There can be a great variation from one place in a forest to another. Soils, moisture, temperature, canopy structure, elevation, vegetation, all can differ over fairly small distances.
That makes forest monitoring difficult.
Field surveys are still one of the most important means of learning what's actually happening on the ground. Walking through a forest, measuring trees, taking samples, and sensing the environment aren't things you can fully replace with remote sensing or GIS technologies.
There are, however, practical limitations to field measurements. Forests occupy the large areas, while environmental conditions change constantly.
So how can forestry, conservation and research groups fill in the forest portrait?
More and more, the answer is adding field observation to environmental sensors, remote sensing, LiDAR, GIS, and data analysis.
Why having one data source is never enough
A field survey can give a lot of detail about a specific site. The hard part is knowing how well that site reflects the whole landscape.
Field crews could be collecting data on tree size, canopy health, soil moisture, temperature or other measures at sites within the refuge. Those numbers matter, but they are specific to times and places.
Other technologies provide different perspectives.
Satellite can give extensive landscape coverage. Drones can provide finer resolution observations at specific locations. In ground sensors can monitor environmental factors over time.
The question of which technology is best is irrelevant, but I think it is more usefully to ask:
How have each of the sources been combined (joined, linked, relinked) for the question.
The aim is not to substitute fieldwork, but rather to integrate various sources of observation, so that the weaknesses of one are compensated by the strengths of another.
What LiDAR brings to the table
LiDAR (Light Detection and Ranging) Is useful when forest structure is important.
Unlike a conventional photograph, LiDAR can produce three-dimensional information about vegetation and terrain. Depending on the system and data quality, this can help characterize canopy structure, vegetation distribution, terrain elevation, and other physical characteristics.
This can be used for purposes such as forest inventory, biomass estimation, habitat analysis and wildfire planning.
It may also provide answers to questions that cannot be adequately answered by two-dimensional imaging.
- Where are dense canopy areas located?
- How does vegetation structure vary across a landscape?
- How does terrain influence forest conditions?
- What are the structural differences that are possibly relevant to habitat?
LiDAR is not the silver bullet for forest monitoring. What it does do is to add another layer of data to field measurements and other spatial data sets.
Why microclimate is important
There isn't just one climate for a forest.
Elevation, slope, canopy density, exposure to the sun, plant growth, and soil properties can all result in significant variations of temperature and moisture even at small distances.
These local variations could be detected by using distributed environmental sensors.
Rather than sampling only intermittent field measurements, networks of sensors are available to document additional conditions - solar radiation, soil moisture, humidity and temperature.
This time-series data provides useful insights into areas and seasons of varying conditions for researchers and land managers.
For example, monitoring might be more effective for questions about: Drought stress, regeneration, conditions in your habitat, and trends in your environment.
A second example is the use of wireless sensor grids to gather information on microclimates within a forest. This technology could add to the information gathered through remote sensing.
More data doesn't automatically lead to better decisions
A crucial difference exists between collecting data and utilizing data.
A forestry group may have satellite data, LiDAR data, field data, sensor data, and GIS data. Each data set can be valuable on its own.
The challenge is connecting them.
When data are held in isolated systems, it makes them harder to compare measurements, recognize patterns, and convert observations into management decisions.
Through GIS platforms, data bases, dashboards, and analytical tools, all the types of data can be integrated.
A useful monitoring workflow should therefore move beyond:
Data More data More data
and instead aim for:
Data Interpretation Decision
The function of monitoring is in the end to enhance understanding and to underpin an informed action.
Where AI fits into the picture
AI & Machine learning can be particularly useful when environmental datasets are so large or complicated they are impossible to process manually.
For instance, models can examine images for signatures of vegetation change, stress, disease, or other disturbance. Trends can be extracted from time-series data that may not be apparent if individual measurements are considered alone.
However, one should never replace ecologically experienced people.
The model might detect the change, but an environmental professional will still need to work out the significance of the change.
Is it drought?
Disease?
Seasonal variation?
A disturbance?
A strange environment?
Or maybe the data is the problem?
Therefore validation by field experts and interpretation by experts are of crucial importance even in the case where automated analysis is involved.
A manageable framework for selecting monitoring technologies
Before implementing new monitoring tools, you can begin by considering the following five essential questions:
What are we trying to measure?
Specify the forest or environmental variable which is in relevant.What spatial scale matters?
Is the question either pertaining to a single stand or site, or the entire landscape?
What temporal resolution is necessary?
Some questions can be measured occasionally, some need to be assessed all the time.Which set of data sources to combine?
Think about if field observations, sensors, satellite images, drones, LiDAR, and GIS data can supplement each other.
- How will information inform a decision?
A monitoring is most informative when the data that it gathers is relevant to the decision that it is meant to facilitate.
It can also deter firms from acquiring extensive data just because of the availability of new technologies.
The future will be hybrid?
Perhaps the most promising advancement in forest monitoring will not be a technology at all.
Perhaps it's the ability to make connections.
Field observations can provide ground-level context.
Sensors can provide continuous local measurements.
Satellite imagery can provide broad coverage.
Aerial photography Drones are capable of capturing detailed images of a specific area.
LiDAR can provide three-dimensional structural information.
GIS can connect spatial datasets.
Analytics can help identify patterns and changes.
These devices combined to offer a more comprehensive picture of forest ecosystems.
This is important because forest management practices today go beyond simply managing for timber productivity. In specific landscapes and situations, monitoring can be used to address questions related to biodiversity, wildfire risk and water, carbon, ecological restoration, and climate adaptation and resilience.
Technology still needs ecological context
More advanced technology does not always lead to better choices about the environment.
Technology becomes valuable when it is chosen to address a particular question.
Prior to the purchase of any sensors, imagery or analytical systems, an organization should establish what they need to be measuring, the purpose of such measurement, how often it will be taken and how it will be validated.
No single index or data source can capture the complexity of a forest.
So it seems that the future of forest monitoring will not be about gathering more data, but rather, about gathering the right information, at the right spatial scale and connecting it in ways that enables people to make sense of what is happening in their ecosystems.
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