Net primary productivity of vegetation in the Mu Us Sandy Land in 2001-2020

doi:10.7522/j.issn.1000-694X.2025.00027

Journal of Desert Research ›› 2025, Vol. 45 ›› Issue (6): 258-268.DOI: 10.7522/j.issn.1000-694X.2025.00027

Net primary productivity of vegetation in the Mu Us Sandy Land in 2001-2020

Yueru Guan(), Junwu Bai(), Yiqiong Li, Zhaohui Yang, Haonan Shi

School of Geography Science and Geomatics Engineering，Suzhou University of Science and Technology，Suzhou 215009，Jiangsu，China

Received:2024-07-22 Revised:2025-02-24 Online:2025-11-20 Published:2025-11-26
Contact: Junwu Bai

Abstract

Abstract:

Quantitatively evaluating the impacts of climate change and human activities on the terrestrial ecosystem carbon cycle is crucial for sustainable natural resource utilization. Using MODIS time series data， meteorological data， and land use data， we employed the CASA model to estimate the net primary productivity （NPP） of vegetation in the Mu Us Sandy Land from 2001 to 2020. Through trend analysis， correlation analysis， and residual analysis， we examined the spatiotemporal variations in vegetation NPP and identified their driving mechanisms. We also quantitatively assessed the relative contributions of climate change and human activities to NPP. Our findings indicate that：（1） From 2001 to 2020， the overall NPP of vegetation in the Mu Us Sandy Land increased at a rate of 3 gC·m⁻²·a⁻¹， showing a spatial trend of increasing from northwest to southeast. （2） Correlation analysis revealed that vegetation NPP in the northern region is more sensitive to temperature changes， whereas in the southern region， NPP is more sensitive to precipitation changes. （3） The variations in vegetation NPP （Net Primary Productivity） in the Mu Us Sandy Land are primarily driven by climate change and human activities， with average contributions of 35.38% and 64.62%， respectively. Climate warming and humidification have promoted vegetation growth， while human activities， though limited in spatial extent， contribute over 60% in 30.75% of the area. The implementation of ecological protection measures has further accelerated vegetation recovery. （4） Looking ahead， 55.05% of the areas exhibit non-persistent trends in vegetation NPP， with 38.95% of the areas showing a shift from increase to decrease， highlighting significant uncertainty in future NPP changes.

Key words: vegetation net primary productivity, Mu Us Sandy Land, climate change, human activities

CLC Number:

Q948

Yueru Guan, Junwu Bai, Yiqiong Li, Zhaohui Yang, Haonan Shi. Net primary productivity of vegetation in the Mu Us Sandy Land in 2001-2020[J]. Journal of Desert Research, 2025, 45(6): 258-268.

Figures/Tables 11

Fig.1 Elevation， meteorological stations， land use in 2020， and location map of the Mu Us Sandy Land

Table 1 Significance test of M-K trend

$β$	$Z$	趋势特征
$β > 0$	$Z > 2.58$	极显著增加
	$1.96 < Z ≤ 2.58$	显著增加
	$1.65 < Z ≤ 1.96$	微显著增加
	$Z ≤ 1.65$	不显著增加
$β = 0$	$Z$	无变化
$β < 0$	$Z ≤ 1.65$	不显著减少
	$1.65 < Z ≤ 1.96$	微显著减少
	$1.96 < Z ≤ 2.58$	显著减少
	$Z > 2.58$	极显著减少

Table 1 Significance test of M-K trend

$β$	$Z$	趋势特征
$β > 0$	$Z > 2.58$	极显著增加
	$1.96 < Z ≤ 2.58$	显著增加
	$1.65 < Z ≤ 1.96$	微显著增加
	$Z ≤ 1.65$	不显著增加
$β = 0$	$Z$	无变化
$β < 0$	$Z ≤ 1.65$	不显著减少
	$1.65 < Z ≤ 1.96$	微显著减少
	$1.96 < Z ≤ 2.58$	显著减少
	$Z > 2.58$	极显著减少

Table 2 Criteria for determining the driving factors of vegetation NPP changes and methods for calculating contribution rates

Slope ( $N P P o b s)$	驱动因素的划分标准		驱动因素的贡献率/%
Slope ( $N P P o b s)$	Slope ( $N P P C C$ )	Slope ( $N P P H A$ )	气候变化	人类活动
>0	>0	>0	$S l o p e (N P P C C) S l o p e (N P P o b s)$	$S l o p e (N P P H A) S l o p e (N P P o b s)$
	>0	<0	100	0
	<0	>0	0	100
<0	<0	<0	$S l o p e (N P P C C) S l o p e (N P P o b s)$	$S l o p e (N P P H A) S l o p e (N P P o b s)$
	<0	>0	100	0
	>0	<0	0	100

Table 2 Criteria for determining the driving factors of vegetation NPP changes and methods for calculating contribution rates

Slope ( $N P P o b s)$	驱动因素的划分标准		驱动因素的贡献率/%
Slope ( $N P P o b s)$	Slope ( $N P P C C$ )	Slope ( $N P P H A$ )	气候变化	人类活动
>0	>0	>0	$S l o p e (N P P C C) S l o p e (N P P o b s)$	$S l o p e (N P P H A) S l o p e (N P P o b s)$
	>0	<0	100	0
	<0	>0	0	100
<0	<0	<0	$S l o p e (N P P C C) S l o p e (N P P o b s)$	$S l o p e (N P P H A) S l o p e (N P P o b s)$
	<0	>0	100	0
	>0	<0	0	100

Table 3 Future trend prediction of vegetation NPP

Hurst	$β$	未来趋势
>0.5	>0	持续增加
>0.5	<0	持续减少
<0.5	>0	由增加变为减少
<0.5	<0	由减少变为增加
=0.5		无法预测

Table 3 Future trend prediction of vegetation NPP

Hurst	$β$	未来趋势
>0.5	>0	持续增加
>0.5	<0	持续减少
<0.5	>0	由增加变为减少
<0.5	<0	由减少变为增加
=0.5		无法预测

Fig.2 Fitting results of simulated vegetation NPP and MODIS vegetation NPP in the Mu Us Sandy Land

Fig.3 Interannual variation of vegetation NPP in the Mu Us Sandy Land from 2001 to 2020

Fig.4 Multi-year average， change trend and significance test of vegetation NPP in Mu Us Sandy Land from 2001 to 2020

Fig.5 Interannual variation of temperature and precipitation in the Mu Us Sandy Land from 2001 to 2020

Fig.6 Correlation between vegetation NPP and temperature and precipitation in the Mu Us Sandy Land from 2001 to 2020

Fig.7 Spatial distribution of the relative contribution rate of climate change and human activities to vegetation NPP in Mu Us Sandy Land

Fig.8 Future trend prediction of vegetation NPP in the Mu Us Sandy Land from 2001 to 2020

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Net primary productivity of vegetation in the Mu Us Sandy Land in 2001-2020

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