## 3 Results
### Methodology and Sources of Error
Likewise, the mixed forest is defined as an area where invasive species co-dominate with native species [42]. Given there is a wide range of species, both native and introduced, that could fit that description, it is understandable that humans and computer algorithms could easily misclassify these areas (error rate = 12% in Cristobal and Floreana, Table 9). Another challenge to mapping the agricultural area is that this is a very dynamic landscape, and its land cover is always changing. For example, both transitory crops and pioneer vegetation are, by definition, short-lived; farmers harvest transitory crops in less than one year, and succession quickly leads other plants to take over disturbed areas where pioneer plants once thrived. This short life probably confused their classification because the spectral life of specific land cover types is shorter than the two-year timeframe when the different satellite images were collected. Short spectral life might be one of the main reasons why the 'pioneer' land cover unit received the lowest \(R^{2}\) score (0.032) during map validation. Despite their ephemeral nature, it was important to include these categories to remain consistent with other data sources and to try to capture a snapshot of current vegetation cover. Furthermore, temporary vegetation covers like 'pioneer' or heterogeneous categories like'mixed forest' might signal that these areas are currently in flux, so their presence helps represent the dynamic nature of the agricultural areas. The landscape and the classification units themselves are not homogenous, so traditional methods for assessing accuracy are limited by the assumption that each area in the map can be unambiguously assigned to a single category, and by expressing classification errors as either \"right\" or \"wrong\" without reference to the magnitude of the error [87]. We initially attempted a point-based accuracy assessment, but this yielded misleadingly low results because GPS control points of individual plant species could not be unequivocally assigned to any one category. Therefore, we opted for a validation method that could accommodate for the landscape's and our classification scheme's ambiguity. Expressing our accuracy in terms of percentages and correlations \(RMSE\) and \(R^{2}\) was more relevant to the users of our classification because we are comparing manually-classified reference polygons with our classification results. However, we included other metrics common in land cover classification and machine learning (KIA, AUC) to reach readers that are more accustomed to them. Our accuracy assessment results are in a similar range as those from similar object-based land cover classifications [24,69]. Our overall KIA value of 0.7 suggests that our classification results are significant and are useful overall. Categories with KIA or AUC values \(>\)0.7 correspond to land cover types with a forest or pasture structure (Table A5). The land covers that were confused most often were bare ground, freshwater, and pioneer cover types. (Table A5). All three land cover types covered relatively small surface area extensions, so their representation in the validation reference polygons was minimal. This error was especially strong for freshwater (KIA: 0.34, AUC: 0.314) because most visible freshwater occurs in microreservoirs, for which reflectance can change drastically depending on whether or not they are filled. Only one microreservoir fell within the reference polygons for classification. Bare ground (KIA: 0.271, AUC: 0.386) was understandably confused with transitory crops (KIA: 0.446, AUC 0.628) because these areas are usually denuded from other vegetation, and so the spectral response of the bare soil gets confused with that of the crops themselves. Bare ground is also hard to distinguish from built environment (KIA: 0.516, AUC: 0.685) because most roads of the humid highlands remain dirt roads and are not paved yet.