More Like this

1. Divergent urban land trajectories under alternative population projections within the Shared Socioeconomic Pathways

   https://doi.org/10.1088/1748-9326/ad2eec  

   McManamay, Ryan A; Raad, Alen; Vernon, Chris R; Thurber, Travis; Gao, Jing; Powers, Stephen; O’Neill, Brian (March 2024, Environmental Research Letters)

   Abstract Population change is a main driver behind global environmental change, including urban land expansion. In future scenario modeling, assumptions regarding how populations will change locally, despite identical global constraints of Shared Socioeconomic Pathways (SSPs), can have dramatic effects on subsequent regional urbanization. Using a spatial modeling experiment at high resolution (1 km), this study compared how two alternative US population projections, varying in the spatially explicit nature of demographic patterns and migration, affect urban land dynamics simulated by the Spatially Explicit, Long-term, Empirical City development (SELECT) model for SSP2, SSP3, and SSP5. The population projections included: (1) newer downscaled state-specific population (SP) projections inclusive of updated international and domestic migration estimates, and (2) prevailing downscaled national-level projections (NP) agnostic to localized demographic processes. Our work shows that alternative population inputs, even those under the same SSP, can lead to dramatic and complex differences in urban land outcomes. Under the SP projection, urbanization displays more of an extensification pattern compared to the NP projection. This suggests that recent demographic information supports more extreme urban extensification and land pressures on existing rural areas in the US than previously anticipated. Urban land outcomes to population inputs were spatially variable where areas in close spatial proximity showed divergent patterns, reflective of the spatially complex urbanization processes that can be accommodated in SELECT. Although different population projections and assumptions led to divergent outcomes, urban land development is not a linear product of population change but the result of complex relationships between population, dynamic urbanization processes, stages of urban development maturity, and feedback mechanisms. These findings highlight the importance of accounting for spatial variations in the population projections, but also urbanization process to accurately project long-term urban land patterns. 

   more » « less
2. Global urban growth between 1870 and 2100 from integrated high resolution mapped data and urban dynamic modeling

   https://doi.org/10.1038/s43247-021-00273-w  

   Li, Xuecao; Zhou, Yuyu; Hejazi, Mohamad; Wise, Marshall; Vernon, Chris; Iyer, Gokul; Chen, Wei (December 2021, Communications Earth & Environment)

   Abstract Long term, global records of urban extent can help evaluate environmental impacts of anthropogenic activities. Remotely sensed observations can provide insights into historical urban dynamics, but only during the satellite era. Here, we develop a 1 km resolution global dataset of annual urban dynamics between 1870 and 2100 using an urban cellular automata model trained on satellite observations of urban extent between 1992 and 2013. Hindcast (1870–1990) and projected (2020–2100) urban dynamics under the five Shared Socioeconomic Pathways (SSPs) were modeled. We find that global urban growth under SSP5, the fossil-fuelled development scenario, was largest with a greater than 40-fold increase in urban extent since 1870. The high resolution dataset captures grid level urban sprawl over 200 years, which can provide insights into the urbanization life cycle of cities and help assess long-term environmental impacts of urbanization and human–environment interactions at a global scale. 

   more » « less
3. Four-century history of land transformation by humans in the United States (1630–2020): annual and 1 km grid data for the HIStory of LAND changes (HISLAND-US)

   https://doi.org/10.5194/essd-15-1005-2023  

   Li, Xiaoyong; Tian, Hanqin; Lu, Chaoqun; Pan, Shufen (January 2023, Earth System Science Data)

   Abstract. The land of the conterminous United States (CONUS) hasbeen transformed dramatically by humans over the last four centuries throughland clearing, agricultural expansion and intensification, and urban sprawl.High-resolution geospatial data on long-term historical changes in land useand land cover (LULC) across the CONUS are essential for predictiveunderstanding of natural–human interactions and land-based climatesolutions for the United States. A few efforts have reconstructed historicalchanges in cropland and urban extent in the United States since themid-19th century. However, the long-term trajectories of multiple LULCtypes with high spatial and temporal resolutions since the colonial era(early 17th century) in the United States are not available yet. Byintegrating multi-source data, such as high-resolution remote sensingimage-based LULC data, model-based LULC products, and historical censusdata, we reconstructed the history of land use and land cover for theconterminous United States (HISLAND-US) at an annual timescale and 1 km × 1 km spatial resolution in the past 390 years (1630–2020). The results showwidespread expansion of cropland and urban land associated with rapid lossof natural vegetation. Croplands are mainly converted from forest, shrub,and grassland, especially in the Great Plains and North Central regions.Forest planting and regeneration accelerated the forest recovery in theNortheast and Southeast since the 1920s. The geospatial and long-termhistorical LULC data from this study provide critical information forassessing the LULC impacts on regional climate, hydrology, andbiogeochemical cycles as well as achieving sustainable use of land in thenation. The datasets are available at https://doi.org/10.5281/zenodo.7055086 (Li et al., 2022). 

