An innovative lightweight 1D-CNN model for efficient monitoring of large-scale forest composition: a case study of Heilongjiang Province, China

Large-scale forest composition mapping and change monitoring are essential for regional and national forest resource management, monitoring, and carbon stock assessment. However, the existing large-scale mapping methods are not effective enough in terms of efficiency and accuracy. To address this limitation, this study proposes a lightweight one-dimensional convolutional neural network (LW-CNN) model for forest composition mapping. The LW-CNN model is developed using Landsat imagery covering 470,700 km² obtained from Google Earth Engine (GEE) collected during two periods (2007 and 2018). The proposed LW-CNN is compared with a visual geometry group with 16 convolutional layers (VGG16), a residual network with 34 convolutional layers (Resnet34), and a residual network with 50 convolutional layers (Resnet50) in terms of model accuracy and efficiency. The factors influencing forest composition change are analyzed using the structural equation model (SEM). The results show that the proposed LW-CNN model can outperform the other three models in terms of model accuracy, achieving a mean overall accuracy (OA) of: 0.75 and efficiency of 7–22-fold. The changed forest composition from 2007 to 2018 accounts for 29.6% of the total forest area. The SEM results show that the climate factors have the most significant effect on the forest composition change. This study presents an innovative model for large-scale forest composition mapping, which is proven to be both efficient and accurate. This study also provides insights into the factors that affect the forest composition change, which could be valuable for forest resource management, monitoring, and carbon stock assessment.

大规模森林组成制图与变化监测，对于区域及国家级森林资源管理、监测与碳储量评估至关重要。然而现有大规模制图方法在效率与精度层面仍存在明显不足。为解决这一局限，本研究提出一种轻量化一维卷积神经网络（LW-CNN）模型，用于森林组成制图。该模型基于谷歌地球引擎（Google Earth Engine, GEE）获取的、覆盖470700平方千米的陆地卫星（Landsat）影像构建，影像采集时段涵盖2007年与2018年两个时期。本研究将所提LW-CNN模型与16层卷积视觉几何组网络（VGG16）、34层残差网络（ResNet34）、50层残差网络（ResNet50）从模型精度与效率两方面开展对比实验。同时借助结构方程模型（SEM, structural equation model）分析森林组成变化的影响因子。结果表明，所提LW-CNN模型在精度上优于其余三款对比模型，平均总体精度（Overall Accuracy, OA）达0.75，运算效率提升7至22倍。2007年至2018年间森林组成发生变化的区域面积占森林总面积的29.6%。结构方程模型分析结果显示，气候因子对森林组成变化的影响最为显著。本研究提出了一种适用于大规模森林组成制图的创新模型，经证实兼具高效性与精准性。本研究同时揭示了森林组成变化的关键影响因子，可为森林资源管理、监测及碳储量评估提供重要参考价值。