Multi-sensor Satellite Images Research Articles

Integrated Remote Sensing for Geological and Mineralogical Mapping of Pb-Zn Deposits: A Case Study of Jbel Bou Dahar Region Using Multi-Sensor Imagery

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.

Multi-Sensor Satellite Images for Detecting the Effects of Land-Use Changes on the Archaeological Area of Giza Necropolis, Egypt

The World Heritage Committee has been meeting to discuss the arrangements of existing World Heritage Sites, and, on 22–26 October, the area from Giza to the Dahshur was included in the list of World Heritage Sites. According to the Egyptian Antiquities Authority (EAA), the groundwater levels at the Pyramids Plateau are too shallow, which threatens the ancient Sphinx and Pyramids in Giza, Egypt. In addition, many geophysical studies have been carried out in the archaeological area of Giza, which prove that the area is facing the risk of a high level of groundwater, specifically threatening the Sphinx. Recent developments in Earth observation have helped in the field of land monitoring such as land use changes, risk observation, and the creation of models for protecting cultural heritage sites. This study aimed to examine the impact of land use changes on on the archaeological sites of the Giza Necropolis area by integrating various data sources including optical satellite imagery and SRTM data during the period of 1965–2019. A historical database of Corona 1965 and Landsat 2009 data was investigated along with the new acquisitions of Sentinel-2 2016 and Sentinel-1 2016 and 2019. In addition, the radar Sentinel-1 SLC data were collected and analyzed for calculating the land subsidence value in the area of interest through two periods between 6–30 July 2016 and 30 July–15 December 2016. Various methods were implemented, including cluster outliers, the Moran index, and spatial autocorrelation to examine the changes in urban masses. Additionally, the relationship between groundwater leakage and land subsidence in the region was investigated. The analysis was carried out using Envi5.3, ArcMap10.6.1, and SNAP6.0 software to extract spatial data from the raw data. The results from our investigation highlighted rapid changes in urban areas between 1965 and 2019. The data obtained and analyzed from optical and radar satellite imagery showed that changes in land use can cause changes in the topographic situation by decreasing the level of groundwater, which adversely affects Egyptian monumental pyramids and the Sphinx. Land use analysis showed that the urban area represented 7.63% of the total area of the study area in 1965, however it reached 32.72% in 2009, approximately half of the total area in 2016, and in 2019, the urban mass area increased to nearly two-thirds of the total area. The annual growth rate between 1965 and 2019 was estimated by nearly 0.642 km2/year. These land-use changes possibly affected the land subsidence value (−0.0138 m), causing the rising groundwater level close to the Sphinx. Using the information obtained from our RS- and GIS-based analysis, mitigation strategies have also been identified to support archaeological area preservation.

Detecting Changes in Impervious Surfaces Using Multi-Sensor Satellite Imagery and Machine Learning Methodology in a Metropolitan Area

This study utilizes multi-sensor satellite images and machine learning methodology to analyze urban impervious surfaces, with a particular focus on Nanchang, Jiangxi Province, China. The results indicate that combining multiple optical satellite images (Landsat-8, CBERS-04) with a Synthetic Aperture Radar (SAR) image (Sentinel-1) enhances detection accuracy. The overall accuracy (OA) and kappa coefficients increased from 84.3% to 88.3% and from 89.21% to 92.55%, respectively, compared to the exclusive use of the Landsat-8 image. Notably, the Random Forest algorithm, with its unique dual-random sampling technique for fusing multi-sensor satellite data, outperforms other machine learning methods like Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Classification and Regression Trees (CARTs), Maximum Likelihood Classification (Max-Likelihood), and Minimum Distance Classification (Min-Distance) in impervious surface extraction efficiency. With additional satellite images from 2015, 2017, and 2020, the impervious surface changes are tracked in the Nanchang metropolitan region. From 2015 to 2021, they record a notable increase in impervious surfaces, signaling a quickened urban expansion. This study observes several impervious surface growth patterns, such as a tendency to concentrate near rivers, and larger areas in the east of Nanchang. While the expansion was mainly southward from 2015 to 2021, by 2021, the growth began spreading northward around the Gan River basin.

