Tree Canopy Density Research Articles

Exploring the landscape scale influences of tree cover on crop yield in an agroforestry parkland using satellite data and spatial statistics

Trees in agroforestry parklands influence crops both through competitive and facilitative mechanism, but the effects are challenging to disentangle due to the complexity of the system with high variability in tree cover structure and species diversity and crop combinations. Focusing on a landscape in central Burkina Faso dominated by Vitellaria paradoxa and Parkia biglobosa, this paper examines how tree cover influences crop yield at landscape scale using satellite data and spatial statistics. Our analysis is based on data from 2017 to 2018 with differences in rainfall to assess the stability in identified relationships. Our findings showed that tree canopy cover and tree density inside the fields tended to decrease crop yield because of competition, but also that these variables when considering the surrounding landscape exerted an opposite effect because of their buffering effects. The explanatory variables representing soil properties did have limited effects on crop yield in this study. These patterns were consistent during the two years of monitoring. Overall, our results suggest that farmers in this area might manage the tree cover in a way that optimizes sustainable yields as canopy cover and tree density in most parklands is below the limits identified here where competition outweight the facilitative effects.

Construction and testing of a model for predicting droplet deposition based on Gaussian process regression

To realize precision spraying, predicting the pesticide application dose based on the structural characteristics of the plant canopy is a crucial step. However, most of the current precision spraying techniques are based on canopy characteristics with no consideration given to the effect of canopy characteristics on droplet particle population. As a result, the droplet deposition value is far from being ideal. Herein, we explored the effects of canopy density and spraying volume on the distribution of droplet deposition in simulated citrus tree canopies and constructed a prediction model for droplet deposition. In this research, the three-dimensional (3D) reconstruction of fruit tree canopy was obtained using binocular camera and a new method was proposed to describe simulated citrus tree canopy density using three-dimensional point cloud. The optimal description of the interrelationship between 3D point cloud and fruit tree canopy density was determined by comparing the determination coefficients, Akaike information criterion and relative errors of the four fitting functions. Through our experiments, a droplet deposition prediction model based on Gaussian process regression was established by changing the duty cycle of the solenoid valve to adjust the spray volume, with simulated citrus trees as the target and water sensitive paper as the sampling method for collecting droplet deposition data. The experimental results show that the distribution of droplet deposition in the upper and lower layers of the canopy varies, but within the same layer droplets can produce relatively uniform deposition in similar canopy density areas with correlation coefficients greater than 0.6. When the canopy density reached 4.35 m2/m3, the amount of droplet deposition was anomalous. The prediction model, after training, has good ability of predicting droplet deposition with R2 higher than 0.79. At the same time, it was found in the research that differences occurred between the prediction accuracy of the droplet deposition prediction model in the upper canopy layer and that in the lower canopy layers, in which the R2 of the prediction model in the lower layer could reach 0.82, while in the upper layer it was 0.79. It is expected that our research on droplet deposition prediction can provide a new research perspective for precision spraying technology in plant protection operations.

Carbon stocks and footprints of smallholder cacao systems in Polewali Mandar, West Sulawesi

Cacao (Theobroma cacao) is a commodity that plays an important role in supporting economic and social development. However, cacao production can also be a major contributor to carbon emissions, which has stimulated various efforts toward sustainable cacao farm management. There remains a gap in knowledge regarding the links between carbon stocks and carbon footprints, which can serve as indicators of environment “friendliness.” In this study, we investigated carbon stocks and carbon footprints in two cacao cultivation systems, agroforests and monocultural systems, and the biophysical aspects (biotic and abiotic factors) that might contribute to the variability of carbon levels. System inventories, soil samples, and farmer interviews identified the characteristics and management practices of two cacao production systems. Results show that cacao agroforests accumulated more carbon stocks than cacao monocultures, 134.4 Mg C ha−1 and 104.7 Mg C ha−1, respectively, while cacao monocultural systems had higher carbon footprints than cacao agroforests, 1914.4 kg CO2e ha−1 and 932.1 ± 251.6 kg CO2e ha−1, respectively. Canopy cover, tree density, and soil organic carbon were the biophysical aspects that showed a significantly positive correlation with carbon stock levels, while canopy cover had a significantly negative correlation with carbon footprint levels. These results suggest that cacao agroforests are more climate-friendly management systems due to their ability to maintain high carbon stock levels while producing low carbon footprints.

