Landslide Database Research Articles

Versatility Evaluation of Landslide Risk with Window Sizes and Sampling Techniques Based on Deep Learning

Across the globe, landslides cause significant loss of life, injuries, and widespread damage to homes and infrastructure. Therefore, assessing and analyzing landslide hazards is crucial to human, environmental, cultural, economic, and social sustainability. This study utilizes ArcGIS 10.8 and Python 3.9 to create landslide databases for Niigata Prefecture (NIG), Iwate and Miyagi Prefectures (IWT-MYG), and Hokkaido (HKD), drawing on data obtained from the National Research Institute for Earth Science and Disaster Resilience, Japan. A distinguishing feature of this study is the application of a Convolutional Neural Network (CNN), which significantly outperforms traditional machine learning models in image-based pattern recognition by extracting contextual information from surrounding areas, a distinct advantage in image and pattern recognition tasks. Unlike conventional methods that often require manual feature selection and engineering, CNNs automate feature extraction, enabling a more nuanced understanding of complex patterns. By experimenting with CNN input window sizes ranging from 3 × 3 to 27 × 27 pixels and employing diverse sampling techniques, we demonstrate that larger windows enhance the model’s predictive accuracy by capturing a wider range of environmental interactions critical for effective landslide modeling. CNN models with 19 × 19 pixel windows typically yield the best overall performance, with CNN-19 achieving an AUC of 0.950, 0.982 and 0.969 for NIG, HKD, and IWT-MYG, respectively. Furthermore, we improve prediction reliability using oversampling and a random window-moving method. For instance, in the NIG region, the AUC of the oversampling CNN-19 is 0.983, while the downsampling AUC is 0.950). These techniques, less commonly applied in traditional machine learning approaches to landslide detection, help address the issue of data imbalance often seen in landslide datasets, where instances of landslides are far outnumbered by non-landslide occurrences. While challenges remain in enhancing the model’s generalization, this research makes significant progress in developing more robust and adaptable tools for landslide prediction, which are vital for ensuring environmental and societal resilience.

Spatiotemporal variations of non-seismically fatal landslides in Northwest China: A case study from Shaanxi province

Landslides are one of the devastating geo-hazards that result in severe casualties in Northwest China (NW China) every year. However, a comprehensive landslide database is yet to be available for quantitatively assessing the distribution of fatal landslides in NW China. To investigate the spatiotemporal characteristics of non-seismically fatal landslides in NW China, we carried out a study using fatal landslides that occurred in Shaanxi province as an example to construct a new database by incorporating data of fatal landslides that occurred in the period between 1996 and 2018. A total of 332 non-seismically fatal landslides that claimed 1132 lives were compiled in this database. Additionally, a thorough study of spatial and temporal variations of fatal landslides reveals that anthropogenic landslides occur approximately evenly throughout the year. Natural fatal landslides, however, rise noticeably in July, August, and September. The spatial distribution analysis showed that fatal landslides in Shaanxi province of China were mainly distributed in the An’ kang, Yan’an, and Shangluo regions. Additionally, areas with relatively high relief (46.1 to 123 m), steep–slope topography (19.2 to 26.1°), and intense precipitation (465 to 937 mm) are more vulnerable to naturally-triggered landslides. The spatiotemporal analysis of the fatal landslides revealed increasing trends in zones such as Yan’an and Yulin cities (in northern part), and in An’kang city (in southern part). When combined with other information, the Fatal Landslide Database of Shaanxi Province can be used to provide a guide for risk assessment and spatial planning studies to mitigate geo-hazard risks.

Landslide Hazard Is Projected to Increase Across High Mountain Asia

AbstractHigh Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine‐learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2‐4.5 and 5‐8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid‐century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.

