References

  1. J.L. Lovell, D.L.B. Jupp, D.S. Culvenor, N.C. Coops, Using airborne and ground-based ranging LiDAR to measure canopy structure in Australian forests, Can. J. Remote Sens., 29 (2003) 607–622.
  2. J.L. Lovell, D.L.B. Jupp, G.J. Newnham, D.S. Culvenor, Measuring tree stem diameters using intensity profiles from ground-based scanning LiDAR from a fixed viewpoint, ISPRS J. Photogramm. Remote Sens., 66 (2011) 46–55.
  3. A.H. Strahler, D.L.B. Jupp, C.E. Woodcock, C.B. Schaaf, T. Yao, F. Zhao, X.Y. Yang, J. Lovell, D. Culvenor, G. Newnham, W. Ni-Miester, W. Boykin-Morris, Retrieval of forest structural parameters using a ground-based LiDAR instrument (Echidna®), Can. J. Remote Sens., 34 (2008) S426–S440.
  4. B. Koetz, F. Morsdorf, G. Sun, K.J. Ranson, K. Itten, B. Allgower, Inversion of a LiDAR waveform model for forest biophysical parameter estimation, IEEE Geosci. Remote Sens. Lett., 3 (2006) 49–53.
  5. G. Zheng, L.M. Moskal, S.-H. Kim, Retrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning, IEEE Trans. Geosci. Remote Sens., 51 (2013) 777–786.
  6. C.Y. Zhang, Y.H. Zhou, F. Qiu, Individual tree segmentation from LiDAR point clouds for urban forest inventory, Remote Sens., 7 (2015) 7892–7913.
  7. L.M. Moskal, G. Zheng, Retrieving forest inventory variables with terrestrial laser scanning (TLS) in urban heterogeneous forest, Remote Sens., 4 (2012) 1–20.
  8. L.H. Jing, B.X. Hu, J.L. Li, T. Noland, Automated delineation of individual tree crowns from LiDAR data by multi-scale analysis and segmentation, Photogramm. Eng. Remote Sens., 78 (2012) 1275–1284.
  9. Z.Y. Zhang, A. Kazakova, L.M. Moskal, D.M. Styers, Objectbased tree species classification in urban ecosystems using LiDAR and hyperspectral data, Forests, 7 (2016) 122.
  10. R.O. Dubayah, S.L. Sheldon, D.B. Clark, M.A. Hofton, J.B. Blair, G.C. Hurtt, R.L. Chazdon, Estimation of tropical forest height and biomass dynamics using LiDAR remote sensing at La Selva, Costa Rica, J. Geophys. Res. G: Biogeosci., 115 (2010) 272–281.
  11. M.A. Lefsky, W.B. Cohen, T.A. Spies, An evaluation of alternate remote sensing products for forest inventory, monitoring, and mapping of Douglas-fir forests in western Oregon, Can. J. For. Res., 31 (2001) 78–87.
  12. Z.S. Wang, C.B. Schaaf, P. Lewis, Y. Knyazikhin, M.A. Schull, A.H. Strahler, R.B. Myneni, M.J. Chopping, B.J. Blair, Retrieval of canopy height using moderate-resolution imaging spectroradiometer (MODIS) data, Remote Sens. Environ., 115 (2011) 1595–1601.
  13. C. Alexander, A.H. Korstjens, R.A. Hill, Influence of microtopography and crown characteristics on tree height estimations in tropical forests based on LiDAR canopy height models, Int. J. Appl. Earth Obs. Geoinf., 65 (2018) 105–113.
  14. M.W. He, H.J. Zhao, F. Davoine, J.S. Cui, H.B. Zha, Pairwise LiDAR Calibration Using Multi-Type 3D Geometric Features in Natural Scene, IEEE/RSJ International Conference on Intelligent Robots and Systems, Tokyo, Japan, 3–7 November 2013.
  15. J.-H. Song, S.-H. Han, K. Yu, Y.-I. Kim, Assessing the possibility of land-cover classification using LiDAR intensity data, Int. Soc. Photogramm. Remote Sens., 34 (2012) 9–13.
  16. C.X. Cao, M. Xu, Y.F. Bao, H. Zhang, Synchronous Retrieval of Forest Canopy Cover by Airborn LiDAR and Optical Remote Sensing, IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010.
  17. I. Fayad, N. Baghdadi, J.S. Bailly, N. Barbier, V. Gond, B. Hérault, M. El Hajj, J. Lochard, J. Perrin, Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 26-31 July 2015.
  18. X.Y. Wang, D.H. Xie, G.J. Yan, W.M. Zhang, Y. Wang, Y.M. Chen, 3D Reconstruction of a Single Tree From Terrestrial LiDAR Data, IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014, pp. 796–799.
