APPLYING NIJOS LANDSCAPE INDICATORS

TO THE RIBATEJO E OESTE REGION [1]

By Rui Rosário (INIAP), Ana Antunes, Isabel Escada (GPPAA)

[1] Rosário, Rui; Ana Antunes e Isabel Escada, 2001. Elementos Paisagísticos do Ribatejo e Oeste, Estação Agronómica Nacional, Instituto Nacional de Investigação Agrária Introduction

Research project :

• to use GIS to produce information associated with land use

• integrate data obtained by photointerpretation of already existing digital orthophotomaps (CAP control system) Introduction

Strategy:

• launching a sample of territorial sections, based on statistical criteria

• data gathered using photointerpretation techniques validated through field work Indicators Framework

Agro-environmental indicators should: • have the ability to desaggregate values spatially - geographical scale

• have time scales associated with each particular issue - time scale

• identify trends and variation intervals for each indicator - the relative expression of an indicator is usually more important than its absolute value Project

• Team: 2.25 research units

• Duration: 18 months

• Variable cost: $19.000.00 (USD) Project Outline

Heterogeneous geographical area

Open land Small plots Project Outline

Open forest area Softwood forest with agriculture Project Outline

Urban settlements Mixed urban/rural settlements Project Methodology

• A non-stratified aligned systematic sample

• A methodology that allows an uniform coverage of the region

• Observation without any intervention at farm level Project Methodology Segment location and identification 221221 356356 Project Methodology

The sample:

• is made up of 483 segments of 25 hectares each

• segments are 5 Km from each other

• the total area sampled is 12.075 hectares

• the sampling rate is 1% Project Methodology Sample rate by municipality Photointerpretation

Photointerpretation techniques were used to identify plot structures

CPCP_P_POO • Parceling criteria URBURB_P_POOVV TLTL EstEst • Soil occupation CPCP_V_V classes EstEst CPCP_V_V TLTL Photointerpretation

Photointerpretation data was compared with field data in 80 segments corresponding to 520 plots

• 45% had been correctly classified

• 55% of the plots the initial parceling and classification criteria required revision : - by plot aggregation (less detailed) - by occupation reclassification Photointerpretation

FLFL TLTLTlTlssSS TlTlss TLS TLS TLTL TLTL TLTL Tls Sb_PasSb_PasTlTlss Tls TLTL TLTLSS

Initial parceling (6 plots) Final parceling (1 plot) Photointerpretation

After this work, the initial parceling and classification criteria were revised

With the new criteria the degree of precision reached 76% of the plots Photointerpretation

Persistent deviations were due to:

• differences between the time when aerial photographs were taken (1995) and when field data was collected (1999)

• highest effectiveness of this work was obtained in Agro-Forestry (93%) and Open Land (81%)

• a residual source of error (3 in 520 plots) was due to misunderstandings of occupation type Results

Soil Occupation Indicator Defined taking into account the type of occupation and the corresponding area in each segment

Ferreira do Zezere Ferreira do Zezere Vila Nova de Ourém Vila Nova de Ourém

Nazaré Nazaré Tomar Alcobaça Alcobaça Sardoal

Torres Novas V. Nova da Barquinha EntroncamV.ent Nova da Barquinha Alcanena Gavião Constância Gavião Caldas da Rainha Constância Abrantes Caldas da Rainha Golegâ Óbidos Golegâ Óbidos Peniche Santarém Peniche Rio Maior Santarém Bombarral Bombarral Lourinhã Chamusca Lourinhã Alpiarça Cadaval Alpiarça

Permanent Crop-Vineyard Almeirim Azambuja Cartaxo Azambuja Alenquer Torres Vedras Alenquer

Salvaterra de Mag os Salvaterra de Mag os

Sobral de Monte Agraço Sobral deA Mrrountdae doAgsr Vaçinhoo s Mafra Mafra Vila Franca de Xira Coruche

Benavente Benavente Loures Sintra

Amadora AmadoraLisboa Lisboa Montijo Oeiras Alcochete Montijo Cascais Oeiras Alcochete Montijo Montijo Moita Almada Moita Almada Barreiro Barreiro Palmela Seixal Palmela Seixal

Setúbal Sesimbra Setúbal Sesimbra Results

Forestry Agro-Forestry

Ferreira do Zezere Ferreira do Zezere Ferreira do Zezere Vila Nova de Ourém Vila Nova de Ourém Vila Nova de Ourém

Tomar Tomar Nazaré Nazaré Nazaré Tomar Alcobaça Sardoal Alcobaça Sardoal Alcobaça Sardoal

