Land use inventories are sound measures to provide information on the area occupied by different land use or land cover types and their changes, although less widespread than traditional mapping; as such, they are distinctively well-established tools for generating statistics on the state and the dynamics of land use in the European Union. Italy has recently set up a land use inventory system (IUTI) as a key instrument for accounting removals and emissions of greenhouse gases (GHG) associated to land use, land use change and forestry (LULUCF) activities elected by Italy under the Kyoto Protocol. IUTI adopts a statistical sampling procedure to estimate the area covered by LULUCF land use categories in Italy, and associated uncertainty estimates. Estimates of land use have been so far processed for the period 1990-2008 and highlight three interlinked land use change patterns in Italy: (i) increase in forest land for a total uptake of 1.7% of the Italian territory; forest cover estimates, with a standard error of 0.1%, indicate an annual increase of forestland higher over the period 1990-2000 (32 901 ha year-1) than in 2000-2008 (22 857 ha year-1); surprisingly, also a significant deforestation rate is observed (-7000 ha year-1), due to forest land conversion mainly into artificial areas; (ii) consumption of arable land (-4.2% of the Italian territory) primarily due to land uptake by urban areas and to conversions to permanent crops (mainly orchards and vineyards); (iii) urban sprawl uptakes 1.6% of the Italian territory in this period, with a total coverage of settlements reaching 7.1% of total land surface in Italy in 2008. Overall, land use dynamic results in land uptake by forest land is of the same magnitude of land uptake by urban areas, but the effects of these processes on GHG removals (by forest sinks) and emissions (by urban areas) is expected to be significantly different. In a broader perspective, IUTI methodology, by providing reliable estimates and well-defined levels of statistical uncertainty for assessing stocks and flows of land use at national level, can be further implemented to frame other key questions for sustainable development policies, like the set up of environmental-economic accounting systems.
Land use inventories are sound measures to provide information on the area occupied by different land use or land cover types and their changes, although less widespread than traditional mapping. The use of formal statistical procedures allows land use inventory to straightforwardly provide area figures along with uncertainty estimates: this is an important advantage in comparison to other land-use area assessment methods, as the reliability of such figures can be quantitatively evaluated (
Distinctively, land use inventories are well-established tools for generating statistics on the state and the dynamics of land use in the European Union: for instance, the Program Land Use/Cover Area frame statistical Survey (LUCAS, Decision N°1445/2000/EC of the European Parliament and the Council) provides harmonized data on land use/cover and their changes over time in the 27 EU countries based on direct observations gathered through ground survey in the framework of area-frame sampling scheme.
Italy is one of the first European countries that have adopted statistical systems to monitor land use changes earlier than the proliferation of mapping initiatives, thanks to the AGRIT project (http://www.itacon.it/). More recently, the Italian Ministry of Environment, Land and Sea has implemented the land use inventory (Inventario dell’Uso delle Terre - IUTI) as a key instrument of the National Registry for forest carbon sinks. The Registry is part of the national system for the Italian greenhouse gas inventory, which includes all institutional, legal and procedural arrangements for accounting anthropogenic emissions by sources and removals by sinks of greenhouse gases (GHG) under the United Nation Framework Convention on Climate Change and its Kyoto Protocol (
The National Registry for carbon sinks integrates the NFI sample set within a bundle of integrated tools aimed at estimating greenhouse gases removals/emissions associated to activities under articles 3.3 (afforestation, reforestation and deforestation) and 3.4 (forest management) of the Kyoto Protocol, in accordance with the relevant decisions of the Meeting of Parties of the Kyoto Protocol and with the Good Practices Guidance for Land Use, Land Use Change and Forestry (GPG-LULUCF) of the Intergovernmental Panel on climate change (
The current paper is aimed at introducing IUTI methodological framework as a relevant example, and its current applications and future perspectives as a commentary discussion. Specific goals of the paper are: introducing the most relevant methodological features of IUTI inventory (see chapt. “IUTI methodological framework”); presenting IUTI land use estimates for the years 1990 and 2008, focusing on national and subnational trends in forest land use category (see Results); discussing how the IUTI inventory can be used to gain insights on the processes underpinning land use change in Italy, and future perspectives (see Discussion and final remarks).
