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63 results for “tree crowns”
UAV time series and tree crowns
<p>This dataset contains:</p><p>-A UAV time series of mosaicked images of a woodland in Northeast UK. Complete detaisl are given in: "Elias Fernando Berra, Rachel Gaulton, Stuart Barr, Assessing spring phenology of a temperate woodland: A multiscale comparison of ground, unmanned aerial vehicle and Landsat satellite observations, Remote Sensing of Environment, Volume 223, 2019, Pages 229-242, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2019.01.010." </p><p>-Manual (reference) and automatic delinetaed tree crowns for the area covered by the UAV time series data. Complete details in: Elias F. Berra. Individual tree crown detection and delineation across a woodland using leaf-on and leaf-off imagery from a UAV consumer-grade camera. Journal of Applied Remote Sensing, Vol. 14, Issue 3, 034501 (July 2020). https://doi.org/10.1117/1.JRS.14.034501</p>
Crown Traits of Broadleaf Deciduous Trees at NEON Forest Sites (2018-2022)
Using NEON Airborne Observation Platform (AOP) measurements collected in 2018-2022 from nine broadleaf deciduous NEON forest sites, we quantified a broad suite of structural metrics and spectral reflectance indices for 305 tree crowns that were delineated in the field by NEON and met our data quality criteria. For each tree crown, we used 1-m^3 voxelated AOP LiDAR data to compute structural metrics, including plant area index (PAI), leaf area index (LAI), top rugosity, maximum canopy height (MAXCH), mean outer canopy height (MOCH), rumple, accumulative plant area density and accumulative LiDAR intensity at multiple tree heights. We used AOP imaging spectrometer to compute several spectral indices, including NDVI, NIRv, EVI, NDWI and chlorophyll index of red edge/green. The data are suitable for ecophysiological studies at tree crown and/or species level. The broad spatial extent allows for the exploration of variability in structure and function of common north American tree species across wide environmental gradients.
Point clouds from terrestrial laser scanning from crowns of individual Scots pine trees
<p>Trees adapt to their growing conditions by regulating the sizes of their parts and their relationships. For example, removal or death of adjacent trees increases the growing space and the amount of light received by the remaining trees enabling their crowns to expand. Knowledge about the effects of silvicultural practices on crown size and shape as well as about the quality of branches affecting the shape of a crown is, however, still limited. Laser scanning (or Light detecting and ranging LiDAR) has provided new opportunities for characterizing trees in more detail in three-dimensional space. Especially terrestrial laser scanning (TLS) has increasingly been used in producing a variety of tree attributes. This data set includes 3D reconstruction of crowns of Scots pine (<em>Pinus sylvestris</em> L.) trees from sample plots with different thinning treatments. The thinning treatments include two intensities of thinning, three thinning types as well as control (i.e. no thinning treatment since the establishment). This data set can be used in developing point cloud processing algorithms for single tree crown characterization and for investigating variation in crown size and shape as well as the effects of various thinning treatments on crown size and shape of Scots pine trees grown in boreal forests.</p>
Outputs of the Jupyter Notebook - Tree crown detection using DeepForest
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li> <p>Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></p> </li> <li> <p>Matt Allen (reviewer), Department of Geography - University of Cambridge, <a href="https://github.com/mja2106">@mja2106</a></p> </li> </ul> <p><em>Modelling codebase</em></p> <ul> <li> <p>Ben Weinstein (maintainer & developer), University of Florida, <a href="https://github.com/bw4sz">@bw4sz</a></p> </li> <li> <p>Henry Senyondo (support maintainer), University of Florida, <a href="https://github.com/henrykironde">@henrykironde</a></p> </li> <li> <p>Ethan White (PI and author), University of Florida, <a href="https://github.com/ethanwhite">@weecology</a></p> </li> <li> <p>Other contributors are listed in the <a href="https://github.com/weecology/DeepForest/graphs/contributors">GitHub repo</a></p> </li> </ul> <p><em>Modelling