<p>The widespread use of diameter at breast height (DBH) and tree height attributes as a non-destructive indirect estimation of tree parameters (e.g., above-ground biomass, volume, age, and carbon stock) demands efficient and accurate surveying methods. However, traditional surveys, which are primarily manual, are often time-consuming, inaccurate, inconsistent, and might suffer from observer-bias. This study applies an agile quadruped robot, Spot from Boston Dynamics, and a mounted LiDAR system for mapping and measuring tree height, diameter at breast height (DBH), and tree volume. This project uses the Spot Enhanced Autonomy Payload (EAP) navigation module as the source of LiDAR data. The use of this module has two main advantages. First, Spot EAP's VLP-16 sensor is a low-beam LiDAR that, as demonstrated in previous research, is capable of estimating tree structural parameters while consuming less time and data than robust systems such as Terrestrial Laser Scanning (TLS). Second, using an existing payload as the primary source of data without disabling its default function results in more efficient payload capacity utilization and, as a result, lower energy consumption, in addition to making room for additional payloads. The experiment was conducted for 41 trees (23<em> Erythrina variegata</em> and 18 <em>Ficus altissima</em>) in a park on the campus of King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. TLS data were used to compute the height and volume reference data, while manual measurements were used to obtain DBH reference data. The robot-derived point cloud generation methodology was based on a multiway registration approach in which a total of 76 scans were acquired from 4 different locations using multiple poses of the robot to overcome the short field of view of the LiDAR sensor. As a result of processing the scans, a point cloud for each of the trees was obtained. The height estimations, which consist of a difference within Z coordinates, obtained a mean absolute error (MAE) and a mean percentage error (MPE) of 6.71 cm and 1.31% respectively. The DBH estimation based on circle-fitting algorithms obtained an MAE and an MPE of 2.55 and 12.99% respectively. The volume estimation obtained a coefficient of determination of 0.93. When compared to the most recent approaches available in the literature, the results for height and volume were satisfactory, yielding higher accuracy than other studies in some cases. The results for DBH estimation were also comparable to those in the literature. The main sources of error were tree occlusion and inclined trees, both of which are solvable by including more scanning locations and increasing the robustness of software estimation. Consequently, the acquisition system is not a barrier to future improvements. This work successfully introduced one of the first methods for using agile robots in high throughput field phenotyping. The use of agile robots addresses some of the major challenges for deploying ground-based robotics in high throughput field phenotyping, allowing for a higher assessment frequency without causing soil compaction and damage, as well as bringing unprecedented adaptation to difficult terrains.</p> <p> </p>

<p>Land surface temperature (LST) is crucial information that helps to understand and assess the interactions between the surface and the atmosphere. LST is a key parameter used in various applications including studies of irrigation, water use, vegetation health, urban heat island effects, and building insulation. In addition to several satellites that provide periodic images of surface temperature, unmanned aerial vehicle (UAV) platforms have been adapted to obtain higher spatio-temporal resolution thermal infrared (TIR) data. In fact, numerous research studies have investigated the accuracy and the processing method of UAV-based TIR images given its complexity and sensitivity to ambient conditions. However, the surface temperature is characterized by continuous and rapid variation over time, which is difficult to take into consideration in the processing of UAV-based orthomosaics. Here, we quantify this variation and discuss the environmental factors that lead to its amplification. Thermal images were collected over a fixed hovering position during periods of 15-20 min, representing the common duration of UAV flights. At different times of the day, we flew at different altitudes over sand, water, grass and olive trees. Before the quantification of the surface temperature variation, the thermal infrared data were evaluated against field-based measurements using calibrated Apogee sensors. The evaluation showed a significant error in the UAV-based thermal infrared data linked to wind speed, which increased the bias from -1.02 to 3.86 °C for 0.8 to 8.5 m/s winds, respectively. The assessment of the LST values collected over the different surfaces showed a temperature variation while hovering ranging between 1.4 and 5 °C. In addition to wind effects, temperature variations while hovering were strongly linked to solar radiation, specifically radiation fluctuations occurring after sunrise and before sunset. This research provides insights into the LST variation expected for standard UAV flights of 15-20 min under different environmental conditions, which should be taken into account during UAV-based thermal infrared data processing and may help interpret and quantify inconsistencies in UAV-based orthomosaics of LST.</p>

<p>Hyperspectral (HS) images obtained from space are useful for monitoring different natural phenomena on regional to global scales. The Environmental Mapping and Analysis Program (EnMAP) is a satellite recently launched by Germany to monitor the environment and explore the capabilities of hyperspectral sensors in the 420 and 2450 nm range of the spectrum. However, the data captured by the EnMAP mission have a ground sampling distance (GSD) of 30 m. This limits the use of the data for some applications that require higher spatial resolution (<10 m). This study examines the potential for improving the resolution of hyperspectral data using high resolution multispectral (MS) data obtained by Cubesats. Specifically, this work uses the data captured by the PlanetScope constellation, which has more than 150 CubeSats in low Earth orbit, with a high spatial and temporal resolution. The approach adopted leverages (1) the spectral capability of the hyperspectral EnMAP sensor, with a bandwidth of 6.5 nm in the visible and near infrared (VNIR) range (420–1000 nm) and 10 nm in the SWIR range (900–2450 nm), and (2) the spatial capability of the multispectral PlanetScope data, with a GSD of 3 meters, to enable significant spatial improvements due to its high spatial resolution. The main components of this work include: (i) area of interest clipping (ii) data co-registration, (iii) HS-MS data fusion, and (iv) quality assessments using the Jointly Spectral and Spatial Quality Index (QNR). In this study, a 2 km x 2 km area of interest was selected in the Malaucene region of France, where six state-of-the-art HS-MS fusion methods were evaluated: (1) fast multi-band image fusion algorithm (FUSE), (2) coupled nonnegative matrix factorization (CNMF), (3) smoothing filtered-based intensity modulation (SFIMHS), (4) maximum a posteriori stochastic mixing model (MAPSMM), (5) Hyperspectral Superresolution (HySure), and (6) generalized laplacian pyramid hypersharpening (GLPHS). Quality assessments of the enhanced data showed that high spectral and spatial fidelity are maintained, with the best performing fusion method being FUSE with a QNR of 0.625 followed by the MAPSMM method with a QNR of 0.604. Overall, this study advocates the benefits associated with the fusion of hyperspectral and multispectral data to obtain enhanced EnMAP data at 3 m GSD. </p>