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Dr. Qinghua Guo
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

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0 GIS
0 Remote Sensing
0 UAV
0 LiDAR applications
0 Climate change and terrestrial ecosystem

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Journal article
Published: 23 March 2021 in International Journal of Electrical Power & Energy Systems
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In recent decades, a substantial increase in electricity demand has put pressure on powerline systems to ensure an uninterrupted power supply. In order to prevent power failures, timely and thorough powerline inspections are needed to detect possible anomalies in advance. In the past few years, the emerging unmanned aerial vehicle (UAV)-mounted sensors (e.g. light detection and ranging/lidar, optical cameras, infrared cameras, and ultraviolet cameras) have provided rich data sources for comprehensive and accurate powerline inspections. A challenge that still hinders the use of UAVs in powerline inspection is that their operation is highly dependent on the pilot’s experience, which may pose risks to the safety of the powerline system and reduce inspection efficiency. An intelligent automatic inspection solution could overcome the limitations of current UAV-based inspection solutions. The main objective of this paper is to provide a contemporary look at the current state-of-the-art UAV-based inspections as well as to discuss a potential lidar-supported intelligent powerline inspection concept. Overall, standardized protocols for lidar-supported intelligent powerline inspections include four data analysis steps, i.e., point cloud classification, key point extraction, route generation, and fault detection. To demonstrate the feasibility of the proposed concept, we implemented a workflow using a dataset of 3536 powerline spans, showing that the inspection of a single powerline span could be completed in 10 min with only one or two technicians. This demonstrates that lidar-supported intelligent inspection can be used to inspect a powerline system with extremely high efficiency and low costs.

ACS Style

Hongcan Guan; Xiliang Sun; Yanjun Su; Tianyu Hu; Haitao Wang; Heping Wang; Chigang Peng; Qinghua Guo. UAV-lidar aids automatic intelligent powerline inspection. International Journal of Electrical Power & Energy Systems 2021, 130, 106987 .

AMA Style

Hongcan Guan, Xiliang Sun, Yanjun Su, Tianyu Hu, Haitao Wang, Heping Wang, Chigang Peng, Qinghua Guo. UAV-lidar aids automatic intelligent powerline inspection. International Journal of Electrical Power & Energy Systems. 2021; 130 ():106987.

Chicago/Turabian Style

Hongcan Guan; Xiliang Sun; Yanjun Su; Tianyu Hu; Haitao Wang; Heping Wang; Chigang Peng; Qinghua Guo. 2021. "UAV-lidar aids automatic intelligent powerline inspection." International Journal of Electrical Power & Energy Systems 130, no. : 106987.

Journal article
Published: 28 December 2020 in Remote Sensing
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Accurate and repeated forest inventory data are critical to understand forest ecosystem processes and manage forest resources. In recent years, unmanned aerial vehicle (UAV)-borne light detection and ranging (lidar) systems have demonstrated effectiveness at deriving forest inventory attributes. However, their high cost has largely prevented them from being used in large-scale forest applications. Here, we developed a very low-cost UAV lidar system that integrates a recently emerged DJI Livox MID40 laser scanner (~$600 USD) and evaluated its capability in estimating both individual tree-level (i.e., tree height) and plot-level forest inventory attributes (i.e., canopy cover, gap fraction, and leaf area index (LAI)). Moreover, a comprehensive comparison was conducted between the developed DJI Livox system and four other UAV lidar systems equipped with high-end laser scanners (i.e., RIEGL VUX-1 UAV, RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE). Using these instruments, we surveyed a coniferous forest site and a broadleaved forest site, with tree densities ranging from 500 trees/ha to 3000 trees/ha, with 52 UAV flights at different flying height and speed combinations. The developed DJI Livox MID40 system effectively captured the upper canopy structure and terrain surface information at both forest sites. The estimated individual tree height was highly correlated with field measurements (coniferous site: R2 = 0.96, root mean squared error/RMSE = 0.59 m; broadleaved site: R2 = 0.70, RMSE = 1.63 m). The plot-level estimates of canopy cover, gap fraction, and LAI corresponded well with those derived from the high-end RIEGL VUX-1 UAV system but tended to have systematic biases in areas with medium to high canopy densities. Overall, the DJI Livox MID40 system performed comparably to the RIEGL miniVUX-1 UAV, HESAI Pandar40, and Velodyne Puck LITE systems in the coniferous site and to the Velodyne Puck LITE system in the broadleaved forest. Despite its apparent weaknesses of limited sensitivity to low-intensity returns and narrow field of view, we believe that the very low-cost system developed by this study can largely broaden the potential use of UAV lidar in forest inventory applications. This study also provides guidance for the selection of the appropriate UAV lidar system and flight specifications for forest research and management.

ACS Style

Tianyu Hu; Xiliang Sun; Yanjun Su; Hongcan Guan; Qianhui Sun; Maggi Kelly; Qinghua Guo. Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications. Remote Sensing 2020, 13, 77 .

AMA Style

Tianyu Hu, Xiliang Sun, Yanjun Su, Hongcan Guan, Qianhui Sun, Maggi Kelly, Qinghua Guo. Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications. Remote Sensing. 2020; 13 (1):77.

Chicago/Turabian Style

Tianyu Hu; Xiliang Sun; Yanjun Su; Hongcan Guan; Qianhui Sun; Maggi Kelly; Qinghua Guo. 2020. "Development and Performance Evaluation of a Very Low-Cost UAV-Lidar System for Forestry Applications." Remote Sensing 13, no. 1: 77.

Review
Published: 25 December 2020 in IEEE Geoscience and Remote Sensing Magazine
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ACS Style

Qinghua Guo; Yanjun Su; Tianyu Hu; Hongcan Guan; Shichao Jin; Jing Zhang; Xiaoxia Zhao; Kexin Xu; Dengjie Wei; Maggi Kelly; Nicholas C. Coops. Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective. IEEE Geoscience and Remote Sensing Magazine 2020, 9, 232 -257.

