基于kNDVI的广东省植被动态变化分析

doi:10.16258/j.cnki.1674-5906.2025.04.001

生态环境学报 ›› 2025, Vol. 34 ›› Issue (4): 499-510.DOI: 10.16258/j.cnki.1674-5906.2025.04.001

• 研究论文【生态学】 • 下一篇

基于kNDVI的广东省植被动态变化分析

陈鹏¹(), 马育军¹^,^*(), 张梦雅¹, 陈婉婷¹, 江晓鹏²

1.中山大学地理科学与规划学院，广东广州 510006
2.梅州市国有梅南林场，广东梅州 514777

收稿日期:2024-09-23 出版日期:2025-04-18 发布日期:2025-04-24
通讯作者: ^*马育军。E-mail: mayujun3@mail.sysu.edu.cn
作者简介:陈鹏（2000年生），女，硕士研究生，研究方向为森林碳循环。E-mail: chenp278@mail2.sysu.edu.cn
基金资助:
国家自然科学基金项目(41930646)

Analysis of Vegetation Dynamic in Guangdong Province Based on kNDVI

CHEN Peng¹(), MA Yujun¹^,^*(), ZHANG Mengya¹, CHEN Wanting¹, JIANG Xiaopeng²

1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510006, P. R. China
2. Meinan State-owned Forest Farm in Meizhou City, Meizhou 514777, P. R. China

Received:2024-09-23 Online:2025-04-18 Published:2025-04-24

摘要/Abstract

摘要：

广东省经济发展迅速，植被资源丰富，研究其植被动态变化对于实现社会经济发展与生态环境保护之间的平衡具有重要意义。基于2000－2022年的核标准化植被指数（kNDVI），探讨了广东省植被的时空变化特征，通过SHAP方法探究了影响kNDVI变化的主导因子，并利用LSTM模型与Hurst指数预测了未来植被变化趋势。结果表明，1）广东省kNDVI整体呈现“中间低、四周高”的格局，粤北地区年平均kNDVI最高，反映了区域较高的植被覆盖率；珠三角地区年平均kNDVI最低，这可能是由于快速的城市化进程导致了自然植被减少。2）研究时段内，广东省仅0.04%的区域植被状况保持稳定；约84.6%的区域植被状况改善，这可能得益于一系列生态保护政策与城市化积极效应的共同作用；15.4%的区域出现植被退化，主要集中在珠三角及粤东地区，这与当地频繁的土地利用变化和城市扩张紧密相关。相较于自然因素（如气温和降水），人为因素（如GDP增长和人口增加）对广东省植被变化的影响更显著。3）未来，广东省植被呈现反持续性像元占比为66.6%，持续性像元占比为33.4%，表明大部分地区植被覆盖的变化趋势可能发生逆转。

关键词: kNDVI, 植被时空变化, 人类活动, 未来趋势, 广东省

Abstract:

