Wetlands are important natural resources due to their numerous ecological services. Consequently, identifying their locations and extents is imperative. The stability, repeatability, ...cost-effectiveness, multi-scale coverage, and proper spatial resolution imagery of satellites provide a valuable opportunity for their use in various large-scale applications, such as provincial wetland mapping. To do so, it is required to (1) process and classify big geo data (i.e. a large amount of satellite datasets) in a time- and computationally-efficient approach and (2) collect a large amount of field samples. In this study, Google Earth Engine (GEE) and machine learning algorithms were utilized to process thousands of remote sensing images and produce provincial wetland inventory maps of the three Canadian provinces of Manitoba, Quebec, and Newfoundland and Labrador (NL). Additionally, using GEE, a generalized supervised classification method is proposed to produce a regional wetland map from a large area (e.g., a province) when lacking field samples. In fact, using the field data from only Manitoba and assuming that all wetlands in Canada have similar characteristics, the wetland maps were generated for the other two provinces. The overall classification accuracies for Manitoba, Quebec, and NL were 84%, 78%, and 82%, respectively, indicating the high potential of the proposed method for aiding provincial wetland inventory systems.
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.
Displacement time series (TS) provides temporal and spatial information related to ground deformation. This study aims to investigate temporal behavior of ground deformation TS, including ...classification of displacement trends and periodicity evaluation, which ease the interpretation of movements. To this end, we propose several modifications to an existing automatic classification workflow of Persistent Scatterers Interferometry (PSI) TS using new tests to classify ground deformations into seven main trends: Stable, Linear, Quadratic, Bilinear, Phase Unwrapping Errors (PUE), Discontinuous with constant and different velocities. We illustrate our approach over 1500 km
2
of the Granada region and the metropolitan area of Barcelona, which were monitored using Sentinel-1 images and a PSI technique. This study provided the spatial distribution of different ground movement types and was useful to detect several TS anomalies due to PUE. The proposed approach also identified stable targets, which were wrongly classified as moving scatterers by the existing classification method. A periodicity analysis was finally performed using the Welch's power spectral density estimator to investigate seasonal and yearly fluctuations. The method was validated using simulated data, where the classified TSs characterized by probable phase unwrapping errors were verified by PSI experts. The overall classification accuracy was 77.8%, indicating that the proposed method has a considerable TS classification potential.
Synthetic Aperture Radar (SAR) is the most widely used sensor for retrieving soil surface parameters. This study was performed in two steps. In the first step, estimated backscattering coefficients ...using three models, Oh, Dubois, and IEM, in three bands of P, L, and C and two polarizations of HH and VV were compared with those extracted from SAR data acquire from AIRSAR over LWREW experimental site located in southwestern Oklahoma with a sub-humid climate. The results showed that the Oh model in band C had the best accuracy in both polarizations (RMSE_HH=1.48 and RMSE_VV=1.1). Dubois and IEM models were appropriately accurate in band L; however, both were less accurate compared with the Oh model in band C. In the second step, ground truth measurements of soil roughness, dielectric constant, and correlation length were compared with the corresponding results of inversion backscattering models. Based on the findings, it was concluded that IEM performed better at estimating soil roughness with RMSE=0.37, while Oh more accurately assessed dielectric constant in all three bands and at depths of 0-3 cm and 3-6 cm. All results confirmed that band P was not appropriate for retrieving soil surface parameters using backscattering models compared with the bands of C and L.
This study focuses on deformation mapping and monitoring using Sentinel-1 radar data and the DInSAR (Differential Interferometric Synthetic Aperture Radar) technique. In particular, a Persistent ...Scatterer Interferometry technique was used over 15000 km2 of the Pyrenees, located in Spain, Andorra and France. The main goal is to monitor deformations over a vegetated and mountainous region by exploiting the wide-area coverage, the high coherence and temporal sampling provided by the Sentinel-1 satellites. All possible interferograms were generated using 150 images covering five years. The velocity map of the entire region is presented considering the characteristics of the study area, including vegetation and severe steep mountains. Two areas of deformation are shown, which are characterized by velocity values ranging between -20 to -40 mm/year.
•The Temperature-Vegetation-soil Moisture-Precipitation Drought Index was proposed.•Validation was performed using meteorological data.•More than 15,206 MODIS, TRMM and GPM datasets were ...utilized.•The overall drought trend across Iran gradually decreased over the past 21 years.•Significant drought was observed in south-west, north, and central parts of Iran.
