Gli indicatori per la stima della vulnerabilità alla land degradation da fattori antropici:...

Sesta Conferenza Nazionale in Informatica e Pianificazione Urbana e Territoriale Potenza, 13-15 settembre 2010

GLI INDICATORI PER LA STIMA DELLA VULNERABILITÀ ALLA LAND DEGRADATION DA

FATTORI ANTROPICI: STRUMENTI PER UNA EFFICACE PIANIFICAZIONE TERRITORIALE

IMBRENDA V.1,2, D’EMILIO M.2, LANFREDI M.2, RAGOSTA M.1, SIMONIELLO T.2

1 Università degli Studi della Basilicata, Dipartimento di Ingegneria e Fisica dell'Ambiente (DIFA)

2 Istituto di Metodologie per l’Analisi Ambientale (IMAA-CNR)

Difa Dipartimento di Ingegneria e Fisica dell’Ambiente

Difa Dipartimento di Ingegneria e Fisica dell’Ambiente


land degradation means reduction or loss of the biological or economic

productivity (UN/FAO 2003)

DESERTIFICATION L’individuazionefenomeni di degradoL’individuazionefenomeni di degrado

DESERT


LAND DEGRADATION

Salinization

Overgrazing


Dessication of Aral Sea

Soil erosion

LAND DEGRADATION


Excessive tillage

Calanchi landscape

LAND DEGRADATION


Anthropogenic causes hold a role of equal importance (in some countries even higher) than climatic conditions in generating land degradation processes (coupled H-E systems)

MONITORING: repeated collection and archiving of data

ASSESSMENT: critical evaluation of information

Human response against land degradation phenomenaHuman response against land degradation phenomena

Identification of vulnerable areas and determination of vulnerability levels

Background


Indicators must be SMART (specific, measurable, achievable, relevant, time bound) enabling:• a synthetic information in GIS systems to determine spatial extension and geographic distribution of degraded areas• an easily usable data for the public or policy-makers

INDICATORS: simplified, synthetic information on the state andtendency of complex processes (Rubio and Bochet, 1998)

Many national and international projects (e.g., MEDALUS, Desert-Net, DISMED, Ladamer, RIADE) have been developed

during the last decades in order to characterize land degradation processes by adopting vulnerability models based on indicators

accounting for different influencing factors

Background


INDICATORS

Spatial and temporal resolution Reliability

Dynamic and spatially detailed information

Efficient and flexible tool for land management

Drafting and implementation of SLM (Sustainable Land Management) plans

Early warning systems

Background


A widely adopted vulnerability model is theESAs (ENVIRONMENTALLY SENSITIVE AREAS, Kosmas, 1999)

Background


Land management indexes

UAA_VAR - Percentage of variation of cultivated surfaces

Methods

1990

19902000

UAA

100*) UAA-UAA(= UAA_Var

A set of land management indicators was selected in

order to accurately depict the impacts of the anthropic

pressure in Southern Italy (APAT, 2000, Genesio et al., 2004, Motroni et al., 2004).

They were computed at municipal level

A marked UAA increase implies a larger extension of cultivated lands (agricultural intensification). A

strong UAA decrease is a sign of land transformation process leading to land abandonment or soil sealing

LMI

Land Management Index

ML

MechanizationLevel

GI

Grazing Index

PP_UAA

Permanent Grassand Pasture on

Utilized AgriculturalArea

UAA_Var

UtilizedAgricultural

Area Variation

LMI

Land Management Index

ML

MechanizationLevel

GI

Grazing Index

PP_UAA

Permanent Grassand Pasture on

Utilized AgriculturalArea

UAA_Var

AgriculturalArea Variation


Permanent grass and pasture are less vulnerable to degradation processes than cultivated lands. Moreover, herbage serves as protection against erosion

A high grazing intensity induces both plant and soil degradation. Grass are consumed and trampled, generating a reduction of leaf apparatus and soil compaction.

