Tag Info

10

According to Wien's law, the wavelength of the peak emission depends on the temperature. wavelength_of_peak = 2.898*10^(-3)/T where T is the temperature in degree Kelvin. So the wavelength of the peak emission decrease when the temperature increases. For the temparature of the surface of the Earth, you are around 300 K, so the peak is about 10 ...

6

Per https://lpdaac.usgs.gov/dataset_discovery/modis, the viewing swath width of MODIS is 2,330 km, thus a large portion of the image is off-nadir in some way. https://modis.gsfc.nasa.gov/about/specifications.php The following forum post gives an explanation of how to calculate pixel size based on viewing position. (Note: still an estimate due to factors ...

5

I thoroughly edited the answer to give a more complete picture. The edited answer also reflects what I discussed in the comments. ====================================================== There is not only a single answer to your question. First of all, I have to say that it would indeed be possible to use the observed range measurements (which are encoded ...

5

First, I would reword the question to "Why is fire "visible" in the short-wave infrared portion of electromagnetic spectrum ?" Second, I would like to add my 2 cents to @radouxju answer. These 2 examples could make the explanation clearer: A piece of metal heated by a blow torch first becomes "red hot" as the very longest visible wavelengths appear ...

4

Such a tool already exists. It is called Sentinelsat and the source is available on GitHub. It offers a command line interface and a Python API. It works with Sentinel 1 and 2. The spatial query is based on a polygon and not a point, but otherwise this is exactly what you need. EDIT: 1) you can return the product ID (or product ID list) using the query ...

4

Here is a modification of the first example from this presentation about tables and vectors. Note that you can "transpose" the table if there are other properties in the points that are of interest: var rectangle = ee.Geometry.Rectangle(96.01669, 18.52621, 96.04819, 18.49634); Map.centerObject(rectangle); Map.addLayer(rectangle, {}, 'rectangle') var ...

4

Short answer: No. And more specifically lidR is designed for ALS primarily, if ever I add a function for noise removal it will be for ALS first.

3

Based on this post How can i create a Digital Elevation Model from satellite Images? If you have stereo pair of satellite images or overlapping aerial photographs or even UAV acquired imagery, you can use a standard GIS software to generate a DEM based on the principles of Photogrammetry. There are open source as well as commercially available softwares ...

3

You should be able to import GeoJSON geometry objects directly. Here is an example of a MultiPolygon: feature_geometry = { 'type': 'MultiPolygon', 'coordinates': [[[ [-90, 24], [-90.001, 40], [-90.001, 39.001], [-90, 31.001], [-90, 27] ]]] } Also, as mentioned in the comments, you can directly convert geojson into a shapefile using ...

3

You are likely observing the difference between pixel based classification and object oriented classification. The Photoshop algorithm likely incorporates some form of image segmentation (i.e. a type of object oriented image analysis), whereas the ArcGIS pixel based classifier uses the Maximum Likelihood classification algorithm. Esri defines the difference ...

3

You can create a raster mask using this: from osgeo import gdal import numpy as np ds = gdal.Open("Norway_WD4O_Copy.tif") ulx, xres, xskew, uly, yskew, yres =ds.GetGeoTransform() band_ds = ds.GetRasterBand(1) DEM = band_ds.ReadAsArray() Mask_Zero = DEM < 1000 #In units of the raster, e.g. meters Ones_array = np.ones((DEM.shape[0],DEM.shape[1]),dtype=np....

3

You can mosaic a collection of pan-sharpened images as follows: // Function to mask clouds using the quality band of Landsat 8. var maskL8 = function(image) { var qa = image.select('BQA'); /// Check that the cloud bit is off. // See https://landsat.usgs.gov/collectionqualityband var mask = qa.bitwiseAnd(1 << 4).eq(0); return image.updateMask(...

3

It would help if you could provide more information on your case. Are you struggling with a particular application? How does feature scaling relate to the rest of your workflow? Based on what information you provided, I'll attempt a general answer: I suggest using the first option, i.e. scaling each band individually. Some background information: I assume ...

3

You do not need to do any further atmospheric correction with Landsat OLI/TIRS Level-2 data products as they are already corrected to surface reflectance. These data will be sufficient for time-series analysis as well as work involving multiple scenes. However, make sure any older imagery (i.e. earlier than Landsat 8 OLI/TIRS) is also corrected to surface ...

