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Fine-resolution forest tree height estimation across the Sierra Nevada through the integration of spaceborne LiDAR, airborne LiDAR, and optical imagery

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International Journal of Digital Earth 2017年第3期10卷 307-323页

作者： yanjun su Qin Ma Qinghua Guo Sierra Nevada Research Institute School of EngineeringUniversity of California at MercedMercedCAUSA State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijingPeople’s Republic of China

Forests of the Sierra Nevada(SN)mountain range are valuable natural heritages for the region and the country,and tree height is an important forest structure parameter for understanding the SN forest *** is still a need in the accurate estimation of wall-to-wall SN tree height distribution at fine spatial *** this study,we presented a method to map wall-to-wall forest tree height(defined as Lorey’s height)across the SN at 70-m resolution by fusing multi-source datasets,including over 1600 in situ tree height measurements and over 1600 km^(2) airborne light detection and ranging(LiDAR)*** tree height estimates within these airborne LiDAR boundaries were first computed based on in situ measurements,and then these airborne LiDAR-derived tree heights were used as reference data to estimate tree heights at Geoscience Laser Altimeter System(GLAS)***,the random forest algorithm was used to model the SN tree height from these GLAS tree heights,optical imagery,topographic data,and climate *** results show that our fine-resolution SN tree height product has a good correspondence with field *** coefficient of determination between them is 0.60,and the root-mean-squared error is 5.45 m.

关键词： Tree height Sierra Nevada LiDAR integration fine resolution

Quantifying individual tree growth and tree competition using bi-temporal airborne laser scanning data:a case study in the Sierra Nevada Mountains,California

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International Journal of Digital Earth 2018年第5期11卷 485-503页

作者： Qin Ma yanjun su Shengli Tao Qinghua Guo School of Engineering Sierra Nevada Research InstituteUniversity of California at MercedMercedCAUSA State Key Laboratory of Vegetation and Environmental Change Chinese Academy of SciencesInstitute of BotanyBeijingPeople’s Republic of China Department of Ecology College of Urban and Environmental Sciencesand Key Laboratory for Earth Surface Processes of the Ministry of EducationPeking UniversityBeijingPeople’s Republic of China

Improved monitoring and understanding of tree growth and its responses to controlling factors are important for tree growth *** Laser Scanning(ALS)can be used to enhance the efficiency and accuracy of large-scale forest surveys in delineating three-dimensional forest structures and under-canopy *** study proposed an ALSbased framework to quantify tree growth and ***-temporal ALS data were used to quantify tree growth in height(ΔH),crown area(ΔA),crown volume(ΔV),and tree competition for 114,000 individual trees in two conifer-dominant Sierra Nevada *** analyzed the correlations between tree growth attributes and controlling factors(*** sizes,competition,forest structure,and topographic parameters)at multiple *** the individual tree level,ΔH had no consistent correlations with controlling factors,ΔA andΔV were positively related to original tree sizes(R>0.3)and negatively related to competition indices(R0.7),ΔV was positively related to original tree sizes(|R|>0.8).Multivariate regression models were simulated at individual tree level forΔH,ΔA,andΔV with the R2 ranged from 0.1 to *** ALS-based tree height estimation and growth analysis results were consistent with field measurements.

关键词： Airborne Laser Scanning change detection tree growth tree competition Sierra Nevada

Automatic segmentation of stem and leaf components and individual maize plants in field terrestrial LiDAR data using convolutional neural networks

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The Crop Journal 2022年第5期10卷 1239-1250页

作者： Zurui Ao Fangfang Wu Saihan Hu Ying sun yanjun su Qinghua Guo Qinchuan Xin Guangdong Key Laboratory for Urbanization and Geo-simulation Sun Yat-sen UniversityGuangzhou 510275GuangdongChina State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China Department of Ecology College of Urban and Environmental Scienceand Key Laboratory of Earth Surface Processes of the Ministry of EducationPeking UniversityBeijing 100871China

