# Research progress on multimodal data fusion in forest resource monitoring

## TL;DR

This paper reviews advances in using combined satellite, drone, and ground data to monitor forests, aiming to improve accuracy and efficiency in tracking forest resources.

## Contribution

The study constructs a standardized data chain and proposes a scenario-adaptable fusion framework for forest monitoring.

## Key findings

- A complete technology chain for multimodal data fusion has been established, covering data acquisition to multi-strategy fusion.
- Applications show significant improvements in tree species classification and forest disaster monitoring.
- Technical challenges include LiDAR accuracy loss in dense forests and high annotation costs during preprocessing.

## Abstract

Dynamic monitoring of forest resources is crucial for safeguarding global ecological security. However, traditional monitoring methods, limited by single data sources, struggle to meet the demands of refined management. The global forest loss area in 2024 surged by 80% compared with that in 2023, further highlighting the urgency of technological upgrading. Multimodal data fusion technology has emerged as a core solution by establishing an “air-space-ground” collaborative network integrating “satellite remote sensing (macro-scale) + UAV hyperspectral (meso-scale) + ground sensors (micro-scale)”. This technology integrates multi-source heterogeneous data such as optical, radar, and LiDAR data, and achieves cross-modal information complementarity by combining traditional machine learning and deep learning. Based on the framework of “technical characteristics-scenario applications-challenge breakthroughs”, this study systematically reviews the research progress from 2020 to 2025. Technically, a complete technology chain is established, covering data acquisition, data preprocessing (including key links such as “data cleaning-spatiotemporal registration-feature dimensionality reduction”), and multi-strategy fusion. Significant application effects have been achieved in scenarios including tree species classification, land resource monitoring, forest structure parameter estimation and ecological monitoring, as well as forest disaster monitoring and tree health assessment. Meanwhile, the study identifies key technical bottlenecks: in data acquisition, the accuracy of LiDAR point clouds in dense forest areas decreases by 15%-20%; in preprocessing, issues such as spatiotemporal registration errors and high annotation costs exist; in fusion strategies, the accuracy of early fusion decreases by 12% when the number of features exceeds 500 dimensions; in model deployment, the inference latency of edge devices increases by 20%-30%. The core contributions of this study are as follows: constructing a standardized “air-space-ground” data technology chain, proposing a scenario-adaptable fusion framework, and clarifying future directions such as model lightweighting and edge computing. These contributions provide support for the engineering application of this technology and promote the transformation of forestry monitoring from “experience-driven” to “intelligent data-driven”.