Above-Ground Biomass Estimation of Plantation with Complex Forest Stand Structure Using Multiple Features from Airborne Laser Scanning Point Cloud Data

Accurate forest above-ground biomass (AGB) estimation is important for dynamic monitoring of forest resources and evaluation of forest carbon sequestration capacity. However, it is difficult to depict the forest’s vertical structure and its heterogeneity using optical remote sensing when estimating forest AGB, for the reason that electromagnetic waves cannot penetrate the canopy’s surface to obtain low vegetation information, especially in subtropical and tropical forests with complex layer structure and tree species composition. As an active remote sensing technology, an airborne laser scanner (ALS) can penetrate the canopy surface to obtain three-dimensional structure information related to AGB. This paper takes the Jiepai sub-forest farm and the Gaofeng state-owned forest farm in southern China as the experimental area and explores the optimal features from the ALS point cloud data and AGB inversion model in the subtropical forest with complex tree species composition and structure. Firstly, considering tree canopy structure, terrain features, point cloud structure and density features, 63 point cloud features were extracted. In view of the biomass distribution differences of different tree species, the random forest (RF) method was used to select the optimal features of each tree species. Secondly, four modeling methods were used to establish the AGB estimation models of each tree species and verify their accuracy. The results showed that the features related to tree height had a great impact on forest AGB. The top features of Cunninghamia Lanceolata (Chinese fir) and Eucalyptus are all related to height, Pinus (pine tree) is also related to terrain features and other broadleaved trees are also related to point cloud density features. The accuracy of the stepwise regression model is best with the AGB estimation accuracy of 0.19, 0.76, 0.71 and 0.40, respectively, for the Chinese fir, pine tree, eucalyptus and other broadleaved trees. In conclusion, the proposed linear regression AGB estimation model of each tree species combining different features derived from ALS point cloud data has high applicability, which can provide effective support for more accurate forest AGB and carbon stock inventory and monitoring.

Formamide deionized accelerates the somatic embryogenesis of Cunninghamia lanceolata

Aim of the study: To improve the efficiency of the somatic embryogenesis (SE) in Cunninghamia lanceolata. Area of the study: The study was conducted at Nanjing Forestry University (Nanjing, China). Material and methods: Immature cones of C. lanceolata, genotype 01A1 which was planted in Yangkou State-owned Forest Farm (Fujian, China), were used to induced callus. These calli were used to induce SE, concentration gradients of 0 g/L, 0.01134 g/L, 0.1134 g/L, 1.1134 g/L and 11.34 g/L of FD was added, to explore the optimal concentration for promoting SE of C. lanceolata. Main results: Low concentration of FD promoted the maturation of somatic embryos, while high concentration of FD lead to browning of embryogenic callus. The seedling rate and rooting number of seedlings induced by different concentrations of FD were significantly different. Research highlights: This study may aid in the rapid maturation of C. lanceolata somatic embryos and is useful for accelerated C. lanceolata breeding.

Influence of durative drainage melioration on the soil macrofauna (The Leningrad region)

The soil-zoological research took place in 2018 on the territory of the soil-hydromeliorative station “Malinovsky” of Lisinsky educational-experimental forest farm (Tosnensky district, Leningrad region, Russia) which was drained in 1973-1974. The purpose of this research is a comparative evaluation of the density of the population and taxonometric variety of eco-functional groups of macrofauna on the Gleysols Histic Drainic after 45 year draining period and the similar biotop without drainage. The constitution of group and species and the trophic structure of soil macrofauna have been researched. 118 individuals of geobionts out of 3 types and 6 classes have been collected. It’s been stated that zoophages are the trophic dominating group. After 45 year draining period a poor groups and species constitution, a low number of large soil invertebrates and a small share of saprophages have survived which corresponds to the fauna of the soil in oligotrophic bogs.

Conversion of natural forests to farmlands and its associated woody species diversity and carbon stocks in a span of 33 years (1984 to 2016): in the case of southwestern Ethiopia

Background Gura-Ferda forest is one of the Afromontane rainforests in the southwestern region of Ethiopia. However, since 1984, large parts of this forest have become increasingly disturbed and fragmented due to forest conversion into forest farm interface and farmlands. The study was conducted to assess changes of woody species diversity and carbon stock in association with the conversion of natural forest to forest farm interface and farmlands. Methods Data were collected from natural forest, forest farm interface and farmland which are historically forest lands before 1984. A total of 90 nested plots (20m×20m for natural forest and forest farm interface; 50m*100m for farmland)) were established for inventory of woody species. Three 1m×1m subplots were established to collect litter and soil samples. A total of 180 soil samples were collected. The total carbon stocks were estimated by summing carbon stock in the biomass and soil (0-60 cm depth). Results Results showed that Shannon-Wiener diversity (H’) in forest farm interface (H’ = 1.42±0.49) is significantly lower than that of natural forest (H’ = 2.72±0.31) but significantly higher than farmland (H’ = 1.08±0.57). The total carbon stocks of natural forest (388.54±161.63 Mg C ha-1) were approximately 1.53 and 2.67 times higher than that of forest farm interface (252.95±41.86 Mg C ha-1) and farmland (145.58±25.94 Mg C ha-1). Conclusion Our study revealed that along the conversion gradient of natural forest to forest farm interface and farmland there was a significant change of woody species diversity and carbon stocks.

