Consideration of Scale in Remote Sensing of Biodiversity
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A coherent and effective remote sensing (RS) contribution to biodiversity monitoring requires careful consideration of scale in all its dimensions, including spatial, temporal, spectral, and angular, along with biodiversity at different levels of biological organization. Recent studies of the relationship between optical diversity (spectral diversity) and biodiversity reveal a scale dependence that can be influenced by the RS methods used, vegetation type, and degree and nature of disturbance. To better understand these issues, we call for multi-scale field campaigns that test the effect of sampling scale, vegetation type, and degree of disturbance on the ability to detect different kinds of biodiversity, along with the development of improved models that incorporate both physical and biological principles as well as ecological and evolutionary theory. One goal of these studies would be to more closely match instrumentation and sampling scales to biological definitions of biodiversity and so improve optical diversity (spectral diversity) as a proxy for biodiversity. The ultimate goal would be to design and implement a truly effective, “scale-aware” global biodiversity monitoring system employing RS methods. Such a system could improve our understanding of the distribution and functional importance of biodiversity and enhance our ability to manage ecosystems for resilience and sustainability in a changing world.
KeywordsScale Scaling Spatial scale Temporal scale Spectral scale Angular scale Optical remote sensing Optical diversity Spectral diversity Biological diversity
Biodiversity is critical to ecosystem function and provides many goods and services essential to human well-being (Hooper et al. 2012; Tilman et al. 2012). Despite centuries of effort, we lack a comprehensive account of global biodiversity, at a time the world is facing a sixth mass extinction due to human disturbance and climate change (Barnosky et al. 2011). Effective management of biological resources to preserve diversity and maintain ecosystem function in a rapidly changing world remains difficult, in part due to sampling challenges and lack of globally consistent data sets. Sampling biodiversity using traditional field methods alone simply cannot address this need, leading to recent calls for remote sensing (RS) as part of a global biodiversity monitoring system (Scholes et al. 2012; Pereira et al. 2013; Turner 2014; Jetz et al. 2016; Geller et al. Chap. 20).
Examples of biodiversity-related studies using different methods of optical remote sensing
Community composition (dominant species mapping)
Plant trait assessment
Optical diversity assessment
With the advent of hyperspectral sensors and imaging spectrometers, a growing number of studies have utilized optical diversity, or the variability in vegetation optical properties (also called spectral diversity) to assess species diversity (typically alpha or beta diversity), or to address plant traits related to functional diversity. These methods offer the opportunity to directly detect species and functional diversity, but also require close attention to scale (Asner et al. 2015). In this chapter, our primary focus is on these latter RS methods involving optical RS, with the understanding that other RS methods can also make important contributions to our understanding of biodiversity.
16.1.1 Why a Chapter on Scale?
A key thesis of this chapter is that much of the uncertainty in the RS of biodiversity arises from the lack of attention to sampling scale, which affects both traditional biodiversity metrics and our ability to detect biodiversity remotely. In most cases, the sampling scales of typical satellite or airborne RS methods do not match those of our biological definitions or biodiversity measurements on the ground, confounding our interpretation of biodiversity from RS. In part because of scale mismatches, the interpretation of remotely sensed data from one time or place often cannot be applied to another, and we lack a universal, operational approach to RS of biodiversity. For RS of biodiversity to be meaningful, a careful consideration of scale is essential.
