North American Regional Climate Change Assessment Program (NARCCAP)

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Climate change is expected to have wide-ranging impacts on the Mekong basin, with implications for food security and the frequency of natural disasters such as droughts, extreme rainfall events, and typhoons. The Mekong delta is identified as one of the regions in the world that is most vulnerable to climate change impacts (International Panel on Climate Change [IPCC], 2007), in part because of its high population density and limited capacity for adaptation. Climate change is expected to affect the timing of the onset of the monsoon season, which could impact fisheries and rice production. For example, the IPCC projects that for every 1°C increase in growing season minimum temperature, rice yields are expected to decrease by 10% (IPCC, 2007). Climate projections are an important driver for ecological, hydrological, and agricultural productivity models that are crucial for long-term climate change adaptation planning. In addition, climate projections can be used to enhance public awareness of climate change and provides a scientific basis to integrate climate change issues into regional and local development strategies.

In a process called “downscaling”, global-scale climate predictions can be linked to regional climate dynamics. Downscaling is a technique for generating climate forecasts that are specific to a certain region. USGS partnered with the North American Regional Climate Change Assessment Program (NARCCAP) to obtain downscaled Global Climate Model data for the entire Mekong basin. NARCCAP has developed a set of regional climate models (RCMs) driven by a set of atmosphere-ocean general circulation models (AOGCMs). The AOGCMs have been forced with the Special Report on Emissions Scenarios ( SRES) A2 emissions scenario (a medium-high scenario) for the 21st century. Simulations with these models were also produced for the current (historical) period. The RCMs are nested within the AOGCMs for the current period 1971-2000 and for the future period 2041-2070. All the RCMs are run at a spatial resolution of 50 km. Time slice experiments were conducted using the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric model (AM2.1; Figure 1). In a timeslice experiment, the atmospheric component of an AOGCM is run using observed sea surface temperatures and sea ice boundaries for the historical run, and those same observations combined with perturbations from the future AOGCM for the scenario run (NARCCAP, 2010).

For more information about NARCCAP: http://www.narccap.ucar.edu/

For more information on climate change in the Mekong basin and climate data based on the PRECIS model, visit the Southeast Asia START Regional center for climate change studies website: http://www.start.or.ch/

References:

IPCC. 2007. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, 2007. M.L. Parry, O.F. Canziani, J.P. Palutikof, P.J. van der Linden and C.E. Hanson (eds) Cambridge University Press , Cambridge, United Kingdom and New York, NY, USA. Available online here.

NARCCAP. 2010. North American Regional Climate Change Assessment Program: About the Program. Available from http://www.narccap.ucar.edu/about/index.html.

Normalized Difference Vegetation Index (NDVI)

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Characterizing changes in the Mekong basin vegetation and surface water cover is important to understanding environmental patterns and processes, and informing ecosystem management decisions. Satellite remote sensing can provide invaluable geospatial information and techniques for studying spatially and temporally variable phenomena such as flooding regimes, vegetation communities, and crop growth. Satellite remote sensing has become one of the most valuable and readily available tools for regional- and ecosystem-scale research and management applications. Spaceborne sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) provide daily data at regional to global scales, making these sensors preferred data sources for aiding regional scale assessments of spatially and temporally variable terrestrial and aquatic phenomena.

