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  • 1
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    Content of soil organic carbon and labile fractions depend on local combinations of mineral-phase characteristics
    Copernicus GmbH ; 2022
    In:  SOIL Vol. 8, No. 1 ( 2022-02-24), p. 113-131
    Details
    In: SOIL, Copernicus GmbH, Vol. 8, No. 1 ( 2022-02-24), p. 113-131
    Abstract: Abstract. Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The PCA clusters were differentiated into total clusters that contain the entire dataset or major proportions of it and local clusters representing only a smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the total clusters, we found significant relationships, by bivariate models, between SOC, its labile fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91) and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total models to local clusters resulted in less explained variance and a higher RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC 〉 HWEC 〉 MBC, showing that labile fractions depend less on soil properties but presumably more on processes such as organic carbon input and turnover in soil.
    Type of Medium: Online Resource
    ISSN: 2199-398X
    URL: Article
    URL: Article
    DOI: 10.5194/soil-8-113-2022
    DOI: 10.5194/soil-8-113-2022-supplement
    Language: English
    Publisher: Copernicus GmbH
    Publication Date: 2022
    detail.hit.zdb_id: 2834892-8
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  • 2
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    Remote Sensing of Geomorphodiversity Linked to Biodiversity—Part III: Traits, Processes and Remote Sensing Characteristics
    MDPI AG ; 2022
    In:  Remote Sensing Vol. 14, No. 9 ( 2022-05-09), p. 2279-
    Details
    In: Remote Sensing, MDPI AG, Vol. 14, No. 9 ( 2022-05-09), p. 2279-
    Abstract: 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.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs14092279
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2513863-7
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  • 3
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    Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 22 ( 2020-11-10), p. 3690-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 22 ( 2020-11-10), p. 3690-
    Abstract: The status, changes, and disturbances in geomorphological regimes can be regarded as controlling and regulating factors for biodiversity. Therefore, monitoring geomorphology at local, regional, and global scales is not only necessary to conserve geodiversity, but also to preserve biodiversity, as well as to improve biodiversity conservation and ecosystem management. Numerous remote sensing (RS) approaches and platforms have been used in the past to enable a cost-effective, increasingly freely available, comprehensive, repetitive, standardized, and objective monitoring of geomorphological characteristics and their traits. This contribution provides a state-of-the-art review for the RS-based monitoring of these characteristics and traits, by presenting examples of aeolian, fluvial, and coastal landforms. Different examples for monitoring geomorphology as a crucial discipline of geodiversity using RS are provided, discussing the implementation of RS technologies such as LiDAR, RADAR, as well as multi-spectral and hyperspectral sensor technologies. Furthermore, data products and RS technologies that could be used in the future for monitoring geomorphology are introduced. The use of spectral traits (ST) and spectral trait variation (STV) approaches with RS enable the status, changes, and disturbances of geomorphic diversity to be monitored. We focus on the requirements for future geomorphology monitoring specifically aimed at overcoming some key limitations of ecological modeling, namely: the implementation and linking of in-situ, close-range, air- and spaceborne RS technologies, geomorphic traits, and data science approaches as crucial components for a better understanding of the geomorphic impacts on complex ecosystems. This paper aims to impart multidimensional geomorphic information obtained by RS for improved utilization in biodiversity monitoring.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12223690
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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  • 4
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    Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale
    MDPI AG ; 2023
    In:  Sensors Vol. 23, No. 2 ( 2023-01-06), p. 662-
    Details
    In: Sensors, MDPI AG, Vol. 23, No. 2 ( 2023-01-06), p. 662-
    Abstract: Previous studies investigating multi-sensor fusion for the collection of soil information have shown variable improvements, and the underlying prediction mechanisms are not sufficiently understood for spectrally-active and -inactive properties. Our objective was to study prediction mechanisms and benefits of model fusion by measuring mid-infrared (MIR) and X-ray fluorescence (XRF) spectra, texture, total and labile organic carbon (OC) and nitrogen (N) content, pH, and cation exchange capacity (CEC) for n = 117 soils from an arable field in Germany. Partial least squares regression models underwent a three-fold training/testing procedure using MIR spectra or elemental concentrations derived from XRF spectra. Additionally, two sequential hybrid and two high-level fusion approaches were tested. For the studied field, MIR was superior for organic properties (ratio of prediction to interquartile distance of validation (RPIQV) for total OC = 7.7 and N = 5.0)), while XRF was superior for inorganic properties (RPIQV for clay = 3.4, silt = 3.0, and sand = 1.8). Even the optimal fusion approach brought little to no accuracy improvement for these properties. The high XRF accuracy for clay and silt is explained by the large number of elements with variable importance in the projection scores 〉 1 (Fe ≈ Ni 〉 Si ≈ Al ≈ Mg 〉 Mn ≈ K ≈ Pb (clay only) ≈ Cr) with strong spearman correlations (±0.57 〈 rs 〈 ±0.90) with clay and silt. For spectrally-inactive properties relying on indirect prediction mechanisms, the relative improvements from the optimal fusion approach compared to the best single spectrometer were marginal for pH (3.2% increase in RPIQV versus MIR alone) but more pronounced for labile OC (9.3% versus MIR) and CEC (12% versus XRF). Dominance of a suboptimal spectrometer in a fusion approach worsened performance compared to the best single spectrometer. Granger-Ramanathan averaging, which weights predictions according to accuracy in training, is therefore recommended as a robust approach to capturing the potential benefits of multiple sensors.