Deriving individual tree crown (ITC) information from light detection and ranging (LiDAR) data is of great significance to forest resource assessment and smart management. After proof-of-concept studies, advanced deep learning methods have been shown to have high efficiency and accuracy in remote sensing data analysis and geoscience problem solving. This study proposes a novel concept for synergetic use of the YOLO-v4 deep learning network based on heightmaps directly generated from airborne LiDAR data for ITC segmentation and a computer graphics algorithm for refinement of the segmentation results involving overlapping tree crowns. This concept overcomes the limitations experienced by existing ITC segmentation methods that use aerial photographs to obtain texture and crown appearance information and commonly encounter interference due to heterogeneous solar illumination intensities or interlacing branches and leaves. Three generative adversarial networks (WGAN, CycleGAN, and SinGAN) were employed to generate synthetic images. These images were coupled with manually labeled training samples to train the network. Three forest plots, namely, a tree nursery, forest landscape and mixed tree plantation, were used to verify the effectiveness of our approach. The results showed that the overall recall of our method for detecting ITCs in the three forest plot types reached 83.6%, with an overall precision of 81.4%. Compared with reference field measurement data, the coefficient of determination R2 was ≥ 79.93% for tree crown width estimation, and the accuracy of our deep learning method was not influenced by the values of key parameters, yielding 3.9% greater accuracy than the traditional watershed method. The results demonstrate an enhancement of tree crown segmentation in the form of a heightmap for different forest plot types using the concept of deep learning, and our method bypasses the visual complications arising from aerial images featuring diverse textures and unordered scanned points with irregular geometrical properties.

Individual Tree Crown Segmentation and Crown Width Extraction From a Heightmap Derived From Aerial Laser Scanning Data Using a Deep Learning Framework

Accurate and automatic identification of tree species information at the individual tree scale is of great significance for fine-scale investigation and management of forest resources and scientific assessment of forest ecosystems. Despite the fact that numerous studies have been conducted on the delineation of individual tree crown and species classification using drone high-resolution red, green and blue (RGB) images, and Light Detection and Ranging (LiDAR) data, performing the above tasks simultaneously has rarely been explored, especially in complex forest environments. In this study, we improve upon the state of the Mask region-based convolution neural network (Mask R-CNN) with our proposed attention complementary network (ACNet) and edge detection R-CNN (ACE R-CNN) for individual tree species identification in high-density and complex forest environments. First, we propose ACNet as the feature extraction backbone network to fuse the weighted features extracted from RGB images and canopy height model (CHM) data through an attention complementary module, which is able to selectively fuse weighted features extracted from RGB and CHM data at different scales, and enables the network to focus on more effective information. Second, edge loss is added to the loss function to improve the edge accuracy of the segmentation, which is calculated through the edge detection filter introduced in the Mask branch of Mask R-CNN. We demonstrate the performance of ACE R-CNN for individual tree species identification in three experimental areas of different tree species in southern China with precision (P), recall (R), F1-score, and average precision (AP) above 0.9. Our proposed ACNet–the backbone network for feature extraction–has better performance in individual tree species identification compared with the ResNet50-FPN (feature pyramid network). The addition of the edge loss obtained by the Sobel filter further improves the identification accuracy of individual tree species and accelerates the convergence speed of the model training. This work demonstrates the improved performance of ACE R-CNN for individual tree species identification and provides a new solution for tree-level species identification in complex forest environments, which can support carbon stock estimation and biodiversity assessment.

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ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

A novel algorithm of individual tree crowns segmentation considering three-dimensional canopy attributes using UAV oblique photos

The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management. Given the increasing availability of UAV images with limited spectral information, the high intra-class variance of date palm trees, the variations in the spatial resolutions of the data, and the differences in image contexts and backgrounds, accurate mapping of date palm trees from very-high spatial resolution (VHSR) images can be challenging. This study aimed to investigate the reliability and the efficiency of various deep vision transformers in extracting date palm trees from multiscale and multisource VHSR images. Numerous vision transformers, including the Segformer, the Segmenter, the UperNet-Swin transformer, and the dense prediction transformer, with various levels of model complexity, were evaluated. The models were developed and evaluated using a set of comprehensive UAV-based and aerial images. The generalizability and the transferability of the deep vision transformers were evaluated and compared with various convolutional neural network-based (CNN) semantic segmentation models (including DeepLabV3+, PSPNet, FCN-ResNet-50, and DANet). The results of the examined deep vision transformers were generally comparable to several CNN-based models. The investigated deep vision transformers achieved satisfactory results in mapping date palm trees from the UAV images, with an mIoU ranging from 85% to 86.3% and an mF-score ranging from 91.62% to 92.44%. Among the evaluated models, the Segformer generated the highest segmentation results on the UAV-based and the multiscale testing datasets. The Segformer model, followed by the UperNet-Swin transformer, outperformed all of the evaluated CNN-based models in the multiscale testing dataset and in the additional unseen UAV testing dataset. In addition to delivering remarkable results in mapping date palm trees from versatile VHSR images, the Segformer model was among those with a small number of parameters and relatively low computing costs. Collectively, deep vision transformers could be used efficiently in developing and updating inventories of date palms and other tree species.

