The dehesa is an anthropic complex ecosystem typical of some areas of Spain and Portugal, with a key role in soil and biodiversity conservation and in the search for a balance between production, conservation and ecosystem services. For this reason, it is essential to have tools that allow its characterization, as well as to monitor and support decision-making to improve its sustainability. A multipurpose and scalable tool has been developed and validated, which combines several low-cost technologies, computer vision methods and RGB aerial orthophotographs using open data sources and which allows for automated agroforestry inventories, identifying and quantifying units with important ecological significance such as: trees, groups of trees, ecosystem corridors, regenerated areas and sheets of water. The development has been carried out from images of the national aerial photogrammetry plan of Spain belonging to 32 dehesa farms, representative of the existing variability in terms of density of trees, shrub species and the presence of other ecological elements. First, the process of obtaining and identifying areas of interest was automated using WMS services and shapefile metadata. Then, image analysis techniques were used to detect the different ecological units. Finally, a classification was developed according to the OBIA approach, which stores the results in standardized files for Geographic Information Systems. The results show that a stable solution has been achieved for the automatic and accurate identification of ecological units in dehesa territories. The scalability and generalization to all the dehesa territories, as well as the possibility of segmenting the area occupied by trees and other ecological units opens up a great opportunity to improve the construction of models for interpreting satellite images.

The aim of this study was to develop and validate a methodology to carry out olive grove inventories based on open data sources and automatic photogrammetric and satellite image analysis techniques. To do so, tools and protocols have been developed that have made it possible to automate the capture of images of different characteristics and origins, enable the use of open data sources, as well as integrating and metadating them. They can then be used for the development and validation of algorithms that allow for improving the characterization of olive grove surfaces at the plot and cadastral polygon scales. With the proposed system, an inventory of the Andalusian olive grove has been automatically carried out at the level of cadastral polygons and provinces, which has accounted for a total of 1,519,438 hectares and 171,980,593 olive trees. These data have been contrasted with various official statistical sources, thus ensuring their reliability and even identifying some inconsistencies or errors of some sources. Likewise, the capacity of the Sentinel 2 satellite images to estimate the FCC at the cadastral polygon, parcel and 10 × 10 m pixel level has been demonstrated and quantified, as well as the opportunity to carry out inventories with temporal resolutions of approximately up to 5 days.

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This paper aims to evaluate whether an automatic analysis with deep learning convolutional neural networks techniques offer the ability to efficiently identify olive groves with different intensification patterns by using very high-resolution aerial orthophotographs. First, a sub-image crop classification was carried out. To standardize the size and increase the number of samples of the data training (DT), the crop images were divided into mini-crops (sub-images) using segmentation techniques, which used a different threshold and stride size to consider the mini-crop as suitable for the analysis. The four scenarios evaluated discriminated the sub-images efficiently (accuracies higher than 0.8), obtaining the largest sub-images (H = 120, W = 120) for the highest average accuracy (0.957). The super-intensive olive plantings were the easiest to classify for most of the sub-image sizes. Nevertheless, although traditional olive groves were discriminated accurately, too, the most difficult task was to distinguish between the intensive plantings and the traditional ones. A second phase of the proposed system was to predict the crop at farm-level based on the most frequent class detected in the sub-images of each crop. The results obtained at farm level were slightly lower than at the sub-images level, reaching the highest accuracy (0.826) with an intermediate size image (H = 80, W = 80). Thus, the convolutional neural networks proposed made it possible to automate the classification and discriminate accurately among traditional, intensive, and super-intensive planting systems.