Human settlement plays a key role in understanding social processes such as urbanization and interactions between human and environmental systems but not much is known about the landscape evolution before the era of operational remote sensing technology. In this study, housing and property databases are used to create new gridded settlement layers describing human settlement processes at fine spatial and temporal resolution in the conterminous United States between 1810 and 2015. The main products are a raster composite layer representing the year of first settlement, and a raster time series of built-up intensity representing the sum of building areas in a pixel. Several accompanying uncertainty surfaces are provided to ensure the user is informed about inherent spatial, temporal and thematic uncertainty in the data. A validation study using high quality reference data confirms high levels of accuracy of the resulting data products. These settlement data will be of great interest in disciplines in which the long-term evolution of human settlement represents crucial information to explore novel research questions.

5-lipoxygenase (5-LOThe enzyme 5-lipoxygenase (5-LO, arachidonate:oxygen 5-oxidoreductase, EC 1.13.11.34) 1 catalyzes the conversion of arachidonic acid to (5S)-hydroperoxy-6-trans-8,11,14-cis-eicosatetraenoic acid (5-HPETE) and further to leukotriene A 4 ((5S)-6-oxido-7,9-trans-11,14-cis-eicosatetraenoic acid) (1, 2).5-LO is expressed in a variety of immune competent cells including B-lymphocytes, granulocytes, monocytes, mast cells, and dendritic cells (3). Depending on the cell type, several cytokines have been shown to be inducers of the 5-LO pathway. In granulocytes 5-LO expression is stimulated by granulocytemacrophage colony-stimulating factor (GM-CSF) (4), whereas interleukin-3 regulates the development of the 5-LO pathway in mouse mast cells (5). In the human myeloid leukemic cell lines HL-60 and Mono Mac 6, cell differentiation by 1,25-dihydroxyvitamin D 3 (1,25(OH) 2 D 3 ) and transforming growth factor-beta (TGF␤) leads to a strong induction of the 5-lipoxygenase pathway (6, 7). In Mono Mac 6 cells, the induction of 5-LO protein expression and activity by TGF␤ and 1,25(OH) 2 D 3 was accompanied by a 64-fold up-regulation of mature 5-LO mRNA and an up to 5-fold increase in 5-LO primary transcripts (7, 8), whereas no significant induction of 5-LO transcription was found in nuclear run-off assays (9). The human 5-LO gene promoter was first characterized by Hoshiko et al. (10). Several features of the putative 5-LO promoter region (such as the lack of TATAA or CCAAT boxes and repeated (GϩC)-rich elements) are characteristic for so-called housekeeping genes. Previous data suggest that the transcription factors Egr-1 and/or Sp1 are required for basal 5-LO transcription and that they functionally interact with the 5-LO promoter and activate it via repeated response elements located between positions Ϫ212 and Ϫ88 bp, relative to the translational start site (10, 11). Interestingly, naturally occurring mutations were found in the 5-LO promoter consisting of the deletion of one or two, or the addition of one Sp1 binding site (12). These mutations only slightly alter 5-LO promoter activity in reporter gene assays but have a significant impact on the response of asthma patients to 5-LO inhibitors (13).As yet, no data are available on the mechanisms involved in the cell type-specific activation of the 5-LO promoter in response to cell differentiation signals and inflammatory stimuli. Expression of several genes with (GϩC)-rich promoters has been shown to be regulated by DNA methylation (14). Whereas promoters of (GϩC)-rich housekeeping genes are usually unmethylated, methylation of promoters of tissue-specific genes is usually linked with silencing of the respective genes.Therefore, it was of interest to study the role of DNA methylation in the regulation of 5-lipoxygenase expression. The human myeloid cell lines Mono Mac 6 and HL-60 show prominent 5-LO gene expression and 5-LO activity after differentiation by TGF␤ and 1,25(OH) 2 D 3 (6, 7), whereas U937 cells and the HL-60TB cell line, a subline of the HL-60 ce...

