High Canopy Density Research Articles

Stability Detection of Canopy RGB Images for Different Underlying Surfaces Based on SVM

This study aims to investigate the impact of different environmental conditions on the stability of RGB images in ecological sites and the anti-interference properties of vegetation images on different underlying surfaces. Three vegetation types including sugarcane, forest, and karst (mainly shrub and grass) are used to segment green vegetation using machine learning, and the RGB vegetation indices are calculated using color channel data. Then, The effect of weather, season, and time period on different types of vegetation indices are studied, which provide technical references for quantitative application of RGB image data. The results indicate the following: ① For the vegetation with high canopy density, the SVM machine learning segmentation algorithm used in this study is more applicable, as the RGB image segmentation accuracy of sugarcane and forest is significantly higher than karst. For different weather conditions, the segmentation accuracy of sugarcane and forest RGB images on sunny or cloudy days is higher than that on rainy or foggy days, but the effect on sparse vegetation in karst is not obvious. Additionally, the segmentation accuracy of different vegetation types has a small increase with NLM filter processing. ② Change in weather, season, and time can affect the stability of the image index. For different weather conditions, the vegetation indices of sugarcane and forest images on sunny or cloudy days are the more stable (ExGR, outlier proportion between 3.25% and 5.63%), while on rainy and foggy days they are less stable (ExR, outlier proportion between 17.60% and 21.59%). For different seasons, the stability of the sugarcane and karst image index obtained in spring is better (ExG, outlier proportion between 3.32% and 6.88%), while the stability of the forest image index obtained in summer is better (ExR, outlier proportion = 10.32%). For different times within a day, the sugarcane image index obtained in the morning is more stable (ExGR, outlier proportion = 4.62%), while the stability of the forest image index obtained in the afternoon is better (ExGR, outlier proportion = 9.24%). ③ The stability of the sugarcane image index is more affected by weather and season. For forest, the influence of the weather is more obvious than the season. But, for karst, the effect of season on the vegetation index is greater than that of weather.

CONDITIONS OF THE OAK FORESTS GROWTH AND CULTIVATION ON THE TOP SURFACES AND SLOPES OF THE NEAR-VOLGA AND ERGENI UPLANDS AND THE PLAINS OF THE SAL-MANYCH INTERFLUVE

Planted oak forests in the south of European Russia are of great conservation and economic importance. The widespread practice of their creation in dry steppes and semideserts dates back to the 1950-1970s and allows to form oak (Quercus robur L.) plantations with longevity up to 40-60 years. The aim of the study is to investigate the growth features of oak trees under different soil-climatic and biocenotic conditions and to suggest methods to increase their longevity.The research was carried out in 2011-2016 in the south of the Near-Volga Upland, the northern part of the Ergeni Upland and lowland plains of the Sal-Manych interfluve at 28 temporary sample plots using common methods of forest inventory and regression data analysis.It has been established that the viability of oak is limited by unfavorable topographic position of plantations, increased climate dryness (HC 0,4-0,6), high content of soil carbonates and shallow-lying horizon of their accumulation, as well as the presence of shrub layer and other competitors for moisture. Under these conditions, afforestation of depressions on the plains and shady slopes with pure plantations of oak and other species without shrubs should gain advantage. The methods of their creation should be modified depending on the topographic position of particular forest area and the climate dryness.So, the main soil cultivation in depressions could last 1-2 years, applying the system of black fallow with plantation plowing. It is advisable to keep 3,5-4,0-meter row spacing, frequent tending, and intensive clearing of young tree stands. Maturer plantations need careful sanitary-selective cuttings, mainly the low-type ones, and preservation of high canopy density. Poor water availability and deterioration of plantations should be prevented by construction of dams.Reclamation plantations of low marketability are created on flat areas after 2-3 years of deeper basic tillage. The carbonate horizon should be loosened and a large moisture reserve is needed. Row spacing of 2,5-3,0 meters is reasonable, accelerating the canopy closure. The area of tree nutrition is expanded by low-intensive uniformly selective cuttings, forming more open stands. The longevity of oak in already existing plantations could be increased by eliminating its competitors, such as living ground cover, shrubs and associated species.

