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Rapid remote recognition of habitat changes
Preventive FeterinaryMedicine, 11 ( 1991 ) 315-323
315
Elsevier Science Publishers B.V., Amsterdam
Rapid remote recognition of habitat changes Arthur P. Cracknell Department of Applied Physics and Electronic and Manufacturing Engineering, University of Dundee, Dundee DD1 4HN, UK
ABSTRACT Cracknell, A.P., 1991. Rapid remote recognition of habitat changes. Prey. Vet. Med., 11: 315-323. An attempt is made to give a realistic appraisal of the possibilities and shortcomings of remote sensing techniques in the context of detecting habitat changes. Consideration has to he given to the various spatial scales at which remote sensing data are exploited, and to the relationship between the size of the object of an investigation and the intrinsic limitations on the ground resolution (instantaneous field of view, IFOV) of the detecting system. Features larger than the IFOV are commonly studied and, in certain circumstances, features that are smaller than the IFOV can also be detected. However, the question of obtaining information about objects or features that are very small compared with the IFOV is much more difficult. Examples include sheep distribution, locust prediction and the development of toxic algal blooms at sea. The monitoring of blooms, once they have begun to appear, is now feasible. However, it is much more difficult to study the conditions leading to the development of blooms and to give advance warning of their growth. The study of this type of problem is still in its infancy. The other question that has to be addressed concerns the timeliness of the extraction of information from remotely sensed data. With the exception of the meteorological community, the philosophy for the last decade or two has been simply to capture remotely sensed data from satellites, to archive it on magnetic tape and to hope that someone, somewhere, someday, will retrieve the data from the archive, analyse it and possibly make use of it. In many applications of remotely sensed data, such as oil pollution monitoring, agricultural yield prediction, and epidemic and plague prevention, the remotely sensed data must be analysed, interpreted and the results distributed to the end users in something approaching real time. It is pointless, other than as an academic exercise, to predict a plague or epidemic 5 years after it has happened!
INTRODUCTION
Two points are addressed in this paper. The first is to survey what remote sensing has achieved, not in its totality but in those aspects which may be of some relevance to epidemiology and parasitology. The second is to address the question of the timeliness of the production and distribution of results from remote sensing data analysis. This is an aspect in which remote sensing has often been deficient in the past.
© 1991 Elsevier Science Publishers B.V. All rights reserved 0167-5877/91/$03.50
316
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REMOTE SENSING OF LARGE OBJECTS
The term remote sensing is used to mean gathering data about the surface of the Earth from an aircraft or a spacecraft. Aerial photographs have long been used in mapping, so much so that the majority of the information on the topographic maps that are produced these days has come from aerial photograph interpretation and photogrammetric analysis, and relatively little has come from field surveys. Similarly, one can regard the use of space photographs or scanner images obtained from space as an extension of this work. Mapping of large areas of inaccessible or hostile territory can, in many cases, be achieved more conveniently with space imagery than with aerial photographs. This, therefore, forms one of the very important uses of remotely sensed data. The limit to the spatial resolution of the imagery is determined by the grain size of a photograph, by the instantaneous field of view (IFOV) of a scanner, or by the theoretical diffraction limit of the aperture of the instrument. For mapping purposes at any given scale, one necessarily chooses a source of imagery in which the features which are to be m a p p e d are significantly larger than the limit of resolution of the data. In the context of a small country that has been well m a p p e d for a long time, such as most western European countries, the resolution of satellite data is such that data from space are of little use in m a p revision. However, in many parts of the world where there are large areas that are very poorly mapped, satellite imagery forms a convenient and highly suitable source of data for m a p making. Examples are large parts of the Sahara, China, South America and Antarctica. REMOTE SENSING OF SMALL OBJECTS
People who are familiar with satellite data will be aware that the size of the IFOV of the c o m m o n LANDSAT multispectral scanner (MSS) data is about 80 m; the LANDSAT thematic m a p p e r ( T M ) has an IFOV of about 30 m, while the images from the SPOT satellite system have a resolution corresponding to an IFOV of 20 m (or 10 m in the panchromatic m o d e ) . When we speak of a scanner having an IFOV of 80 m, we mean that the instrument gathers all the radiation coming from an area of about 80 m X 80 m on the ground and focuses it on to a detector which gives a response characterising the total a m o u n t of radiation received from that area. No detailed structure will be discernible within that area. For instance, one would not expect to be able to distinguish individual houses, trees or automobiles in LANDSAT MSS imagery because, in terms of area, each covers only a tiny fraction of the IFOV. This illustrates what is meant by 'small', i.e. an object which covers a small, but not entirely negligible, fraction of the IFOV. In some cases, there are ways in which information can be obtained from satellite data about objects which are small compared with the size of the IFOV. Three examples of cases in
