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Second Progress Report (2015-2016) for NASA Science of Terra & Aqua Project:

Change in our MIDST: Detection and analysis of land surface dynamics in North

and South America using multiple sensor datastreams. [NNX14AJ32G]

Submitted 11 April 2016 by

PI: Geoffrey M. Henebry PhD CSE

Geospatial Sciences Center of Excellence (GSCE) South Dakota State University 1021 Medary Ave., Wecota Hall 506B Brookings, SD 57007-3510 Email: geoffrey.henebry@sdstate.edu Office: +1-605-688-5351 (-5227 fax) Project Team Geoffrey M. Henebry1, PI Kirsten M. de Beurs2, Co-I Xiaoyang Zhang1, Co-I

Cole Krehbiel1, Geospatial Analyst Lan Nguyen1, PhD Student Pedro Valle de Carvalho e Oliveira1, Geospatial Analyst Braden Owsley2, Geospatial Analyst Baojuan Zheng1, Post-doctoral Fellow

John S. Kimball3, Collaborator Christopher Small4, Collaborator 1South Dakota State University 2University of Oklahoma 3University of Montana, 4Columbia University, Lamont-Doherty Earth Observatory

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A. Project Timeline 4/2015-4/2016

2015

04/01 Henebry talks about “Change in our MIDST: Toward Detection and Analysis of Urban Land Dynamics in North and South America” with co-authors X Zhang, KM de Beurs, JS Kimball, C Small at the Joint Urban Remote Sensing Event (JURSE 2015) in Lausanne Switzerland.

04/02 Pedro Valle de Carvalho e Oliveira formally joins project to work on characterization of cerrado dynamics using passive microwave data.

04/20-23 Henebry presents poster “Change in our MIDST: Toward Detection and Analysis of Urban Dynamics in CONUS” with co-authors KM de Beurs, CP Krehbiel, L Nguyen, B Owsley, X Zhang, and B Zheng at the NASA Carbon Cycle & Ecosystems Joint Science Workshop in College Park, MD.

05/28 de Beurs talks about “Using Big Data for Earth Observation” to the Faculty of Geo-Information Science and Earth Observation, University of Enschede, The Netherlands.

10/06 Henebry talks about “Phenologies in cool earthlight: How passive microwave time series can reveal land surface phenologies and more” with co-authors WG Alemu, P Valle De Carvalho E Oliveira at Phenology 2015, Kuşadası, Turkey.

11/06 Presentations at SWAAG: (1) Holtzman & de Beurs, “Breakpoint Analysis with the BFAST Algorithm in Global Vegetation Index”; and (2) Owsley & de Beurs, “Multiple Remote Sensing Products for Trend Detection and Analysis in South America”.

11/17 MIDST midway project meeting part 1, via GoToMeeting.

11/24 MIDST midway project meeting part 2, via GoToMeeting.

12/04 Henebry gives the Harold and Florence Mayer Distinguished Lecture in

Geography at the University of Wisconsin-Milwaukee entitled “Remote Sensing of Land Surface Phenologies and Seasonalities Using Hot, Warm, and Cool Earthlight”.

12/13-18 Presentations at the AGU Fall Meeting: (1) Henebry et al. “Comparative perspectives on recent trends in land surface dynamics in the grasslands of North and South America”; (2) Henebry et al. “New perspectives on longwave imaging of urban heat islands: Middle infrared to microwaves”; (3) Krehbiel & Henebry “The dynamics of cities: Assessing scaling relations of past and projected urban population and infrastructure to analyze trajectories

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of urbanization in the 21st century”; (4) Nguyen et al. “Urban expansion of major cities in the US Great Plains from 2000 to 2009 using scatterometer data”; and (5) Zheng et al. “Monitoring carbon monoxide pollution over the largest ten cities in the US using satellite observations”.

2016

04/04 Invited talk at US-IALE: de Beurs KM et al. “Using multiple remote sensing perspectives to identify and attribute land surface dynamics in the changing grassland of the Western Hemisphere”.

04/06 Project meeting at US-IALE.

04/11 Second Progress Report submitted.

