5 years ago

Forest Restoration in Landscapes

Forest Restoration in Landscapes

The spatial

The spatial configuration of the restoration landscape is of critical importance for biodiversity conservation for several reasons. One, the long-term survival of many species often depends directly on the size and connectivity of available habitat. The reasons for this are generally (a) individuals and populations require sufficient outbreeding opportunities that are only available in habitat blocks of a particular size, and (b) the species in question has ecological requirements (e.g., seasonal migration) that require large connected blocks of habitat. In both cases, research may be necessary to assess the habitat configuration necessary for the target species. Two, many environmental and ecological processes will not be maintained once habitat fragments drop below a particular threshold of isolation or fragmentation. The maintenance of natural hydrological flows in watersheds, for example, can depend on the size and connectivity of intact forest blocks. 1.1.2. Socioeconomic Targets The second major class of targets are socioeconomic. In some cases, socioeconomic targets will have been specified when the landscape was identified within a priority setting exercise (e.g., the visioning process in ecoregion conservation), though this is less often the case than with biological targets. Socioeconomic targets that require spatial data generally specify target amounts of land uses within the landscape. This may involve zoning one portion of the landscape for a particular land use. For example, participants may wish to have one third of the landscape devoted to community forestry. In other cases, the entire landscape (apart from those areas reserved for biodiversity conservation) may be zoned for particular land uses, akin to a traditional land-use plan or zoning map. Mapping areas to meet socioeconomic targets requires a detailed and up-to-date landcover map. This map shows the current distribution of natural and human-oriented areas in as much detail and at as fine a scale as possible and it can be derived from existing land-use/ land-cover maps for the area, or may be created 16. Mapping and Modelling 117 from aerial and remote sensing sources coupled with ground truth.The map of current land uses serves as the starting point; a map of future land uses shows those areas where changes in land uses will be necessary to meet socioeconomic targets. 1.1.3. Land Tenure and Land Value The legal status and ownership of land (land tenure) within the landscape, and the economic value of that land are also important for planning forest landscape restoration. Sometimes this information can be derived from existing maps available from local or national government organisations, particularly in the case of land tenure. In other cases, ground surveys will need to be conducted to establish tenure and land value of unknown areas. Spatial economic modelling has also been used to estimate land value. Rules are constructed that allow one to estimate the value of every parcel of land within the area of interest, based on variables such as market access, for example. 1.3. Mapping Opportunities: Integrating Biological and Socioeconomic Data to Meet Targets and Map Opportunities Some areas are more suitable than others for particular uses. Analysis of spatial data has the potential to efficiently allocate areas to one use or another. This idea is formalised in land-use plans or more formally via suitability modelling otherwise known as multicriteria evaluation (MCE). 152 Suitability modelling or MCE using GIS can be used to systematically combine spatial, biological, physical and socioeconomic data detailed above in order to meet biological and socioeconomic objectives via restoration. Here are two generic examples: 1. Map suitability for a single biological or socioeconomic target. As an example, imagine 152 Eastman et al, 1993.

118 T.F. Allnutt one biological target for the landscape is to maintain a viable population of a primate. It is estimated that the target primate requires 25,000 hectares of habitat between 1000 and 3000m in elevation, in a single, connected block of forest. There are currently only 15,000 hectares of suitable forest within the landscape, in two disconnected blocks. Therefore, the challenge is to map at least 10,000 hectares to restore based on the habitat criteria required for the species: elevation, size, and connectivity. Three maps are created. One shows all areas in the target range of 1000 to 3000m, one ranks areas according to their potential to rejoin the disconnected blocks, and one ranks areas by their proximity to existing good habitat for the primate. These three maps are standardised to a common numeric range, and then combined by means of a weighted average, to produce a continuous map of suitability.The most suitable areas are those that are close to existing intact habitat, connect the two blocks, and are the right elevation.The highest scoring areas (those that come close to meeting all three criteria) are selected until the target of 10,000 hectares is met.These form the priority restoration areas for this biological target. The same process may be used to map suitable areas for socioeconomic targets. 2. Incorporating socioeconomic data as a constraint on suitable areas for biological targets. Just as physical and biological criteria may be combined to identify suitable restoration areas to meet biological targets, socioeconomic criteria, such as land use or land value, can also be incorporated in the process. For example, imagine two parcels of land that, when restored, would be equal in every way for meeting the above biological target. They are equivalent in elevation, in proximity to existing forest, and in terms of connecting the two forest blocks. One parcel is currently actively used for agricultural production, whereas the other has been abandoned for several years. For several reasons, it would likely be easier to restore the abandoned parcel. Thus, including socioeconomic data in the MCE process can help to efficiently identify restoration priorities when there are choices of areas to meet biological targets. 1.4. Monitoring A key benefit of using quantitative spatial data and targets for both biological and socioeconomic variables throughout the planning and implementation process is that it facilitates long-term monitoring as the project proceeds. Remote sensing in particular provides a relatively quick and inexpensive, synoptic, repeatable view of large-scale changes to land uses and land cover over time within the landscape. Clearly this will have to be paired with reviews of progress toward those biological and socioeconomic targets that cannot be measured remotely. A current disadvantage is the lack of long-term large-scale attempts at systematic monitoring of conservation programmes, though efforts are currently underway at a number of places and institutions. 2. Examples Examples abound of the use of maps and GIS in the fields of planning and conservation. 153 Generally speaking, however, there are few examples of its application to forest restoration planning. One exception is the recent work of J. Halperin, in which GIS was used for participatory, community-based, large-scale restoration planning in Uganda. 154 The WWF network has only recently begun to apply GIS to its restoration initiatives. The United Nations’ Environment Programme- World Conservation Monitoring Centre (UNEP-WCMC) used GIS to prioritise areas for WWF-based restoration projects in North Africa. 155 Biological attributes such as species’ richness, forest integrity, and patch size were balanced against human pressures including road density, grazing pressure, and resource use. As of early 2004, there are two additional projects underway. In one, in the Andresito landscape (Argentina) of the Atlantic Forest, there are plans to use suitability modelling with IDRISI to identify key restoration corridors in 153 see e.g., Eghenter, 2000; Herrman and Osinski, 1999. 154 Halperin et al, 2004. 155 UNEP-WCMC, 2003.

Forest Landscape Restoration - IUCN