Tree Location Research Articles

Effects of sampling approaches on quantifying urban forest structure

Researchers and practitioners obtain information on urban forest structure mainly through sampling a small percentage of the total population of trees. Nevertheless, people seldom examine the suitability of sampling designs for this task. In this study, we evaluated the performance of simple random sampling (SRS), stratified random sampling (STR), systematic sampling (SS), and generalized random-tessellation stratified sampling (GRTS) for obtaining information on urban forests in Philadelphia, United States and Beijing, China. We determined the spatial locations of all urban trees in the two cities from high-resolution aerial images. We ran simulated surveys on the data set using the four sampling designs with varied sample sizes to estimate structural variables of urban forests, including the total number of trees, species richness, and species abundance. We conducted accuracy assessments by comparing the estimates to the real values. In the end, we located 1,381,903 and 2,295,794 urban trees in Philadelphia and Beijing, respectively. The results of simulated surveys showed that SRS had a more stable performance than other sampling designs when used for estimating the total number of trees. STR and GRTS performed better than SRS and SS in estimating species richness and detecting rare species. For estimating species abundance, SRS again displayed a stable performance among all designs. Also, we found that small sample sizes (≤200) used in existing studies would lead to estimates of urban forest structure with low accuracy. Our findings provide useful guidance to researchers and practitioners on sampling urban forests.

Detection of New Zealand Kauri Trees with AISA Aerial Hyperspectral Data for Use in Multispectral Monitoring

The endemic New Zealand kauri trees (Agathis australis) are of major importance for the forests in the northern part of New Zealand. The mapping of kauri locations is required for the monitoring of the deadly kauri dieback disease (Phytophthora agathidicida (PTA)). In this study, we developed a method to identify kauri trees by optical remote sensing that can be applied in an area-wide campaign. Dead and dying trees were separated in one class and the remaining trees with no to medium stress symptoms were defined in the two classes “kauri” and “other”. The reference dataset covers a representative selection of 3165 precisely located crowns of kauri and 21 other canopy species in the Waitakere Ranges west of Auckland. The analysis is based on an airborne hyperspectral AISA Fenix image (437–2337 nm, 1 m2 pixel resolution). The kauri spectra show characteristically steep reflectance and absorption features in the near-infrared (NIR) region with a distinct long descent at 1215 nm, which can be parameterised with a modified Normalised Water Index (mNDWI-Hyp). With a Jeffries–Matusita separability over 1.9, the kauri spectra can be well separated from 21 other canopy vegetation spectra. The Random Forest classifier performed slightly better than Support Vector Machine. A combination of the mNDWI-Hyp index with four additional spectral indices with three red to NIR bands resulted in an overall pixel-based accuracy (OA) of 91.7% for crowns larger 3 m diameter. While the user’s and producer’s accuracies for the class “kauri” with 94.6% and 94.8% are suitable for management purposes, the separation of “dead/dying trees” from “other” canopy vegetation poses the main challenge. The OA can be improved to 93.8% by combining “kauri” and “dead/dying” trees in one class, separate classifications for low and high forest stands and a binning to 10 nm bandwidths. Additional wavelengths and their respective indices only improved the OA up to 0.6%. The method developed in this study allows an accurate location of kauri trees for an area-wide mapping with a five-band multispectral sensor in a representative selection of forest ecosystems.

The Effect of Location on the Value of Ornamental Trees Using the Example of a Green Space in Lublin

Abstract This paper examines and identifies, using a specific example, how the location of trees influences their value. For the valuation of trees outside a forest area, appraisers often employ methods recommended for estimating forest stands, which significantly reduces the value of trees and valued properties. The tree-valuation method developed by SZCZEPANOWSKA et al. (2010) was used in our study. First, we compiled an inventory of trees located on plots intended for expropriation. The study covered all specimens with trunk circumferences of over 25 centimetres, which amounted to a total of 76 trees. We considered the value of the trees in the conditions of the actual location, which means that our calculations included house gardens in the city and the values of the same trees in hypothetical growth conditions: tree stands in rural and urban areas, green spaces in urban and rural areas, housing estates, roads and streets (both urban and rural), and historic areas, health resorts and health-resort protection zones. Our study has shown that the tree valuation method based on differentiating coefficients is a very useful tool for establishing the value of trees in outlying areas.

