. Our analysis highlights the finding that changes in oil structure and size are influenced by strong surface winds, while surface currents predominantly affect the spread of oil spills. Moreover, to detect oil slicks across different datasets, we propose an innovative unsupervised algorithm that combines a Bayesian approach used to detect oil and look-alike objects with an oil contours approach distinguishing oil from look-alikes. This algorithm can be applied to both SAR and optical data, and the results demonstrate its ability to accurately identify oil slicks, even in the presence of oil look-alikes and under varying met–ocean conditions. [ABSTRACT FROM AUTHOR]

Estuaries are one of the most productive ecosystems in the world, supporting a variety of flora and fauna. Primary productivity by phytoplankton is a rich source of organic carbon, substantial for the aquatic food web. Monitoring phytoplankton (i.e., chlorophyll-a) is essential to assess the health of estuaries and other continental shelves subjected to constant anthropogenic stress (e.g., developmental activities). In this study, a three-endmember combination Spectral Decomposition Algorithm (SDA) was developed to estimate the phytoplankton in the micro-tidal Yura estuary of Japan using Landsat-8 (30 m), and Sentinel − 2A (10 m). The endmember water, phytoplankton, and submerged aquatic vegetation (SAV) yielded the best results with both the satellite sensors (R2 > 0.80) owing to the limited influence of non-phytoplankton suspended solids (NPSS) in the estuary. Chlorophyll-a was used as the proxy for phytoplankton. The estimated root mean square error (RMSE) was relatively higher in Landsat-8 (RMSE = 0.187 µg/L) than the Sentinel-2A (RMSE = 0.162 µg/L). The results were validated using the ground truth data of the Yura Estuary (26 sampling points). Furthermore, the results indicate low chlorophyll-a concentration in the Yura estuary (< 2µg/L) except near the shorelines (~ 6 µg/L). A good fit (R2 = 0.79) between observed chlorophyll-a and turbidity indicated phytoplankton-dominated turbidity in the tide-less estuary of Japan. The estimated maximum turbidity was 1.4 FTU using both sensors, suggesting a low anthropogenic influence on the Yura Estuary. The study demonstrates a successful application of the spectral decomposition algorithm (SDA) in the coastal waters which could further be used to assess the horizontal and temporal variability in phytoplankton in estuarine water. [ABSTRACT FROM AUTHOR]

Global warming and associated weather changes, notably the El Niño Southern Oscillation (ENSO), significantly impact marine ecosystems by altering water quality parameters such as chlorophyll-a (Chl-a) and sea surface temperature (SST). These changes are crucial in understanding the biogeochemical and ecological dynamics of marine environments, especially in regions affected by upwelling. This study aims to monitor upwelling events on Satonda Island, a volcanic island with unique central lake and status as a protected area using remote sensing. Utilizing Landsat-8 imagery and machine learning regression techniques--Random Forest (RF), Support Vector Machine (SVM), and Classification and Regression Tree (CART)--this research evaluates the water quality in Satonda waters over a decade (2013-2023). The RF method emerged as the most accurate in estimating Chl-a and SST, indicating its efficacy in monitoring marine ecosystems with the result (RMSE = 0.309 and 0.274). The analysis reveals seasonal upwelling patterns, characterized by decreased SST and increased Chl-a concentration, with peaks varying annually between June and November. This study highlights the crucial role of remote sensing and machine learning in monitoring the effects of climate change on marine biodiversity. It provides valuable insights into the temporal dynamics of upwelling in the shallow waters of Indonesia. [ABSTRACT FROM AUTHOR]

The South Rifain ridges are an example of tectonic-sedimentation interaction in the Mio-Plio-Quaternary foreland basins at the front of the Rif chain. is an elongated mountain zone-oriented E-W and N-S, forming the most frontal part of the Rif belt. The morphotectonic study carried out in this area is based on Landsat-8 OLI image processing techniques to determine the contribution of these images to structural mapping. The results obtained reveal a predominant E-W orientation, which is widely present throughout the study area. This is followed by a second N-S direction, a third NW-SE direction, and a fourth NE-SW direction. The NW-SE lineaments are also mapped in kilometres. Their equivalent on the ground shows a sinister movement but does not show a significant horizontal displacement of more than a few metres. Together with the NE-SW faults, these faults form a conjugate system of dextral and sinistral faults, compatible with a palaeostress field where the maximum shortening stress is submeridian. [ABSTRACT FROM AUTHOR]