An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland

AbstractAt the time of its development, GeoSure was created using expert knowledge based on a thorough understanding of the engineering geology of the rocks and soils of Great Britain. The ability to use a data-driven methodology to develop a national-scale landslide susceptibility was not possible due to the relatively small size of the landslide inventory at the time. In the intervening 20 years, the National Landslide Database has grown from around 6000 points to over 18,000 records today and continues to be added to. With the availability of this additional inventory, new data-driven solutions could be utilised. Here, we tested a Bernoulli likelihood model to estimate the probability of debris flow occurrence and a log-Gaussian Cox process model to estimate the rate of debris flow occurrence per slope unit. Scotland was selected as the test site for a preliminary experiment, which could potentially be extended to the whole British landscape in the future. Inference techniques for both of these models are applied within a Bayesian framework. The Bayesian framework can work with the two models as additive structures, which allows for the incorporation of spatial and covariate information in a flexible way. The framework also provides uncertainty estimates with model outcomes. We also explored consideration on how to communicate uncertainty estimates together with model predictions in a way that would ensure an integrated framework for master planners to use with ease, even if administrators do not have a specific statistical background. Interestingly, the spatial predictive patterns obtained do not stray away from those of the previous GeoSure methodology, but rigorous numerical modelling now offers objectivity and a much richer predictive description.

Parsimonious High‐Resolution Landslide Susceptibility Modeling at Continental Scales

AbstractLandslide susceptibility maps are fundamental tools for risk reduction, but the coarse resolution of current continental‐scale models is insufficient for local application. Complex relations between topographic and environmental attributes characterizing landslide susceptibility at local scales are not transferrable across areas without landslide data. Existing maps with multiple susceptibility classifications under‐represent landslide potential in moderate and gently sloping terrain. We leverage an extensive landslide database (N = 613,724), a high‐resolution digital elevation model (10‐m), and high‐performance computing resources, to develop a new nationwide susceptibility map for the contiguous United States, Hawaii, Alaska, and Puerto Rico. We calculate four alternative linear and nonlinear thresholds of topographic slope and relief using an objective split‐sample calibration. We down‐sample our results to a 90‐m grid to account for uncertainty in the digital elevation model and landslide position, and evaluate these thresholds' ability to differentiate areas of greater susceptibility. The less conservative nonlinear model optimally balances our priorities of capturing observed landslides (99%) while minimizing area covered by susceptible terrain (43%). Independent evaluation with four statewide landslide inventories (N = 172,367) reinforces our model selection but highlights spatially variable performance. Therefore, we propose a novel approach to susceptibility classification using the concentration of landslide‐prone terrain within each down‐sampled grid. While landslides are possible within any cells containing susceptible terrain, those with the highest concentration capture the majority of observed landslides. Our new map characterizes landside susceptibility more consistently than prior models; our transparent classification approach also provides flexibility for accommodating different tolerances in risk reduction measures.

Refined Intelligent Landslide Identification Based on Multi-Source Information Fusion

Landslides are most severe in the mountainous regions of southwestern China. While landslide identification provides a foundation for disaster prevention operations, methods for utilizing multi-source data and deep learning techniques to improve the efficiency and accuracy of landslide identification in complex environments are still a focus of research and a difficult issue in landslide research. In this study, we address the above problems and construct a landslide identification model based on the shifted window (Swin) transformer. We chose Ya’an, which has a complex terrain and experiences frequent landslides, as the study area. Our model, which fuses features from different remote sensing data sources and introduces a loss function that better learns the boundary information of the target, is compared with the pyramid scene parsing network (PSPNet), the unified perception parsing network (UPerNet), and DeepLab_V3+ models in order to explore the learning potential of the model and test the models’ resilience in an open-source landslide database. The results show that in the Ya’an landslide database, compared with the above benchmark networks (UPerNet, PSPNet, and DeepLab_v3+), the Swin Transformer-based optimization model improves overall accuracies by 1.7%, 2.1%, and 1.5%, respectively; the F1_score is improved by 14.5%, 16.2%, and 12.4%; and the intersection over union (IoU) is improved by 16.9%, 18.5%, and 14.6%, respectively. The performance of the optimized model is excellent.

