Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

Parviz Fatehi,Michael Schaepman,Mathias Kneubühler,Alexander Damm

doi:10.3390/rs71215830

Abstract

Spatial information of forest structural variables is crucial for sustainable forest management planning, forest monitoring, and the assessment of forest ecosystem productivity. We investigate a complex alpine forest ecosystem located in the Swiss National Park (SNP) and apply empirical models to retrieve the structural variables canopy closure, basal area, and timber volume at plot scale. We used imaging spectrometer (IS) data from the Airborne Prism EXperiment (APEX) in combination with in-situ measurements of forest structural variables to develop empirical models. These models are based on simple and stepwise multiple regressions, while all potential two narrow-band combinations of the Simple Ratio (SR), the Normalized Difference Vegetation Index (NDVI), the perpendicular vegetation index (PVI), the second soil-adjusted vegetation index (SAVI2), and band depth indices were tested. The accuracy of the estimated structural attributes was evaluated using a leave-one-out cross-validation technique. Using stepwise multiple regression models, we obtained a moderate to good accuracy when estimating canopy closure (R2 = 0.81, rRMSE = 10%), basal area (R2 = 0.68, rRMSE = 20%), and timber volume (R2 = 0.73, rRMSE = 22%). We discuss the reliability of empirical approaches for estimates of canopy structural parameters considering the causality of light interaction and surface information.

Highlights

Forest inventories seek to enumerate trees over a defined area and to gain forest structural attributes, such as volume, basal area, canopy cover, stem density, diameter at breast height (DBH), and maximum height at individual tree, plot, stand, regional, national, and global scale [1]
We conclude that Airborne Prism EXperiment (APEX) data in combination with empirical-statistical approaches allow estimating forest structural attributes including canopy closure, basal area, and volume in heterogeneous alpine ecosystems
The Shortwave Infrared (SWIR) region of the reflectance spectrum was found to be sensitive to forest structural attributes and could be used to predict relevant parameters

Summary

Introduction

Forest inventories seek to enumerate trees over a defined area and to gain forest structural attributes, such as volume, basal area, canopy cover, stem density, diameter at breast height (DBH), and maximum height at individual tree, plot, stand, regional, national, and global scale [1]. Forest structure data have been collected by means of field surveys [15]. Field surveys are the most accurate way to collect forest structural data [16,17], but require in-situ measurements that are generally limited to a small area (plot area). They do not provide continuous spatial and temporal information on forest structure variables across scales [18]. Remote sensing (RS) data, acquired from either airborne or space-borne platforms, have been taken into consideration as a practical means to estimate forest attributes at different scales [21,22]. RS provides the advantage of spatially explicit mapping of forest attributes [23], repeatedly over large and remote areas [24]

Methods

Results

Discussion

Conclusion