Mapping Alpine Aboveground Biomass From Imaging Spectrometer Data: A Comparison of Two Approaches

Abstract

Aboveground biomass (AGB) of terrestrial ecosystems is an important constraint of global change and productivity models and used to assess carbon stocks and thus the contribution of vegetated ecosystems to the global carbon cycle. Although an indispensable and important requirement for decision makers, coherent and accurate estimates of grassland and forest AGB especially in complex environments are still lacking. In this study, we aim to assess the capability of two strategies to map grassland and forest AGB in a complex alpine ecosystem, i.e., using a discrete as well as a continuous field (CF) mapping approach based on imaging spectroscopy (IS) data. In situ measurements of grassland and forest AGB were acquired in the Swiss National Park (SNP) to calibrate empirical models and to validate AGB retrievals. The selection of robust empirical models considered all potential two narrow-band combinations of the simple ratio (SR) and the normalized difference vegetation index (NDVI) generated from Airborne Prism Experiment (APEX) IS data and in situ measurements. We found a narrow-band SR including spectral bands from the short-wave infrared (SWIR) (1689 nm) and near infrared (NIR) (851 nm) as the best regression model to estimate grassland AGB. Forest AGB showed highest correlation with an SR generated from two spectral bands in the SWIR (1498, 2112 nm). The applied accuracy assessment revealed good results for estimated grassland AGB using the discrete mapping approach [R 2 of 0.65, mean RMSE (mRMSE) of 0.91 t · ha −1 , and mean relative RMSE (mrRMSE) of 26%]. The CF mapping approach produced a higher R 2 ( R 2 = 0.94 ), and decreased the mRMSE and the mrRMSE to 0.55 t · ha −1 and 15%, respectively. For forest, the discrete approach predicted AGB with an R 2 value of 0.64, an mRMSE of 67.8 t · ha −1 , and an mrRMSE of 25%. The CF mapping approach improved the accuracy of forest AGB estimation with R 2 = 0.85 , mean RMSE = 55.85 t · ha −1 , and mean relative RMSE = 21%. Our results indicate that, in general, both mapping approaches are capable of accurately mapping grassland and forest AGB in complex environments using IS data, whereas the CF-based approach yielded higher accuracies due to its capability to incorporate subpixel information (abundances) of different land cover types.

Estimation of Alpine Forest Structural Variables from Imaging Spectrometer Data

“…Measurements of reflected radiance in the optical spectral domain are per se less or not sensitive for forest structural variables, particularly for the variables basal area or timber volume. In fact, optical measurements are mostly representative for the upper canopy part, and derived information tends, for example, to saturate with increasing canopy volume [40,117,118]. This indicates that results obtained in our study are likely based on secondary relationships.…”

“…The forest is classified as boreal type forests with European larch (Larix decidua L.) as a dominant tree. The Norway spruce (Picea abies (L.) Karst) and Swiss stone pine (Pinus cembra L.) are associated species [40]. The Norway spruce is present at a lower altitude, whereas the Swiss stone pine occurs at a high altitude.…”

“…Vegetation indices (VIs) are widely used to estimate biophysical vegetation properties, i.e., biomass [40,83], LAI [55,59], canopy closure [84,85], or stand volume [17,61]. In this study, we used two main categories of vegetation indices: ratio-based and soil-based indices.…”

“…Therefore, 193 APEX spectral bands were used to generate correlograms, allowing to compute 37,249 different narrow-band combinations. The optimal band combination was selected based on R 2 (i.e., we used an R 2 threshold of 0.5 with a confidence level of 95%), considering multi-collinearity issues, causality explanations, and focusing on "hot-spot areas" (spectral range) [40]. The selected band combinations were then used in regression analysis.…”

Estimating Aboveground Biomass in Dense Hyrcanian Forests by the Use of Sentinel-2 Data

“…On the one hand, extending the canopy surface will increase the size and number of holes in the canopy. Tree growth will increase in terms of volume, so trees will make a shadow that will cause a reduction in reflection [99]. On the other hand, spreading water on the leaves and increasing the water availability will also reduce the reflectance [99].…”