Geoinformation - Science topic

Sven Schade Many thanks for the reply. I am very grateful for always responding, as are colleagues who have also responded. Your statements are fundamental to elaborate this representation. The suggestion to categorize the policies and forms of geoinformation that are used is a strategy I can follow. Your comments were very important. Thank you very much.

You didn't provided sufficient information, therefore I am constrained to assume that you are intending to work with an independent HS image. So, if you are going to analyse spectral values within a single scene of a single time, DN values would serve the purpose. You should apply atmospheric correction in case you are interested to derive vegetation indices such as NDVI for their onward use as supplementary information aimed for optimising model performance. Similarly geometric correction is considered when image is required to be registered with master image (e.g. in change detection) or needed to be adjusted with available GCPs to maintain precision and accuracy standards.

Spatial Data Infrastructures provide a platform that offers an interaction between spatial data, with networks, standards and policies. This framework can be used to develop institutional arrangements, legal and political tools, social resources to facilitate the integration of spatial data and its maximum potential use. To do this, there must be a channel involving data providers, resellers and users, helping to interact with technical, policy and standards tools to coordinate and use the data correctly. SDI offers minimization of costs and time when integrating data from multiple sources. However, this has not yet been achieved. More research is needed to achieve efficient data integration in the context of the SDI project; the results can assist in the development of technical, political, institutional and management tools. When this is achieved, data integration will facilitate sustainability.

Are we able to establish some interest group to speak about this?

Hi,

I suggest to use a regression function. Use x, y coordinates AND height as depending variables and the rainfall as independent variable. Based on the regression function, you may calculate for each raster cell the interpolated rainfall value.

I've got some good results with this method, as it will really consider the height dependancy of the rainfall.

Have a nice day

Dietrich

Dear Yu Sheng Zhu,

if you are on Windows, I would propose that you use our R package called GCDkit.

As one of the authors I cannot judge whether it is "convenient". But what I can say is that it is a freeware, available to download from www.gcdkit.org. It also leaves the possibility of using plain R, with the wealth of its statistical functions, open.

We have recently published a book dealing with using GCDkit, with or without R, in plotting/interpretation of geochemical data and protogenetic modelling that should give you a head start::

Janoušek, V., Moyen, J. F., Martin, H., Erban, V. & Farrow, C. M. (2016). Geochemical Modelling of Igneous Processes – Principles and Recipes in R Language. Bringing the Power of R to a Geochemical Community. Springer-Verlag, Berlin, Heidelberg, 346 pp. doi:10.1007/978-3-662-46792-3

I trust that this helps,

Vojtech Janousek

Hi Pratiman Patel

NCOs are really good thank you

you are most welcome, please vote up my answer if it help you

Please overlay UTM grid on your area and see which zone covers most of your area. Select that zone for projected coordinate system of your dem.

Look at Thm 3.5.1 page 117 of these notes

The fundamental equations of submanifolds in a Riemannian manifold are the equations of Gauss, Codazzi and Ricci. These equations relate the curvature tensors, second fundamental form, and normal connection of the submanifolds with the curvature tensor of the ambient space. In particular, when the ambient space is a real space form, the fundamental equations give rise the the existence and uniqueness theorem, i.e. the fundamental theorems of submanifolds.

Dear Ronald,

FLDWAV can model single channel or dendritic systems, straight or meandering channels, or divided channels.

You can download it at the following site

 

With my best regards

Prof. Bachir ACHOUR

Hi,

ArcHydro is fully dependent on ArcGIS, so no stand-alone use. And it's not comparable to Spatial Analyst in any way, as the tools in the latter are quite basic and don't help for hydrology-related studies (while they are okay for basic land system mapping).

As wrote Daniel, you can use HEC-HMS, but many models exist, more or less using ArcHydro or Spatial Analyst to generate inputs or processes. A very popular yet complicated model is SWAT (Soil Water Assessment Tool) that has an ArcGIS extension, but also WEPP, and the list is quite long, for extensions and for stand alone applications as well.

It finally depend if you're interested in process-based (hydrological model) modeling or empirical modeling (ArcHydro).

If you are new to the topic of Spatial Optimization then I recommend that you have a look at 2009 R.L. Church and A.T. Murray. Business Site Selection, Location Analysis and GIS (New York: Wiley). Church and Murray are two of the leading experts in the area of spatial optimization and their book is very approachable for starters. You can also look up their papers if you are looking for more advanced spatial optimization models.

Hi Gopal,

If you're not set on doing the entire thing in MATLAB there is a paper we have just published which describes a Python based system for object oriented classification which may be suitable: 

Clewley, D.; Bunting, P.; Shepherd, J.; Gillingham, S.; Flood, N.; Dymond, J.; Lucas, R.; Armston, J.; Moghaddam, M. A Python-Based Open Source System for Geographic Object-Based Image Analysis (GEOBIA) Utilizing Raster Attribute Tables. Remote Sensing 2014, 6, 6111-6135.

For segmentation, I'd recommend using only a subset of bands. You could also use OTB (http://www.orfeo-toolbox.org/otb/) to perform the segmentation step. Once you have attributed objets you could export as a text file and import this into MATLAB for the classification or read in as a HDF5 file.

This blog post has some more information on how to install the software (unfortunately you need to run under Linux or OS X at the moment) and some example code:

Hope this helps.

@Karel Charvat good to see you here.

Indeed SDIs have social dimension because at the end of the day these are meant to deliver benefits to social communities. Local knowledge that will come though social groups would help not only to validate the specific needs of an SDI and its rationale for a particular region and but also to fetch innovations from the region itself.

I'm not sure that we can compare KML and WFS.

KML is a format just like GML, JSON, CityGML, Shapefile, etc, but WFS is a service. For example WFS outputformat can be GML, Shapefile, JSON, CSV and so on, I assume also KML is possible.

We can compare KML vs GML, JSON, CSV, Shapefile and other formats.

Thanks for the information.