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which such information can be obtained are concerned with: (i) sheep; (ii) gas flares, agricultural strawburning and forest fires; (iii) waves on the surface of the sea. Some work on sheep population was carried out by T h o m s o n and Milner ( 1989 ) with data relating to the uplands of North Wales. This is a part of the U K where there is a range of topography, where the altitude varies from 100 to 1000 m above sea level, and where a high annual rainfall ( 1750-4500 m m ) ensures that the area is well covered with a variety of semi-natural grassland and heathland vegetation. Sheep are the only large herbivores that are present in large numbers in this area and so sheep grazing is the d o m i n a n t biotic factor that controls these ecosystems. Different vegetation communities support different population densities of sheep and the soil, rainfall and altitude affect sheep distribution. These factors affect the satellite-received radiance and, therefore, also the vegetation index. Thus, one could expect to be able to use satellite-derived vegetation indices to study the population distribution of sheep. In practice, better correlation was obtained for sheep population density with near-infrared (0.76-0.90 a m ) radiance than with the normalised difference vegetation index ( N D V I ) . It was suggested that perhaps in undulating terrain near-infrared radiance is less influenced by non-vegetational factors, such as soils and topography, than radiance in other wavebands. Perhaps, we should attempt to distinguish between cause and effect. In this case, the differences in vegetation cause both the differences in the sheep population density and in the satellite-received near-infrared radiance. We observe the correlation between two quantities which share a c o m m o n cause. The second example is a little different and is mostly relevant to data from Band 3 of the advanced very high resolution radiometer ( A V H R R ) which is flown on the National Oceanic and Atmospheric Administration (NOAA) series of meteorological satellites. A gas flare produced by an oil or gas well, or by an oil refinery, for example, may be a few metres or tens of metres in dimension and is thus very small in relation to the size of the IFOV of the A V H R R which is about l k m × 1 km. However, such flares show up extremely clearly in the Band 3 data from the AVHRR. The peak of the Planck distribution function of a typical flame occurs at a wavelength of about 3.5 or 4.0 a m , which is exactly the wavelength of Band 3 of the AVHRR. Such a small source as a glas flare produces such a large a m o u n t of heat energy as to increase very significantly the total radiation from the IFOV. Indeed, the amount of energy is often so large that it actually saturates the sensor for that pixel. For example, if one looks at a Band 3 A V H R R image of the North Sea, one notices a n u m b e r of isolated black dots, either single pixels or else a cluster of a very small n u m b e r of pixels (less than ten). It should be realised that even though the flare is very small compared with the IFOV, it can affect the signal not only of the IFOV in which it is located, but also a few nearby IFOV as well. By careful geometrical rectification of such a scene and comparison with
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a map of the concessions and drilling operations in the North Sea, one can identify the particular drilling platforms which are responsible for each of the flares (Muirhead and Cracknell, 1984). In this instance, the small object is rendered directly apparent because it contrasts so extremely sharply with the background. Similar arguments apply to fires of various sorts, such as those of agricultural strawburning or forest fires. There is a fairly extensive literature on this subject. Again, even if the area of the conflagration is smaller than the IFOV of the AVHRR, the fire is still likely to be apparent in the A V H R R Band 3 data. The third example concerns the study of waves and the determination of the wave vector, i.e. the wavelength and the direction of propagation, of the waves on the surface of the sea. This may be done with synthetic aperture radar (SAR), such as those carried on the u n m a n n e d SEASAT satellite or on the m a n n e d Shuttle missions. The ground resolution of the radar, if it is used in the context of mapping objects on the ground (land), depends on the manner in which the data are processed to produce an image. The SEASAT SAR was designed to achieve a spatial resolution of 25 m; however, a wavelength that is smaller than 25 m can be detected. In this case, the success in measuring something smaller than the spatial resolution of the system arises from the fact that the wave is extended and, therefore, the wavelength is repeated over an area that is large with respect to the ground resolution. In this case, the wavelength is most easily detected and measured by taking a Fourier transform of the image generated by the SAR. (Sea waves are also visible in LANDSAT TM imagery. ) VERY SMALL OBJECTS - LOCUSTS AND ALGAE
In the previous section, we considered three examples of situations in which the simple concept of the ground resolution of remotely sensed data does not necessarily correspond to the lowest limit of the size of an object about which information may be obtained with the data. In this section, we turn to even smaller objects and have chosen to discuss locusts and algae. Although locusts and algae are much smaller than sheep, they do tend to occur in large aggregations and can give rise to directly observable dramatic effects in remotely sensed data. In that sense, they may be relevant to the problems addressed in this conference. However, detecting the ravages of locusts or detecting a large bloom of toxic algae, once it has occurred, is only of limited use. By that stage, it is often too late to take preventive action and a great deal of damage has already been done. What would be m u c h more valuable would be to use remotely sensed data to study the conditions that lead to the explosive expansion of the populations in certain locations at certain times and to be able to give advance warning of impending plagues or blooms. This is much more difficult. A great deal of work has been carried out on locusts in this context (see e.g. Tucker et al., 1985; Hielkema et al., 1986; Bryceson, 1989). The detection of
