| 摘要: |
| 为精准估算亚热带低起伏城市辖区植被地上生物量，本研究以南宁市兴宁区为研究对象，以GEDI星载激光雷达L4A数据与Landsat 8多光谱影像为数据源，通过构建随机森林回归模型建立植被特征与地上生物量密度的耦合关系，对南宁市兴宁区的生物量进行估算，并通过地理探测器对生物量变化驱动因素进行了综合分析。研究结果表明，南宁市兴宁区生物量呈“北高南低”空间格局，2019年、2021年和2023年兴宁区平均生物量分别达459.62 t/hm2、524.61 t/hm2和479.61 t/hm2，总生物量分别为3.67×108 t、4.19×108 t和3.83×108 t。研究区海拔250 m以下范围内，森林 AGB 密度随海拔的升高而增大；坡度分析显示半平坡和缓坡的森林 AGB 密度较高；坡向的变化对森林 AGB密度不敏感，但在平地处有明显减小。在构建模型过程中，遥感变量中NDVI、近红外波段及多光谱指数贡献突出，地形因子因研究区地势平缓影响较小，植物覆盖、土地覆盖、夜间灯光、海拔、初级生产力、降水、土壤pH、坡度等自然与人为驱动因子对生物量的变化作用显著。本文结果为南宁市兴宁区城市生态功能优化与可持续发展提供了科学依据。 |
| 关键词: GEDI星载激光雷达 生物量估算 兴宁区 驱动因子 |
| DOI： |
| 投稿时间：2026-03-10修订日期：2026-05-15 |
| 基金项目:桉树采伐活动对“社会-生态”多层异构网络的动态影响研究项目 |
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| Study on the Spatial-temporal Pattern of Vegetation Aboveground Biomass and Its Influencing Factors in Urban Area ——Taking Xingning District of Nanning City as an Example |
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ZhangYiming, SU Kai, Li Ziming, Lin Bin, Huang Chuqi, Wang Jin, Song Liyuan
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| (Forestry College of Guangxi University) |
| Abstract: |
| To accurately estimate the aboveground biomass of vegetation in subtropical low-elevation urban areas.Its accurate estimation is crucial for optimizing urban ecological functions and sustainable development. This study focuses on Xingning District of Nanning City, using GEDI satellite laser radar L4A data and Landsat 8 multispectral imagery as data sources. By constructing a random forest regression model to establish the coupling relationship between vegetation characteristics and aboveground biomass density, the study estimates the biomass in Xingning District of Nanning City and comprehensively analyzes the driving factors of biomass changes through geograpHic detectors. The results indicate that the biomass in Xingning District of Nanning City exhibits a spatial pattern of "high in the north and low in the south." The average biomass in Xingning District reached 459.62 t/hm2 in 2019,524.61 t/hm2 in 2021, and 479.61 t/hm2 in 2023, with total biomass values of 3.67×108 t、4.19×108 t and 3.83×108 t , respectively. Within the study area below 250 m in altitude, forest AGB density increases with rising elevation. Slope analysis reveals higher forest AGB density on semi-level and gentle slopes. While slope orientation shows no significant impact on forest AGB density, it demonstrates a marked decrease in flat terrain. During the model construction process,among the remote sensing variables, NDVI, near-infrared bands, and multispectral indices demonstrated significant contributions. Terrain factors had limited impact due to the study area's gentle topograpHy. Natural and anthropogenic drivers—including vegetation coverage, land use, nighttime lighting, altitude, primary productivity, precipitation, soil pH, and slope—exerted notable effects on biomass changes. These findings provide a scientific basis for optimizing urban ecological functions and promoting sustainable development in Xingning District, Nanning. |
| Key words: LiDAR satellite Biomass estimation Xingning district Driving factor |
