Forest Vitality

Forest vitality

Development of methodology for assessment of forest vitality index

Every year approximately 783 000 ha of forests in Nordic and Baltic countries suffer from the negative effects caused by insect infestation, a root-rot disease, flooding, drought and other factors. It is estimated, that in European countries the economic loss caused by the root-rot alone is 500 million EUR every year. In Latvia 1100-3500 EUR per hectare annually are lost due to different negative stressors. Therefore, forest owners and managers are still in need for an effective method and tool for the assessment of tree vitality and early detection of trees under stress.

1.image. The image acquired by using LiDAR laser scanner. Spruce are marked by different color points depending on the degree of the rot. Green - 0.degree of rot; pink - 1.degree; yellow - 2.degree; orange - 3.degree of the rot.

1.image. The image acquired by using LiDAR laser scanner. Spruce are marked by different color points depending on the degree of the rot. Green – 0.degree of rot; pink – 1.degree; yellow – 2.degree; orange – 3.degree of the rot.

The Aim

To develop a commercially operational methodology for acquisition, analysis and presentation of forest vitality data based on synergetic use of airborne LiDAR laser scanner and hyperspectral sensors.

Results

The work primarily focused on a methodology for vitality data for Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H.Karst.) based on airborne hyper spectral images and airborne laser scanner data. The data collection, especially the remote sensing data collection was designed and performed to imitate the situation in a true commercial project. A methodology that can be operational in a commercial context was designed. It consists of a step-wise workflow with well-defined work packages.

The overall idea for this methodology is that vegetation indices can be used to indicated health condition by connecting ground truth data for healthy and non-healthy trees with indices values and locking for a subset of indices that can discriminate healthy and non-health trees. Next, apply the selected indices to the whole area to identify the unhealthy trees based on the spectral responses.

2.image. Assessment of tree-crowns (white borders). Trees fixed in the nature field (The trees in green - healthy; in orange - trees with damaged trunks; in pink - trees with damaged roots). Data acquired by using LiDAR laser scanner

2.image. Assessment of tree-crowns (white borders). Trees fixed in the nature field (The trees in green – healthy; in orange – trees with damaged trunks; in pink – trees with damaged roots). Data acquired by using LiDAR laser scanner

Project Duration: 07.2009 – 10.2010

Project Manager: Ulf Söderman, PhD

Project is financed by: European Regional Development Fund (ERDF)

Beneficiary:

Coordinating beneficiary: FORAN Baltic Ltd.

Associated beneficiaries: LLC “ForestOwnersConsultingCenter”, Latvian State Forest Research Institute “Silava”, Institute for Environmental Solutions