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Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.

Accurate and reliable information on tree species composition and distribution is crucial in operational and sustainable forest management. Developing a high-precision tree species map based on time series satellite data is an effective and cost-efficient approach. However, we do not quantitatively know how the time scale of data acquisitions contributes to complex tree species mapping. This study aimed to produce a detailed tree species map in a typical forest zone of the Changbai Mountains by incorporating Sentinel-2 images, topography data, and machine learning algorithms. We focused on exploring the effects of the three-year time series of Sentinel-2 within monthly, seasonal, and yearly time scales on the classification of ten dominant tree species. A random forest (RF) and support vector machine (SVM) were compared and employed to map continuous tree species. The results showed that classification with monthly datasets (overall accuracy (OA): 83.38–87.45%) outperformed that with seasonal and yearly datasets (OA:72.38–85.91%), and the RF (OA: 81.70–87.45%) was better than the SVM (OA: 72.38–83.38%) at processing the same datasets. Short-wave infrared, the normalized vegetation index, and elevation were the most important variables for tree species classification. The highest classification accuracy of 87.45% was achieved by combining RF, monthly datasets, and topography information. In terms of single species’ accuracy, the F1 scores of the ten tree species ranged from 62.99% (Manchurian ash) to 97.04% (Mongolian Oak), and eight of them obtained high F1 scores greater than 87%. This study confirmed that monthly Sentinel-2 datasets, topography data, and machine learning algorithms have great potential for accurate tree species mapping in mountainous regions.

Interest in drone solutions in forestry applications is growing. Using drones, datasets can be captured flexibly and at high spatial and temporal resolutions when needed. In forestry applications, fundamental tasks include the detection of individual trees, tree species classification, biomass estimation, etc. Deep neural networks (DNN) have shown superior results when comparing with conventional machine learning methods such as multi-layer perceptron (MLP) in cases of huge input data. The objective of this research is to investigate 3D convolutional neural networks (3D-CNN) to classify three major tree species in a boreal forest: pine, spruce, and birch. The proposed 3D-CNN models were employed to classify tree species in a test site in Finland. The classifiers were trained with a dataset of 3039 manually labelled trees. Then the accuracies were assessed by employing independent datasets of 803 records. To find the most efficient set of feature combination, we compare the performances of 3D-CNN models trained with hyperspectral (HS) channels, Red-Green-Blue (RGB) channels, and canopy height model (CHM), separately and combined. It is demonstrated that the proposed 3D-CNN model with RGB and HS layers produces the highest classification accuracy. The producer accuracy of the best 3D-CNN classifier on the test dataset were 99.6%, 94.8%, and 97.4% for pines, spruces, and birches, respectively. The best 3D-CNN classifier produced ~5% better classification accuracy than the MLP with all layers. Our results suggest that the proposed method provides excellent classification results with acceptable performance metrics for HS datasets. Our results show that pine class was detectable in most layers. Spruce was most detectable in RGB data, while birch was most detectable in the HS layers. Furthermore, the RGB datasets provide acceptable results for many low-accuracy applications.

The Sentinel-2 program provides the opportunity to monitor terrestrial ecosystems with a high temporal and spectral resolution. In this study, a multi-temporal Sentinel-2 data set was used to classify common tree species over a mature forest in central Sweden. The tree species to be classified were Norway spruce (Picea abies), Scots pine (Pinus silvestris), Hybrid larch (Larix × marschlinsii), Birch (Betula sp.) and Pedunculate oak (Quercus robur). Four Sentinel-2 images from spring (7 April and 27 May), summer (9 July) and fall (19 October) of 2017 were used along with the Random Forest (RF) classifier. A variable selection approach was implemented to find fewer and uncorrelated bands resulting in the best model for tree species identification. The final model resulting in the highest overall accuracy (88.2%) came from using all bands from the four image dates. The single image that gave the most accurate classification result (80.5%) was the late spring image (27 May); the 27 May image was always included in subsequent image combinations that gave the highest overall accuracy. The five tree species were classified with a user’s accuracy ranging from 70.9% to 95.6%. Thirteen of the 40 bands were selected in a variable selection procedure and resulted in a model with only slightly lower accuracy (86.3%) than that using all bands. Among the highest ranked bands were the red edge bands 2 and 3 as well as the narrow NIR (near-infrared) band 8a, all from the 27 May image, and SWIR (short-wave infrared) bands from all four image dates. This study shows that the red-edge bands and SWIR bands from Sentinel-2 are of importance, and confirms that spring and/or fall images capturing phenological differences between the species are most useful to tree species classification.

