(1)      国家自然科学基金委员会面上项目, 中国森林叶面积密度空间分布规律及其对森林固碳能力的影响研究（在研）

(2)      国家重点研发计划子课题, 主要造林树种与珍贵树种低成本高精度检测平台构建（在研）

(3)      国家自然科学基金委员会青年科学基金项目, 基于无人机近地面遥感的红松种群空间格局分析（已结题）

论文专著：

Cheng, K., Chen, Y., Xiang, T., Yang, H., Liu, W., Ren, Y., Guan, H., Hu, T., Ma, Q., Guo, Q., 2023a. 2020 forest age map for China with 30 m resolution. Earth System Science Data Discussions, 1–26.

Cheng, K., Su, Y., Guan, H., Tao, S., Ren, Y., Hu, T., Ma, K., Tang, Y., Guo, Q., 2023b. Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples. ISPRS Journal of Photogrammetry and Remote Sensing 196, 356–371.

Dai, J., Liu, H., Wang, Y., Guo, Q., Hu, T., Quine, T., Green, S., Hartmann, H., Xu, C., Liu, X., 2020. Drought-modulated allometric patterns of trees in semi-arid forests. Communications Biology 3, 405.

Guan, H., Su, Y., Hu, T., Chen, J., Guo, Q., 2019a. An object-based strategy for improving the accuracy of spatiotemporal satellite imagery fusion for vegetation-mapping applications. Remote Sensing 11, 2927.

Guan, H., Su, Y., Hu, T., Wang, R., Ma, Q., Yang, Q., Sun, X., Li, Y., Jin, S., Zhang, J., 2019b. A novel framework to automatically fuse multiplatform LiDAR data in forest environments based on tree locations. IEEE Transactions on Geoscience and Remote Sensing 58, 2165–2177.

Guan, H., Sun, X., Su, Y., Hu, T., Wang, Haitao, Wang, Heping, Peng, C., Guo, Q., 2021. UAV-lidar aids automatic intelligent powerline inspection. International Journal of Electrical Power & Energy Systems 130, 106987.

Guo, Q., Su, Y., Hu, T., Guan, H., Jin, S., Zhang, J., Zhao, X., Xu, K., Wei, D., Kelly, M., 2020. Lidar boosts 3D ecological observations and modelings: A review and perspective. IEEE Geoscience and Remote Sensing Magazine 9, 232–257.

Guo, Q., Su, Y., Hu, T., Zhao, X., Wu, F., Li, Y., Liu, J., Chen, L., Xu, G., Lin, G., 2017. An integrated UAV-borne lidar system for 3D habitat mapping in three forest ecosystems across China. International journal of remote sensing 38, 2954–2972.

Guo, Q.-H., Hu, T.-Y., Ma, Q., Xu, K.-X., Yang, Q.-L., Sun, Q.-H., Li, Y.-M., Su, Y.-J., 2020. Advances for the new remote sensing technology in ecosystem ecology research. Chinese Journal of Plant Ecology 44, 418.

Hu, T., Ma, Q., Su, Y., Battles, J.J., Collins, B.M., Stephens, S.L., Kelly, M., Guo, Q., 2019. A simple and integrated approach for fire severity assessment using bi-temporal airborne LiDAR data. International Journal of Applied Earth Observation and Geoinformation 78, 25–38.

Hu, T., Su, Y., Xue, B., Liu, J., Zhao, X., Fang, J., Guo, Q., 2016. Mapping global forest aboveground biomass with spaceborne LiDAR, optical imagery, and forest inventory data. Remote Sensing 8, 565.

Hu, T., Sun, X., Su, Y., Guan, H., Sun, Q., Kelly, M., Guo, Q., 2020a. Development and performance evaluation of a very low-cost UAV-LiDAR system for forestry applications. Remote Sensing 13, 77.

Hu, T., Wei, D., Su, Y., Wang, X., Zhang, J., Sun, X., Liu, Y., Guo, Q., 2022. Quantifying the shape of urban street trees and evaluating its influence on their aesthetic functions based on mobile lidar data. ISPRS Journal of Photogrammetry and Remote Sensing 184, 203–214.