Estimation of Picea Schrenkiana Canopy Density at Sub-Compartment Scale by Integration of Optical and Radar Satellite Images

This study proposes a novel approach to estimate canopy density in Picea Schrenkiana var. Tianschanica forest sub-compartments by integrating optical and radar satellite data. This effort is aimed at enhancing methodologies for forest resource surveys and monitoring, particularly vital for the sustainable development of semi-arid mountainous areas with fragile ecological environments. The study area is the West Tianshan Mountain Nature Reserve in Xinjiang, which is characterized by its unique dominant tree species, Picea Schrenkiana. A total of 411 characteristic factors were extracted from Gaofen-2 (GF-2) sub-meter optical satellite imagery, Gaofen-3 (GF-3) multi-polarization synthetic aperture radar satellite imagery, and digital elevation model (DEM) data. Consequently, 17 characteristic parameters were selected based on their correlation with canopy density data to construct an estimation model. Three distinct models were developed, including a multiple stepwise regression model (a linear approach), a Back Propagation (BP) neural network model (a neural network-based method), and a Cubist model (a decision tree-based technique). The results indicate that combining optical and radar image characteristics significantly enhances accuracy, with an Average Absolute Percentage Precision (AAPP) value improvement in estimation accuracy from 76.50% (with optical image) and 78.50% (with radar image) to 78.66% (with both). Of the three models, the BP neural network model achieved the highest overall accuracy (79.19%). At the sub-component scale, the BP neural network model demonstrated superior accuracy in low canopy density estimation (75.37%), whereas the Cubist model, leveraging radar image characteristics, excelled in medium density estimations (87.46%). Notably, the integrated Cubist model combining optical and radar data achieved the highest accuracy for high canopy density estimation (89.17%). This study highlights the effectiveness of integrating optical and radar data for precise canopy density assessment, contributing significantly to ecological resource monitoring methodologies and environmental assessments.

A new method for individual treetop detection with low-resolution aerial laser scanned data

In the past decade, the use of three-dimensional forest information from airborne Light Detection and Ranging (LiDAR) has become widespread in forest inventories. Accurate Individual Treetop Detection (ITD) and crown boundary delineation using LiDAR data are critical for obtaining precise inventory metrics. To address this need, we introduced a novel growing tree region (GTR)-driven ITD method that utilizes canopy height models (CHM) derived from very low-resolution airborne LiDAR data. The GTR algorithm consists of three key stages: (i) preserving all height layers through incremental cutting and stacking of CHM; (ii) employing a three-layer concept to identify individual treetops; and (iii) refining the detected treetops using a distance-based filter. Our method was tested in five temperate forests across Central Europe and was compared against the widely-used local maxima (LM) search combined with an optimized variable window filtering (VWF) technique. Our results showed that the GTR method outperformed LM with VWF, particularly in forests with high canopy density. The achieved root mean square accuracies were 74% for the matching rate, 19% for commission errors, and 27% for omission errors. In comparison, the LM with the VWF method resulted in a matching rate of 71%, commission errors of 20%, and omission errors of 31%. To facilitate the application of our algorithm, we developed an R package called TREETOPS, which seamlessly integrates with the lidR package, ensuring compatibility with existing treetop-based segmentation methods. By introducing TREETOPS, we provide the most accurate open-source tool for detecting treetops using low-resolution LiDAR-derived CHM.

Crown Density Relationship to Microclimate and Comfort Index in Telaga Sari City Forest, Balikpapan, East Kalimantan, Indonesia

The development of urban areas that are increasingly developing causes the area of green open space to decrease, and urban buildings that are increasingly denser result in an increase in local temperatures in the city. The presence of vegetation will provide cooler air. Furthermore, the urban microclimate is an interesting thing to correlate with the presence of vegetation somewhere. The Telaga Sari City Forest is one of Balikpapan’s green open space places. This research analyz the relationship between crown density microclimate and comfort index. The method for the percentage of crown density used Habitapp App. To measure the microclimate used Thermohygrometer, and Lux Meter. From the research results, it is known that, at the Telaga Sari City Forest there is a strong relationship between crown density and microclimate, namely light intensity, temperature, humidity, and the comfort index/THI, successively the multiple R values of 0.87, 0.83, 0.78 and 0.81. The linear equations namely y = -23.677x + 1986, y = -0.0317x + 31.096, y = 0.119x + 65.891 and y = -0.0232x + 29.007. In order to create a favorable microclimate in the city and provide green open space, it required to plant trees with high canopy density values.

