TWO EXAMPLES OF REMOTE SENSING USE IN LOCATING AND MEASURING EARTH RESOURCES: FOREST COVER CLASSES AND PEATLANDS

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1 Ecosystem Service and Sustainable Watershed Management in North China International Conference, Beijing, P.R. China, August 23-25, 2000 TWO EXAMPLES OF REMOTE SENSING USE IN LOCATING AND MEASURING EARTH RESOURCES: FOREST COVER CLASSES AND PEATLANDS Jean-Jacques FORTIER SMA Inc., Quebec city, Canada Fax: PRELIMINARY NOTE It is in the context of the International Conference in Beijing and the ensuing case study project on watershed management in North-Eastern China (Changbai reserve and Songhua Jiang catchment), that we were asked to present possible uses of remote sensing. Forests, wetlands or peatlands, being in abundance in the area, we thought our examples were pertinent for the project. They have been developed by our company, whose products and history are described on our web site: For those not familiar with French, they should consult the English version of the site under OVERVIEW, which is a technical description of the forestry example. Also we have included an appendix which is a brief description of SMA s services in remote sensing 59

2 Abstract The examples concern the identification, surface measurements and thematic representation of 1) forest cover classes and 2) peatlands, by Bayesian and other methods developed by us in remote sensing, using, in this instance, TM mapper Landsat data. The first example is taken from the Temiscamingue area in North-Western Quebec, Canada. The second experiment (peatland) concerns plains south of the St Lawrence river, upstream from Quebec city. The first work was done for a forest product company, TEMBEC, and the second one, for a group at Laval University, led by Monique Poulin. The forest example deals with 67 different all inclusive covers including clear cuts, fires, water, wetlands, different tree species, at different levels of maturity and density. The peatland example concerns 17 identified kinds of peatlands to be isolated by masking other covers. Our examples are preceded by considerations on the important role of remote sensing in a geographical information system (GIS) and the objectives of such a system. Keywords Bayesian classification, case studies, forest cover classes, GIS, GPS, Landsat, multitemporal images, peatland classes, remote sensing, software 60

3 OBJECTIVES OF A GIS AND THE IMPORTANCE OF REMOTE SENSING TO ATTAIN THEM A GIS makes full sense only if it is used to take appropriate decisions on the basis of the information it provides. Therefore, that information should be timely, dynamic, and global. Though remote sensing does not necessarily provides all the information a GIS is composed of, the one it brings possesses the above qualities at an affordable cost. One information that usually is not provided by remotely-sensed data is a high resolution base map. However, remote sensing may well do so, in the future, for example, using recent radar images taken by the space shuttle. Passive or active remote sensors, aboard satellites, are capable of creating images from a wide variety of different electro-magnetic frequencies typical of different phenomena, both on the surface of the earth, and in the atmosphere. Only a small fraction of these collected data is used, for lack of systematic near real time decision processes based on organized up-datable data banks which are the basis of GIS s. Therefore we see that a GIS system, fed by remote sensing, and other essential georeferenced data, is a dynamic decision tool for the watershed project, that will allow: 1) analysis of past events through multitemporal images 2) prediction of events to come 3) interventions to be made on the basis of 1) and 2) In summary, only remote sensing can provide global, timely, dynamic and economical information, by being able to cover large areas, frequently, at a reasonable cost, in a format readily digestible by a GIS. Finally, we note that an advantage of such a remote sensing-gis process, for the local administrators and researchers in the project area, is that it may remain operational in their hands at the end of the project. 61

