remote-sensing-resistance
Fire resistance study
Investigating how forest heterogeneity affects wildfire severity by analyzing remote sensing data and GIS analysis
Does heterogeneity in forest structure make a forest resistant to wildfire? That is, does greater heterogeneity decrease wildfire severity when a fire inevitably occurs? A collaborative effort co-authored by: Michael J. Koontz, Malcolm P. North, Chhaya M. Werner, Stephen E. Fick, and Andrew M. Latimer
9 stars
3 watching
10 forks
Language: Jupyter Notebook
last commit: almost 4 years ago
Linked from 1 awesome list
acgeospatial/awesome-earthobservation-codeRelated projects:
| Repository | Description | Stars |
|---|---|---|
| mikoontz/local-structure-wpb-severity | An analysis of drone imagery to understand the relationship between forest structure and tree mortality due to an insect infestation. | 6 |
 datadesk/nasa-wildfires | Downloads wildfire hotspots and associated data from NASA satellites | 33 |
 pyronear/pyro-risks | Provides machine learning-based wildfire forecasting capabilities | 25 |
 azvoleff/gfcanalysis | Tools and analyses for working with Hansen et al.'s Global Forest Change dataset | 17 |
 mratsim/amazon-forest-computer-vision | A repository providing tools and techniques for training computer vision models on satellite images of the Amazon forest. | 368 |
 ytarazona/forestoolboxrs | Tools and algorithms for processing satellite images to detect deforestation and monitor forest health. | 54 |
 heatherkmtb/q_research | This project investigates the relationship between forest canopy height and density using LiDAR data from ICESat GLAS, GEDI, and ICESat-2. | 8 |
 ltrr-arizona-edu/burnr | A package for analyzing forest fire history data in R. | 15 |
 perceptilabs/wildfire-detection | Develops machine learning models to automatically detect wildfires from images | 7 |
| pyronear/pyro-vision | A Python-based computer vision library for detecting wildfires using deep learning models. | 52 |
 hsg-aiml/industrialsmokeplumedetection | Detects industrial smoke plumes from satellite images using machine learning models and remote sensing data. | 40 |
 alan-turing-institute/monitoring-ecosystem-resilience | Develops tools to analyze satellite imagery and simulate vegetation patterns for understanding ecosystem resilience | 23 |
 orion-ai-lab/mesogeos | A multi-purpose dataset and code repository for training deep learning models to predict wildfire behavior in the Mediterranean region. | 44 |
 ghislainv/forestatrisk-tropics | This project models and forecasts tropical deforestation using spatial analysis and machine learning techniques | 29 |
 johntruckenbrodt/pyrosar | A framework for processing SAR satellite data from various sources | 512 |