AMA Style

Qinghua Guo, Yanjun Su, Tianyu Hu, Hongcan Guan, Shichao Jin, Jing Zhang, Xiaoxia Zhao, Kexin Xu, Dengjie Wei, Maggi Kelly, Nicholas C. Coops. Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective. IEEE Geoscience and Remote Sensing Magazine. 2020; 9 (1):232-257.

Chicago/Turabian Style

Qinghua Guo; Yanjun Su; Tianyu Hu; Hongcan Guan; Shichao Jin; Jing Zhang; Xiaoxia Zhao; Kexin Xu; Dengjie Wei; Maggi Kelly; Nicholas C. Coops. 2020. "Lidar Boosts 3D Ecological Observations and Modelings: A Review and Perspective." IEEE Geoscience and Remote Sensing Magazine 9, no. 1: 232-257.

Journal article
Published: 24 December 2020 in SCIENTIA SINICA Vitae
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Vegetation maps are important for understanding the spatial distribution and geographical pattern of vegetation. Vegetation maps also provide important auxiliary information for natural resource research and management decision-making. Vegetation mapping is based on vegetation survey data. To realize vegetation mapping in continuous areas, the data are supplemented by 3S technology, i.e., remote sensing, geographic information systems, and global positioning systems. Despite the rapid development of 3S technology, obtaining massive distribution information of vegetation groups remains a crucial prerequisite and also represents a bottleneck for large-scale vegetation mapping. With the increased availability of smart terminals and personal remote sensing devices, combined with the rise of citizen science, the crowdsourcing model has created new opportunities for vegetation formation investigation. In this study, we developed a collection and analysis system for vegetation group information based on smartphones and web services, which we refer to as LiVegetation. The LiVegetation application supports Android and iOS systems, and the main functions include data collection, layer management, point of interest management, trajectory management, user information, and data display. The LiVegetation application can efficiently collect photo and text records with geographic information in the field. The LiVegetation web system provides management functions that correspond to the LiVegetation application as well as a data inspection function with hierarchical experts. Since July 2018, the LiVegetation system has collected 108,083 data records, which are widely distributed across 34 provinces, municipalities, and autonomous regions in China. These data cover seven vegetation group types and 582 vegetation formation types. The maximum number of data uploaded by the system in a single day is 5,358, and the maximum total number of data uploaded by a single person in a single day is 2,543. Data collected by the LiVegetation system have been successfully applied to update the 1:1,000,000 Vegetation Map of China and are serving the mapping of the 1:500,000 Vegetation Map of China. In future, it is expected that LiVegetation data will continue to support vegetation ecology research and policy management.

ACS Style

Shichao JIN; Tianyu HU; Yanjun SU; Qin Ma; Hongcan GUAN; Mohan YANG; Qinghua GUO. LiVegetation: an investigative tool for vegetation mapping in the era of citizen science. SCIENTIA SINICA Vitae 2020, 51, 362 -374.

AMA Style

Shichao JIN, Tianyu HU, Yanjun SU, Qin Ma, Hongcan GUAN, Mohan YANG, Qinghua GUO. LiVegetation: an investigative tool for vegetation mapping in the era of citizen science. SCIENTIA SINICA Vitae. 2020; 51 (3):362-374.

Chicago/Turabian Style

Shichao JIN; Tianyu HU; Yanjun SU; Qin Ma; Hongcan GUAN; Mohan YANG; Qinghua GUO. 2020. "LiVegetation: an investigative tool for vegetation mapping in the era of citizen science." SCIENTIA SINICA Vitae 51, no. 3: 362-374.

Short review
Published: 06 December 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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Plant phenomics is a new avenue for linking plant genomics and environmental studies, thereby improving plant breeding and management. Remote sensing techniques have improved high-throughput plant phenotyping. However, the accuracy, efficiency, and applicability of three-dimensional (3D) phenotyping are still challenging, especially in field environments. Light detection and ranging (lidar) provides a powerful new tool for 3D phenotyping with the rapid development of facilities and algorithms. Numerous efforts have been devoted to studying static and dynamic changes of structural and functional phenotypes using lidar in agriculture. These progresses also improve 3D plant modeling across different spatial–temporal scales and disciplines, providing easier and less expensive association with genes and analysis of environmental practices and affords new insights into breeding and management. Beyond agriculture phenotyping, lidar shows great potential in forestry, horticultural, and grass phenotyping. Although lidar has resulted in remarkable improvements in plant phenotyping and modeling, the synthetization of lidar-based phenotyping for breeding and management has not been fully explored. We identify three main challenges in lidar-based phenotyping development: 1) developing low cost, high spatial–temporal, and hyperspectral lidar facilities, 2) moving into multi-dimensional phenotyping with an endeavor to generate new algorithms and models, and 3) embracing open source and big data.

ACS Style

Shichao Jin; Xiliang Sun; Fangfang Wu; Yanjun Su; Yumei Li; Shiling Song; Kexin Xu; Qin Ma; Frédéric Baret; Dong Jiang; Yanfeng Ding; Qinghua Guo. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 171, 202 -223.

AMA Style

Shichao Jin, Xiliang Sun, Fangfang Wu, Yanjun Su, Yumei Li, Shiling Song, Kexin Xu, Qin Ma, Frédéric Baret, Dong Jiang, Yanfeng Ding, Qinghua Guo. Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 171 ():202-223.