Trees, Wood, and Paper: Materialities of Urban Arboriculture in Modern Berlin

The systematization of street tree planting in twentieth-century cities went hand in hand with the development of the modern city’s managerial system. In the systematization process, tree planting and care was serialized and standardized. To facilitate these procedures, tree locations and conditions began to be surveyed, planned, and recorded on paper. Focusing on street tree planting in East and West Berlin, this article examines the role of different paper formats, technologies, and representations such as lists, plans, maps, and index and edge-notched cards in the evolution of an increasingly regimented urban arboriculture. Paper was intricately related to both the life and death of the trees that it helped to document. As much as the malleable and variable nature and materiality of street trees contributed to an evolution of paper formats to capture the trees’ changeable conditions, so the use of paper to control, regulate, and systematize also contributed to standardizing trees, their planting, and their care.

Age and site index evaluations for loblolly pine in urban environments

Conventional site index (tree height at base age) may not be suitable for urban trees, therefore it is important to assess its applicability in urban environments. In addition, rapid and non-destructive tree age evaluation methods need to be developed. The objectives of this study were to: (1) develop a rapid and non-destructive method to determine tree age in urban environments, (2) examine the relationship between tree height, age, location, and soil type, and (3) compare the measured site indices with traditional site indices reported in Soil Survey Geographic Database (SSURGO). The study was conducted at the Clemson Experimental Forest (CEF) using conventionally-managed loblolly pine (Pinus taeda) stands, and loblolly pines in the urban environment of the Clemson University campus (CUC). Tree age obtained with a recording resistance drilling method and stump tree ring counting were comparable (R2 = 0.98, n = 14). Tree age estimates from recording resistance drilling and from increment boring were also similar (R2 = 0.93, n = 25). Replicate drilling samples had a low standard deviation, which indicated the high precision of the methodology. An additional benefit of the recording resistance drilling method is the ability to enable rapid evaluation of tree age across an urban forest. There was no discernible relationship between tree height and recording resistance drill age estimate for loblolly pines on the CUC (n = 25). In contrast, there was a relationship between measured diameter at breast height (DBH, cm) and recording resistance drill age estimates. A portion of the loblolly pine trees in the urban environment on the CUC appeared to have reduced productivity, as measured by the height/age relationship when predominantly surrounded by pavement or grass areas.

Assessing the Use of Sentinel-2 Time Series Data for Monitoring Cork Oak Decline in Portugal

In Portugal, cork oak (Quercus suber L.) stands cover 737 Mha, being the most predominant species of the montado agroforestry system, contributing to the economic, social and environmental development of the country. Cork oak decline is a known problem since the late years of the 19th century that has recently worsened. The causes of oak decline seem to be a result of slow and cumulative processes, although the role of each environmental factor is not yet established. The availability of Sentinel-2 high spatial and temporal resolution dense time series enables monitoring of gradual processes. These processes can be monitored using spectral vegetation indices (VI) as their temporal dynamics are expected to be related with green biomass and photosynthetic efficiency. The Normalized Difference Vegetation Index (NDVI) is sensitive to structural canopy changes, however it tends to saturate at moderate-to-dense canopies. Modified VI have been proposed to incorporate the reflectance in the red-edge spectral region, which is highly sensitive to chlorophyll content while largely unaffected by structural properties. In this research, in situ data on the location and vitality status of cork oak trees are used to assess the correlation between chlorophyll indices (CI) and NDVI time series trends and cork oak vitality at the tree level. Preliminary results seem to be promising since differences between healthy and unhealthy (diseased/dead) trees were observed.