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a swarm of locusts as a black cloud on a satellite image, or the detection of the massive devastation caused to an agricultural area by a swarm of locusts, is possible because the effect in the image is large enough to detect; but that is not very useful. What one has to do is to try to predict potential swarming of the locusts and take appropriate preventive action before damage to crops occurs. When the locusts are present in low numbers in their natural (desert) habitat or recession area they present no problem. When rainfall occurs, the locusts multiply and, for a while, live on the ephemeral vegetation that appears after the rainfall. When the locust population has grown large and the local vegetation is exhausted, the locusts swarm and migrate, often over very large distances, in search of further supplies of vegetation. Prevention strategy is thus based on locating areas where rain has fallen (see Levizzani et al., 1990), monitoring the locust population build-up and then controlling the population by spraying where necessary. Given that the recession areas are large and generally not well supplied with meteorological stations on the ground, satellite remote sensing provides an important source of data for monitoring rainfall in these areas. There are techniques based on the study of clouds in satellite images to locate areas of precipitation. Following precipitation, one can attempt to detect its occurrence indirectly from studies of soil moisture or of the green vegetation biomass. The study of soil moisture from satellites, although possible in principle using microwave or thermal infrared data, is not presently possible on an operational basis. However, the use of visible-band and near-infrared-band data for the determination of vegetation indices in now a very widespread and common practice in remote sensing. Nevertheless, it is one thing to say that satellite data can be used to determine vegetation indices, but it is quite another thing actually to be able to do so in an operational programme. Several relevant questions arise concerning the spatial resolution, the frequency and area of coverage, and the cost and delivery time of the data. While on the subject of pests, it is worth mentioning some work related to river blindness (Onchocerciasis) which is a parasitic worm affliction spread by the bite of the female black fly (Simulium damnosum). However, in this case the remotely sensed data have not been used in the attempts to control or eradicate the disease, but in some related aspects of the programme. River blindness, which afflicted about 10% of the population of the Volta River system basin, was the target of a special project in that area by the World Health Organisation (WHO), the Food and Agriculture Organisation ( FAO ) and the United Nations Development Programme ( U N D P ) . The countries concerned in this area were Benin, Burkino Faso (Upper Volta) and Ghana. The problem was attacked in three stages which involved: (i) spraying; (ii) study of land-cover and land-resources development potential; (iii) redevelopment. The first stage, the Onchocerciasis Vector Control Program, involved antilarval spraying and was apparently successful. LANDSAT data
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were then used in the second stage, the Regional Onchocerciasis-free Area Planning (ROAP) project, over a 2 year period from early 1978. This was designed as a flexible planning mechanism to enable assistance agencies to respond to requests from African governments for help in planning and developing areas recently freed from Onchocerciasis. In this context, LANDSAT data were used in a fairly conventional way to study land cover and land resources development potential. For further details, see the paper by Heinzenknecht (1980). Subsequently, an Onchocerciasis Control Program (OCP) has been set up by WHO ( 1985 ) involving nine West African countries. This has involved the use of high-resolution remote sensing satellite data in the initial stages for mapping purposes. It now uses about 200 stations measuring river flow rate and transmitting the data via the ARGOS data collection system; this enables decisions to be made on the applications of doses of larvicide to the river breeding grounds. The programme has been highly successful in controlling Onchocerciasis in the area. Let us turn now to the subject of algae. We are very much nearer the start of the road in terms of studying the conditions of blooms of toxic algae and the prediction of their development; indeed, it is fair to say that research on this aspect is only just beginning. A considerable milestone in the use of satellite remote sensing data for the study of algal blooms was the flight of Nimbus-7 carrying the Coastal Zone Colour Scanner (CZCS) which supplied data from 1978 until 1986. Some observations of algal blooms had been made previously with LANDSAT MSS data (see Ulbricht, 1981 ), but the CZCS was designed specifically for studying ocean colour and was more suitable than the LANDSAT MSS for this. However, oceanographers were, generally speaking, slow to appreciate the potential significance of the CZCS data until it was too late. Thus, there were very little field data gathered contemporaneously with the CZCS data and, consequently, the validation of much of the interpretation of the archived CZCS data is, sadly, not now possible. Thus, for the present, until a successor to CZCS is launched, we have to make do with existing sensors, which for practical purposes means LANDSAT MSS and TM, SPOT HRV and NOAA AVHRR. Thus, as an example, a more recent study which used one of these ongoing systems and for which field data were gathered has been chosen. There was a very large unexpected toxic bloom of Chrysochromulina polylepis which appeared in Skagerrak in May 1988. This caused very severe damage to the Norwegian fish-farming industry. Johannessen et al. (1989) described the use of combined remote sensing and model studies of the mesoscale circulation of the Norwegian coastal current in support of the daily forecasts of the advance of the algal front. The importance of these forecasts was that they enabled many fish farmers to tow their cages away from the advancing bloom to safety in low-salinity water deep in the various fiords along the coast. Otherwise, the damage and financial loss caused by the bloom would have
