I have downloaded L8 OLI/TIRS C1 Higher Level Data from USGS Earth Explorer to map Chlorophyll content in lake. The file contains original images and surface reflectance images. When I calculate NDWI using the original images, the lakes and surrounding area can be distinguised clearly. However, when I use the surface reflectance images, they produce a map of single colour. I have used the same NDWI formula for both the images. I have used the following code to calculate NDWI:    LS_ndwi &lt;- (tmp3-tmp5)/(tmp3+tmp5)  plot(LS_ndwi)    Here tmp3 is the Green band and tmp5 is the NIR band. I wanted to know, should I use the original images or the surface reflectance images for my analysis? I have to retrieve the reflectance data from the images for my analysis, so wanted to use the surface reflectance imageries. I checked my data type and it is in "FLT8S". So the problem of integer is not valid here I think.
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I have made atmospheric corrections to my Landsat 8-OLI raster using 'ENVI FLAASH' having surface reflectance values scaled between 1-100.     In ArcGIS I am using simple expression to compute NDVI using 'Raster Calculator' tool as illustrated below.      However, the results are not respectable. The same results are faced using 'ENVI Band Math' tool. Interestingly, this is not the case with the outcome from  'Spectral Indices' or 'NDVI' tools built within ENVI.     Why there is great discrepancy in results in all these cases?
33773
Recently, I found this , that allows you to generate a 3D model from a 2D image.   It works very well and you can download an OBJ file. Vertex are colored and when I import it using Blender, I can't see any Vertex color.   Using Cycle (I prefer a internal solution if it exists) according to some answers I've found on Blender Stack Exchange, I patched some nodes : Attribute -> Diffuse BDSF -> Material Output.  Unfortunately, I can't get it works, the model stills black.    When I open the OBJ file, I see that color per vertex are there :  v 43.747554 107.000000 69.000000 0.470588 0.380392 0.309804 v 43.780163 115.000000 64.500000 0.427451 0.337255 0.266667 v 43.782527 107.000000 68.500000 0.470588 0.380392 0.309804 v 43.801902 115.000000 65.000000 0.427451 0.337255 0.266667 v 43.806461 107.000000 69.500000 0.470588 0.380392 0.309804 v 43.821703 115.000000 64.000000 0.427451 0.337255 0.266667 v 43.838352 108.000000 68.500000 0.466667 0.376471 0.305882 v 43.854670 106.000000 69.000000 0.482353 0.392157 0.321569 v 43.856176 108.000000 69.000000 0.466667 0.376471 0.305882 v 43.873876 106.000000 69.500000 0.482353 0.392157 0.321569 v 43.888855 110.000000 68.000000 0.466667 0.376471 0.305882 v 43.891471 109.000000 68.000000 0.466667 0.376471 0.305882 v 43.892733 110.000000 67.500000 0.466667 0.376471 0.305882 v 43.895170 108.000000 68.000000 0.466667 0.376471 0.305882 v 43.900904 109.000000 68.500000 0.466667 0.376471 0.305882 v 43.907584 107.000000 68.000000 0.470588 0.380392 0.309804 v 43.917898 115.000000 65.500000 0.427451 0.337255 0.266667 v 43.925000 111.000000 67.500000 0.454902 0.364706 0.294118   According to some answers here, Blender import more easily the vertex color of a PLY file so I tried to convert to PLY with two different sites :   1. meshconvert .com : Give me a 3.5 Mo file  2. greentoken.de/onlineconv : Give me a 2.3 Mo file  (Of course, if you know some other sites I could try, it interest me)  I guess that color data are lost with greentoken (not sure).   When I import the 3.5 Mo PLY file in blender, I need to be in textured display mode, then I think I see my vertex color value but in black &amp; white instead of RGB, that is weird. Anyway, I can't get it work  EDIT 1 : I opened PLY files and color vertex aren't there... I need to find a better converter   EDIT 2 : I tried www.ofoct.com/3d-model-file-for-3d-printer-converter it doens't take vertex color into account. In fact, in each PLY files I converted with the various sites here, there is a common line :      comment Created by Open Asset Import Library -    (v3.2.0)   (the version varies)  EDIT 3 : In fact it seems that model converted by Green token, keep the vertex color. That is weird because that's the smallest file :   ply format ascii 1.0 comment Created by Open Asset Import Library - http://assimp.sf.net (v3.1.187496374) element vertex 29793 property float x property float y property float z property float nx property float ny property float nz element face 59582 property list uint uint vertex_index end_header 82 103 6.37528 0.40963 0.0331735 0.911648 82 102 6.57043 0.272087 0.235193 0.933088 82.6603 103 6.5 -0.14071 0.043367 0.9891 82 103.935 6.5 0.201161 -0.136681 0.969976 81.5475 103 7 0.825371 0.0489484 0.562465 83 102 6.62244 -0.248588 0.147929 0.957247 83 103 6.56417 -0.411487 -0.0867124 0.907281 81.5819 104 7 0.787841 -0.0338904 0.614946 82 105 6.63886 0.0715144 -0.131383 0.988749 83 104 6.77321 -0.431167 -0.171652 0.885794 83.6781 102 7 -0.470137 0.061235 0.880467 83.6327 103 7 -0.543939 -0.0905203 0.834228 83 105 6.96709 -0.445324 -0.163091 0.880391 84 103 7.23724 -0.520118 -0.141377 0.842313 84 104 7.47775 -0.546517 -0.195833 0.814229 84 102 7.16782 -0.416323 -0.021798 0.908955 83.3508 101 7 -0.314426 0.156435 0.936304 84 101 7.22581 -0.280388 0.0301462 0.959413 84.9009 101 7.5 -0.339848 -0.0219793 0.940223 84.6939 102 7.5 -0.435242 -0.0925323 0.895546   As you can see, nx, ny and nz correspond to RGB values..
