**6. Conclusions**

38 Biodiversity Loss in a Changing Planet

**HAB CLC MODEL CLC MODEL** 

**3110** 0,00 77,78 0,00 35,14 **3120** 0,00 100,00 0,00 100,00 **3130** 0,00 83,33 0,00 70,00 **3140** 0,00 100,00 0,00 100,00 **3150** 0,00 100,00 0,00 80,00 **3160** 0,00 100,00 0,00 50,00 **3260** 6,67 73,33 1,40 5,83 **3270** 6,67 66,67 2,41 9,64 **4020\*** 90,91 100,00 18,63 88,24 **4030** 91,67 100,00 26,16 78,48 **6410** 38,46 100,00 1,78 100,00 **6430** - 100,00 - 91,29 **6510** 46,15 100,00 2,08 100,00 **7110\*** 0,00 100,00 0,00 89,47 **7230** 0,00 100,00 0,00 100,00 **8220** 0,00 100,00 0,00 100,00 **91D0\*** - 100,00 - 88,89 **91E0\*** 0,00 100,00 0,00 78,88 **91F0** 0,00 100,00 0,00 96,97 **9230** 100,00 100,00 30,77 99,04 **Total gap**: less than 10% of coincidences **Very high gap**: coincidences between 10-30 % **High gap**: coincidences between 30-50% **Moderate gap**: coincidences between 50-90%

**No gap**: coincidences upper 90% Table 10. Spatial correlations between CD 92/43/EEC habitats cartography, Corine Land Cover cartography (5th level) and the MODEL *Terras do Miño* in the SCI Parga-Ladra-

At 10km CLC shows a total gap in the most part of the habitats (Table 10). Only heaths and the woodlands with *Quercus spp.* (9230) have good correspondence. At 1 km CLC shows total or very high gap in any case. On the other hand, the model of *Terras do Miño* shows good results at 10km. At 1km, the most part of the habitats present good correspondence or moderate gap. Only two habitats present total gap which corresponds to water courses (3260 and 3270) which can be assigned to the constraints of the spatial resolution of the

**1 KM GRID (UTM)** 

**10 KM GRID (UTM)** 

Tamoga (NW Spain)

images.

To meet the requirements of European policies such as *Natura 2000* Network and the 2020 EU Biodiversity Strategy the development of more cost and time effective monitoring strategies are mandatory. Remote sensing (RS) techniques contribute significantly to biodiversity monitoring and several approaches have been proposed to get on-going requirement for spatially explicit data on the ecological units, and the value and threats against natural and semi-natural habitats (Bock et al. 2005; Weiers et al. 2004), but no definite nor any that has been standardized across Europe.

The major obstacles to get standardized scientific monitoring methodologies for habitat monitoring form a complex patchwork. The immense versatility of RS, the full range of RS techniques and products, has led to numerous potential approaches but all of them are dependent of many factors: i) firstly the large variability in the quality of input variables, their semantic, thematic and geometrical accuracy; many approaches have assumed the suitability and representativity of the selected geospatial data; ii) secondly, the possible variability of the spectral, spatial and temporal resolutions; iii) finally, the availability of suitable RS and ancillary data.

There is no a simple relationship between habitats and biophysical parameters like land covers (Groom et al. 2006). *Habitat classes* are not the same that *land cover classes* and the inconsistencies and gaps when a land cover map, as CORINE Land Cover, is used as a surrogate of a habitat map are significant and it should be evaluated in each case. It is necessary to develop *ad hoc* criteria to get the objective of identifying and monitoring habitats from remote sensing. It should be found the optimal way (cost effective and in an acceptable time, and with an optimal level of accuracy) to get from one unit of land cover (which can definitely be detected directly by remote sensing) to a unit of habitat (which may be, at least not in a direct way).

At the European Community level the appropriate criteria for getting that relation should be achieved through EUNIS system (Martínez et al. 2010; Moss and Davies 2002) since it is a common denominator that is compatible with the requirements of Annex I of the Habitat Directive. It will support the standardization because it makes possible cross-comparable data: at spatial and temporal levels.

In regard to habitat identification through RS recent researches have suggested different relevant considerations and requirements: study areas specific approaches; ecological expert knowledge implemented as decision rules; the implementation/inclusion of key input variables selected following specific characteristics of individual habitats; the integration of ancillary data into the classification processes, related to shape, texture, context; the use of non-parametric algorithms implemented through binary classifications or decision trees that allow to include nominal, derived and ancillary geospatial data and also are advantageous with scarce training samples; (Bock et al. 2005; Boyd et al. 2006; Foody et al. 2007; Franklin et al. 2001; Kerr and Ostrovsky 2003; Martínez et al. 2010; Mücher et al. 2009).

