शैडोइंग अभ्यास: Google Earth Engine Tutorial 1: Lake Area Estimation - वीडियो के साथ अंग्रेजी बोलना सीखें

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Hello everyone my name is Amrsen Ahri.

I'm a Google Earth Engine expert

and here I'm going to show you how to calculate the water area for the inland waters and also the lakes.

Let's go to the Google Earth Engine platform code editor.

Here I'm going to show you to calculate lake area by JavaScript in the code editor

and then for the first step we should identify the study area.

My study area in this tutorial is the Urmia lake in the northwest of Iran.

By the drawing tools here

you can set your study area for example geometry helping me to draw the study area first

and then we can start programming in the code editor.

Map .center objects helps to have focus on the study area

or the location of the interest

and the location of interest already entitled as geometry in this bar as you can see here.

The next step you need to call your desired satellite data.

There are different satellites data enabling us to calculate the lake area for example Landsat,

MODIS, Sentinel and on and on it goes but the most important

or the most applicable satellite images nowadays we use for calculate the area are MODIS

and Sentinel and also the Landsat series data.

For example here I'm gonna use Landsat images.

The process of programming is same for Landsat,

MODIS and Sentinel just you should know about the bands and also the threshold and how to set them.

If you had any questions or any problem regarding to the programming steps you can

add your comments in my youtube page and then I will back to you

and answer them and help you and help you to solve their problems.

So first of all I make a variable for the landsat images

entitled Landsat equals to through the search places and data set we can find Landsat collection 2 level 2 data.

Here

if you type Landsat 8 for example Landsat 8 collection 2 level 2 helping

you to find those of Landsat data which is radiometrically and atmospherically corrected.

Landsat Collection 2 and Level 2 is the newest product

which is available in 2024 enabling us to have Landsat images with great quality

and no need to any atmospheric radiometric corrections just you have to implement

or apply some scale and factor to receive the surface effectance value.

It's wonderful for the calculations and doing image processing by Landsat Images.

So by the import image collection, Landsat Image Collection imported to the code entitled as Image Collection here, then I write

Image Collection or I assign

Image Collection to the Landsat Images

and for all of the satellite data

you are going to do calculations in the Google Earth Engine platform you have to set filter bones

and filter dates which is very very important filters

and before any programming you need to define this so

that filter date enabled us to call satellite images for a specific period for example

I make two variables like time start and time

and time starts like this time start equals to for example 2020

I want to get the area of the Urmia Lake in the north east of Iran for 2020

and time and would be here

2021 2021 for the 2020 I want to get an average landsat images and then calculate the lake area for this region.

So filter date starts with time start and time end.

Just like that.

And then dot filter bones enabled us to limit our narrow

down lands of collections to the region of the interest that already defined here.

The filter bones is a geometry like this

and then by select you can call your desired bands for the calculations

and also the computations or something like that so for now just I want to write print like Landsat,

Print Landsat Collections to see how many images are available for the 2020 from Landsat scenes for this area.

As you can see for these regions only 12 elements are available

and also it's a little weird I think there is a problem in calling a Landsat data.

Oh the problem is here I should call Landsat 8 collection to level 2 tier 1 not 2.

So that's the problem.

Collection 2 tier 1.

So just like that and we remove collection and the collection already imported and then

if I run code again you can see 98 elements are available 98 images are available for the 2020

and you can see different features and by and also different dates

and also the difference frame code and also pass

and rows showing here and each features included 19 elements included a surface reflectance bands

and also the complementary bands that are useful for do some calendar calculations or something like that we don't need them,

just we need some multi -spectral bands from visible and near infrared regions to calculate normalized difference of water indices to

separate or mask all of the non -water pixels from the satellite image.

So back to the programming.

So far we defined filter date and filter bounds

and in the next step i need to do some process on each collection

or on each element or on each feature

during the time series to select the desired bands to calculate

the desired spectral indices such as normalized difference water index

or something like that to do this we make a loop by dot map functions.

Dot map works very well and enabled us to do calculations over the time series images.

Dot map in the map define a function each input images considered as an IMG here

and then in the parentheses we can in the a new parenthesis we can write the specific

or any considerate equations

that we need to do on each input color input images in the collections first of all

I need to select a specific bands and implement a scale and factor

that already defined for the lens of images variable bands equals to from each imported image into the collection

or into the loop which is entitled already as an IMG .Select.

