TY - JOUR A1 - Dhillon, Maninder Singh A1 - Dahms, Thorsten A1 - Kübert-Flock, Carina A1 - Steffan-Dewenter, Ingolf A1 - Zhang, Jie A1 - Ullmann, Tobias T1 - Spatiotemporal Fusion Modelling Using STARFM: Examples of Landsat 8 and Sentinel-2 NDVI in Bavaria JF - Remote Sensing N2 - The increasing availability and variety of global satellite products provide a new level of data with different spatial, temporal, and spectral resolutions; however, identifying the most suited resolution for a specific application consumes increasingly more time and computation effort. The region’s cloud coverage additionally influences the choice of the best trade-off between spatial and temporal resolution, and different pixel sizes of remote sensing (RS) data may hinder the accurate monitoring of different land cover (LC) classes such as agriculture, forest, grassland, water, urban, and natural-seminatural. To investigate the importance of RS data for these LC classes, the present study fuses NDVIs of two high spatial resolution data (high pair) (Landsat (30 m, 16 days; L) and Sentinel-2 (10 m, 5–6 days; S), with four low spatial resolution data (low pair) (MOD13Q1 (250 m, 16 days), MCD43A4 (500 m, one day), MOD09GQ (250 m, one-day), and MOD09Q1 (250 m, eight day)) using the spatial and temporal adaptive reflectance fusion model (STARFM), which fills regions’ cloud or shadow gaps without losing spatial information. These eight synthetic NDVI STARFM products (2: high pair multiply 4: low pair) offer a spatial resolution of 10 or 30 m and temporal resolution of 1, 8, or 16 days for the entire state of Bavaria (Germany) in 2019. Due to their higher revisit frequency and more cloud and shadow-free scenes (S = 13, L = 9), Sentinel-2 (overall R\(^2\) = 0.71, and RMSE = 0.11) synthetic NDVI products provide more accurate results than Landsat (overall R\(^2\) = 0.61, and RMSE = 0.13). Likewise, for the agriculture class, synthetic products obtained using Sentinel-2 resulted in higher accuracy than Landsat except for L-MOD13Q1 (R\(^2\) = 0.62, RMSE = 0.11), resulting in similar accuracy preciseness as S-MOD13Q1 (R\(^2\) = 0.68, RMSE = 0.13). Similarly, comparing L-MOD13Q1 (R\(^2\) = 0.60, RMSE = 0.05) and S-MOD13Q1 (R\(^2\) = 0.52, RMSE = 0.09) for the forest class, the former resulted in higher accuracy and precision than the latter. Conclusively, both L-MOD13Q1 and S-MOD13Q1 are suitable for agricultural and forest monitoring; however, the spatial resolution of 30 m and low storage capacity makes L-MOD13Q1 more prominent and faster than that of S-MOD13Q1 with the 10-m spatial resolution. KW - Landsat KW - Sentinel-2 KW - NDVI KW - fusion KW - agriculture KW - grassland KW - forest KW - urban KW - water Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-323471 SN - 2072-4292 VL - 14 IS - 3 ER - TY - JOUR A1 - Dhillon, Maninder Singh A1 - Kübert-Flock, Carina A1 - Dahms, Thorsten A1 - Rummler, Thomas A1 - Arnault, Joel A1 - Steffan-Dewenter, Ingolf A1 - Ullmann, Tobias T1 - Evaluation of MODIS, Landsat 8 and Sentinel-2 data for accurate crop yield predictions: a case study using STARFM NDVI in Bavaria, Germany JF - Remote Sensing N2 - The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km\(^2\)) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R\(^2\) = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R\(^2\) = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R\(^2\) = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R\(^2\) = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world. KW - MODIS KW - Sentinel-2 KW - Landsat 8 KW - sustainable agriculture KW - decision-making KW - winter wheat KW - oil seed rape KW - resolution Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311132 SN - 2072-4292 VL - 15 IS - 7 ER - TY - JOUR A1 - Fisser, Henrik A1 - Khorsandi, Ehsan A1 - Wegmann, Martin A1 - Baier, Frank T1 - Detecting moving trucks on roads using Sentinel-2 data JF - Remote Sensing N2 - In most countries, freight is predominantly transported by road cargo trucks. We present a new satellite remote sensing method for detecting moving trucks on roads using Sentinel-2 data. The method exploits a temporal