The purpose of this chapter is to address this need, with the goal of contributing to the design of an effective, operational global biodiversity monitoring system. Our primary focus is on optical diversity (a.k.a. “spectral diversity”) using passive optical RS in the visible to shortwave-infrared (VIS-SWIR) range (400–2500 nm) because this approach allows species and functional diversity assessment. However, we acknowledge that other methods, including lidar (Asner et al. 2012; Lausch et al., Chap. 13, this volume), can also make important contributions to our understanding of biodiversity. Our key examples involve optical studies of terrestrial vegetation primarily at the level of alpha and beta diversity (Whittaker 1972), with the underlying assumption that vegetation diversity may be related to the diversity of other trophic levels via surrogacy (Magurran 2004) or to belowground diversity via biogeochemical cycling (Madritch et al. 2014; Madritch et al., Chap. 8). Similarly, in aquatic environments optical diversity (often expressed as “ocean color”) can reveal dynamic structure related to the distribution of phytoplankton (Moses et al. 2016; Muller-Karger et al. 2018) and benthic organisms (Goodman and Ustin 2007), and scaling principles discussed here may apply in these cases. While a comprehensive assessment of all aspects of biodiversity in all environments is beyond the scope of this review, our hope is that the principles discussed here with a primary focus on terrestrial vegetation will enable progress toward an operational global biodiversity monitoring system involving RS.
16.1.2 What Is Optical (Spectral) Diversity?
Examples of methods and metrics used to assess optical diversity, several of which are based on information content
Values or variance of vegetation indices (e.g., the Normalized Difference Vegetation Index, NDVI, or Enhanced Vegetation Index, EVI)
Measures of variance (e.g., coefficient of variation, CV)
Measures of spectral angle (e.g., Spectral Angle Mapper, SAM)
Measures of area or volume in spectral or principle components space (e.g., convex hull area or volume)
Principal components analysis (PCA)
Regression methods (e.g., partial least squares regression, PLSR)
Fava et al. (2010)
Spectral classification (clustering) methods
Many spectral variability methods derive from information theory, which provides a rich array of methods for assessing the abstract “information content” or “entropy” in a given data set (Table 16.2). One simple method expresses spectral variation as the coefficient of variation (CV) spectrum for a given region, which can then be averaged into a single metric (Fig. 16.1). At this point, it is not entirely clear if there is a “best” method, because most of these methods work to some degree and their strength of correlation may vary with the circumstances (e.g., Gholizadeh et al. 2018).
The fundamental reason spectral patterns reveal underlying biological diversity is that plant reflectance spectra contain information on plant structure and chemical composition (Ustin and Jacquemoud, Chap. 14) that can differ slightly between species or functional types (Ustin and Gamon 2010) and can indicate different evolutionary histories (Schweiger et al. 2018; Meireles et al., Chap. 7, this volume). Thus, another way to utilize spectral information is to directly relate spectral patterns to plant functional traits (Serbin and Townsend, Chap. 3), providing a link between optical diversity and functional diversity (Cavender-Bares et al. 2017; Schweiger et al. 2018).
The topic of how to measure spectral diversity remains an active area of research, and the “best” metric is likely to vary depending upon the particular context and purpose. Rather than provide a detailed summary of all the possible methods used, we present the concept of spectral information content as a viable proxy for multiple dimensions of biodiversity at the species, functional, or genetic and phylogenetic levels (Cavender-Bares et al. 2017; Schweiger et al. 2018; Cavender-Bares et al., Chap. 2, this volume) and note that recent reviews (e.g., Wang and Gamon 2019) consider this topic in more detail.
16.2 What Is “Scale” and Why Is It Important?
“Scale” has several definitions and is used as both a noun and a verb. As a noun, it refers to a level of observation and can have several dimensions, including spatial, temporal, spectral, and biological. As a verb, it refers to the act of examining a phenomenon at multiple levels, usually referring to transcending spatial scales, as in “upscaling” (extrapolation from fine-scale data to a coarser scale) or “downscaling” (interpreting underlying patterns or mechanisms from coarse-scale data).
Discussions of scale have a rich history in both biology and RS. In biology, scale typically refers to levels of organization (genetic, cellular, organismal, species or population, community or ecosystem, etc.). Biodiversity can be defined at many of these levels, requiring different study approaches (Bonar et al. 2011). Biological systems typically exhibit complex, nonlinear interactions, and feedbacks, resulting in emergent properties and thus requiring evaluation across multiple levels of organization (Heffernan et al. 2014). Scale can also refer to sampling scale, as in the grain size and extent of field sampling (Turner et al. 1989). There is an abundant literature on the importance of considering scale when exploring ecological phenomena (Ehleringer and Field 1993; Levin 1992) and a number of “scaling rules” have emerged. For example, Levin (1992) stated the importance of matching the scale of observation to the phenomenon (and grain size or patch size) in question, and specific rules of sample grain size have been developed (e.g., O’Neill et al. 1996). However, these rules are often violated when remotely sensing biodiversity.
