Dataset Information

High-resolution land use/cover forecasts for Switzerland in the 21st century.

ABSTRACT: We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21^st century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using > 200 predictors and accounting for climate and policy changes. We improved model performance by using a data augmentation algorithm that synthetically increased the number of cells of underrepresented classes, resulting in an overall quantity disagreement of 0.053 and allocation disagreement of 0.15, which indicate good prediction accuracy. These class-specific spatial suitability maps outputted by the NN were then merged in a single LULC map per time-step using the CLUE-S algorithm, accounting for LULC demand for the future and a set of LULC transition rules. As the first LULC forecast for Switzerland at a thematic resolution comparable to available LULC maps for the past, this product lends itself to applications in land-use planning, resource management, ecological and hydraulic modelling, habitat restoration and conservation.

SUBMITTER: Butikofer L

PROVIDER: S-EPMC10891137 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

High-resolution land use/cover forecasts for Switzerland in the 21st century.

Bütikofer Luca L Adde Antoine A Urbach Davnah D Tobias Silvia S Huss Matthias M Guisan Antoine A Randin Christophe C

Scientific data 20240223 1

We present forecasts of land-use/land-cover (LULC) change for Switzerland for three time-steps in the 21<sup>st</sup> century under the representative concentration pathways 4.5 and 8.5, and at 100-m spatial and 14-class thematic resolution. We modelled the spatial suitability for each LULC class with a neural network (NN) using > 200 predictors and accounting for climate and policy changes. We improved model performance by using a data augmentation algorithm that synthetically increased the num ...[more]

PMID: 38396146

Similar Datasets

Project description:There is no good science in bad models. Cell culture is especially prone to artifacts. A number of novel cell culture technologies have become more broadly available in the 21st century, which allow overcoming limitations of traditional culture and are more physiologically relevant. These include the use of stem-cell derived human cells, cocultures of different cell types, scaffolds and extracellular matrices, perfusion platforms (such as microfluidics), 3D culture, organ-on-chip technologies, tissue architecture, and organ functionality. The physiological relevance of such models is further enhanced by the measurement of biomarkers (e.g., key events of pathways), organ specific functionality, and more comprehensive assessment cell responses by high-content methods. These approaches are still rarely combined to create microphysiological systems. The complexity of the combination of these technologies can generate results closer to the in vivo situation but increases the number of parameters to control, bringing some new challenges. In fact, we do not argue that all cell culture needs to be that sophisticated. The efforts taken are determined by the purpose of our experiments and tests. If only a very specific molecular target to cell response is of interest, a very simple model, which reflects this, might be much more suited to allow standardization and high-throughput. However, the less defined the end point of interest and cellular response are, the better we should approximate organ- or tissue-like culture conditions to make physiological responses more probable. Besides these technologic advances, important progress in the quality assurance and reporting on cell cultures as well as the validation of cellular test systems brings the utility of cell cultures to a new level. The advancement and broader implementation of Good Cell Culture Practice (GCCP) is key here. In toxicology, this is a major prerequisite for meaningful and reliable results, ultimately supporting risk assessment and product development decisions.

Project description:Hereby presented data consists of a land use / land cover map of an 84 km2 part of the Vavatenina district, in the Analanjirofo Region of the east coast of Madagascar, where the landscape is dominated by woody vegetation. This map was obtained by processing very high spatial resolution multispectral images acquired by the Pleiades satellite sensor in 2018. Pleiades data were produced by Airbus defence & Space (France), and distributed by the French National Space Agency (CNES, France). An object-based approach was chosen to exploit the advantages of such images, allowing the use of various texture indices derived from the image to discriminate between different surfaces sharing similar radiometric properties while being structurally different. The object-based image analysis consists of iterative segmentations of the image into homogeneous regions, called objects, which can afterwards be classified using radiometric and textural variables. The reference database, computed out of field knowledge and used to train the mapping methodology, is also described here. It includes 2105 georeferenced points labelled with one of the 9 following classes: Built up/road/bare areas, Annual crops/pasture/short vegetation, Clove dominated park, Clove monoculture, Diversified agroforest, Diversified park, Plantation of woody species, Shrubby fallow, Woody fallow. It is formatted as a geographical information system, accessible for any other purpose like further methodology development. These ground-truth data also helped to the definition of the 13 thematic land use / land cover types to be distinguished in the area. Multiple segmentation levels were thus necessary to gradually derive these classes, with different classification algorithms depending on the level. The final map provides an exhaustive description of the area with a good overall accuracy, reaching 80%. It is openly available as an ESRI shapefile to allow quantitative spatial analysis in key applications such as agriculture, environment, or food security.

Dataset Information

High-resolution land use/cover forecasts for Switzerland in the 21st century.

Publications

High-resolution land use/cover forecasts for Switzerland in the 21st century.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets