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Forests Sourcebook - HCV Resource Network

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Assessment (www.millenniumassessment.org). Another<br />

good framework, although still being piloted and not yet<br />

implemented at regional and global levels, is the “Manual<br />

for Environmental and Economic Accounts” (FAO 2004).<br />

Using existing and new methodologies. Advancements<br />

in methodology and technology are a constant phenomenon<br />

in forest monitoring, as evidenced by the advancements<br />

in remote sensing, a particularly useful tool for studying forest<br />

change comprehensively and uniformly across time and<br />

space. Remote sensing multiplies the value of field-plot data<br />

and permits complete, wall-to-wall analyses. Spatial images<br />

from Landsat have been a workhorse for scientists interested<br />

in measuring changes in the distribution and condition of<br />

forests. A number of satellites have come online since Landsat,<br />

several of which are useful for monitoring forests<br />

(USDA 2007). Other advancements in forest monitoring<br />

approaches have been based on imaging radar data, which<br />

works in all weather conditions. Methodological advancements,<br />

too, such as simplifying and automating mapping of<br />

forest disturbances (USDA 2007), have been important.<br />

Older technologies, such as mobile data loggers and<br />

mobile communication, could be more effectively used for<br />

monitoring forest activities. Mobile telephone communication<br />

can serve two purposes: It can increase the safety of<br />

field crews by giving them access to communication in cases<br />

of emergency, while also enabling online data entry to a central<br />

database. Advancements in software, such as GIS 2 , also<br />

offer many possibilities for presentation of results and<br />

improved access to information.<br />

The use of some of the newer technologies is not without<br />

shortcomings. For example, the availability of reference data<br />

for digital image analysis or visual interpretation is one of<br />

the key problems in remote sensing–aided surveys. The<br />

parameters that can be estimated using remote-sensing surveys<br />

often depend on the intensity of the field sampling.<br />

Area of forests, other wooded land, and other land types, as<br />

well as their changes, can be adequately measured with<br />

available remote sensing–aided surveys. In contrast, tree<br />

stem volume and biomass, which are critical variables in<br />

determining the status of the world’s forests, require thorough<br />

field measurements for their estimation. Such limitations<br />

are constantly being dealt with.<br />

Cost of monitoring. Cost is often an important driver in<br />

designing monitoring systems. A frequently asked question<br />

is when and whether satellite imagery–based forest cover<br />

mapping can substitute for forest inventory. Mapping studies<br />

cost less than field work (if the necessary hardware and<br />

software are available); need less planning, smaller teams,<br />

and less broad expertise; are partly independent of weather;<br />

and provide maps as the major product, which are usually<br />

more easily accepted and “marketed” than statistics and<br />

tables with error specifications.<br />

The type of analysis required often influences the cost.<br />

High-resolution images are notably more expensive than<br />

coarse images. Tomppo and Czaplewski (2002) estimated<br />

costs for remote sensing under different resolutions and<br />

sampling options for regional and global surveys using<br />

remote sensing and field data (see tables 7.2 and 7.3). The<br />

costs have since changed significantly, but this information<br />

provides an indication of the differences in costs.<br />

Selection of indicators. Assessing all benefits from<br />

forests over time is complicated, and only feasible if simplifications<br />

and approximations, such as indicators, are<br />

accepted. Several international processes have developed<br />

criteria and indicators for sustainable forest management.<br />

Table 7.2<br />

Example of Number of Images and Estimated Costs for a Remote-Sensing Survey with<br />

Different Resolution and Sampling Options<br />

Number of images needed<br />

Imaging cost (US$ thousand)<br />

MODIS Landsat Ikonos Ikonos Landsat Ikonos Ikonos<br />

full 10% 0.1% 1% 10% 0.1% 1%<br />

Region coverage coverage coverage coverage coverage coverage coverage<br />

Africa 6 97 331 3,309 58 951 8,992<br />

Asia 6 100 343 3,428 60 986 9,315<br />

Europe 4 73 251 2,511 44 722 6,824<br />

North and Central America 4 69 237 2,374 42 683 6,453<br />

Oceania 2 28 94 943 17 271 2,564<br />

South America 3 57 195 1,950 34 561 5,299<br />

Total 25 424 1,451 14,515 255 4,174 39,447<br />

Source: Tomppo and Czaplewski 2002.<br />

Note: MODIS and Landsat are NASA satellite imaging programs; Ikonos is is a commercial Earth observation satellite.<br />

254 CHAPTER 7: MONITORING AND INFORMATION SYSTEMS FOR FOREST MANAGEMENT

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