publications</em></p> <ul> <li> <p>Ben G Weinstein, Sergio Marconi, Mélaine Aubry-Kientz, Gregoire Vincent, Henry Senyondo, and Ethan P White. Deepforest: a python package for rgb deep learning tree crown delineation. <em>Methods in Ecology and Evolution</em>, 11:1743–1751, 2020. URL: <a href="https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472">https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/2041-210X.13472</a>, <a href="https://doi.org/https://doi.org/10.1111/2041-210X.13472">doi:https://doi.org/10.1111/2041-210X.13472</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, and Ethan White. Individual tree-crown detection in rgb imagery using semi-supervised deep learning neural networks. <em>Remote Sensing</em>, 2019. URL: <a href="https://www.mdpi.com/2072-4292/11/11/1309">https://www.mdpi.com/2072-4292/11/11/1309</a>, <a href="https://doi.org/10.3390/rs11111309">doi:10.3390/rs11111309</a>.</p> </li> <li> <p>Ben G Weinstein, Sergio Marconi, Stephanie A Bohlman, Alina Zare, and Ethan P White. Cross-site learning in deep learning rgb tree crown detection. <em>Ecological Informatics</em>, 56:101061, 2020. URL: <a href="https://www.sciencedirect.com/science/article/pii/S157495412030011X">https://www.sciencedirect.com/science/article/pii/S157495412030011X</a>, <a href="https://doi.org/https://doi.org/10.1016/j.ecoinf.2020.101061">doi:https://doi.org/10.1016/j.ecoinf.2020.101061</a>.</p> </li> </ul>
Outputs of the Jupyter Notebook - Tree crown delineation using detectreeRGB
<p>The dataset contains the outputs of the notebook "Tree crown detection using DeepForest" published in The Environmental Data Science Book.</p> <p><strong>Contributions</strong></p> <p><em>Notebook</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge, <a href="https://github.com/shmh40">@shmh40</a></li> <li>Alejandro Coca-Castro (reviewer), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a></li> </ul> <p><em>Modelling codebase</em></p> <ul> <li>Sebastian H. M. Hickman (author), University of Cambridge <a href="https://github.com/shmh40">@shmh40</a></li> <li>James G. C. Ball (contributor), University of Cambridge <a href="https://github.com/PatBall1">@PatBall1</a></li> <li>David A. Coomes (contributor), University of Cambridge</li> <li>Toby Jackson (contributor), University of Cambridge</li> </ul>
Data of Cerrado´s Tree Crown Networks
<p>Information about the architecture of the woody crown obtained through representations in the form of a network (graphs). The essential components of these networks are nodes and connectors. Decomposition, topology, and properties calculated for analyzing the strategies of crown airspace acquisition in any environment. The networks represented in a two-dimensional space follow the general laws of network theory, but with specific meanings for the crown architecture. Thus, a dataset generated and included information about five individuals from fifteen tree species growing under the natural conditions of the Cerrado vegetation. We presented the types and the total number of nodes. Initial node (IN) was the node that starts the network, regular node (RN) was the vast majority of nodes with three connectors. Emission node (EN) showed four connectors, and the final node (FN) was the last in leafy axes. There are data about the distances between the initial and final nodes (IN-IF), and initial and emission nodes (IN-IE). Decomposition and topological combinations permitted to disclose the properties (navigability, vulnerability, symmetry, and complexity). The data presented can be used by researchers from all over the world in works that investigate the behavior of networks in biological systems, in addition to the specific applications of studies of functional ecology and plant ecophysiology. We obtained the data directly from a skeletonized representation of the woody crown in a two-dimensional space in the form of a drawing. Subsequently, the nodes counted, and their proportions (decomposition), the distances between the different types of nodes (topology), and the values of network properties (the combination of decomposition and topology) obtained.</p>
Figure 7 in Additions to the British list of Megaselia Rondani (Diptera: Phoridae), including two new species, from the crowns of ancient pollarded trees
Figure 7. Megaselia russellsmithi male, hypopygium. (A) Left face; (B) right face (minus penis complex). Scale bar: 0.1 mm.
