Wilson Lara Henao

Post Doctor

(+47) 920 78 042
wilson.lara@nibio.no

Place
Steinkjer

Visiting address
Innocamp Steinkjer, Skolegata 22, 7713 Steinkjer

To document

Abstract

Spatial resources accessible for the derivation of biodiversity indicators of the class ecosystem structure are sparse and disparate, and their integration into computer algorithms for biodiversity monitoring remains problematic. We describe ecochange as an R-package that integrates spatial analyses with a monitoring workflow for computing routines necessary for biodiversity monitoring. The ecochange comprises three modules for data integration, statistical analysis and graphics. The first module currently downloads and integrates diverse remote sensing products belonging to the essential biodiversity class of structure. The module for statistical analysis calculates RasterStack ecosystem-change representations across areas of interest; this module also allows focusing on species habitats while deriving changes in a variety of indicators, including ecosystem areas, conditional entropy and fractal dimension indices. The graphics module produces level and bar plots that ease the development of indicator reports. Its functionality is described with an example workflow to calculate ecosystem-class areas and conditional entropy across an area of interest contained in the package documentation. We conclude that ecochange features procedures necessary to derive ecosystem structure indicators integrating the retrieval of spatially explicit data with the use of workflows to calculate/visualize biodiversity indicators at the national/regional scales.

To document

Abstract

To meet international and national commitments to decrease emissions of fossil fuels, cities around the world must obtain information on their historical levels of emissions, identifying hotspots that require special attention. Direct atmospheric measurements of pollution sources are almost impossible to obtain retrospectively. However, tree rings serve as an archive of environmental information for reconstructing the temporal and spatial distribution of fossil-fuel emissions in urban areas. Here, we present a novel methodology to reconstruct the spatial and temporal contribution of fossil-fuel CO2 concentration ([CO2F]) in the urban area of Medellin, Colombia. We used a combination of dendrochronological analyses, radiocarbon measurements, and statistical modeling. We obtained annual maps of [CO2F] from 1977 to 2018 that describe changes in its spatial distribution over time. Our method was successful at identifying hotspots of emissions around industrial areas, and areas with high traffic density. It also identified temporal trends that may be related to socioeconomic and technological factors. We observed an important increase in [CO2F] during the last decade, which suggests that efforts of city officials to reduce traffic and emissions did not have a significant impact on the contribution of fossil fuels to local air. The method presented here could be of significant value for city planners and environmental officials from other urban areas around the world. It allows identifying hotspots of fossil fuels emissions, evaluating the impact of previous environmental policies, and planning new interventions to reduce emissions.