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# DTM - Science topic

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I'm trying to redo an article (file attached) that uses the hybrid finite difference method - the differential transformation method (DTM FDM) to solve heat conduction problems. I have used Maple but there is something wrong. Can someone help me fix the code?
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Hello Friends, I want to calculate deep thermal mass of my car for the Air conditioning system design...... I need to find the exact DTM, Please help me to compute this one
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What are possible applications of Machine Learning in ArcGIS ?
Assuming we have very detailed DTM Data about surface around lets say a glacier impending to fall into a lake resulting in a deadly outburst flood (GLOF).
a) Forecasting the hazard of this melting glacier one could predict the ice-thickness.
Do you have other ideas?
Tags: Remote Sensing, QGis, ArcGIS, Drone Images, Climate Change, Risk Management, Machine Learning, AI
Thanks for the question. The answers also interest me
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I had a point file of an area around 70 km^2 collected through field survey using Auto Level. The survey was carried out in the lines 25 m apart. I converted the readings from auto level into point shapefile and then converted the point shapefile into DTM using Grid- IDW (nearest neighbour selection) tool with search radius of 25 m in QGIS. However, in the DTM prepared through GIS has lot of discontinuity in form of strips at the distance of 25 m. I wish to smoothen these strips. Can you suggest the methods.
If you are using a search radius of only 25m, you have not smoothing at all, as no information from the neighboring cells are used. It even looks that you have not only discontinuities but gaps in the border from one stripe too the other. You should use a large search distance, that points from the neighboring stripes are used in the interpolation
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I hope everyone is doing well! I have a question regarding measurements of river through LiDAR or IfSAR. I have IfSAR DTM data of a certain domain, I need to know if the elevation data on a river point is measured as the river bed elevation or the water level elevation at the time the data was taken? Thank you in advance!
Matthew Mabey Thank you for taking time to explain really well, I appreciate it! I asked because I have an IFSAR data that says 1.5m elevation within the river surface. The river is far away from the sea and sort of a receiving catchment from a mountainous region, so I was wondering if the river surface is really low (relating to MSL measurements) and probably one of the reason why that area is flood prone?
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-Required are DEM/DTM satellite data from mountain ranges without glaciers. For example SRTM 30m.
-The data has 3 parameters only. X, Y and Elevation. There is no need for georeference or labels
-I use python 3.6 with tensorflow.keras
-What data type is most beneficial: .png, .jpg, .tiff, .hgt, ... ?
-Are there other open access opportunities on the web like Google Earth Engine and USGS's Earth Explorer?
-Is it better to download the data to my local storage or import and process it directly from the python notebook via "import srtm" ?
Cheers!
You can read some papers presenting our datasets: https://data.world/kietnv. Our papers show how to create datasets and control their quality.
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Dear Colleagues
As it is known from literature the differential transform method (DTM) is a numerical as well as analytical method for solving integral equations,ordinary, partial differential equations and differential equation systems
I am looking for colleagues who are experts in this technique
So we can cooperate in solving certain problems
I am not an expert in this technique but I will love to be a part of the Collaboration.
Meanwhile, I am already looking at the method.
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I am looking for a new iterative method or analytic-approximate method to solve ODE\PDE not the already known methods such as:ADM, VIM, HAM, HPM, DJM, DTM,,,,,but new method.
Best regards,
please see Picard-Green and Picard-Green methods.
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In preparing a series of academic illustrations we're looking for an open source DTM for the Croatian islands of Palagruza. If anyone has, or knows of such a model, we would be very grateful
Hi Leandro,
Many thanks indeed for this
Vince
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An Unmanned Aerial Veichle (UAV) based survey is being conducted for an area which is more or less a flat terrain type. The post processing of the UAV images results into a digital elevation map which shows the height of buildings along with the surrounding agricultural areas.
So, could anyone please suggest the procedure for separation of the surface heights from the raster file to create the actual elevation of the region?
Hi Saroj Kumar Dash, you could work directly with UAV point clouds and exporting them in CloudCompare, after that try to get the DTM using the CSF plugin.
