Tree destruction induced by heavy rainfall, an overlooked type of forest degradation, has been exacerbated along with global climate change. On the Chinese Loess Plateau, especially in afforested gully catchments dominated by Robinia pseudoacacia, destructive rainfall events have increasingly led to widespread forest damage. Previous study has manifested the severity of heavy rainfall-induced tree destruction and its association with topographic change, yet the contributions of tree structure and forest structure remain poorly understood. In this study, we quantified the destroyed trees induced by heavy rainfall using light detection and ranging (LiDAR) techniques. We assessed the influence of tree structure (tree height, crown diameter, and crown area), forest structure (tree density, gap fraction, leaf area index, and canopy cover), and terrain parameters (elevation, slope, and terrain relief) using machine learning models (random forest and logistic regression). Based on these, we aimed to clarify the respective and combined contributions of structural and topographic factors to rainfall-induced tree destruction. Key findings revealed that when considered in isolation, greater tree height, crown diameter, crown area, leaf area index (LAI), and canopy cover suppressed tree destruction, whereas higher gap fractions increased the probability of tree destruction. However, the synergistic increases of tree structural factors (tree height, crown diameter, and crown area) and forest structural factors (LAI and canopy cover) significantly promoted tree destruction, which can counteract the inhibitory effect of terrain on destruction. In addition, increases in tree structure or canopy density (LAI and canopy cover) also increased the probability of tree destruction at the same elevation. Our findings challenge conventional assumptions in forest management by demonstrating the interaction of tree structure and canopy density can significantly promote tree destruction during heavy rainfall. This highlights the need to avoid overly dense afforestation in vulnerable landscapes and supports more adaptive, climate-resilient restoration strategies.

This study evaluated the usability and effectiveness of robotic platforms working together with foresters in the wild on forest inventory tasks using LiDAR scanning. Emphasis was on the Universal Access principle, ensuring that robotic solutions are not only effective but also environmentally responsible and accessible for diverse users. Three robotic platforms were tested: Boston Dynamics Spot, AgileX Scout, and Bunker Mini. Spot's quadrupedal locomotion struggled in dense undergrowth, leading to frequent mobility failures and a System Usability Scale (SUS) score of 78 +/- 10. Its short, battery life and complex recovery processes further limited its suitability for forest operations without substantial modifications. In contrast, the wheeled AgileX Scout and tracked Bunker Mini demonstrated superior usability, each achieving a high SUS score of 88 +/- 5. However, environmental impact varied: Scout's wheeled design caused minimal disturbance, whereas Bunker Mini's tracks occasionally damaged young vegetation, highlighting the importance of gentle interaction with natural ecosystems in robotic forestry. All platforms enhanced worker safety, reduced physical effort, and improved LiDAR workflows by eliminating the need for human presence during scans. Additionally, the study engaged forest engineering students, equipping them with hands-on experience in emerging robotic technologies and fostering discussions on their responsible integration into forestry practices. This study lays a crucial foundation for the integration of Artificial Intelligence (AI) into forest robotics, enabling future advancements in autonomous perception, decision-making, and adaptive navigation. By systematically evaluating robotic platforms in real-world forest environments, this research provides valuable empirical data that will inform AI-driven enhancements, such as machine learning-based terrain adaptation, intelligent path planning, and autonomous fault recovery. Furthermore, the study holds high value for the international research community, serving as a benchmark for future developments in forestry robotics and AI applications. Moving forward, future research will build on these findings to explore adaptive remote operation, AI-powered terrain-aware navigation, and sustainable deployment strategies, ensuring that robotic solutions enhance both operational efficiency and ecological responsibility in forest management worldwide.

