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Instytut Kształtowania i Ochrony Środowiska, Uniwersytet Przyrodniczy we Wrocławiu, pl. Grunwaldzki 24, 50-363 Wrocław
Publication date: 2015-07-25
Inż. Ekolog. 2015; 43:166–171
Research on the possibility of using remote sensing methods to evaluate condition of vegetation on the green walls were performed on experimental models in 2010–2011. Two models that differ from one another with vegetation layer were analyzed: a retention model (MR I) with substrate soil and an economic model (ME II) with hydroponic felt. In the individual panels plants representing shrubs, perennials and grasses were planted. In total, on experimental models 60 plant species was applied depending on the exhibition of the walls. The evaluation of the plants condition was performed based on field observations and the analysis of Normalized Difference Vegetation Index (NDVI). Evaluation of vegetation condition using remote sensing methods leads to the conclusion that the vegetation on retention model (MR I) have a much higher NDVI index value compared with the economic model (ME II). The comparison of the percent coverage of panels on retention model (MR I) and economic model (ME II) by the plants was done by separating the background plane from the plant surface. As a division criterion NDVI ratio in the range from -1 to 0.2 was taken. The results showed a clear contrast between the level of plant coverage of the examined models for individual facades. On the retention model (MR I) panels plant covering was significantly higher than on an economic model (ME II) where plant growth was limited. The growth of vegetation on the retention model (MR I) using substrate soil in plant panels was normal suggesting the potential use of such solutions in the climatic conditions of Lower Silesia. Vegetation on the economic model (ME II) is characterized by a worse growth throughout the growing season, which is why it is not recommended. The study showed that it is possible to evaluate the conditions of vegetation on the green walls with the use of remote sensing methods based on a modified photographic camera.