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Pollen-based normalized difference vegetation index reconstruction and its application to two Holocene sites of the Tibetan Plateau

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GLOBAL AND PLANETARY CHANGE 2025年 251卷

作者： Han, Haoshuang Qin, Feng Liang, Chen Yin, Yunhe Zhao, Yan Chinese Acad Sci Inst Geog Sci & Nat Resources Res Key Lab Land Surface Pattern & Simulat Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Henan Acad Sci Aerosp Informat Res Inst Zhengzhou 450046 Peoples R China Hebei GEO Univ Dept Land Resource & Urban Planning Shijiazhuang 050031 Peoples R China

The Tibetan Plateau (TP) vegetation exhibits high sensitivity to climate fluctuations and disturbances, making it a focal area for studying alpine ecosystem dynamics. normalized difference vegetation index (NDVI), as an indicator of vegetation growth and health status, is one of the most effective satellite-based vegetation parameters that are used to monitor vegetation dynamics. However, long-term NDVI data are not available due to the short temporal span of instrumental observations. Quantitative reconstructions of past vegetation based on pollen data can provide crucial evidence for understanding long-term vegetation evolution and validating the vegetation/ climate model to enhance the accuracy of future projection. On the basis of the relationships between modern pollen data and remote sensing NDVI, we apply the modern analogue technique (MAT) to reconstruct vegetation NDVI from pollen data of the TP. The accuracy of the reconstruction was evaluated by comparing the reconstructions with remote sensing observations. Results showed that a search window of 10 km radius around sampling sites combined with the inverse-distance weighting method were the most suitable for extracting vegetation parameters to establish the pollen-NDVI reconstruction model. Under the above parameter settings, the pollen-based modern NDVI reconstructions were in good agreement with the observed ones (R2 = 0.79). The pollen-based reconstructions accurately reflect the spatial pattern of NDVI observed by remote sensing on the TP, although NDVI values were overestimated in sparsely vegetated areas and underestimated in dense forest areas in a few cases. The Holocene NDVI changes of the Zoige Basin and Hurleg Lake were then reconstructed based on their fossil pollen records by applying the pollen-NDVI reconstruction model. The reconstructed paleo-NDVI changes at the two sites were highly consistent with their Holocene regional vegetation and climate patterns, which further confirmed the reliab

关键词： Pollen normalized difference vegetation index Modern analogue technique Paleovegetation reconstruction Tibetan Plateau

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The use of multi-temporal Landsat normalized difference vegetation index (NDVI) data for mapping fuel models in Yosemite National Park, USA

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2003年第8期24卷 1639-1651页

作者： Van Wagtendonk, JW Root, RR US Geol Survey Western Ecol Res Ctr Yosemite Field Stn El Portal CA 95318 USA US Geol Survey Rocky Mt Mapping Ctr Denver Fed Ctr Denver CO 80225 USA

The objective of this study was to test the applicability of using normalized difference vegetation index (NDVI) values derived from a temporal sequence of six Landsat Thematic Mapper (TM) scenes to map fuel models for Yosemite National Park, USA. An unsupervised classification algorithm was used to define 30 unique spectral-temporal classes of NDVI values. A combination of graphical, statistical and visual techniques was used to characterize the 30 classes and identify those that responded similarly and could be combined into fuel models. The final classification of fuel models included six different types: short annual and perennial grasses, tall perennial grasses, medium brush and evergreen hardwoods, short-needled conifers with no heavy fuels, long-needled conifers and deciduous hardwoods, and short-needled conifers with a component of heavy fuels. The NDVI, when analysed over a season of phenologically distinct periods along with ancillary data, can elicit information necessary to distinguish fuel model types. Fuels information derived from remote sensors has proven to be useful for initial classification of fuels and has been applied to fire management situations on the ground.

关键词： EVERGREEN Yosemite National Park heavy oil Fuel models Conifers Remote sensing equipment ancillary data normalized difference vegetation index Sorting algorithm

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Influence of Variations of Hydrothermal Conditions on normalized difference vegetation index in Typical Temperature Zones Along the East Coast of China

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FRONTIERS IN EARTH SCIENCE 2020年 8卷

