### Remote Sensing of Island Agroecosystems  
Remote sensing techniques have become essential tools for agronomists and conservation scientists alike because they allow for systematic, non-intrusive, and uniform collection of data that can be interpreted from a biophysical standpoint, even in places that are hard to reach [13; 14; 15; 16]. Despite its usefulness, remote sensing is still relatively underused because, until recently, access to high-resolution imagery was prohibitively expensive, and the long revisit time of high-resolution satellites was impractical for applications that required close monitoring, especially in regions with persistent cloud coverage [14; 15]. However, this has changed with the introduction of small satellite constellations that have short revisit times and with the availability of freely-accessible high spatial and spectral resolution image collections [13; 15; 17]. The persistent cloud coverage, high biological diversity, and high landscape heterogeneity of island agroecosystems like those in the Galapagos make them challenging regions to classify. Among the plethora of methods, sensors, vegetation indices, and classification algorithms used for mapping island ecosystems and agricultural landscapes, methods that combine multiple data sources of complementary resolutions have been among the most successful at accurately classifying land cover [15; 18; 19; 20; 21]. The inclusion of vegetation indices is useful for differentiating land cover types that have variable spectral responses based on local conditions, such as moisture and vegetation health [16; 22]. Object-based classification methods have been found to handle nuanced classification of high-resolution images better than pixel-based classification methods [15; 18; 23; 24]. Machine learning methods, like decision trees [25], have become common and have been shown to have higher accuracy than parametric classification methods like maximum likelihood classifiers as they do not require assuming a particular statistical model for the distribution of the training data [24; 25; 26]. Object-based classification of high-resolution images using machine learning algorithms, like random forest, is an efficient method for classifying smallholder agricultural systems and mapping invasive species with high accuracy results [23; 24; 26; 27; 28]. Taking advantage of newly-available high-resolution sensors, commonly-used vegetation indices, object-based approaches, and contemporary machine learning methods, we can create an easily-replicable methodology that addresses the challenges of creating high-quality classification maps of regions like the humid highlands of the Galapagos. Past and Present Efforts to Map the Humid Highlands and Agroecosystems of the Galapagos Using Remote Sensing  
Despite the importance of the agricultural region for human wellbeing and environmental conservation, the land cover of these critical habitats remains mostly undocumented in detail for this tropical archipelago. Table 1 summarizes previous and current efforts to map the Galapagos using remote sensing. Most previous studies either exclude the agricultural areas or suffer from high cloud cover concentrations over the humid highlands (Table 1). Only the survey performed by the National System of Information of Rural Lands and Technological Infrastructure (SIGTIERRAS) has high-resolution images where the agricultural zone is visible. However, these images are a decade old, and the sensor was sensitive only to wavelengths within the visible spectrum. Furthermore, two of the primary studies that are used by the Galapagos National Park (GNP) and government institutions (CLIRSEN and TNC, 2006, and SIGTIERRAS 2010) are not published, so wider access to this valuable information is greatly restricted.