Start of vegetation growing season on the Tibetan Plateau inferred from multiple methods based on GIMMS and SPOT NDVI data

Ding, M. D.; Li, Lanhui; Zhang, Yili; Sun, Xiaomin; Liu, Linshan; Gao, Jungang; Wang, Zhaofeng; Li, Yingnian

doi:10.1007/s11442-015-1158-y

Cited by 51 publications

(30 citation statements)

References 51 publications

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“…White et al [28] found that individual methods differed in average SOS by ±60 days, and the spatial phenological patterns derived from different methods often differed among ecoregions by comparing 10 commonly used methods for estimating the SOS based on the Advanced Very High Resolution Radiometer (AVHRR) NDVI in North America. Such differences in phenology among methods were also found in other previous studies [33][34][35]. In our study, the differences between phenology derived from C5 and C6 NDVI varied among methods (Tables 2 and 3, Figures 6-9).…”

Section: Discussionsupporting

confidence: 89%

Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau

Zheng

Zhu

2017

Remote Sensing

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Vegetation phenology is considered a sensitive indicator of climate change, which controls carbon, nitrogen, and water cycles within terrestrial ecosystems. The Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) is an important moderate resolution remote sensing data for monitoring vegetation phenology. However, Terra MODIS Collection 5 (C5) vegetation index products were identified to be affected by sensor degradation, which has been addressed in the recently released MODIS Collection 6 (C6) vegetation index products. In order to compare the difference between MODIS C5 and C6 NDVI in monitoring vegetation phenology, the start and end of growing season (SOS and EOS) of the alpine grassland on the Tibetan Plateau (TP) were extracted using four common methods. Then, the C5 and C6 NDVI-derived SOS (SOS C5 and SOS C6 ) and EOS (EOS C5 and EOS C6 ) were compared with ground-observed phenology data. Results showed that the multi-year average growing season NDVIs of C6 were lower than those of C5 in most areas, while the inter-annual variation patterns of regional average SOS C5 and SOS C6 (EOS C5 and EOS C6 ) were consistent. However, large spatial differences in phenological trends were found between C5 and C6 NDVI products. From C5 to C6, pixels with a SOS (EOS) trend shifting from significant to insignificant or from insignificant to significant accounted for at least 14.58% (9.07%) of the total pixels. SOS C5 was more consistent than SOS C6 with the ground-observed green-up dates. C5 NDVI may be more appropriate for monitoring SOS than C6 NDVI in the study region, but more ground-observed phenology records are needed to confirm it due to only four observational sites in this study. However, large differences and poor correlations existed between EOS C5 (EOS C6 ) and the ground-observed beginning of leaf coloring. To further evaluate the uncertainty of MODIS C5 and C6 NDVI in monitoring vegetation phenology, higher resolution near-surface remote sensing data and corresponding validation methods should be applied.

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Section: Discussionsupporting

confidence: 89%

Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau

Zheng

Zhu

2017

Remote Sensing

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“…Although our results were consistent with those studies, our results could not fully capture cropland phenology, which is characterized by a second growing season in some areas of China. However, when we compared our results with those of Xiao et al [52], who based their conclusions on field observations, the trends in both SOS and EOS determined by the present study were the same as those identified by Xiao et al [52] for most stations. This suggests that even though some errors might be associated with remote-sensing data, the trend we inferred in crop phenology is still reliable.…”