Tree canopy density thresholds for improved forests cover estimation in protected areas of Madagascar

Abstract The rich endemic biodiversity of Madagascar is concentrated in different types of natural forests primarily conserved within the network of protected areas (PAs). Since 1990, remote sensing has been utilized to monitor forest cover. The latest forest cover map generated using these techniques provides accurate estimates of natural forest cover within the PAs network. However, the standardized application of Tree Canopy Density (TCD), as used in global assessments of forest cover, yields erroneous estimates for different forest types in Madagascar because the standard TCD cannot be globally applied to all types of forests. Our study aims to utilize global remote sensing data at the scale of PAs to identify specific TCD thresholds for individual PAs. Starting from the year 2000 data, the application of these thresholds will allow us to estimate deforestation in subsequent years at reduced costs. We used the official PA boundaries, a reliable forest cover map at the national scale, and the TCDs published at a global scale to infer the values of TCD to be applied in each PA. The standard TCD threshold above 30% overestimates humid and dry forests and underestimates dry spiny forests in Madagascar. Our specific TCD thresholds inferred for each PA accurately estimate the forest cover in the vast majority of PAs. Using these specific TCD thresholds will allow for improved monitoring of forest cover within the network of PAs. The methodology detailed here can also be applied in other geographic regions, and future improvements in data on forest cover—both remotely sensed and field-collected—will enhance our ability to estimate forest cover and its changes over time.

Лось (Alces alces) на південній межі ареалу: стан популяції Центрального Полісся, хижацтво вовка та вразливість до потепління клімату

In the latter less hot years after the decline in the number of elk (2019–2020), the stabilisation and increasing trend in the number of this species have been observed throughout the study area. Due to migrations, the winter elk population exceeds the summer one and amounts to 2 individuals/thousand hectares (40 individuals; against 1.3 individuals/1 thousand hectares in summer) in the Polissia Reserve, and 1–6 individuals/1 thousand hectares (940 individuals) in the Chornobyl Reserve. Data on the vulnerability of elk to climate change are presented. In hot weather at different times of the year, elks may show signs of heat stress. In summer, elk can be inactive, and in the leafless period, when chased by wolves, they can get heat stress and die. In the heat of the day, elk choose swamps, waterlogged forests with a dense tree canopy and better cooling, which are comfortable for these ungulates in the face of global warming. Climate change causes marshes to dry up and overgrow, reduces the moisture content of habitats, and worsens the fodder capacity of the land. In case of even partial restoration of lowland marshes in the Chornobyl Biosphere Reserve, the reserves of summer and winter food for elk may increase by 2–3 times. In the Polissia Reserve, in 2011–2013, lowland bogs with bush willows (Salix) were restored on fallow land near the Zholobnytsia drainage system, creating highly productive habitats for the elk, which is a promising measure for different areas of Polissia. Climate warming has led to an increase in the red deer (Cervus elaphus) population and a decrease in the elk population in the Polissia, and if no special measures are taken, these trends will intensify. Outside of protected areas, given the lack of a high legal status for wolves (Canis lupus), a strategy for conserving the elk population and reducing predation mortality in combination with other elk habitat management strategies can be recommended to hunting ground users, including limiting poaching, restoring the natural regime of forest fires in reserves, improving winter provision of branch fodder, reducing the disturbance factor, and introducing a ban on staying in the forest with dogs for berry and mushroom pickers.