sensing offset of the Sentinel-2 multispectral instrument, causing spatially and spectrally distorted signatures of moving objects. A random forest classifier was trained (overall accuracy: 84%) on visual-near-infrared-spectra of 2500 globally labelled targets. Based on the classification, the target objects were extracted using a developed recursive neighbourhood search. The speed and the heading of the objects were approximated. Detections were validated by employing 350 globally labelled target boxes (mean F\(_1\) score: 0.74). The lowest F\(_1\) score was achieved in Kenya (0.36), the highest in Poland (0.88). Furthermore, validated at 26 traffic count stations in Germany on in sum 390 dates, the truck detections correlate spatio-temporally with station figures (Pearson r-value: 0.82, RMSE: 43.7). Absolute counts were underestimated on 81% of the dates. The detection performance may differ by season and road condition. Hence, the method is only suitable for approximating the relative truck traffic abundance rather than providing accurate absolute counts. However, existing road cargo monitoring methods that rely on traffic count stations or very high resolution remote sensing data have limited global availability. The proposed moving truck detection method could fill this gap, particularly where other information on road cargo traffic are sparse by employing globally and freely available Sentinel-2 data. It is inferior to the accuracy and the temporal detail of station counts, but superior in terms of spatial coverage. KW - Sentinel-2 KW - truck detection KW - road traffic KW - machine learning Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-267174 SN - 2072-4292 VL - 14 IS - 7 ER - TY - JOUR A1 - Kacic, Patrick A1 - Thonfeld, Frank A1 - Gessner, Ursula A1 - Kuenzer, Claudia T1 - Forest structure characterization in Germany: novel products and analysis based on GEDI, Sentinel-1 and Sentinel-2 data JF - Remote Sensing N2 - Monitoring forest conditions is an essential task in the context of global climate change to preserve biodiversity, protect carbon sinks and foster future forest resilience. Severe impacts of heatwaves and droughts triggering cascading effects such as insect infestation are challenging the semi-natural forests in Germany. As a consequence of repeated drought years since 2018, large-scale canopy cover loss has occurred calling for an improved disturbance monitoring and assessment of forest structure conditions. The present study demonstrates the potential of complementary remote sensing sensors to generate wall-to-wall products of forest structure for Germany. The combination of high spatial and temporal resolution imagery from Sentinel-1 (Synthetic Aperture Radar, SAR) and Sentinel-2 (multispectral) with novel samples on forest structure from the Global Ecosystem Dynamics Investigation (GEDI, LiDAR, Light detection and ranging) enables the analysis of forest structure dynamics. Modeling the three-dimensional structure of forests from GEDI samples in machine learning models reveals the recent changes in German forests due to disturbances (e.g., canopy cover degradation, salvage logging). This first consistent data set on forest structure for Germany from 2017 to 2022 provides information of forest canopy height, forest canopy cover and forest biomass and allows estimating recent forest conditions at 10 m spatial resolution. The wall-to-wall maps of the forest structure support a better understanding of post-disturbance forest structure and forest resilience. KW - forest KW - forest structure Germany KW - canopy height KW - Global Ecosystem Dynamics Investigation KW - GEDI KW - Sentinel-1 KW - Sentinel-2 KW - random forest regression Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-313727 SN - 2072-4292 VL - 15 IS - 8 ER - TY - JOUR A1 - Kanmegne Tamga, Dan A1 - Latifi, Hooman A1 - Ullmann, Tobias A1 - Baumhauer, Roland A1 - Thiel, Michael A1 - Bayala, Jules T1 - Modelling the spatial distribution of the classification error of remote sensing data in cocoa agroforestry systems JF - Agroforestry Systems N2 - Cocoa growing is one of the main activities in humid West