The occurrence of hurricanes in the southern U.S. is on the rise, and assessing the damage caused to forests is essential for implementing protective measures and comprehending recovery dynamics. This work aims to create a novel data integration framework that employs LANDSAT 8, drone-based images, and geographic information system data for change detection analysis for different forest types. We propose a method for change vector analysis based on a unique spectral mixture model utilizing composite spectral indices along with univariate difference imaging to create a change detection map illustrating disturbances in the areas of McDowell County in western North Carolina impacted by Hurricane Helene. The spectral indices included near-infrared-to-red ratios, a normalized difference vegetation index, Tasseled Cap indices, and a soil-adjusted vegetation index. In addition to the satellite imagery, the ground truth data of forest damage were also collected through the field investigation and interpretation of post-Helene drone images. Accuracy assessment was conducted with geographic information system (GIS) data and maps from the National Land Cover Database. Accuracy assessment was carried out using metrics such as overall accuracy, precision, recall, F score, Jaccard similarity, and kappa statistics. The proposed composite method performed well with overall accuracy and Jaccard similarity values of 73.80% and 0.6042, respectively. The results exhibit a reasonable correlation with GIS data and can be employed to assess damage severity.

Ability of remotely sensed solar-induced chlorophyll fluorescence (SIF) to serve as a vegetation productivity and stress indicator is impaired by confounding factors, such as varying crop-specific canopy structure, changing solar illumination angles, and SIF-soil optical interactions. This study investigates two normalisation approaches correcting diurnal top-of-canopy SIF observations retrieved from the O2-A absorption feature at 760 nm (F 760 hereafter) of summer barley crops for these confounding effects. Nadir SIF data was acquired over nine breeding experimental plots simultaneously by an airborne imaging spectrometer (HyPlant) and a drone-based highperformance point spectrometer (AirSIF). Ancillary measurements, including leaf pigment contents retrieved from drone hyperspectral imagery, destructively sampled leaf area index (LAI), and leaf water and dry matter contents, were used to test the two normalisation methods that are based on: i) the fluorescence correction vegetation index (FCVI), and ii) three versions of the near-infrared reflectance of vegetation (NIRV). Modelling in the discrete anisotropic radiative transfer (DART) model revealed close matches for NIRv-based approaches when corrected canopy SIF was compared to simulated total chlorophyll fluorescence emitted by leaves (R2 = 0.99). Normalisation with the FCVI also performed acceptably (R2 = 0.93), however, it was sensitive to variations in LAI when compared to leaf emitted chlorophyll fluorescence efficiency. Based on the results modelled in DART, the NIRvH1 normalisation was found to have a superior performance over the other NIRv variations and the FCVI normalisation. Comparison of the SIF escape fractions suggests that the escape fraction estimated with NIRvH1 matched escape fraction extracted from DART more closely. When applied to the experimental drone and airborne nadir canopy SIF data, the agreement between NIRvH1 and FCVI produced chlorophyll fluorescence efficiency was very high (R2 = 0.93). Nevertheless, NIRvH1 showed higher uncertainties for areas with low vegetation cover indicating an unaccounted contribution of SIF-soil interactions. The diurnal courses of chlorophyll fluorescence efficiency for both approaches differed not significantly from simple normalisation by incoming and apparent photosynthetically active radiation. In conclusion, SIF normalisation with NIRvH1 more accurately compensates the effects of canopy structure on top of canopy far red SIF, but when applied to top of canopy in-situ data of spring barley, the effects of NIRvH1 and FCVI on the diurnal course of SIF had a similar influence.

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.

Timber skidding is one of the many causes of destruction or damage to forest stands. Any sustainable forestry system requires a suitable skidding trail layout that minimises damage to trees and soils as well as economic losses. Imagery of six teak plantation plots in Thong Pha Phum was captured with a DJI Mavic Pro unmanned aerial vehicle and further processed with Agisoft Metashape software. Single trees could be distinguished in the canopy height model thus created, and understory trees for thinning from below were identified from a weighted Voronoi diagram. This approach does not include a field survey and therefore needs to include an assessment of the health and quality of the trees, but it can be considered as a basis for accelerating the process of marking trees for thinning. Rasterisation was applied to produce an estimate of tree density based on Kernel Density Estimation. Given the irregular spacing of the teak trees, a subjective approach was applied to plot a skidding trail layout, with the emphasis on shortening skidding distances and reducing potential damage to soils and remaining trees. This study could help to improve access to forest stands by improving the quality of skidding and reducing damage to standing trees and to the timber itself.

Forests are essential to our planet's well-being, playing a vital role in climate regulation, biodiversity preservation, and soil protection, thus serving as a cornerstone of our global ecosystem. The threat posed by forest fires highlights the critical need for early detection systems, which are indispensable tools in safeguarding ecosystems, livelihoods, and communities from devastating destruction. In combating forest fires, a range of techniques is employed for efficient early detection. Notably, the combination of drones with artificial intelligence, particularly deep learning, holds significant promise in this regard. Image segmentation emerges as a versatile method, involving the partitioning of images into multiple segments to simplify representation, and it leverages deep learning for fire detection, continuous monitoring of high-risk areas, and precise damage assessment. This study provides a comprehensive examination of recent advancements in semantic segmentation based on deep learning, with a specific focus on Mask R-CNN (Mask Region Convolutional Neural Network) and YOLO (You Only Look Once) v5, v7, and v8 variants. The emphasis is placed on their relevance in forest fire monitoring, utilizing drones equipped with high-resolution cameras.

Laohugou glacier No. 12 (LHG12), located in the northeast of the Qinghai-Tibet Plateau, is the largest valley glacier in the Qilian mountains. Since 1957, LHG12 has shrunk significantly. Due to the limitations of in situ observations, simulations and investigations of LHG12 have higher levels of uncertainty. In this study, consumer-level, low-altitude microdrones were used to conduct repeated photogrammetry at the lower part of LHG12, and a digital orthophoto map (DOM) and a digital surface model (DSM) with a resolution at the centimeter scale were generated, from 2017 to 2021. The dynamic parameters of the glacier were detected by artificial and automatic extraction methods. Using a combination of GNSS and drone-based data, the dynamic process of LHG12 was analyzed. The results show that the terminus of LHG12 has retreated by 194.35 m in total and by 19.44 m a(-1) on average during 2008-2021. The differential ablation leading to terminus retreat distance markedly increased during the study period. In 2019-2021, the maximum annual surface velocity was 6.50 cm day(-1), and during ablation season, the maximum surface velocity was 13.59 cm day(-1), 52.17% higher than it is annually. The surface parameters, motion, and mass balance characteristics of the glacier had significant differences between the west and east branches. The movement in the west branch is faster than it is in the east branch. Because of the extrusion of the two ice flows, there is a region with a faster surface velocity at the ablation area. The ice thickness of LHG12 is decreasing due to intensified ablation, leading to a deceleration in the surface velocity. In large glaciers, this phenomenon is more obvious than it is in small glaciers in the Qilian mountains.