Applications in agroforestry and environment

To Provide the student with the knowledge related to:
The principles and applications of UAS in the management of natural resources
Capacity for the design of operations and data management in the realization of inventories.
Know-how on the main applications of UAS in the field of agriculture and forestry.
Ability to design operations and data management in precision agriculture.

The official memory of the master contains the following contents for the course:
Applications of UAS to the characterization, evaluation and 2D and 3D monitoring of the vegetation cover. Extraction of quantitative variables, classification and analysis of changes from multi and hyperspectral data, LiDAR and SfM point clouds.
UAS applications to species monitoring. Sampling methods and population calculation
UAS applications in the agroforestry sector. Precision farming. Determination of the degree of crop coverage, biomass, yield, water and erosion status of crops.
Control of pests and diseases through the use of UAS.
Microscale structure and spatial pattern analysis from ultra-high resolution UAS data.

These contents will be developed in the following theoretical and practical sessions:
Theoretical sessions (12 hours face-to-face + 20 hours autonomous work of the student):
Topic 1. Introduction. Natural resources, ecosystem services and biodiversity: Evaluation and monitoring through RPAS. Specific platforms and sensors. Integration with other data sources. 1 h face-to-face + 1 h autonomous work
Topic 2. Project sfor the evaluation and monitoring of vegetation cover. Image processing and generation of 2D and 3D products. 1 h face-to-face + 2 h autonomous work
Topic 3. Applications of UAVs to the characterization, evaluation and monitoring of vegetation cover: Landscape and habitats. (radiometry, spectral response, classification systems. Multispectral classifications. Verification of results). 1 h face-to-face + 2 h autonomous work
Topic 4. Applications of UAVs to the characterization, evaluation and monitoring of vegetation cover: Landscape and habitats. (3D classification, LiDAR,). 1 h face-to-face + 2 h autonomous work
Topic 5. Applications of UAVs to the characterization, evaluation and monitoring of vegetation cover: Landscape and habitats. (analysis of changes). 1 h face-to-face + 2 h autonomous work
Topic 6. Applications of UAVs to species monitoring. Sampling methods and population estimation. 1 h face-to-face + 2 h autonomous work
Unit 8. Analysis of spatial patterns. 2 hours face-to-face + 2 hours autonomous work
Unit 9. Precision agriculture (determination of the degree of crop coverage, biomass and prediction of yield, irrigation and fertilization). 2 h face-to-face + 3 h autonomous work
Unit 10. Viticulture. 1 h face-to-face + 2 h autonomous work
Unit 11. Control and fight against pests. 1 h face-to-face + 2 h autonomous work

Laboratory and field work (30 face-to-face hours + 82 hours of autonomous student work):
Practice 1. Preparation of a project for the evaluation and monitoring of vegetation cover. Image processing and generation of 2D and 3D products. 3D, multispectral and hyperspectral information extraction. 2 hours face-to-face + 4 hours autonomous work
Practice 2. Classification and monitoring of the vegetation cover using very high resolution images. (multispectral classification). 2 h face-to-face + 6 h autonomous work
Practice 3. Classification and monitoring of the vegetation cover by means of very high resolution images. (3d classification). 3 hours face-to-face + 10 hours autonomous work
Practice 4. Classification and monitoring of the vegetation cover by means of very high resolution images. (changes analysis). 3 hours face-to-face + 10 hours autonomous work
Practice 5. Classification and monitoring of the vegetation cover by means of very high resolution images. (2d and 3d verification). 2 hours face-to-face + 10 hours autonomous work
Practice 6. Analysis of spatial patterns. 2 h face-to-face + 6 h autonomous work
Practice 7. Field data and images collection for the evaluation and monitoring of the vegetation cover. 6 h. Field trip face-to-face + 4 h autonomous work
Practice 8. Precision agriculture (determination of the degree of crop coverage, biomass and prediction of yield, irrigation and fertilization). 4 hours face-to-face + 12 hours autonomous work
Practice 9. Viticulture. 4 hours face-to-face. 3 hours face-to-face + 10 hours autonomous work
Practice 10. Control and fight against pests. 3 hours face-to-face + 10 hours autonomous work