Africa, which is mainly grown in pure stands. It is the main driver of deforestation and encroachment in protected areas. Cocoa agroforestry systems which have been promoted to mitigate deforestation, needs to be accurately delineated to support a valid monitoring system. Therefore, the aim of this research is to model the spatial distribution of uncertainties in the classification cocoa agroforestry. The study was carried out in Côte d’Ivoire, close to the Taï National Park. The analysis followed three steps (i) image classification based on texture parameters and vegetation indices from Sentinel-1 and -2 data respectively, to train a random forest algorithm. A classified map with the associated probability maps was generated. (ii) Shannon entropy was calculated from the probability maps, to get the error maps at different thresholds (0.2, 0.3, 0.4 and 0.5). Then, (iii) the generated error maps were analysed using a Geographically Weighted Regression model to check for spatial autocorrelation. From the results, a producer accuracy (0.88) and a user’s accuracy (0.91) were obtained. A small threshold value overestimates the classification error, while a larger threshold will underestimate it. The optimal value was found to be between 0.3 and 0.4. There was no evidence of spatial autocorrelation except for a smaller threshold (0.2). The approach differentiated cocoa from other landcover and detected encroachment in forest. Even though some information was lost in the process, the method is effective for mapping cocoa plantations in Côte d’Ivoire. KW - cocoa mapping KW - geographically weighted regression KW - Sentinel-1 KW - Sentinel-2 KW - Shannon entropy KW - spatial error assessment Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-324139 SN - 0167-4366 VL - 97 IS - 1 ER - TY - JOUR A1 - Khare, Suyash A1 - Latifi, Hooman A1 - Khare, Siddhartha T1 - Vegetation growth analysis of UNESCO World Heritage Hyrcanian forests using multi-sensor optical remote sensing data JF - Remote Sensing N2 - Freely available satellite data at Google Earth Engine (GEE) cloud platform enables vegetation phenology analysis across different scales very efficiently. We evaluated seasonal and annual phenology of the old-growth Hyrcanian forests (HF) of northern Iran covering an area of ca. 1.9 million ha, and also focused on 15 UNESCO World Heritage Sites. We extracted bi-weekly MODIS-NDVI between 2017 and 2020 in GEE, which was used to identify the range of NDVI between two temporal stages. Then, changes in phenology and growth were analyzed by Sentinel 2-derived Temporal Normalized Phenology Index. We modelled between seasonal phenology and growth by additionally considering elevation, surface temperature, and monthly precipitation. Results indicated considerable difference in onset of forests along the longitudinal gradient of the HF. Faster growth was observed in low- and uplands of the western zone, whereas it was lower in both the mid-elevations and the western outskirts. Longitudinal range was a major driver of vegetation growth, to which environmental factors also differently but significantly contributed (p < 0.0001) along the west-east gradient. Our study developed at GEE provides a benchmark to examine the effects of environmental parameters on the vegetation growth of HF, which cover mountainous areas with partly no or limited accessibility. KW - Hyrcanian forest KW - NDVI KW - phenology KW - Sentinel-2 KW - TNPI KW - World Heritage Sites KW - Google Earth Engine Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-248398 SN - 2072-4292 VL - 13 IS - 19 ER - TY - JOUR A1 - Klein, Igor A1 - Cocco, Arturo A1 - Uereyen, Soner A1 - Mannu, Roberto A1 - Floris, Ignazio A1 - Oppelt, Natascha A1 - Kuenzer, Claudia T1 - Outbreak of Moroccan locust in Sardinia (Italy): a remote sensing perspective JF - Remote Sensing N2 - The Moroccan locust has been considered one of the most dangerous agricultural pests in the Mediterranean region. The economic importance of its outbreaks diminished during the second half of the 20th century due to a high degree of agricultural industrialization and other human-caused transformations of