The Distribution of Oceanographic Parameter Conditions in the Swordfish (Xiphias gladius) Fishing Ground in the Southern Indian Ocean off Java

The Southern Indian Ocean off Java is one of the potential locations for catching swordfish. One crucial factor in improving fishermen's productivity is the availability of information regarding the characteristics of waters related to potential fishing areas. This research aimed to determine the distribution conditions of several oceanographic parameters such as sea surface temperature, salinity, chlorophyll-a, and surface currents using remote sensing data at the swordfish (Xiphias gladius) fishing ground in the Southern Indian Ocean off Java. This research was conducted from April 27, 2022, to December 12, 2022, in the Indian Ocean. Data for several oceanographic parameters were obtained from the data.marine.copernicus.eu website from April 2022 to December 2022, and the coordinates of the swordfish (Xiphias gladius) fishing ground were obtained from KM Lulu Marina 08, which operates in WPPD 57. Data from data.marine.copernicus.eu are the result of reanalysis methods developed by the Copernicus Marine Service (CMS) using multisensor satellite images data such as MODIS-AQUA, NOAA20-VIIRS, NPP-VIIRS, and Sentinel 3A-OLCI. The spatial resolution of the data was standardized to 0.001 degrees using resampling techniques with B-Spline interpolation. The highest sea surface temperature (SST) at each fishing ground was recorded in April 2022 at the fishing ground ST 1, reaching 27.52°C. The lowest SST was observed in November 2022 at fishing ground ST 6, measuring 21.70°C. The lowest salinity values at each fishing ground were recorded in June 2022 at fishing ground ST 1, measuring 34.09 psu, while the highest salinity values were found in April 2022 at fishing ground ST 6, measuring 35.38 psu. The lowest chlorophyll-a concentration values at each fishing ground were recorded in December 2022 at fishing ground ST 6, measuring 0.062 mg/m3, while the highest concentration values were found in September 2022 at fishing ground ST 6, measuring 0.357 mg/m3. The lowest catch was recorded in September 2022, with only 2 fish caught, while the highest catch was recorded in November 2022, with a total of 42 fish caught. The optimal swordfish catch rate falls within the range of SST 23.47-24.74°C, salinity 34.45-34.78 psu, and chlorophyll-a concentration in the range of 0.079-0.124 mg/m3.

Deep learning and data fusion to estimate surface soil moisture from multi-sensor satellite images

We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11–19, 2019 and March 01–06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio, respectively. We compared the performance of ANN model with ten different machine learning algorithms [i.e., Generalised Regression Neural Network (GRNN), Radial Basis Network (RBN), Exact RBN (ERBN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), Boosting Ensemble Learning (Boosting EL), Recurrent Neural Network (RNN), Binary Decision Tree (BDT), and Automated Machine Learning (AutoML)]. We observed that the ANN model accurately predicts the soil moisture and outperforms all the benchmark algorithms with correlation coefficient (R = 0.80), Root Mean Square Error (RMSE = 0.040 mathrm {m^3/m^3}), and bias = 0.004 mathrm {m^3/m^3}. Finally, for an unbiased and robust conclusion, we performed spatial distribution analysis by creating thirty different sets of training-validation-testing datasets. We observed that the performance remains consistent in all thirty scenarios. The outcomes of this study will foster new and existing applications of soil moisture.

A Multirule-Based Relative Radiometric Normalization for Multisensor Satellite Images

A Multirule-Based Relative Radiometric Normalization for Multisensor Satellite Images

Object-Based Informal Settlement Mapping in Google Earth Engine Using the Integration of Sentinel-1, Sentinel-2, and PlanetScope Satellite Data

