Robert E. Froese

Forest Monitoring Using Landsat Time Series Data: A Review

Abstract Unique among Earth observation programs, the Landsat program has provided continuous earth observation data for the past 41 years. Landsat data are systematically collected and archived following a global acquisition strategy. The provision of free, robust data products since 2008 has spurred a renaissance of interest in Landsat and resulted in an increasingly widespread use of Landsat time series (LTS) for multitemporal characterizations. The science and applications capacity has developed steadily since 1972, with the increase in sophistication offered over time incorporated into Landsat processing and analysis practices. With the successful launch of Landsat-8, the continuity of measures at scales of particular relevance to management and scientific activities is ensured in the short term. In particular, forest monitoring benefits from LTS, whereby a baseline of conditions can be interrogated for both abrupt and gradual changes and attributed to different drivers. Such benefits are enabled by data availability, analysis-ready image products, increased computing power and storage, as well as sophisticated image processing approaches. In this review, we present the status of remote sensing of forests and forest dynamics using LTS, including issues related to the sensors, data availability, data preprocessing, variables used in LTS, analysis approaches, and validation issues.

A validation and evaluation of the Prognosis individual-tree basal area increment model

We subjected the individual-tree, aspatial basal area increment model developed for the Inland Empire Variant of the Forest Vegetation Simulator to validation and evaluation tests. We used a large set of independent data from the Forest Inventory and Analysis program that covers the geographic extent to which the model is usually applied. Equivalence tests did not validate the model as a predictive tool using nominated criteria, though they usually did validate the model structure as a theory. Design-unbiased estimates of prediction error suggest that the model overpredicts diameter and volume increment by 14% and 2%, respectively. Relationships between species, bias, and predictor variables suggest the model may overpredict most on productive sites. We spatially interpolated the model performance across the study area using thin-plate splines. The observed regional patterns are examined using a selection of cross-sectional transects, and reveal a complex relationship between bias and the way climate effe...

Optimizing Variable Radius Plot Size and LiDAR Resolution to Model Standing Volume in Conifer Forests

Abstract The conventional approach to LiDAR-based forest inventory modeling depends on field sample data from fixed-radius plots (FRP). Because FRP sampling is cost intensive, combining variable-radius plot (VRP) sampling and LiDAR data has the potential to improve inventory efficiency. The overarching goal of this study was to evaluate the integration of LiDAR and VRP data. FRP and VRP plots using different basal-area factors (BAF) were colocated in 6 conifer stands near Alberta, Michigan, in the United States. A suite of LiDAR metrics was developed for 24 different resolutions at each plot location, and a number of nonparametric prediction models were evaluated to identify an optimal LiDAR resolution and an optimal scale of VRP to spatially extend the data. An FRP-based model had root mean square error (RMSE) of 31.8 m3 ha−1, whereas the top VRP-based models were somewhat less precise, with RMSE of 38.0 m3 ha−1 and 45.8 m3 ha−1 using BAF 2.06 m2 ha−1 and BAF 2.29 m2 ha−1, respectively. The optimal LiDAR resolution for the VRP data was found to be 18 m for the selected stands, and plot-level estimates based on a model using BAF 2.29 m2 ha−1 were statistically equivalent to the FRP measurements. The use of VRP data shows promise and can substitute for FRP measurements to improve efficiency.

Multiscale validation of an operational model of forest inventory attributes developed with constrained remote sensing data

Abstract. This paper summarizes the output of an imputation model that simultaneously estimates multiple operational-scale forest inventory attributes in the Laurentian mixed forest type of the United States. The model was constrained by national forest inventory privacy protocols and temporal uncertainties in feature and reference data. Estimates were most accurate at the county level and more variable across smaller spatial extents. Model development and validation highlighted that performance and reliability were influenced by our approach of using publicly available remote sensing predictors and ground reference data in model building. Comprehensive validation included diagnostics of the chosen model and leveraged multiscale independent data for analysis of lack-of-fit spatially and by individual feature variables. Relatively poor performance in some forest types pointed to an impact of temporal mismatch in the estimation of forest stocking in typically even-aged stands dominated by fast-growing species. Résumé. Cet article résume la sortie d’un modèle d’imputation qui estime simultanément plusieurs attributs à l’échelle opérationnelle d’inventaire forestier dans la forêt mixte Laurentienne des États-Unis. Le modèle a été contraint par la politique de confidentialité de l’inventaire forestier national et des incertitudes temporelles des données de caractéristiques et de référence. Les estimations étaient les plus précises au niveau du comté et plus variables dans les petites étendues spatiales. Le développement et la validation du modèle ont mis en évidence que la performance et la fiabilité ont été influencées par notre approche qui utilise, pendant la construction du modèle, des données disponibles publiquement de prédicteurs de la télédétection et de référence au sol. La validation complète a inclus les diagnostics du modèle choisi, et a utilisé des données multiéchelles indépendantes pour l’analyse spatiale des erreurs d’ajustement et par des variables de caractéristiques individuelles. La performance relativement médiocre dans certains types de forêts a mis en lumière un impact de décalage temporel dans l’estimation de la densité relative dans des peuplements typiquement équiens dominés par des espèces à croissance rapide.

Business viability of biomass co-firing and gasification for electricity generation

Greenhouse gas reduction occurs as a result of substitution of woody biomass for coal. Coal is the worst amongst all the fossil fuels in terms of greenhouse gas emission per unit of electricity generated. Recent concerns related to the environmental impact of greenhouse gases from using fossil based feedstock, like coal, for the generation of power, specifically electricity have driven the need to identify alternative bio-based energy technologies in the USA, UK and Germany. The objective of this paper is to determine the business attractiveness of utilising biomass resources to produce electricity through biomass co-firing and gasification through case study analysis.

Modelling juvenile height in mixed species, even aged interior cedar-hemlock stands

University of Idaho: PhD in Forestry, Wildlife and Range Sciences 2003 Dissertation: “Re-engineering the Prognosis basal area increment model for the Inland Empire” Advisor: A.P. Robinson University of British Columbia: Master of Forestry 2000 Thesis: “Modelling juvenile height in mixed species, even aged interior cedar-hemlock stands” Advisor: P.L. Marshall University of British Columbia: Bachelor of Science in Forestry 1995 Thesis: “A case study of understory composition and diversity within several 30-year-old silvicultural system research trials at the Aleza Lake Research Forest” Advisor: J.P. Kimmins