MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland

Gong, Zhe; Kawamura, Kensuke; Ishikawa, Naoto; Goto, Masakazu; Wulan, Tuya; Alateng, Dalai; Yin, Ting; Ito, Yukihiro

doi:10.5194/se-6-1185-2015

Cited by 104 publications

(89 citation statements)

References 52 publications

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“…However, by comparing the multi-year mean SOS/EOS of 2000-2004 and that of 2011-2015, we still found a significant advance of SOS in typical steppe and desert steppe, and a significant advance of EOS in desert steppe, which have also been identified by other studies with different data and methods [62,74]. SOS experienced a larger shift than EOS.…”

Section: What Factors Contributed To Sos/eos Trendssupporting

confidence: 79%

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Ren

Peichl

et al. 2017

Remote Sensing

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Vegetation phenology in temperate grasslands is highly sensitive to climate change. However, it is still unclear how the timing of vegetation phenology events (especially for autumn phenology) is altered in response to climate change across different grassland types. In this study, we investigated variations of the growing season start (SOS) and end (EOS), derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016), for meadow steppe, typical steppe, and desert steppe in the Inner Mongolian grassland of Northern China. Using gridded climate data (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015), we further analyzed correlations between SOS/EOS and pre-season average air temperature and total precipitation (defined as 90-day period prior to SOS/EOS, i.e., pre-SOS/EOS) in each grid. The results showed that both SOS and EOS occurred later in desert steppe (day of year (doy) 114 and 312) than in meadow steppe (doy 109 and 305) and typical steppe (doy 111 and 307); namely, desert steppe has a relatively late growing season than meadow steppe and typical steppe. For all three grasslands, SOS was mainly controlled by pre-SOS precipitation with the sensitivity being largest in desert steppe. EOS was closely connected with pre-EOS air temperature in meadow steppe and typical steppe, but more closely related to pre-EOS precipitation in desert steppe. During 2000-2015, SOS in typical steppe and desert steppe has significantly advanced by 2.2 days and 10.6 days due to a significant increase of pre-SOS precipitation. In addition, EOS of desert steppe has also significantly advanced by 6.8 days, likely as a result from the combined effects of elevated preseason temperature and precipitation. Our study highlights the diverse responses in the timing of spring and autumn phenology to preceding temperature and precipitation in different grassland types. Results from this study can help to guide grazing systems and to develop policy frameworks for grasslands protection.

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Section: What Factors Contributed To Sos/eos Trendssupporting

confidence: 79%

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Ren

Peichl

et al. 2017

Remote Sensing

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“…Other studies also confirm that the LST and NDVI changes are due to changes of the vegetation cover and residential areas. Studies by Gong et al (2015) and Valor and Caselles (1996) showed that NDVI and LST values show trends in vegetation cover and phenology changes. A study by Sandra et al (2015) showed that land degradation and regeneration areas can be investigated using NDVI satellite data.…”

Section: Discussionmentioning

confidence: 99%

“…The Landsat Thematic Mapper (TM) and ETM+ (Enhanced Thematic Mapper Plus) sensor images can be used to study relationship between surface temperature and land use types using thermal quantitative indicators (Weng, 2003;Streutker, 2003). Land surface temperature can be retrieved using data from NOAA-11 (National Oceanic and Atmospheric Administration) AVHRR (Advanced Very High Resolution Radiometer) channels 4 and 5 by emissivity calculation (France and Cracknell, 1994).…”

Section: S Zareie Et Al: Using Landsat Thematic Mapper (Tm) Sensormentioning

confidence: 99%

“…Changes in land use and land cover can be evaluated by analysis of the vegetation cover and NDVI trends. The vegetation phenology was detected using Terra MODIS NDVI data by Gong et al (2015). Land use changes, vegetation cover and soil moisture have strong effects on the land surface temperature; therefore, surface temperature can be applied to study land use changes, urbanization and desertification.…”

Section: S Zareie Et Al: Using Landsat Thematic Mapper (Tm) Sensormentioning

confidence: 99%

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Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

