SES parameters

Biodiversity indices - forests & shrubland

medesdesire@googlemail.com (Jane Brandt) — Mon, 17 Sep 2012 12:13:43 +0000

Biodiversity is related to species richness, density, and diversity of an ecosystem. If an ecosystem is overexploited, the consequence is the loss of biodiversity potentially expressed by direct loss of vegetation and animal species. In forest/shrubland ecosystems, biodiversity indices are usually applied both at forest and landscape level. At forest level several indexes are commonly used, depending on data availability (field surveys, forest inventory with qualitative and quantitative information (Corona et al., 1989), forest map with qualitative information based on field surveys (Costantini et al. 2006), forest map based on supervised remote sensed classification, etc.). In this ambit a widely used index is the Diversity index as proposed by Shannon and Weaver (1949). At regional level a well known index is the γ diversity (Whittaker, 1972).

Aim of the method/technique	The proposed techniques aim at characterizing the species composition of the forest component both at forest and regional level.
Scale – spatial and temporal	Scale can vary from local to regional, based on availability of forest surveys. Updates of forest maps data collection usually vary from 10 to 20 years.
Brief description	Forest level. Starting from the information related to a forest map (type and percentage of species present in each elementary unit), it is possible to characterize the forest biodiversity by using Shannon and Weaver (1949) diversity index: Where, D is the diversity index; i is the i-th species in the elementary unit; n is the number of species in the elementary unit; Ei is the proportion of i-th species in the elementary unit; ; logEi is the logarithm (base 10) of Ei . Regional level. An index to assess the biodiversity at landscape level is the γ diversity (that measures the overall diversity within a large region) as proposed by Whittaker (1972).
Data requirements	Vegetation maps, Forestry maps with single species composition content (type and percentage of species present in each elementary area mapped), remote sensed data (Modis, Landsat TM), land cover maps.
Main applications in forests & shrubland regions	Description of biodiversity levels in forest/shrubland ecosystems, both at forest and regional level, can be useful in assessing the impacts of various responses to LEDD.
Strengths and weaknesses	Strengths are referred to the capability to apply very simple analysis also with opportunity for diachronic analysis. Possible weaknesses stand in the (sometimes restricted) spatial and temporal availability of data.

Soil water availability

medesdesire@googlemail.com (Jane Brandt) — Mon, 17 Sep 2012 11:39:15 +0000

In the last decades several numerical hydrologic models has been developed to answer to the questions related to the water resource availability in forested soils due to the impacts that have on forest growth and management. In this ambit it is possible to evaluate a soil moisture index through the hydrological model used by several authors (Coops et al., 2005; Coops and Waring, 2001a; Coops and Waring, 2001b; Waring and Running, 2007).

Aim of the method/technique

The soil moisture index provides assessments of the water available in forest soil both over space and time.

Scale – spatial and temporal

The model has been originally designed for site specific applications, but due to its simple modelling approach it has been modified and implemented in a GIS environment from local to regional scale with a variable temporal resolution from days to years.

Brief description

To assess the soil moisture the model integrates a simplified physiological based hydrological balance routine, implemented in a GIS environment. In particular the model assesses the soil water availability represented by a soil moisture index, as the difference between monthly evapotranspiration and monthly precipitation starting from the simple Penman-Monteith approach, Landsberg & Waring, (1997). The water balance model is parameterized as a function of the main ecosystem characteristics involved in the main processes as edaphic features (soil texture and depth), forest types and forest cover, land use and leaf area index (LAI). According to this methodological approach the model can be implemented at different scale and vegetation types by introducing modified Penman-Monteith functions to assess evapotranspiration for different vegetation types. In figure 8 an example of soil moisture estimate is reported.