In recent years, intensified climate change and human activity have posed severe threats to terrestrial ecosystems stability. Guangdong, one of the most important provinces in China’s economy, is rich in natural resources and has an extensive vegetation cover. However, rapid urbanization and industrialization have led to complex vegetation changes in the Guangdong Province. In the soil-plant-atmosphere continuum, vegetation plays a crucial role in balancing socioeconomic development and environmental protection. To better monitor and understand the characteristics of vegetation change in Guangdong, this study introduced a new vegetation index: the Kernel Normalized Difference Vegetation Index (kNDVI). Compared with traditional NDVI, kNDVI addresses the challenges in monitoring vegetation dynamics owing to vegetation saturation, bias, and complex phenological dynamics. It also excels in noise resistance and data stability, making it ideal for evaluating vegetation change across diverse biomes and climate zones. To verify the applicability of kNDVI in Guangdong, we first compared the changes in monthly NDVI and kNDVI between 2000 and 2022, particlarly from June to September 2021, during which the vegetation cover reached its peak. Based on this comparison, we used the NDVI saturation threshold of 0.80 as a reference, and found that the saturation effect of NDVI may prevent accurate representation of vegetation change in approximately 30% of Guangdong. In contrast, kNDVI reflects the spatiotemporal distribution of vegetation in Guangdong better than NDVI, particularly in areas with high vegetation cover. Using kNDVI as the main vegetation indicator, this study investigated the spatiotemporal characteristics of vegetation dynamics in Guangdong from 2000 to 2022 through Theil-Sen Median trend analysis and Mann-Kendall tests. The Shapley Additive Explanation (SHAP) method was adopted to explore the primary driving factors of kNDVI change. Finally, we analyzed future trends in vegetation change based on the Theil-Sen Median trend analysis of the Hurst index and verified the results with predictions from the Long Short-Term Memory (LSTM) model. Our results indicate the following. 1) Spatial distribution of kNDVI: The annual average kNDVI values in Guangdong ranged from 0.005 to 0.718, showing obvious spatial heterogeneity due to the combined influence of natural conditions and economic development. Generally, the kNDVI gradually increases outward from the Pearl River Delta region. Northern Guangdong, an ecological protection area, recorded the highest annual average kNDVI (0.542), indicating a high vegetation coverage. Western and eastern Guangdong had annual average kNDVI values of 0.507 and 0.446, respectively. In contrast, the Pearl River Delta had the lowest annual average kNDVI (0.373), particularly around Foshan, Dongguan, and Zhongshan. 2) Temporal change in kNDVI: During the tudy period, the proportions of areas with vegetation that experienced extremely significant degradation, significant degradation, non-significant degradation, stability, non-significant improvement, significant improvement, and extremely significant improvement in Guangdong were 3.62%, 1.75%, 10.0%, 0.04%, 24.8%, 12.9%, and 46.9%, respectively. Overall, 84.6% of Guangdong’s area has experienced vegetation improvement, driven mainly by the implementation of ecological protection policies, such as returning farmland to forests and tree planting, as well as the positive effects of green space construction during urbanization. Nonetheless, 15.4% of Guangdong still experiences vegetation degradation. Although the total proportion of significantly and extremely significantly degraded areas was limited, they mostly occurred in the Pearl River Delta and eastern Guangdong due to rapid economic development and the conversion of considerable farmland and natural land into urban residential and commercial areas. 3) Factors influencing kNDVI change: In Guangdong Province, human activity factors, such as GDP growth and population increase, surpassed natural factors, such as precipitation and air temperature, in determining vegetation dynamics. For the entire province, the contribution order of the different factors to vegetation dynamics was GDP>air temperature>population>precipitation. In different sub-regions of Guangdong, while GDP is always the primary factor influencing vegetation dynamics, the contribution order of the other factors varies. For example, in the Pearl River Delta, the contribution ratios of GDP, population, air temperature, and precipitation to vegetation dynamics were 36%, 22.1%, 22%, and 20%, respectively. 4) Future trends of kNDVI: Based on the analysis of the Sen-Hurst index, future vegetation change trends were categorized into four groups: continuous degradation (6.03%), continuous improvement (27.4%), reversal from continuous degradation (9.35%), and reversal from continuous improvement (57.2%). The regions expected to experience an increasing trend of vegetation cover accounted for 36.7% of Guangdong, whereas the remaining 63.3% of the regions were expected to experience a degradation of vegetation cover. Notably, future vegetation trends in different regions of Guangdong may revert from continuous improvements to degradation. This is consistent with the kNDVI predictions of the LSTM model over the next 50 years (2000‒2072). In addition, according to the significance test of future kNDVI changes simulated by the LSTM model, vegetation in Guangdong may degrade over the next 13 years, even without any changes in current natural and human factors. In conclusion, this study revealed spatiotemporal variations in vegetation in Guangdong from 2000 to 2022 by analyzing the kNDVI index using multiple methods. We also identified the main factors driving vegetation change and predicted future vegetation trends. Our results emphasize the remarkable impact of economic development and human activities on vegetation change, and reveal the potential risk of vegetation degradation in the future. To effectively protect and restore the ecological environment in Guangdong and ensure a balance between socioeconomic development and environmental protection, the government and relevant departments must strengthen the implementation of ecological protection policies, make better land use plans, and execute green and sustainable development.

