Data-Driven Artificial Intelligence Model of Meteorological Elements Influence on Vegetation Coverage in North China

Huimin Bai,Zhiqiang Gong,Guiquan Sun,Li Li

doi:10.3390/rs14061307

Abstract

Based on remote sensing data of vegetation coverage, observation data of basic meteorological elements, and support vector machine (SVM) method, this study develops an analysis model of meteorological elements influence on vegetation coverage (MEVC). The variations for the vegetation coverage changes are identified utilizing five meteorological elements (temperature, precipitation, relative humidity, sunshine hour, and ground temperature) in the SVM model. The performance of the SVM model is also evaluated on simulating vegetation coverage anomaly change by comparing with statistical model multiple linear regression (MLR) and partial least squares (PLS)-based models. The symbol agreement rates (SAR) of simulations produced by MLR, PLS, and SVM models are 55%, 57%, and 66%, respectively. The SVM model shows obviously better performance than PLS and MLR models in simulating meteorological elements-related interannual variation of vegetation coverage in North China. Therefore, the introduction of the intelligent analysis method in term of SVM in model development has certain advantages in studying the internal impact of meteorological elements on regional vegetation coverage. It can also be further applied to predict the future vegetation anomaly change.

Highlights

Published: 8 March 2022Vegetation is the key element for the interaction between land and atmosphere, and its change is caused by topographic factors [1], human activities, and climate change [2–5].Among them, climate is one of the main determinants of vegetation type distribution and dynamic change [6,7].Remote sensing data, such as vegetation coverage (VC), normalized difference vegetation index (NDVI), and net primary productivity (NPP), is widely applied in vegetation greenness, agriculture, forestry, hydrology, and drought detection [8–11]
2005, multiple linear regression (MLR) and partial least squares (PLS) simulated error is quite large in Shanxi, and support vector machine (SVM) simulated error north of Hebei
The meteorological elements influence on vegetation coverage (MEVC) SVM model showed better performance with regard to vegetation coverage change than the MLR and PLS models, using remotely sensed summer vegetation coverage data combined with meteorological elements of MJJ and JJA (Figures 8 and 9)

Summary

Introduction

Climate is one of the main determinants of vegetation type distribution and dynamic change [6,7]. Remote sensing data, such as vegetation coverage (VC), normalized difference vegetation index (NDVI), and net primary productivity (NPP), is widely applied in vegetation greenness, agriculture, forestry, hydrology, and drought detection [8–11]. Numerous studies explored the response of regional and global vegetation to climate change. Revealed that growing-season NDVI depends largely on water in arid and semiarid areas, temperature in high northern latitude areas, and radiation in the Amazon and Eastern and Southern Asia. Li et al [13] used linear correlation and showed that there was a strong correlation between grassland and precipitation, forest, farmland, and temperature in the

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