## 3 Results
### Methodology and Sources of Error  
In this paper, we presented a replicable methodology that uses free and open-source data to monitor land use and land cover change in complex agricultural systems. Agricultural landscapes are a complex patchwork of food crops and other vegetation types, where even small-scale patches of vegetation can be a valuable habitat for animals of different kinds [82; 83; 84; 85; 86]. Having images with high spatial resolution is crucial for capturing small habitat fragments in the analysis, so methods that use freely-available high-resolution image collections provide a much-needed and inexpensive alternative to purchasing commercial high-resolution data for fine-scale analysis. Mapping an agricultural matrix in high resolution is a challenge because categorically different vegetation types can have similar spectral signatures and be cultivated in proximity with other vegetation types; our object-oriented random forest classification of fused images tackles this challenge by combining data sources with complementing resolutions, taking into account the geometrical properties of landscape features in addition to their reflectance, and by classifying elements with an algorithm that provides reliable results with limited training datasets [24; 27]. Unfortunately, two land cover types, _Lantana_ and _Erythrina_, had to be dropped from the final mapping outcomes because we did not have enough reference points to have consistent results during classification. In the case of _Erythrina_, their proximity to other dense vegetation types, such as permanent crops, _Cedrela_, and _Psidium_, was an issue during segmentation. _Erythrina_ trees are usually planted in long stands as living fences are often pruned, so their crowns tend to be relatively small. Therefore, the size of _Erythrina_ stands was insufficient to be adequately captured by the sensors, even at 3 m resolution. The small size and proximity to other vegetation types caused segments to include neighboring pixels of different land cover types and confuse their spectral signatures. Similarly, in the case of _Lantana_, we did not have enough examples of patches that were large enough to produce a clear spectral response. Additionally, we were limited in our reference data available because some areas lacking information were too remote or of difficult access to reach them by foot to collect GPS control points. On the other hand, weather conditions in the highlands also made image collection with a UAV difficult and sometimes impossible within the timeframe available for fieldwork, as was the case with Isabela's highest regions within our study area. Therefore, areas that were easier to access or had favorable weather conditions were more represented in our samples. Additionally, land cover units grouping several species, instead of mapping individual plants, were adopted because these types of general categories are used in policy documents or previous studies. While this makes our results relevant to the intended users of the map (i.e., MAG) and compatible with existing data sources, these categories forced us to group together vegetation types into land cover units that likely had a wide range of spectral signatures. For example, permanent crops, transitory crops, silvopastures, and mixed forest are land cover units that are heterogeneous by definition. For instance, permanent crops are often tree crops, but MAG also includes pineapples in this category since they are technically perennial crops [40]. Pineapple plantations have a very different spectral signature than tree stands. This inclusion might explain why permanent crops were the most frequently misclassified category (error rate = 28% in Santa Cruz, Table 9). Similarly, silvopastures are, by definition, a mixture of grass and tree species, both of which could be of different types and have vastly different reflectance, and this probably contributed to their misclassification and high error rate (error rate = 38% in Santa Cruz, Table 9).