Methods

100*UAA

Sur_PP= PP_UAA

2000

2000

Sur_PP

ABU of No=GI

PP_UAA - Percentage of pasture and grass on UAA

GI - Grazing index

10

sheep n°+

10

goats n°+ equines °n + buffalos °n + bovines °n=)ABU( UnitBovineAdult

Land management indexes


The MLI considers that persistent mechanical interventions strongly alter soil chemical-physical properties since repeated passes causes a stratum of compacted soil having a low permeability (ploughsole formation). It limits the root expansion and water penetration, and in the worse situation creates asphyxiation conditions that bring on vegetation cover degradation and reduction in crop production

Non compacted soil Compacted soil

MethodsMLI - Level of agricultural mechanization

[Duiker, 2004]


The most diffused index is:

UAA of ha 100MLAPAT

(APAT, 2000)

N = the number of machineriesUAA = Utilized Agricultural Area

It is easy to compute, but can largely misestimates the vulnerability since there are many agricultural areas managed with machineries of a third party property or join ownership.Moreover, it does not take into account the type of grip (tyres or tracks) and the actual number of passes required for different types of cultivations.

Methods


Tracks induce a higher compaction effects in the superficial soil, which are easier recoverable than those caused by tyred vehicles in medium-deep subsoil layers.

Many studies (see e.g., Pagliai et al., 2003; Keller et al., 2002) enhanced different compaction effects between tyred and tracked vehicles.

Methods

Moreover, tyres are not typically utilized in presence of steep slope (>18-20%).


Corine Land Cover 2000 Number of passes for different cultivations(data from ENAMA – Ente Nazionale per la

Meccanizzazione Agricola)

To evaluate the different number of passes for each land cover class

MethodsNew Mechanization Level Index

0Other classes

3Land principally occupied by agriculture, with significant areasof natural vegetation

4Complex cultivation patterns

Annual crops associated with permanent crops

3Pastures - 2.3.1

7Permanent crops (rice fields, vineyards, fruit trees and berry plantations, olive groves)

7,5Arable Land (cereals, legumes, crops, vegetables)

Number of

passes

Different cultivations type(source -ENAMA National Agency of Agricultural Mechanization)

0Other classes


Complex cultivation patterns

Annual crops associated with permanent crops

7Permanent crops (rice fields, vineyards, fruit trees and berry plantations, olive groves)

Arable Land (cereals, legumes, crops, vegetables)

Number of

passes

Different cultivations type(source -ENAMA National Agency of Agricultural Mechanization)

0Other classes

1


4

5

3

7Permanent crops (vineyards, fruit trees, olive groves)

7,5

Number of

passes

Different cultivations type(source - ENAMA National Agency of Agricultural Mechanization)

0Other classes

1

3Land principally occupied by agriculture, with natural areas - 2.4.3

4– 2.4.2

5– 2.4.1

3

7

7,5

Number of

passes

Different cultivations type(source ENAMA - National Agency of Agricultural Mechanization)

- 2.1.1, 2.1.2- 2.2.1, 2.2.2, 2.2.3

Agro-forestry areas – 2.4.4


To evaluate the difference in compaction effects between tyred and tracked vehicles

- tyred vehicles are used on the plains (<18-20%)- tracked vehicles are used on steep slopes (>18-20%)

DEMSLOPE

Correction factors

Slope gradient Correction factor

>20% - tracked 1

<20% - tyred 1.5



Np = the average number of machinery passes for single land cover class

f = the correction factor according to tyres (1.5) or tracks (1) use

fNMLI pp

The analytic expression of the new mechanization level index is:

The index is evaluated at pixel detail;

Its resolution depends on the land cover detail



2.01.71.51.31.0

2.01.71.51.31.0

MLIpCLC 2000

2.01.71.51.31.0

2.01.71.51.3

2.01.71.51.3

MLIAPAT

2.1.1. Non-irrigated arable land

2.1.2. Permanently irrigated land

2.1.3. Rice fields

2.2.1. Vineyards

2.2.2. Fruit trees and berry plantations

2.2.3. Olive groves

2.3.1. Pastures

2.4.1. Annual crops associated with permanent crops

2.4.2. Complex cultivation patterns

2.4.3. Land principally occupied by agriculture with natural areas

2.4.4. Agro-forestry areas

3.2.1. Natural grassland

Percentage of surface per vulnerability class

Results: MLI comparison

Vulnerability Level MLIAPAT MLIp

1 (low ) 79.5 0.1

1.3 15.2 5.0

1.5 3.4 13.3

1.7 1.5 35.9

2 (high ) 0.3 45.6

Very Low

Very High

Vulnerability Levels

Very Low

Very High



1990

19902000

UAA

100*) UAA-UAA( UAA_Var =

UAA_Var (Variation of Utilized Agricultural Area)

UAA_Var


1.0

1.3

1.5

1.7

2.0

Very Low

Very High

Results


100*UAA

Sur_PP PP_UAA

2000

2000=

PP_UAA (Permanent grass and Pasture on Utilized Agricultural Area)

