INTRODUCING A LOW-COST MINI-UAV FOR THERMAL- AND MULTISPECTRAL-IMAGING

The trend to minimize electronic devices also accounts for Unmanned Airborne Vehicles (UAVs) as well as for sensor technologies and imaging devices. Consequently, it is not surprising that UAVs are already part of our daily life and the current pace of development will increase civil applications. A well known and already wide spread example is the so called flying video game based on Parrot's AR.Drone which is remotely controlled by an iPod, iPhone, or iPad (http://ardrone.parrot.com). The latter can be considered as a low-weight and low-cost Mini-UAV. In this contribution a Mini-UAV is considered to weigh less than 5 kg and is being able to carry 0.2 kg to 1.5 kg of sensor payload. While up to now Mini-UAVs like Parrot's AR.Drone are mainly equipped with RGB cameras for videotaping or imaging, the development of such carriage systems clearly also goes to multi-sensor platforms like the ones introduced for larger UAVs (5 to 20 kg) by Jaakkolla et al. (2010) for forestry applications or by Berni et al. (2009) for agricultural applications. The problem when designing a Mini-UAV for multi-sensor imaging is the limitation of payload of up to 1.5 kg and a total weight of the whole system below 5 kg. Consequently, the Mini-UAV without sensors but including navigation system and GPS sensors must weigh less than 3.5 kg. A Mini-UAV system with these characteristics is HiSystems' MK-Okto (www.mikrokopter.de). Total weight including battery without sensors is less than 2.5 kg. Payload of a MK-Okto is approx. 1 kg and maximum speed is around 30 km/h. The MK-Okto can be operated up to a wind speed of less than 19 km/h which corresponds to Beaufort scale number 3 for wind speed. In our study, the MK-Okto is equipped with a handheld low-weight NEC F30IS thermal imaging system. The F30IS which was developed for veterinary applications, covers 8 to 13 μm, weighs only 300 g, and is capturing the temperature range between −20 °C and 100 °C. Flying at a height of 100 m, the camera's image covers an area of approx. 50 by 40 m. The sensor's resolution is 160 x 120 pixel and the field of view is 28° (H) x 21° (V). According to the producer, absolute accuracy for temperature is ±1 °C and the thermal sensitivity is >0.1 K. Additionally, the MK-Okto is equipped with Tetracam's Mini MCA. The Mini MCA in our study is a four band multispectral imaging system. Total weight is 700 g and spectral characteristics can be modified by filters between 400 and 1000 nm. In this study, three bands with a width of 10 nm (green: 550 nm, red: 671 nm, NIR1: 800 nm) and one band of 20 nm width (NIR2: 950 nm) have been used. Even so the MK-Okto is able to carry both sensors at the same time, the imaging systems were used separately for this contribution. First results of a combined thermal- and multispectral MK-Okto campaign in 2011 are presented and evaluated for a sugarbeet field experiment examining pathogens and drought stress

REGIONALIZATION OF AGRICULTURAL MANAGEMENT BY USING THE MULTI-DATA APPROACH (MDA)

Regional process-based (agro-)ecosystem modelling depends mainly on data availability of land use, weather, soil, and agricultural management. While land use, weather, and soil data are available from official sources or can be captured with monitoring systems, management data are usually derived from official statistics for administrative units. For numerous spatial modeling approaches, these data are not satisfying. Especially for process-based agro-ecosystem modeling on regional scales, spatially disaggregated and land use dependent information on agricultural management is a must. Information about date of sowing, dates of fertilization, dates of weeding etc. are required as input parameters by such models. These models consider nitrogen (N)- and carbon (C)-matter fluxes but essential amounts of N-/C-input and N-/C-output are determined by crop management. Therefore, in this contribution a RS- and GIS-based approach for regional generation of management data is introduced. The approach is based on the Multi-data Approach (MDA) for enhanced land use/land cover mapping. The MDA is a combined RS and GIS approach. The retrieved information from multitemporal and multisensoral remote sensing analysis is integrated into official land use data to enhance both the information level of existing land use data and the quality of the land use classification. The workflow of the MDA to generate enhanced land use and land cover data consists basically of two components: (a) the methods and data of the remote sensing analysis and (b) the methods and data of the GIS analysis. The MDA results in disaggregated land use data which can be used to link crop management information about the major crops and especially crop rotations like date of sowing, fertilization, irrigation, harvest etc. to the derived land use classes. Consequently, depending on the land use, a distinct management is given in a spatial context on regional scale. In this contribution, three case studies of different regions in Germany will be presented: (i) the dairy farm region "Württembergisches Allgäu", (ii) the arable land region "Kraichgau", and (iii) the diverse Rur-Watershed in Western Germany. For each of the study regions, a different MDA-based approach for regionalizing agricultural management is applied and will be discussed

EUROPEAN MINING DATABASE NORTH RHINE – WESTPHALIA (EMD-NRW): A MASHUP FOR INTUITIVE USAGE OF ARCHIVE DATA

The European Mining Database for North Rhine Westphalia (EMD-NRW) has the objective to present information about the industrial heritage of mining in an intuitive web mapping application. With the increasing abandonment of these industrial objects, especially in the area of NRW, the digital preservation of information, locations and relations between and about these objects is important. These historic sites and the transformation from an industrial area to a new polycentric metropolis is also a key focus of the European Capital of Culture 2010 "Essen for the Ruhr". The EMD-NRW system is capable of representing the complex, hierarchical structure of archive data and connecting this data to their geographic location. Comprehensive information is available about every object. A combination (mashup) of own geodata and data of Microsoft’s Bing Maps is used. For beginners and experts information are easy to retrieve because of the intuitive design

EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed

EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed

Evaluating the potential of consumer-grade smart cameras for low-cost stereo-photogrammetric Crop-Surface Monitoring

Crop-Surface-Models (CSMs) are a useful tool for monitoring in-field crop growth variability, thus enabling precision agriculture which is necessary for achieving higher agricultural yields. This contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions. An application that automates image acquisition and transmission was developed. Automated image acquisition took place throughout the growing period of barley in 2013. For three dates where both automated image acquisition and manual measurements of plant height were available, CSMs were generated using a combination of AgiSoft PhotoScan and Esri ArcGIS. The coefficient of determination R2 between the average of the manually measured plant heights per plots and the average height of the developed crop surface models was 0.61 (n = 24). The overall correlation between the manually measured heights and the CSM-derived heights is 0.78. The average per plot of the manually measured plant heights in the timeframe covered by the generated CSMs range from 19 to 95 cm, while the average plant height per plot of the generated CSMs range from 2.1 to 69 cm. These first results show that the presented approach is feasible

EVALUATION OF RGB-BASED VEGETATION INDICES FROM UAV IMAGERY TO ESTIMATE FORAGE YIELD IN GRASSLAND

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