Soil and water conservation structures are vital for environmental resilience but present maintenance challenges due to their wide distribution and remote locations. To tackle these issues, a method using unmanned aerial vehicles (UAVs) combined with 360 degree photography was developed. UAVs captured images that were processed into panoramic and 3D models, enabling precise inspections of structural damage. These models were integrated into the disaster environment review and update (DER&U) rating system, enhanced by a fuzzy inference classification mechanism for improved damage estimation. Additionally, a management platform was created to boost overall efficiency and provide decision-making support for relevant authorities. The UAV-assisted inspection method demonstrated promising results, though certain limitations were also noted.

Vegetation indices (VIs) are widely applied to estimate leaf area index (LAI) for monitoring vegetation vigor and growth dynamics. However, the saturation issues in VIs caused by crown closure during the growing season pose significant challenges to the application of VIs in LAI estimation, particularly at the individual tree level. To address this, the feasibility of common VIs for LAI estimation at the individual tree level throughout the growing season was analyzed using data from digital hemispherical photography (DHP) and Unmanned Aerial Vehicle (UAV) acquisition. Additionally, the physical mechanisms underlying a generic VI-based estimation model were explored using the PROSAIL model and Global Sensitivity Analysis (GSA). Furthermore, the relationships between observed LAI derived from DHP and UAV-based VIs across different phenological development phases throughout the growing season were analyzed. The results suggested that the normalized difference vegetation index (NDVI) and its faster substitute infrared percentage vegetation index (IPVI) exhibited the best capabilities for LAI estimation (R2 = 0.55 and RMSE = 0.77 for both) across the entire growing season. The LAI-VI relationship varied seasonally due to the saturation issues on VIs, with R2 values increasing from the leaf budburst to the growing stage, decreasing during maturation, and rising again in the senescence stage. This indicated that seasonal effects induced by phenological changes should be considered when estimating LAI using VIs. Additionally, the saturation of VIs was influenced by soil background, leaf properties (especially leaf chlorophyll content [Cab] and dry matter content [Cm]), and canopy structures (especially average leaf inclination angle, ALA). Compared to satellites, UAV-based sensors were more effective at mitigating spectral saturation at finescale due to their finer spatial resolution and narrower bandwidth. The drone-based VIs used in this study provided reliable estimates and effectively described temporal variability in LAI, contributing to a better understanding of VI saturation effects.

Exposed surfaces following glacial retreat are ideal field laboratories for studying primary vegetation succession. Many related studies based on ground sampling methods have been performed worldwide in proglacial zones, but studies on species diversity and vegetation succession using aerial photography have been rare. In this study, we investigated soil organic carbon (SOC), total nitrogen (TN), plant species diversity, and fractional vegetation cover (FVC) along a chronosequence within the foreland of Urumqi Glacier No. 1 by combining field sampling and aerial photography. We then analysed soil development and vegetation succession along distance (distance from glacier terminus) and time (terrain age) gradients as well as the relationships between topographic and environmental variables (aspect, slope, SOC, and TN), distance, time, and species distributions. The results indicated that: (1) plant diversity and FVC showed increasing trends with increases in distance and terrain age, whereas soil nutrient content varied nonlinearly; (2) Silene gonosperma, Leontopodium leontopodioides, and Saussurea gnaphalodes were the dominant species in the early, transient, and later succession stages, respectively. Cancrinia chrysocephala occurred in all stages and had a high abundance in the early and later stages; and (3) the relationships of FVC with soil nutrient content were nonlinear. Moreover, distance and site age played important roles in species distribution. These findings confirm that distance and terrain age positively affect vegetation succession. The increase in FVC facilitated the accumulation of soil nutrition, but this trend was affected by the rapid growth of plants. Caryophyllaceae and Asteraceae were the most common plants during the succession stages, and the former tended to colonise in the early succession stage. We conclude that the UAV-based method exhibits a high application potential for assessing vegetation dynamics in glacier forelands, which has a significance for long-term and repeated monitoring on the process of vegetation colonisation and succession in deglariated areas. (C) 2021 Elsevier B.V. All rights reserved.

Climate-induced changes in vegetation phenology at northern latitudes are still poorly understood. Continued monitoring and research are therefore needed to improve the understanding of abiotic drivers. Here we used 14 years of time lapse imagery and climate data from high-Arctic Northeast Greenland to assess the seasonal response of a dwarf shrub heath, grassland, and fen, to inter-annual variation in snow-cover, soil moisture, and air and soil temperatures. A late snowmelt and start of growing season is counterbalanced by a fast greenup and a tendency to higher peak greenness values. Snow water equivalents and soil moisture explained up to 77% of growing season duration and senescence phase, highlighting thatwater availability is a prominent driver in the heath site, rather than temperatures. We found a significant advance in the start of spring by 10 days and in the end of fall by 11 days, resulting in an unchanged growing season length. Vegetation greenness, derived from the imagery, was correlated to primary productivity, showing that the imagery holds valuable information on vegetation productivity.