Mapping informal settlements’ diverse morphological patterns remains intricate due to the unavailability and huge costs of high-resolution data, as well as the spatial heterogeneity of urban environments. The accessibility to high-spatial-resolution PlanetScope imagery, coupled with the convenience of simple non-iterative clustering (SNIC) algorithm within the Google Earth Engine (GEE), presents the potential for Geographic Object-Based Image Analysis (GEOBIA) to map the spatial morphology of deprivation pockets in a complex built-up environment of Durban. Such advances in multi-sensor satellite image inventories on GEE also afford the possibility to integrate data from sensors with different spectral characteristics and spatial resolutions for effective abstraction of informal settlement diversity. The main objective is to exploit Sentinel-1 radar data, Sentinel-2 and PlanetScope optical data fusion for more accurate and precise localization of informal settlements using GEOBIA, within GEE. The findings reveal that the Random Forests classification model achieved informal settlement identification accuracy of 87% (F-score) and overall accuracy of 96%. An assessment of agreement between observed informal settlement extents and ground truth dimensions was conducted through regression analysis, yielding root mean square log error (RMSLE) = 0.69 and mean absolute percent error (MAPE) = 0.28. The results demonstrate reliability of the classification model in capturing variability of spatial characteristics of informal settlements. The research findings confirm efficacy of combined advantages of GEOBIA within GEE, and integrated datasets for more precise capturing of characteristic morphologic informal settlement features. The outcomes suggest a shift from standard static conventional approaches towards more dynamic, on-demand informal settlement mapping through cloud computing, a powerful analysis platform that simplifies access to and the processing of voluminous data. The study has important implications for identifying the most effective ways to map informal settlements in a complex urban landscape, thus providing a benchmark for other regions with significant landscape heterogeneity.

A multisensor satellite image classification for the detection of mangrove forests in Qeshm Island (Southern Iran)

A multisensor satellite image classification for the detection of mangrove forests in Qeshm Island (Southern Iran)

Pollen in the Baltic Sea as viewed from space

Multi-sensor satellite images often show recurrent image slicks in the Baltic Sea. Based on historical reports, laboratory experiments, and spectral diagnostics, these image slicks are determined to be caused by pine (Pinus sylvestris) pollen aggregations as opposed to surface scums of cyanobacteria or other floating matters. This attribution is because the reflectance spectral shapes of these image slicks resemble those of pollen grains, which are all different from other floating matters. They all show rapid reflectance increase from ∼400 to ∼500 nm, beyond which the reflectance shape appears to plateau. During the 22-year period of 2000–2021, satellite images indicated large amounts of pollen grains in 14 years, with the earliest day being May 10 (in 2000) and the latest day being June 16 (in 2006). The longest duration in a single year is 22 days, from May 12 to June 2 of 2018. The waters containing pollen grains have expanded significantly in recent years, encompassing nearly the entire Baltic Sea. Because pollen grains contain a significant amount of carbon and have distinctive optical properties from other particles, these findings have significant implications on carbon sequestration, marine ecology, bio-optics, ocean color calibration/validation, and remote sensing of marine debris.

Multi-sensor satellite images and resistivity survey for the evolution of Sutlej Palaeochannels and groundwater aquifer potential in Punjab, Northwest India

Remote Sensing technique with multi-sensor satellite images is found to be a useful tool in delineation of the palaeochannel network in arid to semi-arid regions in India. Principal Component Analysis method with fused images (optical and microwave data) shows a wide N-S trending Sutlej Palaeochannels (SPC) and narrow NE-SW trending palaeochannels in central Punjab. High-resolution Electrical Resistivity Tomography (HERT) survey and litholog data analysis provide good groundwater prospect zone of the palaeochannel. Interpretation of 2D resistivity tomograms with Wenner-Schlumberger method exhibit a thick zone of low resistivity layer (saturated coarse-grained sand) at a depth of 40 m. Integrated geo-tectonic study provides clue that the river migration of Sutlej River from N-S to E-W direction was due to a major tectonic upliftment in the Himalaya. Geochronological data analysis indicates that the avulsed channel of SPC was the main course of old Ghaggar River, linked to the ‘Lost Saraswati River’ in northwest India.

Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches

Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture.

Monitoring Fractional Floating Algae Cover Over Eutrophic Lakes Using Multisensor Satellite Images: MODIS, VIIRS, GOCI, and OLCI

Coarse-resolution sensors have been used operationally to monitor floating algal blooms with near daily revisit in coastal and inland waters. Most of the current methods in estimating fractional floating algae cover (FAC) were based on the linear pixel un-mixing assumption. In this study, a new FAC model following logistic curve was developed and applied to multisensor satellite data in two large shallow eutrophic lakes, Lake Taihu and Lake Chaohu, in China. The results indicated that after resampling to 250 m, match-up pairs of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (The Visible and Infrared Imager/Radiometer Suite), GOCI (Geostationary Ocean Color Imager) and OLCI (Ocean and Land Color Instrument) possessed consistent Rayleigh corrected reflectance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R_rc</i> ) and FAI (floating algae index) or AFAI (alternative floating algae index). The FAC model was developed based on the simulated AFAI data of GOCI using point spread function (PSF) and bloom percent derived from OLI (Operational Land Imager), and then was applied to MODIS, VIIRS, and OLCI. Compared with the linear pixel un-mixing method, the FAC model reflects the asymptotic reflectance saturation in the NIR (near infrared) band with the accumulation of blooms. Besides, the equivalent bloom area (EBA) of GOCI was validated using OLI matched pairs with UPD 37.6% (N=39, R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> =0.96). The spatial-temporal dataset of FAC (2002-2020) shows that Lake Taihu and Lake Chaohu experienced severe algal blooms after 2010, partly resulting from the higher frequency of multisensor data. This study provides a method in building a lasting and comparable FAC dataset using multisensors.

Mapping Freshwater Chlorophyll-a Concentrations at a Regional Scale Integrating Multi-Sensor Satellite Observations with Google Earth Engine

Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

Characteristics of Tsunami Fragility Functions Developed Using Different Sources of Damage Data from the 2018 Sulawesi Earthquake and Tsunami

We developed tsunami fragility functions using three sources of damage data from the 2018 Sulawesi tsunami at Palu Bay in Indonesia obtained from (i) field survey data (FS), (ii) a visual interpretation of optical satellite images (VI), and (iii) a machine learning and remote sensing approach utilized on multisensor and multitemporal satellite images (MLRS). Tsunami fragility functions are cumulative distribution functions that express the probability of a structure reaching or exceeding a particular damage state in response to a specific tsunami intensity measure, in this case obtained from the interpolation of multiple surveyed points of tsunami flow depth. We observed that the FS approach led to a more consistent function than that of the VI and MLRS methods. In particular, an initial damage probability observed at zero inundation depth in the latter two methods revealed the effects of misclassifications on tsunami fragility functions derived from VI data; however, it also highlighted the remarkable advantages of MLRS methods. The reasons and insights used to overcome such limitations are discussed together with the pros and cons of each method. The results show that the tsunami damage observed in the 2018 Sulawesi event in Indonesia, expressed in the fragility function developed herein, is similar in shape to the function developed after the 1993 Hokkaido Nansei-oki tsunami, albeit with a slightly lower damage probability between zero-to-five-meter inundation depths. On the other hand, in comparison with the fragility function developed after the 2004 Indian Ocean tsunami in Banda Aceh, the characteristics of Palu structures exhibit higher fragility in response to tsunamis. The two-meter inundation depth exhibited nearly 20% probability of damage in the case of Banda Aceh, while the probability of damage was close to 70% at the same depth in Palu.

Integration of in-situ and multi-sensor satellite observations for long-term water quality monitoring in coastal areas