Hu, T., Zhang, Y., Su, Y., Zheng, Y., Lin, G., Guo, Q., 2020b. Mapping the global mangrove forest aboveground biomass using multisource remote sensing data. Remote sensing 12, 1690.

Hu, T., Zhou, G., 2014. Drivers of lightning-and human-caused fire regimes in the Great Xing’an Mountains. Forest Ecology and Management 329, 49–58.

Jin, S., Su, Y., Gao, S., Hu, T., Liu, J., Guo, Q., 2018a. The transferability of Random Forest in canopy height estimation from multi-source remote sensing data. Remote Sensing 10, 1183.

Jin, S., Su, Y., Gao, S., Wu, F., Ma, Q., Xu, K., Hu, T., Liu, J., Pang, S., Guan, H., 2019. Separating the structural components of maize for field phenotyping using terrestrial LiDAR data and deep convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing 58, 2644–2658.

Jin, S., Su, Y., Song, S., Xu, K., Hu, T., Yang, Q., Wu, F., Xu, G., Ma, Q., Guan, H., 2020a. Non-destructive estimation of field maize biomass using terrestrial lidar: an evaluation from plot level to individual leaf level. Plant Methods 16, 1–19.

Jin, S., Su, Y., Wang, D., 2018b. Deep learning: individual maize segmentation from terrestrial lidar data using faster R-CNN and regional growth algorithms. Frontiers in plant science 9, 365925.

Jin, S., Su, Y., Wu, F., Pang, S., Gao, S., Hu, T., Liu, J., Guo, Q., 2018c. Stem–leaf segmentation and phenotypic trait extraction of individual maize using terrestrial LiDAR data. IEEE Transactions on Geoscience and Remote Sensing 57, 1336–1346.

Jin, S., Su, Y., Zhao, X., Hu, T., Guo, Q., 2020b. 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, 3958–3974.

Li, Y., Su, Y., Hu, T., Xu, G., Guo, Q., 2018. Retrieving 2-D leaf angle distributions for deciduous trees from terrestrial laser scanner data. IEEE Transactions on Geoscience and Remote Sensing 56, 4945–4955.

Li, Y., Su, Y., Zhao, X., Yang, M., Hu, T., Zhang, J., Liu, J., Liu, M., Guo, Q., 2020. Retrieval of tree branch architecture attributes from terrestrial laser scan data using a Laplacian algorithm. Agricultural and Forest Meteorology 284, 107874.

Liu, B.-B., Wei, J.-X., Hu, T.-Y., Yang, Q.-L., Liu, X.-Q., Wu, F.-Y., Su, Y.-J., Guo, Q.-H., 2022. Validation and uncertainty analysis of satellite remote sensing products for monitoring China’s forest ecosystems—Based on massive UAV LiDAR data. Chinese Journal of Plant Ecology 46, 1305.

Liu, X., Ma, Q., Wu, X., Hu, T., Dai, G., Wu, J., Tao, S., Wang, S., Liu, L., Guo, Q., 2022a. Nonscalability of fractal dimension to quantify canopy structural complexity from individual trees to forest stands. Journal of Remote Sensing 2022, 0001.

Liu, X., Ma, Q., Wu, X., Hu, T., Liu, Z., Liu, L., Guo, Q., Su, Y., 2022b. A novel entropy-based method to quantify forest canopy structural complexity from multiplatform lidar point clouds. Remote Sensing of Environment 282, 113280.

Liu, X., Su, Y., Hu, T., Yang, Q., Liu, B., Deng, Y., Tang, H., Tang, Z., Fang, J., Guo, Q., 2022c. Neural network guided interpolation for mapping canopy height of China’s forests by integrating GEDI and ICESat-2 data. Remote Sensing of Environment 269, 112844.

