Monitoring Spatiotemporal Expansion Dynamics of Short-Rotation Eucalyptus Plantations over Large Scales Using Landsat Time-Series Data

Yuanzheng Yang; Wen H. Cai; Qiuxia Huang; Le Yu; Jiaxing Zu; Jiali Wang; Jian Yang

doi:10.1109/JSTARS.2024.3472008

Monitoring Spatiotemporal Expansion Dynamics of Short-Rotation Eucalyptus Plantations over Large Scales Using Landsat Time-Series Data

Yuanzheng Yang
, Wen H. Cai
, Qiuxia Huang
, Le Yu
, Jiaxing Zu
, Jiali Wang
, Jian Yang

Forestry and Natural Resources

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Eucalyptus, valued for its rapid growth and economic potential, has been widely introduced in China to address timber demands while conserving natural forests. Precisely estimating the spatiotemporal expansion of short-rotation eucalyptus plantations is crucial for evaluating their ecological and social value and formulating effective sustainable forestry policies. Medium-resolution satellite images, such as Landsat data, offer a cost-effective tool for large-scale forest mapping compared with the traditional forest inventories. This study used pixel-level time-series analysis to identify annual eucalyptus plantation distributions across Guangxi, China, from 2004 to 2019, based on the standard temporal vegetation index curves derived from the characteristics of short-rotation and fast-growing eucalyptus. Furthermore, an image segmentation method, coupled with an empirical relationship linking patch-level landscape indices to optimal thresholds, was employed to eliminate isolated pixels and reduce omission errors arising from the above time-series analysis. The established thresholds increased the accurate identification of eucalyptus patches within segments. Our proposed eucalyptus detection algorithm achieved an overall accuracy exceeding 80%, demonstrating its effectiveness. The analysis revealed eucalyptus plantations increased from 0.42 × 10⁶ ha in 2004 to 2.47 × 10⁶ ha in 2019, exhibiting a pronounced northward expansion. Initially concentrated in upland areas, plantations subsequently expanded into flatter terrains, raising concerns about potential agricultural conflicts. Annual eucalyptus plantation maps offer critical information for sustainable forest management and policymaking. This study highlights the potential of medium-resolution satellite data and time-series analysis for robust and cost-effective monitoring of annual short-rotation timber forest expansion dynamics over large scales.

Original language	English
Pages (from-to)	18915-18925
Number of pages	11
Journal	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Volume	17
DOIs	https://doi.org/10.1109/JSTARS.2024.3472008
State	Published - 2024

Bibliographical note

Publisher Copyright:
© 2008-2012 IEEE.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 32460289, Grant 41961037, and Grant 42001216, in part by the Special Research Project of Guangxi for Research Bases and Talents under Grant AD19110143, Grant AD20297066, and Grant AD21220088, and in part by the BaGui Scholars Program of Guangxi Zhuang Autonomous Region. eliminate isolated pixels andreduceomission errors arising from I. INTRODUCTION accurateidentification of eucalyptus patches within segments.above timeseries analysis.The established thresholdsincreased n recent decades, short-rotation eucalyptus plantations Our proposed eucalyptus detection algorithm achieved an overall Ihave experienced rapid expansion to meet the growing demand for wood resources [1], [2]. While this expansion offers significant economic benefits [3], [4], its potential National Natural Science FoundationofChina under Grant 32460289, GrantManuscript receivedApril8,2024. Thiswork wassupported inpart by allelopathic effects and intensive cultivation practices, such as 41961037, and Grant 42001216, in part by Special Research Project of short rotations and clearcut logging, also raise concerns about Guangxi for ResearchBases andTalentsunder Grant AD19110143, Grant ecological impacts [5] – [7]. Accurate and up-to-date ProgramofGuangxi ZhuangAutonomous Region(Correspondingauthor: AD20297066, andGrant AD21220088, and inpart byBaGuiScholars monitoring eucalyptus plantation distribution is crucial for JianYang). assessing their environmental consequences [8]. Previous YuanZ.Yangis withthe KeyLaboratoryofEnvironment Change and studies have reported statistical data about eucalyptus China(e-mail:[email protected]).Resources Use inBeibuGulf, NanningNormal University,Nanning530001, plantation area in specific years or at local scales [9] – [11].

