Pratica di Shadowing: Google Earth Engine Tutorial 1: Lake Area Estimation - Impara a parlare inglese con i video

Scegli una modalità di pratica

Ogni modalità apre una sala di pratica a schermo intero.

326 frasi

Se le frasi sono troppo corte o troppo lunghe, clicca su Edit per modificarle.

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

100

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

101

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

102

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

103

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

104

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

105

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

106

normalized difference water index.

107

It's very simple.

108

It's a normalized equations.

109

Just we use bands dot normalized difference just like that.

110

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

111

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

112

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

113

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

114

Oh sorry my mistake.

115

so band number five here selected

116

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

117

so this is NDWI normalize difference water index

118

and then return NDWI as a final calculation in each date

119

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

120

What is the properties?

121

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

122

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

123

and the system time start system time end

124

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

125

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

126

and available for all of the satellite images

127

so now we need to copy

128

or have all of this information for the further processing

129

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

130

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

131

but however I use the copy properties here to copy these

132

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

133

that already calculated

134

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

135

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

136

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

137

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

138

Now we have one image entitled as normalized difference water indices

139

that's the first step in the next step

140

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

141

and to do this in their properties

142

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

143

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

144

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

145

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

146

Here is that filter.

147

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

148

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

149

Cloth covers less than a specific threshold.

150

Less than the property is cloth cover from the properties.

151

I called or I select the cloth cover and I say

152

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

153

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

154

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

155

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

156

here we have after filter cloud cover you can see

157

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

158

and then we can make

159

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

160

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

161

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

162

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

163

accordingly earth engine earth engine

164

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

165

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

166

or only for winter just in the calendar range

167

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

168

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

169

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

170

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

171

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

172

or minimum or maximum or something like that.

173

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

174

and also the shadow effects in the satellite data

175

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

176

So then if I print again you can see

177

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

178

if you want to see the final image here

179

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

180

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

181

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

182

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

183

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

184

False helps us to manage the visualization process for example

185

when you run your code

186

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

187

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

188

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

189

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

190

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

191

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

192

and you can see

193

that the image is available by the inspector

194

you can check the different values in the images NDWI is

195

very helpful to extract the water area from other phenomena

196

or the land covers in your study area

197

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

198

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

199

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

200

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

201

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

202

But those of non -water pixels.

203

or for example you can see that uh clear it

204

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

205

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

206

so now we need to set a threshold to differentiate

207

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

208

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

209

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

210

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

211

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

212

Just like that.

213

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

214

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

215

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

216

we can see

217

that the dose of values greater than zero is a suitable

218

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

219

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

220

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

221

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

222

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

223

so the actually here we have a dark regions

224

that all labeled by zero value but we can remove them

225

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

226

we get a thr or threshold image

227

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

228

so again by map .add layer

229

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

230

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

231

again execute your code and here we have NDWI masked

232

that remove all of the zero values

233

so as you can see by the inspector you can see

234

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

235

and non -water pixels are labeled by NAN

236

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

237

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

238

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

239

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

240

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

241

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

242

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

243

that pixel area and it is based on the square meter

244

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

245

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

246

So next we use map .addLayer again.

247

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

248

So just here we have the pixel area that clip geometry

249

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

250

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

251

The only thing matter here is the pixel area.

252

The pixel area is based on the square kilometer.

253

If we remove the divide one million

254

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

255

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

256

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

257

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

258

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

259

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

260

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

261

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

262

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

263

which is a geometry the argument is geometry

264

and the location of interest already entitled as geometry

265

and the next is scale the scale of Landsat images defined

266

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

267

so a scale should be 30 but for those of region

268

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

269

or issues for the computations or something like that in

270

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

271

if we face any issues regarding to the computation speed

272

or something like that we will check it

273

if we get error we can increase it to 100

274

or 200 or something like that

275

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

276

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

277

that we are doing actually so this is it and

278

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

279

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

280

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

281

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

282

That's what I said before.

283

We can increase the pixel size for scales here.

284

I hope it works.

285

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

286

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

287

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

288

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

289

get the area value as a square kilometer here

290

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

291

1 is 6 not 16 actually.

292

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

293

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

294

Again with the new threshold you can see

295

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

296

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

297

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

298

For example another lake is in Turkey.

299

You can see that this is another lake here

300

and we run the code for the new region

301

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

302

and also for example this is like that 3000 square kilometer

303

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

304

This is NDWI image for summer

305

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

306

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

307

and also we can also change the time

308

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

309

because more pixels are available and the possibility of NAND values

310

and also the cloud covers and the noises reducing the computations

311

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

312

that yes now we have lower noises in the final images

313

and also the threshold works very well some years

314

because of the noises or limitation of the data

315

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

316

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

317

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

318

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

319

My name is Amritsen Ahri.

320

I'm a Google Earth Engine expert.