Chicago/Turabian Style

Shichao Jin; Xiliang Sun; Fangfang Wu; Yanjun Su; Yumei Li; Shiling Song; Kexin Xu; Qin Ma; Frédéric Baret; Dong Jiang; Yanfeng Ding; Qinghua Guo. 2020. "Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects." ISPRS Journal of Photogrammetry and Remote Sensing 171, no. : 202-223.

Journal article
Published: 23 November 2020 in Sensors
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Imbalanced learning is a common problem in remote sensing imagery-based land-use and land-cover classifications. Imbalanced learning can lead to a reduction in classification accuracy and even the omission of the minority class. In this paper, an impartial semi-supervised learning strategy based on extreme gradient boosting (ISS-XGB) is proposed to classify very high resolution (VHR) images with imbalanced data. ISS-XGB solves multi-class classification by using several semi-supervised classifiers. It first employs multi-group unlabeled data to eliminate the imbalance of training samples and then utilizes gradient boosting-based regression to simulate the target classes with positive and unlabeled samples. In this study, experiments were conducted on eight study areas with different imbalanced situations. The results showed that ISS-XGB provided a comparable but more stable performance than most commonly used classification approaches (i.e., random forest (RF), XGB, multilayer perceptron (MLP), and support vector machine (SVM)), positive and unlabeled learning (PU-Learning) methods (PU-BP and PU-SVM), and typical synthetic sample-based imbalanced learning methods. Especially under extremely imbalanced situations, ISS-XGB can provide high accuracy for the minority class without losing overall performance (the average overall accuracy achieves 85.92%). The proposed strategy has great potential in solving the imbalanced classification problems in remote sensing.

ACS Style

Fei Sun; Fang Fang; Run Wang; Bo Wan; Qinghua Guo; Hong Li; Xincai Wu. An Impartial Semi-supervised Learning Strategy for Imbalanced Classification on VHR Images. Sensors 2020, 20, 6699 .

AMA Style

Fei Sun, Fang Fang, Run Wang, Bo Wan, Qinghua Guo, Hong Li, Xincai Wu. An Impartial Semi-supervised Learning Strategy for Imbalanced Classification on VHR Images. Sensors. 2020; 20 (22):6699.

Chicago/Turabian Style

Fei Sun; Fang Fang; Run Wang; Bo Wan; Qinghua Guo; Hong Li; Xincai Wu. 2020. "An Impartial Semi-supervised Learning Strategy for Imbalanced Classification on VHR Images." Sensors 20, no. 22: 6699.

Journal article
Published: 17 November 2020 in SCIENTIA SINICA Vitae
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Vegetation maps serve as the key source information for ecological studies, biodiversity conservation, and vegetation management and restoration. The latest version of the Vegetation Map of China (1:1,000,000) was generated in the 1980s. Since then, the vegetation distribution pattern of China has changed dramatically during these 40 years. Classification errors and time lag have limited the applications of Vegetation Map of China (1:1,000,000), and it is in great demand to make the new generation of national vegetation map to fulfill the needs of ecological studies and government policy making. The development of satellite remote sensing technology provides a practical and economical approach to achieve vegetation mapping in large scale. In this article, we reviewed methods of vegetation mapping at national scale and the progress of satellite remote sensing technology on vegetation classification and mapping, and summarized the current bottleneck in vegetation mapping from satellite images. Further, we introduced a vegetation mapping strategy through the combination of crowdsource sample collection, object-based segmentation, and deep learning techinique from multi-source data. Over 50 taxonomists across China participated in the validation and calibration process through a self-developed online mapping system.

ACS Style

Qinghua GUO; Hongcan GUAN; Tianyu HU; Shichao JIN; Yanjun SU; Xuejing WANG; Dengjie WEI; Qin Ma; Qianhui SUN. Remote sensing-based mapping for the new generation of Vegetation Map of China (1:500,000). SCIENTIA SINICA Vitae 2020, 51, 229 -241.

AMA Style

Qinghua GUO, Hongcan GUAN, Tianyu HU, Shichao JIN, Yanjun SU, Xuejing WANG, Dengjie WEI, Qin Ma, Qianhui SUN. Remote sensing-based mapping for the new generation of Vegetation Map of China (1:500,000). SCIENTIA SINICA Vitae. 2020; 51 (3):229-241.

Chicago/Turabian Style

Qinghua GUO; Hongcan GUAN; Tianyu HU; Shichao JIN; Yanjun SU; Xuejing WANG; Dengjie WEI; Qin Ma; Qianhui SUN. 2020. "Remote sensing-based mapping for the new generation of Vegetation Map of China (1:500,000)." SCIENTIA SINICA Vitae 51, no. 3: 229-241.

Journal article
Published: 01 October 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Space-Earth Integrated Stereoscopic Mapping promotes the progress of earth observation technologies. The method which combined remote sensing images with zenith perspectives and ground-level landscape photos with slanted viewing angles improves the efficiency and accuracy of land surveys. Recently, numerous efforts have been devoted to combining deep learning and remote sensing images for the classification of land use scenes. However, improvement of classification accuracy has been limited because of the lack of sectional representation. Landscape photos can describe the cross-sections in detail. For this reason, this study constructed a Land-use Semantic Photo Dataset (LSPD) and proposed a Land-use Classification Framework for Photos (LUCFP) based on Inception-v4. LSPD was constructed through semantic planning, scene segmentation, supervised iteration transfer learning and augmentation of photos. LSPD has 1.4 million photos collected from seven geographic regions of China, and covers 13 land-use categories and 44 semantic categories. LUCFP adapts scene segmentation based on depth of field, multi-semantic block labeling, and weighting of semantic joint spatial ranges to determine the land use category. To validate LUCFP, nine semantic samples (9 3 2000 photos) were chosen from LSPD, obtaining an overall accuracy of 97.64%. The best photo cropping method was masking, which crops the boundary of the scene labeled by the photo, leading to an accuracy of 90.32%. The optimal pixel size that balances speed and accuracy is 675 675, with speed reaching 30 photos per second with an average accuracy of 93.80%. LUCFP has been successfully applied to the automatic verification of land surveys in China.

