● Size. It is important to note the scale of the image during visual inspection, as this has a major bearing on how objects are interpreted. Both the relative and absolute sizes of objects are important in their identification. The absolute size of an object is determined by reference to the scale of the image.
● Shadow. The presence of shadows can greatly complicate image interpreta- tion, as shaded features generally appear to be dark and difficult to discern.
On the other hand, shadows can be used to help interpret features: for example, the length of a shadow cast by an individual tree can give an indication of its height relative to other objects on the image. Shadows can also display the shape of an object on the ground.
● Shape. The shape of objects can be highly diagnostic. Roads, rivers, and urban development can readily be identified because of their characteristic shapes, but it is also possible to differentiate some individual tree species on the basis of crown shape, as well as different kinds of disturbance to which a forest has been subjected—a fire or logging coup may produce a canopy gap with a very different shape to one caused by a natural windthrow.
Visual interpretation of aerial photographs can be used to map a wide variety of forest features, including variation in species composition, degree of crown clos- ure, height class and density, and pattern of disturbance. Typically, forests are mapped as stands, which may be defined as areas with relatively homogeneous characteristics. Determining the boundaries between forest stands can, however, be difficult in practice. As with all other aspects of visual interpretation, decisions made by the analyst are subjective and may therefore be subject to a degree of error that can be difficult to quantify. However, experienced practitioners are able to discern features with a very high degree of accuracy, which can potentially be validated by reference to field surveys, and for this reason aerial photography is still the main technique of choice for producing maps in support of forest manage- ment. For example, in Finland, data for forest management planning are generally gathered by field surveys of forest stands that are first delineated by reference to air photos (Pekkarinen and Tuominen 2003). Many studies have shown that digitized aerial photographs can do better than satellite remote sensing data for a variety of forest mapping applications (Franklin 2001, Hyyppäet al. 2000, Poso et al. 1999).
expertise to be used effectively. Second, the development of satellite remote sens- ing methods has been driven largely by rapid technological advancement sup- ported by a great deal of scientific research. It can be argued that these developments have been led by producers of the imagery and the research com- munity, rather than by its potential users. In reality, only a tiny proportion of avail- able imagery has been put to any practical use; much of it resides in very extensive data archives, some of which are only rarely accessed. These problems have some- times been compounded by a lack of understanding among practitioners of the strengths and weaknesses of satellite imagery, leading to unrealistic expectations of what such imagery can deliver. There are many examples of forest projects that have made substantial investment in satellite imagery, only for the results to be dis- appointing and of little practical value (Franklin 2001).
The barriers to effective use of satellite imagery in forest assessment are increas- ingly being overcome. Software tools are now available that enable images to be processed and classified relatively easily, and combined with other spatial data to produce highly visual outputs that can easily be understood by forest managers.
Dialogue between the developers and potential users of this technology has helped improve the practical application of remote sensing methods in ways that can genuinely support decision-making. Experience of applying different methods to particular problems has enabled ready identification of the situations where use of satellite imagery is most likely to be valuable, and those where results are less likely to be satisfactory. As a result, it is now feasible for someone with no prior experience of the methods (such as a postgraduate research student, perhaps) to be producing highly accurate analyses of satellite remote sensing data with just a few weeks of appropriate training.
On the other hand, even though satellite imagery and the requisite analytical software are increasingly becoming available at little or no cost, use of these methods still requires substantial investment of time and effort, as well as capital expenditure. Careful consideration should be given to whether satellite imagery offers the most cost-effective way of addressing the issue at hand, or whether some cheaper, alternative method might be available. A valuable critique of the use of remote sensing in forest planning is provided by Holmgren and Thuresson (1998), which highlights a series of limitations in the technique, such as the difficulty of differentiating more than a small number of different forest types, and the high levels of inaccuracy that are often associated with vegetation classifications based on remote sensing imagery. Satellite sensors are only able to detect the canopy from above, and cannot directly measure the age, structure, height, or volume of forest stands, particularly when crown cover is complete. These methods should not be viewed as a quick technical fix; they can require a great deal of technical expertise and resources to be used effectively. It is worth remembering that better and less expensive ways of obtaining the necessary information might often be available (Holmgren and Thuresson 1998). Just because aerial photographs are a relatively mature technology does not mean they are no longer of value: they may often be the most cost-effective method available. Yet there may be no realistic alternative
to the use of satellite data, particularly where the forest areas to be assessed are large and inaccessible (Figure 2.2).