作者： He, Guangxin Zhen, Xiaoju Li, Zhongliang Shen, Weishou Han, Jing Zhang, Lu Li, Xun Zhang, Ran Nanjing Univ Informat Sci & Technol Key Lab Meteorol Disaster Minist Educ KLME Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Int Joint Res Lab Climate & Environm Change ILCEC Nanjing Peoples R China Nanjing Univ Informat Sci & Technol Collaborat Innovat Ctr Forecast & Evaluat Meteoro Nanjing Peoples R China China Meteorol Adm Guangzhou Inst Trop Marine Meteorol Guangzhou Peoples R China Hebei Prov Inst Meteorol Sci Key Lab Meteorol & Ecol Environm Hebei Prov Shijiazhuang Hebei Peoples R China Key Lab South China Sea Meteorol Disaster Prevent Haikou Hainan Peoples R China Nanjing Univ Informat Sci & Technol Coll Geog Sci Nanjing Peoples R China Minist Environm Protect Nanjing Inst Environm Sci Nanjing Peoples R China Nanjing Meteorol Bur Nanjing Peoples R China China Meteorol Adm Meteorol Observat Ctr Beijing Peoples R China

The eastern coastal areas of China span multiple climatic zones, and the impacts of climate warming on their ecological environment show regional differences. In this research, the normalized difference vegetation index (NDVI) was used as the indicator to characterize the ecological environment, and selected Guangdong, Jiangsu, and Liaoning as its typical research areas. The authors selected the NDVI, average temperature, and precipitation data of the yearly growth season, respectively, from 1982 to 2016. This study adopted the copula functions model based on Markov Chain Monte Carlo to carry out the research of bivariate joint distribution so as to calculate the joint probability, the joint exceedance probability, the joint return period and the co-occurrence return period. The results showed that 1) the temperature and precipitation in the three regions were respectively related to the NDVI sequence showing the characteristic that was correlated at the upper tail and asymptotically independent at the lower tail, which demonstrated that the temperature and precipitation had little effect on NDVI when they reached their minimum values, and the temperature and precipitation had obvious effect on NDVI when they reached their maximum values. 2) The shorter the return period was, the wider the ranges of the climate factor and the NDVI were, showing that when the climate factor was constant, the probability of the NDVI having a shorter return period was higher. The greater the climate factor was, the longer the return period was, indicating that the probability of plant growth inhibition was higher when the climate factor exceeded a certain threshold. 3) The suitable temperature and precipitation for vegetation growth in the three regions gradually decreased from south to north. These results provide some theoretical guidance and scientific foundation for the protection of regional ecological environment and enhance the understanding of the impact of climate change on th

关键词： temperature precipitation normalized difference vegetation index copula function joint probability distribution

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Mapping the normalized difference vegetation index for the Contiguous US Since 1850 Using 391 Tree-Ring Plots

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REMOTE SENSING 2024年第21期16卷 3973-3973页

作者： Li, Hang Thapa, Ichchha Xu, Shuang Yang, Peisi Univ Northern British Columbia Dept Ecosyst Sci & Management Prince George BC V2N 4Z9 Canada Michigan State Univ Dept Forestry E Lansing MI 48824 USA Southwest Univ Sch Geog Sci Chongqing 400715 Peoples R China Hohai Univ Coll Water Conservancy & Hydropower Engn Nanjing 210098 Peoples R China

The forests and grasslands in the U.S. are vulnerable to global warming and extreme weather events. Current satellites do not provide historical vegetation density images over the long term (more than 50 years), which has restricted the documentation of key ecological processes and their resultant responses over decades due to the absence of large-scale and long-term monitoring studies. We performed point-by-point regression and collected data from 391 tree-ring plots to reconstruct the annual normalized difference vegetation index (NDVI) time-series maps for the contiguous U.S. from 1850 to 2010. Among three machine learning approaches for regressions-Support Vector Machine (SVM), General Regression Neural Network (GRNN), and Random Forest (RF)-we chose GRNN regression to simulate the annual NDVI with lowest Root Mean Square Error (RMSE) and highest adjusted R2. From the Little Ice Age to the present, the NDVI increased by 6.73% across the contiguous U.S., except during some extreme events such as the Dust Bowl drought, during which the averaged NDVI decreased, particularly in New Mexico. The NDVI trend was positive in the Northern Forest, Tropical Humid Forest, Northern West Forest Mountains, Marin West Coast Forests, and Mediterranean California, while other ecoregions showed a negative trend. At the state level, Washington and Louisiana had significantly positive correlations with temperature (p < 0.05). Washington had a significantly negative correlation with precipitation (p < 0.05), whereas Oklahoma had a significantly positive correlation (p < 0.05) with precipitation. This study provides insights into the spatial distribution of paleo-vegetation and its climate drivers. This study is the first to attempt a national-scale reconstruction of the NDVI over such a long period (151 years) using tree rings and machine learning.

