In the heart of Europe’s most densely forested nation, an invisible sentinel never sleeps. High above the pine canopies and misty river valleys of Latvia, a constellation of European Space Agency satellites sweeps overhead at 786 kilometres, capturing images in thirteen spectral bands every five days. On the ground or rather, beneath the clouds algorithms trained on millions of pixels are reading the land in wavelengths no human eye can perceive, scanning for the faintest chemical whisper of a tree under stress: the imperceptibly fading green of a spruce whose bark has been breached, the subtle reddening of waterlogged roots, the infrared signature of a smouldering ember not yet visible as smoke.
Latvia is, by any measure, a forested country. With forests covering more than 50% of the country’s territory almost double the world average Latvia is one of the most forested EU member states, with a standing volume of 677 million cubic metres reached over the last 80 years. These are not merely scenic assets. Forest product exports were worth €3.3 billion in 2023, comprising 17.3% of Latvia’s total export value, making forestry the nation’s leading export sector. The health of every hectare, every stand, every individual spruce tree is therefore both an ecological and an economic imperative and Latvia is now deploying a technologically remarkable arsenal to protect it.
Bark beetle outbreaks remain invisible until forests begin to collapse
To understand why Latvia has invested so aggressively in AI-driven forest surveillance, one must first appreciate the biological adversary at the centre of the drama: Ips typographus, the European spruce bark beetle. Small enough to fit on a fingernail, this insect is capable of killing trees across thousands of hectares in the time it takes a forest manager to file a survey report.
In 2023, an emergency situation was declared in Latvia due to the massive infestation of the bark beetle in Latvian forests. The larvae of this small insect feed on the inner layer of spruce bark where organic matter is transported, and damage to this part of the plant permanently weakens the tree. The damage caused by the bark beetle has increased particularly as a result of climate change and the intensification of forestry.
The insidious genius of this insect lies in its temporal invisibility. In the early stage of infestation the beetles are undergoing development under the bark, and symptoms are hardly visible. Symptoms are mainly visible at the latest stage of infestation, when the hosts are usually already abandoned by the beetles and it is too late for management. Remote sensing is the approach with the highest potential to overcome the limitations of field surveys, and its importance in bark beetle detection is gradually growing. The applicability of remote sensing relies on significant differences in spectral reflectance of healthy and infested trees.
The consequences of delayed detection are severe. In Central Europe, bark beetle-induced forest degradation since 2018 caused approximately 110,000 hectares of mainly spruce stands to be clear-cut in Thuringia, Germany alone. Across the region, salvage felling volumes climbed from under 3 million m³ per year before 2015 to approximately 22 million m³ in 2019 an eightfold increase in four years, directly linked to drought and above-average temperatures. Latvia watches this trajectory with alarm.
Latvia’s State Forests Strike Back: The Algorithm Approach
Due to the increase in damage by the eight-toothed spruce bark beetles in the managed territory, Latvia’s State Forests (LVM) started work on the development of an algorithm for identifying potentially damaged spruce stands, so that in the long term economic activity could be planned to limit damage by bark beetles in the designated stands. The algorithm is based on high-resolution Sentinel-2 satellite images covering the entire territory of the country.
The data collected by VMD in November 2024 were published in the State Forest Register as a separate layer titled “2024 Detected Potential Damage in Spruce Stands.” To make the information about the algorithm-identified threatened or damaged forest stands available to as many forest owners as possible, LVM published the data in the “LVM GEO Mobile” application and in the map viewer solution at lvmgeo.lvm.lv.
As Edijs Leišavnieks, Head of Forest Protection and Fire Safety at LVM, explained: “The information generated by the algorithm about potential damage to spruce stands is primarily informative. The data represent changes in various vegetation indices of specific spruce stands over several years, indicating changes in the vitality of the stand. These changes can be caused by spruce bark beetle damage, increased water levels in the forest, windthrow, partial forestry activities, and other factors.”
Crucially, the algorithm displays potential damage locations in the forest, which may be individual groups of 5 to 10 trees or can cover areas up to 7 hectares, indicating long-term damage to the forest stand over several years.
