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Rockfalls are incidents of nature that take place when rocks or boulders break from a steep slope and fall to the ground. They can pose considerable threats to buildings placed in high-risk zones. Despite the fact that the impact of a rockfall on a building can cause structural and non-structural damage, few studies have been undertaken to investigate the danger associated with this event. Most of these studies indicated that the risk resulting from rockfall hazards is hard to forecast and assess. A comprehensive quantitative risk assessment approach for rockfalls on buildings is developed and described in this paper and applied for the Mtein village in Mount Lebanon. This method employs a 3D model to simulate the rockfall trajectories using a combination of digital elevation data, field surveys, and orthorectified aerial photographs. The spatial and temporal probability of rockfalls were evaluated using the analysis of historical data in two triggering-factor scenarios: earthquake and precipitation. The findings show that, during the period of 1472 years between the years 551 (the first observed large earthquake in Lebanon) and the current year of the study (2023), the temporal probability will potentially be equal to 0.002 and 0.105 in the cases of earthquake- and rainfall-triggered rockfalls, respectively, while the maximal damage values are expected to be 232 USD and 10,511 USD per year, respectively. The end result is a final map presenting the risk values assigned to each building that could be damaged by rockfalls. [ABSTRACT FROM AUTHOR]

Along with the concept of improving reforestation efforts in Lebanon, this study aimed to provide a land suitability analysis for forest species in Lebanon while considering the effect of climate change. Herein, the soil evaluation criteria developed by FAO (The Food and Agriculture Organization) for land suitability classification were implemented through the weighted overlay method to produce suitability maps based on natural variables (soil, climate, and topography) influencing the presence of the species on the land. Cedrus libani, Quercus calliprinos, Ceratonia siliqua, Eucalyptus globulus, and Pinus halepensis are the species considered in this study. The results of this study provide useful information to inform reforestation activities in Lebanon, considering the expected climate change projections for medium- (2050) and long-term (2070) periods, according to two different scenarios (RCP4.5 and RCP8.5) and three General Circulation Models: CCSM4, GFDL-CM3, and HadGEM2-ES. The suitability maps showed a generally critical situation for the spatial distribution of forest species under future climate change compared to the current situation (1970–2000). The distribution of thermophilic species, which tolerate high temperatures (over 20 °C), was projected to expand compared to the current situation. In contrast, the expansion of cold-adapted species may be limited by future climate change conditions. It is crucial to consider the expected effects of climate change to better select species for reforestation and, therefore, to maintain forest cover in Lebanon. [ABSTRACT FROM AUTHOR]

Urbanization has led to the degradation of green areas and natural resources, which are critical for preserving biodiversity and natural benefits. Sustainable urban development (SUD) practices aim to balance urbanization with preserving and protecting green land and natural resources to achieve greater sustainability. This study evaluated land suitability for SUD in the Nabatiyeh area of Lebanon using a multi-criteria decision analysis technique, the analytical hierarchy process, combined with a geographic information system. The elevations and slopes were the most important criteria, followed by the distances from urban agglomeration and industrial areas, accessibility, and land cover types, with corresponding weights of 34% and 36%, 12%, 9%, 6%, and 3%, respectively. The study created a suitability map for future SUD. This identification assists urban planners and policymakers in making informed decisions regarding the development of urban areas. The study's findings highlight the efficiency of combining multi-criteria decision analysis with geospatial techniques for urban planning and environmental management in promoting sustainable development and protecting the environment. Future research could explore the possibility of integrating administrative reforms to promote SUD, including improving governance, enhancing the efficiency of governmental agencies responsible for urban development, building scientific capacities, and increasing officials' accountability. [ABSTRACT FROM AUTHOR]

Rockfall hazard gains popularity nowadays among researchers in different scientific fields, decision-makers and urban planners. The assessment of rockfall hazard requires detection, mapping and estimating the maximum travel distance that rock boulders may reach, commonly known as "rockfall runout". This latter can change significantly under the effects of different triggering factors such as soil conditions, chemical, physical and geological rock properties. However, comparing and analyzing these different effects represents, to the best of our knowledge, one of the newest scientific challenges that need to be addressed. This paper presents a complete methodologic approach aiming to assess the rockfall hazard through runout estimation in three different conditions: (i) gravity, (ii) earthquakes, and (iii) the presence of moisture along the slope. The "Mtein" Village and its surrounding areas in the Mount Lebanon region were chosen as the study area because there have been numerous historic rockfalls and various-sized rocks, such as cobbles and boulders, scattered throughout the area. Thus, three-dimensional simulations were conducted using the Rockyfor3D software and aerial photos for the year 1999 to assess the rockfall runout, the energy curves, and the number of deposited rocks. The results reveal that earthquakes have the highest triggering effect on rockfall and that moisture has a damping effect on RFs by decreasing the kinetic energy. The study shows the importance of taking into consideration the influence of triggering factors as well as rock density on rockfall runout and hazard. [ABSTRACT FROM AUTHOR]