Zareie¹,

Khosravi²,

Nasiri³

et al. 2016

Solid Earth

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Abstract. Land surface temperature (LST) is one of the key parameters in the physics of land surface processes from local to global scales, and it is one of the indicators of environmental quality. Evaluation of the surface temperature distribution and its relation to existing land use types are very important to the investigation of the urban microclimate. In arid and semi-arid regions, understanding the role of land use changes in the formation of urban heat islands is necessary for urban planning to control or reduce surface temperature. The internal factors and environmental conditions of Yazd city have important roles in the formation of special thermal conditions in Iran. In this paper, we used the temperature-emissivity separation (TES) algorithm for LST retrieving from the TIRS (Thermal Infrared Sensor) data of the Landsat Thematic Mapper (TM). The root mean square error (RMSE) and coefficient of determination (R 2 ) were used for validation of retrieved LST values. The RMSE of 0.9 and 0.87 • C and R 2 of 0.98 and 0.99 were obtained for the 1998 and 2009 images, respectively. Land use types for the city of Yazd were identified and relationships between land use types, land surface temperature and normalized difference vegetation index (NDVI) were analyzed. The Kappa coefficient and overall accuracy were calculated for accuracy assessment of land use classification. The Kappa coefficient values are 0.96 and 0.95 and the overall accuracy values are 0.97 and 0.95 for the 1998 and 2009 classified images, respectively. The results showed an increase of 1.45 • C in the average surface temperature. The results of this study showed that optical and thermal remote sensing methodologies can be used to research urban environmental parameters. Finally, it was found that special thermal conditions in Yazd were formed by land use changes. Increasing the area of asphalt roads, residential, commercial and industrial land use types and decreasing the area of the parks, green spaces and fallow lands in Yazd caused a rise in surface temperature during the 11-year period.

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“…A total of 368 NDVI data were first clipped and subsequently re-projected to an Albers equal-area projection by standard output projection parameters with the WGS84 datum using the MODIS re-projection Tool (MRT) downloaded from the National Aeronautics and Space Administration (NASA) website. The TIMESAT software was used to remove the noise in NDVI time series datasets by applying a Savitzky-Golay filter [27] which provided a simplified least-squares-fit convolution for smoothing and computing derivatives of a set of consecutive values [28]. Finally, the vegetation growing season (i.e., between April and October) was divided into spring (April and May), summer (between June and August), and autumn (September and October) [29].…”

Section: Data Sourcesmentioning

confidence: 99%

The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

et al. 2017

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Vegetation dynamics in relation to climatic changes and anthropogenic activities is critical for terrestrial ecosystem management. The objective of this study was to investigate spatiotemporal change of vegetation and their driving forces during growing seasons (between April and October and including the spring, summer and autumn) in the Tumen River Basin (TRB) using Normalized Difference Vegetation Index (NDVI) and climate data spanning from 2000 to 2015. A linear regression, Pearson correlation coefficients and the residual trend (RESTREND) was applied for this study. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years. Our results demonstrate that vegetation increased during different periods of the growing season in most of the areas of the TRB over 16 years; those in growing season (spring, summer, and autumn) were characterized by the increase in rates by 0.0012/year, 0.0022/year, 0.0011/year, and 0.0019/year, respectively. Forested regions are characterized by the largest increase (0.0021/year) in NDVI compared with other vegetation types across the entire study area. The trends in NDVI across the study area were influenced by both climatic variations and human disturbances. The human activities such as reforestation and agricultural practices are the primary driver, greater than climatic factors, during growing season, including summer and autumn. Temperature and precipitation has had a significant influence on NDVI in a limited area (temp = 0.86%, p < 0.05 and precipitation = 1.93%, p < 0.05) during growing season. The significant role of precipitation on NDVI change throughout growing season and the summer is larger than that of temperature across the TRB, although the influence of the latter becomes most significant during the spring and autumn. The RESTREND method shows that human activity during the growing season, including the spring, summer, and autumn, have led to enhancements in NDVI across more than 70% of the TRB over the last 16 years, with the most significant improvements seen in forested land and farmland. At the same time, a significant reduction in residual (i.e., degraded areas) NDVI values for different growing seasons had characterized farmland and urban land at low altitudes. This study provides important background information regarding the influence of human activities on land degradation and provides a scientific foundation for the development of ecological restoration policies within the TRB. We found that the RESTREND method can be used to detect human drivers of vegetation in the regions with semi-humid and humid monsoon, where the significant correlation between NDVI and climatic factors exists.

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MODIS normalized difference vegetation index (NDVI) and vegetation phenology dynamics in the Inner Mongolia grassland

Cited by 104 publications

References 52 publications

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Diverse Responses of Vegetation Phenology to Climate Change in Different Grasslands in Inner Mongolia during 2000–2016

Using Landsat Thematic Mapper (TM) sensor to detect change in land surface temperature in relation to land use change in Yazd, Iran

The Driving Force Analysis of NDVI Dynamics in the Trans-Boundary Tumen River Basin between 2000 and 2015

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