321

I will be appreciated if you like

322

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

323

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

324

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

325

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

326

Thank you for watching

Scarica l'app

Tutto ciò che serve per parlare fluentemente

Pronuncia con IAVoto per ogni frase

Pratica IPAPadroneggia ogni suono

VocabolarioCostruisci il tuo lessico

Gioco di vocabolarioImpara giocando

Scansiona QR

Download on theApp Store Get it onGoogle Play

FOR YOU

Video consigliato

197 frasi

Vocabolario e note di pronuncia per questa lezione

Questa lezione di conversazione si basa sul video “Google Earth Engine Tutorial 1: Lake Area Estimation”. Le parole che ritornano più spesso: image, pixel, band, landsat, lake. Questo video contiene 326 frasi e 4053 parole da ripetere con lo shadowing. Il parlato dura 33:14. Chi parla mantiene un ritmo costante di circa 122 parole al minuto, comodo per lo shadowing. Il 83% delle parole rientra nelle 3.000 più comuni dell’inglese; conviene studiare le altre prima di iniziare.

Vocaboli chiave di questo video

15 parole del video che vale la pena imparare, con pronuncia e significato:

Parola	Pronuncia	Significato
pixel sostantivo	/ˈpɪk.səl/	pixel
calculate verbo	/ˈkælkjʊleɪt/	calcolare
filter sostantivo	/ˈfɪltɚ/	filtro
select verbo	/sɪˈlɛkt/	scegliere, selezionare
mask sostantivo	/mæsk/	maschera, mascherina
threshold sostantivo	/ˈθɹɛʃ(h)oʊld/	soglia
calculation sostantivo	/ˌkælkjuˈleɪʃn̩/	conto, calcolo
satellite sostantivo	/ˈsætəlaɪ̯t/	satellite
define verbo	/dɪˈfaɪn/	definire, determinare
cloud sostantivo	/ˈklaʊ̯d/	nuvola, nube
enable verbo	/ɪˈneɪ.bəl/	permettere
geometry sostantivo	/d͡ʒiˈɑ.mə.tɹi/	geometria
input sostantivo	/ˈɪnpʊt/	immissione
variable aggettivo	[ˈvæɹ.i.ə.bl̩]	variabile, regolabile
cloth sostantivo	/klɒθ/	stoffa, tessuto

La grammatica di questo video

Le strutture che chi parla usa di più, con le parole esatte del video:

Struttura	Nel video
Forma passiva be + participio passato — conta ciò che accade, non chi lo fa	is entitled · be applied · can be seen
Frasi relative who / which + frase — un’informazione in più su una persona o una cosa	data which is · dates which is · loop which is

Pronuncia a cui fare attenzione

Chi parla usa 13 contrazioni e forme ridotte, come I'm, doesn't, don't. Pronunciale nella forma breve, così come le senti.

I suoni “th”: threshold /ˈθɹɛʃ(h)oʊld/, cloth /klɒθ/, northwest /nɔɹθˈwɛst/, thermal /ˈθɜːməl/, parenthesis /pəˈɹɛn.θə.sɪs/
I suoni “sh” e “zh”: threshold /ˈθɹɛʃ(h)oʊld/, calculation /ˌkælkjuˈleɪʃn̩/, collections /kəˈlɛkʃənz/, equation /ɪˈkweɪ.ʒən/, spatial /ˈspeɪ.ʃəl/
Parole lunghe — attenzione all’accento: geometry /d͡ʒiˈɑ.mə.tɹi/, visibility /ˌvɪz.əˈbɪl.ɪ.ti/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/, acquisition /ˌæk.wɪˈzɪʃ.ən/, accuracy /ˈæk.jɚ.ə.si/

I suoni difficili per chi parla italiano:

/h/ — va soffiata, non è muta: threshold /ˈθɹɛʃ(h)oʊld/, helpful /ˈhɛlp.fl̩/, highlight /ˈhaɪˌlaɪt/
Consonante finale — senza aggiungere una vocale dopo: calculate /ˈkælkjʊleɪt/, select /sɪˈlɛkt/, mask /mæsk/, threshold /ˈθɹɛʃ(h)oʊld/, satellite /ˈsætəlaɪ̯t/
/æ/ — più aperta della “e”: calculate /ˈkælkjʊleɪt/, mask /mæsk/, calculation /ˌkælkjuˈleɪʃn̩/, satellite /ˈsætəlaɪ̯t/, variable [ˈvæɹ.i.ə.bl̩]

Come esercitarsi con questo video

Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
Fai shadowing frase per frase a velocità normale, ripetendo ognuna finché il tuo ritmo coincide con quello di chi parla.
Registrati e confronta con l’originale, facendo attenzione a parole come pixel, calculate, filter.

Cos'è la tecnica dello Shadowing?

Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.

Tecnica dello shadowing: leggi la guida completa passo dopo passo →

Pratica di Shadowing: Google Earth Engine Tutorial 1: Lake Area Estimation - Impara a parlare inglese con i video

Scegli una modalità di pratica

Scarica l'app

How I use AI to learn engineering (anti brain rot studying)

Why Is So Much of the Amazon Still Unexplored?

LTEC 782: Design-Based Research in Education (Session 01)

Petroleum - 06 - Drilling Fluid Laboratory

🎧Luyện Nghe Tiếng Anh Level C1 | 📍Topic: Hoan Kiem Lake - Hồ Gươm

Executing Monitoring, Evaluation and Learning (MEAL) | Complete Practical Guide

1. Introduction to statistics - Lecture 1

IELTS Listening - Renewable energy by sea waves

Civil 3D course for beginners - Part 2 of 3

The Complete Guide to Hybrid Search in RAG (BM25 + Embeddings + Reranker)

Architectural Design Process Explained

Inside the Upstream Process: How Oil & Gas Is Found