B. Project Personnel & Project Management

The current project team at South Dakota State University includes PI Henebry, Co-I Zhang, post-doctoral fellow Zheng, PhD student Nguyen, and geospatial analyst Krehbiel; all are housed in the Geospatial Sciences Center of Excellence. Krehbiel graduated with an MS in Geography from SDSU and joined the team in November. Krehbiel’s MS research was funded by Henebry’s NASA IDS project. Pedro Valle de Carvalho e Oliveira, a Brazilian geospatial analyst who earned his MS at INPE, worked with AMSR-E/AMSR2 data in the cerrado during his visit to SDSU. He returned to Brazil in October 2015 but he remains in touch and is expected to return to SDSU to begin a PhD program with Henebry in Fall 2016. The team at the University of Oklahoma includes Co-I de Beurs and geospatial analyst Owsley. Collaborators Kimball and Small are engaged periodically as the situation warrants as they are not receiving any funding from the project.

To date we have had three project meetings: two via GoToMeeting in November and one in April at the US-IALE meeting in Asheville, NC.

C. Project Synopsis [same text as Year 1]

We propose to build, implement, and refine an innovative system to quantify and localize change, characterize environmental processes, and examine the function of land surface change within the Earth System, and to demonstrate its use in two contrasting environments.

Our science question is broad: Where in the western hemisphere is the vegetated land surface changing significantly during the past 15 years in response to direct human impacts?

We will use the Human Influence Index (HII, Sanderson et al. 2003) and Anthromes v2.0 (Ellis & Ramankutty 2008; Ellis et al. 2013) to partition the Americas spatially. Both are cross-

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disciplinary indicators of direct human influence on terrestrial ecosystems. In addition, we will use the complementary spatial partitioning provided by the World Wildlife Fund (WWF) global ecoregions (Olson et al. 2001).

We formulate our question as a suite of testable hypotheses about changes in the vegetated land surface, as follows:

[H1] Areas of significant positive change occur in areas of moderate human impact, due predominantly to agricultural land uses;

[H2] Areas of significant negative change that occur in areas with low human impact, arise predominantly from forest pests and forest fires; and

[H3] Areas of significant negative changes that occur in areas with high human impact, appear predominantly associated with the expansion of human settlements, particularly cities.

We will use MIDST to highlight areas manifesting significant (p0.05) and highly significant (p0.01) changes in land surface properties in North, Central, and South America to assess changes since 2001, with particular focus on the Brazilian cerrado and the hemisphere’s six megacities. We will exclude from consideration most of the Caribbean region and other islands in the Western Hemisphere due to issues of scale. However, we will include the four largest islands in the Caribbean, each of which has an area of more than 9000 sq km: Cuba, Hispaniola, Jamaica, and Puerto Rico.

Works Cited Ellis EC, N Ramankutty. 2008. Putting people in the map: anthropogenic biomes of the world. Frontiers in

Ecology and the Environment 6:439-447. Ellis EC, et al. 2013. Used planet: A global history. Proceedings of the National Academy of Sciences 110:7978–

7985. Olson DM, et al. 2001. Terrestrial ecoregions of the world: a new map of life on earth. BioScience 51:933–938. Sanderson EW, et al. 2002. The Human Footprint and the Last of the Wild: The human footprint is a global map of

human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience 52:891–904.

D. Research Progress during Project Year 2

Much of this second Project Year has been focused on two activities: (1) improving the ingestion and processing of image time series, and (2) communicating initial results. We have made tremendous progress this year, both with respect to method optimization and data processing.

Workflow Optimization Although highly automated, a lot of our initial image processing workflows were not optimized. This past year we have made great strides in optimizing our workflows. As a result, the processing is now much more streamlined allowing us to process many more image tiles simultaneously. Below we describe the original workflow and the subsequent optimizations.

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Figure 1: Original processing workflow

The original process worked well, but it was not optimized with respect to data storage and throughput. Data were stored in both their original form, and as the calculated index. As a result, all data was basically stored twice. This approach was not problematic for smaller datasets and global MODIS data, but we are now also processing the 500m / 1000m MODIS tiles for both North and South America, which is a lot of data to store.

Figure 2: Optimized processing workflow

The change in workflow is relatively minor, but greatly reduces storage. Instead of storing all the original MODIS hdf files, we now store stacks of the original bands. These stacks are imported in our custom trend analysis program to calculate indices on the fly before applying the trend calculation. This change has enabled us to process many trend analyses at once. For example, we calculate all the MODIS trend data simultaneously for one tile (NDVI, EVI, etc.). We no longer store the original MODIS hdf files and the stacks of individual bands are easily updated when more data becomes available. We now also incorporate the MODIS water mask and do not calculate any trends inside the mask.

