High Spatial Resolution Aerial Images Research Articles

Self-Adaptive-Filling Deep Convolutional Neural Network Classification Method for Mountain Vegetation Type Based on High Spatial Resolution Aerial Images

The composition and structure of mountain vegetation are complex and changeable, and thus urgently require the integration of Object-Based Image Analysis (OBIA) and Deep Convolutional Neural Networks (DCNNs). However, while integration technology studies are continuing to increase, there have been few studies that have carried out the classification of mountain vegetation by combining OBIA and DCNNs, for it is difficult to obtain enough samples to trigger the potential of DCNNs for mountain vegetation type classification, especially using high-spatial-resolution remote sensing images. To address this issue, we propose a self-adaptive-filling method (SAF) to incorporate the OBIA method to improve the performance of DCNNs in mountain vegetation type classification using high-spatial-resolution aerial images. Using this method, SAF technology was employed to produce enough regular sample data for DCNNs by filling the irregular objects created by image segmenting using interior adaptive pixel blocks. Meanwhile, non-sample segmented image objects were shaped into different regular rectangular blocks via SAF. Then, the classification result was defined by voting combining the DCNN performance. Compared to traditional OBIA methods, SAF generates more samples for the DCNN and fully utilizes every single pixel of the DCNN input. We design experiments to compare them with traditional OBIA and semantic segmentation methods, such as U-net, MACU-net, and SegNeXt. The results show that our SAF-DCNN outperforms traditional OBIA in terms of accuracy and it is similar to the accuracy of the best performing method in semantic segmentation. However, it reduces the common pretzel phenomenon of semantic segmentation (black and white noise generated in classification). Overall, the SAF-based OBIA using DCNNs, which is proposed in this paper, is superior to other commonly used methods for vegetation classification in mountainous areas.

Improving the performance of learned descriptors in the matching of high spatial resolution aerial images by proposing a large-scale dataset of vertical images

Improving the performance of learned descriptors in the matching of high spatial resolution aerial images by proposing a large-scale dataset of vertical images

Spatiotemporal changes of coastal land use land cover and its drivers in Shanghai, China between 1989 and 2015

The coastal zone, an ecotone that provides vital diverse ecosystem services, is one of the most ecologically fragile and sensitive areas on the Earth. It has been strongly influenced by human activities and climate change. Thus, understanding the spatiotemporal changes of land use land cover in the coast is an essential prerequisite for the comprehensive evaluation of coastal ecosystems and the promotion of sustainable development. This study investigated the spatial and temporal changes of land use land cover in the coastal zone of mainland Shanghai by using high spatial resolution aerial images, and the associated driving factors obtained from statistical yearbooks between 1989 and 2015. Our results show that the total land area in the coastal zone exhibited an increasing trend at an annual rate of 7.6% on average. The coastal urban land use also experienced substantial increases, with the degree of urban expansion reaching 42.1% in 2015. The coastal urbanization was at the cost of natural and semi-natural lands. Nearly 64.4% of agricultural land, 24.4% of ocean area within the buffer, 84.9% of fresh water, and 92.8% of the tidal flat had been converted to urban land during 1989–2015. The gradient analysis of land use land cover change along the north-to-south coastal line revealed the spatiotemporal patterns of total land, urbanization degree, and natural and semi-natural lands. The majority of the socioeconomic factors influenced land use land cover change in the coastal zone, positively contributing to the increase of public facilities’ use of land, but negatively affecting the freshwater areas. These findings can provide insights for decision-making in the future for coastal land use land cover planning and management in Shanghai.

Abrupt increase in thermokarst lakes on the central Tibetan Plateau over the last 50 years

Thermokarst lakes are an important ecosystem component in permafrost terrains; therefore, quantifying changes in these lakes is important for evaluating the water balance and carbon budget on the Qinghai-Tibet Plateau (QTP). In this study, we utilized high spatial resolution aerial and satellite images from 1969, 2010, and 2019 to quantify changes in thermokarst lakes > 0.1 ha across four regions on the central QTP. The results indicated an overall significant increase in the lake number (+158%) and surface area (+123%) for all study regions over the last five decades, despite variations in change in these trends among the different landform types. Changes in individual lake processes indicated that the above lake changes were mainly characterized by a significant increase in the number of small lakes and area enlargements of large lakes, whereas thermokarst lake drainage only occurred in some gentle slope areas. By analyzing the potential factors that drive changes in thermokarst lakes in the study region, we found that persistent climate warming and the increasing of precipitation were the most likely explanations for the observed results. With continued climate warming and permafrost degradation, we expect a persistent increase in the number and surface area of thermokarst lakes; however, the probability of lake drainage may also increase with lake expansion.

