## 4 Discussion  
This present study has examined the use of EO and geospatial datasets with a particular focus on the additional insight that VHR imagery (0.7-4 m resolution) can bring to the understanding of the availability, connectivity, and assessment at various scales of habitat suitable for five key species in the SH-AONB. Efforts to use the capabilities of EO for the purposes examined here depend crucially on combining knowledge on the habitat needs of the species in question (acquired from expert consultations and published sources) with that on EO technology and image interpretation. There are clearly important synergies here between these groups and use of the EO-derived data needed to have the input of experts who have in-depth knowledge of the ecology of the species. Similar image classifications have been accomplished in the literature [37]. The present research has provided several examples of the ways in which such a combination can be applied to habitat assessment. EO-derived land-cover and land-use products have been used successfully for mapping and monitoring biodiversity and modelling species habitat suitability [36; 37; 43; 44; 64; 108] and for assessing ecosystem services [108; 109]. Hence, at the scale of the whole SH-AONB, as well as for our three intensive study sites, the readily-available EO and geospatial datasets provide valuable resources enabling identification of the areas of broadly appropriate habitat for these species, albeit at a fairly 'coarse' level of resolution. In general terms, this provides a semiquantitative snapshot of potential habitat availability for the various species which may be particularly useful as a broad guide to the prevalence and location of the habitats across the whole SH-AONB. At the level of individual farms and similar landholdings, the readily available EO and geospatial datasets again provide the capability to broadly map suitable habitat at the individual property scale, e.g., several hectares. However, as is apparent from the habitat needs of these species outlined in Table S3 [98; 102; 103] and in the detailed analyses, the high-level land classifications in the very useful LCM2019 data and similar/complementary resources only permit a quite general assessment of the availability and distribution of suitable habitat. A key question driving our research was whether information potentially available from the VHR satellite imagery could provide the level of detail that would enable a much more confident assessment of the quantity and quality of the habitat present for the given species. Further questions about such VHR imagery and its interpretation were related to whether such information would be relevant for habitat management and, potentially, for MRV systems for habitat provision under policies such as ELMs. These aspects form the basis of the discussion below. VHR satellite data have proven efficient and accurate in assessing habitat quality [41; 42]. The VHR data (0.7-4 m) used in Step 3 of this research allowed detailed analysis of image texture, patterns, colours, meanings, and identification and quantification of areas of scrub, hedges, field margins, different tree species types, etc. This was a considerable extension to the 'granularity' of habitat assessment over that available from the readily available resources analysed in Step 2 (which relied upon resolutions of 10 m or more and other geospatial datasets). Specifically, the benefits of using the VHR imagery as demonstrated in our results were a considerably extended capability to:  
* Identify a wider range of landcover types relevant to the habitat preferences of the species than the range of classes in LCM2019. Notable amongst these was scrub (an aspect of preferred habitat of small blue butterfly, Silver-washed fritillary butterfly, and Hazel dormouse), 'patchiness' small open spaces in broadleaved woodland (Silver-washed fritillary butterfly), and recognition of small areas of single trees (Hazel dormouse, Silver-washed fritillary butterfly, and small blue butterfly).