Vegetation type change in California’s Northern Bay Area: A comparison of contemporary and historical aerial imagery

The North Bay area of California is a populous and ecologically diverse area that has experienced significant changes in the past century, as well as a series of recent wildfires, after over a century of fire suppression practices. While much research has been conducted quantifying drivers and patterns of vegetation change in conifer-dominated ecosystems, and how such changes have influenced current trends in fire behavior, studies of similar focus and scale are rarer in non-conifer ecosystems, including mixed-hardwood forests or shrub-dominated ecosystems in central and coastal Northern California. This is despite the fact that ecosystems other than conifer forests make up the majority of area burned in California wildfires. As such, expanding research focused on patterns of large-scale vegetation change as a possible driver of this trend in this area is a priority. In this study, we sought to map the overall extent and patch sizes of broad vegetation classes across a 52,000 ha study area based on historical (1948) and contemporary (2014) aerial imagery and to investigate shifts in vegetation patterns, as well as potential pathways and drivers of detected changes. We classified vegetation types through segmenting our imagery into homogenous polygons, and assigning broad vegetation categories using a random forest algorithm. We then analyzed patterns of change using spatial statistics and conditional inference tree analyses. We detected a large increase (12%) in the relative landscape proportion and average patch size of the forest class, characterized by dense tree canopy cover. Woodlands and shrub patches were most susceptible to type change, with the majority (57% and 65%, respectively) of converted areas subsequently identified as denser forest stands. By contrast, herbaceous and forest patches were most persistent. We additionally found that disturbance history, specifically whether an area burned or not, and topographic variables, including elevation and aspect, were important influences on the likelihood of vegetation persistence, while slope and water availability were not. Historical aerial imagery, which provides fine resolution and accurate data over a large spatial scale, is a useful tool for detecting landscape-scale vegetation shifts in ecosystems where widespread vegetation monitoring was not common historically. The marked increase in dense forest we detected, specifically due to the conversion of large areas of shrub and woodland vegetation may correspond to higher surface and ladder fuel load and continuity, and potentially higher wildfire risk. Fuel reduction treatments typically implemented in conifer-dominated forests may also be warranted in these mixed hardwood forests. However, more research is needed to understand drivers of change in non-conifer-dominated ecosystems in California and how such change influences wildfire behavior.

Navigation of an Autonomous Spraying Robot for Orchard Operations Using LiDAR for Tree Trunk Detection

Traditional Japanese orchards control the growth height of fruit trees for the convenience of farmers, which is unfavorable to the operation of medium- and large-sized machinery. A compact, safe, and stable spraying system could offer a solution for orchard automation. Due to the complex orchard environment, the dense tree canopy not only obstructs the GNSS signal but also has effects due to low light, which may impact the recognition of objects by ordinary RGB cameras. To overcome these disadvantages, this study selected LiDAR as a single sensor to achieve a prototype robot navigation system. In this study, density-based spatial clustering of applications with noise (DBSCAN) and K-means and random sample consensus (RANSAC) machine learning algorithms were used to plan the robot navigation path in a facilitated artificial-tree-based orchard system. Pure pursuit tracking and an incremental proportional–integral–derivative (PID) strategy were used to calculate the vehicle steering angle. In field tests on a concrete road, grass field, and a facilitated artificial-tree-based orchard, as indicated by the test data results for several formations of left turns and right turns separately, the position root mean square error (RMSE) of this vehicle was as follows: on the concrete road, the right turn was 12.0 cm and the left turn was 11.6 cm, on grass, the right turn was 12.6 cm and the left turn was 15.5 cm, and in the facilitated artificial-tree-based orchard, the right turn was 13.8 cm and the left turn was 11.4 cm. The vehicle was able to calculate the path in real time based on the position of the objects, operate safely, and complete the task of pesticide spraying.