The ASI algorithm uses the same sea ice and seawater tie-points when inverting polar sea ice concentration (SIC), but this approach does not fully consider the differences between different polar sea regions and the impact of different sea ice characteristics on SIC results. To make up for this deficiency, the SIC inversion algorithm based on different types of Arctic sea ice is proposed. The proposed algorithm selects pure ice and pure water sample points in different sea regions to derive SIC inversion formulas, and subsequently obtains SIC retrieval results for the entire Arctic. Compare the results of this study with those of traditional ASI algorithm, and perform local validation based on the sea ice distribution obtained from Landsat-8 data. The results show that compared with the traditional ASI algorithm, the proposed algorithm has improved the accuracy of SIC inversion in different sea ice regions by 2%-6%, with an average improvement of 3.3%. Overall, our research has improved the ASI algorithm, which is of great significance for obtaining higher precision polar SIC. [ABSTRACT FROM AUTHOR]

Vegetation plays a crucial role in terrestrial ecosystems, and the FVC (Fractional Vegetation Coverage) is a key indicator reflecting the growth status of vegetation. The accurate quantification of FVC dynamics and underlying driving factors has become a hot topic. However, the scale effect on FVC changes and driving factors has received less attention in previous studies. In this study, the changes and driving factors of FVC at multiple scales were analyzed to reveal the spatial and temporal change in vegetation in the Henan section of the Yellow River basin. Firstly, based on the pixel dichotomy model, the FVC at different times and spatial scales was calculated using Landsat-8 data. Then, the characteristics of spatial and temporal FVC changes were analyzed using simple linear regression and CV (Coefficient of Variation). Finally, a GD (Geographic Detector) was used to quantitatively analyze the driving factors of FVC at different scales. The results of this study revealed that (1) FVC showed an upward trend at all spatial scales, increasing by an average of 0.55% yr−1 from 2014 to 2022. The areas with an increasing trend in FVC were 10.83% more than those with a decreasing trend. (2) As the spatial scale decreased, the explanatory power of the topography factors (aspect, elevation, and slope) for changes in FVC was gradually strengthened, while the explanatory power of climate factors (evapotranspiration, temperature, and rainfall) and anthropogenic activities (night light) for changes in FVC decreased. (3) The q value of evapotranspiration was always the highest across different scales, peaking notably at a spatial scale of 1000 m (q = 0.48). [ABSTRACT FROM AUTHOR]

Understanding the phenology of urban trees can help mitigate the heat island effect by strategically planting and managing trees to provide shade, reduce energy consumption, and improve urban microclimates. In this study, we carried out the first evaluation of high spatial resolution satellite images from Landsat-8, Sentinel-2, and PlanetScope images to quantify urban street tree phenology in downtown Beijing. The major research goals are to evaluate the consistency in pixel-level spring–summer growth period phenology and to investigate the capacity of high-resolution satellite observations to distinguish phenological transition dates of urban street trees. At the city scale, Landsat-8, Sentinel-2, and PlanetScope show similar temporal NDVI trends in general. The pixel-level analysis reveals that green-up date consistency is higher in areas with medium (NDVI > 0.5) to high (NDVI > 0.7) vegetation cover when the impacts of urban surfaces on vegetation reflectance are excluded. Similarly, maturity date consistency significantly increases in densely vegetated pixels with NDVI greater than 0.7. At the street scale, this study emphasizes the efficacy of NDVI time series derived from PlanetScope in quantifying the phenology of common street tree genera, including Poplars (Populus), Ginkgos (Ginkgo), Chinese Scholars (Styphnolobium), and Willows (Salix), in downtown Beijing to improve urban vegetation planning. Based on PlanetScope observations, we found that the four street tree genera have unique phenological patterns. Interestingly, we found that the trees along many major streets, where Chinese Scholars are the major tree genus, have later green-up dates than other areas in downtown Beijing. In conclusion, the three satellite observation datasets prove to be effective in monitoring street tree phenology during the spring–summer growth period in Beijing. PlanetScope is effective in monitoring tree phenology at the street scale; however, Landsat-8 may be affected by the mixture of land covers due to its relatively coarse spatial resolution. [ABSTRACT FROM AUTHOR]

