Residual trend method based on regional modeling and machine learning for attribution of vegetation changes

doi:10.6046/zrzyyg.2023258

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Abstract

Existing residual trend methods utilize a pixel-by-pixel modeling strategy, in which the ordinary least squares method is employed. These methods suffer certain limitations. On the one hand, the pixel-by-pixel modeling strategy causes each model to contain signal interference from human activities in local space. On the other hand, the ordinary least squares method is unfavorable for simulating commonly observed nonlinear characteristics. This study proposed an entirely new residual trend method based on regional modeling and machine learning. Besides, this study compared two types of environmental variables used to express spatial heterogeneity: ①direct-environmental variables (DEVs) such as terrain, hydrology, and land use; and ②proxy-environmental variables (PEVs) that combine the spatiotemporal series of vegetation and climate. First, a regional modeling strategy was adopted. After DEVs and PEVs were introduced individually, models for the vegetation-climate relationship were built using machine learning. Second, residuals were determined based on the definition of the residual trend method. Finally, the contributions of anthropogenic and climatic factors to vegetation change were assessed. The results indicate that compared to the previous pixel-by-pixel residual trend method that utilizes ordinary least squares, the new residual trend method can simulate the nonlinear features of the vegetation-climate relationship and exhibits enhanced resistance to human signal interference. For the new method, significantly higher performance can be achieved using PEVs compared to DEVs. PEVs can fully utilize the original modeling data, without increasing difficulties with data acquisition and avoiding additional data errors. The residual trend method based on regional modeling and machine learning proposed in this study allows for more effective attribution of vegetation changes.

Keywords attribution of vegetation change regional modeling machine learning algorithm residual trend method spatial heterogeneity

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Issue Date: 17 February 2025

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	Boyang HU
	Jianguo SUN
	Qian ZHANG
	Yunrui YANG

Cite this article:

Boyang HU,Jianguo SUN,Qian ZHANG, et al. Residual trend method based on regional modeling and machine learning for attribution of vegetation changes[J]. Remote Sensing for Natural Resources, 2025, 37(1): 46-53.

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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2023258 OR https://www.gtzyyg.com/EN/Y2025/V37/I1/46

Fig.1 Overview of Gansu Province

Tab.1 DEVs in regional machine learning method

植被变化类型	$S L O o b e$	$S L O e s t$	$S L O r e s$	贡献率/%
植被变化类型	$S L O o b e$	$S L O e s t$	$S L O r e s$	气候因素	人为因素
植被恢复	$0$ (且 $P 0.05$ )	$0$	$0$	$S L O e s t S L O o b e × 100$	$S L O r e s S L O o b e × 100$
		$0$	$0$	100	0
		$0$	$0$	0	100
植被退化	$0$ (且 $P 0.05$ )	$0$	$0$	$S L O e s t S L O o b e × 100$	$S L O r e s S L O o b e × 100$
		$0$	$0$	100	0
		$0$	$0$	0	100

Tab.2 Calculation method for contribution of climate and human factors

Fig.2 The relationship between a single climate factor and the simulated values of aNDVI in regional machine learning method

Fig.3 Effect of vegetation change and its attribution in the research area

Fig.4 Relationship between climate and the simulated values of aNDVI in typical areas with differences in output between regional machine learning method using direct environment variables and pixel by pixel least squares method

Fig.5 High resolution images of Region I and Region II in 2009 and 2020

Fig.6 Actual land cover types of typical areas with different output differences between regional machine learning method using direct environment variables and regional machine learning method using PEVs

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