Assessment of a UAV Radar System for Reconstructing Vertical Structure of Forests by Single Pass | IEEE Journals & Magazine | IEEE Xplore

Chinese Researchers Develop Lightweight UAV Radar System for Forest Structure Mapping

A team from the Aerospace Information Research Institute of the Chinese Academy of Sciences has developed a novel unmanned aerial vehicle (UAV) radar system that can map forest vertical structure in a single flight. The system, weighing just 0.92 kg, achieved a root-mean-square error of 1.33 meters in measuring vegetation height compared to ground measurements.

Led by Ping Zhang and Zhipeng Wu, the researchers tested their system across three different forest environments in China's Hainan Province - mangrove reserves, terrestrial vegetation areas, and mixed vegetation zones. The radar operates in the S-band frequency range and uses a modular design incorporating a main control unit, signal processing unit, and data recording unit.

The study, published in IEEE Transactions on Geoscience and Remote Sensing, demonstrates advantages over traditional forest measurement methods like LiDAR and synthetic aperture radar (SAR), offering a more cost-effective and operationally simpler solution for forest structure mapping. The researchers propose this technology could be valuable for forest resource management, monitoring biomass, and understanding forest ecosystems.

The work builds on the team's preliminary research published in 2021, where they first introduced the radar system concept. This latest study includes significant improvements to signal processing methods and comprehensive validation across different forest types.

 

Assessment of a UAV Radar System for Reconstructing Vertical Structure of Forests by Single Pass

Assessment of a UAV Radar System for Reconstructing Vertical Structure of Forests by Single Pass

Ping Zhang, Zhipeng Wu, Zhen Li, Lei Huang, Chang Liu, Shuo Gao, Jianmin Zhou, Haiwei Qiao, Shiqun Zhang

https://doi.org/10.1109/TGRS.2024.3518567

 

Abstract: Forests are the most extensive terrestrial ecosystems in the world. Their vertical structure information not only reflects the spatial structure characteristics of the forest but also the physiological and ecological processes. The existing studies on forest vertical structure through remote sensing often encounter challenges such as high costs, difficulties in acquiring effective data, or complexities in data processing. In this study, a new technology for detecting the vertical structure of vegetation is proposed and validated, which can obtain the vertical structure of vegetation by single flight. In order to adopt the new technology, a radar system was developed, incorporating a modular design scheme that emphasizes high integration and lightweight characteristics, and it was deployed on an unmanned aerial vehicle (UAV) flight platform. It consists of a main control unit, a signal processing unit, and a data recording unit, which weighs only 0.92 kg in total. Meanwhile, a novel algorithm is proposed to reconstruct the vertical structure of targets, effectively addressing critical challenges in UAV radar signal processing, such as strong system coupled signal, significant noise in radar signals, and high sidelobes in images. In order to validate the capability of the new technology, UAV flight experiments, as well as in situ observation, were carried out in typical vegetation areas. The proposed algorithm was used to process the acquired radar echoes, yielding a root-mean-square error (RMSE) of 1.33 m for the vegetation height compared to the ground synchronous measurement.

 

:Dec 15, 2024

 Background of the study:
The paper discusses the development of a new technology for detecting the vertical structure of vegetation using an unmanned aerial vehicle (UAV) radar system. This is an important topic as information about forest height and canopy structure is crucial for various applications, such as forest resource management, monitoring, and carbon storage estimation.

Research objectives and hypotheses:
The main objectives of this study are to develop a lightweight and integrated UAV radar system, and to propose a new data processing method to reconstruct the vertical structure of vegetation from the radar data. The authors hypothesize that this UAV radar system can accurately retrieve vegetation height and canopy information.

Methodology:
The researchers developed a modular UAV radar system weighing only 0.92 kg. The system uses a PulsOn 440 broadband signal processor and log-periodic antennas. The data processing method involves three key steps: (1) suppressing the system coupled signal using principal component analysis (PCA), (2) denoising the data using wavelet decomposition, and (3) enhancing the image using a cross-correlation backprojection (BP) algorithm.

Results and findings:
The proposed data processing method was applied to radar data acquired from three different vegetation areas in Hainan, China, including mangrove forests, rubber plantations, and mixed vegetation. The results show that the UAV radar system can accurately retrieve the vertical structure of vegetation, including the forest canopy height and the underlying surface. The root-mean-square error (RMSE) for vegetation height retrieval was 1.33 m compared to ground measurements.

Discussion and interpretation:
The authors discuss the influence of different underlying surfaces (water vs. soil) and terrain on the radar responses and the resulting vertical profile reconstruction. They found that the radar signals can penetrate the sparse canopy of the rubber forests and retrieve information about the lower branches and the ground surface, while for the dense mangrove forests, the radar mainly captures the canopy information due to the strong scattering from the water surface.

Contributions to the field:
This study provides a new approach for retrieving vertical vegetation structure using a lightweight and integrated UAV radar system, which is an alternative to the commonly used LiDAR and SAR-based methods.

Achievements and significance:
The developed UAV radar system and data processing method can accurately reconstruct the vertical structure of different vegetation types, including forest height and canopy information, with a relatively high precision compared to other microwave-based methods. This technology offers advantages in terms of cost, mobility, and ease of use.

Limitations and future work:
The current UAV radar system is limited in the coverage area compared to 3D imaging techniques like tomographic SAR. Future work should focus on further improving the system and exploring the feasibility of extending this technology to satellite-based platforms.