As noted by Franklin (2001), satellite remote sensing should not be viewed as a panacea, and cannot be expected to meet all needs relating to forest monitoring and assessment. However, it can be considered as one of the most important sources of information available to those involved in forest conservation and man- agement, and has the potential to be of even greater value in the future. A key chal- lenge is to integrate remote sensing information with field observations, and to identify the appropriate role for remote sensing methods. Perhaps the most useful approach is to consider how field observations, aerial photography and satellite data can be used in a complementary way (Franklin 2001).
Satellite remote sensing | 41
Fig. 2.2 A map of forest cover produced using satellite remote sensing (raster) data. The image is the island of Borneo and was created using MODIS data at a spatial resolution of 500 m. The depth of shading on the image relates to the density of tree cover; areas in white are largely absent of trees. These MODIS data have been used in a variety of different forest conservation assessments (e.g.
Miles et al. 2006, DeFries et al. 2005). (Data from Hansen et al. 2003.)
How can the risk of failure be minimized? The following sections are designed to help identify some of the pitfalls and limitations in use of satellite imagery. A clear definition of the problem or objective in question is very important. Consider the level of accuracy and precision that is actually required in the analysis; it may be that relatively low-resolution data will be adequate, which could save much time and resources. Above all, discuss your intended approach with experienced practitioners if you possibly can, or better still, collaborate with someone who has experience of using these methods.
In which situations might satellite remote sensing be preferable? Common situations where this method is used include (Franklin 2001):
● mapping spatial distribution of forest cover and different types of forest
● assessing forest condition and forest health
● monitoring changes in forest extent and structure, for example in response to some management intervention or another form of environmental change;
some believe that the greatest strength of remote sensing techniques lies in their use for environmental monitoring
● assessing forests at multiple scales and resolutions, enabling plot-based data to be scaled up to landscapes and regions
● assessing landscape pattern and structure by using quantitative methods
● assessing forest growth and biomass, which can be of value for estimating the environmental services provided by forests, such as carbon sequestration
● monitoring threats to forests (such as fire, flooding, or deforestation).
Remote sensing data derived from satellite-borne sensors first need to be acquired, then subjected to various forms of image processing. Typically, the image is then classified by using additional data, such as information derived from a field survey or forest inventory. Each of these steps in the use of satellite imagery is considered in separately in the following sections. The limitations and common problems associated with using satellite imagery for forest assessment are then profiled. Examples of applying satellite remote sensing to practical problems relat- ing to forest ecology and conservation are considered in a subsequent section (2.5).
2.3.1 Image acquisition
The provision of satellite remote sensing data is highly dynamic, with new data sources continually becoming available as new satellites are placed in orbit.
Traditionally, the main source of such data has been the national and international space agencies involved in developing and launching earth observation satellites, such as NASA, the European Space Agency, and the space agencies of countries such as Russia and India. Data can be purchased directly from these sources, but increasing amounts of data are now being made available for free download over the Internet. Researchers in academic institutions are particularly well placed to take advantage of educational discounts and academic agreements when accessing data, and much information is now available to researchers free of charge. Increasingly, however, remote sensing data are being provided by private
corporations. For example, the very high-resolution data provided by Ikonos and Quickbird are available from Space Imaging and Digital Globe, respectively, typically at relatively high cost. Such high-resolution imagery is now so detailed that it can compete effectively with aerial photography, and can be used to map individual trees (see, for example, Koukal and Schneider 2003). The cost of obtaining remote sensing imagery is typically a major factor influencing purchas- ing decisions. Although an increasing number of archives are offering free access to satellite imagery, much of it is at low (less than 250 m) resolution and therefore of limited value for application at the scale of forest stands or landscapes. Before purchasing imagery, it is worth checking whether anyone else (perhaps a researcher in an institute or university, the forest service or other government agency) has already analysed imagery for the area in which you are interested—they may be prepared to share their data.
Some widely used sources of satellite imagery are listed in Table 2.2. The choice of which satellite data are most appropriate for a particular task depends on the nature of the problem being addressed, and the characteristics of available data.
Different sources of satellite data vary in their resolution, which refers to the abil- ity of the sensor to acquire data with specific characteristics and comprises the following components (Franklin 2001):
Satellite remote sensing | 43
Table 2.2 Selected sources of satellite imagery.