Remote Sensing (RS) offers efficient tools for drought monitoring, especially in countries with a lack of reliable and consistent in-situ multi-temporal datasets. In this study, a novel RS-based Drought Index (RSDI) named Temperature-Vegetation-soil Moisture-Precipitation Drought Index (TVMPDI) was proposed. To the best of our knowledge, TVMPDI is the first RSDI using four different drought indicators in its formulation. TVMPDI was then validated and compared with six conventional RSDIs including VCI, TCI, VHI, TVDI, MPDI and TVMDI. To this end, precipitation and soil temperature in-situ data have been used. Different time scales of meteorological Standardized Precipitation Index (SPI) index have also been used for the validation of the RSDIs. TVMPDI was highly correlated with the monthly precipitation and soil temperature in-situ data at 0.76 and 0.81 values respectively. The correlation coefficients between the RSDIs and 3-month SPI ranged from 0.07 to 0.28, identifying the TVMPDI as the most suitable index for subsequent analyses. Since the proposed TVMPDI could considerably outperform the other selected RSDIs, all spatiotemporal drought monitoring analyses in Iran were conducted by TVMPDI over the past 21 years. In this study, different products of the Moderate Resolution Imaging Spectrometer (MODIS), Tropical Rainfall Measuring Mission(TRMM), and Global Precipitation Measurement (GPM) datasets containing 15,206 images were used on the Google Earth Engine (GEE) cloud computing platform. According to the results, Iran experienced the most severe drought in 2000 with a 0.715 TVMPDI value lasting for almost two years. Conversely, the TVMPDI showed a minimum value equal to 0.6781 in 2019 as the lowest annual drought level. The drought severity and trend in the 31 provinces of Iran have also been mapped. Consequently, various levels of decrease over the 21 years were found for different provinces, while Isfahan and Gilan were the only provinces showing an ascending drought trend (with a 0.004% and 0.002% trendline slope respectively). Khuzestan also faced a worrying drought prevalence that occurred in several years. In summary, this study provides updated information about drought trends in Iran using an advanced and efficient RSDI implemented in the cloud computing GEE platform. These results are beneficial for decision-makers and officials responsible for environmental sustainability, agriculture and the effects of climate change.
Early alarm systems can activate vital precautions for saving lives and the economy threatened by natural hazards and human activities. Interferometric synthetic aperture radar (InSAR) products ...generate valuable ground motion data with high spatial and temporal resolutions. Integrating the InSAR products and forecasting models make possible to set up early alarm systems to monitor vulnerable areas. This study proposes a technical support to early warning detection tools of ground instabilities using machine learning and InSAR time series that is capable of forecasting regions affected by potential collapses. A long short-term memory (LSTM) model is tailored to predict ground movements in three forecast ranges (i.e., SAR observations): 3, 4, and 5 multistep. A contribution of the proposed strategy is utilizing adjacent time series to decrease the possibility of falsely detecting safe regions as significant movements. The proposed tool offers ground motion-based outcomes that can be interpreted and utilized by experts to activate early alarms to reduce the consequences of possible failures in vulnerable infrastructures, such as mining areas. Three case studies in Spain, Brazil, and Australia, where fatal incidents happened, are analyzed by the proposed early alert detector to illustrate the impact of chosen temporal and spatial ranges. Since most early alarm systems are site dependent, we propose a general tool to be interpreted by experts for activating reliable alarms. The results show that the proposed tool can identify potential regions before collapse in all case studies. In addition, the tool can suggest an optimum selection of InSAR temporal (i.e., number of images) and spatial (i.e., adjacent measurement points) combinations based on the available SAR images and the characteristics of the study area.
This study proposes modifications to an existing automatic classification method of Persistent Scatterers Interferometry (PSI) time series (TS) and a new procedure to classify ground movements into ...seven classes. We also represent a technique to detect TSs affected by phase unwrapping errors and a reclassification part to detect stable points, which are incorrectly classified as moving points using the original method. Around 60 km2 of Catalunya were classified using Sentinel-1 images and a PSI technique. The proposed method classified 78359 PS TS. This study provided the spatial distribution of ground movement classes and detected several time series anomalies.
River eelgrass has a vital role in the coastal ecosystem, which can be threatened by human activities and climate change. Accurate mapping of eelgrass habitats is important for sustainable eelgrass ...conservation and management. A large portion of the Shediac river in the province of New Brunswick, Canada is covered by eelgrass, which can be threatened by different natural and anthropogenic activities, such as bridge construction. Thus, mapping and monitoring eelgrass in this river are of interest to the province. In this study, a very high-resolution Worldview-3 image along with an Iterative Self-Organizing Data Analysis Technique (ISODATA) unsupervised classification algorithm was applied to map eelgrass over a portion of Shediac river. The results indicated a reasonable classification accuracy, and it was observed that 90,416.43 m 2 of the study area was covered by river eelgrass.
PDF