I selected the surface reflectance bands.

They say all of the surface reflectance bands starting with SR and underline as you can see.

And I write to select those bands starting with SR underline

and then to include all of the bands starting with this prefix type dot

and this sign here and then dot multiply and dot add help us to implement the scale and factor.

What is the scale and factor?

If you go to the data descriptions in the Google Earth Engine data catalog there are descriptions and also in the bands.

In the bands you can see every bands all of the bands listed here

and also here we have a scale and offsets

that we need to multiply each images on own scale

and own offset and add to own offsets just copy a scale value to the code

and multiply to the scale

and the offset value was minus 0 .2 minus 0 .2 the scale

and offset for the surface reflectance bands are same

and also no need to change it for different bands

because we only need to do calculations based on the surface of the test bands

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and no need to do thermal bands or something like that.

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So that's the first equation or that's the first variable

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that selects surface reflectance bands and implement scale and factor by multiply and add functions.

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It's a linear equations actually that needs to be applied on each band before any further processing.

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So in the next step, you should define your or you should calculate the normalized difference water indices.

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The normalized difference water index NDWI enable us to highlight the water and inland lakes in the satellite images variable NDWI,

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normalized difference water index.

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It's very simple.

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It's a normalized equations.

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Just we use bands dot normalized difference just like that.

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It's a normalization relationship between the near infrared and red.

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Just like NDVI but in the NDVI we have a normalization between near infrared

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and red but here the normalization is between green and near infrared.

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So the green band is band number three surface reflectance three and near infrared band is surface reflectance band five.

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Oh sorry my mistake.

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so band number five here selected

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and then by rename I use for example a new name for the NDWI images

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so this is NDWI normalize difference water index

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and then return NDWI as a final calculation in each date

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and then copy we need to have the properties of each input images to the output.

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What is the properties?

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As we talked about that previously in each feature we have different bands and for each features we have different properties.

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The properties including the metadata information, the time of acquisitions the scale and factors values

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and the system time start system time end

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that are necessary for the time series analysis in the google earth engine

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or different informations that you can see all of them listed here

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and available for all of the satellite images

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so now we need to copy

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or have all of this information for the further processing

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if you want to do the time series analysis or something like

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that but in this tutorial it doesn't matter if we don't have them

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but however I use the copy properties here to copy these

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kind of properties from the input images for the all NDWI images

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that already calculated

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that copy properties from img img .property names it means take all of the property names

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and copy them to the new outputs that we calculated before.

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So if you can save your code or and also you can run it it doesn't matter.

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Execute code by run and here again we have 98 elements and throw the 98 elements in each date.

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Now we have one image entitled as normalized difference water indices

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that's the first step in the next step

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I need to select those of Landsat images with the lowest for example cloud cover

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and to do this in their properties

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we have some information about the cloud cover in each scene for example here

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we have called cloud cover percentage that's mentioned here for example this image contain 47 % of image covered by the cloth.

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But we need to call those of images which are almost or approximately cloth free.

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And to do this we need to define a new filter or less than filter.

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Here is that filter.

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For example I add a new filter filter earth engine filter .lt. The filter for less than.

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We need to filter input image collections based on the cloth covers.

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Cloth covers less than a specific threshold.

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Less than the property is cloth cover from the properties.

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I called or I select the cloth cover and I say

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that I need those of images with cloth cover less than for example 10 % totally clot -free

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because to have accurate the area value for the lake we need to have all of the pixels clear

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without any noise and also very perfect for the calculations to reduce the amount of the uncertainties

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that may occur by the cloud cover or something like that so if click on the run again

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here we have after filter cloud cover you can see

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that only 47 elements are available and these are almost clot -free images

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and then we can make

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it we can get a median value from all input images to have a single band that's media

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if you want to for example narrow down your calculation for a specific

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or a specific period in during the year you can use the calendar range just like

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that for example I want to use those of satellite data blinds to for example summer season

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accordingly earth engine earth engine

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that filter dot calendar range helps me to select a specific months during a year to collect their data

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and do calculations based on if you want to have focus only for summer

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or only for winter just in the calendar range

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you can choose your desired period for example from june to september i selected here

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and uh based on the months from months number six to months number nine from june to uh september

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i wanna call landsat images those of landsat images with less than a 10 percent cloud cover here

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and then get a median of all uh all of the summer images

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and you can use different types of the statistical operation instead of the median like average

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or minimum or maximum or something like that.

