Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1075
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3195
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Heilongjiang Province, a key ecological barrier in Northeast China, is crucial for regional ecosystem stability. Previous vegetation index research in this region primarily focused on annual or growing-season scales, without comprehensive comparisons of seasonal and interannual variations. This study addresses this gap by analyzing spatiotemporal vegetation dynamics and their driving forces in Heilongjiang Province using MODIS data (2000-2021). The findings reveal: (1) Analysis of MODIS-derived Fractional Vegetation Cover (FVC) from 2000 to 2021 revealed decreasing trends in spring, autumn, and winter, alongside an increasing summer trend. Spatially, FVC was higher in the northwest, central, and southeast regions, indicating significant heterogeneity. (2) Theil-Sen trend and Hurst exponent analyses indicated a declining annual FVC trend in 61.8% of the area, with 54.7% projected for continued future decline. A centroid shift model showed an overall westward FVC movement, except in spring. Coefficient of variation analysis demonstrated highest FVC stability in summer and lowest in winter. The global Moran's I index indicates that FVC exhibits a highly spatially concentrated distribution. Local Moran's I analysis primarily reveals two clustering patterns: "high-high" and "low-low" aggregations.(3) Random Forest SHAP analysis identified altitude, land cover type, evapotranspiration (ET), and slope as primary factors influencing FVC. Furthermore. The geographical detector analysis demonstrates that the interactions among factors strengthen their overall impact on FVC.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12328808 | PMC |
http://dx.doi.org/10.1038/s41598-025-14182-x | DOI Listing |