Predicting the location of larval habitats of Anopheles mosquitoes using remote sensing and soil type data

Malaria is a mosquito-borne infectious disease transmitted by the bite of Anopheles mosquitoes. Accurate and timely identification of Anopheles larval habitats and analysis of environmental factors affecting the formation and stability of these locations are very effective in the prevention and spread of malaria. In the absence of sufficient field observations and environmental parameters required in a suitable spatial coverage, remote sensing data can be effective in predicting the habitats of Anopheles mosquitoes. In this article, high-risk depressions that have the potential for Anopheles larval habitats had been identified by fusing a Digital Surface Model (DSM) extracted from very high spatial resolution aerial stereo images with land-use and soil type maps. land-useFinally, the high-risk map of malaria based on the prone larval habitats of Anopheles was created. To evaluate the results, 22 important depressions were identified using field observations in the 1.5 km buffer around Qaleh-Ganj city, Kerman Province, Iran. Of these 22 samples, 14 of them were stable for more than one month at temperatures above 30 °C and the rest were able to store water for three to four weeks. 12 out of 14 samples were consistent with the identified 130 high-risk depressions in the buffer range. Also, 19 of these 22 samples were compatible with the optimal depressions in the buffer range. By comparing the proposed method with previous methods based on data with medium and low spatial resolution or meteorological data, it was concluded that the correct and accurate seasonal and temporary position of Anopheles larval habitats depend on higher spatial resolution of remote sensing data. In addition, this study demonstrated that the use of vegetation and water indices cannot predict the exact location of all habitats of Anopheles. Because many of these habitats were temporary and these indices could not estimate and predict their exact location. The proposed method can be applied to search for suitable larval habitats of Anopheles around local residential areas, where neither medium and low spatial resolution data nor limited field observations can be used.

Superpixel-Based Attention Graph Neural Network for Semantic Segmentation in Aerial Images

Semantic segmentation is one of the significant tasks in understanding aerial images with high spatial resolution. Recently, Graph Neural Network (GNN) and attention mechanism have achieved excellent performance in semantic segmentation tasks in general images and been applied to aerial images. In this paper, we propose a novel Superpixel-based Attention Graph Neural Network (SAGNN) for semantic segmentation of high spatial resolution aerial images. A K-Nearest Neighbor (KNN) graph is constructed from our network for each image, where each node corresponds to a superpixel in the image and is associated with a hidden representation vector. On this basis, the initialization of the hidden representation vector is the appearance feature extracted by a unary Convolutional Neural Network (CNN) from the image. Moreover, relying on the attention mechanism and recursive functions, each node can update its hidden representation according to the current state and the incoming information from its neighbors. The final representation of each node is used to predict the semantic class of each superpixel. The attention mechanism enables graph nodes to differentially aggregate neighbor information, which can extract higher-quality features. Furthermore, the superpixels not only save computational resources, but also maintain object boundary to achieve more accurate predictions. The accuracy of our model on the Potsdam and Vaihingen public datasets exceeds all benchmark approaches, reaching 90.23% and 89.32%, respectively.

Spatiotemporal Analysis of Sea Ice Leads in the Arctic Ocean Retrieved from IceBridge Laxon Line Data 2012–2018

The ocean and atmosphere exert stresses on sea ice that create elongated cracks and leads which dominate the vertical exchange of energy, especially in cold seasons, despite covering only a small fraction of the surface. Motivated by the need of a spatiotemporal analysis of sea ice lead distribution, a practical workflow was developed to classify the high spatial resolution aerial images DMS (Digital Mapping System) along the Laxon Line in the NASA IceBridge Mission. Four sea ice types (thick ice, thin ice, open water, and shadow) were identified, and relevant sea ice lead parameters were derived for the period of 2012–2018. The spatiotemporal variations of lead fraction along the Laxon Line were verified by ATM (Airborne Topographic Mapper) surface height data and correlated with coarse spatial resolution sea ice motion, air temperature, and wind data through multiple regression models. We found that the freeboard data derived from sea ice leads were compatible with other products. The temperature and ice motion vorticity were the leading factors of the formation of sea ice leads, followed by wind vorticity and kinetic moments of ice motion.

