As described in the previous sections, a wide variety of methods are available for analysing remote sensing data. Choosing an appropriate method for a particular task can be a daunting exercise. The best way of making an appropriate choice is to learn from the experience of others, particularly in a rapidly developing field such as this. This section describes the approaches that have been adopted in a range of published studies, to illustrate how remote sensing methods have been applied to address specific questions relating to forest ecology and conservation. Problems that may be encountered when using particular techniques and general limitations of these methods are also highlighted.
The selection of an appropriate method is governed by the overall objectives of the investigation, and how the data are to be used. Once a map has been produced from remote sensing data, it can be used as simply as a visual aid for forest planning purposes or to inform a stakeholder consultation process. Alternatively, a range of different quantitative analyses can be carried out, including species habitat model- ling, deforestation modelling, landscape pattern and forest fragmentation analysis, and conservation priority-setting. Some of these methods are considered in subse- quent chapters of this book. In the current chapter, methods are presented that are useful for assessing changes in forest area and in forest condition.
2.5.1 Analysing changes in forest cover
The use of remote sensing imagery to assess changes in forest cover is of great inter- est to forest conservationists. Such analyses can be used to estimate deforestation rates and patterns, which can be of value in identifying conservation priorities and potential sites for forest restoration. In addition, analysis of changes in forest cover can be used to infer changes over time in the availability of habitat for forest- dwelling species.
Aerial photography can be used for very detailed assessment of rates and patterns of change in forest area (Price 1986); as photographs are often obtained relatively easily, this is frequently the method of choice. Aerial photographs have the added advantage of being potentially available over longer time periods than other types of Applying remote sensing | 55
imagery, enabling forest changes over longer timescales to be evaluated. The main problem with this method is the difficulty in mapping the boundaries of vegetation types, which do not always coincide with those observed during field surveys (Franklin 2001). Despite this problem, aerial photography continues to be very widely used for assessments of forest change (see, for example, Lowell et al. 1996).
Typically, satellite imagery is used to evaluate changes in forest cover by produ- cing classified maps that illustrate the distribution of different change classes, such as forest to non-forest, forest unchanged, non-forest to forest, and non-forest unchanged (Horning 2004). The commonest way of producing a map of changes in forest cover is to compare two classified images produced for different dates. If the amount of change is large then this approach can be highly effective (Franklin 2001). A variety of different algorithms can be used to calculate the difference between two images, ranging from simple subtraction to more complex statistical manipulations, which are available in image-processing software. Details of these algorithms are provided by Gong and Xu (2003).
The main problem with this post-classification approach is the fact that errors associated with each of the individual land cover maps are accumulated into the final map illustrating forest cover change, which therefore tends to be less accurate than either of the individual land-cover maps (Horning 2004). As an alternative, the images from the two different dates can be combined into a single image, which can then be classified by using the approaches described earlier. This method can potentially enable classes of land-cover change to be mapped directly, produ- cing lower errors than the post-classification method. The main problem with this approach is the potential difficulty of identifying changes in forest cover if there is variation within the images that is not directly related to changes in forest cover (Horning 2004).
Other methods that can be used include (Horning 2004):
● Image difference or ratio. This involves the analysis of individual bands or sin- gle-band image products, such as vegetation indices (such as NDVI). Images from different dates can be compared by subtracting them, and the values analysed to determine changes in forest cover. Although rapid, this method does enable changes between specific land-cover types (for example, conver- sion of forest cover to different non-forest land-cover types) to be determined.
● Spectral change vector analysis. This involves assessing changes in spectral composition and intensity of pixels between different dates. For example, conversion of forest cover to bare ground is likely to result in an increase in the brightness of an image as well as a change in colour. This method is typically employed with multispectral imagery.
● Manual methods. Visual interpretation, supported by on-screen digitizing, can be used to manually produce maps of changes in forest cover. For example, polygons can be drawn on screen by using suitable GIS software rep- resenting different classes of land-cover change. Although relatively simple, the work can be arduous, and is subject to biases introduced by the analyst.