Tree architecture is an important component of forest community dynamics - taller trees with larger crowns often outcompete their neighbors, but they are generally at higher risk of wind-induced damage. Yet, we know little about wind impacts on tree architecture in natural forest settings, especially in complex tropical forests. Here, we use airborne light detection and ranging (LiDAR) and 30 yr of forest inventory data in Puerto Rico to ask whether and how chronic winds alter tree architecture. We randomly sampled 124 canopy individuals of four dominant tree species (n = 22-39). For each individual, we measured slenderness (height/stem diameter) and crown area (m2) and evaluated whether exposure to chronic winds impacted architecture after accounting for topography (curvature, elevation, slope, and soil wetness) and neighborhood variables (crowding and previous hurricane damage). We then estimated the mechanical wind vulnerability of trees. Three of four species grew significantly shorter (2-4 m) and had smaller crown areas in sites exposed to chronic winds. A short-lived pioneer species, by contrast, showed no evidence of wind-induced changes. We found that three species' architectural acclimation to chronic winds resulted in reduced vulnerability. Our findings demonstrate that exposure to chronic, nonstorm winds can lead to architectural changes in tropical trees, reducing height and crown areas. La arquitectura de los & aacute;rboles es un componente importante de la din & aacute;mica de la comunidad forestal: los & aacute;rboles m & aacute;s altos con copas m & aacute;s grandes suelen sobrepasar a sus vecinos, pero por lo general corren m & aacute;s riesgo de sufrir da & ntilde;os inducidos por el viento. Sin embargo, es poco lo que se sabe sobre el impacto del viento en la arquitectura de los & aacute;rboles en entornos forestales naturales, sobre todo en bosques tropicales complejos. En este caso, utilizamos LiDAR y 30 a & ntilde;os de datos de campo en Puerto Rico para preguntarnos si los vientos cr & oacute;nicos alteran la arquitectura de los & aacute;rboles. Se tom & oacute; una muestra aleatoria de 124 individuos del dosel de cuatro especies arb & oacute;reas dominantes (n = 22-39). De cada individuo, medimos la esbeltez (altura/di & aacute;metro) y el & aacute;rea de la copa (m2) y evaluamos si la exposici & oacute;n a vientos cr & oacute;nicos influ & iacute;a en la arquitectura teniendo en cuenta la topograf & iacute;a (curvatura, elevaci & oacute;n, pendiente, humedad del suelo) y las variables del vecindario (aglomeraci & oacute;n y da & ntilde;os previos por huracanes). Luego, estimamos la vulnerabilidad mec & aacute;nica de los & aacute;rboles al viento. En los lugares expuestos a vientos cr & oacute;nicos, tres de las cuatro especies crecieron mucho menos (2-4 m) y tuvieron & aacute;reas de copa m & aacute;s peque & ntilde;as. Cecropia schreberiana, en cambio, no mostr & oacute; indicios de cambios inducidos por el viento. La aclimataci & oacute;n arquitect & oacute;nica de tres especies a los vientos cr & oacute;nicos llevaba a una reducci & oacute;n de la vulnerabilidad. Nuestros hallazgos demuestran que la exposici & oacute;n a vientos cr & oacute;nicos puede provocar cambios arquitect & oacute;nicos en los & aacute;rboles tropicales, reduciendo su altura y la superficie de sus copas.

We present a multi-year study of Saharan dust intrusions on a mountainous site located in the central Mediterranean Basin regarding their aerosol optical and geometrical properties. The observations were carried out at the Consiglio Nazionale delle Ricerche-Istituto di Metodologie per l'Analisi Ambientale (CNR-IMAA) located in Potenza (40,360N, 15,440E), Italy, from March 2010 to October 2022, using ACTRIS (Aerosol Clouds and Trace Gases Research InfraStructure). A total of 101 night-time lidar measurements of dust intrusions were identified. The following properties were calculated for the periods December, January, February (DJF), March, April, May (MAM), June, July, August (JJA) and September, October, November (SON): aerosol layer center of mass altitude, particle lidar ratio at 355 and 532 nm, particle depolarization ratio at 532 nm and backscattering & Aring;ngstr & ouml;m exponent at 532-1064 nm. Both geometrical and optical aerosol properties vary considerably with the seasons. During SON and DJF, air masses transporting dust travel at lower altitudes, and become contaminated with local continental particles. In MAM and JJA, dust is also likely to travel at higher altitudes and rarely mix with other aerosol types. As a result, aerosols are larger in size and irregular in shape during the warm months. The ratio of the lidar ratios at 355 and 532 nm is 1.11 +/- 0.15 in DJF, 1.12 +/- 0.07 in SON, 0.94 +/- 0.12 in MAM, and 0.92 +/- 0.08 in JJA. The seasonal radiative effect estimated using the Fu-Liou-Gu (FLG) radiative transfer model indicates the most significant impact during the JJA period. A negative dust radiative effect is observed both at the surface (SRF) and at the top of the atmosphere (TOA) in all the seasons, and this could be related to a minimal contribution from black carbon. Specifically, the SRF radiative effect estimation is -14.48 +/- 1.32 W/m2 in DJF, -18.00 +/- 0.89 W/m2 in MAM, -22.08 +/- 1.36 W/m2 in JJA, and -13.47 +/- 1.12 W/m2 in SON. Instead, radiative effect estimation at the TOA is -22.23 +/- 2.06 W/m2 in DJF, -38.23 +/- 2.16 W/m2 in MAM, -51.36 +/- 3.53 W/m2 in JJA, and -22.57 +/- 2.11 W/m2 in SON. The results highlight how the radiative effects of the particles depend on the complex relationship between the dust load, their altitude in the troposphere, and their optical properties. Accordingly, the knowledge of aerosols optical property profiles is of primary importance to understand the radiative impact of dust.