Object-Based Mangrove Mapping Comparison on Visible and NIR UAV Sensor

Mangrove ecosystems are natural resources that have potential value for development due to their high productivity. Mapping and identification of mangroves have always played a crucial role in mangrove ecosystem conservation efforts, especially to support the sustainable development goal of coastal resources and climate change issues. Several attempts have been made using Unmanned Aerial Vehicle (UAV) techniques acquisition of high spatial resolution aerial images data with various sensors and object-based classification for image processing with various levels of success. This study aims to identify mangrove objects using UAV with true color and NIR false-color sensors using the OBIA approach. The UAV used in this study was DJI Phantom 3 Pro with a true-color sensor (default) and NIR false-color (modified Canon IXUS 160 cameras). The comparison between the two types of sensor of aerial photographs as a source for mangrove mapping proved that the latter performed better than the former because of the near-infrared band can optimally discriminate between mangrove and non-mangrove objects. This will assist future research directions in the mangrove ecosystems mapping method.

PREPARATION OF THE AGRICULTURAL REGISTER BASED ON UAV TECHNOLOGY AND GIS TECHNIQUES. CASE STUDY

In the agricultural field and especially in precision agriculture, drones can be used for various activities such as: monitoring crop health, identifying diseases and pests, monitoring irrigation equipment, generating images underlying the determination of fertilization doses, monitoring livestock. From high spatial resolution aerial images, but also from three-dimensional digital models can be extracted information particularly useful for inventory of natural resources, monitoring of risk phenomena, complex territorial analysis or mapping and representation of the territory. In this context, the aim of the study is to create a complex agricultural register for experimental sites, by applying a modern working methodology that includes high-precision data acquired through UAV technology and processed in the GIS environment. The working methodology involved: locating the area of interest; data acquisition with DJI Phantom 4 Pro UAV equipment; extracting information in vector format, in the AutoCAD Map 3D 2016 software and processing data in the GIS environment - in this stage the descriptive database was created and the cartographic materials were generated. The resulting agricultural register provides a true picture of the reality on the ground but also an important volume of descriptive data associated with each entity in the experimental site. Geospatial data obtained through UAV and GIS techniques can be integrated into studies or analyzes together with other environmental data (climatic, pedological, geological, socio-economic data) and can be a support in making management or organization decisions.

Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning

Automated fruit identification or recognition using image processing is a key element in precision agriculture for performing object detection in large crop plots. Automation of fruit recognition for the captured top-view of RGB based images using an unmanned aerial vehicle (UAV) is a challenge. Image analysis demonstrated the difficulty of processing the captured image under variant illumination in natural environment and with textured objects of non-ideal geometric shapes. However, this is subjected to certain consideration settings and image-processing algorithms. The study presents an automatic method for identifying and recognising the pineapple’s crown images in the designated plot using image processing and further counts the detected images using machine learning classifiers namely artificial neural network (ANN), support vector machine (SVM), random forest (RF), naive Bayes (NB), decision trees (DT) and k-nearest neighbours (KNN). The high spatial-resolution aerial images were pre-processed and segmented, and its extracted features were analysed according to shape, colour and texture for recognising the pineapple crown before classifying it as fruit or non-fruit. Feature fusion using one-way analysis of variance (ANOVA) was incorporated in this study to optimise the performance of machine learning classifier. The algorithm was quantitatively analysed and validated for performance via accuracy, specificity, sensitivity and precision. The detection for the pineapple’s crown images with ANN-GDX classification has demonstrated best performance fruit counting with accuracy of 94.4% and has thus demonstrated clear potential application of an effective RGB images analysis for the pineapple industry.

Utility and optimization of LANDSAT-derived burned area maps for southern California