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Elevations in S0 Terrengmodell Svalbard (dataset by Norwegian Polar Institute, 10.21334/npolar.2014.dce53a47) are above sea level. I have some GNSS measurements above WGS84 ellipsoid and want to compare them with S0 DTM, hence I need to know the conversion from original above-sea-level altitudes to WGS84 elevations. I am not sure what conversion was used to construct S0 Terrengmodell - was it one of global geoid models (EGM96, maybe?) or some local height system corrections. I can't find any data myself (in Eglish at least). Does anybody know this technical details?
as Elevations in S0 Terrengmodell based on mean sea level or the geoid, and since WGS84 ellepsoidal heights were based on the ellepsoid surface, you have to know the geoid height (i.e the difference between the ellepsoid surface and the surface of the geoid - mean sea level surface) to convert the ellepsoidal heights to mean sea level heights which are comparable with the S0 DTM.
For accurate conversion ask for the Norwegian geoid model for the area under consideration, otherwise you can get the estimated geoid from the EGM2008 global geoid model.
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Dear all
What is the best way of the (DTM, MADM, GDQM, or VIM) to obtain the natural frequencies of the vibration of a pipe conveying fluid in terms of the accuracy of the results and the time of the least implementation?
Thank you
analytical solution
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I am working with ArcGIS. I have several vectorial data of a territory such as Geology, Land Use, Landslide and flood susceptibility map (polygon data), InSAR data, Earthquakes (Point data), river and road network (line data) and raster data such as DTM, Slope, Aspect.
In order to detect and then predict critical road sections on my studied road network I have to assign all these feature to the road network itself. The aim is to build a database that I can use to develop some predictive models (such as Support Vector Machines or other Machine Learning Algorithm).
Dividing the road network in several fixed-length sections, and then assign the features, does not work, because I can not take into account the spatial heterogeneity of the feature inside the homogeneous sections (that are not "so homogeneous").
I am thinking on sectioning the road network each time a certain feature changes its value (dynamic segmentation), or to build a cell-based database (so convert all the data into raster, and then overlay these resulting raster file).
Is these a good ideas? Have you got other ideas or suggestion?
Thanks,
Nicholas.
Dear Srini,
The "Intersect" operation was one of the first I tried. And it was good enough. However, I think that the "Identity" operation works better since the output maintains not only the intersecting features but also the input features that don't have any attribute in common with the overlay layer.
Anyway, all of these is good enough for categorial polygon data. For raster such as Slope or Aspect in which we have continuous values, the "Identity" operation (or the "Intersect") doesn't work. I should convert continuous raster features into vectorial categorical ones, but it would require several assumptions in order to split the values in some interval.
In order to preserve the spatial heterogeneity, what would you do to assign these continuous features?
Thanks,
Nicholas.
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My goal is to extract diches (small stream) from a LiDAR DTM in a flat area. The problem is that I have a big data (13 GB) and some process like "breach depression" are too slow to be run in Whitebox GAT
The DEM's resolution is 0,5 m (square pixel) and I would like to keep it like this. What can I do?
Jens Kleb Thank you for your consideration
The DEM i'm using has a max resolution of 25 cm but I change it to 50 cm for make the elaboration quicker. Maybe I have to consider to use the 25 cm considering what you wrote, but is a think that I will think about later if the final output (ditch network) is not enough correct. Unfortunally I don't have time for re create the DEM from las files but the DEM I have is well made (using LASTools).
Do you have experience using Whitebox and using morphologic approach like the tool "Map Gully Depht" for extract ditches?
Best regards
Franco
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I am looking for the DTM of the Queensland state, Australia. Where do you guys think I can get it from? I really need it for my project!
Hi,
Two things must be considered:
1. A free DTM or paid one;
2. What is the spatial resolution of the DTM required for your project.
For free you can get AW3D30 - a very good model (one sigma approx. 0.8m), and a much more popular SRTM somehow less accurate (one sigma approx. 2m). Both are at 1" resolution and both are DSMs.
Best regards,
KB
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Hello!
I work with photogrammetry and LiDAR data, but now I have a problem with some data.
I have this LiDAR point cloud (4.5 points/m2) that I need to filter and get a DTM. The problem is that the zone has a lot of dense forest and some places without trees (glades).
How can I filter this point cloud correctly? I have access to globalmapper, but I don't know exactly what parameters I should use. Or maybe if you recommend to me another software.
Thank you and have a nice weekend.
Sebastián
Sebastian, recomiendo que uses MicroStation y TerraSolid y sus modulos. Puedes crear reglas de decision para limpiar la NDP...
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Hi everyone.
I have a technical question about Topographic Wetness Index, specifically about DTM preparation for calculating this index.
I have DTM 1m for a very large area.
I know the formula for calculating TWI. I need Flow Accumulation for this.
There is only one important question. Is it enough that I generate Flow direction and then flow accumulation from it.