Monthly estimate of soil moisture in Basilicata Region (Source: Nolè et al., 2008)

Data requirements

The model routine requires daily meteorological data:

Precipitation (mm)
Maximum air temperature (°C)
Minimum air temperature (°C)
Relative humidity (%)
Soil temperature, ave. 24-h (°C)
Short-wave radiation (kJ m-² day-¹)
Leaf area index (m² m-²)
Daylight average air temperature (°C)
Average night minimum temperature (°C)
Vapor pressure deficit (mbar)
Absolute humidity deficit (g m-³)
Daylength (s)
Canopy daily average radiation (kJ m-² day-¹)

Main applications in forests & shrubland regions

The model has been applied over a wide range of forest types in North America, New Zealand, Australia and Europe as subroutine to assess the soil moisture of forest stands in a wider framework of ecosystem productivity assessment (Coops and Waring 2001a, 2001b; Coops et al. 2001, 2005; Law et al., 2000; Sands and Landsberg, 2002; Tickle et al. 2001; Xenakis et al., 2008), including the Italian forest systems for the desertnet (Nolè et al., 2008, 2009).

Strengths and weaknesses

Strengths:

High flexibility in temporal scale and coverage, ranging from days to millennia
Flexible in spatial coverage, ranging from single site-specific application to regional scale
Provide crucial links between biophysical conditions and human activities, especially crops and forestry

Weaknesses:

Some ecological functions request the use of empirical formulations
Site-specific parameterisation
Availability of spatial input information

Air temperature

medesdesire@googlemail.com (Jane Brandt) — Wed, 05 Sep 2012 11:10:09 +0000

The air temperature an area is usually described in terms of average (daily or monthly) values, average monthly minimum and maximum values, and monthly absolute minimum and maximum values. These characteristics are presented in graphs showing the change of air temperature with time.

Aim of the method /technique

The method aims to describe basic characteristics of air temperature which are: (a) average daily and monthly values, (b) daily and monthly average minimum and maximum values and (c) absolute monthly minimum and maximum values.

Scale – spatial and temporal

Air temperature data are based on point observations (meteorological stations). The link with soils, vegetation, and landscape requires the allocation of the geographical area covered by each data collection station. Thiessen polygons can be used for regionalization of point climatic data. Thiessen network can be corrected taking into consideration topographic features. A period of at least 30 years is necessary to describe the prevailing air temperatures of an area.

Brief description

Air temperature data are recorded on hourly or daily basis. Altitude and geographical coordinates of the observation station in meters are necessary for environmental studies. The average daily or monthly values are calculated by the following equation (Steel et al. 1997):

Where xi is one value from a set of numbers i=1 to n, and n is the number of observations.

Data requirements

Data required to calculate mean daily and monthly characteristic values of air temperature are provided by the regional or national Meteorological Services or the regional administration. The necessary data are: (a) daily air temperature, (b) daily minimum air temperature, and (c) daily maximum air temperature. Recently, meteorological data are collected with automatic meteorological stations in which data are collected continuously in a data logger and transmitted in central administration office for elaboration (Figure 1).

Figure 1. A data logger in which the various sensors of climate parameters are connected for continuous monitoring (Source: C. Kosmas)

Main applications in cropland and forests & shrubland regions

Air temperature characteristics of an area constitute a basic part of natural resources inventory and ecosystem performance in cropland regions such as plant growth, soil water balance, soil water evaporation, ecosystem resilience, biodiversity, etc.

Strengths and weaknesses

The proposed method includes standard basic statistical analysis used for biological studies. The main weakness of the proposed method is usually attributed to the basic data required (quality and time period of data available) for the analysis.

Rainfall

medesdesire@googlemail.com (Jane Brandt) — Thu, 06 Sep 2012 04:30:54 +0000

Rainfall is a basic component of climatic characteristics of an area. Rainfall characteristics of an area for the purpose of this project are: (a) graphs of daily and monthly rainfall, (b) graphs of mean monthly rainfall change with time including standard deviation values.