High-throughput maize phenotyping at both organ and plant levels plays a key role in molecular breeding for increasing crop yields. Although the rapid development of light detection and ranging(Li DAR) provides a new way to characterize three-dimensional(3 D) plant structure, there is a need to develop robust algorithms for extracting 3 D phenotypic traits from Li DAR data to assist in gene identification and selection. Accurate 3 D phenotyping in field environments remains challenging, owing to difficulties in segmentation of organs and individual plants in field terrestrial Li DAR data. We describe a two-stage method that combines both convolutional neural networks(CNNs) and morphological characteristics to segment stems and leaves of individual maize plants in field environments. It initially extracts stem points using the Point CNN model and obtains stem instances by fitting 3 D cylinders to the points. It then segments the field Li DAR point cloud into individual plants using local point densities and 3 D morphological structures of maize plants. The method was tested using 40 samples from field observations and showed high accuracy in the segmentation of both organs(F-score =0.8207) and plants(Fscore =0.9909). The effectiveness of terrestrial Li DAR for phenotyping at organ(including leaf area and stem position) and individual plant(including individual height and crown width) levels in field environments was evaluated. The accuracies of derived stem position(position error =0.0141 m), plant height(R^(2)>0.99), crown width(R^(2)>0.90), and leaf area(R^(2)>0.85) allow investigating plant structural and functional phenotypes in a high-throughput way. This CNN-based solution overcomes the major challenges in organ-level phenotypic trait extraction associated with the organ segmentation, and potentially contributes to studies of plant phenomics and precision agriculture.

关键词： Terrestrial LiDAR Phenotype Organ segmentation Convolutional neural networks

Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

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Forest Ecosystems 2022年第5期9卷 617-629页

作者： Qiuli Yang yanjun su Tianyu Hu Shichao Jin Xiaoqiang Liu Chunyue Niu Zhonghua Liu Maggi Kelly Jianxin Wei Qinghua Guo State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing100093China University of Chinese Academy of Sciences Beijing100049China Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesCollaborative Innovation Centre for Modern Crop Production co-sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China Department of Environmental Sciences Policy and ManagementUniversity of CaliforniaBerkeleyCA94720-3114USA Division of Agriculture and Natural Resources University of CaliforniaBerkeleyCA94720-3114USA College of Geography and Remote Sensing Sciences Xinjiang UniversityUrumqiXinjiang830017China Xinjiang Lidar Applied Engineering Technology Research Center UrumqiXinjiang830002China Xinjiang Land and Resources Information Center UrumqiXinjiang830002China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing100871China

Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing ***,combining the advantages of active and passive data sources to improve estimation accuracy remains ***,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral *** 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed *** results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum *** with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests *** proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf ***,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random *** proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

关键词： Forest aboveground biomass Drone LiDAR Allometric relationship Power law Tree height Vegetation index

Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing

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The Crop Journal 2022年第5期10卷 1334-1345页

作者： Qing Li Shichao Jin Jingrong Zang Xiao Wang Zhuangzhuang sun Ziyu Li Shan Xu Qin Ma yanjun su Qinghua Guo Dong Jiang Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesRegional Technique Innovation Center for Wheat ProductionMinistry of AgricultureKey Laboratory of Crop Physiology and Ecology in Southern ChinaMinistry of AgricultureCollaborative Innovation Centre for Modern Crop Production co-sponsored by Province and MinistryJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095JiangsuChina Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System SciencesNanjing UniversityNanjing 210023JiangsuChina Department of Forestry Mississippi State UniversityMississippi StateMS 39759USA State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China Department of Ecology College of Environmental Sciencesand Key Laboratory of Earth Surface Processes of the Ministry of EducationPeking UniversityBeijing 100871China

Accurate, efficient, and timely yield estimation is critical for crop variety breeding and management optimization. However, the contributions of proximal sensing data characteristics(spectral, temporal, and spatial) to yield estimation have not been systematically evaluated. We collected long-term, hypertemporal, and large-volume light detection and ranging(Li DAR) and multispectral data to(i) identify the best machine learning method and prediction stage for wheat yield estimation,(ii) characterize the contribution of multisource data fusion and the dynamic importance of structural and spectral traits to yield estimation, and(iii) elucidate the contribution of time-series data fusion and 3 D spatial information to yield estimation. Wheat yield could be accurately(R^(2)= 0.891) and timely(approximately-two months before harvest) estimated from fused Li DAR and multispectral data. The artificial neural network model and the flowering stage were always the best method and prediction stage, respectively. Spectral traits(such as CIgreen) dominated yield estimation, especially in the early stage, whereas the contribution of structural traits(such as height) was more stable in the late stage. Fusing spectral and structural traits increased estimation accuracy at all growth stages. Better yield estimation was realized from traits derived from complete 3 D points than from canopy surface points and from integrated multi-stage(especially from jointing to heading and flowering stages) data than from single-stage data. We suggest that this study offers a novel perspective on deciphering the contributions of spectral, structural, and timeseries information to wheat yield estimation and can guide accurate, efficient, and timely estimation of wheat yield.

