The study found that AET, slope/elevation, and changes in population density were all important explanatory factors for changes in tree cover. Decreased population density was linked to increased forest cover, and the authors suggest that this might be explained by marginal agriculture land abandonment. Cangxi County, Sichuan Prefecture demonstrates this trend, showing increased forest cover (Image 2) and decreased population density (Image 3) from 2000–2010. Meanwhile, Wenchuan County, Sichuan Prefecture shows decreased forest (Image 4) and increased population density (Image 5). Overall, the study’s findings show that increases in tree cover in eastern China are associated with low or declining human pressure, steep terrain (slope), and climatic conditions that favor tree growth. China also has enacted measures such as the Slope Land Conversion program, which aims to take marginal farmland with high slopes and convert the land back to forest. Forest cover change is of great concern across the planet as forests are sources of biodiversity and provide myriad ecosystem services to humans. Combining remote sensing-derived datasets from tree cover to elevation to evapotranspiration, along with socioeconomic data such as population density, provides rich opportunities to understand land surface dynamics and the various roles that humans and the environment play in changes in vegetation. AppEEARS simplifies the process of combining geospatial datasets from different sources to help users better understand the dynamics of our planet.
Example of AppEEARS Extract Area Sample form. This request is using a shapefile of counties in Sichuan Prefecture to subset data spatially from 2000–2010 for NASA MEaSUREs SRTM (SRTM v3), Terra MODIS Vegetation Continuous Fields (VCF) Percent Tree Cover (MOD44B.006), GPW UN-Adjusted Population Density (GPW v4), and Terra MODIS Evapotranspiration (MOD16A2.006).
Center for International Earth Science Information Network, CIESIN, Columbia University, 2015, Gridded population of the world, Version 4 (GPWv4)—Population density adjusted to match 2015 revision UN WPP country totals dataset: NASA Socioeconomic Data and Applications Center (SEDAC), accessed March 7, 2018, at https://doi.org/10.7927/H4SF2T42.
Dimiceli, C., Carroll, M., Sohlberg, R., Kim, D.H., Kelly, M., and Townshend, J.R.G., 2015, MOD44B—MODIS/Terra vegetation continuous fields yearly L3 global 250m SIN grid V006 dataset: NASA EOSDIS Land Processes DAAC, accessed March 6, 2018, at https://doi.org/10.5067/MODIS/MOD44B.006.
Nüchel, J., and Svenning, J.C., 2017, Recent tree cover increases in eastern China linked to low, declining human pressure, steep topography, and climatic conditions favoring tree growth: PLoS ONE, v. 12, no. 6, e0177552, accessed March 7, 2018, at https://doi.org/10.1371/journal.pone.0177552.
Reuter, H.I., Nelson, A., and Guevara, E., 2008, Hole-filled SRTM for the globe Version 4 dataset: CGIAR-CSI SRTM 90m Database, accessed March 7, 2018, at http://srtm.csi.cgiar.org.
Trabucco, A., and Zomer, R.J., 2010, Global high-resolution soil-water balance database: CGIAR Consortium for Spatial Information, accessed March 6, 2018, at http://www.cgiar-csi.org/data/global-high-resolution-soil-water-balance.
Material written by Cole Krehbiel1
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 G15PD00467 for LP DAAC2.
2 LP DAAC Work performed under NASA contract NNG14HH33I.