  19. H.B. Xiang, C.X. Cao, H.C. Jia, M. Xu, R.B. Myneni, The Analysis on the Accuracy of DEM Retrieval by the Ground LiDAR Point Cloud Data Extraction Methods in Mountain Forest Areas, IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012, pp. 6067–6070.
  20. C. Paris, L. Bruzzone, A growth-model-driven technique for tree stem diameter estimation by using airborne LiDAR data, IEEE Trans. Geosci. Remote Sens., 57 (2019) 76–92.
  21. J.F. Wu, H.L. Wang, N. Li, P. Yao, Y. Huang, Z.K. Su, Y. Yu, Distributed trajectory optimization for multiple solar-powered UAVs target tracking in urban environment by Adaptive Grasshopper Optimization Algorithm, Aerosp. Sci. Technol., 70 (2017) 497–510.
  22. Y. Yu, H.L. Wang, X.L. Shao, Y. Huang, The Attitude Control of UAV in Carrier Landing Based on ADRC, IEEE Chinese Guidance, Navigation and Control Conference, Nanjing, China, 12–14 Aug 2016, pp. 832–837.
  23. Z.B. Zhou, L. Yang, Y. Li, An Adaptive Dual Kalman Filtering Algorithm for Locata/GPS/INS Integrated Navigation, 4th China Satellite Navigation Conference (CSNC) 2013 Proceedings, Wuhan, China, 13–17 May 2013, pp. 527–541.
  24. M.-J. Yu, INS/GPS integration system using adaptive filter for estimating measurement noise variance, IEEE Trans. Aerosp. Electron. Syst., 48 (2012) 1786–1792.
  25. J.B. Wu, An Innovative Neural-Fuzzy Adaptive Kalman Filter for Ultra-Tightly Coupled GPS/INS Integrated System, Beijing Institute of Remote Sensing Equipment, Beijing, China, 2017.
  26. R. Babu, J.L. Wang, Ultra-tight GPS/INS/PL integration: a system concept and performance analysis, GPS Solutions, 13 (2009) 75–82.
  27. L. Zhao, H.Y. Qiu, Y.M. Feng, Analysis of a robust Kalman filter in loosely coupled GPS/INS navigation system, Measurement, 80 (2016) 138–147.
  28. G.Q. Wang, Y. Han, J. Chen, S.B. Wang, Z.C. Zhang, N. Du, Y.J. Zheng, A GNSS/INS integrated navigation algorithm based on Kalman filter, IFAC-PapersOnLine, 51 (2018) 232–237.
  29. N.C. Yadav, A. Shanmukha, B.M. Amruth, Basavaraj, Development of GPS/INS Integration Module Using Kalman Filter, International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies, Chennai, India, 16–18 February 2017.
  30. Y. Zhou, C. Zhang, Y.F. Zhang, J.Z. Zhang, A new adaptive square-root unscented Kalman filter for nonlinear systems with additive noise, Int. J. Aerosp. Eng., 2015 (2015), https://doi. org/10.1155/2015/381478.
  31. O. Derbel, M.L. Cherif, R. Landry Jr., Driver Behavior Assessment Based on Loosely Coupled GPS/INS Integration in Harsh Environment, IEEE/ION Position, Location and Navigation Symposium, Monterey, CA, USA, 23–26 April. 2018, pp. 1362–1367.
  32. S.H. Oh, D.-H. Hwang, Low-cost and high performance ultratightly coupled GPS/INS integrated navigation method, Adv. Space Res., 60 (2017) 2691–2706.
  33. R. Chen, K-Means Aided Kalman Filter Noise Estimation Calibration for Integrated GPS/INS Navigation, IEEE International Conference on Intelligent Transportation Engineering, Singapore, 20–22 August 2016, pp. 156–161.
  34. I.-U. Lee, H. Li, N.-M. Hoang, J.-M. Lee, Navigation System Development of the Underwater Vehicles Using the GPS/ INS Sensor Fusion, 14th International Conference on Control, Automation and Systems, Seoul, South Korea, 22–25 October 2014, pp. 610–612.
  35. C. Liu, H.L. Wang, N. Li, Y. Yu, Sensor Fault Diagnosis of GPS/INS Tightly Coupled Navigation System Based on State Chi-square Test and Improved Simplified Fuzzy ARTMAP Neural Network, IEEE International Conference on Robotics and Biomimetics (ROBIO), Macau, China, 5–8 December 2017, pp. 2527–2532.
  36. R. Eberhart, J. Kennedy, A New Optimizer Using Particle Swarm Theory, Proceedings of the Sixth International Symposium on Micro Machine and Human Science, Nagoya, Japan, Japan, 4–6 October 1995, pp. 39–43.
