Landsats Aff

Landsats 1AC

Advantage 3 is Biodiversity –
Landsat is key to quantifying biodiversity loss, and enables successful habitat preservation
Turner et al 3 (Woody Turner1, Sacha Spector2, Ned Gardiner2, Matthew Fladeland3, Eleanor Sterling2 and Marc Steininger4 1NASA Office of Earth Science, 2Center for Biodiversity and Conservation, American Museum of Natural History, 3Earth Science Division, NASA Ames Research Center, 4Center for Applied Biodiversity Science at Conservation International, TRENDS in Ecology and Evolution Vol.18 No.6 June, p. 306-314 “Remote sensing for biodiversity science and conservation” JMB)

The potential for modern sensors to identify areas of significance to biodiversity, predict species distributions and model community responses to environmental and anthropogenic changes is an important research topic. Underlying this effort is the assumption that certain key environmental parameters, with remotely detectable biophysical properties, drive the distribution and abundance of species across landscapes and determine how they occupy habitats. New imagery and data sets are now enabling remote sensing, in conjunction with ecological models, to shed more light on some of the fundamental questions regarding biodiversity. These tools should prove useful to those seeking to generate basic knowledge about why organisms are found where they are, as well as those asking the more applied question of where to invest conservation funds. Here, we use the term ‘biodiversity’ in its organismal sense to refer to species and certain characteristics of species, in particular their distribution and number within a given area. We also use ‘biodiversity’ more broadly to mean species assemblages and ecological communities (i.e. groups of interacting and interdependent species). There are two general approaches to the remote sensing of biodiversity. One is the direct remote sensing of individual organisms, species assemblages, or ecological communities from airborne or satellite sensors. New spaceborne systems with very high spatial (also known as hyperspatial) resolutions are now available from commercial sources. For the first time, the direct remote sensing of certain large organisms and many communities is possible with unclassified satellite imagery. Likewise, new hyperspectral sensors slice the electromagnetic spectrum into many more discrete spectral bands, enabling the detection of spectral signatures that are characteristic of certain plant species or communities. The other approach is the indirect remote sensing of biodiversity through reliance on environmental parameters as proxies. For example, many species are restricted to discrete habitats, such as a woodland, grassland, or seagrass beds that can be clearly identified remotely. By combining information about the known habitat requirements of species with maps of land cover derived from satellite imagery, precise estimates of potential species ranges and patterns of species richness are possible. Just such an approach has been employed extensively in the US GAP analysis program [1]. Of course, it is probable that no single environmental parameter drives patterns of species distribution and richness. Many possible drivers have been proposed (Table 1). Here, we focus on three often-cited environmental parameters that now lend themselves particularly well to detection because of recent advances in remote-sensing technology: primary productivity, climate and habitat structure (including topography) [2–5]. For the conservation biologist, remotely sensed imagery exposes land-cover changes at spatial scales from local to continental, letting one monitor the pace of habitat loss and conversion [6,7]. These measurements of habitat loss can be converted into quantitative estimates of biodiversity loss through the use of the species–area relationship (Box 2), which underlies many current estimates of biodiversity decline [8–12].Remote sensing provides the area component of the equation. Public and nongovernmental conservation organizations worldwide leverage their understanding of species–area relationships with imagery-based habitat classifications to estimate species losses associated with changes inland cover and land use(Box3).The challenge is to go beyond this approach to a more detailed understanding of which species are being lost and why. How can we match existing and emerging remote-sensing technologies to parameters that have clear implications for organisms and ecosystems? Here, we review evidence that indicates that we might be close to improving greatly the detection of species, ecological communities and patterns of species richness with remote sensing. We explore recent advances in technology, addressing direct and indirect approaches to the remote sensing of biodiversity. Following the discussion of each technology, we offer examples of applications of that technology to the issue at hand.

Landsats 1AC

Conservation efforts require data – limited resources mean effective choices must be made
Harris et al 5 (Grant M., Clinton N. Jenkins, and Stuart L. Pimm, Nicholas School of the Environment and Earth Sciences at Duke, http://www.terpconnect.umd.edu/~cnjenkin/Harris_et_al_2005.pdf, accessed 7-6-11, JMB)

Tropical forest destruction is severe, resulting in the highest extinction rates of any global ecosystem ( Wilson 1992; Skole & Tucker 1993; Pimm et al. 1995; Myers et al. 2000; Pimm & Raven 2000). In large part, stemming these losses requires protecting what forest remains and setting priorities for such actions. Globally, we know where the priorities are. There is close agreement among the hotspots of Myers et al. (2000), the endemic bird area (EBA) analyses by BirdLife (Stattersfield et al. 1998), ecoregions (Olson et al. 2001), and other quantitative mapping exercises (Wege & Long 1995; Manne et al. 1999; Jetz & Rahbek 2002; Myers 2003). The next course of action is to refine conservation priorities down to scales at which managers can work. There is already an extensive literature on prioritizing areas for conservation. Some computationally sophisticated methods prioritize areas based on a detailed knowledge of species distributions (e.g., Jennings 2000; Cowling et al. 2003a, 2003b). These approaches, so compelling for species-rich and taxonomically well-surveyed places (such as the United States and South Africa), rarely extend to tropical forests, where distributional data are few. With rare exceptions, they have not been applied to hotspots, where, by definition, there are high levels of both species endemism and habitat loss (Myers et al. 2000). Here, we describe a method that helps identify areas of a practical size to help prioritize, conserve, and manage species-rich tropical forests. To exemplify the approach, we focused on threatened birds endemic to Brazil’s Atlantic Forest. Our procedure advances the science of conservation prioritization by identifying forest fragments of a few tens of square kilometers that contain the most threatened birds from an ecoregion of more than 1 million km2 . The process is simple, intuitive, and relatively fast. The method also helps with generating practical goals to produce concrete results. These characteristics will facilitate its understanding and appeal for people charged with managing tropical biodiversity. Moreover, because production costs are low, it eliminates quibbling over whether conservation dollars are better spent on improved prioritization schemes or on protecting more land. Determining what areas are important for conservation requires knowing where habitat remains. Information on species distributions is also vital. Detailed knowledge of species ranges, however, is not necessarily required. A more moderate approach is to assume one must know both the detailed distribution of species and remaining habitats. Even if one accepts this approach, a key practical consideration is how expensive (in time or resources) it will be to uncover the distribution of species versus the distribution of remaining habitats. The expense of the former is self-evident, but what about the latter? In some cases the task of setting priorities is disconcertingly simple. As an extreme example, Cebu in the Philippines has only one small patch of forest remaining (Pimm 2001). It holds the island’s known endemics and, almost certainly, its unknown ones too. When habitat loss becomes this acute, whatever habitat remains becomes the priority. On average, tropical forest hotspots covered roughly 1 million km2 , of which 100,000 km2 remain (Myers et al. 2000). Protecting the remainder is the priority (Pimm et al. 2001) and probably the most influential action that can reduce future extinctions (Pimm & Raven 2000). Unfortunately, the costs of protecting hotspots are high (Pimm et al. 2001) because the remaining habitat is still too large for immediate protection. Is all remaining habitat equally important? The answer is surely, no. Even within a hotspot certain areas hold more threatened species than others. In addition, some fraction of the remaining forest may be in patches too small and isolated to have much conservation value (Brooks et al. 1999; Ferraz et al. 2003). Unless special circumstances warrant their attention (e.g., the last refuge of an endemic species), small fragments should receive lower priority relative to larger, more connected areas