Global data on settlements, built-up land and population distributions are becoming increasingly available and represent important inputs to a better understanding of key demographic processes such as urbanization and interactions between human and natural systems over time. One persistent drawback that prevents user communities from effectively and objectively using these data products more broadly, is the absence of thorough and transparent validation studies. This study develops a validation framework for accuracy assessment of multi-temporal built-up land layers using integrated public parcel and building records as validation data. The framework is based on measures derived from confusion matrices and incorporates a sensitivity analysis for potential spatial offsets between validation and test data as well as tests for the effects of varying criteria of the abstract term built-up land on accuracy measures. Furthermore, the framework allows for accuracy assessments by strata of built-up density, which provides important insights on the relationship between classification accuracy and development intensity to better instruct and educate user communities on quality aspects that might be relevant to different purposes. We use data from the newly-released Global Human Settlement Layer (GHSL), for four epochs since 1975 and at fine spatial resolution (38m), in the United States for a demonstration of the framework. The results show very encouraging accuracy measures that vary across study areas, generally improve over time but show very distinct patterns across the rural-urban trajectories. Areas of higher development intensity are very accurately classified and highly reliable. Rural areas show low degrees of accuracy, which could be affected by misalignment between the reference data and the data under test in areas where built-up land is scattered and rare. However, a regression analysis, which examines how well GHSL can estimate built-up land using spatially aggregated analytical units, indicates that classification error is mainly of thematic nature. Thus, caution should be taken in using the data product in rural regions. The results can be useful in further improving classification procedures to create measures of the built environment. The validation framework can be extended to data-poor regions of the world using map data and Volunteered Geographic Information.

Over the past 200 years, the population of the United States grew more than 40-fold. The resulting development of the built environment has had a profound impact on the regional economic, demographic, and environmental structure of North America. Unfortunately, constraints on data availability limit opportunities to study long-term development patterns and how population growth relates to land-use change. Using hundreds of millions of property records, we undertake the finest-resolution analysis to date, in space and time, of urbanization patterns from 1810 to 2015. Temporally consistent metrics reveal distinct long-term urban development patterns characterizing processes such as settlement expansion and densification at fine granularity. Furthermore, we demonstrate that these settlement measures are robust proxies for population throughout the record and thus potential surrogates for estimating population changes at fine scales. These new insights and data vastly expand opportunities to study land use, population change, and urbanization over the past two centuries.

With the increasing availability and rapidly improving the spatial resolution of synthetic aperture radar (SAR) images from the latest and future satellites like TerraSAR-X and TanDEM-X, their applicability in remote sensing applications is set to be paramount. Considering challenges in the field of point feature-based multisensor/multimodal SAR image matching/registration and advancements in the field of computer vision, we extend the applicability of the scale invariant feature transform (SIFT) operator for SAR images. In this article, we have analysed the feature detection, identification and matching steps of the original SIFT processing chain. We implement steps to counter the speckle influence, which deteriorates the SIFT operator performance for SAR images. In feature identification, we evaluate different local gradient estimating techniques and highlight the fact that giving up the SIFT's rotation invariance characteristic increases the potential number of matches when the multiple SAR images from different sensors have been acquired with the same geometrical acquisition parameters. In the feature matching stage, we propose to assist the standard SIFT matching scheme to utilise the SIFT operator capability for effective results in challenging SAR image matching scenarios. The results obtained for SAR images acquired by different sensors using different incidence angles and orbiting directions over both rural and semi urban land cover, highlight the SIFT operator's capability for point feature matching in SAR imagery.

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Abstract. The collection, processing, and analysis of remote sensing data since the early 1970s has rapidly improved our understanding of change on the Earth's surface. While satellite-based Earth observation has proven to be of vast scientific value, these data are typically confined to recent decades of observation and often lack important thematic detail. Here, we advance in this arena by constructing new spatially explicit settlement data for the United States that extend back to the early 19th century and are consistently enumerated at fine spatial and temporal granularity (i.e. 250 m spatial and 5-year temporal resolution). We create these time series using a large, novel building-stock database to extract and map retrospective, fine-grained spatial distributions of built-up properties in the conterminous United States from 1810 to 2015. From our data extraction, we analyse and publish a series of gridded geospatial datasets that enable novel retrospective historical analysis of the built environment at an unprecedented spatial and temporal resolution. The datasets are part of the Historical Settlement Data Compilation for the United States (https://dataverse.harvard.edu/dataverse/hisdacus, last access: 25 January 2021) and are available at https://doi.org/10.7910/DVN/YSWMDR (Uhl and Leyk, 2020a), https://doi.org/10.7910/DVN/SJ213V (Uhl and Leyk, 2020b), and https://doi.org/10.7910/DVN/J6CYUJ (Uhl and Leyk, 2020c).

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