Section: Spatial Distribution In Phenology Parameterssupporting

confidence: 84%

Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014

Luo

2017

Remote Sensing

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Abstract:Land surface phenology is a highly sensitive and simple indicator of vegetation dynamics and climate change. However, few studies on spatiotemporal distribution patterns and trends in land surface phenology across different climate and vegetation types in China have been conducted since 2000, a period during which China has experienced remarkably strong El Niño events. In addition, even fewer studies have focused on changes of the end of season (EOS) and length of season (LOS) despite their importance. In this study, we used four methods to reconstruct Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) dataset and chose the best smoothing result to estimate land surface phenology. Then, the phenophase trends were analyzed via the Mann-Kendall method. We aimed to assess whether trends in land surface phenology have continued since 2000 in China at both national and regional levels. We also sought to determine whether trends in land surface phenology in subtropical or high altitude areas are the same as those observed in high latitude areas and whether those trends are uniform among different vegetation types. The result indicated that the start of season (SOS) was progressively delayed with increasing latitude and altitude. In contrast, EOS exhibited an opposite trend in its spatial distribution, and LOS showed clear spatial patterns over this region that decreased from south to north and from east to west at a national scale. The trend of SOS was advanced at a national level, while the trend in Southern China and the Tibetan Plateau was opposite to that in Northern China. The transaction zone of the SOS within Northern China and Southern China occurred approximately between 31.4 • N and 35.2 • N. The trend in EOS and LOS were delayed and extended, respectively, at both national and regional levels except that of LOS in the Tibetan Plateau, which was shortened by delayed SOS onset more than by delayed EOS onset. The absolute magnitude of SOS was decreased after 2000 compared with previous studies, and the phenophase trends are species specific.

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“…The tree line ecotone ranges between 3000-4300 m altitude [85] and also has a good NDVI distribution. It can be said that the tree line area is experiencing good ecological restoration with more seedlings and saplings [86].The NDVI distribution and trends in different ecological zones indicate a good ecological status with a positive level of photosynthetic activities, biomass, productivity and ecosystem balance [19,23,87,88], as well as a lengthening of the growing season [89][90][91].…”

Section: Discussionmentioning

confidence: 99%

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

Baniya

Tang

Huang

et al. 2018

Forests

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Abstract:Nepal is a country of contrast, with varying altitude, climate and vegetation from the top of high mountains to the tropical forest in low lands. The terrestrial vegetation has rapidly been altered by climate change in Nepal. The spatial and temporal evolution of vegetation and its linkage to climatic variables were analyzed using the Normalized Difference Vegetation Index (NDVI) obtained from Advanced Very High Resolution Radiometer (AVHRR) sensors. A linear regression model and Sen's slope method were used to estimate NDVI trends and the Pearson correlation between NDVI and climatic variable, i.e., temperature and precipitation were calculated to identify the role of climate in vegetation changes. The carbon dynamics were also measured using a biomass carbon density estimation model. Results showed that NDVI experienced an overall increasing trend in Nepal from 1982-2015. The NDVI significantly increased at the rate of 0.0008 year −1 (p < 0.05) with seasonal variation of 0.0004 year −1 , p > 0.05; 0.0007 year −1 , p < 0.05; 0.0008 year −1 , p < 0.05 and 0.0007 year −1 , p > 0.05 in winter, pre-monsoon, monsoon and post-monsoon seasons, respectively. The NDVI relative change ratio (RCR) was 6.29% during last 34 years in Nepal. The correlation between NDVI and temperature was significantly positive (r = 0.36, p = 0.03), but there was a negative correlation with precipitation (r = −0.21, p = 0.28). Altogether, 82.20% of the study areas showed a positive correlation with temperature in which 34.97% was significant and 69.23% of the area had a negative correlation (16.35% significant, p < 0.05) with precipitation. In addition, NDVI-based carbon estimation showed that Nepal's forest total carbon stock is 685.45 × 10 6 t C (i.e., an average of 115.392 t C/ha) with an annual carbon sequestration rate of 0.10 t C/ha from 1982-2015. The results suggest that NDVI variation is more sensitive to temperature than precipitation and it is valuable to measure carbon dynamics in Nepal.

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Start of vegetation growing season on the Tibetan Plateau inferred from multiple methods based on GIMMS and SPOT NDVI data

Cited by 51 publications

References 51 publications

Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau

Uncertainty of Remote Sensing Data in Monitoring Vegetation Phenology: A Comparison of MODIS C5 and C6 Vegetation Index Products on the Tibetan Plateau

Spatiotemporal Variability of Land Surface Phenology in China from 2001–2014

Spatial and Temporal Variation of NDVI in Response to Climate Change and the Implication for Carbon Dynamics in Nepal

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