Methodology for the Automatic Inventory of Olive Groves at the Plot and Polygon Level

Abstract

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.

Advancements in Remote Sensing Imagery Applications for Precision Management in Olive Growing: A Systematic Review

“…Abozeid et al [231] presented SwinTUnet, achieving 98.3% accuracy in olive tree detection, with challenges noted in dense areas. Martínez-Ruedas et al [113] developed an automated methodology for inventory using Sentinel-2 imagery, characterizing 92% of Andalusian olive orchards. Martínez-Ruedas et al [232] also validated a DL approach using convolutional neural networks (CNNs), achieving 95.7% accuracy for sub-images and 82.6% at the farm level.…”

Detection of Planting Systems in Olive Groves Based on Open-Source, High-Resolution Images and Convolutional Neural Networks

“…(i) The olive groves with different planting systems were identified through an observer in SIGPAC [52]. (ii) After being identified, they were automatically downloaded from the PNOA through the module "Automatic Image Acquisition" developed in the study [53] which made use of the Web Map Service (WMS) [54] provided by the IGN [55]. (iii) Finally, the FCC of the olive groves was extracted using the method "Identification of elements of interest" developed and validated in the study [53].…”