Traditional field surveys are expensive, time-consuming, laborious, and difficult to perform, especially in mountainous and dense forests, which imposes a burden on forest management personnel and researchers. This study focuses on predicting forest growing stock, one of the most significant parameters of a forest resource assessment. First, three schemes were designed—Scheme 1, based on the study samples with mixed tree species; Scheme 2, based on the study samples divided into dominant tree species groups; and Scheme 3, based on the study samples divided by dominant tree species groups—the evaluation factors are fitted by least-squares equations, and the non-significant fitted-factors are removed. Second, an overall evaluation indicator system with 17 factors was established. Third, remote sensing images of Landsat Thematic Mapper, digital elevation model, and the inventory for forest management planning and design were integrated in the same database. Lastly, a backpropagation neural network based on the Levenberg–Marquardt algorithm was used to predict the forest growing stock. The results showed that the group estimation precision exceeded 90%, which is the highest standard of total sampling precision of inventory for forest management planning and design in China. The prediction results for distinguishing dominant tree species were better than for mixed dominant tree species. The results also showed that the performance metrics for prediction could be improved by least-squares equation fitting and significance filtering of the evaluation factors.

It is quite necessary to explore some more efficient and reliable estimation models which could integrate or, in some cases, substitute the traditional and expensive measuring techniques in forest resources management owing to the rising investigation costs. Thanks to their flexibility and adaptability, artificial neural networks (ANN) constitute a valid approach for modelling complex long-lived dynamic forest ecosystems.The evaluation indexes set was established, including 17 factors: elevation, slope, aspect, surface curvature, solar radiation index, topographic humidity index, tree ages, the soil depth, the A-layer depth of soil, canopy density, Normalized Difference Vegetation Index (NDVI), and the spectral characteristics of the bands from Enhaced Thematic Mapper (ETM+) or Thematic Mapper (TM), Band 1 to Band 5, and Band 7 from Landsat. Then, integrating the remote sensing images of ETM+ or TM, Digital Elevation Model (DEM), and forest resource planning investigation data of fir of the key forestry city of Longquan, Zhejiang Province, China, the membership of each factor was empirically fitted by polynomials, and the forest volumes were estimated via an improved back propagation (BP) neural network (NN) model with Levenberg-Marquardt (LM) optimization algorithm (LM-BP). The results showed that the average individual relative errors (IARE) were from 26.38% to 34.41%; the group relative errors (GRE) were from 2.04% to 6.69%, and all of the group estimation precisions were more than 90% which is the highest standard of overall sampling accuracy about volume of forest resource inventory in China.

The diameter at breast height (DBH) is an important factor used to estimate important forestry indices like forest growing stock, basal area, biomass, and carbon stock. The traditional DBH ground surveys are time-consuming, labor-intensive, and expensive. To reduce the traditional ground surveys, this study focused on the prediction of unknown DBH in forest stands using existing measured data. As a comparison, the tree age was first used as the only independent variable in establishing 13 kinds of empirical models to fit the relationship between the age and DBH of the forest subcompartments and predict DBH growth. Second, the initial independent variables were extended to 19 parameters, including 8 ecological and biological factors and 11 remote sensing factors. By introducing the Spearman correlation analysis, the independent variable parameters were dimension-reduced to satisfy very significant conditions (p ≤ 0.01) and a relatively large correlation coefficient (r ≥ 0.1). Finally, the remaining independent variables were involved in the modeling and prediction of DBH using a multivariate linear regression (MLR) model and generalized regression neural network (GRNN) model. The (root-mean-squared errors) RMSEs of MLR and GRNN were 1.9976 cm and 1.9655 cm, respectively, and the R2 were 0.6459 and 0.6574 respectively, which were much better than the values for the 13 traditional empirical age–DBH models. The use of comprehensive factors is beneficial to improving the prediction accuracy of both the MLR and GRNN models. Regardless of whether remote sensing image factors were included, the experimental results produced by GRNN were better than MLR. By synthetically introducing ecological, biological, and remote sensing factors, GRNN produced the best results with 1.4688 cm in mean absolute error (MAE), 13.78% in MAPE, 1.9655 cm for the RMSE, 0.6574 for the R2, and 0.0810 for the Theil’s inequality coefficient (TIC), respectively. For modeling and prediction based on more complex tree species and a wider range of samples, GRNN is a desirable model with strong generalizability.

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