Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R2\u3e 0.95) in the testing and (R2\u3e 0.93) in the evaluation process

Investigating Decadal Changes of Multiple Hydrological Products and Land-Cover Changes in the Mediterranean Region for 2009–2018

Land-cover change is a critical concern due to its climatic, ecological, and socioeconomic consequences. In this study, we used multiple variables including precipitation, vegetation index, surface soil moisture, and evapotranspiration obtained from different satellite sources to study their association with land-cover changes in the Mediterranean region. Both observational and modeling data were used for climatology and correlation analysis. Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Global Land Data Assimilation System (GLDAS) were used to extract surface soil moisture and evapotranspiration data. Intercomparing the results of FLDAS and GLDAS suggested that FLDAS data had better accuracy compared to GLDAS for its better coherence with observational data. Climate Hazards Group Infra-Red Precipitation with Station Data (version 2.0 final) (CHIRPS Pentad) were used to extract precipitation data while Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to extract the vegetation indices used in this study. The land-cover change detection was demonstrated during the 2009–2018 period using MODIS Land-Cover data. Some of the barren and crop lands in Euphrates-Tigris and Algeria have converted to low-vegetated shrublands over the time, while shrublands and barren areas in Egypt’s southwestern Delta region became grasslands. These observations were well explained by changing trends of hydrological variables which showed that precipitation and soil moisture had higher values in the countries located to the east of the Mediterranean region compared to the ones on the west. For evapotranspiration, the countries in the north had lower values except for countries in Europe such as Bosnia, Romania, Slovenia, and countries in Africa such as Egypt and Libya. The enhanced vegetation index appeared to be decreasing from north to south, with countries in the north such as Germany, Romania, and Czechia having higher values, while countries in the south such as Libya, Egypt, and Iraq having lower trends. Time series analysis for selected countries was also done to understand the change in hydrological parameters, including Enhanced Vegetation Index, evapotranspiration, and soil moisture, which showed alternating drop and rise as well as stagnant values for different parameters in each country

A STUDY ON EVACUATION SIMULATION FOR GUIDING TOURISTS IN HIMEJI CASTLE BASED ON A SURVEY OF TOURISTS’ INTENTIONS IN EVACUATION AFTER EARTHQUAKE

Many tourists tend to visit historic areas. Nevertheless, their knowledge about these areas, disaster prevention, and evacuation is not sufficient. Japan has met with several large-scale disasters, namely the Great East Japan Earthquake in 2011, the Great Hanshin-Awaji Earthquake in 1995, and will potentially face the Nankai Trough Quake in the future. This paper, based on a survey of tourists’ intentions in evacuation after an earthquake in Himeji castle, shows an evacuation simulation and the measures for supporting tourists’ evacuation. Himeji Castle, the area investigated by this study, is one of the world heritage sites in Japan. First, this study revealed decision-making rules and used these to categorize tourists. This paper investigated the sources of information that tourists consider before starting evacuation. According to the results of the questionnaire survey, four groups were categorized by analytic hierarchy process and cluster analysis. As a result, many tourists set a high value on information from sign boards and staff of the Himeji castle before starting evacuation. Next, in a similar manner, using analytic hierarchy process, this survey found that many tourists consider information from signboard and staff when choosing evacuation routes, and the respondents were categorized into four groups using cluster analysis. Second, this study developed an evacuation simulation taking into account the tourists’ intentions about evacuation. This study used SOARS, Spot Oriented Agent Role Simulator, as a simulation platform and adopted a Spot-Link type model. Third, this study simulated six cases that have different evacuee flows near “Bizen-gate” and routes in sightseeing, and evaluated them by transition of the number of evacuees who were able to reach an evacuation area and the number of evacuees who could not move because of bottlenecks. As a result, we found two effective measures for guiding tourists

Multidecadal Analysis of Beach Loss at the Major Offshore Sea Turtle Nesting Islands in the Northern Arabian Gulf