Spatiotemporal variability in water sources of urban soils and trees in the semiarid, irrigated Salt Lake Valley

AbstractPlant transpiration is a critical process in ecohydrology and urban water budgets. Urban forests in the semiarid cities of the western United States are composed largely of non‐native species sustained by irrigation. Given that the major end use of water in these cities is usually landscape irrigation, the efficiency of irrigation practices is essential in future water conservation measures. The dependence of urban trees on irrigation is driven not only by the availability of alternative water sources (e.g., precipitation) but also by patterns of infiltration and mobility of soil water. We used hydrogen and oxygen stable isotopes to identify spatiotemporal dynamics of trees, of varied sizes and species, and shallow soil water apportioning of irrigation versus winter snowmelt in urban parks in the semiarid Salt Lake Valley. We estimate for this ecosystem that, on average, >70% of tree transpiration and soil moisture during the summer originate from irrigation, with lower proportions of snowmelt (<30%) from the preceding winter. This implies that a fraction of summer transpiration is originated from out of phase precipitation inputs. Irrigation fractions are dominant throughout the growing season for soil and stem water, but snowmelt fractions increase in stem water towards the end of summer. Tree size, location, and species were not strongly associated with water sources under well‐irrigated conditions. These results show that urban tree management practices should consider the impact of both irrigation practices and residual precipitation inputs, highlighting the importance of examining interannual time scales when calculating the local water budget.

A GIS-Based Water Balance Approach Using a LiDAR-Derived DEM Captures Fine-Scale Vegetation Patterns

Topography exerts strong control on microclimate, resulting in distinctive vegetation patterns in areas of moderate to high relief. Using the Thornthwaite approach to account for hydrologic cycle components, a GIS-based Water Balance Toolset is presented as a means to address fine-scale species–site relationships. For each pixel within a study area, the toolset assesses inter-annual variations in moisture demand (governed by temperature and radiation) and availability (precipitation, soil storage). These in turn enable computation of climatic water deficit, the amount by which available moisture fails to meet demand. Summer deficit computed by the model correlates highly with the Standardized Precipitation–Evapotranspiration Index (SPEI) for drought at several sites across the eastern U.S. Yet the strength of the approach is its ability to model fine-scale patterns. For a 25-ha study site in central Indiana, individual tree locations were linked to summer deficit under different historical conditions: using average monthly climatic variables for 1998–2017, and for the drought year of 2012. In addition, future baseline and drought-year projections were modeled based on downscaled GCM data for 2071–2100. Although small deficits are observed under average conditions (historical or future), strong patterns linked to topography emerge during drought years. The modeled moisture patterns capture vegetation distributions described for the region, with beech and maple preferentially occurring in low-deficit settings, and oak and hickory dominating more xeric positions. End-of-century projections suggest severe deficit, which should favor oak and hickory over more mesic species. Pockets of smaller deficit persist on the landscape, but only when a fine-resolution Light Detection and Ranging (LiDAR)-derived Digital Elevation Model (DEM) is used; a coarse-resolution DEM masks fine-scale variability and compresses the range of observed values. Identification of mesic habitat microrefugia has important implications for retreating species under altered climate. Using readily available data to evaluate fine-scale patterns of moisture demand and availability, the Water Balance Toolset provides a useful approach to explore species–environment linkages.

Classification of the tree for aerial image using a deep convolution neural network and visual feature clustering

In recent years, over-exploitation has led to the accelerated destruction of rural and natural environments for urban development, making an understanding of land use and land cover changes, one of the most urgently required and important tools for urban land planning. To this end, before any land planning begins, the distribution ratio of trees for a given piece of land is determined by calculating the area of that land covered by trees. This study proposes the use of supervised machine learning methods to classify treed areas and combines unsupervised color clustering techniques to achieve optimum classification results. First, the YOLO (you only look once) classification model is used to obtain tree features and location information. The ‘K-means’ and ‘Flood fill algorithm’ methods were tested with tree classification experiments, measuring precision rate, accuracy rate, and recall rate, with shape, illumination, and angle of tree species, and color differences affecting classification results.

Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery

Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.83.

Surface markers: An identity card of endothelial cells.

All endothelial cells have the common characteristic that they line the vessels of the blood circulatory system. However, endothelial cells display a large degree of heterogeneity in the function of their location in the vascular tree. In this article, we have summarized the expression patterns of a number of well-accepted endothelial surface markers present in normal microvascular endothelial cells, arterial and venous endothelial cells, lymphatic endothelial cells, tumor endothelial cells, and endothelial precursor cells.

