Remotely sensed data can provide information about conditions on the ground that may affect food supply chains and food security during pandemics. Your challenge is to demonstrate the agricultural socio-economic impacts from COVID-19.
There are more than a 100 species of identified mangroves in the world out of which there are 16 true mangrove species, rest are associated mangroves. For example, The Godavari mangroves(Coringa Mangroves)in our region consist of 35 mangrove species, out of which all the 16 true mangroves are found. In this project, we developed a code that can identify mangroves. Only a test code is submitted as there is not a lot of time for us. We've considered the desired conditions in which the mangroves grow to write the code. We have only identified the Godavari mangroves using the code and can further develop and debug the code in the future*LINK TO REST OF OUR SUMMARY: http://shorturl.at/gklGP
The aim of our project is to develop a way by which the unidentified mangroves can be mapped using remote sensing data from the satellites. Thus by mapping the mangroves, we can have a strategy to conserve them, and also we can improve the vegetation over there. A strategy to map the mangroves using the landcover data is developed in this project.
The aim of our project is to develop a way by which the unidentified mangroves can be mapped using remote sensing data from the satellites. Thus by mapping the mangroves, we can have a strategy to conserve them, and also we can improve the vegetation over there. A strategy to map the mangroves using the landcover data is developed in this project.
Generally, mangrove forests surround an area where the river and sea meets. Mangrove forests won't form where everywhere the river and sea meet. They require unique and favorable conditions. For instance, The Godavari mangroves (known as the coringa mangroves) are formed where the River Godavari drains into the Bay of Bengal. This Coringa Mangrove Forest surrounds the city ‘Kakinada’ and acts as a natural storm barrier, that protects the city from cyclones and tidal effects.
Our project identifies these type of mangroves which are unidentified. After identifying them can aware the people in those regions about them and we can conserve them. we can do that by considering the factors like soil Ph value, the temperature of the region, Ph value of water, and other climatic conditions.
**(The above picture shows the Godavari mangrove river mouth, which is captured by Sohan Seemakurthy)
Our project works by identifying the points where the river and sea meet thereby classifying the Ph value of soil, type of soil, Oxygen present in the atmosphere, the temperature of the region, and other considerable factors. By sorting out all the factors, we will run a test code that can identify such areas and click the pictures of desired areas. We are only attaching a sample code in the references and not a full version due to a lack of time.
Our sole aim is to conserve the tidal forests as they constitute a unique ecosystem. Many endangered flora and fauna are found in such regions. The animals present in such types of forests are poached and hunted for false use. Mangrove crops have a lot of uses. Their tannin has an antibacterial and vermifuge effect. Somengroves are often used to treat snake bites.
discovering new mangrove crops, we can add up the species population. Finally, We hope to identify all the mangroves present on the earth.
Mangroves are a habitat for numerous species. The droppings of the migratory birds present in mangroves make the swamps much fertile, thereby providing the plants’ nutrition for survival. These droppings also create fertile breeding grounds for fish, crabs, and shrimp.
In my mangrove region, we can find a lot of endangered species like Fishing cats, Smooth Coated Otters, Golden Jackals, and some more terrestrial mammals are found. The total number of terrestrial mammals is 16. A total of 575 finfish species, 26 species of reptiles, 6 species of whales and dolphins, and 277 benthic organisms are found in Coringa mangroves. By this, we can clearly conclude that mangrove swamps are an excellent habitat for flora and fauna.
**(the animal in the above picture is a Fishing cat, it is clicked by Srikanth Mannepuri. He's a wildlife photographer and a good friend of us)
**(this picture is a yawing Smooth-coated Otter, clicked by Srikanth Mannepuri)
**(these birds are lesser sand plovers captured by Srikanth Mannepuri)
We people in our team are living in an area which is soo close to mangroves. We have faced some circumstances like cutting down mangroves by the government and occupying the land for industrial use last year in the month of May. We were shaken after looking at such devastating incidents.
