Geography - Science topic

An alternative approach would be to use LiDAR (or photogrammetry) to build a DEM and then model your river network over the surface. In many environments water in streams is obscured by vegetation and cannot be detected directly by RS. Moreover in arid environments stream courses can be intermittent, so the effectiveness of the optical / radar approaches will depend on capturing data at the right time. We have compared a stream network derived from a DEM modelling approach to a network derived from conventional manual photogrammetric interpretation over the same area and found remarkable agreement in the results (sorry, unpublished). True, the LiDAR DEM approach is a model of the environment, although so too are networks derived from other means. The best approach will depend on the environment, scale and accuracy you are aiming to achieve together with your budget. Hope this helps.

Hi Sainath, Yes there is. You need SAR platforms for this and perform interferometry of the SAR stereocouples to produce an interferogram. This geodetic method uses two or more synthetic aperture radar (SAR) images to generate maps of surface deformation or digital elevation, using differences in the phase of the waves returning to the satellite or aircraft. The technique can potentially measure centimetre-scale changes in deformation over spans of days to years. It has applications for geophysical monitoring of natural hazards, for example earthquakes, volcanoes and landslides, and in structural engineering, in particular monitoring of subsidence and structural stability. You should preferable do the observations with an airplane acquiring the stereocouple, because you need high spatial resolution as well, at least for local landslides. Cheers, good luck and success, Frank

I have some experience with waste management.  Fascinating idea, mapping wastes.  Would be very useful and interesting.  Seems like it would raise numerous questions we may not be prepared to answer.  Which wastes are going where?  Why?  For how long?  Consequences?  How do and how should distribution and management of different waste streams (industrial, municipal, household, agricultural,...) differ?  In the US most waste management regulation and monitoring is done at the state level.  One would think the available data could be assembled to generate maps.  I have seen some reports for specific kinds of wastes (e.g., coal ash) prepared by organizations and activists with specific interests, but I have not seen a compilation for multiple kinds of wastes.  Also, experience shows there is considerable illegal, illicit, reckless, or clandestine waste disposal that is unknown to the state authorities until discovered by accident, and often, even when discovered, it is not reported to regulators.  Mapping unknown wastes disposal practices and sites could only be extrapolated from the number and types of illicit sites discovered, which I think has been done to a limited extent by some US states.  I think I recall some presentations by state waste regulators on numbers and locations of illicit waste dumps, and they have data for permitted waste disposal facilities.  Perhaps you would want to contact some state waste agencies in US.  Unfortunately, government budget cuts in environmental agencies in US have constrained ability of those agencies to collect, track and provide such information in recent years.  Nevertheless, it would seem a place to start.  

Greetings, although the following paper is not fully related to waste management per se, it proposes a risk-assessment based model to manage sustainable tourism development with a focus on water-based resources and their quality. An empirical study with impact measures was conducted.

Dear Thomas, I agree with Volker that your topic is "the typical phenomena of an oasis when teaching themes in geographical education." My starting point is "Why fertile soil (oasis) is in the middle of desert?". And the second one "Why do people live in a desert?" I think the open topic is biomes and the climate in the Earth. I used climate graphs and schemas of tree vegetation in the parallel zones of Berlin's Technical University. Pupils could find deserts and strange little area (oases) in the middle of the areas. And the next question "What is a source of water?" Yes, an artesian basin/water. I explained my pupils why water of artesian bores goes up, close to the surface of desert. In an oasis everything revolves around the water. Is it reasonable to use irrigation in the place? Pupils should realise that evaporation is very high and danger for soil (minerals leave in the soil, salinization). The people in oases make barters among the places and it is important to cross over the desert. The people use camels which are adapted to the extreme dry climate. They move between oases and the edges of deserts. Another questions: Which food staff did the people produce in the oasis? And next questions How to navigate cross the desert? How to survive in the desert, out of oasis?  I must say I represent the view of Central European individual about Africa ;-)  Best regards, Jaroslav

Here's some R code to achieve what you're after. It reads in an ESRI raster, takes a random sample of n cells weighted by the raster's values, and exports the resulting points and corresponding population densities as an ESRI shapefile. It requires the raster package and its dependencies. You'll also need GDAL installed on your system. I've attached the code as well, in case there are encoding errors with copy/pasting. I've also added detail to the comments here as per @Tyler's request. (It's a shame we can't style these answers for clarity...) First, the code without comments: install.packages('raster') library(raster) r <- raster('path/to/raster') # note forward slashes in path, not backslashes n <- 1000 cells <- sample(x=seq_len(ncell(r)), size=n, prob=values(r)) d <- data.frame(popdens=values(r)[cells]) coords <- xyFromCell(r, cells, spatial=TRUE) # NB: CRS here is WGS84... you can get the appropriate proj4 string from www.spatialreference.org pts <- SpatialPointsDataFrame(coords, d, proj4string=CRS('+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')) writeOGR(pts, '.', 'randpts', 'ESRI Shapefile') Now, with comments to explain what's going on: install.packages('raster') library(raster) ## Create dummy raster (see commented line below for reading in true data) # This creates a 10000 x 100 matrix of random uniform numbers between 0 and 1000. r <- raster(matrix(runif(1000000, 0, 1000), ncol=100)) # r <- raster('path/to/raster') # note forward slashes in path, not backslashes ## Random sample of n raster cells, weighted by pop density (i.e. weighted by the values of the raster). # Here, we take a sample of size n from the numbers 1 through 1,000,000 (seq_len(ncell(r)) produces a vector of the integers from 1 to the number of cells of r), where the probability weights of each of those numbers is equal to the values of r, i.e. our dummy population density data. n <- 1000 cells <- sample(x=seq_len(ncell(r)), size=n, prob=values(r)) # We then create a data.frame with a single column, popdens, which contains just the pop density for the sampled cells. values(r)[cells] subsets the vector of pop densities, values(r), to just those values that correspond to the sampled cells. d <- data.frame(popdens=values(r)[cells]) ## Extract lat & lon of the chosen cells and return as a SpatialPoints object # The xyFromCell function identifies the cell-center coordinates for given cells (in this case, cells, the cell numbers of the sampled cells) of the raster object (in this case r, our raster of pop densities). Setting spatial=TRUE returns the object as a SpatialPoints object, which facilitates writing out to a shapefile. coords <- xyFromCell(r, cells, spatial=TRUE) ## combine coords with data to create SpatialPointsDataFrame object # NB: CRS here is WGS84... you can get appropriate proj4 string from www.spatialreference.org pts <- SpatialPointsDataFrame(coords, d, proj4string=CRS('+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs')) ## write out to ESRI shapefile # requires gdal installed on system writeOGR(pts, '.', 'testpopdens', 'ESRI Shapefile')

Probably a very accurate geometrically corrected data is useful for census.