The Spatio-Temporal Evolution Characteristics of the Vegetation NDVI in the Northern Slope of the Tianshan Mountains at Different Spatial Scales

The purposes of this study are to reveal the spatial pattern and dynamic changes of NDVI in the northern slope of the Tianshan Mountains for an extended period and to explore whether the spatial and temporal evolution of NDVI in different spatial scales is consistent so as to provide a reasonable th...

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Bibliographic Details
Published in:Sustainability 2023-04, Vol.15 (8), p.6642
Main Authors: Fan, Jie, Fan, Yanmin, Cheng, Junhui, Wu, Hongqi, Yan, Yang, Zheng, Kai, Shi, Mingjie, Yang, Qiangjun
Format: Article
Language:English
Subjects:
Annual variations
Climate change
Computer centers
Datasets
Environmental science
Evolution
Mountains
Remote sensing
Spatial data
Spatial discrimination
Spatial distribution
Spatial resolution
Sustainability
Topography
Vegetation
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Summary:The purposes of this study are to reveal the spatial pattern and dynamic changes of NDVI in the northern slope of the Tianshan Mountains for an extended period and to explore whether the spatial and temporal evolution of NDVI in different spatial scales is consistent so as to provide a reasonable theoretical basis for the selection of appropriate remote sensing spatial resolution in the study area. The GIMMS NDVI remote sensing data set was used to resample the NDVI data with three spatial resolutions of 0.5 km × 0.5 km, 1 km × 1 km, and 8 km × 8 km. The Mann-Kendall method was used to analyze the spatial-temporal evolution characteristics of vegetation NDVI on the NTSM from 1981 to 2015. The results showed that the interannual variation trend and spatial distribution of vegetation NDVI were consistent at different spatial scales. The change of NDVI displayed an increasing trend with changes concentrated in the middle of the NTSM. Five distinct trends were observed: no significant change (35% of the area), significant positive change (26%), significant single peak change (15%), a significant U-shaped change relationship (12%), and significant negative change (11%). Remote sensing NDVI data with a spatial resolution of 8 km could be used to analyze the long-term interannual variation trend of vegetation NDVI on the NTSM.
ISSN:2071-1050
2071-1050
DOI:10.3390/su15086642