We have also translated our optimized IDL code into Julia, which is a new high-level, high-performance dynamic programming language from MIT. It provides a sophisticated compiler and allows for parallel execution. Julia integrates open source C and Fortran libraries for linear algebra. Translating our code from IDL to Julia has sped up the computational processing by ~5X or more!

Status of Data Processing We are making good progress with the data processing. Currently, the data listed in Tables 1 and 2 have been completely processed and are awaiting interpretation and further analysis. We are currently working on the data listed in Table 3. Trend results are available on our website: http://tethys.dges.ou.edu/GlobalChange/

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Table 1: Global Data (MODIS)

Sensor Variable Spatial

Resolution

Product Time period

MODIS NDVI, EVI, TC Greenness, TC

Brightness, TC Wetness

0.05° MCD43C4 2001001 – 2014361 (8-

day data)

MODIS Land Surface Temperature (DAY),

Land Surface Temperature (NIGHT)

0.05° M{O|Y}D11C2 2001001 – 2014361 (8-

day data)

MODIS white sky albedo (VIS, NIR,

shortwave)

0.05° MCD43C3 2001001 – 2014361 (8-

day data)

MODIS evapotranspiration 0.05° MOD16A2 2001/01 – 2014/12

(monthly data)

Table 2: Global data (non-MODIS)

Sensor Variable Spatial

Resolution

Product Time period

AMSR-E to

AMSR2

fraction of water, surface air

temperature, vegetation canopy

transmittance, volumetric soil

moisture, atmospheric water vapor,

vegetation optical depth, growing

degree days, daytime growing degree

days, nighttime growing degree days,

accumulated growing degree days,

accumulated day time growing

degree days, accumulated night time

growing degree days

25 km x 25

km

AMSR Land

Surface

Parameters v1

(Kimball’s

MEaSUREs)

2002185 –

2014361 (8-

day data)

V6

AIRS+AMSU

TqJoint

gridded data

water vapor mass mixing ratio;

Relative humidity; air temperature (all

at multiple pressure levels surface,

850, 700, 600, 500 hPa)

1 x 1

AIRX3STM

2003/01 –

2014/12

(Monthly data)

V6 thermal

only, MOPITT

CO gridded

monthly

means

retrieved CO mixing Ratio Profiles at

500, 600, 700, 800 hPa; Retrieved CO

surface mixing ratio

1 x 1

MOP03TM

2004/04 –

2014/12

(Monthly data)

Northern

Hemisphere

1999001 –

2012353 (16-

7

State of

Cryosphere

snow covered land; snow free land

25 km x 25

km

Robinson’s

MEaSUREs

day data)

TRMM precipitation; relative error 0.25 x

0.25

TRMM 3B43 1999001 –

2012353 (16-

day data)

CERES

toa_sw_all_mon; toa_lw_all_mon;

toa_net_all_mon; toa_sw_clr_mon;

toa_lw_clr_mon; toa_net_clr_mon;

sfc_net_sw_all_mon;

sfc_net_sw_clr_mon;

sfc_net_lw_all_mon;

sfc_net_lw_clr_mon;

sfc_net_tot_all_mon;

sfc_net_tot_clr_mon

1 x 1

EBAF

2000/04 –

2014/03

(monthly data)

North and South America are covered by ~60 MODIS 10°x10° tiles. Table 3 provides an overview of our current progress with respect to the processing of the tiled datasets.

Table 3: MODIS tiles

Sensor Variable Spatial

Resolution

Product Time period Tiles

MODIS NDVI, EVI, TC Greenness, TC

Brightness, TC Wetness

500m MCD43C4 2001001 –

2014361 (8-

day data)

Complete

MODIS Land Surface Temperature

(DAY), Land Surface

Temperature (NIGHT)

1000m M{O|Y}D11A2 2001001 –

2014361 (8-

day data)

25 tiles

stacked,

none

trended

MODIS White sky albedo (VIS, NIR,

SWIR)

500m MCD43C3 2001001 –

2014361 (8-

day data)

All tiles

stacked

none

trended

Key Initial Findings

Two areas of our key findings to date to highlight include (1) the use of MOPITT CO time series for comparison of urban areas, and (2) the conceptualization and development of a new approach to longwave remote sensing of urban areas.

