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Weinstein BG, Marconi S, Zare A, Bohlman SA, Singh A, Graves SJ, Magee L, Johnson DJ, Record S, Rubio VE, Swenson NG, Townsend P, Veblen TT, Andrus RA, White EP. Individual canopy tree species maps for the National Ecological Observatory Network. PLoS Biol 2024; 22:e3002700. [PMID: 39013163 PMCID: PMC11251727 DOI: 10.1371/journal.pbio.3002700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 06/05/2024] [Indexed: 07/18/2024] Open
Abstract
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 km2 shapefiles 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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Affiliation(s)
- Ben G. Weinstein
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
| | - Sergio Marconi
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
| | - Alina Zare
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Stephanie A. Bohlman
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Aditya Singh
- Department of Agricultural & Biological Engineering, University of Florida, Gainesville, Florida, United States of America
| | - Sarah J. Graves
- Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Lukas Magee
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Daniel J. Johnson
- School of Forest, Fisheries, and Geomatics Sciences, University of Florida, Gainesville, Florida, United States of America
| | - Sydne Record
- Department of Wildlife, Fisheries, and Conservation Biology, University of Maine, Orono, Maine, United States of America
| | - Vanessa E. Rubio
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Nathan G. Swenson
- Department of Biological Sciences, University of Notre Dame, Notre Dame, Indiana, United States of America
| | - Philip Townsend
- Department of Forest & Wildlife Ecology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America
| | - Thomas T. Veblen
- Department of Geography, University of Colorado, Boulder, Colorado, United States of America
| | - Robert A. Andrus
- Department of Geography, University of Colorado, Boulder, Colorado, United States of America
- School of Environment, Washington State University, Pullman, Washington, United States of America
| | - Ethan P. White
- Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida, United States of America
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Estrada JS, Fuentes A, Reszka P, Auat Cheein F. Machine learning assisted remote forestry health assessment: a comprehensive state of the art review. FRONTIERS IN PLANT SCIENCE 2023; 14:1139232. [PMID: 37332724 PMCID: PMC10272373 DOI: 10.3389/fpls.2023.1139232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 05/08/2023] [Indexed: 06/20/2023]
Abstract
Forests are suffering water stress due to climate change; in some parts of the globe, forests are being exposed to the highest temperatures historically recorded. Machine learning techniques combined with robotic platforms and artificial vision systems have been used to provide remote monitoring of the health of the forest, including moisture content, chlorophyll, and nitrogen estimation, forest canopy, and forest degradation, among others. However, artificial intelligence techniques evolve fast associated with the computational resources; data acquisition, and processing change accordingly. This article is aimed at gathering the latest developments in remote monitoring of the health of the forests, with special emphasis on the most important vegetation parameters (structural and morphological), using machine learning techniques. The analysis presented here gathered 108 articles from the last 5 years, and we conclude by showing the newest developments in AI tools that might be used in the near future.
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Affiliation(s)
- Juan Sebastián Estrada
- Department of Electronic Engineering, Universidad Tecnica Federico, Santamaria, Valparaíso, Chile
| | - Andrés Fuentes
- Department of Industrial Engeneering, Universidad Tecnica Federica, Santamaria, Valparaíso, Chile
| | - Pedro Reszka
- Faculty on Engineering and Science, Universidad Adolfo Ibáñez, Santiago, Chile
| | - Fernando Auat Cheein
- Department of Electronic Engineering, Universidad Tecnica Federico, Santamaria, Valparaíso, Chile
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Abstract
Robotics navigation and perception for forest management are challenging due to the existence of many obstacles to detect and avoid and the sharp illumination changes. Advanced perception systems are needed because they can enable the development of robotic and machinery solutions to accomplish a smarter, more precise, and sustainable forestry. This article presents a state-of-the-art review about unimodal and multimodal perception in forests, detailing the current developed work about perception using a single type of sensors (unimodal) and by combining data from different kinds of sensors (multimodal). This work also makes a comparison between existing perception datasets in the literature and presents a new multimodal dataset, composed by images and laser scanning data, as a contribution for this research field. Lastly, a critical analysis of the works collected is conducted by identifying strengths and research trends in this domain.
