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Introduction

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Our mission is to predict and monitor tree health with unparalleled accuracy, addressing a critical challenge in environmental conservation. By merging high-resolution satellite imagery, historical data, and meteorological insights, we provide a framework that equips stakeholders with the knowledge needed for informed decisions in sustainable land management.

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Study

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Four reforestation sites in Madagascar were carefully chosen for their unique characteristics and objectives. These sites offer valuable insights into long-term tree growth and ecosystem development.

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Methodology

We acquired satellite data from Copernicus, (Sentinel 2 using Google Earth Engine), and Planet imagery accessed via the QGIS plugin. Meteorological and climate assimilation data were sourced from Copernicus Climate Data Store (ERA5). Our approach leveraged a suite of Open Source tools including Google Earth Engine, Facebook Prophet, and xclim: Climate Services Library. These instrumental tools facilitated the processing and analysis of satellite data, while also enabling us to forecast temperature and precipitation patterns. In addition, we conducted a comprehensive computation of key vegetation indices such as NDVI, NDWI, and mSAVI-2. Furthermore, we calculated meteorological indices SPI (Standardized Precipitation Index) and KBDI (Keetch-Byram Drought Index) to establish customized growing conditions and evaluate site-specific weather patterns

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Analysis and Results

Our comprehensive analysis using satellite data assessed vegetation health, providing valuable insights into the developmental stages of vegetation across the analyzed sites. Predictions of NDVI using PlanetScope imagery showed consistent average values, indicating stable vegetation dynamics.

Climate and Weather Analysis:

We derived essential indices (KBDI and SPI) from downscaled ERA5 Global Climate and Weather reanalysis data, enhancing our localized understanding of climate and weather conditions.

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Prophet Model Prediction:

Time series models predicted temperature and precipitation at specific sites, effectively capturing trends and aiding in understanding future weather conditions.

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Results & Findings

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Our workflow offers a versatile framework for regional forecasting, adaptable to diverse sites and scenarios. We focus on NDVI as a robust predictor of tree health and stress. While meteorological indicators like SPI and KBDI provide insights, they are not currently integrated due to noise and lack of seasonality.

Integrating data-driven approaches, including satellite imagery and meteorological data, enables effective tree health monitoring and prediction. Our workflow, incorporating Facebook Prophet and combining NDVI, temperature, and precipitation predictions, yields promising results. Ongoing efforts aim to enhance accuracy by expanding regions, incorporating additional indices, and validating with field teams.

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Conclusion

This proposed workflow showcases the effective utilization of diverse data sources and tools to forecast NDVI six months ahead. Although data preparation can be time-consuming, the incorporation of Facebook Prophet and the combination of NDVI, temperature, and precipitation predictions as regressors yielded favorable results. The straightforward approach of comparing the site’s NDVI with the regional average allowed for direct sub-regional comparisons.

Overall, this workflow holds promise for predicting NDVI in various areas within a specified timeframe, while also highlighting the value of Facebook Prophet in forecasting and regression analysis. Moving forward, further research and development will focus on enhancing the accuracy and precision of predictions. This includes expanding the workflow to encompass larger regions, incorporating additional vegetation indexes that exhibit strong signals at specific sites, and integrating more data sources or extending the time series for improved analysis. Furthermore, collaboration with field teams in validating the method through ground truth forest tree growth inventories will be pursued over the next month, serving as a benchmark for the predictions.

More details can be found here: https://sites.google.com/view/treehealthmonitoring/

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Meet the Team

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GeoSpatial Scientist

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Machine Learning Engineer

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Ecologist and Forest Biodiversity Expert

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Project Support

Plan Adapt Coordination Hub member

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