Abstract

Understanding the spatiotemporal dynamics of bamboo forests is of critical importance as it characterizes the interaction between forest and agricultural ecosystems and provides essential information for sustainable ecosystem management and decision-making. Thus far, the specific dynamics of moso bamboo (Phyllostachys edulis) and other bamboo are still unknown. In this study, we used temporal information extracted from Landsat time series stacks with Google Earth Engine (GEE) to characterize the spatiotemporal dynamics of bamboo forests, including moso bamboo and other bamboo, in Lin’an County, China, from 2000 to 2019. The bamboo forests were mapped in four periods: the early 2000s (2000–2004), the late 2000s (2005–2009), the early 2010s (2010–2014), and the late 2010s (2015–2019). The overall accuracy of these maps ranged from 97% to 99%. We then analyzed the spatiotemporal dynamics of the bamboo forests at the county and subdistrict/township scales, and probed the bamboo forest gain and loss with respect to the terrain features. Our findings show that bamboo forests increased by 4% from 2000 to 2014, followed by a sharp decrease of 13% in the late 2010s. The decrease was mainly caused by the loss of other bamboo. Approximately 69% of the bamboo forest gain occurred in non-bamboo forest areas, and the rest occupied non-forest areas. Bamboo forest loss was mainly due to conversion into orchard (59%) and forest plantation (22%). Compared to bamboo forest gain, bamboo forest loss was typically observed in areas with lower elevations and steeper slopes. Our study offers spatially explicit and timely insight into bamboo forest changes at the regional scale. The derived maps can be applied to study the drivers, consequences, and future trends of bamboo forest dynamics, which will contribute to sustainable ecosystem management.

Highlights

  • Intensive forestry practices in the 21st century have resulted in dramatic changes to the world’s forested areas, especially subtropical forests [1]

  • Accurate spatiotemporal distribution data for bamboo forests can be pivotal for supporting sustainable ecosystem management and biodiversity conservation

  • Long-term dynamic maps of moso and other bamboo forests at the regional scale have been unavailable, limiting our ability to understand the spatiotemporal dynamic characteristics of bamboo forests and to predict their dynamic trends. We have addressed this problem by using a pixel-based machine learning method to map these two types of bamboo forests in a subtropical area from 2000 to 2019 on the basis of 1003 Landsat 5/7/8 images and the Google Earth Engine (GEE) platform

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Summary

Introduction

Intensive forestry practices in the 21st century have resulted in dramatic changes to the world’s forested areas, especially subtropical forests [1]. Bamboo is one of the major types of forest in subtropical and tropical regions. A continuous long-term expansion of bamboo forests has generally occurred in mountainous areas during the intense forestry process, influencing forest and agricultural land cover changes as well as causing considerable challenges for eco-environmental protection [3,4]. It is necessary to produce multiyear maps of bamboo forests to support studies on the drivers, impacts, and consequences of the related changes at the regional and continental scales

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