***(this map is taken from the SENTINEL-5P mapping service in the EO dashboard)
Though we have a vast number of industries in our region, our area is still unaffected by Carbon and Nitrogen pollutants, due to the mangrove forest present nearby. The above two maps show the mean CO2 concentration and CO volume mixing ratio. Our mean CO2 is moderate and CO ppbv is low.
By observing our current geographical factors, we would like to map the mangroves around the globe.
The below map shows Brahmapur, a place in Orissa, which has few mangroves located on the coast. We identified this by using the GLOBE visualization system. The Ph of the soil in that area is found to be 8, which is quite suitable for the sustainability of Mangroves.
The characterization of the soil near Brahmapur is present in the chart above. The above observation is recorded by a co-citizen scientist over there. More data is provided in reference 6.
Our approach is to consider the favorable conditions for the growth of mangroves and thereby integrating them with the code so that can let us know the location of the mangroves. Already a there’s a version for mangrove mapping that can be integrated into the EO Dashboard. That works even more accurately by considering more factors provided by us. Python(deep learning) can be used to make such software that can identify mangroves. An example link to the code is provided in the references[No.5].
We have used the data of NASA and ESA satellites. Namely, Sentinel-5P and NASA OMI. We have also used data from the GLOBE observation visualization system. Other data sources were taken from google. Most of the data about our region are gathered from our research work on mangroves. The data provided in the EO dashboard was quite useful to make good comparisons about the concerning factors about mangrove development.
Well, the prediction is quite complex and requires factors that can be found from space and on land, by means of some geological conditions. The more the factors match the existence of the mangroves the more the predictability.
Some factors or favorable conditions can include such as:-
1.The PH (“potential of hydrogen” or “Power of hydrogen”) of water in the area where the prediction is taking place. Which can be between around 7.3 - 8.1
2.The factor of soil PH value is also considered for the favorable growth of mangroves. Where the value for mangrove swamps to grow can be 7.4 – 7.61, also can be similar to that of the PH of the water.
3.Not only the PH values, but the prediction can gain accuracy by identifying the climatic conditions, to find the favorable area where the mangrove is situated. Usually, the mangroves grow in the temperatures like a minimum of 18 degrees – a maximum of 25 degrees, and in well-lit areas for the process of photosynthesis.
4.Land covers can also add a boost to the prediction as mangroves can be wide stretched biosystems. In India, the mangrove land covers are about 4,975 sq km, which can show the economical changes in the areas nearby.
From the above conditions or factors, the locations of mangroves can be approximately located with the chance of finding at 80% - 85%.
But as the factors increase the predictability can also be increased. We can do that by means of geostationary satellites for the accuracy of locations.
There’s also another possible way to identify the mangroves. We can do that by using the global river remote sensing data. We could use the endpoints of the rivers where the river gets mixed in the sea to identify any swamps and mudflats. If the recorded data matches the desired condition of the mangroves, then the existence of mangroves can be predicted. This is a more accurate way to predict the existence of mangroves.
The major complication faced by us is lack of time and lack of coding experience in python. We are all C++ programmers and are not so profound at handling python as we are still in the basics of engineering. Though we lack experience in coding, we have good knowledge about mangroves and also about mangrove conservation.
The biggest achievement is that we have educated the judges of this hackathon and a mob of people about the importance of mangroves.
We have developed a way by which we can predict the presence of mangroves by using remote sensing data from satellites. Data like the Ph of soil and water, the temperature of the region, and land cover of the region are the major factors considered for identifying such wilderness swamps. Mangroves are a rich source of biodiversity. Many species are still survived on earth due to such swamps.
Our concern is not about the way which we developed to map the mangroves. Our concern is about environmental conservation. Despite the unavoidable free services provided by the earth to humans, we are not able to pay off her kindness. It is not only the responsibility of environmental activists and departments that conserve such wilderness areas. It is the responsibility of every citizen on the globe to conserve our mother earth.
Yet we have found a decent way to map the mangroves but still, we are not pro-level coders to conclude a way that how can we integrate it in the EO dashboard. If we have the required potential to integrate it, we’ll definitely do so.
#mangrove #conservation #lungcancer #covid #mapping
This project has been submitted for consideration during the Judging process.
