- Видео 70
- Просмотров 77 225
Google Earth Engine with Amirhossein Ahrari
Финляндия
Добавлен 2 мар 2015
Welcome to Amirhossein Ahrari's Earth Engine Hub! I'm Amirhossein, a passionate remote sensing expert with a specialization in Google Earth Engine. On this channel, my goal is to sharing useful codes for a community of learners and creators interested in mastering Earth Engine. Whether you're a beginner or an experienced user, here you'll find in-depth tutorials, insightful analyses, and creative applications of Google Earth Engine.
Join me on a journey of learning, where we explore the endless possibilities that Earth Engine offers. My commitment is not just to share knowledge but to empower you with the skills needed to harness the full potential of this powerful tool.
Join me on a journey of learning, where we explore the endless possibilities that Earth Engine offers. My commitment is not just to share knowledge but to empower you with the skills needed to harness the full potential of this powerful tool.
Google Earth Engine Tutorial-70: ASTER Lithological Indices
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/3f432a9df06c3e8c022919e545789d6aaaf5b9a0/00067_aster_lithology
Просмотров: 194
Видео
Google Earth Engine Tutorial-69: Volcano Monitoring using Sentinel-5 Images
Просмотров 1947 часов назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/2f3276effbd95af00334dca297f81cc0b51d523a/00066_volcano_sulfure
Google Earth Engine Tutorial-68: 70-Years Snowy Days Monitoring
Просмотров 25021 час назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/a3e363438a095f03f503323a47b7208dd8a84144/00065_snow_collection
Google Earth Engine Tutorial-67: Climate Data Classification, using Machine Learning Techniques
Просмотров 741День назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/a2c132bf878df1f362822128f3371d1d7fca4007/00064_climate_classification
Google Earth Engine Tutorial-66: ERA-5 Temperature Downscaling, using Machine Learning Technique
Просмотров 52614 дней назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/f656ee73449ca4a41ef825f56a56386fee1d04de/00063_era5_downscaling
Google Earth Engine Tutorial-65: Crop Type Detection using Multi-Temporal SAR Images
Просмотров 89714 дней назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/32be9f9cf1d5a9da45ed33ce117a87bc112bc4dd/00062_sentinel1_croptype
Google Earth Engine Tutorial-64: Soil Moisture Estimation using Sentinel-1
Просмотров 1,1 тыс.21 день назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/9d92287bf1eee238d86244f989ef3dfb5218aac3/00061_sen1_soil_moisture
Google Earth Engine Tutorial-63: Urban Flood Detection, using SAR and Precipitation Data
Просмотров 1,2 тыс.28 дней назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/5e4d7bfac64ab7609c1b4b536e4115d01bc1efae/00060_urban_flood
Google Earth Engine Tutorial-62: Drought Temporal Classification, using SPEI
Просмотров 68828 дней назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/e9737c021426fac5d06502254c989e3fc693ce25/00059_spei_classification
Google Earth Engine Tutorial-61: Landcover Downscaling, using Machine Learning
Просмотров 1,2 тыс.Месяц назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/2ab0eb848d5d66feab515ea3616826d11165f8fc/00058_modis_landcover_downscaling
Google Earth Engine Tutorial-60: Biomass Prediction, using Machine Learning
Просмотров 1,7 тыс.Месяц назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/afb67ab044886bd187f13d2134078f9e2c537b3a/00057_biomass_prediction
Google Earth Engine Tutorial-59: GRACE Data Downscaling, using Machine Learning Techniques
Просмотров 837Месяц назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/bb3d3f040d72deabe8f19454c5e3fca7b8adb1ec/00056_grace_downscaling
Google Earth Engine Tutorial-58: Soil Moisture Downscaling, using Machine Learning Techniques
