Marine mucilage that threatens marine habitats is one of the natural disasters, mainly resulting from global warming and marine pollution. Monitoring sea surface mucilage formations and mapping their spatial distributions provide valuable information to the local authorities and decision-makers in developing prevention and rehabilitation strategies. This study proposes a new spectral index called Automated Mucilage Extraction Index (AMEI) that allows effective and accurate detection of surface mucilage aggregates using Sentinel-2 satellite imagery. The index uses four bands of Sentinel-2 Level-2A imagery (Bands 3, 4, 8, and 12) covering visible, near-infrared, and shortwave infrared regions. The index was formulated considering the image acquired on 19 May 2021, when mucilage formations were most intensively observed in the Sea of Marmara. The performance of the developed index was then evaluated using the images acquired on 14 and 24 May and 13 June 2021. The Jenks Natural Breaks (JNB) algorithm was applied to estimate the threshold values for separating mucilage formations from the water surface background and classify index maps into two classes: ‘mucilage’ and ‘others’. To statistically analyse the effectiveness of the indices in distinguishing water background and mucilage formations, the proposed index was compared with 21 widely used water indices by applying Bhattacharyya Distance, Jeffries-Matusita Distance, and M-Statistic measures. Results confirm the robustness of the proposed spectral index, offering superior separation performance (above 1.5 in terms of M-Statistic) compared to other water indices on both cloud-free images and images with cumulus clouds. Visual interpretations also verified that boundaries of the mucilage formations in the cloudless and thin cloudy images were accurately identified by the proposed index, and different types of mucilage (i.e. yellow and white) can be identified when an appropriate histogram thresholding is applied.

Advancements in the geometric resolution of space images have improved the conditions for generations of large-scale topographic maps. Using WorldView-1, WorldView-2, and GeoEye-1, images can now be captured from space with a 0.5 m ground sampling distance (GSD). Geometric accuracy and information content are the most significant components of mapping from space images. Depending on the resolution, image quality, and shadows, the identification and classification of ground objects may prove challenging. In this research, the geometric accuracy and information content, of panchromatic WorldView-1 images, were analyzed by covering parts of Istanbul and Zonguldak in Turkey. Each of these locations has various topographic characteristics. For the orientation and investigation of the geometric accuracies of images, a number of ground control points (GCPs) were developed as independent checkpoints. Based on bias-corrected rational polynomial coefficients with one GCP, a standard deviation of independent checkpoints on the range of one GSD was obtained. The information content of images was analyzed by mapping all buildings, in both test areas, and comparing the results with reference $1/5000$ scaled topographic maps. The results verified that the WorldView-1 images can be utilized for generating and updating $1/5000$ scaled topographic maps of urban areas.

Lately, unmanned aerial vehicle (UAV) become a prominent technology in remote sensing studies with the advantage of high-resolution, low-cost, rapidly and periodically achievable three-dimensional (3D) data. UAV enables data capturing in different flight altitudes, imaging geometries, and viewing angles which make detailed monitoring and modelling of target objects possible. Against earlier times, UAVs have been improved by integrating real-time kinematic (RTK) positioning and multispectral (MS) imaging equipment. In this study, positioning accuracy and land cover classification potential of RTK equipped MS UAVs were evaluated by point-based geolocation accuracy analysis and pixel-based ensemble learning algorithms. In positioning accuracy evaluation, ground control points (GCPs), pre-defined by terrestrial global navigation satellite system (GNSS) measurements, were used as the reference data while Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms were applied for land cover classification. In addition, the spectral signatures of some major land classes, achieved by UAV MS bands, were compared with reference terrestrial spectro-radiometer measurements. The results demonstrated that the positioning accuracy of MS RTK UAV is ±1.1 cm in X, ±2.7 cm in Y, and ±5.7 cm in Z as root mean square error (RMSE). In RF and XGBoost pixel-based land cover classification, 13 independent land cover classes were detected with overall accuracies and kappa statistics of 93.14% and 93.37%, 0.92 and 0.93, respectively.