Fire is a recurrent disturbance in Mediterranean ecosystems. Data assemblage from forest fire services can provide useful information for understanding climate controls on daily fire hazard or long term trends. Located at the driest range of the Mediterranean bioclimate, and with contrasting socio-political systems compared to the European area, the southern Mediterranean ecosystems are subjected to more extreme climate and social events. This could potentially lead to unique fire regimes and trends worth being characterized for prevention plans and ecosystem management. However, the region is far less documented, due to missing or inhomogeneous fire records, leaving local authorities with no management strategies when large fires happen. We filled this knowledge gap for Lebanon by combining high spatial resolution Landsat data with high temporal resolution VIIRS (S-NPP and NOAA-20) and MODIS (MCD14ML) hotspots to characterize the seasonal and interannual fire regime over the 2001–2020 period. Numerous small fires were hardly detected by global remote sensing. We estimated that 2044 ha burn annually, representing 0.58% of the wildland cover, with no significant trend over the period, but with non climate-related fires detected during the year experiencing socio-political troubles. The spatial and temporal resolution of this dataset identified a particular prolonged fire season up to November, and an unusual bimodal fire season peaking in July and November. We related these features to the prolonged autumnal soil drought and high August air humidity in the region. This updated fire regime in Lebanon illustrates the benefits of this combined approach for data-scarce regions and provides new insights on the variability of fire weather types in the Mediterranean basin. [ABSTRACT FROM AUTHOR]

Global wheat production reached 754.8 million tons in 2017, according to the FAO database. While wheat is considered as a staple food for many populations across the globe, mapping wheat could be an effective tool to achieve the SDG2 sustainable development goal—End Hunger and Secure Food Security. In Lebanon, this crop is supported financially, and sometimes technically, by the Lebanese government. However, there is a lack of statistical databases, at both national and regional scales, as well as critical information much needed in the subsidy and compensation system. In this context, this study proposes an innovative approach, named Simple and Effective Wheat Mapping Approach (SEWMA), to map the winter wheat areas grown in the Bekaa plain, the primary wheat production area in Lebanon, in the years of 2016 and 2017. The proposed methodology is a tree-like approach relying on the Normalized Difference Vegetation Index (NDVI) values of four-month period that coincides with several phenological stages of wheat (i.e., tillering, stem extension, heading, flowering and ripening). The usage of the freely available Sentinel-2 imageries, with a high spatial (10 m) and temporal (5 days) resolutions, was necessary, particularly due to the small sized and overlapped plots encountered in the study area. Concerning the wheat areas, results show that there was a decrease from 11,063 ± 1309 ha in 2016 to 7605 ± 1184 in 2017. When SEWMA was applied using 2016 ground truth data, the overall accuracy reached 87.0% on 2017 data, whereas, when implemented using 2017 ground truth data, the overall accuracy was 82.6% on 2016 data. The novelty resides in executing early classification output (up to six weeks before harvest) as well as distinguishing wheat from other winter cereal crops with similar NDVI yearly profiles (i.e., barley and triticale). SEWMA offers a simple, yet effective and budget-saving approach providing early-season classification information, very crucial to decision support systems and the Lebanese government concerning, but not limited to, food production, trade, management and agricultural financial support. [ABSTRACT FROM AUTHOR]

Wildfires occur in different climatic zones, forest cover types and eras. Wildfire or forest fire has always shaped the landscape. Different methodologies and indexes have emerged to determine the likelihood of wildfire, commonly confused with the wildfire hazard. However, none of these are universal or portable. In this paper, we have gone through several articles, projects and books. The aim was to identify factors related to the ignition of a wildfire. Consequently, 28 factors were presented and categorized into climatic, topographic, in-situ, historical and anthropogenic factors. It is the first step in building a generalized, acceptable and portable method to determine the wildfire risk. Its creation is strongly related to the prevention and better assessment of this phenomenon. [ABSTRACT FROM AUTHOR]