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But here median is very helpful because it reduces the effects of the remains clods

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and also the shadow effects in the satellite data

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which is very very useful for specifically in the mountainous regions and also some regions that are really really cloudy.

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So then if I print again you can see

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that we have one median images from the clot -free data for the summer of 2020. And now

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if you want to see the final image here

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you can use map .addLayer map .addLayer helping us to visualize the Landsat images calculated

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and then I want to clip it based on the geometry already drawn

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and then for the visualization I leave it free a little bit empty here

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and then for the name of the data sets we can set a name for example Landsat summer

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or NDWI summer is a name of the image that we want to visualize it and then insert false.

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False helps us to manage the visualization process for example

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when you run your code

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but here we have a problem before yes it's a Landsat yes

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when you run your code false helping you to control the visualization process.

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If you don't use false, the image automatically showed in the Google map or the map display

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that we have here but false enabling us to control the visibility or non -visibility of the final images.

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You can choose when visible or when not visualize it. So

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if you zoom in on your study area you can see the NDWI calculated for this region

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and you can see

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that the image is available by the inspector

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you can check the different values in the images NDWI is

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very helpful to extract the water area from other phenomena

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or the land covers in your study area

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it's a global index for the inland lakes that's already developed by the MAG feature

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and also it's one of the most cited spectral indices in the remote sensing

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that helps you to make it differentiate between the water and non -water pixels.

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Here if you click on you can see that the value of the water is those of values above zero.

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From zero to one we have water and also this is the specific range.

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But those of non -water pixels.

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or for example you can see that uh clear it

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or uh can be the screen can be seen uh by the

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minus values or those of values zero uh blow to the zero value actually here

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so now we need to set a threshold to differentiate

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or mask non -water pixels from the final images for example here

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I can set a mask I can copy map dot add layer into a new line

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but in this time we want to show the threshold values applications here for example here

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we are after clipping process we want to select those of pixels that covered the inland lakes or the water area.

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So we need to select those of pixels greater than 0 .1 or greater than 0.

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Just like that.

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So again run the code and this is the NDWI already calculated

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and this one is the mask we created for the inland lakes

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and you can see that we mask the non -water pixels by the thresholds and here

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we can see

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that the dose of values greater than zero is a suitable

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threshold to make a separate between separation between the lake and non -lake areas.

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So to calculate the lake area first of all we need to make a threshold just like

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that from the Landsat image median for the summertime 2020 we want to select those of pixels greater than zero.

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So and then in the next step we should define a mask.

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Mask process enables us to remove the zero values for the calculation of the lake areas

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so the actually here we have a dark regions

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that all labeled by zero value but we can remove them

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or turn them to the no NAN values by the update mask function in Google Earth engine here

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we get a thr or threshold image

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and then using the update mask by threshold we can see the study area without the dark region

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so again by map .add layer

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you can see the result of the computations here map .addLayer here we want to see the mask already computed

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so and instead of ndwi .thr we have ndwi mask so

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again execute your code and here we have NDWI masked

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that remove all of the zero values

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so as you can see by the inspector you can see

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that the lake area or the regions covered by the water all of them labeled by one

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and non -water pixels are labeled by NAN

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and in the next step we need a function to calculate the area of each pixels just like

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that here we make a new variable entitled as pixel area for example here

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we have a new variable pixelarea equals to get mask and mask dot multiply and multiply here means implement the new functions.

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New functions is that earthengin .pixelarea earthengin .image .pixelarea.

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The pixelarea is a function as you can see generate an image in

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which the value of each pixels is the area of that pixels in square meters.

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It's very important things and it's very very helpful for doing the calculations during

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that pixel area and it is based on the square meter

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but if you want to convert it to the square kilometer you can divide it to the

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1e6 or 1 million to convert it from the square meter to a square kilometer.

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So next we use map .addLayer again.

