Ning Li
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Ning Li has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Haoyu Wang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
2 shared publications - Bin Jia
School of Electronic Information and Automation, Guilin University of Aerospace Technology, Guilin 541004, China
1 shared publication - Bin Wang
School of Mathematics and Statistics, Guilin University of Technology, Guilin 541004, China
1 shared publication - Bolin Fu
College of Geomatics and Geoinformation, Guilin University of Technology
1 shared publication - Cheng Wang
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
1 shared publication - Ertao Gao
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541004, China
1 shared publication
Latest Publications
- Open Access
- 24 April 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (9) , 1305
Snow-Covered Filter-Enhanced Canopy Surface Points: A Lightweight and Efficient Framework for Individual Tree Segmentation from LiDAR Data
As fundamental units of forest ecosystems, individual trees provide essential structural characteristics for forest resource assessment. However, existing LiDAR-based individual tree segmentation methods are often limited by a trade-off between information preservation and computational efficiency. This study proposes a novel framework for individual tree segmentation from LiDAR data based on canopy surface points (CSP), aiming to balance this trade-off. The framework introduces a Snow-Covered Filter (SCF) that simulates snow deposition to extract surface points from the point cloud. After removing ground points from these surface points, the resulting CSP retains the core 3D structure of the canopy while significantly reducing data volume. We validate the proposed framework on four multi-platform datasets using four algorithms that represent the evolution of individual tree segmentation methods: Dalponte2016, K-means, Li2012, and SegmentAnyTree. The results demonstrate that: (a) the SCF effectively extracts surface points, with an average F1-score of 0.703; (b) segmentation using CSP achieves accuracy comparable to that obtained using all points or raster data (mean ), with the primary gap observed for SegmentAnyTree (maximum F-score reduction of 0.259); (c) the framework offers substantial efficiency gains: >40% point reduction, ~38.4% average runtime reduction (maximum saving ~4660 s), and lower memory consumption. By providing a lightweight yet structurally rich data representation, this work presents an innovative and efficient approach to individual tree segmentation, with promising potential for large-scale forest resource management.- 24 April 2026
- Published by MDPI AG in Remote Sensing
- Vol. 18 (9) , 1305
Open AccessSnow-Covered Filter-Enhanced Canopy Surface Points: A Lightweight and Efficient Framework for Individual Tree Segmentation from LiDAR Data
As fundamental units of forest ecosystems, individual trees provide essential structural characteristics for forest resource assessment. However, existing LiDAR-based individual tree segmentation methods are often limited by a trade-off between information preservation and computational efficiency. This study proposes a novel framework for individual tree segmentation from LiDAR data based on canopy surface points (CSP), aiming to balance this trade-off. The framework introduces a Snow-Covered Filter (SCF) that simulates snow deposition to extract surface points from the point cloud. After removing ground points from these surface points, the resulting CSP retains the core 3D structure of the canopy while significantly reducing data volume. We validate the proposed framework on four multi-platform datasets using four algorithms that represent the evolution of individual tree segmentation methods: Dalponte2016, K-means, Li2012, and SegmentAnyTree. The results demonstrate that: (a) the SCF effectively extracts surface points, with an average F1-score of 0.703; (b) segmentation using CSP achieves accuracy comparable to that obtained using all points or raster data (mean ), with the primary gap observed for SegmentAnyTree (maximum F-score reduction of 0.259); (c) the framework offers substantial efficiency gains: >40% point reduction, ~38.4% average runtime reduction (maximum saving ~4660 s), and lower memory consumption. By providing a lightweight yet structurally rich data representation, this work presents an innovative and efficient approach to individual tree segmentation, with promising potential for large-scale forest resource management. - Open Access
- 8 January 2025
- Published by MDPI AG in Sustainability
- Vol. 17 (2) , 414
Spatiotemporal Detection of Ecological Environment Quality Changes in the Lijiang River Basin Using a New Dual Model
Detecting spatiotemporal changes in ecological environment quality (EEQ) is of great importance for maintaining regional ecological security and supporting sustainable economic and social development. However, research on EEQ detection from a remote sensing perspective is insufficient, especially at the basin scale. Based on two indices, namely, the Ecological Index (EI) and the Remote Sensing Ecological Index (RSEI), we established a dual model, combining the remote sensing ecological comprehensive index (RSECI) and its differential change model, to study the spatiotemporal evolutionary characteristics of EEQ in the Lijiang River Basin (LRB) from 2000 to 2020. The RSECI combines the following five indicators: greenness, wetness, heat, dryness, and aerosol optical depth. The results of this study show that the area of good and excellent EEQ in the LRB decreased from 3676.22 km2 in 2000 to 2083.89 km2 in 2020, while the area of poor and fair EEQ increased from 80.81 km2 in 2000 to 1375.91 km2 in 2020. From 2000 to 2020, the change curve of the EEQ difference in the LRB first rose, fell, and then rose again. The wetness and greenness indicators had positive effects on promoting EEQ, while the heat, aerosol optical depth, and dryness indicators had restraining effects. The results of stepwise regression analysis showed that, among the selected indicators, wetness and greenness were the key factors for improving the EEQ in the LRB during the study period. The RSECI approach and the difference change model proposed in this study can be used to quantitatively evaluate the EEQ and facilitate the analysis of the spatial and temporal dynamic changes and difference changes in EEQ.
- 8 January 2025
- Published by MDPI AG in Sustainability
- Vol. 17 (2) , 414
Open AccessSpatiotemporal Detection of Ecological Environment Quality Changes in the Lijiang River Basin Using a New Dual Model
Detecting spatiotemporal changes in ecological environment quality (EEQ) is of great importance for maintaining regional ecological security and supporting sustainable economic and social development. However, research on EEQ detection from a remote sensing perspective is insufficient, especially at the basin scale. Based on two indices, namely, the Ecological Index (EI) and the Remote Sensing Ecological Index (RSEI), we established a dual model, combining the remote sensing ecological comprehensive index (RSECI) and its differential change model, to study the spatiotemporal evolutionary characteristics of EEQ in the Lijiang River Basin (LRB) from 2000 to 2020. The RSECI combines the following five indicators: greenness, wetness, heat, dryness, and aerosol optical depth. The results of this study show that the area of good and excellent EEQ in the LRB decreased from 3676.22 km2 in 2000 to 2083.89 km2 in 2020, while the area of poor and fair EEQ increased from 80.81 km2 in 2000 to 1375.91 km2 in 2020. From 2000 to 2020, the change curve of the EEQ difference in the LRB first rose, fell, and then rose again. The wetness and greenness indicators had positive effects on promoting EEQ, while the heat, aerosol optical depth, and dryness indicators had restraining effects. The results of stepwise regression analysis showed that, among the selected indicators, wetness and greenness were the key factors for improving the EEQ in the LRB during the study period. The RSECI approach and the difference change model proposed in this study can be used to quantitatively evaluate the EEQ and facilitate the analysis of the spatial and temporal dynamic changes and difference changes in EEQ.