EcoReviveTX: Ecological Restoration Analysis Tool

EcoReviveTX is a comprehensive platform that empowers ecological restoration efforts across Texas through advanced machine learning and environmental simulation tools. Developed in partnership with the Texas Parks and Wildlife Department, our platform leverages research from Wang et al. (2020)—a study conducted in regions of China with remarkably similar climate patterns, soil composition, and ecosystem characteristics to Texas. By adapting this data to Texas landscapes, we provide park rangers, restoration practitioners, and environmental professionals with powerful predictive tools for estimating restoration timelines and modeling soil carbon dynamics under various environmental conditions. Our mission is to accelerate ecosystem recovery and promote sustainable management of Texas's diverse natural habitats. You can explore the full source code and technical implementation here.

Machine Learning Prediction Tool

Generate site-specific restoration predictions for your land. By inputting the geographic coordinates, climate data, soil characteristics, and ecosystem type of a particular site, this tool estimates: (1) Restoration Time - the number of years required for that specific area to achieve ecosystem recovery, or (2) Soil Carbon Response (lnRR.SOC) - the expected change in soil organic carbon levels for that location.

Latitude (decimal degrees)

Longitude (decimal degrees)

Mean Annual Temp (°C)

Mean Annual Precip (mm)

Soil Depth (cm)

Ambient SOC (g/kg)

Climate Zone

Ecosystem Type

Prediction Target

Environmental Simulation Tool

Run computational simulations to predict soil organic carbon (SOC) dynamics and model ecosystem restoration trajectories under different environmental conditions. Choose between: (1) Basic Ecosystem Model - deterministic simulation providing direct SOC predictions over time, or (2) Monte Carlo Simulation - probabilistic modeling that quantifies uncertainty and variability in SOC recovery across multiple scenarios.