Basic bibliography (in spanish)
Cancela, JJ y González, XP (2018) Uso de drones y satélites en agricultura: actas de horticultura de III Symposium Nacional de Ingeniería Hortícola y I Symposium Ibérico de Ingeniería Hortícola: celebrado del 21 al 23 de febrero de 2018, en Lugo 978-84-697-9314-5
Díaz, J., & Cervigón, J. (2015). Estudio de Índices de vegetación a partir de imágenes aéreas tomadas desde UAS/RPAS y aplicaciones de estos a la agricultura de precisión. Universidad Complutense de Madrid.
Basic bibliography (in english)
Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), 1110.
Colomina, I., Molina, P., 2014. Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote Sens. 92, 79–97.
Díaz-Varela, R.A., Calvo Iglesias, S., Cillero Castro, C., Díaz Varela, E.R., 2018. Sub-metric analisis of vegetation structure in bog-heathland mosaics using very high resolution rpas imagery. Ecol. Indic. 89, 861–873.
Dörnhöfer, K., Oppelt, N., 2016. Remote sensing for lake research and monitoring – Recent advances. Ecol. Indic. 64, 105–122.
Gonzalez, L., Montes, G., Puig, E., Johnson, S., Mengersen, K., Gaston, K., 2016. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation. Sensors 16, 97.
Malveaux, C., Hall, S. G., Price, R. (2014). Using drones in agriculture: unmanned aerial systems for agricultural remote sensing applications. In 2014 Montreal, Quebec Canada July 13–July 16, 2014 (p. 1). American Society of Agricultural and Biological Engineers.
Pádua, L., Vanko, J., Hruška, J., Adão, T., Sousa, J. J., Peres, E., & Morais, R. (2017). UAS, sensors, and data processing in agroforestry: a review towards practical applications. International Journal of Remote Sensing, 38(8-10), 2349-2391.
Tang, L., Shao, G. (2015). Drone remote sensing for forestry research and practices. Journal of Forestry Research, 26(4), 791-797.
Terms, F., 2017. Unmanned aerial vehicles for environmental applications. Int. J. Remote Sens. 38, 2029–2036.
Complementary bibliography (in spanish)
Garcia Carazo, J., Alvarez Alvarez, P., Garrote Haigermoser, J., 2016. Aplicaciones de QGIS en la ordenacion de montes Manual practico. Editorial Académica Española.
Complementary bibliography (in english)
Anderson, K., Gaston, K.J., 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front. Ecol. Environ.
Berni, J., Zarco-Tejada, P.J., Suarez, L., Fereres, E., 2009. Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring from an Unmanned Aerial Vehicle. IEEE Trans. Geosci. Remote Sens. 47, 722–738.
d’Oleire-Oltmanns, S., Marzolff, I., Peter, K., Ries, J., 2012. Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco. Remote Sens. 4, 3390–3416.
Díaz-Varela, R.A., Ramil-Rego, P., Calvo-Iglesias, M.S., Diaz-Varela, R.A., Ramil-Rego, P., Calvo-Iglesias, A.S., 2007. Strategies of remote sensing monitoring of changes in NATURA 2000 sites: a practical assessment in coastal mountains of NW Iberian Peninsula, in: Ehlers, M., Michel, U. (Eds.), Remote Sensing for Environmental Monitoring, Gis Applications, and Geology Vii. p. 74932.
Diaz-Varela, R.A., Zarco-Tejada, P.J., Angileri, V., Loudjani, P., 2014. Automatic identification of agricultural terraces through object-oriented analysis of very high resolution DSMs and multispectral imagery obtained from an unmanned aerial vehicle. J. Environ. Manage. 134, 117–126.
dOleire-Oltmanns, S., Eisank, C., Dragut, L., Blaschke, T., 2013. An Object-Based Workflow to Extract Landforms at Multiple Scales from Two Distinct Data Types. Geosci. Remote Sens. Lett. IEEE 10, 947–951.
Gonçalves, J., Henriques, R., Alves, P., Sousa-Silva, R., Monteiro, A.T., Lomba, Â., Marcos, B., Honrado, J., 2016. Evaluating an unmanned aerial vehicle-based approach for assessing habitat extent and condition in fine-scale early successional mountain mosaics. Appl. Veg. Sci. 19, 132–146.
Jones, G.P., Pearlstine, L.G., Percival, H.F., 2006. An Assessment of Small Unmanned Aerial Vehicles for Wildlife Research. Wildl. Soc. Bull. 34, 750–758.
Kachamba, D., Ørka, H., Gobakken, T., Eid, T., Mwase, W., 2016. Biomass Estimation Using 3D Data from Unmanned Aerial Vehicle Imagery in a Tropical Woodland. Remote Sens. 8, 968.
Laliberte, A.S., Goforth, M.A., Steele, C.M., Rango, A., 2011. Multispectral remote sensing from unmanned aircraft: Image processing workflows and applications for rangeland environments. Remote Sens. 3, 2529–2551.
Michez, A., Piégay, H., Lisein, J., Claessens, H., Lejeune, P., 2016. Classification of riparian forest species and health condition using multi-temporal and hyperspatial imagery from unmanned aerial system. Environ. Monit. Assess. 188, 146.
Mulero-Pázmány, M., Stolper, R., Van Essen, L.D., Negro, J.J., Sassen, T., 2014. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa. PLoS One 9.
Reichardt, T.A., Collins, A.M., McBride, R.C., Behnke, C.A., Timlin, J.A., 2014. Spectroradiometric monitoring for open outdoor culturing of algae and cyanobacteria. Appl. Opt. 53, F31-45.
Shahbazi, M., Sohn, G., Théau, J., Menard, P., 2015. Development and Evaluation of a UAV-Photogrammetry System for Precise 3D Environmental Modeling. Sensors (Basel). 15, 27493–524.
Turner, D., Lucieer, A., Malenovský, Z., King, D., Robinson, S., 2014. Spatial Co-Registration of Ultra-High Resolution Visible, Multispectral and Thermal Images Acquired with a Micro-UAV over Antarctic Moss Beds. Remote Sens. 6, 4003–4024.
Zahawi, R.A., Dandois, J.P., Holl, K.D., Nadwodny, D., Reid, J.L., Ellis, E.C., 2015. Using lightweight unmanned aerial vehicles to monitor tropical forest recovery. Biol. Conserv. 186.
Web resources:
Earth Lab 2020. Document Your Science Using R Markdown and R. https://www.earthdatascience.org/courses/earth-analytics/document-your-…. Acceso 05/05/2022.
Humboldt State University, 2014. GSP 216 Introduction to Remote Sensing. Accuracy Metrics. https://gsp.humboldt.edu/olm_2019/courses/GSP_216_Online/lesson6-2/metr…. Acceso 05/05/2022.
Prado Ortega, E. 2020. Empleo de los Modelos de Simulación de Reflectividad para la docencia de la Teledetección. Universidad de Alcalá. Departamento de Geografía. http://geogra.uah.es/rtm/. Acceso 05/05/2022.
Robert J. Hijmans, 2016-2020. Spatial Data Science with R. https://rspatial.org/raster/#. Acceso 05/05/2022.
The IDB Project, 2011-2020. Index DataBase. A database for remote sensing indices. https://www.indexdatabase.de/. Acceso 05/05/2022.
VV.AA. 2016. UAS 4 ENVIRO. Small Unmanned Aerial Systems for Environmental Research – 5th Edition. 28 – 30 June 2017. UTAD. Vila Real. PO, http://uas4enviro2017.utad.pt/index.html%3Fp=1.html. Acceso 05/05/2022.