Aim of the method /technique	The method aims to describe: (a) the detail change of rainfall year by year, (b) the monthly rainfall change with time within hydrological year, and (c) the average monthly rainfall with time in years.
Scale – spatial and temporal	Rainfall data are based on point observations (meteorological stations). The link with landscape characteristics requires the assessment of the geographical area covered by each meteorological station. Thiessen polygons can be used for regionalization of point rainfall data. Thiessen network can be corrected taking into consideration topographic features. A period of at least 30 years is necessary to describe the prevailing amount of rainfall in a study area.
Brief description	Average values of rainfall for a certain period are calculated by the equation given above. Deviation (SN) of rainfall per month or year is calculated by the following equation (Steel et al., 1997): Monthly rainfall can be plotted in the same graph with monthly air temperature for deriving the umbrothermic diagram. For drawing this diagram, time is plotted in the x-axis in months, the left y-axis indicates rainfall in mm, and the right y-axis indicates air temperature. The precipitation scale must be twice the scale of air temperature. The area in which air temperature curve is above rainfall curve gives the Bagnouls-Gaussen aridity index.
Data requirements	Longitudinal daily or monthly data are required. Such data are provided by the regional or national Meteorological Services or the regional administration.
Main applications in cropland and forests & shrubland regions	Rainfall change with time and average values for an cropland region are basic characteristics for natural resources inventory and ecosystem performance such as plant growth, soil water balance, soil erosion, land desertification, ecosystem resilience, biodiversity, etc.
Strengths and weaknesses	The proposed method includes standard basic statistical analysis used for biological studies. The main weakness of the proposed method is usually attributed to the availability of raw data required for the analysis.

Rainfall seasonality

medesdesire@googlemail.com (Jane Brandt) — Sat, 08 Sep 2012 12:48:06 +0000

Rainfall seasonality is related to the temporal distribution of rainfall on a monthly basis. Rainfall seasonality can be estimated by the Walsh and Lawler (1981) index.

Aim of the method/technique

The method aims to characterize the distribution of precipitation throughout the year and to classify the climate of an area. For example the climate of an area can be characterized as rather seasonal with a short dry season or marked seasonal with a long dry season, depending on the distribution of rainfall during the year.

Scale – spatial and temporal

The calculation of rainfall seasonality requires climatic data. Therefore, the spatial scale of the index requires regionalization according to the method proposed for the raw climatic data. A period of at least 30 years data is necessary.

Brief description

The Seasonality Index (SI) (Walsh and Lawler, 1981) can be estimated using the following equation:

Where Ri is the total annual precipitation for the particular year under study and Xin is the actual monthly precipitation for month n. The various categories of climate based on rainfall seasonality can be assessed by using the following values of the SI index.

SI	Precipitation Regime
<0.19	Precipitation spread throughout the year
0.20-0.39	Precipitation spread throughout the year, but with a definite wetter season
0.40-0.59	Rather seasonal with a short dry season
0.60-0.79	Seasonal
0.80-0.99	Marked seasonal with a long dry season
1.00-1.19	Most precipitation in <3 months

Data requirements

Data required for the calculation of the rainfall seasonality index are: (a) average monthly rainfall, and (b) average annual rainfall.

Main applications in cropland and forests & shrubland regions

Rainfall seasonality index is a critical environmental factor affecting the evolution of natural vegetation. The seasonality index classifies the type of climate in relation to water availability. The higher the seasonality index of a region the greater the water resources variability and scarcity in time, the more vulnerable the area to desertification.

Strengths and weaknesses

The proposed method is very simple with few data required for calculation. However, since meteorological data are point observations the seasonality index has to be regionalized based on the Thiessen method. A period of at least 30 years of rainfall data are required to describe the prevailing rainfall seasonality index of an area.

Potential evapotranspiration

medesdesire@googlemail.com (Jane Brandt) — Sat, 08 Sep 2012 12:53:31 +0000

Evaporation of water from the soil and plant surfaces and transpiration from the stomata cavities is difficult to measure, since the rate of water vapour movement from several surfaces into a dynamic environment varies with time. Furthermore, the process of taking measurements can alter the local climate around the plant and change the actual rate of evaporation or transpiration. Therefore, evaporation and transpiration fluxes are combined and called evapotranspiration (ETo).