Patterns of Reforestation Successions on Abandoned Agricultural Lands of the Bashkir Cis-Urals

The analysis and forecasting of the course of restoration successions on abandoned agricultural lands of the Bashkir Cis-Urals are presented. It was shown that the consequences of the complex influence of various economic uses of arable land after the cessation of plowing remain for a long time in the successional series. This maintains the differences between the communities of each fallow and is the reason for the high phytocoenotic diversity. Subsequently, strong shading as a result of active reforestation leads to a decrease in species richness, leveling of floristic differences between fallow communities and convergence of phytocenoses. The rate of succession and the dynamics of the floristic composition of communities are the highest in the first 12–15 years after the withdrawal of land from agricultural use before the onset of the formation of long-derived phytocenoses with a dense tree canopy. It is predicted that succession on the fallows will be influenced by the forest vegetation directly in contact with the fallows after the formation of a dense forest stand. However, the final formation of the floristic composition of the lower layers may take more than 100–150 years.

Boosting Ensemble Learning Technique for Landslide Activity Classification using Vegetation Anomalies Indicators

Having detailed information and inventories about landslides is important for studying landslides. These inventories have been created for various purposes. However, detecting and mapping landslides in highly dense vegetated areas and evaluating their activity state is a major challenge due to various factors, such as the dense tree canopy, undulating terrain, and fast-growing vegetation. Therefore, this paper presents a new technique for categorising landslide activity using vegetation anomalies indicators (VAIs) extracted from high-resolution remote sensing data. The data were utilised to support manual landslide inventory and VAI production. The landslide inventory map was divided randomly into two groups of datasets, one for training (70%) and the other for validation (30%). The classification process used a boosting ensemble learning approach, specifically Decision Tree (DT) and Stochastic Gradient Boosting (SGB), with seven primary VAIs as inputs. The study compared the classification models’ performance against various parameters, including spatial resolution and landslide depth. To evaluate the accuracy of the classification methods, metrics such as overall accuracy, kappa, producer’s accuracy, and user’s accuracy were measured from the validation dataset. The results demonstrated that both methods performed best under high spatial resolution. Among the two approaches, DT performed better, with an overall accuracy value of 89.6% for deep-seated translational, 64.0% for shallow translational, 67.0% for deep-seated rotational, and 80.0% for shallow rotational. This reliable accuracy that has been attained in landslide activity classification from VAI allows (i) to map and classify the landslide activity in the forested area, (ii) characterise the different types of vegetation characteristics to specific landslide activity, and (iii) permits for the continuous landslide activity monitoring in the area where monitoring activity is not practically feasible to be conducted.

Abstract C092: Proximity to green space and inflammation among breast cancer patients

Abstract It is important to establish if exposure to green space provides a protective effect for breast cancer development and progression to advocate for expanded access to green space areas for 1) those at-risk and 2) cancer patients receiving treatment. There is an association of residential proximity to green space with decreased breast cancer incidence. Current studies have not evaluated how patient variables may affect this relationship. We sought to determine if patient-level green space proximity correlated to social determinants of health such as poverty or minority status, or patient physiologic or inflammatory molecular markers in breast cancer patients. A retrospective analysis of baseline demographic data (including addresses) and cytokine profiles from breast cancer patients who had enrolled on investigator initiated trials at our institution was performed. National Land Cover Database and Philadelphia Parks & Recreation data was used to identify green space indicators such as patient proximity and neighborhood density of green space and tree canopy and was correlated to the patient’s systemic inflammation. Pearson coefficients were performed to identify relationships between green space indicators and inflammation. Patients were accrued to investigator initiated trials between 2013-2019. All had a diagnosis of ductal carcinoma in situ or invasive breast cancer. The median age of the women on the study was 63 years old, 42% were black, and 64% were considered of low socioeconomic status. The fraction of total area within a 250-meter radius of each patient’s address that has low vegetation or tree canopy was calculated. More women with above the median percentage of low vegetation within 250 meters of their residence had elevated levels of the anti-inflammatory cytokine TGF-B (p <0.001). In addition, living with above the median percentage of low vegetation within 250 meters of their residence was associated with having higher levels of IGF-BP3 (p <0.001). In our diverse patient population who experience adverse social determinants of health, an inverse correlation was noted between residential proximity to greenspace indicators and inflammation. These results could be used to advocate for expanded access to green space, especially among those with lowest resources, as increased inflammation is associated with breast cancer incidence and progression. Citation Format: Taylor Micua, Joseph Lombardo, Alex M DesJarlais, Benjamin C. Keating, Melissa Lazar, Charnita Zeigler-Johnson, Adeseye Adekeye, Edith Mitchell, Russell K. McIntire, Nicole L. Simone. Proximity to green space and inflammation among breast cancer patients [abstract]. In: Proceedings of the 15th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2022 Sep 16-19; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2022;31(1 Suppl):Abstract nr C092.

