Forest Potential Productivity Mapping by Linking Remote-Sensing-Derived Metrics to Site Variables

Parinaz Rahimzadeh-Bajgiran,Aaron Weiskittel,Sean Lamb,Chris Hennigar

doi:10.3390/rs12122056

Abstract

A fine-resolution region-wide map of forest site productivity is an essential need for effective large-scale forestry planning and management. In this study, we incorporated Sentinel-2 satellite data into an increment-based measure of forest productivity (biomass growth index (BGI)) derived from climate, lithology, soils, and topographic metrics to map improved BGI (iBGI) in parts of North American Acadian regions. Initially, several Sentinel-2 variables including nine single spectral bands and 12 spectral vegetation indices (SVIs) were used in combination with forest management variables to predict tree volume/ha and height using Random Forest. The results showed a 10–12 % increase in out of bag (OOB) r2 when Sentinel-2 variables were included in the prediction of both volume and height together with BGI. Later, selected Sentinel-2 variables were used for biomass growth prediction in Maine, USA and New Brunswick, Canada using data from 7738 provincial permanent sample plots. The Sentinel-2 red-edge position (S2REP) index was identified as the most important variable over others to have known influence on site productivity. While a slight improvement in the iBGI accuracy occurred compared to the base BGI model (~2%), substantial changes to coefficients of other variables were evident and some site variables became less important when S2REP was included.

Highlights

Forest productivity is an important measurement for sustainable forest planning and management and carbon sequestration studies and can be estimated from gross primary productivity (GPP) or potential site quality
The prediction of stand-level total bole inside-bark volume (TV) based on age, species composition, Mgmt, and biomass growth index (BGI) yielded an out of bag (OOB) r2 of 68.8%, whereas the addition of the July and September Sentinel-2 data increased the OOB r2 to 80.5%
Random Forest models using age, Mgmt, BGI, and only b3, b8a, and Sentinel-2 red-edge position (S2REP) resulted in a slight reduction in OOB r2

Summary

Introduction

Forest productivity is an important measurement for sustainable forest planning and management and carbon sequestration studies and can be estimated from gross primary productivity (GPP) or potential site quality. SI estimation in regions dominated by multi-cohort and mixed-species can be challenging In these regions, a variety of other approaches can be used to determine and quantify potential site productivity [2]. Fine-resolution, region-wide mapping of forest productivity is desired for improved comparison of productivity among and within regions as well as large-scale planning and management, with respect to evaluating the effect of climate change on ecosystems. These maps are generally not available for many regions yet. The production of these maps can be done through process-based models, remote sensing techniques and the merger of both approaches [4,5]

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Conclusion