Jeongmook Park
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View article: Comparative Analysis of Machine Learning and Deep Learning Models for Individual Tree Structure Segmentation Using Terrestrial LiDAR Point Cloud Data
Comparative Analysis of Machine Learning and Deep Learning Models for Individual Tree Structure Segmentation Using Terrestrial LiDAR Point Cloud Data Open
This study aims to segment individual tree structures (stem, crown, and ground) from terrestrial LiDAR-derived point cloud data (PCD) and to compare the segmentation accuracy between two models: XGBoost (machine learning) and PointNet++ (d…
View article: ResTreeNet: A Height-Aware LiDAR Tree Classification Model with Explainable AI for Forestry Applications
ResTreeNet: A Height-Aware LiDAR Tree Classification Model with Explainable AI for Forestry Applications Open
Tree species classification plays a crucial role in forest management, biodiversity conservation, and ecological monitoring. Light detection and ranging (LiDAR) technology, capturing highly detailed 3D structural information of vegetation,…
View article: Assessing Forest Resources with Terrestrial and Backpack LiDAR: A Case Study on Leaf-On and Leaf-Off Conditions in Gari Mountain, Hongcheon, Republic of Korea
Assessing Forest Resources with Terrestrial and Backpack LiDAR: A Case Study on Leaf-On and Leaf-Off Conditions in Gari Mountain, Hongcheon, Republic of Korea Open
In Republic of Korea, the digital transformation of forest data has emerged as a critical priority at the governmental level. To support this effort, numerous case studies have been conducted to collect and analyze forest data. This study …
View article: Pipeline Approach to Segmenting Individual Trees from Terrestrial LiDAR Forest Data
Pipeline Approach to Segmenting Individual Trees from Terrestrial LiDAR Forest Data Open
LiDAR technology has revolutionized the analysis of forest structure by offering three-dimensional insights essential for effective forest management. This study proposes an innovative pipeline-based tree separation algorithm, specifically…
View article: Detection and Analysis of Forest Clear-Cutting Activities Using Sentinel-2 and Random Forest Classification: A Case Study on Chungcheongnam-do, Republic of Korea
Detection and Analysis of Forest Clear-Cutting Activities Using Sentinel-2 and Random Forest Classification: A Case Study on Chungcheongnam-do, Republic of Korea Open
This study provides the methodology for the development of sustainable forest management activities and systematic strategies using national spatial data, satellite imagery, and a random forest machine learning classifier. This study condu…
View article: Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data
Automated Segmentation of Individual Tree Structures Using Deep Learning over LiDAR Point Cloud Data Open
Deep learning techniques have been widely applied to classify tree species and segment tree structures. However, most recent studies have focused on the canopy and trunk segmentation, neglecting the branch segmentation. In this study, we p…
View article: Evaluation of Hyperparameter Combinations of the U-Net Model for Land Cover Classification
Evaluation of Hyperparameter Combinations of the U-Net Model for Land Cover Classification Open
The aim of this study was to select the optimal deep learning model for land cover classification through hyperparameter adjustment. A U-Net model with encoder and decoder structures was used as the deep learning model, and RapidEye satell…
View article: Analysis of Factors Influencing Forest Loss in South Korea: Statistical Models and Machine-Learning Model
Analysis of Factors Influencing Forest Loss in South Korea: Statistical Models and Machine-Learning Model Open
Analyzing the current status of forest loss and its causes is crucial for understanding and preparing for future forest changes and the spatial pattern of forest loss. We investigated spatial patterns of forest loss in South Korea and asse…
View article: Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data
Assessment of Machine Learning Algorithms for Land Cover Classification Using Remotely Sensed Data Open
The purpose of this study was to apply the random forest (RF), XGBoost, and LightGBM machine learning (ML) algorithms to land cover classification, and to present the model tuning process for each algorithm.Sentinel-2 satellite images were…
View article: Assessment of REDD+ Suitable Area for Sustainable Forest Management in Paraguay
Assessment of REDD+ Suitable Area for Sustainable Forest Management in Paraguay Open
This study extracted deforestation area and degraded forestland area, which are potential REDD+ (Reducing Emissions from Deforestation and Forest Degradation) project candidate areas in Paraguay using Land Cover Map (LCM) and Tree Cover Ma…
View article: Object-based Land Cover Change Detection and Landscape Structure Analysis of Demilitarized Zone in Korea
Object-based Land Cover Change Detection and Landscape Structure Analysis of Demilitarized Zone in Korea Open
The demilitarized zone (DMZ) in Korea, a 4-km-wide military-free zone at the border between South Korea and North Korea, has been well preserved for half a century and has a high ecological value.However, temporal and spatial data are lack…
View article: Spatial Distribution Characteristics of Species Diversity Using Geographically Weighted Regression Model
Spatial Distribution Characteristics of Species Diversity Using Geographically Weighted Regression Model Open
The objective of this study is to evaluate the spatial distribution patterns of species diversity at different spatial scales, focusing on the Baekdudaegan Protected Area, which is a biodiversity hotspot in the Republic of Korea.The tree s…
View article: Detection of Individual Tree Species Using Object-Based Classification Method with Unmanned Aerial Vehicle (UAV) Imagery
Detection of Individual Tree Species Using Object-Based Classification Method with Unmanned Aerial Vehicle (UAV) Imagery Open
This study was performed to construct tree species classification map according to three information types (spectral information, texture information, and spectral and texture information) by altitude (30 m, 60 m, 90 m) using the unmanned …
View article: Evaluation of Suitable REDD+ Sites Based on Multiple-Criteria Decision Analysis (MCDA): A Case Study of Myanmar
Evaluation of Suitable REDD+ Sites Based on Multiple-Criteria Decision Analysis (MCDA): A Case Study of Myanmar Open
In this study, the deforestation and forest degradation areas have been obtained in Myanmar using a land cover lamp (LCM) and a tree cover map (TCM) to get the CO2 potential reduction and the strength of occurrence was evaluated by using t…
View article: Evaluation of a Land Use Change Matrix in the IPCC’s Land Use, Land Use Change, and Forestry Area Sector Using National Spatial Information
Evaluation of a Land Use Change Matrix in the IPCC’s Land Use, Land Use Change, and Forestry Area Sector Using National Spatial Information Open
This study compared and analyzed the construction of a land use change matrix for the Intergovernmental Panel on Climate Change’s (IPCC) land use, land use change, and forestry area (LULUCF). We used National Forest Inventory (NFI) perma…
View article: Prediction of Land Use/Land Cover Change in Forest Area Using a Probability Density Function
Prediction of Land Use/Land Cover Change in Forest Area Using a Probability Density Function Open
This study aimed to predict changes in forest area using a probability density function, in order to promote effective forest management in the area north of the civilian control line (known as the Minbuk area) in Korea. Time series anal…
View article: Detection of Trees with Pine Wilt Disease Using Object-based Classification Method
Detection of Trees with Pine Wilt Disease Using Object-based Classification Method Open
In this study, regions infected by pine wilt disease were extracted by using object-based classification method (OB-infected region), and the characteristics of special distribution about OB-infected region were figured out. Scale 24, Shap…