ABSTRACT Maps of burned area derived from Land Remote-Sensing Satellite (LANDSAT) system imagery may be more reliable than fire perimeter records based on ground observation or manual cartographic delineation. This study evaluates accuracy of LANDSAT-derived burned area maps associated with 19 fires (65 to 86,776 ha) within shrublands of southern California in the period 1996–2018. High spatial resolution aerial images collected soon after these 19 fires were used to verify burned fractions within LANDSAT ground resolution elements. Validated burned fractions were used to optimize classification thresholds applied to burn severity metrics based on LANDSAT Normalized Burn Ratio (NBR), Normalized Burn Ratio 2 (NBR2), and Normalized Difference Vegetation Index (NDVI). The burn severity metrics included post-fire spectral index data (e.g., NBR), pre-fire to post-fire differences (e.g., dNBR), and relative differences (e.g., RdNBR). Optimized classifications of dNBR had the greatest overall accuracy (94%) compared to dNBR2 (93%) and dNDVI (92%). Classifications based on standard difference metrics yielded burn area maps that were 1% more accurate than those based on relative-difference metrics for each spectral index. Classification products based solely on post-fire imagery were 85% to 90% accurate, depending on the spectral index utilized. In comparison, perimeter data from the California Fire and Resource Assessment Programme (FRAP) and Monitoring Trends in Burn Severity (MTBS) substantially overestimated burned extent (by about 44%). The MTBS severity maps excluding the ‘unburned/low severity’ class slightly overestimated burned extent (11%). Commission error in the FRAP data set was attributed to low cartographic detail and inclusion of internal unburned patches. Site-specific differences in unburned soil and vegetation fractions (within partly-burned areas) correlated strongly with overestimation of burned area in the optimized LANDSAT-derived maps; areal overestimates of 10%–15% resulted from data grain size rather than map commission error. The main innovation presented in this study is an empirical method to predict dNBR thresholds based on local (pre-fire) NDVI mean and variance, which produced burned area maps of 90% median accuracy. This LANDSAT-based method could support an efficient reconstruction of fire history in recent decades, which would be more comprehensive and accurate than available fire records for southern California.

Does short-interval fire inhibit postfire recovery of chaparral across southern California?

Regrowth after fire is critical to the persistence of chaparral shrub communities in southern California, which has been subject to frequent fire events in recent decades. Fires that recur at short intervals of 10 years or less have been considered an inhibitor of recovery and the major cause of ‘community type-conversion’ in chaparral, primarily based on studies of small extents and limited time periods. However, recent sub-regional investigations based on remote sensing suggest that short-interval fire (SIF) does not have ubiquitous impact on postfire chaparral recovery. A region-wide analysis including a greater spatial extent and time period is needed to better understand SIF impact on chaparral.This study evaluates patterns of postfire recovery across southern California, based on temporal trajectories of Normalized Difference Vegetation Index (NDVI) derived from June-solstice Landsat image series covering the period 1984–2018. High spatial resolution aerial images were used to calibrate Landsat NDVI trajectory-based estimates of change in fractional shrub cover (dFSC) for 294 stands. The objectives of this study were (1) to assess effects of time between fires and number of burns on recovery, using stand-aggregate samples (n = 294) and paired single- and multiple-burn sample plots (n = 528), and (2) to explain recovery variations among predominant single-burn locations based on shrub community type, climate, soils, and terrain. Stand-aggregate samples showed a significant but weak effect of SIF on recovery (p < 0.001; R2 = 0.003). Results from paired sample plots showed no significant effect of SIF on dFSC among twice-burned sites, although recovery was diminished due to SIF at sites that burned three times within 25 years. Multiple linear regression showed that annual precipitation and temperature, chaparral community type, and edaphic variables explain 28% of regional variation in recovery of once-burned sites. Many stands that exhibited poor recovery had burned only once and consist of xeric, desert-fringe chamise in soils of low clay content.

THE ABANDONMENT OF THE GRASSLANDS IN THE MOUNTAIN AREA OF TIMIŞ COUNTY. CASE STUDY: ATU PIETROASA

In the context of a dynamic environment, affected by various natural and / or anthropogenic factors and processes, monitoring and evaluation of grassland areas are essential. The purpose of this study is to capture the changes in time and space, in the surface of some meadows in the mountainous area of Timiş County, against the background of poor management and negative demographic indicators. The researches were carried out in the period 2006 - 2013 - 2018, on high spatial resolution aerial images (orthophotos) which allowed highlighting the details in accordance with the reality on the ground. Geospatial data were processed in the GIS environment, according to the specific methodology (vectorization, complex spatial analysis, change analysis). In all the analyzed cases, the useful surface of the meadows was considerably reduced, mainly as a result of the invasion of the forest vegetation. The changes produced in the structure and the surface occupied by the meadows at the level of the study area, are mostly related to the human factor through the depopulation phenomenon, specific to the western area of Romania and especially to the mountainous areas, defined on the one hand by the balance. natural demographic negative, and on the other hand by large migratory phenomena.

