基于GEE的鄂尔多斯长时序植被NPP时空演变

doi:10.13866/j.azr.2025.02.10

Abstract

Abstract:

Utilizing the Google Earth Engine (GEE) cloud computing platform, the Net Primary Productivity (NPP) of Ordos was calculated based on an improved CASA model. Sen’s slope analysis and MK trend analysis methods were used to analyze the spatiotemporal changes in NPP from 2001 to 2020 and estimate the carbon sequestration capacity of Ordos City. (1) NPP in Ordos City displayed a significant seasonal variation from 2001 to 2020, with the highest values in July and August and an average annual NPP of 78.04 g C·m^-2·a^-1, following an overall fluctuating upward trend. (2) Spatially, NPP demonstrated clear heterogeneity, with higher values in the northeast and lower values in the northwest; high values were concentrated in Dalate Banner and Jungar Banner, while low values were mainly in Hanggin Banner. (3) The implementation of ecological projects and NPP changes were not fully synchronized, with a general trend of initially slow then accelerating growth; NPP change rates significantly increased after 2011 in most areas, but areas with harsher ecological conditions, such as Hanggin Banner, exhibited a lower improvement and some lag. (4) In 2011, Ordos displayed a widespread negative carbon sequestration rate. Yet, by 2020, the spatial heterogeneity in carbon sequestration had significantly increased, with higher values in the east and lower values in the west. The carbon sequestration capacity in Hanggin Banner’s western region still requires reinforcement, while Dalate Banner significantly improved its carbon sequestration capacity.

Key words: Ordos City, Google Earth Engine, CASA model, net primary productivity, carbon sequestration capacity

LIU Ge, ZHAO Hengqian, HUANGFU Xiadan, FU Hancong, WANG Pan, XU Fei, HAN Tian. Spatiotemporal evolution of long-term vegetation NPP in Ordos based on GEE[J].Arid Zone Research, 2025, 42(2): 299-311.

Figures/Tables 15

Fig. 1

Tab. 1

Fig. 2

Tab. 2

Maximum light energy utilization for different land cover types"

土地覆盖类型	最大光能利用率 $ε m a x$ /(g C·MJ^-1)
常绿针叶林	0.389
常绿阔叶林	0.985
落叶针叶林	0.485
落叶阔叶林	0.692
灌木	0.429
其他	0.542

Tab. 2

Tab. 3

Mann-Kendall test for trend categories"

$S e n i j$	$Z$	趋势类别	趋势特征
$S e n i j 0$	$Z 2.58$	4	极显著增加
	$1.96 Z ≤ 2.58$	3	显著增加
	$1.65 Z ≤ 1.96$	2	微显著增加
	$Z ≤ 1.65$	1	不显著增加
$S e n i j = 0$	$Z ∈ R$	0	无变化
$S e n i j 0$	$Z ≤ 1.65$	-1	不显著减少
	$1.65 Z ≤ 1.96$	-2	微显著减少
	$1.96 Z ≤ 2.58$	-3	显著减少
	$Z 2.58$	-4	极显著减少

Tab. 3

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Fig. 11

Fig. 12

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数据	所选产品	时间分辨率	空间分辨率
土地利用数据	MCD12Q1 V6.1 IGBP分类系统	1 a	500 m
PAR数据	MCD18C2 V6.1	3 h	500 m
FPAR数据	MOD15A2H V6.1	8 d	500 m
气象数据	ECMWF ERA5-Land	30 d	11132 m
蒸散量数据	MOD16A2 V6	8 d	500 m
验证数据	MOD17A3HGF V6.1	8 d	500 m

Spatiotemporal evolution of long-term vegetation NPP in Ordos based on GEE

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