Evapotranspiration can be measured or calculated from existing meteorological data. There are several methods for calculating ETo from meteorological data. The simplest method uses the average air temperature. The most complex methods require hourly data, such as solar radiation, air temperature, wind speed, and the vapour pressure. The Penman-Monteith method as modified by Allen (1986) has been proposed for the LEDDRA project.

Aim of the method/technique

The method aims to calculate the amount of water required from a crop for normal growth.

Scale – spatial and temporal

Meteorological data are based on point observations (meteorological stations). The link with landscape characteristics requires the assessment of the geographical area covered by each meteorological station. The regionalization of ETo can be made by drawing the Thiessen polygons. A period of at least 30 years is necessary to describe the prevailing evapotranspiration rates in a study area.

Brief description

The potential evapotranspiration (ETo) can be estimated by the following equation (http://www.fao.org/docrep/X0490E/x0490e06.htm):

Where:

ETo	reference evapotranspiration [mm day-¹],
Rn	net radiation at the crop surface [MJ m-² day-¹]
G	soil heat flux density [MJ m-² day-¹]
T	mean daily air temperature at 2 m height [°C],
u2	wind speed at 2 m height [m s-¹],
es	saturation vapour pressure [kPa],
ea	actual vapour pressure [kPa],
es - ea	saturation vapour pressure deficit [kPa],
Δ	slope vapour pressure curve [kPa °C-¹],
Υ	psychrometric constant [kPa °C-¹].

The web page with details for calculating ETo is: http://www.fao.org/nr/water/eto.html.

Data requirements

The modified Penman-Monteith equation requires the following meteorological data: air temperature (minimum and maximum), relative humidity, wind speed, and solar radiation. The method can also be adjusted to the physical features of the local weather station.

Main applications in cropland and forests & shrubland regions

Potential evapotranspiration rate is basic characteristics for natural ecosystem performance such as plant growth, soil water balance, soil erosion, land desertification, ecosystem resilience, land vulnerability to degradation, biodiversity, etc. In cropland, irrigation water requirements for a crop can be roughly defined as the difference between ETo and precipitation. The amount of rainfall water runoff from the soil surface is greatly affected by ETo values.

Strengths and weaknesses

The method for calculating ETo is easily used. However, several climatic parameters are required compared to other methods. The modified Penman method was frequently found to overestimate ETo, even by up to 20% for low evaporative conditions. It may require local calibration of the wind function to achieve satisfactory results. It is a method with strong likelihood of correctly predicting ETo in a wide range of locations and climates.

Aridity index

medesdesire@googlemail.com (Jane Brandt) — Sat, 08 Sep 2012 12:55:42 +0000

Aridity index (AI) is an estimate of the average water available in the soil. This index is usually defined as the ratio between mean annual precipitation (P) and mean annual evapotranspiration (ETo). The aridity index can be estimated by the Bagnouls-Gaussen index (BGI) that is also a component of the Climate Quality Index, as described below.

Aim of the method/technique	The proposed method aims to classify the climate in relation to water availability. The higher the BGI, the greater the water deficiency for the growing plants in an area. For example areas with BGI greater than 150 is considered as extremely dry.
Scale – spatial and temporal	The calculation of BGI requires climatic data. Therefore, the spatial scale of the index requires regionalization according to the method proposed for the raw climatic data. A period of at least 30 years is necessary to describe the prevailing aridity in a study area.
Brief description	The Bagnouls-Gaussen aridity index (BGI) can been calculated by the following equation: Where: ti is the mean air temperature for month i in 0°C, Pi is the total precipitation for month i in mm; and k represents the proportion of month during which 2ti - Pi >0. The k value can be determined by the umbro-thermic diagram. Based on the DESERTLINKS European research project, the following classes are distinguished for this index: (a) very low aridity, BGI<50, (b) low aridity, BGI ranging from 50-75, (c) moderate aridity, BGI ranging from 75-100, (d) high aridity, BGI ranging from 100-125, (e) very high aridity, BGI ranging from 125=150, and (f) extremely dry aridity, BGI>150.
Data requirements	The data required for the calculation of BGI are: (a) average monthly air temperature, and (b) average monthly amount of rainfall.
Main applications in cropland and forests & shrubland regions	Aridity is a critical environmental factor affecting the evolution of natural vegetation. The aridity index classifies the type of climate in relation to water availability. The higher the aridity index of a region the greater the water resources variability and scarcity over time, the more vulnerable to desertification is the area.
Strengths and weaknesses	The proposed method is very simple with low data requirements. However, since the meteorological data are point observations the BGI has to be regionalized based on the Thiessen method. A period of at least 30 years of meteorological data are required.

