22.2.8.3.2 Climate Zones

Chapter Contents (Back)
Climate Zones.
See also Climate Data.

Bechtel, B.[Benjamin], Alexander, P.J.[Paul J.], Böhner, J.[Jürgen], Ching, J.[Jason], Conrad, O.[Olaf], Feddema, J.[Johannes], Mills, G.[Gerald], See, L.[Linda], Stewart, I.[Iain],
Mapping Local Climate Zones for a Worldwide Database of the Form and Function of Cities,
IJGI(4), No. 1, 2015, pp. 199-219.
DOI Link 1502
BibRef

Geletic, J.[Jan], Lehnert, M.[Michal], Dobrovolný, P.[Petr],
Land Surface Temperature Differences within Local Climate Zones, Based on Two Central European Cities,
RS(8), No. 10, 2016, pp. 788.
DOI Link 1609
BibRef

Hu, J.L.[Jing-Liang], Ghamisi, P.[Pedram], Zhu, X.X.[Xiao Xiang],
Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification,
IJGI(7), No. 9, 2018, pp. xx-yy.
DOI Link 1810
BibRef

Qiu, C.P.[Chun-Ping], Schmitt, M.[Michael], Mou, L.[Lichao], Ghamisi, P.[Pedram], Zhu, X.X.[Xiao Xiang],
Feature Importance Analysis for Local Climate Zone Classification Using a Residual Convolutional Neural Network with Multi-Source Datasets,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Wang, C.Y.[Chu-Yuan], Middel, A.[Ariane], Myint, S.W.[Soe W.], Kaplan, S.[Shai], Brazel, A.J.[Anthony J.], Lukasczyk, J.[Jonas],
Assessing local climate zones in arid cities: The case of Phoenix, Arizona and Las Vegas, Nevada,
PandRS(141), 2018, pp. 59-71.
Elsevier DOI 1806
Local climate zone, Land use land cover, Sky view factor, Land surface temperature, Phoenix, Las Vegas BibRef

Vandamme, S.[Stéphanie], Demuzere, M.[Matthias], Verdonck, M.L.[Marie-Leen], Zhang, Z.M.[Zhi-Ming], van Coillie, F.[Frieke],
Revealing Kunming's (China) Historical Urban Planning Policies Through Local Climate Zones,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Yoo, C.[Cheolhee], Han, D.[Daehyeon], Im, J.[Jungho], Bechtel, B.[Benjamin],
Comparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images,
PandRS(157), 2019, pp. 155-170.
Elsevier DOI 1911
Local climate zone, Convolutional neural networks, Random forest, Urban climate, Landsat BibRef

Zhao, N.[Nan], Ma, A.[Ailong], Zhong, Y.F.[Yan-Fei], Zhao, J.[Ji], Cao, L.Q.[Li-Qin],
Self-Training Classification Framework with Spatial-Contextual Information for Local Climate Zones,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Zhang, G., Ghamisi, P., Zhu, X.X.,
Fusion of Heterogeneous Earth Observation Data for the Classification of Local Climate Zones,
GeoRS(57), No. 10, October 2019, pp. 7623-7642.
IEEE DOI 1910
geophysical image processing, geophysical signal processing, geophysical techniques, image classification, image fusion, satellite images BibRef

Johnson, B.A.[Brian Alan], Jozdani, S.E.[Shahab Eddin],
Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef
And: Reply to Comments:
Confusion Matrices Help Prevent Reader Confusion,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
Comments:
See also Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment. Remote Sens. 2019, 11, 2420, A. BibRef

Bechtel, B.[Benjamin], Demuzere, M.[Matthias], Stewart, I.D.[Iain D.],
A Weighted Accuracy Measure for Land Cover Mapping: Comment on Johnson et al. Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment. Remote Sens. 2019, 11, 2420,
RS(12), No. 11, 2020, pp. xx-yy.
DOI Link 2006
Article and reply to comments
See also Local Climate Zone (LCZ) Map Accuracy Assessments Should Account for Land Cover Physical Characteristics that Affect the Local Thermal Environment. BibRef

Chen, Y.[Yanan], Gu, H.F.[Hong-Fan], Wang, M.[Munan], Gu, Q.[Qing], Ding, Z.[Zhi], Ma, M.G.[Ming-Guo], Liu, R.Y.[Rong-Yuan], Tang, X.G.[Xu-Guang],
Contrasting Performance of the Remotely-Derived GPP Products over Different Climate Zones across China,
RS(11), No. 16, 2019, pp. xx-yy.
DOI Link 1909
GPP: gross primary production. BibRef

