Accurate estimates of tree cover are critical for forest resource assessment, timber inventory management, and carbon stock monitoring. However, traditional methods of counting trees, such as manual surveys or aerial photography, can be time-consuming, labor-intensive, and prone to error. Recent advances in artificial intelligence (AI) offer new opportunities to automate and improve tree counting in diverse forest ecosystems. This blog post provides an overview of AI techniques for tree counting and assesses their potential to expand forest monitoring efforts.
Read more: The Surprising Value of Plant Counting
Counting trees is critical for quantifying aboveground biomass, modeling carbon budgets, and making informed forest management decisions. However, manual counting of individual trees over large areas is generally not feasible, while visual interpretation of aerial imagery is inefficient and subject to observer bias. Lidar and other active remote sensing technologies can estimate tree density, but remain costly at regional scales. Computer vision and machine learning algorithms offer promising new tools for analyzing forest structure from both passive and active remote sensing data sources.
Importance of Counting Trees
Accurate tree counts enable the calculation of key forest metrics such as stand density, basal area, and biomass. These underpin timber inventories, carbon accounting, fire modeling, and biodiversity monitoring. For example, combining tree count data with allometric equations and wood density allows estimation of carbon stored in aboveground woody biomass. This is critical for carbon market programs such as REDD+ that aim to mitigate climate change by avoiding forest loss and degradation.
At larger scales, repeated tree counts using statistical sampling can track changes in forest cover, productivity, and disturbance effects. By quantifying tree mortality, recruitment, and growth rates, managers can assess sustainability, plan harvest cycles, and evaluate silvicultural practices. Without accurate tree population data, sustainable forest management is virtually impossible.
Historically, ground-based surveys have provided estimates of the number of trees in local stands. However, complete systematic inventories over large regions are prohibitively time and resource intensive. Manual interpretation of aerial photographs also requires considerable expertise and is prone to subjective bias. These limitations motivate the development of automated AI techniques.
How Do You Count Trees?
There are several standard approaches to quantifying tree populations, with trade-offs between precision, cost, and feasibility at different scales. Field sampling using fixed-area plots is the basis of most forest inventory programs. Statistical methods such as distance sampling are then used to scale plot data to larger areas. However, ground-based techniques remain impractical for continuous regional monitoring.
Aerial photography enables efficient sampling across broader extents. Stereoscopic photo interpretation facilitates tree detection based on height and crown dimensions. However, occlusion and interpreter bias limit accuracy. Photogrammetric techniques can estimate tree heights but not necessarily distinct stems. Lidar provides more direct 3D structure measurements via ranging laser pulses. However, airborne acquisition costs currently constrain Lidar’s scalability.
Satellite imagery facilitates wall-to-wall regional mapping but lacks fine details for distinguishing individuals. Pixel sizes of 1-5 meters are typical for commercial systems. No single existing method satisfies all data needs across scales from stands to national inventories. This motivates the development of automated AI techniques that can synergistically integrate diverse data streams.
Counting Trees using AI
A range of AI techniques leveraging both passive optical data and active lidar show promise for automated counting trees. Here we highlight some leading examples:
Machine Learning Image Classification
Recent studies have applied convolutional neural networks (CNNs) to classify overhead imagery into forest/non-forest or estimate tree density and crown size distributions. Data scientists trained a CNN on National Agriculture Imagery Program (NAIP) data across multiple biomes. They found it significantly improved tree crown delineation compared to traditional classifiers. Such semantic segmentation facilitates tree counting but depends on very high-resolution imagery.
Object Recognition
Object detection models like You Only Look Once (YOLO) can pinpoint individual trees in aerial or satellite photos. YOLO trained on plantation images, achieving over 90% accuracy. However, performance declined on novel test sites, indicating challenges generalizing across environments. Data augmentation via rotations and added noise can enhance model transferability.
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Active Machine Learning
Lidar provides key structural parameters like canopy height and density, even under vegetation. This active 3D information powerfully complements passive optical data. For example, in 2015, Lidar and aerial imagery combined to segment individual trees with 85% accuracy. The fusion of lidar, optical, and radar data is a promising area of research.
Recent studies also apply unsupervised learning to cluster Lidar returns into individual crowns based on tree architecture. Supervised lidar analysis can also classify returns as tree vs. non-tree points and inform optical image analysis.