● Hybrid approaches. These incorporate elements of both the automated and manual methods described above. For example, an image might first be produced by using an automated method, then edited visually to produce the final map.
One of the biggest challenges to the analysis of changes in forest cover is the fact that the characteristics of the imagery often differ over time. Satellite remote sens- ing data are only available from the early 1970s onwards, and therefore detection of change that has occurred over longer timespans than the past 30 years requires other imagery, such as aerial photographs. Even in this case, earlier photographs are likely to be black-and-white rather than colour images, which can complicate interpretation and comparative analysis. Comparison between aerial photographs and satellite data can be achieved by using visual methods; this is most readily achieved by digitizing (scanning) and georeferencing the photograph and display- ing it together with the satellite image in a GIS.
In the case of satellite imagery, a common problem is encountered when com- paring Landsat images from different dates. Earlier images obtained from the Landsat MSS sensor are at a coarser resolution than more recent Landsat TM imagery, and the number of spectral bands is fewer. To overcome this problem, the lower-resolution data set can be resampled to match the resolution of the other data set so that the pixel sizes for the two data sets are equal, making automated analysis possible (Horning 2004). Some software packages now enable imagery with different resolutions to be combined, avoiding the need for resampling.
Another key problem relates to validating the results obtained. Whereas current maps of forest cover can be validated relatively easily by comparing the results with recent field surveys or forest inventories, validating images from the past can pre- sent significant difficulties. Determining the pattern and distribution of different forest types even just a few decades previously can be highly problematic, particu- larly in areas undergoing rapid deforestation. A range of different sources of infor- mation might be used to help validate the maps produced, such as aerial photographs, interviews with local people familiar with the area, evidence from cut stumps or logging records, and information from field plots or forest inventories that originate from the time in question. Sometimes it is possible to infer the kind of forest a particular site is likely to have supported given the current environmen- tal characteristics of the site, such as soil type, altitude, aspect, and drainage. None of these methods is without problems, and inferring historical patterns of forest cover is always likely to be subject to a high degree of error. This uncertainty should obviously be taken into account when applying the results of the analyses.
Any assessment of forest cover change must carefully consider the type of change classes that are to be mapped. For example, what are the possible land-cover types that might have replaced forest? Is it possible that some land-cover types have reverted to forest through a process of succession, and if so, how might successional vegetation types be classified? Have natural forests been replaced by plantation forests, and if so, are their spectral characteristics likely to differ? It is important to Applying remote sensing | 57
select images that adequately cover the period of interest, and that provide information at an appropriate spatial and spectral resolution to allow the detection of significant changes in forest cover. Often, investigations are significantly limited by the available imagery, and the quality of the analyses depends primarily on mak- ing the best of what can be obtained. Some of the issues that should be considered when selecting imagery for analysing changes in forest cover are listed in Table 2.6.
It should be noted that remote sensing imagery can be used to assess forest recovery as well as loss. Regeneration surveys based on field survey, supported by aerial photography, are a routine approach in many forest areas, for example to determine the density of young trees and the success of planting initiatives.
Ground-based and airborne sensors have been used for direct estimation of cover, seedling, and stem counts. However, satellite remote sensing of forest regeneration assessment is much less common. One approach is to consider the changes in reflectance characteristics as the forest stand develops over time. For example, in Tanzania, Prins and Kikula (1996) reported that detection of coppicing from roots and stumps in miombo woodland (BrachystegiaandJulbernardia) was possible by using Landsat MSS data acquired during the dry season.
Here are some examples of the use of satellite remote sensing methods to detect changes in forest area:
● Alves et al. (1999) used Landsat imagery to assess tropical deforestation by comparing separately classified images from 1977, 1985, and 1999.