Recently, there have been significant efforts in the integration of in-situ and satellite observations for effective monitoring of coastal areas (e.g., the Copernicus program of the European Space Agency). In this study, a 15-year diurnal variation of Water Constituent Concentrations (WCCs) was retrieved from multi-sensor satellite images and in-situ hyperspectral measurements using Radiative Transfer (RT) modeling in the Dutch Wadden Sea. The existing RT model 2SeaColor was inverted against time series of in-situ hyperspectral measurements of water leaving reflectances (Rrs [sr−1]) for the simultaneous retrieval of WCCs (i.e., Chlorophyll-a (Chla), Suspended Particulate Matter (SPM), Dissolved Organic Matter (CDOM)) on a daily basis between 2003 and 2018 at the NIOZ jetty station (the NJS) located in the Dutch part of the Wadden Sea. At the same time, the existing coupled atmosphere-hydro-optical RT model MOD2SEA was used for the simultaneous retrieval of WCCs from time series of multi-sensor satellite images of the MEdium Resolution Imaging Spectrometer (MERIS) onboard ENVISAT, Multispectral Instrument (MSI) onboard Sentinel-2 and Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 between 2003 and 2018 over the Dutch Wadden Sea. At the NJS, a direct comparison (Taylor diagram and statistical analysis) showed strong agreement between in-situ and satellite-derived WCC values (Chla: R2 ≥ 0.70, RMSE ≤7.5 [mg m−3]; SPM: R2 ≥ 0.72, RMSE ≤5.5 [g m−3]; CDOM absorption at 440 nm: R2 ≥ 0.67, RMSE ≤1.7 [m−1]). Next, the plausibility of the spatial variation of retrieved WCCs over the study area was evaluated by generating maps of Chla [mg m−3], SPM [g m−3], and CDOM absorption at 440 nm [m−1] from MERIS and OLCI images using the MOD2SEA model. The integration of the spatio-temporal WCC data obtained from in-situ measurements and satellite images in this study finds applications for the detection of anomaly events and serves as a warning for management actions in the complex coastal waters of the Wadden Sea.

Hybrid Canonical Correlation Analysis and Regression for Radiometric Normalization of Cross-Sensor Satellite Imagery

Radiometric normalization is a fundamental preprocessing step for the analysis of multitemporal and multisensor satellite images. Some of the previous radiometric normalization studies addressed the elimination of spectral differences between bitemporal or cross-sensor images by performing canonical correlation analysis (CCA) or its generalization called kernel CCA (KCCA). The KCCA-based normalization methods, which assume that the relation between at-sensor radiances is spatially nonlinear, can successfully extract pseudoinvariant features (PIFs) for bitemporal images with nonlinear differences caused by seasonal variation and for bitemporal images acquired by different sensors with different spectral centers and bandwidths of spectral bands. However, these methods did not consider the problems of high computation and storage costs induced by the huge size of kernel matrices and the overfitting problem caused by nonlinear regression, making the methods infeasible for satellite images of large sizes. To solve these problems, a hybrid CCA with hybrid regression model is proposed, which can preserve the characteristics of nonlinear CCA and regression while reducing time and space complexity and solving overfitting problems. Cross-sensor images acquired by Landsat-7 Enhanced Thematic Mapper Plus and Landsat-8 Operational Land Imager sensors were used to evaluate the proposed method. Experimental results demonstrate the superiority of the proposed method to the related CCA-based methods in terms of computational performance and PIF quality.

Structure and Dynamics of Waters in the Region of the Shantar Islands National Park (Sea of Okhotsk) Based on the Data of Satellite Observations

The Shantar Islands National Park (northwestern shelf of the Sea of Okhotsk) was established to preserve the biological diversity of unique natural marine and terrestrial ecosystems. The water structure and dynamics in the basin of the Shantar Islands has been studied based on the analysis of multisensor satellite images (Terra/Aqua MODIS, Landsat-7 ETM+, Landsat-8 OLI/TIRS, Sentinel-2A MSI) and field oceanographic observations. The circulation over the shelf in the Shantar Islands region is strongly determined by strong tidal currents and freshwater discharge. The water structure is formed under the influence of intense local tidal mixing and the propagation of low-saline river plumes in the shallow parts of the main bays of the Shantar archipelago. The interaction of tidal currents with the complex topography and bathymetry (peninsulas, capes, islands, straits, and shallow-water regions of the shelf) leads to a high level of submesoscale activity and the generation of various types of submesoscale structures, including jet currents and eddies.

