Imagery (Psychotherapy) - Science topic

The best source for getting started with removing clouds from Sentinel-2 imagery using ESA SNAP is the ESA SNAP tutorial page.

This page contains video tutorials, as well as written tutorials and example workflows, to help you get started with cloud masking using ESA SNAP.

The video tutorial titled “Sentinel-2 Cloud Masking” is a great place to start. This video covers the basics of cloud masking using the Sentinel-2 Level-2A product, including how to apply the S2 Cloudless product and the S2 Cloud Probability product to generate a cloud mask.

The written tutorials and example workflows on the ESA SNAP tutorial page can also be very helpful for understanding the cloud masking process and for getting the most out of ESA SNAP.

Finally, the ESA SNAP User Forum is a great resource for getting answers to any questions you may have about using ESA SNAP for cloud masking.

Touseef Ahmed Yes, convolutional neural networks (CNNs) may be used to categorize PlanetScope images in Google Earth Engine (GEE) Code Editor. GEE has a number of tools and resources for doing image classification using CNNs, such as the ability to train and export models with TensorFlow and Keras, as well as the ability to apply models to image data with the GEE Image and FeatureCollections classes.

Here's an example of how you may use GEE with a CNN to identify PlanetScope imagery:

1. Load your PlanetScope imagery as an ImageCollection into GEE.

2. To extract the day of the year band from the picture collection, use GEE's Chart.image.doy() method.

3. Make training and validation data by drawing polygons on the picture and using the sampleRegions() function to retrieve the band values for each polygon.

4. Train a CNN model with TensorFlow or Keras using the exported training and validation data.

5. Use the trained CNN model to categorize all of the PlanetScope images.

6. To export the categorized picture, use GEE's export.image() method.

The GEE code editor provides a JavaScript-based environment for running GEE scripts and accessing GEE functions and classes. You may easily handle massive datasets, do complicated analyses, and visualize findings with the GEE code editor. You may also utilize the GEE code editor to export your results for usage in other platforms or applications like Google.

Remember that this is a high-level example, and you may need to modify the code to meet the unique needs of your project.

attachments

Best option is to avoid Landsat 7 and try to use Landsat 4/5 Data.

If your study area is well known, visually distinguishible, it does not matter at all, because you would select the training area as per your perception. But the problem is to use image curve of any pixel for recognition of any parameter.

Dear Joseph Olumoyegun

You may go through following publication:

I got it! I got it!

Only took me 3 days, but I have it:

// Import and filter dataset

var MyCollection = ee.ImageCollection("NASA/GRACE/MASS_GRIDS/LAND")

.filterBounds(One)

.filterDate('2010-01-01', '2010-12-31')

.select('lwe_thickness_csr','lwe_thickness_gfz','lwe_thickness_jpl');

//Write a function to add an extra band with mean to image

function addMean(image){

var selected = image.select('lwe_thickness_csr','lwe_thickness_gfz','lwe_thickness_jpl');

var mean = selected.expression('(b(0)+b(1)+b(2))/3');

return image.addBands(mean).rename('csr','gfz','jpl','mean');

}

//Map function over entire time-series collection

var with_mean = MyCollection.map(addMean);

Map.addLayer (with_mean, {min:0, max:3}, 'G_Mean2');

//Produce time-series chart

print(Chart.image.series(with_mean.select('mean'), One));

Will still appreciate any input, comments and recommendations.

Be blessed!

Just use the google earth to view the historical images then apply this process.

Dear Syed,

What do you mean by 'data quality'? Because if you mean visual quality, there is no problem to reduce its size. You have to differentiate the data obtained by a drone depending on the type of sensor used. If this sensor is a conventional camera (visible range), and you only need the images for visual support (not for SfM photogrammetry), you can reduce them without any visual loss (jpg2000, ecw, or other compression formats).

A different matter is if you are working with data obtained by a multispectral sensor, and you need the image information for digital processing. In this case, you will have to work with uncompressed data. So a good solution is as Dorian Gaboriau has told you, which is to use smaller parts.

Regards

Thank you

Yes, well log analysis can give good results in geomechanical studies, and that results will be more reliable if calibrated with laboratory tests on core samples.