关键词： normalized difference vegetation index tree rings contiguous US point-by-point regression

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Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery

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REMOTE SENSING OF ENVIRONMENT 2007年第3期106卷 375-386页

作者： Yuan, Fei Bauer, Marvin E. Mankato State Univ Dept Geog Mankato MN 56001 USA Univ Minnesota Dept Forest Resources St Paul MN 55108 USA

This paper compares the normalized difference vegetation index (NDVI) and percent impervious surface as indicators of surface urban heat island effects in Landsat imagery by investigating the relationships between the land surface temperature (LST), percent impervious surface area (%ISA), and the NDVI. Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data were used to estimate the LST from four different seasons for the Twin Cities, Minnesota, metropolitan area. A map of percent impervious surface with a standard error of 7.95% was generated using a normalized spectral mixture analysis of July 2002 Landsat TM imagery. Our analysis indicates there is a strong linear relationship between LST and percent impervious surface for all seasons, whereas the relationship between LST and NDVI is much less strong and varies by season. This result suggests percent impervious surface provides a cornplementary metric to the traditionally applied NDVI for analyzing LST quantitatively over the seasons for surface urban heat island studies using thermal infrared remote sensing in an urbanized environment. (c) 2006 Elsevier Inc. All rights reserved.

关键词： impervious surface area land surface temperature normalized difference vegetation index spectral mixture analysis surface urban heat island Landsat TM/ETM

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Multi-platform comparisons of MODIS and AVHRR normalized difference vegetation index data

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REMOTE SENSING OF ENVIRONMENT 2005年第3期99卷 221-231页

作者： Gallo, K Li, L Reed, B Eidenshink, J Dwyer, J NOAA NESDIS Camp Springs MD 20746 USA Colorado State Univ CIRA Ft Collins CO 80525 USA USGS EROS SAIC Sioux Falls SD 57198 USA

The relationship between AVHRR-derived normalized difference vegetation index (NDVI) values and those of future sensors is critical to continued long-term monitoring of land surface properties. The follow-on operational sensor to the AVHRR, the Visible/Infrared Imager/Radiometer Suite (VIIRS), will be very similar to the NASA Earth Observing System's Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. NDVI data derived from visible and near-infrared data acquired by the MODIS (Terra and Aqua platforms) and AVHRR (NOAA-16 and NOAA-17) sensors were compared over the same time periods and a variety of land cover classes within the conterminous United States. The results indicate that the 16-day composite NDVI values are quite similar over the composite intervals of 2002 and 2003, and linear relationships exist between the NDVI values from the various sensors. The composite AVHRR NDVI data included water and cloud masks and adjustments for water vapor as did the MODIS NDVI data. When analyzed over a variety of land cover types and composite intervals, the AVHRR derived NDVI data were associated with 89% or more of the variation in the MODIS NDVI values. The results suggest that it may be possible to successfully reprocess historical AVHRR data sets to provide continuity of NDVI products through future sensor systems. (C) 2005 Elsevier Inc. All rights reserved.

关键词： AVHRR MODIS VIIRS normalized difference vegetation index composite data continuity

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Urban new construction land parcel detection with normalized difference vegetation index and PanTex information

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JOURNAL OF APPLIED REMOTE SENSING 2017年第2期11卷

作者： Huang, Qingqing Wang, Chengyi Meng, Yu Yue, Anzhi Chen, Jingbo Yuan, Yuan Chinese Acad Sci Inst Remote Sensing & Digital Earth Beijing Peoples R China Nanjing Univ Posts & Telecommun Coll Geog & Biol Informat Nanjing Jiangsu Peoples R China

An urban new construction land parcel detection method based on normalized difference vegetation index (NDVI) and built-up area presence index is proposed for high-resolution remote sensing images. The method consists of three main steps: construction land detection using NDVI and PanTex, false change removal, and new construction land parcel extraction. More specifically, a change proportion index is raised to convert the pixel-based change detection map to parcels in combination with a segmentation process. From experimental results validated using two cases of high-resolution optical satellite images, the proposed method is demonstrated to be efficient and achieves a per-object overall accuracy rate beyond 95%, significantly superior to the traditional postclassification change detection method. Furthermore, the proposed method avoids errors resulting from classification in the method of postclassification comparison. (C) 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： new construction land normalized difference vegetation index PanTex change proportion index

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SAR2NDVI: a pix2pix generative adversarial network for reconstructing field-level normalized difference vegetation index time series using Sentinel-1 synthetic aperture radar data