关键词： LiDAR Multispectral Yield Phenotype Hyper-temporal

Application of deep learning in ecological resource research:Theories, methods, and challenges

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Science China Earth Sciences 2020年第10期63卷 1457-1474页

作者： Qinghua GUO Shichao JIN Min LI Qiuli YANG Kexin XU Yuanzhen JU Jing ZHANG Jing XUAN Jin LIU yanjun su Qiang XU Yu LIU State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China University of Chinese Academy of Sciences Beijing 100049China State Key Laboratory of Systematic and Evolutionary Botany Institute of BotanyChinese Academy of SciencesBeijing 100093China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of TechnologyChengdu 610059China Institute of Remote Sensing and Geographical Information Systems Peking UniversityBeijing 100871China

Ecological resources are an important material foundation for the survival,development,and self-realization of human ***-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human *** in observation technology have improved the ability to acquire long-term,cross-scale,massive,heterogeneous,and multi-source *** resource research is entering a new era driven by big *** statistical learning and machine learning algorithms have problems with saturation in dealing with big *** 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 *** 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 ***,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 ***,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 *** 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.

关键词： Ecological resources Deep learning Neural network Big data Theory and tools Application and challenge

Nonscalability of Fractal Dimension to Quantify Canopy Structural Complexity from Individual Trees to Forest Stands

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Journal of Remote Sensing 2022年第1期2022卷 21-32页

作者： Xiaoqiang Liu Qin Ma Xiaoyong Wu Tianyu Hu Guanhua Dai Jin Wu Shengli Tao Shaopeng Wang Lingli Liu Qinghua Guo yanjun su State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China China National Botanical Garden Beijing 100093China University of Chinese Academy of Sciences Beijing 100049China Key Laboratory of Forest Ecology and Management Institute of Applied EcologyChinese Academy of SciencesShenyang 110164China School of Biological Sciences The University of Hong KongPokfulamHong KongChina State Key Laboratory of Agrobiotechnology Chinese University of Hong KongHong KongChina Institute of Ecology College of Urban and Environmental SciencePeking UniversityBeijing 100871China Institute of Remote Sensing and Geographical Information Systems School of Earth and Space SciencesPeking UniversityBeijing 100871China

Canopy structural complexity is a critical emergent forest attribute,and light detection and ranging(lidar)-based fractal dimension has been recognized as its powerful measure at the individual tree ***,the current lidar-based estimation method is highly sensitive to data characteristics,and its scalability from individual trees to forest stands remains *** study proposed an improved method to estimate fractal dimension from lidar data by considering Shannon entropy,and evaluated its scalability from individual trees to forest stands through mathematical ***,a total of 280 forest stand scenes simulated from the terrestrial lidar data of 115 trees spanning large variability in canopy structural complexity were used to evaluate the robustness of the proposed method and the scalability of fractal *** results show that the proposed method can significantly improve the robustness of lidar-derived fractal *** mathematical derivations and experimental analyses demonstrate that the fractal dimension of a forest stand is equal to that of the tree with the largest fractal dimension in it,manifesting its nonscalability from individual trees to forest *** nonscalability of fractal dimension reveals its limited capability in canopy structural complexity quantification and indicates that the power-law scaling theory of a forest stand underlying fractal geometry is determined by its dominant tree instead of the entire ***,we believe that fractal dimension is still a useful indicator of canopy structural complexity at the individual tree level and might be used along with other stand-level indexes to reflect the“tree-to-stand”correlation of canopy structural complexity.

关键词： fractal dimension lidar

Exploring Seasonal and Circadian Rhythms in Structural Traits of Field Maize from LiDAR Time Series

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Plant Phenomics 2021年第1期3卷 389-403页

