Abstract The southeastern U.S. produces the most industrial roundwood in the U.S. each year, largely from commercial pine plantations. The extent of plantation forests and management dynamics can be difficult to ascertain from periodic forest inventories, yet short-rotation tree plantations also present challenges for remote sensing. Here, we integrated spectral, temporal, and structural information from airborne and satellite platforms to distinguish pine plantations from natural forests and evaluate the contribution from planted forests to regional forest cover in the southeastern U.S. Within flight lines from NASA Goddard's Lidar, Hyperspectral, and Thermal (G-LiHT) Airborne Imager, lidar metrics of forest structure had the highest overall accuracy for pine plantations among single-source classifications (90%), but the combination of spectral and temporal metrics from Landsat generated comparable accuracy (91%). Combined structural, temporal, and spectral information from G-LiHT and Landsat had the highest accuracy for plantations (92%) and natural forests (88%). At a regional scale, classifications using Landsat spectral and temporal metrics had between 74 and 82% mean class accuracy for plantations. Regionally, plantations accounted for 28% of forest cover in the southeastern U.S., a result similar to plot-based estimates, albeit with greater spatial detail. Regional maps of plantation forests differed from existing map products, including the National Land Cover Database. Combining plantation extent in 2011 with Landsat-based forest change data identified strong regional gradients in plantation dynamics since 1985, with distinct spatial patterns of rotation age (east-west) and plantation expansion (interior). Our analysis demonstrates the potential to improve the characterization of dynamic land cover classes, including economically important timber plantations, by integrating diverse remote sensing datasets. Critically, multi-source remote sensing provides an approach to leverage periodic forest inventory data for annual monitoring of managed forest landscapes.

中文翻译：

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使用结构、光谱和时间遥感数据绘制美国东南部的松树种植园图

摘要 美国东南部每年生产美国最多的工业圆木，主要来自商业松树种植园。人工林的范围和管理动态可能难以从定期森林清查中确定，但短轮伐期人工林也给遥感带来了挑战。在这里，我们整合了来自机载和卫星平台的光谱、时间和结构信息，以区分松树人工林和天然林，并评估人工林对美国东南部区域森林覆盖的贡献。 (G-LiHT) 机载成像仪，森林结构的激光雷达指标在单源分类中对松林具有最高的整体准确度 (90%)，但是来自 Landsat 的光谱和时间指标的组合产生了相当的准确度 (91%)。来自 G-LiHT 和 Landsat 的组合结构、时间和光谱信息对人工林 (92%) 和天然林 (88%) 具有最高的准确度。在区域范围内，使用 Landsat 光谱和时间指标进行分类的人工林平均分类准确率在 74% 到 82% 之间。从区域来看，人工林占美国东南部森林覆盖率的 28%，结果类似于基于地块的估计，但具有更多的空间细节。人工林区域地图不同于现有的地图产品，包括国家土地覆盖数据库。将 2011 年的人工林范围与基于 Landsat 的森林变化数据相结合，确定了自 1985 年以来人工林动态的强烈区域梯度，具有明显的轮作年龄（东西向）和种植园扩张（内部）的空间模式。我们的分析表明，通过整合不同的遥感数据集，可以改善动态土地覆盖类别的特征，包括具有重要经济意义的木材种植园。至关重要的是，多源遥感提供了一种利用定期森林清查数据对管理的森林景观进行年度监测的方法。