Das Fachkräftebarometer Frühe Bildung bietet eine umfassende Bestandsaufnahme zur Situation des Personals und zu den Entwicklungen im Ausbildungssystem sowie einen vergleichenden Blick auf den ...Arbeitsmarkt der Kindertagesbetreuung. Grundlage sind überwiegend Daten der amtlichen Statistik, die in Zeitreihen deutschlandweit und auch in Ländervergleichen aufbereitet wurden. Anhand der Datenbasis können folgende Fragen eingeordnet werden: Bringt das Ausbildungssystem genügend Nachwuchskräfte hervor, um das Angebot der Kindertagesbetreuung weiter auszubauen? Wie wirkt sich die Situation auf die Qualitätsentwicklung in den Einrichtungen aus? Steuert die Frühe Bildung in naher Zukunft auf eine Krise zu?Erstmalig beleuchtet der Datenbericht die Situation von Kita-Leitungen mit Blick auf deren Bedeutung für die Steuerung der Einrichtungen.
Topographic and geomorphological surveys of coastal areas usually require the aerial mapping of long and narrow sections of littoral. The georeferencing of photogrammetric models is generally based ...on the signalization and survey of Ground Control Points (GCPs), which are very time-consuming tasks. Direct georeferencing with high camera location accuracy due to on-board multi-frequency GNSS receivers can limit the need for GCPs. Recently, DJI has made available the Phantom 4 Real-Time Kinematic (RTK) (DJI-P4RTK), which combines the versatility and the ease of use of previous DJI Phantom models with the advantages of a multi-frequency on-board GNSS receiver. In this paper, we investigated the accuracy of both photogrammetric models and Digital Terrain Models (DTMs) generated in Agisoft Metashape from two different image datasets (nadiral and oblique) acquired by a DJI-P4RTK. Camera locations were computed with the Post-Processing Kinematic (PPK) of the Receiver Independent Exchange Format (RINEX) file recorded by the aircraft during flight missions. A Continuously Operating Reference Station (CORS) located at a 15 km distance from the site was used for this task. The results highlighted that the oblique dataset produced very similar results, with GCPs (3D RMSE = 0.025 m) and without (3D RMSE = 0.028 m), while the nadiral dataset was affected more by the position and number of the GCPs (3D RMSE from 0.034 to 0.075 m). The introduction of a few oblique images into the nadiral dataset without any GCP improved the vertical accuracy of the model (Up RMSE from 0.052 to 0.025 m) and can represent a solution to speed up the image acquisition of nadiral datasets for PPK with the DJI-P4RTK and no GCPs. Moreover, the results of this research are compared to those obtained in RTK mode for the same datasets. The novelty of this research is the combination of a multitude of aspects regarding the DJI Phantom 4 RTK aircraft and the subsequent data processing strategies for assessing the quality of photogrammetric models, DTMs, and cross-section profiles.
Due to their location in tropical latitudes, mangrove forests are susceptible to the impact of hurricanes and can be vastly damaged by their high-speed winds. Given the logistic difficulties ...regarding field surveys in mangroves, remote sensing approaches have been considered a reliable alternative. We quantified trends in damage and early signs of canopy recovery in a fringe Rhizophora mangle area of Marismas Nacionales, Mexico, following the landfall of Hurricane Willa in October 2018. We monitored (2016–2021) broad canopy defoliation using 21 vegetation indices (VI) from the Google Earth Engine tool (GEE). We also mapped a detailed canopy fragmentation and developed digital surface models (DSM) during five study periods (2018–2021) with a consumer-grade unmanned aerial vehicle (UAV) over an area of 100 ha. Based on optical data from the GEE time series, results indicated an abrupt decline in the overall mangrove canopy. The VARI index was the most reliable VI for the mangrove canopy classification from a standard RGB sensor. The impact of the hurricane caused an overall canopy defoliation of 79%. The series of UAV orthomosaics indicate a gradual recovery in the mangrove canopy, while the linear model predicts at least 8.5 years to reach pre-impact mangrove cover conditions. However, the sequence of DSM estimates that the vertical canopy configuration will require a longer time to achieve its original structure.
