## 1 Introduction
Integrating nature-based solutions has been well debated in the literature within a range of applications on land-use management [28], land and soil degradation [28], urban planning [26; 29], and polluted land [30]. Likewise, delivering nature-based interventions can maximise ecosystem services and lead towards net zero greenhouse gas emissions [28; 31]. As part of the ELM policy development, DEFRA established 57 Test and Trails with stakeholders to understand how ELMs could be shaped in order to work in a real-life environment across a wide range of circumstances and to help understand the barriers and opportunities of new approaches [32]. The Surrey Hills Area of Outstanding Natural Beauty (SH-AONB) Test and Trial, entitled 'Making Space for Nature', is one of these 57 Test and Trails and is one of the 12 AONB-based ones that together make up the largest Defra Test and Trial project group for the ELM scheme. The main aim of this test and trial is to understand what local nature recovery and enhanced access represent for a 'designated landscape' and how this can be delivered through ELMs [33]. Currently, DEFRA [16; 32] is exploring a range of ways for monitoring, reporting, and verification (MRV) of ELM scheme agreements. They are aware that ground-based surveys and physical inspections can be prohibitively expensive, and they want to consider the use of satellite and geospatial data, alongside other technologies that can offer cost-effective processes for the MRV of ELM and similar activities [16; 34; 35]. The spectral, spatial, and temporal resolution capabilities of modern Earth Observation (EO) satellites certainly appear to offer an unparalleled opportunity relevant to such ELM and MRV application through their capacities to assess:* biodiversity and habitat type [36; 37; 38; 39; 40]
* habitat quality [41; 42] and model habitat suitability [43; 44]
* species identity and diversity [45; 46; 47; 48; 49; 50]
* species behaviour [49; 51]. Habitat suitability models seek to predict the optimal habitat for a species based on the assessment of habitat attributes such as habitat structure, habitat type, and spatial arrangements between habitat features. The models use established correlations between the presence of particular species and known ecological niche elements to analyse geographical space (e.g., from earth observation at various scales) to locate those same niche elements. This can then be used to create predictive location maps and other representations of the distribution and quantities of relevant habitat for a given species or group of species [44; 52]. According to [PERSON] et al. [53], there are two general approaches to quantify and model biodiversity using satellite and airborne remote sensing: direct mapping of species composition (based on tree canopy) and land use and cover [54; 55; 56] and indirect sensing by using environmental variables proxies such as: primary productivity (chlorophyll and ocean colour), climate (rainfall, soil moisture, and phenology), habitat structure (topography and vertical canopy structure) [44]. These approaches can be performed using visual photo/image interpretation [57] and computing/automated methods [57; 58]. Visual interpretation is a'manual' method based on the visual detection, identification, and spatial localisation of different objects and terrain shapes [59], whilst the automated methods typically use supervised and unsupervised image classification approaches (object and pixel image analysis) via classification algorithms (e.g., maximum-likelihood, support vector machine, random forest, and neural networks) [55; 60; 61; 62]. Spatial resolution is an important attribute for the remote assessment of opportunities for biodiversity as it describes the level of spatial detail which can be seen in the image. Generally, higher spatial detail requires a sensor with a narrower field of view, hence less spatial coverage per image scene.