The measurement of tree height has long been an important tree attribute for the purpose of calculating tree growth, volume, and biomass, which in turn deliver important ecological and economical information to decision makers. Tree height has traditionally been measured by indirect field-based techniques, however these methods are rarely contested. With recent advances in Unmanned Aerial Vehicle (UAV) remote sensing technologies, the possibility to acquire accurate tree heights semi-automatically has become a reality. In this study, photogrammetric and field-based tree height measurements of a Scots Pine stand were validated using destructive methods. The intensive forest monitoring site implemented for the study was configured with permanent ground control points (GCPs) measured with a Total Station (TS). Field-based tree height measurements resulted in a similar level of error to that of the photogrammetric measurements, with root mean square error (RMSE) values of 0.304 m (1.82%) and 0.34 m (2.07%), respectively (n = 34). A conflicting bias was, however, discovered where field measurements tended to overestimate tree heights and photogrammetric measurements were underestimated. The photogrammetric tree height measurements of all trees (n = 285) were validated against the field-based measurements and resulted in a RMSE of 0.479 m (2.78%). Additionally, two separate photogrammetric tree height datasets were compared (n = 251), and a very low amount of error was observed with a RMSE of 0.138 m (0.79%), suggesting a high potential for repeatability. This study shows that UAV photogrammetric tree height measurements are a viable option for intensive forest monitoring plots and that the possibility to acquire within-season tree growth measurements merits further study. Additionally, it was shown that negative and positive biases evident in field-based and UAV-based photogrammetric tree height measurements could potentially lead to misinterpretation of results when field-based measurements are used as validation.

The mapping of forest stands and individual trees affected by drought stress is a crucial step in targeted forest management, aimed at fostering resilient and diverse forests. Unoccupied aerial vehicle (UAV)-based thermal sensing is a promising method for obtaining high-resolution thermal data. However, the reliability of typical low-cost sensors adapted for UAVs is compromised due to various factors, such as internal sensor dynamics and environmental variables, including solar radiation intensity, relative humidity, object emissivity and wind. Additionally, accurately assessing drought stress in trees is a complex task that usually requires laborious and cost-intensive methods, particularly in field settings. In this study, we investigated the feasibility of using the thermal band of theMicasense Altummultispectral sensor, while also assessing the potential for modelling tree water deficit (TWD) through point dendrometers and UAV-derived canopy temperature. Our indoor tests indicated that using a limited number of pixels (< 3) could result in temperature errors exceeding 1 K. However, enlarging the spot-size substantially reduced the mean difference to 0.02 K, validated against leaf temperature sensors. Interestingly, drought-treated (unwatered) leaves exhibited a higher root mean squared error (RMSE) (RMSE = 0.66 K and 0.73 K) than watered leaves (RMSE = 0.55 K and 0.53 K), likely due to lower emissivity of the dry leaves. Comparing field acquisition methods, the mean standard deviation (SD) for tree crown temperature obtained from typical gridded flights was 0.25 K with a maximum SD of 0.59 K (n= 12). In contrast, a close-range hovering method produced a mean SD of 0.09 K and a maximum SD of 0.1 K (n= 8). Modelling the TWD from meteorological and point dendrometer data for the 2021 growth season (n= 2928) yielded anR²= 0.667 using a generalised additive model (GAM) with vapor pressure deficit (VPD), wind speed, and solar radiation as input features. A point dendrometer lag of one hour was also implemented. When predicting individual tree TWD with UAV-derived tree canopy temperature, relative humidity, and air temperature, an RMSE of 4.92 (μm) andR²of 0.87 were achieved using a GAM. Implementing leaf-to-air pressure deficit (LVPD) as an input feature resulted in an RMSE of 6.87 (μm) and anR²of 0.71. This novel single-shot approach demonstrates a promising method to acquire thermal data for the purpose of mapping TWD of beech trees on an individual basis. Further testing and development are imperative, and additional data from drought periods, point dendrometers, and high-resolution meteorological sources are required.

GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published. Starting 2016, GI_Forum publishes two issues a Year. Joumal Information is available at: GI-Forum GI_Forum is listed on the Directory of Open Access Journals (DOAJ)

Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observation (EO) data could enhance our understanding of these processes through an added spatial component. However, translating traditional ground-based phenological observations into reliable ground truthing for training and validating EO mapping applications remains challenging. This study explored the feasibility of predicting high-resolution phenological phase data for European beech (Fagus sylvatica) using unoccupied aerial vehicle (UAV)-based multispectral indices and machine learning. Employing a comprehensive feature selection process, we identified the most effective sensors, vegetation indices, training data partitions, and machine learning models for phenological phase prediction. The model that performed best and generalized well across various sites utilized Green Chromatic Coordinate (GCC) and Generalized Additive Model (GAM) boosting. The GCC training data, derived from the radiometrically calibrated visual bands of a multispectral sensor, were predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model demonstrated capability in predicting phenological phases on unseen datasets within a root mean squared error threshold of 0.5. This research highlights the potential interoperability among common UAV-mounted sensors, particularly the utility of readily available, low-cost RGB sensors. However, considerable limitations were observed with indices that implement the near-infrared band due to oversaturation. Future work will focus on adapting models to better align with the ICP Forests phenological flushing stages.