•Mangroves are constantly impacted by hurricanes and can be vastly damaged.•Google Earth Engine and UAV are reliable alternatives to field endeavors.•Hurricane category 3 Willa severely impacted mangrove canopy structure.•VARI index was the most feasible index for mangrove canopy classification.
This study explores the use of small commercial drones in tactical-level military actions, with a particular focus on optimizing ISR (Intelligence, Surveillance, and Reconnaissance) capabilities. ...During a 3-day tactical exercise, we analyzed the performance of 9 commercial drones, including DJI Mini 3, DJI Avata, DJI Mini 4, and DJI Mavic. The study demonstrated that these drones provide essential capabilities for real-time information gathering, facilitating rapid decision-making and improving the efficiency of military operations. Significant challenges were also identified, such as limited battery autonomy and vulnerability to adverse weather conditions. The results highlight the need for specialized training programs and an integrated approach that combines advanced technology with military tactics. Our findings suggest that commercial drones can significantly enhance the efficiency of military operations, but increased attention is required to understand the challenges that may arise due to the technological limitations of small commercial drones.
Unmanned Aerial Vehicles (UAVs) also known as drones have created many challenges to the digital forensic field. These challenges are introduced in all processes of the digital forensic investigation ...(i.e., identification, preservation, examination, documentation, and reporting). From identification of evidence to reporting, there are several challenges caused by the data type, source of evidence, and multiple components that operate UAVs. In this paper, we comprehensively reviewed the current UAV forensic investigative techniques from several perspectives. Moreover, the contributions of this paper are as follows: (1) discovery of personal identifiable information, (2) test and evaluation of currently available forensic software tools, (3) discussion on data storage mechanism and evidence structure in two DJI UAV models (e.g., Phantom 4 and Matrice 210), and (4) exploration of flight trajectories recovered from UAVs using a three-dimensional (3D) visualization software. The aforementioned contributions aim to aid digital investigators to encounter challenges posed by UAVs. In addition, we apply our testing, evaluation, and analysis on the two selected models including DJI Matrice 210, which have not been presented in previous works.
This study estimated biometric attributes of individual trees from the automated measurement of tree height (THUV) by using images from unmanned aerial vehicles (UAVs). An experiment was carried out ...in a natural forest stand in the north of Mexico by using a DJI P4 multispectral equipment and regression analysis. The results show that total tree height (TH) is successfully estimated from UAV images, as the automated estimation of total height (THUV) reaches a R2 = 0,95 and a RMSE = 0,36 m. Consequently, THUV was statistically reliable to generate allometric equations (R2 > 0,57) regarding the canopy height model (CH), diameter at breast height (DBH), basal diameter (BD), above-ground biomass (AGB), volume (V), and carbon contents (C). It is concluded that the estimation of total height with UAVs is a viable option to improve efficiency in forest inventories. However, increased efforts towards the configuration of modern technologies and statistical algorithms are needed; future research challenges remain, particularly in the densest forests areas.
Este estudio estimó los atributos biométricos de árboles individuales a partir de la altura total estimada automáticamente mediante el uso de imágenes de vehículos aéreos no tripulados (VANT). Se llevó a cabo un experimento en un rodal de bosque natural en el norte de México utilizando un equipo multiespectral DJI P4 y análisis de regresión. Los resultados muestran que la altura total del árbol (TH) se estima con éxito a partir de imágenes de VANT, ya que la estimación automática de la altura total (THUV) alcanza un R2 = 0,95 y un RMSE = 0,36 m. En consecuencia, el THUV fue estadísticamente confiable para generar ecuaciones alométricas (R2 > 0,57) con respecto al modelo de altura del dosel (CH), el diámetro a la altura del pecho (DAP), el diámetro basal (BD), la biomasa superficial (AGB), el volumen (V) y el contenido de carbono (C). Lo que se concluye es que la estimación de altura total con VANT es una opción viable para mejorar la eficiencia en los inventarios forestales. Sin embargo, se requieren más esfuerzos orientados a la configuración de tecnologías modernas y algoritmos estadísticos; persisten los desafíos de la investigación futura, particularmente en las áreas boscosas más densas.
