| [1] |
Yang C, Li MY, Li T, Ren F, Li DP, et al. 2023. Scenic beauty evaluation of forests with autumn-colored leaves from aerial and ground perspectives: a case study in Qixia mountain in Nanjing, China. |
| [2] |
Bai Q, Su S, Lin Z, Leng P, Wang W. 2016. The variation characteristics and blooming phenophase of monoecious Pistacia chinensis Bunge. |
| [3] |
Guo Y, Zhao Z, Zhu F, Gao B. 2023. The impact of global warming on the potential suitable planting area of Pistacia chinensis is limited. |
| [4] |
Li HL, Zhang ZX, Lin SZ, Li XX. 2007. Research advances in the study of Pistacia chinensis Bunge, a superior tree species for biomass energy. |
| [5] |
Kim SL, Chung YS, Silva RR, Ji H, Lee H, et al. 2019. The opening of phenome-assisted selection era in the early seedling stage. |
| [6] |
Krings A. 2011. Pistacia chinensis (Anacardiaceae) naturalized in north Carolina, USA. Journal of the Botanical Research Institute of Texas 5(2):867−869 |
| [7] |
Fan J, Zhang Y, Wen W, Gu S, Lu X, et al. 2021. The future of internet of things in agriculture: plant high-throughput phenotypic platform. |
| [8] |
Zhang M, Xu S, Han Y, Li D, Yang S, et al. 2023. High-throughput horticultural phenomics: the history, recent advances and new prospects. |
| [9] |
Fan L, Yang J, Wang X, Liu Z, Xu B, et al. 2025. Combining UAV multisensor field phenotyping and genome-wide association studies to reveal the genetic basis of plant height in cotton (Gossypium hirsutum). |
| [10] |
Jin S, Su Y, Zhang Y, Song S, Li Q, et al. 2021. Exploring seasonal and circadian rhythms in structural traits of field maize from LiDAR time series. |
| [11] |
Li L, Liu S, Wang Z, Zhao X, Qi J, et al. 2025. Seeing into individual trees: tree-specific retrieval of tree-level traits using 3D radiative transfer model and spatial adjacency constraint from UAV multispectral imagery. |
| [12] |
Li L, Huang H, Mu X, Yan G, Qi J, et al. 2025. Low-altitude UAV-based quantitative remote sensing of vegetation: advances, challenges, and prospects. |
| [13] |
Xie C, Yang C. 2020. A review on plant high-throughput phenotyping traits using UAV-based sensors. |
| [14] |
Zhao X, Qi J, Yu Z, Yuan L, Huang H. 2024. Fine-scale quantification of absorbed photosynthetically active radiation (APAR) in plantation forests with 3D radiative transfer modeling and LiDAR data. |
| [15] |
Jin S, Su Y, Wu F, Pang S, Gao S, et al. 2019. Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data. |
| [16] |
Zhu Y, Sun G, Ding G, Zhou J, Wen M, et al. 2021. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. |
| [17] |
Gao W, Yang X, Cao L, Cao F, Liu H, et al. 2023. Screening of ginkgo individuals with superior growth structural characteristics in different genetic groups using terrestrial laser scanning (TLS) data. |
| [18] |
Chen H, Zhang M, Xiao S, Wang Q, Cai Z, et al. 2024. Quantitative analysis and planting optimization of multi-genotype sugar beet plant types based on 3D plant architecture. |
| [19] |
Liu X, Ma Q, Wu X, Hu T, Liu Z, et al. 2022. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds. |
| [20] |
Bhandari M, Ibrahim AMH, Xue Q, Jung J, Chang A, et al. 2020. Assessing winter wheat foliage disease severity using aerial imagery acquired from small Unmanned Aerial Vehicle (UAV). |
| [21] |
Kerkech M, Hafiane A, Canals R. 2018. Deep leaning approach with colorimetric spaces and vegetation indices for vine diseases detection in UAV images. |
| [22] |