Liu, Z., Jin, S., Liu, X., Yang, Q., Li, Q., Zang, J., Li, Z., Hu, T., Guo, Z., Wu, J., 2023. Extraction of Wheat Spike Phenotypes From Field-Collected Lidar Data and Exploration of Their Relationships With Wheat Yield. IEEE Transactions on Geoscience and Remote Sensing 61, 1–13.

Ma, Q., Su, Y., Hu, T., Jiang, L., Mi, X., Lin, L., Cao, M., Wang, X., Lin, F., Wang, B., 2022. The coordinated impact of forest internal structural complexity and tree species diversity on forest productivity across forest biomes. Fundamental Research.

Ma, Q., Su, Y., Niu, C., Ma, Q., Hu, T., Luo, X., Tai, X., Qiu, T., Zhang, Y., Bales, R.C., 2023. Tree mortality during long-term droughts is lower in structurally complex forest stands. Nature communications 14, 7467.

Ren, Y., Tao, S., Hu, T., Yang, H., Guan, H., Su, Y., Cheng, K., Chen, M., Wan, H., Guo, Q., 2022. The outlook and system construction for monitoring Essential Biodiversity Variables based on remote sensing: The case of China. Biodiversity Science 30, 22530.

Su, Y., Guo, Q., Collins, B.M., Fry, D.L., Hu, T., Kelly, M., 2016a. Forest fuel treatment detection using multi-temporal airborne lidar data and high-resolution aerial imagery: a case study in the Sierra Nevada Mountains, California. International Journal of Remote Sensing 37, 3322–3345.

Su, Y., Guo, Q., Guan, H., Hu, T., Jin, S., Wang, Z., Liu, L., Jiang, L., Guo, K., Xie, Z., 2022. Human‐climate coupled changes in vegetation community complexity of China since 1980s. Earth’s Future 10, e2021EF002553.

Su, Y., Guo, Q., Hu, T., Guan, H., Jin, S., An, S., Chen, X., Guo, K., Hao, Z., Hu, Y., 2020a. An updated vegetation map of China (1: 1000000). Science Bulletin 65, 1125–1136.

Su, Y., Guo, Q., Jin, S., Guan, H., Sun, X., Ma, Q., Hu, T., Wang, R., Li, Y., 2020b. The development and evaluation of a backpack LiDAR system for accurate and efficient forest inventory. IEEE Geoscience and Remote Sensing Letters 18, 1660–1664.

Su, Y., Guo, Q., Xue, B., Hu, T., Alvarez, O., Tao, S., Fang, J., 2016b. Spatial distribution of forest aboveground biomass in China: Estimation through combination of spaceborne lidar, optical imagery, and forest inventory data. Remote Sensing of Environment 173, 187–199.

Su, Y., Hu, T., Wang, Y., Li, Y., Dai, J., Liu, H., Jin, S., Ma, Q., Wu, J., Liu, L., 2020c. Large‐scale geographical variations and climatic controls on crown architecture traits. Journal of Geophysical Research: Biogeosciences 125, e2019JG005306.

Sun, H., Bond‐Lamberty, B., Hu, T., Li, J., Jian, J., Xu, Z., Jia, B., 2023. Forest soil carbon efflux evaluation across China: A new estimate with machine learning. Global Biogeochemical Cycles 37, e2023GB007761.

Sun, Z., Sonsuthi, A., Jucker, T., Ali, A., Cao, M., Liu, F., Cao, G., Hu, T., Ma, Q., Guo, Q., 2023. Top Canopy Height and Stem Size Variation Enhance Aboveground Biomass across Spatial Scales in Seasonal Tropical Forests. Plants 12, 1343.

Wang, B., Fang, S., Wang, Y., Guo, Q., Hu, T., Mi, X., Lin, L., Jin, G., Coomes, D.A., Yuan, Z., 2022. The shift from energy to water limitation in local canopy height from temperate to tropical forests in China. Forests 13, 639.

