1018 
can be derived from the training samples. The act 
ual accuracies obtained after an analysis session 
can be found using the test areas. 
For a TM scene of 8000 by 8000 pixels and 7 
channels, the processing times on LDIASl and LDIAS2 
are too long. Therefore, we use the FMPS to reduce 
the analysis time . Table 2 is a summary of the 
FMPS performance for selected functions. These 
functions are: 1) finding the histogram of all chan 
nels of a full TM scene; 2) applying lookup tables; 
3) computing ratios (normalization corresponds to 
each channel being divided by the sum of all chan 
nels); 4) parallelepiped classification, up to 256 
classes; 5) maximum likelihood classification, up to 
256 classes. These processing times are suffic 
iently fast that the LDIAS project can achieve its 
objective. 
TABLE 2 
FMPS PROCESSING PERFORMANCE 
TM Image: 8000 lines by 8000 pixels 
and 7 channels. 
Function 
Time 
Histogram 
Lookup Tables 
Ratios (seven) 
Parallelepiped classification 
(256 classes) 
Maximum Likelihood 
Classification (32 classes) 
10 minutes 
20 
30 
35 
150 minutes 
3 A FORESTRY INTEGRATION EXPERIMENT 
3.1 Methodology 
The Canada Centre for Remote Sensing, with its CCRS 
Image Analysis System (Goodenough 1979) developed in 
the 70's, has demonstrated how remotely sensed data 
can aid in the management of Canada's resources by 
identifying geological structures, range-land 
inventory, crop production estimation, forest cover 
mapping, and environmental monitoring. CCRS, with 
the support of the British Columbia Ministry of 
Forests and Lands (BCMOFL), has conducted a series 
of forest clear-cut recognition experiments to 
demonstrate the feasibility of updating forest 
inventory maps with imagery data. Mr. Frank Hegyi, 
Director of the Planning and Inventory Branch, 
BCMOLF, (Hegyi and Quinet 1983) supports these 
experiments by providing the necessary digital 
inventory maps, associated map labels, documenta 
tion, and interpreted aerial photography. CCRS uses 
this information to extract forest cover change 
information from satellite, multispectral images, 
and then updates the geographic information. The 
updated information is returned to BCMOFL for eval 
uation. The experiments have been so successful 
that BCMOFL now uses LDIAS software and the proce 
dures outlined below on its own computers. 
The overview of the remote sensing analysis and 
GIS integration approach is shown in Figure 3. The 
GIS is assumed to be remote; that is, the GIS of 
BCMOFL. The three main processing steps are: 1) 
extract and process the GIS data; 2) analyze the 
combination of the remote sensing imagery and geo 
graphic information; 3) generate the updated 
geographic information. The dashed line in Figure 3 
shows the intended application area of our artifi 
cial intelligence research. The elements of Figure 
3 are expanded upon in Figures 4, 5, and 6. 
The forest inventory digital maps used are at a 
scale of 1:20,000 covering areas of 12.6 km by 12.6 
km. The maps are in the UTM projection and are based 
upon the topographic maps produced by British 
Columbia's Survey and Mapping Branch. The attribute 
information of these maps are stored separately in a 
database at BCMOFL and are sent to CCRS as label 
files. An overview of the geographic information 
extraction and processing is shown in Figure 4. 
Once these digital maps and label files are loaded 
onto LDIAS2, the attribute information is extracted 
and inserted into an attribute information database 
(DMRS file) which is attached to the maps graphi 
cally. The maps are displayed and the forest 
classes of interest are identified. The maps 
usually contain more than 256 classes. Therefore, 
the forest classes must be grouped based on less 
important attributes. This has not been a limita 
tion yet, but it will be. For that reason, LDIAS is 
being changed to handle a much higher number of 
classes. The selected forest and non-forest classes 
are then converted to raster or grid representation. 
The gridded geographic information is used subse 
quently for training and to mask out forested and 
non-forested areas of the images. 
The forest clear-cut detection is done with 
single-date or multi-temporal images from the MSS or 
the TM sensor, or both. Figure 5 is a summary of 
the analysis methodology for multi-date, multi 
sensor imagery. The images have been processed on 
MOSAICS to eliminate radiometric errors and 
rectified to the Universal Transverse Mercator (UTM) 
projection. Rectification is performed by acquiring 
ground control points from the image and calibrating 
them to a UTM map. The map is produced by the 
Surveys and Mapping Branch, Department of Energy, 
Mines, and Resources. These maps are the standard 
used for image rectification at CCRS. The map scale 
is 1:50,000, and the image is rectified to a pixel 
size of 25 meters. The gridded forest information 
is combined with the remote sensing imagery. 
Training areas can be selected manually based on 
forest polygon classes or more recent ground infor 
mation. The training or truth files are used to 
generate the statistics required by subsequent clas 
sifiers. The combined data set is classified within 
the forested regions (based on the forest - non 
forest mask). The classification is filtered using 
a spatial, contextual filter (Goldberg and 
Goodenough 1976). The classified image is assessed 
for accuracy. 
The GIS is updated following the procedure 
outlined in Figure 6. The classified image is 
converted to another grid format. This grid file is 
changed to a vector representation with smooth 
polygon boundaries and linkage to the DMRS database 
of attribute information. The attributes are placed 
in a label file, the graphics in a digital map file, 
and the two files sent to BCMOFL for evaluation. In 
the next two sections we report on one of our 
experiments and the results obtained as an example 
of GIS and IAS integration. 
3.2 The Experiment 
The particular imagery used for the experiment 
reported here is a LANDSAT-5 TM image of an area 
near Cranbrook, British Columbia in the Kootenay 
Mountains. This cloud-free image was obtained on 
August 15, 1984. The clear-cut areas lie on a river 
valley at an altitude of about 1600m, surrounded by 
mountain ridges up to 3000m. The forested areas are 
covered mainly with spruce and balsam with ages 
ranging from 150 to 200 years old. Non-coniferous 
forest occupies less than 15 percent in any of the 
forest polygons. The sizes of the clear-cut areas 
vary from 12 he 
the boundary p 
18% to 43%. 
The preprocess: 
are: 
A) 
reflectiv 
not used; 
B) 
normalize 
banc 
i 
band i + 
where i, j ar 
Only four ba 
were used as 
correlated to g 
Two methods 
training method 
training areas 
The user judge< 
geneous, perha 
training area 1 
ted training ar 
by the user, 
ing method wei 
were 5 to 40 y< 
less than 5 ye 
greater than 4( 
were performed 
given previousl 
The second 
selection of p 
except new cle 
training methoi 
than the data 
Because of the 
multi-modal cl 
polygons for t 
failed to pi 
acceptable clas 
3.3 Results 
Over 1800 poly 
map, but we ar 
or less. Hen 
database befoi 
grouped into tl 
of the follow 
forest species 
more of the pi 
average age of 
stratifications 
productivity o] 
the site condil 
categories. Tb 
ses, and then 
in Section 3.2. 
Table 3 list 
cies obtained 
preprocessing n 
Table 3. Avera 
Cursor Selecti 
of Training Ar 
Point Selectio 
of GIS Polygon