Using drones with remote sensing technologies for forest management

Forest Fires

The very destructive forest fires that started on 18 April 2021 on the slopes of Table Mountain in Cape Town South Africa destroyed a number of historical buildings and inevitable brought attention back to responsible forest management practices.

In this case there are 2 main contributing factors:

  1. climate change
    1.  data from the South African Weather Service Cape Point monitoring station show clearly that average temperatures have been rising steadily over the past six decades.
the effect of climate change on forest fires
Rising temperatures in Cape Town
  • The steady increase in hot, dry weather will dry out the vegetation, making it more likely to catch fire, and for fire to spread. This phenomenon is not restricted to the Cape, and has been widely reported in other parts of the world.
  •  Alien trees
    • The introduction of alien trees, which increase the fuel available to burn, and that burn with a much higher intensity than the natural fynbos.

The ability to closely monitor the bio mass of alien vegetation is a key element in forest fire prevention.

Forest Management Applications

How remote sensing technologies make a difference

Remote sensing technologies can enhance forest managers’ ability to manage forestry operations in an increasingly complex and challenging environment

Forest health

Forest health assessments conducted using broad scale aerial reconnaissance and followed by ground truthing exercises to verify the results is limited because it is qualitative, subjective and dependent on the skill of the surveyor

Forest structure

Assessing the structural attributes of even-aged forest plantations is only possible using small-footprint discrete return lidar data.

Forest productivity

A key objective is to predict the leaf area index (LAI) is a key input variable into process based models to rapidly assess forest productivity.

Forest species composition

Multispectral satellite image data were used to carry out genus level classifications in forestry but with  airborne hyperspectral image data to discriminate not only even aged tree species, not only discriminate between different species but also differentiate among the age classes of the same species.

Forest change detection

While medium resolution image data can accurately identify clear-felled stands, high resolution image data captured by remote sensors on an airborne platform, can identify replanted compartments, and the weed status of those compartments.

Micro earth observation satellite bands and applications

Wavelength      Range                           Intended Application

440-510 nm      (blue)                           Water bodies, soil/vegetation, deciduous/coniferous.

520-540 nm      (xanthophyll)               Silt in water and deforested lands, urban areas

520-590 nm      (green)                         Green reflectance peak for plant vigour

620-680 nm      (red)                             Chlorophyll absorption, roads, bare soil

690-730 nm      (red-edge)                    Plant stress

840-890 nm      (near-infrared)            Plant-biomass estimates, water bodies, vegetation

Surveying forest stands and single trees using drones

Using different sensors on airborne platforms tree heights, crown radii and crown base heights can be very accurately derived.

This expands the use of UAVs for forest applications such as forest inventory, forest growth, forest health, forest production, wildlife ecology and forest protection.

The following data can be collected by UAVs with a very high spatial resolution (between one and a few centimetres).

  • Multispectral- and hyperspectral cameras register different wavelengths and can indicate tree condition by characteristics of the tree crown reflection.
  • Cameras, which operate in the visible spectrum, can be used for detailed assessment of forest structure or single tree properties.
  • A laser-sensor on a UAV provides very high point densities and therefore a detailed image of the stand and crown structure as well as the ground surface structure.
  • Thermal imaging cameras register the different thermal reflections and can probably be useful for ecological topics.

The quality of analysis options is influenced by the sensors for positioning and orientation of the platform during data capture. In general: The more precise the data, the better the subsequent analysis can as a high point density is an important factor for successfully deriving single tree attributes. This can only be achieved with UAV based surveying.

UAV LiDAR making a difference in forestry management

The benefits of UAV LiDAR in forest management are wide and varied.

The ability to simultaneously visualize the ground and model the canopy structure provides significant advantages to the forestry industry. Traditionally, foresters and land managers have relied on topographic maps for terrain classification and time consuming field-based surveys to obtain tree volumes and height information. LiDAR data provides significant improvements over both these techniques.

Application Specific benefits of UAV LiDAR for Forest Management

Forest management     

LiDAR data is hugely effective in understanding the forest canopy and terrain, which assists with assessing forest health, calculating forest biomass, classifying terrain and identifying drainage patterns.

Vegetation mapping within forests       

LiDAR data is also used to measure the three-dimensional characteristics of plant canopies and estimate the vertical structure of vegetation communities.

Woodland valuation     

LiDAR data is used to assess the volume and growth rate of trees to calculate the value of the forest.

Unlike other aerial surveys such as photography, LiDAR can ‘see through’ woodland and produce 3D models of the forest:

  • the canopy,
  • the undergrowth
  • the floor itself.

Case study: Forest management with drones in controlling palm oil deforestation of rain forests of Indonesia and Malaysia

Countries around the world are challenged with decreasing deforestation and increasing agricultural productivity on existing farmland to prevent forest clearing for cropland expansion.

It is estimated by the world bank that to meet vegetable oil demand in 2020, would require 6.3 Million hectares of oil plantation. And using soybean oil instead would require an additional 42 Million hectares. An area roughly the size of California.

Forest loss in Indonesia

Growth in palm oil plantations are contributing to a large portion of Indonesia’s forest loss in the last two decades .

Oil palm farming is very labour intensive, providing more jobs per hectare than any other farming, all year round rather than seasonal.

Palm trees yields are achieved at the age of 9-18 after which they gradually decline.

Due to labour shortages the backlog of underperforming trees is growing and the increase in palm oil prices have climbed over recent years, and growers are resisting the option of replacing the old but producing trees in a time of record profits.

The government is committed over US$135 million to jump-start a national oil palm replanting program targeting smallholder producers:

  • replacement rate of 100,000 hectares per annum by providing grants to smallholders covering the cost of replanting.
  • commercial palm oil companies will also replant upwards of 100,000 hectares per annum, resulting in a net 200,000 hectare per annum total replanting scheme.

But there is a labour shortage.

  • Most trees are hand-harvested, fertilized, pruned individually and otherwise cared for by a largely immigrant labour force.
  • Fruit bunches that are left unharvested rot on the trees.
  • The labour pool is roughly 491,000 workers, of which 76 percent were from Indonesia. 
Forest management with drones

UAVs are now also being seen as a critical element in solutions to the Oil Palm/Deforestation problem.

Using sensors on an airborne platform such as multispectral, infrared and thermal sensors, offer the possibility to research such aspects as plant health, offering possibilities for highly targeted responses to disease and growth variations, including the capturing and geo-tagging of high resolution images.