Atmospheric temperature affects the growth and development of plants and has an important impact on the sustainable development of forest ecological systems. Predicting atmospheric temperature is crucial for forest management planning.Artificial neural network (ANN) and deep learning models such as gate recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network (CNN), CNN-GRU, and CNN-LSTM, were utilized to predict the change of monthly average and extreme atmospheric temperatures in Zhengzhou City. Average and extreme atmospheric temperature data from 1951 to 2022 were divided into training data sets (1951–2000) and prediction data sets (2001–2022), and 22 months of data were used as the model input to predict the average and extreme temperatures in the next month.The number of neurons in the hidden layer was 14. Six different learning algorithms, along with 13 various learning functions, were trained and compared. The ANN model and deep learning models were evaluated in terms of correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE), and good results were obtained. Bayesian regularization (trainbr) in the ANN model was the best performing algorithm in predicting average, minimum and maximum atmospheric temperatures compared to other algorithms in terms of R (0.9952, 0.9899, and 0.9721), and showed the lowest error values for RMSE (0.9432, 1.4034, and 2.0505), and MAE (0.7204, 1.0787, and 1.6224). The CNN-LSTM model showed the best performance. This CNN-LSTM method had good generalization ability and could be used to forecast average and extreme atmospheric temperature in other areas. Future climate changes were projected using the CNN-LSTM model. The average atmospheric temperature, minimum atmospheric temperature, and maximum atmospheric temperature in 2030 were predicted to be 17.23 °C, −5.06 °C, and 42.44 °C, whereas those in 2040 were predicted to be 17.36 °C, −3.74 °C, and 42.68 °C, respectively. These results suggest that the climate is projected to continue warming in the future.

Prediction of monthly average and extreme atmospheric temperatures in Zhengzhou based on artificial neural network and deep learning models

Harvesting fog is a potential and effective way to alleviate the crisis of water resource shortage. A highly efficient and economical fog harvesting method has always been a global and common goal. Here, a promising fog harvesting method by coupling plasma and micro/nano materials is proposed, which can achieve 93% fog collection efficiency with consuming power of only 0.76 W/0.04 m2. The basic method is to utilize nanoparticles to decorate both the discharge electrode and the collecting electrode of the micro/nano electrostatic fog collector. For the discharge electrode, the nanoparticles can achieve an order of magnitude higher electric field strength and a 28.6% decrease in the operating voltage (14 kV decreases to 10 kV). For the collecting electrode, a novel composite structure of hydrophobic/hydrophilic (HB/HL) is proposed. The core advantage is the directional droplet transport at the junction of HB and HL caused by surface tension can adjust the accumulated droplets on the two sides, which avoids the droplet residue and mesh blockage in the general structure. This technology provides an innovative approach for the collection of microdroplets and a new design idea for the fog collector to deal with the water crisis.

Advanced Fog Harvesting Method by Coupling Plasma and Micro/Nano Materials.

Due to the complexity and difficulty of forest resource ground surveys, remote-sensing-based methods to assess forest resources and effectively plan management measures are particularly important, as they provide effective means to explore changes in forest resources over long time periods. The objective of this study was to monitor the spatiotemporal trends of the wood carbon stocks of the standing forests in the southeastern Xiaoxinganling Mountains by using Landsat remote sensing data collected between 1989 and 2021. Various remote sensing indicators for predicting carbon stocks were constructed based on the Google Earth Engine (GEE) platform. We initially used a multiple linear regression model, a deep neural network model and a convolutional neural network model for exploring the spatiotemporal trends in carbon stocks. Finally, we chose the convolutional neural network model because it provided more robust predictions on the carbon stock on a pixel-by-pixel basis and hence mapping the spatial distribution of this variable. Savitzky–Golay filter smoothing was applied to the predicted annual average carbon stock to observe the overall trend, and a spatial autocorrelation analysis was conducted. Sen’s slope and the Mann–Kendall statistical test were used to monitor the spatial trends of the carbon stocks. It was found that 59.5% of the area showed an increasing trend, while 40.5% of the area showed a decreasing trend over the past 33 years, and the future trend of carbon stock development was plotted by combining the results with the Hurst exponent.

/pdf/simulation-of-spatial-and-temporal-distribution-of-forest-2vkr9lmk.pdf

Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning




The tree crown is a useful measure of tree vigour and is highly relevant to a tree's environmental adaptability. Crown allometry depends on environmental and stand conditions. Several studies have focussed on the effects of climate change and competitive intensity on the crown, but the regulatory role of soil resources and diversity on crown allometry and carbon allocation has been neglected.

Data from 20,994 trees in 232 mixed forests collected between 2011 and 2019 were located near four major mountain ranges in northeast China. The proposed crown width model includes the stand developmental stage, soil, climate, competition intensity, species mixture, species diversity, structural diversity and their interactions.

We observed that the cross‐species allometric scaling exponent does not conform to the universal scaling law. Our results showed that crown width increased with increasing soil bulk density, quadratic mean diameter and coefficient of diameter variation but decreased with increasing de Martonne aridity index, basal area, Simpson index and species mixture. The interaction between quadratic mean diameter and soil bulk density had a significant negative effect on crown width. The influence of a particular factor within the interaction term on crown width was modulated by the gradients of other factors. Furthermore, soil bulk density contributed more to crown width modelling than the aridity index, and structural diversity had a greater effect on crown width than species diversity.

