Drohnenbasierte Schätzung der räumlichen Variabilität von Luzerne-Ertragsanteilen in Luzerne-Gras-Gemengen

Mithilfe drohnebasierter multispektraler Aufnahmen wurde die räumliche Variabilität des Luzerneanteils in Luzerne-Gras-Gemenge auf zwei Schlägen in Hessen, Deutschland, mit hoher Genauigkeit geschätzt. Daraus erstellte Karten ermöglichen die räumliche Analyse der Bestände hinsichtlich N-Fixierungpotenzial

Geometric quality assessment of multi-rotor unmanned aerial vehicle borne remote sensing products for precision agriculture

Sub-meter level spatial resolution remote sensing products are essential for Precision Agriculture (PA) applications. Recent development of Unmanned Aerial Vehicle (UAV) with imaging sensors provides opportunity to generate sub-meter level, timely, and cloud free remote sensing products. This study is a preliminarily assessment of a multi-rotor UAV with consumer grade optical camera and five spectral band multispectral camera for PA applications especially in geometric aspect. The UAV was flown over the agriculture area and images from both cameras were acquired and Digital Surface Models (DSM) and Ortho-Mosaics were derived from the collected image data. Geometric and visual quality of the derived products were assessed and limitations were identified regarding to PA applications. The optical camera images derived 2.1 cm spatial resolution orthomosaic while multispectral ortho-mosaic from the UAV multipsectral images gave 5.6 cm spatial resolution. The horizontal geometric accuracies of the optical camera product and multispectral camera product were 2 pixels and less than one pixel respectively. Relative average elevation difference of agriculture crop area and non-crop area were 0.27 m and 0.14 m in derived DSM from optical images and multispectral images respectively. Blurriness of the UAV-borne images was identified as a limitation of the UAV remote sensing exercise and UAV motion blur, cloud shadow, and wind were noted as possible causes for the blurriness in this study.Miniaturization of the electronic devices opens doors for remote sensing using Unmanned Aerial Vehicle (UAV) with small imaging sensors. Cloud free, very high spatial resolution, real time remote sensing products can be obtained from the UAV remote sensing. Vegetation monitoring is the most applied remote sensing application using UAV. Precision Agriculture (PA) is a new level of farming strategy which used remote sensing as a main tool. UAV remote sensing provides the opportunity to collect accurate, timely data over agriculture field s to conduct PA applications. This study focus on geometric accuracy of the UAV remote sensing products from two different cameras that attached to the UAV for the PA. Moreover, remote sensing product generation form UAV mages and difficulties of the process were identified from the study. A multi-rotor UAV with a consumer grade optical camera and a five band multispectral camera were used in this study. UAV was flew over test agriculture fields and image were acquired from both cameras. Acquired images were tested for light condition and blurriness effects. Based on the quality level UAV images were processed to obtain Digital Surface Models (DSM) and Ortho-rectified Image Mosaics from each image set. Geometric accuracy assessment using Ground Control Points (GCPs) were performed with different number of GCP and different pattern GCP distribution. Vertical accuracy of the products were not evaluated but average relative height difference of the two DSMs were evaluated. The DSM with 4.2 cm resolution and the ortho-mosaic with 2.1 cm resolution were derived from optical camera images. Similarly, the DSM with 22.6 cm and 5-band ortho-mosaic with 5.6 cm resolutions were obtained from multispectral images. Relative average elevation difference of agriculture crop area and non-crop area were 0.27 m and 0.14 m in derived DSM. The horizontal geometric accuracies of the optical camera product and multispectral camera product were 2 pixels and less than one pixel respectively. According to the area of 0.07 square kilometres, 06 GCPs were enough to obtain required geometric accuracy for PA. Blurriness of the UAV-borne images was identified as a limitation of the UAV remote sensing exercise and UAV motion blur, cloud shadow, and wind were noted as possible causes for the blurriness in this study