Rapid Localization and Mapping Method Based on Adaptive Particle Filters.

With the development of autonomous vehicles, localization and mapping technologies have become crucial to equip the vehicle with the appropriate knowledge for its operation. In this paper, we extend our previous work by prepossessing a localization and mapping architecture for autonomous vehicles that do not rely on GPS, particularly in environments such as tunnels, under bridges, urban canyons, and dense tree canopies. The proposed approach is of two parts. Firstly, a K-means algorithm is employed to extract features from LiDAR scenes to create a local map of each scan. Then, we concatenate the local maps to create a global map of the environment and facilitate data association between frames. Secondly, the main localization task is performed by an adaptive particle filter that works in four steps: (a) generation of particles around an initial state (provided by the GPS); (b) updating the particle positions by providing the motion (translation and rotation) of the vehicle using an inertial measurement device; (c) selection of the best candidate particles by observing at each timestamp the match rate (also called particle weight) of the local map (with the real-time distances to the objects) and the distances of the particles to the corresponding chunks of the global map; (d) averaging the selected particles to derive the estimated position, and, finally, using a resampling method on the particles to ensure the reliability of the position estimation. The performance of the newly proposed technique is investigated on different sequences of the Kitti and Pandaset raw data with different environmental setups, weather conditions, and seasonal changes. The obtained results validate the performance of the proposed approach in terms of speed and representativeness of the feature extraction for real-time localization in comparison with other state-of-the-art methods.

DYNAMICS OF FOREST ECOSYSTEM COMPONENTS IN THE BLACK SEA COAST OF THE CAUCASUS AGAINST THE BACKGROUND OF BOXWOOD DESTRUCTION AS A RESULT OF <i>CYDALIMA PERSPECTALIS</i> WALKER (LEPIDOPTERA, CRAMBIDAE) INVASION

The situation when the invasion of one species indirectly leads to ecosystem consequences of a larger scale is considered. The work was carried out in the relict ecosystem of Colchis, the flora and fauna of which was formed more than 15 million years ago. Disappearance of boxwood, the edifier of Colchian ecosystems, in the short term has not led to a significant change in soil characteristics. The succession of plant communities was expressed by an increase in vegetation cover of native plant species under the active introduction of alien species. The rates of seed regeneration of boxwood decreased, especially in the areas with low density of tree canopy. The study of the distribution of earthworms showed the general topical preferences of boxwood and most species of large soil oligochaetes that prefer shaded stations and moistened habitats.

Predicting the Forest Canopy Height from LiDAR and Multi-Sensor Data Using Machine Learning over India

Forest canopy height estimates, at a regional scale, help understand the forest carbon storage, ecosystem processes, the development of forest management and the restoration policies to mitigate global climate change, etc. The recent availability of the NASA’s Global Ecosystem Dynamics Investigation (GEDI) LiDAR data has opened up new avenues to assess the plant canopy height at a footprint level. Here, we present a novel approach using the random forest (RF) for the wall-to-wall canopy height estimation over India’s forests (i.e., evergreen forest, deciduous forest, mixed forest, plantation, and shrubland) by employing the high-resolution top-of-the-atmosphere (TOA) reflectance and vegetation indices, the synthetic aperture radar (SAR) backscatters, the topography and tree canopy density, as the proxy variables. The variable importance plot indicated that the SAR backscatters, tree canopy density and the topography are the most influential height predictors. 33.15% of India’s forest cover demonstrated the canopy height <10 m, while 44.51% accounted for 10–20 m and 22.34% of forests demonstrated a higher canopy height (>20 m). This study advocates the importance and use of GEDI data for estimating the canopy height, preferably in data-deficit mountainous regions, where most of India’s natural forest vegetation exists.