Topographic maps used as the cartographic basis of thematic maps are created on the basis of data from the certain time period and the existing topographic base for the study area was published in 1975-1990, which does not allow visualizing recent changes of the area condition. One way to solve this problem is to use up-to-date data sources. The problem is successfully solved on the basis of visual and automated use of satellites remote sensing data of medium, high and ultra-high spatial resolution. The purpose of this article is to conduct comparative analysis of the Earth remote sensing data using the example of Sentinel 2, Landsat-8 satellite images, to consider methods for processing satellite images and use of remote sensing data to update vector layers of the cartographic basis of thematic maps. As part of the tasks of actualization of the vector layers for updating the cartographic basis of the study area, in this work was used archival and operational medium-resolution data obtained from Landsat and Sentinel-2 series satellites and Sentinel-2 image services, as well as Airbus WorldDEM4Ortho DEM. The work used the methods of GIS technologies and remote sensing, classification of remote sensing data and comparative analysis, visual and automated interpretation. A set of Python scripts has been generated to filter the collection of Sentinel-2 images and to determine cloudiness and cloud shadow. The use of remote sensing data made it possible to cover spacious and inaccessible areas of mountainous regions, significantly reducing the costs of data and processing. As a result, the updated cartographic basis of the studied area was created. The cartographic basis can be used for subsequent analysis of the dynamics and trends in changes of natural and social-economic objects. [ABSTRACT FROM AUTHOR]

Copyright of Jurnal Tanah dan Sumberdaya Lahan is the property of Brawijaya University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The exploration of Pb-Zn mineralization in carbonate complexes during field campaign is a challenging process that consumes high expenses and time to discover high prospective zones for a detailed exploration stage. In this study, multi-sensor remote-sensing imagery from Landsat-8, Sentinel-2, and ASTER were utilized for Pb-Zn mineralization prospectivity mapping in the Akhlamad carbonate complex area, Razavi Khorasan, NE Iran. Due to the presence of carbonate formations and various evidence of Pb-Zn mineralization, this area was selected. Band composition, band ratio, principal component analysis (PCA), and SAM techniques for mapping alteration minerals as well as lineament analysis were implemented. Subsequently, a fuzzy logic model for identifying the prospective zones of Pb-Zn mineralization using multi-sensor remote-sensing satellite images was designed. The weight of each exploratory layer was determined using the fuzzy hierarchical method and the integration process of the information layers was performed using fuzzy operators. Finally, the existing mineral indications were used to evaluate and validate the obtained mineral potential map. The outcome of this investigation suggested several high-potential zones for Pb-Zn exploration in the study area. [ABSTRACT FROM AUTHOR]

This research applies remote sensing methodologies for the first time to comprehensively explore the geological and mineralogical characteristics of the Jbel Bou Dahar region. An integrated approach with multi-sensor satellite images, including ASTER, Landsat-8, and Sentinel-2 was applied with the aim to discriminate the different lithological units in the study area. We implemented a suite of well-established image processing techniques, including Band Ratios, Principal Component Analysis, and Spectral Angle Mapper, to successfully identify, classify, and map the spatial distribution of carbonate minerals, OH-bearing minerals, and iron oxide minerals. Due to its high spectral resolution in the short-wave infrared region (SWIR), the ASTER sensor provided the most accurate results for mapping carbonate and OH-bearing minerals compared to the Sentinel-2 and Landsat-8 sensors. Conversely, Sentinel-2 offers high spectral and spatial resolution in visible and near-infrared (VNIR) corresponding to the regions where iron oxide minerals exhibit their characteristic absorption peaks. The results confirm the advantages of remote sensing technologies in the geological and mineralogical exploration of the study area and the importance of selecting the appropriate sensors for specific mapping objectives. [ABSTRACT FROM AUTHOR]

The exploration of Pb-Zn mineralization in carbonate complexes during field campaign is a challenging process that consumes high expenses and time to discover high prospective zones for a detailed exploration stage. In this study, multi-sensor remote-sensing imagery from Landsat-8, Sentinel-2, and ASTER were utilized for Pb-Zn mineralization prospectivity mapping in the Akhlamad carbonate complex area, Razavi Khorasan, NE Iran. Due to the presence of carbonate formations and various evidence of Pb-Zn mineralization, this area was selected. Band composition, band ratio, principal component analysis (PCA), and SAM techniques for mapping alteration minerals as well as lineament analysis were implemented. Subsequently, a fuzzy logic model for identifying the prospective zones of Pb-Zn mineralization using multi-sensor remote-sensing satellite images was designed. The weight of each exploratory layer was determined using the fuzzy hierarchical method and the integration process of the information layers was performed using fuzzy operators. Finally, the existing mineral indications were used to evaluate and validate the obtained mineral potential map. The outcome of this investigation suggested several high-potential zones for Pb-Zn exploration in the study area.

