Forests Sourcebook - HCV Resource Network

NOTE 7.3
Spatial Monitoring of Forests
Remote sensing (RS) has become part of most forest
management strategy implementations. While the
technology for RS has evolved, aerial photography
has been in use for almost a century, and satellites have been
recording forest change for more than 35 years. Landsat,
launched in 1972, was one of the first satellites widely used
for remote sensing. Since then, Landsat has been a workhorse
for scientists interested in measuring changes in the
distribution and condition of forests. Remote sensing (aerial
flights, aerial photography, optical and radar satellite
imagery) and GIS have aided forest mapping and forest
inventories for many years.
Two trends have stimulated the rise of spatial monitoring.
First, technological advances in RS/GIS make these
tools more accessible; human capacity to manipulate this
data has also increased while information has become more
accurate, less expensive, and more freely circulated. In addition,
there is a wider choice of satellites, prices of satellites
have deceased significantly, 1 the Internet is widely used to
share data, and more affordable and more user-friendly
RS/GIS software is now readily available.
Second, in addition to these technical advances, a variety
of needs are increasing demand for forest monitoring. The
impetus to monitor comes from, among other things, the
need to assess national-level compliance with international
conventions, and measure global public goods (for example,
carbon sequestration, area under protection for conservation
purposes, and the like). RS/GIS technology provides
the data and the tools needed for monitoring by enabling
precise overlays between different time periods within specific
boundaries, and by storing and analyzing the data.
However, because inventories and mapping are frequently
time intensive and expensive, they are often carried out only
in specific regions or for specific purposes. To date, only six
attempts to map forests worldwide exist.
Various local, national, and international entities systematically
collect, examine, and disseminate data about
forest resources throughout the world. Nevertheless, efforts
at these different levels to systematically collect, examine,
and disseminate data about forest resources are done independently
of one another, and use different definitions and
measurements. Accordingly, comparison of results is difficult,
major knowledge gaps remain in large areas, and
duplications exist in others. Only a few countries use spatial
information for national forest inventories that are updated
and suitable to address current environmental issues (see
boxes 7.16 and 7.17 for examples).
Spatial monitoring has become increasingly important
in the context of REDD. For REDD, even minimum
requirements to develop national deforestation databases
using typical and internationally agreed on methods will
require using RS data. The data would help assess gross
deforestation, possibly develop a map of national forest
area, and present a visual representation of forest cover
change. Spatial monitoring, in some cases, is already part of
discussions for monitoring land use, land-use change and
forestry (LULUCF) (see box 7.18).
Independent of demand, there are challenges to effective
spatial monitoring of forests:
■
Availability of RS data. Detailed data (that is, certain
satellite sensors and aerial photographs) must be
ordered in advance and may not be available in the short
term. Clouds often hamper monitoring in the tropics
and in mountainous areas, and can become a major
problem, especially for data acquired with less frequency
(for example, it can take more than a year to get a
detailed resolution image with less than 30 percent cloud
cover in Indonesia). Because higher temporal resolution
satellites acquire data more often, they have better opportunities
to acquire imagery with fewer clouds. Another
alternative is the use of radar satellites because radar sensors
“see” through clouds. Nevertheless, radar applications
for forest monitoring are still uncommon.
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