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s23020662
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2052857-7
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  • 5
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    Quantification of soil organic carbon at regional scale: Benefits of fusing vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches
    Elsevier BV ; 2022
    In:  Geoderma Vol. 405 ( 2022-01), p. 115426-
    Details
    In: Geoderma, Elsevier BV, Vol. 405 ( 2022-01), p. 115426-
    Type of Medium: Online Resource
    ISSN: 0016-7061
    URL: Article
    DOI: 10.1016/j.geoderma.2021.115426
    RVK:
    RA 2235.4
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 281080-3
    detail.hit.zdb_id: 2001729-7
    SSG: 13
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  • 6
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    Online Resource
    Strategies for the efficient estimation of soil organic carbon at the field scale with vis-NIR spectroscopy: Spectral libraries and spiking vs. local calibrations
    Elsevier BV ; 2019
    In:  Geoderma Vol. 354 ( 2019-11), p. 113856-
    Details
    In: Geoderma, Elsevier BV, Vol. 354 ( 2019-11), p. 113856-
    Type of Medium: Online Resource
    ISSN: 0016-7061
    URL: Article
    DOI: 10.1016/j.geoderma.2019.07.014
    RVK:
    RA 2235.4
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 281080-3
    detail.hit.zdb_id: 2001729-7
    SSG: 13
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  • 7
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    Soil moisture effects on predictive VNIR and MIR modeling of soil organic carbon and clay content
    Elsevier BV ; 2022
    In:  Geoderma Vol. 427 ( 2022-12), p. 116103-
    Details
    In: Geoderma, Elsevier BV, Vol. 427 ( 2022-12), p. 116103-
    Type of Medium: Online Resource
    ISSN: 0016-7061
    URL: Article
    DOI: 10.1016/j.geoderma.2022.116103
    RVK:
    RA 2235.4
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2022
    detail.hit.zdb_id: 281080-3
    detail.hit.zdb_id: 2001729-7
    SSG: 13
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  • 8
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    Performance of field‐scale lab vs in situ visible/near‐ and mid‐infrared spectroscopy for estimation of soil properties
    Wiley ; 2022
    In:  European Journal of Soil Science Vol. 73, No. 1 ( 2022-01)
    Details
    In: European Journal of Soil Science, Wiley, Vol. 73, No. 1 ( 2022-01)
    Abstract: Comparison of laboratory versus in situ visible/near‐ (visNIR) and mid‐infrared (MIR) spectroscopy for prediction of various soil properties is required to demonstrate trade‐offs between accuracy and efficiency. Field measurements were made on an arable field in Germany (silt loam Haplic Luvisol) using visNIR (ASD FieldSpec 3 Hi‐Res) and MIR (Agilent Technologies 4300 Handheld FTIR) and material was collected for lab visNIR (Foss XDS Rapid Content Analyzer) and MIR (Bruker‐TENSOR 27) measurements on dried and ground soil and determination of total, labile ( 〉 63 μm light), stabilized ( 〉 63 μm heavy + 〈 63 μm oxidizable) and resistant organic carbon (OC) content, total nitrogen (N t ), pH and texture. Partial least squares regression models were calculated for five repeated partitions of the dataset ( n  = 238) into training (75%) and test (25%) sets. Lab spectral models outperformed in situ models for total OC (root mean squared error [RMSE] = 0.24–1.0 g kg −1 ), N t (RMSE = 0.026–0.088 g kg −1 ), pH (RMSE = 0.12–0.28) and texture (RMSE = 0.53–1.5%). For both lab and field spectra, the accuracy of visNIR models was comparable or slightly better than MIR for sand, silt and clay. Spectral estimations for labile (RMSE = 0.34–0.47 g kg −1 ) and stabilized OC (RMSE = 0.41–0.85 g kg −1 ) were slightly (lab spectra) to substantially (field spectra) inferior to estimations from multiple linear regressions using total OC, N t , clay and pH as predictors. Variable importance in the projection scores elucidated differences in spectral prediction mechanisms by spectrometer and OC fraction, and found mineral spectral signatures were highly important. For this field‐scale study with 14% median soil gravimetric water content (GWC), the loss of accuracy from lab to field measurement was lower for visNIR than MIR. Analysis of the driest soils ( 〈 9% GWC) found field MIR outperformed field visNIR for OC and N t estimation and vice versa for the wettest soils ( 〉 18%), demonstrating the moisture dependence of performance rankings. Highlights Lab vs field visible/near‐ (visNIRS) and mid‐infrared (MIRS) spectroscopy require comparison for prediction of soil C fractions, N, pH and texture. Lab MIRS prediction of C, N and pH were superior, while texture estimations were comparable or slightly inferior to lab visNIRS. At 14% median soil water content, the loss of accuracy from lab to field measurement was lower for visNIRS than MIRS. The ranking of field visNIRS vs MIRS performance for C and N estimation is moisture dependent.