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Large-Scale Date Palm Tree Segmentation from Multiscale UAV-Based and Aerial Images Using Deep Vision Transformers

Sour cherry (Prunus cerasus L.) is an economically important allotetraploid cherry species believed to have evolved in the Caspian Sea and Black Sea regions. How, when and where exactly the evolution of this species took place is unclear. It resulted from a hybridization of the tetraploid ground cherry (Prunus fruticosa Pall.) and an unreduced (2n) pollen of the diploid ancestor sweet cherry (P. avium L.). Some indications implement that the genome of sour cherry is segmental allopolyploid, but how it is structured and to what extent is unknown. To get an insight, the genome of the sour cherry cultivar ‘Schattenmorelle’ was sequenced at ~400x using Illumina NovaSeqTM short-read and Oxford Nanopore long-read technologies (ONT R9.4.1 PromethION). Additionally, the transcriptome of ‘Schattenmorelle’ was sequenced using PacBio Sequel II SMRT cell sequencing at ~300x. The final assembly resulted in a ~629 Mbp long pseudomolecule reference genome, which could be separated into two subgenomes each split into eight chromosomes. Subgenome PceS_a which originates from P. avium has a length of 269 Mbp, whereas subgenome PceS_f which originates from P. fruticosa has a length of 299.5 Mbp. The length of unassembled contigs was 60 Mbp. The genome of the sour cherry shows a size-reduction compared to the genomes of its ancestral species. It also shows traces of homoeologous sequence exchanges throughout the genome. Comparative positional sequence and protein analyses provided evidence that the genome of sour cherry is segmental allotetraploid and that it has evolved in a very recent event in the past.

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The structure of the tetraploid sour cherry ‘Schattenmorelle’ (Prunus cerasus L.) genome reveals insights into its segmental allopolyploid nature

Forest above-ground biomass (AGB) is an important index to evaluate forest carbon sequestration capacity, which is very important to maintain the stability of forest ecosystems. At present, the wide use of remote sensing technology makes it possible to estimate the large-scale forest AGB accurately and efficiently. Airborne hyperspectral remote sensing data can obtain rich spectral information and spatial structure information on the forest canopy with the characteristics of high spatial and hyperspectral resolution. Airborne LiDAR data can describe the three-dimensional structure characteristics of a forest and provide vertical structure information related to biomass. Based on the characteristics of the two data sources, this study takes Gaofeng forest farm in Nanning, Guangxi, as the study area, Chinese fir, pine tree, eucalyptus and other broadleaved trees as the research object, and constructs the AGB estimation models of different tree species by fusing airborne LiDAR and hyperspectral features. Firstly, spectral features, texture features, vegetation index, wavelet transform features and edge features are extracted from hyperspectral data. Canopy structure features, point cloud structure features, point cloud density features and terrain features are extracted from airborne LiDAR data. Secondly, the random forest (RF) method is used to screen the features of the two sets of data, and the features with the highest importance are selected. Finally, based on the optimal features of the two data sources, the forest AGB model is constructed using the multiple stepwise regression method. The results show that the texture features extracted by wavelet transform can be used for AGB modeling. The AGB of eucalyptus has high correlation with height features derived from airborne LiDAR, the AGB of other broadleaved trees mostly depends on the wavelet transform texture features from airborne hyperspectral data, while the AGB of Chinese fir and pine tree has high correlation with both height features and spectral features. Feature-fusion-based LiDAR and hyperspectral data can greatly improve the accuracy of the AGB models. The accuracy of the optimal AGB models of Chinese fir, pine tree, eucalyptus and other broadleaved trees is 0.78, 0.95, 0.72 and 0.89, respectively. In conclusion, more accurate estimation results can be obtained by combining active and passive remote sensing data for forest AGB estimation, which provides a solution for carbon storage assessment and forest ecosystem assessment.

