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Airborne Laser Scanning Helps Identify Old-Growth Mediterranean Forests

In an article published in Remote Sensing, researchers used geostatistical analysis, low‐density airborne laser scanning (ALS) metrics, and forest inventory data to estimate oldgrowth indices (OGIs) as indicators of oldgrowth forest conditions.

Study: Identification of Old-Growth Mediterranean Forests Using Airborne Laser Scanning and Geostatistical Analysis. Image Credit: Atstock Productions/

What are Old-Growth Forests (OGFs)?

Old-growth forests are unique and have a high level of biodiversity, which has sparked interest in locating and maintaining them. This has made protecting and conserving old-growth forests a worldwide issue of forest management. However, different researchers use different parameters for defining old-growth forests.

In general, there are four categories for defining old-growth forests: utilizing an economic threshold, emphasizing stand development, minimum age, and emphasizing a lack of human disturbance. Therefore, old-growth forests can be identified by indicators related to the forest's functional and structural aspects, presence of dead wood, natural regeneration, stand dynamics, and age structure.

Old-growth forests are a valuable source of information for restoration, conservation tactics, and sustainable management because they provide crucial information about threats to forest biodiversity throughout the forests' lifespan. Conversions to managed plantations, agriculture or active management, significant disturbances and deforestation have all been linked to a decline in these rare forests around the globe.

European Forests Situation

Most of Europe's forests are now classified as seminatural, with just four percent of the continent's total forest area consisting of natural forests. In addition, just 0.7 percent of European forests are considered undisturbed, defined as forests that have not been altered and are home to natural species, and 46 percent are strictly protected.

Given forests' complexity, geographical variety, and developmental phases, an ecological understanding of old-growth forests needs a multiscale approach, from individual trees to landscape sizes.

Old-growth forests also contribute significantly to the fight against climate change since they store more carbon per unit area than any other successional stage and continue sequestering carbon for extended periods. Old-growth forests may also provide useful information on the adaptability of forest ecosystems to climate change.

Aerial Laser Scanning (ALS)

The optimal old-growth index (OGI) for each forest species may be determined, and its prediction using spatial modeling might help locate old-growth forests in the landscape. These old-growth indices and mapping of old-growth forests may be produced by combining them with methods such as aerial laser scanning (ALS).

Several attributes related to old-growth forests description, such as forest successional stages, structural canopy complexity, forest canopy gaps, stand age, and standing dead tree class distributions, can be estimated using aerial laser scanning, which provides data that can be used to predict the 3D structure of vegetation at various scales. However, fewer studies are documenting and mapping old-growth forests with aerial laser scanning, and none have targeted the Mediterranean ecosystems.

Research Area

This study aimed to identify the old-growth forests' structural attributes, select the best old-growth forest via field‐measured stand variables, and model the selected old-growth forest via aerial laser scanning data.

The researchers chose the southwest of the Cazorla Mountains, within the Cazorla, Segura, and Las Villas Natural Park, northwest of the Andalusian region. The research location has a Mediterranean climate with an 11.7 °C average temperature and 1100 mm per year average rainfall.

The Navahondona forest in the Natural Park was chosen as a test site, and the researchers concentrated on the management units of the forest where Pinus nigra is the predominant tree species.

Old‐Growth Indices Calculations and Geostatical Modeling

The study utilized information from management plans on the forest inventory circular plots of 148 m2, evenly spaced out in a grid with 200 m sides, to compute the old‐growth Indices. Prefield screening was not carried out to see if the plots were in forested areas. Every tree's total height (HT) and diameter at breast height (DBH) were included in the inventory.

The researchers examined the geographical correlation of old‐growth indices data in the pilot region after choosing the optimal old‐growth index. The OGI had three stand structure components representing forest variations over various successional stages.

To examine the importance of spatial relationships, they utilized a linear mixed model using the inventory plots' orientation, slope, and height, as model variables. After that, aerial laser scanning data was utilized to calculate height distribution and canopy cover-related metrics.


Using the geostatistical old-growth forest model, the study provides a map of the OGFs' distribution in which the placement of old-growth stands was unrelated to slope, exposition or height. Therefore, the geostatistical model's spatial prediction of OGIs is a solid starting point for identifying old growth features in forested regions.

The aerial laser scanning model showed that the second-order moment and 95th percentile for height were the key factors associated with old-growth features. The low density of the aerial laser scanning data hindered the performance of the aerial laser scanning model. However, it is anticipated that this situation will soon be resolved by integrating data with greater resolution.

The researchers anticipate that the technique and process described in this study can be used to identify old-growth forests of additional conifer and broad-leaved species in other ecosystems, track changes in OGFs' characteristics over time, and determine the most important old-growth forests to conserve and manage.


Andrea Hevia, Anabel Calzado, Reyes Alejano and Javier Vázquez‐Piqué (2022) Identification of Old-Growth Mediterranean Forests Using Airborne Laser Scanning and Geostatistical Analysis. Remote Sensing.

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Taha Khan

Written by

Taha Khan

Taha graduated from HITEC University Taxila with a Bachelors in Mechanical Engineering. During his studies, he worked on several research projects related to Mechanics of Materials, Machine Design, Heat and Mass Transfer, and Robotics. After graduating, Taha worked as a Research Executive for 2 years at an IT company (Immentia). He has also worked as a freelance content creator at Lancerhop. In the meantime, Taha did his NEBOSH IGC certification and expanded his career opportunities.  


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