## 5 Conclusions  
In this paper, we have presented a replicable and reliable methodology for land cover classification combining freely-available high-resolution data sources and taking advantage of their complementing spectral and spatial resolutions. The methods described are well suited to classify complex agricultural landscapes by taking into account the geometry of scene elements in addition to their reflectance. We found that our spatial resolution was insufficient to reliably detect rows of living fences of _Erythrina_ since their crown size was often less than the 3 m pixel size of our data sources. However, our methods were able to reliably identify stands of invasive plants like _C. odorata_, _S. jambos_, and _P. guajava_. Our results suggest that out of the 25,261 ha of the agricultural zone, invasive plants cover most of the surface area (28%), mostly dominated by _P. guajava_ (4959 ha). Pastures for raising cattle cover 22% of the agricultural zones, the majority of which are located in Santa Cruz. Most native vegetation is distributed outside of the agricultural areas, but almost 19% of the agricultural zone's land cover was identified as native vegetation, mainly located in San Cristobal (2535 ha). Food crops of different kinds cumulatively cover a similar percentage (18%) of the surface area, and about 944 ha of these are dedicated to coffee cultivation, mostly located in Santa Cruz. About 12% of the agricultural sector is covered by vegetation that could not be clearly identified as either native or invasive vegetation. Multiple institutions and researchers have expressed the need for a high-resolution and updated and land cover classification of Galapagos agroecosystems. Local agencies in the agricultural sector can now use these data for identifying current agricultural resources and planning land management projects. For example, the classification maps will inform the GNP and the MAG about priority areas for intervention to control invasive species; having clear estimates of surface area covered by different invasive plants allows institutions to make realistic estimates of approximate costs for their control as part of long-term plans for the region. Authorities can now ascertain the trends of the region's food basket with greater confidence, to ascertain whether food crops and pastures have increased or diminished in relation to previous years as well as in relation to future assessments. These data help local authorities plan strategic interventions, such as estimating how many people are necessary to make the most of land that is already being planted, as well as how many people would be necessary to reactivate areas currently dominated by invasive plants. Additionally, researchers can use these baseline data to anticipate changes in land cover as socioeconomic and environmental drivers continue to change observed land use and climate patterns. The land cover categorization and mapping strategy is designed to be reproducible so that it can be applied to future images to track changes in these agroecosystems. The dataset presented here is designed to suit the needs of both agricultural and conservation practitioners. We aim to encourage collaboration between these two sectors to ensure the well-being of Galapagos inhabitants and the sustainable preservation of its ecosystems and to serve as a model for land use mapping in other environments globally where agroecosystems host essential species for conservation. The complete land cover dataset is available online at [[http://www.mdpi.com/2072-4292/12/1/65/s1](http://www.mdpi.com/2072-4292/12/1/65/s1)]([http://www.mdpi.com/2072-4292/12/1/65/s1](http://www.mdpi.com/2072-4292/12/1/65/s1)), Figure S1: Land cover classification of Galapagos agroecosystems and their surrounding protected areas (2018). Conceptualization, F.J.L. ; data curation, F.J.L., F.L.B. and C.S. ; formal analysis, F.J.L., F.L.B., and C.S. ; funding acquisition, F.J.L. ; investigation, F.J.L., G.R.-T. and J.A.-N.; methodology, F.J.L. and F.L.B.