The ever-increasing availability of remote sensing data allows production of forest attributes maps, which are usually made using model-based approaches. These map products are sensitive to various bias sources, including model extrapolation. To identify, over a case study forest, the proportion of extrapolated predictions, we used a convex hull method applied to the auxiliary data space of an airborne laser scanning (ALS) flight. The impact of different sampling efforts was also evaluated. This was done by iteratively thinning a set of 487 systematic plots using nested sub-grids allowing to divide the sample by two at each level. The analysis were conducted for all alternative samples and evaluated against 56 independent validation plots. Residuals of the extrapolated validation plots were computed and examined as a function of their distance to the model calibration domain. Extrapolation was also characterized for the pixels of the area of interest (AOI) to upscale at population level. Results showed that the proportion of extrapolated pixels greatly reduced with an increasing sampling effort. It reached a plateau (ca. 20% extrapolation) with a sampling intensity of ca. 250-calibration plots. This contrasts with results on model’s root mean squared error (RMSE), which reached a plateau at a much lower sampling intensity. This result emphasizes the fact that with a low sampling effort, extrapolation risk remains high, even at a relatively low RMSE. For all attributes examined (i.e., stand density, basal area, and quadratic mean diameter) estimations were generally found to be biased for validation plots that were extrapolated. The method allows an easy identification of map pixels that are out of the calibration domain, making it an interesting tool to evaluate model transferability over an area of interest (AOI). It could also serve to compare “competing” models at a variable selection phase. From a model calibration perspective, it could serve a posteriori, to evaluate areas (in the auxiliary space) that merit further sampling efforts to improve model reliability.

遥感数据不断增加的可用性允许生成森林属性图，这些图通常使用基于模型的方法制作。这些地图产品对各种偏差源很敏感，包括模型外推。为了在案例研究森林中确定外推预测的比例，我们使用了应用于机载激光扫描 (ALS) 飞行的辅助数据空间的凸包方法。还评估了不同抽样工作的影响。这是通过使用嵌套子网格迭代细化一组 487 个系统图来完成的，允许在每个级别将样本一分为二。对所有替代样品进行了分析，并针对 56 个独立的验证图进行了评估。外推验证图的残差被计算并检查为它们与模型校准域的距离的函数。还对感兴趣区域 (AOI) 的像素进行外推以在人口水平上进行升级。结果表明，随着采样工作量的增加，外推像素的比例大大降低。它达到了一个平台（约 20% 外推），采样强度为 ca。250 个校准图。这与模型的均方根误差 (RMSE) 的结果形成对比，后者在低得多的采样强度下达到了一个平台。这一结果强调了这样一个事实，即在抽样工作量较低的情况下，外推风险仍然很高，即使在相对较低的 RMSE 下也是如此。对于检查的所有属性（即 还对感兴趣区域 (AOI) 的像素进行外推以在人口水平上进行升级。结果表明，随着采样工作量的增加，外推像素的比例大大降低。它达到了一个平台（约 20% 外推），采样强度为 ca。250 个校准图。这与模型的均方根误差 (RMSE) 的结果形成对比，后者在低得多的采样强度下达到了一个平台。这一结果强调了这样一个事实，即在抽样工作量较低的情况下，外推风险仍然很高，即使在相对较低的 RMSE 下也是如此。对于检查的所有属性（即 还对感兴趣区域 (AOI) 的像素进行外推以在人口水平上进行升级。结果表明，随着采样工作量的增加，外推像素的比例大大降低。它达到了一个平台（约 20% 外推），采样强度为 ca。250 个校准图。这与模型的均方根误差 (RMSE) 的结果形成对比，后者在低得多的采样强度下达到了一个平台。这一结果强调了这样一个事实，即在抽样工作量较低的情况下，外推风险仍然很高，即使在相对较低的 RMSE 下也是如此。对于检查的所有属性（即 250 个校准图。这与模型的均方根误差 (RMSE) 的结果形成对比，后者在低得多的采样强度下达到了一个平台。这一结果强调了这样一个事实，即在抽样工作量较低的情况下，外推风险仍然很高，即使在相对较低的 RMSE 下也是如此。对于检查的所有属性（即 250 个校准图。这与模型的均方根误差 (RMSE) 的结果形成对比，后者在低得多的采样强度下达到了一个平台。这一结果强调了这样一个事实，即在抽样工作量较低的情况下，外推风险仍然很高，即使在相对较低的 RMSE 下也是如此。对于检查的所有属性（即、林分密度、基底面积和二次平均直径）估计通常被发现对外推的验证图有偏差。该方法可以轻松识别校准域之外的地图像素，使其成为评估模型在感兴趣区域 (AOI) 上的可迁移性的有趣工具。它还可以用于在变量选择阶段比较“竞争”模型。从模型校准的角度来看，它可以用于后验评估（在辅助空间中）值得进一步采样工作以提高模型可靠性的区域。