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11

ESRI has a pretty good help section on LiDAR (below). For more formal details on LiDAR, I would recommend the following books: Topographic Laser Ranging and Scanning: Principles and Processing Airborne and Terrestrial Laser Scanning Remote Sensing and Image Interpretation LiDAR Laser Returns Laser pulses emitted from a lidar system reflect from ...


7

Think of the geometry. The incidence angle refers to the angle from nadir, or directly beneath the satellite, which would be 0°. As the sensor looks out to the sides from this nadir, the angle of incidence increases as does the fov (field of view). This is why the resolution decreases with increase in incidence angle. This illustration from the Sentinel ...


7

The project website hosts the MOD16 dataset on an FTP server. As FTPs allow directory listings you can easily download complete folders without having to click individual links. This can be done with most FTP clients - a popular one would be FileZilla. Just right click the folder you want and select download. edit: The question now specifies that only one ...


7

PDAL doesn't provide anything like FUSION's "GridMetrics" at this time. We've been interested in useful statistics or metrics that PDAL could compute for algorithm builders, but we haven't gotten around to implementing anything yet. It would be straightforward to implement a custom PDAL stage to compute these. It will be more productive to ask on the mailing ...


6

Your understanding is correct. Obviously, you aren't limited to just two points in time, but that is only a minor variation. Multi temporal information is generally used for change detection, but it also provides a good tool to take phenological information into account when doing vegetation classification.


6

One flaw in your approach. You don't need to go through DN to radiance. You can go straight to the DN to reflectance. Just stick to ((B1*0.00002)-0.1)/0.74457226676389733207607359928648.


5

Yes, there are free object-oriented (segmentation) software available. A few that come to mind are Spring, ITK, Orfeo toolbox and GRASS GIS. I would however point out that image segmentation is a poor direction to peruse when trying to model fractional cover. A segmentation algorithm is designed to minimize within unit variance and maximize between unit ...


5

Firstly, welcome to the site! Numpy arrays don't have a concept of coordinate systems inbuilt into the array. For a 2D raster they are indexed by column and row. Note I'm making the assumption that you're reading a raster format that is supported by GDAL. In Python the best way to import spatial raster data is with the rasterio package. The raw data ...


5

There are multiple things to respond to in your question. 1. The title of your question refers to atmospheric correction, while the information you refer to in the text is focused around converting to radiance. These two things should not be confused. 2. It does not make physical sense to atmospherically correct pansharpend data using standard methods due ...


5

I hacked together a solution for this and wrote a blog article a while back on a very similar topic, which I will summarize here. The script is intended to extract a river from a 4-band NAIP image using an image segmentation and classification approach. Convert image to a numpy array Perform a quick shift segmentation (Image 2) Convert segments to raster ...


5

I am the main developer of MGET. The first step in your problem is to obtain values of the covariates that you will use to fit the model to your 90 GPS points. It sounds like you want to use the 8 bands as your covariates. You need to add 8 fields to your shapefile (one for each band) and populate them using a tool such as Extract Multi Values to Points ...


5

Sentinel-2 Level 1C data are expressed in reflectance with a scaling factor, not in radiance. You have to divide by 10000 to get the reflectance. In the preliminary products shown this autumn, you had to divide by 1000. The scaling factor is given in the xml file at the root of the product directory. <QUANTIFICATION_VALUE ...


5

Kogan (2004) (p. 2891) provides the following formula for the Vegetation Condition Index (VCI): VCI = 100 * (NDVI - NDVImin) / (NDVImax - NDVImin) where, NDVI = Smoothed weekly NDVI value NDVImin = Multiyear minimum NDVI value NDVImax = Multiyear maximum NDVI value As you know, NDVI ranges from -1 to 1 and functionally ranges from 0 - 1. VCI ...


5

I am not a specialist of orbits, but I'll try to answer. Given a theoretical overpass time on a sun synchronous orbit, the exact one is not that easy to determine, as it depends on a lot of factors. the theoretical overpass time is valid at the equator, and for the local time under the satellite track when it crosses the equator (which is called the ...


4

The full details can be found on the USGS FAQ. Here is a short answer. Band numbers in Landsat 5 TM and Landsat 7 ETM+ correspond with the same wavelength (approximately). Unfortunately, ​Landsat 5 Thematic Mapper (TM) operational imaging ended in November 2011. Landsat 5 MSS was powered back on in 2012 and collected data until January 2013. However, there ...


