Results 191 to 200 of about 167,285 (319)

A 37 years [1984-2021] Landsat/Sentinel-2 derived snow cover time-series for Switzerland. [PDF]

Sci Data
Poussin C, Peduzzi P, Chatenoux B, Giuliani G.   +3 more
europepmc   +1 more source

Assessing Long Term Stability of Landsat 5 TM, Landsat 7 ETM+ and Landsat 8 OLI

, 2017
The radiometric calibration stability of Landsat 5 (L5) Thematic Mapper (TM), Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) and Landsat 8 (L8) Operation Land Imager (OLI) is continuously monitored, trended, and updated using various on-board and vicarious calibration techniques. The on-board calibrators in L5 TM and L7 ETM+ were deemed unreliable
Micijevic, Esad, Mishra, Nischal, Helder, Dennis   +2 more
openaire   +1 more source

Assessing the Effects of Climate Change and Land‐Use Changes on Extreme Discharge in the Western Watershed of Lake Urmia, Iran

Journal of Flood Risk Management, Volume 18, Issue 2, June 2025.
ABSTRACT This study investigates the impacts of climate change and land‐use changes on peak discharge and runoff behavior in the western watersheds of Lake Urmia, Iran. Employing machine learning algorithms (e.g., SVM), stochastic models (e.g., CA‐MARKOV), ERA5 reanalysis climate data, and the large‐scale hydrological VIC model, we assessed these ...
Ghasem Farahmand, Behzad Hessari, Hossein Salehi   +2 more
wiley   +1 more source

Landsat Time Series Reconstruction Using a Closed-Form Continuous Neural Network in the Canadian Prairies Region. [PDF]

Sensors (Basel)
Babadi Ataabadi M, Pouliot D, Chen D, Oluwadare TS.   +3 more
europepmc   +1 more source

CNPq/INPE-LANDSAT system report of activities [PDF]

The status of the Brazilian LANDSAT facilities and the results achieved are presented. In addition, a LANDSAT product sales/distribution analysis is provided.
Barbosa, M. N., Parada, N. D. J.
core   +1 more source

Analysis of the Utilization of Machine Learning to Map Flood Susceptibility

Journal of Flood Risk Management, Volume 18, Issue 2, June 2025.
ABSTRACT This article provides an analysis of the utilization of Machine Learning (ML) models in Flood Susceptibility Mapping (FSM), based on selected publications from the past decade (2013–2023). Recognizing the challenge that some stages of ML modeling inherently rely on experience or trial‐and‐error approaches, this work aims at establishing a ...
Ali Pourzangbar, Peter Oberle, Andreas Kron, Mário J. Franca   +3 more
wiley   +1 more source

Multitask semantic change detection guided by spatiotemporal semantic interaction. [PDF]

Sci Rep
Wang Y, Zhao L, Hu Y, Dai H, Zhang Y.
europepmc   +1 more source

Urban Flood Susceptibility Mapping for Toronto, Canada, Using Supervised Regression and Machine Learning Models

Journal of Flood Risk Management, Volume 18, Issue 2, June 2025.
ABSTRACT Floods are one of the most devastating natural hazards, causing adverse effects on human life, well‐being, property, and the environment. The application of five machine‐learning techniques in pluvial flood susceptibility mapping was investigated using the case study of two severe storms (2005 and 2013) in Toronto, Canada.
Baljeet Kaur, Andrew Binns, Edward McBean, Dan Sandink, Karen Castro, Bahram Gharabaghi   +5 more
wiley   +1 more source

Remote sensing with machine learning for multi-decadal surface water monitoring in Ethiopia. [PDF]

Sci Rep
Tesfaye M, Breuer L.
europepmc   +1 more source

Flood Inundation Mapping of a River Stretch Using Machine Learning Algorithms in the Google Earth Engine Environment

Journal of Flood Risk Management, Volume 18, Issue 2, June 2025.
ABSTRACT Floods are among the most common natural disasters in India, causing significant socio‐economic and environmental impacts. This study focuses on a frequently flooded stretch of the Godavari River in Telangana, India, to analyze the flood event that occurred between 14th July 2022 and 20th July 2022.
Maaz Ashhar, Venkata Reddy Keesara, Venkataramana Sridhar   +2 more
wiley   +1 more source