ACS Style

Shiwu Xu; Shihui Zhang; Jue Zeng; Tingyu Li; Qinghua Guo; Shichao Jin. A Framework for Land Use Scenes Classification Based on Landscape Photos. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 6124 -6141.

AMA Style

Shiwu Xu, Shihui Zhang, Jue Zeng, Tingyu Li, Qinghua Guo, Shichao Jin. A Framework for Land Use Scenes Classification Based on Landscape Photos. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):6124-6141.

Chicago/Turabian Style

Shiwu Xu; Shihui Zhang; Jue Zeng; Tingyu Li; Qinghua Guo; Shichao Jin. 2020. "A Framework for Land Use Scenes Classification Based on Landscape Photos." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 6124-6141.

Journal article
Published: 21 September 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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One-class classification is a common situation in remote sensing, where researchers aim to extract a single land type from remotely sensed data. Learning a classifier from labeled positive and unlabeled background data, which is the case-control sampling scenario, is efficient for one-class remote sensing classification because labeled negative data are not necessary for model training. In this study we propose a novel positive and background learning with constraints (PBLC) algorithm to address this one-class classification problem. With user-specified information of maximum probability as the constraint, PBLC infers the posterior probability of positive class directly in one-step model training. We test PBLC on a synthetic dataset and a real aerial photograph to perform different one-class classification tasks. Experimental results demonstrate that PBLC can successfully train linear and nonlinear classifiers including generalized linear model (GLM), artificial neural network (ANN), and convolutional neural network (CNN). Probabilistic and binary predictions by PBLC are more similar to the gold-standard positive-negative method, outperforming the two-step positive and background learning (PBL) algorithm that post-calibrates a naive classifier based on an estimated constant. Hence, the proposed PBLC algorithm has the potential to solve one-class classification problems in the case-control sampling scenario.

ACS Style

Wenkai Li; Qinghua Guo; Charles Elkan. One-Class Remote Sensing Classification From Positive and Unlabeled Background Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 14, 730 -746.

AMA Style

Wenkai Li, Qinghua Guo, Charles Elkan. One-Class Remote Sensing Classification From Positive and Unlabeled Background Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 14 (99):730-746.

Chicago/Turabian Style

Wenkai Li; Qinghua Guo; Charles Elkan. 2020. "One-Class Remote Sensing Classification From Positive and Unlabeled Background Data." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14, no. 99: 730-746.

Journal article
Published: 26 August 2020 in Plant Methods
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Background Identification and characterization of new traits with sound physiological foundation is essential for crop breeding and production management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of identification of physiological traits. Taking the advantage of deep learning, this study aims to develop a novel trait of canopy structure that integrate source and sink in japonica rice. Results We applied a deep learning approach to accurately segment leaf and panicle, and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice canopy during grain filling stage. Images of training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation and the azimuth angles of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy was then selected to check the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPR displayed large spatial and temporal variations as well as genotypic differences. In addition, it was responsive to agronomical practices such as nitrogen fertilization and spraying of plant growth regulators. Conclusion Deep learning technique can achieve high accuracy in simultaneous detection of panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable to detect and quantify crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.

ACS Style

Zongfeng Yang; Shang Gao; Feng Xiao; Ganghua Li; Yangfeng Ding; Qinghua Guo; Matthew J. Paul; Zhenghui Liu. Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning. Plant Methods 2020, 16, 1 -15.

AMA Style

Zongfeng Yang, Shang Gao, Feng Xiao, Ganghua Li, Yangfeng Ding, Qinghua Guo, Matthew J. Paul, Zhenghui Liu. Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning. Plant Methods. 2020; 16 (1):1-15.

Chicago/Turabian Style

Zongfeng Yang; Shang Gao; Feng Xiao; Ganghua Li; Yangfeng Ding; Qinghua Guo; Matthew J. Paul; Zhenghui Liu. 2020. "Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning." Plant Methods 16, no. 1: 1-15.

Journal article
Published: 12 August 2020 in IEEE Access
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Microfossils, tiny fossils whose study requires the use of a microscope, have been widely applied in many fields of earth, life, and environmental sciences. The abundance and high diversity of microfossils, as well as the need for rapid identification, call for automated methods to classify microfossils. In this study, we constructed an open dataset of three-dimensional (3D) microfossils and proposed a deep learning-based approach for microfossil classification. The dataset, named ‘Archives of Digital Morphology’ (ADMorph), currently contains more than ten thousand 3D models from five classes of 410 million-year-old fishes. The deep learning-based method includes data preprocessing, feature extraction, and 3D microfossil model classification. To assess the method performance and dataset representability, we performed extensive experiments. Compared with multiview convolutional neural networks (MVCNN) (91.54%), PointNet (64.13%), and VoxNet (78.15%), the method proposed herein had higher accuracy (97.60%) on the experimental dataset. We also verified data preprocessing (92.36%) and feature extraction (97.10%). We combined them to obtain the macroaveraging accuracy of 97.60%, the highest accuracy of 100%, and the lowest accuracy of 88.78%. We suggest that the proposed method can be applied to other 3D fossils and biomorphological research fields. The fast-accumulating 3D fossil models might become a source of information-rich datasets for deep learning.

ACS Style

Yemao Hou; Xindong Cui; Mario Canul-Ku; Shichao Jin; Rogelio Hasimoto-Beltran; Qinghua Guo; Min Zhu. ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning. IEEE Access 2020, 8, 148744 -148756.

