Kim et al. (2025) Retrieving Woody Components from Time-Series Gap-Fraction and Multispectral Satellite Observations over Deciduous Forests

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Remote Sensing
• Year: 2025
• Date: 2025-12-19
• Authors: Woohyeok Kim, Jaese Lee, Yoojin Kang, Jungho Im, Bokyung Son, Jiwon Lee
• DOI: 10.3390/rs18010010

Research Groups

Not available from the provided text.

Short Summary

This study introduces a novel method to estimate the woody-to-total-area ratio (α) using Sentinel-2-based Normalized Difference Vegetation Index (NDVI) and time-series Plant Area Index (PAI) measurements, effectively mitigating the overestimation of Leaf Area Index (LAI) from gap-fraction-based PAI by accounting for woody components. The adjusted LAI (LAIadjusted) shows good agreement with Sentinel-2 LAI and accurately captures seasonal dynamics across various forest types.

Objective

• To develop and demonstrate a novel method for estimating the woody-to-total-area ratio (α) using Sentinel-2-based Normalized Difference Vegetation Index (NDVI) and time-series Plant Area Index (PAI) measurements to reduce the influence of woody components in PAI and derive more accurate Leaf Area Index (LAI) estimates.

Study Configuration

• Spatial Scale: Forest ecosystems, including deciduous broadleaf forests (DBF) and evergreen needleleaf forests (ENF).
• Temporal Scale: Seasonal to annual, encompassing growing seasons and winter periods, utilizing time-series measurements.

Methodology and Data

• Models used: PROSAIL model (used to derive effective LAI from Sentinel-2).
• Data sources: Sentinel-2-based Normalized Difference Vegetation Index (NDVI), time-series gap-fraction-based Plant Area Index (PAI) field observations.

Main Results

• A novel method was developed to estimate the woody-to-total-area ratio (α) using Sentinel-2 NDVI and time-series PAI measurements.
• The estimated α values effectively reduced the influence of woody components in PAI within deciduous broadleaf forests (DBF).
• The adjusted LAI (LAIadjusted) showed good agreement with Sentinel-2 LAI, which represents effective LAI derived from the PROSAIL model.
• The spatial distribution of α effectively captured expected seasonal dynamics across various forest types.
• In DBF, α values increased during winter due to leaf fall compared to the growing season, while seasonal variations were relatively small in evergreen needleleaf forests (ENF).
• The proposed method demonstrated greater robustness when using NDVI compared to other vegetation indices that are more susceptible to topographic variation.

Contributions

• Presents a novel framework for estimating the woody-to-total-area ratio (α) by integrating remote sensing (Sentinel-2 NDVI) with time-series field PAI measurements.
• Significantly enhances the accuracy of Leaf Area Index (LAI) estimates by providing a robust method to mitigate biases caused by woody components in gap-fraction-based PAI measurements.
• Improves vegetation structural assessments, which are crucial for broader ecological and climate-related applications.

Funding

Not available from the provided text.

Citation

@article{Kim2025Retrieving,
  author = {Kim, Woohyeok and Lee, Jaese and Kang, Yoojin and Im, Jungho and Son, Bokyung and Lee, Jiwon},
  title = {Retrieving Woody Components from Time-Series Gap-Fraction and Multispectral Satellite Observations over Deciduous Forests},
  journal = {Remote Sensing},
  year = {2025},
  doi = {10.3390/rs18010010},
  url = {https://doi.org/10.3390/rs18010010}
}