Product Comments URL
EOMOnline Sells a variety of imagery, www.eomonline.com/
such as Landsat 5, IRS Imagery and Landsat 7
Terraserver www.terraserver.com/
ResMap Provides free online access www.resmap.com to satellite data, including
over 10 terabytes of Landsat imagery
Ikonos www.spaceimaging.com/
SPOT www.spotimage.fr/
Landsat 7 Available from USGS http://landsat.usgs.gov/
Orbimage www.orbimage.com/
AVHRR Available from USGS http://edc.usgs.gov/products/satellite/
avhrr.html
Quickbird www.digitalglobe.com/
Global Land Cover Provides access to Landsat http://landcover.org/
Facility TM images for much of (GLCF) the world for download
● Spectral resolution. The number and dimension of specific wavelength intervals in the electromagnetic spectrum to which a sensor is sensitive. Particular wave- length intervals are selected for specific applications; for example, the red region of the spectrum can be related to the chlorophyll content of leaves.
Hyperspectral sensors detect very many narrow (2–4 nm) intervals (Figure 2.3).
● Spatial resolution. A measure of the smallest separation between objects that can be distinguished by the sensor. A system with higher spatial resolution can detect smaller objects. Spatial resolution can best be understood as the size of a pixel in terms of dimensions on the ground, and is usually presented as a single value rep- resenting the length of one side of a square (for example, a spatial resolution of 30 m indicates that one pixel represents an area of 3030 m on the ground).
● Temporal resolution. The image frequency of a particular area recorded by the sensor. Satellite sensors vary in the time interval between visits to a particular area.
● Radiometric resolution. The sensitivity of the detector to differences in the energy received at different wavelengths. Greater resolution enables smaller differences in radiation signals to be discriminated.
Some of the most important sources of satellite remote sensing data are listed in Table 2.3, together with an indication of their spatial resolution. There are often trade-offs between different forms of resolution; for example, an increase in the number of bands that the sensor is able to detect increases spectral resolution, but often decreases spatial resolution. There may also be trade-offs in the design of sen- sors between spectral and spatial resolution (Franklin 2001). Imagery should be
Hyperspectral sensors
Multispectral sensors
Photographic sensors
300 14000 Radar and passive
microwave sensors
300 700 900
0.3m
Wavelength
Frequency
1 mm 1 m Infrared
Colour
300 2400
Fig. 2.3 Electromagnetic spectrum with regions of interest for remote sensing of forests. The main regions of interest are the optical/ infrared and microwave portions of the spectrum. (Copyright 2001, From Remote sensing for sustainable forest management, by S. E. Franklin. Reproduced by permission of Taylor and Francis, a division of Informa plc.)
selected with a spatial resolution (pixel size) that is appropriate to the scale of the pattern being assessed. Collection of an unnecessary degree of detail can hinder interpretation of the imagery and slow the analytical process. Imagery should also be selected with a spectral resolution that is appropriate to measurement of the phenomenon of interest.
Variation between sources of remote sensing imagery in their resolution influ- ences the scale to which they can be usefully applied. Scalerefers to the area over which a pattern or process is mapped or assessed. According to cartographic con- vention, ‘small-scale’ refers to a large area of coverage, whereas ‘large-scale’ refers to a small area of coverage and consequently greater detail. The scale on a map or image refers to the difference in size between features represented in the image and Satellite remote sensing | 45
Table 2.3 Characteristics of selected sources of satellite remote sensing data that are of value for forest assessment and monitoring (adapted from Franklin 2001).
Satellite name Sensor Number of bands Spatial resolution (m)
Landsat-5 TM 7 30–120
MSS 4 82
Landsat-7 ETM 7 15–30
SPOT-2 HRV 4 10–20
SPOT-4 HRV 5 10–20
VI 4 1150
SPOT-5 HRG 4 2.5
HRS 4 10
VEGETATION 2 4 1150–1700
IRS-1B LISS 4 36–72
IRS-1C, -1D LISS 4 23–70
PAN 1 5.8
ERS-1, -2 AMI (SAR) 1 26
ATSR 4 1000
Space Imaging Ikonos-2 5 1–4
NOAA-15 AVHRR 5 1100
NOAA-14 AVHRR 5 1100
NOAA-L AVHRR 5 1100
Terra (EOS AM-1) ASTER 14 15, 30, 90
MODIS 36 250, 500, 1000
MISR 4 275
DigitalGlobe Quickbird2 5 0.61–2.44
Orbview-3 Orbview 4 1–4
IRS P6 LISS 3 4 23
AWiFS 3 56
on the ground, and is usually expressed as a ratio of image distance over ground distance. For example, a scale of 1 : 100 000 indicates that a distance of 1 cm on the map represents a distance of 100 000 cm (1 km) on the ground. On an image of this scale, an area of 5 5 cm would represent an area of 2500 ha on the ground.
