Publications

Zhou, DC; Zhang, LX; Hao, L; Sun, G; Xiao, JF; Li, X (2023). Large discrepancies among remote sensing indices for characterizing vegetation growth dynamics in Nepal. AGRICULTURAL AND FOREST METEOROLOGY, 339, 109546.

Abstract
Mountain ecosystems provide multiple ecosystem services and are natural laboratories to understand ecosystem responses to global change. Because of the inaccessibility and the high cost of field surveys, remote sensing indices are the major and sometimes the only measures to monitor the vegetation growth dynamics in mountains. However, there are large discrepancies in those indices that should be quantified in mountainous regions. This case study in Nepal, a highly mountainous region, explores the consistency and inconsistency of six widely used remote sensing indices in monitoring vegetation growth from 2000 to 2020. The study considers three greenness indices of normalized difference vegetation indices (NDVI), enhanced vegetation index (EVI), and near-infrared reflectance of vegetation (NIRv), one cover index of leaf area index (LAI), and two productivity indices of gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF). We find high spatial consistency in the multiyear means (r = 0.79 similar to 1, N = 4300, p < 0.01), especially in the highlands and between EVI and NIRv, and a logarithmic relationship between greenness indices or GOSIF and LAI or GPP. In contrast, the long-term trends differ substantially by index and space. Only 7% of the lands show synchronized significant increase though all the indices show a widespread increasing tendency (77 similar to 87% of the lands). The prevalent non-significant changes of all the indices primarily contribute to the trend uncertainties, especially in the highlands. The inconsistencies between greenness and productivity indices and in them further exaggerate the uncertainties. Our results emphasize the large discrepancies of remote sensing indices in quantifying mountain vegetation growth dynamics. Larger inconsistency is expected if we consider disparities among the quality-control schemes, study seasons, remote sensing models, satellite platforms, and sensors. Reinforced remote sensing data, model improvements and/or new indices are needed for an accurate quantification of the vegetation growth dynamics in mountain regions.

DOI:
10.1016/j.agrformet.2023.109546

ISSN:
1873-2240