● Cushman and Wallin (2000) used Landsat imagery to assess landscape change in the central Sikhote-alin mountains of the Russian Far East. Maximum likelihood classification of the satellite imagery identified four broad cover types (hardwood, conifer, mixed, and non-forest); multitemporal principal components analysis was used to describe the magnitude and direction of landscape change in six watersheds.
● Parmenter et al. (2003) analysed land-cover change in the Greater Yellowstone Ecosystem in the USA. Classification tree regression analysis was used to define land use and land-cover classes in the landscape, and to produce maps from Landsat TM scenes.
● Hayes and Sader (2001) used three dates of Landsat TM imagery to assess land-cover change in Guatemala’s Maya Biosphere Reserve (MBR). Three change-detection methods were evaluated: NDVI image differencing, princi- pal components analysis, and RGB–NDVI change detection. A technique to generate reference points by visual interpretation of colour composite Landsat images, for kappa-optimizing thresholding and accuracy assessment, was employed. The highest overall accuracy was achieved with the RGB–NDVI method (85%).
● Zhanget al. (2005) used Landsat TM and MSS to assess deforestation in cen- tral Africa.
● Mayaux et al. (2005) provide an overview of the results of recent research that has employed satellite remote sensing data to assess tropical deforestation.
Applying remote sensing|59 Table 2.6 Some of the variables that should be considered when selecting imagery for assessing changes in forest cover (adapted
from Horning 2004 and Franklin 2001).
Sensor Ideally, images from different dates that are selected for comparison should be obtained from the same sensor, so that characteristics sensor characteristics are consistent between the images. However, even if imagery from the same sensor is used, this is (spatial and no guarantee that sensor characteristics will be directly comparable, as sensors change over time. Such changes may need spectral resolution) to be corrected by applying published radiometric correction factors or by procuring radiometrically corrected imagery.
Solar illumination Images should be selected that are similar in terms of solar illumination angles, to ensure that areas under shadow are similar in all images that are to be compared. This can be achieved by selecting images acquired at the same season and time of day. It is also possible to use a DEM to correct for the influence of different illumination angles.
Atmospheric Images to be compared should ideally have been acquired under similar atmospheric conditions, although this is often conditions hard to achieve. Selecting images acquired at the same season and time of day can help, but again there may be a need
to use some form of correction algorithm.
Soil moisture Variation in soil moisture can greatly complicate comparison of different images, particularly when image bands sensitive to water (such as Landsat TM band 5) are used in the analysis. Variation in soil moisture availability can also influence the spectral characteristics of vegetation.
Acquisition date Select imagery acquired at a time of year when the features of greatest interest can be accurately differentiated from and frequency other features. For example, if there is a need to map areas of deciduous forest, it might be preferable to use images
obtained when the forests are leafless, enabling them to be differentiated from evergreen vegetation types. However, images acquired during seasons when refoliation or leaf senescence occurs can be difficult to compare over time, because the forest type of interest is changing rapidly. Usually, images obtained at the same time of year are used as the basis of comparison. Lambin (1999) emphasizes the need for long-term data sets for monitoring forest degradation in tropical regions.
2.5.2 Mapping different forest types
A forest ecologist is typically interested in mapping the distribution of different forest communities or ecosystems, and perhaps different types of forest stand. Assessment of the distribution or status of particular forest types often forms an important part of any forest conservation project. Some forest types, such as tropical montane cloud forests or tropical dry forests, are considered as globally threatened and a high priority for conservation. Determination of where such forests occur within a particular area may therefore be a high priority. Alternatively, particular species of conservation concern may be associated with particular types of forest, and estimation of the extent and distribution of potential habitat for such species may therefore form an important part of conservation planning. Can analysis of remote sensing data enable different types of forest community to be resolved?
Remote sensing methods can be used to map different forest types according to a range of different classification methods, based on consideration of different attributes. However, some forest attributes of particular interest to forest ecologists and conservationists are poorly differentiated by remote sensing methods, and therefore the potential use of these methods should be critically considered before implementation. It should also be noted that the classifications typically used in assessments of land cover may have limited value in terms of illustrating the distri- bution of ecological communities. A land-cover type is not necessarily equivalent to an ecological community. Many existing forest maps were not developed with ecological objectives in mind, and may therefore be of limited use for applications relating to forest ecology or conservation.
Mapping the distribution of different ecological communities, defined in terms of species composition, is generally done by field survey, which can often usefully be supported by interpretation of aerial photographs (Avery 1968). Forest stands, or areas with relatively homogeneous species composition, can often be differenti- ated on photographs through differences in colour and texture. In forests with a relatively high diversity of tree species, this method is less reliable. Although esti- mates of accuracy are rarely provided, the use of aerial photographs to assist in the process of identifying tree species and mapping forest communities is well accepted. Experienced human practitioners can be very effective at interpreting photographic images, in a way that is difficult to duplicate with automated proced- ures (Franklin 2001). In some areas, keys have been developed to assist in the identification of tree species from air photos. For example, in the Dominican Republic, Hudson (1991) described how tree species can be identified from such photographs by using criteria such as crown shape, crown margin (smooth or serrate), tone (light or dark grey), and texture (rough or smooth).
Identification of individual tree species can also be achieved by the use of other remote sensing methods, such as field spectroradiometric techniques and airborne digital imagery, by examining illuminated tree crowns at different times to detect phenological differences between species. Satellite imagery has proved to be less useful for mapping the distribution of individual tree species; in low- to medium-resolution
imagery, such as Landsat, the pixel size is too large to differentiate the characteristics of individual trees. However, the increasing availability of high-resolution satellite data, such as Ikonos and Quickbird, should facilitate mapping individual tree species and forest communities in future. Analytical methods for analysing such imagery are still at a relatively early stage of development, but it can be amenable to the visual inter- pretation methods used with aerial photographs.
Potentially, if appropriate field data are available, a satellite image could be classified according to the communities of tree species present in the canopy. A key point made by Horning (2004) is that the accuracy of any classification tends to decline as the number of classes increases. In other words, the higher the precision for the class definitions, the lower the accuracy for the individual classes. Horning (2004) provides a useful general guideline of how classification accuracy varies with the number of classes used, based on experience with Landsat imagery:
● With a simple classification, such as forest/non-forest, water/no water, soil/
vegetated, accuracies of over 90% can be achieved.
● If conifer and hardwood forest types are differentiated, accuracies decline to 80–90%.
● Classifications based on presence of different tree genera give accuracies of 60–70%.
● If individual species are included in the classification, accuracies are likely to fall in the range 40–60%. Landsat imagery is limited to detecting the dom- inant tree species in forest canopies.
● Classification accuracies can be improved if a DEM is used in the classification (Franklin 2001, Horning 2004). This can help define ecological communities associated with different topographical characteristics, such as slope or aspect.
One method that appears to offer particular promise for mapping forest compo- sition by using satellite imagery is spectral mixture analysis. Many forest ecosystems show small-scale heterogeneity, which results in many individual pixels representing forest areas that are mixed in terms of their species composition. Mixture modelling approaches assume that the reflectance of a pixel is a combination of the spectral reflectance of different cover-type objects (or endmembers). The resulting spectra are thus a composite of the endmembers of pure spectra of objects in a pixel, weighted by their area proportion (Corona et al. 2003). Methods of spectral mix- ture analysis are described in detail by Asner et al. (2003). As an example, Köhl and Lautner (2001) found that for a test site in the Ore mountains, Germany, spectral mixture analysis provided accurate results for the assessment of mixture proportions of deciduous and coniferous trees, enabling classification of stand types and differ- entiation of tree species groups by using a maximum likelihood algorithm.
Examples of studies that have used satellite remote sensing data to map different forest types include:
● Ramirez-Garcia et al. (1998) mapped 10 land-cover classes in Mexico, includ- ing two mangrove communities, with over 90% accuracy by using a Landsat Applying remote sensing | 61