33271
I am looking for a method to process a remote sensing image and extract the crown areas of the individual trees from the image.   I have both visual wavelength areal imagery, and lidar data from the area. The location in question is a desert area, so the tree cover isn't as dense as a forest area. The resolution of the aerial imagery is 0.5 feet by 0.5 feet. The lidar resolution is approximately 1 x 1 feet. Both the visual data and the lidar come from a Pima County, Arizona dataset. A sample of the type of aerial imagery I have is at the end of this post.   This question  seems to be the same issue, but there does not seem to be a good answer there.  I can obtain a reasonable classification of the vegetation types (and information about the overall percent cover) in the area by using the Iso Cluster classification in Arcmap, but this provides little information on individual trees. The closest I have to what I want is the results of passing the output of the isocluster classification through the Raster to Polygon feature in Arcmap. The problem is that this method merges near by trees into a single polygon.   Edit: I probably should have included some more detail about what I have. The raw datasets I have are:    Full las data, and a tiff raster generated from it. Visual imagery (like the sample image shown, but covering a much wider area)  Manual direct measurements of a subset of the trees in the area.    From these I have generated:    The ground/vegetation classifications. The DEM/DSM rasters.
53122
I've got some aerial photography (format: IMAGINE Image) which is detailed enough to see to fire hydrants and a shapefile of fire hydrants which was digitized based on some CAD data.  My project is to QC the fire hydrant shapefile locations by spot checking with the imagery. Is it possible to extract RGB values from the imagery that match the fire hydrants RGB values?  Fire hydrant (red):    I'm looking to select cell values (which i have written down) that are similar to the cell values of the fire hydrant and then extract those cells (either a raster file of those cells or a shapefile which plots location of similar cells). I'm assuming that there are very few features in the imagery that have similar reddish RGB Values.  After looking at Arron's Answer and playing around with both the supervised and unsupervised approach (see my comment below), i was unable to get the tool to perform exactly what i wanted until I started too look at the confidence raster output from the  tool.  I am not to sure what exactly the raster output is supposed to symbolize but from just looking at the level 14 cell value, but it captured the all the fire hydrants.    raster output:  location of fire hydrants:   My next step is to use the tool  convert the fire hydrant raster footprint to a polygon.  I am running the tool right now and in the past hour it has only made it to 11%.  The area I am working in is large, at 1x1 mile so I understand that it could take some time if there is a lot of small raster cells to convert to a polygon.  Are there any suggestions on running some tools to clean up some of the data so it speeds up the process of converting the raster to a polygon? I may not have a need to run this tool this since the raster footprint did such a good job at capturing the fire hydrants but I am interested in speeding up the process since I foresee this being used in other applications for the future.
3294
I have calculated NDVI and SAVI vegetation indices for a landsat-8 image. I am unexpectedly getting higher values for the NDVI than SAVI. What may be causing these unusual results?  NDVI=0.46 SAVI=0.34   Specific band pixel values are:  toa b4- 0.130 toa b5- 0.365 L= 0.5 taken
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I am using MOD09Q1 now and I would like to calculating NDVI whole year data. One year, I have 46 band 1 images and 46 band 2 images.     list.files   I creating list of band 1 and list of band 2.  Band1list  &lt;- list.files(path = ".", pattern = "*.b01.tif", all.files = FALSE,                          full.names = FALSE, recursive = FALSE,                          ignore.case = FALSE)    Band2list  &lt;- list.files(path = ".", pattern = "*.b02.tif", all.files = FALSE,                          full.names = FALSE, recursive = FALSE,                          ignore.case = FALSE)    raster::stack     Stacking all 46 band 1 (band 2) images to 'StackBand1' ('StackBand2')  StackBand1 &lt;- stack(Band1list)  StackBand2 &lt;- stack(Band2list)    NDVI calculation via for loop     I try to stack images of same date (e.g. "2012.01.01.Smb01.tif" and "2012.01.01.Smb02.tif") and calculate NDVI  for (i in 1:length(Band1list)) {   StackBand1[[i]] == i    StackBand2[[i]] == i     Stack &lt;- brick(StackBand1[[i]],StackBand2[[i]]) }   In here, I could not execute loop for all 46 images. It can be run all and result is showed for the last couple only.