    Type of Medium: Online Resource
    ISSN: 1351-0754 , 1365-2389
    URL: Issue
    URL: Article
    DOI: 10.1111/ejss.v73.1
    DOI: 10.1111/ejss.13180
    Language: English
    Publisher: Wiley
    Publication Date: 2022
    detail.hit.zdb_id: 240830-2
    detail.hit.zdb_id: 2020243-X
    detail.hit.zdb_id: 1191614-X
    SSG: 13
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  • 9
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    Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics
    MDPI AG ; 2019
    In:  Remote Sensing Vol. 11, No. 20 ( 2019-10-11), p. 2356-
    Details
    In: Remote Sensing, MDPI AG, Vol. 11, No. 20 ( 2019-10-11), p. 2356-
    Abstract: In the face of rapid global change it is imperative to preserve geodiversity for the overall conservation of biodiversity. Geodiversity is important for understanding complex biogeochemical and physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystem organization. Despite the great importance of geodiversity, there is a lack of suitable monitoring methods. Compared to conventional in-situ techniques, remote sensing (RS) techniques provide a pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, as well as standardized monitoring of continuous geodiversity on the local to global scale. This paper gives an overview of the state-of-the-art approaches for monitoring soil characteristics and soil moisture with unmanned aerial vehicles (UAV) and air- and spaceborne remote sensing techniques. Initially, the definitions for geodiversity along with its five essential characteristics are provided, with an explanation for the latter. Then, the approaches of spectral traits (ST) and spectral trait variations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging), thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soil characteristics and soil moisture are also presented. Furthermore, the paper discusses current and future satellite-borne sensors and missions as well as existing data products. Due to the prospects and limitations of the characteristics of different RS sensors, only specific geotraits and geodiversity characteristics can be recorded. The paper provides an overview of those geotraits.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs11202356
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2513863-7
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  • 10
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    Underwater Use of a Hyperspectral Camera to Estimate Optically Active Substances in the Water Column of Freshwater Lakes
    MDPI AG ; 2020
    In:  Remote Sensing Vol. 12, No. 11 ( 2020-05-29), p. 1745-
    Details
    In: Remote Sensing, MDPI AG, Vol. 12, No. 11 ( 2020-05-29), p. 1745-
    Abstract: Freshwater lakes provide many important ecosystem functions and services to support biodiversity and human well-being. Proximal and remote sensing methods represent an efficient approach to derive water quality indicators such as optically active substances (OAS). Measurements of above-ground remote and in situ proximal sensors, however, are limited to observations of the uppermost water layer. We tested a hyperspectral imaging system, customized for underwater applications, with the aim to assess concentrations of chlorophyll a (CHLa) and colored dissolved organic matter (CDOM) in the water columns of four freshwater lakes with different trophic conditions in Central Germany. We established a measurement protocol that allowed consistent reflectance retrievals at multiple depths within the water column independent of ambient illumination conditions. Imaging information from the camera proved beneficial for an optimized extraction of spectral information since low signal areas in the sensor’s field of view, e.g., due to non-uniform illumination, and other interfering elements, could be removed from the measured reflectance signal for each layer. Predictive hyperspectral models, based on the 470 nm–850 nm reflectance signal, yielded estimates of both water quality parameters (R² = 0.94, RMSE = 8.9 µg L−1 for CHLa; R² = 0.75, RMSE = 0.22 m−1 for CDOM) that were more accurate than commonly applied waveband indices (R² = 0.83, RMSE = 13.2 µg L−1 for CHLa; R² = 0.66, RMSE = 0.25 m−1 for CDOM). Underwater hyperspectral imaging could thus facilitate future water monitoring efforts through the acquisition of consistent spectral reflectance measurements or derived water quality parameters along the water column, which has the potential to improve the link between above-surface proximal and remote sensing observations and in situ point-based water probe measurements for ground truthing or to resolve the vertical distribution of OAS.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs12111745
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2513863-7
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