Upon completion of this course, students must be competent in several aspects:
BASIC AND GENERAL
CG5 – To apply, in the field of unmanned aerial systems, the principles and methodologies of research such as bibliographic searches, data collection and analysis and interpretation of these, as well as the presentation of conclusions, in a clear, concise and rigorous way.
CB6 – To possess and understand knowledge that provides a basis or opportunity to be original in the development and / or application of ideas, often in a research context
CB7 – Know how to apply the knowledge acquired and their ability to solve problems in new or unfamiliar environments within broader (or multidisciplinary) contexts related to their area of study
CB8 – To integrate knowledge and face the complexity of formulating judgments based on information that, being incomplete or limited, includes reflections on social and ethical responsibilities linked to the application of their knowledge and judgments
CB9 - Know how to communicate their conclusions and the knowledge and ultimate reasons that support them to specialized and non-specialized audiences in a clear and unambiguous way
CB10 – To dominate the learning skills that allow them to continue studying in a way that will be largely self-directed or autonomous.
TRANSVERSAL
CT3 - Sustainability and environmental commitment. Equitable, responsible and efficient use of resources
CT4 - Development of the innovative and entrepreneurial spirit.
CT6 - Ability to work in a team.
CT7 - Capacity for organization and planning
CT8 - Capacity for analysis and synthesis.
CT9 - Ability for critical reasoning and creativity.
CT10 - Orientation to quality and continuous improvement.
SPECIFIC
CE2 - Knowledge of geomatic, photogrammetric and cartographic principles, navigation, aerial triangulation, interpretation and digital processing of images necessary in the operation of unmanned aerial systems and know how to apply the regulations in force.
CE5 - Ability to apply data from unmanned aerial systems to obtain key information for the management of natural and agroforestry resources.

