Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits-A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R-2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R-2: 0.75 to 0.84). These results indicate the imaging system's potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed.Peer reviewe

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed

Investigating the Potential of a Newly Developed UAV-Mounted VNIR/SWIR Imaging System for Monitoring Crop Traits—A Case Study for Winter Wheat

UAV-based multispectral multi-camera systems are widely used in scientific research for non-destructive crop traits estimation to optimize agricultural management decisions. These systems typically provide data from the visible and near-infrared (VNIR) domain. However, several key absorption features related to biomass and nitrogen (N) are located in the short-wave infrared (SWIR) domain. Therefore, this study investigates a novel multi-camera system prototype that addresses this spectral gap with a sensitivity from 600 to 1700 nm by implementing dedicated bandpass filter combinations to derive application-specific vegetation indices (VIs). In this study, two VIs, GnyLi and NRI, were applied using data obtained on a single observation date at a winter wheat field experiment located in Germany. Ground truth data were destructively sampled for the entire growing season. Likewise, crop heights were derived from UAV-based RGB image data using an improved approach developed within this study. Based on these variables, regression models were derived to estimate fresh and dry biomass, crop moisture, N concentration, and N uptake. The relationships between the NIR/SWIR-based VIs and the estimated crop traits were successfully evaluated (R2: 0.57 to 0.66). Both VIs were further validated against the sampled ground truth data (R2: 0.75 to 0.84). These results indicate the imaging system’s potential for monitoring crop traits in agricultural applications, but further multitemporal validations are needed

Scientific Research Data Management for Soil-Vegetation-Atmosphere Data – The TR32DB

The implementation of a scientific research data management system is an important task within long-term, interdisciplinary research projects. Besides sustainable storage of data, including accurate descriptions with metadata, easy and secure exchange and provision of data is necessary, as well as backup and visualisation. The design of such a system poses challenges and problems that need to be solved.This paper describes the practical experiences gained by the implementation of a scientific research data management system, established in a large, interdisciplinary research project with focus on Soil-Vegetation-Atmosphere Data

Olympia’s Harbour Site Pheia (Elis, Western Peloponnese, Greece) Destroyed by Tsunami Impact

It is well known from historic catalogues that the Greek coast has repeatedly been struck by large earthquakes and associated tsunami events during the past millennia. The seismically highly active Hellenic Arc, where the African plate is being subducted by the Aegean microplate, is considered to be the most significant tsunami source in the wider region. The study presented in this paper focuses on sedimentary and geomorphological tsunami traces encountered at Pheia, western Peloponnese (Greece), one of the harbours of the nearby cult site Olympia. Sedimentological, pedological, geoarchaeological and geochemical analyses revealed tsunami sand and gravel of mostly marine origin reaching far inland. Wave refraction and channeling effects seem to have steepened tsunami waters up to 18-20 m above present sea level and induced tsunami water passage across the narrow Katakolo Pass into adjacent coastal plains. Tsunami deposits that were accumulated onshore were partly cemented and later exposed in the form of beachrock. By radiocarbon dating and archaeological age estimation of ceramic fragments, three distinct tsunami events were found, namely for the 6th millennium BC, for the time around 4300 ± 200 cal BC and for the Byzantine to post-Byzantine period. Olympia’s harbour site Pheia was finally destroyed by tsunami landfall, most probably in the 6th century AD and accompanied by co-seismic submergence

Monitoring costs of result-based payments for biodiversity conservation: Will UAV-based remote sensing be the game-changer?

Paying landowners for conservation results rather than paying for the measures intended to provide such results is a promising approach for biodiversity conservation. However, a key roadblock for the widespread implementation of such result-based payment schemes are the frequent difficulties to monitor target species for whose presence a landowner is supposed to receive a remuneration. Until recently, the only conceivable monitoring approach would be conventional monitoring techniques, by which qualified experts investigate the presence of target species on-site. With the rise of remote sensing technologies, in particular increased capabilities and decreased costs of unmanned aerial vehicles (UAVs), technological monitoring opportunities enter the scene. We analyse the costs of monitoring an ecological target of a hypothetical result-based payments scheme and compare the monitoring cost between conventional monitoring and UAV-assisted monitoring. We identify the underlying cost structure and cost components of both monitoring approaches and use a scenario analysis to identify the influence of factors like UAV and analysis costs, area size, and monitoring frequency. We find that although conventional monitoring is the least-cost monitoring approach today, future cost developments are likely to render UAV-assisted monitoring more cost-effective

Estimating Nitrogen from Structural Crop Traits at Field Scale-A Novel Approach Versus Spectral Vegetation Indices

A sufficient nitrogen (N) supply is mandatory for healthy crop growth, but negative consequences of N losses into the environment are known. Hence, deeply understanding and monitoring crop growth for an optimized N management is advisable. In this context, remote sensing facilitates the capturing of crop traits. While several studies on estimating biomass from spectral and structural data can be found, N is so far only estimated from spectral features. It is well known that N is negatively related to dry biomass, which, in turn, can be estimated from crop height. Based on this indirect link, the present study aims at estimating N concentration at field scale in a two-step model: first, using crop height to estimate biomass, and second, using the modeled biomass to estimate N concentration. For comparison, N concentration was estimated from spectral data. The data was captured on a spring barley field experiment in two growing seasons. Crop surface height was measured with a terrestrial laser scanner, seven vegetation indices were calculated from field spectrometer measurements, and dry biomass and N concentration were destructively sampled. In the validation, better results were obtained with the models based on structural data (R-2 < 0.85) than on spectral data (R-2 < 0.70). A brief look at the N concentration of different plant organs showed stronger dependencies on structural data (R-2: 0.40-0.81) than on spectral data (R-2: 0.18-0.68). Overall, this first study shows the potential of crop-specific across-season two-step models based on structural data for estimating crop N concentration at field scale. The validity of the models for in-season estimations requires further research