The use of UAVs in monitoring yellow sigatoka in banana

Monitoring pests and diseases is an extremely important activity for increasing productivity in agriculture. In this scenario, remote sensing, coupled with techniques of machine learning, offer new prospects for monitoring and identifying characteristic specific patterns, such as manifestations of diseases, pests, and water and nutritional stress. The aim was to use high spatial resolution aerial images to monitor the extent of an attack of yellow sigatoka in a banana crop, following the basic assumptions of identification, classification, quantification and prediction of phenotypic factors. Monthly flights were carried out on a commercial banana plantation using an unmanned aerial vehicle, equipped with a 16-megapixel RGB camera (GSD of 0.016781 m pixel−1). Five classification algorithms were used to identify and quantify the disease while field evaluations were also made following traditional methodology. The results showed that, for September 2017, the Support Vector Machine algorithm achieved the best performance (99.28% overall accuracy and 97.13 Kappa Index), followed by the Artificial Neural Network and Minimum Distance algorithms. In quantifying the disease, the SVM algorithm was more effective than other algorithms compared to the conventional methodology used to estimate the extent of yellow sigatoka, demonstrating that the tools used for monitoring leaf spots can be handled by remote sensing, machine learning and high spatial-resolution RGB images.

Postfire evaluation of fuelwood loss and restoration priority in a mixed, community-managed conifer forest of central New Mexico, United States

The causes and consequences of severe fires must be understood to strategically manage forest resources, including timber and fuelwood. Floods and resulting infrastructure damages are intensified due to soil erosion and debris exhumed by recently-burned catchments. A mixed Ponderosa and Piñon-Juniper forest managed by a rural New Mexico community burned in 2016, destroying harvestable fuelwood and damaging access roads. The objectives of this study were to: (1) estimate lost fuelwood value; (2) identify burned catchments that contributed to road damages; (3) develop criteria to prioritize a restoration area; and 4) recommend possible forest management actions based on assessment of fire regime, natural resource usage, ecological dynamics, and feasibility. Field reconnaissance facilitated image-based mapping of forest structure, burn severity, and early-successional recovery by 2018. High spatial resolution aerial images collected before fire were used to map tree density, as well as height based on shadow lengths and sun angle, and to infer stem volumes from empirical diameter-height relationships. Results suggest that 1.5–2.5 million USD of fuelwood was lost from a 1053-ha area. A remediation priority area (112 ha) was identified within a severely-burned, erosive catchment with limited successional plant cover. Econometrics and landscape-scale optimizations for restoration are proposed. Potential fire management strategies are outlined in order to maximize ecosystem products and services while enhancing safety. Techniques developed in this study – involving open-source data and minimal field work – may be useful to conifer forest managers with limited scientific funding.

Mapping plant growth forms using structure-from-motion data combined with spectral image derivatives

ABSTRACTMapping plant growth forms are useful to classify and monitor chaparral shrubland community types in southern California. This study evaluates the utility of plant height information, derived from high spatial resolution aerial images and structure-from-motion photogrammetry, for the purpose of distinguishing tree, shrub and sub-shrub growth forms from herbs and bare ground. Canopy height models (CHMs) were derived for two chaparral sites on Santa Rosa Plateau which contain intermixed growth forms. A multi-criterion, knowledge-based thresholding approach was used to classify growth forms based on spectral data (Normalized Difference Vegetation Index, hue, intensity, and focal texture) alone, CHM data alone, and hybrid combinations of spectral and CHM data. Overall accuracies were 66.0–69.0% from spectral data, 72.0–75.5% from CHM data, and 80.5–82.5% from the hybrid data sets. This study highlights the utility in combining multi-spectral and canopy height data for characterizing Mediterranean-type plant communities and changes therein.