Timely and accurate information on tree species is of great importance for the sustainable management of natural resources, forest inventory, biodiversity detection, and carbon stock calculation. The advancement of remote sensing technology and artificial intelligence has facilitated the acquisition and analysis of remote sensing data, resulting in more precise and effective classification of tree species. A review of the remote sensing data and deep learning tree species classification methods is lacking in its analysis of unimodal and multimodal remote sensing data and classification methods in this field. To address this gap, we search for major trends in remote sensing data and tree species classification methods, provide a detailed overview of classic deep learning-based methods for tree species classification, and discuss some limitations of tree species classification.

Timely and accurate information on the spatial distribution of urban trees is critical for sustainable urban development, management and planning. Compared with satellite-based remote sensing, Unmanned Aerial Vehicle (UAV) remote sensing has a higher spatial and temporal resolution, which provides a new method for the accurate identification of urban trees. In this study, we aim to establish an efficient and practical method for urban tree identification by combining an object-oriented approach and a random forest algorithm using UAV multispectral images. Firstly, the image was segmented by a multi-scale segmentation algorithm based on the scale determined by the Estimation of Scale Parameter 2 (ESP2) tool and visual discrimination. Secondly, spectral features, index features, texture features and geometric features were combined to form schemes S1–S8, and S9, consisting of features selected by the recursive feature elimination (RFE) method. Finally, the classification of urban trees was performed based on the nine schemes using the random forest (RF), support vector machine (SVM) and k-nearest neighbor (KNN) classifiers, respectively. The results show that the RF classifier performs better than SVM and KNN, and the RF achieves the highest accuracy in S9, with an overall accuracy (OA) of 91.89% and a Kappa coefficient (Kappa) of 0.91. This study reveals that geometric features have a negative impact on classification, and the other three types have a positive impact. The feature importance ranking map shows that spectral features are the most important type of features, followed by index features, texture features and geometric features. Most tree species have a high classification accuracy, but the accuracy of Camphor and Cinnamomum Japonicum is much lower than that of other tree species, suggesting that the features selected in this study cannot accurately distinguish these two tree species, so it is necessary to add features such as height in the future to improve the accuracy. This study illustrates that the combination of an object-oriented approach and the RF classifier based on UAV multispectral images provides an efficient and powerful method for urban tree classification.

Recent advances in terrestrial laser scanning (TLS) technology have enabled the automatic capture of three‐dimensional vegetation structure at high resolution, but the scalability of using these data for large‐scale forest monitoring is limited by reliance on intensive manual data processing, including the use of stem maps generated in the field to determine tree species. New methods from data science have the capacity to automate this identification process, reducing the hurdles towards automated inventories with TLS. In particular, contemporary developments in point cloud processing methods, alongside large increases in the computing power of consumer‐level graphics processing units, provide new opportunities. Here, we apply a deep learning‐based approach, based on joint classification from multiple viewpoints for each stem, to automatically classify tree species directly from laser scanning data obtained in structurally complex Mediterranean forests. We also explore the use of data augmentation techniques to maximise performance for a fixed number of manually labelled stems. Our method does not require expensive pre‐processing such as leaf‐wood separation or quantitative reconstructions. Using modern network architectures and data augmentation techniques, and without extensive pre‐processing, we are able to achieve high overall and per‐species accuracy that is comparable or higher than in existing work while using data from a water‐limited ecosystem complicated by structural convergence and multi‐stem trees. Our findings demonstrate the power of deep learning to remove a major TLS data processing obstacle—individual species identification—and to minimise the bottleneck created by manual data labelling requirements in the use of TLS for standard forest monitoring.