More than sedum: Colonizing weedy species can provide equivalent green roof ecosystem services

Green roofs are typically constructed for their provision of both environmental and associated economic benefits including increased habitat, thermal regulation, mitigation of air and noise pollution, and stormwater retention. The provision of these various ecosystem services is sharply influenced by plant species composition, with particular species and traits known to excel at specific services. For this reason, increased biodiversity can improve the overall provision of ecosystem services. However, one key contributor to green roof biodiversity, colonizing species, is understudied. In this study we examine the contribution of common colonizing species (lawn weeds) and one green roof species, to three important ecosystem services: stormwater retention, temperature reduction, and nitrogen retention. This experiment used replicated green roof modules to examine 13 different treatments: eight colonizing species in monoculture, one green roof species monoculture, two treatments with a mix of colonizing species, one treatment where vegetation was allowed to spontaneously colonize, and one substrate only control. Results from this study show that mixtures of colonizing species performed well for all three monitored ecosystem services, with a few of the monocultures, especially those with high biomass or canopy density, performing similarly. While it is unlikely that green roof installations would proceed equally well without adding vegetation, our results indicate that colonizing species can support a viable green roof ecosystem. This study shows that spontaneous growth and/or allowing new species to colonize may be a viable design alternative for green roofs, decreasing cost while maintaining the desired ecosystem services.

A Simulation Study of the Impact of Urban Street Greening on the Thermal Comfort in Street Canyons on Hot and Cold Days

The urban heat island effect has become a widely concerning issue worldwide. Many researchers have made great efforts to improve the summer thermal comfort of urban street canyons by optimizing street greening. Relatively less research has focused on how to improve the thermal comfort of street canyons by optimizing street greening during cold days. Many researchers have proposed strategies to improve the summer thermal comfort of street canyons using road greening. This may have a significant negative impact on the winter thermal comfort of street canyons due to the lack of consideration of the impact on hot and cold days simultaneously, especially when the road green space is mainly composed of evergreen tree species. We aimed to explore the impacts of urban street greening on thermal comfort on hot and cold days at the same time. We used Zhutang West Road in Changsha, China, as an example and built six different models to explore the impacts of the street vegetation types, number of street trees, tree heights, crown widths, and Leaf Area Index on the thermal comfort of the street canyon. In addition, we also considered the impact of different building features and wind directions on the thermal comfort of the street canyon. We employed ENVI-met (version 5.5.1) to simulate different urban street greening models. The results show that the model with a high tree canopy density, tall trees, large and dense crowns, and sufficient building shade has good thermal comfort on hot days (the average physiological equivalent temperature (PET) is 31.1 °C for the study period) and bad thermal comfort on cold days (the PET is 13.3 °C) when it is compared with the other models (the average PETs are 36.2 °C, 31.5 °C, 41.5 °C, 36.2 °C, and 35.5 °C, respectively, on hot days and for other models). In addition, the model with a very large number of short hedges has a positive impact on thermal comfort during hot days (the PET is 31.1 °C). The PET value of another comparable model which does not have hedges is 31.5 °C. Even if the model with a small building area has good ventilation, the small building shade area in the model has a more obvious impact and the model has relatively good thermal comfort during cold days (the PET is 14.2 °C) when it is compared to models with bigger building areas (the PET is 13.9 °C). In summer, when the wind is parallel to the direction of the street canyon, the wind speed in the street canyon is high and the model has relatively good thermal comfort (the PET is 35.5 °C) compared with another model which has different wind direction and lower wind speed at the street canyon (the PET is 36.2 °C). In winter, when the wind is perpendicular to the direction of the street canyon, buildings and trees have a strong windproof effect and this is beneficial to the improvement of thermal comfort (the PET is 15.3 °C for this model and 13.9 °C for another comparable model). This research lays a solid foundation and encourages people to think about the impact of building and tree composition and configuration on the thermal comfort of street canyons during hot and cold days simultaneously.