Просмотров 1,1 тыс.Месяц назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/b2fa5aeceb654e63eebac763e1aa7a461b75f81c/00055_soilmoisture_downscaling
Google Earth Engine Tutorial-57: Precipitation Downscaling, using Machine Learning Techniques
Просмотров 2,1 тыс.Месяц назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/29a75e312054e2faf8311372b2aa17fc6908f0e3/00054_precipitation_downscaling
Google Earth Engine Tutorial-56: Snow Duration Estimation, using MODIS Snow Product
Просмотров 359Месяц назад
code link: github.com/AmirhosseinAhrari/GoogleEarthEngine/blob/d69af970bdc6efacef4ac9f16332c3eec8187fc3/00053_modis_snow_duration
Google Earth Engine Tutorial-55: Vegetation Moisture Content, using Landsat Images
Просмотров 715Месяц назад
Google Earth Engine Tutorial-55: Vegetation Moisture Content, using Landsat Images
Google Earth Engine Tutorial-54: Earth Engine Learning Resources
Просмотров 518Месяц назад
Google Earth Engine Tutorial-54: Earth Engine Learning Resources
Google Earth Engine Tutorial-53: Wildfire Detection and Mapping, using VIIRS and Landsat
Просмотров 1,2 тыс.2 месяца назад
Google Earth Engine Tutorial-53: Wildfire Detection and Mapping, using VIIRS and Landsat
Google Earth Engine Tutorial-52: Crop Type Detection, using NDVI
Просмотров 1,9 тыс.2 месяца назад
Google Earth Engine Tutorial-52: Crop Type Detection, using NDVI
Google Earth Engine Tutorial-51: Deforestation Monitoring, using Sentinel-2 Images
Просмотров 1,1 тыс.2 месяца назад
Google Earth Engine Tutorial-51: Deforestation Monitoring, using Sentinel-2 Images
Google Earth Engine Tutorial-50: Urban Green Space Monitoring, using Sentinel-2
Просмотров 8822 месяца назад
Google Earth Engine Tutorial-50: Urban Green Space Monitoring, using Sentinel-2
Google Earth Engine Tutorial-49: MODIS LST Downscaling, From 1000 to 100m
Просмотров 1,2 тыс.2 месяца назад
Google Earth Engine Tutorial-49: MODIS LST Downscaling, From 1000 to 100m
Google Earth Engine Tutorial-48: Methane (CH4) Mapping and Monitoring using Sentinel-5 Images
Просмотров 1 тыс.2 месяца назад
Google Earth Engine Tutorial-48: Methane (CH4) Mapping and Monitoring using Sentinel-5 Images
Google Earth Engine Tutorial-47: MODIS NDVI Downscaling, from 500m to 30m
Просмотров 1,2 тыс.2 месяца назад
Google Earth Engine Tutorial-47: MODIS NDVI Downscaling, from 500m to 30m
Google Earth Engine Tutorial-46: Carbon Monoxide Monitoring over Urban Area
Просмотров 1,1 тыс.3 месяца назад
Google Earth Engine Tutorial-46: Carbon Monoxide Monitoring over Urban Area
Google Earth Engine Tutorial-45: Sentinel-3 NDVI Estimation using Maximum Value Composite Method
Просмотров 6863 месяца назад
Google Earth Engine Tutorial-45: Sentinel-3 NDVI Estimation using Maximum Value Composite Method
Google Earth Engine Tutorial-44: Daily NDVI Time Series Smoothing
Просмотров 8003 месяца назад
Google Earth Engine Tutorial-44: Daily NDVI Time Series Smoothing
Google Earth Engine Tutorial-43: Snow Parameters Monitoring, using ERA-5 Product
Просмотров 5283 месяца назад
Google Earth Engine Tutorial-43: Snow Parameters Monitoring, using ERA-5 Product
Google Earth Engine Tutorial-42: Precipitation Spatial Clustering
Просмотров 1 тыс.3 месяца назад
Google Earth Engine Tutorial-42: Precipitation Spatial Clustering
Google Earth Engine Tutorial-41: Urban Heat Island Detection in Arid Region, using MODIS LST Night
Просмотров 7913 месяца назад
Google Earth Engine Tutorial-41: Urban Heat Island Detection in Arid Region, using MODIS LST Night
Thank you, your lessons are very useful and valuable
Hello. How export the result please.
Sir which is more accurate using the SMAP or sentinel 1
Radiometrically SMAP, and spatially sentinel-1.
I am very happy to see this tutorial as my phd topic is Soil moisture retrieval using microwave RS. It help me a lot... Thank you so much for this wonderful tutorial.😊😊😊❤❤
happy to hear. hope it works for you. keep follwing and share the channel with your community.
