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Data Brief. 2019 Jun 25;25:104187. doi: 10.1016/j.dib.2019.104187. eCollection 2019 Aug.

Dataset for landscape pattern analysis from a climatic perspective.

Author information

1
Department of Physical Geography and Geoinformatics, Faculty of Science and Technology, University of Debrecen, Egyetem tér 1, H-4032, Debrecen, Hungary.
2
MTA-DE Biodiversity and Ecosystem Services Research Group, Egyetem tér 1, H-4032, Debrecen, Hungary.
3
University of Debrecen, Doctoral School of Earth Sciences, Hungary.
4
Geographical Institute, Research Centre for Astronomy and Earth Sciences of the Hungarian Academy of Sciences, Budaörsi str. 45., H-1112, Budapest, Hungary.

Abstract

Revealing the driving forces of changes in landscape pattern is a key question of landscape ecology and landscape analysis. Temperature and precipitation as climatic variables have a dominant role in triggering vegetation changes; thus, a database, which contain their interaction, can support the understanding of spatio-temporal changes in vegetation patterns even on a large scale. The dataset provided in this article contain the R-squared values of bivariate linear regression analysis between the Normalized Difference Vegetation Index (target variable; as a general quantitative descriptor of surface greenness) of the TERRA satellite's MODIS sensor and the climatic variables of the CarpatClim database (predictor variables; maximum monthly temperature, aridification index, evapotranspiration and precipitation). Environmental variables are also included to support further analysis: terrain height, macro regions, land cover classes. The dataset has a spatial projection (i.e. maps) and covers the area of Hungary. Tabular version provides the possibility of traditional statistical analysis, while maps allow the investigation to involve the spatial characteristics of absolute and relative position of the data points. This data article is related to the paper "NDVI dynamics as reflected in climatic variables: spatial and temporal trends - a case study of Hungary" (Szabo et al., 2019).

KEYWORDS:

Climatic factors; NDVI; Pattern; R-squared; Trend

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