357 Results Found

Accurate detection of tropical cyclones (TCs) is important to prevent and mitigate natural disasters associated with TCs. Deep transfer learning methods have advantages in detection tasks, because they can further improve the stability and accuracy o...

In this study, deep convolutional neural network (CNN) models of stimulated tropical cyclone intensity (TCI), minimum central pressure (MCP), and maximum 2 min mean wind speed at near center (MWS) were constructed based on ocean and atmospheric reana...

Accurate crop type identification and crop area estimation from remote sensing data in tropical regions are still considered challenging tasks. The more favorable weather conditions, in comparison to the characteristic conditions of temperate regions...

Under global climate change, the frequency of typhoons and their strong wind, heavy rain, and storm surge increase, seriously threatening the life and property of human society. However, traditional tropical cyclone track prediction methods have diff...

Accurate forecasting of tropical cyclone (TC) tracks is critical for disaster preparedness and risk mitigation. While traditional numerical weather prediction (NWP) systems have long served as the backbone of operational forecasting, they face limita...

This study develops an objective deep-learning-based model for tropical cyclone (TC) intensity estimation. The model’s basic structure is a convolutional neural network (CNN), which is a widely used technology in computer vision tasks. In order...

Tropical cyclone (TC) motion has an important impact on both human lives and infrastructure. Predicting TC intensity is crucial, especially within the 24 h warning time. TC intensity change prediction can be regarded as a problem of both regression a...

Tropical forests are essential ecosystems recognized for their carbon sequestration and biodiversity benefits. As the world undergoes a simultaneous data revolution and climate crisis, accurate data on the world’s forests are increasingly impor...

Tropical cyclone (TC) detection is essential to mitigate natural disasters, as TCs can cause significant damage to life, infrastructure and economy. In this study, we applied the deep learning object detection model YOLOv3 to detect TCs in the North...

Tropical cyclones (TCs) rank among the most destructive natural hazards globally, with core damaging potential originating from regions of intense wind shear and steep wind speed gradients within the eyewall and spiral rainbands. Accurately character...

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Pre...

Environmental heat stress on buildings through façades contributes to indoor overheating and thus increases demand for energy consumption. The study analyzed the problem, heat gain risk, of modern air-conditioned multi-level office buildings i...

The Quasi-Biennal Oscillation (QBO) and the El Niño-Southern Oscillation (ENSO) largely modulate the zonal wind in the tropics. Previous studies showed that QBO phases produce changes in deep convection through an increase/decrease in the trop...

Precambrian tropical glaciation is an enigma of Earth’s climate. Overlooking fundamental difference of land/sea icelines, it was equated with a global frozen ocean, which is at odds with the sedimentary evidence of an active hydrological cycle,...

Tropical cyclones (TCs) are dangerous weather events; accurate monitoring and forecasting can provide significant early warning to reduce loss of life and property. However, the study of tropical cyclone intensity remains challenging, both in terms o...

Tropical forests are of vital importance for maintaining biodiversity, regulating climate and material cycles while facing deforestation, agricultural reclamation, and managing various pressures. Remote sensing (RS) can support effective monitoring a...

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting informat...

Tropical cyclones (TCs) seriously threaten the safety of human life and property especially when approaching a coast or making landfall. Robust, long-lead predictions are valuable for managing policy responses. However, despite decades of efforts, se...

Mediterranean hurricanes (Medicanes) are characterized by the presence of a quasi-cloud-free calm eye, spiral-like cloud bands, and strong winds around the vortex center. Typically, they reach a tropical-like cyclone (TLC) phase characterized by an a...

This paper presents a novel semi-supervised approach for accurate counting and localization of tropical plants in aerial images that can work in new visual domains in which the available data are not labeled. Our approach uses deep learning and domai...

Synthetic Aperture Radar (SAR) imagery plays an important role in observing tropical cyclones (TCs). However, the C-band attenuation caused by rain bands and the problem of signal saturation at high wind speeds make it impossible to retrieve the fine...

Farmland abandonment is becoming a growing land use challenge in the Brazilian Cerrado, yet its extent, spatial distribution, and underlying drivers remain poorly understood. This study addresses the following question: Can deep learning methods reli...

Rapid Intensification (RI) in Tropical Cyclone (TC) development is one of the most difficult and still challenging tasks in weather forecasting. In addition to the dynamical numerical simulations, commonly used techniques for RI (as well as TC intens...

A CatBoost-based intelligent tropical cyclone (TC) intensity-detecting model was built to quantify the intensity of TCs over the Western North Pacific (WNP) with the cloud-top brightness temperature (CTBT) data of Fengyun-2F (FY-2F) and Fengyun-2G (F...