Or rather, follow the procedure proposed in the ArcHydro module for determining the basin? First, burn the streams in DTM (DEM Reconditioning), and fill the outflow areas (fill sinks function), and only then make the flow direction on the DTM prepared in this way. I wonder if fill sinks will not destroy the TWI results, because the outflow areas should have the largest TWI and this procedure will rule out.
What do you think, what ideas do you have?
Czesc Pawel,
You are right, the results are very method-dependent:
Kind regards, Balázs
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I would like to know if it is better to state in a technical report that DTM is semi-analytical method or approximate analytical method ?
I think, Semi analytical method is attached to solving PDEs especially when the model is linear while approximate analytical methods are used for solving ODEs. In summary, methods such as DTM, HPM, VIM, VPM, DJM, ADM etc when used to handle ODEs generate approximate solutions.
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Dear all,
I'm looking for DTM or DSM with geometrical resolution below 10 meters for pixel of Dead Sea region (Israel). Priority for free information sources but also ready to pay for any appropriate data.
I heard that Spot-5 DTM should be free to download but I can't find the sources.
Semion
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Dear All,
I would apply the Benthic Terrain Modeler BTM to a seabed DTM, using ArcGIS 10.0. I downloaded the tool from the following link
but it doesn't work properly. It just generates the broad and fine scale BTI, but all the others function (Standardize, classification, etc.) do not work. They generate no output, in spite of I get no message error.
Is there some other tool to perform BTM on ArcGIS 10.0?
Thanks
Francesco
The problem was fixed by an upgrade to ArcGis 10.3. Thanks to all.
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Hi all,
I just received a LiDAR dataset consisting in five different folders. I am reporting the label of those folder in case there is a standard nomenclature: c_1, c_2, c_3 ,c_4 and c_5.
All the datasets cover the same area. Provided that c_1 represents the DTM (floating) and c_2 represents the DSM (floating), the issue I have is identifying the remaining three layers, and specifically:
- c_3 has a 'unsigned integer' pixel type and a classification range from 0 to 255, maybe they are co-surface point groups?
- c_4 has a 'unsigned integer' pixel type with just 4 groups under the value 'Area'
-c_5 is almost identical to c_1 (the DTM) with slightly different values.
Any help on the meaning of all these layers??
Alessandro
Hello-
My recommendation would be to contact the vendor for an explanation. There are no standard folder naming conventions for LiDAR data. The standards referenced by the ASPRS website are in regards to the las files, not folder names. I also wouldn't assume that c1 and c2 are the DTM and DSM only because there's no standard for folder naming. Of course all of this could be resolved if there was metadata written, but I'm assuming this doesn't exist either.
Good luck
-al
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The SRTM90m v4.1 is a digital elevation model with a resolution of 90 metres (3'' Arc-Sec) and available through the CGIAR-CSI data centre web page at http://srtm.csi.cgiar.org/SELECTION/inputCoord.asp.
While, the SRTM30_Plus v10 is a 30'' Arc-Sec resolution global topography and bathymetry model and available through the Satellite Geodesy Research Group web page at http://topex.ucsd.edu/WWW_html/srtm30_plus.html
Hi,
what you mean by synonymous? As you pointet out they have a different spatial resolution. Are you asking what is the source of original data? It's the famous SRTM mission.
Best.
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While trying to generate DSM using UAV acquired data, I am confused with step by step processes that are carried out. I believe, it all starts with extracting keypoints/tiepoints, then we get multiple image pairs and we extract Z for each image pair? How do we extract single Z for a single pixel in final orthomosaicked image? Then where do exactly bundle block adjustment fit-in in this process? Please help me to understand.
Hi Sandura,
if your workflow is based on a Structure from Motion pipeline (many are these days), there is indeed first a feature detection step. In past decades, a wide variety of feature detectors have been developed. Aside from their effectiveness, they vary widely in computational complexity and the type of features they detect.
Afterwards, these features (which can be corners, blobs or lines) have to be described by so-called feature descriptors or feature vectors. Such a descriptor computes a feature vector with local characteristics to describe a local patch (whose size can vary) of pixels around each feature. Just as the detected feature itself, this vector should be invariant (i.e. robust to image noise and photometric plus geometric deformations).
Then, all descriptor vectors are matched between different images. To compute a match, a distance between the descriptors is generally used (e.g. the Euclidean distance). The dimension of the descriptor has a direct impact on the time this takes, and fewer dimensions are desirable for fast matching. However, lower-dimensional descriptor vectors are generally less distinctive than their high-dimensional counterparts.