Torres Novas Torres Novas Torres Novas V. Nova da Barquinha Alcanena V. Nova da Barq uinha EntroncamV.e ntNova da Barquinha Alcanena Alcanena Entroncamento Entroncamento Gavião Gavião Constância Abrantes Gavião Constância Abrantes Caldas da Rainha Constância Abrantes Caldas da Rainha Caldas da Rainha Golegâ Golegâ Óbidos Golegâ Óbidos Óbidos Peniche Santarém Peniche Santarém Peniche Rio Maior Rio Maior Rio Maior Santarém Bombarral Bombarral Bombarral Chamusca Chamusca Lourinhã Chamusca Lour inhã Lourinhã Cadaval Alpiarça Cadaval Alpiarça Cadaval Alpiarça

Almeirim Almeirim Cartaxo Almeirim Cartaxo Azambuja Cartaxo Azambuja Azambuja Torres Vedras Alenquer Torres Vedr as Torres Vedras Alenq uer Alenquer

Salvaterra de Magos Salvaterra de Magos

Sobral de Monte Agraço Sobral de Monte Ag raço Sobral deAr Mrountdae doAgsr Viaçnohos Arruda dos Vinhos Mafra Arruda dos Vinhos Mafra Mafra Vila Franca de Xira Coruche Vila Franca de Xira Coruche Vila Franca de Xira Coruche

Benavente Benavente Loures Benavente Loures Loures Sintra Sintra Sintra

Amadora Amadora AmadoraLisboa Lisboa Cascais Lisboa Montijo Montijo Oeiras Alcochete Montijo Cascais Oeiras Alcochete Cascais Oeiras Alcochete Montijo Montijo Montijo Moita Moita Almada Moita Almada Almada Barreiro Barreiro Barreiro Palmela Palmela Seixal Palmela Seixal Seixal

Setúbal Setúbal Sesimbra Setúbal Sesimbra Sesimbra Results

Diversity Indicator

Ferreira do Zezere Ferreira do Zezere Vila Nova de Ourém Vila Nova de Ourém

Nazaré Tomar Nazaré Tomar Alcobaça Sardoal NO Alcobaça Sardoal NO Torres Novas Torres Novas V. Nova da Barquinha Alcanena V. Nova da Barquinha Alcanena Entroncamento Entroncamento Diversity = Gavião Diversity = Constância Abrantes Gavião Caldas da Rainha Constância Abrantes Caldas da Rainha Golegâ Óbidos Golegâ Óbidos Peniche NS Peniche Rio Maior Santarém NS Rio Maior Santarém Bombarral Bombarral Chamusca Lourinhã Chamusca Lourinhã Cadaval Alpiarça Cadaval Alpiarça

Almeirim Cartaxo Almeirim Azambuja Cartaxo Azambuja Torres Vedras Torres Vedras Alenquer Alenquer

Salvaterra de Magos Salvaterra de Magos

Sobral de Monte Agraço Sobral de Monte Agraço Arruda dos Vinhos Mafra Arruda dos Vinhos NO - total number of different Ma fr a Vila Franca de Xira Coruche Vila Franca de Xira Coruche

Benavente occupations Loures Benavente Loures Sintra Sintra

Amadora Amadora Lisboa Cascais Lisboa Montijo NS - total number of segments Oeiras Alcochete Montijo Cascais Oeiras Alcochete Montijo Montijo

Moita Almada Moita Almada Barreiro Barreiro Palmela Seixal Palmela Seixal

Setúbal Sesimbra Setúbal Sesimbra Results

Landscape Structure Indicator or Perimeter/Area ratio

Ferreira do Zezere Vila Nova de Ourém

Nazaré Tomar EE Alcobaça Sardoal

Torres Novas ED = V. Nova da Barquinha ED = Alcanena Entroncamento Gavião Constância Abrantes Caldas da Rainha A Golegâ A Óbidos Peniche Rio Maior Santarém

Bombarral Chamusca Lourinhã Cadaval Alpiarça

Almeirim Cartaxo E - Total perimeter of the plots Azambuja Torres Vedras Alenquer

Salvaterra de Magos

A - Total area of the reference unit Sobral de Monte Agraço Arruda dos Vinhos Mafra Vila Franca de Xira Coruche

Benavente Loures Sintra

Amadora Lisboa Montijo Cascais Oeiras Alcochete Montijo

Moita Almada Barreiro Palmela Seixal

Setúbal Sesimbra Conclusions

Sampling:

• different sizes of segments in the stratified sample

• Progressive area frame sampling to adapt the sample to different landscape characteristics Conclusions

Photointerpretation techniques

are quite adequate when dealing with large groups of territorial occupation

the larger the scale of the photographic material, the more efficient the photointerpreting process with more detail nomenclature Thank you very much