its habitat. Nevertheless, in Sardinia (Italy) from 2019 on, a growing invasion of this locust species is ongoing, being the worst in over three decades. Locust swarms destroyed crops and pasture lands of approximately 60,000 ha in 2022. Drought, in combination with increasing uncultivated land, contributed to forming the perfect conditions for a Moroccan locust population upsurge. The specific aim of this paper is the quantification of land cover land use (LCLU) influence with regard to the recent locust outbreak in Sardinia using remote sensing data. In particular, the role of untilled, fallow, or abandoned land in the locust population upsurge is the focus of this case study. To address this objective, LCLU was derived from Sentinel-2A/B Multispectral Instrument (MSI) data between 2017 and 2021 using time-series composites and a random forest (RF) classification model. Coordinates of infested locations, altitude, and locust development stages were collected during field observation campaigns between March and July 2022 and used in this study to assess actual and previous land cover situation of these locations. Findings show that 43% of detected locust locations were found on untilled, fallow, or uncultivated land and another 23% within a radius of 100 m to such areas. Furthermore, oviposition and breeding sites are mostly found in sparse vegetation (97%). This study demonstrates that up-to-date remote sensing data and target-oriented analyses can provide valuable information to contribute to early warning systems and decision support and thus to minimize the risk concerning this agricultural pest. This is of particular interest for all agricultural pests that are strictly related to changing human activities within transformed habitats. KW - agricultural pests KW - food security KW - remote sensing KW - locust outbreak KW - abandoned land KW - Sentinel-2 KW - Dociostaurus maroccanus Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-297232 SN - 2072-4292 VL - 14 IS - 23 ER - TY - JOUR A1 - Mayr, Stefan A1 - Klein, Igor A1 - Rutzinger, Martin A1 - Kuenzer, Claudia T1 - Systematic water fraction estimation for a global and daily surface water time-series JF - Remote Sensing N2 - Fresh water is a vital natural resource. Earth observation time-series are well suited to monitor corresponding surface dynamics. The DLR-DFD Global WaterPack (GWP) provides daily information on globally distributed inland surface water based on MODIS (Moderate Resolution Imaging Spectroradiometer) images at 250 m spatial resolution. Operating on this spatiotemporal level comes with the drawback of moderate spatial resolution; only coarse pixel-based surface water quantification is possible. To enhance the quantitative capabilities of this dataset, we systematically access subpixel information on fractional water coverage. For this, a linear mixture model is employed, using classification probability and pure pixel reference information. Classification probability is derived from relative datapoint (pixel) locations in feature space. Pure water and non-water reference pixels are located by combining spatial and temporal information inherent to the time-series. Subsequently, the model is evaluated for different input sets to determine the optimal configuration for global processing and pixel coverage types. The performance of resulting water fraction estimates is evaluated on the pixel level in 32 regions of interest across the globe, by comparison to higher resolution reference data (Sentinel-2, Landsat 8). Results show that water fraction information is able to improve the product's performance regarding mixed water/non-water pixels by an average of 11.6% (RMSE). With a Nash-Sutcliffe efficiency of 0.61, the model shows good overall performance. The approach enables the systematic provision of water fraction estimates on a global and daily scale, using only the reflectance and temporal information contained in the input time-series. KW - earth observation KW - landsat KW - MODIS KW - remote sensing KW - probability KW - Sentinel-2 KW - subpixel KW - water Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-242586 SN - 2072-4292 VL - 13 IS - 14 ER - TY - JOUR A1 - Philipp, Marius B. A1 - Levick, Shaun R. T1 - Exploring the potential of C-Band SAR in contributing to burn severity mapping in tropical savanna JF - Remote Sensing N2 - The ability to map burn severity and to understand how it varies as a function of time of year and return frequency is an important tool for landscape management and carbon accounting in tropical savannas. Different indices based on optical satellite imagery are typically used for mapping fire scars and for estimating burn severity. However, cloud cover is a major limitation for analyses using optical data over tropical landscapes. To address this pitfall, we explored the suitability of C-band Synthetic Aperture Radar (SAR) data for detecting vegetation response to fire, using experimental fires in northern Australia. Pre- and post-fire results from Sentinel-1 C-band backscatter intensity data were compared to those of optical satellite imagery and were corroborated against structural changes on the ground that we documented through terrestrial laser scanning (TLS). Sentinel-1 C-band backscatter (VH) proved sensitive to the structural changes imparted by fire and was correlated with the Normalised Burn Ratio (NBR) derived from Sentinel-2 optical data. Our results suggest that C-band SAR holds potential to inform the mapping of burn severity in savannas, but further research is required over larger spatial scales and across a broader spectrum of fire regime conditions before automated products can be developed. Combining both Sentinel-1 SAR and Sentinel-2 multi-spectral data will likely yield the best results for mapping burn severity under a range of weather conditions. KW - burn severity KW - Sentinel-1 KW - Sentinel-2 KW - terrestrial LiDAR Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-193789 SN - 2072-4292 VL - 12 IS - 1 ER - TY - THES A1 - Reinermann, Sophie T1 - Earth Observation Time Series for Grassland Management Analyses – Development and large-scale Application of a Framework to detect Grassland Mowing Events in Germany T1 - Erdbeobachtungszeitserien zur Analyse der Grünlandbewirtschaftung – Entwicklung und großflächige Anwendung einer Prozessierungsarchitektur zur automatisierten Detektion von Grünlandmahden N2 - Grasslands shape many landscapes of the earth as they cover about one-third of its surface. They are home and provide livelihood for billions of people and are mainly used as source of forage for animals. However, grasslands fulfill many additional ecosystem functions next to fodder production, such as storage of carbon, water filtration, provision of habitats and cultural values. They play a role in climate change (mitigation) and in preserving biodiversity and ecosystem functions on a global scale. The degree to what these ecosystem functions are present within grassland ecosystems is largely determined by the management. Individual management practices and the use intensity influence the species composition as well as functions, like carbon storage, while higher use intensities (e.g. high mowing frequencies) usually show a negative impact. Especially in Central European countries, like in Germany, the determining influence of grassland management on its physiognomy and ecosystem functions leads to a large variability and small-scale alternations of grassland parcels. Large-scale information on the management and use intensity of grasslands is not available. Consequently, estimations of grassland ecosystem functions are challenging which, however, would be required for large-scale assessments of the status of grassland ecosystems and optimized management plans for the future. The topic of this thesis tackles this gap by investigating the major grassland management practice in Germany, which is mowing, for multiple years, in high spatial resolution and on a national scale. Earth Observation (EO) has the advantage of providing information of the earth’s surface on multi-temporal time steps. An extensive literature review on the use of EO for grassland management and production analyses, which was part of this thesis, showed that in particular research on