Integrating Landsat pixel composites and change metrics with lidar plots to predictively map forest structure and aboveground biomass in Saskatchewan, Canada

Forest inventory and monitoring programs are needed to provide timely, spatially complete (i.e. mapped), and verifiable information to support forest management, policy formulation, and reporting obligations. Satellite images, in particular data from the Landsat Thematic Mapper and Enhanced Thematic Mapper (TM/ETM +) sensors, are often integrated with field plots from forest inventory programs, leveraging the complete spatial coverage of imagery with detailed ecological information from a sample of plots to spatially model forest conditions and resources. However, in remote and unmanaged areas such as Canada's northern forests, financial and logistic constraints can severely limit the availability of inventory plot data. Additionally, Landsat spectral information has known limitations for characterizing vertical vegetation structure and biomass; while clouds, snow, and short growing seasons can limit development of large area image mosaics that are spectrally and phenologically consistent across space and time. In this study we predict and map forest structure and aboveground biomass over 37 million ha of forestland in Saskatchewan, Canada. We utilize lidar plots—observations of forest structure collected from airborne discrete-return lidar transects acquired in 2010—as a surrogate for traditional field and photo plots. Mapped explanatory data included Tasseled Cap indices and multi-temporal change metrics derived from Landsat TM/ETM + pixel-based image composites. Maps of forest structure and total aboveground biomass were created using a Random Forest (RF) implementation of Nearest Neighbor (NN) imputation. The imputation model had moderate to high plot-level accuracy across all forest attributes (R2 values of 0.42–0.69), as well as reasonable attribute predictions and error estimates (for example, canopy cover above 2 m on validation plots averaged 35.77%, with an RMSE of 13.45%, while unsystematic and systematic agreement coefficients (ACuns and ACsys) had values of 0.63 and 0.97 respectively). Additionally, forest attributes displayed consistent trends in relation to the time since and magnitude of wildfires, indicating model predictions captured the dominant ecological patterns and processes in these forests. Acknowledging methodological and conceptual challenges based upon the use of lidar plots from transects, this study demonstrates that using lidar plots and pixel compositing in imputation mapping can provide forest inventory and monitoring information for regions lacking ongoing or up-to-date field data collection programs

Process-Based Modeling to Assess the Effects of Recent Climatic Variation on Site Productivity and Forest Function across Western North America

A process-based forest growth model, 3-PG (Physiological Principles Predicting Growth), parameterized with values of soil properties constrained by satellite-derived estimates of maximum leaf area index (LAI[subscript max]), was run for Douglas-fir (Pseudotsuga menziesii) to contrast the extent to which site growth potential might vary across western North America between a cool, wet period (1950–1975) and a more recent, generally warmer and drier one (2000–2009). LAI[subscript max] represents a surrogate for overall site growth potential, as demonstrated from a strong correlation between the two variables, with the latter based on the culmination of mean annual increment estimates made at 3356 ground-based U.S. Forest Service survey plots across the states of Oregon and Washington. Results indicate that since 2000, predicted LAI[subscript max] has decreased more than 20% in portions of the Southwest USA and for much of the forested area in western Alberta. Similar percentage increases in LAI[subscript max] were predicted for parts of British Columbia, Idaho and Montana. The modeling analysis included an assessment of changes in seasonal constraints on gross primary production (GPP). A general reduction in limitations caused by spring frost occurred across the entire study area. This has led to a longer growing season, along with notable increases in summer evaporative demand and soil drought for much of the study area away from the maritime influence of the Pacific Ocean.Keywords: Douglas-fir, FIA survey plots, Climate change, Leaf area index, 3-PG model, Productivit

sgsR: a structurally guided sampling toolbox for LiDAR-based forest inventories

Establishing field inventories can be labor intensive, logistically challenging and expensive. Optimizing a sample to derive accurate forest attribute predictions is a key management-level inventory objective. Traditional sampling designs involving pre-defined, interpreted strata could result in poor selection of within-strata sampling intensities, leading to inaccurate estimates of forest structural variables. The use of airborne laser scanning (ALS) data as an applied forest inventory tool continues to improve understanding of the composition and spatial distribution of vegetation structure across forested landscapes. The increased availability of wall-to-wall ALS data is promoting the concept of structurally guided sampling (SGS), where ALS metrics are used as an auxiliary data source driving stratification and sampling within management-level forest inventories. In this manuscript, we present an open-source R package named sgsR that provides a robust toolbox for implementing various SGS approaches. The goal of this package is to provide a toolkit to facilitate better optimized allocation of sample units and sample size, as well as to assess and augment existing plot networks by accounting for current forest structural conditions. Here, we first provide justification for SGS approaches and the creation of the sgsR toolbox. We then briefly describe key functions and workflows the package offers and provide two reproducible examples. Avenues to implement SGS protocols according to auxiliary data needs are presented

Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data

Precise knowledge of fuel conditions is important for predicting fire hazards and simulating fire growth and intensity across the landscape. We present a methodology to retrieve and map forest canopy fuel and other forest structural parameters using small-footprint full-waveform airborne light detection and ranging (LiDAR) data. Full-waveform LiDAR sensors register the complete returned backscattered signal through time and can describe physical properties of the intercepted objects. This study was undertaken in a mixed forest dominated by Douglas-fir, occasionally mixed with other conifers, in north-west Oregon (United States). We extracted two sets of LiDAR metrics using pulse detection and waveform modelling and then constructed several predictive models using forward stepwise multiple linear regression. The resulting models explained ~80% of the variability for many of the canopy fuel and forest structure parameters: aboveground biomass (R2 = 0.84), quadratic mean diameter (R2 = 0.82), canopy height (R2 = 0.79), canopy base height (R2 = 0.78) and canopy fuel load (R2 = 0.79). The lowest performing models included basal area (R2 = 0.76), stand volume (R2 = 0.73), canopy bulk density (R2 = 0.67) and stand density index (R2 = 0.66). Our results indicate that full-waveform LiDAR systems show promise in systematically characterising the structure and canopy fuel loads of forests, which may enable accurate fire behaviour forecasting that in turn supports the development of prevention and planning policies.This paper was developed as a result of two mobility grants funded by the Erasmus Mundus Programme of the European Commission under the Transatlantic Partnership for Excellence in Engineering (TEE Project) and the Generalitat Valenciana (BEST/2012/235). The authors appreciate the financial support provided by the Spanish Ministry of Science and Innovation in the framework of the project CGL2010-19591/BTE. In addition, the authors thank the Panther Creek Remote Sensing and Research cooperative program for the data provided for this research, Jim Flewelling (Seattle Biometrics) and George McFadden (Bureau of Land Management) for their help in data availability and preparation.Hermosilla Gómez, T.; Ruiz Fernández, LÁ.; Kazakova, AN.; Coops, N.; Moskal, LM. (2014). Estimation of forest structure and canopy fuel parameters from small-footprint full-waveform LiDAR data. International Journal of Wildland Fire. 23(2):224-233. https://doi.org/10.1071/WF13086S224233232Akaike, H. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 19(6), 716-723. doi:10.1109/tac.1974.1100705Andersen, H.-E., McGaughey, R. J., & Reutebuch, S. E. (2005). Estimating forest canopy fuel parameters using LIDAR data. 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Optic disc classification by the Heidelberg Retina Tomograph and by physicians with varying experience of glaucoma

PurposeTo compare the diagnostic accuracy of the Heidelberg Retina Tomograph's (HRT) Moorfields regression analysis (MRA) and glaucoma probability score (GPS) with that of subjective grading of optic disc photographs performed by ophthalmologists with varying experience of glaucoma and by ophthalmology residents.MethodsDigitized disc photographs and HRT images from 97 glaucoma patients with visual field defects and 138 healthy individuals were classified as either within normal limits (WNL), borderline (BL), or outside normal limits (ONL). Sensitivity and specificity were compared for MRA, GPS, and the physicians. Analyses were also made according to disc size and for advanced visual field loss.ResultsForty-five physicians participated. When BL results were regarded as normal, sensitivity was significantly higher (P<5%) for both MRA and GPS compared with the average physician, 87%, 79%, and 62%, respectively. Specificity ranged from 86% for MRA to 97% for general ophthalmologists, but the differences were not significant. In eyes with small discs, sensitivity was 75% for MRA, 60% for the average doctor, and 25% for GPS; in eyes with large discs, sensitivity was 100% for both GPS and MRA, but only 68% for physicians.ConclusionOur results suggest that sensitivity of MRA is superior to that of the average physician, but not that of glaucoma experts. MRA correctly classified all eyes with advanced glaucoma and showed the best sensitivity in eyes with small optic discs