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wofost-study

workshop

A hands-on workshop on deep neural networks

worldview2_forestry

Codes of paper: Fassnacht, F. E., Mangold, D., Schäfer, J., Immitzer, M., Kattenborn, T., Koch, B., Latifi, H. (2017): Estimating stand density, biomass and tree species from very high resolution stereo-imagery – towards an all-in-one sensor for forestry applications? Forestry, pp. 1–19

wrf-fire

A coupled weather-fire forecasting model built on top of Weather Research and Forecasting (WRF).

wrf_mml

This project development study the performance the machine learning algorithmic to downscaling data of air temperature from WRF model. The objective generate grads with good resolution and low computing cost

wrfxctrl

A website that enables users to submit jobs to the wrfxpy framework for fire simulation.

wrfxpy

WRF fire forecasting and data assimilation in python

wrfxweb

Web-based visualization system for imagery generated by wrfxpy.

xgb_vegetation_mapping

Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially at conditions of intensive human activities and accelerating global changes. However, it is still challenging today to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping technology in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study, using extensive features and abundant vegetation survey data, created a workflow by adopting a promising classifier, eXtreme Gradient Boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases: Dzungarian Basin in China and New Zealand. For Dzungarian Basin, a vegetation map with 7 vegetation types, 17 subtypes, and 43 associations was produced, with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced, at an overall accuracy of 0.946, 0.703, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field surveying and remote sensing based methods in terms of accuracy as well as efficiency. Besides, it opens the possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs.

xlur

A Python toolbox for ArcGIS Pro that enables the development and application of land use regression models.

yaron

yaron1000.github.io

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