Mediterranean hurricanes, or medicanes, are tropical-like cyclones forming once or twice per year over the waters of the Mediterranean Sea. These mesocyclones pose a serious threat to coastal infrastructure and lives because of their strong winds and...

Fluvial floods endure as one of the most catastrophic weather-induced disasters worldwide, leading to numerous fatalities each year and significantly impacting socio-economic development and the environment. Hence, the research and development of new...

The U-net is nowadays among the most popular deep learning algorithms for land use/land cover (LULC) mapping; nevertheless, it has rarely been used with synthetic aperture radar (SAR) and multispectral (MS) imagery. On the other hand, the discriminat...

This research investigated the source and fate of different chemical species of N and P on a deep tropical urban reservoir, the artificial Lake Paranoá, located in the city of Brasilia (Brazil). To determine an N and P budget, nutrient input f...

Tropical cyclones (TC) are dynamic atmospheric phenomena featuring extreme low-pressure systems and powerful winds, known for their devastating impacts on weather and the environment. The main purpose of this paper is to consider the subtle involveme...

Schistosomiasis is a debilitating parasitic disease of poverty that affects more than 200 million people worldwide, mostly in sub-Saharan Africa, and is clearly associated with the construction of dams and water resource management infrastructure in...

Monitoring changes in tree cover for assessment of deforestation is a premise for policies to reduce carbon emission in the tropics. Here, a U-net deep learning model was used to map monthly tropical tree cover in the Brazilian state of Mato Grosso b...

The prediction of severe weather events such as hurricanes is always a challenging task in the history of climate research, and many deep learning models have been developed for predicting the severity of weather events. When a disastrous hurricane s...

Commercial buildings in hot and humid tropical climates rely significantly on cooling systems to maintain optimal occupant comfort. A well-accurate day-ahead load profile prediction plays a pivotal role in planning the energy requirements of cooling...

We assessed the performance of Convolutional Neural Network (CNN)-based approaches using mobile phone images to estimate regrowth density in tropical forages. We generated a dataset composed of 1124 labeled images with 2 mobile phones 7 days after th...

This study revisited the association of African easterly waves (AEWs) to Atlantic tropical cyclone (TC) development using weather states (WSs) from the International Satellite Cloud Climatology Project, National Hurricane Center best track hurricane...

Tropical deforestation has been recognized as a major and multi-faceted sustainability issue, frequently analyzed in terms of its economic drivers, the effectiveness of protection policies, or broader political dynamics. Meanwhile, the role of values...

The TanDEM-X synthetic aperture radar (SAR) system allows for the recording of bistatic interferometric SAR (InSAR) acquisitions, which provide additional information to the common amplitude images acquired by monostatic SAR systems. More concretely,...

Estimating the intensity of tropical cyclones (TCs) is usually involved as a critical step in studies on TC disaster warnings and prediction. Satellite cloud images (SCIs) are one of the most effective and preferable data sources for TC research. Des...

It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining...

Global climate and oceanic water masses have undergone profound changes during the middle Pleistocene transition; however, due to a lack of foraminiferal fossils, the nonfossiliferous pelagic deposits were less detected in previous reports. In this w...

Accurate tropical cyclone (TC) intensity estimation is crucial for prediction and disaster prevention. Currently, significant progress has been achieved for the application of convolutional neural networks (CNNs) in TC intensity estimation. However,...

This study focuses on optimizing precipitation forecast induced by tropical cyclones (TCs) in the Northwest Pacific region, with lead times ranging from 6 to 72 h. The research employs deep learning models, such as U-Net, UNet3+, SE-Net, and SE-UNet3...

To investigate the mechanisms underlying the continuous failure of deep foundation pits in tropical water-rich sandy strata, this study comprehensively examines a foundation pit project in Haikou city, China. Using the PLAXIS3D 24.1 software, a three...

Frequent forest fires are causing severe harm to the natural environment, such as decreasing air quality and threatening different species; therefore, developing accurate prediction models for forest fire danger is vital to mitigate these impacts. Th...

Singapore is a tiny city-state located in maritime Southeast Asia. Weather systems such as localized thunderstorms, squalls, and monsoon surges bring extreme rainfall to Singapore, influencing the day-to-day conduct of stakeholders in many sectors. N...

Land use and land cover (LULC) mapping is a powerful tool for monitoring large areas. For the Amazon rainforest, automated mapping is of critical importance, as land cover is changing rapidly due to forest degradation and deforestation. Several resea...

Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they l...

Clouds play a central role in regulating incoming solar radiation and outgoing terrestrial emission; hence, they must be internally constrained to prognose Earth’s temperature. At the same time, planetary fluids are inherently turbulent, so the...

Detecting early deforestation is a fundamental process in reducing forest degradation and carbon emissions. With this procedure, it is possible to monitor and control illegal activities associated with deforestation. Most regular monitoring projects...

In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations du...