Ecosystem_Modeling
This repository contains the code and methodology used to model the distribution of the Caretta Caretta (Loggerhead Sea Turtle) using remote sensing data and the Google Earth Engine (GEE) platform. The analysis focuses on understanding the key environmental factors that influence the distribution of this species, such as temperature, ocean currents, and chlorophyll-a concentrations, using satellite imagery and global datasets.
Model Caretta Caretta Distribution: Using the updated code, the goal was to develop a model that can predict the potential habitat suitability for the Loggerhead Sea Turtle. Environmental Factor Analysis: The GEE platform was used to analyze environmental factors such as sea surface temperature, ocean color (chlorophyll-a), and other oceanographic data to understand their impact on the species' distribution. Update and Customize the Code: The initial GEE code was updated to meet the specific objectives of this study, with new features added for more accurate spatial and temporal analysis. Data Sources GBIF Dataset: The primary data used for this analysis comes from the Global Biodiversity Information Facility (GBIF), which provides georeferenced species occurrence data. This data was utilized to model the distribution of Caretta Caretta. Remote Sensing Data: Satellite data such as MODIS chlorophyll-a, sea surface temperature, and other oceanographic data were sourced to support the analysis. Contributor: The data utilized in this analysis was provided by Lucas Barera, whose contributions have been critical in advancing the research. Google Earth Engine Analysis This analysis utilizes Google Earth Engine, a powerful cloud-based platform for processing and analyzing geospatial data. The code in this repository uses Earth Engine's extensive data archive and processing capabilities to:
Data Collection: Obtain remote sensing data such as MODIS chlorophyll-a, sea surface temperature, and other environmental variables.
Analysis: Perform spatial and temporal analysis of the environmental variables to identify patterns and correlations with the sea turtle's distribution. Modeling: Generate predictive models based on environmental factors that can help predict the distribution of Caretta Caretta. Visualization: Create visualizations and maps to represent the distribution patterns and insights from the analysis.
MODIS satellite data (e.g., chlorophyll-a concentration, sea surface temperature) Oceanographic data from global datasets available on GEE Environmental data layers for model inputs Analysis in Google Earth Engine:
The analysis was carried out entirely on the Google Earth Engine platform, leveraging its cloud-based resources to process large-scale geospatial data. The GEE code was tailored to incorporate relevant spatial and temporal dimensions to suit the analysis objectives.
The code in this repository was modified and updated from the original version to meet the specific needs of this study, including the addition of custom functions to handle satellite data and model environmental factors.
The analysis produced various maps and visualizations showing the predicted distribution of Caretta Caretta across different seasons and environmental conditions. The key findings from the study include:
Habitat Suitability: Identified key areas where the environmental conditions are most favorable for Caretta Caretta. Seasonal Variability: Analyzed how seasonal changes in oceanographic variables influence sea turtle distribution patterns.
Model Refinement: Further refinement of the habitat suitability model based on new data and additional environmental factors. Broader Application: Extending the model to other regions and species for broader marine conservation efforts.