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Efficient monitoring of crop traits such as biomass and nitrogen uptake is essential for an optimal application of nitrogen fertilisers. However, currently available remote sensing approaches suffer from technical shortcomings, such as poor area efficiency, long postprocessing requirements and the inability to capture ground and canopy from a single acquisition. To overcome such shortcomings, LiDAR scanners mounted on unmanned aerial vehicles (UAV LiDAR) represent a promising sensor technology. To test the potential of this technology for crop monitoring, we used a RIEGL Mini-VUX-1 LiDAR scanner mounted on a DJI Matrice 600 pro UAV to acquire a point cloud from a winter wheat field trial. To analyse the UAV-derived LiDAR point cloud, we adopted LiDAR metrics, widely used for monitoring forests based on LiDAR data acquisition approaches. Of the 57 investigated UAV LiDAR metrics, the 95th percentile of the height of normalised LiDAR points was strongly correlated with manually measured crop heights (R2 = 0.88) and with crop heights derived by monitoring using a UAV system with optical imaging (R2 = 0.92). In addition, we applied existing models that employ crop height to approximate dry biomass (DBM) and nitrogen uptake. Analysis of 18 destructively sampled areas further demonstrated the high potential of the UAV LiDAR metrics for estimating crop traits. We found that the bincentile 60 and the 90th percentile of the reflectance best revealed the relevant characteristics of the vertical structure of the winter wheat plants to be used as proxies for nitrogen uptake and DBM. We conclude that UAV LiDAR metrics provide relevant characteristics not only of the vertical structure of winter wheat plants, but also of crops in general and are, therefore, promising proxies for monitoring crop traits, with potential use in the context of Precision Agriculture.

The Atacama Desert is the driest and oldest desert on Earth. Despite the abundance evidence for long-term landscape stability, there are subtle signs of localised fluvial erosion and deposition since the onset of hyperaridity in the rock record. In the dry core of the Atacama Desert, pluvial episodes allowed antecedent drainage to incise into uplifting fault scarps, which in turn generated sinuous to meandering channels. Incision of ancient alluvial fan surfaces occurred during intermittent fluvial periods, albeit without signs of surface erosion. Fluvial incision during predominantly hyperarid climate periods is evident from these channels in unconsolidated alluvium. The absence of dense vegetation to provide bank stability and strength led us to investigate the potential role of regionally ubiquitous CaSO

Remote sensing systems based on unmanned aerial vehicles (UAVs) are well suited for airborne monitoring of small to medium-sized farmland in agricultural applications. An imaging system is often used in the form of a multispectral multi-camera system to derive well-established vegetation indices (VIs) efficiently. This study investigates the potential of such a multi-camera system with a novel approach to extend spectral sensitivity from visible-to-near-infrared (VNIR) to short-wave infrared (SWIR) (400–1700 nm) for estimating forage mass from an aerial carrier platform. The system test was performed in a grassland fertilizer trial in Germany near Cologne in late July 2019. Within 37 min, a spectral response in four different wavelength bands in the NIR and SWIR range was acquired during two consecutive flights. Spectral image data were calibrated to reflectance using two different methods. The resulting reflectance data sets were processed to orthomosaics for each wavelength band. From these orthomosaics for both calibration methods, the four-band NIR/SWIR GnyLi VI and the two-band NIR/SWIR Normalized Ratio Index (NRI), were calculated. During both UAV flights, spectral ground truth data were recorded with a spectroradiometer on 12 plots in total for validation of camera-based spectral data. The camera and spectroradiometer data sets were directly compared in resulting reflectance and further analyzed with simple linear regression (SLR) models to predict dry matter (DM) yield. In the camera-based SLRs, the NRI performed best with $$R^2$$ R 2 of 0.73 and 0.75 (RMSE: 0.18 and 0.17) before the GnyLi with $$R^{2}$$ R 2 of 0.71 and 0.73 (RMSE: 0.19 and 0.18). These results clearly indicate the potential of the camera system for applications in forage mass monitoring.