Seasonal Variation in the Thermal Environment and Health-Related Factors in Two Clustered Recreational Bamboo Forests

Forest thermal environments and health-related factors have a significant impact on user experience and physical benefits. Therefore, it is important to study changes in the thermal environment and health-related factors in recreational forests. Clustered bamboo forests have unique structures featuring high canopy density and extensive understory spaces suitable for recreational activities. However, there is no relevant report on the recreational use of these forests. This study investigated seasonal characteristics in the thermal comfort and health-related factors in two clustered bamboo forests in Southwest China. Microenvironmental parameters and health-related factors (negative air oxygen ions (NAI), airborne particulate matter, airborne microorganisms, and biogenic volatile organic compounds (BVOCs)) were measured in four seasons. The microenvironmental parameters were converted into a physiological equivalent temperature (PET) for each period. The results showed that (1) most of the time, the thermal comfort, air particle, NAI, and bacteria concentrations in the two bamboo forests were superior to the controls and met the standard for recreational activities; (2) thermal comfort environments and health-related factors levels varied between two bamboo forests; and (3) the most abundant compounds in the two bamboo forests in each season were leaf alcohol and 2-hexenal. The two clustered bamboo forests provided a comfortable thermal environment and had clean air and bactericidal abilities in all seasons. The forests emitted BVOCs with fresh grass and leaf fragrances, helping to alleviate the sense of depression among visitors. The results confirm that clustered bamboo forests can provide suitable recreational conditions. The results can be used to guide the management of recreational forests and provide support for the development of bamboo forests.

Efficient detection and picking sequence planning of tea buds in a high-density canopy

Real-time robotic tea picking ensures the economic benefits of the well-known high-quality tea industry. Efficient tea bud detection and picking sequence planning are two challenges that hinder the development of these robots. To this end, two lightweight neural networks are investigated to tackle these two problems. A state-of-the-art detection network YOLOX-S is first deployed to quickly identify tea buds. Second, the picking sequence planning of the detected tea buds is formulated as a traveling salesman problem (TSP). Then a modified pointer network is proposed to solve the TSP, improving the pointer network by replacing its recurrent neural network with a simple self-attention layer and optimizing its parameters using a reinforcement learning algorithm. The experimental results show that YOLOX-S achieves an average precision of 0.642 and an average running time of 17.43 ms; for the problem of up to 100 tea buds in the two-dimensional interval [0, 1] × [0, 1], the modified pointer network solves the TSP with an average path length of 8.30 and an average running time of 1.69 ms. These results demonstrate that YOLOX-S and the modified pointer network can efficiently solve the tea bud detection and picking sequence planning problem without losing too much accuracy, which provides technical support for real-time tea-harvesting robots.

Towards Intricate Stand Structure: A Novel Individual Tree Segmentation Method for ALS Point Cloud Based on Extreme Offset Deep Learning

Due to the complex structure of high-canopy-density forests, the traditional individual tree segmentation (ITS) algorithms based on ALS point cloud, which set segmentation threshold manually, is difficult to adequately cover a variety of complex situations, so the ITS accuracy is unsatisfactory. In this paper, a top-down segmentation strategy is adopted to propose an adaptive segmentation method based on extreme offset deep learning, and the ITS set aggregation strategy based on gradient change criterion is designed for the over-segmentation generated by random offset, and the precise ITS is realized. Firstly, the segmentation sub-plot is set as 25 m × 25 m, the regional point cloud and its treetop are marked, and the offset network is trained. Secondly, the extreme offset network is designed to carry out spatial transformation of the original point cloud, and each point is offset to the position near the treetop to obtain the offset point cloud with a high density at the treetop, which enhances the discrimination among individual trees. Thirdly, the self-adaptive mean shift algorithm based on average neighboring distance is designed to divide and mark the offset point cloud. Fourthly, the offset point cloud, after clustering, is mapped back to the original space to complete the preliminary segmentation. Finally, according to the gradient change among different canopies, the ITS aggregation method is designed to aggregate adjacent canopies with a gentle gradient change. In order to investigate the universality of the proposed method on different stand structures, two coniferous forest plots (U1, U2) in the Blue Ridge area of Washington, USA, and two mixed forest plots (G1, G2) in Bretten, Germany, are selected in the experiment. The learning rate of the deep network is set as 0.001, the sampled point number of the sub-plot is 900, the transformer dimension is 512 × 512, the neighboring search number of points is 16, and the number of up-sampling blocks is 3. Experimental results show that in mixed forests (G1, G2) with complex structures, the F-score of the proposed method reaches 0.89, which is about 4% and 7% higher than the classical SHDR and improved DK, respectively. In high-canopy-density areas (U2, G2), the F-score of the proposed method reaches 0.89, which is about 3% and 4% higher than the SHDR and improved DK, respectively. The results show that the proposed method has high universality and accuracy, even in a complex stand environment with high canopy density.