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Deep Convolutional Neural Network for Large-Scale Date Palm Tree Mapping from UAV-Based Images. REMOTE SENSING 2021. [DOI: 10.3390/rs13142787] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datasets, based on a deep learning approach. A U-Shape convolutional neural network (U-Net), based on a deep residual learning framework, was developed for the semantic segmentation of date palm trees. A comprehensive set of labeled data was established to enable the training and evaluation of the proposed segmentation model and increase its generalization capability. The performance of the proposed approach was compared with those of various state-of-the-art fully convolutional networks (FCNs) with different encoder architectures, including U-Net (based on VGG-16 backbone), pyramid scene parsing network, and two variants of DeepLab V3+. Experimental results showed that the proposed model outperformed other FCNs in the validation and testing datasets. The generalizability evaluation of the proposed approach on a comprehensive and complex testing dataset exhibited higher classification accuracy and showed that date palm trees could be automatically mapped from VHSR UAV images with an F-score, mean intersection over union, precision, and recall of 91%, 85%, 0.91, and 0.92, respectively. The proposed approach provides an efficient deep learning architecture for the automatic mapping of date palm trees from VHSR UAV-based images.
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Canopy Top, Height and Photosynthetic Pigment Estimation Using Parrot Sequoia Multispectral Imagery and the Unmanned Aerial Vehicle (UAV). REMOTE SENSING 2021. [DOI: 10.3390/rs13040705] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
Remote sensing is one of the modern methods that have significantly developed over the last two decades and, nowadays, it provides a new means for forest monitoring. High spatial and temporal resolutions are demanded for the accurate and timely monitoring of forests. In this study, multi-spectral Unmanned Aerial Vehicle (UAV) images were used to estimate canopy parameters (definition of crown extent, top, and height, as well as photosynthetic pigment contents). The UAV images in Green, Red, Red-Edge, and Near infrared (NIR) bands were acquired by Parrot Sequoia camera over selected sites in two small catchments (Czech Republic) covered dominantly by Norway spruce monocultures. Individual tree extents, together with tree tops and heights, were derived from the Canopy Height Model (CHM). In addition, the following were tested: (i) to what extent can the linear relationship be established between selected vegetation indexes (Normalized Difference Vegetation Index (NDVI) and NDVIred edge) derived for individual trees and the corresponding ground truth (e.g., biochemically assessed needle photosynthetic pigment contents) and (ii) whether needle age selection as a ground truth and crown light conditions affect the validity of linear models. The results of the conducted statistical analysis show that the two vegetation indexes (NDVI and NDVIred edge) tested here have the potential to assess photosynthetic pigments in Norway spruce forests at a semi-quantitative level; however, the needle-age selection as a ground truth was revealed to be a very important factor. The only usable results were obtained for linear models when using the second year needle pigment contents as a ground truth. On the other hand, the illumination conditions of the crown proved to have very little effect on the model’s validity. No study was found to directly compare these results conducted on coniferous forest stands. This shows that there is a further need for studies dealing with a quantitative estimation of the biochemical variables of nature coniferous forests when employing spectral data that were acquired by the UAV platform at a very high spatial resolution.
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Individual Sick Fir Tree (Abies mariesii) Identification in Insect Infested Forests by Means of UAV Images and Deep Learning. REMOTE SENSING 2021. [DOI: 10.3390/rs13020260] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Insect outbreaks are a recurrent natural phenomenon in forest ecosystems expected to increase due to climate change. Recent advances in Unmanned Aerial Vehicles (UAV) and Deep Learning (DL) Networks provide us with tools to monitor them. In this study we used nine orthomosaics and normalized Digital Surface Models (nDSM) to detect and classify healthy and sick Maries fir trees as well as deciduous trees. This study aims at automatically classifying treetops by means of a novel computer vision treetops detection algorithm and the adaptation of existing DL architectures. Considering detection alone, the accuracy results showed 85.70% success. In terms of detection and classification, we were able to detect/classify correctly 78.59% of all tree classes (39.64% for sick fir). However, with data augmentation, detection/classification percentage of the sick fir class rose to 73.01% at the cost of the result accuracy of all tree classes that dropped 63.57%. The implementation of UAV, computer vision and DL techniques contribute to the development of a new approach to evaluate the impact of insect outbreaks in forest.
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