In this repository I process, agregate and do estimates with MapBiomas land use transition data (30 meter resolution).

App Deployed by Mu'azu Gital with content credit to Ramadhan

Code repository for project modelling scenarios of future Land Use Land Cover Change, Ecosystem services and Biodiversity in Peru

A R script that runs Boosted Regression Trees(BRT) on epochs of land use datasets with random points to model land use changes and predict and determine the main drivers of change

The elaborated documentation imported from the previous Confluence wiki (https://www.wiki.ed.ac.uk/display/CRAFTY)

Code and data used during the hackathon Code4Green by the team team_cli07_landscapeoptimizer

An open dataset for pixel level classification of Landsat 7 and Landsat Imagery. The repo contains the code for classification as well as the error correction methods on top of it.

This repository houses scripts to accompany Crawford et al. 2021 (Ecological Applications), in which we apply a range of biodiversity indices to an agricultural land-use prioritization model, using Zambia as a case study to investigate how variation in how biodiversity is represented affects the results of land-use prioritization analyses.

Tools for extracting and preparing Digital Earth Australia Satellite Multi-Spectral Images for use in Deep Learning Machine models.

Data and R scripts accompanying the paper "Forecasting deforestation in the Brazilian Amazon to prioritize conservation efforts"

Valentin Lucet // Msc Thesis // 2018-2020

An interface for managing SWATPlus input and output files to aid in implementing, and visualizing the impact of land use changes on catchment hydrology in the SWATPlus model

We provide a pixel level training dataset for landuse classification (four categories - Green, Water, Barren land and Built up Areas) using google earth engine for India. All associated scripts are also provided.

R scripts mapping global wetland loss (1700-2000)

A Google Earth Engine API (interactive dashboard) for satellite-based global climate hazard analysis (urban heat, landcover changes, etc). Project under World Bank Group. ⬇️ ⬇️

The Mixed-Cell Cellullar Automata (MCCA) provides a new approach to enable more dynamic mixed landuse modeling to move away from the analysis of static patterns. One of the biggest advantages of mixed-cell CA models is the capability of simulating the quantitative and continuous changes of multiple landuse components inside cells.