IUTI estimates the coverage of the six land use categories identified in the GPG-LULUCF over the Italian territory. The methodology falls under approach 3 of the GPG-LULUCF (“geographically explicit land use data”),
The localization of sampling points is carried out according to a tessellation stratified sampling design (also known as unaligned systematic sampling), preferable than simple random or systematic grid sampling (
The set of sample points was extracted using a 0.5 km square grid, for a total of about 1 206 000 geo-referenced points randomly located in each square cell and fully covering the Italian territory (
The GPG-LULUCF identifies six broad categories for land-use classification: settlements, cropland, forest land, grassland, wetland, other lands. In IUTI the six categories are furtherly subdivided following a hierarchical approach (
Each sample point is photo-interpreted in order to classify the sample into IUTI land use classes (
The classification of the sample set is currently completed for the years 1990, 2000 and 2008; photo-interpretation was based on the following set of multi-temporal orthophotos: (i) 1990, the black and white high resolution full national coverage aerial photography database of TerraItaly was used to produce orthophotos in scale 1:75.000, spatial resolution of 1 m (the aerial photos, taken on 1988/89, have the same image acquisition standard adopted by USGS-National High Altitude Program at that time: panchromatic film, 400 lines per millimeter); (ii) 2000, TerraItaly 2000 dataset, digital color aerial orthophotos with spatial resolution of 1 m; (iii) 2008, TerraItaly 2008 dataset, digital color aerial orthophotos with spatial resolution of 0.5 m. Furthermore, visual interpretation was supported by ancillary information from GPPHMF EASUI® and available thematic forest and land use maps at regional and sub-regional scales.
The estimate of the area of each land use class at national level and its breakdown by administrative districts was performed on the basis of the photo-interpretation results. The estimation procedures refer to the methodology proposed by
Let
(
(
Furthermore, the quantities (
(
(
Since the sample points are selected on the area
(
The calibrated estimators are approximately unbiased, with variances and covariances which can be estimated by (
(
where
and where (
Finally, the calibrated estimates of the surfaces of the
where
(
The land use estimates derived from IUTI database provide a statistically sound information basis to quantify land use changes in Italy over the period 1990-2008. Main results at national level are here presented, with a focus on the forest category at district level (19 Regions and 2 autonomous Provinces).
The allocation of different land use classes shows some key changes between 1990 and 2008 (
On the whole, the most significant changes in the stocks of different land use categories expressed as percentage of Italian land surface are:
consumption of arable land (-4.2%) and of grasslands (-1%);
formation of forests (+1.7%), urban areas (+1.6%) and permanent crops (+1.4%).
The land use change matrix helps understanding the type of processes that have resulted in the observed land use changes (
consumption of arable land is primarily due to land uptake by urban areas and to conversion to permanent crops (mainly orchards and vineyards);
consumption of grasslands is to a large extent associated to formation of other wooded land;
forest formation is the outcome of other wooded land succession pathways, a transitional state to forest, or it is due to the creation of new forests in former arable land due to afforestation/reforestation activities or to natural forest expansion after withdrawal of farming.
Flows between forest land and other land uses result always in a positive balance (net formation of forest) except for the “other land” category and the settlements: the former mainly due to catastrophic events, mainly landslides, the latter caused by deforestation due to the creation of new mines, infrastructures and urban expansion (
Looking in more detail at the dynamics of the categories of forest interest, the most remarkable figure is the increase in forest land cover during 1990-2008, equal to 511 861 ha; forest cover estimates, characterized by a small standard error of 0.1%, indicate a coverage rising from 9,141,355 ha in 1990 up to 9,653,216 ha in 2008. Also other wooded land increased its area of about 124,000 ha in the examined period.
Net formation of forest during 1990-2008 results from the balance between gains due to afforestation/reforestation and natural dynamics (639,099 ha, around 35,000 ha gained per year) and the losses due to deforestation (127,238 ha, around 7000 ha lost per year). The annual increase of forestland is higher over the period 1990-2000 (32,901 ha per year) than in 2000-2008 (228 57 ha per year). The slowdown in the annual value of forest expansion between the periods is a trend common to all districts (although with different magnitudes), except for islands (Sicilia and Sardegna) as well as for other regions as Abruzzo, Bolzano Province and Basilicata that show an opposite trend (
The highest ranked regions in term of forest cover in 2008 are Toscana, Piemonte and Lombardia; instead, Valle d’Aosta, Puglia and Molise show the lowest forest cover (
IUTI provides a statistically sound methodology for tracking changes in the land use assets in Italy; accordingly, it offers an analytical framework for addressing some key questions about sustainability of land use change,
The asset accounts for 1990-2008 highlight three interlinked land use change patterns in Italy, commented below.
