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We assessed landslide susceptibility using Chi-square Automatic Interaction Detection (CHAID), exhaustive CHAID, and Quick, Unbiased, and Efficient Statistical Tree (QUEST) decision tree models in Jumunjin-eup, Gangneung-si, Korea. A total of 548 landslides were identified based on interpretation of aerial photographs. Half of the 548 landslides were selected for modeling, and the remaining half were used for verification. We used 20 landslide control factors that were classified into five categories, namely topographic elements, hydrological elements, soil maps, forest maps, and geological maps, to determine landslide susceptibility. The relationships of landslide occurrence with landslide-inducing factors were analyzed using CHAID, exhaustive CHAID, and QUEST models. The three models were then verified using the area under the curve (AUC) method. The results showed that the CHAID model (AUC = 87.1%) was more accurate than the exhaustive CHAID (AUC = 86.9%) and QUEST models (AUC = 82.8%). The verification results showed that the CHAID model had the highest accuracy. There was high susceptibility to landslides in mountainous areas and low susceptibility in coastal areas. Analyzing the characteristics of the landslide control factors in advance will enable us to obtain more accurate results.

The logistic regression (LR) and decision tree (DT) models are widely used for prediction analysis in a variety of applications. In the case of landslide susceptibility, prediction analysis is important to predict the areas which have high potential for landslide occurrence in the future. Therefore, the purpose of this study is to analyze and compare landslide susceptibility using LR and DT models by running three algorithms (CHAID, exhaustive CHAID, and QUEST). Landslide inventory maps (762 landslides) were compiled by reference to historical reports and aerial photographs. All landslides were randomly separated into two data sets: 50% were used to establish the models (training data sets) and the rest for validation (validation data sets). 20 factors were considered as conditioning factors related to landslide and divided into five categories (topography, hydrology, soil, geology, and forest). Associations between landslide occurrence and the conditioning factors were analyzed, and landslide-susceptibility maps were drawn using the LR and DT models. The maps were validated using the area under the curve (AUC) method. The DT model running the exhaustive CHAID algorithm (prediction accuracy 90.6%) was better than the DT CHAID (AUC = 90.2%), LR (AUC = 90.1%), and DT QUEST (84.3%) models. The DT model running the exhaustive CHAID algorithm is the best model in this study. Therefore, all models can be used to spatially predict landslide hazards.

Leaf area index is a key structural parameter for biological and physical processes. Korea is planning to launch CAS500-4 in 2025, so in situ data is needed to validate the leaf area index. Unlike other networks (e.g., NEON and TERN), establishing an elementary sampling unit is difficult in Korea due to the complex forest structure and rugged terrain. Therefore, pixel-level correspondence between the satellite product and fisheye footprints is the best way to verify in complex terrain. In this study, we analyzed the spatial footprint of fisheye lenses in different forest types using terrestrial LiDAR data for the first time. The three-dimensional forest structure was analyzed at various viewing zenith angles, and the footprint radius was approximately 3 m at view zenith angle (VZA) 20° and approximately 10 m at VZA 90°. We also analyzed the Z-values from terrestrial laser data and the plant area index on leafless seasons to assess the impact of obstacles, such as tree trunks, under various viewing zenith angles. The analysis showed that the influence of woody components increases dramatically as the VZA exceeds 40°. Such factors influenced the increase in LAI and the decrease in the clumping index as the VZA increased. Overall, we concluded that narrowing VZA between 20° and 40° is appropriate for Korean forests with complex structures.

Forest change detection is essential to prevent the secondary damage occurring by landslides causing profound results to the environment, ecosystem, and human society. The remote sensing technique is a solid candidate for identifying the spatial distribution of the forest. Even though the acquiring and processing of remote sensing images are costly and time- and labor-consuming, the development of open source data platforms relieved these burdens by providing free imagery. The open source images also accelerate the generation of algorithms with large datasets. Thus, this study evaluated the generalizability of forest change detection by using open source airborne images and the U-Net model. U-Net model is convolutional deep learning architecture to effectively extract the image features for semantic segmentation tasks. The airborne and tree annotation images of the capital area in South Korea were processed for building U-Net input, while the pre-trained U-Net structure was adopted and fine-tuned for model training. The U-Net model provided robust results of the segmentation that classified forest and non-forest regions, having pixel accuracies, F1 score, and intersection of union (IoU) of 0.99, 0.97, and 0.95, respectively. The optimal epoch and excluded ambiguous label contributed to maintaining virtuous segmentation of the forest region. In addition, this model could correct the false label images because of showing exact classification results when the training labels were incorrect. After that, by using the open map service, the well-trained U-Net model classified forest change regions of Chungcheong from 2009 to 2016, Gangwon from 2010 to 2019, Jeolla from 2008 to 2013, Gyeongsang from 2017 to 2019, and Jeju Island from 2008 to 2013. That is, the U-Net was capable of forest change detection in various regions of South Korea at different times, despite the training on the model with only the images of the capital area. Overall, this study demonstrated the generalizability of a deep learning model for accurate forest change detection.