Artificial Intelligence and Urban Green Space Facilities Optimization Using the LSTM Model: Evidence from China

Urban road green belts, an essential component of Urban Green Space (UGS) planning, are vital in improving the urban environment and protecting public health. This work chooses Long Short-Term Memory (LSTM) to optimize UGS planning and design methods in urban road green belts. Consequently, sensitivity-based self-organizing LSTM shows a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE) of 1.75, 1.12, and 6.06, respectively. These values are superior to those of LSTM, XGBoost, and SVR. Furthermore, we configure three typical plant community models using the improved LSTM model and found that different plant community configurations have distinct effects on reducing PM 2.5 concentrations. The experimental results show that other plant community configuration models have specific effects on reducing PM 2.5 concentrations, and the multi-layered green space with high canopy density in the community has a better impact on PM 2.5 reduction than the single-layer green space model with low canopy density. We also assess the reduction function of green road spaces on PM 2.5, which revealed that under zero pollution or slight pollution (PM 2.5 < 100 μg.m−3), the green space significantly reduces PM 2.5. In UGS planning, the proposed model can help reveal UGS spatial morphology indicators that significantly impact PM 2.5 reduction, thereby facilitating the formulation of appropriate green space planning strategies. The finding will provide primary data for selecting urban road green space plant configuration.

Characterization of the layered SIF distribution through hyperspectral observation and SCOPE modeling for a subtropical evergreen forest

Solar-induced chlorophyll fluorescence (SIF) has been widely used as a prospective proxy of plant photosynthesis to accurately detect the response of vegetation to climate change and study terrestrial ecosystem carbon cycle. However, the challenge of estimating vegetation gross primary product (GPP) based on SIF observations remains unresolved due to the confounding effects of leaf physiology and canopy structure, especially the vertical heterogeneity of plant attributes. Radiative transfer models are ideally suited to investigate the variability in radiance signals including fluorescence, but previous studies lack vertically layered spectra to validate the model. In this study, we developed a vertically layered hyperspectral system and proposed an innovative framework for assessing vertical characteristics of SIF and improving the estimation of GPP based on SIF in a subtropical evergreen forest. The global sensitivity analysis on Soil-Canopy-Observation of Photosynthesis and Energy fluxes (SCOPE) model with vertical profiles showed that chlorophyll content (Cab), leaf inclination distribution (LIDFa), leaf area index (LAI), and senescent material (Cs) dominate the vertical variations in reflectance (Ref) and SIF. We found that the vertical characteristics of SIF were mainly impacted by these parameters of the adjacent vertical layer, except for the effects of LAI across the vertical layers on the observed understory SIFU, which were approximately equivalent. 7.8 ± 1.7% of the SIFU was transferred to the top of canopy, contributing up to 17% of the SIF observed on the top of canopy (SIFTOC) during the year. Furthermore, our findings suggest that substituting the observed SIFTOC with the total emitted SIF, particularly from the simulated overstory and understory SIF, could enhance the correlation between GPP and SIF with an increased R2 (ΔR2 = 0.09). This study highlights the importance of accounting for the contribution of the layered SIF in the quantification of the total SIF emission and can benefit the GPP estimation based on SIF signals in subtropical evergreen forests with high canopy density.

Land-use effects on aquatic macroinvertebrate diversity in subtropical highland grasslands streams

Knowledge of the frequency and occurrence of macroinvertebrates throughout landscapes may clarify the effects of anthropic impacts on aquatic systems and help guide conservation actions for watersheds. We evaluated macroinvertebrate α and β diversity in streams in four different subtropical phytophysiognomies: highland grasslands with arboreal riparian vegetation, highland grasslands without arboreal riparian vegetation, mixed ombrophilous forest and silviculture. We also evaluated how environmental factors influence α diversity. We sampled macroinvertebrate communities (by Surber sampler), litter input (by nets) and physicochemical variables of the water (by multiparameter and chemical analysis) at 10 sites in riparian zones of a highland system. A total of 2124 individuals were sampled representing 41 taxa, with high α and β diversity in mixed ombrophilous forest. The results indicate that environmental heterogeneity increases food resource availability, as well as macroinvertebrate diversity. Furthermore, the β diversity was found to increase with distance among the unconnected streams. Areas of silviculture had the highest density values among the sampled areas due high organic material stock in the soil. Therefore, higher leaf litter input may decrease the toxicity effect of secondary compounds released by leaf plant decomposition compared to environments with low systemic leaf litter input, such as highland grasslands. The presence of arboreal riparian vegetation drives canopy openness, water temperature, dissolved oxygen, and orthophosphate in streams, which control the frequency and occurrence of benthic macroinvertebrates. Homogeneous systems, as naturally observed in highland grasslands, plus high canopy openness decrease macroinvertebrate richness and density. The naturally low richness of highland grasslands, although with endemic species, may be an important guideline for legislation on anthropic impacts, management, and conservation of streams in this specific subtropical zone.