A Multi-Threshold Segmentation for Tree-Level Parameter Extraction in a Deciduous Forest Using Small-Footprint Airborne LiDAR Data

The development of new approaches to tree-level parameter extraction for forest resource inventory and management is an important area of ongoing research, which puts forward high requirements for the capabilities of single-tree segmentation and detection methods. Conventional methods implement segmenting routine with same resolution threshold for overstory and understory, ignoring that their lidar point densities are different, which leads to over-segmentation of the understory trees. To improve the segmentation accuracy of understory trees, this paper presents a multi-threshold segmentation approach for tree-level parameter extraction using small-footprint airborne LiDAR (Light Detection And Ranging) data. First, the point clouds are pre-processed and encoded to canopy layers according to the lidar return number, and multi-threshold segmentation using DSM-based (Digital Surface Model) method is implemented for each layer; tree segments are then combined across layers by merging criteria. Finally, individual trees are delineated, and tree parameters are extracted. The novelty of this method lies in its application of multi-resolution threshold segmentation strategy according to the variation of LiDAR point density in different canopy layers. We applied this approach to 271 permanent sample plots of the University of Kentucky’s Robinson Forest, a deciduous canopy-closed forest with complex terrain and vegetation conditions. Experimental results show that a combination of multi-resolution threshold segmentation based on stratification and cross-layer tree segments merging method can provide a significant performance improvement in individual tree-level forest measurement. Compared with DSM-based method, the proposed multi-threshold segmentation approach strongly improved the average detection rate (from 52.3% to 73.4%) and average overall accuracy (from 65.2% to 76.9%) for understory trees. The overall accuracy increased from 75.1% to 82.6% for all trees, with an increase of the coefficient of determination R2 by 20 percentage points. The improvement of tree detection method brings the estimation of structural parameters for single trees up to an accuracy level: For tree height, R2 increased by 5.0 percentage points from 90% to 95%; and for tree location, the mean difference decreased by 23 cm from 105 cm to 82 cm.

Attributing drivers to spatio-temporal changes in tree density across a suburbanizing landscape since 1944

Urban expansion, especially suburbanization, represents a major social, economic and environmental shift that has escalated since the mid-1900s in North America. Suburban development leads to corresponding changes in the treed environment of urban-rural fringes. It is important to understand where, when and why trees change in response to development over many decades, but this is difficult since long-term data are scarce. We used 70+ years (1944–2017) of leaf-off aerial photographs, often representing the only long-term landscape record, to quantify and map spatio-temporal changes in tree density through the entirety of the agricultural-suburban transitional period. Photo-interpretation of individual tree locations, along with recording observable drivers of change, was completed across six different modern landscapes in Mississauga, Ontario, Canada. Results indicate that tree density often recovers, or even increases, post-development. There are differences between landscapes, but most tree density gains are associated with forest expansion and tree planting, while most losses are associated with building and road construction. The influence of these drivers, along with the temporal trajectory of tree density changes, are shaped by a landscape’s socioecological legacy and the length, scope and intensity of development (as decided by decision makers). Processes include initial tree losses followed by recovery from tree planting, and forest succession in abandoned fields after land purchase and nearby development. We assert that the spatio-temporal changes in tree density and related drivers quantified here can be generalized to gain knowledge on how tree density and distribution across agricultural landscapes will change under different development scenarios.

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure–from–motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below.

Interaplant Distribution of the Citrus Rust Mite, Phyllocoptruta oleivora (ASHMEAD) on Citrus Trees

Experiments were conducted to evaluate the preferring location of the citrus rust mite, Phyllocoptruta oleivora inhabiting citrus trees on different heights level, high, medium and low and four sides of the citrus tree locations east, north, west and south. One orange cultivated area Balady variety was chosen for the experimental study at Enshas region located in Sharkia Governorate (latitude 52°38'11.7N and longitude 5°49'37.66E). The lower, medium and high trees levels were selected at 80cm, 80-150cm., and more than 150 cm. from the ground level, respectively. The study was conducted during two successive years starting from January 2017 till end of December 2018.The results showed that P. oleivora preferred the warmth and high relatively humid conditions, allocated at tree surfaces near the soil on contrast the plant surfaces far of soil. That is where the steaming rate of irrigation water provides the predator with the optimal high humidity for population increase. In addition, the study indicates that the citrus rust mite prefers living the east side of the tree was exposed to sunlight more than the other sides

Tree diversity drives associational resistance to herbivory at both forest edge and interior.