33416
I am having a hard time finding a world relief map with "lighter" ocean colors that will actually display as such in Arc.   This is the image I download from the web    but this is how it displays in ArcGIS.
7781
I'm working on a software project that involves creating a visualizer for flood simulations. As part of this project, I've created a water gradient that shows water depth at particular points. To set what values will represent what colors, I go through the data and get the minimum and maximum values that occur and evenly distribute the colors according to that scale.  However, there are often times points in these simulations that have significantly deeper water at them than anywhere else in the simulation. This causes most of the points on the map to have very similar colors and this is not very informative and makes the areas where the water is deeper very hard to see.  My goal is to dedicate a larger range of colors to depths that occur more frequently. For example, if depths go from 0 to 12 but most depths are between 1 and 2, I want more color variation to occur within that range than does between say 11 and 12 or 4 and 5. It seems I need to use standard deviation or something involving normal distribution to do this, but I'm a bit fuzzy on how these things work and how I can use them to accomplish my goal.  Any help that can be provided will be appreciated. Thank you.
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I have the following two files:   LiDAR las file1 Let's give it a name LiDAR las file2, contains the same file1 data, where number of points randomly removed.   So if file1 contains X number of points, file2 would contain Y number of points, where Y is less than X.  Now, I am using ArcGIS "LAS to Multipoint" to create from both files above their shape counterparts. Next I am creating two empty TIN(triangulated irregular network) files using ArcGIS. Then I edit both TINs, let's say tin1 and tin2, with the appropriate input feature classes, i.e. file1 to tin1 and file2 to tin2.  Is there any tool/package/library or any software that would let me calculate the RMSE(root mean square error) or any difference information that would give me information on the accuracy of the second file, i.e. file2, with respect to file1? I tried to use the TIN difference but it gives system error and cannot obtain the result. I would really appreciate if anyone could show how to obtain accuracy information and interpretation of the results obtained.
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We are doing the research about applying remote sensed data on hydro logical model . After delineating, the river basin from DEM data using Argis9.3, We have to map the remote sensed data derived from products like PERSIANN. Our task now is to create the grid with the same resolution with the available remote sensed data( for example 0.25degree x 0.25 degree). and have a certain corner that matches with the obtained data.   Does anyone know how to deal with our problem?
95427
Some months ago I watched  video of Blenderguru about "How to create a realistic earth in blender". In this video blender render is used and  now that I understand texturing in cycles a little better I am trying to rebuild this "realistic earth" with cycles.    In the video - starting at about 11:30 min - it is shown how to map the "earth day" and "earth night" image textures. Depending on the lights shining on the earth the earth or night image is projected. I tried to rebuild that in cycles but I think I a making mistakes. In the circled box the "night" nodes are found. What Is my mistake ?   P.S When you see other mistakes in this node tree, please let me know.  OK ... I now have implemented the advice of Uncle Snail (except moving the black slider). Below an image of the earth and the updated node tree (I also rearranged nodes for better overview).    In the Color Ramp, I did not move the slider. Is that really necessary ??
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Recently I came across a debate about whether it's fair to call RAW 'unprocessed' or 'unaltered' sensor data of a DSLR. As far as I know, the analog sensor data is processed into a digital RAW file using the ISO setting, a process during which the Bayer-sensor data is demosaiced as well. However, those are just the necessary steps for saving a digital file while maintaining maximum quality.  Are those assumptions correct? If so, are there any additional steps that need to be performed between the sensor receiving light and the saving of the RAW file? And can those be reasonably be described as an 'alteration' of the sensor data, or is it fair to say that a RAW file holds the unaltered sensor data?
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Explanation of the problem:     On 31 May 2003 the Landsat 7 Enhanced Thematic Mapper (ETM+) sensor had a failure of the Scan Line Corrector (SLC). Since that time all Landsat ETM+ images have had wedge-shaped gaps on both sides of each scene, resulting in approximately 22% data loss. These images are available for free download from the USGS GloVis website and are found in the L7 SLC-off collection.   source:   Can I use Qgis instead of proprietary software (ENVI, Erdas....) to fill the gaps?
