
Christoph HüttUniversity of Cologne | UOC · Institute of Geography
Christoph Hütt
Dr. rer. nat.
About
40
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Introduction
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June 2009 - present
Publications
Publications (40)
In 2019, we introduced a novel VNIR-SWIR multi-camera system for UAV-based SWIR spectral imaging, making narrowband 2D spectral image data more accessible for agricultural field analysis. Despite its success and robust performance, it faced limitations, such as the need for multiple flights for comprehensive spectral captures. To address these shor...
This study uses a cost-benefit analysis to compare the economic effects of using three dig-ital technologies for herbage mass estimation: Rising Plate Meter (RPM), Unmanned Aerial Vehicle with Structure from Motion (UAV SfM) and Portable Light Detection and Ranging (UAV LiDAR) systems in small-scale farms in mountainous regions of southern Germany....
In Precision Agriculture applications, the knowledge of the N-uptake of crops in a spatial context is a precondition for precise N-fertilization. Therefore, non-destructive methods to determine N concentration or N-uptake of crops are of key interest. In this contribution, we will present an approach based on UAV-based crop height analysis to estim...
Remote sensing approaches using Unmanned Aerial Vehicles (UAVs) have become an established method to monitor agricultural systems. They enable data acquisition with multi- or hyperspectral, RGB, or LiDAR sensors. For non-destructive estimation of crop or sward traits, photogrammetric analysis using Structure from Motion and Multiview Stereopsis (Sf...
Sustainable utilisation of the available grazing area acts to increase the profitability and productivity of livestock grazing and should consider animals and grass sward. The labour-intensive and time-consuming tasks of fencing, animal monitoring, and controlling forage availability on pasture are general obstacles to the wider implementation of g...
Plant height (PH) is a helpful parameter for understanding plant development and stress. Therefore, extensive experimental fields can benefit by collecting different plant parameters, including PH, using unoccupied aerial vehicle (UAV) borne sensor data. Principally, three-dimensional (3D) data of the plant canopy requires to derive PH. A plant can...
Grasslands are one of the world’s largest ecosystems, accounting for 30% of total terrestrial biomass. Considering that aboveground biomass (AGB) is one of the most essential ecosystem services in grasslands, an accurate and faster method for estimating AGB is critical for managing, protecting, and promoting ecosystem sustainability. Unmanned aeria...
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 singl...
Grazing causes disturbance to the grass sward which is used as an indicator for management decisions based on herbage disappearance. Cattle grazing on a pasture move primarily to identify feeding stations that fulfil their daily dietary requirements. However, possible stress caused by new virtual fencing technology could also affect daily movement....
Non-destructive monitoring of sward traits is of interest for grassland management. Remote sensing methods using sensors mounted on Unmanned Aerial Vehicles (UAVs) can provide timely and detailed information. Photogrammetric analysis of UAV-based image data can measure sward height which is used to estimate forage mass. In this contribution, we inv...
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...
The monitoring of managed grasslands with remote sensing methods is becoming more important for spatial decision support. Various remote sensing data acquisition techniques are applied for that purpose in different spatial resolutions ranging from UAV-borne to satellite-based remote sensing. In the last decade, UAV-borne imaging and analysis techni...
UAV imaging provides data in ultra-high spatial resolution of smaller than 3 cm. Although such data contains valuable information such as green cover and sward height, lower resolutions of e.g. 0.5 m meet the demands of monitoring pasture biomass or quality for management purposes. In the spatial analysis workflow of field experiment data, zonal st...
Remote sensing, especially from unmanned aerial vehicles (UAVs), has gained popularity for monitoring grassland growth dynamics over space and time, enabling location-specific management optimization. A new generation of LiDAR sensors mounted on UAVs could potentially overcome the drawbacks of using optical imaging as the information basis. For thi...
Accurate crop-type maps are urgently needed as input data for various applications, leading to improved planning and more sustainable use of resources. Satellite remote sensing is the optimal tool to provide such data. Images from Synthetic Aperture Radar (SAR) satellite sensors are preferably used as they work regardless of cloud coverage during i...
Crop distribution information is essential for tackling some challenges associated with providing food for a growing global population. This information has been successfully compiled using the Multi-Data Approach (MDA). However, the current implementation of the approach is based on optical remote sensing, which fails to deliver the relevant infor...