Tree diversity is increasingly acknowledged as an important driver of insect herbivory. However, there is still a debate about the direction of associational effects that can range from associational resistance (i.e., less damage in mixed stands than in monocultures) to the opposite, associational susceptibility. Discrepancies among published studies may be due to the overlooked effect of spatially dependent processes such as tree location within forests. We addressed this issue by measuring crown defoliation and leaf damage made by different guilds of insect herbivores on oaks growing among conspecific versus heterospecific neighbors at forest edges versus interior, in two closed sites in SW France forests. Overall, oaks were significantly less defoliated among heterospecific neighbors (i.e., associational resistance), at both forest edge and interior. At the leaf level, guild diversity and leaf miner herbivory significantly increased with tree diversity regardless of oak location within stands. Other guilds showed no clear response to tree diversity or oak location. We showed that herbivore response to tree diversity varied among insect feeding guilds but not between forest edges and interior, with inconsistent patterns between sites. Importantly, we show that oaks were more defoliated in pure oak plots than in mixed plots at both edge and forest interior and that, on average, defoliation decreased with increasing tree diversity from one to seven species. We conclude that edge conditions could be interacting with tree diversity to regulate insect defoliation, but future investigations are needed to integrate them into the management of temperate forests, notably by better understanding the role of the landscape context.

Influence of Scanner Position and Plot Size on the Accuracy of Tree Detection and Diameter Estimation Using Terrestrial Laser Scanning on Forest Inventory Plots

This research tested how different scanner positions and sample plot sizes affect the tree detection and diameter measurement in forest inventories. For this, a multistage density-based clustering approach was further developed for the automatic mapping of tree positions and simultaneously applied with automatic measurements of tree diameters. This further development of the algorithm reduced the proportion of falsely detected tree locations by about 64%. The algorithms were tested in different settings with respect to the number and spatial alignment of scanner positions and under manifold forest conditions, covering different age classes and a mixture of scenarios, and representing a broad gradient of structural complexity. For circular sample plots with a maximum radius of 20 m, the tree mapping algorithm showed a detection rate of 82.4% with seven scanner positions at the vertices of a hexagon plus the center coordinates, and 68.3% with four scanner positions aligned in a triangle plus the center. Detection rates were significantly increased with smaller maximum radii. Thus, with a maximum radius of 10 m, the hexagon setting yielded a detection rate of 90.5% and the triangle 92%. Other alignments of scanner positions were also tested, but proved to be either unfavorable or too labor-intensive. The commission rates were on average less than 3%. The root mean square error (RMSE) of the dbh (diameter at breast height) measurement was between 2.66 cm and 4.18 cm for the hexagon and between 3.0 cm and 4.7 cm for the triangle design. The robustness of the algorithm was also demonstrated via tests by means of an international benchmark dataset. It has been shown that the number of stems per hectare had a significant impact on the detection rate.

ARBOR: A new framework for assessing the accuracy of individual tree crown delineation from remotely-sensed data

To assess the accuracy of individual tree crown (ITC) delineation techniques the same tree needs to be identified in two different datasets, for example, ground reference (GR) data and crowns delineated from LiDAR. Many studies use arbitrary metrics or simple linear-distance thresholds to match trees in different datasets without quantifying the level of agreement. For example, successful match-pairing is often claimed where two data points, representing the same tree in different datasets, are located within 5 m of one another. Such simple measures are inadequate for representing the multi-variate nature of ITC delineations and generate misleading measures of delineation accuracy. In this study, we develop a new framework for objectively quantifying the agreement between GR and remotely-sensed tree datasets: the Accuracy of Remotely-sensed Biophysical Observation and Retrieval (ARBOR) framework. Using common biophysical properties of ITC delineated trees (location, height and crown area), trees represented in different data sets were modelled as overlapping Gaussian curves to facilitate a more comprehensive assessment of the level of agreement. Extensive testing quantified the limitations of some frequently used match-pairing methods, in particular, the Hausdorff distance algorithm. We demonstrate that within the ARBOR framework, the Hungarian combinatorial optimisation algorithm improves the match between datasets, while the Jaccard similarity coefficient is effective for measuring the correspondence between the matched data populations. The ARBOR framework was applied to GR and remotely-sensed tree data from a woodland study site to demonstrate how ARBOR can identify the optimum ITC delineation technique, out of four different methods tested, based on two measures of statistical accuracy. Using ARBOR will limit further reliance on arbitrary thresholds as it provides an objective approach for quantifying accuracy in the development and application of ITC delineation algorithms.

