Université de Picardie Jules Verne, EDYSAN (UMR CNRS-UPJV 7058), 1 rue des Louvels, 80037, Amiens Cedex, France.
INRAE, UR LESSEM, 2 rue de la Papeterie, BP 76 38402, Saint Martin d'Hères Cedex, France.
J Environ Manage. 2024 Feb;351:119865. doi: 10.1016/j.jenvman.2023.119865. Epub 2023 Dec 29.
Old-growth forests provide a broad range of ecosystem services. However, due to poor knowledge of their spatiotemporal distribution, implementing conservation and restoration strategies is challenging. The goal of this study is to compare the predictive ability of socioecological factors and different sources of remotely sensed data that determine the spatiotemporal scales at which forest maturity attributes can be predicted. We evaluated various remotely sensed data that cover a broad range of spatial (from local to global) and temporal (from current to decades) extents, from Airborne Laser Scanning (ALS), aerial multispectral and stereo-imagery, Sentinel-1, Sentinel-2 and Landsat data. Using random forests, remotely sensed data were related to a forest maturity index available in 688 forest plots across four ranges of the French Alps. Each model also includes socioecological predictors related to topography, socioeconomy, pedology and climatology. We found that the different remotely sensed data provide information on the main forest structural characteristics as defined by ALS, except for Landsat, which has a too coarse resolution, and Sentinel-1, which responds differently to vegetation structure. The predictions were quite similar considering aerial remotely sensed data, on the one hand, and satellite remotely sensed data, on the other hand. Socioecological variables are the most important predictors compared to the remote sensing metrics. In conclusion, our results indicate that a wide range of remotely sensed data can be used to study old-growth forests beyond the use of ALS and despite different abilities to predict forest structure. Accounting for socioecological predictors is indispensable to avoid a significant loss of predictive accuracy. Remotely sensed data can allow for predictions to be made at different spatiotemporal resolutions and extents. This study paves the way to large-scale monitoring of forest maturity, as well as for retrospective analyses which will show to what extent predicted maturity change at different dates.
原始森林提供了广泛的生态系统服务。然而，由于对其时空分布的了解不足，实施保护和恢复策略具有挑战性。本研究的目的是比较社会生态因素和不同遥感数据源的预测能力，以确定可以预测森林成熟度属性的时空尺度。我们评估了各种遥感数据，这些数据涵盖了广泛的空间（从局部到全球）和时间（从当前到几十年）范围，包括机载激光扫描（ALS）、航空多光谱和立体图像、Sentinel-1、Sentinel-2 和 Landsat 数据。使用随机森林，将遥感数据与在法国阿尔卑斯山四个范围内的 688 个森林样地中可用的森林成熟度指数相关联。每个模型还包括与地形、社会经济、土壤学和气候学相关的社会生态预测因子。我们发现，不同的遥感数据提供了与 ALS 定义的主要森林结构特征有关的信息，除了 Landsat 分辨率太粗，以及 Sentinel-1 对植被结构的响应不同。考虑到航空遥感数据，一方面，以及卫星遥感数据，另一方面，预测结果非常相似。与遥感指标相比，社会生态变量是最重要的预测因子。总之，我们的结果表明，广泛的遥感数据可用于研究原始森林，而不仅仅是使用 ALS，尽管预测森林结构的能力不同。考虑社会生态预测因子是必不可少的，以避免预测精度的显著损失。遥感数据可以在不同的时空分辨率和范围进行预测。这项研究为大规模监测森林成熟度以及回溯分析奠定了基础，回溯分析将显示在不同日期预测的成熟度变化在多大程度上。