Rainstorm events are becoming increasingly frequent due to the impacts of global warming, which results in widespread erosion disasters and related tree destruction. However, previous corresponding studies of forest damage have focused on typhoons or wildfires, ignoring the increasing risk of rainstorm erosion-induced tree destruction. It is unclear what scale of tree destruction can be caused by heavy rainfall. In this study, we used a tree segmentation method based on airborne light detection and ranging (LiDAR) technology to accurately quantify the tree destruction during heavy rainfall in a representative afforested catchment on the Chinese Loess Plateau. Additionally, topographic changes were calculated using pre- and post-heavy rainfall LiDAR datasets, and tree destruction was assessed by combining terrain information and tree structural parameters. The results showed that 3253 trees in the catchment (0.9 km2) were destroyed due to rainstorm erosion, among which 2845 trees were located on gully slope landform, accounting for 87.4 % of all destroyed trees. Tree destruction on steep gully slope (slope: 45.5 degrees-50.5 degrees) was mainly induced by rainstorm erosion, while that on both sides of the gully bed (altitude: 1137 m-1147 m) was mainly induced by sediment deposition. In the catchment, the deposition area that resulted in tree destruction (21265 m2) was greater than the erosion area (20020 m2). However, the damage caused by erosion was more destructive than that caused by deposition. There was a significant linear relationship between tree structural parameters and terrain in the forestland catchment. Our study provides a reference methodology for studies of forest damage due to extreme weather events worldwide, and has significant implications for ecosystem management and reforestation in the context of global change.

古冰川地貌是研究第四纪环境演变的重要依据,无人机Li DAR技术的高精度数据使得古冰川地貌研究具有更高的分辨率,可显著提升古冰川地貌分析、冰川地貌制图的精度。青藏高原东南部的稻城古冰帽保存了大量古冰川遗迹,是研究冰川地貌的理想区域。本研究应用无人机Li DAR技术,对稻城古冰帽南缘的库照日地区槽谷出口的冰碛垄进行航测,获得库照日冰碛垄的数字高程模型(DEM)、数字正射影像(DOM)和三维点云数据,并进一步对比12.5 m、30 m分辨率的DEM成像效果,分析库照日冰碛垄的地形特征、库照日槽谷出口冰碛垄围成谷地的形态参数等。结果表明:(1)无人机Li DAR技术能快速获得高质量、高分辨率的数据,适用于小区域、地貌较复杂的地区,结合三维模型可提高对冰碛垄地貌形态的认识;(2)对库照日冰碛垄的地形特征统计可知,最内侧的K-M1垄拥有第二高的坡度平均值,K-M6垄作为独立垄拥有最大的坡度平均值;(3)库照日槽谷出口冰碛垄围成谷地的幂函数指数b值范围为0.24至0.54,小于多数槽谷的b值;V指数结果范围为0.52～0.69。本研究为基于无人机Li DAR技术的冰川地貌定量分析提供了较好的研究案...

古冰川地貌是研究第四纪环境演变的重要依据,无人机Li DAR技术的高精度数据使得古冰川地貌研究具有更高的分辨率,可显著提升古冰川地貌分析、冰川地貌制图的精度。青藏高原东南部的稻城古冰帽保存了大量古冰川遗迹,是研究冰川地貌的理想区域。本研究应用无人机Li DAR技术,对稻城古冰帽南缘的库照日地区槽谷出口的冰碛垄进行航测,获得库照日冰碛垄的数字高程模型(DEM)、数字正射影像(DOM)和三维点云数据,并进一步对比12.5 m、30 m分辨率的DEM成像效果,分析库照日冰碛垄的地形特征、库照日槽谷出口冰碛垄围成谷地的形态参数等。结果表明:(1)无人机Li DAR技术能快速获得高质量、高分辨率的数据,适用于小区域、地貌较复杂的地区,结合三维模型可提高对冰碛垄地貌形态的认识;(2)对库照日冰碛垄的地形特征统计可知,最内侧的K-M1垄拥有第二高的坡度平均值,K-M6垄作为独立垄拥有最大的坡度平均值;(3)库照日槽谷出口冰碛垄围成谷地的幂函数指数b值范围为0.24至0.54,小于多数槽谷的b值;V指数结果范围为0.52～0.69。本研究为基于无人机Li DAR技术的冰川地貌定量分析提供了较好的研究案...