Forest Farm Fire Drone Monitoring System Based on Deep Learning and Unmanned Aerial Vehicle Imagery

Forest fires represent one of the main problems threatening forest sustainability. Therefore, an early prevention system of forest fire is urgently needed. To address the problem of forest farm fire monitoring, this paper proposes a forest fire monitoring system based on drones and deep learning. The proposed system aims to solve the shortcomings of traditional forest fire monitoring systems, such as blind spots, poor real-time performance, expensive operational costs, and large resource consumption. The image processing techniques are used to determine whether the frame returned by a drone contains fire. This process is accomplished in real time, and the resultant information is used to decide whether a rescue operation is needed. The proposed method has simple operations, high operating efficiency, and low operating cost. The experimental results indicate that the relative accuracy of the proposed algorithm is 81.97%. In addition, the proposed technique provides a digital ability to monitor forest fires in real time effectively. Thus, it can assist in avoiding fire-related disasters and can significantly reduce the labor and other costs of forest fire disaster prevention and suppression.

Mechatronics design of self-adaptive under-actuated climbing robot for pole climbing and ground moving

Climbing robots have broad application prospects in aerospace equipment inspection, forest farm monitoring, and pipeline maintenance. Different types of climbing robots in existing research have different advantages. However, the self-adaptability and stability have not been achieved at the same time. In order to realize the self-adaptability of holding and climbing stability, this work proposes a new type of climbing robot under the premise of minimizing the driving source. The robot realizes stable multifinger holding and wheeled movement through two motors. At the same time, the robot has two different working modes, namely pole climbing and ground crawling. The holding adaptability and climbing stability are realized by underactuated holding mechanism and model reference adaptive controller (MRAC). On the basis of model design and parameter analysis, a prototype of the climbing robot is built. Experiments prove that the proposed climbing robot has the ability to stably climb poles of different shapes. The holding and climbing stability, self-adaptability, and climbing and crawling speed of the proposed climbing robot are verified by experiments.

Biomass Estimation, Nutrient Content, and Decomposition Rate of Shoot Sheath in Moso Bamboo Forest of Yixing Forest Farm, China

The biomass, nutrient content and decomposition rate of shoot sheaths remain unexplored in the study of Moso bamboo forests. The rapid growth of shoots means many bamboo sheaths are produced each year, and therefore should not be neglected in the study of the Moso bamboo ecosystem. In our study, we selected 160 bamboo shoots of different sizes in Yixing forest farm, Jiangsu Province. Our analysis was based on the allometric growth equation, using diameter at breast height (DBH), internode length of bamboo at breast height (IL), and bamboo height (H) as independent variables to establish the biomass model of shoot sheaths using all samples. In addition, we also measured the nutrient content of shoots and estimated the decomposition rate of shoots by setting up litter decomposition bags. Our results found that logarithmic regression should be used to fit the biomass model of shoot sheaths. From the perspective of practical application, model W3 fitting DBH and IL was determined. The order of the nutrient elements in the shoot sheath is C > N > K > P. Decomposition tests showed that it took 0.47 years for 50% of sheaths to decompose, and 3.15 years for all sheaths to decompose.

Using a Simple Model to Determine the Best Management Regimes for Plantations at the Stand Level: A Case Study of Moshao Forest Farm in the Red-Soil Hilly Region of Southern China

Plantations in Southern China are experiencing several major problems concerning even-aged forest structures and dwindling ecosystem services under traditional forest management. The objective of this study was to determine the best management regimes (BMRs) for sustainable forest management using the Moshao forest farm as a case study. We constructed a framework for BMR modeling characterized by highly scheduled timber production (STP), low fluctuations in periodically scheduled timber harvest levels (FPS), and age class structure (ACS) at the end of the planning horizon. A paired analysis was conducted between the three indicators to identify suitable management planning for long-term timber production. Our results suggest that STP, FPS, and ACS are correlated, enabling the control of these forest performance indicators by setting various harvesting intensities in a planning horizon. We found that management regimes (MRS) with cutting area percentages from 20% to 40% and a cutting period of 10 years combined with small-area clear-cutting (≤5 ha) are optimal (MR6–MR10) for the Moshao forest farm in Southern China. In particular, MR with a cutting area percentage of 35% is the best option (MR9). These findings suggest that an applicable MR is designed by identifying the optimal harvesting intensity. The current local harvesting intensity can be properly increased to balance between timber production and ecological impacts on plantations.

Evaluation of Five Gas Diffusion Models Used in the Gradient Method for Estimating CO2 Flux with Changing Soil Properties

The gradient method used to estimate soil CO2 flux is distinctive because it can provide additional information about CO2 production and consumption of soil profile. However, choosing an appropriate gas diffusion model with confidence with the gradient method is a big challenge. There is no universal optimal diffusion model but only the most suitable model in specific soils. This paper evaluates the applicability of five commonly used diffusion models in laboratory with changing soil properties and in a forest farm, respectively. When soil moisture, bulk density and fertility status were changed in the laboratory, the applicability of the five diffusion models was discussed. Moreover, this paper shows diurnal variation of soil CO2 flux estimated by the gradient method under four different climatic conditions in the forest farm, and the applicability of the five models was also analyzed. Both laboratory and forest experimental results confirm that the estimating accuracy of the Moldrup model is the highest, followed by the Millington–Quirk model, while those of the Penman, Marshall and Penman–Millington–Quirk models are poor. Furthermore, the results indicate that soil CO2 flux estimated by the gradient method is highly sensitive to the diffusion model and insensitive to the changes of soil properties. In general, the gradient method can be used as a practical, cost-effective tool to study soil respiration only when the appropriate diffusion model is first determined.