The mapping of forest stands and individual trees affected by drought stress is an important step in targeted forest management with the aim of creating resilient and diverse forests. UAV-based thermal sensing is a promising method to acquire high-resolution thermal data, yet the performance of typical UAV-adapted low-cost sensors are somewhat limited in deriving accurate temperature measurements. Uncertainty is evident in the effects of internal sensor dynamics as well as environmental variables such as solar radiation intensities, relative humidity, object emissivity and wind to name a few. Furthermore, the accurate assessment of drought stress in trees is challenging to quantify, and typical research station methods can be laborious and cost-intensive and particularly challenging when carried out in the field. In this study, we explored the possibility to acquire reliable tree canopy temperature using the thermal band of theMicasense Altummultispectral sensor while examining the prospect of quantifying drought stress by implementing point dendrometers and UAV-derived tree canopy temperature to model Tree Water Deficit (TWD). In an indoor environment we showed that the usage of a limited number of pixels (< 3) can result in temperature errors of over 1°C whereas increasing the spot size can reduce the mean difference to 0.02°C when using leaf temperature sensors as validation. Interestingly, leaves which were subjected to drought treatment (unwatered) resulted in a higher root mean squared error ((RMSE) (RMSE = 0.66°C and 0.73°C) than watered leaves (RMSE = 0.55°C and 0.53°C) due to most probably a lower emissivity of the dryer leaves. In a comparison of field acquisition methods, measuring the tree crown temperature of a selected tree from various incidence angles derived from typical gridded flights resulted in a mean standard deviation (SD) of 0.25°C and a maximum SD of 0.59°C (n = 12), where as a close-range hovering method resulted in a mean SD of 0.09°C and a maximum SD of 0.1°C (n = 8). Modelling the TWD from meteorological and point dendrometer data from the 2021 growth season (n = 2928) resulted with a R² = 0.667 using a Generalised Additive Model (GAM) with the Vapor Pressure Deficit (VPD), wind speed and solar radiation as input features and a point dendrometer lag of one hour. When predicting individual tree TWD with UAV-derived tree canopy temperature, relative humidity and air temperature as input features, a RMSE of 4.92 (µm) and R²of 0.87 was achieved with a GAM. The GAM with the Leaf-to-Air Pressure Deficit (LVPD) as an input feature resulted in a RMSE of 6.87 (µm) and a R²of 0.71. This study presents a promising method to acquire thermal data for the purpose of mapping TWD of beech on an individual tree basis. Further testing and development is an imperative and more drought period point dendrometer data as well as higher resolution meteorological data is required.

The acquisition of phenological events play an integral part in investigating the effects of climate change on forest dynamics and assessing the potential risk involved with the early onset of young leaves. Large scale mapping of forest phenological timing using earth observation data, could facilitate a better understanding of phenological processes due to an added spatial component. The translation of traditional phenological ground observation data into reliable ground truthing for the purpose of the training and validation of Earth Observation (EO) mapping applications is a challenge. In this study, we explored the possibility of predicting high resolution phenological phase data for European beech ( Fagus sylvatica ) with the use of Unmanned Aerial Vehicle (UAV)-based multispectral indices and machine learning. Using a comprehensive feature selection process, we were able to identify the most effective sensors, vegetations indices, training data partitions, and machine learning models for phenological phase prediction. The best performing model that generalised well over various sites was the model utilising the Green Chromatic Coordinate (GCC) and Generalized Addictive Model (GAM) boosting. The GCC training data was derived from the radiometrically calibrated visual bands from a multispectral sensor and predicted using uncalibrated RGB sensor data. The final GCC/GAM boosting model was capable in predicting phenological phases on unseen datasets within a RMSE threshold of 0.5. This research shows the potential of the interoperability among common UAV-mounted sensors in particular the utility of readily available low cost RGB sensors. Considerable limitations were however discovered with indices implementing the near-infrared (NIR) band due to oversaturation. Future work involves adapting models to facilitate the ICP Forests phenological flushing stages.