Evaluating the spatial–temporal transferability of models for agricultural land cover mapping using Landsat archive

Gefördert im Rahmen des Projekts DEA

Comparison of Spaceborne and UAV-Borne Remote Sensing Spectral Data for Estimating Monsoon Crop Vegetation Parameters

Gefördert durch den Publikationsfonds der Universität Kasse

Mapping Invasive Lupinus polyphyllus Lindl. in Semi-natural Grasslands Using Object-Based Image Analysis of UAV-borne Images

Gefördert im Rahmen des Projekts DEA

Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data

Grassland ecosystems can be hotspots of biodiversity and act as carbon sinks while at the same time providing the basis of forage production for ruminants in dairy and meat production. Annual grassland dry matter yield (DMY) is one of the most important agronomic parameters reflecting differences in usage intensity such as number of harvests and fertilization. Current methods for grassland DMY estimation are labor-intensive and prone to error due to small sample size. With the advent of unmanned aerial vehicles (UAVs) and miniaturized hyperspectral sensors, a novel tool for remote sensing of grassland with high spatial, temporal and radiometric resolution and coverage is available. The present study aimed at developing a robust model capable of estimating grassland biomass across a gradient of usage intensity throughout one growing season. Therefore, UAV-borne hyperspectral data from eight grassland sites in North Hesse, Germany, originating from different harvests, were utilized for the modeling of fresh matter yield (FMY) and DMY. Four machine learning (ML) algorithms were compared for their modeling performance. Among them, the rule-based ML method Cubist regression (CBR) performed best, delivering high prediction accuracies for both FMY (nRMSEp 7.6%, Rp2 0.87) and DMY (nRMSEp 12.9%, Rp2 0.75). The model showed a high robustness across sites and harvest dates. The best models were employed to produce maps for FMY and DMY, enabling the detailed analysis of spatial patterns. Although the complexity of the approach still restricts its practical application in agricultural management, the current study proved that biomass of grassland sites being subject to different management intensities can be modeled from UAV-borne hyperspectral data at high spatial resolution with high prediction accuracies

Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

The timely knowledge of forage quality of grasslands is vital for matching the demands in animal feeding. Remote sensing (RS) is a promising tool for estimating field-scale forage quality compared with traditional methods, which usually do not provide equally detailed information. However, the applicability of RS prediction models depends on the variability of the underlying calibration data, which can be brought about by the inclusion of a multitude of grassland types and management practices in the model development. Major aims of this study were (i) to build forage quality estimation models for multiple grassland types based on an unmanned aerial vehicle (UAV)-borne imaging spectroscopy and (ii) to generate forage quality distribution maps using the best models obtained. The study examined data from eight grasslands in northern Hesse, Germany, which largely differed in terms of vegetation type and cutting regime. The UAV with a hyperspectral camera on board was utilised to acquire spectral images from the grasslands, and crude protein (CP) and acid detergent fibre (ADF) concentration of the forage was assessed at each cut. Five predictive modelling regression algorithms were applied to develop quality estimation models. Further, grassland forage quality distribution maps were created using the best models developed. The normalised spectral reflectance data showed the strongest relationship with both CP and ADF concentration. From all predictive algorithms, support vector regression provided the highest precision and accuracy for CP estimation (median normalised root mean square error prediction (nRMSEp) = 10.6%), while cubist regression model proved best for ADF estimation (median nRMSEp = 13.4%). The maps generated for both CP and ADF showed a distinct spatial variation in forage quality values for the different grasslands and cutting regimes. Overall, the results disclose that UAV-borne imaging spectroscopy, in combination with predictive modelling, provides a promising tool for accurate forage quality estimation of multiple grasslands

Multisite and Multitemporal Grassland Yield Estimation Using UAV-Borne Hyperspectral Data

Gefördert durch den Publikationsfonds der Universität Kasse

Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

Gefördert durch den Publikationsfonds der Universität Kasse