Ground-Based Hyperspectral Characterization of Alaska Tundra Vegetation along Environmental Gradients

Abstract

Abstract:Remote sensing has become a valuable tool in monitoring arctic environments. The aim of this paper is ground-based hyperspectral characterization of Low Arctic Alaskan tundra communities along four environmental gradients (regional climate, soil pH, toposequence, and soil moisture) that all vary in ground cover, biomass, and dominating plant communities. Field spectroscopy in connection with vegetation analysis was carried out in summer 2012, along the North American Arctic Transect (NAAT). Spectral metrics were extracted, including the averaged reflectance and absorption-related metrics such as absorption depths and area of continuum removal. The spectral metrics were investigated with respect to "greenness", biomass, vegetation height, and soil moisture regimes. The results show that the surface reflectances of all sites are similar in shape with a reduced near-infrared (NIR) reflectance that is specific for low-growing biomes. The main spectro-radiometric findings are: (i) Southern sites along the climate gradient have taller shrubs and greater overall vegetation biomass, which leads to higher reflectance in the NIR. (ii) Vegetation height and surface wetness are two antagonists that balance each other out with respect to the NIR reflectance along the toposequence and soil moisture gradients. (iii) Moist acidic OPEN ACCESSRemote Sens. 2013, 5 3972 tundra (MAT) sites have "greener" species, more leaf biomass, and green-colored moss species that lead to higher pigment absorption compared to moist non-acidic tundra (MNT) sites. (iv) MAT and MNT plant community separation via narrowband Normalized Difference Vegetation Index (NDVI) shows the potential of hyperspectral remote sensing applications in the tundra.

Mapping Arctic Tundra Vegetation Communities Using Field Spectroscopy and Multispectral Satellite Data in North Alaska, USA

“…For this analysis, classifications were conducted on the plant community level following the approach of Buchhorn et al [28] and Bratsch et al [39]. Although PFTs are commonly used in Earth System and other carbon flux models to prescribe vegetation parameters and system response to changing environmental conditions [61], the spatial heterogeneity of the tundra ecosystem is evident even at sub-meter scales, making single PFT classifications not good representatives of vegetation composition, distribution and properties.…”

“…2016, 8,978 3 of 24 observe tundra ecosystems at daily to bi-weekly repeat cycles. These remote sensing data have been used successfully to map and monitor regional vegetation change across Arctic tundra systems [27,28], including decadal increases in biomass and greenness [29][30][31]. Although coarse spatial resolution imagery (>100 m) may be suitable for determining broad-scale ecosystem distributions and regional change, it is insufficient to capture the high spatial heterogeneity occurring across many Arctic tundra landscapes [16,32,33].…”

“…Field spectroscopy data are not widely collected and used in Arctic tundra vegetation studies [44] due to the often difficult logistics and weather related constraints for field sampling in remote high latitude environments [45]. None-the-less, recent studies using field spectroscopy report improved spectral differentiation of vegetation communities at the plot scale [28,39,46]. For example, Bratsch et al [39] successfully used field spectroscopy to discriminate between four tussock tundra communities at Ivotuk, Alaska with overall classification accuracies of 84%-94%.…”

“…Satellite data are often collected bi-weekly to monthly in comparison to daily repeat cycles using moderate resolution (e.g., MODIS) products. Finally, hyperspectral data have also been used successfully to map tundra vegetation, and has worked across different geographies [28,[34][35][36][37], but it is only available through hand held or airborne sensors.…”

An interactive tool for semi-automatic feature extraction of hyperspectral data

“…Multivariate techniques require large numbers of cases; however, expensive analyses of the examined materials in the laboratories can limit the number of measurements. Our approach considers all bands and we can use any kind of equations involving a [27] in EnMap-Box software [28] and the approach was successfully applied in a study by Buchorn et al [29] using field spectroscopy. Beside the similarity (as HypDA also determines indices based on regression between a measured variable and the spectral bands), HypDA provides several methods to investigate the prerequisites of regression analysis.…”