2

MODIS imagery is publicly available via satellite imagery service called LandViewer. You can download it with ease via the toolbar on the right or use dozens of tools for image analysis directly on the platform. Besides that there are already ready-made tools for obtaining multispectral indices, flexible processing of data on AOI, elementary clustering, ...

2

You may compare Landsat imagery and analyze it with ease via LandViewer. There are dozens of built-in tools available for image analysis and compare thousands of images with ease. Here’s a brief guide to free satellite data that can be found on LandViewer Landsat 4 - archive 1982-1993 Landsat 5 - archive 1984-2013 Landsat 7 - archive since 1999 MODIS - ...

2

Set geometries to true, then export to a table asset. Snippet: var training = landcover_roi.select(bands).sampleRegions({ collection: newfc, properties: ['class'], scale: 30, geometries: true }); Export.table.toAsset({ collection: training, description: 'foo', assetId: 'foo' });

2

If you are working in range-Doppler (not projected), you can exploit the information contained in the calibration gains. RS-2 products are generated using an "Application LUT", which aims to correct incidence angle effects to produce a nicely viewable DN image. The calibration gains contain both the calibration factor, but also the factor necessary to ...

2

According to the Landsat documentation, "Landsat data acquisition times are expressed in Greenwich Mean Time (GMT) standard."

2

For the ee.Geometry.Point() constructor, the dimension ordering of the coord parameter is longitude,latitude (or x,y), rather than latitude,longitude. Once you reverse your parameters, you should see some resulting images in the filtered collection. Space_Set = ee.ImageCollection( L8.filterBounds(ee.Geometry.Point(lon, lat)) ) print('Number of images in ...

2

According to Wikipedia an object would need to be at a temperature of at least 2000K (Kelvin) to emit light in the near infra-red spectrum, not even lava flows are that hot according to Wikipedia lava flows are at most 1200C (Celsius, which when converted to 1473.15 Kelvin). In order to see objects, like with a thermal camera, at night you would need to be ...

2

Thanks to commentators above... the solution to this seems to be pretty straightforward. based on answer here and here : It seems like the easiest way to convert ccap data to rasters in R is through the raster package ex: Land_cover <- raster("landcoverfile.img" and then later exporting the file as a .tiff Leaving this up in case other beginners need ...

2

I have found the answer: For a classification task, feature scaling should be done for each individual pixel, not for the individual band. That means, we need to compute mean for the individual pixel over all bands since each pixel in B dimension represents a specific object which we want to be classified. Therefore we need to scale the observation not ...

2

If you prefer to use Landsat, then Landsat 5 ran until 2013 (when Landsat 8 was launched). This imagery doesn't suffer from the scan-line error and would cover your time period of interest.

2

It depends on what is your purpose. If you are looking for a very high resolution forest analysis may be that noise means something and must be analyzed in a particular way. If you just want to "simplify" (decimation) your data and get an homogenized distribution of points you can use the following functions (here is the complete guide): library(lidR) las ...

2

I would recommend investigating Synthetic Aperture Radar (SAR) data and soil indices such as Normalized Radar Backscatter soil Moisture Index (NBMI). Radar has the benefit of being able to penetrate clouds, unlike spectral sensors. The following are some resources to get you started. Shoshany, M., Svoray, T., Curran, P. J., Foody, G. M., & Perevolotsky,...

2

For the LS4-7 level 2 surface reflectance products which are supplied as Int16, you need to multiply the values by the scale factor 0.0001 to convert the values to 0.0-1.0. This is documented in the product guide.

2

In this case, your feature space is tiny - only four variables. Any reasonable classification method is designed to deal with a significantly larger feature space, so there is no need to reduce the data set. As such, go ahead and use all the available data. Many methodologies will also provide a 'ranking' of features, which will indicate how useful a given ...

2

First, your file sizes are as expected. When you go from 1000m per pixel, to 30m per pixel, you get 1111-times increase in size, which is close to what you see from 2 MB to 3.4 GB, once you also consider compression etc. The calculation is: (1000 m/ 30m)^2 = 1111.1 It is squared due to the raster being a 2d array. The whole point of STARFM, or ESTARTFM (a ...

1

I think you are getting wrong altitude values not because the focal distance. The altitudes computations depends of some factors: Numbers of photos for intersection calculus: If you have few photos for each point, errors in the intersection will be absorbed in Z coordinate. Number and distribution of GCP's: The ground control points must to be correctly ...

Only top voted, non community-wiki answers of a minimum length are eligible