Rapid and accurate assessment of yield and nitrogen use efficiency (NUE) is essential for growth monitoring, efficient utilization of fertilizer and precision management. This study explored the ...potential of a consumer-grade DJI Phantom 4 Multispectral (P4M) camera for yield or NUE assessment in winter wheat by using the universal vegetation indices independent of growth period. Three vegetation indices having a strong correlation with yield or NUE during the entire growth season were determined through Pearson’s correlational analysis, while multiple linear regression (MLR), stepwise MLR (SMLR), and partial least-squares regression (PLSR) methods based on the aforementioned vegetation indices were adopted during different growth periods. The cumulative results showed that the reciprocal ratio vegetation index (repRVI) had a high potential for yield assessment throughout the growing season, and the late grain-filling stage was deemed as the optimal single stage with R
2
, root mean square error (RMSE), and mean absolute error (MAE) of 0.85, 793.96 kg/ha, and 656.31 kg/ha, respectively. MERIS terrestrial chlorophyll index (MTCI) performed better in the vegetative period and provided the best prediction results for the N partial factor productivity (NPFP) at the jointing stage, with R
2
, RMSE, and MAE of 0.65, 10.53 kg yield/kg N, and 8.90 kg yield/kg N, respectively. At the same time, the modified normalized difference blue index (mNDblue) was more accurate during the reproductive period, providing the best accuracy for agronomical NUE (aNUE) assessment at the late grain-filling stage, with R
2
, RMSE, and MAE of 0.61, 7.48 kg yield/kg N, and 6.05 kg yield/kg N, respectively. Furthermore, the findings indicated that model accuracy cannot be improved by increasing the number of input features. Overall, these results indicate that the consumer-grade P4M camera is suitable for early and efficient monitoring of important crop traits, providing a cost-effective choice for the development of the precision agricultural system.
Das ERiK-Projekt erarbeitet durch bundesweite Befragungen die empirische Grundlage zum Monitoring des KiQuTG. Entlang der Handlungsfelder des Gesetzes werden u.a. Daten zu den Bedarfen von Eltern, ...zur Personalsituation aber auch zu Themen wie Gesundheit von Kindern, sprachliche Bildung oder dem Thema Raum in der Kindertagesbetreuung analysiert. Im vorliegenden Bericht werden - ergänzend zu deskriptiven Auswertungen hauptsächlich amtlicher Daten - Forschungsfragen zur Qualität im Feld der FBBE, die sich aus den deskriptiven Analysen der Daten aus den ERiK-Surveys 2020 ergeben haben, in Vertiefungsanalysen multivariat ausgewertet. Eine Veröffentlichung des Deutschen Jugendinstituts e.V. München (DJI)
RAD-DAR is a frequency-modulated-continuous-wave radar demonstrator, working at X-band, completely designed and constructed by the authors’ research group following the ubiquitous-radar concept. ...After introducing its main hardware and software blocks, with special emphasis to its off-line signal processing and data processing, this study presents the results of a drone detection test, where RAD-DAR achieved a DJI-Phantom 4 detection and tracking at a range up to 3 km. The system performance is discussed with different flights including an attack manoeuvre, and a free flight, which helps to highlight their advantages in surveillance tasks due to the RAD-DAR staring nature. Furthermore, range, speed and azimuth accuracies are discussed, considering the drone global positioning system data. Finally, this study shows a statistical radar cross-section study based on the processed data of the drone and attempts to classify this target by means of Swerling models.
Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ...ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.
PDF
