This study aims to examine the effect of urban spatial patterns on heat exposure in the city of Tel Aviv using multiple methodologies, Local Climate Zones (LCZ), meteorological measurements, and remote sensing. A Local Climate Zone map of Tel Aviv was created using Geographic Information System (GIS), and satellite images were used to identify the spatial patterns of the urban heat island (UHI). Climatic variables were measured by fixed meteorological stations and by mobile cross-section. Surface and wall temperatures were obtained by satellite images and a hand-held infrared camera. Meteorological measurements at a height of 2 m showed that during midday the city is ~3.6 °C warmer than the surrounding rural area. The cooling effect of parks was evident only during the hot hours of the day (9:00–17:00). Land Surface Temperature in the southern part of the city was hotter by ~7–9 °C compared to the northern part due to lack of urban vegetation. Hot spots were found in compact midrise forms (LCZ 2) that are not ideal from the climatological perspective. Whereas compact low-rise forms (LCZ 3) were less heat vulnerable. The results of this study suggest that climatologists can provide planners and architects with scientific insight into the causes of and solutions for urban climatic heat exposure.

In recent years, hyperspectral remote sensing (HRS) has become common practice for remote analyses of the physiognomy and composition of forests. Supervised classification is often used for this purpose, but demands intensive sampling and analyses, whereas unsupervised classification often requires information retrieval out of the large HRS datasets, thereby not realizing the full potential of the technology. An improved principal component analysis-based classification (PCABC) scheme is presented and intended to provide accurate and sequential image-based unsupervised classification of Mediterranean forest species. In this study, unsupervised classification and reduction of data size are performed simultaneously by applying binary sequential thresholding to principal components, each time on a spatially reduced subscene that includes the entire spectral range. The methodology was tested on HRS data acquired by the airborne AisaFENIX HRS sensor over a Mediterranean forest in Mount Horshan, Israel. A comprehensive field-validation survey was performed, sampling 257 randomly selected individual plants. The PCABC provided highly improved results compared to the traditional unsupervised classification methodologies, reaching an overall accuracy of 91%. The presented approach may contribute to improved monitoring, management, and conservation of Mediterranean and similar forests.

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Mineral nutrition is essential for optimal plant growth. Phosphorus (P) is a relatively small component of leaf dry weight, with a concentration in plant foliage of less than 1%. Despite its low concentration, P is an essential element in plants, mainly used for energy transfer. Mapping P concentration using traditional methods is expensive and usually limited to a small area; it is timeconsuming and covers only a few plant individuals or species. In this study, we demonstrate the use of remote-sensing (RS) data acquired from the feld and airborne hyperspectral sensors to predict and map the P concentration in leaves of different woody Mediterranean plant species. Comprehensive field work included leaf sampling, laboratory analyses, and spectral measurements using a visible, near-infrared and shortwave-infrared (VIS-NIR-SWIR) field spectrometer. Using different spectral configurations, we built accurate models to predict P concentration in leaf samples. The models were built using a NIR data analysis technique with the data mining software PARACUDA II. This software allowed us to identify the correlative wavelengths for P-bearing molecules in selected woody Mediterranean plant species. The hyperspectralbased model for leaf P concentration was extracted from the reflectance data acquired using a manned aircraft carrying a hyperspectral sensor (Specim AisaFenix 1 K). The model gave a reliable correlation between points extracted from the hyperspectral image and samples measured in the field. We believe that the methodology used in this study will help forest ecologists better understand the concentration of P in the foliage of woody Mediterranean plant species.

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