基于kNDVI的时空演变、预测及生态安全格局构建——以水土流失的黄土高原为例

doi:10.13474/j.cnki.11-2246.2024.0820

摘要/Abstract

摘要： 作为中国重要的生态区域,黄土高原面临严重的环境挑战。如何准确监测和预测植被变化已成为当前研究的焦点。本文利用更适合研究黄土高原的核归一化差异植被指数(kNDVI),对2000—2019年该地区的植被变化进行探究。结果显示,2001年和2013年是生态结构转型的分水岭,高和低类型发生显著变化。此外,为更全面了解未来植被的演变,引入BP神经网络和GeoSOS-FLUS模型进行时空预测,首次验证了GeoSOS-FLUS模型在kNDVI空间预测中的适用性。预测显示,2020—2022年更低和低类型将显著增加。值得注意的是,尽管kNDVI的斜率较过去翻了一番,但其峰值(8月)略有下降,而初春和冬季的数值则有所增加。最后,利用kNDVI和NDVI构建了黄土高原的生态安全格局。比较分析结果显示,由kNDVI构建的生态安全格局优于NDVI,结果进一步揭示了黄土高原西北部的生态更为脆弱,更受人类活动的影响。

关键词: 黄土高原, kNDVI, 时空预测, BP神经网络, GeoSOS-FLUS模型, 生态安全格局

Abstract: As a crucial ecological region in China, the Loess Plateau faces serious environmental challenges. How to accurately monitor and predict vegetation changes has become the focus of current research. This paper uses the kernel normalized difference vegetation index (kNDVI), which is more suitable for studying the Loess Plateau, to conduct a new exploration of the vegetation changes in this area from 2000 to 2019. The results reveal that 2001 and 2013 are the watershed of ecological structure transformation, and the high and low vegetation types show significant changes. In addition, to understand the evolution of vegetation in the future more comprehensively, we introduce the BP neural network and the GeoSOS-FLUS model for spatio-temporal prediction. We verify the applicability of the GeoSOS-FLUS model in kNDVI spatial prediction for the first time. We also find a significant increase in low and lower vegetation types predicted for 2020—2022. It is worth noting that although the slope of kNDVI has doubled compared to the past, its peak value (August) has slightly decreased, while the values in early spring and winter have increased. Finally, we use kNDVI and NDVI to construct the ecological security pattern of the Loess Plateau, and the comparative analysis results show that the ecological security pattern by kNDVI is better than NDVI. Further results reveal that the ecology of the northwestern Loess Plateau is more fragile and more affected by human activities.

Key words: Loess Plateau, kNDVI, spatio-temporal prediction, BP neural network, GeoSOS-FLUS model, ecological security pattern

中图分类号:

P208

周康胜, 杨德宏, 韩杨, 周鹏, 姜昀呈. 基于kNDVI的时空演变、预测及生态安全格局构建——以水土流失的黄土高原为例[J]. 测绘通报, 2024, 0(8): 115-121.

ZHOU Kangsheng, YANG Dehong, HAN Yang, ZHOU Peng, JIANG Yuncheng. Spatio-temporal evolution, prediction and ecological security pattern construction based on kNDVI: a case study of the Loess Plateau with severe soil erosion[J]. Bulletin of Surveying and Mapping, 2024, 0(8): 115-121.

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