Being a course with a strong practical and procedural component, in the lectures the student will be provided with knowledge on the introduction -theoretical bases- that they must know to apply in practical applications. Through these methodologies the following competences will be addressed: CG5, CB6, CB7, CB9, CB10, CE2, CE5, CT4, CT7, CT8, CT9, CT10
The practical activities include a compulsory field trip (field practice) to collect data on site. In case of impossibility of carrying out this activity, other alternative activities will be considered. Through these methodologies the following competences will be addressed: CB6, CB8, CE2, CE5, CT3, CT4, CT6, CT7, CT8, CT9, CT10
The autonomous work will deepen the management of data sources, analysis techniques and procedures through the application of ICT to case studies and supervised work. Through these methodologies the following competences will be addressed: CG5, CB6, CB7, CB8, CB10, CE2, CE5, CT3, CT4, CT7, CT8, CT9, CT10
All the previous activities (expositive, interactive sessions and tutorials) will be supported by the virtual environment (virtual classroom of the course) that will facilitate and allow continuity throughout the learning process (guide, materials, communications, delivery of work, forums of debate, collaboration spaces, etc.).
The attendance at face-to-face sessions may be done physically (EPSE) or through a synchronous connection to the Microsoft Teams platform created for the subject.

The achievement of these competences will be evaluated continuously throughout the school period. The final score will take into account:
- Written test (20% of the final grade): CG5, CB6, CB7, CB9, CB10, CE2, CE5, CT4, CT7, CT8, CT9, CT10
- Delivery of course works / assignments and/or practical/lab results (70% of the final grade): CG5, CB6, CB7, CB9, CB10, CE2, CE5, CT4, CT7, CT8, CT9, CT10
- attendance and participation in scheduled activities including the fieldwork trip (10% of the final grade): CB6, CB8, CE2, CE5, CT3, CT4, CT6, CT7, CT8, CT9, CT10
The subject will be passed with a minimum grade of 5 (out of a maximum of 10) computed globally. The described evaluation system will be the one used both in the first and second opportunity.
In the case of repeating students, the evaluation system described for ordinary students will be followed, and attendance to the practical trip if the previous year has been completed may be validated.
For cases of fraudulent performance of exercises or tests, the one set out in the Regulations for the evaluation of the academic performance of students and for the review of grades will apply.
Students who have been granted a waiver of attendance to any of the teaching activities programmed in accordance with the provisions of Instruction 1/2017 of the General Secretariat, must take into account that to pass this subject it is mandatory to attend practical activities, both laboratory and field, indicated in the class schedule and scheduled in the teaching guide, as well as the written test.

The course consists of 6 ECTS credits (42 contact hours), with a total load of autonomous work of the student of 108 hours. The distribution of hours for each activity is shown below.
Face-to-face work
Theoretical-practical lectures: 12 hours
Interactive classes (laboratory, field work, case studies, problem solving, assignments, tutorials, evaluation): 30 hours
TOTAL PRESENTIAL WORK: 42 hours
PERSONAL WORK
Reading and preparation of lectures, case study: 20 hours
Preparation of practices and course work: 82 hours
Evaluation: 3 h
Individual tutorials 3 h
TOTAL PERSONAL WORK: 108 hours
TOTAL NUMBER OF HOURS OF COURSE WORK: 150 HOURS

- To attend participatively the theoretical and practical lectures
- To analyze the bibliography provided