Soil Organic Matter Fractions in Relation to Root Characteristics of Different Tree Species in Altitude Gradient of Temperate Forest in Carpathian Mountains

The roots are a key functional component of belowground systems and one of the main factors influencing the quality and quantity of soil organic matter. Our research aimed to determine the fractional composition of the soil organic matter (SOM) in soils under various tree species on an altitude gradient. In our research, we related the SOM fractions with the root characteristics. There is a lack of information on the relationship between the SOM fractions and the root properties. We assessed labile and heavy fractions of SOM content in forest mountain soils with a climosequence approach. The study plots were established at 600, 800, and 1000 m above sea level in a beech stand (Fagus sylvatica L.) and a fir stand (Abies alba Mill.). In this case, three research plots with beech and fir were designated in each altitude variant. Forest stands growing in the same soil conditions were selected for the study. The research used stands of similar age with the same tree canopy density. The basic physicochemical properties (pH, hydrolytic acidity, carbon and nitrogen content, base cations content) and the fractional composition of the SOM were determined from soil samples. In addition, we determined the basic characteristics of the roots (diameter, length, biomass, decomposition, production). The correlation between soil organic matter fractions and root characteristics was recorded. This study confirmed the importance of climatic conditions in shaping the fractional composition of forest soils. In the highest locations, characterized by lower temperatures, the light fraction of the SOM exhibited the highest C and N content, which is the effect of slower decomposition processes. Apart from climatic conditions, the stabilization of SOM is influenced by the tree species composition of a forest stand. Beech forest stands lead to a larger accumulation of a heavy fraction of SOM. This study indicates a positive correlation between the light fraction of SOM, root biomass, and decomposition rate of roots. Our research shows that avoiding single-species coniferous stands and introducing admixtures of deciduous species, which increase the heavy SOM fraction, is justified in forest management.

Imageseg: An R package for deep learning‐based image segmentation

Abstract Convolutional neural networks (CNNs) and deep learning are powerful and robust tools for ecological applications, and are particularly suited for image data. Image segmentation (the classification of all pixels in images) is one such application and can, for example, be used to assess forest structural metrics. While CNN‐based image segmentation methods for such applications have been suggested, widespread adoption in ecological research has been slow, likely due to technical difficulties in implementation of CNNs and lack of toolboxes for ecologists. Here, we present R package imageseg which implements a CNN‐based workflow for general purpose image segmentation using the U‐Net and U‐Net++ architectures in R. The workflow covers data (pre)processing, model training and predictions. We illustrate the utility of the package with image recognition models for two forest structural metrics: tree canopy density and understorey vegetation density. We trained the models using large and diverse training datasets from a variety of forest types and biomes, consisting of 2877 canopy images (both canopy cover and hemispherical canopy closure photographs) and 1285 understorey vegetation images. Overall segmentation accuracy of the models was high with a Dice score of 0.91 for the canopy model and 0.89 for the understorey vegetation model (assessed with 821 and 367 images respectively). The image segmentation models performed significantly better than commonly used thresholding methods, and generalized well to data from study areas not included in training. This indicates robustness to variation in input images and good generalization strength across forest types and biomes. The package and its workflow allow simple yet powerful assessments of forest structural metrics using pretrained models. Furthermore, the package facilitates custom image segmentation with single or multiple classes and based on colour or grayscale images, for example, for applications in cell biology or for medical images. Our package is free, open source and available from CRAN. It will enable easier and faster implementation of deep learning‐based image segmentation within R for ecological applications and beyond.