This article is dedicated to the study of airborne GNSS-R signal processing techniques for water body detection and edge localization using a low-altitude airborne carrier with high rate reflectivity measurements. A GNSS-R setup on-board a carrier with reduced size and weight was developed for this application. We develop a radar technique for automatic GNSS signal segmentation in order to differentiate in-land water body surfaces based on the reflectivity measurements associated to different areas of reflection. Such measurements are derived from the GNSS signal amplitudes. We adapt a transitional model to characterize the changes in the measurements of the reflected GNSS signals from one area to another. We propose an on-line/off-line change detection algorithm for GNSS signal segmentation. A real flight experimentation took place in the context of this work obtaining reflections from different surfaces and landforms. We show, using the airborne GNSS measurements obtained, that the proposed radar technique detects in-land water body surfaces along the flight trajectory with high temporal (50 Hz) and spatial resolution (order of 10 to 100 m 2 ). We also show that we can localize the edges of the detected water body surfaces at meter accuracy. [ABSTRACT FROM AUTHOR]

Surface soil moisture (SSM) is a key variable for many environmental studies, including hydrology and agriculture. Synthetic aperture radar (SAR) data in the C-band are widely used nowadays to estimate SSM since the Sentinel-1 provides free-of-charge C-band SAR images at high spatial resolution with high revisit time, whereas the use of L-band is limited due to the low data availability. In this context, the main objective of this paper is to develop an operational approach for SSM estimation that mainly uses data in the C-band (Sentinel-1) with L-bands (ALOS/PALSAR) as additional data to improve SSM estimation accuracy. The approach is based on the use of the artificial neural networks (NNs) technique to firstly derive the soil roughness (Hrms) from the L-band (HH polarization) to then consider the L-band-derived Hrms and C-band SAR data (VV and VH polarizations) in the input vectors of NNs for SSM estimation. Thus, the Hrms estimated from the L-band at a given date is assumed to be constant for a given times series of C-band images. The NNs were trained and validated using synthetic and real databases. The results showed that the use of the L-band-derived Hrms in the input vector of NN in addition to C-band SAR data improved SSM estimation by decreasing the error (bias and RMSE), mainly for SSM values lower than 15 vol.% and regardless of Hrms values. Based on the synthetic database, the NNs that neglect the Hrms underestimate and overestimate the SSM (bias ranges between −8.0 and 4.0 vol.%) for Hrms values lower and higher than 1.5 cm, respectively. For Hrms <1.5 cm and most SSM values higher than 10 vol.%, the use of Hrms as an input in the NNs decreases the underestimation of the SSM (bias ranges from −4.5 to 0 vol.%) and provides a more accurate estimation of the SSM with a decrease in the RMSE by approximately 2 vol.%. Moreover, for Hrms values between 1.5 and 2.0 cm, the overestimation of SSM slightly decreases (bias decreased by around 1.0 vol.%) without a significant improvement of the RMSE. In addition, for Hrms >2.0 cm and SSM between 8 to 22 vol.%, the accuracy on the SSM estimation improved and the overestimation decreased by 2.2 vol.% (from 4.5 to 2.3 vol.%). From the real database, the use of Hrms estimated from the L-band brought a significant improvement of the SSM estimation accuracy. For in situ SSM less than 15 vol.%, the RMSE decreased by 1.5 and 2.2 vol.% and the bias by 1.2 and 2.6 vol.%, for Hrms values lower and higher than 1.5 cm, respectively. [ABSTRACT FROM AUTHOR]

This paper details a new extrinsic calibration method for scanning laser rangefinder that is precisely focused on the geometrical ground plane-based estimation. This method is also efficient in the challenging experimental configuration of a high angle of inclination of the LiDAR. In this configuration, the calibration of the LiDAR sensor is a key problem that can be be found in various domains and in particular to guarantee the efficiency of ground surface object detection. The proposed extrinsic calibration method can be summarized by the following procedure steps: fitting ground plane, extrinsic parameters estimation (3D orientation angles and altitude), and extrinsic parameters optimization. Finally, the results are presented in terms of precision and robustness against the variation of LiDAR's orientation and range accuracy, respectively, showing the stability and the accuracy of the proposed extrinsic calibration method, which was validated through numerical simulation and real data to prove the method performance. [ABSTRACT FROM AUTHOR]