Biodiversity-ecosystem functioning relationships of overstorey versus understorey trees in an old-growth temperate forest

The results of this study, based on a large dataset with mapped tree locations, suggest that the relationship between biodiversity and ecosystem functioning (BEF) is different between the over- and understorey in the investigated forest community. Neither the functional nor the phylogenetic diversity showed a significant advantage in predicting aboveground biomass and biomass production (aboveground biomass increment) when compared with species richness. Consistent conclusions have still not been reached regarding the relative importance of the different diversity indices in predicting biomass production. Furthermore, the effects of certain forest strata on the BEF relationship remain unclear. The woody plant species in the study area were classified as two vertical strata and were referred to as the “overstorey” (trees with dbh > 10 cm) and “understorey” (trees with dbh ≤ 10 cm). The relationships between different metrics of tree diversity and biomass production were quantified using linear models. The set of the best predictors for tree biomass and productions were determined using a multi-model selection approach. The differences in diversity-ecosystem functioning relationships between the two forest vertical strata, and the effects of canopy trees on the understorey trees, were investigated using structural equation models. The relative importance of the species diversity, phylogenetic diversity, and functional diversity in predicting the aboveground biomass production (ABP) was examined to compare the potential of phylogenetic and functional diversity indices and species richness in predicting biomass productivity and compare the influence of niche complementarity, selection effects, or mass-ratio effects on aboveground biomass variables and to assess possible differences in the BEF relationships between the overstorey and understorey layers in the studied ecosystems. Also, this study investigated possible differences in the BEF relationships between the overstorey and understorey layers in the studied ecosystems. The results confirmed a positive diversity-productivity relationship in the natural coniferous and broadleaved mixed forest. However, the relationship in the overstorey stratum was statistically stronger than that in the understorey layer. The combination of diversity indices and functional traits could explain more of the variations in the biomass and productivity than when examined separately. The correlation between the richness of the overstorey and understorey species was found to be positive. However, the aboveground woody biomass of the overstorey layer had negative effects on the understorey biomass. In summary, this study found a stronger positive correlation between the woody plant diversity and productivity in the overstorey than in the understorey stratum. These findings indicate that the BEF relationships are hierarchically dependent, and supported by niche complementarity. Also, a much weaker relationship was observed between the functional diversity and biomass production than between the species diversity and biomass production due to a selection effect. The aboveground woody biomass was found to be enhanced in communities dominated by functional traits correlated to smaller leaf areas, greater maximum tree heights, and higher leaf carbon content. These findings suggest that the BEF relationships were driven by a mass-ratio hypothesis.

Increasing the profitability of propolis production in honey bee colonies by utilizing remote sensing techniques to spot locations of trees as potential sources of resin

ABSTRACTPropolis is produced by honey bees utilizing resins from different plants especially trees. Propolis is incorporated in many industries and has many medicinal uses, and is also considered among valuable products from honey bee colonies. Beekeepers can produce propolis from their colonies after the end of honey seasons. Locations containing high numbers of trees are very suitable for propolis production by honey bee colonies. Searching for suitable locations to obtain high amounts of propolis from bee colonies is very crucial especially when no forests are available as in Egypt. In this study, geographical information system (GIS) and Landsat satellite images were utilized to identify potential locations for propolis production in the study area (Kafr Al-Shiekh Governorate, Egypt). The analysis depended on unsupervised and supervised image classification followed by using spatial analyst tools. Real images were used to verify the correctness of the classified locations. It is possible to predict the potential locations for propolis production using remote sensing to increase the profitability from honey bee colonies.