Climate quality index

medesdesire@googlemail.com (Jane Brandt) — Sat, 08 Sep 2012 12:59:32 +0000

Climate quality is a composite indicator assessed by using parameters that influence water availability to plants, as well as climatic hazards such as frost that inhibit or even prohibit plant growth.

Aim of the method/technique	The method aims to evaluate the quality of the climate with respect to the sensitivity of an area to land degradation and desertification. It is an index proposed by the MEDALUS III European research project for defining environmentally sensitive areas to desertification (ESA).
Scale – spatial and temporal	The scale depends on the scales of the input data and can vary from local to regional (from 30m to 1km of resolution). Meteorological data are usually collected daily and thus averaged on monthly basis. A period of at least 30 years is necessary to describe the prevailing climate quality index of an area.
Brief description	Climate Quality Index (CQI) is assessed using parameters affecting water availability to plants, such as amount of rainfall, aridity, and slope aspect. Slope aspect is introduced here since it affects micro-climate characteristics of hilly or mountainous areas. This index is calculated by the equation (Kosmas et al. 1999): CQI= (Total annual precipitation * Bagnouls & Gaussen aridity index * Aspect)¹/³
Data requirements	The data required to calculate CQI are monthly and annual rainfall in mm, monthly air temperature in °C, and topographic characteristics of the study area in relation to slope orientation (slope aspect).
Main applications in cropland and forests & shrubland regions	The Climate Quality Index can be used to evaluate the climate quality of cropland and forest regions and to obtain to evaluate sensitivity to land degradation and desertification as in the ESA index, developed within the project MEDALUS (Kosmas et al., 1999) and applied in several forest environments (Ferrara et al., 2008; Ferrara et al. 2009)
Strengths and weaknesses	The possibility to combine climatic data to obtain a CQI map allows verifying regions that are subjected to drier climatic conditions and require more attention for management planning. The strengths are about the possibility to condense climate properties in a index that can be referred to land degradation. The main weaknesses are associated with the availability of input data in time and space.

Geology

medesdesire@googlemail.com (Jane Brandt) — Sat, 08 Sep 2012 13:00:42 +0000

The knowledge of the geology of an area requires mapping through field surveying, sampling, laboratory analysis and processing of the data using existing classification systems. One of the crucial points of geology mapping is the scale of the map.

Aim of the method/technique

The aim of the geological mapping is to derive a geological map presenting the various geological formations. Geological information can be used for environmental studies such as understanding basic properties of the formed soils, the hydrology of an area, and land and environmental vulnerability to degradation. Geology is used as a parameter in land use planning for agricultural areas and environmental impact assessments. Natural hazards such as landslides and slope stability is highly related to the stratigraphy of the geological formations.

Scale – spatial and temporal

At European level the implementation of geological mapping is delegated to the official national Geological Surveys. As example of a national level, the Geological Map of Italy (scale 1:100,000), is currently the only official geological map of the country, whilst the CARG project is currently working on the completion of the new Geological Map at 1:50,000 scale, which is the common used scale.
The National Geological Surveys of EU countries provided the geological synthesis for the European Map at 1:1,000,000 scale, as required by European Directive INSPIRE (May 2007). Following the INSPIRE Directive the "One Geology-Europe” project has been implemented to identify and share digital geological data across Europe.
The information of the existing geological maps is usually not enough for detailed studies of land degradation processes. In such cases, field surveys are conducted by observing the geological materials on existing cuts. As an example, the geological map of Crete (Figure 1) presents the geological parent materials of the uppermost earth geological layer that affects soil characteristics.

