Recent Advances in Observations of Land Cover and Land Use Change and Impacts from Multi-Sourced Data

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1 International Meeting on Land Use and Emissions in South/Southeast Asia - Ho Chi Minh City, Vietnam, October, 2016 Recent Advances in Observations of Land Cover and Land Use Change and Impacts from Multi-Sourced Data Presentation by S. V. Nghiem 1 and C. Small 2 1 NASA Jet Propulsion Laboratory, California Institute of Technology, Pasadena, California, U.S.A. 2 Lamont-Doherty Earth Observatory, Columbia University Palisades, New York, U.S.A. Technical Session - V. Forests and LCLUC - 19 October All Rights Reserved.

2 International Science Team PI IDS: Son V. Nghiem, Jet Propulsion Laboratory, CA, USA. PI MuSLI: Chris Small, LDEO, Columbia University, NY, USA Co-Is and Collaborators: M. Jacobson, Stanford University, CA, USA; D. Balk, City University of New York, USA, M. Masetti, University of Milan, Italy; A. Sorichetta and A. Tatem, University of Southampton, UK; C. Linard, University of Brussels, Belgium; D. Hall, NASA GSFC/UM, USA; A. Richter, University of Bremen, Germany; U. Deichmann, The World Bank; K. Gurney, Arizona State University, USA, P. Shepson, Purdue University, USA; C. Field, Carnegie Institution, USA; P. Fabbri and M. Pola, University of Padova, Italy; A. Gargini, University of Bologna, Italy; F. Celico, University of Parma, Italy; M. Deserti, ARPA Emilia Romagna, Italy; F. Catani, University of Florence; Italy, P. Pampaloni and S. Paloscia, IFAC-CNR, Italy; G. R. Brakenridge, University of Colorado, USA; P. Morin, U. of Minnesota, USA; E. Strauss, LMU/LA Center for Urban Res., USA; M. Kafatos, Chapman U.; G. Henebry, SDSU; P. Stych, Charles University, Czech Republic; A. Mathews, Oklahoma State University, USA.

3 Challenges in Mapping and Monitoring LCLUC LCLUC sciences across multiple scales in time and in space, with 3 dimensions Require mapping extensively across space (S/SE Asia) & time (e.g., decades) Require multiple satellite sensors with different spatial and temporal resolution and coverage & ancillary data Time-space continuum paradigm to address rural-urban transformation Require new advances and break-through methods

4 Dense Sampling Method Patent US , US , WO A3 Enable global urban observations without gaps in times/space, 1-km grid, Dynamic Atlas of Global Continuum of LCLUC

5 Oklahoma City in 2006 Building volume overlaid on DSM radar signature Building volume from Oklahoma State University in collaboration with JPL under a NASA EPSCoR project

6 Building volume from Oklahoma State University in collaboration with JPL under a NASA EPSCoR project

7 Oklahoma City in 2006 Crosstown Corridor in E-W axis along I-40 High correlation between BDV vs DSM Building volume from Oklahoma State University in collaboration with JPL under a NASA EPSCoR project Demonstrate that DSM signature represents building volume

8 Rate of Change of Urban Vertical Built-Up in WUI in the Decade of the 2000s (in % per decade) 14 cities and JPL from Ventura to Mentone, CA Etiwanda 44.7% Ventura 9.9% Camarillo 6.9% Simi Valley 6.2% Chatworth 5.1% San Fernando 19.5% Sun Valley 10.0% Burbank 5.3% JPL 6.5% Sierra Madre 7.4% Monrovia 1.7% Glendora 5.7% San Antonio Heights 13.2% Highland 17.4% Mentone 23.5%

9 Colby Fire in January 2014 Fuel dry-up map derived with NASA MODIS EVI on 1/8/2014. Red represents a severe dry-up by ~80% decrease in LFM from a level of 140% LFM on 2/25/2013. LFM dropped <60% (critical danger).

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11 Saigon Regional Change in

12 Saigon Urban Hot Spot Detection using DSM rate of change across rural-urban continuum Hiep Phuoc new urban area and Cat Lai Port

13 Beijing Urban Extent from DSM Paper in preparation

14 Beijing Urban Extent from DSM Annual change captures policy change Beijing growth reduction in 2005 as China s President Hu Jintao gained power, imposing policies to slow land confiscations, reduce demolitions, and freeze industrial land conversion to address mounting tensions and appease social discontent [Broudehoux, A.M., 2007, J. Urban Affairs, 29(4)].

15 Beijing Urban Change and NO 2 / Olympic Paper in preparation

16 DSM Time-Space Continuum Allow consistent assessment of Beijing population pattern and dynamics Paper in preparation, Nghiem, Sorichetta, Linard, et al., 2016

17 DSM Y-Intercept Continuum with OSM Road Network and SRTM Water Bodies

18 DSM Y-Intercept Continuum with OSM Road Network and SRTM Water Bodies Satellite observations of New Delhi (and other cities across Asia) as a time-space continuum, laterally and vertically, will provide critical inputs to GATOR-GCMOM, a model nesting urban and climate scales to assess urbanization impacts on urban heating, soil drying, pollutant vertical mixing, air stagnation, and near-surface oxidant pollution (Jacobson, Nghiem, Sorichetta, and Whitney, 2015).

19 DSM Decadal Mean Patterns over the Indian Subcontinent Intensity Variability

20 Vector-Borne Diseases (Malaria, Zika) over the Indian Subcontinent Malaria Study (Courtesy of A. Sorichetta) Consider distributions of Anopheles (nighttime active; malaria) and Aedes Aegypti (daytime active; Zika) Require layer of rural/urban/peri-urban as a continuum of built-up density rather than binary, together with ambient population dynamics.

21 Increase Information Content with Multiple Satellite Data Beijing Tri-Temporal: DMSP-OLS Raw Filtered Beijing Beijing Moscow Infrastructure Index SRTM + Landsat Landsat and DSM composite in 2000 Miyun, China, 2000 Small et al., 2014, 2015

22 Multi-Sensor Infrastructure Index and Night Light

23 Polarization Diversity Sentinel-1A SAR VV 4 April 2016 NY Sentinel1A- VH Sentinel-1A SAR VH 4 April 2016

24 Sentinel-1A SAR on 9 June 2016 with SRTM water bodies and road network from Google There will be a hands-on practice on this in the training session tomorrow on Satellite Remote Sensing Theory and Practical Use with GE Pro

25 Summary Challenges in LCLUC science & appl. New method and multi-sourced data Assessment of urban change in 3D Urban sprawl and wildfire in Calif. Saigon expansion and build-up Beijing change and air pollution Continuum over Indian subcontinent Infectious diseases (malaria, zika) High-resolution data compositions

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