Ocean remote sensing techniques and applications: a review (Part II)

As discussed in the first part of this review paper, Remote Sensing (RS) systems are great tools to study various oceanographic parameters. Part I of this study described different passive and active RS systems and six applications of RS in ocean studies, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD). In Part II, the remaining nine important applications of RS systems for ocean environments, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery are comprehensively reviewed and discussed. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed.Peer ReviewedPostprint (published version

A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery

Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were categorized as Component Substitution (CS-based), Multi-Resolution Analysis (MRA), Variational Optimization-based (VO), and Hybrid and were tested on a collection of 21 case studies. These include images from WorldView-2, 3 & 4, GeoEye-1, QuickBird, IKONOS, KompSat-2, KompSat-3A, TripleSat, Pleiades-1, Pleiades with the aerial platform, and Deimos-2. Neural network-based methods were excluded due to their substantial computational requirements for operational mapping purposes. The methods were evaluated based on four Spectral and three Spatial quality metrics. An Analysis Of Variance (ANOVA) was used to statistically compare the pan-sharpening categories. Results indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based methods had the highest spatial quality and CS-based methods had the lowest results both spectrally and spatially. The revisited version of the Additive Wavelet Luminance Proportional Pan-sharpening method had the highest spectral quality, whereas Generalized IHS with Best Trade-off Parameter with Additive Weights showed the highest spatial quality. CS-based methods generally had the fastest run-time, whereas the majority of methods belonging to MRA and VO categories had relatively long run times

Citrus orchard mapping in Juybar, Iran: Analysis of NDVI time series and feature fusion of multi-source satellite imageries

Nowadays crop mapping as an interdisciplinary hot topic attracted both agriculture and remote sensing researchers' interests. This study proposed an automatic method to map citrus orchards in Juybar, Iran, where planting citrus trees is booming there. In this regard, 148 Sentinel-1, Sentinel-2, and ALOS Digital Surface Model (DSM) tiles are processed in Google Earth Engine to provide a hybrid feature set including initial satellite images, Gray Level Co-occurrence Matrix (GLCM) textural features, and spectral features such as vegetation, built-up, bare-soil indices, and the proposed Vegetation Dynamic Index (VDI). A semi-automatic sample selection paradigm is also developed based on a time-series analysis of 12 monthly Normalized Difference Vegetation Indices (NDVIs), Otsu thresholding, multi-level thresholding (MLT), and using two proposed indices called Evergreenness Index (EGI) and Water-covered or No-vegetation (WCNV) index, and finally human post-revision. The output of the Support Vector Machine (SVM) classification using 60,000 samples and the post-classification operation showed that the classified map has an average overall accuracy (OA) and an average kappa coefficient (KC) equal to 99.7% and 0.992, respectively. The results show that multispectral bands lonely extracted orchards with high accuracy (OA: 99.55%, KC: 0.986), and the rest of the features only made a slight improvement to that. For the year 2019, an area of about 4351 ha is estimated as citrus orchards, which is in accordance with the agriculture department's reports (~4700 ha). The results indicate that from 2016 to 2019, despite a “citrus to non-citrus” land-use conversion of about 754 ha, the citrus orchards area was totally expanded by about 17%. Comparing the results with the Google Earth images indicates the precise extraction of orchards with a 10 m spatial resolution. To use the proposed method for extensive cases or areas with other types of evergreen trees, it is recommended to use high-resolution normalized DSMs (nDSMs) and textural features

Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression

Flood has long been known as one of the most catastrophic natural hazards worldwide. Mapping flood-prone areas is an important part of flood disaster management. In this study, a flood susceptibility mapping framework was developed based on a novel integration of nature-inspired algorithms into support vector regression (SVR). To this end, various remote sensing (RS) and geographic information system (GIS) datasets were applied to the hybridized SVR models to map flood susceptibility in Ahwaz township, Iran. The proposed framework has two main steps: 1) updating the flood inventory (historical flooded locations) using the proposed RS-based flood detection method developed within the google earth engine (GEE) platform. The mosaicked images of multi-temporal Sentinel-1 synthetic aperture radar (SAR) data have been used in this step; 2) producing flood susceptibility map using the standalone SVR and hybridized model of SVR. The hybridized methods were derived from a novel integration of SVR with meta-heuristic algorithms, hence forming the SVR-bat algorithm (SVR-BA), SVR-invasive weed optimization (SVR-IWO), and SVR-firefly algorithm (SVR-FA). A spatial database of flood locations and 11 conditioning factors (altitude, slope angle, aspect, topographic wetness index, stream power index, normalized difference vegetation index (NDVI), distance to stream, curvature, rainfall, soil type, and land use/cover) were built for the susceptibility modelling. The accuracy of the proposed model was evaluated using the statistical and sensitivity indices, such as root mean square error (RMSE), receiver operating characteristic (ROC) and area under the ROC curve (AUROC) index. The results indicated that all hybridized models outperformed the standalone SVR. According to AUROC values, the predictive power of the SVR-FA was the highest with the value of 0.81, followed by SVR-IWO, SVR-BA, and SVR with values of 0.80, 0.79, and 0.77, respectively.</p

Flood susceptibility mapping using multi-temporal SAR imagery and novel integration of nature-inspired algorithms into support vector regression

Flood has long been known as one of the most catastrophic natural hazards worldwide. Mapping flood-prone areas is an important part of flood disaster management. In this study, a flood susceptibility mapping framework was developed based on a novel integration of nature-inspired algorithms into support vector regression (SVR). To this end, various remote sensing (RS) and geographic information system (GIS) datasets were applied to the hybridized SVR models to map flood susceptibility in Ahwaz township, Iran. The proposed framework has two main steps: 1) updating the flood inventory (historical flooded locations) using the proposed RS-based flood detection method developed within the google earth engine (GEE) platform. The mosaicked images of multi-temporal Sentinel-1 synthetic aperture radar (SAR) data have been used in this step; 2) producing flood susceptibility map using the standalone SVR and hybridized model of SVR. The hybridized methods were derived from a novel integration of SVR with meta-heuristic algorithms, hence forming the SVR-bat algorithm (SVR-BA), SVR-invasive weed optimization (SVR-IWO), and SVR-firefly algorithm (SVR-FA). A spatial database of flood locations and 11 conditioning factors (altitude, slope angle, aspect, topographic wetness index, stream power index, normalized difference vegetation index (NDVI), distance to stream, curvature, rainfall, soil type, and land use/cover) were built for the susceptibility modelling. The accuracy of the proposed model was evaluated using the statistical and sensitivity indices, such as root mean square error (RMSE), receiver operating characteristic (ROC) and area under the ROC curve (AUROC) index. The results indicated that all hybridized models outperformed the standalone SVR. According to AUROC values, the predictive power of the SVR-FA was the highest with the value of 0.81, followed by SVR-IWO, SVR-BA, and SVR with values of 0.80, 0.79, and 0.77, respectively.Geo-engineerin

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Oceans cover over 70% of the Earth&rsquo;s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study