AMA Style

Yemao Hou, Xindong Cui, Mario Canul-Ku, Shichao Jin, Rogelio Hasimoto-Beltran, Qinghua Guo, Min Zhu. ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning. IEEE Access. 2020; 8 (99):148744-148756.

Chicago/Turabian Style

Yemao Hou; Xindong Cui; Mario Canul-Ku; Shichao Jin; Rogelio Hasimoto-Beltran; Qinghua Guo; Min Zhu. 2020. "ADMorph: A 3D Digital Microfossil Morphology Dataset for Deep Learning." IEEE Access 8, no. 99: 148744-148756.

Journal article
Published: 30 July 2020 in Communications Biology
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Tree allometry in semi-arid forests is characterized by short height but large canopy. This pattern may be important for maintaining water-use efficiency and carbon sequestration simultaneously, but still lacks quantification. Here we use terrestrial laser scanning to quantify allometry variations of Quercus mongolica in semi-arid forests. With tree height (Height) declining, canopy area (CA) decreases with substantial variations. The theoretical CA-Height relationship in dynamic global vegetation models (DGVMs) matches only the 5th percentile of our results because of CA underestimation and Height overestimation by breast height diameter (DBH). Water supply determines Height variation (P = 0.000) but not CA (P = 0.2 in partial correlation). The decoupled functions of stem, hydraulic conductance and leaf spatial arrangement, may explain the inconsistency, which may further ensure hydraulic safety and carbon assimilation, avoiding forest dieback. Works on tree allometry pattern and determinant will effectively supply tree drought tolerance studying and support DGVM improvements.

ACS Style

Jingyu Dai; Hongyan Liu; Yongcai Wang; Qinghua Guo; Tianyu Hu; Timothy Quine; Sophie Green; Henrik Hartmann; Chongyang Xu; Xu Liu; Zihan Jiang. Drought-modulated allometric patterns of trees in semi-arid forests. Communications Biology 2020, 3, 1 -8.

AMA Style

Jingyu Dai, Hongyan Liu, Yongcai Wang, Qinghua Guo, Tianyu Hu, Timothy Quine, Sophie Green, Henrik Hartmann, Chongyang Xu, Xu Liu, Zihan Jiang. Drought-modulated allometric patterns of trees in semi-arid forests. Communications Biology. 2020; 3 (1):1-8.

Chicago/Turabian Style

Jingyu Dai; Hongyan Liu; Yongcai Wang; Qinghua Guo; Tianyu Hu; Timothy Quine; Sophie Green; Henrik Hartmann; Chongyang Xu; Xu Liu; Zihan Jiang. 2020. "Drought-modulated allometric patterns of trees in semi-arid forests." Communications Biology 3, no. 1: 1-8.

Preprint content
Published: 29 July 2020
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Background : Identification and characterization of new traits with a sound physiological foundation is essential for crop breeding and management. Deep learning has been widely used in image data analysis to explore spatial and temporal information on crop growth and development, thus strengthening the power of the identification of physiological traits. This study aims to develop a novel trait that indicates source and sink relation in japonica rice based on deep learning.Results : We applied a deep learning approach to accurately segment leaf and panicle and subsequently developed the procedure of GvCrop to calculate the leaf to panicle ratio (LPR) of rice populations during grain filling. Images of the training dataset were captured in the field experiments, with large variations in camera shooting angle, the elevation angle and the azimuth angle of the sun, rice genotype, and plant phenological stages. Accurately labeled by manually annotating all the panicle and leaf regions, the resulting dataset were used to train FPN-Mask (Feature Pyramid Network Mask) models, consisting of a backbone network and a task-specific sub-network. The model with the highest accuracy is then selected to study the variations in LPR among 192 rice germplasms and among agronomical practices. Despite the challenging field conditions, FPN-Mask models achieved a high detection accuracy, with Pixel Accuracy being 0.99 for panicles and 0.98 for leaves. The calculated LPRs showed large spatial and temporal variations as well as genotypic differences.Conclusion : Deep learning techniques can achieve high accuracy in simultaneously detecting panicle and leaf data from complex rice field images. The proposed FPN-Mask model is applicable for detecting and quantifying crop performance under field conditions. The newly identified trait of LPR should provide a high throughput protocol for breeders to select superior rice cultivars as well as for agronomists to precisely manage field crops that have a good balance of source and sink.

ACS Style

Zongfeng Yang; Shang Gao; Feng Xiao; Ganghua Li; Yangfeng Ding; Qinghua Guo; Matthew J. Paul; Zhenghui Liu. Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning. 2020, 1 .

AMA Style

Zongfeng Yang, Shang Gao, Feng Xiao, Ganghua Li, Yangfeng Ding, Qinghua Guo, Matthew J. Paul, Zhenghui Liu. Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning. . 2020; ():1.

Chicago/Turabian Style

Zongfeng Yang; Shang Gao; Feng Xiao; Ganghua Li; Yangfeng Ding; Qinghua Guo; Matthew J. Paul; Zhenghui Liu. 2020. "Leaf to panicle ratio (LPR): a new physiological trait indicative of source and sink relation in japonica rice based on deep learning." , no. : 1.

Journal article
Published: 10 July 2020 in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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Airborne laser scanning (ALS) data is one of the most commonly used data for terrain products generation. Filtering ground points is a prerequisite step for ALS data processing. Traditional filtering methods mainly use handcrafted features or predefined classification rules with pre-processing/post-processing operations to filter ground points iteratively, which is empirical and cumbersome. Deep learning provides a new approach to solve classification and segmentation problems because of its ability to self-learn features, which has been favored in many fields, particularly remote sensing. In this study, we proposed a point-based fully convolutional neural network (PFCN) which directly consumed points with only geometric information and extracted both point-wise and tile-wise features to classify each point. The network was trained with 37449157 points from 14 sites and evaluated on 6 sites in various forested environments. Additionally, the method was compared with five widely used filtering methods and one of the best point-based deep learning methods (PointNet++). Results showed that the PFCN achieved the best results in terms of mean omission error (T1 = 1.10%), total error (Te = 1.73%), and kappa coefficient (93.88%), but ranked second for the root mean square error of the digital terrain model caused by the worst commission error. Additionally, our method was on par with or even better than PointNet++ in accuracy. Moreover, our method consumes one-third of the computational resource and one-seventh of the training time. We believe that PFCN is a simple and flexible method that can be widely applied for ground point filtering.

