Preprint Article Version 1 This version not peer reviewed

Development of a Regional Lidar-Derived Forest Inventory Model with Bayesian Model Averaging for use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA

Version 1 : Received: 29 January 2018 / Approved: 30 January 2018 / Online: 30 January 2018 (04:05:36 CET)

How to cite: Tenneson, K.; Patterson, M.S.; Mellin, T.; Nigrelli, M.; Joria, P.; Mitchell, B. Development of a Regional Lidar-Derived Forest Inventory Model with Bayesian Model Averaging for use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA. Preprints 2018, 2018010275 (doi: 10.20944/preprints201801.0275.v1). Tenneson, K.; Patterson, M.S.; Mellin, T.; Nigrelli, M.; Joria, P.; Mitchell, B. Development of a Regional Lidar-Derived Forest Inventory Model with Bayesian Model Averaging for use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA. Preprints 2018, 2018010275 (doi: 10.20944/preprints201801.0275.v1).

Abstract

Historical forest management practices in the southwestern US have left forests prone to high intensity, stand-replacement fires. Effective management to reduce the cost and impact of forest-fire management and allow fires to burn freely without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to reduce costs and increase ability of managers to monitor AGB and other forest metrics. Using Bayesian Model Averaging (BMA), we develop a regionally applicable lidar-based statistical model for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. The selected regional model includes a mid and low canopy height metric, a canopy cover, and height distribution term. It explains 72% of the variability in field estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model developed is structured in accordance with previously described models fit to local data, and performs equivalently to models designed for smaller scale application. Developing regional models for broad scale application provides a cost-effective, robust approach for managers to monitor and plan adaptively at the landscape scale.

Subject Areas

forest biomass; aboveground biomass; airborne lidar; monitoring; regional forest inventory; variable selection; Bayesian model averaging; multiple linear regression

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