meodensi

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About meodensi

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  1. Some basic rules for a WebGIS to my opinion. From Aministrator's site: - Should have UI for administrator; - Able to host GIS data (vector, raster) in the cloud storage; - Able to display GIS data from WMS/WFS/WMST services; - Able to create thematic maps from GIS data; - Able to create <embed> object into HTML website/blog/forum; - Manage users by different security levels; - Allow multi-user working environment. From User's site: - Very fast feedback (high speed map display, searching, info query...); - Should have Layer Control; - Able to display map legend/attribute tables; - Able to show pop-up information by mouse-click; - Able to search for information by conditions; - Able to analyze/summarize information by request; - Able to do some other tasks: measure, print, export....
  2. You may be interested in this paper "Mapping forest canopy height globally with spaceborne lidar" http://onlinelibrary.wiley.com/doi/10.1029/2011JG001708/pdf Cheers
  3. MapInfo is getting bigger, heavier and slower and it just serves "information" as its name. Previously, it was good to use MapInfo for vector editing, map layer preperation, printing and database conversion. Nowadays, all these things can be done with QGIS or ArcGIS. If it's fast, compact, light-weighted and cheap, MapInfo can be used for some standard GIS applications. One important thing MapInfo is left behind other applications, which is "Internet" thing. Poor WMS, WFS and no WMST service with this app. Direct data format reading is poor as well. Of course, no raster processing is the biggest negative thing. I prefer MapInfo to be compact and cheap, not like now.
  4. For dealing with cloud, please refer to: 1. Description the use of Band-9 of Landsat-8 http://landsat.gsfc.nasa.gov/?page_id=5377 2. Fmask tool https://code.google.com/p/fmask/ 3. USGS Landsat-8 quality assessment http://landsat.usgs.gov/L8QualityAssessmentBand.php 4. Example for Landsat-7 http://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20110007294.pdf Hope it helps!
  5. I guess you have to sub-divide Kansan into sub-level such as district. If one scheme is not applicable for the whole Kansan, then make a few schemes for each district or group of districts that have similar conditions. After that merge the results together.
  6. Don't forget the TIR (Band 61, 62) from Landsat. The infrastructure is of higher temperature than the others. In reverese, water bodies are of low temperature. ENVI 5.x is also able to segment the image with lots of parameters: shape, size, perimeter... have you tried? Lastly, don't try to rely completely on automated solution from image processing softwares. You may think of post-processing algorithms, in which lots of eCognition's functions can be implemented by ArcGIS/ArcView/MapInfo easily. Regards.
  7. You mention about a specific software which has never been used by most of us here. Hopefully somebody who has tried SuperGeo GIS can give feedback to the case. In case you don't find the answer, try ArcGIS for an easy answer. Regards.
  8. Hi Enganga, The common method is to create buffers for all the points (health care centers) to show how they cover the region. Somehow, it's not efficient to advanced health care service management. Two basic information you need includes: - Health care facilities, and - Road network - and (optional) DEM. A simple method (but efficient) you can try is with following steps: 1. Create distance map from road network. - Grid data from vector map of roads - If you have DEM (free from JAXA), you can incorporate slope as obstacle feature with the distance map to make it irregular (more realistic). 2. Create distance map from health care points (based on its importance, you'll have different distance coverage from each point) 3. Overlay (or sum up) Point distance map with Road distance map. 4. Reclassify the result to display health care coverage. From this simple example, you can add up more parameters to make it closed to realistic. The best method is to run accessibility analysis with GIS to: Calculate accessibility index from any residential point to health care service within a certain distance and certain means of transport. Can't tell you much about it here, perhaps you can find it from Internet, they're available. Cheers.
  9. It might not be a very good idea to use ArcGIS but somehow we can use this software for tree counting! My method is quite simple: 1. Get a piece of hi-res image from Google Earth 2. Open the image in ArcGIS 3. Make sure toolbar "Image Classification" is checked (turned on). 4. From "Image Classification" toolbar, select "Classification" --> "Iso Cluster Unsupervised Classification" command. 5. Chose "2" for "Number of Classes" in the dialogbox. By doing it, you will have only trees (or something else similar) and non-tree objects in the result. Run it and see the result (below). C'mon, it also shows "Google Earth" trademark on the result (LOL). 6. Now go to ArcGIS ToolBox, select "Conversion" --> "From Raster" --> "Raster to Polygon". In the dialog box, check to "Simplify Polygion (optional)" checkbox. 7. The result is as below 8. Mask the area where you want to count the trees, then count the number of polygons within that mask. Bingo!!!! If you want to do it better, you can do some pre-processing steps for your image. Also, when you have the polygon layer, you can try to simplify the layer again (using Eliminate, Integrate... functions) before counting trees. Enjoy ESRI.
  10. Oil palm tree counting is a quite simple application. You can simply use following methods: 1. Ravine extraction (Honda Kyioshi): Based on the fact that the top canopy is the brightest point. The ravine (or the valley between 2 trees) looks darker. Doing it, you wil be able to locate the central point of each tree. Then counting is some simple works to do aftewards. 2. Local threshold texture matching: Requires some programming tasks. Get the individual trees as samplings (with different sizes of canopy) and then match them over the whole image. The matching algorithm can be image correlation or histograming matching. I already did it with VB programming and it works perfectly. By doing this, you can also be able to map individual trees. All of this is based on RGB image. For better results, mask the plantation areas before running the procedure. Of course there are several other options to do. If you want to operate the procedure in an automatic way, the programming skill is needed.
  11. Good project. Keep challenging!
  12. Why not? It depends on the algorithm. As far as your algorithm is good enough which can simulate human's brain (like what you're thinking and doing with visual interpretation), you can use Google Earth images for classification. An important thing of this data source is it's been changed in color (spectral) but kept its texture. You know already damage site is mostly related to texture. You have to use two dates of data (even from Google Earth) to compare. The damage sites look "different" from the previous date. There are two possibilities: - The "difference" is a newly built-up area - The "difference" is the damage site After detecting the difference, you can check the shape of them. If the shape is round/square/rectangle... lets say it's a newly built-up. Otherwise, it's a damage. In this case, you have to think of Feature Extraction function as Lurker already referred to.
  13. Global Mapper does not provide Google Earth image, unfortunately. It's working well with Landsat, OSM, BingMaps...
  14. Frankly speaking, this new interface is just "changing the cloth" only. Somehow it creates more troubles to ones who try to produce/overlay new map layers on it. For surfing people, maybe it's fun enough.
  15. This is food and medicine for MapObjects 2.2. https://dl.dropboxusercontent.com/u/22507978/MapObjects%202.2/MapObjects%202.2.rar Usually, Mo20.ocx is install in Program Files\Common\ESRI.... locate and point on it for C.R.A.C.K. Welcome to the world of MapObjects's programmers.