Key words: kNDVI, spatiotemporal change of vegetation, human activity, future trend, Guangdong Province

中图分类号:

Q948
X173

陈鹏, 马育军, 张梦雅, 陈婉婷, 江晓鹏. 基于kNDVI的广东省植被动态变化分析[J]. 生态环境学报, 2025, 34(4): 499-510.

CHEN Peng, MA Yujun, ZHANG Mengya, CHEN Wanting, JIANG Xiaopeng. Analysis of Vegetation Dynamic in Guangdong Province Based on kNDVI[J]. Ecology and Environment, 2025, 34(4): 499-510.

图/表 11

图1 广东省区域概况该图基于国家地理信息公共服务平台（天地图）（https://cloudcenter.tianditu.gov）网站下载的审图号GS(2024)0650号的标准地图制作，底图无修改（发布时间：2024年4月）

Figure 1 Overview of Guangdong Province

表1 研究区植被变化趋势分类

Table 1 Classification of vegetation change trend in the study area

类别	β	\|Z\|	类型
1	<0	\|Z\|>2.58	极显著退化
2	<0	1.96<\|Z\|≤2.58	显著退化
3	<0	\|Z\|≤1.96	不显著退化
4	=0	‒	稳定
5	>0	\|Z\|≤1.96	不显著改善
6	>0	1.96<\|Z\|≤2.58	显著改善
7	>0	\|Z\|>2.58	极显著改善

表2 研究区植被未来变化趋势分类

Table 2 Classification of future vegetation change trends in the study area

类别	β	Hurst指数	类型
1	<0	H<0.50	反持续退化
2	<0	H>0.50	持续退化
3	/	H=0.50	不确定
4	=0	‒	基本稳定
5	>0	H>0.50	持续改善
6	>0	H<0.50	反持续改善

图2 广东省2000－2022年NDVI与kNDVI对比

Figure 2 Comparison of NDVI and kNDVI in Guangdong Province from 2000 to 2022

图3 广东省2000－2022年均kNDVI概况

Figure 3 Overview of the average kNDVI in Guangdong Province from 2000 to 2022

图4 广东省2000－2022年植被动态变化

Figure 4 Vegetation dynamics in Guangdong Province from 2000 to 2022

图5 广东省2000－2022年植被变化影响因子贡献度统计

Figure 5 Statistical analysis of the contribution of influencing factors to vegetation change in Guangdong Province from 2000 to 2022

图6 Sen-Hurst预测广东省植被未来变化趋势

Figure 6 Sen-hurst predicts the future change trends of vegetation in Guangdong Province

图7 LSTM预测广东省植被未来50年变化趋势

Figure 7 LSTM predicts the change trends of vegetation in Guangdong Province over the next 50 years

表3 广东省2000－2022年土地利用转移矩阵

Table 3 Land use change Matrix from 2000 to 2022 in Guangdong Province km2

2000	2022
2000	农田	森林	灌木	草地	水域	裸地	不透水面	总计
农田	2.94×10⁴	1.05×10⁴	0.625	28	1.08×10³	13.3	4.17×10³	4.52×10⁴
森林	1.28×10⁴	1.01×10⁵	34.4	29.8	566	2.5	765	1.15×10⁵
灌木	4.13	61.6	3.88	0.75	0	0	0.188	70.6
草地	91.2	52.9	0.375	4.19	13.1	1.38	55.4	219
水域	2.37×10³	717	0	4.81	2.32×10³	3.63	679	6.09×10³
裸地	8.94	1.19	0	1.38	5.38	1.31	11.4	29.6
不透水面	2.77×10³	508	6.25×10⁻²	6.13	305	7.19	5.14×10³	8.74×10³
总计	4.75×10⁴	1.13×10⁵	39.3	75	4.28×10³	29.3	1.08×10⁴	1.76×10⁵

图8 kNDVI模拟值显著性（p<0.05）像元占比统计

Figure 8 Statistical analysis of the proportion of kNDVI pixels with significant simulation values (p<0.05) in Guangdong Province

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基于kNDVI的广东省植被动态变化分析

Analysis of Vegetation Dynamic in Guangdong Province Based on kNDVI

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