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Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data

Chinese fir (Cunninghamia lanceolata (Lamb.) Hook) is one of the important tree species in plantation in southern China. Rapid and accurate acquisition of individual tree above-ground biomass (IT-AGB) information is of vital importance for precise monitoring and scientific management of Chinese fir forest resources. Unmanned Aerial Vehicle (UAV) oblique photogrammetry technology can simultaneously obtain high-density point cloud data and high spatial resolution spectral information, which has been a main remote sensing source for obtaining forest fine three-dimensional structure information and provided possibility for estimating IT-AGB. In this study, we proposed a novel approach to estimate IT-AGB by introducing the color space intensity information into a regression-based model that incorporates three-dimensional point cloud and two-dimensional spectrum feature variables, and the accuracy was evaluated using a leave-one-out cross-validation approach. The results demonstrated that the intensity variables derived from the color space were strongly correlated with the IT-AGB and obviously improved the estimation accuracy. The model constructed by the combination of point cloud variables, vegetation index and RGB spatial intensity variables had high accuracy (R2 = 0.79; RMSECV = 44.77 kg; and rRMSECV = 0.25). Comparing the performance of estimating IT-AGB models with different spatial resolution images (0.05, 0.1, 0.2, 0.5 and 1 m), the model was the best at the spatial resolution of 0.2 m, which was significantly better than that of the other four. Moreover, we also divided the individual tree canopy into four directions (East, West, South and North) to develop estimation models respectively. The result showed that the IT-AGB estimation capacity varied significantly in different directions, and the West-model had better performance, with the estimation accuracy of 67%. This study indicates the potential of using oblique photogrammetry technology to estimate AGB at an individual tree scale, which can support carbon stock estimation as well as precision forestry application.

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Estimating Individual Tree Above-Ground Biomass of Chinese Fir Plantation: Exploring the Combination of Multi-Dimensional Features from UAV Oblique Photos

Malus sieversii is a precious wild fruit tree resource, and its sustainable reproduction is of great significance to the conservation of wild fruit tree germplasm resources and the stability of wild fruit forest ecosystems. In recent years, the natural population number and area of distribution of the Malus sieversii have been declining due to pests, water limitations, and human activities. Root sprouts are a primary means of rejuvenation of the Malus sieversii. A reasonable spatial distribution pattern is conducive to the growth of Malus sieversii plants and the ecological restoration of wild fruit forest populations. However, the spatial distribution pattern of root sprouts still needs to be discovered, which constrains our understanding of the mechanisms underlying the damage and management of Malus sieversii. Therefore, this paper examines the study area of the Gilgalang River Malus sieversii forest in Gongliu County, Ili Valley, Xinjiang. The topographic data and high-resolution images were first obtained using ultra-low-altitude photogrammetry and total station measurement techniques, then spatial pattern analysis and standard deviation ellipse analysis were used to investigate the spatial distribution pattern of root sprouts, and, finally, the factors affecting the spatial distribution pattern of root sprouts were investigated by principal component analysis and grey correlation analysis. The results show that: (1) Under-canopy Malus sieversii root sprouts are clustered and randomly distributed along the root system, with the degree of clustering decreasing with increasing distance; (2) Spatial orientation and distance from the maternal plant are the main factor affecting the sprouting of Malus sieversii roots, explaining 73.69% of the total variance; (3) Under sediment accumulation and water erosion, the root sprouts under the canopy are mainly distributed in the downslope direction. The shape is similar to the “clover type”. The results of this study can provide a theoretical basis for conserving Malus sieversii germplasm resources and a solid scientific basis for the ecological restoration of plants under anthropogenic disturbance.

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Spatial Distribution Pattern of Root Sprouts under the Canopy of Malus sieversii in a Typical River Valley on the Northern Slopes of the Tianshan Mountain

Xiaoli Zhang

Papers

ACE R-CNN: An Attention Complementary and Edge Detection-Based Instance Segmentation Algorithm for Individual Tree Species Identification Using UAV RGB Images and LiDAR Data

Above-Ground Biomass Estimation of Plantation with Different Tree Species Using Airborne LiDAR and Hyperspectral Data

Estimating Individual Tree Above-Ground Biomass of Chinese Fir Plantation: Exploring the Combination of Multi-Dimensional Features from UAV Oblique Photos

A novel algorithm of individual tree crowns segmentation considering three-dimensional canopy attributes using UAV oblique photos

Spatial Distribution Pattern of Root Sprouts under the Canopy of Malus sieversii in a Typical River Valley on the Northern Slopes of the Tianshan Mountain