时间过得好快，上次写林业定量遥感进展还是2022年的元月份（交叉融合 开拓创新 持续推动林业定量遥感），一晃都进入2025年的深秋时节了。当然，期间也写了一点别的，关于AI的到来对定量遥感的改变思考。不过，毕竟主题不同，心里总是过意不去，还是插空写点东西纪念一下。

  国庆节的时候提笔开了个头，又就搁置了。今天终于忙完大讲堂的事情，继续我的思考了。从哪里开头呢？还是从成长的角度来吧。随着年龄和阅历的增加，我感觉科研上有3大显著变化。

  一是团队越来越大，事情越来越多，自己不得不从科研的单点作业转向系统的团队规划和管理。前几年还自己写程序，维护RAPID代码。现在只能想想而已，实在没有整块的时间去思考技术细节。同时，我还发现一个很有意思的现象，那就是每当自己沉迷于某个算法、论文，并亲自下场去干的时候，多半会忘了其他重要的事情而出现差池。慢慢地，我开始有转变了，逼迫自己去布局，比如给出几个可能研究生长点，给团队老师布置几个事情，给学生一些情绪价值，多和老师同学聊天等。所以，沟通成了现在很重要的部分。记得抖音有段时间老推送管理的本质，比如学会定目标、抓过程、重考核等，还有高情商说话什么的。还有什么阿里管理七剑。虽然学不会，潜移默化的也开始从简单的模仿转向领悟后的自觉。我必须承认自己角色变了，不是带几个学生的问题，而是带若干老师和几十个学生的问题。大家都看着你怎么发展。行政管理和团队管理本质上类似，可以相互借鉴。

  二是论文越来越卷，项目越来越难，自己要费很大心力在坚守阵地和拓展资源上平衡。论文的风向上从追求行业顶刊转向了NCS，何去何从呢？做辐射传输模型太小众，但是像几位师弟那样走出小圈让更多人理解，能发子刊就很成功，也是一条路子。纯粹的辐射传输，受到AI冲击很大，申请重点项目太难。当前，研究项目要强调服务国家战略，个人感兴趣的科研也可以做，但是也要面向国家需求。学院里面很多老师未能适用这种变化，以传统的惯性在申请，转弯太慢。如何守正创新，既发挥研究基础优势，又能融入新技术新方法，才有可能拓展方向获得资源。不仅要考虑国家项目，还是考虑和地方合作、企业合作，才能渡过难关，也有可能趟出新路。

  三是科研越来越“极"，交叉合作越来越重要，自己要为团队谋划出有前瞻性的方向和国内外合作。遥感是适合极宏观拓展的，但也有和极微观的基因结合做表型。林业的极端条件是什么？复杂地形已经考虑过了，透视地球项目也有，深时地球等大科学计划也正在开展，那么林业遥感要服务什么极端条件的监测呢？这个可能就是突破口。

  实际上过去3年，成果还是不错的，部分论文见附录。但是感觉还差得很远，还要突破。林业定量遥感团队的下一步发展思路就是智能化、多源化和即时化。

  1.智能化：就是要将三维辐射传输模型变得更加逼真、快速，更加适合AI学习。反之，用AI加速辐射传输模型的场景构建和参数设置。在此基础上，要解决林业应用场景中的关键问题，不能停留在模型本身。比如森林生长问题，碳汇问题，多样性问题等。

  2.多源化：就是发展无人机、地面平台的一些监测装备等，为极端条件下的森林监测和作业提供“眼睛”和“触手”。普通的研究，如平替人工一般很难出彩，“极陡坡”“极海拔”“极大面积”“极密闭树冠”“极难到达”“极危险”等都则可能重点关注的条件，需要逐一去解决。

  3.即时化：就是解决重大突发事件的即时发现和决策支持。目前，即时的主体实现是卫星智能星座，理论基础则是背后的知识，而且是极其庞大又极其浓缩的知识。火灾监测是相对成熟，但是其他突发的林区灾害如何识别快？如何知道是什么类型的灾害？干旱、采伐、病虫害、风倒等都很难区分。因此，还有很多需要进一步研究。

  以“三化”为引导，推动整个团队做重要的事情，做系统的工作，做完整的链条管理。这样成果应该也就水到渠成了吧。

附录几篇近3年的论文，仅供参考：

Gao, G., Qi, J., Lin, S., Hu, R., Huang, H., 2023. Estimating plant area density of individual trees from discrete airborne laser scanning data using intensity information and path length distribution. International Journal of Applied Earth Observation and Geoinformation 118, 103281. https://doi.org/10.1016/j.jag.2023.103281

He, S., Qi, J., Wang, D., Yan, K., Huang, H., 2024. Estimation of canopy photon recollision probability from airborne laser scanning. REMOTE SENSING OF ENVIRONMENT 311. https://doi.org/10.1016/j.rse.2024.114264

Huang, H., Qi, J., Li, L., 2023. Enhanced Branch Simulation to Improve RAPID in Optical Region Using RAMI Scenes. JOURNAL OF REMOTE SENSING 3. https://doi.org/10.34133/remotesensing.0039