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I'm trying to compute the shadow of 3D objects on the ground. To do this I'm assuming parallel rays from Sun origin.   Let's assume the Sun direction is given by azimuth $\varphi$ and elevation $\theta$ angles. So the direction unit vector is retrieved as:  $$ \hat{\mathbf{s}} = \mathbf{R}_y\left(\theta\right)\mathbf{R}_z\left(\varphi\right)\hat{\boldsymbol{\imath}}$$  where $\mathbf{R}_{x/y/z}$ are the rotation matrices along the $x, y, z$ axes given by, for example, Euler-Rodrigues equation, and $\hat{\boldsymbol{\imath}}$ is the unit vector along $x$-axis.  A point on a ray-line has the equation:  \begin{equation} \mathbf{r} = \mathbf{r}_p + \lambda\hat{\mathbf{s}}  \tag{1} \end{equation}  being $\mathbf{r}_p$ a point on that line.  A point on a general plane is given by the equation:  \begin{equation} \left(\mathbf{r} - \mathbf{r_0}\right)\cdot \hat{\mathbf{n}} = 0 \tag{2} \end{equation}  being $\mathbf{r}_0$ a point on the plane and $\hat{\mathbf{n}}$ the unit vector normal to that plane.   Ensuring the validity of both equations (1) and (2) allows to compute the $\lambda$ multiplier and retrieve the position vector of the projected point (writing $\tilde{\mathbf{r}} = \mathbf{r_p} - \mathbf{r_o}$)  \begin{gather} \lambda = - \left(\frac{\tilde{\mathbf{r}}\cdot \hat{\mathbf{n}}} {\hat{\mathbf{s}}\cdot\hat{\mathbf{n}}}\right) \\ \mathbf{r}_\text{proj} = \mathbf{r}_p - \left(\frac{\tilde{\mathbf{r}}\cdot \hat{\mathbf{n}}} {\hat{\mathbf{s}}\cdot\hat{\mathbf{n}}}\right)\hat{\mathbf{s}} \tag{3} \end{gather}  Applying eq. (3) to all the vertices of a mesh $\left\{\mathbf{r}_{p_i}\right\}$ .   A problem can be noted: $\hat{\mathbf{s}}\cdot \hat{\mathbf{n}}$ can be zero (e.g. Sun is horizontal, i.e. the ray is ortogonal to the normal of plane).   Are there some conceptual mistakes in my reasoning? Is there a way to rewrite the operation as a linear operator applied to the set of vertices position vectors?   $$ \left[\mathbf{A}\right]\left\{\mathbf{r}_{p_i}\right\} = \mathbf{r}_\text{proj}$$
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I am reprojecting a SRTM 3 arc-second raster file from WGS84 geographic coordinate system, to projected coordinate system which is using Azimuthal Equidistant projection:  gdalwarp -s_srs EPSG:4326 -t_srs "+proj=merc +lat_ts=40.81266 +lon_0=14.414252" -r near -of GTiff C:/vesuvius2_wgs84.tif C:/vesuvius2_aeqd.tif   I attached the initial raster file in here ().  The issue is that if I use the Nearest neighbor resampling method (-r near), some sort of "stripes" appear on terrain once I reproject the file to Azimuthal Equidistant projection. Here is the how the reprojected raster looks like:    And here is its 3d representation:    Interestingly similar "stripes" appear in both direction if I reproject the initial raster to UTM:  gdalwarp -s_srs EPSG:4326 -t_srs EPSG:32633 -r near -of GTiff C:/vesuvius2_wgs84.tif C:/vesuvius2_epsg32633.tif   The "stripes" go away when I use Bilinear or Cubic resampling methods (-r bilinear, -r cubic) instead of the Nearest Neighbor one.  Why do these "stripes" appear when Nearest Neighbor resampling method is used?
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I am new in remote sensing field. I sometimes got confused when I read about image pre-processing. Can someone please elaborate to me the difference and some example methods to do radiometric correction and radiometric calibration ?  Is converting DN values to Top Of Atmospheric (TOA) reflectance values from 2 sets of images (e.g Landsat 7) from different time counted as correcting or calibrating ? Are DOS (Dark Object Subtraction) and Sun-angle correction parts of radiometric correction ?
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I have a series of landsat wanting to process in raster calculator, but after doing the tool it happened these output images only contain one single band. They used to be RGB 3-band images.  It is on arcmap 10.3. I was using raster calculator to remove a certain value in my 3-band landsat image. After I type in the conditional statement to remove all 0 value from the inpur RGB image, it only returns an image of 1band, black and white. How can I preserve the RGB value in output.