Monitoring biomass yield in grassland is of key importance to support sustainable management decisions. Especially the high spatio-temporal variety in grasslands requires rapid and cost-efficient data acquisition with a high spatial and temporal resolution. Therefore, this study aims to evaluate the comparability of UAV-based simultaneously acquired vegetation indices from a consumer-grade RGB-camera (Sony Alpha 6000) and a well-calibrated narrow-band multispectral camera (MicaSense RedEdge-M) to estimate dry matter biomass yield. The study site is an experimental grassland field in Germany with four nitrogen fertilizer levels. Biomass yield and UAV-based data for the first cut in May 2018 was analysed in this study. From the RGB-data the Plant Pigment Ratio Index (PPR) and the Normalized Green Red Difference Index (NGRDI) and from the multispectral data the Normalized Difference Vegetation Index (NDVI) are calculated as predictors for dry biomass yield. The NGRDI and NDVI perform moderately well with cross-validation R2 of 0.57 and 0.63 respectively, while the PPR performs better with an R2 of 0.70. These results indicate the potential of low-cost UAV-based methods for rapid assessment of grasslands.

[Abstract] The Atacama Desert in northern Chile is one of the driest deserts on Earth. Hyperaridity persists at least since the Miocene and was punctuated by pluvial phases. However, very little is known about the timing, regional spread and intensities of precipitation changes. Here, we present a new precipitation record from a sedimentary sequence recovered in a tectonically blocked endorheic basin that is located in the hyperarid core of the Atacama Desert. The chronostratigraphic framework of the record is given by a multi-disciplinary dating approach, suggesting an age of ca. 68 ka BP for the core base. The sequence consists of three sediment types, whose sedimentological and geochemical characteristics suggest different depositional processes that reflect different degrees in humidity. First, particularly fine-grained sediments with high clastic but low calcium sulfate and carbonate contents reflect a particularly dry climate with only sporadic precipitation events and fluvial supply via channel systems. Second, more coarse-grained sediments with lower clastic and higher calcium sulfate and carbonate contents reflect more moist conditions with stronger precipitation events that lead to fluvial activity not restricted to the channels but involving the slopes and plains in the catchment. Third, normally graded layers with an equally high proportion of calcium sulfate and carbonate reflect occasional high-precipitation events that caused sediment supply also from most distant parts of the catchment via severe flash floods. The sedimentary succession suggests that precipitation changes took place on orbital but also on millennial time scales. Rather moist periods occurred during most of MIS 2, several shorter periods within MIS 3 and parts of MIS 4. Comparison of the findings from the Huara record with selected climate records from continental and marine sites in South America suggests a strong precipitation heterogeneity across the Atacama. This heterogeneity is caused by pronounced differences in the dominating climate patterns and a shift from predominant summer rain in the north to winter rain in the south. Precipitation supply to the Huara clay plan is controlled by the atmospheric circulation rather than the surface temperature of the adjacent ocean.

Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R2 values of 0.62.

Short-wave infrared (SWIR) imaging systems with unmanned aerial vehicles (UAVs) are rarely used for remote sensing applications, like for vegetation monitoring. The reasons are that in the past, sensor systems covering the SWIR range were too expensive, too heavy, or not performing well enough, as, in contrast, it is the case in the visible and near-infrared range (VNIR). Therefore, our main objective is the development of a novel modular two-channel multispectral imaging system with a broad spectral sensitivity from the visible to the short-wave infrared spectrum (approx. 400 nm to 1700 nm) that is compact, lightweight and energy-efficient enough for UAV-based remote sensing applications. Various established vegetation indices (VIs) for mapping vegetation traits can then be set up by selecting any suitable filter combination. The study describes the selection of the individual components, starting with suitable camera modules, the optical as well as the control and storage parts. Special bandpass filters are used to select the desired wavelengths to be captured. A unique flange system has been developed, which also allows the filters to be interchanged quickly in order to adapt the system to a new application in a short time. The characterization of the system was performed in the laboratory with an integrating sphere and a climatic chamber. Finally, the integration of the novel modular VNIR/SWIR imaging system into a UAV and a subsequent first outdoor test flight, in which the functionality was tested, are described.

In the context of an increasing world population, the demand for agricultural crops is continuously rising. Especially rice plays a key role in food security, not only in Asia. To increase crop production of rice, either productivity of plants has to be improved or new cultivation areas have to be found. In this context, our study investigated crop growth of paddy rice (Oryza Sativa J.) in Germany. An experimental field in the vegetation period of 2014 with two nitrogen treatments was conducted using remote sensing methods. The research project focussed on two main aspects: (1) the potential of UAV-based and hyperspectral remote sensing methods to monitor selected growth parameters at different phenological stages; (2) the potential of paddy rice cultivation under the present climate condition in western Germany. We applied a low-cost UAV-system (Unmanned Aerial Vehicle) to generate high resolution Crop Surface Models (CSM). These were compared with hyperspectral in-field measurements and directly measured agronomic parameters (fresh and dry aboveground biomass (AGB), leaf-area-index (LAI) and plant nitrogen concentration (PNC)). For all acquisition dates we could determine single in-field structures in the CSM (e.g. distribution of hills) and different growth characteristics between the nitrogen treatments. Especially in the second half of the growing season, the plants with higher nitrogen availability were about 25 – 30 % larger. The plant height in the CSM correlates particularly with fresh AGB and the LAI (R2 > 0.8). Thus, the conducted methods for plant growth monitoring can be a contribution for precision agriculture approaches.

