Geo-detector based spatio-temporal variation characteristics and driving factors analysis of NDVI in Central Asia

doi:10.6046/gtzyyg.2019.04.05

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Abstract

Vegetation is an important nexus connecting atmosphere, pedosphere, hydrosphere and biosphere. Therefore, the relationship between the temporal and spatial variation characteristics of vegetation and its driving factors is of great significance in the study of regional ecological environment changes. Based on multiple data sets such as GIMMS NDVI and CRU, the authors analyzed the temporal and spatial variation characteristics of NDVI in Central Asia from 1991 to 2015, using trend analysis and geo-detector model that included factor detection, risk detection and interaction analysis. The results show that the vegetation activities in Central Asia have remained stable and volatile on the whole in the past 25 years. In detail, NDVI in the middle and low altitude areas of the Kazakh hills has increased significantly, while the NDVI in the southwestern part of Aral Sea has been significantly reduced because of the close diffusion of salt dust in the Aral Sea basin. In addition, because of the contradiction between water resources development and utilization among Central Asian countries, the trend of NDVI in the midstream of the Syr Darya and the downstream has been reversed. The non-irrigated farmland in northern Kazakhstan has a large decline in NDVI, and the results are not significant (P≥0.1) due to the phenomenon of re-cultivation. According to the results of geo-detector model, the water factor dominates the vegetation growth pattern in Central Asia, and the temperature is negatively correlated with the NDVI change. The difference in spatial and temporal variation of NDVI between different terrains, elevations, soil types and land use types is also significant. In terms of the interaction factor, the bi-factor interaction has enhanced the interpretation of spatial distribution and temporal and spatial variation of NDVI. The synergistic effect of potential evapotranspiration and elevation on the spatial distribution of NDVI is over 64%.

Keywords NDVI Central Asia geographical detector trend analysis temporal and spatial variation

TP79

Corresponding Authors: Abuduwaili Jilili E-mail: jilil@ms.xjb.ac.cn

Issue Date: 03 December 2019

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	Wei WANG
	Samat Alim
	Abuduwaili Jilili

Cite this article:

Wei WANG,Samat Alim,Abuduwaili Jilili. Geo-detector based spatio-temporal variation characteristics and driving factors analysis of NDVI in Central Asia[J]. Remote Sensing for Land & Resources, 2019, 31(4): 32-40.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.04.05 OR https://www.gtzyyg.com/EN/Y2019/V31/I4/32

Fig.1 Map of research area

数据集	数据来源	时间分辨率	空间分辨率	数据预处理
NDVI数据集	NASA艾姆斯研究中心生态预测实验室发布的GIMMS NDVI 3g v1数据产品(1991—2015年)	15 d	8 km	最大化合成法合成逐年NDVI数据
气候数据集	英国东英吉利大学CRU小组发布的cru_ts_4.01(1991—2015年),数据包括: 气温(最高气温、最低气温、平均气温)、降水量、湿日频率、潜在蒸散发、云量和日较差	30 d	0.5° $×$ 0.5°	双线性内插法进行重采样,重采样后的空间分辨率为0.15° $×$ 0.15°
土壤类型数据集	FAO世界土壤数据库的土壤类型数据	—	1 km	按照FAO90标准对土壤类型进行重分类,共分为13类
土壤湿度数据集	欧洲中期天气预报中心表层土壤湿度再分析数据	30 d	0.25° $×$ 0.25°	双线性内插法进行重采样,重采样后的空间分辨率为0.15° $×$ 0.15°
土地覆被数据集	欧空局的ESA_CCI的土地覆被数据(1991—2015年)	1 a	300 m	对土地覆被类型重分类为8类
高程数据	NASA的STRM(shuttle Radar topographic mission)数字高程数据	—	90 m	—

Tab.1 Data source and data preprocessing

Tab.2 Types of NDVI change trend

Tab.3 Types of two-factor interaction result

Fig.2 Spatial distribution of NDVI, NDVI changes, land cover types and soil types

Fig.3 Changes of NDVI values corresponding to different terrain areas and land cover types

Fig.4 Change trend of NDVI in Central Asia during the period from 1991 to 2015

Tab.4 Results of factor detector

Fig.5 Results of risk detector and interaction detector

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