Xu, K., Su, Y., Liu, J., Hu, T., Jin, S., Ma, Q., Zhai, Q., Wang, R., Zhang, J., Li, Y., 2020. Estimation of degraded grassland aboveground biomass using machine learning methods from terrestrial laser scanning data. Ecological Indicators 108, 105747.

Xue, B., Guo, Q., Hu, T., Xiao, J., Yang, Y., Wang, G., Tao, S., Su, Y., Liu, J., Zhao, X., 2017. Global patterns of woody residence time and its influence on model simulation of aboveground biomass. Global Biogeochemical Cycles 31, 821–835.

Xue, B.-L., Guo, Q., Gong, Y., Hu, T., Liu, J., Ohta, T., 2016. The influence of meteorology and phenology on net ecosystem exchange in an eastern Siberian boreal larch forest. Journal of Plant Ecology 9, 520–530.

Xue, B.-L., Guo, Q., Hu, T., Wang, G., Wang, Y., Tao, S., Su, Y., Liu, J., Zhao, X., 2017. Evaluation of modeled global vegetation carbon dynamics: Analysis based on global carbon flux and above-ground biomass data. Ecological Modelling 355, 84–96.

Yang, Q., Su, Y., Hu, T., Jin, S., Liu, X., Niu, C., Liu, Z., Kelly, M., Wei, J., Guo, Q., 2022. Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes. Forest Ecosystems 9, 100059.

Yang, Q., Su, Y., Jin, S., Kelly, M., Hu, T., Ma, Q., Li, Y., Song, S., Zhang, J., Xu, G., 2019. The influence of vegetation characteristics on individual tree segmentation methods with airborne LiDAR data. Remote Sensing 11, 2880.

Yang, Z., Su, Y., Li, W., Cheng, K., Guan, H., Ren, Y., Hu, T., Xu, G., Guo, Q., 2023. Segmenting Individual Trees From Terrestrial LiDAR Data Using Tree Branch Directivity. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 17, 956–969.

Yi, X., Wang, N., Ren, H., Yu, J., Hu, T., Su, Y., Mi, X., Guo, Q., Ma, K., 2022. From canopy complementarity to asymmetric competition: The negative relationship between structural diversity and productivity during succession. Journal of Ecology 110, 457–465.

Zhao, X., Feng, Y., Xu, K., Cao, M., Hu, S., Yang, Q., Liu, X., Ma, Q., Hu, T., Kelly, M., 2023. Canopy structure: An intermediate factor regulating grassland diversity-function relationships under human disturbances. Fundamental Research 3, 179–187.

Zhao, X., Su, Y., Hu, T., Cao, M., Liu, X., Yang, Q., Guan, H., Liu, L., Guo, Q., 2022. Analysis of UAV lidar information loss and its influence on the estimation accuracy of structural and functional traits in a meadow steppe. Ecological Indicators 135, 108515.

Zhao, X., Su, Y., Hu, T., Chen, L., Gao, S., Wang, R., Jin, S., Guo, Q., 2018a. A global corrected SRTM DEM product for vegetated areas. Remote Sensing Letters 9, 393–402.

Zhao, X., Su, Y., Li, W., Hu, T., Liu, J., Guo, Q., 2018b. A comparison of LiDAR filtering algorithms in vegetated mountain areas. Canadian Journal of Remote Sensing 44, 287–298.

Zhu, J., Qiu, L., Su, Y., Guo, Q., Hu, T., Bao, H., Luo, J., Wu, S., Xu, Q., Wang, Z., 2022. Disentangling the effects of the surrounding environment on street-side greenery: Evidence from Hangzhou. Ecological Indicators 143, 109153.

王嘉童, 牛春跃, 胡天宇, 李文楷, 刘玲莉, 郭庆华, 苏艳军, 2022. 三维辐射传输模型在森林生态系统研究中的应用与展望. 植物生态学报 , 46, 1200–1218.

胡天宇, 周广胜, 贾丙瑞, 2012. 大兴安岭林区 10 小时时滞可燃物湿度的模拟. 生态学报 , 32, 6984–6990.