Author(s)

Zijia Liu ¹

Affiliation(s)

¹ School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China

Corresponding Author

Zijia Liu

ABSTRACT

To analyze the spatiotemporal variation characteristics of NPP in Henan Province, this paper uses the MODIS Remote Sensing Data combined with segmented linear regression, Theil-Sen Median trend analysis, Mann-Kendall test, and coefficient of variation methods, resulting in the following conclusions:(1) From 2001 to 2023, the annual average NPP in Henan Province increased at a rate of 3.87 g C·m^-2·a^-1. The year 2004 was a mutation year, with the rates of increase before and after the mutation being 47.34 g C·m^-2·a^-1 and 2.89 g C·m^-2·a^-1, respectively. (2) The NPP in Henan Province shows a spatial distribution pattern declining from the southwest to the northeast. The area proportions of regions with multi-year average NPP below 200 g C·m^-2·a^-1, between 200-400 g C·m^-2·a^-1, 400-600 g C·m^-2·a^-1, and above 600 g C·m^-2·a^-1 are 0.5%, 37.7%, 58.9%, and 2.9%, respectively. (3) From 2001 to 2023, 94.2% of Henan Province's regions exhibited an increasing trend in NPP, with the significance of the trend decreasing from west to east. The area proportions of regions with significant decrease, non-significant decrease, stable, non-significant increase, and significant increase NPP are 0.9%, 2.4%, 2.5%, 44.6%, and 49.6%, respectively. (4) From 2001 to 2023, the NPP change trend in Henan Province displayed a spatial distribution pattern of stability decreasing from southwest to northeast, overall showing moderate stability. The area proportions of regions with low stability, medium-low stability, moderate stability, medium-high stability, and high stability are 4.9%, 25.8%, 62.4%, 6.5%, and 0.4%, respectively.

KEYWORDS

MODIS, NPP, spatio-temporal variation characteristics, Trend analysis

CITE THIS PAPER

Zijia Liu, Spatio-temporal Variation Analysis of Net Primary Productivity of Vegetation in Henan Based on MODIS Remote Sensing Data. Geoscience and Remote Sensing (2025) Vol. 8: 21-28. DOI: http://dx.doi.org/10.23977/geors.2025.080103.

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