1.0

1.2

1.4

1.6

1.8

2.0

Very Low

Very High

PP_UAA


Results


10

sheep

10

goats equines n buffalos n bovines n)ABU( Unit BovineAdult

n°+

n°+°+°+°=

Sur_PP

ABU of NoGI =

GI (Grazing Index)

GI


Very Low

Very High

1.0

1.2

1.4

1.6

1.8

2.0

Results


4/1)__( MLIGIUAAPPVARUAALMI

Land Management IndexResults

LMIOLD

1.0

2.0

1.31.5

1.7

Very Low

Very High


LMINEW

1.0

2.0

1.31.5

1.7

Very Low

Very High



4/1)__( MLIGIUAAPPVARUAALMI

Percentage distribution of the vulnerability levels of theLMI NEW (Southern Italy)

Percentage distribution of the vulnerability levels of theLMI OLD (Southern Italy)

18.26

25.71

33.75

18.82

3.46

05

101520253035

1.0 1.3 1.5 1.7 2.0

26.18

44.06

25.33

4.100.33

0

10

20

30

40

50

1.0 1.3 1.5 1.7 2.0

LMINEW

1.0

2.0

1.31.5

1.7

1.0

2.0

1.31.5

1.7

LMIOLD

1.0

2.0

1.31.5

1.7

1.0

2.0

1.31.5

1.7



Distribution of the LMINEW

in vulnerability

levels for Southern Italy regions

BASILICATA3.3%4.3%

26.3%

34.3%

31.9%LOW

MIDDLE-LOW

MIDDLE

MIDDLE-HIGH

HIGH

CALABRIA

2.2%12.2%

25.4%32.8%

27.3% LOW

MIDDLE-LOW

MIDDLE

MIDDLE-HIGH

HIGH

CAMPANIA

7.4%17.7%

30.0% 25.2%

19.8%LOW

MIDDLE-LOW

MIDDLE

MIDDLE-HIGH

HIGH

SICILIA

15.9%

26.5%34.0%

22.1% 1.5%LOW

MIDDLE-LOW

MIDDLE

MIDDLE-HIGH

HIGH

PUGLIA

6.1%15.0%

46.5%

4.4%28.0%

LOW

MIDDLE-LOW

MIDDLE

MIDDLE-HIGH

HIGH



The analysis of the anthropic indicators enhanced that:

About 25% of the Southern Italy is marked by medium-high or high vulnerability levels due to land management;

The factors playing a major role in determining LD vulnerability are the mechanization level (MLI) and the variation of agricultural areas (UAA_VAR);

The most vulnerable regions to land degradation due to land management are Puglia, Campania and Sicily respectively.

The new mechanization index gives several advantages:

Possibility of a frequent updating; Capability to discriminate among different vulnerability values within the municipal areas; Easy exportability at different monitoring scales using other land cover maps (e.g. satellite-based) having a higher resolution for both the number of classes and spatial detail.

Final remarks


Thank you for attention

Contact: [email protected]


UAA_VAR values Vulnerability class Decreases Increases

2 (high) < - 50 > 50 1.7 -50 : -20 20 : 50 1.5 -20 : -10 10 : 20 1.3 -10 : -5 5 : 10

1 (low) -5 : 5 -5 : 5

Vulnerability class MLI p MLI APAT

2 (high) >9 >5

1.7 7 : 9 3 : 5

1.5 5 : 7 2 : 3

1.3 3 : 5 1 : 2

1 (low) <3 0 : 1

Vulnerability class PP_UAAAA GI

2 (high) 0 : 5 > 100

1.8 5 :10 30 : 100

1.6 10 : 20 10 : 30

1.4 20 : 30 3 : 10

1.2 30 : 40 1 : 3

1 (low) 40 :100 0 :1


Vulnerability class LMI

2 (high) 1.8 : 2

1.7 1.6 : 1.8

1.5 1.4 : 1.6

1.3 1.2 : 1.4

1 (low) 1 : 1.2

S

NWkWWs

5

(Bazzoffi et al., 2003)

N = the number of machineriesWs = weight summationW = average weight of machineries 5 = average passes number per yearS = area of arable land and permanent crops

Gli indicatori per la stima della vulnerabilità alla land degradation da fattori antropici:...

Documents

Transcript of Gli indicatori per la stima della vulnerabilità alla land degradation da fattori antropici:...