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The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai-Tibet Plateau

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Abstract

The trend estimate of vegetation change is essential to understand the change rule of the ecosystem. Previous studies were mainly focused on quantifying trends or analyzing their spatial distribution characteristics. Nevertheless, the uncertainties of trend estimates caused by spatiotemporal scale effects have rarely been studied. In response to this challenge, this study aims to investigate spatiotemporal scale effects on trend estimates using Moderate-Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) and Gross Primary Productivity (GPP) products from 2001 to 2019 in the Qinghai-Tibet Plateau (QTP). Moreover, the possible influencing factors on spatiotemporal scale effect, including spatial heterogeneity, topography, and vegetation types, were explored. The results indicate that the spatial scale effect depends more on the dataset with a coarser spatial resolution, and temporal scale effects depend on the time span of datasets. Unexpectedly, the trend estimates on the 8-day and yearly scale are much closer than that on the monthly scale. In addition, in areas with low spatial heterogeneity, low topography variability, and sparse vegetation, the spatiotemporal scale effect can be ignored, and vice versa. The results in this study help deepen the consciousness and understanding of spatiotemporal scale effects on trend detection.

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References

  • Alcaraz-Segura D O, Chuvieco E, Epstein H E et al., 2010. Debating the greening vs. browning of the North American boreal forest: Differences between satellite datasets. Global Change Biology, 16(2): 760–770.

    Article  Google Scholar 

  • Anderson K, Fawcett D, Cugulliere A et al., 2020. Vegetation expansion in the subnival Hindu Kush Himalaya. Global Change Biology, 26(3): 1608–1625.

    Article  Google Scholar 

  • Beck H E, McVicar T R, van Dijk A I et al., 2011. Global evaluation of four AVHRR–NDVI data sets: Intercomparison and assessment against Landsat imagery. Remote Sensing of Environment, 115(10): 2547–2563.

    Article  Google Scholar 

  • Chen K, Li S, Zhou Q et al., 2007. Multi-scale study on climate change for recent 50 years in Dari county in the source regions of the Yangtze and Yellow rivers. Geographical Research, 26(3): 526. (in Chinese)

    Google Scholar 

  • Chen X, Wang D, Chen J et al., 2018. The mixed pixel effect in land surface phenology: A simulation study. Remote Sensing of Environment, 211: 338–344.

    Article  Google Scholar 

  • Cheng C, Li B, Ma T, 2003. The application of very high resolution satellite image in urban vegetation cover investigation: A case study of Xiamen City. Journal of Geographical Sciences, 13(2): 265–270.

    Article  Google Scholar 

  • De Beurs K M, Wright C K, Henebry G M, 2009. Dual scale trend analysis for evaluating climatic and anthropogenic effects on the vegetated land surface in Russia and Kazakhstan. Environmental Research Letters, 4(4): 045012.

    Article  Google Scholar 

  • Fagua J C, Ramsey R D, 2019. Comparing the accuracy of MODIS data products for vegetation detection between two environmentally dissimilar ecoregions: The Chocó-Darien of South America and the Great Basin of North America. GIScience & Remote Sensing, 56(7): 1046–1064.

    Article  Google Scholar 

  • Fensholt R, Proud S R, 2012. Evaluation of earth observation based global long term vegetation trends: Comparing GIMMS and MODIS global NDVI time series. Remote Sensing of Environment, 119: 131–147.

    Article  Google Scholar 

  • Fensholt R, Rasmussen K, Nielsen T T et al., 2009. Evaluation of earth observation based long term vegetation trends: Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113(9): 1886–1898.

    Article  Google Scholar 

  • Ghazaryan G, 2015. Analysis of temporal and spatial variations of forest. A case of study in northeastern Armenia 9 [D].

  • Gorelick N, Hancher M, Dixon M et al., 2017. Remote Sensing of Environment, 202: 18–27.

    Article  Google Scholar 

  • Hu Z, Islam S, 1997. A framework for analyzing and designing scale invariant remote sensing algorithms. IEEE Transactions on Geoscience and Remote Sensing, 35(3): 747–755.

    Article  Google Scholar 

  • Ichii K, Kawabata A, Yamaguchi Y, 2002. Global correlation analysis for NDVI and climatic variables and NDVI trends: 1982–1990. International Journal of Remote Sensing, 23(18): 3873–3878.

    Article  Google Scholar 

  • Jiang Z, Huete A R, Chen J et al., 2006. Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 101(3): 366–378.

    Article  Google Scholar 

  • Li P, Hu Z, Liu Y, 2020. Shift in the trend of browning in southwestern Tibetan Plateau in the past two decades. Agricultural and Forest Meteorology, 287: 107950.

    Article  Google Scholar 

  • Liang D, Zuo Y, Huang L et al., 2015. Evaluation of the consistency of MODIS Land Cover Product (MCD12Q1) based on Chinese 30 m GlobeLand30 datasets: A case study in Anhui province, China. ISPRS International Journal of Geo-Information, 4(4): 2519–2541.

    Article  Google Scholar 

  • Liu L, Liu L, Liang L et al., 2014. Effects of elevation on spring phenological sensitivity to temperature in Tibetan Plateau grasslands. Chinese Science Bulletin, 59(34): 4856–4863.