(i) Increase of the stocks of forest and other wooded land reaching 11,644,416 ha in 2008, corresponding to 38.6% of the national territory. The same figure is reported by the last LUCAS survey campaign in 2009, confirming forest cover to be as high as 38% in Italy. On the other hand, these figures significantly differ from that by NFI: for a detailed discussion about possible causes of such discrepancies, see
(ii) Decrease of the stocks of arable land, only partially counterbalanced by an increase in the stocks of permanent crops (orchards, vineyards, forest plantations for timber production). Distinctively, carbon sequestration potential by forest plantations and other wood formations like trees outside forest (
(iii) While land use dynamics (i) and (ii) are contributing to increase the natural capital for delivering ecosystems services (carbon sequestration among others), urban sprawl is a fast driver of natural resource depletion and, implicitly, source of GHG emissions. IUTI estimates settlements coverage to be 7.1% of total land surface in 2008, in agreement with LUCAS 2009 (7.3%
IUTI land use inventory is planned to be repeated at the end of 2012 for estimating forest and land use assets at the end of the first Kyoto commitment period. Overall, although land use dynamic results in land uptake by forest land of the same magnitude as land uptake by urban areas, the impacts of these changes on GHG removals (by forest sinks) and emissions (by urban areas) are expected to be significantly different.
In a broader perspective, IUTI methodology, by providing reliable estimates and well-defined levels of staistical uncertainty for assessing stocks and flows of land use at national level, can support also other kinds of environmental assessment. The future challenge for land use inventory lies in shifting the focus from a straightforward quantification of land use stocks and flows to targeting the analysis on key questions for sustainable development policies (
This article is a scientific research commentary of the information retrievable by the
Exemplification of IUTI tessellation stratified sampling scheme.
Annual variation of forest cover at district level in the periods 1990-2000 and 2000-2008.
Distribution of forest cover at district level in 2008. Data are expressed as percentage of total district land area (the line shows the average national forest coverage value).
IUTI land use classification system.
GPG-LULUCF class | IUTI category/subcategory | IUTI code | |
---|---|---|---|
Forest land | - | 1 | |
Cropland | Arable land | 2.1 | |
Permanent crops | Orchards, vineyards and nurseries | 2.2.1 | |
Forest plantations | 2.2.2 | ||
Grassland | Natural grassland and pastures | 3.1 | |
Other wooded land | 3.2 | ||
Wetlands | - | 4 | |
Settlements | - | 5 | |
Other land | Bare rock and sparsely vegetated areas | 6 |
IUTI land use estimates for the years 1990 and 2008. (se%): percent standard error.
IUTI land use category / subcategory | 1990 | 2008 | Variation 1990-2008 (% of Italy land area) | ||
---|---|---|---|---|---|
Area(ha) | se% | Area(ha) | se% | ||
Forest land | 9 141 355 | 0.1 | 9 653 216 | 0.1 | +1.70 |