Construction and preliminary analysis of landslide database triggered by heavy storm in the parallel range-valley area of western Chongqing, China, on 8 June 2017

Construction and preliminary analysis of landslide database triggered by heavy storm in the parallel range-valley area of western Chongqing, China, on 8 June 2017

CFTIlandslides, Italian database of historical earthquake-induced landslides

Knowing the location, the extent and the characteristics of any earthquake-induced environmental phenomenon is becoming an increasingly pressing need for civil protection agencies and local administrations. In particular, earthquake-triggered landslides are known for being among the most important sources of secondary hazard, as they may cause significant losses and may delay rescue operations across large areas. The combination of the relatively frequent seismic release with a very high landslide susceptibility makes the Italian territory especially prone to the occurrence of earthquake-induced landslides. The CFTIlandslides dataset features over 1,000 landslides triggered by historical Italian earthquakes (up to 1997). The landslides effects are subdivided into classes based on location accuracy and type of movement. Knowing the distribution of the past earthquake-induced landslides provides the input information for assessing the related hazard. This dataset is addressed to a large audience of potential users, including researchers and scholars, administrators and technicians belonging to local institutions, and civil protection authorities.

Landslide Distribution and Development Characteristics in the Beiluo River Basin

The Beiluo River Basin, situated in the central region of the Loess Plateau, frequently experiences landslide geological disasters, posing a severe threat to local lives and property. Thus, establishing a detailed database of historical landslides and analyzing and revealing their development characteristics are of paramount importance for providing a foundation for geological hazard risk assessment. First, in this study, landslides in the Beiluo River Basin are interpreted using Google Earth and ZY-3 high-resolution satellite imagery. Combined with a historical landslide inventory and field investigations, a landslide database for the Beiluo River Basin is compiled, containing a total of 1781 landslides. Based on this, the geometric and spatial characteristics of the landslides are analyzed, and the relationships between the different types of landslides and landslide scale, stream order, and geomorphological types are further explored. The results show that 50.05% of the landslides have a slope aspect between 225° and 360°, 68.78% have a slope gradient of 16–25°, and 38.97% are primarily linear in profile morphology. Areas with a high landslide density within a 10 km radius are mainly concentrated in the loess ridge and hillock landform region between Wuqi and Zhidan Counties and in the loess tableland region between Fu and Luochuan Counties, with a significant clustering effect observed in the Fu County area. Loess–bedrock interface landslides are relatively numerous in the northern loess ridge and hillock landform region due to riverbed incision and the smaller thickness of loess in this area. Intra-loess landslides are primarily found in the southern loess tableland region due to headward erosion and the greater thickness of loess in this area. Loess–clay interface landslides, influenced by riverbed incision and the limited exposure of red clay, are mainly distributed in the northern part of the southern loess tableland region and on both sides of the Beiluo River Valley in Ganquan County. These results will aid in further understanding the development and spatial distribution of landslides in the Beiluo River Basin and provide crucial support for subsequent landslide susceptibility mapping and geological hazard assessment in the region.

Addressing the Effect of Intra-Seasonal Variations in Developing Rainfall Thresholds for Landslides: An Antecedent Rainfall-Based Approach

We developed a rainfall threshold model with the objective of limiting the effects of uncertainties typically associated with them, such as a lack of robust landslide database, the selection of the contributing rain gauge, seasonal variations in rainfall patterns, and the effect of extreme rainfall conditions. With the aid of gauge-corrected satellite precipitation data and a landslide database compiled from various sources, separate rainfall thresholds were developed for two waves of the monsoon season in the Western Ghats, India. The daily vs. antecedent rainfall distributions for different scenarios of antecedent rainfall were analyzed for landslide occurrence. The different scenarios considered included 1, 2, 3, 5, 10-, 20-, 30- and 40-day antecedent rainfalls along with the monsoon antecedent defined as the cumulative rainfall from the start of the monsoon to the day prior to landslide occurrence, and the event antecedent defined as the cumulative rainfall from the start of a rainfall event to the day prior to landslide occurrence. A statistically defined critical value was used to define the thresholds for extreme rainfall conditions, while ordinary least squares and quantile regression models were compared to identify the best-fit model for the non-extreme rainfall threshold. Receiver Operating Characteristic (ROC) analysis was performed on all these models and the best model was chosen based on the efficiency values. The daily vs. monsoon antecedent threshold was the best model for the first monsoon wave, and the daily vs. event antecedent model was the best model for the second monsoon wave. A separate rainfall threshold was defined for the entire monsoon without subdivision into separate waves, and corresponding ROC statistics were compared with the former approach to analyze the efficacy of intra-seasonal variations in rainfall threshold development. The results suggest that cumulative rainfall makes a significant contribution towards landslide initiation and that intra-seasonal variations should be necessarily considered in rainfall threshold modeling.