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CO Trends with MOPITT We have been exploring MOPITT CO time series to investigate if these data could be useful to the LCLUC community for comparing and contrasting urbanization. While the atmospheric sciences community has focused on trends in total column CO (e.g., Buchwitz et al. 2007; Clerbaux et al. 2008; Li & Liu 2011; Pommier et al. 2013; Worden et al. 2013), we are more interested in concentrations at higher pressure level retrievals nearer to the ground where the urban contributions predominate.

Figure 1. MOPITT TIR-only CO vertical profiles for (a) the ten largest MSAs in the USA with the legend ordered by 2013 MSA population estimate from the highest to the lowest and (b) the seven sparsely populated remote areas. Each plotted value is a 12-year average of monthly MOPITT CO time series. Note that due to the higher elevation of remote sites, atmospheric pressure level at the surface is between 700 to 800 hPa for S.W. New Mexico and 800 to 900 hPa for the other remote sites. Note that rapid decrease in the high CO over urban areas with decreasing pressure as well as the variation among MSAs.

Surface

900

800

700

600

500

50 100 150 200 250 300 350 400

Pre

ss

ure

(h

Pa

)

MOPITT CO Mixing Ratio (ppbv)

New York

Los Angeles

Chicago

Dallas-Fort Worth

Houston

Philadelphia

Washington D.C.

Miami

Atlanta

Boston

Surface

800

700

600

500

50 100 150 200 250 300 350 400

Pre

ss

ure

(h

Pa

)

MOPITT CO Mixing Ratio (ppbv)

Sandhills, NE

E. Colorado

S.W. New Mexico

S.E. Utah

S.E. Oregon

Central Idaho

E. Montana

b

a

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Figure 2. Comparison of Seasonal Kendall (SK) scores of MOPITT CO trends over (a) urban areas and (b) remote sites using TIR-only CO data with scores over (c) cities and (d) remote sites using TIR+NIR CO data. Note that all trends are decreasing and highly significant; however, the TIR+NIR trends are less strong near the urban surface.

Thus far we have encountered some resistance from the atmospheric science community to our use of nonparametric trend analysis in contrast to the conventional linear trend analysis used in the papers cited above.

Based on our results, MOPITT TIR-only data is a better satellite product for monitoring urban CO trends than either the MOPITT TIR+NIR data or the AIRS CO data. Although the spatial scale of 1 degree is rather too coarse to monitor specific smaller urban areas, we have found that it has merit for megacities and major conurbations. Given the urbanizing trends across the planet, it may be an appropriate scale for those interested in urban dynamics to pay attention to the influences of urban areas on the local and regional atmospheric environment.

Works Cited Buchwitz M, I Khlystova, H Bovensmann, JP Burrows. 2007. Three years of global carbon monoxide from

SCIAMACHY: comparison with MOPITT and first results related to the detection of enhanced CO over cities. Atmospheric Chemistry and Physics 7:2399–2411.

Clerbaux C, DP Edwards, M Deeter, L Emmons, J-F Lamarque, XX Tie, ST Massie, J Gille. 2008. Carbon monoxide pollution from cities and urban areas observed by the Terra/MOPITT mission. Geophysical Research Letters 35:L03817.

-600

-500

-400

-300

-200

-100

0

Surface 800 700 600 500

SK

Sc

ore

Pressure Grid (hPa)

Sandhills, NEE. ColoradoS.W. New MexicoS.E. UtahS.E. OregonCentral IdahoE. Montana

-600

-500

-400

-300

-200

-100

0

Surface 900 800 700 600 500

SK

Sc

ore

Pressure (hPa)

New YorkChicagoLos AngelesHoustonBostonAtlantaPhiladelphiaWashington D.C.Dallas-Fort WorthMiami

a) Urban TIR-only

c) Remote TIR-only

-600

-500

-400

-300

-200

-100

0

Surface 800 700 600 500

Pressure Grid (hPa)

Sandhills Nebraska

E. Colorado

S.W. New Mexico

S.E. Utah

S.E. Oregon

Central Idaho

E. Montana

d) Remote TIR+NIR

-600

-500

-400

-300

-200

-100

0

Surface 900 800 700 600 500

Pressure (hPa)

New York

Los Angeles

Chicago

Dallas-Fort Worth

Houston

Philadelphia

Washington D.C.

