Performance Capabilities and Detection Efficiency of Vehicle Backup Proximity Sensors for Narrow Objects

ABSTRACT. This paper investigates performance capabilities and potential safety effectiveness of ultrasonic backup proximity sensors with special attention on cylindrical and cubical shaped narrow obstacles with the dimensions varies from 2.6 cm to 11.0 cm. The experiment was performed using a commercially available parking aid system consisting of two ultrasonic sensors, and a rear bumper model constructed in a laboratory environment together with a test surface divided into grids having cells of 6 cm x 6 cm. It can be observed that there are considerable differences in detection zone patterns when comparing observations for cylinders and cubes even for same dimensions. Using detection zone maps, it can be seen that, when the size of the test objects become smaller, there are large blind spot areas in the space between the sensors and also near the right and left edges of the rear bumper. In average, when reducing the obstacle dimensions from 11.0 cm to 2.6 cm, the detection efficiency changes from 62.6% to 33.1% for cylindrical objects and for cubical shaped objects it was 39.3% to 21.6%. Average detection efficiency is lower than 50% for objects less than 5 cm in dimension irrespective of the object geometry

Lightning Climatology and Human Vulnerability to Lightning Hazards in a School Community: A Case Study in Sri Lanka using LIS Data from TRMM Satellite

This study reported lightning climatology and human vulnerability to lightning in a 20 km × 20 km high-density school area in Colombo city in Sri Lanka from 1998 to 2014 using Lightning Imaging Sensor (LIS) flash data of NASA’s Tropical Rainfall Measuring Mission (TRMM). An average annual flash density recorded over the study area was 9.43 flashes km-2 year-1. A maximum of 49% lightning flashes happened during the first inter-monsoon season. There were only 4% lightning flashes that occurred during 06.00-12.00 LT and during 18.00-24.00 LT, it was 67%, whereas 94% of lightning flashes within a day had occurred after 14.00 LT. It is recommended that, without having proper lightning hazard preventive measures, schools in the study area should avoid or minimalize scheduling their outdoor activities in high lightning risk months of April and November. Especially, after-school outdoor activities should be planned with proper safety measures during the aforementioned months as per the diurnal analysis. Moreover, May to September and December to February were the months with the least lightning risk levels. It is recommended to follow the proposed five-level lightning safety guideline which includes, schedule outdoor activities by considering the variation of lightning activities, follow the 30-30 rule whenever required, avoid staying at the most hazardous locations which are vulnerable to lightning accidents, crouching action if required and providing first-aid whenever necessary. Not only for the Sri Lankan context but also the study is crucial and highly applicable for all schools and other institutes especially in other tropical countries

Novel Index for Hydrological Drought Monitoring Using Remote Sensing Approach: Standardized Water Surface Index (SWSI)

Most of the drought indices designed for hydrological drought monitoring use location-specific data, while there are only a handful of indices designed for hydrological drought monitoring using remote sensing data. This study revealed a novel drought index, Standardized Water Surface Index (SWSI), developed for hydrological drought monitoring. The water surface areas required to calculate the SWSI can be extracted from remote sensing data entirely using both the optical (Landsat 5, 7, and 8) and SAR (Sentinel-1). Furthermore, the developed index was applied to five major reservoirs/tanks; Iranamadu, Mahavilachchiya, Kantale, Senanayaka Samudhraya, and Udawalawa, located in Sri Lanka to monitor respective hydrological drought status for the period from 2000 to 2020. Cloud computing platform such as Google Earth Engine (GEE) provides a good basement to use this index effectively, as it can extract long-term water surface area covering a large geographical area efficiently and accurately. The surface water area extraction from satellite data of those tanks shows an accuracy of more than 95%, and in the event of a severe hydrological drought, the water surface area of the tanks is less than 25% of the total and lasts for more than three to four months. It was also determined that in some years, the surface water area of tanks dropped to as low as 7%. The strong correlation observed between the Standardized Precipitation Index (SPI) and SWSI is indicated by the Pearson correlation coefficient ranging from 0.58 to 0.67, while the correlation between the Vegetation Condition Index (VCI) and SWSI ranges from 0.75 to 0.81. Timely drought monitoring over large geographical areas can be more accurately performed with the SWSI index compared to existing hydrological drought monitoring indices. The SWSI could be more useful for areas that do not have measurable field data

Novel Index for Hydrological Drought Monitoring Using Remote Sensing Approach: Standardized Water Surface Index (SWSI)