Multispecies individual tree crown extraction and classification based on BlendMask and high-resolution UAV images

Accurate detection and segmentation of individual trees from unmanned aerial vehicle images is critical for forestry resource surveys and accurate forest management. Deep learning methods have been used for studies of individual tree crown segmentation, classification, and number of trees in mixed coniferous and broad-leaved forests, but the accuracy needs to be improved. Therefore, this study uses BlendMask, a simpler and more efficient algorithm that combines Mask R-CNN and Yolact algorithms to effectively combine instance-level information with semantic information at a finer granularity level, greatly improving crown segmentation accuracy and classification results. Three coniferous species and five broad-leaved species unmanned aerial vehicle images collected from the Jing Yue multispecies ecological forestry site in Changping District, Beijing, were used as the dataset, and the results were compared with Yolact and Mask R-CNN. The results show that the method described in this work has the highest Kappa coefficient (0.89) and overall accuracy (92.14%) in the test set. For segmentation accuracy, coniferous species’ producer’s accuracy was 0.91 to 0.95, whereas that of broad-leaved species was 0.89 to 0.92. For species classification, the F1-score and mean average precision for coniferous species were greater than 91%, whereas those for broad-leaved species were 77.64% to 85.63%. The accuracy of extracting stand density in low and medium canopy density stands was 0.9909 and 0.9422, respectively, whereas that in high canopy density stands was 0.8913. This study shows that the BlendMask model has a good effect in studying the classification of multiple tree species, the segmentation of individual tree crowns, and the statistics of the number of trees in complex forest areas. Compared with broad-leaved forests and high canopy density stands, this model is more suitable for coniferous forest and medium and low canopy density stand scenarios. This study provides an important tool for obtaining more accurate species classification, canopy segmentation, and resource inventory results in complex forest areas.

Temporal and Spatial Characteristics of Soundscape Ecology in Urban Forest Areas and Its Landscape Spatial Influencing Factors

We explored the spatial and temporal characteristics of the urban forest area soundscape by setting up monitoring points (70 × 70 m grid) covering the study area, recorded a total of 52 sound sources, and the results showed that: (1) The soundscape composition of the park is dominated by natural sounds and recreational sounds. (2) The diurnal variation of sound sources is opposite to that of temperature, 6:00–9:00 is the best time for the public to perceive birdsong, and after 18:00, the park is dominated by insect chirps. (3) The PSD (power spectral density) and the SDI (soundscape diversity index) of the park are greatly affected by public recreation behaviors, and some recreation behaviors may affect the vocal behavior of organisms such as birds. (4) Spaces with high canopy density can attract more birdsong and recreational sounds in summer, and the combination of “tree + lake” can attract more birdsong. Vegetation has a significant dampening effect on traffic sound. (5) Landscape spatial elements, such as the proportion of hard ground, sky, trees, and shrubs, have a significant impact on changes in the PSD, the SDI and different kinds of sound sources. The research results provide effective data support for improving the soundscape of urban forests.

