## 2 Materials and Methods
### Land Cover Classification
#### 2.3.1 Data Acquisition  
Multispectral Data  
New freely-available sensors, such as the 10-meter spatial resolution Sentinel-2 images from the UE's Copernicus Programme [54], and 3-meter spatial resolution 4-Band PlanetScope images from Planet Labs [55], can help overcome the challenges that have limited mapping of these areas. Planet Labs is a commercial venture, but its image collections are accessible to university students and faculty for free through its education and research program. PlanetScope sensors have a high spatial resolution (3 m) and are sensitive to both visible and Near Infrared (NIR) wavelengths (Figure 4). The high spatial resolution of PlanetScope images allows visualizing considerably more details than NASA's freely available Landsat collections or even commercially-available SPOT (Satellite Pour l'Observation de la Terre) sensors. Meanwhile, Sentinel-2 sensors have a high spectral resolution (Aerosol, Visible, Red Edge, NIR, Narrow NIR, Water Vapor, and Short Wave Infrared or SWIR) and have a 10, 20, and 60-meter spatial resolution (Figure 4). The multispectral images used to make the mosaic and land cover classification are listed in Table 4. These images were chosen by searching through Planet and Copernicus image collections for low (usually \(<\)10%) cloud cover images from the warm/humid season (January-May) that were recorded less than a month apart from each other for each island to avoid variability in the island mosaics. Even if cloud cover was greater than 10%, the images were considered useful if land areas were visible. Images from 2018 were used as a reference, and we included images a maximum of one year apart to complete areas that suffered from cloud coverage. Figure 4: Comparison of spectral bands between Sentinel-2 and PlanetScope satellites. The axes depict the wavelength in nanometers and the terrestrial atmospheric transmission (grey) in percent (adapted from NASA, [[https://landsat.gsfc.nasa.gov/sentinel-2a-launches-our-compliments-our-complements/](https://landsat.gsfc.nasa.gov/sentinel-2a-launches-our-compliments-our-complements/)]([https://landsat.gsfc.nasa.gov/sentinel-2a-launches-our-compliments-our-complements/](https://landsat.gsfc.nasa.gov/sentinel-2a-launches-our-compliments-our-complements/))). Topographical Data  
We used a 10-meter spatial resolution Digital Topographic Model (DTM) generated from SIGTIERRAS orthophotos (2010). The DTMs were provided by authorities from the Galapagos Government Council (CGREG). Elevation and Slope are useful predictors of vegetation variability [56; 57]. We calculated the slope from the DTM layer in ArcMap. From 2017-2019, we surveyed about 83 agricultural production units (UPA) on all four islands. Most surveyed UPAs were recommended for mapping by MAG because they are considered to be representative of agrarian production on each island. Additionally, some UPAs were chosen because they are located in known tortoise habitat. During the surveys, we collected high-resolution (<2 cm per pixel) visible wavelength images of the UPAs with an unmanned aerial vehicle (UAV--DJI Mavic Pro Platinum, 1/2.3 CMOS Sensor), as well as 826 control points of the most salient landscape features and vegetation types within the UPAs that were visible from above, wherever terrain accessibility allowed, using a handheld GPS (Garmin Map64, accuracy: +/ - 3 m). We covered an area of \(\sim\)1252 ha with our drone flights, corresponding to about 2% of the study area (Table 5, Figure 5). We aligned the UAV images and generated a three-dimensional mesh that we used to stitch together a high-resolution (\(\sim\)0.04 m) orthomosaic using AgiSoft Photoscan (version 1.4.5) for each UPA.