  37. O.P. Rahi, A.K. Chandel, M.G. Sharma, Optimization of Hydro Power Plant Design by Particle Swarm Optimization (PSO), Procedia Eng., 30 (2012) 418–425.
  38. A. Jaini, I. Musirin, N. Aminudin, M.M. Othman, T.K.A. Rahman, Particle Swarm Optimization (PSO) Technique in Economic Power Dispatch Problems, 4th International Power Engineering and Optimization Conference, Shah Alam, Malaysia, 23–24 June 2010, pp. 308–312.
  39. K.M. Gharaibeh, A. Yaqot, Target Classification in Wireless Sensor Network Using Particle Swarm Optimization (PSO), 2012 IEEE Sensors Applications Symposium Proceedings, Brescia, Italy, 7–9 February 2012.
  40. Y. Marinakis, M. Marinaki, G. Dounias, A hybrid particle swarm optimization algorithm for the vehicle routing problem, Eng. Appl. Artif. Intell., 23 (2010) 463–472.
  41. S.H. Cai, M.L. Li, Cost optimization problem of hybrid flowshop based on PSO algorithm, Adv. Mater. Res., 532 (2012) 1616–1620.
  42. L.D. Li, X.H. Yu, X.D. Li, W. Guo, A Modified PSO Algorithm for Constrained Multi-objective Optimization, 2009 Third International Conference on Network and System Security, Gold Coast, QLD, Australia, 19–21 October 2009, pp. 462–467.
  43. C.-A. Liu, “New Dynamic Constrained Optimization PSO Algorithm, 2008 Fourth International Conference on Natural Computation, Jinan, China, 18–20 October 2008, pp. 650–653.
  44. J.H. Qu, Z.Z. Shao, X.Y. Liu, PSO clustering algorithm based on cooperative evolution, J. Donghua Univ. (English Edition), 27 (2010) 285–288.
  45. C.M. Gu, Y. Liu, D.B. Liu, Z.G. Li, I. Mohamed, R.H. Zhang, M. Brooks, F. Chen, Distribution and ecological assessment of heavy metals in irrigation channel sediments in a typical rural area of south China, Ecol. Eng., 90 (2016) 466–472.
  46. M.B. Huang, T.H. Dang, J. Gallichand, M. Goulet, Effect of increased fertilizer applications to wheat crop on soil-water depletion in the loess plateau, china, Agric. Water Manage., 58 (2003) 267–278.
  47. F.-M. Li, P. Wang, J. Wang, J.-Z. Xu, Effects of irrigation before sowing and plastic film mulching on yield and water uptake of spring wheat in semiarid Loess Plateau of China, Agric. Water Manage., 67 (2004) 77–88.
  48. S. Kaneko, K. Tanaka, T. Toyota, S. Managi, Water efficiency of agricultural production in china: regional comparison from 1999 to 2002, Int. J. Agric. Resour. Governance Ecol., 3 (2004) 231–251.
  49. P.C. Phondani, A. Bhatt, E. Elsarrag, Y.M. Alhorr, A. El-Keblawy, Criteria and indicator approach of global sustainability assessment system for sustainable landscaping using native plants in Qatar, Ecol. Indic., 69 (2016) 381–389.
  50. B. Das, A. Singh, S.N. Panda, H. Yasuda, Optimal land and water resources allocation policies for sustainable irrigated agriculture, Land Use Policy, 42 (2015) 527–537.
  51. Z. Wang, D. Zerihun, J. Feyen, General irrigation efficiency for field water management, Agric. Water Manage., 30 (1996) 123–132.
  52. H. Ali, L.T. Shui, K.C. Yan, A.F. Eloubaidy, K.C. Foong, Modeling water balance components and irrigation efficiencies in relation to water requirements for double-cropping systems, Agric. Water Manage., 46 (2000) 167–182.
  53. C. Qian, W.Y. Lin, J.H. Chen, Z. Li, H.J. Gao, Point Cloud Trajectory Planning Based on Octree and K-dimensional Tree Algorithm, 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC), Wuhan, China, 11–13 November 2016, pp. 213–218.
  54. Z.C. Zhang, Y. Han, J. Chen, S.B. Wang, G.Q. Wang, N.N. Du, Information extraction of ecological canal system based on remote sensing data of unmanned aerial vehicle, J. Drain. Irrig. Mach. Eng., 36 (2018) 1006–1011.
  55. S.B. Wang, Y. Han, J. Chen, Y. Pan, Y. Cao, H. Meng, Weed classification of remote sensing ecological irrigation area by UAV based on deep learning, J. Drain. Irrig. Mach. Eng., 11 (2018) 1137–1141.