In parallel, Latvian State Forests has adopted CollectiveCrunch’s early-stage bark beetle detection solution. CollectiveCrunch has recently adopted its solutions to address the Baltic forestry context, with very good results. Bark beetle outbreaks have become a devastating problem in Central Europe and increasingly in the Nordics. Understanding the risks and responding to outbreaks as early as possible can protect forests and maintain forest values.
How Satellites Read a Dying Tree
The technical foundation of Latvia’s approach is vegetation index analysis derived from multispectral satellite imagery. Among the most powerful of these indices is the Normalised Difference Vegetation Index (NDVI), which exploits the contrast between how healthy vegetation absorbs red light and reflects near-infrared (NIR) radiation. A healthy spruce is a strong NIR reflector; a beetle-infested spruce progressively loses this capacity as its needles die.
A landmark 2024 paper by Latvian and international researchers published in the peer-reviewed journal Forests provides granular evidence of this approach working at landscape scale. Research developed a Long Short-Term Memory (LSTM) Autoencoder model for real-time anomaly detection with 10-metre spatial resolution applied to Sentinel-2 data. The best model achieved a detection accuracy of 87% on test data and was able to detect 61% of all anomalies at a very early stage more than a month before visible signs of forest degradation. Critically, the approach uses unsupervised deep learning independent from labelled training data, making it accessible for a wide range of users.
AI DETECTION ACCURACY TABLE
| Method | Platform | Best Season | Accuracy |
|---|---|---|---|
| NDVI Threshold | UAV / Drone | Any (leaf-on) | 97% |
| NDVI Binary Mask | Sentinel-2 | June–July | 85% |
| LSTM Autoencoder | S-2 Time Series | Multi-temporal | 87% |
| Tree Species (RF) | S-2 (3 images) | Multi-temporal | 92–94% |
| Green Attack | UAV Multispectral | Weeks 5–10 | ~90% |
ForestRadar: Latvia’s Commercial Intelligence Layer
Operating in parallel to state forestry management is a commercially innovative company that has quietly become one of the most technically sophisticated forest surveillance providers in Northern Europe. Baltic Satellite Service (BSS), founded in 2009 with its office premises in Riga, Latvia, develops proprietary ForestRadar technology for large forest management companies to monitor clear-cut processes, windfalls, floods and fires using both optical and radar satellite imagery. Commercial services have been provided since 2018.
High-quality and guaranteed weekly data updates regarding new clear-cuts and windfalls starting from 0.25 ha are provided with proven detection accuracy exceeding 95%. Alerts with detected changes are sent via email and the result is delivered as a change polygon in ForestRadar’s web application. API integration is available for easy incorporation into existing management systems.
BSS delivers and maintains the weekly/monthly forest alert service covering clear-cuts, windfalls, fires, and floods, as well as property status detection to forest customers via app.forestradar.com, flood.forestradar.com, and fire2.forestradar.com.
Baltic Satellite Service has developed fully automated technology for Sentinel-1 and Sentinel-2 imagery processing to provide immediate access to each image for further analysis, applying machine learning algorithms to provide fast access to forest alert data on changes including clear-cuts, wind-falls, and high moisture levels based on free and open Copernicus data.
For fire specifically, the Fire Monitoring App leverages cutting-edge technology to offer a robust solution for early wildfire detection and management. By integrating terrestrial and satellite image processing, it provides a suite of tools designed to enhance forest fire preparedness and response capabilities. Developed during a three-year Eurostars project, the app represents a successful collaborative effort between Baltic Satellite Service, AD TELECOM (Spain), and AXSYSNAV (France).
When Drones Become the Eyes on the Ground
Satellites excel at landscape-scale surveillance but are physically limited in resolution. A Sentinel-2 pixel covers 100 square metres. Individual trees particularly in the critical early weeks of infestation — fall below the detection threshold. This is where unmanned aerial vehicles (UAVs) enter the surveillance hierarchy.
