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Title: Focal accuracy estimates and landscape metrics for built-up areas from the GHS-BUILT R2018A in Massachusetts, USA.
These files are supplementary data for this publication: Uhl JH & Leyk S (2022). "Assessing the relationship between morphology and mapping accuracy of built-up areas derived from global human settlement data (https://doi.org/10.1080/15481603.2022.2131192). Each geopackage (GPKG) file contains a set of point locations (in EPSG:3857) attributed with focal accuracy metrics of the GHS-BUILT-R2018A epochs 1975 and 2014, calculated within different levels of spatial support (i.e., focal window size) and for different analytical units (i.e., 30m grid cells, and 3x3 grid cell blocks). Moreover, each location is attributed with focal landscape metrics of built-up areas calculated in the same focal windows using the software Fragstats. These landscape metrics are calculated based on both, GHS built-up areas and reference built-up areas. Reference built-up areas were derived from the Multi-temporal building footprint database for 33 U.S. counties (MTBF-33). These datasets can be used for spatially explicit predictive modeling of the GHS-BUILT R2018A data accuracy using landscape metrics as predictor variables. File nomenclature: lsm_ref_accuracy_sample_2014_1000.gpkg : landscape metrics calculated from the reference built-up areas, for the epoch 2014, using a quadratic focal window of 1,000m x 1,000m. lsm_ghs_accuracy_sample_1975_10000.gpkg : landscape metrics calculated from the ghs built-up areas, for the epoch 1975, using a quadratic focal window of 10,000m x 10,000m. Data processing: Johannes H. Uhl, University of Colorado Boulder (USA), 2020-2022.  more » « less
Award ID(s):
1924670
PAR ID:
10482845
Author(s) / Creator(s):
;
Publisher / Repository:
figshare
Date Published:
Subject(s) / Keyword(s):
Urban analysis and development Photogrammetry and remote sensing Landscape ecology
Format(s):
Medium: X Size: 477894312 Bytes
Size(s):
477894312 Bytes
Sponsoring Org:
National Science Foundation
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This allows a characterization of the estimated loads for each day relative to all past/future loads near that day, in order to bolster the sample size and provide more robust parametric estimates [similar to Ford et al., (2016)]; this is a key difference between our GDI derivation and that presented by Tang et al. (2023). Figure 1d illustrates the representative distribution for 01 December of each year at the grid cell co-located with GPS station P349 for the daily TWS solution. Here all epochs between between 16 November and 16 December of each year (red dots), are compiled to form the distribution presented in Figure 1e. This approach allows inter-annual variability in the phase and amplitude of the signal to be retained (which is largely driven by variation in the hydrologic cycle), while removing the primary annual and semi-annual signals. 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