Li, L., Liu, Shangbo, Wang, Z., Zhao, X., Qi, J., Zeng, Y., Li, D., Guo, P., Yu, Z., Lin, S., Liu, Shouyang, Huang, H., 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. Remote Sensing of Environment 318, 114616. https://doi.org/10.1016/j.rse.2025.114616

Li, L., Mu, X., Chianucci, F., Qi, J., Jiang, J., Zhou, J., Chen, L., Huang, H., Yan, G., Liu, S., 2022. Ultrahigh-resolution boreal forest canopy mapping: Combining UAV imagery and photogrammetric point clouds in a deep-learning-based approach. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 107. https://doi.org/10.1016/j.jag.2022.102686

Li, L., Mu, X., Jiang, H., Chianucci, F., Hu, R., Song, W., Qi, J., Liu, S., Zhou, J., Chen, L., Huang, H., Yan, G., 2023. Review of ground and aerial methods for vegetation cover fraction (fCover) and related quantities estimation: definitions, advances, challenges, and future perspectives. ISPRS Journal of Photogrammetry and Remote Sensing 199, 133–156. https://doi.org/10.1016/j.isprsjprs.2023.03.020

Li, X., Li, L., Ni, W., Mu, X., Wu, X., Laurin, G.V., Vangi, E., Sterenczak, K., Chirici, G., Yu, S., Huang, H., 2024. Validating GEDI tree canopy cover product across forest types using co-registered aerial LiDAR data (vol 207, pg 326, 2024). ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING 209, 35–36. https://doi.org/10.1016/j.isprsjprs.2024.01.019

Li, X., Shabanov, N., V., Chen, L., Zhang, Y., Huang, H., 2022. Modeling solar-induced fluorescence of forest with heterogeneous distribution of damaged foliage by extending the stochastic radiative transfer theory. REMOTE SENSING OF ENVIRONMENT 271. https://doi.org/10.1016/j.rse.2022.112892

Lin, S., He, Z., Huang, H., Chen, L., Li, L., 2022. Mixed forest specific calibration of the 3-PGmix model parameters from site observations to predict post-fire forest regrowth. FOREST ECOLOGY AND MANAGEMENT 515. https://doi.org/10.1016/j.foreco.2022.120208

Lin, S., Li, L., Liu, S., Gao, G., Zhao, X., Chen, L., Qi, J., Shen, Q., Huang, H., 2024a. Stratified burn severity assessment by integrating spaceborne spectral and waveform attributes in Great Xing’an Mountain. REMOTE SENSING OF ENVIRONMENT 307. https://doi.org/10.1016/j.rse.2024.114152

Lin, S., Li, L., Liu, S., Yang, S., Lin, D., Zhao, X., Chen, L., Huang, H., 2024b. Predicting post-fire forest recovery using the 3-PG model with bi-temporal Landsat imagery in high-severity burned areas of Great Xing’an Mountain. FOREST ECOLOGY AND MANAGEMENT 563. https://doi.org/10.1016/j.foreco.2024.121991

Qi, J., Jiang, J., Zhou, K., Xie, D., Huang, H., 2023. Fast and Accurate Simulation of Canopy Reflectance under Wavelength-Dependent Optical Properties Using a Semi-Empirical 3D Radiative Transfer Model. JOURNAL OF REMOTE SENSING 3. https://doi.org/10.34133/remotesensing.0017

Qi, J., Xie, D., Jiang, J., Huang, H., 2022. 3D radiative transfer modeling of structurally complex forest canopies through a lightweight boundary-based description of leaf clusters. REMOTE SENSING OF ENVIRONMENT 283. https://doi.org/10.1016/j.rse.2022.113301

Yu, Z., Qi, J., Liu, S., Zhao, X., Huang, H., 2024a. Evaluating forest aboveground biomass estimation model using simulated ALS point cloud from an individual-based forest model and 3D radiative transfer model across continents. JOURNAL OF ENVIRONMENTAL MANAGEMENT 372. https://doi.org/10.1016/j.jenvman.2024.123287

Yu, Z., Qi, J., Zhao, X., Huang, H., 2024b. Evaluating the reliability of bi-temporal canopy height model generated from airborne laser scanning for monitoring forest growth in boreal forest region. INTERNATIONAL JOURNAL OF DIGITAL EARTH 17. https://doi.org/10.1080/17538947.2024.2345725

Zhao, X., Qi, J., Jiang, J., Liu, S., Xu, H., Lin, S., Yu, Z., Li, L., Huang, H., 2024a. Fine-scale retrieval of leaf chlorophyll content using a semi-empirically accelerated 3D radiative transfer model. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 135. https://doi.org/10.1016/j.jag.2024.104285

Zhao, X., Qi, J., Xu, H., Yu, Z., Yuan, L., Chen, Y., Huang, H., 2023. Evaluating the potential of airborne hyperspectral LiDAR for assessing forest insects and diseases with 3D Radiative Transfer Modeling. Remote Sensing of Environment 297, 113759. https://doi.org/10.1016/j.rse.2023.113759

Zhao, X., Qi, J., Yu, Z., Yuan, L., Huang, H., 2024b. Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data. PLANT PHENOMICS 6. https://doi.org/10.34133/plantphenomics.0166