The Spatial Distribution Characteristics and Possible Influencing Factors of Landslide Disasters in the Zhaotong Area, Yunnan Province of China

The Zhaotong area in Yunnan Province stands out as one of the most susceptible areas to landslide disasters. The landslide susceptibility of the Zhaotong area can be attributed to its steep terrain, fractured rock formations and strong rainfall, compounded by its frequent seismic activity. This study utilized landslide data provided by the Zhaotong City Natural Resources and Planning Bureau and visually interpreted from high-resolution satellite images of Google Earth to establish the landslide database of the Zhaotong area, including 161 landslides and 3646 potential geological disasters. The distribution characteristics and possible influencing factors of landslides within the Zhaotong area were analyzed using the aforementioned data. The results show that the spatial distribution of landslides and potential geological disasters is roughly consistent; the most concentrated landslides occurred at the junction of Yiliang County, Zhaotong City, and Daguan County, indicating the necessity to enhance surveillance of these landslide-prone areas. The relationship of landslide locations and different influencing factors suggests that elevation, slope angle, and distance to rivers are closely related to landslide occurrence. Landslides are more likely to occur in areas with lower elevations with slope angles ranging from 10° to 40° and near river channels.

The Interconnection between Climate Cycles and Geohazards in Urban Areas of the Tourist Island of Mallorca, Spain

The island of Mallorca has experienced major interventions and transformations of the territory, with unprecedented urban development related to growing tourism activity. In this paper, we present a spatio-temporal analysis—by using spectral analysis techniques—of climate cycles on the island of Mallorca (Spain) and their correlation with the occurrences of landslides and flash floods. Both geohazards are closely related to wet periods, which are controlled by different, well-known natural cycles: ENSO, the NAO, sunspot, etc. Geostatistical methods are used to map the distribution of rainfall, as well as a spatial representation of the spectral confidence of the different natural cycles, to define the hazardous areas on the island. The cycles with the greatest influence on rainfall in Mallorca are El Niño–Southern Oscillation (ENSO) (5.6 y and 3.5 y), the North Atlantic Oscillation (NAO) (7.5 y), and Quasi-Biennial Oscillation (QBO). Recorded events of both rockfalls and flash floods exhibit a strong correlation with the climate indices of QBO, ENSO, the NAO, and sunspot activity. This correlation is particularly pronounced with QBO, as this cycle has a higher frequency than the others, and QBO is observed as part of the other cycles in the form of increases and decreases during periods of higher ENSO, NAO, and sunspot values. However, the impact of flash floods is also significant in the southeast part of the island, despite its lower levels of rainfall. The most dangerous episodes are related to ENSO (6.4 y) and the NAO. The validation of the methodology employed is strengthened by incorporating information from the flash flood data, as it offers comprehensive coverage of the entire island, compared to the landslide database, which is confined to the Serra de Tramuntana region. The study reveals that the city of Palma and the municipality of Calvià, as well as the central and eastern urban areas of the island, are the most vulnerable regions to intense rainfall and its consequences.

