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

Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services

The appropriate locations of road emergency stations (RESs) can help to decrease the impact of traffic accidents that cause around 50 million injuries per year worldwide. In this research, the appropriateness of existing RESs in the Khuzestan province, Iran, was assessed using an integrated fuzzy analytical hierarchy process (FAHP) and geographic information system (GIS) approach. The data used in this research were collected from different sources, including the department of roads, the department of health, the statistics organization, forensics, police centers, the surveying and geological department, remotely-sensed and global positioning system (GPS) data of accident high crash zones. On the basis of previous studies and the requirements of the Ministry of Health and Medical Education, as well as the department of roads of Iran for the location of RESs, nine criteria and 19 sub-criteria were adopted, including population, safety, environmental indicators, compatible area in RES, incompatible area in RES, type of road, accident high crash zones, traffic level and performance radius. The FAHP yielded the criteria weights and the ideal locations for establishing RESs using GIS analysis and aggregation functions. The resulting map matched the known road accident and high crash zones very well. The results indicated that the current RES stations are not distributed appropriately along the major roads of the Khuzestan province, and a re-arrangement is suggested. The finding of the present study can help decision-makers and authorities to achieve sustainable road safety in the case study area

A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions

Distribution of Land Cover (LC) classes is mostly imbalanced with some majority LC classes dominating against minority classes in mountainous areas. Although standard Machine Learning (ML) classifiers can achieve high accuracies for majority classes, they largely fail to provide reasonable accuracies for minority classes. This is mainly due to the class imbalance problem. In this study, a hybrid data balancing method, called the Partial Random Over-Sampling and Random Under-Sampling (PROSRUS), was proposed to resolve the class imbalance issue. Unlike most data balancing techniques which seek to fully balance datasets, PROSRUS uses a partial balancing approach with hundreds of fractions for majority and minority classes to balance datasets. For this, time-series of Landsat-8 and SRTM topographic data along with various spectral indices and topographic data were used over three mountainous sites within the Google Earth Engine (GEE) cloud platform. It was observed that PROSRUS had better performance than several other balancing methods and increased the accuracy of minority classes without a reduction in overall classification accuracy. Furthermore, adopting complementary information, particularly topographic data, considerably increased the accuracy of minority classes in mountainous areas. Finally, the obtained results from PROSRUS indicated that every imbalanced dataset requires a specific fraction(s) for addressing the class imbalance problem, because different datasets contain various characteristics

A review of land use/land cover change mapping in the China-Central Asia-West Asia economic corridor countries

Large-scale projects, such as the construction of railways and highways, usually cause an extensive Land Use Land Cover Change (LULCC). The China-Central Asia-West Asia Economic Corridor (CCAWAEC), one key large-scale project of the Belt and Road Initiative (BRI), covers a region that is home to more than 1.6 billion people. Although numerous studies have been conducted on strategies and the economic potential of the Economic Corridor, reviewing LULCC mapping studies in this area has not been studied. This study provides a comprehensive review of the recent research progress and discusses the challenges in LULCC monitoring and driving factors identifying in the study area. The review will be helpful for the decision-making of sustainable development and construction in the Economic Corridor. To this end, 350 peer-reviewed journal and conference papers, as well as book chapters were analyzed based on 17 attributes, such as main driving factors of LULCC, data collection methods, classification algorithms, and accuracy assessment methods. It was observed that: (1) rapid urbanization, industrialization, population growth, and climate change have been recognized as major causes of LULCC in the study area; (2) LULCC has, directly and indirectly, caused several environmental issues, such as biodiversity loss, air pollution, water pollution, desertification, and land degradation; (3) there is a lack of well-annotated national land use data in the region; (4) there is a lack of reliable training and reference datasets to accurately study the long-term LULCC in most parts of the study area; and (5) several technical issues still require more attention from the scientific community. Finally, several recommendations were proposed to address the identified issues

Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services / Traffic Accident Spatial Simulation Modeling for Planning of Road Emergency Services

