Exploring the potential of Artificial intelligence (AI) and machine learning (ML) in Vegetation Dynamics.

Introduction

Artificial intelligence (AI) and machine learning (ML) techniques have been increasingly used in studying vegetation dynamics. These techniques enable the analysis of large datasets and complex patterns in vegetation cover and changes over time. Here are some examples of AI/ML applications in vegetation dynamics, along with cited journal articles:

1. Land cover change analysis: AI/ML techniques enable the analysis of land cover changes at a large scale, providing valuable information for land management and conservation. These techniques can detect and classify land cover changes based on historical satellite imagery. For example, researchers have applied deep learning models, such as convolutional neural networks, to analyze time series satellite imagery and detect land cover changes. A study by Xie et al. (2016) titled "Detecting Land Cover Change in Urban Areas Using Deep Learning and Auxiliary Geospatial Data" used deep learning models to detect land cover changes in urban areas.
2. Vegetation classification and mapping: AI/ML algorithms can be employed to classify and map different vegetation types based on remote sensing data. For instance, convolutional neural networks (CNNs) have been used to classify land cover types using satellite imagery. A study by Deng et al. (2018) titled "DeepGlobe 2018: A Challenge to Parse the Earth through Satellite Images" used deep learning techniques to classify land cover types, including vegetation, from satellite imagery.
3. Vegetation change detection: AI/ML techniques are utilized to detect changes in vegetation cover over time, which can be crucial for understanding ecosystem dynamics and monitoring land degradation. For example, researchers have employed change detection algorithms, such as support vector machines (SVM), to analyze multi-temporal satellite imagery and detect deforestation or vegetation recovery. A study by Torres et al. (2019) titled "Deforestation Detection with Fully Convolutional Networks in the Amazon Forest from Landsat-8 and Sentinel-2 Images" applied deep learning models to identify deforestation areas using satellite images.
4. Phenological studies: AI/ML approaches aid in analyzing phenological patterns, such as the timing of plant growth stages, and their responses to environmental factors. These analyses provide insights into climate change impacts and ecosystem functioning. For instance, researchers have used ML algorithms, such as decision trees and random forests, to model the relationships between climate variables and vegetation phenology. A study by Dai et al. (2019) titled "Detecting temporal changes in the temperature sensitivity of spring phenology with global warming: Application of machine learning in phenological model" used ML models to predict the start of the vegetation active season based on climate data. Researchers at the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center used machine learning algorithms to develop multi-years "C6 Aqua 250-m eMODIS Remote Sensing Phenology Metrics across the conterminous U.S.".
5. Species distribution modeling: AI/ML techniques are employed to model and predict the distribution of plant species based on environmental variables. This helps understand species-environment relationships and forecast potential range shifts due to climate change. For example, researchers have used MaxEnt, a popular ML algorithm, to model the distribution of plant species. The scientist at the United States Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center used machine learning algorithms particularly XGBoost and TensorFlow to map "Fractional Estimates of Multiple Exotic Annual Grass (EAG) Species and Sandberg bluegrasses in the Sagebrush Biome, USA, 2016 - 2021 (ver. 2.0, December 2022)".
6. Biomass estimation: AI/ML algorithms can be used to estimate vegetation biomass, which is crucial for carbon cycle studies and assessing ecosystem productivity. Remote sensing data, such as satellite imagery and LiDAR data, are often combined with ML models. For example, a study by Santoro et al. (2011) titled "Estimating Above-Ground Biomass in Tropical Forests: Field Methods and Error Analysis for the Calibration of Remote Sensing Observations" used ML regression models to estimate above-ground biomass in tropical forests based on remote sensing data.
7. Disease detection: AI/ML techniques can assist in detecting diseases affecting vegetation, such as plant pathogens or pests. These techniques can analyze multispectral or hyperspectral data to identify disease symptoms or stress indicators in plants. For instance, researchers have used ML algorithms, including random forests and support vector machines, to detect plant diseases based on spectral data. A study by Mahlein et al. (2010) titled "Spectral Characteristics of Plants Infected with Rust Fungus" used hyperspectral data and ML models to identify rust fungus infection in wheat plants.
8. Vegetation forecasting: AI/ML techniques can be utilized to forecast vegetation dynamics, such as future vegetation growth or vegetation response to changing environmental conditions. These models incorporate historical data, environmental variables, and climate projections to predict vegetation patterns. For example, researchers have used ML algorithms, including artificial neural networks, to forecast vegetation growth and productivity. A study by Buckland et al. (2019) titled "Using artificial neural networks to predict future dryland responses to human and climate disturbances" used artificial neural networks to forecast vegetation cover in dryland environments.

These examples highlight the diverse applications of AI/ML techniques in vegetation dynamics. However, it is essential to note that the field of AI/ML in vegetation dynamics is rapidly evolving, and new studies and techniques continue to emerge. The combination of advanced algorithms and remotely sensed data has the potential to greatly enhance our understanding of vegetation patterns, dynamics, and ecosystem functioning.