Site Index Curves for White Fir in the Southwestern United States Developed Using a Guide Curve Method

Mathiasen, Robert L.; Olsen, W. K.; Edminster, Carleton B.

doi:10.1093/wjaf/21.2.87

Cited by 8 publications

(12 citation statements)

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“…We used this information to develop a chronosequence of height estimates. To develop the potential site productivity model, we applied the guide curve method, following the methodology presented by Mathiasen et al (2006) and Edminster et al (1991). In this approach, a curve is first fit to the height-age measurements, and confidence intervals are calculated.…”

Section: Chronosequence Analysis and Curve Fittingmentioning

confidence: 99%

“…To derive the final model curves for given height and age values, we again followed the method presented by Mathiasen et al (2006). The distance proportion (p) for given age was calculated as the difference between the guide curve height value and given height value, divided by the difference between the guide curve and modeled lower 95% confidence interval:…”

Section: Chronosequence Analysis and Curve Fittingmentioning

confidence: 99%

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Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series

Tompalski

Coops

White

et al. 2015

Canadian Journal of Remote Sensing

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Site productivity, an important measure of the capacity of land to produce wood biomass, is traditionally estimated by applying species-specific, locally designed models that describe the relation between stand age and dominant height. In this article, we present an approach to derive chronosequences of stand age and height estimates from remotely sensed data to develop site productivity estimates. We first utilized an annual Landsat time series to identify areas of stand replacing disturbances and to estimate the time-since-disturbance, a proxy for stand age. Airborne laser scanning data were used to provide estimates of dominant height for these stands. Nonlinear regression was used to fit a site productivity guide curve for stands aged 7 to 32 years. Existing and developed productivity models, together with remote sensing and inventory data as inputs, were used to validate the site productivity model in three different comparisons. Site productivity was overestimated by 0.70 m (RMSE = 5.55 m) relative to existing forest inventory estimates; further, 89% of remote sensing estimates were within ±1 derived site class of the forest inventory estimates. We conclude that the presented approach is suitable for estimating site productivity for young stands in areas that lack wall-to-wall forest inventory data. Résumé. Le potentiel du site, une mesure importante de la capacité des terresà produire de la biomasse de bois, est traditionnellement estimé en appliquant des modèles spécifiques d'espèces, conçus localement, qui décrivent la relation entre l'âge du peuplement et la hauteur dominante. Dans cet article, nous présentons une approche pour dériver des séquences chronologiques d'estimations de l'âge du peuplement et de la hauteurà partir de données de télédétection pourétablir des estimations du potentiel du site. Nous avons d'abord utilisé une série temporelle annuelle Landsat pour identifier les zones de perturbations menant au remplacement des peuplements et pour estimer le tempsécoulé depuis la perturbation, un estimateur pour l'âge du peuplement.Des données laser aéroportées ontété utilisées pour fournir des estimations de la hauteur dominante de ces peuplements. Une régression non linéaire aété utilisée pour obtenir une courbe de potentiel du site pour les peuplementsâgés de 7à 32 ans. Les modèles de productivité existants ainsi que des données de télédétection et d'inventaire ontété utilisés comme entrées pour valider le modèle du potentiel du siteà partir de trois comparaisons différentes. Le potentiel du site aété surestimé de 0,70 m (RMSE = 5,55 m) par rapport aux estimations existantes d'inventaires forestiers. De plus, 89% des estimations de télédétectionétaientà ±1 classe dérivée du site des estimations de l'inventaire forestier. Nous concluons que l'approche présentée est appropriée pour estimer le potentiel du site pour les jeunes peuplements dans les zones avec des données incomplètes d'inventaire forestier.

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Section: Chronosequence Analysis and Curve Fittingmentioning

confidence: 99%

Section: Chronosequence Analysis and Curve Fittingmentioning

confidence: 99%

Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series

Tompalski

Coops

White

et al. 2015

Canadian Journal of Remote Sensing

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“…Site productivity is usually evaluated using the site index, determined using top height at a predetermined age, referred to as base age [2][3][4][5]. Site index models are also used to project forest growth [1,6,7]. The development of site index models is thus one of the essential tasks in forest management and forest research.…”

Section: Introductionmentioning

confidence: 99%

“…TSPs offer one of the most readily available data sources for site index modelling; however, TSP measurements do not provide growth series. Until now, the guide curve method has usually been applied for the development of site index models from TSP data [7,14,18]. The guide curve method is a method of modelling time series based on cross-sectional data collected by measuring many sample plots at the same point of time.…”

Section: Introductionmentioning

confidence: 99%

“…A guide curve is an outdated method of analysing panel data with a single curve, which constitutes its main fault. Moreover, the guide curve allows for adjustments to a set of growth curves that are anamorphic (proportional) or semi-polymorphic-calculated using the distance proportion between the guide curve and confidence bounds for a height-age sample [7]. Moreover, the guide curve system, which is the method of analysing panel data with a single curve, is potentially biased [8].…”

Section: Introductionmentioning

confidence: 99%

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A Method for the Development of Dynamic Site Index Models Using Height–Age Data from Temporal Sample Plots

Socha¹,

Tymińska-Czabańska²

2019

Forests

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Knowledge of the potential productivity of forest sites is fundamental for making strategic decisions in forest management. Site productivity is usually evaluated using the site index, and therefore the development of site index models is one of the crucial tasks in forest research and forest management. This research aims to develop an effective method for building top-growth and site index models using data from temporary sample plots (TSP). Exploiting the advantages of the generalised algebraic difference approach (GADA), the proposed method overcomes the limitations of the guide curve method that has been to date used in site index modelling using TSPs data and allows to obtain only a set of anamorphic site index curves. The proposed approach enables the construction of dynamic site index models with polymorphism and variable asymptotes. Such models better reflect local, site-specific height growth trajectories and therefore allow more appropriate site index estimation. We tested the proposed method using data collected from 5105 temporary sample plots in Poland. Our results indicate that growth trend estimates using height–age measurements of TSPs may be valuable data for modelling top height growth. For these reasons, the proposed method can be very useful in forest management.

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Modelling top height growth and site index using repeated laser scanning data

Socha

Pierzchalski

Bałazy

et al. 2017

Forest Ecology and Management

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Site Index Curves for White Fir in the Southwestern United States Developed Using a Guide Curve Method

Cited by 8 publications

References 0 publications

Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series

Estimating Forest Site Productivity Using Airborne Laser Scanning Data and Landsat Time Series

A Method for the Development of Dynamic Site Index Models Using Height–Age Data from Temporal Sample Plots

Modelling top height growth and site index using repeated laser scanning data

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