Spatiotemporal Characteristics and Rainfall Thresholds of Geological Landslide Disasters in ASEAN Countries

Spatiotemporal Characteristics and Rainfall Thresholds of Geological Landslide Disasters in ASEAN Countries

Marine Geohazards of the Bay of Naples (Southern Tyrrhenian Sea, Italy): A Review Integrating Morpho-Bathymetric and Seismo-Stratigraphic Analysis

Marine geohazards in the Bay of Naples, an eruptive region during the late Quaternary, have been assessed based on both morpho-bathymetric and seismic data. Previously identified areas of high marine hazard with slide potential (northern Ischia slope, Naples canyons, and Sorrento Peninsula–Capri slope) have been confirmed and integrated through the seismo-stratigraphic analysis of selected seismic sections. We evaluated the occurrence of important fossil submarine landslides in the stratigraphic record. Several kinds of submarine landslides have been individuated through morpho-bathymetric and seismic interpretation, including creeping, debris avalanches, and debris flows, among others, often controlled by volcanic eruptions. Submarine landslides of Naples Bay are primary geohazards in the marine and coastal areas, which has been ascertained with significant volcanic and tsunami hazards involving the gulf. Despite previous studies on these topics, much work is still needed to compile a systematic database of the submarine landslides of the Bay of Naples, representing a future step of this research.

Spatiotemporal Prediction of Rainfall-induced Landslides Using Machine Learning Techniques

Landslides cause significant damage to infrastructure and loss of life. Landslide susceptibility map (LSM), as an important reference for landslide hazard assessment, indicates prone areas of landslides. However, conventional LSM only predicts the spatial distribution of potential landslides, which cannot fully explain the occurrence of landslides at different times. In the present study, a spatiotemporal LSM is conducted to predict landslides both in space and time. A landslide database containing 223 recorded landslide events in southwestern Pennsylvania is used. Fourteen topographic spatial factors and eight rainfall temporal factors are used for machine learning (ML). Four ML models are applied in the study, including Logistic regression (LR), Support vector machine (SVM), Random forest (RF), and Gradient boosting machine (GBM). The results show that through cross-validation, RF outperforms the other algorithms with a value 0.86 of the area under the receiver operating characteristic curve (AUC score). The optimal model is used to generate spatiotemporal LSMs. It is concluded that by introducing spatial and temporal information simultaneously, ML models have the capability of learning the pattern of landslide occurrence both in space and time, providing an effective assessment tool to reduce catastrophic loss of landslide hazards.

Landslide Mapping in Calitri (Southern Italy) Using New Multi-Temporal InSAR Algorithms Based on Permanent and Distributed Scatterers

Landslides play a significant role in the morpho-evolutional processes of slopes, affecting them globally under various geological conditions. Often unnoticed due to low velocities, they cause diffuse damage and loss of economic resources to the infrastructure or villages built on them. Recognizing and mapping mass movements is crucial for mitigating economic and social impacts. Conventional monitoring techniques prove challenging for large areas, necessitating resource-intensive ground-based networks. Leveraging abundant synthetic aperture radar (SAR) sensors, satellite techniques offer cost-effective solutions. Among the various methods based on SAR products for detecting landslides, multi-temporal differential interferometry SAR techniques (MTInSAR) stand out for their precise measurement capabilities and spatiotemporal evolution analysis. They have been widely used in several works in the last decades. Using information from the official Italian landslide database (IFFI), this study employs Sentinel-1 imagery and two new processing chains, E-PS and E-SBAS algorithms, to detect deformation areas on the slopes of Calitri, a small town in Southern Italy; these algorithms assess the cumulated displacements and their state of activity. Taking into account the non-linear trends of the scatterers, these innovative algorithms have helped to identify a dozen clusters of points that correspond well with IFFI polygons.

Temporal Clustering of Precipitation Driving Landslides Over the Italian Territory

