Forest inventories are essential to accurately estimate different dendrometric and forest stand parameters. However, classical forest inventories are time consuming, slow to conduct, sometimes inaccurate and costly. To address this problem, an efficient alternative approach has been sought and designed that will make this type of field work cheaper, faster, more accurate, and easier to complete. The implementation of this concept has required the development of a specifically designed software called “Artificial Intelligence for Digital Forest (AID-FOREST)”, which is able to process point clouds obtained via mobile terrestrial laser scanning (MTLS) and then, to provide an array of multiple useful and accurate dendrometric and forest stand parameters. Singular characteristics of this approach are: No data pre-processing is required either pre-treatment of forest stand; fully automatic process once launched; no limitations by the size of the point cloud file and fast computations.

To validate AID-FOREST, results provided by this software were compared against the obtained from in-situ classical forest inventories. To guaranty the soundness and generality of the comparison, different tree species, plot sizes, and tree densities were measured and analysed. A total of 76 plots (10,887 trees) were selected to conduct both a classic forest inventory reference method and a MTLS (ZEB-HORIZON, Geoslam, ltd.) scanning to obtain point clouds for AID-FOREST processing, known as the MTLS-AIDFOREST method. Thus, we compared the data collected by both methods estimating the average number of trees and diameter at breast height (DBH) for each plot. Moreover, 71 additional individual trees were scanned with MTLS and processed by AID-FOREST and were then felled and divided into logs measuring 1 m in length. This allowed us to accurately measure the DBH, total height, and total volume of the stems.

When we compared the results obtained with each methodology, the mean detectability was 97% and ranged from 81.3 to 100%, with a bias (underestimation by MTLS-AIDFOREST method) in the number of trees per plot of 2.8% and a relative root-mean-square error (RMSE) of 9.2%. Species, plot size, and tree density did not significantly affect detectability. However, this parameter was significantly affected by the ecosystem visual complexity index (EVCI). The average DBH per plot was underestimated (but was not significantly different from 0) by the MTLS-AIDFOREST, with the average bias for pooled data being 1.8% with a RMSE of 7.5%. Similarly, there was no statistically significant differences between the two distribution functions of the DBH at the 95.0% confidence level.

Regarding the individual tree parameters, MTLS-AIDFOREST underestimated DBH by 0.16 % (RMSE = 5.2 %) and overestimated the stem volume (Vt) by 1.37 % (RMSE = 14.3 %, although the BIAS was not statistically significantly different from 0). However, the MTLS-AIDFOREST method overestimated the total height (Ht) of the trees by a mean 1.33 m (5.1 %; relative RMSE = 11.5 %), because of the different height concepts measured by both methodological approaches. Finally, AID-FOREST required 30 to 66 min per ha⁻¹ to fully automatically process the point cloud data from the *.las file corresponding to a given hectare plot. Thus, applying our MTLS-AIDFOREST methodology to make full forest inventories, required a 57.3 % of the time required to perform classical plot forest inventories (excluding the data postprocessing time in the latter case). A free trial of AID-FOREST can be requested at dielmo@dielmo.com.

森林清查对于准确估计不同的树木测量和林分参数至关重要。然而，传统的森林清查耗时、执行缓慢、有时不准确且成本高昂。为了解决这个问题，已经寻求并设计了一种有效的替代方法，使这种类型的现场工作更便宜、更快、更准确、更容易完成。这一概念的实施需要开发一种专门设计的软件，称为“数字森林人工智能 (AID-FOREST)”，该软件能够处理通过移动地面激光扫描 (MTLS) 获得的点云，然后提供一系列有用且准确的树木测量和林分参数。这种方法的独特特点是：林分预处理均无需数据预处理；启动后全自动流程；不受点云文件大小和快速计算的限制。

为了验证 AID-FOREST，将该软件提供的结果与从原位经典森林清单中获得的结果进行了比较。为了保证比较的合理性和普遍性，对不同的树种、地块大小和树密度进行了测量和分析。总共选择了 76 个地块（10,887 棵树）进行经典森林清单参考方法和 MTLS（ZEB-HORIZON，Geoslam，ltd.）扫描，以获得用于 AID-FOREST 处理的点云，称为 MTLS-AIDFOREST方法。因此，我们比较了两种方法收集的数据，估计了每个地块的平均树木数和胸高 (DBH) 直径。此外，另外 71 棵单独的树木用 MTLS 扫描并由 AID-FOREST 处理，然后被砍伐并分成长度为 1 m 的原木。

当我们比较每种方法获得的结果时，平均可检测性为 97%，范围为 81.3 至 100%，每块地块的树木数量存在偏差（MTLS-AIDFOREST 方法低估），相对根均方误差 (RMSE) 为 9.2%。物种、地块大小和树木密度没有显着影响可检测性。然而，该参数受到生态系统视觉复杂度指数（EVCI）的显着影响。MTLS-AIDFOREST 低估了每个地块的平均 DBH（但与 0 没有显着差异），汇总数据的平均偏差为 1.8%，RMSE 为 7.5%。同样，在 95.0% 的置信水平下，DBH 的两个分布函数之间没有统计学上的显着差异。

关于单个树木参数，MTLS-AIDFOREST 将 DBH 低估了 0.16 % (RMSE = 5.2 %)，并将茎体积 (Vt) 高估了 1.37 % (RMSE = 14.3 %，尽管 BIAS 与 0 没有统计学上的显着差异)。然而，由于两种方法测量的高度概念不同，MTLS-AIDFOREST 方法平均高估了树木的总高度 (Ht) 1.33 m（5.1 %；相对 RMSE = 11.5 %）。最后，AID-FOREST 每公顷需要 30 到 66 分钟^-1全自动处理与给定公顷地块相对应的 *.las 文件中的点云数据。因此，应用我们的 MTLS-AIDFOREST 方法来制作完整的森林清单，需要执行经典地块森林清单所需时间的 57.3%（不包括后一种情况下的数据后处理时间）。可以通过 dielmo@dielmo.com 请求免费试用 AID-FOREST。