Suchergebnisse

The use of Land use and Land cover (LULC) data is gradually becoming more widely spread in studies relating the environment to human health. However, little research has acknowledged the compositional nature of these data. The goal of the present study is to explore, for the first time, the independent effect of eight LULC categories (agricultural land, bare land, coniferous forest, broad-leaved forest, sclerophyll forest, grassland and shrubs urban areas, and waterbodies) on three selected common health conditions: type 2 diabetes mellitus (T2DM), asthma and anxiety, using a compositional methodological approach and leveraging observational health data of Catalonia (Spain) at area level. We fixed the risk exposure scenario using three covariates (socioeconomic status, age group, and sex). Then, we assessed the independent effect of the eight LULC categories on each health condition. Our results show that each LULC category has a distinctive effect on the three health conditions and that the three covariates clearly modify this effect. ; Quim Zaldo-Aubanell was supported by AGAUR FI fellowship (DOGC num. 7720, of 5.10.2018). Isabel Serra acknowledges support from FIS2015-71851-P and PGC-FIS2018-099629-B-I00 from Spanish MINECO and MICINN, and was partially funded by the grant RTI2018- 096072-B-I00 from the Spanish Ministry of Science, Innovation and Universities. Jordina Belmonte was supported by the Spanish Ministry of Science and Technology through the project CTM2017-86565-C2-1-O and by the Catalan Government AGAUR through 2017SGR1692. Pepus Daunis-i-Estadella acknowledges support from the project RTI2018- 095518-B-C21 Methods for Compositional analysis of Data (CODAMET), Ministerio de Ciencia, Innovación y Universidades, Spain.

The use of Land use and Land cover (LULC) data is gradually becoming more widely spread in studies relating the environment to human health. However, little research has acknowledged the compositional nature of these data. The goal of the present study is to explore, for the first time, the independent effect of eight LULC categories (agricultural land, bare land, coniferous forest, broad-leaved forest, sclerophyll forest, grassland and shrubs, urban areas, and waterbodies) on three selected common health conditions: type 2 diabetes mellitus (T2DM), asthma and anxiety, using a compositional methodological approach and leveraging observational health data of Catalonia (Spain) at area level. We fixed the risk exposure scenario using three covariates (socioeconomic status, age group, and sex). Then, we assessed the independent effect of the eight LULC categories on each health condition. Our results show that each LULC category has a distinctive effect on the three health conditions and that the three covariates clearly modify this effect. This compositional approach has yielded plausible results supported by the existing literature, highlighting the relevance of environmental heterogeneity in health studies. In this sense, we argue that different types of environment possess exclusive biotic and abiotic elements affecting distinctively on human health. We believe our contribution might help researchers approach the environment in a more multidimensional manner integrating environmental heterogeneity in the analysis. ; Quim Zaldo-Aubanell was supported by AGAUR FI fellowship (DOGC num. 7720, of 5.10.2018). Isabel Serra acknowledges support from FIS2015-71851-P and PGC-FIS2018-099629-B-I00 from Spanish MINECO and MICINN, and was partially funded by the grant RTI2018-096072-B-I00 from the Spanish Ministry of Science, Innovation and Universities. Jordina Belmonte was supported by the Spanish Ministry of Science and Technology through the project CTM2017-86565-C2-1-O and by the Catalan Government AGAUR through 2017SGR1692. Pepus Daunis-i-Estadella acknowledges support from the project RTI2018-095518-B-C21 Methods for Compositional analysis of Data (CODAMET), Ministerio de Ciencia, Innovación y Universidades, Spain. The funding sources did not participate in the design or conduct of the study, the collection, management, analysis, or interpretation of the data, or the preparation, review, or approval of the manuscript. We want to thank Marcel Bach-Pagès for his insights in the manuscript. ; Peer Reviewed ; Postprint (published version)