Sir! GRACE data spatial resolution is 1* X 1* then it will cover how much area? As GLDAS data of 0.25 degree into .25 degree resolution i want to use. But i don't know how to resample the data set as i want do in python but the think is that i resampled the data upto 0.025 into 0.025 degree grid to do grid based analysis is it fine. And if so then how much area is cover by GRACE pre sampling and post sampling, on the same side that about GLDAS dataset? If i resample the data upto 0.5 into 0.5 degree grid size then it will cover how much area?
one degree = 100 km in equatorial region.
Hello Amir...Thank you for sharing the amazing tutorial. I have a question for you. In the time 24:11 min, when you show the bands : List (19 elements) I have from the 12 elements, but only some months has the list (19 elements) while others I do not have it. What could be the implication for having or not the 19 elements?
HI, thanks for the comment. the difference is because of region of interest, cloud filter I think.
Good job Amirhossein, could CART or Randomforest be used instead of linear regression method?
thanks for the comment. couldn't find a method for this question for now. maybe in future. keep following please. you will receive notification, in case such tutorial published.
Can we use mean instead of mode?
For landcover no it is impossible. technically yes, but conceptually not true. because turns the landcover value from integer to float.
thank you for tutorial. Do sentinel-1 dataset (S1_GRD) in GEE need any pre-processing steps like radiometric correction, geometric correction........?
no need any preprocessing steps. s1 data completely corrected radiometrically and geometrically but speckle impacts.
Thank you for the Amazing job you are doing, if you don't mind would you make a Video about Climate scenarios like (RCPs) ..etc.
thanks for the nice feedback. unfortunately GEE is not suitable for such models to my knowledge.
Thank you for this tutorial, it's very helpful. please add code for analyzing urban growth and correlating with NO2 mean.
Thanks for the comment and suggestion. will share in near future. keep following and share the channel with your community please.
Zabardast means brilliantly done.
Thanks a lot
Thank you so much
welcome. please share the channel with your community.
Very nice. Plz add for LST and its monthly time series.
Thanks for the comment. check this out: ruclips.net/video/JLFcyTt-das/видео.html
❤❤❤ hello too Mr ahrari🎉
Hi, thanks for your feedback. please share the content and tutorials with your community.
Thank you, Amir! You're doing an excellent job. I hope you'll continue your focus on climate-related analysis. I have a question for you: would it be possible for you to provide a tutorial on predicting droughts for the coming years? That would be incredibly helpful.
thanks for nice feedback. will try to share more information. working on prediction methods, will share as soon as I find a reliable method. keep following and share the tutorials with your community.
thanks for the informative tutorial. may I use Landsat images instead of SAR images
Yes, I have a tutorial about optical satellite imagery for this propose: ruclips.net/video/am-5y0VWI9I/видео.html
Thank you very much for this very explicit and educational video, I learned a lot from you. I don't know if you can share the link to the reference document you used to calculate VCI (seven classes).
thanks for the comment. These are well-established methods. You will find several papers about VCI and related indices, if you search it on google scholar.
لطفا درباره محصولات کشاورزی و موضوعات کشاورزی در سنجش از راه دور بپردازید.
آموزش های متعدد تا کنون در این رابطه تولید کردم. بازهم منتشر خواهد شد. فعلا آموزش های کشاورزی رو از طریق این پلی لیست دریافت کنید ruclips.net/p/PLMFUNkj5z2c5w_9f7CklGb9Y8f35EQmYq
دکتر احراری ، متشکریم متشکریم
ممنون از شما. لطفا آموزش ها رو با کاربران و علاقه مدان سنجش از دور به اشتراک بزارید.
