Live fuel moisture content (LFMC) is an important index used to evaluate the wildfire risk and fire spread rate. In order to further improve the retrieval accuracy, two ensemble models combining deep learning models were proposed. One is a stacking ensemble model based on LSTM, TCN and LSTM-TCN models, and the other is an Adaboost ensemble model based on the LSTM-TCN model. Measured LFMC data, MODIS, Landsat-8, Sentinel-1 remote sensing data and auxiliary data such as canopy height and land cover of the forest-fire-prone areas in the Western United States, were selected for our study, and the retrieval results of different models with different groups of remote sensing data were compared. The results show that using multi-source data can integrate the advantages of different types of remote sensing data, resulting in higher accuracy of LFMC retrieval than that of single-source remote sensing data. The ensemble models can better extract the nonlinear relationship between LFMC and remote sensing data, and the stacking ensemble model with all the MODIS, Landsat-8 and Sentinel-1 remote sensing data achieved the best LFMC retrieval results, with $R^2$ = 0.85, RMSE = 18.88 and ubRMSE = 17.99. The proposed stacking ensemble model is more suitable for LFMC retrieval than the existing method.

Understanding the responses of C:N:P stoichiometry and nutrient resorption to thinning is essential to evaluate the effects of management practices on biogeochemical cycling in plantation forest ecosystems. However, nutrient resorption and C:N:P stoichiometry do not always respond in the same way to various thinning intensities, and the underlying mechanisms are not well understood. In this study, we aimed to examine the mechanisms underlying the impacts of thinning on C:N:P stoichiometry in a Pinus massoniana plantation, focusing on interactions among soils, plant tissues (leaves and litter), and soil properties. We conducted four different thinning treatments to determine the effects of thinning on the C:N:P stoichiometric ratios in leaves, litter, and soil in a Pinus massoniana plantation ecosystem. Thinning significantly increased the C, N, and P content of leaves, litter, and soil (p < 0.05). The effects of thinning on C:N:P stoichiometry varied strongly with thinning intensity. Specifically, thinning significantly decreased all C:N:P stoichiometry except leaf N:P and litter C:N (p < 0.05). The N resorption efficiency (NRE) showed no significant change, but thinning significantly decreased the P resorption efficiency (PRE, p < 0.05). This suggests that thinning has inconsistent impacts on N and P cycling in Pinus massoniana plantations. In addition, these different responses suggest that soil physicochemical processes play a crucial role in regulating the effects of thinning. Thinning intensity regulates the biogeochemical cycles of C, N, and P in Pinus massoniana plantation ecosystems by affecting nutrient resorption and soil physicochemical processes. The inconsistent results obtained can be attributed to the complexities of stand environments and the redistribution of site resources following thinning. Therefore, incorporating the effects of thinning intensity into nutrient cycling models may improve predictions related to achieving long-term forest management strategies.

The Species-area relationship is one of the core issues in community ecology and an important basis for scale transformation of biodiversity. However, the effect of scale on this relationship, together with the selection of an optimal species-area model for different sampling methods, is still controversial. This study is based on the data from two sampling areas of 40 km² in size, one in a Korean pine (Pinus koraiensis Sieb. et Zucc) broad-leaved mixed forest in Mt. Changbai and the other in Jiaohe, Jilin Province. The logarithmic, power, and logistic model were established on a scale of 10 km², 20 km², and 30 km², respectively, using a nested sampling plot and random sampling plot. The goodness of the species-area model was tested by the Akaike information criterion (AIC). The results show that the sampling method affected the relationship between species and area, and the data were fitted better under random sampling compared with nested sampling. The construction of the relationship between species and area was closely related to the upper limit of the sampling area size. On a small scale (10 km²), the data were fitted best with the logarithmic and logistic model, whereas the logistic model was the best fit on a medium (20 km²) and large scale (30 km²). We evaluated the scale dependence of species-area relationship in two forests with nested and random sampling methods. We further showed that the logistic model based on the random sampling plot can explain most soundly the species-area relationship in Jiaohe and Mt. Changbai. More studies are needed in other regions to develop models to optimize sampling designs for different forest types under different density constraints at different spatial scales, and for a more accurate estimation of forest dynamics under long-term observations.

Efficiency assessment is a pivotal instrument in the pursuit of sustainable operations. It is imperative to evaluate government-funded afforestation initiatives to ensure the optimal utilisation of resources, thereby enhancing sustainability. In this study, a framework for measuring afforestation efficiency at the sub-compartment scale was established based on a Bootstrap-modified Data Envelopment Analysis (DEA) model. The empirical study included 48 afforestation sub-compartments from six districts involved in the Beijing Plains Afforestation Project. The results of the study indicate that the efficiency of the afforestation sub-compartment has much room for improvement and significant individual differences. The mean scores for comprehensive efficiency, pure technical efficiency, and scale efficiency of the sample sub-compartments were 0.646, 0.664, and 0.973, respectively. Compared to the pure technical efficiency, the scale efficiency is higher. Notably, prioritising native or climate-resilient species, adopting long-term ecological maintenance protocols, and fostering financially self-sustaining mechanisms were identified as key drivers for boosting efficiency. These findings underscore the need to embed sustainability principles—including resource optimisation and economic viability—into afforestation planning and governance to strengthen ecological restoration resilience and long-term project continuity.