grasslands consisting of small parcels with a large variety of management and use intensity, like common in Central Europe, is underrepresented. Especially the launch of the Sentinel satellites in the recent past now enables the analyses of such grasslands due to their high spatial and temporal resolution. The literature review specifically on the investigation of grassland mowing events revealed that most previous studies focused on small study areas, were exploratory, only used one sensor type and/or lacked a reference data set with a complete range of management options. Within this thesis a novel framework to detect grassland mowing events over large areas is presented which was applied and validated for the entire area of Germany for multiple years (2018–2021). The potential of both sensor types, optical (Sentinel-2) and Synthetic Aperture Radar (SAR) (Sentinel-1) was investigated regarding grassland mowing event detection. Eight EO parameters were investigated, namely the Enhanced Vegetation Index (EVI), the backscatter intensity and the interferometric (InSAR) temporal coherence for both available polarization modes (VV and VH), and the polarimetric (PolSAR) decomposition parameters Entropy, K0 and K1. An extensive reference data set was generated based on daily images of webcams distributed in Germany which resulted in mowing information for grasslands with the entire possible range of mowing frequencies – from one to six in Germany – and in 1475 reference mowing events for the four years of interest. For the first time a observation-driven mowing detection approach including data from Sentinel-2 and Sentinel-1 and combining the two was developed, applied and validated on large scale. Based on a subset of the reference data (13 grassland parcels with 44 mowing events) from 2019 the EO parameters were investigated and the detection algorithm developed and parameterized. This analysis showed that a threshold-based change detection approach based on EVI captured grassland mowing events best, which only failed during periods of clouds. All SAR-based parameters showed a less consistent behavior to mowing events, with PolSAR Entropy and InSAR Coherence VH, however, revealing the highest potential among them. A second, combined approach based on EVI and a SARbased parameter was developed and tested for PolSAR Entropy and InSAR VH. To avoid additional false positive detections during periods in which mowing events are anyhow reliably detected using optical data, the SAR-based mowing detection was only initiated during long gaps within the optical time series (< 25 days). Application and validation of these approaches in a focus region revealed that only using EVI leads to the highest accuracies (F1-Score = 0.65) as combining this approach with SAR-based detection led to a strong increase in falsely detected mowing events resulting in a decrease of accuracies (EVI + PolSAR ENT F1-Score = 0.61; EVI + InSAR COH F1-Score = 0.61). The mowing detection algorithm based on EVI was applied for the entire area of Germany for the years 2018-2021. It was revealed that the largest share of grasslands with high mowing frequencies (at least four mowing events) can be found in southern/south-eastern Germany. Extensively used grassland (mown up to two times) is distributed within the entire country with larger shares in the center and north-eastern parts of Germany. These patterns stay constant in general, but small fluctuations between the years are visible. Early mown grasslands can be found in southern/south-eastern Germany – in line with high mowing frequency areas – but also in central-western parts. The years 2019 and 2020 revealed higher accuracies based on the 1475 mowing events of the multi-annual validation data set (F1-Scores of 0.64 and 0.63), 2018 and 2021 lower ones (F1-Score of 0.52 and 0.50). Based on this new, unprecedented data set, potential influencing factors on the mowing dynamics were investigated. Therefore, climate, topography, soil data and information on conservation schemes were related to mowing dynamics for the year 2020, which showed a high number of valid observations and detection