Qiu, C.P.[Chun-Ping], Mou, L.[Lichao], Schmitt, M.[Michael], Zhu, X.X.[Xiao Xiang],
Local climate zone-based urban land cover classification from multi-seasonal Sentinel-2 images with a recurrent residual network,
PandRS(154), 2019, pp. 151-162.
Elsevier DOI 1907
Land cover, Local climate zones (LCZs), Sentinel-2, Multi-seasonal, Recurrent neural network (RNN) BibRef

Liu, S.J.[Sheng-Jie], Shi, Q.[Qian],
Local climate zone mapping as remote sensing scene classification using deep learning: A case study of metropolitan China,
PandRS(164), 2020, pp. 229-242.
Elsevier DOI 2005
Local climate zone, Convolutional neural network, Scene classification, Metropolitan China, Urban climate BibRef

Yoo, C.[Cheolhee], Lee, Y.[Yeonsu], Cho, D.J.[Dong-Jin], Im, J.H.[Jung-Ho], Han, D.[Daehyeon],
Improving Local Climate Zone Classification Using Incomplete Building Data and Sentinel 2 Images Based on Convolutional Neural Networks,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Lehnert, M.[Michal], Savic, S.[Stevan], Miloševic, D.[Dragan], Dunjic, J.[Jelena], Geletic, J.[Jan],
Mapping Local Climate Zones and Their Applications in European Urban Environments: A Systematic Literature Review and Future Development Trends,
IJGI(10), No. 4, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Chen, C.M.[Chao-Min], Bagan, H.[Hasi], Xie, X.[Xuan], La, Y.[Yune], Yamagata, Y.[Yoshiki],
Combination of Sentinel-2 and PALSAR-2 for Local Climate Zone Classification: A Case Study of Nanchang, China,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105
BibRef

Zeng, L.L.[Ling-Lin], Hu, Y.[Yuchao], Wang, R.[Rui], Zhang, X.[Xiang], Peng, G.Z.[Guo-Zhang], Huang, Z.Y.[Zhen-Yu], Zhou, G.Q.[Guo-Qing], Xiang, D.X.[Da-Xiang], Meng, R.[Ran], Wu, W.X.[Wei-Xiong], Hu, S.[Shun],
8-Day and Daily Maximum and Minimum Air Temperature Estimation via Machine Learning Method on a Climate Zone to Global Scale,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Kim, M.H.[Min-Ho], Jeong, D.[Doyoung], Kim, Y.[Yongil],
Local climate zone classification using a multi-scale, multi-level attention network,
PandRS(181), 2021, pp. 345-366.
Elsevier DOI 2110
Local climate zone, Scene classification, Multi-scale multi-level attention, Urban climate BibRef

Yu, Z.W.[Zhao-Wu], Jing, Y.C.[Yong-Cai], Yang, G.Y.[Gao-Yuan], Sun, R.H.[Ran-Hao],
A New Urban Functional Zone-Based Climate Zoning System for Urban Temperature Study,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101
BibRef

Li, N.[Nana], Yang, J.[Jun], Qiao, Z.[Zhi], Wang, Y.[Yongwei], Miao, S.G.[Shi-Guang],
Urban Thermal Characteristics of Local Climate Zones and Their Mitigation Measures across Cities in Different Climate Zones of China,
RS(13), No. 8, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Stredová, H.[Hana], Chuchma, F.[Filip], Rožnovský, J.[Jaroslav], Streda, T.[Tomáš],
Local Climate Zones, Land Surface Temperature and Air Temperature Interactions: Case Study of Hradec Králové, the Czech Republic,
IJGI(10), No. 10, 2021, pp. xx-yy.
DOI Link 2110
BibRef

Shi, G.X.[Guang-Xun], Ye, P.[Peng], Yang, X.W.[Xian-Wu],
Spatio-Temporal Variation Analysis of the Biological Boundary Temperature Index Based on Accumulated Temperature: A Case Study of the Yangtze River Basin,
IJGI(10), No. 10, 2021, pp. xx-yy.
DOI Link 2110
BibRef


Das, M., Ghosh, S.K.,
Detection of climate zones using multifractal detrended cross-correlation analysis: A spatio-temporal data mining approach,
ICAPR15(1-6)
IEEE DOI 1511
climatology BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Growing Season Dates, Spring .


Last update:Nov 30, 2021 at 22:19:38