基于PLUS-InVEST模型的大兴安岭农林交错区碳储量时空变化及驱动力分析
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内蒙古自治区重点研发计划项目(2023YFDZ0026);内蒙古自治区科研基础平台项目(2023KYPT0001)


Spatiotemporal changes and driving forces of carbon storage in the forest-agricultural interlacing zone of Greater Khingan Mountains using PLUS-InVEST model
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    摘要:

    通过分析林地与耕地交错地带的土地利用时空变化及其对碳储量的影响,探索提升碳汇能力的有效途径。该研究运用PLUS-InVEST模型,以及地理探测器(Geodetector)的分析方法,对大兴安岭农林交错区1990—2030年土地利用时空变化对陆地生态系统碳储量时空变化特征的影响,并分析其碳储量变化的驱动因素。结果表明:1)1990—2020年,土地类型的转出面积较多的是林地、草地与耕地;转入率占比较高的分别是裸地75%、水体61.33%、不透水面61.15%。到2030年,自然保护和生态保护情景下林地和草地面积显著增加;耕地保护情景增加耕地和草地面积,林地面积减少;城市发展情景中,耕地面积减少。2)研究区碳储量在过去20年中总体呈下降趋势,共减少了6.08×107t,林地碳储量减少最多,其次为草地和耕地,不透水面的碳储量有所增加;与2020年相比,2030年不同情景下的碳储量,除耕地保护情景外,其他情景下均呈增加趋势,碳储量总量分别为1.555×109、1.562×109、1.562×109和1.561×109t;3)高程、人口、坡度是研究区碳储量空间分异的主要驱动因子;且各驱动因子间的交互作用强度均强于单一因子,其中坡度和人口密度交互效应最为显著为0.74。该研究揭示了大兴安岭农林交错区碳储量时空变化特征以及驱动力,以及模拟未来不同情景下的碳储量,研究结果可为研究区土地利用合理规划及碳汇能力提升提供数据支撑。

    Abstract:

    This study aims to analyze the spatiotemporal land-use patterns in the forest-farmland ecotone of ecologically sensitive areas, together with their effects on carbon storage. Effective approaches were proposed to enhance carbon sequestration. The PLUS-InVEST model was also utilized along with the Geodetector. A systematic investigation was implemented to assess the impacts of spatiotemporal land-use patterns from 1990 to 2030 on the carbon storage of terrestrial ecosystems in the forest-farmland ecotone of the Greater Khingan Mountains. Additionally, the driving factors were determined responsible for the changes in carbon storage. The results were summarized as follows:1) The land-use types that experienced the largest area of transition were forests, grasslands, and croplands between 1990 and 2020. Notably, the highest conversion rates were observed in bare land (75%), water bodies (61.33%), and impervious surfaces (61.15%). The forest and grassland areas were expected to significantly increase under natural conservation and ecological protection scenarios by 2030. Furthermore, the areas of cropland and grassland were projected to increase under the cropland protection scenario, while the forest areas decreased greatly. The area of cropland declined in the urban development scenario, due to the urban expansion and land-use changes. 2) The total carbon storage in the study area exhibited a general downward trend, decreasing by 6.08×107 t over the past two decades. The largest decrease in carbon storage occurred in forests, followed by grasslands and croplands. In contrast, there was the increasing carbon storage in the impervious surfaces. The carbon storage in 2020 was compared to the projections for 2030. The total carbon storage for 2030 was projected as 1.555×109, 1.562×109, 1.562×109, and 1.561×109 t, respectively, under the forests, grasslands, croplands, and impervious surfaces. All scenarios indicated an increase in the total carbon storage, except for the cropland protection. Different scenarios of land-use management varied greatly in the potential of overall carbon sequestration at regional. Natural conservation and ecological protection share the most promise to increase carbon storage. 3) The elevation, population density, and slope were identified as the primary driving factors behind the spatial differentiation of carbon storage in the study area. Among them, there was the most significant interaction between slope and population density, with an interaction strength of 0.74. There was a much stronger impact of multiple driving factors on carbon storage, compared with any single factor. Multi-factorial approaches were also required to analyze the spatial dynamics of carbon storage. Therefore, the carbon dynamics of the ecosystem depended mainly on the land-use transitions in the ecotonal regions, like forest-farmland interfaces. The important implications were gained to develop the data-driven strategies of land management. Carbon sinks were also optimized to support biodiversity conservation. Overall, the significant role of targeted interventions was highlighted for the land-use changes in carbon storage and sequestration at regional scales. The findings can greatly contribute to ecosystem management and climate adaptation in the Greater Khingan Mountains.

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李斯佳,王冰,王子昊,张秋良.基于PLUS-InVEST模型的大兴安岭农林交错区碳储量时空变化及驱动力分析[J].农业工程学报,2024,40(21):232-241. DOI:10.11975/j. issn.1002-6819.202406067

LI Sijia, WANG Bing, WANG Zihao, ZHANG Qiuliang. Spatiotemporal changes and driving forces of carbon storage in the forest-agricultural interlacing zone of Greater Khingan Mountains using PLUS-InVEST model[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE),2024,40(21):232-241. DOI:10.11975/j. issn.1002-6819.202406067

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  • 收稿日期:2024-06-17
  • 最后修改日期:2024-09-27
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  • 在线发布日期: 2024-11-01
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