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JOURNAL OF APPLIED REMOTE SENSING 2023年第2期17卷

作者： Ramathilagam, Arun Balaji Natarajan, Sudha Kumar, Anil SASTRA Univ Sch Comp Thanjavur Tamil Nadu India Indian Inst Remote Sensing Dehra Dun Uttarakhand India

The normalized difference vegetation index (NDVI) is essential for monitoring urban green space, forest cover, and crop growth from sowing to harvesting. High-resolution images from Sentinel-2 and Landsat-8 satellites are available nowadays to get field-level phenological information at short intervals. However, cloud cover is one of the biggest hindrances in vegetation monitoring using NDVI, resulting in data gaps. To address this issue, we propose an NDVI gap-filling methodology to generate cloud-free NDVI time series from Sentinel-1 SAR data using the pix2pix generative adversarial network (GAN) model. Pix2pix GANs with generators based on U-Net and ResNet were designed, and the performance of the models was compared for both the VV and VH polarizations. The generalization capability of the models was studied using synthetic aperture radar (SAR) and NDVI image pairs with different vegetation, field sizes, and shapes. Experimental results show that the generated synthetic NDVI images can effectively substitute the cloudy images for gap-filling. Compared to ResNet, the U-Net generator has given the best results with PSNR = 29.447 dB, RMSE = 0.055, and Pearson correlation coefficient ? = + 0.909 for VH polarization.

关键词： pix2pix generative adversarial network normalized difference vegetation index synthetic aperture radar

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Predicting the normalized difference vegetation index (NDVI) by training a crop growth model with historical data

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2019年 161卷 305-311页

作者： Berger, Andres Ettlin, Guillermo Quincke, Christopher Rodriguez-Bocca, Pablo Inst Nacl Invest Agr Ruta 50 Km 11 Colonia 70006 Uruguay Univ Republica Fac Ingn Inst Computac Julio Herrera & Reissig 565 Montevideo 11300 Uruguay

normalized difference vegetation index (NDVI) is an important remote measurement in agriculture because it has a high correlation with crop growth and yield result. In this paper, we present a methodology to predict the NDVI by training a crop growth model with historical data. Although we use a very simple soybean growth model, the methodology could be extended to other crops and more complex models. The training process is an optimization problem, that is solved using the spectral projected gradient method. The quality of the prediction is measured by computing the Root-Mean-Square Error (RMSE) between predicted and true values, obtaining an error lower than 9%, which improves the results obtained by simple forecast techniques used as baseline estimators.

关键词： normalized difference vegetation index Predictive analysis Optimization

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Simple method for extracting the seasonal signals of photochemical reflectance index and normalized difference vegetation index measured using a spectral reflectance sensor

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Journal of Integrative Agriculture 2021年第7期20卷 1969-1986页

作者： Jae-Hyun RYU Dohyeok OH Jaeil CHO Department of Applied Plant Science Chonnam National UniversityGwangju 61186Republic of Korea

A spectral reflectance sensor(SRS)fixed on the near-surface ground was developed to support the continuous monitoring of vegetation indices such as the normalized difference vegetation index(NDVI)and photochemical reflectance index(PRI).NDVI is useful for indicating crop growth/phenology,whereas PRI was developed for observing physiological ***,the seasonal change patterns of NDVI and PRI are two valuable pieces of information in a crop-monitoring ***,capturing the seasonal patterns is considered challenging because the vegetation index values estimated by the reflection from vegetation are often governed by meteorological conditions,such as solar irradiance and ***,unlike growth/phenology,the physiological condition has diurnal changes as well as seasonal *** study proposed a novel filtering method for extracting the seasonal signals of SRS-based NDVI and PRI in paddy rice,barley,and ***,the measurement accuracy of SRSs was compared with handheld spectrometers,and the R^(2)values between the two devices were 0.96 and 0.81 for NDVI and PRI,***,the experimental study of threshold criteria with respect to meteorological variables(i.e.,insolation,cloudiness,sunshine duration,and precipitation)was conducted,and sunshine duration was the most useful one for excluding distorted values of the vegetation *** data processing based on sunshine duration,the R^(2)values between the measured vegetation indices and the extracted seasonal signals of vegetation indices increased by approximately 0.002–0.004(NDVI)and 0.065–0.298(PRI)on the three crops,and the seasonal signals of vegetation indices became noticeably *** method will contribute to an agricultural monitoring system by identifying the seasonal changes in crop growth and physiological conditions.

关键词： photochemical reflectance index normalized difference vegetation index vegetation remote sensing spectral reflectance sensor

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