A robust outlier detection algorithm such as RANSAC (RANdom SAmple Consensus) will ensure the rejection of probable false matches by testing them for consistency. This is done for all possible image pairs by checking if their putative matches fulfil the so-called epipolar geometry constraint : i.e. that the displacements of image features are a possible result solely of the motion of the camera between both images. At the end of this process, the fundamental matrices F of the image pairs are obtained: each of them is a 3 × 3 matrix depending on seven parameters that describes the motion (i.e. relative orientation) from the first to the second image.
Finally, the complete set of image correspondences (called tie points) for the whole image sequence is obtained after considering all meaningful image pairs. The set of corresponding image features thus obtained functions, together with the fundamental matrices, as input for the last steps of the structure form Motion (SfM) computation.
Relying on the algorithms that detect, describe and match local feature points throughout the multiple images, SfM computes the locations of those feature points in a local coordinate frame, creating a sparse 3D point cloud that represents the geometry/structure of the scene. This determination of a point’s 3D position when observed from two or more cameras is called image triangulation. However, image triangulation requires the knowledge of the images’ interior and exterior orientation (called camera intrinsics and extrinsics in computer vision).
These are obtained after combining all the relative orientations of the image pairs in form of their fundamental matrices. SfM can accomplish this as it is based on the projective reconstruction theorem, which states: "given a set of point correspondences in two views defined by the fundamental matrix, the 3D scene geometry and images’ projection matrices (which comprise all the orientation parameters) may be reconstructed from these correspondences alone, and any two such reconstructions from these correspondences are projectively equivalent".
However, rather than a projective reconstruction, a metric reconstruction is wanted: i.e. one in which orthogonal planes are at right angles, parallel lines stay parallel and the reconstructed 3D model is a scaled version of reality. This can be accomplished by running a simultaneous self-calibration / auto-calibration to define the camera’s interior orientation. The latter is stored for each image in the intrinsic parameter matrix (in photogrammetry, these are called the interior camera orientations).
Up to now all images were dealt with in pairs, for each of which a fundamental matrix was computed. Afterwards the oriented image pairs were combined to form the complete block of images and to yield the structure of the scene. The results obtained this way are, however, suboptimal because not all overlapping images are used at the same time and the discrepancies in the structure (caused by small errors during the feature measurement phase) are not optimally minimised. To overcome these problems, the final stage of most SfM algorithms is bundle adjustment. Bundle adjustment iteratively optimises the 3D structure and the projection matrices of all images simultaneously by performing a robust non-linear minimisation of the actual measurement errors, also known as re-projection errors. The technique was developed half a century ago in the field of photogrammetry but is now also largely applied in the computer vision community. The term bundle adjustment comes from the fact that the bundles of rays connecting camera/projection centres to 3D scene points are adjusted to minimise the sum of squared differences between the observed and re-projected image points.
This means that an SfM approach can recover the scene structure and camera projection matrices from image correspondences alone without prior knowledge about camera poses or interior orientation. There is thus no real need to use calibrated cameras and optics during the image acquisition stage, which makes the procedure very flexible and well suited for almost any kind of imagery. It needs to be noted, though, that a proper image network geometry is essential for reliably obtaining the exterior and interior orientation parameters.
Finally, it is It is essential to understand that the SfM output is characterised by a scale ambiguity. This means that if the entire scene is scaled by some factor and the distance between the camera positions is simultaneously scaled by the same scale factor, the projections of the scene points in the image will remain exactly the same. The reconstructed 3D scene obtained after a standard SfM approach is thus expressed in a local coordinate framework and equivalent to the real-world scene up to a global scaling, rotation and translation. These parameters can only be recovered via the use of additional data, which in turn define a coordinate reference system. Ideally, one should this import highly accurate camera positions or a minimum of three ground control points or known distances and use them as constraints in the bundle adjustment. This approach can correct for errors such as drift in the recovered camera and point locations, avoids instability of the bundle solution while the SfM output is directly georeferenced.
At this stage a georeferenced sparse 3D reconstruction of the scene is available. ‘Sparse’ because it is only based on the reconstructed set of image features. However, with the now known orientation of the images, it becomes possible to create a dense 3D model and compute orthophotographs. The essential step in this process is the computation of this denser 3D model. Alternatively, one could interpolate the sparse set of 3D points, but this would yield a far from optimal result. Therefore, it is better to run a multi-view stereo algorithm to compute a dense estimate of the surface geometry of the observed scene. Because these solutions operate on pixel values instead of on feature points, this additional step enables the generation of detailed 3D point clouds (which can be meshed) from the initially calculated sparse point clouds, hence reproducing fine details present in the scene.