4

Step 1: Convert from digital numbers (DN) to radiance This is done by applying the multiplier and addition numbers as found in the metadata (.MTL) file. For the thermal bands (B10 and B11), the values are usually, but you should check the file: Add: 0.1 Multiply by: 0.0003342 (3.3420E-04) In ENVI you can apply this correction using 'band math': ...


4

The simple answer is that lidar sensors coupled with NIR cameras can collect point cloud data that can then have the NIR values "embedded" with them, the same way RGB values can be assigned to point cloud data collected with high res photos.


4

It's normal that azimuth and range resolution of SAR sensors differ, because they depend on different variables: The azimuth resolution (AR) of a SAR system is: AR=Length_of_antenna/2 The slant range resolution (SRR) of a SAR system is: SRR=(Speed_of_light*pulse_length)/2 The ground range resolution (GRR) of a SAR system is: ...


4

There are many multi-look algorithms. At the most basic, the process reduces noise (or "speckle") in SAR images by averaging adjacent pixels. Often SAR processors allow the user to define some N x N window over which to average. However, other algorithms include using median values rather than mean values. For a comparison of other algorithms, see ...


4

Suppose that we have two images that we want to co-register or one image that we want to register to earth: First step is to remove the errors in each image both geometrically and radiometrically. Each image has some geometric errors due to: Earth rotation Scan time skew Aspect ratio Panoramic effect (bowtie error) Earth curvature These errors will ...


4

It is technically possible to use the pansharpening algorithm with different sensors, and all your tagged software have pansharpening tools (sometime . However, the quality of the outputs will depend: 1) on the pixel number ratio. In your case, it will be very large (15*15 = 225). IMHO this will be too large, in the litterature you hardly find successful ...


4

In Windows (run OSGeo4W shell): Scaling: for %i in (*.tif) DO gdal_translate -scale -2000 10000 -0.2 1 %i outputs\%i You might find recalculating instead better: for %i in (*.tif) DO gdal_calc.bat -A %i --outfile=outputs\%i --calc="A*0.0001" --NoDataValue=0 In Ubuntu looping through files is slightly different: for i in *.tif; do gdal_calc -A $i ...


4

No, the NDVI threshold value will not be the same for the time series due to differences in phenology and unique conditions on the ground. As Kersten mentioned in the comments, you may want to consider using Global Forest Watch data, which is well respected in the environmental community. You have uncovered one of the limitations of working with ...


4

fiducial mark are used to define the coordinate system of the photograph. With film photograph, the paper moves under the objective and can get further distorted during storage and development, so you need to localise the image on each frame. On the other hand, digital sensors are fixed, so you don't need any mark on the image to define the coordinate ...


3

First, there is problably a typo in your question, but here are the sun angles solar zenith angle = 90 - meanSunEl solar azimuth angle = meanSunAz this is also valid for the mean satellite angles MEAN satellite zenith angle = 90 - meanSatEl MEAN satellite azimuth angle = meanSatAz That being said, it is important to realize that you can consider ...


3

It is possible and actually in practice. For example, Owyhee Air Research conducts wildlife surveys using aircraft mounted Forward Looking Infrared (FLIR) Sensors. The following is a video link highlighting the capabilities of using FLIR for grouse surveys: https://vimeo.com/130870170 The screenshot shows a Sagegrouse lek filmed in 1080p from a fixed wing ...


3

In the example you are referencing, NDVI is included as a predictor variable along with all of the band values. The response variable is the class (vegetation type). In your case, you could simply have a binary response (cover, or non-cover). Random forests is a very valuable machine learning algorithm because you can incorporate any type of predictor you ...


3

The STATION_ID refers to the satellite ground station. A ground station is used to upload/download data to/from the satellite. You can think of it like a cellphone tower - when the satellite is in range of a ground station it sends the imagery to earth. Here is a map which shows the location of the ground stations participating in the International Ground ...


3

In short, yes. You can do that. The sensors in most cameras are sensitive to light from UV to IR. To change the information into the standard RGB, most cameras use a Bayer Filter (see the Bayer Filter wikipedia for more info on how this is done) approach to filter the visible light into red, green and blue, while throwing away UV and IR information. As ...


3

I would strongly recommend you use the MCD43 product, instead of calculating the albedo product by yourself, for a couple of reasons: 1) Albedo products are not that easy to calculate. Based on your previous question asking if that could be achieved by using Band Math, i'd assume you, at least at that time, didn't fully understand the algorithm that goes ...



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