Improving the Land Degradation Assessment

An accurate assessment of land degradation at a flexible scale combining socioeconomic and biophysical aspects and driving forces is needed to plan actions and investments to reverse land degradation to improve socioeconomic livelihoods and to conserve dryland ecosystems and their unique biological diversity. A further advance is the use of the ecosystem approach as a framework for action under the Convention on Biological Diversity (www.biodiv.org) and as a strategy for the integrated management of land, water and biological resources.

Recent advances in participatory planning and management of resources, integrated ecosystem approaches to resources assessment using economic-ecological zoning, remote sensing and Geographic Information System (GIS) techniques, as well as the economic valuation of soil loss and strategic impact assessments of policies and interventions provide an opportunity for developing an improved land degradation assessment system.

In addition to a Global Water Resources Assessment and Global Biodiversity Assessment, the International Institute for Applied Systems Analysis and FAO have developed a system for rational land-use planning on the basis of agroecological zones methodology (Fischer et al., 2000). This methodology can be applied at national, regional, and local levels. The International Food Policy Research Institute and World Resources Institute have undertaken a comprehensive assessment of the earth's ecosystems, such as a Pilot Analysis of Global Ecosystems and Millennium Ecosystem Assessment, sponsored by the Global Environment Facility, the United Nations Foundation, the Packard Foundations, and the World Bank (Reid, 2000). UNEP and FAO have developed guidelines for erosion and desertification control management with particular reference to Mediterranean coastal areas (UNEP/MAP/PAP, 2000). Socioeconomic data sets (Global Farming System Study, Food Insecurity and Vulnerability Mapping systems) have also become increasingly available (www.fao.org).

The vegetation index based on data from the National Oceanic and Atmospheric Administration (NOAA) (chapters 5 and 6), high-resolution satellite data (e.g., Landsat TM, SPOT, RADARSAT, etc.), meteorological satellite (METEOSAT) data (chapters 19 and 32), and GIS techniques that are now available and have been used cost effectively for mapping risk of erosion or land degradation using GIS by overlaying different thematic layers and using a model have been tested in a number of studies, such as the FAO study in Parana, Brazil (FAO, 1997). The method of assessing degradation depends largely on the spatial scaleā€”for example, regional scale versus continental scale, as shown in table 33.1.

Table 33.1 A comparison of various methods for land degradation assessment

Method

Advantages

Problems

Scales applicable

Factors to which applicable

Relative cost

Expert

Rapid, low

Subjectivity,

Applied at

Soil,

Low

opinion

cost

reliability

global, but

vegetation,

potential for

water

all scales

Remote

Fairly rapid,

Separating

Global,

Vegetation,

Moderately

sensing

objective,

change in

regional,

land cover

low

Large area

land use from

national,

changes

coverage

degradation

local

Field

Directly

Slowness and

Local, plus

Soil,

Relatively

monitoring

indicates

high cost

district to

vegetation,

high

change in

national on

water,

condition

sampling

biodiversity

of land,

basis

quantitative

Productivity

Degradation

Variations in

National,

Crop

Low at

changes

is defined in

management

district, local

production,

national, high

these terms

practices

animal

at local levels

production

Field criteria

Acquires

Slowness,

Local; district

Soil (includ

Relatively

and talking

grass-roots

subjectivity

on sampling

ing erosion),

high,

with farmers

view, links

basis

vegetation,

practical only

degradation

water, bio-

on sampling

with cause,

diversity, so-

basis

impact,

cioeconomic

response

indicators

Modeling

Rapid,

Danger that

All scales

Soil, water

Relatively

low cost,

users confuse

very low

potential for

modeling

extrapolation

with results

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