Figure 22 in Additions to the British list of Megaselia Rondani (Diptera: Phoridae), including two new species, from the crowns of ancient pollarded trees
Figure 22. Megaselia veluitinicavus male. (A) Left face of hypopygium; (B) tips of right paraphysis and posteroventral region of epandrium; (C) anterior face of hind basitarsus; (D) the internal hairy cavity of the hind basitarsus (anterior focal plane). Scale bars: 0.1 mm.
Figure 8 in Additions to the British list of Megaselia Rondani (Diptera: Phoridae), including two new species, from the crowns of ancient pollarded trees
Figure 8. Megaselia russellsmithi female, details of abdomen. (A) Tergites 5–7; (B) sternite 7 and lobes at rear of sternum 8; (C) right cercus. Scale bars: 0.1 mm.
Figure 4 in Additions to the British list of Megaselia Rondani (Diptera: Phoridae), including two new species, from the crowns of ancient pollarded trees
Figure 4. Megaselia henrydisneyi male, hypopygium. (A) Left face; (B) right face. Scale bar: 0.1 mm.
Figure 1 in Additions to the British list of Megaselia Rondani (Diptera: Phoridae), including two new species, from the crowns of ancient pollarded trees
Figure 1. Megaselia crassipes male. (A) Posterior face of front tarsus; (B) left face of hypopytgium. Scale bar: 0.1 mm.
Tree crowns of the north-west corner of the permanent sample area in the Kaluzhskiye Zaseki Nature Reserve
<p>The studies were conducted in the Kaluga Zaseki Nature Reserve on a permanent sample plot (PSP) established in an old-growth broadleaved forest. The stand on the PSP has a complex structure, consisting of several tiers. There are 6 species of broad-leaved trees in the stand: oak (Quercus robur), ash (Fraxinus excelsior), elm (Ulmus glabra), sharp-leaved maple (Acer platanoides), field maple (A. campestre), linden (Tilia cordata) and 2 small-leaved trees - birch (Betula spp.) and aspen (Populus tremula). The oldest oak trees are about 300 years old. The size of the PPP is 440 × 200 m, this work was done on a 40 × 40 m plot located in the northwest corner of the PSP. For tree detection, orthophotomaps were used based on aerial photography materials taken with a DJI Phantom IV Pro quadcopter. Photogrammetric processing was carried out in Agisoft Metashape software (version 1.6.1.10009).</p> <p>The data set contains two fragments of multi-season orthophotos in tif format and corresponding files in shp., dbh. and shx. formats, which contain information on crown boundaries and species of marked trees.</p>
Decomposition, topology, properties, and graphs of woody crown networks of 15 tree species of Cerrado vegetation
<p>Data of decomposition, topology, properties, and the corresponding graphs of 15 adult tree species of Cerrado vegetation, <em>sensu stricto</em> physiognomy. The woody crown networks (WCN) representations in a bidimensional space were obtained by drawing followed the methodology described by Prado et al. (2020, Prado, C.H.B.A., Trovão, D.M.B.M., Souza, J.P.<strong>,</strong> 2020. A network model for determining the woody crown's decomposition, topology, and properties. Journal of Theoretical Biology, v. 499, p. 110318. https://doi.org/<a href="https://www.x-mol.com/paperRedirect/1258515479077781504">10.1016/j.jtbi.2020.110318</a>.). The branching regions were the nodes, and the woody crown segments connecting the nodes or merely emerging from them were the connectors. Those trees grew under natural conditions in a most common (<em>sensu stricto</em>) physiognomy of Cerrado vegetation, in a reservoir of 86 ha, located at 850 m above sea level in São Carlos city, São Paulo state, Brazil, at 21°58'- 22°00'S and 47°51'-47°52'W. Following the Köppen climatic classification, this region is between Aw and Cwa, a tropical climate with dry winter and wet summer. The rainy season occurs between October-March, and the dry season between April and September. </p>
Рис. 1. ГнезΑо гусениц непарного шеΛкопряΑа в кроне Αерева. Фото Δ. Куренщикова Fig. 1. The nest of gypsy moth caterpillars in the tree crown. Photo by D. Kurenshchikov in Sensitivity of caterpillars of the gypsy moth (Lymantria dispar, Erebidae) from the South of Khabarovsk Territory to various strains of nuclear polyhedrosis virus