The terrestrial environment of this tropical-subtropical region can be densely vegetated, depending on the location and season. The Normalized Difference Vegetation Index (NDVI) is a multi-band spectral index used to detect, quantify and assess vegetation canopy greenness for single dates and/or over time. This use is in large part due to NDVI being chlorophyll sensitive (Huete et al. 2002). Since its initial use in the 1970s, satellite-based NDVI has been employed to aid regional studies of vegetation productivity, leaf area index, fraction of photosynthetically active radiation, percent canopy cover, phenology, land cover classification and disturbance recognition and tracking (Gamon et al. 1995; Rundquist 2002; Filella et al. 2004; Pettorelli et al. 2005; Hargrove et al. 2009; Spruce et al. 2011; Ramsey et al. 2011). The NDVI is a normalized ratio of two spectral reflectance bands that exploits varying absorption and reflection characteristics of red and near-infrared (NIR) wavelengths of light in relation to observed locations on the earth’s surface. The NDVI is calculated according to the following formula: [NDVI = (Xnir – Xred)/(Xnir + Xred)], where Xnir refers to the near-infrared band (MODIS band 2) and Xred refers to the red band (MODIS band 1). Given its long multi-decadal history of use involving multiple satellite sensors, NDVI has also been called the continuity index (Huete et al. 1999; Huete et al. 2002).

References:

Filella, I., Penuelas, J., Llorens, L., and Estiarte, M. (2004). Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought. Remote Sensing of Environment, 90(3):308-318.
Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Geeske, J., Penuelas, J., and Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5:28-41.
Hargrove, W. W., Spruce, J. P., Gasser, G.E., and Hoffman, F. M. (2009). Toward a national early warning system for forest disturbances using remotely sensed canopy phenology. Photogrammetric Engineering & Remote Sensing, 75:1150-1156.
Huete, A, Justice, C., and van Leeuwen, W. (1999). MODIS VEGETATION INDEX (MOD 13) Algorithm Theoretical Basis Document, Version 3, On-line at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (accessed 2-3-2011).
Huete, A. Didan, K. Miura, T. Rodriguez, E. P, Gao, X., and Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-213.
McFeeters, S. K. (1996). The use of Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17(7):1425–1432.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J., Tucker, C. J., and Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20(9):503-510.
Ramsey, E., Spruce, J., Rangoonwala, A., Suzuoki, Y., Smoot, J., Gasser, J., and Bannister, T. (2011). Daily MODIS data trends of hurricane-induced forest impact and early recovery, Photogrammetric Engineering and Remote Sensing, 77 (11), 133-143.
Rundquist, B. C. (2002). The influence of canopy vegetation fraction on spectral measurements over native tallgrass prairie. Remote Sensing of Environment, 81(1):129-135.
Spruce, J. P., Sader, S., Ryan, R. E., Smoot, J., Kuper, P., Ross, K., Prados, D., Russell, J., Gasser, G., McKellip, R., and W. Hargrove, W. (2011). Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment, 115:427-437.
Vermote E.F., and Vermeulen, A. (1999). MODIS Algorithm Technical Background Document, Atmospheric Correction Algorithm: Spectral Reflectances (MOD09), Version 4.0, April 1999. Online at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod08.pdf (accessed 2-18-2011).

Normalized Difference Vegetation Index (NDVI) Change

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The vegetation change products are based on MODIS NDVI time series data from 2003-2011. These change products represent the percent change in maximum value NDVI compared to the average maximum value across a multiyear baseline. The NDVI values are computed utilizing MODIS 09A1 reflectance products. These MOD/MYD09A1 products are actually composites of the original daily MODIS Terra and Aqua reflectance data that have undergone atmospheric correction and cloud removal algorithms (Vermote and Vermeulen 1999) in addition to temporal data processing with a software package known as the Time Series Product Tool (TSPT). TSPT subjects the data to additional noise reduction and data void interpolation on an as needed “decision rule” basis. The highest quality pixel during an 8-day period is contained within the input MODIS reflectance data. Though these products have already gone through cloud exclusion, noise reduction, and void interpolation algorithms, some residual contamination still exists, particularly in portions of the Mekong River Basin, such as elfin forests, where cloud cover can frequently persist,. To further mitigate atmospheric contamination and noise, monthly composites were aggregated from the original 8-day products that were temporally processed with TSPT.

For each observed monthly interval, the NDVI-based change products compare maximum NDVI for a current monthly date to NDVI from a historical baseline (i.e. mean of the maximum NDVI), expressing the results of that comparison in terms of % NDVI change. Comparing current versus historical NDVI composites is useful for monitoring change compared to “normal” (i.e. average) conditions at monthly intervals during the vegetation growing season. Subsequent monthly composites can then be compared to the corresponding baseline composite to create a departure-from-average dataset (NDVI departure). Comparing current maximum NDVI imagery to a historical baseline (mean of the maximum NDVI) on a monthly basis minimizes the detection of change due to variability in the timing of senescence periods and other intra-annual variations, facilitating the isolation of change typically due to abnormal episodic events.

Done at monthly intervals, the change products presented here specifically report the % change in a given “current” NDVI to the mean of the maximum NDVI across the 2003-2011 time series. The historical baseline was calculated using 2003-2011 data, since this time frame includes observations by both MODIS Aqua and Terra sensors. Above and below % NDVI change values represent areas experiencing higher or lower vegetation canopy greenness (respectively) than is typical for that area during the given month. For example, negative % NDVI change (shown in hot color shades on the map with dark red tones for the most extreme) indicate below-average vegetation conditions, which can be due to atypical flooding, drought, vegetation decline, or vegetation clearing. Studying these above and below % change in NDVI values provides a means for obtaining a greater understanding of vegetation condition throughout the year and across years in a complex, diverse ecosystem such as the Mekong River basin.

In using these products, note that the monthly temporal composite products are actually 32 day products that approximate monthly products. Also, the products for 2011 are complete through the mid-way point of 2011, due to the processing requirements for TSPT. These MODIS NDVI and NDVI change products have a spatial resolution of approximately 232 meters per pixel.

References:

Filella, I., Penuelas, J., Llorens, L., and Estiarte, M. (2004). Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought. Remote Sensing of Environment, 90(3):308-318.
Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Geeske, J., Penuelas, J., and Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5:28-41.
Hargrove, W. W., Spruce, J. P., Gasser, G.E., and Hoffman, F. M. (2009). Toward a national early warning system for forest disturbances using remotely sensed canopy phenology. Photogrammetric Engineering & Remote Sensing, 75:1150-1156.
Huete, A, Justice, C., and van Leeuwen, W. (1999). MODIS VEGETATION INDEX (MOD 13) Algorithm Theoretical Basis Document, Version 3, On-line at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (accessed 2-3-2011).
Huete, A. Didan, K. Miura, T. Rodriguez, E. P, Gao, X., and Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-213.
McFeeters, S. K. (1996). The use of Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17(7):1425–1432.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J., Tucker, C. J., and Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20(9):503-510.
Ramsey, E., Spruce, J., Rangoonwala, A., Suzuoki, Y., Smoot, J., Gasser, J., and Bannister, T. (2011). Daily MODIS data trends of hurricane-induced forest impact and early recovery, Photogrammetric Engineering and Remote Sensing, 77 (11), 133-143.
Rundquist, B. C. (2002). The influence of canopy vegetation fraction on spectral measurements over native tallgrass prairie. Remote Sensing of Environment, 81(1):129-135.
Spruce, J. P., Sader, S., Ryan, R. E., Smoot, J., Kuper, P., Ross, K., Prados, D., Russell, J., Gasser, G., McKellip, R., and W. Hargrove, W. (2011). Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment, 115:427-437.
Vermote E.F., and Vermeulen, A. (1999). MODIS Algorithm Technical Background Document, Atmospheric Correction Algorithm: Spectral Reflectances (MOD09), Version 4.0, April 1999. Online at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod08.pdf (accessed 2-18-2011).

