Spatio-temporal evolution, prediction and ecological security pattern construction based on kNDVI: a case study of the Loess Plateau with severe soil erosion

doi:10.13474/j.cnki.11-2246.2024.0820

Abstract

Abstract: As a crucial ecological region in China, the Loess Plateau faces serious environmental challenges. How to accurately monitor and predict vegetation changes has become the focus of current research. This paper uses the kernel normalized difference vegetation index (kNDVI), which is more suitable for studying the Loess Plateau, to conduct a new exploration of the vegetation changes in this area from 2000 to 2019. The results reveal that 2001 and 2013 are the watershed of ecological structure transformation, and the high and low vegetation types show significant changes. In addition, to understand the evolution of vegetation in the future more comprehensively, we introduce the BP neural network and the GeoSOS-FLUS model for spatio-temporal prediction. We verify the applicability of the GeoSOS-FLUS model in kNDVI spatial prediction for the first time. We also find a significant increase in low and lower vegetation types predicted for 2020—2022. It is worth noting that although the slope of kNDVI has doubled compared to the past, its peak value (August) has slightly decreased, while the values in early spring and winter have increased. Finally, we use kNDVI and NDVI to construct the ecological security pattern of the Loess Plateau, and the comparative analysis results show that the ecological security pattern by kNDVI is better than NDVI. Further results reveal that the ecology of the northwestern Loess Plateau is more fragile and more affected by human activities.

Key words: Loess Plateau, kNDVI, spatio-temporal prediction, BP neural network, GeoSOS-FLUS model, ecological security pattern

CLC Number:

P208

ZHOU Kangsheng, YANG Dehong, HAN Yang, ZHOU Peng, JIANG Yuncheng. Spatio-temporal evolution, prediction and ecological security pattern construction based on kNDVI: a case study of the Loess Plateau with severe soil erosion[J]. Bulletin of Surveying and Mapping, 2024, 0(8): 115-121.

