No abstract

Sustainable forest management heavily relies on the accurate estimation of tree parameters. Among others, the diameter at breast height (DBH) is important for extracting the volume and mass of an individual tree. For systematically estimating the volume of entire plots, airborne laser scanning (ALS) data are used. The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans (STLS) of sample plots. Although reliable, this method is time-consuming, which greatly hampers its use. Here, a handheld mobile terrestrial laser scanning (HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH. Different data acquisition techniques were applied at a sample plot, then the resulting parameters were comparatively analysed. The calculated DBH values were comparable to the manual measurements for HMTLS, STLS, and ALS data sets. Given the comparability of the extracted parameters, with a reduced point density of HTMLS compared to STLS data, and the reasonable increase of performance, with a reduction of acquisition time with a factor of 5 compared to conventional STLS techniques and a factor of 3 compared to manual measurements, HMTLS is considered a useful alternative technique.

Surface moisture plays a key role in limiting the aeolian transport on sandy beaches. However, the existing measurement techniques cannot adequately characterize the spatial and temporal distribution of the beach surface moisture. In this study, a mobile terrestrial LiDAR (MTL) is demonstrated as a promising method to detect the beach surface moisture using a phase-based Z&F/Leica HDS6100 laser scanner mounted on an all-terrain vehicle. Firstly, two sets of indoor calibration experiments were conducted so as to comprehensively investigate the effect of distance, incidence angle and sand moisture contents on the backscattered intensity by means of sand samples with an average grain diameter of 0.12 mm. A moisture estimation model was developed which eliminated the effects of the incidence angle and distance (it only relates to the target surface reflectance). The experimental results reveal both the distance and incidence angle influencing the backscattered intensity of the sand samples. The standard error of the moisture model amounts to 2.0% moisture, which is considerably lower than the results of the photographic method. Moreover, a field measurement was conducted using the MTL system on a sandy beach in Belgium. The accuracy and robustness of the beach surface moisture derived from the MTL data was evaluated. The results show that the MTL is a highly suitable technique to accurately and robustly measure the surface moisture variations on a sandy beach with an ultra-high spatial resolution (centimeter-level) in a short time span (12 × 200 m per minute).

Worldwide, flood events frequently have a dramatic impact on urban societies. Time is key during a flood event in order to evacuate vulnerable people at risk, minimize the socio-economic, ecologic and cultural impact of the event and restore a society from this hazard as quickly as possible. Therefore, detecting a flood in near real-time and assessing the risks relating to these flood events on the fly is of great importance. Therefore, there is a need to search for the optimal way to collect data in order to detect floods in real time. Internet of Things (IoT) is the ideal method to bring together data of sensing equipment or identifying tools with networking and processing capabilities, allow them to communicate with one another and with other devices and services over the Internet to accomplish the detection of floods in near real-time. The main objective of this paper is to report on the current state of research on the IoT in the domain of flood detection. Current trends in IoT are identified, and academic literature is examined. The integration of IoT would greatly enhance disaster management and, therefore, will be of greater importance into the future.

Spatio-temporal ground-movement measurements and mappings have been carried out in the Campine coalfield in Belgian Limburg since the closure of the mines to document post-mining effects. MT-InSAR measurements are compared to groundwater head changes in the overburden and to height data from the closest GNSS stations. Radar interferometry is used to estimate the extension and the velocity of ground movements. In particular, the MT-InSAR technique has been applied to SAR acquisitions of the satellites ERS-1/2 (1991–2005), ENVISAT (2003–2010), COSMO-SkyMed (2011–2014), and Sentinel-1A (2014–2022). The images were processed and used to highlight a switch from subsidence to uplift conditions in the western part of the coal basin, while the eastern part had already been affected by a rebound since the beginning of the ERS-1/2 acquisitions. Following the closure of the last active colliery of Zolder in 1992 and the subsequent cease of mine-water pumping, a recharge of mine-water aquifers occurred in the western part of the basin. This process provoked the change from subsidence to uplift conditions that was recorded during the ENVISAT period. In the center of the coal-mining area, measured uplift velocities reached a maximum of 18 mm/year during the ENVISAT period, while they subsided at -12 mm/year during the ERS-1/2 period. Mean velocities in the western and eastern parts of the coalfield area have decreased since the last MT-InSAR measurements were performed using Sentinel-1A, while the Zolder coal mine continues to rise at a faster-than-average rate of a maximum of 16 mm/year. The eastern part of the coalfield is still uplifting, while its rate has been reduced from 18 mm/year (ERS-1/2) to 9 mm/year (Sentinel-1A) since the beginning of the radar–satellite observations. Time-series data from the two GNSS stations present in the study area were used for a local comparison with the evolution of ground movements observed by MT-InSAR. Two leveling campaigns (2000, 2013) were also used to make comparisons with the MT-InSAR data. The station’s measurements and the leveling data were in line with the MT-InSAR data. Overall, major ground movements are obviously limited to an extension of the actual underground-mining works and rapidly diminish outside of them.

<p><strong>Abstract.</strong> Increasing urbanisation, changes in land use (e.g., more impervious area) and climate change have all led to an increasing frequency and severity of flood events and increased socio-economic impact. In order to deploy an urban flood disaster and risk management system, it is necessary to know what the consequences of a specific urban flood event are to adapt to a potential event and prepare for its impact. Therefore, an accurate socio-economic impact assessment must be conducted. Unfortunately, until now, there has been a lack of data regarding the design and construction of flood-prone building structures (e.g., locations and dimensions of doors and door thresholds and presence and dimensions of basement ventilation holes) to consider when calculating the flood impact on buildings. We propose a pipeline to detect the dimension and location of doors and windows based on mobile LiDAR data and 360° images. This paper reports on the current state of research in the domain of object detection and instance segmentation of images to detect doors and windows in mobile LiDAR data. The use and improvement of this algorithm can greatly enhance the accuracy of socio-economic impact of urban flood events and, therefore, can be of great importance for flood disaster management.</p>

Highlights High-resolution beach surface moisture estimation from the LiDAR intensity data. No need to calibrate the intensity data in advance based on the Machine Learning. Detailed investigation of the impacts of SVR training samples’ size and density.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.