5 years ago

Forest Restoration in Landscapes

Forest Restoration in Landscapes


16 Mapping and Modelling as Tools to Set Targets, Identify Opportunities, and Measure Progress Thomas F. Allnutt Key Points to Retain Forest landscape restoration can benefit from mapping and use of geographical information systems (GIS) in several key ways, but in particular by measuring and monitoring progress toward meeting biological and socioeconomic targets via restoration. Many potential methods exist to utilise maps and GIS for landscape-scale restoration, from the simple to the highly customised and experimental. 1. Background and Explanation of the Issue Successfully planning, implementing, and monitoring projects that aim to restore forest landscapes involves the management and analysis of spatial information, that is, quantitative and qualitative two-dimensional data covering the area of interest. For example, understanding how a potential restoration site may or may not meet a biodiversity goal such as “increase overall habitat connectivity from x to y to maintain the viability of species z” requires maps and basic statistics (size, isolation, etc.) for all forest patches that occur across the landscape. Many other spatial variables influence the suitability and likely success of a given area for restoration.Therefore, map-based technologies, such as satellite remote sensing, aerial photo- graphy, and geographic information systems (GIS) have and will continue to provide many benefits to forest landscape restoration. There are many ways GIS and other spatial technologies can assist forest landscape restoration projects. At one end of the spectrum, simple maps of forest cover, elevation, rivers, communities, and roads are inherently useful for understanding the ecological and human context of the landscape. At the other extreme, sophisticated and custom spatial models may be constructed to simulate, for example, the hydrological effects of forest restoration on downstream watersheds. Here we focus on the use of spatial data to develop spatial scenarios that meet biological and socioeconomic targets. Known as “suitability modelling” or “multicriteria evaluation,” this approach is one type of GIS-based modelling utilising readily available commercial GIS packages. Specifically, in this chapter we provide (1) examples of the types of spatial data and some common map-based measures useful for planning and monitoring restoration of forest landscapes, (2) examples of spatial tools and technologies for deriving this information, and (3) reviews of several recent applications of spatial technologies to restoration. 1.1. Mapping Areas to Meet or Set Targets The targets and goals of the project determine the types of spatial data to collect and spatial analyses to conduct. There are two main types 115

116 T.F. Allnutt of targets, biological and socioeconomic. Although not all targets are spatial in nature (e.g., “prevent the extinction of species x”), many are. Some examples of spatial targets include “Protect x hectares of habitat y” or “Establish x hectares of community forest reserves.” Planning for and evaluating progress toward a target such as the latter type requires appropriate spatial data. 1.1.1. Biological Targets Often, biological targets are derived directly from existing large-scale conservation planning processes such as ecoregion conservation (ERC). 146 An initial product of an ERC vision is a set of priority landscapes designed to meet specific biological objectives, such as the conservation of an endangered primate.Where this is the case, these targets can be used directly to prioritise and implement restoration areas, for example, preferentially conduct restoration adjacent to known populations of the target primate. In other cases, no such information may exist. Here, participants may rely on basic principles of biological conservation to guide what targets to select, and thus what spatial data sets are needed. In general, space-based biological targets involve individual species (e.g., cheetah), 147 habitat, or vegetation types (e.g., wetlands), or ecological and evolutionary processes (e.g., migration, hydrology). 148 Targets for these features are typically expressed as quantitative areas or percentages of the total distribution of the biological element in question (e.g., 1000 hectares of oak-savannah). Once biological targets are established, several classes of spatial data are necessary to map where they may be achieved on the ground. In many cases, existing map sources may be used; in others, maps will have to be created using modelling or technologies such as remote sensing. 146 Dinerstein et al, 2000. 147 Lambeck, 1997. 148 Pressey et al, 2003. To evaluate species-based targets, one first needs to know the current distribution of all target species within the landscape at the finest level of detail possible. Range maps are one potential surrogate for this information and they are increasingly available for a number of taxa worldwide. 149 In other cases, modelling may be used to predict species’ distributions from field collections coupled with environmental data. 150 Often, and particularly at fine scales, field-based inventories will be required to assess the presence or absence of certain key species. Another common type of biological target involves particular habitat and/or vegetation types. Several sources of data are available to evaluate this type of target. Existing maps and classifications are often used, from national or regional inventories, for example. In other cases, new maps may be created from raw photographs or the processing of photographs or digital images. The most widespread source is remote sensing—typically photographs or digital imagery from airplanes or satelliteborne sensors. New, high-resolution imagery (submetre) provides a good source for mapping natural habitats as well as human land uses, though cost can be a significant constraint. In areas of high species and habitat heterogeneity, optical remote-sensing may not be able to distinguish biological differences to a necessary degree. Forest that is indistinguishable spectrally—from the perspective of a camera or satellite—is often very diverse biologically. Here, habitat modelling can be used to map areas where one expects species to differ significantly. A range of approaches are available, from the quick and approximate, to more formal statistical methods. 151 Elevation, for example, is often used as a proxy for species’ distributions, and can be used to quickly divide a continuously mapped forest type into several or more forest habitats (lowland, sub-montane, montane, etc.). 149 Ridgely et al, 2003. 150 Boitani et al, 1999. 151 Ferrier et al, 2002.

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