The Finnish National Forest Inventory

Tomppo, Erkki

doi:10.1007/1-4020-4381-3_11

Cited by 49 publications

(36 citation statements)

References 146 publications

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“…The data utilised in this study was based on the Finnish National Forest Inventory Korhonen et al, 2001;Tomppo, 2006). The measurements in the inventory were done from systematically located rectangular or L-shape clusters, each cluster containing 10-18 sample plots.…”

Section: Sample Plot Datamentioning

confidence: 99%

Climate Change and Energy Systems

Björnsson¹,

Þorsteinsson²

2012

TemaNord

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Section: Sample Plot Datamentioning

confidence: 99%

Climate Change and Energy Systems

Björnsson¹,

Þorsteinsson²

2012

TemaNord

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“…Defoliation level was visually estimated using the same procedure utilized by the National Forest Inventory (NFI) of Finland [49]. However, visual interpretation could easily cause deviation in the results if the surveyors were not professionals.…”

Section: Rfmentioning

confidence: 99%

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Kantola

Vastaranta

et al. 2010

Remote Sensing

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Climate change and rising temperatures have been observed to be related to the increase of forest insect damage in the boreal zone. The common pine sawfly (Diprion pini L.) (Hymenoptera, Diprionidae) is regarded as a significant threat to boreal pine forests. Defoliation by D. pini can cause severe growth loss and tree mortality in Scots pine (Pinus sylvestris L.) (Pinaceae). In this study, logistic LASSO regression, Random Forest (RF) and Most Similar Neighbor method (MSN) were investigated for predicting the defoliation level of individual Scots pines using the features derived from airborne laser scanning (ALS) data and aerial images. Classification accuracies from 83.7% (kappa 0.67) to 88.1% (kappa 0.76) were obtained depending on the method. The most accurate result was produced using RF with a combination of data from the two sensors, while the accuracies when using ALS and image features separately were 80.7% and 87.4%, respectively. Evidently, the combination of ALS and aerial images in detecting needle losses is capable of providing satisfactory estimates for individual trees.

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“…As such, capturing factors that perturb the capacity to accumulate fiber, such as defoliation, is increasingly important. The Finnish Forest Institute carries out the National Forest Inventory (NFI) [17], through which information on forest health is collected and monitored on a strategic level. While useful for some purposes, more precise (spatial, temporal, and categorical) information on forest disturbances is required to meet the current and growing needs of forest health monitoring.…”

Section: Introductionmentioning

confidence: 99%

Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR

Vastaranta

Kantola

Lyytikäinen‐Saarenmaa

et al. 2013

Remote Sensing

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Abstract:The mapping of changes in the distribution of insect-caused forest damage remains an important forest monitoring application and challenge. Efficient and accurate methods are required for mapping and monitoring changes in insect defoliation to inform forest management and reporting activities. In this research, we develop and evaluate a LiDAR-driven (Light Detection And Ranging) approach for mapping defoliation caused by the Common pine sawfly (Diprion pini L.). Our method requires plot-level training data and airborne scanning LiDAR data. The approach is predicated on a forest canopy mask created by detecting forest canopy cover using LiDAR. The LiDAR returns that are reflected from the canopy (that is, returns > half of maximum plot tree height) are used in the prediction of the defoliation. Predictions of defoliation are made at plot-level, which enables a direct integration of the method to operational forest management planning while also providing additional value-added from inventory-focused LiDAR datasets. In addition OPEN ACCESSRemote Sens. 2013, 5 1221 to the method development, we evaluated the prediction accuracy and investigated the required pulse density for operational LiDAR-based mapping of defoliation. Our method proved to be suitable for the mapping of defoliated stands, resulting in an overall mapping accuracy of 84.3% and a Cohen's kappa coefficient of 0.68.

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The Finnish National Forest Inventory

Cited by 49 publications

References 146 publications

Climate Change and Energy Systems

Climate Change and Energy Systems

Classification of Defoliated Trees Using Tree-Level Airborne Laser Scanning Data Combined with Aerial Images

Area-Based Mapping of Defoliation of Scots Pine Stands Using Airborne Scanning LiDAR

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