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Urban vegetation classification with high-resolution PlanetScope and SkySat multispectral imagery
66-75Views:607In 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 spectral 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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Spatial distribution of vegetation cover in Erbil city districts using high-resolution Pléiades satellite image
10-22Views:243Green 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 strategies 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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Mapping aquatic vegetation of the Rakamaz-Tiszanagyfalui Nagy-Morotva using hyperspectral imagery
1-10Views:189Rapid development in remote sensing technologies provides more and more reliable methods for environmental assessment. For most wetlands, it is difficult to walk-in without disturbing the endangered species living there; therefore, application of opportunities provided by remote sensing has a great importance in population-mapping. One effective tool of vegetation pattern estimation is hyperspectral remote sensing, which can be used for association and species level mapping as well, due to high ground resolution. The Rakamaz-Tiszanagyfalui Nagy-morotva is an oxbow lake, located in the north-eastern part of Hungary. For this study, a wetland area of 1.17 km2 containing the original water bad and shoreline was selected. For the image analysis, images taken by an AISA DUAL system hyperspectral sensor were used. At the same time, 7 main vegetation classes were separated, which are typical for the sample plot designated on the test site. Classification was performed by the master areas signed by the most common associations of the Rakamaz-Tiszanagyfalui Nagy-morotva with determined spectrums. During the image analysis, SAM classification method was used, where radian values were optimized by the results of classification performed at the control area.
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Studying floodplain roughness in an Upper Tisza study area
85-90Views:215Floods slowing down due to the significant decrease of the gradient have considerable sediment accumulation capacity in the floodplain. The grade of accumulation is further increased if the width of the floodplain is not uniform as water flowing out of the narrow sections diverge and its speed is decreased. Surface roughness in a study area of 492 hectares in the Upper Tisza region was analysed based on CIR (color-infrared) orthophotos from 2007. An NDVI index layer was created first on which object-based image segmentation and threshold-based image classification were performed. The study area is dominated by land cover / land use types (grassland-shrubs, forest) with high roughness values. It was concluded that vegetation activity based analyses on their own are not enough for determining floodplain roughness.