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Monitoring biomass changes in the Mediterranean ecosystems is important for better understanding their responses to climatic and anthropogenic changes. Multi-frequency SAR data (L-band PASLAR and C-band Sentinel-1) is investigated for developing a biomass estimation model along a climatic gradient in Israel. First, the relationships between biomass and transmissivity at various frequencies are discussed. Multi-frequency polarimetric radar vegetation index (MPRVI) is then proposed utilizing the ensemble average of degree of polarization and cross-polarized backscattering coefficients. After randomly partitioning the data for training set and testing set, the new MPRVI-based biomass model is evaluated. It shows a good agreement with reference biomass data with an r-square of 0.899 and a root-mean-squared error (RMSE) of 0.381 kg/m2 and with a relative RMSE (RRMSE) of 10.8%.

Mediterranean environments are characterized by high spatial and temporal heterogeneity due to their climatological, lithological, soil and vegetation geo-diversity and their high population density which cause growing land-use transformations at the rural-urban fringe. Remote sensing mapping and monitoring land cover in these environments under such conditions is a challenging task. Instead of the common per pixel approach we suggest combining application of an object-oriented classification based on image objects separation through edge detection with unsupervised classification. The main elements of our methodology are: (1) separating image areas into vegetation/ non-vegetation regions utilizing NDVI threshold; (2) calculation of the spatial variance at different bands; (3) image objects extraction through enhancement of the differences between edge pixels and regions of homogeneity; (4) per-object classification for the homogenous areas; (5) overlaying large unclassified image areas by the results of ISODATA (Iterative Self-Organizing Data Analysis) unsupervised classification. Our methodology was applied on multi-spectral images acquired by the VENμS remote sensing system. The study area consists of a typical rural area in semi-arid climate regions undergoing increasing urbanization. Six test areas were selected representing different spatial combinations of natural/ planted forests, agriculture and built-up land-use/ land cover types. While bare fields were poorly classified, areas of low vegetation cover were classified with producer/user accuracies below 60%, built-up areas and roads, cultivated areas, shrublands and bata (dwarf-shrubs) and rocky areas gained good producer/ user classification accuracies.

The climatic gradient between the Judean Lowland and the Negev Desert in Central Israel represents a transition zone between dense shrublands in the North, and desert fringe Batha and Irano-Turanian vegetation in the South, characterizing wide Mediterranean Type Climate regions around the world. Understanding the expected response of these water-limited ecosystems to climate change presents a significant challenge due to the high geodiversity of Mediterranean environments. Studying relationships between vegetation patterns and climatic parameters is fundamental for this purpose, and remote sensing provides a valuable tool for investigating these relationships over large regions. This study aims at examining the relationships between NDVI extracted from Sentinel2 and rainfall and PET accumulated over 1 to 6 months. The analysis was first conducted for 38 sites (100×100 meters) across the climatic gradient for three years representing high (2016), low (2017), and average (2018) rainfall. Results indicate that the highest correlation between NDVI and climatic parameters is achieved for accumulation interval of two months. Least-squares analysis was then utilized for calculating the per-pixel regression coefficients between NDVI and corresponding rainfall and PET. Classification of the multi-temporal NDVI (2016–2018) and of the linear regressions’ coefficients between NDVI and rainfall and PET at accumulation interval of 2 months yielded both high accuracies. Since these slope and intercept coefficients can be perceived as representing the water-use regime at each pixel, the similarity between the classification results suggests that multi-temporal NDVI typologies correspond water-use regime typologies across desert fringe ecosystems at the margins of Mediterranean regions.

A novel classification technique based on definition of unique spectral relations (such as slopes among spectral bands) for all land cover types named (SSF Significant Spectral Features) is presented in the article.A large slopes combination between spectral band pairs is calculated and spectral characterizations that emphasizes the best spectral land cover separation is sought. Increasing in dimensionality of spectral representations is balanced by the simplicity of calculations. The technique has been examined on data acquired by a flown hyperspectral scanner (AISA). The spectral data was narrowed into the equivalent 8 world-view2 channels. The research area was in the city of “Hadera”, Israel, which included 10 land cover types in an urban area, open area and road infrastructure. The comparison between the developed SSF technique and common techniques such as: SVM (Support Vector Machine) and ML (Maximum Likelihood) has shown a clear advantage over ML technique, while produced similar results as SVM. The poorest results of using SSF technique was achieved in an herbaceous area (70%). However, the simplicity of the method, the well-defined parameters it produces for interpreting the results, makes it intuitive over using techniques such as SVM, which is considered as a not explicit classifier.

One-dimensional discrete processes, which can be measured repetitively for discrete values of arguments after discrete periodic or non periodic intervals of time, are discussed in this study. These processes can be under effect of different perturbations with different frequency, amplitudes, with accidental measurement error, etc. The problem investigated, is whether repetitive, local, relatively small effects (RLEs) can be extracted from this data. A simple method for such effects detection is proposed. A model for the simulation of discrete processes with local effects is introduced. Experiments with the method indicate that it is stable in detecting local effects and can be useful for many applications.