Funders	Funder number
Bagui Scholars Program of Guangxi Zhuang Autonomous Region
National Natural Science Foundation of China (NSFC)	32460289, 42001216, 41961037
Specific Research Project of Guangxi for Research Bases and Talents	AD19110143

Keywords

Eucalyptus
Google Earth engine (GEE)
forest monitoring
short-rotation plantations
time-series analysis

ASJC Scopus subject areas

Computers in Earth Sciences
Atmospheric Science

Access to Document

10.1109/JSTARS.2024.3472008

Cite this

@article{62f1d3dabdf24dbab81015846aae39ab,

title = "Monitoring Spatiotemporal Expansion Dynamics of Short-Rotation Eucalyptus Plantations over Large Scales Using Landsat Time-Series Data",

abstract = "Eucalyptus, valued for its rapid growth and economic potential, has been widely introduced in China to address timber demands while conserving natural forests. Precisely estimating the spatiotemporal expansion of short-rotation eucalyptus plantations is crucial for evaluating their ecological and social value and formulating effective sustainable forestry policies. Medium-resolution satellite images, such as Landsat data, offer a cost-effective tool for large-scale forest mapping compared with the traditional forest inventories. This study used pixel-level time-series analysis to identify annual eucalyptus plantation distributions across Guangxi, China, from 2004 to 2019, based on the standard temporal vegetation index curves derived from the characteristics of short-rotation and fast-growing eucalyptus. Furthermore, an image segmentation method, coupled with an empirical relationship linking patch-level landscape indices to optimal thresholds, was employed to eliminate isolated pixels and reduce omission errors arising from the above time-series analysis. The established thresholds increased the accurate identification of eucalyptus patches within segments. Our proposed eucalyptus detection algorithm achieved an overall accuracy exceeding 80\%, demonstrating its effectiveness. The analysis revealed eucalyptus plantations increased from 0.42 × 106 ha in 2004 to 2.47 × 106 ha in 2019, exhibiting a pronounced northward expansion. Initially concentrated in upland areas, plantations subsequently expanded into flatter terrains, raising concerns about potential agricultural conflicts. Annual eucalyptus plantation maps offer critical information for sustainable forest management and policymaking. This study highlights the potential of medium-resolution satellite data and time-series analysis for robust and cost-effective monitoring of annual short-rotation timber forest expansion dynamics over large scales.",

keywords = "Eucalyptus, Google Earth engine (GEE), forest monitoring, short-rotation plantations, time-series analysis",

author = "Yuanzheng Yang and Cai, \{Wen H.\} and Qiuxia Huang and Le Yu and Jiaxing Zu and Jiali Wang and Jian Yang",

note = "Publisher Copyright: {\textcopyright} 2008-2012 IEEE.",

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doi = "10.1109/JSTARS.2024.3472008",

language = "English",

volume = "17",

pages = "18915--18925",

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Monitoring Spatiotemporal Expansion Dynamics of Short-Rotation Eucalyptus Plantations over Large Scales Using Landsat Time-Series Data. / Yang, Yuanzheng; Cai, Wen H.; Huang, Qiuxia et al.
In: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 17, 2024, p. 18915-18925.

Research output: Contribution to journal › Article › peer-review

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T1 - Monitoring Spatiotemporal Expansion Dynamics of Short-Rotation Eucalyptus Plantations over Large Scales Using Landsat Time-Series Data

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AU - Cai, Wen H.

AU - Huang, Qiuxia

AU - Yu, Le

AU - Zu, Jiaxing

AU - Wang, Jiali

AU - Yang, Jian

PY - 2024

Y1 - 2024

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AB - Eucalyptus, valued for its rapid growth and economic potential, has been widely introduced in China to address timber demands while conserving natural forests. Precisely estimating the spatiotemporal expansion of short-rotation eucalyptus plantations is crucial for evaluating their ecological and social value and formulating effective sustainable forestry policies. Medium-resolution satellite images, such as Landsat data, offer a cost-effective tool for large-scale forest mapping compared with the traditional forest inventories. This study used pixel-level time-series analysis to identify annual eucalyptus plantation distributions across Guangxi, China, from 2004 to 2019, based on the standard temporal vegetation index curves derived from the characteristics of short-rotation and fast-growing eucalyptus. Furthermore, an image segmentation method, coupled with an empirical relationship linking patch-level landscape indices to optimal thresholds, was employed to eliminate isolated pixels and reduce omission errors arising from the above time-series analysis. The established thresholds increased the accurate identification of eucalyptus patches within segments. Our proposed eucalyptus detection algorithm achieved an overall accuracy exceeding 80%, demonstrating its effectiveness. The analysis revealed eucalyptus plantations increased from 0.42 × 106 ha in 2004 to 2.47 × 106 ha in 2019, exhibiting a pronounced northward expansion. Initially concentrated in upland areas, plantations subsequently expanded into flatter terrains, raising concerns about potential agricultural conflicts. Annual eucalyptus plantation maps offer critical information for sustainable forest management and policymaking. This study highlights the potential of medium-resolution satellite data and time-series analysis for robust and cost-effective monitoring of annual short-rotation timber forest expansion dynamics over large scales.

KW - Eucalyptus

KW - Google Earth engine (GEE)

KW - forest monitoring

KW - short-rotation plantations

KW - time-series analysis

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JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

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ER -

Monitoring Spatiotemporal Expansion Dynamics of Short-Rotation Eucalyptus Plantations over Large Scales Using Landsat Time-Series Data

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

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