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been very much greater than was actually suffered. The remotely sensed data used came from the NOAA AVHRR and an essential element was its speedy availability and the capability to process it rapidly. For further details and colour-coded AVHRR images of the bloom, see the paper Johannessen et al. (1989). Two lessons emerge from this case study. First, the use of remotely sensed data was only possible because the data were obtained and processed extremely quickly. Secondly, while it is useful to track a bloom using remotely sensed data, the much more challenging task is to be able to predict the onset of a bloom before it occurs and before any damage is done. This is a problem that is still waiting to be solved and its solution is not going to be easy. MONITORING THE HABITATSOF MIGRATORY BIRDS Some very recent work, that is not yet published, has been carried out by Sader et al. ( 1991 ) who have used unsupervised classification of LANDSAT TM data to identify habitats important for migratory birds in Costa Rica and to study habitat conversion over a recent 10 year period, principally associated with forestry clearing followed by the establishment of permanent pasture. Although there has been a considerable amount of work published on using satellite remote sensing in the study of forest clearing, as far as Sader et al. were able to ascertain, no previous studies of the use of satellite remote sensing to monitor changes in migratory bird habitat have been published. THE POTENTIAL OF REMOTE SENSING So far, some relevant studies with remotely sensed data have been described. What of the future? Remote sensing applications are of three types: (i) those which are operational; (ii) those which, in principle, are well understood and could be made operational with sufficient investment in terms of space-borne systems and ground-based distribution, analysis and interpretation systems; (iii) speculative applications. Many things in some remote sensing applications 'shopping lists' are, quite frankly, speculative and severe damage has sometimes been done to the image of remote sensing by people who have pretended otherwise. In the context of this meeting, we must recognise that the prospect of the direct recognition of habitat changes is rather unlikely. If it is possible, it will probably be too late since the signs will almost certainly have already been observed on the ground. It may still be possible to allow changes, as was done for instance in the tracking of the algal bloom off the Norwegian coast in 1988 mentioned above. One key element will be fast access to the remotely sensed data and the capability to analyse it rapidly. We must get away from the traditional way of dealing with much remotely sensed data. For many years now, we have captured data from the sky, put it into an archive and hoped that someone, somewhere, will later come along and make use of it. In practice,
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however, much of the archived data is likely never to be used at all. For many purposes, we must get to the stage where the data are processed, distributed and analysed to produce information in directly usable form in near real-time. This is important since new satellite systems will generate even larger quantities of data than existing systems. The word 'rapid' in the title of this paper is very important; recent developments in the power and speed of computing machinery, and in communications systems, have made possible many things that were not possible a few years ago. Remotely sensed data must be considered as a candidate source of epidemiological information that is not simply a direct observation of habitat changes, but which gives information about parameters that affect the evolution of habitats. In many cases, we are not going to be able to make direct observation of habitat changes. It is going to be important to have a model which depends on a n u m b e r of parameters. Remote sensing may be able to provide information on the values of some of those parameters. ACKNOWLEDGEMENTS
I am grateful to Trevor E. Beaumont for drawing my attention to the work on river blindness, and to Ren6 Le Berre and G. Barrie Heinzenknecht for providing me with copies of papers on that work.
REFERENCES Bryceson, K.P., 1989. The use of Landsat MSS data to determine the distribution of locust eggbeds in the Riverina region of New South Wales, Australia, Int. J. Remote Sensing, 10: 1749-1762. Heinzenknecht, G.B., 1980. Landsat-related study for regional planning in onchocerciasis-free areas in west Africa. Proceedings of 14th International Symposium on Remote Sensing of Environment, 23-30 April 1980, San Jos6, Costa Rica. Environmental Research Institute of Michigan, Ann Arbor, MI, pp. 1849-1857. Hielkema, J.U., Roffey, J. and Tucker, C.J., 1986. Assessment of ecological conditions associated with the 1980/81 Desert Locust plague upsurge in West Africa using environmental satellite data. Int. J. Remote Sensing, 6: 1609-1622. Johannessen, J.A., Johannessen, O.M. and Haugan, P.M., 1989. Remote sensing and model simulation studies of the Norwegian coastal current during the algal bloom in May 1988. Int. J. Remote Sensing, 10: 1893-1906. Levizzani, V., Porc6, F. and Prodi, F., 1990. Operational rainfall estimation using Meteostat infrared imagery: an application in Italy's Arno River Basin - its potential and drawbacks. ESA J., 14: 313-323. Muirhead, K. and Cracknell, A.P., 1984. Identification of gas flares in the North Sea using satellite data. Int. J. Remote Sensing, 5:199-212. Sader, S.A., Powell, G.V.N. and Rappole, J.H., 1991. Migratory bird habitat monitoring through remote sensing. Int. J. Remote Sensing, in press.
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Thomson, A.G. and Milner, C., 1989. Population densities of sheep related to Landsat Thematic Mapper radiance. Int. J. Remote Sensing, 10: 1907-1912. Tucker, C.J. Hielkema, J.U. and Roffey, J., 1985. The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity. Int. J. Remote Sensing, 6: 127-138. Ulbricht, K.A., 1981. Examples of application of digital image processing of remotely sensed phenomena. In: A.P. Cracknell (Editor), Remote Sensing in Meteorology, Oceanography and Hydrology, Ellis Horwood, Chichester, pp. 285-294. World Health Organisation, 1985. 10 years of Onchocerciasis control in West Africa. Review of the work of the Onchocerciasis Control Programme in the Volta River Basin area from 19741984. WHO, Geneva, R 386-786-1086-388.