Unmanned Aerial Vehicles (UAVs, e.g. drones) are often used to acquire spectral information at very high spatial resolution, to be able to isolate trees and observe the spectral changes at single tree level. Remote sensing approaches allow for the monitoring of vast areas, yet these techniques may be less sensitive to the earliest and most subtle symptoms of infestation, particularly when images have coarser spatial resolution.
Studies analysing an extent of 250 ha of forest using time series of UAV-borne spectral reflectance data focusing primarily on early-stage bark beetle detection are extremely rare. This is, among other things, caused by the solar noon conditions allowing only a limited time for acquisition within a day. A fixed-wing UAV system can cover approximately 400 ha in 4 hours.
A 2025 study published in Frontiers in Forests and Global Change extended this approach to large areas of the Italian South-Eastern Alps, demonstrating early detection of Ips typographus infestations using high-resolution UAV multispectral images with single-tree-level analysis, providing useful guidance for management of areas suffering pest outbreaks. The effective performance of this task on large forest areas remains a challenge, and investigating factors that can affect infestation symptom development and enable healthy trees to be separated from trees at the early stage of infestation is extremely important for effective management of epidemic populations.
In perhaps the most operationally innovative development in this field, researchers and companies have begun developing coordinated multi-drone systems that detect and respond in a single mission: a detecting drone equipped with a multispectral camera identifies infested trees while flying above the forest canopy, while a coordinated marking drone follows to physically flag those trees for ground crews compressing the response time that has historically allowed infestations to spread unchecked.
The Third Threat: Flooding and Latvia’s 100,000 Beavers
Bark beetles are not Latvia’s only AI-monitored adversary. Among the more unusual dimensions of the country’s forest health challenge is a problem of excess water and its primary cause: the European beaver (Castor fiber). Excess water is the third most common cause of forest damage in Latvia. When trees not adapted to flooding are submerged for extended periods, they weaken and die and Latvia’s beaver population is estimated at approximately 100,000 animals spread across the entire country.
To address this, Baltic Satellite Service partnered with FruitPunch AI and SUN-Space Hub Network on a machine learning challenge dedicated to flood detection in Latvian forest satellite data. The goal was to build a computer vision model capable of detecting excess water in forests from satellite data, combining open satellite imagery with meteorological data and registry information on forest clearings delivering a real-time map of Latvia with highlighted risk areas, accessible to forest managers via an HTTP API.
📊 PRIMARY FOREST DAMAGE CAUSES BAR CHART
Fire from the Sky – AI Against Forest Conflagration
While pest management dominates Latvia’s current AI deployment, fire detection represents the field with the most rapidly advancing scientific literature. Early detection of wildfires is crucial due to its potential to prevent the rapid spread of fire, protect lives through timely evacuation efforts, preserve biodiversity by minimising habitat destruction, maintain air quality by reducing smoke emissions, mitigate economic losses by minimising property damage and firefighting costs, and contribute to climate change mitigation by reducing greenhouse gas emissions.
Global climate change has triggered frequent extreme weather events, leading to a significant increase in the frequency and intensity of forest fires. Traditional fire monitoring methods such as manual inspections, sensor technologies, and remote sensing satellites have limitations. With the advancement of drone technology and deep learning, using drones combined with artificial intelligence for fire monitoring has become mainstream.
The dominant deep learning architectures for fire detection are variants of the YOLO (You Only Look Once) family of object detectors. A 2025 study in Scientific Reports proposed an improved YOLOv8-based model for drone-based fire detection in complex forest environments. The model incorporates local convolution instead of full convolution in the C2F module and integrates the EMA module to enhance feature channel interaction modeling capability and contextual information utilization, thereby reducing model complexity and increasing efficiency. The AgentAttention module was introduced in the Backbone to address the risk of false positives and missed detections caused by vegetation, terrain, and lighting changes in forests.
A parallel 2024 study in Remote Sensing in Earth Systems Sciences took a hybrid approach. The proposed forest fire hybrid detection model (FFHDM) combines Random Forest, Support Vector Machine, and Convolutional Neural Networks to enhance detection capabilities. Landsat satellite images serve as the primary data source, offering high spatial resolution crucial for detailed land cover analysis and long-term monitoring.