Tree detection and fuel amount and distribution estimation are crucial for the investigation and risk assessment of wildfires. The demand for risk assessment is increasing due to the escalating severity of wildfires. A quick and cost-effective method is required to mitigate foreseeable disasters. In this study, a method for tree detection and fuel amount and distribution prediction using aerial images was proposed for a low-cost and efficient acquisition of fuel information. Three-dimensional (3D) fuel information (height) from light detection and ranging (LiDAR) was matched to two-dimensional (2D) fuel information (crown width) from aerial photographs to establish a statistical prediction model in northeastern South Korea. Quantile regression for 0.05, 0.5, and 0.95 quantiles was performed. Subsequently, an allometric tree model was used to predict the diameter at the breast height. The performance of the prediction model was validated using physically measured data by laser distance meter triangulation and direct measurement from a field survey. The predicted quantile, 0.5, was adequately matched to the measured quantile, 0.5, and most of the measured values lied within the predicted quantiles, 0.05 and 0.95. Therefore, in the developed prediction model, only 2D images were required to predict a few of the 3D fuel details. The proposed method can significantly reduce the cost and duration of data acquisition for the investigation and risk assessment of wildfires.

Pine wood nematode (Bursaphelenchus xylophilus) is an invasive pathogen in South Korea, where it has caused pine wilt disease (PWD) with extremely high mortality of native pine species (Pinus densiflora, Pinus thunbergii, and Pinus koraiensis). Since the disease spreads by its vectors, native pine sawyer beetles (Monochamus alternatus and Monochamus saltuarius), the cost of monitoring the expansion has been rapidly increasing. Furthermore, it is even more costly to eliminate new and isolated infections since unremoved infected trees act as new sources of infection through the preferred oviposition of the beetles on such trees. The methodology of combining unmanned aerial vehicle (UAV) and object detection based on deep learning provides the opportunity to solve such problems, as UAV with RGB camera can provide high spatial resolution aerial image and digital surface model (DSM), which can be used for object detection with excellent results. In this study, we evaluated the performance of this method to detect dead pine trees in PWD-damaged areas. In particular, to ensure low omission error of monitoring, YOLOv3 was employed for object detection as the model design is focused on minimizing the omission error. We also modified the model so that the positions and crown diameter could be estimated. Four detection models were trained using four different combinations between aerial images (R, G, B) and DSM from UAV. Among them, the model from RGB showed the highest performance (recall: 0.9909, precision: 0.8438) and was selected as the optimal model. Our results suggest that our method can contribute to low-cost and effective monitoring of the dead pine trees while maintaining low omission error, which is critical for PWD management.

These days, wildfires are prevalent in almost all areas of the world. Researchers have been actively analyzing wildfire damage using a variety of satellite images and geospatial datasets. This paper presents a method for detailed estimation of wildfire losses using various geospatial datasets and an actual case of wildfire at Kang-Won-Do, Republic of Korea in 2005. A set of infrared (IR) aerial images acquired after the wildfire were used to visually delineate the damaged regions, and information on forest type, diameter class, age class, and canopy density within the damaged regions was retrieved from GIS layers of the Korean national forest inventory. Approximate tree heights were computed from airborne LIDAR and verified by ground LIDAR datasets. The corresponding stand volumes were computed using tree volume equations (TVE). The proposed algorithm can efficiently estimate fire loss using the geospatial information; in the present case, the total fire loss was estimated as $5.9 million, which is a more accurate estimate than $4.5 million based on conventional approach. The proposed method can be claimed as a powerful alternative for estimating damage caused by wildfires, because the aerial image interpretation can delineate and analyze damaged regions in a comprehensive and consistent manner; moreover, LIDAR datasets and national forest inventory data can significantly reduce field work.