Miami

Atlanta

Boston

b) Urban TIR+ NIR

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Li L, Y Liu. 2011. Space-borne and ground observations of the characteristics of CO pollution in Beijing, 2000–2010. Atmospheric Environment 45:2367–2372.

Pommier M, CA McLinden, M Deeter. 2013. Relative changes in CO emissions over megacities based on observations from space. Geophysical Research Letters 40:3766–3771.

Worden HM, MN Deeter, C Frankenberg, et al. 2013. Decadal record of satellite carbon monoxide observations. Atmospheric Chemistry and Physics 13:837–850.

A New Approach to Longwave Remote Sensing of Urban Areas Studies of the urban heat island (UHI) effect have traditionally used networks and transits of air temperature sensors located ~2m above the surface. Thermal infrared (TIR) remote sensing of the radiometric surface temperature reveals the surface UHI (SUHI). The UHI and SUHI are related but distinct phenomena. Much urban remote sensing research has been conducted at finer spatial resolution to reveal the structural and spatial heterogeneities in the urban fabric. Our approach, rather, has focused on coarser spatial but finer temporal resolution observations of urbanized and urbanizing areas in the TIR, but also in the MIR and in the microwave region. The point of this new approach is to enable comparative studies of the urbanized land surface within the matrix of other land uses, rather than a detailed investigation within particular cities.

Figure 3. Decadal (2003–2012): (a) mean Accumulated Nocturnal Degree-Days (ANDD) from MODIS and (b) AminDD from Daymet over the Upper Midwest Region. Areas in in shades of red (blue) indicate higher (lower) values of (a) ANDD or (b) AminDD values. GHCN sites are indicated by pale yellow circles, and eleven focal cities are outlined in black. Notice how major water bodies and river valleys have higher ANDD. Figure from Krehbiel & Henebry 2016.

We have introduced four new metrics of thermal time based on MODIS land surface temperature observations from Terra and Aqua: diurnal degree-days (DDD) and nocturnal degree-days (NDD) and their accumulations, ADDD and ANDD, respectively (Krehbiel & Henebry 2016). DDD is calculated from the LST observations that straddle noon (~1030 for Terra and ~1330 for

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Aqua); whereas, NDD is calculated from LST observations around midnight (~2230 for Terra and ~0130 for Aqua). These times are unlikely to capture the diel thermal extrema, but over extended periods of observation we expect that differences between daytime and nighttime temperatures will be resolved. Thus, the accumulations (ADDD and ANDD) should reveal clearer pictures than any particular 8-day composites of DDD or NDD. Likewise, the difference between ADDD and ANDD should be less noisy.

The portrait of the Upper Midwest US as seen by the MODIS average ANDD for 2003-2012 reveals a surprisingly heterogeneous landscape in contrast to the Daymet 2003-2012 average accumulated minima based on weather station observations and a terrain model (Figure 3; Krehbiel & Henebry 2016). Notice the many nocturnal hotspots across the region, in addition to the expected urbanized areas. Many of these hotspots are associated with larger bodies of water, but river valleys also appear prominently. Notice also how urbanized areas are largely invisible in the Daymet data.

In addition to these new views in the TIR, we have been exploring how urbanized areas appear in the middle infrared (MIR) at 4.08 microns, MODIS band 23 (Tomaszewska et al. 2016). Why the MIR? The longer wavelength is able to pierce urban haze and building materials appear comparably bright in the MIR, whether asphalt, concrete, or roofing materials (Henebry 2007; Krehbiel et al. 2013; Tomaszewska et al. 2016). There are challenges, however, because the MIR is the mixing zone of reflected cool sunlight and emitted hot earthlight. Ms. Tomaszewska is preparing a paper for a special issue of Remote Sensing on Urban Thermal Remote Sensing that introduces a novel technique for contrasting normalized MIR radiance from different land covers.

We are also exploring the limits of characterizing the urbanized portion of continents using the AMSR-E/AMSR2 passive microwave enhanced land parameters that the Dr. John Kimball’s group has developed. It is notable that these data are at very coarse spatial (25 km) but fine temporal (up to twice daily) resolution and include a retrieval of 2 m air temperature data. Mr. Nguyen is preparing a paper for the same special issue of Remote Sensing on how urbanized areas in CONUS appear in the AMSR data. He also introduces the NDATTI (normalized difference in accumulated thermal time index) to attenuate some of the latitudinal effects of seasonal temperature patterns.

