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Measuring and monitoring the extent of human settlements
Published in Ciro Gardi, Urban Expansion, Land Cover and Soil Ecosystem Services, 2017
Daniele Ehrlich, Aneta J. Florczyk, Andreea Julea, Thomas Kemper, Martino Pesaresi, Vasileios Syrris
The multi-temporal medium resolution GHSL is the first geographic dataset that describes the spatial evolution of the human settlements at the global scale and along a time interval covering 40 years (from 1975 to the present). Producing spatio-temporal built-up layers is a demanding task requiring several processing steps and sophisticated modelling. Prototyping and production were fraught with several challenges such as: (1) size, diversity and quality of the input/output datasets, (2) parameterisation and fine-tuning of the information extraction and fusion techniques and (3) computational complexity.
Dasymetric Mapping
Published in Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard, Thematic Cartography and Geovisualization, 2022
Terry A. Slocum, Robert B. McMaster, Fritz C. Kessler, Hugh H. Howard
The mission of the Global Human Settlement Layer project (GHSL; https://ghsl.jrc.ec.europa.eu/about.php) is to produce “global spatial information about the human presence on the planet over time, [including] … built-up maps, population density maps, and settlement maps.” One major GHSL effort has utilized machine learning remote sensing technology to create both 1-kilometer and 250-meter resolution built-up areas for the globe.11 These built-up areas are expressed as a proportion of building footprint area within a grid cell, and the associated database is referred to as GHS-Built (https://ghsl.jrc.ec.europa.eu/ghs_bu2019.php). GHSL has treated the GHS-Built database as ancillary data and used a dasymetric technique to combine GHS-Built with the GPW population data described in Section 16.9.1. Population totals for the vector-based enumeration units in GPW were assigned to 250-meter cells of the GHS-Built database as follows:If the enumeration unit generated 250 m cells and contained built-up areas (BU), the population was disaggregated in proportion to the density of the BU.If the polygon generated 250 m cells and did not contain BU, population was disaggregated using areal weighting.If the polygon did not generate its own 250 m cell, the polygon was converted to a point (based on its centroid), which was then associated with a particular cell (Freire et al. 2016, 3).12
The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL)
Published in International Journal of Digital Earth, 2020
Christina Corbane, Pesaresi Martino, Politis Panagiotis, Florczyk J. Aneta, Melchiorri Michele, Freire Sergio, Schiavina Marcello, Ehrlich Daniele, Naumann Gustavo, Kemper Thomas
The availability of the Landsat data archives combined with advanced image processing and analysis methods has allowed mapping of urbanization processes and the quantitative description of urban physical features including green spaces within cities. The Global Human Settlement Layer (GHSL) using the Landsat archive as a baseline data source, sets out as a key dataset for analysing and monitoring the spatial footprint of human settlements including their built-up areas, their population and their degree of urbanization at the global level (Pesaresi, Melchiorri, et al. 2016). Likewise, using the long-term Landsat data, it is feasible to study interannual greenness dynamics and provide a spatially complete view of the vegetation greenness change for all cities around the world (Ju and Masek 2016). By integrating greenness information derived from Landsat records together with data on city boundaries and built-up areas derived from the GHSL, this work aims at providing a spatially comprehensive picture of greenness changes in the period 1990–2014 for more than 10,000 urban centres worldwide. In this research, we assess differences between greenness within and outside the built-up area for all the urban centres identified by the Global Human Settlement Model (GHS-SMOD). The greenness values are derived from Landsat annual Top-of-Atmosphere (TOA) reflectance composites available as collections in the Google Earth Engine (GEE) platform for the period 1982–2018. These composites are created by considering the highest value of the Normalized Difference Vegetation Index (NDVI) as the composite value. Changes in the amount of greenness within cities are investigated in the periods centred on 1990, 2000 and 2014 by estimating it within the built-up areas. A cross-comparative analysis has been conducted between TOA-based greenness values and NDVI trends based on Landsat surface reflectance for the purpose of checking the suitability of the greenness values for the analysis of greenness changes over time. The surface reflectance-derived trends are taken as a control for checking the consistency in the TOA-based estimates of changes in the amount of greenness. The results from this research provide insights into the changes of greenness within cities at the global level and a framework for monitoring green spaces with open and spatially consistent datasets.