In RS, scale has several aspects or dimensions (Malenovský et al. 2007). The spatial dimension typically refers to the pixel size (grain size) and spatial extent of a remotely sensed image. The temporal dimension can refer to the time of sampling, repeat frequency, or temporal extent of sampling. The spectral dimension includes band position, bandwidth (full width half maximum, FWHM, and sampling interval), and range. The directional or angular dimension, including the illumination or viewing angle, leading to variations in anisotropic reflectance and the bidirectional reflectance distribution function (BRDF), is also an important scale dimension in optical RS, as it strongly influences the ability to detect signals present in reflectance spectra (Schaepman-Strub et al. 2006; Malenovský et al. 2007; Gamon 2015). These scale dimensions often interact, with the effects of one influencing the effects of another, so it is often best to consider multiple scale dimensions together. A consideration of sampling scale in all these dimensions is relevant to a discussion of RS of biodiversity. Sampling across scales often reveals critical information that is not apparent from a single-scale observation alone. Below, we consider these scale effects in more detail by providing examples of how these dimensions impact biodiversity assessment from RS.
16.2.1 Biological Scale
Biodiversity exists across multiple scales of organization from genes to biomes. A detailed discussion of the various “dimensions” of biodiversity at different scales of organization is beyond the scope of this chapter and has been reviewed elsewhere (e.g., Magurran 2004; see also Cavender-Bares et al., Chap. 2, this volume). In the context of this chapter, a key challenge lies in matching the scale of the measurement approach to the biological scale of organization, a topic considered in the sections below.
16.2.2 Spatial Scale
Similar issues of scale mismatch arise when exploring plant traits with RS. It is unclear how well the ability to detect leaf traits transcends spatial scales, with some studies suggesting certain leaf traits (nitrogen content) cannot be detected at the sampling scale of a typical aircraft or satellite pixel (Knyazikhin et al. 2013). However, many leaf traits (if not all) can be detected at the canopy scale (i.e., the scale of an individual plant crown), particularly when appropriate sampling and scaling methods are followed, suggesting that scale-appropriate RS methods can often resolve trait differences associated with functional or species diversity (Townsend et al. 2013; Asner et al. 2015). This is particularly relevant to airborne data, where pixel sizes often approximate those of individual tree crowns, allowing individual traits to be distinguished (Asner et al. 2015; Singh et al. 2015), but calls into question the accuracy of trait retrievals when the pixel sizes are much larger than individual canopies, as is the case for most satellite RS.
These studies have significant implications for attempts to sample optical diversity from aircraft or satellite sensors and illustrate the importance of matching sampling scale (pixel size) to crown size when designing airborne campaigns. Clearly, there is a strong scale-dependence of the optical diversity-biodiversity relationship, but this scale-dependence varies between study sites, even for the same biome (prairie grassland, in this case). These site differences have been attributed to a number of possible factors, including the degree of disturbance (fire regimes, invasion by exotic weeds, or subsequent weed removal), the size of the plot (sampling extent) relative to the pixel (grain) size, and the differences in species richness between studies. The Wood River site was less disturbed, with less bare ground and larger, more species-rich plots than the Cedar Creek site (Gholizadeh et al. 2019). It is likely that multiple features influence the scale dependence of the optical diversity-biodiversity relationship, illustrating that the larger context and experimental design of a study can matter.
We know less about the scale dependence of these relationships for other biomes where explicit scaling experiments involving the RS of biodiversity have not yet been conducted. Despite unanswered questions, these experiments in prairie ecosystems demonstrate the importance of spatial scale and support the idea that canopy crown size is an important factor in scale dependence. These findings have clear implications for studies using airborne and satellite platforms where the pixel sizes often exceed the crown sizes of common plant species.
