PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Digital landscape architecture design using high resolution remote sensing image interpretation

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
With the continuous development of remote sensing technology and the widespread application of high-resolution remote sensing images, digital landscape design based on high-resolution remote sensing image interpretation is gradually becoming a new design concept and method. This study is based on high-resolution remote sensing images to classify gardens, and combined with ground survey data, statistical analysis software is used to invert the landscape elements of gardens. The experimental results showed that the R2 value was relatively large, while the MRE and RMSE values were small, indicating that the analysis results were close to the true values and the fitting effect was relatively ideal. The overall image segmentation was excellent, with an average diameter at breast height of 8.0-17.0 cm, mixing degree of 0.4-0.6, vertical diversity of 0.5-0.8, and a clear forest hierarchy when the average density was between 800-1100 plants/hm2. This indicates that the quality of landscape architecture designed at this landscape scale changes significantly and the effect is good. Digital landscape design based on high-resolution remote sensing image interpretation can not only improve design efficiency and accuracy, but also provide strong support for the sustainable development of urban planning and landscape design.
Rocznik
Strony
99--112
Opis fizyczny
Bibliogr. 21 poz., il., tab.
Twórcy
autor
  • Luxun Academy of Fine Arts, College of Architectural Art Design, Shenyang, China
autor
  • Luxun Academy of Fine Arts, Experimental Art Department, Shenyang, China
  • Luxun Academy of Fine Arts, College of Industrial Design, Shenyang, China
Bibliografia
  • [1] D.U.G. Sekban and C. Acar, “Determining usages in post-mining sites according to landscape design approaches”, Land Degradation and Development, vol. 32, no. 8, pp. 2661-2676, 2021, doi: 10.1002/ldr.3933.
  • [2] S.Y. Cao and X.J. Hu, “Dynamic prediction of urban landscape pattern based on remote sensing image fusion”, International Journal of Environmental Technology and Management, vol. 24, no. 1/2, pp. 18-32, 2021, doi: 10.1504/IJETM.2021.115726.
  • [3] H. Zhou and Z. Dai, “Green urban garden landscape simulation platform based on high-resolution image recognition technology and GIS”, Microprocessors and Microsystems, vol. 82, no. 4, art. no. 103893, 2021, doi: 10.1016/j.micpro.2021.103893.
  • [4] S.H. Lee, K.J. Han, K. Lee, K.J. Lee, and M.J. Lee, “Classification of landscape affected by deforestation using high-resolution remote sensing data and deep-learning techniques”, Remote Sensing, vol. 12, no. 20, art. no. 3372, 2020, doi: 10.3390/rs12203372.
  • [5] T. Hu and W. Gong, “Urban landscape information atlas and model system based on remote sensing images”, Mobile Information Systems, vol. 2021, art. no. 9613102, 2021, doi: 10.1155/2021/9613102.
  • [6] S. Liu, “Spatial distribution characteristics of urban landscape pattern based on multi-source remote sensing technology”, International Journal of Environmental Technology and Management, vol. 24, no. 1/2, pp. 33-48, 2021, doi: 10.1504/IJETM.2021.115727.
  • [7] Z. Li, X. Han, L. Wang, T. Zhu, and F. Yuan, “Feature extraction and image retrieval of landscape images based on image processing”, Traitement Du Signal: Signal Image Parole, vol. 37, no. 6, pp. 1009-1018, 2020, doi: 10.18280/ts.370613.
  • [8] J. Dong, H. Jiang, T. Gu, et al., “Sustainable landscape pattern: a landscape approach to serving spatial planning”, Landscape Ecology, vol. 37 no. 1, pp. 31-42, 2022, doi: 10.1007/s10980-021-01329-0.
  • [9] F. Fu, S. Deng, D. Wu, W. Liu, and Z. Bai, “Research on the spatiotemporal evolution of land use landscape pattern in a county area based on CA-Markov model”, Sustainable Cities and Society, vol. 80, art. no. 103760, 2022, doi: 10.1016/j.scs.2022.103760.