I hope this helps!
Cheers,
Geert
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I am doing my PhD thesis on impact assesement of DTM varieties adoption in Benin. How can you help me in term of article? or follow upt he project in Benin ...
Dear Michel Tchègoun Atchikpa
There are many free GIS sources of agriculture data-set, which may be used for your research. I have a friend from Zambia, moreover, who is working for the government as a geography scholar. But in my opinion, it is better for you to try to contact the Benin Government directly and  tell them your requirement.
BUT, firstly you need to determine your idea and purpose. Maybe you can find ideas from millions of the previous studies about agriculture.
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Hi All,
We have two shape files which are of footprint and roofs respectively and have an oblique imagery along with IMD file and DTM for the same.
When overlay the both shape files on the imagery falling at the same location as footprint location.
We are required to move these roofs on the imagery roof positions.
Can anyone suggest how can I adjust the roofs on oblique imagery roofs position.
Hi  Arnadi Murtiyoso and Jianming Liang
what EO parameters required like OPK  or something else?
yet i not started reading document , once  competed i will try and update you the results.
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I whant to generate a  DEM or DTM from satellite images particulary sentinel-2, there is any methode for this?
You can't derive DEM or DTM from Sentinel-2 images ... However you can use Sentinel-1 dara ( SAR images ) to generate DEMs ( You can find documentation on ESA or Copernicus website).
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Digital Terrian Model (DTM) or Digital Elevation Model (DEM)
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Hello, I am new in working with Pix4DMapper Pro. I need to get a very precise DTM. Can somebody explain how to get it? Also, I am working with landslides and I need their correct measures from images, so calculating of volume is necessary. How can I get the most accurate results from processing?
Dear Maja,
In order to get volumes you need a good and accurate DSM.
Pix4D mapper Pro works with a series of algorithms which process UAV imagery with the so called Structure From Motion (SfM) and Multi-View Stereopsis (MVS) pipelines. I attached you a diagram I created to show you how it generally works and the generic steps required to get accurate DSM and orthophotos.
However, in order to get very precise location and spatial resolution, both influencing volume calculations, here are some good tips that jumps on my mind right now. You need to (summed-up and simplified):
1. use a good network of ground control points GCPs (targets) and record their location with surveyor grade differential gps
2. georeference accuratly your images so that the least x,y RMSE occurs
3. enhance textural heterogeinety in your site (landslide) so that the feature recognition algorithms work efficiently
4. assure at least 80% (rule of thumb) of lateral and frontal overlap between images
Ideally, you should know the distortions parameters of the camera lenses are mounted on your UAV , but if you have good GCPs, the bundle block adjustments within the SfM pipeline will model the camera with a sufficient precision.
Given that you use Pix4D Mapper Pro, I imagine that most of the processing is automatic, but keep in mind that there is A LOT behind the generation of DSM from near-vertical UAV imagery.
I prepared for you an extract of one of my researches about UAV and their potentials for coastal management, which describes accuratly what happens behind the curtains of a user-friendly SfM software like Pix4DMapper Pro. I left you the references of all my research... they might be useful to you.
Another thing.
You should edit the topics of this question so that they match with the expertise you are looking for. For example, try to write Pix4DMapper Pro, Structure from Motion, SfM, UAV, Drones, Mapping, Geomorphology...things like that.
My best regards and good luck with you UAV processing!
Nic
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Steps to WGS1984 ASCii file project to Kertau_RSO_Malaya_Meters ASCii file. Been trying but still not projected well.I cannot open in FMP stated that this file has no projection. Seeking an answer from all experts and professors. Thank you and appreciate your feedback.
I have also attached my original file for reference.
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Hi Team,
Good Day.
I am working on a text mining assignment and have built the document matrix using the tm package.
Now I need to run findAssocs from my dtm with some word say 'like' with a correlation of 0.70 but  as far as i have been researching it tells it this function is only viable when we have more than 1 doc however in my case i only have 1. So please suggest if there is an alternate to this issue.
Hi Shivi,
as far as I can see, your code is passing a single vector into the Corpus function, and from there into the DocumentTermMatrix() function. So essentially, you're creating a matrix with 1 row and as many columns as there are terms. Hence, no correlations.