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Using the map .addLayer you can see the area of each pixel with the based on a square kilometer.

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So just here we have the pixel area that clip geometry

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and remove the threshold instead of NDWi THR NDWi pixel area we have.

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So run code again NDWi pixel area is the same image with a darker color but the color doesn't matter.

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The only thing matter here is the pixel area.

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The pixel area is based on the square kilometer.

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If we remove the divide one million

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and run your code again you can see the pixel areas by the with actually a square meter.

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Here you can see that this is the square meter the area of each pixel not the size of each pixel,

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the area of each pixel based on the square meter showed here

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and then in the next part we need to calculate the lake area by the reducers functions.

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So here we make a new function entitled as lake area.

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Lake area equals to pixels pixel area dot reduce region reduce region

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so there are different arguments for example one of them is reducer we want to get this summation of

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or we want to get the sum of all pixels area for the selected regions

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so here the reducer will be that sum for the location of interest I think

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which is a geometry the argument is geometry

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and the location of interest already entitled as geometry

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and the next is scale the scale of Landsat images defined

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based on the spatial resolution the spatial resolution in Landsat data as you as you know is 30

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so a scale should be 30 but for those of region

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which is a little big sometimes scale and scale 30 makes some problems

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or issues for the computations or something like that in

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that in such cases we need to increase the value of the scale

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if we face any issues regarding to the computation speed

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or something like that we will check it

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if we get error we can increase it to 100

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or 200 or something like that

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but such kind of error just sometimes happened for some of the lakes

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or some of the study areas which are very large regarding regarding to the processing

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that we are doing actually so this is it and

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that values we want to select the values from the zero from the first band

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and show that in the to show the final result in the right panel

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or in the console console part we need to print a final result the final result will be lake area.

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Then if you run your code, oh yes you can see that too many pixels in these regions.

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That's what I said before.

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We can increase the pixel size for scales here.

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I hope it works.

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Such kind of issues is very normal during the computations or something like that.

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Yes you can see that the area of the lake based on the square meter calculated.

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If you want to convert it to the value you can use the number from an object.

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You can use earth engine dot number just like that and if you want to get the value,

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get the area value as a square kilometer here

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you can as I told you before we can divide the pixel area to 1 million of just

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1 is 6 not 16 actually.

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Yes the number is 3000 s square kilometer and also the threshold we defined is very very important here. For example

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if you increase the value of the threshold in the computations sometimes helping you to make the final results more realistic.

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Again with the new threshold you can see

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that we get this value 3000 square kilometer for the region of the interest or the for the lake of interest.

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And then you can use this code for different lakes in all around the world.

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For example I can just you need just change your study area.

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For example another lake is in Turkey.

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You can see that this is another lake here

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and we run the code for the new region

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and you can see the area of this lake with the number format in the right panel

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and also for example this is like that 3000 square kilometer

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and you can see that how much it is accurate for this region.

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This is NDWI image for summer

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and sometimes for some images we have we face some bad pixels

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or noises or something like that and then I try for another region

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and also we can also change the time

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or for example I try to set a longer period of time for the computations

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because more pixels are available and the possibility of NAND values

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and also the cloud covers and the noises reducing the computations

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and we expect to have more accurate calculations in these regards for example this is NDWI summer you can see

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that yes now we have lower noises in the final images

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and also the threshold works very well some years

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because of the noises or limitation of the data

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or cloudy situation we will have some gaps in the computations by changing the timing period,

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cloud cover, or something like that you can increase the accuracy of the computations.

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Yes, I hope this tutorial helping you to calculate inland lakes or water bodies in your works and also in your projects.

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If you face any issues during the programming just feel free to contact me through my YouTube channel.

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My name is Amritsen Ahri.

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I'm a Google Earth Engine expert.

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I will be appreciated if you like

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or share this video with other researchers who need to calculate the lake area for the process.

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And also in the next tutorials I will try to make a video about showing

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that how we can calculate the lake area in the time series

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which is very very helpful for the hydrological studies and climatological studies.