Undocumented historical losses of sea turtle nesting beaches worldwide could overestimate the successes of conservation measures and misrepresent the actual status of the sea turtle population. In addition, the suitability of many sea turtle nesting sites continues to decline even without in-depth scientific studies of the extent of losses and impacts to the population. In this study, multidecadal changes in the outlines and area of Jana and Karan islands, major sea turtle nesting sites in the Arabian Gulf, were compared using available Kodak aerographic images, USGS EROS Declassified satellite imagery, and ESRI satellite images. A decrease of 5.1% and 1.7% of the area of Jana and Karan islands, respectively, were observed between 1965 and 2017. This translated to 14,146 m2 of beach loss at Jana Is. and 16,376 m2 of beach loss at Karan Is. There was an increase of island extent for Karan Is. from 1965 to 1968 by 9098 m2 but comparing 2017 with 1968, Karan Is. lost as much as 25,474 m2 or 2.6% of the island extent in 1968. The decrease in island aerial extent was attributed to loss of beach sand. The southern tips of the island lost the most significant amount of sand. There was also thinning of beach sand along the middle and northern sections that exposed the rock outcrops underneath the beach. The process of beach changes of both islands was tracked by the satellite imagery from Landsat 1,3,5,7 and Sentinel-2 during 1972 to 2020. Other factors including the distribution of beach slope, sea level changes, as well as wind & current from both northward and eastward components were analyzed to show its impact on the beach changes. The loss of beach sand could potentially impact the quality and availability of nesting beach for sea turtles utilizing the islands as main nesting grounds. Drivers of beach loss at the offshore islands are discussed in the context of sea level rise, dust storms, extreme wave heights and island desertification

Does Delay in Breast Irradiation Following Conservative Breast Surgery in Node-Negative Breast Cancer Patients Have an Impact on Risk of Recurrence?

PURPOSE: This retrospective review was conducted to determine if delay in the start of radiotherapy after definitive breast surgery had any detrimental effect on local recurrence or disease-free survival in node-negative breast cancer patients. METHODS AND MATERIALS: A total of 568 patients with T1-T2, N0 breast cancer were treated with breast-conserving surgery and breast irradiation, without adjuvant systemic therapy between January 1, 1985 and December 31, 1992, at the London Regional Cancer Centre. Adjuvant breast irradiation consisted either of 50 Gy in 25 fractions or 40 Gy in 15 or 16 fractions, followed by a boost of 10 Gy or 12.5 Gy to the lumpectomy site. The time intervals from definitive breast surgery to breast irradiation used for analysis were 0-8 weeks (201 patients), \u3e 8-12 weeks (235 patients), \u3e 1216 weeks (91 patients), and \u3e 16 weeks (41 patients). The time intervals of 0-12 weeks (436 patients) and \u3e 12 weeks (132 patients) were also analyzed. Kaplan-Meier estimates of time to local recurrence and disease-free survival rates were calculated. The association between surgery-radiotherapy interval, age (\u3c or = 40, \u3e 40 years), tumor size (\u3c or = 2, \u3e 2cm), Scharf-Bloom-Richardson (SBR) grade, resection margins, lymphatic vessel invasion, extensive intraductal component, and local recurrence and disease-free survival were investigated using Cox regression techniques. RESULTS: Median follow-up was 63.5 months. Patients in all 4 time intervals were similar in terms of age and pathologic features. There was no statistically significant difference between the 4 groups in local recurrence or disease-free survival with surgery-radiotherapy interval (p = 0.189 and p = 0.413, respectively). The 5-year freedom from local relapse was 95.4%. The crude local recurrence rate was 6.9% (7.8% for 436 patients treated within 12 weeks (median follow-up 67 months) and 3.8% for 132 patients treated \u3e 12 weeks from surgery (median follow-up 52 months). In a stepwise multivariable Cox regression model for disease-free survival, allowing for entry of known risk factors, tumour size (p \u3c 0.001), grade (p \u3c 0.001), and age (p = 0.048) entered the model, but the surgery-radiotherapy interval did not enter the model. CONCLUSION: This retrospective study suggests that delay in start of breast irradiation beyond 12 and up to 16 weeks does not increase the risk of recurrence in node-negative breast cancer patients. The certainty of these results are limited by the retrospective nature of this analysis and the lack of information concerning the late local failure rate