Advancements in Tree Species Identification Using Airborne Data

Understanding the types of trees in forests is key for managing these areas, planning conservation efforts, and studying the environment. In the past, collecting data on tree Species has depended on two main methods. The first method involves counting trees in specific plots of land, which gives good quality data but only covers small areas. The second method uses satellite images to predict the diversity of tree species over larger areas, but this method does not provide information about individual trees.

Recent advancements in airborne technology are helping bridge the gap between these two methods. By using high-resolution images from planes and drones, researchers can obtain detailed information about trees over vast landscapes. This technology allows them to gather data from dozens to thousands of hectares, which helps in understanding forest structures and how they change over time due to natural and human influences.

#Detecting Individual Trees

Detecting individual trees from the sky has been a focus in environmental studies. This is important because knowing how many trees are in a specific area helps in various research and management tasks. Identifying where individual trees are located and measuring their crowns (the upper parts of trees) are critical steps in this research. These tasks have led to a lot of research into creating better Algorithms that can accurately detect trees from aerial images.

Deep learning, a type of artificial intelligence, has become a popular approach for this task. By using labeled images and ground data, AI Models can learn to identify tree locations and species. However, a significant challenge is gathering enough training data, which needs to capture the various shapes of Tree Crowns across different environments.

Once researchers have identified tree crowns in the images, the next step is to label each crown with the correct species name. Many models have been suggested to achieve this, but it is still uncertain how well these models perform across different ecosystems with varying tree types and densities. To improve the models, researchers suggested using multiple years of aerial images to get better results.

Another issue that arises is class imbalance. In many environments, a few tree species dominate, making it hard for models to learn about the less common species. This is a significant challenge when training AI models.

#Advancements with the National Ecological Observatory Network (NEON)

The National Ecological Observatory Network (NEON) is making significant strides in understanding regional forests by providing open-access, high-resolution airborne data across the United States. NEON collects standardized data from various sites, which is beneficial for creating detailed maps of tree species.

One major goal is to produce maps that show individual tree crowns to support ongoing research on forests, ecosystems, and wildlife. By utilizing different types of aerial data-like regular photos, height models, and spectral data-researchers have predicted the locations of over 100 million trees from 81 species in 24 NEON sites.

This work builds upon previous datasets by adding details on species and their health status. Having this species information expands the dataset's utility for research focused on biodiversity and managing natural resources.

#Data Collection Methods

NEON collects airborne data annually during periods when trees have leaves. The data is taken at peak greenness to ensure consistency. Different NEON data products include high-resolution camera images, canopy height models, and hyperspectral images, which help distinguish tree species based on how they reflect light.

Field data consists of tree measurements taken from fixed-area plots, providing the necessary information on tree species for connection with airborne data. A significant number of additional trees are collected from various sources to ensure that as many species as possible are included in the model.

To effectively link tree species information from ground data with aerial images, a filtering approach is used to ensure data quality and accuracy. By comparing field measurements with aerial data, researchers can filter out trees that do not meet specific criteria, ensuring only the most reliable data is used for training AI models.

#Predicting Tree Crowns

The DeepForest algorithm is a key tool in predicting tree crowns. This model has been improved over time and is now publicly available, achieving an average recall rate of about 72%. This means that the model is able to correctly identify a significant portion of trees in the images.

However, there are still some limitations. The accuracy of crown predictions can vary based on tree spacing and species diversity. Generally, well-spaced trees in open forests yield better results than those in dense forest environments.

Once the tree crowns are detected, each crown is classified as either alive or dead using deep learning models trained on images from all NEON sites. This classification helps determine the health status of the trees, a crucial element in understanding forest dynamics.

#Species Identification

To label tree crowns with species names, researchers developed unique models for each NEON site. This means that each model is fine-tuned to the specific tree species present at that site. The process involves using hyperspectral data, which measures how trees reflect light at various wavelengths, to classify tree species.

A hierarchical modeling approach is adopted to manage class imbalance better. In this system, species are grouped into submodels based on similarities, allowing the model to handle both common and rare species more effectively. This approach also aims to reduce errors in predicting species that are closely related.

When the model is applied, predictions are made for each available year of data to minimize potential biases and overfitting. Ultimately, this process produces a comprehensive dataset of predicted tree species across multiple sites.

#Results of Species Predictions

The models created for species classification achieved notable success, identifying a diverse range of tree species across the NEON sites. On average, each site had around 6.56 species represented, with a peak of 15 species at Harvard Forest and a low of 3 species at some other sites. Overall, these predictions captured a high percentage of the total tree species richness at the sites.

The accuracy measurements reveal that the models performed well, achieving around 78.8% micro-accuracy and about 75.8% macro-accuracy. Micro-accuracy indicates the overall performance across all predictions while macro-accuracy specifically emphasizes the accuracy of less common species.

Data from the predictions show that certain tree species make up a significant portion of trees at a site, with some species dominating the predictions depending on their abundance. Understanding these dynamics is essential for studying how different species contribute to the ecosystem.

#Challenges and Limitations

While the dataset generated is extensive, there are challenges and limitations to consider. The accuracy of predictions can suffer in areas with high tree diversity or densely packed crowns, where it becomes more difficult to distinguish between species. Additionally, smaller trees, those that are classified as subcanopy trees, are not well represented in the dataset since they are not included in the aerial predictions.

Moreover, uncertainty exists in labeling and categorizing trees accurately. Errors can result from various factors, including misidentification of tree species or inaccuracies in crown delineation. While the algorithms used have been tested and shown to have a good degree of reliability, the predictions are still just estimates.

#Looking Towards the Future

To improve the quality of data and accuracy of models, ongoing data collection will be essential. Researchers can focus on gathering data for underrepresented species, as they often struggle to be accurately predicted. Targeted sampling, where researchers gather data from specific areas with rarer species, will help enhance the model's knowledge base.

As new researchers arrive at NEON sites, collaboration will foster a richer understanding of forest ecology. Combining the canopy tree predictions with other ecological data will provide deeper insights into the interactions within these systems. This entails connecting the predictions to more detailed environmental data and historical records.

Ultimately, the goal is to advance species predictions to a level where they can be reliably used for broader ecological research. By improving models and understanding the forest ecosystem better, researchers hope to aid in conservation efforts and ecological management on a larger scale.