Remote sensing of geomorphodiversity linked to biodiversity — part III: traits, processes and remote sensing characteristics

Remote sensing (RS) enables a cost-effective, extensive, continuous and standardized monitoring of traits and trait variations of geomorphology and its processes, from the local to the continental scale. To implement and better understand RS techniques and the spectral indicators derived from them in the monitoring of geomorphology, this paper presents a new perspective for the definition and recording of five characteristics of geomorphodiversity with RS, namely: geomorphic genesis diversity, geomorphic trait diversity, geomorphic structural diversity, geomorphic taxonomic diversity, and geomorphic functional diversity. In this respect, geomorphic trait diversity is the cornerstone and is essential for recording the other four characteristics using RS technologies. All five characteristics are discussed in detail in this paper and reinforced with numerous examples from various RS technologies. Methods for classifying the five characteristics of geomorphodiversity using RS, as well as the constraints of monitoring the diversity of geomorphology using RS, are discussed. RS-aided techniques that can be used for monitoring geomorphodiversity in regimes with changing land-use intensity are presented. Further, new approaches of geomorphic traits that enable the monitoring of geomorphodiversity through the valorisation of RS data from multiple missions are discussed as well as the ecosystem integrity approach. Likewise, the approach of monitoring the five characteristics of geomorphodiversity recording with RS is discussed, as are existing approaches for recording spectral geomorhic traits/ trait variation approach and indicators, along with approaches for assessing geomorphodiversity. It is shown that there is no comparable approach with which to define and record the five characteristics of geomorphodiversity using only RS data in the literature. Finally, the importance of the digitization process and the use of data science for research in the field of geomorphology in the 21st century is elucidated and discussed

Mapping Specific Constituents of an Ochre-Coloured Watercourse Based on In Situ and Airborne Hyperspectral Remote Sensing Data

It is a well-known fact that water bodies are crucial for human life, ecosystems and biodiversity. Therefore, they are subject to regulatory monitoring in terms of water quality. However, land-use intensification, such as open-cast mining activities, can have a direct impact on water quality. Unfortunately, in situ measurements of water quality parameters are spatially limited, costly and time-consuming, which is why we proposed a combination of hyperspectral data, in situ data and simple regression models in this study to estimate and thus monitor various water quality parameters. We focused on the variables of total iron, ferrous iron, ferric iron, sulphate and chlorophyll-a. Unlike other studies, we used a combination of airborne hyperspectral and RGB data to ensure a very high spatial resolution of the data. To investigate the potential of our approach, we conducted simultaneous in situ measurements and airborne hyperspectral/RGB aircraft campaigns at different sites of the Spree River in Germany to monitor the impact of pyrite weathering on water bodies after open-cast mining activities. Appropriate regression models were developed to estimate the five variables mentioned above. The model with the best performance for each variable gave a coefficient of determination R2 of 64% to 79%. This clearly shows the potential of airborne hyperspectral/RGB data for water quality monitoring. In further investigations, we focused on the use of machine learning techniques, as well as transferability to other water bodies. The approach presented here has great potential for the development of a monitoring method for the continuous monitoring of still waters and large watercourses, especially given the freely available space-based hyperspectral missions via EnMAP

Landsat 5 TM image (Germany, Saxony-Anhalt, path 194, row 24, acquisition date: May 8^th, 2011).

<p>The TERENO study locations are indicated by black frames (FBG: Friedeburg, GFH: Greifenhagen, HAR: Harsleben, SIP: Siptenfelde, SST: Schafstaedt, Wan: Wanzleben), and trapping points are given as yellow filled circles.</p

Modelling patterns of pollinator species richness and diversity using satellite image texture

<div><p>Assessing species richness and diversity on the basis of standardised field sampling effort represents a cost- and time-consuming method. Satellite remote sensing (RS) can help overcome these limitations because it facilitates the collection of larger amounts of spatial data using cost-effective techniques. RS information is hence increasingly analysed to model biodiversity across space and time. Here, we focus on image texture measures as a proxy for spatial habitat heterogeneity, which has been recognized as an important determinant of species distributions and diversity. Using bee monitoring data of four years (2010–2013) from six 4 × 4 km field sites across Central Germany and a multimodel inference approach we test the ability of texture features derived from Landsat-TM imagery to model local pollinator biodiversity. Textures were shown to reflect patterns of bee diversity and species richness to some extent, with the first-order entropy texture and terrain roughness being the most relevant indicators. However, the texture measurements accounted for only 3–5% of up to 60% of the variability that was explained by our final models, although the results are largely consistent across different species groups (bumble bees, solitary bees). While our findings provide indications in support of the applicability of satellite imagery textures for modeling patterns of bee biodiversity, they are inconsistent with the high predictive power of texture metrics reported in previous studies for avian biodiversity. We assume that our texture data captured mainly heterogeneity resulting from landscape configuration, which might be functionally less important for wild bees than compositional diversity of plant communities. Our study also highlights the substantial variability among taxa in the applicability of texture metrics for modelling biodiversity.</p></div

Coordinates (site centroids) and characteristics of the six study sites as specified in a previous work [28].

<p>SN = semi-natural areas; CF = crop fields; For = forest; GL = grassland.</p

Landsat 5 TM image (Germany, Saxony-Anhalt, path 194, row 24, acquisition date: May 8^th, 2011).

<p>The TERENO study locations are indicated by black frames (FBG: Friedeburg, GFH: Greifenhagen, HAR: Harsleben, SIP: Siptenfelde, SST: Schafstaedt, Wan: Wanzleben), and trapping points are given as yellow filled circles.</p

Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements

The prolonged drought of recent years combined with the steadily increasing bark beetle infestation (Ips typographus) is causing enormous damage in Germany’s spruce forests. This preliminary study investigates whether early spruce infestation by the bark beetle (green attack) can be detected using indices based on airborne spatial high-resolution (0.3 m) hyperspectral data and field spectrometer measurements. In particular, a new hyperspectral index based on airborne data has been defined and compared with other common indices for bark beetle detection. It shows a very high overall accuracy (OAA = 98.84%) when validated with field data. Field measurements and a long-term validation in a second study area serve the validation of the robustness and transferability of the index to other areas. In comparison with commonly used indices, the defined index has the ability to detect a larger proportion of infested spruces in the green attack phase (60% against 20% for commonly used indices). This index confirms the high potential of the red-edge domain to distinguish infested spruces at an early stage. Overall, our index has great potential for forest preservation strategies aimed at the detection of infested spruces in order to mitigate the outbreaks.Peer Reviewe

Model-average estimates (EST) of scaled test predictors of bee biodiversity represented by bee count (BC), Shannon diversity (SD), and species richness (SpR) using bumble bees (bb), solitary bees (sb) and all wild bees (nohb).

<p>The R² corresponds to the global model including all predictors that remained after model selection, while the R² of the Null model (R²_NULL) refers to the model including only the fixed effects control predictors. Δ = R²- R²_NULL. IMP = relative importance; SD = Standard deviation; n_model = number of models averaged; P = P-values. con2 = 2^nd order contrast; ent1 = 1^st order entropy; hom2 = 2^nd order homogeneity; NDVI_cv = coefficient of variance of the NDVI; rough = surface roughness; 100/1000 = 100 m or 1000 m scale.</p