This research applies remote sensing methodologies for the first time to comprehensively explore the geological and mineralogical characteristics of the Jbel Bou Dahar region. An integrated approach with multi-sensor satellite images, including ASTER, Landsat-8, and Sentinel-2 was applied with the aim to discriminate the different lithological units in the study area. We implemented a suite of well-established image processing techniques, including Band Ratios, Principal Component Analysis, and Spectral Angle Mapper, to successfully identify, classify, and map the spatial distribution of carbonate minerals, OH-bearing minerals, and iron oxide minerals. Due to its high spectral resolution in the short-wave infrared region (SWIR), the ASTER sensor provided the most accurate results for mapping carbonate and OH-bearing minerals compared to the Sentinel-2 and Landsat-8 sensors. Conversely, Sentinel-2 offers high spectral and spatial resolution in visible and near-infrared (VNIR) corresponding to the regions where iron oxide minerals exhibit their characteristic absorption peaks. The results confirm the advantages of remote sensing technologies in the geological and mineralogical exploration of the study area and the importance of selecting the appropriate sensors for specific mapping objectives.

Total Suspended Solid (TSS) is the phenomenon of transporting solid particles both organic and inorganic on water column which can be an initial parameter in determining water quality. Merah Putih Beach is located in the Madura Strait, which has an active shipping pathway. We used the Gravimetric method to determine the concentration of TSS. We also applied the advances of remote sensing technology for Landsat-8 using Google Earth Engine and based on Jaelani algorithm. The algorithm is considered quite efficient because it only takes a short time and gets maximum results in TSS visualization. Merah Putih Beach has a TSS value 250 - 1508 mg/l. The result of validation for the algorithm estimation obtained an RMSE 866,51 mg/l and R square 0,1076. The advanced empirical modelling tests reveal the Polynomial mathematical equation had a smaller RMSE and a larger R square of 82,34 mg/l and 0.4468. The visualization results show a difference and have a value that is close to the actual TSS.

• Proposed a Hybrid Model for Paddy Crop prediction based on phenological parameters. • CNN-LSTM attained RMSE value of 0.219 t/ha compared to other existing methods. • Random Forest method achieved highest accuracy of 96.6% in paddy classification. Rice is one of the predominant food sources to fulfill the dietary requirements of well-being in India. Therefore, accurate and timely paddy crop yield prediction is crucial to ensure the food security of the country. In this direction, the present study proposed a hybrid deep-learning method based on Conv-1D and LSTM layers using the classification-derived phenological with meteorological parameters for paddy crop yield prediction. The paddy crop classification has been conducted using high-resolution (10 m) multispectral imagery based on GPS coordinates collected during the paddy field visits to extract the phenological parameters for input to the prediction model. In this context, the efficiency of Random Forest, Naïve Bayes, SVM, and Gradient Tree boost classifiers was assessed. Furthermore, we have also analyzed the accuracy of Landsat-8, Sentinel-1 GRD, and Sentinel-2 satellite imagery in paddy crop classification based on area estimation. The Statistical Abstract of Haryana was utilized to validate the paddy crop area estimation and yield prediction. The classification outcomes showed that the Random Forest method attained the highest accuracy of 96.6 % compared to other GEE-based classifiers. The proposed Hybrid Deep learning approach achieved an RMSE value of 0.219 t/ha compared to CNN, LSTM, CNN-Bi-LSTM, and Regression techniques for crop yield prediction. The study conclusion highlighted that the sentinel-2 satellite imagery performed well and found that the proposed hybrid approach provided an alternative for paddy crop yield prediction. [ABSTRACT FROM AUTHOR]

Extreme events related to climate change are increasing in intensity, frequency, and duration worldwide. Europe is identified as a heatwave hotspot, with trends three-to-four time faster than the northern mid-latitudes; effects of heatwaves are combined in urban contexts with the heat island phenomenon, making cities critical for climate risk prevention and management. Land surface temperature represents an essential parameter for assessing the intensity of thermal impact on urban ecosystems and on public health. This parameter is widely used to map and assess urban heat islands in light to support climate-resilient adaptation planning. The general aim of this study is to assess urban heat island intensity, during a significant heatwave, in a critical heat-related risk region in Southern Italy (Salento). Specific objectives are 1) assessing climate change trends for heat-related extremes (hot days and heatwaves), 2) calculating urban heat islands intensity at regional and urban scale, 3) assessing spatial relationships among thermal intensity and urban characteristics (soil sealing and surface albedo). Identification of heatwaves is based on climatological data and statistical analyses; spatial thermal analyses and correlations are based on Landsat-8 imagery while land cover data are derived from ortho-photos. Climate analyses show a notable increase of the maximum annual temperature of 0.5°C per decade, with an increase of eight hot days per decade. Spatial analyses on thermal impact highlight that urban heat island intensity is much lower within cities and towns than in rural areas, showing a "reverse effect" compared to the typical microclimatic characteristics of urban contexts. In fact, thermal intensity in the city of Lecce ranges from -11°C to 5.6°C. Also, by NDVI analyses, we found that permeable surfaces were 2°C higher than impermeable surfaces, with statistically significant differences. Results from albedo analysis suggest that the characteristics of building material in historical sectors of cities may play a crucial role in this "reverse effect" of urban heat islands. Further studies are required to better investigate the contribution of different factors in this context. [ABSTRACT FROM AUTHOR]

