Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning

Solberg, Svein; Næsset, Erik; Hanssen, Kjersti; Christiansen, Erik

doi:10.1016/j.rse.2006.03.001

Cited by 210 publications

(173 citation statements)

References 29 publications

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“…ALS data has proven efficient for determining important forest parameters; it is increasingly used in forest inventory (Kantola et al, 2010) and would provide a good basis for detection of defoliation (Solberg et al, 2006). On the contrary, Synthetic Aperture Radar (SAR) data seems to contribute modestly to remote detection of defoliation.…”

Section: Trends In Remote Monitoring Of Forest Insect Defoliationmentioning

confidence: 99%

Remote Monitoring of Forest Insect Defoliation -A Review-

et al. 2013

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Aim of study: This paper reviews the global research during the last 6 years (2007)(2008)(2009)(2010)(2011)(2012) on the state, trends and potential of remote sensing for detecting, mapping and monitoring forest defoliation caused by insects.Area of study: The review covers research carried out within different countries in Europe and America.Main results: A nation or region wide monitoring system should be scaled in two levels, one using time-series with moderate to coarse resolutions, and the other with fine or high resolution. Thus, MODIS data is increasingly used for early warning detection, whereas Landsat data is predominant in defoliation damage research. Furthermore, ALS data currently stands as the more promising option for operative detection of defoliation.Vegetation indices based on infrared-medium/near-infrared ratios and on moisture content indicators are of great potential for mapping insect pest defoliation, although NDVI is the most widely used and tested.Research highlights: Among most promising methods for insect defoliation monitoring are Spectral Mixture Analysis, best suited for detection due to its sub-pixel recognition enhancing multispectral data, and use of logistic models as function of vegetation index change between two dates, recommended for predicting defoliation.

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Section: Trends In Remote Monitoring Of Forest Insect Defoliationmentioning

confidence: 99%

Remote Monitoring of Forest Insect Defoliation -A Review-

et al. 2013

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“…Korpela (2008) found that lidar intensity values discriminated well between lichens and other ground vegetation. Precise lidar-based estimation of leaf area index (LAI) from small-footprint data (Morsdorf et al, 2006) contributed to the detection of defoliation of Scots pine in Norway due to a severe insect attack (Solberg et al, 2006).…”

Section: Lidar Metrics As Predictors Of Forest Attributesmentioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

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111

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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“…The capability of ALS to map defoliation has been demonstrated for a pine sawfly attack in Norway [31]. ALS data were acquired both before and after the insect attack, and the defoliation was derived from the change in penetration rate and Leaf Area Index (LAI).…”

Section: Introductionmentioning

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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Mapping defoliation during a severe insect attack on Scots pine using airborne laser scanning

Cited by 210 publications

References 29 publications

Remote Monitoring of Forest Insect Defoliation -A Review-

Remote Monitoring of Forest Insect Defoliation -A Review-

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

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

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