--- title: 'Living Document' url: 'https://www.waldki.metronom-leipzig.com/living-document' markdown: 'https://www.waldki.metronom-leipzig.com/living-document.md' lang: de date: '2026-10-23' description: 'Living document' --- [ ![WaldKI Logo](https://www.waldki.metronom-leipzig.com/user/themes/wald-ki/images/waldki-logo.svg) ](https://www.waldki.metronom-leipzig.com/) [ ![WaldKI Logo](https://www.waldki.metronom-leipzig.com/user/themes/wald-ki/images/waldki-logo.svg) ](https://www.waldki.metronom-leipzig.com/)- [DE](https://www.waldki.metronom-leipzig.com/living-document) - [EN](https://www.waldki.metronom-leipzig.com/en/living-document) # Living document ## Einträge Wissenschaftlicher Artikel 2019 ### A Convolutional Neural Network Classifier Identifies Tree Species in Mixed-Conifer Forest from Hyperspectral Imagery Geoffrey A. Fricker, Jonathan D. Ventura, Jeffrey A. Wolf, Malcolm P. North, ​Frank W. Davis, Janet Franklin Aus der Luft: Bilder, Aus der Luft: Laserscanning, Vom Boden: Felddaten · Artenzusammensetzung · CNN-based Model, Tree-based Model #### Region USA #### Datengrundlage Aus der Luft: Bilder, Aus der Luft: Laserscanning, Vom Boden: Felddaten #### Erfasste Waldparameter Artenzusammensetzung #### Waldtyp Nadelmischwald #### KI-Architekturfamilie CNN-based Model, Tree-based Model #### KI-Modelle AlexNet, Unet, random forest #### DOI / Link #### Abstract Abstract: In this study, we automate tree species classification and mapping using field-based training ​data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network ​classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead ​standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using ​training, validation, and testing datasets composed of spatially-explicit transects and plots sampled ​across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo ​true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model ​without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier ​is pixel-based rather than object based, although we use three-dimensional structural information ​from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) ​and the classifier was applied to image pixels that were thus identified as tree crowns. By training a ​CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree ​species and predict their distribution, as well as the distribution of tree mortality, across the landscape. ​Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the ​correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and ​F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance ​increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of ​0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition ​changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer ​forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can ​support forest ecosystem monitoring and management, and identifying dead trees aids landscape ​assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide ​our code to apply deep learning classifiers to tree species identification from geospatial imagery and ​field training data Wissenschaftlicher Artikel 2022 ### A convolution neural network for forest leaf chlorophyll and carotenoid estimation using hyperspectral reflectance Shuo Shi, Lu Xu, Wei Gong, Bowen Chen, Biwu Chen, Fangfang Qu, Xingtao Tang, ​​Jia Sun, Jian Yang Sonstiges · Artenzusammensetzung, Erkennung toter Bäume #### Datengrundlage Sonstiges #### Erfasste Waldparameter Artenzusammensetzung, Erkennung toter Bäume #### DOI / Link #### Abstract Forest leaf chlorophyll (Cab) and carotenoid (Cxc) are key functional indicators for the state of the forest ​​ecosystem. Current machine learning models based on hyperspectral reflectance are widely applied to estimate ​​leaf Cab and Cxc contents at leaf scale. However, these models have certain accuracy for non-independent datasets ​​but have poor generalization for independent datasets when they are used to estimate leaf Cab and Cxc contents. ​​This fact limits that hyperspectral remote sensing completely replaces destructive measurements for leaf Cab and ​​Cxc contents. Thus, the development of an estimation model with high accuracy and satisfactory generalization is ​​necessary. Convolutional neural networks (CNNs) have certain accuracy and generalization in many domains, ​​and have the potential to solve above-mentioned problem. Therefore, this study developed a CNN using one-dimensional hyperspectral reflectance, which aimed to improve the model’s accuracy and generalization in ​​leaf Cab and Cxc content estimation at leaf scale. The proposed CNN was developed by three steps. First, in ​​consideration of the correlation between leaf Cab and Cxc contents in natural leaves, 2500 physical data with leaf ​​reflectance and corresponding Cab and Cxc contents were generated by leaf radiative transfer model and multi​variable gaussian distribution function. Then, the proposed CNN was built by five strategies based on the ar​chitecture of the AlexNet. Finally, five-fold cross validation was performed with 70% of the physical data to ​​determine the best strategy to develop the proposed CNN. These were executed to ensure the proposed CNN with ​​the maximum accuracy and generalization. In addition, the accuracy and generalization of the proposed CNN ​​were tested using a non-independent dataset and an independent dataset, respectively. The proposed CNN was ​​also compared with back propagation neural network (BPNN), support vector regression (SVR) and gaussian ​​process regression (GPR). Results showed that the best CNN could be developed with one input, five convolu​tional, three max-pooling and three fully-connected layers. Comprehensively considering the model’s accuracy ​​and generalization, the proposed CNN was the best model for leaf Cab and Cxc content estimation compared with ​​BPNN, SVR and GPR. This study provides a development strategy of CNN estimation model using one-dimensional hyperspectral reflectance at leaf scale. The proposed CNN could further promote the practical ​​application of hyperspectral remote sensing in leaf Cab and Cxc content estimation. - [Datenschutz](https://www.waldki.metronom-leipzig.com/datenschutz) - [Erklärung zur Barrierefreiheit](https://www.waldki.metronom-leipzig.com/barrierefreiheit) - [Impressum](https://www.waldki.metronom-leipzig.com/impressum) Kontakt Hochschule Anhalt Lohmannstraße 23 06366 Köthen Hochschule Anhalt Strenzfelder Allee 28 06406 Bernburg (Saale) Presse Verena Arndt [03471 355 1280](tel:+4934713551280) Copyright © Hochschule Anhalt ![Sachsen-Anhalt](https://www.waldki.metronom-leipzig.com/user/pages/13.blocks/_footer/sanhalt.png "Sachsen-Anhalt") ![Finanziert durch die Europäische Union](https://www.waldki.metronom-leipzig.com/user/pages/13.blocks/_footer/eu-finanz.png "EU Förderung") ![](https://www.waldki.metronom-leipzig.com/user/pages/13.blocks/_footer/HSA_Logo_1000px_rgb_01_web.png) Folgen Sie uns auf: [ ![Instagram](https://www.waldki.metronom-leipzig.com/user/pages/13.blocks/_footer/insta.png "Instagram") ](https://www.instagram.com/meinewaldki/) --- ## Navigation - Previous: [Erklärung zur Barrierefreiheit](https://www.waldki.metronom-leipzig.com/barrierefreiheit.md) - Next: [Fokusmerkmale](https://www.waldki.metronom-leipzig.com/fokusmerkmale.md)