@@amirhosseinahrarigee سلام یه سوال چطور میشه میوه سیب گندم یا غیره دتکت کرد با هیستوگرام و چارت بای ریجن انجام بدم ؟
I wanted to send the code link of my gee but my comment disappears every time I add the link. I had an error on the code. //Load Area of Nepal var country = ee.FeatureCollection("USDOS/LSIB/2017") var table = country.filter(ee.Filter.eq("COUNTRY_NA","Nepal")) Map.centerObject(table, 6) //Load NASADEM var image = ee.Image("NASA/NASADEM_HGT/001") var elevation = image.select('elevation') Map.addLayer(elevation.clip(table),{},'Elevation') //Elevation above 3600m var elev3600 = elevation.gt(3600) Map.addLayer(elev3600.clip(table).updateMask(elev3600),{},'Elevation3600') //Load Sentinel-2a data and filter by date and region var sentinel2 = ee.ImageCollection("COPERNICUS/S2_SR_HARMONIZED") .filterDate('2022-01-01','2022-12-30') .map(function(img){ var bands = img.select('B.*') var ndwi = bands.normalizedDifference(['B3','B8']).rename('ndwi') var ndwielev3600 = ndwi.updateMask(elev3600) .reproject({ crs: 'EPSG:4326', scale: 500 // Downsample to 500 meters }); return ndwielev3600.copyProperties(img, img.propertyNames()) }) Map.addLayer(sentinel2,{},'Sentinel-2a') // Import OEEL library var oeel=require('users/OEEL/lib:loadAllSF') // Apply Otsu Threshold var otsu = sentinel2.map(function(image) { return oeel.ImageCollection.OtsuThreshold(ee.ImageCollection([image]), 'ndwi'); }).mosaic(); // Combine the thresholded images print(otsu) print( ui.Chart.image.histogram(otsu,table,500) ) var thr = ee.Number(otsu.reduce(ee.Reducer.mean()).reduceRegion({ reducer: ee.Reducer.mean(), geometry:table, scale:500 }).values().get(0)); print ('water body threshold', thr) var water = sentinel2.mean().gt(thr) // Apply threshold to the mean NDWI image Map.addLayer(water.updateMask(water), {palette: 'blue'}, 'Water Bodies')
please send the error message.
User memory exceeded and computation timed out
Can you make video on susceptibility mapping of hazards like landslide, flood etc using machine learning ?
@@utsavpoudel7034 will try. Keep following
Dear Amirhoussien, Can we use GEE for climate prediction?
to my knowledge, running the climate model prediction is impossible for now. will share in case I find sustained methods in GEE.
Yes , you must jupyter note book and use package library in colab gee or leafmap
Thank you very much for your timely and practical topics. Sir, is it possible to perform precipitation downscaling on 30 years of historical precipitation data? Im want to use it for validation of other models.
Yes you can extend your model for a longer period of time.
Great work......will it be possible for you perform MODIS LST Downscaling From 1 Km to 10m on GEE?
Thanks for the comment. no idea for now. will share in case I find a solution.
Impressive tutorial. One thing I did not understand is how I can classify different type of crops using this method. The thing I learned is I can only map one crop which is reflecting most energy back, which is showing in red color. My question is How can I map all different crops?
The most accurate method for crop classification is having crop calendar or samples from each crop. Due to the lack of these data, I only showed the growing differences between crops that is the main input for crop classification. This is the best way to show all the existing crops for your regions of interest.
HELLO.... I need to see the changings in coastline by the effect of tidal waves
Hi, check these dataset in google earth engine: developers.google.com/earth-engine/datasets/tags/tidal-flats
Love this.. ❤
Thanks.
سلام دکتر احراری خسته نباشید ممنون از اموزش فوق کاربردی شما . جناب دکتر شما ایده ای برای فرونشست داخل ارث انجین دارید ؟ میشه ویدیویی برای این مورد درست کنید
با سلام و احترام نه متاسفانه فعلا راهکار مناسبی در این زمینه ندارم. با عرض پوزش
Thank you so much for this fantastic tutorial on ERA-5 Temperature Downscaling using Machine Learning Techniques! Your clear explanations and step-by-step approach made the complex process easy to understand and implement. I've learned a lot, and I appreciate your effort in creating such informative content. Keep up the great work!
Thanks for nice feedback. will try to make more. Please share the tutorials with your community.
this process can do with rainfall downscaling by ERA-5 data?
Yes, it is extendable to other bands, in case their higher resolution data is available.
Can you plz put the reference of methodology so that we can noted for publication
Most of the methodologies back to the typical methods and roles in machine learning and I create them according to my ideas. will share the references, in case I have used a paper.
