The spatio-temporal analysis of land use changes & above-ground carbon stock in Sumbawa mangrove ecosystem, Indonesia

IF 3.7 Q4 ENVIRONMENTAL SCIENCES
Jaka Putra Rahmajati, Vina Nurul Husna, Yulius Yulius, Taslim Arifin, Hadiwijaya L. Salim, Rinny Rahmania, Aprizon Putra, Muhammad Ramdhan, Anang Dwi Purwanto, August Daulat, Joko Prihantono, Hafidzuddin Fihrin, Farel A. Aditama
{"title":"The spatio-temporal analysis of land use changes & above-ground carbon stock in Sumbawa mangrove ecosystem, Indonesia","authors":"Jaka Putra Rahmajati,&nbsp;Vina Nurul Husna,&nbsp;Yulius Yulius,&nbsp;Taslim Arifin,&nbsp;Hadiwijaya L. Salim,&nbsp;Rinny Rahmania,&nbsp;Aprizon Putra,&nbsp;Muhammad Ramdhan,&nbsp;Anang Dwi Purwanto,&nbsp;August Daulat,&nbsp;Joko Prihantono,&nbsp;Hafidzuddin Fihrin,&nbsp;Farel A. Aditama","doi":"10.1007/s44218-025-00116-7","DOIUrl":null,"url":null,"abstract":"<div><p>Mangrove deforestation in Indonesia amounts to roughly 18,209 ha per year (18.26 MtCO₂e/year<sup>−1</sup>). However, spatial and temporal integrative research in eastern Indonesia's coastal areas remains limited. This study aims to analyse mangrove forests' condition and spatio-temporal changes, compute above-ground carbon (AGC) as a basis for sustainable coastal management in Sumbawa Regency and support the 2030 Nationally Determined Contribution (NDC) target. This research merges UAV-RGB data (July 2024) and multitemporal PlanetScope imagery (2016 and 2024) to develop a Canopy Height Model (CHM) and calculate the AGC. Land use classification was accomplished using Object-Based Image Analysis (OBIA)—Random Forest (RF), while changing trends were assessed using Modules for Land Use Change Evaluation/Simulations (MOLUSCE). AGC at the study site was in the range of 21—168 <span>\\({\\text{MgC}.\\text{ha}}^{-1}\\)</span> (mean = 72.3 <span>\\({\\text{MgC}.\\text{ha}}^{-1}\\)</span>, total = 8,999.9 <span>\\(\\text{MgC}\\)</span>. The investigation found a change of 37.89 ha of mangrove area to barren and shrubland, resulting in an AGC loss of 2,739.5 <span>\\(\\text{MgC}\\)</span>. However, there has been an increase in land use and changes in aquaculture and agriculture. This study proposes a novel approach by merging UAV data and time-series satellite images to increase the accuracy of AGC prediction in data-sparse locations. A nature-based Solution (NbS) is advocated as an integrated method for adaptive mangrove restoration and conservation, while still supporting the demands of the local community. This research presents a geographical framework that may be duplicated to create inclusive and adaptable coastal resource management plans and directly contribute to reaching the 2030 NDC targets.</p></div>","PeriodicalId":100098,"journal":{"name":"Anthropocene Coasts","volume":"9 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2026-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s44218-025-00116-7.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Anthropocene Coasts","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s44218-025-00116-7","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

Mangrove deforestation in Indonesia amounts to roughly 18,209 ha per year (18.26 MtCO₂e/year−1). However, spatial and temporal integrative research in eastern Indonesia's coastal areas remains limited. This study aims to analyse mangrove forests' condition and spatio-temporal changes, compute above-ground carbon (AGC) as a basis for sustainable coastal management in Sumbawa Regency and support the 2030 Nationally Determined Contribution (NDC) target. This research merges UAV-RGB data (July 2024) and multitemporal PlanetScope imagery (2016 and 2024) to develop a Canopy Height Model (CHM) and calculate the AGC. Land use classification was accomplished using Object-Based Image Analysis (OBIA)—Random Forest (RF), while changing trends were assessed using Modules for Land Use Change Evaluation/Simulations (MOLUSCE). AGC at the study site was in the range of 21—168 \({\text{MgC}.\text{ha}}^{-1}\) (mean = 72.3 \({\text{MgC}.\text{ha}}^{-1}\), total = 8,999.9 \(\text{MgC}\). The investigation found a change of 37.89 ha of mangrove area to barren and shrubland, resulting in an AGC loss of 2,739.5 \(\text{MgC}\). However, there has been an increase in land use and changes in aquaculture and agriculture. This study proposes a novel approach by merging UAV data and time-series satellite images to increase the accuracy of AGC prediction in data-sparse locations. A nature-based Solution (NbS) is advocated as an integrated method for adaptive mangrove restoration and conservation, while still supporting the demands of the local community. This research presents a geographical framework that may be duplicated to create inclusive and adaptable coastal resource management plans and directly contribute to reaching the 2030 NDC targets.

印度尼西亚松巴哇红树林生态系统土地利用变化及地上碳储量时空分析
印度尼西亚的红树林砍伐量约为每年18,209公顷(18.26 MtCO₂e/年−1)。然而,印度尼西亚东部沿海地区的时空综合研究仍然有限。本研究旨在分析红树林的状况和时空变化,计算地上碳(AGC),作为松巴哇县可持续沿海管理的基础,并为2030年国家自主贡献(NDC)目标提供支持。本研究将无人机- rgb数据(2024年7月)与多时相PlanetScope图像(2016年和2024年)相结合,建立了冠层高度模型(CHM)并计算了AGC。使用基于目标的图像分析(OBIA) -随机森林(RF)完成土地利用分类,同时使用土地利用变化评估/模拟模块(MOLUSCE)评估变化趋势。研究现场的AGC范围为21-168 \({\text{MgC}.\text{ha}}^{-1}\) (mean = 72.3 \({\text{MgC}.\text{ha}}^{-1}\), total = 8,999.9 \(\text{MgC}\))。调查发现,37.89 ha的红树林变为荒地和灌木林,导致AGC损失2739.5 \(\text{MgC}\)。然而,土地利用有所增加,水产养殖和农业也发生了变化。本研究提出了一种将无人机数据与时间序列卫星图像融合的新方法,以提高数据稀疏位置AGC预测的精度。基于自然的解决方案(NbS)被认为是一种综合的适应性红树林恢复和保护方法,同时仍然支持当地社区的需求。这项研究提出了一个地理框架,可以复制,以创建包容性和适应性强的沿海资源管理计划,并直接有助于实现2030年国家自主贡献目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
CiteScore
3.80
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信
小红书