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Heavy metal loads in the soil of Debrecen
Published December 31, 2007
57-67

Results of examinations on the amount, and spatial distribution of heavy metal compounds in the soil
of Debrecen, their geographic, pedologic and ecologic aspects are presented in this study. The effects
of the differences in traffic conditions, build-up/land use and the density of vegetation on the heavy
metal content of the soils hav...e been examined in city of Debrecen and its closer environment.
Cadmium-, cobalt-, nickel-, lead-, and copper-contents of the soil samples taken from 88 sites of the
sample area have been studied after acidic extraction, using atomic absorption spectrometer with the
flame technique. Close-to-background concentrations of heavy metals in unpolluted soils of the
forested area of the Nagyerdő were determined. Spatial differences in the heavy metal content of the
soils for the whole area of Debrecen have been studied. Influence of soil properties (humus, CalciumCarbonate content, pH and grain-size distribution) on the binding and mobility of heavy metals in the
soil has been examined. Vertical distribution and mobility of heavy metal compounds in acid sandy
soils was determined. Heavy metal content of soil in the most sensitive areas, playgrounds,
recreational areas, urban gardens and grazing fields along busy roads has been surveyed.

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Spatial distribution of vegetation cover in Erbil city districts using high-resolution Pléiades satellite image
Published June 30, 2018
10-22

Green spaces are playing an essential role for ecological balance and for human health in the city as well.
They play a fundamental role in providing opportunities for relaxation and enjoying the beauty of nature
for the urban population. Therefore, it is important to produce detailed vegetation maps to assist planners
in designing str...ategies for the optimisation of urban ecosystem services and to provide a suitable plan
for climate change adaptation in one fast growing city. Hence, this research is an investigation using 0.5
m high-resolution multispectral Pléiades data integrated with GIS data and techniques to detect and
evaluate the spatial distribution of vegetation cover in Erbil City. A supervised classification was used
to classify different land cover types, and a normalised difference vegetation index (NDVI) was used
to retrieve it for the city districts. Moreover, to evaluate the accessibility of green space based on their
distance and size, a buffer zone criterion was used. The results indicate that the built-up land coverage
is 69% and vegetation land cover is 14%. Regarding NDVI results, the spatial distribution of vegetation
cover was various and, in general, the lowest NDVI values were found in the districts located in the city
centre. On the other hand, the spatial distribution of vegetation land cover regarding the city districts was
non-equal and non-concentric. The newly built districts and the districts far from the Central Business
District (CBD) recorded the lowest vegetation cover compared with the older constructed districts.
Furthermore, most of the districts have a lack of access to green spaces based on their distance and size.
Distance and accessibility of green areas throughout the city are not equally distributed. The majority of
the city districts have access to green areas within radius buffer of two kilometres, whereas the lowest
accessibility observed for those districts located in the northeast of the city in particular (Xanzad,
Brayate, Setaqan and Raperin). Our study is one of the first investigations of decision-making support
of the spatial planning in a fast-growing city in Iraq and will have a utilitarian impact on development
processes and local and regional planning for Erbil City in the future.

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107
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Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery
Published July 14, 2021
66-75

In this study two high-resolution satellite imagery, the PlanetScope, and SkySat were compared based on their classification capabilities of urban vegetation. During the research, we applied Random Forest and Support Vector Machine classification methods at a study area, center of Rome, Italy. We performed the classifications based on the spect...ral bands, then we involved the NDVI index, too. We evaluated the classification performance of the classifiers using different sets of input data with ROC curves and AUC values. Additional statistical analyses were applied to reveal the correlation structure of the satellite bands and the NDVI and General Linear Modeling to evaluate the AUC of different models. Although different classification methods did not result in significantly differing outcomes (AUC values between 0.96 and 0.99), SVM’s performance was better. The contribution of NDVI resulted in significantly higher AUC values. SkySat’s bands provided slightly better input data related to PlanetScope but the difference was minimal (~3%); accordingly, both satellites ensured excellent classification results.

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