作者： Shichao Jin yanjun su Yongguang Zhang Shilin Song Qing Li Zhonghua Liu Qin Ma Yan Ge LingLi Liu Yanfeng Ding Frédéric Baret Qinghua Guo Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesCollaborative Innovation Centre for Modern Crop Production Co-Sponsored by Province and MinistryJiangsu Key Laboratory for Information AgricultureNanjing Agricultural UniversityNanjing 210095China Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology International Institute for Earth System SciencesNanjing UniversityNanjingJiangsu 210023China State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China University of Chinese Academy of Sciences Beijing 100049China National Technique Innovation Center for Regional Wheat Production/Key Laboratory of Crop Ecophysiology Ministry of AgricultureNanjing Agricultural UniversityNanjing210095 JiangsuChina Department of Forestry Mississippi State UniversityMississippi State 39759USA Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes(EMMAH) Institut National de la Recherche AgronomiqueUnitéMixte de Recherche 1114 Domaine Saint-PaulAvignon Cedex 84914France Department of Ecology College of Environmental Sciencesand Key Laboratory of Earth Surface Processes of the Ministry of EducationPeking UniversityBeijing 100871China

Plant growth rhythm in structural traits is important for better understanding plant response to the ever-changing *** laser scanning(TLS)is a well-suited tool to study structural rhythm under field *** studies have used TLS to describe the structural rhythm of trees,but no consistent patterns have been ***,whether TLS can capture structural rhythm in crops is ***,we aim to explore the seasonal and circadian rhythms in maize structural traits at both the plant and leaf levels from time-series *** seasonal rhythm was studied using TLS data collected at four key growth periods,including jointing,bell-mouthed,heading,and maturity *** rhythms were explored by using TLS data acquired around every 2 hours in a whole day under standard and cold stress *** showed that TLS can quantify the seasonal and circadian rhythm in structural traits at both plant and leaf levels.(1)Leaf inclination angle decreased significantly between the jointing stage and bell-mouthed *** azimuth was stable after the jointing stage.(2)Some individual-level structural rhythms(e.g.,azimuth and projected leaf area/PLA)were consistent with leaf-level structural rhythms.(3)The circadian rhythms of some traits(e.g.,PLA)were not consistent under standard and cold stress conditions.(4)Environmental factors showed better correlations with leaf traits under cold stress than standard *** was the most important factor that significantly correlated with all leaf traits except leaf *** study highlights the potential of time-series TLS in studying outdoor agricultural chronobiology.

关键词： crops heading traits

miR-367-3p靶向ZEB2抑制NSCLC细胞增殖、迁移和侵袭生物学功能研究

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中国肺癌杂志 2022年第11期25卷 782-788页

作者：苏延军张华天津医科大学肿瘤医院肺部肿瘤科国家肿瘤临床医学中心天津市“肿瘤防治”重点实验室天津市恶性肿瘤临床医学研究中心天津市肺癌中心天津300060

背景与目的微小RNA在肺癌的发生发展及生物学表型中发挥重要作用。探讨miR-367-3p在非小细胞肺癌(non-small cell lung cancer,NSCLC)患者中的表达及其对NSCLC细胞增殖、迁移和侵袭的影响。方法选取我院收治并手术治疗的NSCLC患者22例(... 详细信息

背景与目的微小RNA在肺癌的发生发展及生物学表型中发挥重要作用。探讨miR-367-3p在非小细胞肺癌(non-small cell lung cancer,NSCLC)患者中的表达及其对NSCLC细胞增殖、迁移和侵袭的影响。方法选取我院收治并手术治疗的NSCLC患者22例(其中腺癌13例,鳞癌9例),术中留取患者癌组织、癌旁组织及外周血5 mL,同时选取健康查体患者22例,留取外周血5 mL。采用Real-time PCR法检测NSCLC患者癌组织、癌旁组织、外周血及健康查体者外周血中miR-367-3p的相对表达水平。培养肺癌细胞株(A549)与正常支气管上皮细胞(bronchial epithelium transformed with Ad12-SV402B,BEAS-2B)并检测细胞株中miR-367-3p表达水平。转染外源性miR-367-3p后采用MTT和Transwell实验观察转染前后A549细胞增殖和侵袭能力变化。采用生物信息分析miR-367-3p下游靶基因,采用Real-time PCR和Western blot检测转染外源性miR-367-3p前后锌指e-box-binding同源盒2(Zinc finger E-box binding homeobox 2,ZEB2)表达水平。结果22例NSCLC患者,癌组织中miR-367-3p相对表达水平显著低于癌旁组织(Psurvival,OS)和无疾病进展生存(disease free survival,DFS)与低表达组NSCLC差异无统计学意义(P>0.05),但高表达组显示出了OS及DFS降低的趋势。结论miR-367-3p在NSCLC患者中表达下调,并通过靶向ZEB2基因参与NSCLC的增殖和侵袭生物学过程。

关键词：肺肿瘤 miR-367-3p 侵袭转移