ACS Style

Shichao Jin; Yanjun Su; Xiaoqian Zhao; Tianyu Hu; Qinghua Guo. A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 2020, 13, 3958 -3974.

AMA Style

Shichao Jin, Yanjun Su, Xiaoqian Zhao, Tianyu Hu, Qinghua Guo. A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2020; 13 (99):3958-3974.

Chicago/Turabian Style

Shichao Jin; Yanjun Su; Xiaoqian Zhao; Tianyu Hu; Qinghua Guo. 2020. "A Point-Based Fully Convolutional Neural Network for Airborne LiDAR Ground Point Filtering in Forested Environments." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13, no. 99: 3958-3974.

Journal article
Published: 07 July 2020 in IEEE Geoscience and Remote Sensing Letters
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Forest inventory holds an essential role in forest management and research, but the existing field inventory methods are highly time-consuming and labor-intensive. Here, we developed a simultaneous localization and mapping-based backpack light detection and ranging (LiDAR) system with dual orthogonal laser scanners and an open-source Python package called Forest3D for efficient and accurate forest inventory applications. Two key forest inventory variables, tree height and diameter at breast height (DBH), were extracted at six study sites with different tree species compositions. In addition, the vertical point density distribution and leaf area density (LAD) were calculated for two complex natural forest sites. The results showed that the backpack LiDAR system together with the Forest3D package accurately estimated the tree height (R² = 0.65, RMSE = 1.90 m) and DBH (R² = 0.95, RMSE = 0.02 m), which were equivalent to those derived from terrestrial laser scanning (TLS), but with much higher efficiency. The point density of the backpack LiDAR data was higher than or the same as that of the TLS data across all height strata, and the estimated LAD fit well with the TLS estimates (R² > 0.92, RMSE = 0.01 m²/m³). The backpack LiDAR system, along with the Forest3D package, provides an efficient and accurate solution for extracting forest inventory variables, which should be of great interests to forest managers and researchers.

ACS Style

Yanjun Su; Qinghua Guo; Shichao Jin; Hongcan Guan; Xiliang Sun; Qin Ma; Tianyu Hu; Rui Wang; Yumei Li. The Development and Evaluation of a Backpack LiDAR System for Accurate and Efficient Forest Inventory. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1660 -1664.

AMA Style

Yanjun Su, Qinghua Guo, Shichao Jin, Hongcan Guan, Xiliang Sun, Qin Ma, Tianyu Hu, Rui Wang, Yumei Li. The Development and Evaluation of a Backpack LiDAR System for Accurate and Efficient Forest Inventory. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (9):1660-1664.

Chicago/Turabian Style

Yanjun Su; Qinghua Guo; Shichao Jin; Hongcan Guan; Xiliang Sun; Qin Ma; Tianyu Hu; Rui Wang; Yumei Li. 2020. "The Development and Evaluation of a Backpack LiDAR System for Accurate and Efficient Forest Inventory." IEEE Geoscience and Remote Sensing Letters 18, no. 9: 1660-1664.

Journal article
Published: 10 June 2020 in ISPRS Journal of Photogrammetry and Remote Sensing
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Terrestrial laser scanning (TLS) has been recognized as an accurate means for non-destructively deriving three-dimensional (3D) forest structural attributes. These attributes include but are not limited to tree height, diameter at breast height, and leaf area density. As such, TLS has become an increasingly important technique in forest inventory practices and forest ecosystem studies. Multiple TLS scans collected at different locations are often involved for a comprehensive characterization of 3D canopy structure of a forest stand. Among which, multi-scan registration is a critical prerequisite. Currently, multi-scan TLS registration in forests is mainly based on a very time-consuming and tedious process of setting up hand-crafted registration targets in the field and manually identifying the common targets between scans from the collected data. In this study, a novel marker-free method that automatically registers multi-scan TLS data is presented. The main principle underlying our method is to identify shaded areas from the raw point cloud of a single TLS scan and to use them as the key features to register multi-scan TLS data. The proposed method is tested with 17 pairs of TLS scans collected in six plots across China with various vegetation characteristics (e.g., vegetation type, height, and understory complexity). Our results showed that the proposed method successfully registered all 17 pairs of TLS scans with equivalent accuracy to the manual registration approach. Moreover, the proposed method eliminates the process of setting up registration targets in the field, manually identifying registration targets from TLS data, and processing raw TLS data to extract individual tree attributes, which brings it the advantages of high efficiency and robustness. It is anticipated that the proposed algorithms can save time and cost of collecting TLS data in forests, and therefore improves the efficiency of TLS forestry applications.

ACS Style

Hongcan Guan; Yanjun Su; Xiliang Sun; Guangcai Xu; Wenkai Li; Qin Ma; Xiaoyong Wu; Jin Wu; Lingli Liu; Qinghua Guo. A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments. ISPRS Journal of Photogrammetry and Remote Sensing 2020, 166, 82 -94.

AMA Style

Hongcan Guan, Yanjun Su, Xiliang Sun, Guangcai Xu, Wenkai Li, Qin Ma, Xiaoyong Wu, Jin Wu, Lingli Liu, Qinghua Guo. A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments. ISPRS Journal of Photogrammetry and Remote Sensing. 2020; 166 ():82-94.

