F1 2021 jeddah setup3/28/2023 ![]() Normalized difference vegetation index (NDVI) derived from satellites has been ubiquitously utilized in the field of remote sensing. This should have practical relevance for water environment management and governance departments. This study should provide strong methodological and theoretical support for future monitoring of algal blooms in turbid water bodies with vigorous aquatic vegetation, especially in the absence of actual measurement data. The spatial transferability test of the method in the independent lakes with the various optical properties indicated the prospects of its application in other turbid water bodies. The overall identification accuracy of aquatic vegetation and algal blooms using the improved VPF ranged 71.8–84.3%. Based on the above method and process, the information of algal blooms and aquatic vegetation was sufficiently distinguished in five typical lakes in China (Lake Hulun, Lake Hongze, Lake Chaohu, Lake Taihu, and Lake Dianchi), and the spatial distribution was reasonably mapped. It effectively distinguished algal blooms and aquatic vegetation without actual measurement data. By combining the image time series information with the natural phenological characteristics of the aquatic vegetation and algal blooms, an improved Vegetation Presence Frequency (VPF) was developed. Since this method combined the vegetation extraction results from multiple indices, it effectively tackled the mis-extraction when only the Floating Algae Index (FAI) or the Normalized Difference Vegetation Index (NDVI) is used in water with high turbidity. Our results showed that the accuracy of the extraction of vegetation information could reach 96.1%. To address this challenge, this study constructed a method to effectively extract algal blooms and aquatic vegetation from the turbid water bodies using Sentinel 2 images with high spatial resolution. ![]() Due to the similar spectral characteristics shared by algal and aquatic vegetation, both are hardly distinguishable in remote sensing imaging, especially in turbid water bodies. In contrast, aquatic vegetation contributes to water purification. To be more specific, indices using green channel were found to be inferior for extracting mucilage information from the satellite imagesĪlgal blooms frequently occur in numerous lakes in China, risking human health and the environment. Among the applied indices, the Adjusted Floating Algae Index (AFAI) was superior for producing the mucilage maps even for the partly cloudy image, followed by Normalized Difference Turbidity Index (NDTI) and Mucilage Index (MI). Results showed that mucilage aggregates started with the coverage of about 6 km² sea surface on 14 May, reached the highest level on 24 May and diminished at the end of July. In this study, five water indices estimated from cloud-free and partly cloudy Sentinel-2 images acquired from May to July 2021 were employed to effectively map mucilage aggregates on the sea surface in the Izmit Bay using the cloud-based Google Earth Engine (GEE) platform. The use of remote sensing technologies provides significant benefits for detecting, monitoring, and analyzing rapidly occurring and displaced natural phenomena, including mucilage events. ![]() ![]() Periodic monitoring of coastal water quality is of critical importance for the effective management of water resources and the sustainability of marine ecosystems. If confidence is over 80% its prolly safe to let the AI make adjustments in P2 by simulating sessions.Global warming together with environmental pollution threatens marine habitats and causes an increasing number of environmental disasters. Thank you! You may find my on Twitter under and Discord here: (Should you wish to chat) F1 Manager 2022 Setup Templates I was able to take the time to make this guide thanks to my wonderful supporters on Patreon. I recommend writing your own results down somewhere to remember for next time you visit a circuit. This guide is to help you in your early F1 Manager days. What works for one perfectly might not work so well for another. I am not sure if Driver, Team or Season has any baring on these confidence ratings however I have added them to the list to see if any patterns appear down the road.Īll cars, tracks and drivers are different. I have included all of the info I collected on the spreadsheet. Because switching settings in Practice sessions take so long the Confidence stat is the one that you will struggle with the most and using these starting templates will save you a session of testing. ![]() These were established from many different cars, seasons and drivers. Most of these are currently at 85% confidence or above but will be added to over time. Over a period of 10 seasons on the pre-release build of the game I recorded a ‘Good starting template’ spreadsheet for every track. ![]()
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