Blar i 1 USN ansatte på tidsskrift "Remote Sensing"
Viser treff 1-16 av 16
-
A Hybrid GIS Multi-Criteria Decision-Making Method for Flood Susceptibility Mapping at Shangyou, China
(Journal article; Peer reviewed, 2019)Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical ... -
A Semi-empirical Approach Based on Genetic Programming for the Study of Biophysical Controls on Diameter-Growth of Fagus orientalis in Northern Iran
(Journal article; Peer reviewed, 2019)This paper examines the possible ecological controls on the diameter increment of oriental beech (Fagus orientalis Lipsky) in a high altitude forest in northern Iran. The main objectives of the study are computer-generated ... -
Coastal Wetland Classification with Deep U-Net Convolutional Networks and Sentinel-2 Imagery: A Case Study at the Tien Yen Estuary of Vietnam
(Peer reviewed; Journal article, 2020)The natural wetland areas in Vietnam, which are transition areas from inland and ocean, play a crucial role in minimizing coastal hazards; however, during the last two decades, about 64% of these areas have been converted ... -
Comparative Analysis of Deep Learning and Swarm-Optimized Random Forest for Groundwater Spring Potential Identification in Tropical Regions
(Peer reviewed; Journal article, 2023)Identifying areas with high groundwater spring potential is crucial as it enables better decision-making concerning water supply, sustainable development, and the protection of sensitive ecosystems; therefore, it is necessary ... -
Evaluating GIS-Based Multiple Statistical Models and Data Mining for Earthquake and Rainfall-Induced Landslide Susceptibility Using the LiDAR DEM
(Peer reviewed; Journal article, 2019)Landslides are typically triggered by earthquakes or rainfall occasionally a rainfall event followed by an earthquake or vice versa. Yet, most of the works presented in the past decade have been largely focused at the ... -
Hybrid BBO-DE Optimized SPAARCTree Ensemble for Landslide Susceptibility Mapping
(Peer reviewed; Journal article, 2023)This paper presents a new hybrid ensemble modeling method called BBO-DE-STreeEns for land-slide susceptibility mapping in Than Uyen district, Vietnam. The method uses subbagging and random subspacing to generate subdatasets ... -
Improving Accuracy Estimation of Forest Aboveground Biomass Based on Incorporation of ALOS-2 PALSAR-2 and Sentinel-2A Imagery and Machine Learning: A Case Study of the Hyrcanian Forest Area (Iran)
(Journal article; Peer reviewed, 2018)The main objective of this research is to investigate the potential combination of Sentinel-2A and ALOS-2 PALSAR-2 (Advanced Land Observing Satellite -2 Phased Array type L-band Synthetic Aperture Radar-2) imagery for ... -
A New Hybrid Firefly–PSO Optimized Random Subspace Tree Intelligence for Torrential Rainfall-Induced Flash Flood Susceptible Mapping
(Peer reviewed; Journal article, 2020)Flash flood is one of the most dangerous natural phenomena because of its high magnitudes and sudden occurrence, resulting in huge damages for people and properties. Our work aims to propose a state-of-the-art model for ... -
A New Modeling Approach for Spatial Prediction of Flash Flood with Biogeography Optimized CHAID Tree Ensemble and Remote Sensing Data
(Peer reviewed; Journal article, 2020)Flash floods induced by torrential rainfalls are considered one of the most dangerous natural hazards, due to their sudden occurrence and high magnitudes, which may cause huge damage to people and properties. This study ... -
Predicting Habitat Properties Using Remote Sensing Data: Soil pH and Moisture, and Ground Vegetation Cover
(Peer reviewed; Journal article, 2022)Remote sensing data comprise a valuable information source for many ecological landscape studies that may be under-utilized because of an overwhelming amount of processing methods and derived variables. These complexities, ... -
Predicting Habitat Properties Using Remote Sensing Data: Soil pH and Moisture, and Ground Vegetation Cover
(Journal article; Peer reviewed, 2022)Remote sensing data comprise a valuable information source for many ecological landscape studies that may be under-utilized because of an overwhelming amount of processing methods and derived variables. These complexities, ... -
Remote sensing approaches for monitoring mangrove species, structure and biomass: opportunities and challenges
(Journal article; Peer reviewed, 2019)The mangrove ecosystem plays a vital role in the global carbon cycle, by reducing greenhouse gas emissions and mitigating the impacts of climate change. However, mangroves have been lost worldwide, resulting in substantial ... -
Soil Salinity Mapping Using SAR Sentinel-1 Data and Advanced Machine Learning Algorithms: A Case Study at Ben Tre Province of the Mekong River Delta (Vietnam)
(Journal article; Peer reviewed, 2019)Soil salinity caused by climate change associated with rising sea level is considered as one of the most severe natural hazards that has a negative effect on agricultural activities in the coastal areas in most tropical ... -
Spatial Modeling of Snow Avalanche Using Machine Learning Models and Geo-Environmental Factors: Comparison of Effectiveness in Two Mountain Regions
(Peer reviewed; Journal article, 2019)Although snow avalanches are among the most destructive natural disasters, and result in losses of life and economic damages in mountainous regions, far too little attention has been paid to the prediction of the snow ... -
Spatial Prediction of Fluvial Flood in High-Frequency Tropical Cyclone Area Using TensorFlow 1D-Convolution Neural Networks and Geospatial Data
(Peer reviewed; Journal article, 2023)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 ... -
Wildfire Probability Mapping: Bivariate vs. Multivariate Statistics
(Peer reviewed; Journal article, 2019)Wildfires are one of the most common natural hazards worldwide. Here, we compared the capability of bivariate and multivariate models for the prediction of spatially explicit wildfire probability across a fire-prone landscape ...