Index
Visualizing Data	A-Z	Single Raster Analysis Tools Basic descriptive statistics with HISTO

1^st Example

About the data:
the vegetation has been digitized from a analog copy. The original field data were superimposed on a 1:25000 map. The whole digital vegetation data exist in form of a Arc/Info® coverage. The import happened through Arc/Infos UNGENERATE format and IDRISIs ARCIDRIS module. As the latter produces vectors, a rasterizing process followed (resolution has been adjusted to 25 m). The river data originated from 1:50000 maps and were treated the same way as the vegetation information except the rasterizing.
Both data are georeferenced to the same coordinates.

1. First we need to rasterize the river layer

   in IDRISI this requires a two-step procedure: (1) INITIAL to create a new empty image for the data to be filled in during rasterize with (2) LINERAS. If desirable IDRISI allows for copying spatial parameters (rows, cols, min x, min y, ...) from an existing image.

2. Now have IDRISI calculate buffer zones

   the DISTANCE operator looks for non-zero cellvalues, takes them as a target and calculates the distances to these cells. The resulting image now holds euclidian distance values given in reference units. Use SPDIST where spherical distortions should be avoided. Keep in mind, that DISTANCE calculates for a 'flat' surface - results do not take into account slopes hence do not correspond to 'real' distances, especially in steep mountainous areas!
   The darker the color the closer we get to a water streamline. For a better orientation the vector layer rivers has been overlaid.

   

3. Regarding our problem, which areas will be of interest?

   Consider the different data resolutions: vegetation on the basis of 1:25000, but rivers digitized from 1:50000 maps. The inspected region is part of the Alps, the rivers should better be delineated as brooks sometimes forming gorges with their own microclimate. For the sake of demonstration we will inspect the image for 100 resp. 300 m distance buffer zones around rivers.

   By applying RECLASS to the output image of DISTANCE (rivdist) we extract a 100 m buffer zone around the rivers: distance values between 0 and 100 are set to 1, all others to 0. The output image (here named riv100) contains only 1 and 0, 'yes' and 'no', therefor call it a boolean image

   

   We go on with OVERLAY, a very useful and often employed module when it comes to combine two images through addition, subtraction, multiplication, rationing, etc. The module takes the values of the two input images and proceeds with them according to the Overlay options writing the results to the output image cells:

   

   In that we multiply the vegetation with the boolean distance image, only those values (= vegetation classes) survive, that are multiplied with 1 - well, exactly these reside within our desired 100 m buffer zone. Black regions in the illustration below indicate areas outside the 100 m buffer.

   

4. Calculating the area

   We need to stress the module AREA twice - (1) to know the overall area-size of each vegetation class (taurvege as input image for AREA) and (2) to get the areas in the buffer zone (veg100 as input). AREA offers three ways: create an image, where the cells of each vegetation class receive the summed area of that class, produce a values file or present the area for the classes as a table on the screen. In our case it seems appropriate to write out a values file. Further we must choose area measure units. I took kmē (number of cells, mē, acres, hectares, ... are among the choices).
   Repeat the steps starting with RECLASS now with a buffer distance of 300 m.

5. Comparing with DATABASE WORKSHOP

   The DATABASE WORKSHOP should be subject of an own exercise, so only the principal line is sketched here.
   We ground the last step on three values files - that one for the areas of vegetation classes over the whole image, and the others showing the area sizes of the classes within the 100 resp. 300 m zone. Together with a values file of the class names a single database can be built up. The illustration below shows you, how additional database fields can be calculated from existing ones by standard SQL-statements (here the percentage of area within buffer zones related to overall area).

   

   Very high percentages mean, that most of the area of that vegetation class resides within a 100 resp. 300 m distance buffer zone. Notice that water does not occupy 100 percent! This fact may well result from the data heterogeneity. Obviously the vegetation map contains a small lake, etc. that is not part of the rivers dataset.

   Interpretation and ingenuity of the results in detail may be ceded to experts, but without knowing anything about the vegetation classes, we could make careful assumptions about which associations could be influenced by proximity to waters.

   ID	Vegetation Class Name	Overall area	Area within 100 m zone [kmē]	Area within 300 m zone [kmē]	Percentage 100 m zone	Percentage 300 m zone
   1	Androsacion alpinae	56.98687	3.693125	13.6675	6.480659	23.98359
   2	Salicion herbaceae	6.873125	1.083125	3.165	15.75884	46.04892
   3	Caricion curvulae	15.14437	1.741875	5.870625	11.5018	38.76439
   4	Seslerio-Semperviretum	0.44625	0	0	0	0
   5	Aveno-Nardetum	14.87438	1.904375	6.308125	12.80306	42.40934
   6	Agristio-Trifolio-Deschampsietum cespitosum	5.370625	1.90875	3.75	35.54056	69.82427
   7	Agrostio-Trifolio-Deschampsion	2.3525	0.853125	1.9525	36.26461	82.99681
   8	Polygono-Trisetion	0.51	0.324375	0.51	63.60294	100
   9	Dactylo-Poion	0.023125	0.005625	0.023125	24.32433	100
   10	Brachypodio-Koelerietum	0.021875	0	0.0075	0	34.28571
   11	Loiseleurietum	3.27	0.49625	1.56875	15.17584	47.97401
   12	Rhododendretum ferruginei	5.620625	0.94	2.718125	16.72412	48.35984
   13	Erico-Rhododendretum hirsuti	0.00375	0	0	0	0
   14	Junipero-Callunetum	0.721875	0.055	0.25	7.619048	34.63203
   15	Pinetum mugli	1.406875	0.114375	0.39125	8.129721	27.80986
   16	Alnetum viridis	5.616875	1.17625	3.28125	20.94136	58.41771
   17	Cembretum	0.449375	0.026875	0.214375	5.980528	47.70515
   18	Larici-Cembretum	1.659375	0.1075	0.585625	6.478343	35.2919
   19	Vaccinio-Rhododendro-Laricetum	0.198125	0.020625	0.0625	10.4101	31.54574
   20	Larici-Piceetum	4.884375	1.44125	3.6775	29.50736	75.29111
   21	Piceetum subalpinum	0.00625	0	0.00625	0	100
   22	Luzulo-Piceetum	2.3575	0.829375	1.846875	35.18027	78.3404
   23	Alnetum incanae	0.160625	0.1	0.160625	62.25681	100
   24	Salicetum eleagni	0.053125	0.025	0.053125	47.05882	100
   25	Adenostyletalia	1.048125	0.1375	0.603125	13.11866	57.54323
   26	Dryopteridetum	0.323125	0.071875	0.254375	22.24371	78.7234
   27	Caricion fuscae	0.475625	0.28125	0.444375	59.13272	93.4297
   28	very moist soils and fountain areas	0.301875	0.194375	0.301875	64.38924	100
   29	Glaciers	11.6075	0.136875	0.723125	1.179194	6.229808
   30	Waters	0.8275	0.6275	0.78625	75.83082	95.01511
   31	Other regions (scree flora or cultivated areas)	0.404375	0.054375	0.08125	13.44668	20.09274

	Index
Visualizing Data	A-Z	Single Raster Analysis Tools Basic descriptive statistics with HISTO