Figure 1. Geological map of Crete characterized as parent material of the existing soils (Source: LEDDRA project)

Brief description

Based on the Geo-referenced Soil Database for Europe (Finke et al. 1998), the following categories of geological materials are distinguished:
•   Consolidated clastic sedimentary rocks (conglomerate, sandstone, claystone, siltstone, etc.).
•   Calcareous and non clastic siliceous sedimentary rocks and sulphates (limestone, marl, evaporates, chert, etc.)
•   Igneous rocks (acid to intermediate plutonic rocks, acid to intermediate volcanic rocks. Basic plutonic rocks, basic volcanic rocks, ultrabasic rocks, pyroclastics rocks).
•   Metamorphic rocks (non calcareous metamorphic such as gneiss, mica schist, calcaric metamorphic such as marble).
•   Unconsolidated alluvial deposits (marine and estuarine deposits, fluvial and lacustrine deposits, lake deposits).
•   Unconsolidated glacial deposits (glacial drift. glaciofluvial deposits, etc.).
•   Eolian deposits (loess, eolian sands).
•   Residual and redeposited materials (slope deposits, redeposited clay, etc.)
•   Other rocks and deposits (organic material, anthropogenic deposits).

Data requirements

The data required for compiling a geological map of an area are a topographic map and aerial photographs. Geological data for studying land degradation processes or land vulnerability to degradation are required for the uppermost geological layer, considered as parent material of existing soils.

Main applications in cropland and forests & shrubland regions

The geological characteristics of an area are used for assessing: (as) the vulnerability of the land to land degradation and desertification, (b) the soil characteristics such as nutrients availability, amount of rock fragments, etc., (c) the crop production, (e) the ability of an ecosystem for recovering after a strong disturbance such as forest fire, (f) the resilience of an ecosystem under specific land management practices, (g) the density of drainage system, (i) the presence of ground water, etc.

Strengths and weaknesses

The classification of geological materials presented above can be adequately used for assessing land degradation processes and land capability for crop production. Some of the limitations are related to lack of uniformity of existing classification systems and existing data. Furthermore, geology mapping requires many field observations by geology experts. Also the lack, in some cases, of digital mapping support requires a complex work of digitization and geo-referencing.

Geomorphology

medesdesire@googlemail.com (Jane Brandt) — Sat, 08 Sep 2012 13:02:22 +0000

Aim of the method/technique

The aim of geomorphology mapping is to present the surface characteristics of landforms by combining slope gradient, hypsometry, and degree of dissection by various streams. The description of the various geomorphologic characteristics requires direct land observations, as well as existing data or models such as Digital Elevation Model (DEM) (Wood 1996), and satellite images (Fornaciai et al. 2008).
Using geomorphology characteristics of an area, it is possible to define the origin of land features and provide information on their possible evolution, through the geomorphological studies such as the nature of the rocks in which they are modelled (lithological study), the geometric relationships between layers of rock formations or packages of different types (stratigraphic study), the bedding of the layers and the presence of fractures, faults, sliding surfaces, etc. (Structural study).

Scale – spatial and temporal

A geomorphology map is usually presented on a large scale usually 1:50,000, or better a scale 1:5,000. At European level, there is a DEM with pixel resolution of 1km*1km of the EEA (European Environment Agency), derived from GTOPO30, produced by the USGS for the entire globe through the mission SRTM (Shuttle Radar Topography Mission). Recently, the NASA mission "ASTER Global Digital Elevation Model" led to the creation of a DEM with 30 m*30 m resolution covering 99% of the Earth's surface. For the Italian regions it is available a DEM with a resolution of 20 m*20 m, made by the Ministry of Environment, obtained by the interpolation techniques of contour lines and elevation points (Tarquini et al. 2007). Data have low temporal variability.
There is low temporal change on geomorphologic characteristics of an area considering periods of decades or even centuries. Spatial change of geomorphology is greatly affected by land characteristics such as topography, dissection, and geology.

