Remote sensing of climate changes effects on urban green biophysical variables

Maria A. Zoran; Adrian I. Dida

doi:10.1117/12.2241369

18 October 2016 Remote sensing of climate changes effects on urban green biophysical variables

Maria A. Zoran, Adrian I. Dida

Proceedings Volume 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII; 100051K (2016) https://doi.org/10.1117/12.2241369
Event: SPIE Remote Sensing, 2016, Edinburgh, United Kingdom

Abstract

Urban vegetation land cover change is a direct measure of quantitative increase or decrease in sources of urban pollution and the dimension of extreme climate events and changes that determine environment quality. This study addresses climate changes effects and anthropogenic impacts on urban green biophysical variables based on time series satellite data in synergy with in-situ data and new analytical methods. This paper explored the use of time-series MODIS Terra/Aqua Normalized Difference Vegetation Index (NDVI/EVI), Land Surface Temperature (LST) and Leaf Area Index (LAI), land surface albedo data to provide vegetation change detection information for Bucharest test area during 2000- 2015 period. Training and validation are based on a reference dataset collected from Landsat ETM remote sensing data. The mean detection accuracy for investigated period was 89%, with a reasonable balance between change commission errors (19.74%), change omission errors (24.72%), and Kappa coefficient of 0.74. Annual change detection rates across the urban/peri-urban green areas over the study period were estimated at 0.77% per annum in the range of 0.45% (2000) to 0.78% (2015).Vegetation dynamics in urban areas at seasonal and longer timescales reflect large-scale interactions between the terrestrial biosphere and the climate system.

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Maria A. Zoran and Adrian I. Dida "Remote sensing of climate changes effects on urban green biophysical variables", Proc. SPIE 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII, 100051K (18 October 2016); https://doi.org/10.1117/12.2241369

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