Normalized Difference Water Index (NDWI)

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Monthly Normalized Difference Water Index (NDWI) products are available for 2003-2011. These products are derived from MOD09 reflectance data that has been atmospherically corrected, cloud-masked, and temporally processed with the Time Series Product Tool (TSPT) to further mitigate noise and interpolate data voids. The NDWI products shown here are derived using the NDWI formula originally published by McFeeters (1996). The McFeeters’ NDWI is computed as follows: [NDWI = (Xgreen – Xnir)/(Xgreen + Xnir)], where Xgreen refers to the green band (MODIS band 4) and Xnir refers to the nir band (MODIS band 2). This formulation of NDWI produces an image in which the positive data values are typically open water areas; while the negative values are typically non-water features (i.e. terrestrial vegetation and bare soil dominated cover types). Like NDVI, NDWI has a native scaling of -1 to +1. The NDWI products were processed at a spatial resolution of about 463 meters per pixel. These products offer a means to view water bodies and may be useful for assessing flooding impacts. This NDWI was utilized over other available forms in part because the output appears to be cleaner with less apparent noise. The NDWI products can be used in conjunction with NDVI change products to assess context of apparent change areas.

References:

Filella, I., Penuelas, J., Llorens, L., and Estiarte, M. (2004). Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought. Remote Sensing of Environment, 90(3):308-318.
Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Geeske, J., Penuelas, J., and Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5:28-41.
Hargrove, W. W., Spruce, J. P., Gasser, G.E., and Hoffman, F. M. (2009). Toward a national early warning system for forest disturbances using remotely sensed canopy phenology. Photogrammetric Engineering & Remote Sensing, 75:1150-1156.
Huete, A, Justice, C., and van Leeuwen, W. (1999). MODIS VEGETATION INDEX (MOD 13) Algorithm Theoretical Basis Document, Version 3, On-line at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (accessed 2-3-2011).
Huete, A. Didan, K. Miura, T. Rodriguez, E. P, Gao, X., and Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-213.
McFeeters, S. K. (1996). The use of Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17(7):1425–1432.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J., Tucker, C. J., and Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20(9):503-510.
Ramsey, E., Spruce, J., Rangoonwala, A., Suzuoki, Y., Smoot, J., Gasser, J., and Bannister, T. (2011). Daily MODIS data trends of hurricane-induced forest impact and early recovery, Photogrammetric Engineering and Remote Sensing, 77 (11), 133-143.
Rundquist, B. C. (2002). The influence of canopy vegetation fraction on spectral measurements over native tallgrass prairie. Remote Sensing of Environment, 81(1):129-135.
Spruce, J. P., Sader, S., Ryan, R. E., Smoot, J., Kuper, P., Ross, K., Prados, D., Russell, J., Gasser, G., McKellip, R., and W. Hargrove, W. (2011). Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment, 115:427-437.
Vermote E.F., and Vermeulen, A. (1999). MODIS Algorithm Technical Background Document, Atmospheric Correction Algorithm: Spectral Reflectances (MOD09), Version 4.0, April 1999. Online at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod08.pdf (accessed 2-18-2011).

MODIS True Color Composite RGB

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Monthly true color RGB products are available for use in assessing vegetation and water conditions within the region. These products were custom processed with the Time Series Product Tool (TSPT) software to minimize and mitigate cloud contamination that can be problematic with electro-optical data sets collected in the tropics and sub-tropics, especially during the monsoon or rainy season.

With its broad 2,330-km-wide viewing swath, the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on the Terra and Aqua satellites is ideal for monitoring ecosystem changes at broad regional scales. However, frequent cloud cover over the Mekong basin presents tremendous challenges in terms of using remote sensing to study and monitor the region. Though MODIS captures imagery of virtually the entire planet twice per day, there were certain regions of the Mekong River Basin which were almost always cloud contaminated for a given month. Multiple stages of cloud recognition and poor quality data exclusion algorithms were used to create predominantly cloud-free imagery for the entire region on a monthly basis. Fortunately, though use of the TSPT software, improvements were obtained through customized cloud detection and removal methods in conjunction with additional noise mitigation and void interpolation techniques. This process yields predominantly cloud free true color composites which are of the highest quality during the dry season. These true color RGB data sets have a spatial resolution of approximately 463 meters. Such products, like the NDWI, can be used to help assess the context of the NDVI-based change detection products.

