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1. NEON Tree Crowns Dataset

   https://doi.org/10.5281/zenodo.3765871  

   Weinstein, Ben; Marconi, Sergio; Zare, Alina; Bohlman, Stephanie; Graves, Sarah; Singh, Aditya; White, Ethan (January 2020, Zenodo)

   {"Abstract":["Abstract<\/strong><\/p>\n\nThe 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\u2019s Airborne Observation Platform. Each rectangular bounding box crown prediction includes height, crown area, and spatial location. <\/p>\n\nHow can I see the data?<\/strong><\/p>\n\nA web server to look through predictions is available through idtrees.org<\/p>\n\nDataset Organization<\/strong><\/p>\n\nThe 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: https://data.neonscience.org/data-products/explore?search=camera. 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 https://www.neonscience.org/field-sites/field-sites-map. <\/p>\n\nThe 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>\n\nHeight: 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>\n\nArea: The crown area in m^{2<\/sup> of the rectangular bounding box.<\/p>\n\n}Label: All data in this release are "Tree".<\/p>\n\nScore: The confidence score from the DeepForest deep learning algorithm. The score ranges from 0 (low confidence) to 1 (high confidence)<\/p>\n\nHow were predictions made?<\/strong><\/p>\n\nThe DeepForest algorithm is available as a python package: https://deepforest.readthedocs.io/. Predictions were overlaid on the LiDAR-derived canopy height model. Predictions with heights less than 3m were removed.<\/p>\n\nHow were predictions validated?<\/strong><\/p>\n\nPlease see<\/p>\n\nWeinstein, B. G., Marconi, S., Bohlman, S. A., Zare, A., & White, E. P. (2020). Cross-site learning in deep learning RGB tree crown detection. Ecological Informatics<\/em>, 56<\/em>, 101061.<\/p>\n\nWeinstein, B., Marconi, S., Aubry-Kientz, M., Vincent, G., Senyondo, H., & White, E. (2020). DeepForest: A Python package for RGB deep learning tree crown delineation. bioRxiv<\/em>.<\/p>\n\nWeinstein, Ben G., et al. "Individual tree-crown detection in RGB imagery using semi-supervised deep learning neural networks." Remote Sensing<\/em> 11.11 (2019): 1309.<\/p>\n\nWere any sites removed?<\/strong><\/p>\n\nSeveral 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>\n\n# Contact<\/p>\n\nWe 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>"],"Other":["Gordon and Betty Moore Foundation: GBMF4563"]} 

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2. Individual canopy tree species maps for the National Ecological Observatory Network

   https://doi.org/10.1371/journal.pbio.3002700  

   Weinstein, Ben G; Marconi, Sergio; Zare, Alina; Bohlman, Stephanie A; Singh, Aditya; Graves, Sarah J; Magee, Lukas; Johnson, Daniel J; Record, Sydne; Rubio, Vanessa E; et al (July 2024, PLOS Biology)

   Tanentzap, Andrew J (Ed.)

   The ecology of forest ecosystems depends on the composition of trees. Capturing fine-grained information on individual trees at broad scales provides a unique perspective on forest ecosystems, forest restoration, and responses to disturbance. Individual tree data at wide extents promises to increase the scale of forest analysis, biogeographic research, and ecosystem monitoring without losing details on individual species composition and abundance. Computer vision using deep neural networks can convert raw sensor data into predictions of individual canopy tree species through labeled data collected by field researchers. Using over 40,000 individual tree stems as training data, we create landscape-level species predictions for over 100 million individual trees across 24 sites in the National Ecological Observatory Network (NEON). Using hierarchical multi-temporal models fine-tuned for each geographic area, we produce open-source data available as 1 km²shapefiles with individual tree species prediction, as well as crown location, crown area, and height of 81 canopy tree species. Site-specific models had an average performance of 79% accuracy covering an average of 6 species per site, ranging from 3 to 15 species per site. All predictions are openly archived and have been uploaded to Google Earth Engine to benefit the ecology community and overlay with other remote sensing assets. We outline the potential utility and limitations of these data in ecology and computer vision research, as well as strategies for improving predictions using targeted data sampling. 