Mediterranean regions are hot spots of climate change, where the expected decrease in water resources threatens the sustainability of shrublands at their arid margins. Studying spectral vegetation indices' relationships with rainfall and potential evapotranspiration (PET) changes across Mediterranean to arid transition zones is instrumental for developing methods for mapping and monitoring the effects of climate change on desert fringe shrublands. Here we examined relationships between 17 spectral vegetation indices (VIs) and four climate and aridity measures, i.e., rainfall, PET, aridity index (AI), and water deficit (WD), calculated at accumulation lags between 1 and 6 months. For this purpose, VIs for 38 sites (100 × 100 m each) representing less disturbed areas were extracted from Sentinel 2A images for 3 years with high (2016), low (2017), and average (2018) annual rainfall. Most of the VIs had shown the highest correlation with the four climate and aridity measures at 2-month accumulation interval. While NDVI relationships with climate measures gained the widest use, our data suggest that indices combining NIR and SWIR bands better correlate with climate parameters. AI is one of the leading annual measures of dryness worldwide; when calculating it monthly, WD was found to represent better the balance between precipitation and PET across the climate transition zone and to be better correlated with VIs. Relationships between NIR and SWIR VIs and water deficit may thus facilitate improvements in monitoring and mapping desert fringe shrublands' responses to climate change if supported by similar results from other areas., (© 2023. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Following a predicted decline in water resources in the Mediterranean Basin, we used reaction-diffusion equations to gain a better understanding of expected changes in properties of vegetation patterns that evolve along the rainfall transition between semi-arid and arid rainfall regions. Two types of scenarios were investigated: the first, a discrete scenario, where the potential consequences of climate change are represented by patterns evolving at discrete rainfall levels along a rainfall gradient. This scenario concerns space-for-time substitutions characteristic of the rainfall gradient hypothesis. The second, a continuous scenario, represents explicitly the effect of rainfall decline on patterns which evolved at different rainfall levels along the rainfall gradient prior to the climate change. The eccentricity of patterns that emerge through these two scenarios was found to decrease with decreasing rainfall, while their solidity increased. Due to their inverse modes of change, their ratio was found to be a highly sensitive indicator for pattern response to rainfall decline. An eccentricity ratio versus rainfall (ER:R) line was generalized from the results of the discrete experiment, where ERs above this line represent developed (recovered) patterns and ERs below this line represent degraded patterns. For the rainfall range of 1.2 to 0.8 mm/day, the continuous rainfall decline experiment with ERs that lie above the ER:R line, yielded patterns less affected by rainfall decline than would be expected according to the discrete representation of ecosystems' response. Thus, for this range, space-for-time substitution represents an overestimation of the consequences of the expected rainfall decline. For rainfall levels below 0.8 mm/day, eccentricity ratios from the discrete and continuous experiments practically converge to the same trend of pattern change along the ER:R line. Thus, the rainfall gradient hypothesis may be valid for regions characterized by this important rainfall range, which typically include desert fringe ecosystems.

Retrieval of aerosol loading in vertical atmospheric columns is a common product of satellite and ground instruments that measure spectral extinction of solar radiation throughout the entire atmosphere. Here we study ground hyperspectral imaging of artificial light sources as a complementary method for retrieving fine aerosol concentrations along quazi-horizontal ambient open paths. Previously, we reported hyperspectral measurements of the aerosol optical thickness in the 500-900 nm range over urban-scale distances (180 m to 4 km), measuring the extinction of radiation emitted from a halogen source. Here we confirm in a laboratory-setup the basic premise that different accumulation-size aerosols generate distinct hyperspectral signatures in this spectral range. Measured hyperspectral attenuation signatures of fine aerosols were comparable to calculated Mie scattering signatures, suggesting that modal aerosol concentrations can be retrieved. A genetic algorithm was adapted to estimate the aerosol modal concentrations from its hyperspectral extinction signature. Retrievals of aerosol concentrations from measured and synthetic hyperspectral signatures indicated a robust algorithm, with an expected retrieval error of 0.2-22% for typical ambient concentrations along an urban-scale open path. The retrieval accuracy was found to depend on the relative aerosol modal concentrations, especially when there is a substantial overlap between the modal spectral signatures.

Environmental management and planning are instrumental in resolving conflicts arising between societal needs for economic development on the one hand and for open green landscapes on the other hand. Allocating green corridors between fragmented core green areas may provide a partial solution to these conflicts. Decisions regarding green corridor development require the assessment of alternative allocations based on multiple criteria evaluations. Analytical Hierarchy Process provides a methodology for both a structured and consistent extraction of such evaluations and for the search for consensus among experts regarding weights assigned to the different criteria. Implementing this methodology using 15 Israeli experts-landscape architects, regional planners, and geographers-revealed inherent differences in expert opinions in this field beyond professional divisions. The use of Agglomerative Hierarchical Clustering allowed to identify clusters representing common decisions regarding criterion weights. Aggregating the evaluations of these clusters revealed an important dichotomy between a pragmatist approach that emphasizes the weight of statutory criteria and an ecological approach that emphasizes the role of the natural conditions in allocating green landscape corridors.