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  • 1
    Online Resource
    Online Resource
    Reinforcing nature-based solutions through tools providing social-ecological-technological integration (2022)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_29162
    Format: 1 Online-Ressource (19 Seiten)
    Content: While held to be a means for climate change adaptation and mitigation, nature-based solutions (NbS) themselves are vulnerable to climate change. To find ways of compensating for this vulnerability we combine a focused literature review on how information technology has been used to strengthen positive social–ecological–technological feedback, with the development of a prototype decision-support tool. Guided by the literature review, the tool integrates recent advances in using globally available remote sensing data to elicit information on functional diversity and ecosystem service provisioning with information on human service demand and population vulnerability. When combined, these variables can inform climate change adaptation strategies grounded in local social–ecological realities. This type of integrated monitoring and packaging information to be actionable have potential to support NbS management and local knowledge building for context-tailored solutions to societal challenges in urban environments.
    Content: Peer Reviewed
    In: [Dordrecht] : Springer Netherlands, 2023, 52,3, Seiten 489-507
    Language: English
    DOI: 10.18452/28486
    DOI: 10.1007/s13280-022-01801-4
    URN: urn:nbn:de:kobv:11-110-18452/29162-6
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    HU Berlin
  • 2
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    Online Resource
    Linking Remote Sensing and Geodiversity and Their Traits Relevant to Biodiversity—Part I: Soil Characteristics (2019)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_23311
    Format: 1 Online-Ressource (51 Seiten)
    Content: In the face of rapid global change it is imperative to preserve geodiversity for the overallconservation of biodiversity. Geodiversity is important for understanding complex biogeochemicaland physical processes and is directly and indirectly linked to biodiversity on all scales of ecosystemorganization. Despite the great importance of geodiversity, there is a lack of suitable monitoringmethods. Compared to conventional in-situ techniques, remote sensing (RS) techniques providea pathway towards cost-effective, increasingly more available, comprehensive, and repeatable, aswell as standardized monitoring of continuous geodiversity on the local to global scale. This papergives an overview of the state-of-the-art approaches for monitoring soil characteristics and soilmoisture 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 traitvariations (STV) to record geodiversity using RS are defined. LiDAR (light detection and ranging),thermal and microwave sensors, multispectral, and hyperspectral RS technologies to monitor soilcharacteristics and soil moisture are also presented. Furthermore, the paper discusses current andfuture satellite-borne sensors and missions as well as existing data products. Due to the prospectsand limitations of the characteristics of different RS sensors, only specific geotraits and geodiversitycharacteristics can be recorded. The paper provides an overview of those geotraits.
    Content: Peer Reviewed
    In: Basel : MDPI, 11,20
    Language: English
    DOI: 10.3390/rs11202356
    DOI: 10.18452/22703
    URN: urn:nbn:de:kobv:11-110-18452/23311-5
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    HU Berlin
  • 3
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    Online Resource
    Mapping of Soil Total Nitrogen Content in the Middle Reaches of the Heihe River Basin in China Using Multi-Source Remote Sensing-Derived Variables (2019)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_22600
    Format: 1 Online-Ressource (18 Seiten)
    Content: Soil total nitrogen (STN) is an important indicator of soil quality and plays a key role in global nitrogen cycling. Accurate prediction of STN content is essential for the sustainable use of soil resources. Synthetic aperture radar (SAR) provides a promising source of data for soil monitoring because of its all-weather, all-day monitoring, but it has rarely been used for STN mapping. In this study, we explored the potential of multi-temporal Sentinel-1 data to predict STN by evaluating and comparing the performance of boosted regression trees (BRTs), random forest (RF), and support vector machine (SVM) models in STN mapping in the middle reaches of the Heihe River Basin in northwestern China. Fifteen predictor variables were used to construct models, including land use/land cover, multi-source remote sensing-derived variables, and topographic and climatic variables. We evaluated the prediction accuracy of the models based on a cross-validation procedure. Results showed that tree-based models (RF and BRT) outperformed SVM. Compared to the model that only used optical data, the addition of multi-temporal Sentinel-1A data using the BRT method improved the root mean square error (RMSE) and the mean absolute error (MAE) by 17.2% and 17.4%, respectively. Furthermore, the combination of all predictor variables using the BRT model had the best predictive performance, explaining 57% of the variation in STN, with the highest R2 (0.57) value and the lowest RMSE (0.24) and MAE (0.18) values. Remote sensing variables were the most important environmental variables for STN mapping, with 59% and 50% relative importance in the RF and BRT models, respectively. Our results show the potential of using multi-temporal Sentinel-1 data to predict STN, broadening the data source for future digital soil mapping. In addition, we propose that the SVM, RF, and BRT models should be calibrated and evaluated to obtain the best results for STN content mapping in similar landscapes.