Modeling Biomass for Natural Subtropical Secondary Forest Using Multi-Source Data and Different Regression Models in Huangfu Mountain, China

Forest biomass estimation is an important parameter for calculating forest carbon storage, which is of great significance for formulating carbon-neutral strategies and forest resource management measures. We aimed at solving the problems of low estimation accuracy of forest biomass with complex canopy structure and high canopy density, and large differences in the estimation results of the same estimation model under complex forest conditions. The Huangfu Mountain Forest Farm in Chuzhou City was used as the research area. As predictors, we used Gaofen-1(GF-1) and Gaofen(GF-6) satellite high-resolution imaging satellite data, combined with digital elevation model (DEM) and forest resource data. Multiple stepwise regression, BP neural network and random forest estimation models were used to construct a natural subtropical secondary forest biomass estimation model with complex canopy structure and high canopy closure. We extracted image information as modeling factors, established multiple stepwise regression models of different tree types with a single data source and a comprehensive data source and determined the optimal modeling factors. On this basis, the BP neural network and random forest biomass estimation model were established for Pinus massoniana, Pinus elliottii, Quercus acutissima and mixed forests, with the coefficient of determination n (R2) and root mean square error (RMSE) as the judgment indices. The results show that the random forest model had the best biomass estimation effect among different forest types. The R2 of Quercus acutissima was the highest, reaching 0.926, but the RMSE was 11.658 t/hm2. The R2 values of Pinus massoniana and mixed forest were 0.912 and 0.904, respectively. The RMSE reached 10.521 t/hm2 and 6.765 t/hm2, respectively; the worst result was the estimation result of Pinus elliottii, with an R2 of 0.879 and an RMSE of 14.721 t/hm2. The estimation result of the BP neural network was second only to that of the random forest model in the four forest types. From high precision to low precision, the order was Quercus acutissima, Pinus massoniana, mixed forest and Pinus elliottii, with R2s of 0.897, 0.877, 0.825 and 0.753 and RMSEs of 17.899 t/hm2, 10.168 t/hm2, 18.641 t/hm2 and 20.419 t/hm2, respectively. In this experiment, the worst biomass estimation performance was seen for multiple stepwise regression, which ranked the species in the order of Quercus acutissima, Pinus massoniana, mixed forest and Pinus elliottii, with R2s of 0.658, 0.622, 0.528 and 0.379 and RMSEs of 29.807 t/hm2, 16.291 t/hm2, 28.011 t/hm2 and 23.101 t/hm2, respectively. In conclusion, GF-1 and GF-6 combined with data and a random forest algorithm can obtain the most accurate results in estimating the forest biomass of complex tree species. The random forest estimation model had a good performance in biomass estimation of primary secondary forest. High-resolution satellite data have great application potential in the field of forest parameter inversion.

Plant beta-diversity across biomes captured by imaging spectroscopy

Monitoring the rapid and extensive changes in plant species distributions occurring worldwide requires large-scale, continuous and repeated biodiversity assessments. Imaging spectrometers are at the core of novel spaceborne sensor fleets designed for this task, but the degree to which they can capture plant species composition and diversity across ecosystems has yet to be determined. Here we use imaging spectroscopy and vegetation data collected by the National Ecological Observatory Network (NEON) to show that at the landscape level, spectral beta-diversity—calculated directly from spectral images—captures changes in plant species composition across all major biomes in the United States ranging from arctic tundra to tropical forests. At the local level, however, the relationship between spectral alpha- and plant alpha-diversity was positive only at sites with high canopy density and large plant-to-pixel size. Our study demonstrates that changes in plant species composition and diversity can be effectively and reliably assessed with imaging spectroscopy across terrestrial ecosystems at the beta-diversity scale—the spatial scale of spaceborne missions—paving the way for close-to-real-time biodiversity monitoring at the planetary level.

Improved Forest Canopy Closure Estimation Using Multispectral Satellite Imagery within Google Earth Engine

The large area estimation of forest canopy closure (FCC) using remotely sensed data is of high interest in monitoring forest changes and forest health, as well as in assessing forest ecological services. The accurate estimation of FCC over the regional or global scale is challenging due to the difficulty of sample acquisition and the slow processing efficiency of large amounts of remote sensing data. To address this issue, we developed a novel bounding envelope methodology based on vegetation indices (BEVIs) for determining vegetation and bare soil endmembers using the normalized differences vegetation index (NDVI), modified bare soil index (MBSI), and bare soil index (BSI) derived from Landsat 8 OLI and Sentinel-2 image within the Google Earth Engine (GEE) platform, then combined the NDVI with the dimidiate pixel model (DPM), one of the most commonly used spectral-based unmixing methods, to map the FCC distribution over an area of more than 90,000 km2. The key processing was the determination of the threshold parameter in BEVIs that characterizes the spectral boundary of vegetation and soil endmembers. The results demonstrated that when the threshold equals 0.1, the extraction accuracy of vegetation and bare soil endmembers is the highest with the threshold range given as (0, 0.3), and the estimated spatial distribution of FCC using both Landsat 8 and Sentinel-2 images were consistent, that is, the area with high canopy density was mainly distributed in the western mountainous region of Chifeng city. The verification was carried out using independent field plots. The proposed approach yielded reliable results when the Landsat 8 data were used (R2 = 0.6, RMSE = 0.13, and 1-rRMSE = 80%), and the accuracy was further improved using Sentinel-2 images with higher spatial resolution (R2 = 0.81, RMSE = 0.09, and 1-rRMSE = 86%). The findings demonstrate that the proposed method is portable among sensors with similar spectral wavebands, and can assist in mapping FCC at a regional scale while using multispectral satellite imagery.