Brief description

Geomorphology can be described based on the morphology of land forms and not by their genetic origin or processes responsible for their shape (Engelen and Wen 1995). According to Engelen and Wen (1995), the following geomorphologic landforms are distinguished that are suitable for scales smaller than 1:100,000:

Level land with dominant slopes between 0-8% and relief difference is less than 50 m, including level lands, plains, plateau, depressions, low-gradient footslopes, valley floors.
Sloping land with dominant slopes between 8% and 30% combined with a relief usually greater than 50 m, including medium-gradient mountains, medium-gradient hills, medium-gradient escarpment zone, ridges, mountainous highland, and dissected plains.
Steep land with slopes in excess of 30%, including high-gradient mountains, high-gradient hills, high-gradient escarpment zone, and high-gradient valleys.
Land with composite landforms such as level and steep or slopping land which can not be separated, including valleys, narrow plateaus, and major depressions.

Regional landforms are characterized according to the following three criteria: (1) regional slope, (2) hypsometry, and (3) dissection (Engelen and Wen, 1995). Concerning regional slope characteristics the following categories are distinguished: flat, 0-2% slope gradient; gently undulating, 2-5%; undulating, 5-8%; rolling, 8-15%, moderately steep, 15-30%; steep, 30-60%; and very steep, >60%. Hypsometry characteristics are defined on the basis of the above classification as follows: (a) level lands and sloping lands; very low level-<300 m, low level-300-600 m, medium level-600-1500 m, high level-1500-3000 m, and very high level->3000 m; (b) sloping lands; low-<200 m, medium-200-400 m, high->400 m; and (c) steep and sloping lands; low-600-1500 m, medium-1500-3000 m, high-3000-5000 m, and very high->5000 m. The degree of dissection is defined on the basis of drainage density expressed in km length of channels per area in km². The following three categories are distinguished: (a) slightly dissected, <10 km km-²; (b) dissected, 10-25 km km-²; and (c) strongly dissected, >25 km km-². Dissection can be measured using topographic maps, aerial photographs or satellite images. Examples of cropland landforms are given in Figures 1 and 2.

	Figure 1. Sloping rolling land of low level slightly dissected (Source: C. Kosmas)
	Figure 2. Very steep of low level land strongly dissected (Source: C. Kosmas)

Data requirements

Landforms can be distinguished in the field by observing slope gradient and relief differences between lower and highest point expressed in meters per specified distance, and degree of land dissection. Data required are topographic maps or better Digital Elevation Models, aerial photographs or satellite images.

Main applications in cropland and forests & shrubland regions

Geomorphology or landforms are used in assessing land degradation processes such as soil salinization, soil erosion and deposition; mass movement; soil characteristics affecting crop production such as soil drainage, soil texture; type of ecosystems, etc.
Geomorphological information is directly used as input information in risk of land degradation assessment models such as the model ESA (Environmental Sensitive Areas) (Kosmas et al. 1999). Some geomorphology parameters, such as slope length and slope steepness are used in the USLE model (Wischmeier & Smith 1978) to identify the risk of erosion can be obtained through processing of DEM. The morphometric data are also input layers of PESERA model (Kirkby et al. 2004), a physically based model for identifying the erosion risk (Irvine & Kosmas 2004).

Strengths and weaknesses

Some of the weaknesses of geomorphology mapping are related to the lack of uniformity of existing classification systems. Furthermore, geomorphology mapping requires many field observations by geomorphologists. Also, the main limitations of the available geomorphological information are currently due to the unavailability of uniform datasets at various scales (from global to local) and their standards of representation. Another limitation is the lack, in some cases, of digital geo-referenced mapping supports.