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Spatial predictions of biomass production and biodiversity at regional scale in grasslands are critical to evaluate the effects of management practices across environmental gradients. New generations of remote sensing sensors and machine learning approaches can predict these grassland characteristics with varying accuracy. However, such studies frequently fail to cover a sufficiently broad range of environmental conditions, and their prediction models are often case-specific. To address this gap, we have modelled above-ground biomass and species richness in 150 spatially independent grassland plots of three geographical regions in Germany. These regions follow a North-South climate gradient and differ in soil types, topography, elevation, climatic conditions, historical contexts, and management intensities. The predictors tested in this study are Sentinel-1 backscatter, Sentinel-2 time series of surface reflectance along with derived vegetation indices and Rao's Q, and a set of topoedaphic variables. We compared the performance of a feed-forward deep neural network (DNN) with a random forest (RF) regression algorithm. The DNN achieved the best estimations of biomass (r2 = 0.45) when trained with Sentinel-2 surface reflectance only. Moreover, the DNN showed a higher generalizability than RF during spatial cross-validations (i.e., calibrating and validating in different regions, r2 = 0.38 vs. 0.26). Species richness pre-dictions by both algorithms improved when the full time series of Sentinel-2 surface reflectance values were used (highest r2 = 0.42 achieved by the DNN), but both performed poorly during spatial cross-validations. Overall, the DNN-based models were more robust than RF models, showed a lower bias and lower systematic error, and required fewer inputs. Explainability analysis indicated that red-edge and near infrared information from May and October was the most relevant to predict species richness. This study presents an important step forward in generating robust spatially explicit predictions of grassland attributes and biodiversity variables across large areas, environmental gradients, and phenological stages.
Lineament mapping, which is an important part of any structural geological investigation, is made more efficient and easier by the availability of optical as well as radar remote sensing data, such as Landsat and Sentinel with medium and high spatial resolutions. However, the results from these multi-resolution data vary due to their difference in spatial resolution and sensitivity to soil occupation. The accuracy and quality of extracted lineaments depend strongly on the spatial resolution of the imagery. Therefore, the aim of this study was to compare the optical Landsat-8, Sentinel-2A, and radar Sentinel-1A satellite data for automatic lineament extraction. The framework of automatic approach includes defining the optimal parameters for automatic lineament extraction with a combination of edge detection and line-linking algorithms and determining suitable bands from optical data suited for lineament mapping in the study area. For the result validation, the extracted lineaments are compared against the manually obtained lineaments through the application of directional filtering and edge enhancement as well as to the lineaments digitized from the existing geological maps of the study area. In addition, a digital elevation model (DEM) has been utilized for an accuracy assessment followed by the field verification. The obtained results show that the best correlation between automatically extracted lineaments, manual interpretation, and the preexisting lineament map is achieved from the radar Sentinel-1A images. The tests indicate that the radar data used in this study, with 5872 and 5865 lineaments extracted from VH and VV polarizations respectively, is more efficient for structural lineament mapping than the Landsat-8 and Sentinel-2A optical imagery, from which 2338 and 4745 lineaments were extracted respectively.
Remote sensing analysis is a crucial tool for monitoring the extent of mine waste surfaces and their mineralogy in countries with a long mining history, such as South Africa, where gold and platinum have been produced for over 90 years. These mine waste sites have the potential to contain problematic trace element species (e. g., U, Pb, Cr). In our research, we aim to combine the mapping and monitoring capacities of multispectral and hyperspectral spaceborne sensors. This is done to assess the potential of existing multispectral and hyperspectral spaceborne sensors (OLI and Hyperion) and future missions, such as Sentinel-2 and EnMAP (Environmental Mapping and Analysis Program), for mapping the spatial extent of these mine waste surfaces. For this task we propose a new index, termed the iron feature depth (IFD), derived from Landsat-8 OLI data to map the 900-nm absorption feature as a potential proxy for monitoring the spatial extent of mine waste. OLI was chosen, because it represents the most suitable sensor to map the IFD over large areas in a multi-temporal manner due to its spectral band layout; its (183 km x 170 km) scene size and its revisiting time of 16 days. The IFD is in good agreement with primary and secondary iron-bearing minerals mapped by the Material Identification and Characterization Algorithm (MICA) from EO-1 Hyperion data and illustrates that a combination of hyperspectral data (EnMAP) for mineral identification with multispectral data (Sentinel-2) for repetitive area-wide mapping and monitoring of the IFD as mine waste proxy is a promising application for future spaceborne sensors. A maximum, absolute model error is used to assess the ability of existing and future multispectral sensors to characterize mine waste via its 900-nm iron absorption feature. The following sensor-signal similarity ranking can be established for spectra from gold mining material: EnMAP 100% similarity to the reference, ALI 97.5%, Sentinel-2 97%, OLI and ASTER 95% and ETM+ 91% similarity.