Chicago/Turabian Style

Hongcan Guan; Yanjun Su; Xiliang Sun; Guangcai Xu; Wenkai Li; Qin Ma; Xiaoyong Wu; Jin Wu; Lingli Liu; Qinghua Guo. 2020. "A marker-free method for registering multi-scan terrestrial laser scanning data in forest environments." ISPRS Journal of Photogrammetry and Remote Sensing 166, no. : 82-94.

Journal article
Published: 25 May 2020 in Remote Sensing
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Mangrove forest ecosystems are distributed at the land–sea interface in tropical and subtropical regions and play an important role in carbon cycles and biodiversity. Accurately mapping global mangrove aboveground biomass (AGB) will help us understand how mangrove ecosystems are affected by the impacts of climatic change and human activities. Light detection and ranging (LiDAR) techniques have been proven to accurately capture the three-dimensional structure of mangroves and LiDAR can estimate forest AGB with high accuracy. In this study, we produced a global mangrove forest AGB map for 2004 at a 250-m resolution by combining ground inventory data, spaceborne LiDAR, optical imagery, climate surfaces, and topographic data with random forest, a machine learning method. From the published literature and free-access datasets of mangrove biomass, we selected 342 surface observations to train and validate the mangrove AGB estimation model. Our global mangrove AGB map showed that average global mangrove AGB density was 115.23 Mg/ha, with a standard deviation of 48.89 Mg/ha. Total global AGB storage within mangrove forests was 1.52 Pg. Cross-validation with observed data demonstrated that our mangrove AGB estimates were reliable. The adjusted coefficient of determination (R2) and root-mean-square error (RMSE) were 0.48 and 75.85 Mg/ha, respectively. Our estimated global mangrove AGB storage was similar to that predicted by previous remote sensing methods, and remote sensing approaches can overcome overestimates from climate-based models. This new biomass map provides information that can help us understand the global mangrove distribution, while also serving as a baseline to monitor trends in global mangrove biomass.

ACS Style

Tianyu Hu; Yingying Zhang; Yanjun Su; Yi Zheng; Guanghui Lin; Qinghua Guo. Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data. Remote Sensing 2020, 12, 1690 .

AMA Style

Tianyu Hu, Yingying Zhang, Yanjun Su, Yi Zheng, Guanghui Lin, Qinghua Guo. Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data. Remote Sensing. 2020; 12 (10):1690.

Chicago/Turabian Style

Tianyu Hu; Yingying Zhang; Yanjun Su; Yi Zheng; Guanghui Lin; Qinghua Guo. 2020. "Mapping the Global Mangrove Forest Aboveground Biomass Using Multisource Remote Sensing Data." Remote Sensing 12, no. 10: 1690.

Journal article
Published: 13 May 2020 in Plant Methods
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Background Precision agriculture is an emerging research field that relies on monitoring and managing field variability in phenotypic traits. An important phenotypic trait is biomass, a comprehensive indicator that can reflect crop yields. However, non-destructive biomass estimation at fine levels is unknown and challenging due to the lack of accurate and high-throughput phenotypic data and algorithms. Results In this study, we evaluated the capability of terrestrial light detection and ranging (lidar) data in estimating field maize biomass at the plot, individual plant, leaf group, and individual organ (i.e., individual leaf or stem) levels. The terrestrial lidar data of 59 maize plots with more than 1000 maize plants were collected and used to calculate phenotypes through a deep learning-based pipeline, which were then used to predict maize biomass through simple regression (SR), stepwise multiple regression (SMR), artificial neural network (ANN), and random forest (RF). The results showed that terrestrial lidar data were useful for estimating maize biomass at all levels (at each level, R2 was greater than 0.80), and biomass estimation at leaf group level was the most precise (R2 = 0.97, RMSE = 2.22 g) among all four levels. All four regression techniques performed similarly at all levels. However, considering the transferability and interpretability of the model itself, SR is the suggested method for estimating maize biomass from terrestrial lidar-derived phenotypes. Moreover, height-related variables showed to be the most important and robust variables for predicting maize biomass from terrestrial lidar at all levels, and some two-dimensional variables (e.g., leaf area) and three-dimensional variables (e.g., volume) showed great potential as well. Conclusion We believe that this study is a unique effort on evaluating the capability of terrestrial lidar on estimating maize biomass at difference levels, and can provide a useful resource for the selection of the phenotypes and models required to estimate maize biomass in precision agriculture practices.

ACS Style

Shichao Jin; Yanjun Su; Shilin Song; Kexin Xu; Tianyu Hu; Qiuli Yang; Fangfang Wu; Guangcai Xu; Qin Ma; Hongcan Guan; Shuxin Pang; Yumei Li; Qinghua Guo. Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level. Plant Methods 2020, 16, 1 -19.

AMA Style

Shichao Jin, Yanjun Su, Shilin Song, Kexin Xu, Tianyu Hu, Qiuli Yang, Fangfang Wu, Guangcai Xu, Qin Ma, Hongcan Guan, Shuxin Pang, Yumei Li, Qinghua Guo. Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level. Plant Methods. 2020; 16 (1):1-19.

Chicago/Turabian Style

Shichao Jin; Yanjun Su; Shilin Song; Kexin Xu; Tianyu Hu; Qiuli Yang; Fangfang Wu; Guangcai Xu; Qin Ma; Hongcan Guan; Shuxin Pang; Yumei Li; Qinghua Guo. 2020. "Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level." Plant Methods 16, no. 1: 1-19.