Hi!,

Well...I´m pretty sure there is not an easy answer to your question. SAR is usually very sensitive to roughness, but roughness is not an absolute concept. Usually, rms height (s), and the correlation length (l) are used to characterize it. When you say rough terrain, you have to take the account the relationship between the wavelength of the instrument and how it relates to s and l. A certain soil for example can be very rough at the X band and smooth at the L band. There is also another aspect to take into account if the vegetation you are trying to analyze covers completely to soil and is well developed may be the effect of the soil roughness is not that significant, if not you may get backscatter from the soil. We have observed a similar issue even at the X band, that because of its wavelength, penetrates less. Regarding a correlation between LAI and backscatter, depending on the type of crop/land cover, and soil characteristics maybe you can find a correlation but from our experience, it can be tricky.

Hope it helps,

regards

Check out this paper:

Altena, B. & Kääb, A., 2017. Elevation Change and Improved Velocity Retrieval Using Orthorectified Optical Satellite Data from Different Orbits. Remote Sensing, 9(3). DOI:10.3390/rs9030300

Please, use OSM as base map and also use the imagery to verify the map and make it more reliable.

@Nasir Hameed thank you for your timely response. Your answer was helpful.

Look Martijn Arns, Golf and alpha

Thank you so much to all for your great valuable suggestions

See the following article:

Towards LST split-window algorithm FPGA

implementation for CubeSats on-board

computations purposes

In Seto and Eke (2015), on the development of the CPORT, we reported that approximately a third of the sample had NPAI (CGI/anime; not including stories). NPAI was NOT predictive of sexual recidivism in our analysis.

Link to the 2015 article on ResearchGate here:

Hello Dinuke Munasinghe ,

Not sure what type of optical imagery you intend to use, but if you're talking about multi-spectral satellite imagery such as Sentinel-2 or Landsat-8, you could have a look at the approach presented here:

(https://sentinel.esa.int/web/sentinel/news/-/article/copernicus-sentinel-2-helps-track-changes-in-seasonal-water-of-wetlands)

Using optical satellite imagery obviously has its limitations when it comes to 'detecting something under closed canopy' and the quality of the results will largely depend on the type of vegetation, the duration of flooding and the persistence of clouds...

Best,

Sebastian

Okay. thank you very much.

Dear Abdullah Al-Oshoush

The following article may be useful for you:

Cheers

Fatih Bulut

Well noted with thankful,more love

https://earthobservatory.nasa.gov/global-maps/MOD_LSTD_M

The maps shown here were made using data collected during the daytime by the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Terra satellite. Temperatures range from -25 degrees Celsius (deep blue) to 45 degrees Celsius (pinkish yellow). At mid-to-high latitudes, land surface temperatures can vary throughout the year, but equatorial regions tend to remain consistently warm, and Antarctica and Greenland remain consistently cold. Altitude plays a clear role in temperatures, with mountain ranges like the North American Rockies cooler than other areas at the same latitude.

@ Nima, please have a look of the attached files.

Can you please suggest any sources for CSP, FBCSP, SBCSP Matlab codes for multiclass EEG data analysis or please share if you have any?

Qual é o objetivo de sua pesquisa. As bandas 10 e 11 elas são termais. Qual faixa do espectro eletromagnético você deseja utilizar? Aransiola Sesan Abiodun

You can use the Multi-criteria decision model

Dear Bartłomiej,

Thank you for your reply and accepting / proposing initiative for future cooperation. I am dealing and I am very much interested in these issues. I have the opinion that multidisciplinary approaches may bring added values.

I read some of your papers. Nice materials.

In case you are interested, we may do together, as a phase two:

(1) Bartłomiej SZYPUŁA, (2) Velibor SPALEVIC, (3) Janusz GODZIEK, (4) Hydrogeologists/Soil Scientist from Poland, (5) with one of my colleagues:

Impact of land use changes on soil erosion intensity and peak discharge: Case study Ochotnica Valley, Poland

You have ready all the analysis for land use in the past time profiles: 1879, 1934, 1978.

In case you are ready to provide me few more data, we may, using my models do the analysis on the Impact of land use changes on soil erosion intensity and peak discharge.

1-You have land use data

2-You may easily prepare the table with the following data:

2a-Contour line length, Liz, km;

2b-The area between the two neighboring contour lines, fiz, km²;

3- You may ask as the assistance one of your hydrogeologists to provide you data on area permeability, based on geological map:

3a-A part of the river basin consisted of a very permeable products from rocks (limestone, sand, gravel), fp;

3b-A part of the river basin area consisted of medium permeable rocks (slates, marls, brownstone), fpp;

3c-A part of the river basin consisted of poor water permeability rocks (heavy clay, compact eruptive), fo;

4-Soil map of the Study area

And we will do the rest of 40 parameters.

