## 1 Introduction
Satellite sensors with a smaller field of view (e.g., Worldview and Superview have a 16 km swath) are generally constrained by low revisit times, unless the satellite is combined with phased orbit of the satellite constellation, which can offer a two day revisit for any point on Earth. Coarser spatial resolution sensors can image larger areas (e.g., Sentinel-2 can image strips 290 km wide) in one overpass of the satellite sensor with more regular repeat cycles [63]. There are also several important biodiversity trade-offs when considering the spatial resolution of a satellite sensor. For example, low-resolution data are perfectly adequate for monitoring status of broad land cover (LC)/use (LU) [64] and land change at regional level [65], while higher-resolution data are often desirable for monitoring at a local scale, individual protected areas [66; 67] and agricultural practices [68], habitat loss, fragmentation and degradation, and ecosystem services [69; 70] and for substituting for physical inspection [34; 68]. Moreover, [PERSON] et al. [47] demonstrated that VHR satellite data can help mapping biodiversity and modelling habitat across different scales (landscape, plot, and patch) which can be used for identifying specific taxonomic groups. One other benefit of using VHR satellite data (0.5-5 m, depending on the optical sensor) is to discriminate some aspects of the habitat characteristics which normally cannot be seen at the coarser resolution [37]. For instance, case studies have been carried out using VHR optical satellite or aerial imagery for the mapping and monitoring of moorland to map vegetation patterns, evidence of ditching, and degree of peat exposure or burn histories [71; 72]. The potential for assessing biodiversity and ecosystem status can be obtained through EO-derived land-cover and land-use datasets, spatial information, and in situ data [37]. In terms of the land-cover classification system at the global scale (refers to scales smaller than 1:250,000 and using NOAA Advanced Very High Resolution Radiometer (AVHRR) satellites), the most representative examples are USGS product [73], EarthSat GeoCover Land Cover Legend [74], the UN/FAO Land Cover Classification System [75], Global Observation of Forest Cover [76], and Global Observation of Land Cover Dynamics (GOFC/GOLD) [77]. Furthermore, land-cover classification at a regional scale (between 1:250,000 and 1:100,000) often use a medium spatial resolution from satellites such as Landsat TM, SPOTHRV/XS, IRS-1C/LISS, Landsat ETM+, and MODIS, offering classification systems such as CORINE [78] and AFRICOVER [79]. At the national scale, land-cover classification systems are available at smaller scales (between 1:5000-1:10,000) and are commonly based on remote sensing data and in situ surveys. Some products of land-cover classification systems at the national scale are National Land Cover Data Classification System [80], National Land Survey Classification System, and Centre for Ecology and Hydrology (CEH) Land Cover Map [81]. The Centre for Ecology and Hydrology (CEH) has developed its Land Cover Map spatial framework, which uses satellite data from 2015-2019 from the European Space Agency's Copernicus Sentinel-1 and Sentinel-2 satellites. These have a high resolution (10 m) enabling the distinction between 21 land-cover classes on the basis of Sentinel imagery and linked to Biodiversity Action Plan (BAP) Broad Habitats [82]. However, it has been noted [83] that discrepancies can exist in the habitat terminology, revealing the need for additional ecological expert knowledge and potentially VHR data to help resolve such ambiguities (e.g., in plant height estimations). VHR data have a wide application across many sectors, including assessing biodiversity and habitat quality [47].