Journal article
Published: 02 April 2020 in Science Bulletin
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Vegetation maps are important sources of information for biodiversity conservation, ecological studies, vegetation management and restoration, and national strategic decision making. The current Vegetation Map of China (1:1000000) was generated by a team of more than 250 scientists in an effort that lasted over 20 years starting in the 1980s. However, the vegetation distribution of China has experienced drastic changes during the rapid development of China in the last three decades, and it urgently needs to be updated to better represent the distribution of current vegetation types. Here, we describe the process of updating the Vegetation Map of China (1:1000000) generated in the 1980s using a “crowdsourcing-change detection-classification-expert knowledge” vegetation mapping strategy. A total of 203,024 field samples were collected, and 50 taxonomists were involved in the updating process. The resulting updated map has 12 vegetation type groups, 55 vegetation types/subtypes, and 866 vegetation formation/sub-formation types. The overall accuracy and kappa coefficient of the updated map are 64.8% and 0.52 at the vegetation type group level, 61% and 0.55 at the vegetation type/subtype level and 40% and 0.38 at the vegetation formation/sub-formation level. When compared to the original map, the updated map showed that 3.3 million km2 of vegetated areas of China have changed their vegetation type group during the past three decades due to anthropogenic activities and climatic change. We expect this updated map to benefit the understanding and management of China’s terrestrial ecosystems.

ACS Style

Yanjun Su; Qinghua Guo; Tianyu Hu; Hongcan Guan; Shichao Jin; Shazhou An; Xuelin Chen; Ke Guo; Zhanqing Hao; Yuanman Hu; YongMei Huang; Mingxi Jiang; JiaXiang Li; Zhenji Li; Xiankun Li; Xiaowei Li; Cunzhu Liang; Renlin Liu; Qing Liu; Hongwei Ni; Shaolin Peng; Zehao Shen; Zhiyao Tang; Xingjun Tian; Xihua Wang; Renqing Wang; Zongqiang Xie; Yingzhong Xie; Xiaoniu Xu; Xiaobo Yang; Yongchuan Yang; Lifei Yu; Ming Yue; Feng Zhang; Keping Ma. An updated Vegetation Map of China (1:1000000). Science Bulletin 2020, 65, 1125 -1136.

AMA Style

Yanjun Su, Qinghua Guo, Tianyu Hu, Hongcan Guan, Shichao Jin, Shazhou An, Xuelin Chen, Ke Guo, Zhanqing Hao, Yuanman Hu, YongMei Huang, Mingxi Jiang, JiaXiang Li, Zhenji Li, Xiankun Li, Xiaowei Li, Cunzhu Liang, Renlin Liu, Qing Liu, Hongwei Ni, Shaolin Peng, Zehao Shen, Zhiyao Tang, Xingjun Tian, Xihua Wang, Renqing Wang, Zongqiang Xie, Yingzhong Xie, Xiaoniu Xu, Xiaobo Yang, Yongchuan Yang, Lifei Yu, Ming Yue, Feng Zhang, Keping Ma. An updated Vegetation Map of China (1:1000000). Science Bulletin. 2020; 65 (13):1125-1136.

Chicago/Turabian Style

Yanjun Su; Qinghua Guo; Tianyu Hu; Hongcan Guan; Shichao Jin; Shazhou An; Xuelin Chen; Ke Guo; Zhanqing Hao; Yuanman Hu; YongMei Huang; Mingxi Jiang; JiaXiang Li; Zhenji Li; Xiankun Li; Xiaowei Li; Cunzhu Liang; Renlin Liu; Qing Liu; Hongwei Ni; Shaolin Peng; Zehao Shen; Zhiyao Tang; Xingjun Tian; Xihua Wang; Renqing Wang; Zongqiang Xie; Yingzhong Xie; Xiaoniu Xu; Xiaobo Yang; Yongchuan Yang; Lifei Yu; Ming Yue; Feng Zhang; Keping Ma. 2020. "An updated Vegetation Map of China (1:1000000)." Science Bulletin 65, no. 13: 1125-1136.

Review
Published: 24 March 2020 in Science China Earth Sciences
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Ecological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-term, cross-scale, massive, heterogeneous, and multi-source data. Ecological resource research is entering a new era driven by big data. Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data. Deep learning is a method for automatically extracting complex high-dimensional nonlinear features, which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data. To promote the application of deep learning in the field of ecological resource research, here, we first introduce the relationship between deep learning theory and research on ecological resources, common tools, and datasets. Second, applications of deep learning in classification and recognition, detection and localization, semantic segmentation, instance segmentation, and graph neural network in typical spatial discrete data are presented through three cases: species classification, crop breeding, and vegetation mapping. Finally, challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning. It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data, improve the universality and interpretability of algorithms, and enrich applications with the development of hardware.

ACS Style

Qinghua Guo; Shichao Jin; Min Li; Qiuli Yang; Kexin Xu; Yuanzhen Ju; Jing Zhang; Jing Xuan; Jin Liu; Yanjun Su; Qiang Xu; Yu Liu. Application of deep learning in ecological resource research: Theories, methods, and challenges. Science China Earth Sciences 2020, 63, 1 -18.

AMA Style

Qinghua Guo, Shichao Jin, Min Li, Qiuli Yang, Kexin Xu, Yuanzhen Ju, Jing Zhang, Jing Xuan, Jin Liu, Yanjun Su, Qiang Xu, Yu Liu. Application of deep learning in ecological resource research: Theories, methods, and challenges. Science China Earth Sciences. 2020; 63 (10):1-18.

Chicago/Turabian Style

Qinghua Guo; Shichao Jin; Min Li; Qiuli Yang; Kexin Xu; Yuanzhen Ju; Jing Zhang; Jing Xuan; Jin Liu; Yanjun Su; Qiang Xu; Yu Liu. 2020. "Application of deep learning in ecological resource research: Theories, methods, and challenges." Science China Earth Sciences 63, no. 10: 1-18.