ALBERT

Description: Reflectance spectroscopy in the visible-infrared and shortwave infrared (450–2500 nm) wavelength region is a rapid, cost-effective and non-destructive method that can be used to monitor heavy metal (PTE, potential toxic elements) contaminated areas. Due to the PTE pollution that has accumulated in the course of wastewater treatment, the existence of Technosols presents an environmental problem, a potential source for PTE uptake by vegetation, or even the release of PTEs into groundwater. In this study, multivariate procedures using Partial Least Squares Regression (PLSR) and Random Forest Regression (RFR) are applied to quantify relationships between soil heavy metal concentration (Cr, Cu, Ni, Zn) and reflectance data of highly contaminated Technosols from a former sewage farm near Berlin, Germany. Laboratory measurements of 110 soil samples in four different preparation steps were acquired with HySpex hyperspectral cameras. The impact of the different preparation steps, namely “oven-dried”, “sieved”, “ground”, “LOI”, was evaluated for its potential to enhance the method performance or to reduce the time-consuming soil sample preparation. Furthermore, different spectral pre-processing methods were evaluated regarding improvements of spectral modelling performance and their ability to minimise noise and multiple scattering effects. Considering the optimal coefficient of determination (R2), PLSR shows an improving performance and accuracy with increasing preparation steps such as ground or LOI for all metals of interest (R2_Cr: 0.52–0.78; R2_Cu: 0.36–0.73; R2_Ni: 0.19–0.42 and R2_Zn: 0.41–0.74). RFR shows a weaker estimation performance for all metals, even when using higher sample preparation levels (R2_Cr: 0.36–0.62; R2_Cu: 0.17–0.72; R2_Ni: 0.20–0.35 and R2_Zn: 0.26–0.67). The results show that an application of methods such as PLSR for the prediction of PTE concentration in Technosols is still a challenge but provides more robust estimations than the user-friendly RFR method. Additionally, this study shows that PTE estimation performance in heterogeneous soil samples can be improved by increased laboratory soil preparation steps and further spectral pre-processing steps.

Description: Impacts of human civilization on ecosystems threaten global biodiversity. In a changing environment, traditional in situ approaches to biodiversity monitoring have made significant steps forward to quantify and evaluate BD at many scales but still, these methods are limited to comparatively small areas. Earth observation (EO) techniques may provide a solution to overcome this shortcoming by measuring entities of interest at different spatial and temporal scales. This paper provides a comprehensive overview of the role of EO to detect, describe, explain, predict and assess biodiversity. Here, we focus on three main aspects related to biodiversity − taxonomic diversity, functional diversity and structural diversity, which integrate different levels of organization − molecular, genetic, individual, species, populations, communities, biomes, ecosystems and landscapes. In particular, we discuss the recording of taxonomic elements of biodiversity through the identification of animal and plant species. We highlight the importance of the spectral traits (ST) and spectral trait variations (STV) concept for EO-based biodiversity research. Furthermore we provide examples of spectral traits/spectral trait variations used in EO applications for quantifying taxonomic diversity, functional diversity and structural diversity. We discuss the use of EO to monitor biodiversity and habitat quality using different remote-sensing techniques. Finally, we suggest specifically important steps for a better integration of EO in biodiversity research. EO methods represent an affordable, repeatable and comparable method for measuring, describing, explaining and modelling taxonomic, functional and structural diversity. Upcoming sensor developments will provide opportunities to quantify spectral traits, currently not detectable with EO, and will surely help to describe biodiversity in more detail. Therefore, new concepts are needed to tightly integrate EO sensor networks with the identification of biodiversity. This will mean taking completely new directions in the future to link complex, large data, different approaches and models.