Most of the drought indices designed for hydrological drought monitoring use location-specific data, while there are only a handful of indices designed for hydrological drought monitoring using remote sensing data. This study revealed a novel drought index, Standardized Water Surface Index (SWSI), developed for hydrological drought monitoring. The water surface areas required to calculate the SWSI can be extracted from remote sensing data entirely using both the optical (Landsat 5, 7, and 8) and SAR (Sentinel-1). Furthermore, the developed index was applied to five major reservoirs/tanks; Iranamadu, Mahavilachchiya, Kantale, Senanayaka Samudhraya, and Udawalawa, located in Sri Lanka to monitor respective hydrological drought status for the period from 2000 to 2020. Cloud computing platform such as Google Earth Engine (GEE) provides a good basement to use this index effectively, as it can extract long-term water surface area covering a large geographical area efficiently and accurately. The surface water area extraction from satellite data of those tanks shows an accuracy of more than 95%, and in the event of a severe hydrological drought, the water surface area of the tanks is less than 25% of the total and lasts for more than three to four months. It was also determined that in some years, the surface water area of tanks dropped to as low as 7%. The strong correlation observed between the Standardized Precipitation Index (SPI) and SWSI is indicated by the Pearson correlation coefficient ranging from 0.58 to 0.67, while the correlation between the Vegetation Condition Index (VCI) and SWSI ranges from 0.75 to 0.81. Timely drought monitoring over large geographical areas can be more accurately performed with the SWSI index compared to existing hydrological drought monitoring indices. The SWSI could be more useful for areas that do not have measurable field data

Distribution of Lightning Accidents in Sri Lanka from 1974 to 2019 Using the DesInventar Database

The reported lightning accidents that are available in the DesInventar database—which consist of 549 deaths, 498 injured people, 39 destroyed houses, and 741 damaged houses—were analyzed in terms of their geographical and temporal variation. The average lightning flash densities were calculated using zonal statistics using the geographic information system (GIS), referring to the respective raster maps generated based on Lightning Imaging Sensor (LIS) data from the Tropical Rainfall Measurement Mission (TRMM) Satellite. Hence, the variations of the lightning accidents—monthly and climate season-wise—in response to the lightning flash density were also reported. The calculated average lightning flash density in Sri Lanka is 8.26 flashes km−2 year−1, and the maximum average lightning flash density of 31.33 flashes km−2 year−1 is observed in April in a calendar year. April seems to be more vulnerable to lightning accidents, as the maximum number of deaths (150 deaths) and injuries (147 injuries) were recorded in this month. Most of the high-risk lightning accident regions that were identified in Sri Lanka are well known for agricultural activities, and those activities will eventually create the platform for lightning victims. In Sri Lanka, in a year, 12 people were killed and 11 people were injured, based on the reported accidents from 1974 to 2019. Conversely, a substantial increase in the number of deaths, injuries, and incidents of property damage has been observed in the last two decades (2000–2019). On average, for the period from 2000 to 2019, 18 people were killed and 16 people were injured per year. Furthermore, considering the population of the country, 0.56 people per million per year were killed, and 0.51 people per million per year were injured due to lightning accidents based on the reported accidents from 1974 to 2019. Moreover, for the 2000–2019 period, these estimated values are significantly higher; 0.86 people per million per year were killed, and 0.77 people per million per year were injured

Distribution of Lightning Accidents in Sri Lanka from 1974 to 2019 Using the DesInventar Database

The reported lightning accidents that are available in the DesInventar database—which consist of 549 deaths, 498 injured people, 39 destroyed houses, and 741 damaged houses—were analyzed in terms of their geographical and temporal variation. The average lightning flash densities were calculated using zonal statistics using the geographic information system (GIS), referring to the respective raster maps generated based on Lightning Imaging Sensor (LIS) data from the Tropical Rainfall Measurement Mission (TRMM) Satellite. Hence, the variations of the lightning accidents—monthly and climate season-wise—in response to the lightning flash density were also reported. The calculated average lightning flash density in Sri Lanka is 8.26 flashes km−2 year−1, and the maximum average lightning flash density of 31.33 flashes km−2 year−1 is observed in April in a calendar year. April seems to be more vulnerable to lightning accidents, as the maximum number of deaths (150 deaths) and injuries (147 injuries) were recorded in this month. Most of the high-risk lightning accident regions that were identified in Sri Lanka are well known for agricultural activities, and those activities will eventually create the platform for lightning victims. In Sri Lanka, in a year, 12 people were killed and 11 people were injured, based on the reported accidents from 1974 to 2019. Conversely, a substantial increase in the number of deaths, injuries, and incidents of property damage has been observed in the last two decades (2000–2019). On average, for the period from 2000 to 2019, 18 people were killed and 16 people were injured per year. Furthermore, considering the population of the country, 0.56 people per million per year were killed, and 0.51 people per million per year were injured due to lightning accidents based on the reported accidents from 1974 to 2019. Moreover, for the 2000–2019 period, these estimated values are significantly higher; 0.86 people per million per year were killed, and 0.77 people per million per year were injured