Arable land | 11 315 217 | 0.1 | 10 056 141 | 0.1 | -4.18 |
Orchards, vineyards and nurseries | 2 682 761 | 0.3 | 3 114 765 | 0.3 | +1.43 |
Forest plantations | 134 091 | 1.3 | 144 376 | 1.3 | +0.03 |
Natural grassland and pastures | 2 195 754 | 0.3 | 1 874 449 | 0.3 | -1.07 |
Other wooded land | 1 867 138 | 0.3 | 1 991 200 | 0.3 | +0.41 |
Wetlands | 510 061 | 0.7 | 518 586 | 0.7 | +0.03 |
Settlements | 1 644 010 | 0.4 | 2 140 903 | 0.3 | +1.65 |
Other land | 658 288 | 0.6 | 655 040 | 0.6 | -0.01 |
Land use change matrix 1990-2008, values in hectares (see
IUTI code | 2008 | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
1 | 2.1 | 2.2.1 | 2.2.2 | 3.1 | 3.2 | 4 | 5 | 6 | Total | ||
1990 | 1 | 9 014 117 | 30 192 | 13 573 | 975 | 13 446 | 37 213 | 9 497 | 21 118 | 1 225 | 9 141 355 |
2.1 | 184 398 | 9 586 594 | 789 148 | 69 470 | 154 166 | 128 526 | 15 374 | 387 391 | 150 | 11 315 217 | |
2.2.1 | 35 547 | 272 931 | 2 269 752 | 775 | 21 650 | 16 571 | 575 | 64 962 | 0 | 2 682 761 | |
2.2.2 | 3 847 | 51 692 | 1 249 | 67 659 | 2 773 | 2 349 | 1 249 | 3 273 | 0 | 134 091 | |
3.1 | 138 121 | 60 692 | 22 573 | 4 224 | 1 662 343 | 276 904 | 5 349 | 24 998 | 550 | 2 195 754 | |
3.2 | 256 716 | 48 566 | 17 072 | 750 | 9 449 | 1 513 565 | 7 399 | 13 097 | 525 | 1 867 138 | |
4 | 14 696 | 1 225 | 425 | 400 | 2 999 | 11 224 | 476 768 | 1 500 | 825 | 510 061 | |
5 | 5 023 | 4 174 | 950 | 125 | 5 250 | 3 724 | 1 250 | 1 623 439 | 75 | 1 644 010 | |
6 | 750 | 75 | 25 | 0 | 2 373 | 1 125 | 1 125 | 1 125 | 651 691 | 658 288 | |
Total | 9 653 216 | 10 056 141 | 3 114 765 | 144 376 | 1 874 449 | 1 991 200 | 518 586 | 2 140 903 | 655 040 | 30 148 676 |
IUTI forest land estimates at district level for the years 1990, 2000 and 2008. (se%): standard error, in percentage.
Region /autonomous Province | 1990 | 2000 | 2008 | |||
---|---|---|---|---|---|---|
Area(ha) | se% | Area(ha) | se% | Area(ha) | se% | |
Abruzzo | 412 009 | 0.6 | 424 890 | 0.6 | 440 267 | 0.6 |
Basilicata | 312 493 | 0.7 | 322 917 | 0.7 | 331 667 | 0.7 |
Bolzano | 357 365 | 0.6 | 358 015 | 0.6 | 361 115 | 0.6 |
Calabria | 589 350 | 0.5 | 605 270 | 0.5 | 606 969 | 0.5 |
Campania | 458 141 | 0.6 | 463 931 | 0.6 | 470 995 | 0.6 |
Emilia-Romagna | 553 084 | 0.6 | 574 910 | 0.6 | 584 086 | 0.6 |
Friuli Venezia Giulia | 327 880 | 0.7 | 335 476 | 0.6 | 337 575 | 0.6 |
Lazio | 544 532 | 0.6 | 556 261 | 0.5 | 565 514 | 0.5 |
Liguria | 368 511 | 0.5 | 385 703 | 0.4 | 386 253 | 0.4 |
Lombardia | 601 129 | 0.6 | 622 457 | 0.5 | 638 216 | 0.5 |
Marche | 277 506 | 0.8 | 305 869 | 0.7 | 310 367 | 0.7 |
Molise | 135 630 | 1.1 | 150 739 | 1.0 | 154 417 | 1.0 |
Piemonte | 894 270 | 0.4 | 924 895 | 0.4 | 939 733 | 0.4 |
Puglia | 123 576 | 1.4 | 138 176 | 1.3 | 137 451 | 1.3 |
Sardegna | 546 851 | 0.6 | 574 690 | 0.6 | 611 674 | 0.5 |
Sicilia | 294 836 | 0.9 | 308 139 | 0.8 | 329 369 | 0.8 |
Toscana | 1 079 282 | 0.3 | 1 116 936 | 0.3 | 1 133 810 | 0.3 |
Trento | 389 612 | 0.5 | 393 582 | 0.5 | 395 704 | 0.5 |
Umbria | 363 846 | 0.6 | 379 227 | 0.6 | 386 480 | 0.6 |
Valle d’Aosta | 102 102 | 1.3 | 105 723 | 1.3 | 105 673 | 1.3 |
Veneto | 409 351 | 0.7 | 422 555 | 0.7 | 425 881 | 0.7 |
Total | 9 141 355 | 0.1 | 9 470 362 | 0.1 | 9 653 216 | 0.1 |