int. j. remote sensing, 2001, vol. 22, no. 12, 2377 –2400
Review article Using remote sensing to assess biodiversity H. NAGENDRA* Centre for Ecological Sciences, Indian Institute of Science, Bangalore, 560012, India; e-mail:
[email protected] (Received 8 July 1999; in nal form 30 June 2000) Abstract. This review paper evaluates the potential of remote sensing for assessing species diversity, an increasingly urgent task. Existing studies of species distribution patterns using remote sensing can be essentially categorized into three types. The rst involves direct mapping of individual plants or associations of single species in relatively large, spatially contiguous units. The second technique involves habitat mapping using remotely sensed data, and predictions of species distribution based on habitat requirements. Finally, establishment of direct relationships between spectral radiance values recorded from remote sensors and species distribution patterns recorded from eld observations may assist in assessing species diversity. Direct mapping is applicable over smaller extents, for detailed information on the distribution of certain canopy tree species or associations. Estimations of relationships between spectral values and species distributions may be useful for the limited purpose of indicating areas with higher levels of species diversity, and can be applied over spatial extents of hundreds of square kilometres. Habitat maps appear most capable of providing information on the distributions of large numbers of species in a wider variety of habitat types. This is strongly limited by variation in species composition, and best applied over limited spatial extents of tens of square kilometres.
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Introduction As rates of habitat and species destruction continue to rise, the need for conserving biodiversity has become increasingly imperative during the last decade (Wilson 1988, Kondratyev 1998 ). In order to design meaningful conservation strategies, comprehensive information on the distribution of species, as well as information on changes in distribution with time, is required. It is nearly impossible to acquire such information purely on the basis of eld assessment and monitoring (Heywood 1995). Remote sensors provide a systematic, synoptic view of earth cover at regular time intervals, and have been indicated as useful for this purpose (Soule and Kohm 1989, Lubchenco et al. 1991, Roughgarden et al. 1991, Stoms and Estes 1993, Debinski and Humphrey 1997, Innes and Koch 1998). Coupled with Geographical Information Systems (GIS), *Present address: Center for the Study of Institutions, Populations and Environmental Change, Indiana University, Bloomington IN 47408, USA; e-mail:
[email protected]. Internationa l Journal of Remote Sensing ISSN 0143-116 1 print/ISSN 1366-590 1 online © 2001 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/0143116001
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they can provide information about landscape history, topography , soil, rainfall, temperature and other climatic conditions, as well as about present day habitat and soil coverage—factors on which the distribution of species depend (Noss 1996 ). Relationships between species distribution patterns and remotely sensed/GIS data, if known, can be used to predict the distribution of single species or sets of species over large areas (Debinski and Humphrey 1997). This article attempts to provide a review of several studies that assess the utility of remote sensing, or remote sensing coupled with GIS, to understand the distribution of biodiversity. The term ‘biodiversity’ is restricted to its most commonly used form, i.e. species diversity (Stoms and Estes 1993). In the studies reported on here, several terms —biotopes, communities, ecosystems, ecotopes, habitats, landscape elements, vegetation communities—have been used more or less synonymously . While the terms are reported here as used in the original studies, it must be recognized that their meaning in this context is similar (Forman and Godron 1987, Forman 1995). Section 2 discusses issues of scale—spatial, spectral and temporal. Section 3 categorises existing studies into three major kinds that are described in further detail. Finally, section 4 reviews the capabilities of these various techniques for large-scale assessment of biodiversity. 2.
Issues of scale All observations depend upon the scale of study—extent as well as grain (Allen and Starr 1982). Extent refers to the size of the study area investigated, while grain is the resolution of the remote sensor—radiometric, spatial, spectral and temporal. As extent increases, the level of detail (grain) that can be maintained, given constraints on time, eVort and money, will decrease, and vice versa. The amount of information that can be retrieved, on numbers of species or numbers of habitat types, critically depends on these factors. 2.1. Spatial resolution Spatial resolution determines the amount of information in a remotely sensed image of a given area. The spatial resolution used should be such that the information required (adequate accuracy of classi cation) is available using the least amount of data. If spatial resolution is too low, discrimination of object classes becomes diYcult. If too high, orders of magnitude smaller than that of the objects classi ed, intraclass variability may increase and classi cation accuracy decrease correspondingly (Meyer et al. 1996). The ratio of spatial resolution to the size of the objects being classi ed (whether tree crowns, single plant species or patches of a species) thus plays a crucial role in deciding whether species separation during classi cation is adequate or not. Most remote sensing studies of species diversity concentrate on land area, although a few investigations on freshwater and marine ecosystems have been made. Animal species cannot be normally observed using remote sensors, unless of very high spatial resolution. Relatively few studies of animal species distribution have therefore been carried out using remotely sensed data. Most direct observations of individual plants or animals require them to be fairly macroscopic and slow moving, if not sessile. The vast majority of macroscopic sessile organisms on land are colonies of lichens or plants. Thus most, if not all, remote sensing studies of vegetation concentrate on larger plants on land areas. Smaller organisms like mosses or fungi, let along micro-organisms, are usually