Abstract. This work evaluates the influence of land use and land cover (LUC) data with
different properties on the landslide susceptibility zonation of the road
network in the Zêzere watershed (Portugal). The information value method
was used to assess the landslide susceptibility using two models: one
including detailed LUC data (the Portuguese Land Cover Map – COS) and the
other including more generalized LUC data (the CORINE Land Cover – CLC). A
set of fixed independent layers was considered as landslide predisposing
factors (slope angle, slope aspect, slope curvature, slope-over-area ratio,
soil, and lithology) while COS and CLC were used to find the differences in
the landslide susceptibility zonation. A landslide inventory was used as a
dependent layer, including 259 shallow landslides obtained from the
photointerpretation of orthophotos from 2005, and further validated in three
sample areas. The landslide susceptibility maps were assigned to the road
network data and resulted in two landslide susceptibility road network maps.
The models' performance was evaluated with prediction and success rate curves
and the area under the curve (AUC). The landslide susceptibility results
obtained in the two models present a high accuracy in terms of the AUC
(>90 %), but the model with more detailed LUC data (COS)
produces better results in the landslide susceptibility zonation on the road
network with the highest landslide susceptibility.

Land use and land cover (LULC) as well as its changes (LUCC) are an interplay between bio-geophysical characteristics of the landscape and climate as well as the complex human interaction including its different patterns of utilization superimposed on the natural vegetation. LULC is a core information layer for a variety of scientific and administrative tasks (e.g. hydrological modelling, climate models, land use planning). In particular in the context of climate change with its impacts on socio-economic, socio-ecologic systems as well as ecosystem services precise information on LULC and LUCC are mandatory baseline datasets required over large areas. Remote sensing can provide such information on different levels of detail and in a homogeneous and reliable way. Hence, LULC mapping can be regarded as a prototype for integrated approaches based on spaceborne and airborne remote sensing techniques combined with field observations. The book provides for the first time a comprehensive view of various LULC activities focusing on European initiatives, such as the LUCAS surveys, the CORINE land covers, the ESA/EU GMES program and its resulting Fast-Track- and Downstream Services, the EU JRC Global Land Cover, the ESA GlobCover project as well as the ESA initiative on Essential Climate Variables. All have and are producing highly appreciated land cover products. The book will cover the operational approaches, but also review current state-of-the-art scientific methodologies and recommendations for this field. It opens the view with best-practice examples that lead to a view that exceeds pure mapping, but to investigate into drivers and causes as well as future projections.

There are many new land use and land cover (LULC) products emerging yet there is still a lack of in situ data for training, validation, and change detection purposes. The LUCAS (Land Use Cover Area frame Sample) survey is one of the few authoritative in situ field campaigns, which takes place every three years in European Union member countries. More recently, a study has considered whether citizen science and crowdsourcing could complement LUCAS survey data, e.g., through the FotoQuest Austria mobile app and crowdsourcing campaign. Although the data obtained from the campaign were promising when compared with authoritative LUCAS survey data, there were classes that were not well classified by the citizens. Moreover, the photographs submitted through the app were not always of sufficient quality. For these reasons, in the latest FotoQuest Go Europe 2018 campaign, several improvements were made to the app to facilitate interaction with the citizens contributing and to improve their accuracy in LULC identification. In addition to extending the locations from Austria to Europe, a change detection component (comparing land cover in 2018 to the 2015 LUCAS photographs) was added, as well as an improved LC decision tree. Furthermore, a near real-time quality assurance system was implemented to provide feedback on the distance to the target location, the LULC classes chosen and the quality of the photographs. Another modification was a monetary incentive scheme in which users received between 1 to 3 Euros for each successfully completed quest of sufficient quality. The purpose of this paper is to determine whether citizens can provide high quality in situ data on LULC through crowdsourcing that can complement LUCAS. We compared the results between the FotoQuest campaigns in 2015 and 2018 and found a significant improvement in 2018, i.e., a much higher match of LC between FotoQuest Go Europe and LUCAS. As shown by the cost comparisons with LUCAS, FotoQuest can complement LUCAS surveys by enabling continuous collection of ...

Explore the Important Role that the Semantics of Land Use and Land Cover Plays within a Broader Environmental ContextFocused on the information semantics of land use and land cover (LULC) and providing a platform for reassessing this field, Land Use and Land Cover Semantics: Principles, Best Practices, and Prospects presents a comprehensive overview of fundamental theories and best practices for applying semantics in LULC. Developed by a team of experts bridging relevant areas related to the subject (LULC studies, ontology, semantic uncertainty, information science, and earth observation), thi.