The rapid degradation of Xing'an-Baikal permafrost in Northeast China has led to various road engineering problems. Efficient inspection and control of pavement quality are critical for maintaining the structural integrity of roads and driving safety in cold regions. Taking the Jagdaqi-Walagan (JWS) of the Jagdaqi-Mo'he Highway as the object, based on field investigation, unmanned aerial vehicle images and airborne LiDAR data, combined with geographical information system, this study analyzed the pavement damage characteristics in mid- to high-latitude permafrost regions, including quantification of damage ratio, extraction of pavement cracks, and evaluation of pavement roughness and driving quality. The results showed that the average pavement damage ratio was 8.80 %, significantly higher in isolated permafrost regions. A higher damage rate in the Jagdaqi-Mo'he direction than the opposite, with a more concentrated cracking distribution. The worst pavement roughness and most severe pavement bumping at repetitive repair locations. This study provides an effective method for investigating pavement damages and analyzing their mechanisms, and explores the application potential of visible light images combined with LiDAR data in frozen soil engineering. The results provide a scientific basis for assessing current highway conditions, enabling scientific maintenance, and evaluating the risk of engineering damages.

Cyclonic storms (i.e., hurricanes) are powerful disturbance events that often cause widespread forest damage. Storm-related canopy damage reduces rainfall interception and evapotranspiration, but impacts on streamflow regimes are poorly understood. We quantify streamflow changes in Puerto Rico following Hurricane Maria in September 2017, and evaluate whether forest cover and storm-related canopy damage account for the differences. Streams are particularly vulnerable to flooding in early post-disturbance stages during hurricane season, so we focus on 3 months (Oct-Dec) following the hurricane. To discern changes in rainfall responses, we partitioned streamflow into baseflow and quickflow using a digital filter. We collected 2010-2017 streamflow and rainfall data from 18 watersheds and compared the relative magnitude of post- to pre-hurricane double mass curve slopes of baseflow and quickflow volumes against rainfall. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. The magnitude of quickflow increase was greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low initial forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow generally increased, but increases were greater in highly forested watersheds and smaller in highly damaged watersheds. These results suggest that post-storm baseflow increases were due to recharge of hurricane-related rainfall, as well as forest transpiration interruption and soil disturbance enhancing recharge of post-hurricane rainfall, while increases to quickflow are related to loss of canopy rainfall interception and higher soil saturation decreasing infiltration. Our research demonstrates that forest damage from disturbance lowers quickflow and elevates baseflow in highly forested watersheds, and elevates quickflow and lowers baseflow in less-forested watersheds. Less-forested watersheds may be closer to the forest cover loss threshold needed to elicit a streamflow response following disturbance, suggesting higher flooding potential downstream, and a lower storm-related forest disturbance threshold than in heavily forested watersheds. We quantify streamflow component changes following a severe hurricane and relate these changes to watershed forest cover and canopy damage. Several watersheds displayed higher post-hurricane quickflow and baseflow, however, the response was variable. Quickflow increases were greater in watersheds with high forest damage. Under the same level of relative damage, watersheds with low forest cover had greater quickflow increases than highly forested ones. Conversely, baseflow increases were greater in highly forested watersheds and smaller in highly damaged watersheds. image

The Kahramanmaras, seismic sequence of February 6th, 2023, caused extreme damage and a significant number of casualties across a large region of Turkey and Syria. The paper reports on the survey activities carried out by the authors in the city of Golbas,& imath;, where extensive liquefaction took place. The damage to the built environment caused by liquefaction differs from that caused by typical inertial seismic actions, with quasi-rigid body displacement mechanisms, resulting in extreme settlements, tilts, and, in some cases, complete overturning. After a brief introduction to the geological features of the Golbas,& imath; area and a discussion of the seismic effects on the area, the paper reports and comments on the damage observed in one part of the city and provides some statistical interpretations.