   more » « less
4. A global dataset of daily maximum and minimum near-surface air temperature at 1 km resolution over land (2003–2020)

   https://doi.org/10.5194/essd-14-5637-2022  

   Zhang, Tao; Zhou, Yuyu; Zhao, Kaiguang; Zhu, Zhengyuan; Chen, Gang; Hu, Jia; Wang, Li (January 2022, Earth System Science Data)

   Abstract. Near-surface air temperature (Ta) is a key variable in global climatestudies. A global gridded dataset of daily maximum and minimum Ta (Tmax⁡ and Tmin⁡) is particularly valuable and critically needed inthe scientific and policy communities but is still not available. In this paper, we developed a global dataset of daily Tmax⁡ and Tmin⁡at 1 km resolution over land across 50∘ S–79∘ N from 2003 to 2020 through the combined use of ground-station-basedTa measurements and satellite observations (i.e., digital elevation model and land surface temperature) via a state-of-the-artstatistical method named Spatially Varying Coefficient Models with Sign Preservation (SVCM-SP). The root mean square errors in our estimates rangedfrom 1.20 to 2.44 ∘C for Tmax⁡ and 1.69 to 2.39 ∘C for Tmin⁡. We found that the accuracies were affectedprimarily by land cover types, elevation ranges, and climate backgrounds. Our dataset correctly represents a negative relationship betweenTa and elevation and a positive relationship between Ta and land surface temperature; it captured spatial and temporalpatterns of Ta realistically. This global 1 km gridded daily Tmax⁡ and Tmin⁡ dataset is the first of its kind, and weexpect it to be of great value to global studies such as the urban heat island phenomenon, hydrological modeling, and epidemic forecasting. The data havebeen published by Iowa State University at https://doi.org/10.25380/iastate.c.6005185 (Zhang and Zhou, 2022). 

   more » « less
5. Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City (2016-2019)

   https://doi.org/10.6073/pasta/5194fc22aaa4e0a5d58cfc6884498aa0  

   Tellman, Elizabeth (January 2021, Environmental Data Initiative)

   This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021). The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM), at the Institute of Geography via Dr. Armando Peralta Higuera, who provided support for two students, Juan Alberto Guerra Moreno and Kimberly Mendez Gomez for validating the Landsat urbanization algorithm. Fidel Serrano-Candela, at the UNAM Laboratory of the National Laboratory for Sustainability Sciences (LANCIS) also provided support for urbanization algorithm development and validation, and Rodrigo Garcia Herrera, who provided support for hosting data at LANCIS (at: http://patung.lancis.ecologia.unam.mx/tellman/). Additional collaborators include Enrique Castelán, who provided support for the informal urbanization data from SEDEMA (Ministry of the Environmental for Mexico City). Electoral, land titling, and land zoning data were digitized with support from Juana Martinez, Natalia Hernandez, Alexia Macario Sanchez, Enrique Ruiz Durazo, in collaboration with Felipe de Alba, at CESOP (Center of Social Studies and Public Opinion, at the Mexican Legislative Assembly). The data include geospatial time series data regarding changes in urban land cover, digitized electoral results, land titling, land zoning, and public housing. Additional funding for this work was provided by NSF under Grant No. 1414052, CNH: The Dynamics of Multiscalar Adaptation in Megacities (PI H. Eakin), and the NSF-CONACYT GROW fellowship NSF No. 026257-001 and CONACYT number 291303 (PI Bojórquez). References: Tellman, B., Eakin, H., Janssen, M.A., Alba, F. De, Ii, B.L.T., 2021. The Role of Institutional Entrepreneurs and Informal Land Transactions in Mexico City’s Urban Expansion. World Dev. 140, 1–44. https://doi.org/10.1016/j.worlddev.2020.105374 Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A.K., Cheng, W.-H., Balling, R.C., 2018. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205, 253–275. https://doi.org/10.1016/j.rse.2017.11.026 

   more » « less