Değermenci Ahmet Salih, Zengin Hayati, Özcan Mehmet, Çitgez Tarık
Faculty of Forestry, Department of Forest Management and Planning, Düzce University, Düzce, Turkey.
Faculty of Forestry, Department of Watershed Management, Düzce University, Düzce, Turkey.
Environ Monit Assess. 2025 Jul 2;197(8):843. doi: 10.1007/s10661-025-14299-6.
Leaf area ındex (LAI) is a fundamental metric of forest canopy structure, driving photosynthetic capacity, carbon sequestration, and ecosystem productivity. In this study, we quantified LAI variation across six forest stand types (pure fir, pure beech, pure Scots pine, mixed coniferous, mixed deciduous, and mixed deciduous-coniferous) and six developmental stages in the Düzce region of Turkey, an area characterized by mixed, heterogeneous woodlands. Field measurements from 260 systematically distributed sample plots collected in 2015 employed hemispherical photography for LAI, the N-tree method for basal area (BA) and diameter at breast height (DBH), and atmospherically corrected Landsat 8 OLI imagery for NDVI. Descriptive analyses revealed LAI values ranging from 0.36 m/m in pure Scots pine to 6.30 m/m in mixed deciduous stands. One-way ANOVA and Duncan's multiple-range tests confirmed significant differences among stand types (p < 0.01), with mixed and vertically stratified stands exhibiting the highest LAI. Developmental stages showed increasing mean LAI trends from juvenile (ab) to mature (d) classes, though stage-only ANOVA was not significant (p = 0.378) due to high within-stage variability (CV ≈ 31%). Pearson correlations indicated moderate positive relationships between LAI and both DBH (r = 0.49) and BA (r = 0.53), whereas NDVI displayed the strongest association (r = 0.75 overall; up to r = 0.80 in mixed stands). A multiple linear regression model integrating NDVI, DBH, and BA explained 60.6% of LAI variance (F = 129.7, p < 0.001; adjusted R = 0.601), with NDVI emerging as the dominant predictor (standardized β = 0.683), followed by DBH (β = 0.326) and BA (β = 0.187). These findings underscore the complementary value of integrating spectral indices and structural parameters in the estimation of LAI, particularly in heterogeneous forest stands. The structural complexity of mixed stands appears to play a critical role in enhancing canopy development. To improve estimation accuracy in conifer-dominated or high-LAI forests, future studies should consider incorporating alternative vegetation indices and LiDAR-derived structural metrics to overcome limitations such as spectral saturation and insufficient vertical resolution. Such integrated approaches can significantly enhance the scalability and cost-effectiveness of forest health and productivity monitoring efforts.
叶面积指数（LAI）是森林冠层结构的一个基本指标，它驱动着光合能力、碳固存和生态系统生产力。在本研究中，我们量化了土耳其杜兹恰地区六种林分类型（纯冷杉、纯山毛榉、纯苏格兰松、针叶混交林、落叶混交林和落叶针叶混交林）以及六个发育阶段的LAI变化，该地区以混合、异质的林地为特征。2015年从260个系统分布的样地进行的实地测量采用半球摄影法测量LAI，用N树法测量断面积（BA）和胸径（DBH），并用经过大气校正的Landsat 8 OLI影像测量归一化植被指数（NDVI）。描述性分析显示，LAI值范围从纯苏格兰松林的0.36 m²/m²到落叶混交林的6.30 m²/m²。单因素方差分析和邓肯多重范围检验证实林分类型之间存在显著差异（p < 0.01），混合林和垂直分层林分的LAI最高。发育阶段显示从幼龄（ab）到成熟（d）阶段平均LAI呈上升趋势，不过仅阶段的方差分析不显著（p = 0.378），因为阶段内变异性较高（变异系数≈31%）。Pearson相关性表明LAI与DBH（r = 0.49）和BA（r = 0.53）之间存在中等程度的正相关关系，而NDVI显示出最强的相关性（总体r = 0.75；在混交林中高达r = 0.80）。一个整合NDVI、DBH和BA的多元线性回归模型解释了LAI变异的60.6%（F = 129.7，p < 0.001；调整后的R = 0.601），其中NDVI是主要预测因子（标准化β = 0.683），其次是DBH（β = 0.326）和BA（β = 0.187）。这些发现强调了在LAI估计中整合光谱指数和结构参数的互补价值，特别是在异质林分中。混交林的结构复杂性似乎在促进冠层发育方面起着关键作用。为了提高针叶林主导或高LAI森林的估计精度，未来的研究应考虑纳入替代植被指数和激光雷达衍生的结构指标，以克服诸如光谱饱和和垂直分辨率不足等限制。这种综合方法可以显著提高森林健康和生产力监测工作的可扩展性和成本效益。