Please have a look on some examples:

1. Spalevic, V. (2019). Assessment of Soil Erosion Processes by Using the 'IntErO' Model: Case Study of the Duboki Potok, Montenegro. Journal of Environmental Protection and Ecology, 20(2): 657-665. https://docs.google.com/a/jepe-journal.info/viewer?a=v&pid=sites&srcid=amVwZS1qb3VybmFsLmluZm98amVwZS1qb3VybmFsfGd4OjcyNWI4NzkwZjIzNDYzZjM

2. Spalevic, V., Lakicevic, M., Radanovic, D., Billi, P., Barovic, G., Vujacic, D., Sestras, P., Khaledi Darvishan, A. (2017): Ecological-Economic (Eco-Eco) modelling in the river basins of Mountainous regions: Impact of land cover changes on sediment yield in the Velicka Rijeka in Montenegro. Notulae Botanicae Horti Agrobotanici Cluj-Napoca: 45(2):602-610.

3. Chalise, D.; Kumar, L.; Spalevic, V.; Skataric, G. (2019). Estimation of Sediment Yield and Maximum Outflow Using the IntErO Model in the Sarada River Basin of Nepal. Water, 11, 952. https://www.mdpi.com/2073-4441/11/5/952

4. Nikolic, G., Spalevic, V.*, Curovic, M., Khaledi Darvishan, A., Skataric, G., Pajic, M., Kavian, A., Tanaskovik, V. (2019): Variability of Soil Erosion Intensity Due to Vegetation Cover Changes: Case Study of Orahovacka Rijeka, Montenegro. Not Bot Horti Agrobo, 2019, 47(1): 237-248. DOI: 47.15835/nbha47111310

5. Tavares, S, Spalevic, V., Avanzi, J, Nogueira, D., Silva, M.L.N., Mincato, R.L. (2019): Modelling of water erosion by the erosion potential method in a pilot sub basin in southern Minas Gerais [Modelagem da erosão hídrica pelo método de erosão potencial emuma sub-bacia hidrográfica de referência no sul de Minas Gerais]. Semina: Ciências Agrárias, Londrina, 40 (2): 555-572. DOI: 10.5433/1679-0359.2019v40n2p555

6. Vujacic, D., Barovic, G., Djekovic, V., Andjelkovic, A., Khaledi Darvishan, A., Gholami, L., Jovanovic, M. and Spalevic, V. (2017): Calculation of Sediment Yield using the “River Basin” and “Surface and Distance” Models: A Case Study of the Sheremetski Potok Watershed, Montenegro. Journal of Environmental Protection and Ecology, 18(3): 1193-1202.

Looking forward to your reply and position.

Velibor

Dr. Sc. Velibor SPALEVIC,

Novaka Miloseva, 81000 Podgorica, Montenegro

Mobile/Viber/WhatsApp: +382 67 201 222

E-mail: velibor.spalevic@gmail.com

Annex:

Input data

River basin area, F, km²;

The length of the watershed, O, km;

Natural length of the main watercourse, Lv, km;

The shortest distance between the fountainhead and mouth, Lm, km;

The total length of the main watercourse with tributaries of I and II class, ΣL, km;

River basin length measured by a series of parallel lines, Lb, km;

The area of the bigger river basin part, Fv, km²;

The area of the smaller river basin part, Fm, km²;

Contour line length, Liz, km;

The area between the two neighboring contour lines, fiz, km²;

Altitude of the first contour line, h0, m;

Equidistance, Δh, m;

The lowest river basin elevation, Hmin, m;

The highest river basin elevation, Hmax, m;

A part of the river basin consisted of a very permeable products from rocks (limestone, sand, gravel), fp;

A part of the river basin area consisted of medium permeable rocks (slates, marls, brownstone), fpp;

A part of the river basin consisted of poor water permeability rocks (heavy clay, compact eruptive), fo;

A part of the river basin under forests, fš;

A part of the river basin under grass, meadows, pastures and orchards, ft;

A part of the river basin under bare land, plough-land and ground without grass vegetation, fg;

The volume of the torrent rain, hb, mm;

Incidence, Up, years;

Average annual air temperature, t0, °C;

Average annual precipitation, Hgod, mm;

Types of soil products and related types, Y:

Sand, gravel and incoherent soils:

Saline soils...:

Decomposed limestone and marls:

Serpentines, red sand stones, flishe deposits:

Podzols and parapodzols, decomposed schist:

Solid and Schist limestone, Terra Rosa and Humic soil:

Brown forest soils and Mountain soils:

Epieugleysol and Marshlands:

Good structured Chernozems and alluvial well-structured deposits:

Bare, compact igneous:

River basin planning, coefficient of the river basin planning, Xa:

Bare lands:

Plough-lands:

Orchards and vineyards:

Mountain pastures:

Meadows:

Degraded forests:

Well-constituted forests.

