Evaluation of Landslide Susceptibility of Şavşat District of Artvin Province (Turkey) Using Machine Learning Techniques

The aim of this study is to produce landslide susceptibility maps of Şavşat district of Artvin Province using machine learning (ML) models and to compare the predictive performances of the models used. Tree-based ensemble learning models, including random forest (RF), gradient boosting machines (GBM), and extreme gradient boosting (XGBoost), were used in the study. A landslide inventory map consisting of 85 landslide polygons was used in the study. The inventory map comprises 32,777 landslide pixels at 30 m resolution. Randomly selected 70% of the landslide pixels were used for training the models and the remaining 30% were used for the validation of the models. In susceptibility analysis, altitude, aspect, curvature, distance to drainage network, distance to faults, distance to roads, land cover, lithology, slope, slope length, and topographic wetness index parameters were used. The validation of the models was conducted using success and prediction rate curves. The validation results showed that the success rates for the GBM, RF, and XGBoost models were 91.6%, 98.4%, and 98.6%, respectively, whereas the prediction rate were 91.4%, 97.9%, and 98.1%, respectively. Therefore, it was concluded that landslide susceptibility map produced with XGBoost model can help decision makers in reducing landslide-associated damages in the study area

A Novel Performance Assessment Approach Using Photogrammetric Techniques For Landslide Susceptibility Mapping With Logistic Regression, Ann And Random Forest

Prediction of possible landslide areas is the first stage of landslide hazard mitigation efforts and is also crucial for suitable site selection. Several statistical and machine learning methodologies have been applied for the production of landslide susceptibility maps. However, the performance assessment of such methods have conventionally been carried out by utilizing existing landslide inventories. The purpose of this study is to investigate the performances of landslide susceptibility maps produced with three different machine learning algorithms, i.e., random forest, artificial neural network, and logistic regression, in a recently constructed and activated dam reservoir and assess the external quality of each map by using pre- and post-event photogrammetric datasets. The methodology introduced here was applied using digital surface models generated from aerial photogrammetric flight data acquired before and after the dam construction. Aerial photogrammetric images acquired in 2012 and 2018 (after the dam was filled) were used to produce digital terrain models and orthophotos. The 2012 dataset was used for producing the landslide susceptibility maps and the results were evaluated by comparing the Euclidian distances between the two surface models. The results show that the random forest method outperforms the other two for predicting the future landslides.PubMedWoSScopu

Integrating Remote Sensing and Machine Learning to Assess Forest Health and Susceptibility to Pest-induced Damage

Spruce budworm (Choristoneura fumiferana; SBW) outbreaks are cyclically occurring phenomena in the northeastern USA and neighboring Canadian provinces. These outbreaks are often of landscape level causing impaired growth and mortality of the host species namely spruce (Picea sp.) and balsam fir (Abies balsamea (L.) Mill.). Acknowledging the recent SBW outbreak in Canadian provinces like Quebec and New Brunswick neighboring the state of Maine, our study devised comprehensive techniques to assess the susceptibility of Maine forests to SBW attack. This study aims to harness the power of remote sensing data and machine learning algorithms to model and map the susceptibility of forest in terms of host species availability and abundance (basal area per hectare; BAPH, and leaf area index; LAI), their maturity and the defense mechanism prevalent. In terms of host species abundance mapping our study explores the integration of satellite remote sensing data to model BAPH and LAI of two economically vital SBW host species, red spruce (Picea rubens Sarg.) and balsam fir, in Maine USA. Combining Sentinel-1 synthetic aperture radar (SAR), Sentinel-2 multispectral, and site variables, we used Random Forest (RF) and Multi-Layer Perceptron (MLP) algorithms for modeling LAI and BAPH. The results demonstrated the superiority of RF over MLP, achieving smaller normalized root mean square error (nRMSE) by 0.01 and 0.06 for LAI and BAPH, respectively. Notably, Sentinel-2 variables, especially the red-edge spectral vegetation indices, played a significant role in both LAI and BAPH estimation, with the minor inclusion of site variables, particularly elevation. In addition, using various satellite remote sensing data such as Sentinel-1 C-band SAR, PALSAR L-band SAR and Sentinel-2 multispectral, along with site variables, the study developed large-scale SBW stand impact types and susceptibility maps for the entire state of Maine. The susceptibility of the forest was assessed based on the availability of SBW host species and their maturity. Integrating machine-learning algorithms, RF and MLP, the best model, utilizing site (elevation and aspect) and Sentinel-2 data achieved an overall accuracy of 83.4% to predict SBW host species. Furthermore, combining the host species data with age data from Land Change Monitoring, Assessment, and Projection (LCMAP) products we could produce the SBW susceptibility map based on stand impact types with an overall accuracy of 88.3%. Moreover, the work builds upon the assessment of susceptibility of SBW host species taking into account the concentration of several canopy traits using remote sensing and site data. The study focused on various foliar traits affecting insect herbivory, including nutritive such as nitrogen (N), phosphorous (P), potassium (K), and copper (Cu), non-nutritive such as iron (Fe) and calcium (Ca), and defensive parameters such as equivalent water thickness (EWT) and leaf mass per area (LMA). Using Sentinel-2 and site data, we developed trait estimation models using machine-learning algorithms like Random Forest (RF), Extreme Gradient Boosting (XGB), and Support Vector Machine (SVM). The accuracy of the developed model was evaluated based on the normalized root mean square error (nRMSE). Based on the model performances, we selected XGB algorithm to estimate Ca, EWT, Fe, and K whereas Cu, LMA, N, and P were estimated using RF algorithm. Regarding the variables used, almost all the best performing models included Sentinel-2 red-edge indices and depth to water table (DWT) as the most important variables. Ultimately, the study proposed a novel framework connecting the concentrations of foliar traits in SBW host foliage to tree susceptibility to the pest, enabling the assessment of host susceptibility on a landscape level. To sum up, this study highlights the advantages and effectiveness of integrating satellite remote sensing data for enhanced pest management, providing valuable insights into tree attributes and susceptibility to spruce budworm outbreaks in Northeast USA. The findings offer essential tools for forest stakeholders to improve management strategies and mitigate potential forthcoming SBW outbreaks in the region