It looks like your code is merging together a bunch of reviews. So one option would be to consider each review (i.e. each row in the .csv file) a single document, instead of merging them. That way you could find associations between terms across multiple reviews.
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What software you use to prepare orthophotomaps, DTM, DSM, point cloud or 3d model with drones? I've used: Pix4D and APS.
Hello Sebastian, yes we have to have a Remote Pilot Qualification (Ground School and Flight Test) and be approved by the CAA. This enables the correct insurance and for each operation land owner permissions and risk assessments are required. If in controlled airspace we also need to contact air traffic control.
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I am using R for creating DocumentTermMatrix and then converting it to normal matrix for further processing. There are 26,000 records and 4 columns, while converting DocumentTermMatrix to normal matrix it get hang. I am using syntax abc<- as.matrix(dtm), where dtm contains document term matrix. Ram size of my computer is sufficient. Can anybody give me solution.
Probably you have to check the memory status during execution of command as.matrix(), given your operational system (for windows leave open the task manager). What is your RAM in GB? Are you using 32 or 64 bit computer?
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We have developed a half meter resolution landcover map from a pansharpened image Pleiades image. We want to resample the landcover raster to higher resolutions (15 and 30m). What would be the most recommended resampling technique to do this?
This is a landcover map of area not larger than 200 square kilometers. We want it to be used as an input in a hydrologic model. As the remaining of our data sets (DEM and soil raster) are coarser resolution the model will automatically resample our half meter resolution landcover raster to a coarser resolution raster using one of its default resampling methods. We want to avoid this and instead apply a recommended resampling technique. Any experience regarding an ideal method to do the resampling would be much appreciated.
The sampling scheme you choose will probably affect your simulation results. We have investigated this issue in the following paper:
Zhao, S. and Liu, S., 2014. Scale criticality in estimating ecosystem carbon dynamics. Global change biology, 20(7), pp.2240-2251.
The best solution might be using the composition of land cover rather than any individual class in your model. However, most models are not able to deal with multiple land cover classes in a mapping unit or grid cell. So the next option might be the majority resampling approach. Of course, you can derive a set of different land cover maps from the original data using different resampling schemes, and then investigate their impacts on hydrologic modeling to assess the uncertainty derived from land cover resampling.
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Anyone know if DTMs have been generated from SRTM data?
Is there an online repository?  If not, can anyone recommend processing methods for creating DTM from SRTM data?
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I have to find some topographic attributes about a site affected by slope instability and I need a DTM with a good spatial resolution and moreover a good vertical accuracy.
Thanks Simone, for my study area I've found a Lidar! I'm lucky, maybe ... I'm verifying the data
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I am comparing the three different DEMs (extracted from X band SAR Data, ASTER GDEM2 and Cartosat DEM) with reference data (Aerial photogrammetric DEM), it is found that all DEMs gave their best accuracies in shrubs and forest classes.
What may be the region of this? Is it because I have used DEM as reference data instead of DTM_
SRTM-X/C are less noisy over forested areas than over other areas, for example urban areas, because forest canopy is more homogeneous and 'flat' for InSAR or stereo image matching procedures than greatly diversified urban environments.
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As per the PCI tutorial on "Orthorectification and DEM Extraction of CARTOSAT-1 Imagery" a minimum of six GCPs per scene is required for processing cartosat -1 data using rigorous model. But I am unable find any research material which explains why minimum of 6 GCPs are compulsory.
The best answer is from PCI Geomatics itself, so I suggest to write there. For now, I think you should accept that the model used in their software does require at least six control points in order to function properly.
Nevertheless, in my experience more than six GCPs are necessary at a scene level. Their number depends not only on the model, but also on local steepness of the slopes. Related to the last claim, two more problems occur: (i) to find the points measured on the ground also in the image; (ii) to obtain an acceptable error (< 0.5 pixel) at the local level over the entire scene (i.e., the global RMS Error, which is given automatically by any dedicated program, is not sufficient to assess the transformation performance; in addition, RMSE estimations at the sub-scene level are necessary, and for this stage GCPs are needed, which number depends on the slopes steepness; the sub-scenes definition depends on the landscape characterizing each area of interest).
Finally, I consider that the following article may help you with the rationale of GCPs number: "Review article: Geometric processing of remote sensing images: models, algorithms and methods" by T. Toutin, published in 2004 in Int. J. Rem. Sens., 25(10), 1893-1924 (on the web).
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Any open source software able to extract DEM from satellite stereo data i.e. ASTER, Cartosat, PRISM, world view etc.