Search Results (8)

Precise aboveground biomass (AGB) estimation of forests is crucial for sustainable carbon management and ecological monitoring. Traditional methods, such as destructive sampling, field measurements of Diameter at Breast Height with height (DBH and H), and optical remote sensing imagery, often fall short in capturing detailed spatial heterogeneity in AGB estimation and are labor-intensive. Recent advancements in remote sensing technologies, predominantly Light Detection and Ranging (LiDAR), offer potential improvements in accurate AGB estimation and ecological monitoring. Nonetheless, there is limited research on the combined use of UAS (Uncrewed Aerial System) and Backpack-LiDAR technologies for detailed forest biomass. Thus, our study aimed to estimate AGB at the plot level for Picea crassifolia forests in eastern Qinghai, China, by integrating UAS-LiDAR and Backpack-LiDAR data. The Comparative Shortest Path (CSP) algorithm was employed to segment the point clouds from the Backpack-LiDAR, detect seed points and calculate the DBH of individual trees. After that, using these initial seed point files, we segmented the individual trees from the UAS-LiDAR data by employing the Point Cloud Segmentation (PCS) method and measured individual tree heights, which enabled the calculation of the observed/measured AGB across three specific areas. Furthermore, advanced regression models, such as Random Forest (RF), Multiple Linear Regression (MLR), and Support Vector Regression (SVR), are used to estimate AGB using integrated data from both sources (UAS and Backpack-LiDAR). Our results show that: (1) Backpack-LiDAR extracted DBH compared to field extracted DBH shows about (R² = 0.88, RMSE = 0.04 m) whereas UAS-LiDAR extracted height achieved the accuracy (R² = 0.91, RMSE = 1.68 m), which verifies the reliability of the abstracted DBH and height obtained from the LiDAR data. (2) Individual Tree Segmentation (ITS) using a seed file of X and Y coordinates from Backpack to UAS-LiDAR, attaining a total accuracy F-score of 0.96. (3) Using the allometric equation, we obtained AGB ranges from 9.95–409 (Mg/ha). (4) The RF model demonstrated superior accuracy with a coefficient of determination (R²) of 89%, a relative Root Mean Square Error (rRMSE) of 29.34%, and a Root Mean Square Error (RMSE) of 33.92 Mg/ha compared to the MLR and SVR models in AGB prediction. (5) The combination of Backpack-LiDAR and UAS-LiDAR enhanced the ITS accuracy for the AGB estimation of forests. This work highlights the potential of integrating LiDAR technologies to advance ecological monitoring, which can be very important for climate change mitigation and sustainable environmental management in forest monitoring practices. Full article

Individual tree structure and wood density are important indicators of forest quality and key parameters for biomass calculation. To explore the extraction accuracy of individual tree structure parameters based on LiDAR technology, as well as the correlation between individual tree structure parameters, resistance value and wood density can be beneficial for providing new ideas for predicting wood density. Taking a 23-year-old Ginkgo plantation as the research object, the tree QSM (Quantitative Structure Model) was constructed based on terrestrial and backpack LiDAR point clouds, and the individual tree structure parameters were extracted. The accuracy of estimating structure parameters based on two types of point clouds was compared. A wood density prediction model was constructed using principal component analysis based on the resistance, diameter, tree height, and crown width. The accuracy verification was carried out and it showed that the estimation accuracies of individual tree structure parameters (DBH, tree height, and crown width) extracted from tree QSM constructed based on TLS and BLS all had R² > 0.8. The estimation accuracy of DBH based on TLS was slightly higher than that based on BLS, and the estimation accuracy of tree height and crown width based on TLS was slightly lower than that based on BLS. BLS has great potential in accurately obtaining forest structure information, improving forest information collection efficiency, promoting forest resource monitoring, forest carbon sink estimation, and forest ecological research. The feasibility of predicting the wood basic density based on wood resistance (R² = 0.51) and combined with DBH, tree height, and crown width (R² = 0.49) was relatively high. Accurate and non-destructive estimation of the wood characteristics of standing timber can guide forest cultivation and management and promote sustainable management and utilization of forests. Full article

Owing to advancements in satellite remote sensing technology, the acquisition of global land surface parameters, notably, the leaf area index (LAI), has become increasingly accessible. The Sentinel-2 (S2) satellite plays an important role in the monitoring of ecological environments and resource management. The prevalent use of the 20 m spatial resolution band in S2-based inversion models imposes significant limitations on the applicability of S2 data in applications requiring finer spatial resolution. Furthermore, although a substantial body of research on LAI retrieval using S2 data concentrates on agricultural landscapes, studies dedicated to forest ecosystems, although increasing, remain relatively less prevalent. This study aims to establish a viable methodology for retrieving 10 m resolution LAI data in forested regions. The empirical model of the soil adjusted vegetation index (SAVI), the backpack neural network based on simulated annealing (SA-BP) algorithm, and the variational heteroscedastic Gaussian process regression (VHGPR) model are established in this experiment based on the LAI data measured and the corresponding 10 m spatial resolution S2 satellite surface reflectance data in the Saihanba Forestry Center (SFC). The LAI retrieval performance of the three models is then validated using field data, and the error sources of the best performing VHGPR models (R² of 0.8696 and RMSE of 0.5078) are further analyzed. Moreover, the VHGPR model stands out for its capacity to quantify the uncertainty in LAI estimation, presenting a notable advantage in assessing the significance of input data, eliminating redundant bands, and being well suited for uncertainty estimation. This feature is particularly valuable in generating accurate LAI products, especially in regions characterized by diverse forest compositions. Full article