    Article  Google Scholar 

  • Liu S, Cheng F, Dong S et al., 2017. Spatiotemporal dynamics of grassland aboveground biomass on the Qinghai-Tibet Plateau based on validated MODIS NDVI. Scientific Reports, 7(1): 1–0.

    Google Scholar 

  • Liu W, Chen S, Qin X et al., 2012. Storage, patterns, and control of soil organic carbon and nitrogen in the northeastern margin of the Qinghai-Tibetan Plateau. Environmental Research Letters, 7(3): 035401.

    Article  Google Scholar 

  • Liu X, Zhang J, Zhu X et al., 2014. Spatiotemporal changes in vegetation coverage and its driving factors in the Three-River Headwaters Region during 2000–2011. Journal of Geographical Sciences, 24(2): 288–302.

    Article  Google Scholar 

  • Monteith J L, 1972. Solar radiation and productivity in tropical ecosystems. Journal of Applied Ecology, 9(3): 747–766.

    Article  Google Scholar 

  • Mu S, Yang H, Li J et al., 2013. Spatio-temporal dynamics of vegetation coverage and its relationship with climate factors in Inner Mongolia, China. Journal of Geographical Sciences, 23(2): 231–246.

    Article  Google Scholar 

  • Tian F, Fensholt R, Verbesselt J et al., 2015. Evaluating temporal consistency of long-term global NDVI datasets for trend analysis. Remote Sensing of Environment, 163: 326–340.

    Article  Google Scholar 

  • Wen J, You D, Han Y et al., 2022. Estimating surface BRDF/Albedo over rugged terrain using an Extended Multisensor Combined BRDF Inversion (EMCBI) Model. IEEE Geoscience and Remote Sensing Letters, 19: 1–5.

    Google Scholar 

  • Wu H, Li Z L, 2009. Scale issues in remote sensing: A review on analysis, processing and modeling. Sensors, 9(3): 1768–1793.

    Article  Google Scholar 

  • Wu X, Ma D, Wang J et al., 2021. Temporal scale effects on trend estimates for solar radiation, thermal and snow conditions, and their feedbacks: The case from China. Theoretical and Applied Climatology, 146(3): 869–882.

    Article  Google Scholar 

  • Xia J, Niu S, Ciais P et al., 2015. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proceedings of the National Academy of Sciences, 112(9): 2788–2793.

    Article  Google Scholar 

  • Yu Z, Wang J, Liu S et al., 2013. Inconsistent NDVI trends from AVHRR, MODIS, and SPOT sensors in the Tibetan Plateau. In: 2013 Second International Conference on Agro-Geoinformatics (Agro-Geoinformatics), 97–101.

  • Yuan Q, Yuan Q, Ren P, 2021. Coupled effect of climate change and human activities on th restoration/degradation of the Qinghai-Tibet Plateau grassland. Journal of Geographical Sciences, 31(9): 1299–1327.

    Article  Google Scholar 

  • Zhang Y, Xu G, Li P et al., 2019. Vegetation change and its relationship with climate factors and elevation on the Tibetan Plateau. International Journal of Environmental Research and Public Health, 16(23): 4709.

    Article  Google Scholar 

  • Zhao H, Liu S, Dong S et al., 2015. Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau. The Rangeland Journal, 37(1): 77–87.

    Article  Google Scholar 

  • Zhao J, Chen X, Bao A et al., 2009. A method for choice of optimum scale on land use monitoring in Tarim River Basin. Journal of Geographical Sciences, 19(3): 340–350.

    Article  Google Scholar 

  • Zhu X, Pei Y, Zheng Z et al., 2018. Underestimates of grassland gross primary production in MODIS standard products. Remote Sensing, 10(11): 1771.

    Article  Google Scholar 

Download references

Funding

The Second Tibetan Plateau Scientific Expedition and Research Program (STEP), No.2019QZKK0605; National Natural Science Foundation of China, No.42071296

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Correspondence to Xiaodan Wu.

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Author

Ma Dujuan (1994–), Master Candidate, specialized in the scale effect of quantitative remote sensing products and vegetation remote sensing.

Corresponding author

Wu Xiaodan (1987–), PhD and Professor, specialized in the validation of quantitative remote sensing products over heterogeneous land surfaces.

Data availability statement

MODIS Version 6 NDVI products, including MOD13Q1, MOD13A1, and MOD13A2, were obtained from the Google Earth Engine (https://developers.google.com/earth-engine/data-sets/catalog/modis). MODIS GPP product (i.e., MOD17A2H v006), the land cover types products (i.e., MCD12Q1 V6), and the DEM products (SRTM) were also obtained from the Google Earth Engine via the link https://developers.google.com/earthengine/datasets/catal-og/MODIS_006_MOD17A2H, https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1, and https://developers.google.com/earth-engine/datasets/catalog/CGIAR_SRTM90_V4, respectively. MOD13C1 were downloaded from the Land Processes Distributed Active Arcchive Center (LP DAAC) via the link https://lpdaac.usgs.gov/products/mod13c1v006/.

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Ma, D., Wu, X., Wang, J. et al. The spatiotemporal scale effect on vegetation interannual trend estimates based on satellite products over Qinghai-Tibet Plateau. J. Geogr. Sci. 33, 924–944 (2023). https://doi.org/10.1007/s11442-023-2113-y

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  • DOI: https://doi.org/10.1007/s11442-023-2113-y

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