AbstractThe occurrence of multiple precipitation events not‐independent in time, that is, a temporal clustering, is an example of a temporal compounding event. This type of forcing is of great relevance for the occurrence of different natural hazards, like floods and deep‐seated landslides, for which previous soil saturation plays an important role in shaping the associated hazard. Using ERA5‐Land data set and E‐OBS, we firstly investigate the spatial and temporal characteristics of temporal clustering of precipitation over the Italian territory, and we relate it with two oscillation patterns, namely North Atlantic Oscillation (NAO) and Mediterranean Oscillation Index (MOI), and with common synoptic conditions. Then, we explore the role of temporal compounding of precipitation in the generation of different movement types (complex, debris flow, fall, flow, and sliding) using the database of landslides from the Aree Vulnerate Italiane project (in Italian AVI, meaning Areas Affected by Landslides or Floods). From this study it emerges that below average values of NAO and MOI increase the probability of having clustered precipitation events. For all types of landslides, except rock falls, we observed that the majority of the events are preceded by a temporal clustering of precipitation, over longer time windows for complex events, shorter for debris flows. For rock falls, we found also a link with low minimum temperature and freeze‐thaw cycles for winter events and high maximum temperature for summer events. This work contributes to the investigation of temporal clustering of precipitation in connection with natural hazards characterized by a mechanism of saturation.

Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The occurrence of landslide disasters often causes a large number of casualties along with economic losses in the local area, seriously restricting local economic development. Although risk assessment and deformation mechanism analysis for single landslides have been performed for landslide disasters in the Hancheng area, this area lacks a landslide traces database. A complete landslide database comprises the basic data required for the study of landslide disasters and is an important requirement for subsequent landslide-related research. Therefore, this study used multi-temporal high-resolution optical images and human-computer interaction visual interpretation methods of the Google Earth platform to construct a landslide traces database in Hancheng County. The results showed that at least 6785 landslides had occurred in the study area. The total area of the landslides was about 95.38 km2, accounting for 5.88% of the study area. The average landslide area was 1406.04 m2, the largest landslide area was 377,841 m2, and the smallest landslide area was 202.96 m2. The results of this study provides an important basis for understanding the spatial distribution of landslides in Hancheng County, the evaluation of landslide susceptibility, and local disaster prevention and mitigation work.

Landslides of China's Qinling

AbstractThe Qinling Mountains in China frequently experience geological disasters, with large‐scale landslides being particularly prominent, causing severe economic losses to the local area. To gain a comprehensive understanding of the geological disaters distribution in the region, we conducted extensive research on the entire Qinling Mountains, covering an area of approximately 380,000 km2. By employing methods such as literature review, data collection, and interpretation of remote sensing images, we have successfully created a database of landslides. The inventory of landslides includes a total of 169,888 large‐scale landslides, covering a combined area of approximately 1575 km2. The average size of these landslides is approximately 92,734 m2. The scale of these landslides varies widely, with the smallest individual landslide covering an area of 166.25 m2 and the largest reaching 12.9 km2. Upon examining areas with frequent landslides, it was observed that landslides are usually densely distributed along riverbanks or within valleys. Landslide development is also dense in areas prone to frequent historical earthquakes. This comprehensive database provides essential data to support the analysis of spatial distribution patterns of large‐scale landslides in the Qinling Mountains. It also facilitates landslide assessments and serves as a reference for the prevention and control of landslide disasters in the area.

Delineating Non-Susceptible Landslide Areas in China Based on Topographic Index and Quantile Non-Linear Model

Efficient analysis of non-susceptibility to landslides targets regions with minimal or zero landslide probability, thereby obviating the need to estimate the likelihood for low-susceptibility zones. This study assesses the effectiveness of the quantile non-linear (QNL) model in delineating the non-susceptibility of landslides in China through a topographic index. The topographic index encompassed slope angle and topographic relief, which are calculated using a 3 × 3 and 15 × 15 square cell moving window, respectively. Additionally, a global landslide susceptibility model established using a comprehensive global landslide database and fuzzy algorithm was employed for comparative analysis, providing a holistic evaluation of the QNL model’s accuracy. The results show that while the overall distribution of the two QNL models for non-susceptible landslide areas was roughly consistent, notable discrepancies were observed in localized regions, especially in the Southwest and Qinghai-Tibet geological environment areas where landslides are prone to occur. The applicability of the QNL model is significantly limited in these areas. In addition, the predicted results of the QNL_CHN model are closer to those based on the global landslide susceptibility model of the fuzzy algorithm. This study provides valuable insights to enhance the QNL model’s applicability, thereby strengthening forest ecosystem management and mitigating ecological disaster risks.