References:

Filella, I., Penuelas, J., Llorens, L., and Estiarte, M. (2004). Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought. Remote Sensing of Environment, 90(3):308-318.
Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Geeske, J., Penuelas, J., and Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5:28-41.
Hargrove, W. W., Spruce, J. P., Gasser, G.E., and Hoffman, F. M. (2009). Toward a national early warning system for forest disturbances using remotely sensed canopy phenology. Photogrammetric Engineering & Remote Sensing, 75:1150-1156.
Huete, A, Justice, C., and van Leeuwen, W. (1999). MODIS VEGETATION INDEX (MOD 13) Algorithm Theoretical Basis Document, Version 3, On-line at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod13.pdf (accessed 2-3-2011).
Huete, A. Didan, K. Miura, T. Rodriguez, E. P, Gao, X., and Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83(1-2): 195-213.
McFeeters, S. K. (1996). The use of Normalized Difference Water Index (NDWI) in the delineation of open water features, International Journal of Remote Sensing, 17(7):1425–1432.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J., Tucker, C. J., and Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in Ecology and Evolution, 20(9):503-510.
Ramsey, E., Spruce, J., Rangoonwala, A., Suzuoki, Y., Smoot, J., Gasser, J., and Bannister, T. (2011). Daily MODIS data trends of hurricane-induced forest impact and early recovery, Photogrammetric Engineering and Remote Sensing, 77 (11), 133-143.
Rundquist, B. C. (2002). The influence of canopy vegetation fraction on spectral measurements over native tallgrass prairie. Remote Sensing of Environment, 81(1):129-135.
Spruce, J. P., Sader, S., Ryan, R. E., Smoot, J., Kuper, P., Ross, K., Prados, D., Russell, J., Gasser, G., McKellip, R., and W. Hargrove, W. (2011). Assessment of MODIS NDVI time series data products for detecting forest defoliation by gypsy moth outbreaks. Remote Sensing of Environment, 115:427-437.
Vermote E.F., and Vermeulen, A. (1999). MODIS Algorithm Technical Background Document, Atmospheric Correction Algorithm: Spectral Reflectances (MOD09), Version 4.0, April 1999. Online at: http://modis.gsfc.nasa.gov/data/atbd/atbd_mod08.pdf (accessed 2-18-2011).

Joint Operations Graphics (JOG)

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Joint Operations Graphics (JOGs) were produced by the U.S. Defense Mapping Agency (now known as the National Geospatial-Intelligence Agency) for parts of Vietnam, Cambodia, and Laos during the mid-1960’s to the early 1970’s. The maps were produced at 1:50,000 and 1:250,000 scale and contain detailed information on topography, vegetation, hydrography, cities and towns, cultural features, geographic place names, transportation networks, and other infrastructure. JOGs were produced in both ground and air versions to provide common base graphics for use in combined operations by ground and air military forces (Department of the Army, 1961). On the ground version (the type available on this website), elevation and contour values are shown in meters, while on the air version the elevation values are shown in feet. Both versions contain identical data, except the air version contains additional information to aid air navigation.

The historical JOGs for Vietnam, Cambodia, and Laos are now declassified and were obtained from the National Archives II cartographic holdings in College Park, MD. These paper maps were scanned at 300 dots per inch (DPI) resolution, and the digital versions then were georeferenced by Kenneth Then of the USGS Historical Quadrangle Scanning Project team. Next, the georeferenced JOGs were mosaicked and made available for use in Geographic Information System (GIS) software via a web mapping application. The JOGs can also be downloaded individually from this website in GeoTIFF format, which can be imported into a GIS and overlain with other data layers.

The JOGs are georeferenced to the Transverse Mercator projection, Everest spheroid, and the Indian Datum of 1960 (horizontal datum), which is the original projection of the paper maps.

Reference: