Vegetation indices are the simplest and most effective metric parameters representing the features of vegetation cover and growth condition. This paper used Euonymus japonicas Thunb as a study case and collected 200 leaf samples in 20 locations. Using electronic analytical balance and ASD hyperspectral radiometer with Win FOLIA leaf area meter obtained the data of the amount of dust, spectral information and leaf area. Through comparative analysis between dust and clean leaves, differences of spectral curve and vegetation indices were apparent. Then, combined with dust weight and spectral data, dust correction models-for vegetation indices were built. The analysis results showed that the spectral curve between clean and dust leaves have typical characteristics: blue edge and red edge were at 520 and 705 nm; however, dust influenced leaf reflectance significantly in range of 350-700, 750-1 350, 1 500-1 850, 1 900-2 100 nm wavelength, and had a greater impact on vegetation indices. With dust weight increasing, the linear correlation of dust with NDVI AND PRI was non-significant, but that with NDWI, NDII and CAI was still significant. The verification of correction models showed that coefficient of determination (R2) of NDVI, NDII, CAI and PRI were 0.547, 0.430, 0.653 and 0.96 and their root mean square error (RMSE) was 0.035, 0.020, 0.112 and 0.009 respectively. Furthermore, it showed that applying dust correction models can improve the accuracy of vegetation indices calculation.
Download full-text PDF |
Source |
---|
Natl Sci Rev
February 2025
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China.
Climate warming may induce substantial changes in the ecosystem carbon cycle, particularly for those climate-sensitive regions, such as alpine grasslands on the Tibetan Plateau. By synthesizing findings from warming experiments, this review elucidates the mechanisms underlying the impacts of experimental warming on carbon cycle dynamics within these ecosystems. Generally, alterations in vegetation structure and prolonged growing season favor strategies for enhanced ecosystem carbon sequestration under warming conditions.
View Article and Find Full Text PDFSpectral analysis is a widely used method for monitoring photosynthetic capacity. However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has limited representational capacity (partial least squares regression) or uninterpretable (convolution). In this study, we proposed a deep learning model with enhanced interpretability based on attention and vegetation indices calculation for global spectral feature mining to accurately estimate photosynthetic capacity.
View Article and Find Full Text PDFPNAS Nexus
January 2025
Key Laboratory of Geographic Information Science (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China.
Accelerated global urban expansion not only directly occupies surrounding ecosystems, but also induces cascading losses of natural vegetation elsewhere through cropland displacement. Yet, how such effects alter the net primary productivity (NPP) worldwide remains unclear. Here, we quantified the direct and cascading impacts of global urban expansion on terrestrial NPP from 1992 to 2020 and projected the impacts under the shared socioeconomic pathways framework by 2100.
View Article and Find Full Text PDFSci Total Environ
January 2025
Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur 721302, West Bengal, India. Electronic address:
This study investigates the potential impact of future climate scenarios designated by different shared socioeconomic pathways (SSPs) on vegetation health. Considering the entire Indian mainland as the study region, which exhibits a diverse range of climate and vegetation regimes, we analysed long-term past (1981-2020) and future (2021-2100) changes in vegetation greenness across seven vegetation types and four seasons. In order to gain insight into the intricate interrelationships between vegetation and hydroclimatic factors (soil moisture, precipitation, solar radiation, and temperature), a Standardized Vegetation Index (SVI) is used as a proxy for vegetation health, and a bivariate copula-based probabilistic model is developed incorporating a Combined Climate Index (CCI) derived through Supervised Principal Component Analysis (SPCA) and the SVI.
View Article and Find Full Text PDFPlant Cell Environ
January 2025
Remote Sensing Laboratory, Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India.
The generation of spectral libraries using hyperspectral data allows for the capture of detailed spectral signatures, uncovering subtle variations in plant physiology, biochemistry, and growth stages, marking a significant advancement over traditional land cover classification methods. These spectral libraries enable improved forest classification accuracy and more precise differentiation of plant species and plant functional types (PFTs), thereby establishing hyperspectral sensing as a critical tool for PFT classification. This study aims to advance the classification and monitoring of PFTs in Shoolpaneshwar wildlife sanctuary, Gujarat, India using Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and machine learning techniques.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!