PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING
November 2018
679
Special Issue Foreword
Remote Sensing of Urban Environment (I)
The Guest Editors: Xin Huang, Michael Ying Yang, and Rongjun Qin
With the continuous advance of urbanization, some severe ur-
ban environmental problems arise, such as urban congestion,
air pollution, vegetation loss, lake shrinkage, urban heat is-
land, land degradation, etc (Xie
et al.,
2017; Yang
et al.
, 2017).
Timely and accurate information about urban environment is
essential for urban planning and management. Remote sens-
ing technologies can help to monitor urban environment with
up-to-date spatial information. In this special issue, ten papers
focusing on application of remote sensing to urban environ-
ment are presented. The first five papers, addressing the urban
remote sensing from the image processing methods point of
view, are selected and published in the Special Issue (I), and
the remaining ones are to appear in the Issue (II).
Within this foreword, we would like to conduct a summary
about the content of the five papers in the Special Issue (I).
The topics of data analysis and image processing in this issue,
include image segmentation (Ming
et al.
, 2018), spatial feature
extraction (Liang and Weng, 2018), high-spatial-resolution im-
age classification (Simsek and Sertel, 2018), scene-based classi-
fication (Huang
et al.
, 2018), and change detection (Kun
et al.
,
2018). Based on these fundamental techniques and methods,
diversity of tasks and applications such as urban landscape,
land cover/use mapping, change detection, and urban scene
understanding can be tackled to help monitoring urban envi-
ronment. Among the papers of the special issue, four out of five
contributions (Kun
et al.
, 2018; Huang
et al.
, 2018; Ming
et al.
,
2018; Simsek and Sertel, 2018) considered very high-resolu-
tion imagery, which indicates that subtle monitoring of urban
environment have attracted much attention for its capabilities
in exploiting rich spatial details.
The five articles are briefly reviewed below: Ming
et al.
discuss
the coupling relationship between image segmentation and
classification accuracy, providing guidance for parameters tun-
ing in GEOBIA (Geographic Object-Based Image Analysis). Li-
ang
et al.
assess the potential of integrating fractal texture with
spectral information for urban landscape characterization. The
fractal texture derived from a Landsat image was employed
and its performances with different windows sizes were eval-
uated. Simsek and Sertel compare landscape metrics of two
different cities by using SPOT 6/7 images-derived urban land
cover/use maps produced by the object-based classification ap-
proach. They conduct classification by using thematic layers
from OpenStreetMap, spectral indices, objects-based textural
features. Huang
et al.
propose a framework to map tea gardens
including three scene-based methods: bag-of-visual-words
(BOVW) model, supervised latent Dirichlet allocation (sLDA),
and unsupervised convolutional neural network (UCNN).
Tan
et al.
present a heterogeneous ensemble algorithm which
combing stacked generalization system with image segmenta-
tion. They demonstrate that their approach can integrate the
advantages of both pixel-wise ensemble and object-oriented
methods and improve the performance of change detection.
Finally, we would like to thank all the authors and reviewers
for their efforts and contribution to this special issue.
References
Tan, k., Y. Zhang, Q. Du, P. Du, X. Jin, and J. Li, 2018. Change detec-
tion based on stacked generalization system with segmentation
constraint,
Photogrammetric Engineering & Remote Sensing
.
Huang, X., Z. Zhu, Y. Li, B. Wu, and M. Yang, 2018. Tea garden detec-
tion from high-resolution imagery using a scene-based frame-
work,
Photogrammetric Engineering & Remote Sensing
.
Liang, B., and Q. Weng, 2018. Characterizing urban landscape by us-
ing fractal-based texture information,
Photogrammetric Engineer-
ing & Remote Sensing
.
Ming, D., W. Zhou, L. Xu, M. Wang, and Y. Ma, 2018. Coupling
relationship among scale parameter, segmentation accuracy and
classification accuracy in GeOBIA,
Photogrammetric Engineering
& Remote Sensing
.
Simsek, D., and E. Sertel, 2018. Spatial analysis of two different urban
landscapes using satellite images and landscape metrics,
Photo-
grammetric Engineering & Remote Sensing
.
Xie, C., X. Huang, H. Mu, and W. Yin, 2017. Impacts of land-use
changes on the lakes across the Yangtze floodplain in China,
Environmental Science & Technology
, 51(7): 3669-3677.
Yang, J., J. Sun, Q. Ge, and X. Li, 2017. Assessing the impacts of
urbanization-associated green space on urban land surface tem-
perature: a case study of Dalian, China,
Urban Forestry & Urban
Greening
, 22: 1-10.
Photogrammetric Engineering & Remote Sensing
Vol. 84, No. 11, November 2018, pp. 679.
0099-1112/18/679
© 2018 American Society for Photogrammetry
and Remote Sensing
doi: 10.14358/PERS.84.11.679