Paleoenvironmental studies and according information (data) are abundantly published and available in the scientific record. However, GIS-based paleoenvironmental information and datasets are comparably rare. Here, we present an Open Science approach for creating GIS-based data and maps of paleoenvironments, and Open Access publishing them in a web based Spatial Data Infrastructure (SDI), for access by the archaeology and paleoenvironment communities. We introduce an approach to gather and create GIS datasets from published non-GIS based facts and information (data), such as analogous maps, textual information or figures in scientific publications. These collected and created geo-datasets and maps are then published, including a Digital Object Identifier (DOI) to facilitate scholarly reuse and citation of the data, in a web based Open Access Research Data Management Infrastructure. The geo-datasets are additionally published in an Open Geospatial Consortium (OGC) standards compliant SDI, and available for GIS integration via OGC Open Web Services (OWS).

In this study we combined selected vegetation indices (VIs) and plant height information to estimate biomass in a summer barley experiment. The VIs were calculated from ground-based hyperspectral data and unmanned aerial vehicle (UAV)-based red green blue (RGB) imaging. In addition, the plant height information was obtained from UAV-based multi-temporal crop surface models (CSMs). The test site is a summer barley experiment comprising 18 cultivars and two nitrogen treatments located in Western Germany. We calculated five VIs from hyperspectral data. The normalised ratio index (NRI)-based index GnyLi (Gnyp et al., 2014) showed the highest correlation ( R 2 = 0.83) with dry biomass. In addition, we calculated three visible band VIs: the green red vegetation index (GRVI), the modified GRVI (MGRVI) and the red green blue VI (RGBVI), where the MGRVI and the RGBVI are newly developed VI. We found that the visible band VIs have potential for biomass prediction prior to heading stage. A robust estimate for biomass was obtained from the plant height models ( R 2 = 0.80–0.82). In a cross validation test, we compared plant height, selected VIs and their combination with plant height information. Combining VIs and plant height information by using multiple linear regression or multiple non-linear regression models performed better than the VIs alone. The visible band GRVI and the newly developed RGBVI are promising but need further investigation. However, the relationship between plant height and biomass produced the most robust results. In summary, the results indicate that plant height is competitive with VIs for biomass estimation in summer barley. Moreover, visible band VIs might be a useful addition to biomass estimation. The main limitation is that the visible band VIs work for early growing stages only.

Due to today’s broad and often free availability of detailed satellite data, it became possible to examine desert areas on a large scale and, ideally, down to tiny details for old trans-Saharan route systems, which are abandoned since centuries or even thousands of years ago. Additionally, digital elevation models (DEMs) can be used to evaluate the geomorphological situation. In conjunction with historical sources and ground-truth data, these data allow us to reconstruct the position of the desert routes with reasonable accuracy, here exemplified for the Western Desert of Egypt. On the central limestone plateau, where stony ground (hamada, serir) prevails, most of the routes can precisely been seen from space because of their specific natural preservation conditions. In contrast, sandy surfaces usually do not allow recognition of routes in the satellite image. Additionally, the quite narrow donkey tracks of the pharaonic routes are mostly invisible from space. On the other hand, DEMs (ASTER, SRTM) allow calculating ideal routes by means of geographical information systems (GIS), usually applied in cases were traditional routes have not survived. In the present study, we confront both methods using the example of the Darb el-Tawil to test the hypothesis that these old roads largely follow the ideal route. This 250 km long road has been one of the primary arteries between the Nile Valley and the Western Desert oases during the past 4000 years. Due to modern demographic and economic development, it has been selected for the construction of a new paved highway, which will irrevocably destroy the still existing tracks and archaeological objects.