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too small to consider mapping. Barring a few exceptions such as moss and lichen cover on tundra areas, they are usually present in the lower strata of vegetation. Except for microwave imagery, most remotely sensed imagery is incapable of penetrating through the top canopy of vegetation to receive information about lower strata. Thus remote sensing provides little information on the lower strata, such as herbs or shrubs (Verbyla 1995). Therefore, a majority of studies on plant species diversity using remote sensing have been carried out on tree species in the uppermost canopy (McGraw et al. 1998), or crops and weeds in elds where they form larger associations in the topmost strata of vegetation (Dietz and Steinlein 1996, Atkinson 1997, Atkinson and Curran 1997). Most commonly, individuals directly mapped to the species levels through remotely sensed data are trees. Even with these fairly large plants, the spatial resolution required for identi cation is fairly high. Biging et al. (1995) attempted to discriminate tree species based on visual discrimination of RECON 1 acquired video imagery emulating Landsat Thematic Mapper (TM) blue green, red and infrared bands. They conclude that pixel sizes of 0.5 m (at a 1:12 000 scale) are not capable of assigning individual tree crowns to species. Distinguishing conifer species from hardwood also proved diYcult. They suggest a spatial scale higher than 1:12 000: thereby concurring with Ciesla’s (1989) recommendation of a scale of at least 1:8000. However, this is a fairly general prescription. ‘Ideal’ pixel size will obviously depend upon the size of tree crowns, which can vary widely within as well as between species. As spatial resolution increases beyond this point, complications arise. Very high spatial resolution data contains information on several parts of the tree crown, shade leaves, sunlit leaves, bark, even soil and other understorey plants (Gougeon 1995, Biging et al. 1995, Meyer et al. 1996). As spatial resolution decreases to the point where the resolution cell size approximates the natural variation in the canopy structural characteristics of a single tree, variations in canopy structure will contribute signi cantly to the spectral information, and assigning species identity becomes correspondingly diYcult. Also at the level of forest communities or plant associations, factors other than species identity, including ground surface and understorey components, canopy gaps, stand density and crown size, contribute to spectral variation (Treitz et al. 1992, Fuller et al. 1997 ). As a result, high spatial resolution data contains information on the structural characteristics of a plant association, but there is also loss of information on species type and abundance. Indeed, with the advent of very high spatial resolution imagery, each pixel will tend to represent a small part of the object of interest. The problems associated with pixel-wise classi cation of these high-variance images will certainly increase (Lobo et al. 1996, St-Onge and Cavavas 1997 ). Image segmentation into training elds comprising the objects mapped, is a convenient way to avoid these problems (Lobo et al. 1996, McGraw et al. 1998 ). However, an ideal spatial resolution would minimize within-object variance, while maximizing between-object variance (Meyer et al. 1996). Most critical therefore is the relationship between cell size or spatial resolution, the size of the objects being classi ed (tree crowns, plant associations, forest communities), and the size of the individual components at a smaller scale (leaves, bark, soil gaps, tree crowns, canopy openings) that comprise these objects (Simmons et al. 1992 ). What should this relationship be, for maximum classi cation accuracy? The answer will determine the spatial resolution to be desired. This might be an academic discussion in the case of
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satellite remote sensing, where spatial resolution is xed, but assumes practical importance with aerial remote sensing, where spatial resolution can be varied by altering the altitude of the ight pass. Woodcock and Strahler (1987) and Jupp et al. (1988, 1989) developed a technique to determine the optimal spatial resolution of an image. This method is based on the spatial variation encountered within the image, local and global. The spatial resolution of the image is successively degraded, and a graph of local variance (at a one-pixel lag) and spatial resolution plotted. When the size of the objects is much larger than that of the pixels, many adjacent pixels will fall within the object and tend to be alike. Local variance will be low. When the objects are much smaller than the pixels, again local variance will be low because each pixel contains an aggregate of sub-pixel information from objects and the background (Warner and Shank 1996, Atkinson 1997, St-Onge and Cavavas 1997). The maximum variance will be found when pixel size approximates object size: and this technique thus acts as a guide to determining object size. Atkinson (1997 ) extends this to de ne the ‘optimal spatial resolution’ as that when the semivariance at a lag of one pixel is maximum. For mapping, the spatial resolution needs to be much ner than the ‘optimal’ resolution, to fully cover the range of spectral variation within the feature. O’Neill et al. (1996) recommend as a practical rule of thumb, that the spatial resolution should be two to ve times smaller than the objects of interest. This agrees with recommendations of per-pixel classi ers, that state that ground truth input into a classi er should be a minimum of 2–5 pixels per side, for adequate accuracy (Jensen 1986). Hyppanen (1996) demonstrate s that the optimal resolution depends upon the spectral band used. For a boreal forest, the local variance maximum in the infrared and green bands was found at 3 m, and in the red band, at 2 m. The optimal spatial resolution thus varies depending upon what one is classifying. For agricultural elds in the UK, Atkinson (1997 ) recommends a spatial resolution between 0.5 m and 3 m. Patch sizes are therefore 6 m and greater. Habitat patches in Florida scrub range from 20 m in width upwards (Breininger et al. 1995), while in Indian tropical landscapes, habitat heterogeneity is higher and patch size can be as small as 0.1 ha (Nagendra and Gadgil 1999b) . For conifer canopies in the Paci c Northwest region of the United States, Cohen et al. (1990) recommend pixel sizes lower than 1 m. Forest tree crowns typically are of diameters of 1–10 m (Hyppanen 1996, St-Onge and Cavavas 1997, McGraw et al. 1998). Recommended spatial resolution for tree mapping would therefore be of the order of 0.2 m or lesser. These recommendations hold good for visible infrared imaging. With SAR imagery, due to the presence of image speckle, spatial resolution needs to be mich higher than feature size. In fact, with the spatial resolution available currently with spaceborne SAR, classi cation of patch sizes