Finally, in an effort to bridge across LCLUC projects to advance our understanding, we have been exploring the use of Dr. Son Nghiem’s specially processed QuikSCAT microwave scatterometer data (Nghiem et al. 2009) to understand growth in urban areas in the Great Plains. Figures 4 and 5 are from Mr. Nguyen’s 2015 AGU Fall Meeting poster. Figure 4 shows growth in the Dallas-Ft. Worth metroplex from 2001-2011 using the USGS NLCD impervious surface area (%ISA) data overlain with the backscatter contours from 2000-2009. The grey scale background is the %ISA in 2001 and the green polygons represent %ISA increases between 2001 and 2011. Figure 5 relates the change in backscattered energy between 2001 and 2006 (blue,

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green, and yellow hues) with changes in %ISA between 2001 and 2006 (red polygons). A manuscript detailing these analyses is under preparation.

Figure 4. Urban growth and

development in the Dallas-Ft.

Worth metroplex from 2000-2009

as captured by USGS NLCD

Impervious Surface Area (%ISA)

data and QuikSCAT data

processed using the Dense

Sampling Method approach

(Nghiem et al. 2009).

Figure 5. Spatial localization of

change in backscattered energy

from 2001-2006 compared to

change in %ISA in the Dallas-Ft.

Worth metroplex from 2001-

2006.

Works Cited Henebry GM. 2007. Mapping human settlements using the mid-IR: advantages, prospects, and limitations. In:

Urban Remote Sensing (Q Weng, D Quattrochi, eds). CRC Press: Boca Raton. pp 339–355. Krehbiel CP, V Kovalskyy, GM Henebry. 2013. Exploring the middle infrared region for urban remote sensing:

seasonal and view angle effects. Remote Sensing Letters 4:1147–1155. http://doi.org/10.1080/2150704X.2013.853891

Krehbiel CP, GM Henebry. 2016. A Comparison of Multiple Datasets for Monitoring Thermal Time in Urban Areas over the U.S. Upper Midwest. Remote Sensing 8:297. http://doi.org/10.3390/rs8040297

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Nghiem S, et al. 2009. Observations of urban and suburban environments with global satellite scatterometer data. ISPRS Journal of Photogrammetry and Remote Sensing. 64:367–380.

Tomaszewska M, V Kovalskyy, C Small, GM Henebry. 2016. Viewing Global Megacities through MODIS 4 m Radiance: Effects of Time of Year, Latitude, Land Cover, and View Zenith Angle. Journal of Selected Topics in Applied Earth Observations and Remote Sensing http://doi.org/10.1109/JSTARS.2016.2532740

Communicating Research Results

Research output of the team in Project Year 2 included publication of 4 peer-reviewed journal articles and 2 peer-reviewed conference proceedings and the delivery of 6 invited talks, 5 posters, 2 contributed talks, and 1 webinar. Several other manuscripts are in review, in preparation, or under revision following review.

In December 2015, we established social media accounts to promote the project and associated research. Our Facebook page is https://www.facebook.com/GlobalLandChange. Our Twitter handle is @Land_Changes and the related webpage is https://twitter.com/Land_Changes. Our Instagram account is https://www.instagram.com/land_changes/. As of 4/11/2016, we have 757 followers on Twitter, 235 followers on Instagram, and 117 “likes” on Facebook. Our posting to social media has been modest to date.

At the US-IALE annual meeting held 3-6 April 2016 in Asheville, NC, Henebry co-organized a special symposium Phenology and Seasonality as Integrative Indicators of Ecosystem Health:

Recent Developments and Prospects. Henebry and Zhang are serving as guest editors (along with Drs. Forrest Hoffman and Jitendra Kumar of ORNL) for a special issue of Remote Sensing entitled “Land Surface Phenology and Seasonality: Novel Approaches and Applications” (http://www.mdpi.com/journal/remotesensing/special_issues/phenology) that has an initial submission deadline of 31 October 2016.