  • [10] B. Sowińska-Świerkosz and M. Michalik-Śnieżek, “The methodology of landscape quality (LQ) indicators analysis based on remote sensing data: Polish national parks case study”, Sustainability, vol. 12, no. 7, pp. 1-18, 2020, doi: 10.3390/su12072810.
  • [11] Z.M. Miller, J. Hupy, A. Chandrasekaran, G. Shao, and S. Fei, “Application of post processing kinematic methods with UAS remote sensing in forest ecosystems”, Journal of Forestry, vol. 119, no. 5, pp. 454-466, 2021, doi: 10.1093/jofore/fvab021.
  • [12] C. Mulverhill, N.C. Coops, T. Hermosilla, J.C. White, and M.A. Wulder, “Evaluating ICESat-2 for monitoring, modeling, and update of large area forest canopy height products”, Remote Sensing of Environment, vol. 271, no. 3, art. no. 112919, 2022, doi: 10.1016/j.rse.2022.112919.
  • [13] F. Zhang, X. Li, et al., “Retrieval of soil salinity based on multi-source remote sensing data and differential transformation technology”, International Journal of Remote Sensing, vol. 44, no. 3, pp. 1348-1368, 2023, doi: 10.1080/01431161.2023.2179900.
  • [14] Q. Tan, B. Guo, J. Hu, X. Dong, and J. Hu, “Object-oriented remote sensing image information extraction method based on multi-classifier combination and deep learning algorithm”, Pattern Recognition Letters, vol. 141, no. 1, pp. 32-36, 2021, doi: 10.1016/j.patrec.2020.08.028.
  • [15] T. Ni, X. Han, B. He, X. Li, and G. Bi, “Ship detection in panchromatic optical remote sensing images based on visual saliency and multi-dimensional feature description”, Remote Sensing, vol. 12, no. 1, art. no. 152, 2020, doi: 10.3390/rs12010152.
  • [16] C.A. Bouman and M. Shapiro, “A multiscale random field model for Bayesian image segmentation”, IEEE Transactions on Image Processing, vol. 3, no. 2, pp. 162-177, 1994, doi: 10.1109/83.277898.
  • [17] X. Pan, C. Zhang, J. Xu, and J. Zhao, “Simplified object-based deep neural network for very high resolution remote sensing image classification”, ISPRS Journal of Photogrammetry and Remote Sensing, vol. 181, no. 11, pp. 218-237, 2021, doi: 10.1016/j.isprsjprs.2021.09.014.
  • [18] A. Islam, F. Othman, Sakib N. and H.M.H. Babu, “Prevention of shoulder-surfing attack using shifting condition with the digraph substitution rules”, Artificial Intelligence and Applications, vol. 1, no. 1, pp. 58-68, 2023, doi: 10.47852/bonviewAIA2202289.
  • [19] J. Margielewicz, D. Gąska, G. Litak, et al., “Influence of the configuration of elastic and dissipative elements on the energy harvesting efficiency of a tunnel effect energy harvester”, Chaos, Solitons & Fractals, vol. 167, no. 2, art. no. 113060, 2023, doi: 10.1016/j.chaos.2022.113060.
  • [20] H.E. Walter, J. Pagel, H. Cooksley, et al., “Effects of biotic interactions on plant fecundity depend on spatial and functional structure of communities and time since disturbance”, Journal of Ecology, vol. 111, no. 1, pp. 110-124, 2023, doi: 10.1111/1365-2745.14018.
  • [21] M.E. LeFevre, D.J. Churchill, A.J. Larson, et al., “Evaluating restoration treatment effectiveness through a comparison of residual composition, structure, and spatial pattern with historical reference sites”, Forest Science, vol. 66, no. 5, pp. 578-588, 2020, doi: 10.1093/forsci/fxaa014.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-83181ebe-7162-4687-84ec-45c2a43c0280
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.