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Enhancing Forest Canopy Height Retrieval: Insights from Integrated GEDI and Landsat Data Analysis

Author

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  • Weidong Zhu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Shanghai Estuary Marine Surveying and Mapping Engineering Technology Research Center, Shanghai 201306, China
    Key Laboratory of Marine Ecological Monitoring and Restoration Technologies, Shanghai 201306, China)

  • Fei Yang

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)

  • Zhenge Qiu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Shanghai Estuary Marine Surveying and Mapping Engineering Technology Research Center, Shanghai 201306, China)

  • Naiying He

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China
    Shanghai Estuary Marine Surveying and Mapping Engineering Technology Research Center, Shanghai 201306, China)

  • Xiaolong Zhu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)

  • Yaqin Li

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)

  • Yuelin Xu

    (College of Marine Sciences, Shanghai Ocean University, Shanghai 201306, China)

  • Zhigang Lu

    (School of Resources and Architectural Engineering, Gannan University of Science and Technology, Ganzhou 341000, China)

Abstract

Canopy height is a crucial indicator for assessing the structure and function of the forest ecosystems. It plays a significant role in carbon sequestration, sink enhancement, and promoting green development. This study aimed to evaluate the accuracy of GEDI L2A version 2 data in estimating ground elevation and canopy height by comparing it with airborne laser scanning (ALS) data. Among the six algorithms provided by the GEDI L2A data, algorithm a2 demonstrated higher accuracy than the others in detecting ground elevation and canopy height. Additionally, a relatively strong correlation (R-squared = 0.35) was observed between rh95 for GEDI L2A and RH90 for ALS. To enhance the accuracy of canopy height estimation, this study proposed three backpropagation (BP) neural network inversion models based on GEDI, Landsat 8 OLI, and Landsat 9 OLI-2 data. Multiple sets of relative heights and vegetation indices were extracted from the GEDI and Landsat datasets. The random forest (RF) algorithm was employed to select feature variables with a cumulative importance score of 90% for training the BP neural network inversion models. Validation against RH90 of ALS revealed that the GEDI model outperformed the OLI or OLI-2 data models in terms of accuracy. Moreover, the quality improvement of OLI-2 data relative to OLI data contributed to enhanced inversion accuracy. Overall, the models based on a single dataset exhibited relatively low accuracy. Hence, this study proposed the GEDI and OLI and GEDI and OLI-2 models, which combine the two types of data. The results demonstrated that the combined model integrating GEDI and OLI-2 data exhibited the highest performance. Compared to the weakest OLI data model, the inversion accuracy R-squared improved from 0.38 to 0.74, and the MAE, RMSE, and rRMSE decreased by 1.21 m, 1.81 m, and 8.09%, respectively. These findings offer valuable insights for the remote sensing monitoring of forest sustainability.

Suggested Citation

  • Weidong Zhu & Fei Yang & Zhenge Qiu & Naiying He & Xiaolong Zhu & Yaqin Li & Yuelin Xu & Zhigang Lu, 2023. "Enhancing Forest Canopy Height Retrieval: Insights from Integrated GEDI and Landsat Data Analysis," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:13:p:10434-:d:1185411
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    References listed on IDEAS

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    1. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    2. Fernanda Sayuri Yoshino Watanabe & Enner Alcântara & Thanan Walesza Pequeno Rodrigues & Nilton Nobuhiro Imai & Cláudio Clemente Faria Barbosa & Luiz Henrique da Silva Rotta, 2015. "Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images," IJERPH, MDPI, vol. 12(9), pages 1-27, August.
    3. Wei-Dong Zhu & Chu-Yi Qian & Nai-Ying He & Yu-Xiang Kong & Zi-Ya Zou & Yu-Wei Li, 2022. "Research on Chlorophyll-a Concentration Retrieval Based on BP Neural Network Model—Case Study of Dianshan Lake, China," Sustainability, MDPI, vol. 14(14), pages 1-15, July.
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    1. Weidong Zhu & Yaqin Li & Kuifeng Luan & Zhenge Qiu & Naiying He & Xiaolong Zhu & Ziya Zou, 2024. "Forest Canopy Height Retrieval and Analysis Using Random Forest Model with Multi-Source Remote Sensing Integration," Sustainability, MDPI, vol. 16(5), pages 1-21, February.

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