Sankey TT. 2025. UAV hyperspectral-thermal-lidar fusion in phenotyping: genetic trait differences among Fremont cottonwood populations. |
| [23] |
Gu Y, Wang Y, Wu Y, Warner TA, Guo T, et al. 2024. Novel 3D photosynthetic traits derived from the fusion of UAV LiDAR point cloud and multispectral imagery in wheat. |
| [24] |
Zhang W, Qi J, Wan P, Wang H, Xie D, et al. 2016. An easy-to-use airborne LiDAR data filtering method based on cloth simulation. |
| [25] |
Zhang J, Wang J, Dong P, Ma W, Liu Y, et al. 2022. Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN. |
| [26] |
Fu H, Li H, Dong Y, Xu F, Chen F. 2022. Segmenting individual tree from TLS point clouds using improved DBSCAN. |
| [27] |
Gao J, Tang L, Su H, Chen J, Yuan Y. 2025. Extraction of tree branch skeletons from terrestrial LiDAR point clouds. |
| [28] |
Hartley RJL, Jayathunga S, Morgenroth J, Pearse GD. 2024. Tree branch characterisation from point clouds: a comprehensive review. |
| [29] |
Wang K, Pu X, Li B. 2024. Automated phenotypic trait extraction for rice plant using terrestrial laser scanning data. |
| [30] |
Li L, Mu X, Qi J, Pisek J, Roosjen P, et al. 2021. Characterizing reflectance anisotropy of background soil in open-canopy plantations using UAV-based multiangular images. |
| [31] |
Srinivasan S, Popescu S, Eriksson M, Sheridan R, Ku NW. 2015. Terrestrial laser scanning as an effective tool to retrieve tree level height, crown width, and stem diameter. |
| [32] |
Liu F, Song Q, Zhao J, Mao L, Bu H, et al. 2021. Canopy occupation volume as an indicator of canopy photosynthetic capacity. |
| [33] |
Xu B, Wan X, Yang H, Feng H, Fu Y, et al. 2023. TIPS: a three-dimensional phenotypic measurement system for individual maize tassel based on TreeQSM. |
| [34] |
Ustin SL, Gitelson AA, Jacquemoud S, Schaepman M, Asner GP, et al. 2009. Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. |
| [35] |
Gitelson A, Merzlyak MN. 1994. Quantitative estimation of chlorophyll-a using reflectance spectra: experiments with autumn chestnut and maple leaves. |
| [36] |
Gitelson AA, Kaufman YJ, Merzlyak MN. 1996. Use of a green channel in remote sensing of global vegetation from EOS-MODIS. |
| [37] |
Gitelson AA. 2004. Wide dynamic range vegetation index for remote quantification of biophysical characteristics of vegetation. |
| [38] |
Gitelson AA, Kaufman YJ, Stark R, Rundquist D. 2002. Novel algorithms for remote estimation of vegetation fraction. |
| [39] |
Gamon JA, Surfus JS. 1999. Assessing leaf pigment content and activity with a reflectometer. |
| [40] |
Thiel D, Kreyling J, Backhaus S, Beierkuhnlein C, Buhk C, et al. 2014. Different reactions of central and marginal provenances of Fagus sylvatica to experimental drought. |
| [41] |
Sims DA, Gamon JA. 2002. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. |
| [42] |
An N, Welch SM, Cody Markelz RJ, Baker RL, Palmer CM, et al. 2017. Quantifying time-series of leaf morphology using 2D and 3D photogrammetry methods for high-throughput plant phenotyping. |
| [43] |
Liao L, Cao L, Xie Y, Luo J, Wang G. 2022. Phenotypic traits extraction and genetic characteristics assessment of Eucalyptus trials based on UAV-borne LiDAR and RGB images. |
| [44] |
Sinaga KP, Yang MS. 2020. Unsupervised K-means clustering algorithm. |
| [45] |
Lambeth CC. 1980. Juvenile-mature correlations in Pinaceae and implications for early selection. |
| [46] |
Isik K, Kleinschmit J, Steiner W. 2010. Age–age correlations and early selection for height in a clonal genetic test of Norway spruce. |