Numeral equivalents of visible and clearly exposed erosion process, φ:

RESULTS:

Coefficient of the river basin form, A;

Coefficient of the watershed development, m;

Average river basin width, B, km;

(A)symmetry of the river basin, a;

Density of the river network of the basin, G;

Coefficient of the river basin tortuousness, K;

Average river basin altitude, Hsr, m;

Average elevation difference of the river basin, D, m;

Average river basin decline, Isr, %;

The height of the local erosion base of the river basin, Hleb, m;

Coefficient of the erosion energy of the river basin's relief, Er;

Coefficient of the region's permeability, S1;

Coefficient of the vegetation cover, S2;

Analytical presentation of the water retention in inflow, W, m;

Energetic potential of water flow during torrent rains, 2gDF^½, m km s;

Maximal outflow from the river basin, Qmax, m³/s;

Temperature coefficient of the region, T;

Coefficient of the river basin erosion, Z;

Production of erosion material in the river basin Wgod, m³/god;

Coefficient of the deposit retention, Ru;

Real soil losses Ggod, m³/god;

Real soil losses per km2 Ggod/km², m³/km², god;

Spatial analyst tool can be used for adjusting your road layer as per the imagery. If the data density is less then it is better to do it manually for better and accurate results.

Dear Zach,

Thank you for your question. Although Landsat and Sentinel imagery data would be adequate for many studies because of the temporal observing frequency (16-day) and pixel resolution of 20-30 m, cloud cover is a limiting factor to obtaining frequent and bulk of images, especially for the rainy seasons. In my opinion, selecting the scene depends on your RS application that you are going to apply. There is no solid percentage (e.g. 20%) for the selection of such a scene. In my works for water resources management, I utilized Landsat images with cloud cover ≤5% (e.g. ) and others use different percentages. If I were you, I would try to find many of published papers relevant to your application works which selected a cloud cover scene <20% and cite them in your work to defend this number. There are some papers that might help as follow:

thanks Aysar Jameel Abdalkadhum

Dear Md Saimoom Ferdous ,

It's not possible to implement the target decomposition techniques on SLC SAR data and you need to estimate the polarimetric covariance or coherency matrix first. For doing so, a Boxcar filtering (with window size greater than 3) should be applied whereby, the speckle noise will be reduced and an unbiased estimate of the coherency/covariance matrix will be achieved.

Good luck!

Validation

1. Selected field visit collect georeferenced points and check.

2. Get hold of past flooded areas and superimpose if falls within it can assumed to be ok.

3.Go for modelling taking discharge validate with SAR map

thanks

Hi Frank de Morsier thanks! I will do.

Hi Sahar,

Try to use resample function inArcGIS. Before executing this tool, go to environment and set the parameters (projection system, cell size ...) as your tiff file.

Good luck

SRTM, ASTER as well as CARTOSAT DEM is available freely in 30m resolution which can be downloaded from https://earthexplorer.usgs.gov and http://bhuvan.nrsc.gov.in/ . However if we have the stereo pair images of CARTOSAT and ASTER then we can make DEM upto 10m resolution. Since Ganga basin covers 26% of India's area and it is the largest basin in India. Thus to cover the whole basin around 52 scenes of LANDSAT 5,7 or 8 is required or AVHRR or MODIS data can also be used. Details about Ganga river basin can be obtained from Watershed Atlas of India ( ). please see this link for valuable information. https://bhuvan-app1.nrsc.gov.in/mowr_ganga/

Dear Amir,

As I understand your images are not geometrically coincident. This requires that both be transformed geometrically to the same geographic location without the distortion of the spectral value of the pixels. You can after that try Wavelet transform techniques. See the following paper for more information.

CBCT over CT and other methods

use the following links, https://www.harrisgeospatial.com/docs/CalculateCloudMaskUsingFmask.html

Mekonnen Amberber Degefu

Sentinel-2 dose not have Thermal band. However you may use Sentinel-3 SLSTR data. Please see the specification at https://sentinel.esa.int/web/sentinel/technical-guides/sentinel-3-slstr/instrument/specifications

Regards

Dear Sir, in order to determine the weight of significance, I think you should make the PCA analysis to determine the ponderation weight of significance which allow you to determine an index and you can run your predictions.

your question is too wide. Add some (subset of) your data and a precise description of what you want to do.

Excellent

Please check my poster