Dynamic Land Cover Mapping of Urbanized Cities with Landsat 8 Multi-temporal Images: Comparative Evaluation of Classification Algorithms and Dimension Reduction Methods

Uncontrolled and continuous urbanization is an important problem in the metropolitan cities of developing countries. Urbanization progress that occurs due to population expansion and migration results in important changes in the land cover characteristics of a city. These changes mostly affect natural habitats and the ecosystem in a negative manner. Hence, urbanization-related changes should be monitored regularly, and land cover maps should be updated to reflect the current situation. This research presents a comparative evaluation of two classification algorithms, pixel-based support vector machine (SVM) classification and decision-tree-oriented geographic object-based image analysis (GEOBIA) classification, in producing a dynamic land cover map of the Istanbul metropolitan city in Turkey between 2013 and 2017 using Landsat 8 Operational Land Imager (OLI) multi-temporal satellite images. Additionally, the efficiencies of the two data dimension reduction methods are evaluated as part of this research. For dimension reduction, built-up index (BUI) and principal component analysis (PCA) data were calculated for five images during the mentioned period, and the classification algorithms were applied on data stacks for each dimension reduction method. The classification results indicate that the GEOBIA classification of the BUI data set provided the highest accuracy, with a 91.60% overall accuracy and 0.91 kappa value. This combination was followed by the GEOBIA classification of the PCA data set, which highlights the overall efficiency of the GEOBIA over the SVM method. On the other hand, the BUI data set provided more reliable and consistent results for urban expansion classes due to representing physical responses of the surface when compared to the data set of the PCA, which is a spectral transformation method

Comparison of pixel and object-based classification for burned area mapping using SPOT-6 images

On 30 May 2013, a forest fire occurred in Izmir, Turkey causing damage to both forest and fruit trees within the region. In this research, pre- and post-fire SPOT-6 images obtained on 30 April 2013 and 31 May 2013 were used to identify the extent of forest fire within the region. SPOT-6 images of the study region were orthorectified and classified using pixel and object-based classification (OBC) algorithms to accurately delineate the boundaries of burned areas. The present results show that for OBC using only normalized difference vegetation index (NDVI) thresholds is not sufficient enough to map the burn scars; however, creating a new and simple rule set that included mean brightness values of near infrared and red channels in addition to mean NDVI values of segments considerably improved the accuracy of classification. According to the accuracy assessment results, the burned area was mapped with a 0.9322 kappa value in OBC, while a 0.7433 kappa value was observed in pixel-based classification. Lastly, classification results were integrated with the forest management map to determine the effected forest types after the fire to be used by the National Forest Directorate for their operational activities to effectively manage the fire, response and recovery processes

Evaluating BFASTMonitor Algorithm in Monitoring Deforestation Dynamics in Coniferous and Deciduous Forests with LANDSAT Time Series: A Case Study on Marmara Region, Turkey

Time series analysis combined with remote sensing data allows for the study of abrupt changes in the environment due to significant and severe disturbances such as deforestation, agricultural activities, fires, and urban expansion, as well as gradual changes such as climate variability and forest degradation in the ecosystem. The precision of any change detection analysis is highly dependent upon its ability to separate actual changes and fluctuations on a seasonal scale. One of the efficient methods in this context is using the Breaks for Additive Seasonal and Trend (BFAST) set of algorithms. This study aims to perform a comprehensive and comparative evaluation of different Vis’ performance in forest degradation with the Landsat 8 images and BFASTMonitor approach. Through evaluation, the study also considers the potential effects of different forest types and deforestation scales in the Marmara region of Turkey. For this purpose, the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Moisture Index (NDMI), and Normalized Burn Ratio (NBR) vegetation indices (VI) were selected for a comparative evaluation. The overall accuracy of VIs in deciduous forests was around 85% for NDVI, NDMI, and NBR, and 78.80% for EVI, while in coniferous forests, the overall accuracy demonstrated higher values of about 88% for NDVI, NDMI, and EVI, and 87.28% for NBR. Consequently, water-sensitive VIs that utilize shortwave infrared bands proved to be slightly more sensitive in detecting forest disturbances while chlorophyll-sensitive VIs represented lower accuracy for both forest types. Overall, all VIs faced an underestimation error in deforested area detection that was observable through negative BIAS. The results illuminate that BFASTMonitor can be considered as a tool in monitoring forest environments due to its acceptable deforestation determination capability in deciduous and coniferous forests, with slightly higher performance for small-scale deforestation patterned regions

Satellite-Derived Bathymetry Mapping on Horseshoe Island, Antarctic Peninsula, with Open-Source Satellite Images: Evaluation of Atmospheric Correction Methods and Empirical Models

Satellite-derived bathymetry (SDB) is the process of estimating water depth in shallow coastal and inland waters using satellite imagery. Recent advances in technology and data processing have led to improvements in the accuracy and availability of SDB. The increased availability of free optical satellite sensors, such as Landsat missions and Sentinel 2 satellites, has increased the quantity and frequency of SDB research and mapping efforts. In addition, machine learning (ML)- and deep learning (DL)-based algorithms, which can learn to identify features that are indicative of water depth, such as color or texture variations, have started to be used for extracting bathymetry information from satellite imagery. This study aims to produce an initial optical image-based SBD map of Horseshoe Island’s shallow coasts and to perform a comprehensive and comparative evaluation with Landsat 8 and Sentinel 2 satellite images. Our research considers the performance of empirical SDB models (classical, ML-based, and DL-based) and the effects of the atmospheric correction methods ACOLITE, iCOR, and ATCOR. For all band combinations and depth intervals, the ML-based random forest and XGBoost models delivered the highest performance and best fitting ability by achieving the lowest error with MAEs smaller than 1 m up to 10 m depth and a maximum correlation of R2 around 0.80. These models are followed by the DL-based ANN and CNN models. Nonetheless, the non-linearity of the reflectance–depth connection was significantly reduced by the ML-based models. Furthermore, Landsat 8 showed better performance for 10–20 m depth intervals and in the entire range of (0–20 m), while Sentinel 2 was slightly better up to 10 m depth intervals. Lastly, ACOLITE, iCOR, and ATCOR provided reliable and consistent results for SDB, where ACOLITE provided the highest automation

Satellite–Derived Bathymetry in Shallow Waters: Evaluation of Gokturk-1 Satellite and a Novel Approach

For more than 50 years, marine and remote sensing researchers have investigated the methods of bathymetry extraction by means of active (altimetry) and passive (optics) satellite sensors. These methods, in general, are referred to as satellite-derived bathymetry (SDB). With the advances in sensor capabilities and computational power and recognition by the International Hydrographic Organization (IHO), SDB has been more popular than ever in the last 10 years. Despite a significant increase in the number of studies on the topic, the performance of the method is still variable, mainly due to environmental factors, the quality of the deliverables by sensors, the use of different algorithms, and the changeability in parameterization. In this study, we investigated the capability of Gokturk-1 satellite in SDB for the very first time at Horseshoe Island, Antarctica, using the random forest- and extreme gradient boosting machine learning-based regressors. All the images are atmospherically corrected by ATCOR, and only the top-performing algorithms are utilized. The bathymetry predictions made by employing Gokturk-1 imagery showed admissible results in accordance with the IHO standards. Furthermore, pixel brightness values calculated from Sentinel-2 MSI and tasseled cap transformation are introduced to the algorithms while being applied to Sentinel-2, Landsat-8, and Gokturk-1 multispectral images at the second stage. The results indicated that the bathymetric inversion performance of the Gokturk-1 satellite is in line with the Landsat-8 and Sentienl-2 satellites with a better spatial resolution. More importantly, the addition of a brightness value parameter significantly improves root mean square error, mean average error, coefficient of determination metrics, and, consequently, the performance of the bathymetry extraction

Sensor Synergy in Bathymetric Mapping: Integrating Optical, LiDAR, and Echosounder Data Using Machine Learning

Bathymetry, the measurement of water depth and underwater terrain, is vital for scientific, commercial, and environmental applications. Traditional methods like shipborne echosounders are costly and inefficient in shallow waters due to limited spatial coverage and accessibility. Emerging technologies such as satellite imagery, drones, and spaceborne LiDAR offer cost-effective and efficient alternatives. This research explores integrating multi-sensor datasets to enhance bathymetric mapping in coastal and inland waters by leveraging each sensor’s strengths. The goal is to improve spatial coverage, resolution, and accuracy over traditional methods using data fusion and machine learning. Gülbahçe Bay in İzmir, Turkey, serves as the study area. Bathymetric modeling uses Sentinel-2, Göktürk-1, and aerial imagery with varying resolutions and sensor characteristics. Model calibration evaluates independent and integrated use of single-beam echosounder (SBE) and satellite-based LiDAR (ICESat-2) during training. After preprocessing, Random Forest and Extreme Gradient Boosting algorithms are applied for bathymetric inference. Results are assessed using accuracy metrics and IHO CATZOC standards, achieving A1 level for 0–10 m, A2/B for 0–15 m, and C level for 0–20 m depth intervals

An Approach for the Pan Sharpening of Very High Resolution Satellite Images Using a CIELab Color Based Component Substitution Algorithm

Recent very high spatial resolution (VHR) remote sensing satellites provide high spatial resolution panchromatic (Pan) images in addition to multispectral (MS) images. The pan sharpening process has a critical role in image processing tasks and geospatial information extraction from satellite images. In this research, CIELab color based component substitution Pan sharpening algorithm was proposed for Pan sharpening of the Pleiades VHR images. The proposed method was compared with the state-of-the-art Pan sharpening methods, such as IHS, EHLERS, NNDiffuse and GIHS. The selected study region included ten test sites, each of them representing complex landscapes with various land categories, to evaluate the performance of Pan sharpening methods in varying land surface characteristics. The spatial and spectral performance of the Pan sharpening methods were evaluated by eleven accuracy metrics and visual interpretation. The results of the evaluation indicated that proposed CIELab color-based method reached promising results and improved the spectral and spatial information preservation