AUTOMATIC VEHICLE RECOGNITION FOR URBAN TRAFFIC MANAGEMENT

Abstract. Automatic vehicle recognition has an important role for many applications such as supervision, traffic management and rescue tasks. The ability of online supervision on the distribution of vehicles in urban environments prevents traffic, which in turn reduces air pollution and noise. However, this is extremely challenging due to the small size of vehicles, their different types and orientations, and the visual similarity to some other objects in very high resolution images. In this paper, an automatic vehicle recognition algorithm is proposed based on very high spatial resolution aerial images. In the first step of the proposed method, by generating the image pyramid, the candidate regions of the vehicles are recognized. Then, performing reverse pyramid, decision level fusion of the vehicle candidates and the land use/cover classification results of the original image resolution are performed in order to modify recognized vehicle regions. For evaluating the performance of the proposed method in this study, Ultracam aerial imagery with spatial resolution of 11 cm and 3 spectral bands have been used. Comparing the obtained vehicle recognition results from the proposed decision fusion algorithm with some manually selected vehicle regions confirm the accuracy of about %80. Moreover, the %78.87 and 0.71 are respectively the values for overall accuracy and Kappa coefficient of the obtained land use/cover classification map from decision fusion algorithm.

Land Cover Change Detection in Urban Lake Areas Using Multi-Temporary Very High Spatial Resolution Aerial Images

The availability of very high spatial resolution (VHR) remote sensing imagery provides unique opportunities to exploit meaningful change information in detail with object-oriented image analysis. This study investigated land cover (LC) changes in Shahu Lake of Wuhan using multi-temporal VHR aerial images in the years 1978, 1981, 1989, 1995, 2003, and 2011. A multi-resolution segmentation algorithm and CART (classification and regression trees) classifier were employed to perform highly accurate LC classification of the individual images, while a post-classification comparison method was used to detect changes. The experiments demonstrated that significant changes in LC occurred along with the rapid urbanization during 1978–2011. The dominant changes that took place in the study area were lake and vegetation shrinking, replaced by high density buildings and roads. The total area of Shahu Lake decreased from ~7.64 km2 to ~3.60 km2 during the past 33 years, where 52.91% of its original area was lost. The presented results also indicated that urban expansion and inadequate legislative protection are the main factors in Shahu Lake’s shrinking. The object-oriented change detection schema presented in this manuscript enables us to better understand the specific spatial changes of Shahu Lake, which can be used to make reasonable decisions for lake protection and urban development.

The evaluation and application of an urban land cover map with image data fusion and laboratory measurements

High spatial and spectral resolution aerial images make it possible to develop detailed and large-scale (about 1:5,000) urban land cover maps. The main objectives of this study are (1) to evaluate the correlation between laboratory and hyperspectral image spectra to select proper bands and training samples for classification; (2) to develop a classification process to combine the spectral and spatial information of multispectral and hyperspectral images and make an urban land cover map for the study area in Szeged, Hungary; and (3) to examine the effect of different roof types on the modification of surface temperature. Reference materials were collected from the training area and their spectral characteristics were measured by a laboratory spectrometer. The hyperspectral image and laboratory spectral data between 500-800 nm showed a very strong correlation, the correlation coefficient was 0.99. The urban land cover map was produced by the combination of segmentation procedure and Spectral Angle Mapper (SAM) method using the spatial information derived from multispectral image and the spectral information of the hyperspectral image. Eight land cover classes were identified as impervious surfaces (asphalt, 4 types of tiled roof), water, and green vegetation. The overall accuracy of urban land cover map was 87.9 per cent. According to the results, an accurate large-scale urban land cover map can be generated from the fusion of multispectral and hyperspectral images. We presented that certain roof types have significant effect on surface temperature, which is strongly connected to the urban heat island phenomenon, and influences population health.

Using multi-source data to map and model the predisposition of forests to wind disturbance

Natural disturbances such as wind are known to cause threats to ecosystem services as well as sustainable forest ecosystem management. The objective of this research was to better understand and quantify drivers of predisposition to wind disturbance, and to model and map the probability of wind-induced forest disturbances (PDIS) in order to support forest management planning. To accomplish this, we used open-access airborne light detection and ranging (LiDAR) data as well as multi-source National Forest Inventory (NFI) data to model PDIS in southern Finland. A strong winter storm occurred in the study area in December 2011. High spatial resolution aerial images, acquired after the disturbance event, were used as reference data. Potential drivers associated with PDIS were examined using a multivariate logistic regression model. The model based on LiDAR provided good agreement with detected areas susceptible to wind disturbance (73%); however, when LiDAR was combined with multi-source NFI data, the results were more promising: prediction accuracy increased to 81%. The strongest predictors in the model were mean canopy height, mean elevation, and stem volume of the main tree species (Norway spruce and Scots pine). Our results indicate that open-access LiDAR data can be used to model and map the probability of predisposition to wind disturbance, providing spatially detailed, valuable information for planning and mitigation purposes.