The Central Bengkulu Regency possesses a coastline measuring 21.8 km, situated in direct proximity to the Indian Ocean. The heightened wave and ocean current dynamics possess the capacity to induce detrimental effects in the form of coastal abrasion along the coastline. This study conducted an examination of the rate of shoreline changes resulting from coastal abrasion in Central Bengkulu Regency, Bengkulu, Indonesia. Horizontal-to-vertical spectral ratio (HVSR) analysis, multichannel analysis of surface waves (MASW), and Landsat-8 remote sensing were all used in the current investigation. The project gets started with conducting field investigations and measurements of geophysical techniques, including the collection of Landsat-8 satellite imagery spanning the period from 1990 to 2020. Additionally, an examination was conducted on the rate at which the shoreline undergoes changes in velocity. In order to determine the increase in change that occurs along coastlines over a period of several decades, a study was carried out during which Landsat-8 satellite images were analyzed. This study investigates the application of the HVSR technique for assessing the seismic vulnerability of the ground and, additionally, the MASW technique for measuring the shear wave velocity of the coastline's soil. Both of these methodologies are compared and contrasted with one another in this research. The findings indicated that the mean pace of coastline change in Central Bengkulu Regency was 1.5 m/yr, with the maximum velocity recorded at 4.1 m/yr. This high velocity of shoreline change is correlated with the Vs30 value of MASW measurement and Kg of HVSR measurement, where the subsurface soil structure along the coast of Central Bengkulu Regency from Vs30 measurement shows that it is dominated by stiff soil structure that is susceptible to erosion. Outcomes from the study can inform decision-making processes about safeguards and preventative measures to prevent further coastal degradation. [ABSTRACT FROM AUTHOR]

The Brazilian Atlantic Rainforest presents great diversity of flora and stand structures, making it difficult for traditional forest inventories to collect reliable and recurrent information to classify forest succession stages. In recent years, remote sensing data have been explored to save time and effort in classifying successional forest stages. However, there is a need to understand if any of these sensors stand out for this purpose. Here, we evaluate the use of multispectral satellite data from four different platforms (CBERS-4A, Landsat-8/OLI, PlanetScope, and Sentinel-2) and airborne light detection and ranging (LiDAR) to classify three forest succession stages in a subtropical ombrophilous mixed forest located in southern Brazil. Different features extracted from multispectral and LiDAR data, such as spectral bands, vegetation indices, texture features, and the canopy height model (CHM) and LiDAR intensity, were explored using two conventional machine learning methods such as random trees (RT) and support vector machine (SVM). The statistically based maximum likelihood (MLC) algorithm was also compared. The classification accuracy was evaluated by generating a confusion matrix and calculating the kappa index and standard deviation based on field measurements and unmanned aerial vehicle (UAV) data. Our results show that the kappa index ranged from 0.48 to 0.95, depending on the chosen dataset and method. The best result was obtained using the SVM algorithm associated with spectral bands, CHM, LiDAR intensity, and vegetation indices, regardless of the sensor. Datasets with Landsat-8 or Sentinel-2 information performed better results than other optical sensors, which may be due to the higher intraclass variability and less spectral bands in CBERS-4A and PlanetScope data. We found that the height information derived from airborne LiDAR and its intensity combined with the multispectral data increased the classification accuracy. However, the results were also satisfactory when using only multispectral data. These results highlight the potential of using freely available satellite information and open-source software to optimize forest inventories and monitoring, enabling a better understanding of forest structure and potentially supporting forest management initiatives and environmental licensing programs. [ABSTRACT FROM AUTHOR]

Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems. [ABSTRACT FROM AUTHOR]