315
Elsevier Science Publishers B.V., Amsterdam
Rapid remote recognition of habitat changes Arthur P. Cracknell Department of Applied Physics and Electronic and Manufacturing Engineering, University of Dundee, Dundee DD1 4HN, UK
ABSTRACT Cracknell, A.P., 1991. Rapid remote recognition of habitat changes. Prey. Vet. Med., 11: 315-323. An attempt is made to give a realistic appraisal of the possibilities and shortcomings of remote sensing techniques in the context of detecting habitat changes. Consideration has to he given to the various spatial scales at which remote sensing data are exploited, and to the relationship between the size of the object of an investigation and the intrinsic limitations on the ground resolution (instantaneous field of view, IFOV) of the detecting system. Features larger than the IFOV are commonly studied and, in certain circumstances, features that are smaller than the IFOV can also be detected. However, the question of obtaining information about objects or features that are very small compared with the IFOV is much more difficult. Examples include sheep distribution, locust prediction and the development of toxic algal blooms at sea. The monitoring of blooms, once they have begun to appear, is now feasible. However, it is much more difficult to study the conditions leading to the development of blooms and to give advance warning of their growth. The study of this type of problem is still in its infancy. The other question that has to be addressed concerns the timeliness of the extraction of information from remotely sensed data. With the exception of the meteorological community, the philosophy for the last decade or two has been simply to capture remotely sensed data from satellites, to archive it on magnetic tape and to hope that someone, somewhere, someday, will retrieve the data from the archive, analyse it and possibly make use of it. In many applications of remotely sensed data, such as oil pollution monitoring, agricultural yield prediction, and epidemic and plague prevention, the remotely sensed data must be analysed, interpreted and the results distributed to the end users in something approaching real time. It is pointless, other than as an academic exercise, to predict a plague or epidemic 5 years after it has happened!
INTRODUCTION
Two points are addressed in this paper. The first is to survey what remote sensing has achieved, not in its totality but in those aspects which may be of some relevance to epidemiology and parasitology. The second is to address the question of the timeliness of the production and distribution of results from remote sensing data analysis. This is an aspect in which remote sensing has often been deficient in the past.
© 1991 Elsevier Science Publishers B.V. All rights reserved 0167-5877/91/$03.50
316
A.P. CRACKNELL
REMOTE SENSING OF LARGE OBJECTS
The term remote sensing is used to mean gathering data about the surface of the Earth from an aircraft or a spacecraft. Aerial photographs have long been used in mapping, so much so that the majority of the information on the topographic maps that are produced these days has come from aerial photograph interpretation and photogrammetric analysis, and relatively little has come from field surveys. Similarly, one can regard the use of space photographs or scanner images obtained from space as an extension of this work. Mapping of large areas of inaccessible or hostile territory can, in many cases, be achieved more conveniently with space imagery than with aerial photographs. This, therefore, forms one of the very important uses of remotely sensed data. The limit to the spatial resolution of the imagery is determined by the grain size of a photograph, by the instantaneous field of view (IFOV) of a scanner, or by the theoretical diffraction limit of the aperture of the instrument. For mapping purposes at any given scale, one necessarily chooses a source of imagery in which the features which are to be m a p p e d are significantly larger than the limit of resolution of the data. In the context of a small country that has been well m a p p e d for a long time, such as most western European countries, the resolution of satellite data is such that data from space are of little use in m a p revision. However, in many parts of the world where there are large areas that are very poorly mapped, satellite imagery forms a convenient and highly suitable source of data for m a p making. Examples are large parts of the Sahara, China, South America and Antarctica. REMOTE SENSING OF SMALL OBJECTS
People who are familiar with satellite data will be aware that the size of the IFOV of the c o m m o n LANDSAT multispectral scanner (MSS) data is about 80 m; the LANDSAT thematic m a p p e r ( T M ) has an IFOV of about 30 m, while the images from the SPOT satellite system have a resolution corresponding to an IFOV of 20 m (or 10 m in the panchromatic m o d e ) . When we speak of a scanner having an IFOV of 80 m, we mean that the instrument gathers all the radiation coming from an area of about 80 m X 80 m on the ground and focuses it on to a detector which gives a response characterising the total a m o u n t of radiation received from that area. No detailed structure will be discernible within that area. For instance, one would not expect to be able to distinguish individual houses, trees or automobiles in LANDSAT MSS imagery because, in terms of area, each covers only a tiny fraction of the IFOV. This illustrates what is meant by 'small', i.e. an object which covers a small, but not entirely negligible, fraction of the IFOV. In some cases, there are ways in which information can be obtained from satellite data about objects which are small compared with the size of the IFOV. Three examples of cases in
RAPID REMOTE RECOGNITION OF HABITAT CHANGES
317
