Developing a sub-meter phenological spectral feature for mapping poplars and willows in urban environment

Xiangcai Li, Jinyan Tian, Xiaojuan Li, Le Wang, Huili Gong, Chen Shi, Sheng Nie, Lin Zhu, Beibei Chen, Yun Pan, Jijun He, Rongguang Ni, Chunyuan Diao

Research output: Contribution to journalArticlepeer-review


Poplar and willow catkins (PWCs) have caused severe impacts on human health and environmental quality, and accurate poplars and willows (PaWs) mapping with remote sensing is essential to monitor and manage the PWCs. However, two major issues have constrained the urban tree species (e.g. PaWs) identification: (1) the urban tree landscapes are highly fragmented and susceptible to the existence of mixed pixels in the remote sensing imagery; (2) the tree species in urban environment are diverse with high spectral similarity. To this end, this study developed a sub-meter phenological spectral feature (Spsf) with multi-scale and multi-temporal remote sensing imagery for monitoring PaWs at the tree species level. Spsf includes three steps: (1) exploring three key phenological periods of PaWs (leafless period, greenleaf period, and senescence period); (2) selecting one or three spectral indexes to characterize each phenological period; (3) stacking the spectral vegetation indexes from Sentinel-2 SR imagery and freely available sub-meter (0.8 m) Google Earth imagery together. Subsequently, Spsf was taken as the input data to train the deep learning DeepLabv3 + model for predicting the PaWs distribution. The Beijing Plain was chosen as the study area, where the distribution of PaWs was extensive and fragmented. Compared with the field survey reference data, the derived PaWs map achieved the overall accuracy higher than 92 % and the Kappa coefficient of 0.83. The Spsf integrated rich spatial information from sub-meter imagery and phenological spectral information from Sentinel-2 imagery, which may alleviate the impacts of mixed pixels and enhance the spectral separability between PaWs and other tree species effectively. The proposed Spsf-based method provides a new paradigm for sub-meter tree species mapping with multi-source free remote sensing data. The PaWs map can serve as reference data for the relevant departments to monitor and manage the PWCs.

Original languageEnglish (US)
Pages (from-to)77-89
Number of pages13
JournalISPRS Journal of Photogrammetry and Remote Sensing
StatePublished - Nov 2022


  • Deep learning
  • Multi-scale
  • Phenology
  • Sub-meter
  • Tree species classification
  • Urban

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics
  • Engineering (miscellaneous)
  • Computer Science Applications
  • Computers in Earth Sciences


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