Using Multi-Sensor Satellite Images and Auxiliary Data in Updating and Assessing the Accuracies of Urban Land Products in Different Landscape Patterns

Rapid and accurate updating of urban land areas is of great significance to the study of environmental changes. Although there are many urban land products (ULPs) at present, such as GlobeLand30, Global Urban Footprint (GUF), and Global Human Settlement Layer (GHSL), these products are all static data of a certain year, and are not able to provide high-accuracy updating of urban land areas. In addition, the accuracies of these data and their application value in the update of urban land areas need to be urgently proven. Therefore, we proposed an approach to quickly and accurately update urban land areas in the Kuala Lumpur region of Malaysia, and assessed the accuracies of urban land products in different urban landscape patterns. The approach combined the advantages of multi-source data including existing ULPs, OpenStreetMap (OSM) data, Landsat Operational Land Imager (OLI), and Phased Array type L-band Synthetic Aperture Radar (PALSAR) images. Three main steps make up this approach. First, the urban land training samples were selected in the urban areas consistent with GlobeLand30, GUF, and GHSL, and samples of bare land, vegetation, water bodies, and road auxiliary data were obtained by GlobeLand30 and OSM. Then, the random forest was used to extract urban land areas according to the object’s features in the OLI and PALSAR images. Last, we assessed the accuracies of GlobeLand30, GUF, GHSL, and the results of this study (ULC) by using point and area validation methods. The results showed that the ULC had the highest overall accuracy of 90.18% among the four products and could accurately depict urban land in different urban landscapes. The GHSL was the second most accurate of the four products, and the accuracy in urban areas was much higher than that in rural areas. The GUF had many omission errors in urban land areas and could not delineate a large area of complete spatial information of urban land, but it could effectively extract scattered residential land with small patches. GlobeLand30 had the lowest accuracy and could only express rough, large-scale urban land. The above conclusions provide evidence that ULPs and the approach proposed in this study have a great application potential for high-accuracy updating of urban land areas.

Automatic Methodology to Detect the Coastline from Landsat Images with a New Water Index Assessed on Three Different Spanish Mediterranean Deltas

Due to the importance of coastline detection in coastal studies, different methods have been developed in recent decades in accordance with the evolution of measuring techniques such as remote sensing. This work proposes an automatic methodology with new water indexes to detect the coastline from different multispectral Landsat images; the methodology is applied to three Spanish deltas in the Mediterranean Sea. The new water indexes use surface reflectance rather than top-of-atmosphere reflectance from blue and shortwave infrared (SWIR 2) Landsat bands. A total of 621 sets of images were analyzed from three different Landsat sensors with a moderate spatial resolution of 30 m. Our proposal, which was compared to the most commonly used water indexes, showed outstanding performance in automatic detection of the coastline in 96% of the data analyzed, which also reached the minimum value of bias of − 0.91 m and a standard deviation ranging from ±4.7 and ±7.29 m in some cases in contrast to the existing values. Bicubic interpolation was evaluated for a simple sub-pixel analysis to assess its capability in improving the accuracy of coastline extraction. Our methodology represents a step forward in automatic coastline detection that can be applied to micro-tidal coastal sites with different land covers using many multi-sensor satellite images.

An assessment of oil spill detection using Sentinel 1 SAR-C images

Identification of an oil spill is additionally essential to evaluate the potential spread and float from the source to the adjacent coastal terrains. In such manner, usage of Synthetic Aperture RADAR (SAR) information for the recognition and checking of oil spills has gotten extensive consideration as of late, because of their wide zone inclusion, day-night and all-weather capabilities. The present examination studies an oil spill occurred in the Al Khafji region by applying Sentinel 1 SAR-C images. Al Khafji is on the borderline between Saudi Arabia and Kuwait in the Persian Gulf and it is detected as an unbiased zone. Al Khafji region can possibly deliver in excess of 7472.403 m³ barrels of oil for every day (m³/d). Approaches dependent on multi-sensor satellite images examination have been produced for distinguishing oil spills from referred to common leaks just as oil slick procedures. In this paper, one of these techniques is associated with Sentinel 1 images of a known region of natural oil leakage and of an ongoing oil slick incident in Al Khafji zone. The Synthetic Aperture Radar (SAR) is perceived as the most significant remote sensing apparatus for the ocean and ocean waters oil slick examination, recording, documentation and propagation. Specifically, this paper examines oil spills recognition in the Persian Gulf surveyed by utilizing Sentinel-1 (SAR-C) imageries. Results demonstrated the significance of the VV polarization of the Sentinel-1 for recognizing oil-spills just as the diminished utility of the VH polarization in this sole circumstance.