References

[1] GAO Guangyao,FU Bojie,WANG Shuai,et al.Determining the hydrological responses to climate variability and land use/cover change in the Loess Plateau with the Budyko framework[J].The Science of the Total Environment,2016,557/558: 331-342.
[2] ZHAO Anzhou,ZHANG Anbing,LIU Jianhong,et al.Assessing the effects of drought and “Grain for Green” program on vegetation dynamics in China's Loess Plateau from 2000 to 2014[J].CATENA,2019,175: 446-455.
[3] HUETE A,DIDAN K,MIURA T,et al.Overview of the radiometric and biophysical performance of the MODIS vegetation indices[J].Remote Sensing of Environment,2002,83(1/2): 195-213.
[4] VICENTE-SERRANO S M,GOUVEIA C,CAMARERO J J,et al.Response of vegetation to drought time-scales across global land biomes[J].Proceedings of the National Academy of Sciences,2013,110(1): 52-57.
[5] MANOBAVAN M,LUCAS N,BOYD D,et al.Forecasting the interannual trends in terrestrial vegetation dynamics using time series modelling techniques[EB/OL].[2023-10-11].https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=2AEA4C43BAA572326E215D1BA8C148D3?oi=10.1.1.496.8316&rep=rep1&type=pdf.
[6] LIU Huiyu,ZHANG Mingyang,LIN Zhenshan,et al.Spatial heterogeneity of the relationship between vegetation dynamics and climate change and their driving forces at multiple time scales in Southwest China[J].Agricultural and Forest Meteorology,2018,256/257: 10-21.
[7] STEPCHENKO A,CHIZHOV J.Applying Markov chains for NDVI time series forecasting of Latvian regions[J].Information Technology and Management Science,2015,18(1): 57-61.
[8] ZHOU M.An interval fuzzy chance-constrained programming model for sustainable urban land-use planning and land use policy analysis[J].Land Use Policy,2015,42: 479-491.
[9] CHEN Jianyang,YIN Haiwei,KONG Fanhua,et al.Study on the complex eco-network development around Taihu Lake,China[J].Acta Ecologica Sinica,2015,35(9): 3113-3123.
[10] ZHANG Xiaohui,GUO Xiaoyan,JING Gu,et al.Research of improved shortest path algorithm in campus GIS[J].The Open Cybernetics & Systemics Journal,2015,9(1): 1060-1063.
[11] XIANG Hengxing,ZHANG Jian,MAO Dehua,et al.Optimizing ecological security patterns considering zonal vegetation distribution for regional sustainability[J].Ecological Engineering,2023,194: 107055.
[12] GUO Rui,LI Fengmin,HE Wenying,et al.Spatial and temporal variability of annual precipitation during 1958—2007 in Loess Plateau,China[C]//Proceedings of 2011 International Conference on Computer and Computing Technologies in Agriculture.Berlin: Springer,2011: 551-560.
[13] HE Y,YAO W,ZHANG Y,et al.Spatial variability of vegetation restoration on the Loess Plateau based on MODIS/NDVI[J].Science of Soil & Water Conservation,2015,13(2):63-69.
[14] XIAO Yi,WANG Rui,WANG Fan,et al.Investigation on spatial and temporal variation of coupling coordination between socioeconomic and ecological environment: a case study of the Loess Plateau,China[J].Ecological Indicators,2022,136: 108667.
[15] OSTAD-ALI-ASKARI K,GHORBANIZADEH KHARAZI H,SHAYANNEJAD M,et al.Effect of climate change on precipitation patterns in an arid region using GCM models: case study of isfahan-borkhar plain[J].Natural Hazards Review,2020,21(2): 04020006.
[16] CLELAND E E,CHUINE I,MENZEL A,et al.Shifting plant phenology in response to global change[J].Trends in Ecology & Evolution,2007,22(7): 357-365.
[17] ZUO Lijun,ZHANG Zengxiang,CARLSON K M,et al.Progress towards sustainable intensification in China challenged by land-use change[J].Nature Sustainability,2018,1: 304-313.
[18] HAN Yang,LIN Yilin,ZHOU Peng,et al.Assessing dynamic changes,driving mechanisms and predictions of multisource vegetation remote sensing products in Chinese regions[J].Applied Sciences,2023,13(9): 5229.
[19] XU Xiaojuan,LIU Jing,JIAO Fusheng,et al.Ecological engineering induced carbon sinks shifting from decreasing to increasing during 1981—2019 in China[J].The Science of the Total Environment,2023,864: 161037.
[20] MA Bingran,ZENG Weihua,HU Guanzheng,et al.Normalized difference vegetation index prediction based on the delta downscaling method and back-propagation artificial neural network under climate change in the Sanjiangyuan Region,China[J].Ecological Informatics,2022,72: 101883.
[21] GONG Xinglong,DU Shuping,LI Fengyu,et al.Study of mesoscale NDVI prediction models in arid and semiarid regions of China under changing environments[J].Ecological Indicators,2021,131: 108198.
[22] GAO Yan,FENG Qi,LIU Wei,et al.Changes of daily climate extremes in Loess Plateau during 1960—2013[J].Quaternary International,2015,371: 5-21.
[23] ZHOU Peng,ZHAO Dongxue,LIU Xiao,et al.Dynamic change of vegetation index and its influencing factors in Alxa league in the arid area[J].Frontiers in Ecology and Evolution,2022,10: 922739.
[24] PENG Wenjia,DONG Yilin,TIAN Meihui,et al.City-level greenness exposure is associated with COVID-19 incidence in China[J].Environmental Research,2022,209: 112871.
[25] WANG Jian,ZHAO Junsan,ZHOU Peng,et al.Study on the spatial and temporal evolution of NDVI and its driving mechanism based on geodetector and Hurst indexes: a case study of the Tibet Autonomous Region[J].Sustainability,2023,15(7): 5981.
[26] THENKABAIL P S,SMITH R B,DE PAUW E.Hyperspectral vegetation indices and their relationships with agricultural crop characteristics[J].Remote Sensing of Environment,2000,71(2): 158-182.