On the other hand, insufficient integration at different scales is one of the constraints of the current biodiversity monitoring programmes (Pereira and Cooper 2006) and it is also urgent

Assessing Loss of Biodiversity in Europe

**EUNIS CORRELATION:** G3.1

**EUNIS CORRELATION:** E4.4,

**EUNIS CORRELATION:** E2.2

**associations / Moors and heathland** 

**EUNIS CORRELATION:** F4.1 **HC 4030** - European dry heaths **EUNIS CORRELATION:** F4.2

**EUNIS CORRELATION:** F3.1, F3.16

**EUNIS CORRELATION:** F3.1, F3.16

**EUNIS CORRELATION:** B1.3, B1.32

**EUNIS CORRELATION:** H2.5, H2.5

**EUNIS CORRELATION:** B1.4

**EUNIS CORRELATION:** H3.1

**HC 7110\*** - Active raised bogs

**HC 7230** - Alkaline fens **EUNIS CORRELATION:** D4.1

**Beaches, dunes, sands** 

**associations / Sclerophyllous vegetation** 

**EUNIS CORRELATION:** E3.5, E3.51

**associations / Natural grasslands** 

**Bare rocks** 

Through Remote Sensing: The Necessity of New Methodologies 41

**HC 6410** - *Molinia* meadows on calcareous, peaty or clayey-silt-laden soils (*Molinion caeruleae*)

**CLC 321 - Forest and semi natural areas / Scrub and/or herbaceous vegetation** 

**HC 6510** - Lowland hay meadows (*Alopecurus pratensis*, *Sanguisorba officinalis*)

**CLC 322 - Forest and semi natural areas / Scrub and/or herbaceous vegetation** 

**HC 4020\*** - Temperate Atlantic wet heaths with *Erica ciliaris* and *Erica tetralix*

**HC 5130** - *Juniperus communis* formations on heaths or calcareous grasslands

**HC 5130** - *Juniperus communis* formations on heaths or calcareous grasslands

**CLC 331 - Forest and semi natural areas / Open spaces with little or no vegetation /** 

**HC 2130\*** - Fixed coastal dunes with herbaceous vegetation ('grey dunes')

**HC 8130** - Western Mediterranean and thermophilous scree

**CLC 411 - Wetlands / Inland wetlands / Inland marshes** 

**CLC 412 - Wetlands / Inland wetlands / Peat bogs** 

**EUNIS CORRELATION:** C1.4, D1.1, G5.6

**HC 8220** - Siliceous rocky slopes with chasmophytic vegetation

**HC 7120** - Degraded raised bogs still capable of natural regeneration

**CLC 332 - Forest and semi natural areas / Open spaces with little or no vegetation /** 

**HC 2120** - Shifting dunes along the shoreline with *Ammophila arenaria* ('white dunes')

**CLC 323 - Forest and semi natural areas / Scrub and/or herbaceous vegetation** 

**HC 6170** - Alpine and subalpine calcareous grasslands

to advance in this issue. Remote sensing analyses of ecological phenomena at global scale are too general to meet regional and local monitoring requirements. Medium and high spatial resolution remotely sensed data, like Landsat TM and ETM+ sensors, have been widely used in ecological investigations and applications, because their suitability at regional and landscape scales. But there is a mismatch between broad-scale remote sensing and local scale field ecological data (Kerr and Ostrovsky 2003): the synoptic view of the remote sensing should be enhanced with *in situ* data and regional assessments should combine high spatial resolution satellite RS with on-the ground monitoring, and aerial photography or very high spatial resolution satellite RS in studying some habitats which are best monitoring at small scales (Hansen et al. 2004; Pereira and Cooper 2006).

To conclude, the upcoming standardized methodology should incorporate these recommendations. For habitat mapping through RS, expert knowledge and field measurements should be combined with key input variables and optimal algorithms related to each individual target habitat, implemented in a decision structure like a tree. At European level the new methodology should be based on the EUNIS system that meets the objectives and requirements of the Habitat Directive, the Convention of Biological Diversity and the new 2020 biodiversity targets. It should also look at the new possibilities of medium and high resolution satellite images.