E. Cumulative project publications in reverse chronological order i. Zheng B, K de Beurs, B Owsley, GM Henebry. In revision following review. Impact of Urban

Areas on Carbon Monoxide Vertical Profiles: Variation and Trends over Major US Cities

ii. Liu S, B Bond-Lamberty, T Loveland, A Fox, A Steiner, L Boysen, J Ford, T Huntington, J Hatfield, Z Liu, G Henebry, K Gallo, W Yuan, S Zhao, T Sohl, Z Zhang. In review. Grand Challenges in Understanding the Interplay of Climate & Land Changes. Earth Interactions

9. Krehbiel CP, GM Henebry. 2016. A Comparison of Multiple Datasets for Monitoring Thermal Time in Urban Areas over the U.S. Upper Midwest. Remote Sensing 8:297. http://doi.org/10.3390/rs8040297

8. Krehbiel CP, T Jackson, GM Henebry. 2016. Web-Enabled Landsat Data Time Series for Monitoring Urban Heat Island Impacts on Land Surface Phenology. Journal of Selected Topics in Applied Earth Observations and Remote Sensing http://doi.org/10.1109/JSTARS.2015.2496951

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7. Tomaszewska M, V Kovalskyy, C Small, GM Henebry. 2016. Viewing Global Megacities through MODIS 4 m Radiance: Effects of Time of Year, Latitude, Land Cover, and View Zenith Angle. Journal of Selected Topics in Applied Earth Observations and Remote Sensing http://doi.org/10.1109/JSTARS.2016.2532740

6. Zhang X, Q Zhang. 2016. Monitoring interannual variation in global crop yield using long-term AVHRR and MODIS observations. ISPRS Journal of Photogrammetry and Remote Sensing 114:191-205. http://doi.org/10.1016/j.isprsjprs.2016.02.010

5. Tomaszewska M, V Kovalskyy, GM Henebry. 2015. MODIS 4 m Radiance in Global Megacities Depends on Seasonality, Land Cover, and View Zenith Angle. Joint Urban Remote Sensing Event (JURSE 2015), archived at http://igd.unil.ch/jurse2015/Y2RvRKp9/paper-7a-1.pdf.

http://doi.org/10.1109/JURSE.2015.7120507

4. Liang L, X Zhang. 2015. Coupled Spatiotemporal Variability of Temperature and Spring Phenology in the Eastern U.S., International Journal of Climatology http://doi.org/10.1002/joc.4456

3. Krehbiel CP, T Jackson, GM Henebry. 2015. Using Web-enabled Landsat Data time series to analyze the impacts of urban areas on remotely sensed vegetation dynamics. Joint Urban Remote Sensing Event (JURSE 2015), archived at http://igd.unil.ch/jurse2015/Y2RvRKp9/paper-3a-1.pdf.

http://doi.org/10.1109/JURSE.2015.7120469

2. Henebry GM, XY Zhang, KM de Beurs, JS Kimball, C Small. 2015. Change in our MIDST: Toward Detection and Analysis of Urban Land Dynamics in North and South America. Joint Urban Remote Sensing Event (JURSE 2015), archived at http://igd.unil.ch/jurse2015/Y2RvRKp9/paper-5b-4.pdf.

http://dx.doi.org/10.1109/JURSE.2015.7120496

1. de Beurs KM, GM Henebry, BC Owsley, I Sokolik. 2015. Using Multiple Remote Sensing Perspectives to Identify and Attribute Land Surface Dynamics in Central Asia 2001-2013. Remote Sensing of Environment 170:48-56. http://dx.doi.org/10.1016/j.rse.2015.08.018

F. Cumulative project presentations in reverse chronological order 21. de Beurs KM, BC Owsley, GM Henebry. 2016. Using multiple remote sensing perspectives

to identify and attribute land surface dynamics in the changing grassland of the Western Hemisphere. US-IALE annual meeting. Asheville, NC, April 3-7. [invited talk]

20. Henebry GM, P Valle de Carvalho e Oliveira, B Zheng, KM de Beurs, B Owsley. 2015. Comparative perspectives on recent trends in land surface dynamics in the grasslands of North and South America. AGU Fall Meeting. San Francisco, CA, 13-18 December. [invited talk]

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19. Henebry GM, C Krehbiel, B Zheng, L Nguyen, KM de Beurs, B Owsley. 2015. New perspectives on longwave imaging of urban heat islands: Middle infrared to microwaves. AGU Fall Meeting, San Francisco, CA, 13–18 December. [poster]

18. Krehbiel C, GM Henebry. 2015. The dynamics of cities: Assessing scaling relations of past and projected urban population and infrastructure to analyze trajectories of urbanization in the 21st century. AGU Fall Meeting, San Francisco, CA, 13–18 December. [poster]

17. Liu Y, CM MacKenzie, R Primack, X Zhang, C Schaaf, Q Sun, Z Wang. 2015. Phenological monitoring of Acadia National Park using Landsat, MODIS and VIIRS observations and fused data. AGU Fall Meeting, 14–18 December, San Francisco, CA. AGU Fall Meeting, San Francisco, CA, 13–18 December. [poster]

16. Nguyen L, S Nghiem, GM Henebry. 2015. Urban expansion of major cities in the US Great Plains from 2000 to 2009 using scatterometer data. AGU Fall Meeting, San Francisco, CA, 13–18 December. [poster]

15. Zheng B, K de Beurs, B Owsley, C Krehbiel, G Henebry. 2015. Monitoring carbon monoxide pollution over the largest ten cities in the US using satellite observations. AGU Fall Meeting, San Francisco, CA, 13–18 December. [talk]

14. Henebry GM. 2015. Remote sensing of land surface phenologies and seasonalities using hot, warm, and cool earthlight. Harold and Florence Mayer Distinguished Lecture in Geography. University of Wisconsin-Milwaukee. December 4. [invited talk]

13. Holtzman L, KM de Beurs. 2015. Breakpoint analysis with the BFAST algorithm in global vegetation index. SWAAG, San Antonio, TX. November 6. [poster] Awarded 2nd place in the SWAAG graduate student poster competition.

12. Owsley B, KM de Beurs, 2015. Multiple Remote Sensing Products for Trend Detection and Analysis in South America. SWAAG, San Antonio, TX. November 6. [contributed talk]

11. Henebry GM, WG Alemu, P Valle De Carvalho E Oliveira. 2015. Phenologies in cool earthlight: How passive microwave time series can reveal land surface phenologies and more. Phenology 2015, Kuşadası, Turkey, October 5-8. [contributed talk]

10. Zhang X, Y Yu, L Liu, G Henebry, M Friedl, J Gray, C Schaaf, Y Liu, Z Wang. 2015. VIIRS Land Surface Phenology: from Climate Date Record to Real Time Monitoring. Phenology 2015, Kuşadası, Turkey, October 5-8. [contributed talk]

9. Henebry GM. 2015. Introduction to land surface phenology and its relevance to soundscape ecology. Global Sustainable Soundscapes Network (GSSN) Grasslands Workshop. Brookings, SD, 17 July. [invited talk]

8. Henebry GM. 2015. Introduction to Land Surface Phenology. Integrated Geospatial Education and Technology Training (iGETT) workshop. Brookings, SD, June 24. [invited talk]

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7. de Beurs KM. 2015. Using Big Data for Earth Observation. Faculty of Geo-Information Science and Earth Observation, University of Enschede, Enschede, The Netherlands. May 28. [invited seminar]

6. Henebry GM, X Zhang, B Zheng, L Nguyen, KM de Beurs, B Owsley, JS Kimball, C Small. 2015. Change in our MIDST: Detection and analysis of land surface dynamics in North and South America using multiple sensor data streams. Land Discipline Breakout of NASA MODIS/VIIRS Science Team Meeting. Remote delivery via web. May 21. [webinar]

5. Henebry G, K de Beurs, C Krehbiel, L Nguyen, B Owsley, X Zhang, B Zheng. 2015. Change in our MIDST: Toward detection and analysis of urban dynamics in CONUS. NASA Carbon Cycle & Ecosystems Joint Science Workshop in College Park, MD, 20–24 April. [poster]

4. Henebry GM, XY Zhang, KM de Beurs, JS Kimball, C Small. 2015. Change in our MIDST: Toward Detection and Analysis of Urban Land Dynamics in North and South America Joint Urban Remote Sensing Event (JURSE 2015), Lausanne Switzerland, 30 March–01 April. [contributed talk]

3. de Beurs KM, GM Henebry, B Owsley. 2014. Trend detection and analysis in Eastern Europe and European Russia. AGU Fall Meeting, San Francisco, CA, 15–19 December. [poster]

2. Henebry GM, KM de Beurs, XY Zhang, JS Kimball, C Small. 2014. The many hazards of trend evaluation. AGU Fall Meeting, San Francisco, CA, 15–19 December. [contributed talk]

1. Henebry GM. 2014. Observing land surface phenologies: Back to the future with the planetary macroscope. 20th International Conference on Biometeorology. Cleveland, OH, 28 September–1 October. [invited keynote]