which such information can be obtained are concerned with: (i) sheep; (ii) gas flares, agricultural strawburning and forest fires; (iii) waves on the surface of the sea. Some work on sheep population was carried out by T h o m s o n and Milner ( 1989 ) with data relating to the uplands of North Wales. This is a part of the U K where there is a range of topography, where the altitude varies from 100 to 1000 m above sea level, and where a high annual rainfall ( 1750-4500 m m ) ensures that the area is well covered with a variety of semi-natural grassland and heathland vegetation. Sheep are the only large herbivores that are present in large numbers in this area and so sheep grazing is the d o m i n a n t biotic factor that controls these ecosystems. Different vegetation communities support different population densities of sheep and the soil, rainfall and altitude affect sheep distribution. These factors affect the satellite-received radiance and, therefore, also the vegetation index. Thus, one could expect to be able to use satellite-derived vegetation indices to study the population distribution of sheep. In practice, better correlation was obtained for sheep population density with near-infrared (0.76-0.90 a m ) radiance than with the normalised difference vegetation index ( N D V I ) . It was suggested that perhaps in undulating terrain near-infrared radiance is less influenced by non-vegetational factors, such as soils and topography, than radiance in other wavebands. Perhaps, we should attempt to distinguish between cause and effect. In this case, the differences in vegetation cause both the differences in the sheep population density and in the satellite-received near-infrared radiance. We observe the correlation between two quantities which share a c o m m o n cause. The second example is a little different and is mostly relevant to data from Band 3 of the advanced very high resolution radiometer ( A V H R R ) which is flown on the National Oceanic and Atmospheric Administration (NOAA) series of meteorological satellites. A gas flare produced by an oil or gas well, or by an oil refinery, for example, may be a few metres or tens of metres in dimension and is thus very small in relation to the size of the IFOV of the A V H R R which is about l k m × 1 km. However, such flares show up extremely clearly in the Band 3 data from the AVHRR. The peak of the Planck distribution function of a typical flame occurs at a wavelength of about 3.5 or 4.0 a m , which is exactly the wavelength of Band 3 of the AVHRR. Such a small source as a glas flare produces such a large a m o u n t of heat energy as to increase very significantly the total radiation from the IFOV. Indeed, the amount of energy is often so large that it actually saturates the sensor for that pixel. For example, if one looks at a Band 3 A V H R R image of the North Sea, one notices a n u m b e r of isolated black dots, either single pixels or else a cluster of a very small n u m b e r of pixels (less than ten). It should be realised that even though the flare is very small compared with the IFOV, it can affect the signal not only of the IFOV in which it is located, but also a few nearby IFOV as well. By careful geometrical rectification of such a scene and comparison with
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a map of the concessions and drilling operations in the North Sea, one can identify the particular drilling platforms which are responsible for each of the flares (Muirhead and Cracknell, 1984). In this instance, the small object is rendered directly apparent because it contrasts so extremely sharply with the background. Similar arguments apply to fires of various sorts, such as those of agricultural strawburning or forest fires. There is a fairly extensive literature on this subject. Again, even if the area of the conflagration is smaller than the IFOV of the AVHRR, the fire is still likely to be apparent in the A V H R R Band 3 data. The third example concerns the study of waves and the determination of the wave vector, i.e. the wavelength and the direction of propagation, of the waves on the surface of the sea. This may be done with synthetic aperture radar (SAR), such as those carried on the u n m a n n e d SEASAT satellite or on the m a n n e d Shuttle missions. The ground resolution of the radar, if it is used in the context of mapping objects on the ground (land), depends on the manner in which the data are processed to produce an image. The SEASAT SAR was designed to achieve a spatial resolution of 25 m; however, a wavelength that is smaller than 25 m can be detected. In this case, the success in measuring something smaller than the spatial resolution of the system arises from the fact that the wave is extended and, therefore, the wavelength is repeated over an area that is large with respect to the ground resolution. In this case, the wavelength is most easily detected and measured by taking a Fourier transform of the image generated by the SAR. (Sea waves are also visible in LANDSAT TM imagery. ) VERY SMALL OBJECTS - LOCUSTS AND ALGAE
In the previous section, we considered three examples of situations in which the simple concept of the ground resolution of remotely sensed data does not necessarily correspond to the lowest limit of the size of an object about which information may be obtained with the data. In this section, we turn to even smaller objects and have chosen to discuss locusts and algae. Although locusts and algae are much smaller than sheep, they do tend to occur in large aggregations and can give rise to directly observable dramatic effects in remotely sensed data. In that sense, they may be relevant to the problems addressed in this conference. However, detecting the ravages of locusts or detecting a large bloom of toxic algae, once it has occurred, is only of limited use. By that stage, it is often too late to take preventive action and a great deal of damage has already been done. What would be m u c h more valuable would be to use remotely sensed data to study the conditions that lead to the explosive expansion of the populations in certain locations at certain times and to be able to give advance warning of impending plagues or blooms. This is much more difficult. A great deal of work has been carried out on locusts in this context (see e.g. Tucker et al., 1985; Hielkema et al., 1986; Bryceson, 1989). The detection of
