Forest Health Monitoring is a process used to monitor forest ecosystems by measuring several different indicators such as tree growth, disease and pest outbreaks, and other environmental factors that may have an impact on the overall health of the forest. This information helps to identify potential problems, track changes over time, and inform management decisions.
FHM is an important part of forestry practice and forest conservation efforts worldwide. It can help protect the environment, reduce wildfire risk, and ensure that natural resources are properly managed.
Detection and evaluation of forest health issues is carried out at various scales in the United States through the National Forest Health Monitoring Program, a cooperative, long-term effort. The program’s primary product is information and includes research, analysis, and reporting to support national activities that address forest ecosystems.
There are a variety of tools for forest health monitoring, including aerial surveys, ground-based measurements, and drone technology. However, not all techniques are appropriate for every situation.
For example, in some areas invasive species can disrupt the ecosystem and cause significant damage. In others, drought or other environmental issues can impact the forest’s ability to survive.
In order to understand the causes and effects of forest health, many researchers use field survey methods to collect data on the condition of a given area. This can involve walking through the forest and taking photographs of individual trees or collecting other samples from specific plants and animals to determine how they are growing.
While these methods can be expensive and difficult to cover large areas, they provide a detailed overview of the forest’s health and are valuable for tracking trends over time.
Another approach to monitoring a forest’s health is using remote sensing (RS) technologies. The most commonly used RS sensors include satellite imagery (e.g., Landsat and MODIS) or LiDAR systems that send pulses of NIR light to scan the environment and gather positional information.
The resulting point clouds are then processed to produce high-resolution orthomosaics that display vegetation structure, crown density, and other important forest health parameters. These data can also be used to identify structural changes in a forest that could indicate disease or other problems.
Spectral Information in RS Data
A major part of the FHM community sees spectral information in RS data as one of the most useful indicators for forest health, but a great deal of this information is lost during the photogrammetric process of creating orthomosaics. For this reason, the use of SfM is becoming increasingly popular for creating point clouds and integrating spectral information into point cloud-based FHM applications such as detecting defoliation and crown architecture changes.
Moreover, SfM-based point clouds are particularly useful for monitoring a forest’s structural changes over time and for monitoring the health of trees by linking large-scale environmental gradients to local forests’ composition and structure. This technique can be especially beneficial for the early detection of bark beetle attacks and other stresses on trees.
Although the majority of FHM research has focused on RS data, ground surveys still play a critical role in understanding how stressors interact to affect forest ecosystems. As a result, a general replacement of field surveys for RS-based FHM by UAVs is still far from a feasible goal. Instead, the goal should be to develop a monitoring system that incorporates both RS and in-situ terrestrial information.