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Updated: May 9, 2026

Leaf Area Index Estimation Using Three Distinct Methods in Pure Deciduous Stands
Published on: August 29, 2019
[NDVI difference rate recognition model of deciduous broad-leaved forest based on HJ-CCD remote sensing data]
Yan Wang1, Qing-Jiu Tian, Yan Huang
1International Institute for Earth System Science, Nanjing University, Nanjing 210023, China. wangyan_woshiwang@163.com
This study introduces a new method using the Normalized Difference Vegetation Index (NDVI) difference rate to identify deciduous broad-leaved forests. The approach effectively maps forest distribution using remote sensing data.
Area of Science:
- Forestry science
- Remote sensing
- Geographic Information Systems (GIS)
Context:
- Accurate mapping of forest ecosystems is crucial for conservation and management.
- Deciduous broad-leaved forests are significant components of terrestrial biodiversity.
- Remote sensing technologies offer efficient tools for large-scale vegetation monitoring.
Purpose:
- To develop and validate a novel method for recognizing deciduous broad-leaved forests.
- To utilize the Normalized Difference Vegetation Index (NDVI) difference rate for forest identification.
- To apply this method to HJ-CCD satellite imagery for spatial distribution mapping.
Summary:
- A recognition model for deciduous broad-leaved forests was constructed using the NDVI difference rate between leaf expansion and flowering/fruit-bearing stages.
- The model was applied to HJ-CCD remote sensing images acquired on April 1, 2012, and May 4, 2012, in Chuzhou, Anhui Province.
- The method effectively extracted the spatial distribution of deciduous broad-leaved forests, with results verified and evaluated.
Impact:
- The study validates the efficacy of the NDVI difference rate as an extraction method for deciduous broad-leaved forests.
- It confirms the applicability of HJ-CCD data for vegetation classification and recognition.
- Provides a reliable technique for forest mapping and monitoring in regional ecological studies.
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