accuracy. It was revealed that there are no strong linear relationships between the mowing frequency or the timing of the first mowing event and the investigated variables. However, it was found that for intensive grassland usage certain climatic and topographic conditions have to be fulfilled, while extensive grasslands appear on the entire spectrum of these variables. Further, higher mowing frequencies occur on soils with influence of ground water and lower mowing frequencies in protected areas. These results show the complex interplay between grassland mowing dynamics and external influences and highlight the challenges of policies aiming to protect grassland ecosystem functions and their need to be adapted to regional circumstances. N2 - Grünland prägt viele Landschaften der Erde, da es etwa ein Drittel der Erdoberfläche bedeckt. Es ist Heimat und Lebensgrundlage für Milliarden von Menschen und wird hauptsächlich als Futterquelle für die Viehhaltung genutzt. Neben der Futterproduktion erfüllen Grünlandflächen jedoch viele weitere Ökosystemfunktionen, wie die Speicherung von Kohlenstoff, die Wasserfilterung, die Bereitstellung von Lebensräumen, als auch kulturelle Werte. Sie spielen eine Rolle bei der Abschwächung des Klimawandels und bei der Erhaltung der biologischen Vielfalt und der Ökosystemfunktionen auf globaler Ebene. Das Ausmaß, in dem diese Ökosystemfunktionen in Grünlandökosystemen vorhanden sind, wird weitgehend durch die Bewirtschaftung bestimmt. Einzelne Bewirtschaftungspraktiken und die Nutzungsintensität beeinflussen sowohl die Artenzusammensetzung als auch Funktionen wie die Kohlenstoffspeicherung, wobei höhere Nutzungsintensitäten (z. B. hohe Mähfrequenzen) in der Regel einen negativen Einfluss haben. Insbesondere in mitteleuropäischen Ländern wie Deutschland, führt der bestimmende Einfluss der Grünlandbewirtschaftung auf die Physiognomie und die Ökosystemfunktionen zu einer großen Variabilität und kleinräumigen Differenziertheit einzelner Grünlandflächen. Großräumige Informationen über die Bewirtschaftungs- und Nutzungsintensität von Grünland sind nicht verfügbar. Folglich sind Schätzungen der Ökosystemfunktionen von Grünland eine Herausforderung, die jedoch für großräumige Bewertungen des Zustands von Grünlandökosystemen und optimierte Bewirtschaftungspläne für die Zukunft erforderlich wären. Das Thema dieser Arbeit greift diese Lücke auf, indem es die wichtigste Grünlandbewirtschaftungsmethode in Deutschland, die Mahd, über mehrere Jahre, mit hoher räumlicher Auflösung und auf nationaler Ebene untersucht. Die Erdbeobachtung hat den Vorteil, Informationen über die Erdoberfläche in multitemporalen Zeitschritten zu liefern. Eine umfangreiche Literaturrecherche zur Nutzung von Erdbeobachtung für Grünlandmanagement und Produktion, welche Teil dieser Arbeit war, hat gezeigt, dass insbesondere die Forschung zu kleinparzelligem Grünland mit einer großen Vielfalt an Bewirtschaftungs- und Nutzungsintensitäten, wie in Mitteleuropa gängig, unterrepräsentiert ist. Insbesondere die vor wenigen Jahren erfolgte Start der Sentinel-Satellitenmissionen ermöglicht nun auch die Analyse solcher Grünlandflächen aufgrund der hohen räumlichen und zeitlichen Auflösung ihrer Aufnahmen. Die Literaturrecherche speziell zur Untersuchung von Mähereignissen auf Grünland ergab, dass die meisten bisherigen Studien sich auf kleine Untersuchungsgebiete konzentrierten, explorativ waren, nur einen Sensortyp verwendeten und/oder keinen Referenzdatensatz mit einer vollständigen Palette von Managementoptionen enthielten. Im Rahmen dieser Arbeit wird eine neuartige Methodik zur Erkennung von Grünlandmahdereignissen vorgestellt, welches über mehrere Jahre (2018-2021) flächendeckend in Deutschland angewendet und validiert wurde. Beide Sensortypen – optisch (Sentinel-2) und SAR (Sentinel-1) – wurden hinsichtlich ihres Potentials zur Detektion von Grünlandmahdereignissen ausgewertet. Acht EO-Parameter wurden untersucht, nämlich der Enhanced Vegetation Index (EVI), die Rückstreuintensität und die interferometrische zeitliche Kohärenz (InSAR) für beide verfügbaren Polarimetrien (VV und VH), sowie die polarimetrischen (PolSAR) Zerlegungsparameter Entropie, K0 und K1. Ein umfangreicher Referenzdatensatz wurde auf der Basis täglicher Bilder von Webcams generiert, welche über Deutschland verteilt sind. Dieser enthält Mahdinformationen für Grünland mit dem gesamten möglichen Spektrum an Mähfrequenzen – von eins bis sechs Mahden – und 1475 Referenz-Mähereignisse für die Untersuchungsjahre. Zum ersten Mal wurde ein Ansatz basierend auf tatsächlichen Beobachtungen zur Erkennung der Mahd entwickelt, angewandt und großflächig validiert, der Daten von Sentinel - 2 und Sentinel - 1 verwendet und beide miteinander kombiniert. Anhand eines Subset der Referenzdaten (13 Grünlandparzellen) wurden die EO-Parameter untersucht und der Algorithmus zur Mahddetektion entwickelt und parametrisiert. Die Analyse hat gezeigt, dass ein schwellenwertbasierter Ansatz zur Erkennung von Veränderungen auf der Grundlage des EVI die Ereignisse der Grünlandmahd am besten erfasst, und nur während Bewölkungsperioden Mahden nicht erfolgreich detektiert. Alle SAR-basierten Parameter zeigten ein inkonsistenteres Verhalten gegenüber Mähaktivitäten als EVI, wobei PolSAR Entropie und InSAR Kohärenz VH noch das höchste Potenzial aufwiesen. Ein zweiter, kombinierter Ansatz, der auf EVI und einem SAR Parameter basiert, wurde entwickelt und für PolSAR Entropie und InSAR VH getestet. Aufgrund vieler zusätzlicher Veränderungen, die in den Zeitreihen erkennbar sind, wurde die SAR-basierte Mahddetektion nur während langer Lücken in den optischen Zeitreihen (< 25 Tage) initiiert. Die Anwendung und Validierung dieser Ansätze in einer Fokusregion ergab, dass die Verwendung des EVI-Ansatzes zu den höchsten Genauigkeiten führt (F1-Score = 0.65), da die Kombination dieses Ansatzes mit der SAR-basierten Detektion zu einem starken Anstieg der falsch erkannten Mähereignisse und damit zu einer Abnahme der Genauigkeiten führte (EVI + PolSAR ENT F1-Score=0.61; EVI + InSAR COH F1-Score = 0.61). Der auf EVI basierende Mahddetektionsalgorithmus wurde für die gesamte Fläche Deutschlands für die Jahre 2018–2021 angewendet. Es zeigte sich, dass der größte Anteil an Grünland mit hoher Mähfrequenz (mindestens vier Mähereignisse) im Süden/Südosten Deutschlands zu finden ist. Extensiv genutztes Grünland (bis zu zweimal gemäht) ist über das gesamte Bundesgebiet verteilt, mit größeren Anteilen in der Mitte und im Nordosten Deutschlands. Diese Muster bleiben im Allgemeinen konstant, aber es sind kleine Schwankungen zwischen den Jahren erkennbar. Früh gemähtes Grünland findet sich in Süd-/Südostdeutschland - entsprechend den Gebieten mit hoher Mähfrequenz -, aber auch in Mittel- und Westdeutschland. Die Jahre 2019 und 2020 zeigen höhere Genauigkeiten (F1- Scores von 0.64 und 0.63), 2018 und 2021 niedrigere (F1-Score von 0.52 und 0.50). Darüber hinaus wurden mögliche Einflussfaktoren auf die Mahddynamik untersucht. So wurden Klima, Topografie, Bodendaten und Informationen über Schutzmaßnahmen mit der Mahddynamik für das Jahr 2020 in Verbindung gebracht, für welches eine hohe Anzahl gültiger Beobachtungen und eine hohe Erfassungsgenauigkeit erzielt werden konnten. Es zeigte sich, dass es keine starken linearen Beziehungen zwischen der Mahdhäufigkeit oder dem Zeitpunkt der ersten Mahd und den untersuchten Variablen gibt. Es wurde jedoch festgestellt, dass für eine intensive Grünlandnutzung bestimmte klimatische und topografische Bedingungen erfüllt sein müssen, wohingegen extensive Grünlandflächen im gesamten Spektrum dieser Variablen auftreten. Außerdem treten auf Böden mit Grundwassereinfluss höhere und in Schutzgebieten niedrigere Mahdhäufigkeiten auf. Diese Ergebnisse zeigen das komplexe Zusammenspiel zwischen der Dynamik der Grünlandmahd und äußeren Einflüssen und verdeutlichen die Herausforderungen in der gezielten Erstellung von Maßnahmen zum Schutz von Grünland-Ökosystemfunktionen und die Notwendigkeit diese regional anzupassen. KW - Grünland KW - Erdbeobachtung KW - Fernerkundung KW - Mähen KW - Grünlandnutzung KW - Zeitreihe KW - Erde KW - Sentinel-1 KW - Sentinel-2 KW - Enhanced Vegetation Index KW - PolSAR KW - InSAR Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-322737 ER -