For active wildfire detection via satellite, a 2025 study in Natural Hazards confirmed that machine learning algorithms trained on large datasets of labelled imagery learn to differentiate between normal environmental variations and the distinctive signatures of wildfires, enabling them to detect fires in near real-time and provide early warnings to emergency responders. One of the key advantages of machine learning-based wildfire detection systems is their ability to adapt and evolve over time.
📊 AI FIRE/SMOKE DETECTION ACCURACY CHART
(Drone, Sci. Reports 2025)
(Satellite + UAV)
(RF + SVM + CNN)
(Satellite Thermal)
Inspection (baseline)
Integrated satellite and AI systems form a Baltic monitoring network
Latvia’s forest AI system is not operating in isolation. The country has positioned itself within a broader Baltic and European space data ecosystem, drawing on Copernicus satellite infrastructure, ESA funding mechanisms, and cross-border research collaborations to punch significantly above its weight for a nation of fewer than two million people.
Technology Stack Overview:
🛰️ Sentinel-1 & -2 Data Fusion — Combines radar (SAR) and optical multispectral data for cloud-penetrating, all-weather monitoring across Latvia’s forests. The Sentinel-2 constellation, operated under the European Space Agency’s Copernicus programme, provides 10–60 metre spatial resolution with a 5-day revisit cycle.
🤖 Machine Learning Change Detection — Algorithms trained on Copernicus data detect clear-cuts, windfalls, floods, and fire at weekly intervals with >95% accuracy. Deep learning models including LSTM Autoencoders detect anomalies more than a month before visible degradation.
📡 API-First Architecture — Alert data delivered via HTTP API, enabling seamless integration into existing forest management software and state registries including the State Forest Register.
🚁 UAV Dispatch Guidance — Satellite-flagged areas trigger targeted drone deployments for high-resolution single-tree-level confirmation, creating a tiered surveillance hierarchy.
📊 LATVIA FOREST AI TIMELINE
Europe Scales AI Forest Surveillance Through the FORSAID Programme
Latvia’s work sits within an even larger continental initiative. The European Commission’s Horizon Europe research programme has funded FORSAID (Forest Surveillance with Artificial Intelligence and Digital Technologies), a project with an explicit mandate to scale AI-driven forest protection across EU member states.
FORSAID’s overall goal is to develop a comprehensive forest surveillance system, with forest trees increasingly threatened by invasive pests many of them regulated in EU territory in the context of the European Green Deal’s objectives. The project architecture integrates satellite data streams, drone networks, IoT ground sensors, and predictive AI models into a unified surveillance layer, essentially a prototype for what pan-European forest intelligence could look like by 2030.
The Carbon Dimension: Why AI Forest Monitoring Is Financially Revolutionary
Beyond pest management and fire prevention, there is an emerging economic dimension to AI forest surveillance. As carbon markets develop, the ability to verify forest carbon stocks with precision and to detect losses in near-real time becomes essential infrastructure for a functioning carbon credit system.
With increasing CO₂ market activity, forest owners will soon trade carbon credits based on their forests’ carbon-binding capacity. Only well-maintained, verified forests are eligible making the adoption of AI monitoring tools not just an ecological imperative but an economic one. AI is already being applied globally to carbon-emission tracking, deforestation and land-use analysis, detecting changes in forest cover, identifying illegal logging, and monitoring agricultural expansion.
In March 2025, Planet Labs which operates one of the largest continuous Earth observation datasets ever assembled partnered with Anthropic’s Claude to analyse geospatial satellite imaging data, combining daily geospatial data with advanced AI reasoning and pattern-recognition capabilities to analyse complex visual information at scale. This partnership signals how large-scale commercial AI and satellite data are converging toward precisely the kind of near-real-time planetary forest monitoring that Latvia has been building at national scale.
Remote Sensing Remains Limited by Resolution and Environmental Noise
Latvia’s forest AI story is compelling, but rigorous assessment demands attention to its limitations. The scientific literature is candid about these.