The appropriate locations of road emergency stations (RESs) can help to decrease the impact of traffic accidents that cause around 50 million injuries per year worldwide. In this research, the appropriateness of existing RESs in the Khuzestan province, Iran, was assessed using an integrated fuzzy analytical hierarchy process (FAHP) and geographic information system (GIS) approach. The data used in this research were collected from different sources, including the department of roads, the department of health, the statistics organization, forensics, police centers, the surveying and geological department, remotely-sensed and global positioning system (GPS) data of accident high crash zones. On the basis of previous studies and the requirements of the Ministry of Health and Medical Education, as well as the department of roads of Iran for the location of RESs, nine criteria and 19 sub-criteria were adopted, including population, safety, environmental indicators, compatible area in RES, incompatible area in RES, type of road, accident high crash zones, traffic level and performance radius. The FAHP yielded the criteria weights and the ideal locations for establishing RESs using GIS analysis and aggregation functions. The resulting map matched the known road accident and high crash zones very well. The results indicated that the current RES stations are not distributed appropriately along the major roads of the Khuzestan province, and a re-arrangement is suggested. The finding of the present study can help decision-makers and authorities to achieve sustainable road safety in the case study area.(VLID)445082

Land cover dataset of the China Central-Asia West-Asia Economic Corridor from 1993 to 2018

Abstract Land Cover (LC) maps offer vital knowledge for various studies, ranging from sustainable development to climate change. The China Central-Asia West-Asia Economic Corridor region, as a core component of the Belt and Road initiative program, has been experiencing some of the most severe LC change tragedies, such as the Aral Sea crisis and Lake Urmia shrinkage, in recent decades. Therefore, there is a high demand for producing a fine-resolution, spatially-explicit, and long-term LC dataset for this region. However, except China, such dataset for the rest of the region (Kyrgyzstan, Turkmenistan, Kazakhstan, Uzbekistan, Tajikistan, Turkey, and Iran) is currently lacking. Here, we constructed a historical set of six 30-m resolution LC maps between 1993 and 2018 at 5-year time intervals for the seven countries where nearly 200,000 Landsat scenes were classified into nine LC types within Google Earth Engine cloud computing platform. The generated LC maps displayed high accuracies. This publicly available dataset has the potential to be broadly applied in environmental policy and management

RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine

Timely and accurate Land Cover (LC) information is required for various applications, such as climate change analysis and sustainable development. Although machine learning algorithms are most likely successful in LC mapping tasks, the class imbalance problem is known as a common challenge in this regard. This problem occurs during the training phase and reduces classification accuracy for infrequent and rare LC classes. To address this issue, this study proposes a new method by integrating random under-sampling of majority classes and an ensemble of Support Vector Machines, namely Random Under-sampling Ensemble of Support Vector Machines (RUESVMs). The performance of RUESVMs for LC classification was evaluated in Google Earth Engine (GEE) over two different case studies using Sentinel-2 time-series data and five well-known spectral indices, including the Normalized Difference Vegetation Index (NDVI), Green Normalized Difference Vegetation Index (GNDVI), Soil-Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI), and Normalized Difference Water Index (NDWI). The performance of RUESVMs was also compared with the traditional SVM and combination of SVM with three benchmark data balancing techniques namely the Random Over-Sampling (ROS), Random Under-Sampling (RUS), and Synthetic Minority Over-sampling Technique (SMOTE). It was observed that the proposed method considerably improved the accuracy of LC classification, especially for the minority classes. After adopting RUESVMs, the overall accuracy of the generated LC map increased by approximately 4.95 percentage points, and this amount for the geometric mean of producer’s accuracies was almost 3.75 percentage points, in comparison to the most accurate data balancing method (i.e., SVM-SMOTE). Regarding the geometric mean of users’ accuracies, RUESVMs also outperformed the SVM-SMOTE method with an average increase of 6.45 percentage points

Integration of Sentinel-1 and Sentinel-2 Data with the G-SMOTE Technique for Boosting Land Cover Classification Accuracy