3D Deep Learning
New 3D convolutional networks process aerial LiDAR scans and stereo imagery to reconstruct detailed 3D models of trees for trait analysis beyond just crown dimensions.
Multispectral Analysis
Combining optical imagery with infrared, LiDAR, and radar data provides a rich spectrum profile of vegetation for robust tree sensing under variable conditions.
Counting Trees with Machine Learning
Recent computer vision and machine learning breakthroughs offer new tools to revolutionize tree counting from overhead imagery. Machine learning uses statistical algorithms and large labeled datasets to build models that can analyze complex patterns in data. These powerful predictive models are ideal for tackling challenges like identifying tree crowns against diverse background landscapes.
Two main approaches exist: supervised learning uses input data labeled by humans to train models, while unsupervised learning finds hidden patterns in unlabeled data. In supervised learning, the model learns relationships between input image features and output labels indicating the presence or location of a tree. After sufficient training, the model can classify new unlabeled images.
For tree counting, common supervised tasks include image classification to predict if an image patch contains a tree, object detection to pinpoint tree locations within an image and semantic segmentation to delineate tree crown boundaries at the pixel level. Deep learning neural networks now tackle these challenges more accurately than previous techniques.
Data Collection
Remote sensing provides essential data to feed counting tree models. Satellite multispectral imagery offers regional coverage but lacks fine details. Very high-resolution systems like GeoEye provide about 0.5m pixels capable of capturing some crowns. However, frequent revisit cycles to refresh imagery remain costly over large areas.
Low-altitude aerial imagery from drones, planes, or helicopters delivers sub-meter resolution ideal for identifying individuals. However, the limited field of view constrains coverage. Laser scanning yields 3D structures. Lidar penetration capabilities help overcome occlusion. However, airborne acquisition costs are still prohibitive for systematic regional monitoring.
Ground-based Lidar, camera arrays, and terrestrial laser scanning provide high-precision references to calibrate airborne estimates and provide training data. However, in situ collection is sparsely distributed. Fusing these diverse sampling techniques allows for leveraging their complementary strengths.
Advantages of AI Tree Counting
Automated AI analysis offers numerous potential advantages over manual interpretation for counting trees, including:
- Much faster processing of huge volumes of overhead imagery
- Lower costs by reducing or eliminating manual labor
- Higher consistency without subjective human bias
- Scalability across regions and repeat sampling over time
- Increased accuracy via data fusion and integration
By improving counting efficiency, AI can support higher-resolution monitoring to inform carbon accounting, biodiversity assessment, and sustainable forest management. However, some key challenges remain.
Limitations and Future Work for Counting Trees
While AI clearly presents new opportunities for counting trees, several limitations and areas for improvement remain. Many recent studies have focused on simplistic plantation settings. Performance can degrade in complex natural forests with high biodiversity, dense understory vegetation, or uneven age structure. Interpretability continues to be a challenge for deep neural networks. Diagnosing and correcting model errors is difficult.
Data availability and quality remain primary constraints. Well-annotated optical, LiDAR and field data across diverse biomes is essential for training robust models. But accumulating sufficient reference data through manual image labeling and field sampling requires major time and resource investments. Data fusion techniques help maximize information yield from limited samples. But airflow distortion in LiDAR and sun-sensor geometry effects for optical data can introduce artifacts that complicate analysis.
Future priorities include aggregating larger diverse training datasets, testing transferability across sites and conditions, and exploring optimal sensor and data fusion strategies. Ensemble approaches that integrate outputs from multiple models and data sources could improve consistency and accuracy. Canopy penetrating LiDAR may have advantages for occlusion prone regions. Fully automated workflows from data pre-processing through analysis will be needed for operational implementation. And continued benchmarking on field data will be critical for quantifying uncertainties.
Conclusion
Advanced AI techniques show tremendous promise to reinvent counting trees but are not yet ready for full operational deployment. Priorities for unlocking their potential include expanding labeled training data resources across diverse forest ecosystems, improving model transferability, and sustained calibration and validation using field measurements. Combined with new satellite systems and drone data acquisition, AI-based counting trees could transform twenty-first-century forest monitoring. Realizing this future requires cross-disciplinary collaboration between remote sensing experts, ecologists, and AI researchers to develop robust integrated solutions.
