Background
Forests are vital ecosystems that support a rich diversity of flora and fauna. They provide essential ecosystem services such as carbon sequestration, climate regulation, and habitat provision. Forests play a significant role in regulating the global carbon cycle and mitigating climate change, making their preservation crucial for the well-being of the planet.
However, rising population pressure is driving land-use change and forest degradation, which has a negative impact on climate regulation and the delivery of ecosystem services. Regular and effective forest monitoring is required to protect these vital functions and ensure sustainable use of natural resources over time.
Traditional methods of collecting forest data, such as conducting field-based surveys, are often labour-intensive, time-consuming, and difficult to implement in large, inaccessible or topographically complex areas. Because of these limitations and high operational costs, traditional approaches are not suitable for large-scale or regular monitoring.
In contrast, remote sensing technologies are gaining popularity as efficient, scalable, and cost-effective alternatives. They enable forest monitoring over a large and consistent area, which has significant implications for environmental management and decision-making at local, regional and global levels.
In recent years, the availability of remote sensing tools and data at the local and regional levels has greatly increased. Previously, forest mappers and managers had to purchase high-resolution mapping-quality satellite images and lacked access to open-source Earth Observation data. Today, a wide range of pre-processed, analysed, global-scale, ready-to-use satellite data and thematic layers are available, along with historical archives for optical and Synthetic Aperture Radar (SAR) imagery. The United States Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), for example, make Landsat data available for free, while the European Space Agency (ESA) provides Sentinel-1 and Sentinel-2 imagery. Similarly, the Japanese Aerospace Exploration Agency (JAXA) provides Phased Array L-band Synthetic Aperture Radar (PALSAR) images. These image resources provide valuable opportunities for researchers interested in forest ecosystem monitoring. However, data selection is largely determined by the study's objectives and scale. Globally available data may not always be appropriate for monitoring forests at the local scale.
Higher-resolution commercial imagery, such as GeoEye, WorldView, and RapidEye, is also available, which can be considered for more detailed forest mapping; however, the cost of acquiring such data can be high. Purchasing high-resolution commercial imagery monthly or annually is extremely difficult in the least developed countries and, at times, even for developed countries. This can raise concerns related to cost-effectiveness, particularly for annual forest monitoring.
In this context, Unmanned Aerial Vehicles (UAVs), also known as drones, have emerged as a viable option for small-scale or localised studies, such as those carried out at the watershed or protected area level. Drones provide flexible, high-resolution data collection tailored to specific research needs, making them an increasingly valuable tool for forest monitoring.
Use of drones in forest monitoring
Public perception and awareness frequently determine the adoption of new technology. Drones have occasionally received a bad reputation due to their association with non-civilian applications. For example, their use in special surveillance and border patrol operations has led communities to perceive them as a conflict tool or privacy intrusion. However, drones have numerous applications, particularly in environmental management and societal benefits such as delivering medical supplies, search and rescue during a disaster situation. Fortunately, many countries in the region have now adopted policies and protocols for the use of drones for various purposes, such as coastal mangrove plantation monitoring and community-based forest monitoring (CBFM) in Bangladesh, estimation of above-ground biomass and carbon stock in Nepal. With clear guidelines for drone flight operations, an increasing number of organisations are starting to use drones as an alternative or supplement to satellite-based remote sensing.
Use of drones to inform policy and planning
Despite this progress, the use of drones for environmental monitoring in the HKH region is still limited due to a lack of technical training, regulatory clarity, and operational capacity. As a result, drones have primarily been used for recreation and filmmaking, despite their significant potential for environmental mapping and disaster management, forest monitoring, and precision conservation planning remains largely untapped. Drone technology could be used for sustainable environmental management in the region with more technical expertise, supportive policies, and local operational capacity.
Their ability to capture high-resolution imagery greatly improves the accuracy of forest mapping and change detection. This level of detail allows for more accurate analysis of forest cover dynamics, which can inform forest management and planning. One critical application is the detection of illegal logging, for which drone imagery provides timely, actionable evidence, especially in remote or inaccessible locations. Drone data can also be used to make maps of the tops of individual trees. This can help us learn about the structure of the forest, how it is changing, and whether the trees are sick. Such drone-based images are critical for research on forest health, quality, and ecosystem resilience and must receive support from the international community through forest conservation-based incentives. Furthermore, drones can help estimate and track above-ground forest biomass and carbon stocks. When combined with National Forest Inventory (NFI) programs, drone data serves as a reliable source for cross-validation and overall forest estimates.
Drones are becoming increasingly important tools in forest restoration programs. Using drones, we can track seedling survival and growth after planting, evaluate the efficacy of restoration interventions, and develop adaptive management strategies. Regular drone-based monitoring ensures that restoration goals are met, and degraded landscapes are on their way to recovery.
Drones also contribute to biodiversity conservation by identifying and mapping habitats critical to various species. High-resolution spatial data helps locate nesting sites, migration corridors, and other ecologically significant features required to protect species and ecosystems.
Drones are extremely useful in wildfire management. They can be used for both pre-fire risk assessments, such as identifying fire-prone areas, and post-fire damage evaluations, providing quick and comprehensive information about affected forest zones.
Considerations and challenges
There are various types of drones equipped with different sensors for remote sensing and mapping, including Red, Green, Blue (RGB) cameras, thermal sensors, Light Detection and Ranging (LiDAR), and multispectral sensors that capture images across bands such as red, green, blue, and near-infrared. Among these, multispectral drones are frequently the best choice for forest monitoring because they provide critical information on vegetation health, forest structure, and degradation. However, the drones and sensors used must be aligned with the monitoring task's specific objectives, such as forest cover mapping, biomass estimation, or degradation assessment.
Weather conditions are critical in drone operations. Flights should be avoided during rain, fog, haze, or strong winds, as these conditions can jeopardise both safety and data integrity. Sunny and clear days make for ideal flying conditions. Signal loss is also possible in mountainous areas with dense forests and complex terrain, which can affect flight stability and data acquisition. To address these challenges, proper flight planning and strict adherence to safety protocols are essential.
Additionally, technical expertise is needed in drone operation, data processing, and geospatial analysis. As a result, strengthening institutional capacity through targeted training and resource development is critical to ensuring the effective use of drone technology in forest monitoring.
When using drones to fly over forest landscapes, it's important to follow all national rules for the country. Failure to follow established drone operation protocols for civilian purposes can have serious consequences. These protocols typically include registering the drone, getting aviation permits, renewing licenses, and letting local stakeholders know, all of which are mandatory to avoid potential problems or misunderstandings. These processes can take a while, so they should be scheduled early to make sure the data gets collected and analysed on time.
Unfortunately, some environmental agencies that buy drones limit their use to basic photography or videography instead of systematic forest mapping and monitoring. Underutilization of such an investment diminishes its overall impact and value. To get the most out of drone technology, there is a pressing need to work on building the skills needed for drone-based mapping and analysis. Machine learning is now more important for processing drone data.
As drone regulations evolve and sensor technologies advance, it is crucial to strategically plan and prioritize skill development. Enhancing our expertise in drone operation and drone-based mapping is essential. Drones offer a novel and more efficient approach for mapping and monitoring forest health. By integrating data into frameworks such as Measurement, Reporting, and Verification (MRV), Reducing Emissions from Deforestation and Forest Degradation (REDD), and the System of Environmental-Economic Accounting (SEEA), we can generate detailed ecosystem maps and comprehensive climate change reports. Leveraging these technologies has the potential to significantly strengthen efforts to protect and restore the environment.
