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

Weidong Zhu, Fei Yang, Zhenge Qiu, Naiying He, Xiaolong Zhu, Yaqin Li, Yuelin Xu and Zhigang Lu ()
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Weidong Zhu: College of Marine Sciences, Shanghai Ocean University, 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
Naiying He: College of Marine Sciences, Shanghai Ocean University, 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

Sustainability, 2023, vol. 15, issue 13, 1-20

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.

Keywords: canopy height; GEDI; ALS; OLI-2; BP neural network; importance score (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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