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a swarm of locusts as a black cloud on a satellite image, or the detection of the massive devastation caused to an agricultural area by a swarm of locusts, is possible because the effect in the image is large enough to detect; but that is not very useful. What one has to do is to try to predict potential swarming of the locusts and take appropriate preventive action before damage to crops occurs. When the locusts are present in low numbers in their natural (desert) habitat or recession area they present no problem. When rainfall occurs, the locusts multiply and, for a while, live on the ephemeral vegetation that appears after the rainfall. When the locust population has grown large and the local vegetation is exhausted, the locusts swarm and migrate, often over very large distances, in search of further supplies of vegetation. Prevention strategy is thus based on locating areas where rain has fallen (see Levizzani et al., 1990), monitoring the locust population build-up and then controlling the population by spraying where necessary. Given that the recession areas are large and generally not well supplied with meteorological stations on the ground, satellite remote sensing provides an important source of data for monitoring rainfall in these areas. There are techniques based on the study of clouds in satellite images to locate areas of precipitation. Following precipitation, one can attempt to detect its occurrence indirectly from studies of soil moisture or of the green vegetation biomass. The study of soil moisture from satellites, although possible in principle using microwave or thermal infrared data, is not presently possible on an operational basis. However, the use of visible-band and near-infrared-band data for the determination of vegetation indices in now a very widespread and common practice in remote sensing. Nevertheless, it is one thing to say that satellite data can be used to determine vegetation indices, but it is quite another thing actually to be able to do so in an operational programme. Several relevant questions arise concerning the spatial resolution, the frequency and area of coverage, and the cost and delivery time of the data. While on the subject of pests, it is worth mentioning some work related to river blindness (Onchocerciasis) which is a parasitic worm affliction spread by the bite of the female black fly (Simulium damnosum). However, in this case the remotely sensed data have not been used in the attempts to control or eradicate the disease, but in some related aspects of the programme. River blindness, which afflicted about 10% of the population of the Volta River system basin, was the target of a special project in that area by the World Health Organisation (WHO), the Food and Agriculture Organisation ( FAO ) and the United Nations Development Programme ( U N D P ) . The countries concerned in this area were Benin, Burkino Faso (Upper Volta) and Ghana. The problem was attacked in three stages which involved: (i) spraying; (ii) study of land-cover and land-resources development potential; (iii) redevelopment. The first stage, the Onchocerciasis Vector Control Program, involved antilarval spraying and was apparently successful. LANDSAT data
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were then used in the second stage, the Regional Onchocerciasis-free Area Planning (ROAP) project, over a 2 year period from early 1978. This was designed as a flexible planning mechanism to enable assistance agencies to respond to requests from African governments for help in planning and developing areas recently freed from Onchocerciasis. In this context, LANDSAT data were used in a fairly conventional way to study land cover and land resources development potential. For further details, see the paper by Heinzenknecht (1980). Subsequently, an Onchocerciasis Control Program (OCP) has been set up by WHO ( 1985 ) involving nine West African countries. This has involved the use of high-resolution remote sensing satellite data in the initial stages for mapping purposes. It now uses about 200 stations measuring river flow rate and transmitting the data via the ARGOS data collection system; this enables decisions to be made on the applications of doses of larvicide to the river breeding grounds. The programme has been highly successful in controlling Onchocerciasis in the area. Let us turn now to the subject of algae. We are very much nearer the start of the road in terms of studying the conditions of blooms of toxic algae and the prediction of their development; indeed, it is fair to say that research on this aspect is only just beginning. A considerable milestone in the use of satellite remote sensing data for the study of algal blooms was the flight of Nimbus-7 carrying the Coastal Zone Colour Scanner (CZCS) which supplied data from 1978 until 1986. Some observations of algal blooms had been made previously with LANDSAT MSS data (see Ulbricht, 1981 ), but the CZCS was designed specifically for studying ocean colour and was more suitable than the LANDSAT MSS for this. However, oceanographers were, generally speaking, slow to appreciate the potential significance of the CZCS data until it was too late. Thus, there were very little field data gathered contemporaneously with the CZCS data and, consequently, the validation of much of the interpretation of the archived CZCS data is, sadly, not now possible. Thus, for the present, until a successor to CZCS is launched, we have to make do with existing sensors, which for practical purposes means LANDSAT MSS and TM, SPOT HRV and NOAA AVHRR. Thus, as an example, a more recent study which used one of these ongoing systems and for which field data were gathered has been chosen. There was a very large unexpected toxic bloom of Chrysochromulina polylepis which appeared in Skagerrak in May 1988. This caused very severe damage to the Norwegian fish-farming industry. Johannessen et al. (1989) described the use of combined remote sensing and model studies of the mesoscale circulation of the Norwegian coastal current in support of the daily forecasts of the advance of the algal front. The importance of these forecasts was that they enabled many fish farmers to tow their cages away from the advancing bloom to safety in low-salinity water deep in the various fiords along the coast. Otherwise, the damage and financial loss caused by the bloom would have