Effects of Altitude, Plant Communities, and Canopies on the Thermal Comfort, Negative Air Ions, and Airborne Particles of Mountain Forests in Summer

Forest bathing is considered an economical, feasible, and sustainable way to solve human sub-health problems caused by urban environmental degradation and to promote physical and mental health. Mountain forests are ideal for providing forest baths because of their large area and ecological environment. The regulatory mechanism of a mountain forest plant community in a microenvironment conducive to forest bathing is the theoretical basis for promoting physical and mental health through forest bathing in mountain forests. Based on field investigations and measurements, differences in the daily universal thermal climate index (UTCI), negative air ion (NAI), and airborne particulate matter (PM2.5 and PM10) levels in nine elevation gradients, six plant community types, and six plant community canopy parameter gradients were quantitatively analyzed. In addition, the correlations between these variables and various canopy parameters were further established. The results showed the following: (1) Altitude had a significant influence on the daily UTCI, NAI, PM2.5, and PM10 levels in the summer. The daily UTCI, NAI, PM2.5, and PM10 levels gradually decreased with the increase in altitude. For every 100 m increase in altitude, the daily UTCI decreased by 0.62 °C, the daily NAI concentration decreased by 108 ions/cm3, and the daily PM2.5 and PM10 concentrations decreased by 0.60 and 3.45 µg/m3, respectively. (2) There were significant differences in the daily UTCI, NAI, PM2.5, and PM10 levels among different plant communities in the summer. Among the six plant communities, the Quercus variabilis forest (QVF) had the lowest daily UTCI and the best thermal comfort evaluation. The QVF and Pinus tabuliformis forest (PTF) had a higher daily NAI concentration and lower daily PM2.5 and PM10 concentrations. (3) The characteristics of the plant community canopy, canopy density (CD), canopy porosity (CP), leaf area index (LAI), and sky view factor (SVF), had significant effects on the daily UTCI and NAI concentration, but had no significant effects on the daily PM2.5 and PM10 concentrations in the summer. The plant community with higher CD and LAI, but lower CP and SVF, showed a higher daily UTCI and a higher daily NAI concentration. In conclusion, the QVF and PTF plant communities with higher CD and LAI but lower CP and SVF at lower elevations are more suitable for forest bathing in the summer in mountainous forests at lower altitudes. The results of this study provide an economical, feasible, and sustainable guide for the location of forest bathing activities and urban greening planning to promote people’s physical and mental health.

Fish functional responses to local habitat variation in streams within multiple land uses areas in the Amazon

Abstract In this study, we assessed the effects of multiple land uses and local habitat variables on the composition of fish functional trophic groups (FTG’s) and on the ecomorphological traits of fish in Amazonian streams. We evaluated land use types and local habitat variables in 26 streams distributed within a land use gradient. Land use and habitat variables affected the composition of FTG’s, as evidenced by the increased abundance of diurnal channel drift feeders in areas with high dissolved oxygen and deeper thalweg. At the same time, diurnal surface pickers, as well as diggers, and ambush and stalker predators were more abundant in streams with higher canopy density. Only habitat variables affected the ecomorphological characteristics of the species. Fish with higher values of relative caudal peduncle length were positively associated with high canopy density, while fish with greater relative mouth width were negatively associated with the variables impact in the riparian zone and cover of fish shelter. The stream fish functional structure was mainly affected by the impacts caused to the local habitat resulting from different land uses. Thus, preserving forest remnants, as well as recovering degraded areas, is essential for the maintenance of aquatic biodiversity in the region.