High-resolution (30-m) urban land cover projections for Los Angeles California Urban Area: 2010 to 2100 under SSP3 and SSP5 [Updated simulations based on population-driven urban intensity transitions]

These data (v3)  are updated from previous versions (1 and 2) in that they include consider the effects of population on transitions in urban land intensity. This leads to more reasonable differences in urban land projections under variant SSPs.  For the present dataset, both SSP3 and SSP5 are provided.
These data represent simulations of future land use and land cover (LULCC) for Los Angeles urban area
(U.S. Census Bureau defined area) as raster tiff images at a 30-m pixel resolution and at decadal time
steps from 2010 to 2100. LULCC classes in this product follow the National Land Cover Dataset (NLCD)
classification. NLCD 21-24 correspond to open developed, low developed, medium developed, and high
developed urban land classes, respectively. Only urban land cover classes (NLCD class 21, 22, 23, and 24)
are dynamic over time; however, all NLCD classes are included in the final product. Therefore, NLCD
classes that do not convert to an urban class will be similar to year 2000. The products were developed
using a hybridized statistical and cellular automata approach. Linear mixed models (LMMs) were used
to estimate future urban land budgets based on 1-km urban land fraction projections from Gao and
Pesaresi (2021), whereas separate generalized linear mixed models (GLMMs) were used to estimate
shifts in urban land intensities based on retrospective shifts in NLCD urban class intensities over a 20-
year period. Based on urban land allocations from the statistical models, a cellular-automata and
downscaling routine was used to simulate dynamic urban land expansion at a 30-m resolution based on
suitability criteria. Scenarios of future urban landcover change projections include variant solutions for
the Shared Socioeconomic Pathway 5 (SSP5) and SSP 3 based on different population assumptions, different land
use intensification assumptions, variable land zoning constraints, and iterative adjustments to correct
for over allocation of urban expansion across decadal time periods from 2010 to 2100. This results in
320 raster products.