لطفا درباره آتشفشان، آتش سوزی جنگل NBR , بیوماس هم توضیح بدید . منم الان درباره آلودگی صوتی دارم یک مقاله میدم مجبورم با دستگاه نمونه برداری کنم نظر شما چیه جناب دکتر احراری ؟ بعضی جاها ۶۰ دسی بل و بعضی جاها ۸۰ دسی بل هست به نظر شما مقیاس آلودگی صوتی درست هست یا باید دوباره کالیبره کنم تا دقت بالا بره؟
@@Ramilacookware سلام مدل سازی بیومس و آتش سوزی جنگل داخل ویدیوهای کانال هست
با تشکر از توضیحاتتون اگر بخواهیم برای یک بازه زمانی مشخص lst time series را محاسبه کنیم امکان دارد که به انتهای این کد اضافه شود؟
بله قابل استفاده هست چون ریز مقياس سازی بصورت سری زمانی انجام شده. موفق باشید
Thank you for the comprehensive and insightful information provided. Your explanations have been extremely helpful.
Thanks for the nice feedback. please share the tutorials with your community.
Thanks!!!!
welcome and thanks for the feedback. please share the videos with your community.
THANK YOU SO MUCH AMIRHOSSEIN AHRARI. FIRST TIME IN A DECADE THAT I WATCHED A TUTORIAL IN FULL LENGTH TO CATCH UP WITH A TOOL. VERY WELL ARTICULATED AND PRESENTED. CAN ONE ACCESS A TRANSCRIPT?
Thanks for your nice feedback. will do my best to empower remote sensing community about dealing with earth engine. All explanations are my own and didn't prepare transcripts in advance.
@@amirhosseinahrarigee thank you for the reply. I'll stay tuned!
First of all, I would like to thank you for sharing these kinds of videos on your RUclips channel. I would like to ask 2 questions to you: 1) First how we can just detect the groundwater information by downscaling the GRACE Data. After downscaling the GRACE data, should we subtract the downscaled version of snow water equivalent, surface runoff, etc. or do we need to follow another path. 2) The second question is, besides the RandomForest technique, which type of machine learning techniques can we use here?
Thanks for the comment and will by happy if you share them with your community. Ground water information extraction is a little complex and out of my knowledge now. will share its methodology, in case I find it. In edition to random forest, there are couple of methods in GEE such as cart, minimum distance, gradient boost that working as a regression technique too.
@@amirhosseinahrarigee Thank you for your enlightening comment and reply!
Yet I work on noise pollution 🙏 , فکر کنم خودم یک کد درباره میوه گندم جو دیتکشن انواع محصولات نوشتم براتون میفرستم
بسيار ممنون.
How to download the final result in geo tiff?
check this link: developers.google.com/earth-engine/apidocs/export-image-todrive
تشکر 😊😊😊
ممنون لطفا به اشتراک بزارید.
Very informative. ❤ Can you make a video on crop yields prediction?
thanks for the feedback. will try to make, in case I have a suitable reference for.
Thanks you for all. Please make vidéo on ui app in Gee
Thanks, will do in future.
Very nice video. Thanks for sharing. I have one question. The image is saved as single geotif file in the drive with different bands how to get image for each month
The geotiff included monthly data. Unfortunately I have no idea about getting the data separately through a loop.
❤❤❤ please
check this tutorial: ruclips.net/video/E3N88gVQN2c/видео.html
@@amirhosseinahrarigee سپاسگزارم بابت کمکتون و وقت گران بهاتون. ❤❤❤🙏🙏
great work....what if the threshold values is a range within the adjusted histogram .....
put the otsu function in a loop to get the threshold for each time
Could you upload vedio about NBR fire detection?❤❤❤❤
already is created on my channel, please check it out: ruclips.net/video/E3N88gVQN2c/видео.html
Thanks, Mr. Ahrari. Is it possible to calculate the snow area with respect to ellevation? Is it possible to mask a covered area by the intended elevation according to the SRTM map?
Yes, first you must mask out non-related elevations from snow cover map.
Sir you are using 55 km spatial resolution data for training by using FLDAS datasets 10 km spatial resolution. If we consider 1 pixel of grace data contains 5 pixels of FLDAS. In training, you are using same 1 pixel value from GRACE for all those 5 FLDAS pixels. If we develop the model like this it will go for over estimation. what do you think sir??
Recently tried to solve this issue by resampling process, but haven't seen any progress in the results. Still I am working on these methods. I will update the tutorials in future, in case I have update.
Sir have you written any journal paper related to it. if you have written please provide link
I have no paper about grace downscaling. Last year I have published a paper about soil moisture downscaling using the same method but different data: www.tandfonline.com/doi/pdf/10.1080/20964471.2023.2257905