Mapping forest in the Swiss Alps treeline ecotone with explainable deep learning

Forest maps are essential to understand forest dynamics. Due to the increasing availability of remote sensing data and machine learning models like convolutional neural networks, forest maps can these days be created on large scales with high accuracy. Common methods usually predict a map from remote sensing images without deliberately considering intermediate semantic concepts that are relevant to the final map. This makes the mapping process difficult to interpret, especially when using opaque deep learning models. Moreover, such procedure is entirely agnostic to the definitions of the mapping targets (e.g., forest types depending on variables such as tree height and tree density). Common models can at best learn these rules implicitly from data, which greatly hinders trust in the produced maps. In this work, we aim at building an explainable deep learning model for forest mapping that leverages prior knowledge about forest definitions to provide explanations to its decisions. We propose a model that explicitly quantifies intermediate variables like tree height and tree canopy density involved in the forest definitions, corresponding to those used to create the forest maps for training the model in the first place, and combines them accordingly. We apply our model to mapping forest types using very high resolution aerial imagery and lay particular focus on the treeline ecotone at high altitudes, where forest boundaries are complex and highly dependent on the chosen forest definition. Results show that our rule-informed model is able to quantify intermediate key variables and predict forest maps that reflect forest definitions. Through its interpretable design, it is further able to reveal implicit patterns in the manually-annotated forest labels, which facilitates the analysis of the produced maps and their comparison with other datasets.

Is an Unmanned Aerial Vehicle (UAV) Suitable for Extracting the Stand Parameters of Inaccessible Underground Forests of Karst Tiankeng?

Unmanned aerial vehicle (UAV) remote sensing technology is gradually playing a role alternative to traditional field survey methods in monitoring plant functional traits of forest ecology. Few studies focused on monitoring functional trait ecology of underground forests of inaccessible negative terrain with UAV. The underground forests of tiankeng were discovered and are known as the inaccessible precious ecological refugia of extreme negative terrain. The aim of this research proposal is to explore the suitability of UAV technology for extracting the stand parameters of underground forests’ functional traits in karst tiankeng. Based on the multi-scale segmentation algorithm and object-oriented classification method, the canopy parameters (crown width and densities) of underground forests in degraded karst tiankeng were extracted by UAV remote sensing image data and appropriate features collection. First, a multi-scale segmentation algorithm was applied to attain the optimal segmentation scale to obtain the single wood canopy. Second, feature space optimization was used to construct the optimal feature space set for the image and then the k-nearest neighbor(k-NN) classifier was used to classify the image features. The features were classified into five types: canopy, grassland, road, gap, and bare land. Finally, both the crown densities and average crown width of the trees were calculated, and their accuracy were verified. The results showed that overall accuracy of object-oriented image feature classification was 85.60%, with 0.72 of kappa coefficient. The accuracy of tree canopy density extraction was 82.34%, for which kappa coefficient reached 0.91. The average canopy width of trees in the samples from the tiankeng-inside was 5.38 m, while that of the outside samples was 4.83 m. In conclusion, the canopy parameters in karst tiankeng were higher than those outside the tiankeng. Stand parameters extraction of karst tiankeng underground forests based on UAV remote sensing was relatively satisfactory. Thus, UAV technology provides a new approach to explore forest resources in inaccessible negative terrain such as karst tiankengs. In the future, we need to consider UAVs with more bands of cameras to extract more plant functional traits to promote the application of UAV for underground forest ecology research of more inaccessible negative terrain.

The relationship between natural environments and subjective well-being as measured by sentiment expressed on Twitter

There is growing evidence that time spent in nature can affect well-being. Nonetheless, assessing this relationship can be difficult. We used 1,971,045 geolocated tweets sent by 81,140 users from Seattle, Washington, USA to advance our understanding of the relationship between subjective well-being and natural environments. Specifically, we quantified the relationships between sentiment (negative/neutral/positive) expressed in geolocated tweets and their surrounding environments, focusing on three environmental indicators: land-cover type, tree-canopy density, and urban parks. We allowed the relationships to vary according to the broader type of environment (i.e., land-use zoning). We estimated three random-intercept partial proportional odds models corresponding to the three environmental indicators while controlling for multiple covariates. Our results suggested that for a given land-use type, tweets sent from some natural land-cover types were less likely to be negative compared to tweets sent from the urban built land-cover type. For tweets sent in industrial zones, an increase in tree-canopy cover was associated with a lower probability of having negative sentiments and with a higher probability of having positive sentiments; but for tweets sent in commercial/mixed zones, the association was reversed. Also, urban parks were generally associated with a lower probability of having negative sentiments, but tweets sent from large natural parks in residential zones were less likely to be positive. Our results suggest that the relationship between subjective well-being and natural environments depends on where people are situated in the built environment and may be more complex than previously thought. The more nuanced understanding provided by analyzing geolocated social media has potential to inform urban planning and land management.