    Content: Peer Reviewed
    In: Basel : MDPI, 11,24
    Language: Undetermined
    DOI: 10.3390/rs11242934
    DOI: 10.18452/21877
    URN: urn:nbn:de:kobv:11-110-18452/22600-4
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    HU Berlin
  • 4
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    Online Resource
    Linking the Remote Sensing of Geodiversity and Traits Relevant to Biodiversity—Part II: Geomorphology, Terrain and Surfaces (2020)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_25777
    Format: 1 Online-Ressource (61 Seiten)
    Content: 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.
    Content: Peer Reviewed
    In: Basel : MDPI, 12,22
    Language: English
    DOI: 10.3390/rs12223690
    DOI: 10.18452/25097
    URN: urn:nbn:de:kobv:11-110-18452/25777-8
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    HU Berlin
  • 5
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    Online Resource
    Let your maps be fuzzy!—Class probabilities and floristic gradients as alternatives to crisp mapping for remote sensing of vegetation (2020)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_23615
    Format: 1 Online-Ressource (14 Seiten)
    Content: Mapping vegetation as hard classes based on remote sensing data is a frequently applied approach, even though this crisp, categorical representation is not in line with nature's fuzziness. Gradual transitions in plant species composition in ecotones and faint compositional differences across different patches are thus poorly described in the resulting maps. Several concepts promise to provide better vegetation maps. These include (1) fuzzy classification (a.k.a. soft classification) that takes the probability of an image pixel's class membership into account and (2) gradient mapping based on ordination, which describes plant species composition as a floristic continuum and avoids a categorical description of vegetation patterns. A systematic and comprehensive comparison of these approaches is missing to date. This paper hence gives an overview of the state of the art in fuzzy classification and gradient mapping and compares the approaches in a case study. The advantages and disadvantages of the approaches are discussed and their performance is compared to hard classification (a.k.a. crisp or boolean classification). Gradient mapping best conserves the information in the original data and does not require an a priori categorization. Fuzzy classification comes close in terms of information loss and likewise preserves the continuous nature of vegetation, however, still relying on a priori classification. The need for a priori classification may be a disadvantage or, in other cases, an advantage because it allows using categorical input data instead of the detailed vegetation records required for ordination. Both approaches support spatially explicit accuracy analyses, which further improves the usefulness of the output maps. Gradient mapping and fuzzy classification offer various advantages over hard classification, can always be transformed into a crisp map and are generally applicable to various data structures. We thus recommend the use of these approaches over hard classification for applications in ecological research.
    Content: Peer Reviewed
    In: Chichester : Wiley
    Language: English
    DOI: 10.1002/rse2.188
    DOI: 10.18452/22940
    URN: urn:nbn:de:kobv:11-110-18452/23615-5
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  • 6
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    Online Resource
    Mapping European Spruce Bark Beetle Infestation at Its Early Phase Using Gyrocopter-Mounted Hyperspectral Data and Field Measurements (2021)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_24570
    Format: 1 Online-Ressource (30 Seiten)
    Content: 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.
    Content: Peer Reviewed
    In: Basel : MDPI, 13,22
    Language: English
    DOI: 10.3390/rs13224659
    DOI: 10.18452/23939
    URN: urn:nbn:de:kobv:11-110-18452/24570-0
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    HU Berlin
  • 7
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    Online Resource
    Ground Truth Validation of Sentinel-2 Data Using Mobile Wireless Ad Hoc Sensor Networks (MWSN) in Vegetation Stands (2023)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_28198
    Format: 1 Online-Ressource (22 Seiten)
    Content: Satellite-based remote sensing (RS) data are increasingly used to map and monitor local, regional, and global environmental phenomena and processes. Although the availability of RS data has improved significantly, especially in recent years, operational applications to derive value-added information products are still limited by close-range validation and verification deficits. This is mainly due to the gap between standardized and sufficiently available close-range and RS data in type, quality, and quantity. However, to ensure the best possible linkage of close-range and RS data, it makes sense to simultaneously record close-range data in addition to the availability of environmental models. This critical gap is filled by the presented mobile wireless ad hoc sensor network (MWSN) concept, which records sufficient close-range data automatically and in a standardized way, even at local and regional levels. This paper presents a field study conducted as part of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN), focusing on the information gained with respect to estimating the vegetation state with the help of multispectral data by simultaneous observation of an MWSN during a Sentinel-2A (S2A) overflight. Based on a cross-calibration of the two systems, a comparable spectral characteristic of the data sets could be achieved. Building upon this, an analysis of the data regarding the influence of solar altitude, test side topography and land cover, and sub-pixel heterogeneity was accomplished. In particular, variations due to spatial heterogeneity and dynamics in the diurnal cycle show to what extent such complementary measurement systems can improve the data from RS products concerning the vegetation type and atmospheric conditions.
