Overstory and understory leaf area index as indicators of forest response to ice storm damage

Olthof, Ian; King, Douglas J.; Lautenschlager, R. A.

doi:10.1016/s1470-160x(03)00010-4

Cited by 30 publications

(27 citation statements)

References 20 publications

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“…In concurrent research to this study, forest productivity response measured by the change in LAI e in the four summers following the storm has been found to be highly related to the damage sustained (Olthof et al, 2002). In addition, as damage visually appeared to have been greater in agricultural areas than in regions of more continuous forest, evaluation of relations of landscape fragmentation to damage is being conducted.…”

Section: Summary Discussionmentioning

confidence: 88%

Modelling and Mapping Damage to Forests from an Ice Storm Using Remote Sensing and Environmental Data

et al. 2005

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Abstract. An extreme ice storm in January 1998 deposited up to 100 mm of ice and resulted in significant forest damage across eastern North America. Average crown loss of over 75% was recorded in large areas of eastern Ontario and southern Quebec. A primary question that arose following the storm was: can forest damage be effectively assessed using remote sensing and other available environmental data? This paper presents two contrasting studies to address this question. The first involves damage modelling at a local scale in an unmanaged forest using spectral and spatial information in high-resolution airborne imagery. Results of field data analyses are also given that show relations between damage and forest structure and composition as well as changes in forest structure that occurred in the years following the storm. The second study involves regional scale damage mapping in managed and unmanaged forests of eastern Ontario using medium resolution satellite imagery and other environmental data. In comparison of several image classification and data interpolation methods, the best damage map was produced using a neural network classifier and a mix of Landsat and environmental data. The methods and results presented in this paper form the basis for ongoing long-term temporal study of damage impacts on forest condition.

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Section: Summary Discussionmentioning

confidence: 88%

Modelling and Mapping Damage to Forests from an Ice Storm Using Remote Sensing and Environmental Data

et al. 2005

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“…Myneni et al 1997a) to local (e.g. Olthof et al 2003) scales since it responds rapidly to changes in climatic conditions or environmental stress factors.…”

Section: Remote Sensing In Ecologymentioning

confidence: 99%

“…Needles of the Finnish coniferous species are nonflat, typically hemicylindrical or rhomboid (e.g. Palmroth et al 2002, Niinemets & Kull, 1995, and thus a more suitable definition for them is half the total interception area per unit ground area (Chen & Black, 1992, Lang, 1991. For comparing leaf area index values of coniferous and broadleaved canopies, a useful concept is that of effective leaf area index since, as mentioned previously, it assumes a random spatial distribution of leaves instead of the clumped pattern that is observed in reality.…”

Section: Ground Measurementsmentioning

confidence: 99%

“…In Study VII, a comparison of the optical leaf area index estimates was made to allometric leaf area indices derived using a needle biomass model developed in Sweden for Scots pine and Norway spruce (Marklund, 1988) coupled with corresponding specific needle area values (Palmroth et al 2002. A canopy radiation model (Nilson, 1999) (Fig.…”

Section: Ground Measurementsmentioning

confidence: 99%

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The spectral signature of coniferous forests: the role of stand structure and leaf area index

Rautiainen¹

2005

Diss. For.

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“…Plant leaf area indicates crop nutrient availability (Uhart & Andrade, 1995;Heege, Reusch, & Thiessen, 2008) drought stress (Alves & Setter, 2000), physical damage of canopy (Olthof, King, & Lautenschlager, 2003), land degradation/rehabilitation (Doi & Ranamukhaarachchi, 2013), and other factors. As such, the consecutive observation of leaf area is an effective measure that can be employed to find abnormalities in plant growth within a certain period.…”

Section: Introductionmentioning

confidence: 99%

Detection of Cassava Leaves in Multi-Temporally Acquired Digital Images of a Cassava Field Under Different Brightness Levels by Simultaneous Binarization of the Images Based on Indices of Redness/Greenness

Srisutham¹,

Doi²,

Polthanee³

et al. 2014

MAS

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Plant leaf area reveals various types of abnormalities which can enable appropriate plant/crop management actions. The quantification of plant leaf area is now feasible using commonly available digital photographing tools. Changes in brightness, however, make it difficult to compare leaf areas in digital photographs acquired at multiple time points. This difficulty could be overcome by employing an index of redness/greenness (R/G), which was suggested to be one of the best indices to discriminate between plant leaves and other objects such as soils. R/G and other indices were examined when discriminating cassava leaves from other objects in a field. A surveillance camera captured digital photographs on a daily basis. Of these, 183 photographs were stored. They were pasted into a single image file and simultaneously analyzed. The International Commission on Illumination color model's a* was the best index in the discrimination, with a distinctiveness score of 1.36. R/G was the second best, with a distinctiveness score of 0.70. The percentage of leaf-likely pixels followed sigmoidal patterns with time, resulting in great coefficients of determination of 0.981 (a*) and 0.965 (R/G). The percentage of leaf-likely pixels and cassava leaf weight had a real-time response relationship. The range of the 95% confidence limit was narrowed from -16 to +14% of a predicted value of 98% leaf-likely pixels for R/G to ±12% for a*. Thus, the simultaneous binarization and the detection of leaf-likely pixels in the photographs acquired under different brightness levels was enabled with improved discrimination accuracy by employing a*.

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Overstory and understory leaf area index as indicators of forest response to ice storm damage

Cited by 30 publications

References 20 publications

Modelling and Mapping Damage to Forests from an Ice Storm Using Remote Sensing and Environmental Data

Modelling and Mapping Damage to Forests from an Ice Storm Using Remote Sensing and Environmental Data

The spectral signature of coniferous forests: the role of stand structure and leaf area index

Detection of Cassava Leaves in Multi-Temporally Acquired Digital Images of a Cassava Field Under Different Brightness Levels by Simultaneous Binarization of the Images Based on Indices of Redness/Greenness

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