Green Infrastructure Comparing NLCD 2011 to the Clark Labs’ Predicted NLCD 2050
Layer Opacity:
Clark County, Nevada
Phoenix, Arizona
Atlanta, Georgia
New York, New York
Harrison County, Mississippi
Washington County, Alabama
approx. change in developed
186,040,394 acres in 2011 vs 221,092,262 acres in 2050
approx. change in forests
838,577,357 acres in 2011 vs 770,680,982 acres in 2050
approx. change in cultivated crops
548,673,866 acres in 2011 vs 554,709,750 acres in 2050
This application compares changes between aggregated 2011 National Land Cover Database land cover categories with similarly aggregated land cover categories from The Clark Labs 2050 Conterminous US Land Cover Prediction. It also provides a few summary statistics about possible changes in developed, forest and agricultural land cover. Look for the soon to be released Clark Labs American Land Change Explorer application, which provides exhaustive analysis and summaries of potential transitions from each of the NLCD categories to each of the projected 2050 categories.
The Clark Labs 2050 Conterminous US Land Cover Prediction
© 2016 Clark Labs
The Clark Labs’ conterminous US land cover prediction for 2050 was produced as part of the development of the Land Change Explorer – a web application to illustrate the potential of predictive land change modeling and to introduce potential users to the Land Change Modeler – a cloud-based software service for land change modeling to be offered in the ArcGIS Marketplace.
The prediction is based on an empirical modeling of the relationship between land cover change from 2001 to 2011 and a series of explanatory variables. The land cover data were at a 30 meter resolution from the National Land Cover Database (NLCD). The explanatory variables1 were:
  • Elevation
  • Slope
  • Proximity to primary roads
  • Proximity to secondary roads
  • Proximity to local roads
  • Proximity to high intensity development
  • Proximity to open water
  • Proximity to cropland (used only for transitions to cropland)
  • Protected areas
  • County subdivisions or counties/incorporated places (depending on the state)2
The modeling procedure used is a newly developed algorithm suitable for distributed computing in a cloud computing environment3. Briefly, the procedure is based of kernel density estimation of the normalized likelihood of change associated with varying levels of each independent variable. These estimates are then aggregated by means of a locally-weighted average where the weights are based on the degree of conviction each variable has about the outcome at that specific pixel. Testing has shown it to be comparable in skill to a multi-layer perceptron neural network with the added advantages of rapid calculation and capability of being distributed across multiple computer nodes.
Because the drivers of change can vary over space, modeling was done separately for each state. All transitions that met or exceed 2 km2 in area (at the state level) were modeled independently. Within a single state, as many as 128 individual transitions might occur. In total, over the 48 conterminous states, 3330 transitions were modeled. The modeling process initially establishes the potential to transition. This potential is expressed as a continuous value from 0 to 1 at each pixel for each transition. The procedure then uses the Markovian assumption that the rate of transition experienced in the historical period (2001-2011 in this case) will continue into the future. A competitive greedy selection process then allocates the projected change4.
In the training process for each transition, 50% of historical instances of change and 50% of an equal-sized sample of pixels eligible to change, but which did not (e.g., persistence), were reserved for model validation. The median accuracy over all 3330 transitions was 80% with 79% of change validation pixels being correctly predicted and 83% of persistence pixels being correctly predicted. Thus the models, on average, are quite balanced in their ability to predict change and persistence.
The accuracy associated with more specific transitions varied. A key objective was to be able to monitor and project anthropogenic changes and thus the explanatory variables chosen were focused on such drivers. Consequently, the median accuracy of natural to developed transitions (such as deciduous forest to low intensity development) was 92%. Again, accuracy was evenly balanced (93% for change / 91% for persistence).
Accuracy for transitions between developed categories was lower at 77% (80% change and 75% persistence). A large part of this is because of the inconsistent manner in which roads are classified in the NLCD system. Roads are classified as one of the developed categories (high, medium, low and open development) based on the amount of impervious surface detected within pixels. Alignment of image pixels can cause this to vary resulting in roads that frequently switch classes between the years mapped.
Natural transitions, such as forest to shrub, had the lowest overall accuracy at 74%. This was expected because many drivers cannot be predicted with the variables used. An example would be forest fires caused by lightning. This is also reflected in the fact that accuracy for predicting change was only 71% while that for predicting persistence was 78%.
Finally, in states with significant cropland development, natural to cropland transitions were modeled with a 79% overall median accuracy. Accuracies for change and persistence were 78% and 81% respectively.
Note that there are many highly plausible future outcomes and the specific scenario presented is only one of these (albeit judged to be the most plausible). Also note that each state is modeled separately (on the assumption that drivers of change many differ between states). As a consequence, there may be some mismatches at the boundaries between states. Generally, these are only evident for states that have large quantities of natural to natural transitions (e.g., with forest plantation crop cycles or frequent fire) where the accuracy is lower. Also note that the protected areas layer does not include all protected areas. Some local conservation land may be missing. Finally, note that the modeling is based on the assumption that rate of change experienced within the historical period (2001-2011) will persist into the future.

1 Elevation data were from the National Elevation Database while slope was derived from those data. All roads data were acquired from the Census Bureau TIGER line files for 2014. Earlier road data would have been preferred, but errors in earlier TIGER line files were deemed to be unacceptable. Country subdivisions, counties and incorporated places were also acquired from the Census Bureau. Protected areas came from the Protected Areas Database of the USGS National Gap Analysis Program. All proximity layers were derived by Clark Labs.
2 In some states, planning jurisdiction is controlled by county subdivisions (such as in New England), while in others, planning is governed by a combination of counties and incorporated places (such as in many of the western states).
3 Eastman, J.R., Crema, S.C., and Rush, H.R., (forthcoming) A Weighted Normalized Likelihood Procedure for Empirical Land Change Modeling.
4 Greedy selection assumes that the specific pixels that will change are those that are ranked the highest. Conflicts are resolved by assigning them to the transition with the highest marginal transition potential.