Information extraction of temporal and spatial distribution of short-rotation plantations in Guangxi Zhuang Autonomous Region

DUAN Wensheng; CHEN Yuanpeng; WANG Li; HUANG Ni; HE Yuanhuizi; ZHANG Changsai; ZHANG Yangjian; ZHOU Quan; NIU Zheng

doi:10.11834/jrs.20221059

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Information extraction of temporal and spatial distribution of short-rotation plantations in Guangxi Zhuang Autonomous Region
Vol. 27, Issue 11, Pages: 2617-2627(2023)
Received：09 February 2021，

Published：07 November 2023
DOI： 10.11834/jrs.20221059
稿件说明：

移动端阅览

段文胜，陈元鹏，王力，黄妮，贺原惠子，张昌赛，张阳坚，周泉，牛铮.2023.广西壮族自治区短轮伐期人工林时空分布信息提取.遥感学报，27（11）： 2617-2627 DOI： 10.11834/jrs.20221059.

Duan W S，Chen Y P，Wang L，Huang N，He Y H Z，Zhang C S，Zhang Y J，Zhou Q and Niu Z. 2023. Information extraction of temporal and spatial distribution of short-rotation plantations in Guangxi Zhuang Autonomous Region. National Remote Sensing Bulletin， 27（11）：2617-2627 DOI： 10.11834/jrs.20221059.

摘要

短轮伐期人工林SRPs（Short-Rotation Plantations）作为主要的经济型林类，对于生态环境保护以及社会经济发展都有着重要的影响，但精细的SRPs时空分布信息十分缺乏。本研究以中国SRPs种植最为广泛的广西壮族自治区（以下简称广西）为研究区域，基于Google Earth Engine云平台以及1986年—2019年的Landsat影像数据，应用LandTrendr时间序列分割算法对SRPs的时空分布信息进行了提取。结果分析表明：（1）广西SRPs种植面积近30年呈逐年稳定快速增加的态势，1990年种植面积仅1.93×10

ha，到2019年达到了4.04×10

ha，年均增长速度达到1.33×10

ha；（2）从空间分布来看，广西东部、南部SRPs分布较为集中，其主要分布在海拔500 m以下的低海拔地区以及地表坡度在20°左右的坡地，且河池市是广西SRPs种植分布面积最大的地级市；（3）SRPs种植面积变化趋势与林业产值存在很强的相关性（

=0.83，

0.001），表明SRPs是影响林业经济的重要因素。本文提出的基于长时序的SRPs时空变化信息的提取方法，可以为林业管理提供决策支持，并为森林碳循环的研究提供基础数据。

Abstract

Short-Rotation Plantations (SRPs) as the main economic forests have an important impact on ecological environmental protection and social economic development

but detailed information on the temporal and spatial distribution of SRPs is lacking. SRPs have nearly half a century of plantation history and extensive distribution in South China. This study aims to extract the long-term temporal and spatial distribution information of SRPs and analyze its changing trends and driving factors.

The Guangxi Zhuang Autonomous Region

where SRPs are most widely grown in China

was used as the research area in this study. Based on the Google Earth Engine cloud platform and Landsat image data from 1986 to 2019

the 34-year Normalized Burn Ratio (NBR) long-term series data were first reconstructed by per pixel composite method. Then

the LandTrendr time series trajectory segmentation algorithm was used to segment and fit the NBR time series data for extracting the spatiotemporal distribution information of SRPs. Finally

Google Earth high-resolution images were used to select samples for verifying the accuracy of classification and extraction and analyzing the spatiotemporal characteristics and related factors of planting area changes in SRPs.

(1) The accuracy of the SRP information extraction results was evaluated by the confusion matrix: the overall accuracy of the binary classifications reached 80.52%

the mapping accuracy of SRPs was 79.6%

the user accuracy of SRPs was 81.2%

and the kappa coefficient was over 0.6

which indicate that the classification model has a good classification effect. (2) The planting area of SRPs in Guangxi has been increasing steadily and rapidly year by year in the past 30 years. The planting area was only 1.93×10

ha in 1990

and it reached 4.04×10

ha by 2019

with an average annual growth rate of 1.33×10

ha. (3) In terms of spatial distribution

SRPs are concentrated in eastern and southern Guangxi. They are mainly distributed in low-altitude areas below 500 m and slopes with a surface slope of about 20°. Among them

Hechi City is the prefecture-level city with the largest planting and distribution area of SRPs in Guangxi. (4) A strong correlation exists between the change trend of SRP planting area and forestry output value (

=0.830894

0.001)

which suggests that SRPs are an important affecting factor of forestry economy.

The method proposed in this study based on the LandTrendr time series trajectory segmentation algorithm of SRP spatiotemporal information extraction is proven to be very effective. The mapping and analysis of the spatiotemporal distribution of SRPs can provide decision support for forestry management and provide basic data for the research on forest carbon cycle.

关键词

Keywords

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