Hyperspectral remote sensing helps to acquire information about the status of agricultural crops to allow optimized management practices in the context of precision agriculture. Due to technological innovations small and lightweight hyperspectral sensors have become available which may be carried by unmanned aerial vehicles (UAVs). In this paper we give a brief overview over existing hyperspectral sensors for UAVs. We focus on a new type of full-frame sensors which capture hyperspectral information in two dimensional image frames. We then develop a calibration procedure for these sensors and identify challenges in remote sensing of vegetation. The calibration is evaluate by in-field data acquired during a flight campaign. The spectral calibration shows good results with less than three percent difference in reflection for 110 of the 125 bands (458 to 886 nm)., International archives of photogrammetry, remote sensing and spatial information sciences, XL-7, ISSN:1682-1750, ISSN:1682-1777, ISPRS Technical Commission VII Symposium

Monitoring grassland biomass throughout the growing season is of key importance in sustainable, site-specific management decisions. Precision agriculture applications can support these decisions. However, precision agriculture relies on timely and accurate information on plant parameters with a high spatial and temporal resolution. The use of structural and spectral features derived from unmanned aerial vehicle (UAV)-based image data from low-cost sensors is a promising nondestructive approach to assess plant traits such as above-ground biomass or plant height. Therefore, the main objectives were (1) to evaluate the potential of low-cost UAV-based canopy surface models to monitor sward height as an indicator of grassland biomass, (2) to evaluate the potential of vegetation indices from low-cost UAV-based red-green-blue (RGB) digital image data, and (3) to compare the mentioned methods with established methods for biomass monitoring such as rising plate meters and spectroradiometer-based narrowband vegetation indices over the growing season in 2017, including three cuts. We compared the accuracy of each single UAV-based height feature and vegetation index using a combined multivariate approach to estimate fresh and dry biomass. The heterogeneous sward structure with high spatiotemporal variability led to varying performance in biomass estimation depending on the growths (time between two cuts) and choice of predictor variable. The results showed that biomass prediction by height features provided moderate-to-good results (cross-validation R 2 = 0.57 to 0.73 for dry biomass and 0.43 to 0.79 for fresh biomass), but reference measurements based on rising plate meters were more robust when estimating biomass. The spectral features (RGB-based vegetation indices and spectroradiometer-based vegetation indices) yielded varying accuracy and suitability for biomass prediction. Despite the variability, our findings indicate a promising approach for grassland biomass monitoring.

New freely available digital elevation data derived from the Shuttle Radar Topography Mission (SRTM-3) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) allow, for the first time, the use of area-wide high-resolution elevation models. They are useful for various investigations, especially in regions that are devoid of detailed topographic maps. In this contribution, the geomorphological, sedimentological, geochronological, and archaeological results of the Cologne Collaborative Research Centre ACACIA are presented, showing that the southern Great Sand Sea of Egypt (eastern Sahara) is composed of a complex dune pattern of Pleistocene megadunes (draa) and Holocene dunes. A conceptual model is presented suggesting draa, formed by a strong unimodal trade wind regime that acted, most probably, as helical roll-vortices. The draa differ conspicuously in their dimensions and in their evolution from the smaller longitudinal dunes, which arise from the recent bimodal wind regime. First results of comparative investigations in the southern Namib Erg (Namibia) yielded similar conditions and a synchronous landscape development. Based on these results, the second part of the paper evaluates the elevation data for both regions with respect to their availability for the determination of draa and dunes and the morphometric quantification of the draa. Due to their different resolution (SRTM-3: 90 m, ASTER: 30 m) and their different sensor technology (SRTM-3: radar, ASTER: stereo data), both models have assets and drawbacks but can complement each other. With the calculation of geomorphometric parameters (elevation, slope), the ASTER digital elevation model allows the location of the draa of the Great Sand Sea of Egypt, the quantification of their spatial distribution, size, volume, and consequently the Holocene dune movement. In conclusion, the resolution of the ASTER elevation model is applicable for quantitative analyses of well-defined morphological elements with altitudinal variations > 40–50 m. The volume estimation presented provides a tool for a realistic assessment of the sand mass balance of sand seas. This is not possible with the SRTM-3 model and generally impossible with both models if the altitudinal variation is

Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs). The super-high resolution, multi-temporal (1 cm/pixel) CSMs were derived from red, green, blue (RGB) images captured from a small unmanned aerial vehicle (UAV). Comparison with PH reference measurements yielded an R2 of 0.92. The test site with different cultivars and treatments was monitored during “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie” (BBCH) Stages 24–89. A high correlation was found between PH from CSMs and fresh biomass (R2 = 0.81) and dry biomass (R2 = 0.82). Five models for above-ground fresh and dry biomass estimation were tested by cross-validation. Modelling biomass between different N-treatments for fresh biomass produced the best results (R2 = 0.71). The main limitation was the influence of lodging cultivars in the later growth stages, producing irregular plant heights. The method has potential for future application by non-professionals, i.e., farmers.