Рис. 1. ГнезΑо гусениц непарного шеΛкопряΑа в кроне Αерева. Фото Δ. Куренщикова Fig. 1. The nest of gypsy moth caterpillars in the tree crown. Photo by D. Kurenshchikov
'Does crown sheltering effect the vulnerability of trees to wind damage in tropical forests? ' project dataset
<p>The manually delineated tree crowns, polygon-based sheltering indices, canopy height model (CHM) and digital surface model (DSM) generated to investigate the effects of local crown sheltering on wind vulnerability in Barro Colorado Island, Panama. Files include both circular and directional indices at 10m, 20m, 50m and 2 x canopy radius.</p>
Data from: Shade tolerance controls the spectrum of crown sizes and its response to local competition across European and North American tree species: Implications for light interception strategies
Open the record for dataset details and reuse information.
Crown network datasets of Caatinga´s tree Cenostigma pyramidale (Tul.) Gagnon & G.P.Lewis (Leguminosae - Caesalpinoideae)
<p>Properties of woody crown networks of <em>Cenostigma pyramidale</em> (Tul.) Gagnon & G.P.Lewis (Leguminosae - Caesalpinoideae) growing under natural conditions in Caatinga vegetation area. The data are useful in plant ecophysiology, plant functional ecology by researchers investigating the woody crown traits. We obtained the data directly from a skeletonized representation of the woody crown in a two-dimensional space by hand drawing. Subsequently, the nodes counted, and their proportions (decomposition), the distances between the different types of nodes (topology), and the values of network properties (the combination of decomposition and topology) were obtained.Properties of woody crown networks of <em>Cenostigma pyramidale</em> (Tul.) Gagnon & G.P.Lewis (Leguminosae - Caesalpinoideae) growing under natural conditions in Caatinga vegetation area. The data are useful in plant ecophysiology, plant functional ecology by researchers investigating the woody crown traits. We obtained the data directly from a skeletonized representation of the woody crown in a two-dimensional space by hand drawing. Subsequently, the nodes counted, and their proportions (decomposition), the distances between the different types of nodes (topology), and the values of network properties (the combination of decomposition and topology) were obtained.</p>
NEON Tree Crowns Dataset
<p><strong>Abstract</strong></p> <p>The NeonTreeCrowns dataset is a set of individual level crown estimates for 100 million trees at 37 geographic sites across the United States surveyed by the National Ecological Observation Network’s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. </p> <p><strong>How can I see the data?</strong></p> <p>A web server to look through predictions is available through <a href="http://idtrees.org">idtrees.org</a></p> <p><strong>Dataset Organization</strong></p> <p>The shapefiles.zip contains 11,000 shapefiles, each corresponding to a 1km^2 RGB tile from NEON (ID: DP3.30010.001). For example "2019_SOAP_4_302000_4100000_image.shp" are the predictions from "2019_SOAP_4_302000_4100000_image.tif" available from the NEON data portal: <a href="https://data.neonscience.org/data-products/explore?search=camera">https://data.neonscience.org/data-products/explore?search=camera</a>. NEON's file convention refers to the year of data collection (2019), the four letter site code (SOAP), the sampling event (4), and the utm coordinate of the top left corner (302000_4100000). For NEON site abbreviations and utm zones see <a href="https://www.neonscience.org/field-sites/field-sites-map">https://www.neonscience.org/field-sites/field-sites-map</a>. </p> <p>The predictions are also available as a single csv for each file. All available tiles for that site and year are combined into one large site. These data are not projected, but contain the utm coordinates for each bounding box (left, bottom, right, top). For both file types the following fields are available:</p> <p>Height: The crown height measured in meters. Crown height is defined as the 99th quartile of all canopy height pixels from a LiDAR height model (ID: DP3.30015.001)</p> <p>Area: The crown area in m<sup>2</sup> of the rectangular bounding box.