16.2.3 Temporal Scale
The temporal dimension is rarely considered in most RS campaigns; many RS studies are based on a single overpass (e.g., a single aircraft image or satellite image), or at best a few overpasses, limiting the opportunities for examining temporal effects. A typical field campaign might focus on an optimal time to collect data from a RS perspective (e.g., the dry season in the tropics or the summer growing season in higher latitudes). However, biological communities are inherently dynamic, and the visibility of different species changes with ontogeny, season, and over longer time spans due to disturbance, invasion, succession, climate change, and other processes. Consequently, our ability to detect species richness will vary with time, often in ways that are poorly understood. The few studies that have investigated the temporal aspect of biodiversity using RS or spectral reflectance demonstrate the importance of temporal dynamics when examining the optical diversity-biodiversity links.
16.2.4 Spectral Scale
A number of studies have demonstrated that some wavebands provide more information than others, and this information can vary seasonally (Zutta 2003; Chavana-Bryant et al. 2017; see also Fig. 16.4). Similarly, visible bands (revealing pigment composition) and NIR bands (revealing structural information) respond differently to spatial scale (Wang et al. 2018b) or sampling angle (Gamon 2015) illustrating the functionally distinct responses of these different spectral regions (Fig. 16.1) and providing further evidence of interactions between the spectral, spatial, and angular dimensions of scale.
16.2.5 Angular Scale
Illumination and view angle both interact with vegetation structure to influence the shape and intensity (brightness) of reflectance spectra. While these effects have been well-studied in RS and can be characterized by the BRDF for a particular surface (Malenovský et al. 2007), angular information has generally not been employed in detection of biodiversity. A few studies have noted that the BRDF response can help distinguish vegetation types (Gamon et al. 2004), suggesting that angular information may be useful in biodiversity detection. Sensor view angle affects different spectral regions differently, illustrating an interaction between angular scale and spectral scale (Gamon 2015). With the advent of lidar (Asner et al. 2012) and structure-from-motion (SFM; Wallace et al. 2016) to characterize plant 3-D structure, angular information can now be better understood and effectively integrated with optical RS to improve biodiversity or plant trait detection.
16.3 Implementing Scaling Approaches
The complexity of scaling effects involving the transition from alpha to beta diversity is briefly illustrated in Fig. 16.6, which illustrates optical diversity (CV in this case) calculated for different regions along a transect crossing several plant communities, including woodland, grassland, and experimental grassland plots at the Cedar Creek Ecosystem Science Reserve, Minnesota (USA). In this case, the highest optical diversity values occur at edges, points of abrupt landscape transitions marking the boundaries between adjacent communities (i.e., ecotones), an effect commonly seen in remotely sensed images (Paz-Kagan et al. 2017). In this complex landscape, (and in contrast to the plot-level patterns shown in Fig. 16.3), CV generally increases with spatial scale, reflecting a transition from alpha to beta diversity with increasing spatial lag (pixel sizes). Interestingly, this transition occurs at about 10 m × 10 m in the manipulated grassland (see arrow and blue curve, Fig. 16.6), matching the plot sizes in this experiment, but occurs more gradually in the natural grassland (black curve, Fig. 16.6). These patterns seem to contradict findings of declining optical diversity in grasslands with increasing pixel size obtained at finer scales (1 mm to 1 m in Wang et al. 2018a) (see also Fig. 16.3). This comparison illustrates that the patterns of scale dependence are themselves scale dependent and can differ within the same landscape or for different communities depending upon both individual crown size and larger landscape structure. These observations agree with a body of RS literature that show wide variation in patterns of scale dependency for different landscapes using geostatistical approaches, with the local variance a function of the relative sizes of the pixels and the discrete targets themselves (e.g., Woodcock and Strahler 1987). For these reasons, the spatial aggregation approaches often used in other fields (e.g., plant productivity) may actually confound the detection of optical diversity at certain scales. Consequently, optical diversity (alpha or beta diversity) or variation in plant traits from RS requires particular attention to scaling methodologies (e.g., Asner et al. 2015).