Microwave and optical imaging methods react differently to different land surface parameters and, thus, provide highly complementary information. However, the contribution of individual features from these two domains of the electromagnetic spectrum for tree species classification is still unclear. For large-scale forest assessments, it is moreover important to better understand the domain-specific limitations of the two sensor families, such as the impact of cloudiness and low signal-to-noise-ratio, respectively. In this study, seven deciduous and five coniferous tree species of the Austrian Biosphere Reserve Wienerwald (105,000 ha) were classified using Breiman’s random forest classifier, labeled with help of forest enterprise data. In nine test cases, variations of Sentinel-1 and Sentinel-2 imagery were passed to the classifier to evaluate their respective contributions. By solely using a high number of Sentinel-2 scenes well spread over the growing season, an overall accuracy of 83.2% was achieved. With ample Sentinel-2 scenes available, the additional use of Sentinel-1 data improved the results by 0.5 percentage points. This changed when only a single Sentinel-2 scene was supposedly available. In this case, the full set of Sentinel-1-derived features increased the overall accuracy on average by 4.7 percentage points. The same level of accuracy could be obtained using three Sentinel-2 scenes spread over the vegetation period. On the other hand, the sole use of Sentinel-1 including phenological indicators and additional features derived from the time series did not yield satisfactory overall classification accuracies (55.7%), as only coniferous species were well separated.

Context Mapping the distribution of species, especially those that are endemic and endangered like certain tree species, is a vital step in the effective planning and execution of conservation programs and monitoring efforts. This task gains even more significance as it directly contributes to forest conservation by highlighting the importance of species diversity. Objectives Our study objective was to assess the detection accuracy of a specific tree using different remote sensing sources and approaches. Methods Initially, individual trees were identified and classified using a canopy height model derived from UAV data. Next, we carried out the classification of satellite data within the Google Earth Engine. Lastly, we scaled the UAV-RGB dataset to match the spatial resolution of Sentinel-2, which was then employed to train random forest models using the multispectral data from Sentinel-2. Results For the UAV data, we achieved overall accuracies of 56% for automatically delineated tree crowns and 83% for manually delineated ones. Regarding the second approach using Sentinel-2 data, the classification in the Noor forest yielded an overall accuracy of 74% and a Kappa coefficient of 0.57, while in the Safrabasteh forest, the accuracy was 80% with a Kappa of 0.61. In the third approach, our findings indicate an improvement compared to the second approach, with the overall accuracy and Kappa coefficient of the classification rising to 82% and 0.68, respectively. Conclusions In this study, it was found that according to the purpose and available facilities, satellite and UAV data can be successfully used to identify a specific tree species.

The diversity of tree species and the complexity of land use in cities create challenging issues for tree species classification. The combination of deep learning methods and RGB optical images obtained by unmanned aerial vehicles (UAVs) provides a new research direction for urban tree species classification. We proposed an RGB optical image dataset with 10 urban tree species, termed TCC10, which is a benchmark for tree canopy classification (TCC). TCC10 dataset contains two types of data: tree canopy images with simple backgrounds and those with complex backgrounds. The objective was to examine the possibility of using deep learning methods (AlexNet, VGG-16, and ResNet-50) for individual tree species classification. The results of convolutional neural networks (CNNs) were compared with those of K-nearest neighbor (KNN) and BP neural network. Our results demonstrated: (1) ResNet-50 achieved an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.91 for tree species classification on TCC10 and outperformed AlexNet and VGG-16. (2) The classification accuracy of KNN and BP neural network was less than 70%, while the accuracy of CNNs was relatively higher. (3) The classification accuracy of tree canopy images with complex backgrounds was lower than that for images with simple backgrounds. For the deciduous tree species in TCC10, the classification accuracy of ResNet-50 was higher in summer than that in autumn. Therefore, the deep learning is effective for urban tree species classification using RGB optical images.