</p> <p>Label: All data in this release are "Tree".</p> <p>Score: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence)</p> <p><strong>How were predictions made?</strong></p> <p>The DeepForest algorithm is available as a python package: <a href="https://deepforest.readthedocs.io/">https://deepforest.readthedocs.io/</a>. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed.</p> <p><strong>How were predictions validated?</strong></p> <p>Please see</p> <p>Weinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. <em>Ecological Informatics</em>, <em>56</em>, 101061.</p> <p>Weinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. <em>bioRxiv</em>.</p> <p>Weinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." <em>Remote Sensing</em> 11.11 (2019): 1309.</p> <p><strong>Were any sites removed?</strong></p> <p>Several sites were removed due to poor NEON data quality. GRSM and PUUM both had lower quality RGB data that made them unsuitable for prediction. NEON surveys are updated annually and we expect future flights to correct these errors. We removed the GUIL puerto rico site due to its very steep topography and poor sunangle during data collection. The DeepForest algorithm responded poorly to predicting crowns in intensely shaded areas where there was very little sun penetration. We are happy to make these data are available upon request.</p> <p># Contact</p> <p>We welcome questions, ideas and general inquiries. The data can be used for many applications and we look forward to hearing from you. Contact ben.weinstein@weecology.org. </p>
Figure 14 in Additions to the British list of Megaselia Rondani (Diptera: Phoridae), including two new species, from the crowns of ancient pollarded trees
Figure 14. Megaselia serrata female, details of abdomen, furca.
Supplementary data to article "From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey"
<p>This repository provides regression models and annual prediction rasters for tree mortality in Germany. </p> <p><strong>Regression models:</strong><br>Logistic regression models which predict tree mortality for the species (beech = Fagus sylvatica, <br>oak = Quercus petraea and robur, pine = Pinus sylvestris, spruce = Picea abies) and species <br>groups (OB = other broadleaves, OC = other conifers) based on observations of dead trees in the<br>German Crown Condition Survey (Waldzustandserhebung) and a set of environmental predictor <br>variables. The predictors come from the domains of climate (clim), site conditions (site, i.e. <br>topography, soil, land cover, deposition), tree age (age) and some models contain pairwise <br>interaction terms between predictors (inter). All models were fit in R and are represented as <br>objects of the class glm and stored in files of the type rds.</p> <p><strong>Prediction rasters:</strong><br>Spatial predictions of the mortality rate across Germany for each tree species and species group <br>and for each year from 1998 to 2022. The rasters have a spatial resolution of 100 m. Missing values<br>mark areas where the species/group does not occur. The mortality values are given as integers <br>between 0 (no mortality) and 10000 (100% mortality). The coordinate reference system is Lambert <br>Azimuthal Equal Area (LAEA; EPSG:3035). The rasters are provided in the file format GeoTIFF (tif).</p> <p>A detailed description of the data sources and analyses can be found in the following article.</p> <p><strong>Citation:</strong><br><em>Knapp, N., Wellbrock, N., Bielefeldt, J., Dühnelt, P., Hentschel, R., Bolte, A., 2024. </em><br><em>From single trees to country-wide maps: Modeling mortality rates in Germany based on the Crown Condition Survey.</em></p> <p><strong>Contact:</strong><br>nikolai.knapp@thuenen.de</p> <p> </p> <p> </p>
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