By providing both fine-scale patterns (Fig. 16.3) and the larger context of landscape structure, imaging spectroscopy (Fig. 16.6) provides a valuable tool for further, more detailed studies, and can help define the proper scale at which alpha and beta diversity can be most effectively sampled. Remotely sensed data can also be used to define patterns of geodiversity, the physical template influencing biodiversity (Record et al., Chap. 10, this volume). Geostatistical approaches that examine optical diversity as a function of distance, analogous to the use of semivariograms (Curran 1988), can help design appropriate sampling methodologies by illustrating the influence of landscape features (e.g., ecotones or different vegetation types) on optical diversity (Fig. 16.6). Imaging spectrometry can also reveal temporal dynamics and disturbance patterns that can provide additional context for understanding both the drivers and the consequences of biodiversity changes. Furthermore, image spectrometry can be used to assess the relationships between optical diversity and ecosystem function (e.g., productivity) over large areas (Wang et al. 2016; Schweiger et al. 2018). For these reasons, satellite and airborne RS can be a powerful complement to local and regional field studies of biodiversity.
Other approaches to scaling involve the use of models for upscaling or downscaling, including radiative transfer models and statistical models (Knyazikhin et al. 2013; Malenovský et al. 2019; Verrelst et al. 2019). While potentially powerful, such models are generally limited by the lack of suitable data, bringing us back to the need for experimental studies incorporating empirical methods. Ultimately, a combined empirical and theoretical framework that explicitly considers multiple dimensions of scale is needed to advance our knowledge of biodiversity detection with RS.
16.4 Designing a Scale-Aware Biodiversity Monitoring System
To be truly robust, a global biodiversity monitoring system will need to consider scale in all the dimensions discussed here. Biological concepts (e.g., grain size and extent) often have a direct analog to RS concepts (e.g., pixel size and extent). Ideally, we should design our instrument and optical sampling protocols to match our sampling scale to a particular organizational scale of biodiversity. While specific sampling rules have been proposed (e.g., Justice and Townshend 1981), the general rule of thumb that the pixel size must be smaller than the size of the individual sampling unit (e.g., typical plant canopy) is a good starting point, and this has been supported by studies discussed above (see Fig. 16.3).
However, matching instrument to organizational scale remains a challenge; we typically have a poor match between the pixel size and sampling extent and the organism size and distribution on the ground, often due to the practical constraints of field sampling (in the case of biological studies) and instrument design (in the case of RS). Most remote sensing instruments are designed for a particular airborne or spaceborne platform with physics and engineering requirements in mind. The detector response is constrained by the amount of electromagnetic energy available, which in turn determines the sensor design, pixel size, and spectral resolution needed to achieve a given signal-to-noise ratio. Greater signal-to-noise ratios can be attained by reducing the spectral resolution (combining narrow bands into broadbands, e.g., via spectral binning), or by reducing spatial resolution (e.g., pixel binning), but these choices limit the ability to distinguish individuals, species, and functional types due to the degradation of spectral and spatial information. Orbital and altitudinal considerations also determine the pixel size obtainable from a particular sensor platform. Together, these constraints often reduce the ability to properly distinguish individual organisms or vegetation functional types. Adding to this mismatch, field sampling (including plot size, transect size, and location) is often limited by practical considerations of personnel, time, and budget and is rarely designed with RS in mind.
Improvements in sensor design and novel sampling platforms can relax these impediments, but with trade-offs. For example, flexible airborne platforms, emerging unmanned aerial vehicles (UAV) systems, or robotic ground-based systems (Wang et al. 2018a, b) provide useful platforms for testing the effects of spatial scale, but may not always have the temporal or spectral coverage desired for biodiversity detection (Fig. 16.5). Plans to deploy global satellite imaging spectrometers with frequent repeat visits (Schimel et al., Chap. 19) offer new ways to explore the temporal dimension with high spectral resolution and wide spectral range, although at relatively coarse spatial resolution. Consequently, a key application of satellite RS can be to provide the larger context within which effective sampling regimes can then be defined at finer scales.