Understanding vegetation structures in green spaces to regulate atmospheric particulate matter and negative air ions

Green spaces can effectively reduce atmospheric particulate matter (PM) pollution and regulate negative air ions (NAI), this ecological function of green spaces is directly determined by their types and structures. However, the mechanism of the interactions among the types and structures of green spaces, PM, and NAI are still unclear. In the present study, we selected Dongling Park in Shenyang, China as an example of a typical green space. We ran field experiments to analyze the influences of various green space types and structures on PM10, PM2.5, and NAI. The results showed that green spaces had significantly lower PM10 and PM2.5 and higher NAI concentrations than non-green spaces. Among the four vegetation types (tree-shrub-herb, tree-herb, shrub-herb, and tree-shrub), the tree-shrub-herb type most effectively decreased atmospheric PM10 and PM2.5 concentrations (32.54 ± 8.14% and 21.26 ± 4.07%, respectively) and increased NAI concentrations (115.43 ± 10.00%) to a significantly greater extent than the other vegetation types (p < 0.05). Canopy density was strongly and significantly positively correlated with PM2.5 and PM10 concentrations (r = 0.84* and r = 0.86**, respectively). By contrast, shrub layer richness was significantly negatively correlated with PM2.5 and PM10 concentrations (r = −0.68* and r = −0.74*, respectively). Furthermore, significant positive correlations with NAI concentrations were detected in the canopy density and proportion of evergreen tree species significantly (r = 0.40* and r = 0.46*, respectively). The results illustrate that the green space vegetation types and structures, especially the density of tree canopy, the richness of shrub layer, and the proportion of evergreen tree species, can influence the airborne PM10, PM2.5 and NAI concentrations. Therefore, vegetation types and structures should be considered in the process of planning urban green spaces that perform the ecological function of improving air quality.

Integrating effects of neighbor interactions for pollination and abiotic resources on coffee yield in a multi‐strata agroforest

AbstractAnimal‐pollinated plants interact with neighbors for both abiotic resources and pollination, with consequences for reproduction and yield. Yet few studies have compared the relative magnitude of these effects, particularly in agroecosystems. In vertically stratified communities, such as agroforests, neighbor effects may be stratum‐dependent. Understanding the net effects of neighbors on crop yield is important for managing multifunctional agroecosystems to simultaneously support production and biodiversity. This study evaluated the effects of neighboring plants on pollen deposition, fertilization, and yield in Coffea arabica in a shaded organic coffee farm with high non‐crop plant abundance and diversity in Chiapas, Mexico. We assessed the impact of (1) floral resources at three vertical strata (herbs, coffee bushes, and canopy trees) on stigma pollen load (a measure of interaction for pollination), and (2) floral density and canopy cover (proxies for competition for abiotic resources) on yield (final fruit set and per‐fruit weight), using structural equation modeling to evaluate the relative effect of each interaction type. Coffee competed for pollination with neighbors (conspecifics and heterospecifics) across strata. Pollen load influenced final fruit set, but the effect of neighbor competition for pollination was weaker than effects mediated by interactions for abiotic resources. Effects of interactions for abiotic resources were heterogeneous across strata, with negligible effects of herb‐layer or coffee flower density but net positive effects of canopy trees on final fruit set. Overall effects of neighbors on coffee yield were weak, suggesting that coffee agroecosystems can be managed to maintain high plant density and diversity without sacrificing yield.Abstract in Spanish is available with online material.