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3. Training Data for the NeonTreeEvaluation Benchmark

   https://doi.org/10.5281/zenodo.3459803  

   Weinstein, Ben; Marconi, Sergio; White, Ethan (January 2020, Zenodo)

   This dataset is the large training data files for the NeonTreeEvaluation Benchmark for individual tree detection from airborne imagery. For each geographic site, given by the NEON four letter code (e.g HARV -> Harvard Forest), there are three files: a RGB image, a LiDAR tile, and a 426 band hyperpspectral file. For more information on the benchmark, and the corresponding R package, see https://github.com/weecology/NeonTreeEvaluation_package</p> 

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4. Integrating National Ecological Observatory Network (NEON) Airborne Remote Sensing and In-Situ Data for Optimal Tree Species Classification

   https://doi.org/10.3390/rs12091414  

   Scholl, Victoria M.; Cattau, Megan E.; Joseph, Maxwell B.; Balch, Jennifer K. (May 2020, Remote Sensing)

   null (Ed.)

   Accurately mapping tree species composition and diversity is a critical step towards spatially explicit and species-specific ecological understanding. The National Ecological Observatory Network (NEON) is a valuable source of open ecological data across the United States. Freely available NEON data include in-situ measurements of individual trees, including stem locations, species, and crown diameter, along with the NEON Airborne Observation Platform (AOP) airborne remote sensing imagery, including hyperspectral, multispectral, and light detection and ranging (LiDAR) data products. An important aspect of predicting species using remote sensing data is creating high-quality training sets for optimal classification purposes. Ultimately, manually creating training data is an expensive and time-consuming task that relies on human analyst decisions and may require external data sets or information. We combine in-situ and airborne remote sensing NEON data to evaluate the impact of automated training set preparation and a novel data preprocessing workflow on classifying the four dominant subalpine coniferous tree species at the Niwot Ridge Mountain Research Station forested NEON site in Colorado, USA. We trained pixel-based Random Forest (RF) machine learning models using a series of training data sets along with remote sensing raster data as descriptive features. The highest classification accuracies, 69% and 60% based on internal RF error assessment and an independent validation set, respectively, were obtained using circular tree crown polygons created with half the maximum crown diameter per tree. LiDAR-derived data products were the most important features for species classification, followed by vegetation indices. This work contributes to the open development of well-labeled training data sets for forest composition mapping using openly available NEON data without requiring external data collection, manual delineation steps, or site-specific parameters. 

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5. Capturing long‐tailed individual tree diversity using an airborne imaging and a multi‐temporal hierarchical model

   https://doi.org/10.1002/rse2.335  

   Weinstein, Ben G.; Marconi, Sergio; Graves, Sarah J.; Zare, Alina; Singh, Aditya; Bohlman, Stephanie A.; Magee, Lukas; Johnson, Daniel J.; Townsend, Phillip A.; White, Ethan P. (May 2023, Remote Sensing in Ecology and Conservation)

   Abstract Measuring forest biodiversity using terrestrial surveys is expensive and can only capture common species abundance in large heterogeneous landscapes. In contrast, combining airborne imagery with computer vision can generate individual tree data at the scales of hundreds of thousands of trees. To train computer vision models, ground‐based species labels are combined with airborne reflectance data. Due to the difficulty of finding rare species in a large landscape, many classification models only include the most abundant species, leading to biased predictions at broad scales. For example, if only common species are used to train the model, this assumes that these samples are representative across the entire landscape. Extending classification models to include rare species requires targeted data collection and algorithmic improvements to overcome large data imbalances between dominant and rare taxa. We use a targeted sampling workflow to the Ordway Swisher Biological Station within the US National Ecological Observatory Network (NEON), where traditional forestry plots had identified six canopy tree species with more than 10 individuals at the site. Combining iterative model development with rare species sampling, we extend a training dataset to include 14 species. Using a multi‐temporal hierarchical model, we demonstrate the ability to include species predicted at <1% frequency in landscape without losing performance on the dominant species. The final model has over 75% accuracy for 14 species with improved rare species classification compared to 61% accuracy of a baseline deep learning model. After filtering out dead trees, we generate landscape species maps of individual crowns for over 670 000 individual trees. We find distinct patches of forest composed of rarer species at the full‐site scale, highlighting the importance of capturing species diversity in training data. We estimate the relative abundance of 14 species within the landscape and provide three measures of uncertainty to generate a range of counts for each species. For example, we estimate that the dominant species,Pinus palustrisaccounts for c. 28% of predicted stems, with models predicting a range of counts between 160 000 and 210 000 individuals. These maps provide the first estimates of canopy tree diversity within a NEON site to include rare species and provide a blueprint for capturing tree diversity using airborne computer vision at broad scales. 

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