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been very much greater than was actually suffered. The remotely sensed data used came from the NOAA AVHRR and an essential element was its speedy availability and the capability to process it rapidly. For further details and colour-coded AVHRR images of the bloom, see the paper Johannessen et al. (1989). Two lessons emerge from this case study. First, the use of remotely sensed data was only possible because the data were obtained and processed extremely quickly. Secondly, while it is useful to track a bloom using remotely sensed data, the much more challenging task is to be able to predict the onset of a bloom before it occurs and before any damage is done. This is a problem that is still waiting to be solved and its solution is not going to be easy. MONITORING THE HABITATSOF MIGRATORY BIRDS Some very recent work, that is not yet published, has been carried out by Sader et al. ( 1991 ) who have used unsupervised classification of LANDSAT TM data to identify habitats important for migratory birds in Costa Rica and to study habitat conversion over a recent 10 year period, principally associated with forestry clearing followed by the establishment of permanent pasture. Although there has been a considerable amount of work published on using satellite remote sensing in the study of forest clearing, as far as Sader et al. were able to ascertain, no previous studies of the use of satellite remote sensing to monitor changes in migratory bird habitat have been published. THE POTENTIAL OF REMOTE SENSING So far, some relevant studies with remotely sensed data have been described. What of the future? Remote sensing applications are of three types: (i) those which are operational; (ii) those which, in principle, are well understood and could be made operational with sufficient investment in terms of space-borne systems and ground-based distribution, analysis and interpretation systems; (iii) speculative applications. Many things in some remote sensing applications 'shopping lists' are, quite frankly, speculative and severe damage has sometimes been done to the image of remote sensing by people who have pretended otherwise. In the context of this meeting, we must recognise that the prospect of the direct recognition of habitat changes is rather unlikely. If it is possible, it will probably be too late since the signs will almost certainly have already been observed on the ground. It may still be possible to allow changes, as was done for instance in the tracking of the algal bloom off the Norwegian coast in 1988 mentioned above. One key element will be fast access to the remotely sensed data and the capability to analyse it rapidly. We must get away from the traditional way of dealing with much remotely sensed data. For many years now, we have captured data from the sky, put it into an archive and hoped that someone, somewhere, will later come along and make use of it. In practice,
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however, much of the archived data is likely never to be used at all. For many purposes, we must get to the stage where the data are processed, distributed and analysed to produce information in directly usable form in near real-time. This is important since new satellite systems will generate even larger quantities of data than existing systems. The word 'rapid' in the title of this paper is very important; recent developments in the power and speed of computing machinery, and in communications systems, have made possible many things that were not possible a few years ago. Remotely sensed data must be considered as a candidate source of epidemiological information that is not simply a direct observation of habitat changes, but which gives information about parameters that affect the evolution of habitats. In many cases, we are not going to be able to make direct observation of habitat changes. It is going to be important to have a model which depends on a n u m b e r of parameters. Remote sensing may be able to provide information on the values of some of those parameters. ACKNOWLEDGEMENTS
I am grateful to Trevor E. Beaumont for drawing my attention to the work on river blindness, and to Ren6 Le Berre and G. Barrie Heinzenknecht for providing me with copies of papers on that work.
REFERENCES Bryceson, K.P., 1989. The use of Landsat MSS data to determine the distribution of locust eggbeds in the Riverina region of New South Wales, Australia, Int. J. Remote Sensing, 10: 1749-1762. Heinzenknecht, G.B., 1980. Landsat-related study for regional planning in onchocerciasis-free areas in west Africa. Proceedings of 14th International Symposium on Remote Sensing of Environment, 23-30 April 1980, San Jos6, Costa Rica. Environmental Research Institute of Michigan, Ann Arbor, MI, pp. 1849-1857. Hielkema, J.U., Roffey, J. and Tucker, C.J., 1986. Assessment of ecological conditions associated with the 1980/81 Desert Locust plague upsurge in West Africa using environmental satellite data. Int. J. Remote Sensing, 6: 1609-1622. Johannessen, J.A., Johannessen, O.M. and Haugan, P.M., 1989. Remote sensing and model simulation studies of the Norwegian coastal current during the algal bloom in May 1988. Int. J. Remote Sensing, 10: 1893-1906. Levizzani, V., Porc6, F. and Prodi, F., 1990. Operational rainfall estimation using Meteostat infrared imagery: an application in Italy's Arno River Basin - its potential and drawbacks. ESA J., 14: 313-323. Muirhead, K. and Cracknell, A.P., 1984. Identification of gas flares in the North Sea using satellite data. Int. J. Remote Sensing, 5:199-212. Sader, S.A., Powell, G.V.N. and Rappole, J.H., 1991. Migratory bird habitat monitoring through remote sensing. Int. J. Remote Sensing, in press.
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Thomson, A.G. and Milner, C., 1989. Population densities of sheep related to Landsat Thematic Mapper radiance. Int. J. Remote Sensing, 10: 1907-1912. Tucker, C.J. Hielkema, J.U. and Roffey, J., 1985. The potential of satellite remote sensing of ecological conditions for survey and forecasting desert-locust activity. Int. J. Remote Sensing, 6: 127-138. Ulbricht, K.A., 1981. Examples of application of digital image processing of remotely sensed phenomena. In: A.P. Cracknell (Editor), Remote Sensing in Meteorology, Oceanography and Hydrology, Ellis Horwood, Chichester, pp. 285-294. World Health Organisation, 1985. 10 years of Onchocerciasis control in West Africa. Review of the work of the Onchocerciasis Control Programme in the Volta River Basin area from 19741984. WHO, Geneva, R 386-786-1086-388.