Soil organic carbon (SOC) is often heterogeneously distributed in arable fields and so is probably its turnover. We hypothesized that the spatial patterns of SOC turnover are controlled by basic soil properties like soil texture and the amount of rock fragments. To test this hypothesis, we cultivated maize as a C4 plant on a heterogeneous arable fi...
When using microwave remote sensing for land use/land cover (LULC) classifications, there are a wide variety of imaging parameters to choose from, such as wavelength, imaging mode, incidence angle, spatial resolution, and coverage. There is still a need for further study of the combination, comparison, and quantification of the potential of multipl...
Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like Rapid...
Timely availability of crop acreage estimation is crucial for maintaining economic and ecological sustainability or modelling purposes. Remote sensing data has proven to be a reliable source for crop mapping and acreage estimation on parcel-level. However, when relying on a single source of remote sensing data, e.g. multispectral sensors like Rapid...
In this study, first results of retrieving plant heights of maize fields from multitemporal TanDEM-X images are shown. Three TanDEM-X dual polarization spotlight acquisitions were taken over a rural area in Germany in the growing season 2014. By interferometric processing, digital terrain models (DTM) were derived for each date with 5m resolution....
In this study, first results of retrieving plant heights of maize fields from multitemporal TanDEM-X images are shown. Three TanDEM-X dual polarization spotlight acquisitions were taken over a rural area in Germany in the growing season 2014. By interferometric processing, digital terrain models (DTM) were derived for each date with 5m resolution....
The development and application of an algorithm to compute Köppen-Geiger climate classifications from the Coupled Model Intercomparison Project (CMIP) and Paleo Model Intercomparison Project (PMIP) climate model simulation data is described in this study. The classification algorithm was applied to data from the PMIP III paleoclimate experiments fo...
Geodata, including optical remote sensing (RS) images and topographic vector data, can be collected from multiple sources such as surveying and mapping agencies, commercial data acquisition companies, and local research institutes. These multi-source data have been widely used in past RS and geographic information system (GIS) studies in various ap...
This dataset contains a python script that can be used to automatically find the best combination of raster bands for a land use classification. The zip-file contains two python scripts, one for Python version 2.7 and ArcGIS 10.3 and one for Python version 3.4 and ArcGIS Pro.
In this study, images from the satellite system WorldView-2 in combination with terrestrial laser scanning (TLS) over a maize field
in Germany are investigated. Simultaneously to the measurements a biomass field campaigns was carried out. From the point clouds
of the terrestrial laser scanning campaigns crop surface models (CSM) from each scanning...
Over the last decades, the role of remote sensing gained in importance for monitoring applications in precision agriculture. A key factor for assessing the development of crops during the growing period is the actual biomass. As non-destructive methods of directly measuring biomass do not exist, parameters like plant height are considered as estima...
This geospatial dataset, in raster and vector format, is a Kppen-Geiger climate classification of the MPI-ESM-P PreIndustrial r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
This geospatial dataset, in raster and vector format, is a Kppen-Geiger climate classification of the MPI-ESM-P Mid-Holocene (6k yBP) r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
This geospatial dataset, in raster and vector format, is a Köppen-Geiger climate classification of the MPI-ESM-P Last Glacial Maximum (21k yBP) r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status.For this, multi-temporal dual...
In the context of the Collaborative Research Centre 806 "Our way to Europe" (CRC806), a research database is developed for inte-grating data from the disciplines of archaeology, the geosciences and the cultural sciences to facilitate integrated access to heterogeneous data sources. A practice-oriented data integration concept and its implementation...
Questions
Questions (2)
Interferogram generation is done on a TanDEM-X pairs in CoSSC format. Can I now calculate the phase standard deviation and make assumptions how accurate the interferogram is over a specific (distributed) target? Additionally, how does multilooking of the interferogram and different coherence windows affect this accuracy?
Hi Everyone, in my university course for geography students "Introduction to Remote Sensing" the students asked if there are missions and data from other planets than earth (Mars, moons of Saturn, our moon...). We are mainly working with data from sensors like WV-2, Landsat 8, Aster... It would be nice to give them some data from other planets to load into the software (Envi, 5.1), the missing ground data is not a problem as they currently learning how to load data into the software and explore metadata. It would be nice if the data is ready for envi and the metadata can be loaded with a metadatafile. Also the data has to be free of charge. A multispectral or hyperspectral datasets would be ideal as this concept has just been explained. I think it is a interesting topic but have never had the time to really study such data.