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Thank you for watching

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इस पाठ की शब्दावली और बोलने के सुझाव

यह स्पीकिंग पाठ वीडियो “Google Earth Engine Tutorial 1: Lake Area Estimation” पर आधारित है। सबसे ज़्यादा दोहराए गए शब्द ये हैं: image, pixel, band, landsat, lake। इस वीडियो में शैडोइंग के लिए 326 वाक्य और 4053 शब्द हैं। बोला गया हिस्सा 33:14 लंबा है। वक्ता लगभग 122 शब्द प्रति मिनट की स्थिर गति से बोलता है, जो शैडोइंग के लिए आरामदायक है। 83% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
calculate क्रिया	/ˈkælkjʊleɪt/	गणना करना, हिसाब रखना
filter संज्ञा	/ˈfɪltɚ/	फ़िल्टर, साफी
select क्रिया	/sɪˈlɛkt/	चुनना, चयन करना
mask संज्ञा	/mæsk/	मुखौटा, मास्क
threshold संज्ञा	/ˈθɹɛʃ(h)oʊld/	डेवढ़ी
calculation संज्ञा	/ˌkælkjuˈleɪʃn̩/	गणना, परिकलन
satellite संज्ञा	/ˈsætəlaɪ̯t/	उपग्रह, प्राकृतिक उपग्रह
cloud संज्ञा	/ˈklaʊ̯d/	मेघ, बादल
geometry संज्ञा	/d͡ʒiˈɑ.mə.tɹi/	ज्यामिति, रेखागणित
cloth संज्ञा	/klɒθ/	कपड़ा, पारचा
metre संज्ञा	/ˈmiːtəɹ/	मीटर
infrared विशेषण	/ˌɪnfɹəˈɹɛd/	अवरक्त
helpful विशेषण	/ˈhɛlp.fl̩/	मददगार
calendar संज्ञा	/ˈkæl.ən.də/	कालदर्शक, पंचांग
import संज्ञा	/ˈɪm.pɔɹt/	आयात

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	is entitled · be applied · can be seen
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	data which is · dates which is · loop which is

ध्यान देने योग्य उच्चारण

वक्ता 13 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे I'm, doesn't, don't। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: threshold /ˈθɹɛʃ(h)oʊld/, cloth /klɒθ/, northwest /nɔɹθˈwɛst/, thermal /ˈθɜːməl/, parenthesis /pəˈɹɛn.θə.sɪs/
“sh” और “zh” की ध्वनियाँ: threshold /ˈθɹɛʃ(h)oʊld/, calculation /ˌkælkjuˈleɪʃn̩/, collections /kəˈlɛkʃənz/, equation /ɪˈkweɪ.ʒən/, spatial /ˈspeɪ.ʃəl/
लंबे शब्द — बलाघात सही रखें: geometry /d͡ʒiˈɑ.mə.tɹi/, visibility /ˌvɪz.əˈbɪl.ɪ.ti/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/, acquisition /ˌæk.wɪˈzɪʃ.ən/, accuracy /ˈæk.jɚ.ə.si/

हिंदी भाषियों के लिए मुश्किल ध्वनियाँ:

/v/ और /w/ का फ़र्क़ — /v/ में ऊपर के दाँत निचले होंठ को छूते हैं, /w/ में होंठ गोल होते हैं: variable [ˈvæɹ.i.ə.bl̩], equation /ɪˈkweɪ.ʒən/, divide /dɪˈvaɪd/, convert /kənˈvɝt/, visible /ˈvɪzɪb(ə)l/
/z/ — इसे “ज” (/dʒ/) न बनाएँ: collections /kəˈlɛkʃənz/, visible /ˈvɪzɪb(ə)l/, visibility /ˌvɪz.əˈbɪl.ɪ.ti/, acquisition /ˌæk.wɪˈzɪʃ.ən/, regards /ɹɪˈɡɑɹdz/
शब्द की शुरुआत में s + व्यंजन — पहले “इ” न जोड़ें: spatial /ˈspeɪ.ʃəl/, statistical /stəˈtɪs.tɪ.kəl/, spectral /ˈspɛktɹəɫ/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
सामान्य गति पर एक-एक वाक्य की शैडोइंग करें और हर वाक्य तब तक दोहराएँ जब तक आपकी लय वक्ता से न मिल जाए।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर calculate, filter, select जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: Google Earth Engine Tutorial 1: Lake Area Estimation - वीडियो के साथ अंग्रेजी बोलना सीखें

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