4 We emphasize that the present short presentation cannot deal with many technical aspects that are present in the references or elsewhere in other related works. THE FORESTRY EXAMPLE The initial step in a project to analyze remote sensing images, in our approach, is to identify, a sufficient number of points, called control points, in each class, like clear cuts or a categorized tree type, that one wants to locate, measure and thematically represent. These points may be located on the ground and referenced through a ground positioning system (GPS), and then related to pixels in the satellite image, by a geometric transformation on the image. The transformation itself is based on a number of referenced feature points on the base map that are visible in the image, like a cross-road, the tip of a lake, etc. Strip cuts in sugar maple stands (lower left0 Landsat TM 5 image in Temiscamingue region, Quebec However, we found that method to be expensive and rather imprecise, due to location-dependent random discrepancies between the images and the base map. For an economical approach, aerial photographs were used, where an expert interpreter identified several (ideally randomly chosen) points for each class. They were then directly registered on the computer screen satellite image as 62

5 being a located pixel, using corresponding features in the photo and the image, like lakes, terrain shapes, or other easily recognizable land-marks. In our example, we identified, in such a way, 67 classes, and over control points. One such class, for instance, was the mature-dense-sugar maple, as in the lower left corner of the satellite image above as verified by aerial photography. Another class is clear cuts, that are also visible as strips, in the image above, and also identified in an aerial photo. We also measure the proportions of each class and represented them thematically, as in the image below, after standard global classification (on the left of the image) and more appropriate Bayesian local classification (on the right of the image). Each classification, as well as previous area estimation, is based on the multinormal distribution of the spectra of intensities of measured visible or near infrared electro-magnetic frequency bands of the pixels corresponding to the identified sample points in each class. Classification of strip cuts (blue) in sugar maple stands in Temiscamingue region, Quebec Global Local We are not showing here the statistical estimates that are provided with the thematic maps. In fact, they are a lot more precise than the visual representation might suggest. In the visual 63

6 representation, we are forced to make a probabilistic decision on each pixel involving, even in the Bayesian version, a sizable error (see the error or confusion matrix in the peatland example), of the order of 30% or more. By comparison, the estimates of class surfaces or proportions have an error that is usually lower than 5% This is due to the fact that surfaces or proportions are measured in the aggregate (not pixel by pixel) and a sort of law of large numbers enters into play. The present remote sensing application has been installed as a field operational system, in a Temicamingue sawmill compound, and has been used for forest planning, more or less regularly for a number of years. However, no up-dating of the images, or the control points has been done, and the GIS was not completed and/or integrated with remote sensing. Therefore the dynamic and timeliness aspects have been somewhat lost, though the company has given good notes after appraisal of the system (see end of reference 1). THE PEATLAND EXAMPLE This is a somewhat different example, in the sense that the promoters (see reference 2) were not looking for an operational inventory like the forestry one. They stood more at the experimental stage, and were trying to assess the potentialities of remote sensing to locate, and simply classify some 16 classes of peatland. However, more than the promoters asked for was accomplished at the level of an algorithm (described below) and at the level of estimation of areas and Bayesian classification. As in the case of forestry, a sample of control points for each class (67 points on average per class) was identified and located in aerial photos or on the ground using GPS, and registered on the Landsat image. Since the researchers were excluding everything but peatlands from the analysis, the first task consisted in masking, in the Landsat 7 image below, all that was not one the classes of peatlands. This was accomplished by masking a pixel if its average probability of belonging to 64

7 the non-peatland classes was lower than the average probability of belonging to one or another peatland class multiplird by a certain constant (the mask is the grey areas in the image below, where luminosity has been preserved). The second task was to delineate connected sets of pixels (like the colored blobs below). This was accomplished by accreting neighbouring pixels not in the mask, by an appropriate algorithm. Also, the algorithm would connect masked pixels to a peatland set if they were, in some way, isolated in the middle of it. Conversely, the algorithm eliminated sets that consisted of only one, or too few peatland pixels. Bayesian classification of peatlands Landsat image, middle St. Lawrence plain Quebec Then, as is shown above, in different colors, within the delineated peatlands, the 16 classes areas were measured through another algorithm (an analog of the EM algorithm) and the measures were applied to produce a Bayesian classification. This shows clearly the diversity of classes in the peatland regions. Also, a simple maximum likelihood classification was performed, using the sample points multinormal class distributions. It is not shown here, being similar to the one above, but less efficient. 65