The relation between the resolution of imagery and the scale of phenomena that can be detected can be summarized as follows (following Franklin 2001):
● Low spatial resolution(for example NOAA, AVHRR, MODIS, SPOT VEG- ETATION, Landsat data). Usual applications are the study of patterns vary- ing over hundreds or thousands of metres and for mapping at the small scale, for example the distribution of forest types within a region.
● Medium spatial resolution(for example Landsat, SPOT, IRS). Most relevant for assessing patterns that vary over tens of metres and for mapping at the medium scale, such as tree density, size of forest stands, and the characteristics of forest patches.
● High spatial resolution(for example Ikonos, Quickbird). Applicable to the assessment of phenomena that vary over distances of centimetres to metres and for large-scale mapping, such as mapping individual trees, detailed assessment of forest structure, or mapping the distribution of individual species within forest stands.
Other issues that might be considered when selecting imagery include (Franklin 2001, Horning 2004):
● Spectral bands (channels), characterized by the bandwidth (the range of wave- lengths detected), the placement of the bands (the portion of the electromag- netic spectrum that is used), and the number of bands: sensors with more than one band are multispectraland images with very many bands (more than 100) are called hyperspectral.
● The history of the satellite sensor. Analysis of deforestation, for example, requires a time series of images for comparison. Landsat imagery is available from the early 1970s, but most other imagery is only available from much more recent dates, particularly in the case of very high-resolution imagery.
● The surface area covered by an image, which varies between different satellite sensors, and influences the number of scenes that you will need to obtain. If possible, it is preferable to use just a single image to cover the entire study area, as tiling together different images to cover a particular area can pose problems (for example, if the images differ in quality).
How can the most appropriate source of imagery best be selected? The most effective way of making an informed decision is to discuss the alternatives with experienced practitioners, who can help identify successful approaches and poten- tial pitfalls (Horning 2004). There are many relevant discussion fora accessible via the Internet, and specialists within university departments, research institutions, and government agencies might also be able to offer advice. It is also worth con- sulting research publications providing examples of the kind of investigation that
you want to undertake. For example, Corona et al. (2003) provide a large number of recent examples involving application of different remote sensing data to forest management and biodiversity assessment. Most satellite data can be inspected before purchase, enabling appropriate images to be selected. A common problem in selecting imagery for forest assessment is that clouds may obscure the areas of interest; being able to browse the imagery before purchase enables cloud-free images to be selected. Sensors such as Terra MODIS that revisit areas relatively frequently (every 1–2 days) increase the chances of locating imagery that is cloud-free.
How can an appropriate spatial resolution be selected? There may be no substi- tute for trial and error. As a rule of thumb, try to select imagery that has a resolution a factor of 10 higher than the area of the features you are identifying (Horning 2004). For example, in order to differentiate forest patches with a minimum size of 1 ha (100 100 m), imagery with a spatial resolution of 30 m or finer would be preferred. In order to map tree crowns of around 3 m diameter, imagery with a resolution of at least 1 m should be used.
2.3.2 Image processing
Satellite remote sensing data need to be subjected to at least two forms of image processing in order to be of use: radiometric processing, which seeks to ensure that the measured data accurately represent the spectral properties of the target feature, and geometric processing, which aims to relate the locations of pixels to coordinates on the Earth’s surface according to a chosen map projection (Franklin 2001). Radiometric processing removes the effects of sensor errors and/or environmental factors such as atmospheric interference, often using additional data collected when the image was obtained. Geometric processing corrects any errors caused by the curvature of the Earth and movement of the satellite, and involves registering an image to points on a map that has already been rectified.
After radiometric and geometric processing, the image may be further processed in a variety of ways. Unprocessed satellite imagery is generally stored in a grey-scale format, in which the numerical values of each pixel (known as the digital number, DN) are stored as numbers representing shades of grey from black to white, which represent the variation in energy detected. Image-processing techniques can be used to increase the degree of contrast between shades of grey, enabling different objects to be differentiated more readily. Usually, the image is converted from grey- scale to colour, which is achieved by assigning specific DN values to different colours. Colours chosen may be ‘true colour’, representing the spectral bands that were detected, or ‘false colour’, where colours are chosen that are different from the spectral bands detected (Horning 2004).
Many satellites produce multispectral data, or data collected from a number of different spectral bands. Use of multiple spectral bands enables land-cover types to be differentiated more easily, and therefore images from different sensors are often Satellite remote sensing | 47