A novel design of a portable funnel light trap (PFLT) was presented for collecting insects in ecological studies. The trap consists of a compact plastic box equipped with a light source and power source, along with two plastic polypropylene interception vanes. The PFLT costs 18.3 USD per unit and weighs approximately 300 g. A maximum of six PFLT units can be packed in one medium-sized backpack (32 cm × 45 cm × 15 cm, 20 L), making it easier to set up multiple units in remote areas wherein biodiversity research is needed. The low cost and weight of the trap also allows for large-scale deployment. The design is customizable and can be easily manufactured to fit various research needs. To validate the PFLT’s efficacy in collecting insects across different habitat types, a series of field experiments were conducted in South Korea and Laos, where 37 trials were carried out. The PFLT successfully collected 7497 insects without experiencing battery issues or damage by rain or wind. Insect compositions and abundances differed across the three sampled habitat types: forests, grasslands, and watersides. This new FLT trap is an important tool for studying and protecting insect biodiversity, particularly in areas wherein conventional light traps cannot be deployed. Full article

This study aims to explore the correlation between the activity trajectory of Crested Ibis and meteorological changes. The trajectory data of Crested Ibis were obtained using the HQBG3621L backpack-style tracker, and the spatiotemporal characteristics of the trajectory data were analyzed to obtain information on the activity range and habitat of Crested Ibis. The Scheirer–Ray–Hare test and Kruskal–Wallis test were used to investigate the impact of meteorological factors on the activity of Crested Ibis. The study found that meteorological factors have a certain influence on the habitat selection and activity patterns of Crested Ibis. Through this research, a better understanding of the interaction between Crested Ibis and the meteorological environment can be achieved, providing a scientific basis for the conservation and ecological management of Crested Ibis. Full article

Accurate forest biomass estimation at the individual tree scale is the foundation of timber industry and forest management. It plays an important role in explaining ecological issues and small-scale processes. Remotely sensed images, across a range of spatial and temporal resolutions, with their advantages of non-destructive monitoring, are widely applied in forest biomass monitoring at global, ecoregion or community scales. However, the development of remote sensing applications for forest biomass at the individual tree scale has been relatively slow due to the constraints of spatial resolution and evaluation accuracy of remotely sensed data. With the improvements in platforms and spatial resolutions, as well as the development of remote sensing techniques, the potential for forest biomass estimation at the single tree level has been demonstrated. However, a comprehensive review of remote sensing of forest biomass scaled at individual trees has not been done. This review highlights the theoretical bases, challenges and future perspectives for Light Detection and Ranging (LiDAR) applications of individual trees scaled to whole forests. We summarize research on estimating individual tree volume and aboveground biomass (AGB) using Terrestrial Laser Scanning (TLS), Airborne Laser Scanning (ALS), Unmanned Aerial Vehicle Laser Scanning (UAV-LS) and Mobile Laser Scanning (MLS, including Vehicle-borne Laser Scanning (VLS) and Backpack Laser Scanning (BLS)) data. Full article

The paper suggests a sustainable material footprint of eight tons, per person, in a year as a resource cap target for household consumption in Finland. This means an 80% (factor 5) reduction from the present Finnish average. The material footprint is used as a synonym to the Total Material Requirement (TMR) calculated for products and activities. The paper suggests how to allocate the sustainable material footprint to different consumption components on the basis of earlier household studies, as well as other studies, on the material intensity of products, services, and infrastructures. It analyzes requirements, opportunities, and challenges for future developments in technology and lifestyle, also taking into account that future lifestyles are supposed to show a high degree of diversity. The targets and approaches are discussed for the consumption components of nutrition, housing, household goods, mobility, leisure activities, and other purposes. The paper states that a sustainable level of natural resource use by households is achievable and it can be roughly allocated to different consumption components in order to illustrate the need for a change in lifestyles. While the absolute material footprint of all the consumption components will have to decrease, the relative share of nutrition, the most basic human need, in the total material footprint is expected to rise, whereas much smaller shares than at present are proposed for housing and especially mobility. For reducing material resource use to the sustainable level suggested, both social innovations, and technological developments are required. Full article

The article assesses the material footprints of households living on a minimum amount of social benefits in Finland and discusses the consequences in terms of ecological and social sustainability. The data were collected using interviews and a questionnaire on the consumption patterns of 18 single households. The results are compared to a study on households with varying income levels, to average consumption patterns and to decent minimum reference budgets. The low-income households have lower material footprints than average and most of the material footprints are below the socially sustainable level of consumption, which is based on decent minimum reference budgets. However, the amount of resources used by most of the households studied here is still at least double that required for ecological sustainability. The simultaneous existence of both deprivation and overconsumption requires measures from both politicians and companies to make consumption sustainable. For example, both adequate housing and economic mobility need to be addressed. Measures to improve the social sustainability of low-income households should target reducing the material footprints of more affluent households. Furthermore, the concept of what constitutes a decent life should be understood more universally than on the basis of standards of material consumption. Full article