The importance of Land Cover (LC) classification is recognized by an increasing number of scholars who employ LC information in various applications (i.e., address global climate change and achieve sustainable development). However, studying the roles of balancing data, image integration, and performance of different machine learning algorithms in various landscapes has not received as much attention from scientists. Therefore, the present study investigates the performance of three frequently used Machine Learning (ML) algorithms, including Extreme Learning Machines (ELM), Support Vector Machines (SVM), and Random Forest (RF) in LC mapping at six different landscapes. Moreover, the Geometric Synthetic Minority Over-sampling Technique (G-SMOTE) was adopted to deal with the class imbalance problem. In this work, the time-series of Sentinel-1 and Sentinel-2 data were integrated to improve LC mapping accuracy, taking advantage of both data. Moreover, Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was implemented to distinguish the most informative features. Based on the results, the RF integrated with G-SMOTE showed the best result for four landscapes (coastal, cropland, desert, and semi-arid). SVM integrated with G-SMOTE had the highest accuracy in the remaining two landscapes (plain and mountain). Applied ML algorithms showed good performances in various landscapes, ranging Overall Accuracy (OA) from 85% to 93% for RF, 83% to 94% for SVM, and 84% to 92% for ELM. The outcomes exhibit that although applying G-SMOTE may slightly decrease OA values, it generally boosts the results of LC classification accuracies in various landscapes, particularly for minority classes.Peer Reviewe

A Simple and Automatic Method for Detecting Large-Scale Land Cover Changes Without Training Data

Automatic and accurate extraction of land use/cover change (LUCC) is essential for various applications, particularly studies on climate change and sustainable development. However, the automatic and accurate detection of LUCC in large-scale regions remains challenging due to the complexity of LUCC and the high cost of training data acquisition. To address this research gap, a simple and practical unsupervised change detection method based on multi-indices and bitemporal remote sensing image pairs (USCD-MiBi) was proposed to automatically extract LUCC in two heterogeneous experimental sites without training data. Single-index change analysis (SICA), multi-index change analysis (MICA), and bitemporal change analysis (BTCA) form the core of the USCD-MiBi method. In this method, all the selected change indices were integrated by the SICA and MICA to identify potential change regions. The potential false changes, caused by inconsistencies in atmospheric conditions and phenology, were further removed by the BTCA. Verification experiments revealed that the detection accuracy of USCD-MiBi method can exceed 90%, whether the LUCC is concentrated in urban regions or scattered in mountainous areas. It was also observed that the proposed method had higher detection performance than several other change detection methods. The proposed USCD-MiBi method offers high flexibility and adaptability, allowing users to choose the most suitable change indices based on the characteristics of their study area or the selected data sources. This study provides a simple solution for automatically detecting large-scale LUCC without training samples, making it accessible to a wider range of users

Trends of CO and NO2 Pollutants in Iran during COVID-19 Pandemic Using Timeseries Sentinel-5 Images in Google Earth Engine

The first case of COVID-19 in Iran was reported on 19 February 2020, 1 month before the Nowruz holidays coincided with the global pandemic, leading to quarantine and lockdown. Many studies have shown that environmental pollutants were drastically reduced with the spread of this disease and the decline in industrial activities. Among these pollutants, nitrogen dioxide (NO2) and carbon monoxide (CO) are widely caused by anthropogenic and industrial activities. In this study, the changes in these pollutants in Iran and its four metropolises (i.e., Tehran, Mashhad, Isfahan, and Tabriz) in three periods from 11 March to 8 April 2019, 2020, and 2021 were investigated. To this end, timeseries of the Sentinel-5P TROPOMI and in situ data within the Google Earth Engine (GEE) cloud-based platform were employed. It was observed that the results of the NO2 derived from Sentinel-5P were in agreement with the in situ data acquired from ground-based stations (average correlation coefficient = 0.7). Moreover, the results showed that the concentration of NO2 and CO pollutants in 2020 (the first year of the COVID-19 pandemic) was 5% lower than in 2019, indicating the observance of quarantine rules, as well as people’s initial fear of the coronavirus. Contrarily, these pollutants in 2021 (the second year of the COVID-19 pandemic) were higher than those in 2020 by 5%, which could have been due to high vehicle traffic and a lack of serious policy- and law-making by the government to ban urban and interurban traffic. These findings are essential criteria that might be used to guide future manufacturing logistics, traffic planning and management, and environmental sustainability policies and plans. Furthermore, using the COVID-19 scenario and free satellite-derived data, it is now possible to investigate how harmful gas emissions influence air quality. These findings may also be helpful in making future strategic decisions on how to cope with the virus spread and lessen its negative social and economic consequences