Open Access

ARTICLE

Improving the Accuracy of Vegetation Index Retrieval for Biomass by Combining Ground-UAV Hyperspectral Data–A New Method for Inner Mongolia Typical Grasslands

by Ruochen Wang^1,#, Jianjun Dong^2,#, Lishan Jin³, Yuyan Sun³, Taogetao Baoyin², Xiumei Wang^*

1 The College of Resources and Environmental Engineering, Inner Mongolia University of Technology, Hohhot, 010000, China
2 The College of Ecology and Environment, Inner Mongolia University, Hohhot, 010000, China
3 Key Laboratory of Environmental Pollution Control and Remediation at Universities of Inner Mongolia Autonomous Region, Inner Mongolia University of Technology, Hohhot, 010000, China

* Corresponding Author: Xiumei Wang. Email:
# Contributed equally in the manuscript

(This article belongs to the Special Issue: Grassland Ecology in China under Global Change)

Phyton-International Journal of Experimental Botany 2024, 93(2), 387-411. https://doi.org/10.32604/phyton.2024.047573

Received 09 November 2023; Accepted 17 January 2024; Issue published 27 February 2024

Abstract

Grassland biomass is an important parameter of grassland ecosystems. The complexity of the grassland canopy vegetation spectrum makes the long-term assessment of grassland growth a challenge. Few studies have explored the original spectral information of typical grasslands in Inner Mongolia and examined the influence of spectral information on aboveground biomass (AGB) estimation. In order to improve the accuracy of vegetation index inversion of grassland AGB, this study combined ground and Unmanned Aerial Vehicle (UAV) remote sensing technology and screened sensitive bands through ground hyperspectral data transformation and correlation analysis. The narrow band vegetation indices were calculated, and ground and airborne hyperspectral inversion models were established. Finally, the accuracy of the model was verified. The results showed that: (1) The vegetation indices constructed based on the ASD FieldSpec 4 and the UAV were significantly correlated with the dry and fresh weight of AGB. (2) The comparison between measured R² with the prediction R² indicated that the accuracy of the model was the best when using the Soil-Adjusted Vegetation Index (SAVI) as the independent variable in the analysis of AGB (fresh weight/dry weight) and four narrow-band vegetation indices. The SAVI vegetation index showed better applicability for biomass monitoring in typical grassland areas of Inner Mongolia. (3) The obtained ground and airborne hyperspectral data with the optimal vegetation index suggested that the dry weight of AGB has the best fitting effect with airborne hyperspectral data, where y = 17.962e^4.672x, the fitting R² was 0.542, the prediction R² was 0.424, and RMSE and REE were 57.03 and 0.65, respectively. Therefore, established vegetation indices by screening sensitive bands through hyperspectral feature analysis can significantly improve the inversion accuracy of typical grassland biomass in Inner Mongolia. Compared with ground monitoring, airborne hyperspectral monitoring better reflects the inversion of actual surface biomass. It provides a reliable modeling framework for grassland AGB monitoring and scientific and technological support for grazing management.

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Keywords

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