Machine learning based wildfire susceptibility mapping using remotely sensed fire data and GIS: A case study of Adana and Mersin provinces, Turkey
MC Iban, A Sekertekin - Ecological Informatics, 2022 - Elsevier
In recent years, the number of wildfires has increased all over the world. Therefore, mapping
wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire …
wildfire susceptibility is crucial for prevention, early detection, and supporting wildfire …
Operational perspective of remote sensing-based forest fire danger forecasting systems
EH Chowdhury, QK Hassan - ISPRS Journal of Photogrammetry and …, 2015 - Elsevier
Forest fire is a natural phenomenon in many ecosystems across the world. One of the most
important components of forest fire management is the forecasting of fire danger conditions …
important components of forest fire management is the forecasting of fire danger conditions …
A cubesat enabled spatio-temporal enhancement method (cestem) utilizing planet, landsat and modis data
Satellite sensing in the visible to near-infrared (VNIR) domain has been the backbone of
land surface monitoring and characterization for more than four decades. However, a …
land surface monitoring and characterization for more than four decades. However, a …
Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning
An accurate in-field estimate of soil moisture content (SMC) is critical for precision irrigation
management. Current ground methods to measure SMC were limited by the disadvantages …
management. Current ground methods to measure SMC were limited by the disadvantages …
High-Resolution NDVI from planet's constellation of earth observing nano-satellites: A new data source for precision agriculture
Planet Labs (“Planet”) operate the largest fleet of active nano-satellites in orbit, offering an
unprecedented monitoring capacity of daily and global RGB image capture at 3–5 m …
unprecedented monitoring capacity of daily and global RGB image capture at 3–5 m …
Evaluating soil moisture content under maize coverage using UAV multimodal data by machine learning algorithms
Timely and accurate estimation of soil moisture content (SMC) is essential for precise
irrigation management at the farm scale. Unmanned aerial vehicle (UAV) remote sensing …
irrigation management at the farm scale. Unmanned aerial vehicle (UAV) remote sensing …
[HTML][HTML] Prediction of maize crop coefficient from UAV multisensor remote sensing using machine learning methods
In the upcoming irrigation management in agricultural production, accurate mapping of crop
water consumption with a high spatial and temporal resolution at a farm scale is needed. In …
water consumption with a high spatial and temporal resolution at a farm scale is needed. In …
Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices
Rapid and accurate acquisition of crop coefficient (K c) values is essential for estimating field
crop evapotranspiration (ET). The lack of rapid access to the high-resolution spatial and …
crop evapotranspiration (ET). The lack of rapid access to the high-resolution spatial and …
Comparison of Random Forest and XGBoost Classifiers Using Integrated Optical and SAR Features for Mapping Urban Impervious Surface
The integration of optical and SAR datasets through ensemble machine learning models
shows promising results in urban remote sensing applications. The integration of multi …
shows promising results in urban remote sensing applications. The integration of multi …
Developing a random forest algorithm for MODIS global burned area classification
R Ramo, E Chuvieco - Remote Sensing, 2017 - mdpi.com
This paper aims to develop a global burned area (BA) algorithm for MODIS BRDF-corrected
images based on the Random Forest (RF) classifier. Two RF models were generated …
images based on the Random Forest (RF) classifier. Two RF models were generated …