Overview of Lightning Trend and Recent Lightning Variability over Sri Lanka

The study was conducted to analyze spatial and temporal variations of lightning activity over Sri Lanka and the surrounding coastal belt region bounded by 5.75–10.00 N and 79.50–89.00 E. Flash data collected by the Lightning Imaging Sensor (LIS) on NASA’s Tropical Rainfall Measuring Mission (TRMM) satellite from 1998 to 2014 and the Lightning Imaging Sensor placed on the International Space Station (ISS) from 2018 to 2021 were used for the study. The Mann-Kendall test and Sen’s slope estimator were applied to annual and seasonal lightning data from 1998 to 2014 to identify the trends in the TRMM dataset. A positive slope of 0.23 was obtained for annual flash densities, while a slope of 0.956 was obtained for First Inter-Monsoon (FIM) seasonal data. Considering the ISS data, the annual variation of lightning activity in 2020 displays the lowest flash density, whereas the highest is represented in 2019 with a value of 10.48 flashes km−2 year−1. The highest mean flash density is observed in Colombo in 2019 at a value of 34.85 flashes km−2 year−1. Overall, April displayed the highest annual flash distribution from 2018 to 2021, whereas the second peak was mostly viewed around September and November. All districts have displayed a significant amount of lightning during April for the period 2018 to 2021. FIM displayed the highest lightning distribution over the country. When considering the seasonal variation, districts belonging to the wet zone and intermediate zone displayed most flashes during the FIM

Rainfall Variability and Trends over the African Continent Using TAMSAT Data (1983–2020): Towards Climate Change Resilience and Adaptation

This study reveals rainfall variability and trends in the African continent using TAMSAT data from 1983 to 2020. In the study, a Mann–Kendall (MK) test and Sen’s slope estimator were used to analyze rainfall trends and their magnitude, respectively, under monthly, seasonal, and annual timeframes as an indication of climate change using different natural and geographical contexts (i.e., sub-regions, climate zones, major river basins, and countries). The study finds that the highest annual rainfall trends were recorded in Rwanda (11.97 mm/year), the Gulf of Guinea (river basin 8.71 mm/year), the tropical rainforest climate zone (8.21 mm/year), and the Central African region (6.84 mm/year), while Mozambique (−0.437 mm/year), the subtropical northern desert (0.80 mm/year), the west coast river basin of South Africa (−0.360 mm/year), and the Northern Africa region (1.07 mm/year) show the lowest annual rainfall trends. There is a statistically significant increase in the rainfall in the countries of Africa’s northern and central regions, while there is no statistically significant change in the countries of the southern and eastern regions. In terms of climate zones, in the tropical northern desert climates, tropical northern peninsulas, and tropical grasslands, there is a significant increase in rainfall over the entire timeframe of the month, season, and year. This implies that increased rainfall will have a positive effect on the food security of the countries in those climatic zones. Since a large percentage of Africa’s agriculture is based only on rainfall (i.e., rain-fed agriculture), increasing trends in rainfall can assist climate resilience and adaptation, while declining rainfall trends can badly affect it. This information can be crucial for decision-makers concerned with effective crop planning and water resource management. The rainfall variability and trend analysis of this study provide important information to decision-makers that need to effectively mitigate drought and flood risk

Rainfall Variability and Trends over the African Continent Using TAMSAT Data (1983–2020): Towards Climate Change Resilience and Adaptation

This study reveals rainfall variability and trends in the African continent using TAMSAT data from 1983 to 2020. In the study, a Mann–Kendall (MK) test and Sen’s slope estimator were used to analyze rainfall trends and their magnitude, respectively, under monthly, seasonal, and annual timeframes as an indication of climate change using different natural and geographical contexts (i.e., sub-regions, climate zones, major river basins, and countries). The study finds that the highest annual rainfall trends were recorded in Rwanda (11.97 mm/year), the Gulf of Guinea (river basin 8.71 mm/year), the tropical rainforest climate zone (8.21 mm/year), and the Central African region (6.84 mm/year), while Mozambique (−0.437 mm/year), the subtropical northern desert (0.80 mm/year), the west coast river basin of South Africa (−0.360 mm/year), and the Northern Africa region (1.07 mm/year) show the lowest annual rainfall trends. There is a statistically significant increase in the rainfall in the countries of Africa’s northern and central regions, while there is no statistically significant change in the countries of the southern and eastern regions. In terms of climate zones, in the tropical northern desert climates, tropical northern peninsulas, and tropical grasslands, there is a significant increase in rainfall over the entire timeframe of the month, season, and year. This implies that increased rainfall will have a positive effect on the food security of the countries in those climatic zones. Since a large percentage of Africa’s agriculture is based only on rainfall (i.e., rain-fed agriculture), increasing trends in rainfall can assist climate resilience and adaptation, while declining rainfall trends can badly affect it. This information can be crucial for decision-makers concerned with effective crop planning and water resource management. The rainfall variability and trend analysis of this study provide important information to decision-makers that need to effectively mitigate drought and flood risk