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

Abstract

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.

Creating Landscape-Scale Site Index Maps for the Southeastern US Is Possible with Airborne LiDAR and Landsat Imagery

“…This affirms that one can use low pulse density ALS data from disparate projects combined with Landsat-based age maps to reasonably predict site index over large areas. Our accuracy estimates compare favorably with levels reported by Tompaski et al [21] (bias = 0.7 m, RMSE = 5.5 m, base age = 32 years), who used similar remote sensing data sources for site index prediction. Figure 6 shows that dominant heights could be well-predicted from ALS data, which contributed to overall stronger models.…”

“…Although the site index equation used here was from a study covering much of the native range of loblolly pine and age classes [39], it may not be representative for some forest stands. An alternative way to make site index predictions is the guide curve method [21], where a one-time measurement of a large sample of trees is used to construct a site index model. Our methods are more similar to those of Wulder et al [20] where prior site index equations (for specified species) were applied to ALS predictions of stand height and inventory-based stand ages.…”

“…Wulder et al [20] combined ALS-measured tree heights with inventory age information to predict site index for a 2500-ha forested area in British Columbia, Canada. Tompalski et al [21] detailed a more involved study in the same region, where both ALS and Landsat time-series data were used together to predict site index over 100 square km of western hemlock dominated forests (RMSE = 5.55 m). In a recent study, Kandare et al [22] outlined a method by which both age and dominant height were predicted using ALS and hyperspectral data, which was then used for site index estimation.…”

Enhancing Forest Growth and Yield Predictions with Airborne Laser Scanning Data: Increasing Spatial Detail and Optimizing Yield Curve Selection through Template Matching

“…Among all of the input variables used during growth projections, age is the most difficult to estimate with remote sensing tools [39,40]. However, with the increasing length of the available record of satellite imagery, particularly Landsat, forest stand age becomes possible to map at cell level [39,40]. For younger stands, such information can be then incorporated into the presented methodology.…”

“…Although stand age was shown to not be the most important factor influencing the difference between inventory-and ALS-based projections, we acknowledge that information on stand age is crucial for accurate modelling of growth and yield. Among all of the input variables used during growth projections, age is the most difficult to estimate with remote sensing tools [39,40]. However, with the increasing length of the available record of satellite imagery, particularly Landsat, forest stand age becomes possible to map at cell level [39,40].…”