8 Nevertheless, a so called confusion matrix (below) was computed for that classification. At the top of it, one finds a list of the classes, named: A, B, C etc. For each of them the bottom row shows the number of identified control sample points. Each column shows how the corresponding pixels were classified by the likelihood classifier in regards of the same classes listed in the left-most column. For instance, of the 17 known pixels in class A, 14 were correctly classified in class A, 2 were wrongly classified in class B, and 1 was wrongly classified in class D. Error matrix of the maximum likelihood classification of 16 classes of peatlands in St. Lawrence middle plain,quebec (71% total accuracy) A B C D E F G H I J K L M N O P A B C D E F G H I J K L M N O P Thus, the diagonal of the matrix shows the correctly classified pixels, their sum being 769 out of a total number of 1081 trials. That is a total accuracy of 71%. This accuracy may suffice for a good thematic mapping, since errors of commission (pixels wrongly attributed to a class in each row) are often balanced by errors of omission (pixels not attributed to the class they belong to in each column). But, again, it is largely superior to the errors of estimation of the proportions, or areas of classes, as we explained in the forest example. 66

9 CONCLUSION In each of the examples above in forestry and in peatland inventory, the users were satisfied with the results, from a practical point of view in forestry, and a theoretical point of view in peatland research. It is therefore quite probable that similar satisfying results can be obtained for the Songhua Jiang catchment of North-Eastern China for forestry, wetlands, farmlands, etc. using the same methods or analog ones. If, moreover, remote sensing data were to be included in a GIS in a dynamic, global and timely fashion, it is possible to envisage a useful decision process for beneficial environmental interventions. An added advantage of such a system is that it can be left as a permanent tool, to be operated locally, when the case study project is completed. REFERENCES Using a geographic information system and remote sensing for forest management J.J. Fortier, SMA, June 1996 (Federal program: Testing, Experimenting and Technological Transfer in Forestry : Natural Resources Canada, Canadian Forest Service) La conservation des tourbières selon deux échelles spatiales: la sélection d un réseau de tourbières protégées et la représentativité des fragments résiduels au sein des sites exploités Monique Poulin,: Proposition de recherche de doctorat, Université Laval, Avril

10 APPENDIX SMA s REMOTE SENSING SERVICES SMA has been providing services in remote sensing for more than ten years to a number of clients: Forestry industries (Tembec, Roche, Daishowa, Intervact) Universities (Laval) Research centers (National Institute of Optics, Canadian Forest Service, Agriculture Canada) SMA, Société de Mathématiques Appliquées 59, d'auteuil Québec (Québec) G1R 4C2 Tél: (418) Fax: (418) jfortier@sma.qc.ca dcareau@sma.qc.ca To provide these services SMA has used its own human and software resources in conjunction with those of its clients in an atmosphere of collaboration. Its software comprises particularly OVERVIEW in forestry (e.g. classes of trees) and general terrestrial resources identification (e.g. classes of peatland) SURFACES in agriculture (e.g. weed measurement in corn fields) The software provides all the necessary modules to Process and enhance aerial photos, digital photography, satellite images Measure surfaces of cover classes in an optimal way using Bayesian and nonparametric methods with appropriate reports Classify pixels in an unbiased and optimal fashion using prior knowledge of proportions of classes in a given region Measure proportions of classes in an individual or a collection of mixed pixels Delineate polygons of certain classes when others are masked Create thematic maps Interface with popular office software Our software comes with our personnel free of charge. Hourly fees are the lowest on the market, and our personnel has university education up to a Ph.D. degree in mathematical statistics. If so desired, at the end of a project, which can be of any size, the software can be installed on the client s premises permanently for a fee commensurate with the importance of the project, which may be nominal for a sizeable project. 68