    Content: Peer Reviewed
    In: Basel : MDPI, 15,19
    Language: English
    DOI: 10.3390/rs15194663
    DOI: 10.18452/27548
    URN: urn:nbn:de:kobv:11-110-18452/28198-2
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    HU Berlin
  • 8
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    Online Resource
    Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis (2021)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_27096
    Format: 1 Online-Ressource (39 Seiten)
    Content: A reanalysis is a physically consistent set of optimally merged simulated model states and historical observational data, using data assimilation. High computational costs for modeled processes and assimilation algorithms has led to Earth system specific reanalysis products for the atmosphere, the ocean and the land separately. Recent developments include the advanced uncertainty quantification and the generation of biogeochemical reanalysis for land and ocean. Here, we review atmospheric and oceanic reanalyzes, and more in detail biogeochemical ocean and terrestrial reanalyzes. In particular, we identify land surface, hydrologic and carbon cycle reanalyzes which are nowadays produced in targeted projects for very specific purposes. Although a future joint reanalysis of land surface, hydrologic, and carbon processes represents an analysis of important ecosystem variables, biotic ecosystem variables are assimilated only to a very limited extent. Continuous data sets of ecosystem variables are needed to explore biotic‐abiotic interactions and the response of ecosystems to global change. Based on the review of existing achievements, we identify five major steps required to develop terrestrial ecosystem reanalysis to deliver continuous data streams on ecosystem dynamics.
    Content: Peer Reviewed
    In: Washington, DC : AGU, 59,3
    Language: English
    DOI: 10.1029/2020RG000715
    DOI: 10.18452/26415
    URN: urn:nbn:de:kobv:11-110-18452/27096-9
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    HU Berlin
  • 9
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    Online Resource
    Understanding Forest Health with Remote Sensing, Part III: Requirements for a Scalable Multi-Source Forest Health Monitoring Network Based on Data Science Approaches (2018)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_21210
    Format: 1 Online-Ressource (52 Seiten)
    Content: Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.
    Content: Peer Reviewed
    In: Basel : MDPI, 10,7
    Language: English
    DOI: 10.3390/rs10071120
    DOI: 10.18452/20441
    URN: urn:nbn:de:kobv:11-110-18452/21210-2
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    HU Berlin
  • 10
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    Online Resource
    Inferring Grassland Drought Stress with Unsupervised Learning from Airborne Hyperspectral VNIR Imagery (2021)
    Berlin : Humboldt-Universität zu Berlin
    Details
    UID:
    edochu_18452_23837
    Format: 1 Online-Ressource (23 Seiten)
    Content: The 2018–2019 Central European drought had a grave impact on natural and managed ecosystems, affecting their health and productivity. We examined patterns in hyperspectral VNIR imagery using an unsupervised learning approach to improve ecosystem monitoring and the understanding of grassland drought responses. The main objectives of this study were (1) to evaluate the application of simplex volume maximisation (SiVM), an unsupervised learning method, for the detection of grassland drought stress in high-dimensional remote sensing data at the ecosystem scale and (2) to analyse the contributions of different spectral plant and soil traits to the computed stress signal. The drought status of the research site was assessed with a non-parametric standardised precipitation–evapotranspiration index (SPEI) and soil moisture measurements. We used airborne HySpex VNIR-1800 data from spring 2018 and 2019 to compare vegetation condition at the onset of the drought with the state after one year. SiVM, an interpretable matrix factorisation technique, was used to derive typical extreme spectra (archetypes) from the hyperspectral data. The classification of archetypes allowed for the inference of qualitative drought stress levels. The results were evaluated using a set of geophysical measurements and vegetation indices as proxy variables for drought-inhibited vegetation growth. The successful application of SiVM for grassland stress detection at the ecosystem canopy scale was verified in a correlation analysis. The predictor importance was assessed with boosted beta regression. In the resulting interannual stress model, carotenoid-related variables had among the highest coefficient values. The significance of the photochemical reflectance index that uses 512 nm as reference wavelength (PRI512) demonstrates the value of combining imaging spectrometry and unsupervised learning for the monitoring of vegetation stress. It also shows the potential of archetypical reflectance spectra to be used for the remote estimation of photosynthetic efficiency. More conclusive results could be achieved by using vegetation measurements instead of proxy variables for evaluation. It must also be investigated how the method can be generalised across ecosystems.
    Content: Peer Reviewed
    In: Basel : MDPI, 13,10
    Language: English
    DOI: 10.3390/rs13101885
    DOI: 10.18452/23222
    URN: urn:nbn:de:kobv:11-110-18452/23837-0
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    HU Berlin
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