The ability of Synthetic Aperture Radar (SAR) Sentinel-1 data to detect the main wheat phenological phases was investigated in the Bekaa plain of Lebanon. Accordingly, the temporal variation of Sentinel-1 (S1) signal was analyzed as a function of the phenological phases' dates observed in situ (germination; heading and soft dough), and harvesting. Results showed that S1 data, unlike the Normalized Difference Vegetation Index (NDVI) data, were able to estimate the dates of theses phenological phases due to significant variations in S1 temporal series at the dates of germination, heading, soft dough, and harvesting. Particularly, the ratio VV/VH at low incidence angle (32–34°) was able to detect the germination and harvesting dates. VV polarization at low incidence angle (32–34°) was able to detect the heading phase, while VH polarization at high incidence angle (43–45°) was better than that at low incidence angle (32–34°), in detecting the soft dough phase. An automated approach for main wheat phenological phases' determination was then developed on the western part of the Bekaa plain. This approach modelled the S1 SAR temporal series by smoothing and fitting the temporal series with Gaussian functions (up to three Gaussians) allowing thus to automatically detect the main wheat phenological phases from the sum of these Gaussians. To test its robustness, the automated method was applied on the northern part of the Bekaa plain, in which winter wheat is harvested usually earlier because of the different weather conditions. The Root Mean Square Error (RMSE) of the estimation of the phenological phases' dates was 2.9 days for germination, 5.5 days for heading, 5.1 days soft dough, 3.0 days for West Bekaa's harvesting, and 4.5 days for North Bekaa's harvesting. In addition, a slight underestimation was observed for germination and heading of West Bekaa (−0.2 and −1.1 days, respectively) while an overestimation was observed for soft dough of West Bekaa and harvesting for both West and North Bekaa (3.1, 0.6, and 3.6 days, respectively). These results are encouraging, and thus prove that S1 data are powerful as a tool for crop monitoring, to serve enhanced crop management and production handling. [ABSTRACT FROM AUTHOR]

Invasive aquatic plants are a serious global ecological and socio-economic problem because they can cause local extinction of native species and alter navigation and fishing. Eichhornia crassipes (water hyacinth) is a dangerous invasive floating plant that is widely distributed throughout the world. In Lebanon, it has spread since 2006 in the Al Kabir River. Remote sensing techniques have been widely developed to detect and monitor dynamics and extents of invasive plants such as water hyacinth over large areas. However, they become challenging to use in narrow areas such as the Al Kabir River and we developed a new image-analysis method to extract water hyacinth areas on the river. The method is based on a time series of a biophysical variable obtained from Sentinel-2 images. After defining a reference period between two growing cycles, we used the fractional vegetation cover (FVC) to estimate the water hyacinth surface area in the river. This method makes it possible to monitor water hyacinth development and estimate the total area it colonizes in the river corridor. This method can help ecologists and other stakeholders to map invasive plants in rivers and improve their control. [ABSTRACT FROM AUTHOR]

Based on remotely sensed imagery and socioeconomic data, this research analyzes the reasons why farmers choose one crop over another in the Bekaa Valley in Lebanon. This study mapped the area of the cultivated crop in 2017 with Sentinel-2 images. An accurate and new method was developed to extract the field boundaries from the evolution of the normalized difference vegetation index (NDVI) profile throughout the season. We collected 386 GPS locations for fields that are used for crop cultivation, from which the NDVI profile was extracted. The 386 reference fields were separated into two groups: reference locations and test locations. The Euclidean distance (ED) was calculated between these two groups, and the classification was strongly correlated to the known crop type in the field (overall accuracy: 90%). Our study area cultivated wheat (32%), spring potatoes (25%), spring vegetables (27%), orchards (11%), vineyards (7%), and alfalfa (<1%). Socioeconomic surveys showed that farmers favored these crops over others on account of their profitability. Nonetheless, the surveys highlighted a paradox: despite the lack of a political frame for agriculture in Lebanon, farmers' crop choices strongly depend on a few existing policies. [ABSTRACT FROM AUTHOR]

In this paper, we present a probabilistic approach which uses nadir-looking wide-band radar to detect oil spills on rough ocean surface. The proposed approach combines a single-layer scattering model with Bayesian statistics to evaluate the probability of detection of oil slicks, within a plausible range of thicknesses, on seawater. The difference between several derived detection algorithms is defined in terms of the number of frequencies used (within C-to-X-band ranges), as well as of the number of radar observations. Performance analysis of all three types of detectors (single-, dual- and tri-frequency) is done under different surface-roughness scenarios. Results show that the probability of detecting an oil slick with a given thickness is sensitive to the radar frequency. Multi-frequency detectors prove their ability to overcome the performance of the single- and dual-frequency detectors. Higher probability of detection is obtained when using multiple observations. The roughness of the ocean surface leads to a loss in the reflectivity values, and therefore decreases the performance of the detectors. A possible way to make use of the drone systems in the contingency planning is also presented. [ABSTRACT FROM AUTHOR]