The Extraction of Torreya grandis Growing Areas Using a Spatial–Spectral Fused Attention Network and Multitemporal Sentinel-2 Images: A Case Study of the Kuaiji Mountain Region

Yanyan Lyu, Yong Wang () and Xiaoling Shen
Additional contact information
Yanyan Lyu: School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Yong Wang: School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China
Xiaoling Shen: Shaoxing Meteorological Bureau, Shaoxing 312000, China

Agriculture, 2025, vol. 15, issue 8, 1-22

Abstract: Global climate change poses a serious threat to Torreya grandis , a rare and economically important tree species, making the accurate mapping of its spatial distribution essential for forest resource management. However, extracting forest-growing areas remains challenging due to the limited spatial and temporal resolution of remote sensing data and the insufficient classification capability of traditional algorithms for complex land cover types. This study utilized monthly Sentinel-2 imagery from 2023 to extract multitemporal spectral bands, vegetation indices, and texture features. Following minimum redundancy maximum relevance (mRMR) feature selection, a spatial–spectral fused attention network (SSFAN) was developed to extract the distribution of T. grandis in the Kuaiji Mountain area and to analyze the influence of topographic factors. Compared with traditional deep learning models such as 2D-CNN, 3D-CNN, and HybridSN, the SSFAN model achieved superior performance, with an overall accuracy of 99.1% and a Kappa coefficient of 0.961. The results indicate that T. grandis is primarily distributed on the western, southern, and southwestern slopes, with higher occurrence at elevations above 500–600 m and on slopes steeper than 20°. The SSFAN model effectively integrates spectral–spatial information and leverages a self-attention mechanism to enhance classification accuracy. Furthermore, this study highlights the joint influence of natural factors and human land-use decisions on the distribution pattern of T. grandis. These findings aid precision planting and resource management while advancing methods for identifying tree species.

Keywords: Torreya grandis tree; Sentinel-2 image; Kuaiji Mountain; spatial–spectrum fused attention network; growth region extraction (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2077-0472/15/8/829/pdf (application/pdf)
https://www.mdpi.com/2077-0472/15/8/829/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jagris:v:15:y:2025:i:8:p:829-:d:1632360

Access Statistics for this article

Agriculture is currently edited by Ms. Leda Xuan

More articles in Agriculture from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().