Cloud cover pervasive in Baltic climates periodically interrupts satellite observation windows, requiring fusion with radar (SAR) data that carries its own interpretation challenges. The Sentinel-2 spatial resolution of 10 metres limits single-tree detection, requiring UAV deployments for the most critical early-stage assessments.
The use of UAV high-resolution imagery presents some limitations when performing early detection over larger areas, including possibly different light conditions during drone flights and variable conditions between different flights.
LVM acknowledges there may be inaccuracies in data calculated by the algorithm, primarily due to changes in other tree or shrub species and ground vegetation. Therefore, LVM encourages forest owners to carefully examine their territory using current orthophoto maps and then make a decision about conducting on-site inspections.
The LSTM Autoencoder approach was able to detect 61% of all anomalies at a very early stage, meaning 39% of early-stage outbreaks still went undetected underscoring that no single method yet provides complete coverage.
The overarching limitation is one common to all remote sensing: an algorithm cannot substitute for the ecological knowledge of a forester standing at the base of a tree. What these systems can do and do remarkably well is direct where that forester should stand.
Multi-layer Monitoring Is Redefining Modern Forest Protection
Latvia’s experience carries lessons that scale far beyond its boreal borders. The country has demonstrated that a medium-sized economy, deploying existing open-access satellite infrastructure (Copernicus is free to use), can build world-class forest surveillance capabilities through investment in machine learning expertise, cross-sector collaboration, and a willingness to publish findings that benefit the broader scientific community.
The hierarchical surveillance model satellites for landscape-scale weekly monitoring, UAVs for stand-level confirmation, ground crews for intervention has become a template adopted by researchers from Italy to Finland to the Czech Republic. Latvia was not the inventor of this stack, but it has been among its most systematic implementers, with state forestry management, commercial satellite companies, academic institutions, and European space agencies all aligned behind the same objective.
The forest of the near future will not be a passive landscape. It will be an instrumented, monitored, algorithmically analysed ecosystem one in which the first sign of disease, flood, fire, or pest is detected by a satellite passing in silence overhead, confirmed by a drone dispatched automatically to the GPS coordinates, and actioned by a ground crew whose phones already show them which trees to cut before they have left the road. Latvia, improbably, is already living that future.
References
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[2] Bozzini V et al. (2025). Multispectral drone images for the early detection of bark beetle infestations: assessment over large forest areas in the Italian South-Eastern Alps. Frontiers in Forests and Global Change, 8:1532954.
[3] Puchała M, Barga I et al. (2024). Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection. Forests, 15(4), 644.
[4] Kirsch M et al. (2025). Early Detection of Forest Calamities in Homogeneous Stands Deep Learning Applied to Bark-Beetle Outbreaks. arXiv, 2503.12883.
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[6] Lakshmanaswamy P, Sundaram A, Sudanthiran T. (2024). Enhancing Forest Fire Detection and Prevention Through Satellite Data and Machine Learning. Remote Sensing in Earth Systems Sciences, 7, 472–485.
[7] Seydi ST et al. (2025). Active wildfire detection via satellite imagery and machine learning: an empirical investigation of Australian wildfires. Natural Hazards, Springer.
[8] Birinyi E et al. (2024). Forest Disturbance Monitoring Using Cloud-Based Sentinel-2 Satellite Imagery and Machine Learning. Journal of Imaging, 10(1):14.
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[10] European Commission CORDIS. FORSAID Forest Surveillance with Artificial Intelligence and Digital Technologies. Horizon Europe Project ID 101134200.
[11] Latvia’s Investment and Development Agency. Forest Industry: Key Facts and Figures.
[12] ResearchLatvia.gov.lv. Latvia’s State Forests Develops an Algorithm for Damage Detection in Spruce Stands. (2024).
[13] Baltic Satellite Service / ForestRadar. Technology Overview. Latvia Space Agency Directory.
[14] Latvia Space Science. AI for Earth 2: Forest Health Challenge.
