Mapping Global Urban Area from DMSP/OLS Nightlights using a Cluster-based Method

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Mapping Global Urban Area from DMSP/OLS Nightlights using a Cluster-based Method Y. ZHOU, S. SMITH, A. THOMSON, B. BOND-LAMBERTY, M. IMHOFF K. ZHAO, C. ELVIDGE, J. EOM, K.

1 Mapping Global Urban Area from DMSP/OLS Nightlights using a Cluster-based Method Y. ZHOU, S. SMITH, A. THOMSON, B. BOND-LAMBERTY, M. IMHOFF K. ZHAO, C. ELVIDGE, J. EOM, K. CLARKE Joint Global Change Research Institute, PNNL/UMD Ohio State University NOAA National Geophysical Data Center University of California, Santa Barbara LCLUC Program Science Team Meeting, April 23, 2014

2 Outline Project background Motivations Scientific questions Research objectives Methodology Preliminary results Global urban area China urbanization dynamics Summary and future work

3 Motivations Urban population is projected to 70% by 2050 Urbanization shows important impacts on environment Global warming Urban heat island Energy demand Water cycle It is an important parameter in climate and environmental modeling A consistent global urban map series is lacking

4 Scientific questions Where and when did urbanization occur? What are the socioeconomic and natural causes of urbanization? What are the consequences of urbanization for the sustainability of our earth system?

5 Objectives A consistent global urban map series An integrated framework to project urban expansion Future urbanization scenarios and implications

Urban Extent Delineation Y. Zhou et al, 2014.

6 Urban Mapping from nightlights A cluster-based method 1. Data Preprocess 2. Urban Clusters Segmentation 3. Logistic Model 4. Thresholds Estimation 5. Urban Extent Delineation Y. Zhou et al, A cluster-based method to map urban area from DMSP/OLS nightlights, Remote Sensing of Environment, 147 (5):

7 Potential urban clusters 1. Data Preprocess 2. Urban Clusters Segmentation 3. Logistic Model 4. Thresholds Estimation 5. Urban Extent Delineation NTL data Gradient map Foreground markers Background markers Watershed -based segmentation

8 A logistic model 1. Data Preprocess 2. Urban Clusters Segmentation 3. Logistic Model 4. Thresholds Estimation 5. Urban Extent Delineation USA China

9 Estimation of optimal thresholds 1. Data Preprocess 2. Urban Clusters Segmentation 3. Logistic Model 4. Thresholds Estimation 5. Urban Extent Delineation Optimal thresholds

10 Validations of estimated optimal thresholds USA China

11 Urban extent from nightlights NTL Nightlights Google map High- Res Urban High-res Urban from NTL

12 Validations of mapped urban area USA China

13 Global urban areas mapping Parameter in the logistic model

14 Global urban areas in 2000

Global urban areas (evaluation) 0.56 0.18 0.40 0.66 0.54 0.64 0.26 0.24 0.66 0.60 0.52 0.41 0.41 0.53 0.46 0.57 0.54 0.57 0.50 0.64 0.74 0.53 0.71 0.68 0.

35 0.40 0.45 0.50 0.55 0.60 0.65 0.70 0.86 0.88 0.92 0.93 0.92 0.93 0.91 0.86 0.91 0.94 0.93 0.93 0.88 0.94 0.93 0.82 0.92 0.89 0.91 0.92 0.94 0.94 0.86 0.95 0.

95 0.77 0.65 0.66 0.43 0.75 0.72 0.45 0.73 0.57 0.98 0.47 0.73 0.73 0.32 0.92 0.66 0.72 0.74 0.38 0.72 0.74 0.78 0.82 0.51 0.

50 0.60 0.70 0.80 0.42 0.11 0.27 0.54 0.40 0.59 0.16 0.15 0.65 0.45 0.43 0.28 0.29 0.70 0.32 0.47 0.49 0.53 0.71 0.59 0.81 0.80 0.73 0.92 0.43 0.46 0.42 0.39 0.

15 Global urban areas (evaluation) Kappa Coef. MODIS 500m GlobCover GLC2000 Natural Earth IMPSA Overall Accuracy Producer Accuracy (1-Omission) N American S American Europe Africa Austria Russian Asian Global MODIS 500m GlobCover GLC2000 Natural Earth IMPSA User Accuracy (1-Commission) N American S American Europe Africa Austria Russian Asian Global

16 Global urban areas (6 products)

17 Urban dynamics mapping Inter-calibration of the annual nighttime lights products Parameterization of the logistic model Urban dynamics mapping using the cluster based method Zhao, N., Y. Zhou, Correcting incompatible DN values and geometric errors in nighttime lights time series images. IEEE transactions on Geoscience and Remote Sensing. In review.

18 Urbanization in China (evaluation)

19 Urbanization in China ( ) Beijing Guangdong Shanghai

20 Urbanization in each province ( )

21 Urban area & population

22 Summary and future work Summary The cluster-based method performs well in mapping urban extents from nightlights over large areas The method shows its potential to map global urban areas and temporal dynamics The preliminary results indicate that urbanization shows large spatial and temporal heterogeneity Future work Refine global urbanization mapping Predict future global urbanization Evaluate the environmental impacts of global urbanization

23 Acknowledgements NASA ROSES LCLUC Program Collaborators S. Smith A. Thomson B. Bond-Lamberty M. Imhoff K. Zhao C. Elvidge J. Eom K. Clarke Many colleagues and organizations that shared the data used in this project

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