THIS POST IS PART OF OUR ANTHROPOCENE BIOSPHERE PROJECT–A SERIES OF POSTS ON ERLE ELLIS’ ‘ECOLOGY IN AN ANTHROPOGENIC BIOSPHERE‘ (ECOLOGICAL MONOGRAPHS, 85/3 (2015))
Ellis (2015) discusses in detail the idea that to be able to understand long-term ecological patterns and processes it is now necessary to understand human sociocultural processes first. To visualize the direct influence ofhumans on the landscape, Ellis and Ramankutty (2008) developed a dataset of anthropogenic biomes, which they shorten to “anthromes.” Anthromes are human biomes in which the global ecological patterns are classified according to the effect of human interactions with the ecosystems. Ellis et al. (2010) describes the development of these anthromes based on six underlying 5 by 5 minute spatial resolution datasets: population density (persons/km2), % cropland, % irrigated, % rice, % pasture and % urban. The anthromes papers (Ellis 2015; Ellis et al. 2010; Ellis and Ramankutty 2008) show the global distribution of these anthromes; however, to fully understand these datasets it is useful to take a closer look in a familiar area.
Below, I have extracted the anthromes for the four years (1700, 1800, 1900 and 2000) over Oklahoma as an illustration. There are a few things to notice in these maps. First, there is an interesting classification of Oklahoma’s land as ‘Inhabited treeless and barren lands’, while the land in Texas is classified as ‘Wild treeless and barren lands’ in 1700. A similar border can be found between Oklahoma and Arkansas, where the Oklahoma woodlands are classified as “Remote woodlands” while the Arkansas woodlands are classified as “Wild woodlands”. However, in general the 1700 data appear as expected. The subsequent maps show a slow but steady increase in the populated areas with the largest jump, as expected, between 1900 and 2000. By the year 2000 there are no treeless and barren lands left and all such land has been converted to croplands and rangelands or urban land. Similarly, most of the forests in the eastern portion of the state have been converted except for a small area in the southeastern part of the state.
If we graph the different classes over time, we can easily see the rapid decline in semi-natural lands between 1900 and 2000 in favor of croplands and rangelands.
One interesting feature to notice, especially for Oklahoma, is the omission of the increase in surface water on these maps. In a more detailed study for the Lake Thunderbird watershed we showed the change in stream channels for the Lake Thunderbird watershed in Central Oklahoma (Julian et al. 2015). This study shows much more detailed maps for Central Oklahoma and reveals a large increase in open water from less than 1% in the earliest maps (1874) to 6.9% of the watershed by 1975 which is mainly the result of the creation of Lake Stanley Draper in 1963 and Lake Thunderbird in 1965. Note that these lakes are invisible on the 2000 Anthromes map. Interesting especially because the lakes are man-made and one of the most important changes to the natural landscape over the past 100 years.
The spatial resolution of the Anthromes data is 5 arc-minutes. This means that every grid cell measures 5 arc-minutes by 5 arc-minutes, which is approximately 9 by 9 km at the equator. In this day and age, spatial data are available at a range of different spatial resolutions. When evaluating changes on the land surface, it is often difficult to attribute the observed changes to either human or climatological factors. In a previous study we evaluated changes over a short time period (since 2000) at two spatial resolutions, 5.6km and 500m, in Russia and Kazakhstan (de Beurs et al. 2009). We found that the general pattern of observed changes is similar at the two spatial resolutions. The 5.6km spatial resolution is the current limit of regional and mesoscale meteorological models and we found that changes at this coarser scale are relevant to atmospheric boundary layer processes. The finer scale analysis however revealed trends that were more relevant to human decision-making and regional economics. As such we proposed that dual scale analysis might enable the partitioning of change attribution (de Beurs et al. 2009).
To provide a better understanding of the effect of different spatial resolutions, I show two maps with higher spatial resolution data for Oklahoma below. The first map provides the ambient population at a 1km2 spatial resolution from LandScan (Bright et al. 2011). This is a map for the year 2012 with every grid cell (1km by 1km) color coded according to its ambient population.
The second map provides the impervious surface layer from the National Land Cover Dataset for Oklahoma (Homer et al. 2015). The spatial resolution of this data is 30m and every pixel is color coded according to the percentage of impervious surface. While human influence appears incredibly large and almost overpowering in the anthromes maps and even the Landscan data, the 30m resolution makes this influence look almost puny.
5 thoughts on “The effect of humans on the landscape in Oklahoma: Where is the water? And the effect of spatial data resolution”
Nice work Kirsten! It is fascinating to see that anthromes are OK! (sorry, couldn’t resist). I will certainly agree with you that the scale of analysis works better globally than locally, it does provide an interesting view of long-term anthroecological changes across OK.
Welcome to the blog, Kirsten–and thanks for your wonderful post to start off this series!
I loved the maps–and I have two questions about them. First, to my (uneducated) eye, Figures 1-4 (the time sequence for Oklahoma) seem to follow the rough pattern Erle lays out in his Figure 5 (p. 314). Does that seem right to you? That is, do you think the Oklahoma case is consistent with his broad theoretical prediction?
Second, at the very end you say at the finer resolution available in the National Land Cover Dataset the human influence looks small. Are you suggesting that the anthrome images Ellis and Ramankutty overstate human influence, due to data resolution issues? I wonder here because your map shows impervious surface–so the gray is unpaved, un-roofed surface. But this wouldn’t pick up, for example, cultivated land–wouldn’t that count as human influenced?
I’d love to hear your thoughts on these points. In any event, I think you point to a really key question about the inter-relation between methods for making sense of data and the big-picture theoretical claims those methods are used to support.
Pingback: #EnvHist Worth Reading: January 2016 | NiCHE
Great post and cool maps!
I’m intrigued by the differences you show in different scales of analysis and by your suggestion that some scalar boundary might be useful in separating between atmospheric boundary layer processes and more direct outcomes of human behavior. I may be oversimplifying, but do you think some meso-scale of analysis might mediate between those two layers in a way that explores their interrelationships?
If we accept that atmospheric processes affect human decisions and that the outcomes of human decisions can in turn impact atmospheric processes, it seems like the point of recursion between these two sets of processes is where all the really interesting stuff happens. Do you think there’s a particular scale or method of analysis that lends itself to exploring that recursion, or is this something that is going to be highly specific to particular contexts and questions?
Hi Sarah, thank you for your kind words about the post. I actually think that multiscale analyses are the way to get at these processes. Most research that I am familiar with picks one scale and looks only at that scale, either coarse or fine. I think looking at multiple scales simultaneously could really help in getting a better understanding of these processes and interactions.