Highlights from the Literature: January to March 2019

Jun 18, 2019

Data products distributed by the Land Processes Distributed Active Archive Center (LP DAAC) are used in many different Earth Science applications. LP DAAC products play an important role in modeling, detecting changes to the landscape, and assessing ecosystem variables, to name a few. Three of those applications, published between January and March 2019, are highlighted below. A more comprehensive list is available on the LP DAAC Publications webpage.

The MODIS NDVI layer over Spain and the Balearic Islands. Vegetation is shown in various shades from brown to dark green. Areas in north and western Spain are show as green, where as areas in southern and East Spain are more brown.

A view of Spain and the Balearic Islands, the study area from Novillo and others (2019). This image shows the Normalized Difference Vegetation Index (NDVI) layer from the Terra MODIS Vegetation Indices data product during the summer of 2016.

Granule IDs:

Data citation:
Didan, K.. MOD13Q1 MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD13Q1.006

Boundary source:
Global Administrative Unit Layers (GAUL) dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects.

Novillo, C., Arrogante-Funes, P., and Romer-Calcerrada, R., 2019, Recent NDVI trends in mainland Spain—Land-cover and phytoclimatic-type implications: International Journal of Geo-Information, v. 8, no. 1, art. no. 43. [Also available at https://doi.org/10.3390/ijgi8010043.]

Changes in the seasonal development of vegetation are a good indicator of climate change; however, the impact varies from one land-cover type to the next. Thus, Novillo and others (2019) set out to analyze long-term vegetation trends in mainland Spain and the Balearic Islands using Normalized Difference Vegetation Index (NDVI) data from the Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices product (MOD13Q1) from 2001 to 2016. The authors evaluate the changes in trends with the corresponding land-cover type using Co-Ordination of Information of the Environment (CORINE) Land Cover (CLC) maps and phytoclimatic regions based on the Allué Classification. Novillo found that the region’s NDVI data showed more positive trends (11.8 percent) than negative trends (7.6 percent) overall, which they attributed to an increase in photosynthetic activity across Europe from factors such as longer growing seasons and increases in forested areas. However, the negative NDVI trends they found are higher than NDVI trends found in previous regional studies towards the end of the 20th century. The authors found significant negative trends in the Spanish Atlantic and Mediterranean coasts, areas south of the Iberian mountain range, within the Sierra Morena mountain range, and in the Balearic Islands. The authors found positive trends in northeastern Spain, the Northern Plateau, the Central System, and the Guadalquivir Valley. They conclude by stating that remote sensing datasets are among the best options for assessing trends in vegetation dynamics, and that further research is critical to understanding and managing this region in the future.

A map showing nighttime temperatures over the Po Valley in Italy. Temperatures range from -10 to 25 degrees Celsius. Colder temperatures are shown as shades of blue, moderate as shades of green, and warmer as shades of yellow, which are near urban areas.

A Terra MODIS nighttime LST composite of the Po Valley, the research area of the Zullo and others (2019) study. The shades of blue in the Alps indicate cooler temperatures, whereas the shades of green and yellow indicate mild to warmer temperatures in the Po River Valley.

Granule ID:

Data Citation:
Wan, Z., S. Hook, G. Hulley. MOD11A2 MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1km SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD11A2.006

Boundary source:
Global Administrative Unit Layers (GAUL) dataset, implemented by FAO within the CountrySTAT and Agricultural Market Information System (AMIS) projects.

Zullo, F., Fazio, G., Romano, B., Marucci, A., and Fiorini, L., 2019, Effects of urban growth spatial pattern (UGSP) on the land surface temperature (LST)—A study in the Po Valley (Italy): Science of the Total Environment, v. 650, pt. 2, p. 1740–1751. [Also available at https://doi.org/10.1016/j.scitotenv.2018.09.331.]

In this paper, Zullo and others (2019) examine how different urban growth spatial patterns (UGSP) impacted Land Surface Temperatures (LST) in the Po Valley in Italy between 2001 to 2011. For this study, the authors use Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Daytime and Nighttime LST data (MOD11A2) from the LP DAAC, combined with urbanization data from the Italian National Institute of Statistics (ISTAT) database to conduct an Analysis of Variance (ANOVA) test, as well as a post-hoc analysis using the Turkey-Kramer test. The authors found that during the study period, the Po Valley saw an increase in urban area of 630 square kilometers (240 square miles). The average daytime LST in the region increased by 1.36 °C, and the average nighttime LST increased by 0.53 °C, for an overall daily increase of 0.93°C over the 10-year study period. In some areas, the authors found an increase of over 2 °C in daily LST. The study also found that the range between daytime and nighttime temperatures decreased in areas where urban cover progressively increased, which can lead to a lack of cooling relief at night. The authors believe their results provide useful information to raise public awareness about how different UGSPs can impact temperature changes on a local scale.

A thermal ASTER image showing part of Turkey near the Tuzla Village, located in western Turkey. The image is shades of gray and shades of white, including areas where geothermal sources may exist.

One of the Terra ASTER TIR images used by Sekertekin and Arslan (2019) in their research over the Tuzla Village, Turkey. Brighter colors over the land in the image indicate potential sources of heat.

Granule IDs:

Data Citation:
NASA LP DAAC. ASTER Level 1 Precision Terrain Corrected Registered At-Sensor Radiance V003. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/ASTER/AST_L1T.003

Sekertekin, A., and Arslan., N., 2019, Monitoring thermal anomaly and radiative heat flux using thermal infrared satellite imagery—A case study at Tuzla geothermal region: Geothermics, v. 78, p. 243–254. [Also available at https://doi.org/10.1016/j.geothermics.2018.12.014.]

Geothermal energy is a renewable, environmentally-friendly, and reliable energy source that could reduce our reliance on non-renewable energy resources. Therefore, Sekertekin and Arslan (2019) set out to learn more about this energy source using daytime and nighttime Thermal Infrared (TIR) data from the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite sensor (AST_L1T) and Landsat 8. For this study, the authors use satellite data from 2012, 2013, and 2017 to find thermal anomalies in the Tuzla Village geothermal region of Turkey. The authors chose to use satellite data because it is a cost-effective and time-efficient way to detect thermal anomalies on a large scale. The authors use the Mono-window Algorithm (MWA), developed by previous researchers, to retrieve LST images from the ASTER TIR bands. In this paper, the authors specifically used TIR Band 14. The authors first examined these images, then masked the nighttime images to evaluate for geothermal activity. The results were then cross-validated with Moderate Resolution Imaging Spectroradiometer (MODIS) LST data and verified with Radiative Heat Flux images. The authors also examined Normalized Difference Vegetation Index (NDVI) derived from Landsat data, used to retrieve land surface emissivity. The results indicate that nighttime LST data are significantly more efficient in finding thermal anomalies in geothermal areas than daytime LST data. They also found that while geothermal areas had higher LST values, the NDVI values of these areas remained the same as the values of surrounding non-geothermal areas with similar vegetation. Therefore, they were led to believe that these areas are sources of geothermal anomalies. This was also confirmed when the areas overlapped with areas from previous in-situ geothermal studies. The authors believe remote sensing data can be a great resource to study the spatial distribution of geothermal activity. However, since these data only provide information about the surface of the Earth, it would be beneficial to conduct a future study with simultaneous ground data measurements and satellite imagery.

Material written by Danielle Golon​1

1 Innovate!, Inc., contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. Work performed under USGS contract G15PC00012 for LP DAAC2.

2 LP DAAC Work performed under NASA contract NNG14HH33I.