Synthesis. Our results provide new insights into the environmental variability of crown allometry in mixed forests under global change, which is critical for improving regional and global estimates of forest biomass and carbon stocks.



Developing crown width model for mixed forests using soil, climate and stand factors

To investigate the effects of stand conditions and climatic factors on branch growth and the dynamics of branch growth over time, we constructed a mixed-effects model on branch annual length growth using data from 803 branches of 70 sample trees in Korean pine (Pinus koraiensis Sieb. et al. Zucc.) plantations in Heilongjiang Province, China. The results showed that the introduction of branch age, branch depth into crown, branch diameter with bark, height-diameter ratio of the tree, stand average diameter at breast height, summer mean temperature and summer precipitation significantly improve the model fitting effect. The increase in summer mean temperature promoted branch growth, while the increase in the branch depth into the crown, height-diameter ratio of the tree, stand average diameter at breast height, and summer precipitation negatively impacted branch growth. We found that in the second year, the annual length growth reached a maximum, decreased with increasing branch age and stabilized when the branch age was greater than 20. Younger branches responded more strongly to competitive changes. The influence of summer precipitation on branch growth was greater than that of summer mean temperature. Moreover we found that within the appropriate range, climatic factors will not have a significant impact on branch growth until the summer mean temperature is lower than 16 °C and the summer precipitation exceeds 400 mm, which will have a significant inhibitory effect on branch growth. The introduction of random effects significantly improved the predictive ability of the model, and it was reasonable to select 6 branch annual length growths per branch to estimate random parameters. Our research indicated that branch growth is influenced by the combined effects of climate and competition, which helps to make reasonable forest management decisions under global climate change. 

Modelling branch growth of Korean pine plantations based on stand conditions and climatic factors

Climate-sensitive tree height-diameter models for mixed forests in Northeastern China

The impacts of climate, competition, and their interactions on crown width for three major species in Chinese boreal forests

The applicability of height to crown base (HCB) appears to be particularly broad since the important impact on explaining tree growth dynamics. Given the high cost of measuring HCB, accurate prediction with a few trees is popular in forestry practice. In this study, a fixed-effects model (FEM), a mixed-effects model (MEM), and quantile regressions (QR) were adopted to predict HCB using data from natural secondary forests of Dahurian larch (Larix gmelinii Rupr.) in Northeastern China. Corresponding calibration techniques were applied to trees with different sample designs (random selection, the thickest tree selection, the intermediate tree selection, and the thinnest tree selection) and sample sizes (1-10 trees per plot). The results showed that all models achieved accurate HCB predictions. The QR calibration technique of 3-quantiles achieved simple and accurate consequences compared to 5-quantiles and 9-quantiles. MEM displayed the most robust and superior statistics. The selection of the two thickest trees was recommended for the MEM. For FEM and QR, the sample size should be exceeded by 5. Generally, a combination of MEM and sampling two thickest trees per plot to predict HCB is recommended as a win-win solution that neither sacrifices model prediction accuracy nor minimizes the required measurement cost.

Predicting and calibrating height to crown base: a case for Dahurian larch (Larix gmelinii Rupr.) in Northeastern China

Effects of climate and competition on crown width: a case of Korean pine plantations

The accurate assessment of forest biomass is vital to climate change mitigation. Based on forest survey data, stand biomass models can effectively assess forest biomass carbon at large scales. However, traditional stand biomass models have ignored the potential effects of the climate on stand biomass estimation. There is still a lack of research on whether or not and in what ways the effects of the climate reduce uncertainty in biomass estimation and carbon accounting. Therefore, two types of stand biomass models, including basic stand biomass models (BBMs) and climate-sensitive stand biomass models (CBMs), were developed and tested using 311 plantation plots of Korean pine (Pinus koraiensis Siebold & Zucc.), Korean larch (Larix olgensisi A. Henry), and Mongolian pine (Pinus sylvestris var. mongolica Litv.) in Northeast China. The two types of models were developed by applying simultaneous equations based on nonlinear, seemingly unrelated, regression (NSUR) to ensure additivity of the stand total and components biomass (root, stem, branch, and needle). The results of fitting and leave-one-out cross-validation (LOOCV) indicated that the CBMs performed better than the corresponding BBMs. The RMSEs of the stand total biomass decreased by 3.5% to 10.6% for the three conifer species. The influence of temperature-related climate variables on the biomass of stand components was greater than that of precipitation-related climate variables. The sensitivity of the three conifer species to climate variables was ranked as Korean pine > Mongolian pine > Korean larch. This study emphasizes the importance of combining climate variables in stand biomass models to reduce the uncertainty and climate effects in forest biomass estimation, which will play a role in carbon accounting for forest ecosystems.

https://www.mdpi.com/1999-4907/13/12/2022/pdf?version=1671430706

Sensitivity of Stand-Level Biomass to Climate for Three Conifer Plantations in Northeast China

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