Michael S. Watt

Orcid: 0000-0001-6752-9134

According to our database1, Michael S. Watt authored at least 15 papers between 2017 and 2024.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of five.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
Current Status of Remote Sensing for Studying the Impacts of Hurricanes on Mangrove Forests in the Coastal United States.
Remote. Sens., October, 2024

Early Prediction of Regional Red Needle Cast Outbreaks Using Climatic Data Trends and Satellite-Derived Observations.
Remote. Sens., April, 2024

Early Detection of Myrtle Rust on Pōhutukawa Using Indices Derived from Hyperspectral and Thermal Imagery.
Remote. Sens., March, 2024

Automatic Detection of Phytophthora pluvialis Outbreaks in Radiata Pine Plantations Using Multi-Scene, Multi-Temporal Satellite Imagery.
Remote. Sens., January, 2024

2023
Unsupervised Methodology for Large-Scale Tree Seedling Mapping in Diverse Forestry Settings Using UAV-Based RGB Imagery.
Remote. Sens., November, 2023

Climate-Change-Driven Droughts and Tree Mortality: Assessing the Potential of UAV-Derived Early Warning Metrics.
Remote. Sens., 2023

2022
A Mixed Methods Approach for Fuel Characterisation in Gorse (Ulex europaeus L.) Scrub from High-Density UAV Laser Scanning Point Clouds and Semantic Segmentation of UAV Imagery.
Remote. Sens., 2022

2021
Deep Learning and Phenology Enhance Large-Scale Tree Species Classification in Aerial Imagery during a Biosecurity Response.
Remote. Sens., 2021

A New Drone Laser Scanning Benchmark Dataset for Characterization of Single-Tree and Forest Biophysical Properties.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2021

2020
An Assessment of High-Density UAV Point Clouds for the Measurement of Young Forestry Trials.
Remote. Sens., 2020

2019
Preprocessing Ground-Based Visible/Near Infrared Imaging Spectroscopy Data Affected by Smile Effects.
Sensors, 2019

Early Detection of Invasive Exotic Trees Using UAV and Manned Aircraft Multispectral and LiDAR Data.
Remote. Sens., 2019

Comparison of models describing forest inventory attributes using standard and voxel-based lidar predictors across a range of pulse densities.
Int. J. Appl. Earth Obs. Geoinformation, 2019

2018
UAV Multispectral Imagery Can Complement Satellite Data for Monitoring Forest Health.
Remote. Sens., 2018

2017
Combining Airborne Laser Scanning and Aerial Imagery Enhances Echo Classification for Invasive Conifer Detection.
Remote. Sens., 2017


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