Due to these inherent limitations and trade-offs between scale dimensions, we suggest that the ideal global monitoring system would be an integrated, multi-component system, combining RS at different scales (satellite and aircraft sensors) with proximal sensing (ground optical sensors and field sampling of biodiversity) (Fig. 16.2). Such a system would operate within a clearly defined scaling framework, incorporating empirical and modeling approaches, with explicit attention to sampling scale in each of the dimensions mentioned above. Although rarely used in RS, explicit experimental approaches involving cross-scale and cross-instrument comparisons should be a key capability of an effective global biodiversity monitoring system. With a multi-scale system, it would be possible to express results (e.g., spectral variability) as a function of sampling scale (pixel or grain size; see Figs. 16.3 and 16.6), much in the way that semivariograms are used to express landscape structure (Curran 1988). These patterns could be compared to the driving variables, including the underling patterns of “geodiversity,” (Record et al., Chap. 10, this volume). Such a system would reveal ideal spatial scales for sampling alpha and beta diversity and would help reveal the causes of biodiversity patterns at multiple scales, as illustrated in Figs. 16.3 and 16.6.
Such a scale-aware global biodiversity monitoring system could incorporate and integrate many aspects of existing networks such as NEON (Hopkin 2006), Forest GEO (Anderson-Teixeira et al. 2014), and many others (see Geller et al., Chap. 20), but would provide many benefits currently not provided by existing biodiversity monitoring efforts. The system would require global imaging spectrometry with repeat coverage, revealing global patterns in time and space and providing essential context for more detailed studies at higher spatial resolution (see Schimel et al. Chap. 19). More detailed resolution could be achieved by a fleet of regional aircraft carrying imaging spectrometers at a spatial resolution matching the crown sizes of many shrub and tree species (e.g., Kampe et al. 2010). For even more detailed sampling resolution needed for smaller statured vegetation or for resolving individual leaf traits, UAVs, robotic, or tower-mounted imaging spectrometers could be deployed at key sites. These methods would be coupled to systematic ground sampling of species composition (alpha and beta diversity) using traditional field sampling methods, along with leaf and canopy optical properties (using field spectrometry) for a detailed assessment of plant traits. Radiative transfer models and statistical scaling methods (Serbin and Townsend, Chap. 3) could provide a framework for integrating and analyzing data across scales.
A systematic global evaluation of optical diversity across multiple scales could readily detect dynamics in biodiversity, identify causes biodiversity changes, adapt to these changes, and help to identify monitoring and conservation priorities. By integrating diversity metrics with measures of ecosystem function, our understanding of the ecosystem impacts of biodiversity would be enhanced, allowing better management for resilience. On our rapidly changing planet, such a system would enable the monitoring required for sustainable management of ecosystems globally.
The authors wish to acknowledge funding from a NSF and NASA Dimensions grant to JCB (DEB 1342872), PT (DEB 1342778), and JG (DEB 1342823), as well as the Cedar Creek NSF Long-Term Ecological Research grant (DEB 1234162) for supporting many of the examples cited here. We also thank NIMBioS and the Keck Institute of Space Studies (KISS) for sponsoring meetings that helped develop some of the ideas in this chapter. We also acknowledge additional funding support from NSERC, CFI, and from Rangeland Research Institute (U. Alberta) grants to JG. Thanks to Evan Delancey for spectral data (Fig. 16.1), Greg Crutsinger for access to the Parrot Drone data (Fig. 16.5), and to Rick Perk and Brian Leavitt (CALMIT, University of Nebraska-Lincoln) for technical assistance in airborne data collection and processing (Figs. 16.3 and 16.6).
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