Spatiotemporal variations of NDVI in terrestrial ecosystems in China from 1982-2012

Ke, 刘可 Liu; Lingtong, 杜灵通 Du; Jing, 侯静 Hou; Yue, 胡悦 Hu; Yuguo, 朱玉果 Zhu; Fei, 宫菲 Gong

doi:10.5846/stxb201701150122

Cited by 4 publications

(3 citation statements)

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“…NDVI can detect the basic conditions of vegetation, including vegetation growth status and vegetation cover, and can be constructed as a time series curve to show the changes in vegetation in time and space [31][32][33][34][35]. The phenological characteristics and related information can be extracted.…”

Section: Analysis Of the Intra-annual Variation Characteristics Of Nd...mentioning

confidence: 99%

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data

Tian

Cao

et al. 2023

Sensors

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To meet the challenge of food security, it is necessary to obtain information about rice fields accurately, quickly and conveniently. In this study, based on the analysis of existing rice fields extraction methods and the characteristics of intra-annual variation of normalized difference vegetation index (NDVI) in the different types of ground features, the NDVI difference method is used to extract rice fields using Sentinel data based on the unique feature of rice fields having large differences in vegetation between the pre-harvest and post-harvest periods. Firstly, partial correlation analysis is used to study the influencing factors of the rice harvesting period, and a simulation model of the rice harvesting period is constructed by multiple regression analysis with data from 32 sample points. Sentinel data of the pre-harvest and post-harvest periods of rice fields are determined based on the selected rice harvesting period. The NDVI values of the rice fields are calculated for both the pre-harvest and post-harvest periods, and 33 samples of the rice fields are selected from the high-resolution image. The threshold value for rice field extraction is determined through statistical analysis of the NDVI difference in the sample area. This threshold was then utilized to extract the initial extent of rice fields. Secondly, to address the phenomenon of the “water edge effect” in the initial data, the water extraction method based on the normalized difference water index (NDWI) is used to remove the pixels of water edges. Finally, the extraction results are verified and analyzed for accuracy. The study results show that: (1) The rice harvesting period is significantly correlated with altitude and latitude, with coefficients of 0.978 and 0.922, respectively, and the simulation model of the harvesting period can effectively determine the best period of remote sensing images needed to extract rice fields; (2) The NDVI difference method based on sentinel data for rice fields extraction is excellent; (3) The mixed pixels have a large impact on the accuracy of rice fields extraction, due to the water edge effect. Combining NDWI can effectively reduce the water edge effect and significantly improve the accuracy of rice field extraction.

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Section: Analysis Of the Intra-annual Variation Characteristics Of Nd...mentioning

confidence: 99%

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data

Tian

Cao

et al. 2023

Sensors

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“…Although it is not a "real" gap in the strictest sense, the Intergovernmental Panel on Climate Change (IPCC) has called it the "global warming gap" [18,19], and the change in the relationship between vegetation phenology and climatic factors during the warming gap will also be the focus of attention. Liu Ke et al (2018) used GIMMS NDVI3g data [20] to extract the vegetation phenology of the desert steppe transition zone in the northern hemisphere and established that the EOS showed an advancement trend at a rate of 0.41 d/a from 1998 to 2012, which shortened the vegetation growing season length (GSL). Fu et al (2016) [21] believe that the long-term linear trend might mask short-term phenological changes, and their research in 2015 also confirmed that the significant response of the seven mid-latitude tree species in Europe to climate warming decreased during 1999-2013 [22].…”

Section: Introductionmentioning

confidence: 99%

Phenological Changes and Their Influencing Factors under the Joint Action of Water and Temperature in Northeast Asia

Wang,

Meng,

Zhu

et al. 2023

Remote Sensing

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Phenology is an important indicator for how plants will respond to environmental changes and is closely related to biomass production. Due to global warming and the emergence of intermittent warming, vegetation in northeast Asia is undergoing drastic changes. Understanding vegetation phenology and its response to climate change is of great significance to understanding the changes in the sustainable development of ecosystems. Based on Global Inventory Modelling and Mapping Studies (GIMMS), normalized difference vegetation index (NDVI)3g data, and the mean value of phenological results extracted by five methods, combined with climatic data, this study analyzed the temporal changes in phenology and the responses to climatic factors of five vegetation types of broad-leaved, needle-leaf, mixed forests, grassland, and cultivated land in northeast Asia over 33 years (1982–2014). The results showed that, during the intermittent warming period (1999–2014), the start of the growing season (SOS) advancement (Julian days) trend of all vegetation types decreased. During 1982–2014, the average temperature sensitivity of the SOS was 1.5 d/°C. The correlation between the SOS and the pre-season temperature is significant in northeast Asia, while the correlation between the EOS and the pre-season precipitation is greater than that between temperature and radiation. The impact of radiation changes on the SOS is relatively small.

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“…Under the combined influences of ongoing climate change and increasing human activities, terrestrial vegetation cover in China has experienced significant and complex changes [27]. In order to investigate the patterns of vegetation changes in different terrestrial ecosystems in response to climate factors [28], several studies have examined the spatial and temporal distribution changes of NDVI. For instance, Liu et al suggested that moisture was the limiting factor affecting vegetation development within a certain range of temperature variation, according to GIMMS NDVI and climate variables (temperature and precipitation) [29].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Variation Characteristics of NDVI of Forest Ecosystem in China from 1998 to 2015 and its Driving Force of Climate Factors

Bai,

Guo,

Zhang

et al. 2023

Pol. J. Environ. Stud.

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In recent years, research on the relationship between the Normalized Difference Vegetation Index (NDVI) and climate factors has gained significant interest. This study examines vegetation changes in different forest ecosystems in China using the NDVI dataset from 1998 to 2015 and corresponding climate factor data (temperature and precipitation). Trend line analysis and correlation analysis methods are employed to investigate the spatial and temporal change characteristics of NDVI in five representative Chinese forest ecosystems. The driving forces of the two climate factors are simultaneously analyzed.The results indicate that: (1) Over the 18-year study period, the annual maximum NDVI of China's forest ecosystems exhibited a significant increasing trend (P<0.01), with an average annual increase of 0.0043; (2) Vegetation cover changes demonstrated an overall upward trend, with the NDVI of 84.87% of the forest vegetation in the study area increasing. However, a minimal decrease was observed in 0.94% of the study area; (3) Vegetation coverage in China's forest ecosystems has increased over the past 18 years, exhibiting a greening trend. Across the entire study area, the correlations between NDVI and temperature and precipitation were insignificant.

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Spatiotemporal variations of NDVI in terrestrial ecosystems in China from 1982-2012

Cited by 4 publications

References 0 publications

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data

Research on Rice Fields Extraction by NDVI Difference Method Based on Sentinel Data

Phenological Changes and Their Influencing Factors under the Joint Action of Water and Temperature in Northeast Asia

Temporal and Spatial Variation Characteristics of NDVI of Forest Ecosystem in China from 1998 to 2015 and its Driving Force of Climate Factors

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