Automatic analysis of UAS-based multi-temporal data as support to a precision agroforestry management system
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2021-05-18
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Forest and agriculture ecosystems are prone to disturbances caused by human action or natural effects. For instance, climate change is projected to be a key influence on vegetation across the globe. Regarding agriculture, primary climate vectors with a significant impact include temperature, moisture stress, and radiation. Within this context, it is of foremost importance to monitor crops along time, as well as to detect pests, diseases, assess and control irrigation demands. Regular monitoring activities will enable timely measures that may trigger field interventions that are used to preserve health status of crops, achieving both time and economic gains, while assuring a more sustainable activity. Within this scope, precision agriculture (PA) techniques appear as an effective alternative to the traditional agronomy practices. In fact, the technological advances that promote PA are able to enhance support when making decisions, resulting in agronomical processes upgraded by employing site or plant specific management operations. In this regard, the capabilities of unmanned aerial vehicles (UAVs) to provide flexible, efficient, non-destructive, and non-invasive means of acquiring data on agricultural crops and the various agro-environmental factors of the parcel, can be used for PA applications. The high- temporal, radiometric and spatial resolutions achieved by UAV-based aerial imagery make possible to foresee new and important advances in PA practices. In this study it is presented the development of a management support system for the agriculture and forestry sectors, based on the analysis of multi-temporal data obtained through different sensors coupled to UAVs. With a continuous monitoring, it is intended to monitor the vegetative development and to identify, in an early and (semi)automatic way, potential issues, allowing their localized mitigation, through methodologies and algorithms developed for this purpose. To meet these main objectives, two important agricultural crops from the region of Trás-osMontes and Alto Douro (Portugal) economy, were identified: the grapevine (Vitis vinifera L.); and the European chestnut (Castanea Sativa Mill.). Both of these crops have a high socioeconomic relevance for the population of this region and represent an important share of national production. Thus, the work is divided into two parts, one focuses on monitoring chestnut stands and the other focuses on vineyards. The several differences among these two species in the planting typology and their geometry, make the approaches to each of the sectors also different. However, this fact will allow the adaptation of the proposed methodologies to almost all agricultural species, regardless of the type and the way they are arranged, in a grid or in rows. Although there are several approaches to detect and monitor vegetation through aerial imagery, most of them remain dependent of manual extraction of vegetation parameters. This work presents automatic methods that allow—with none or few parametrization—the individual detection of the trees/grapevines and their multi-temporal analysis. The approach for tree detection was applied to several chestnut stands, allowing the automatic estimation of several parameters, such as the number of trees, the canopy coverage, tree height, and crown diameter. A novel methodology that enables the identification of phytosanitary issues from multitemporal analysis of chestnut stands, using UAV-based multispectral imagery, was also developed and it is presented in this thesis. This approach not only allows the absence or presence of phytosanitary issues but also the identification and the classification of biotic or abiotic factors affecting the trees. The developed methodology proved to be effective in automatically detecting and classifying phytosanitary issues in chestnut trees throughout the growing season. Likewise, methods to automatically estimate and extract grapevine vegetation parameters are also proposed. A full pipeline for vineyards management was developed. First, a methodology able to differentiate grapevine canopy between inter-row vegetation cover and soil, and to identify independent vine row was built. Then, the outputs were provided but the former methods were used to create a multi-temporal data analysis of vineyards, enabling the monitoring of vegetation dynamics of a given vineyard plot along the growing season. This way, areas with canopy management operation needs, and with different vigour levels, are identified. The approaches proposed enable to fully exploit the advantages offered from the UAV-based multi-sensor data (RGB, multispectral and thermal infrared), by performing multitemporal analysis of vineyards both at the plot and at the plant scales. Individual grapevine detection permits the estimation of geometrical and biophysical parameters, as well as missing grapevine plants. Thus, the developed methodologies proved to be very effective and can be used in a single epoch, analyzing the data from one individual flight campaign to estimate different parameters (depending on the used sensors), both at parcel-level and at the plant-level. In terms of agricultural plot, the canopy coverage, the estimation of the number of trees/grapevines, and the estimation of other vegetation and bare soil can be reached, as well as mean values of the species under analysis. Regarding the plant-level monitoring, geometrical and biophysical parameters as height, canopy volume, crown diameter, temperature and vegetation indices that correlate with yield, biomass, leaf density and phytosanitary issues are also possible to estimate. Combining data from different flight campaigns, allows a multi-temporal analysis to be performed. Moreover, this multi-temporal analysis can be carried out over a single vegetative cycle and/or over different agricultural years, allowing, in any case, to obtain important management information. Hence, the original methods presented in this work have shown to be effective and have proved that their potential goes beyond vegetation detection, since they can be employed in an operational routine for the automatic monitoring of vineyard plots and chestnut stands. Thus, this work can be seen as an important contribution towards the substitution of time-consuming and costly field campaigns for managing plantations in a quicker and more sustainable way.
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Tese de Doutoramento em Informática
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multi-temporal data analysis , unmanned aerial vehicles