Mapping Crop Cycles in China Using MODIS-EVI Time Series

As the Earth’s population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA’s (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data

Robot navigation in vineyards based on the visual vanish point concept

One of the biggest challenges of autonomous navigation in robots for agriculture is the path following in a large dimension map and various terrains. An important ability is to follow corridors and or vine rows which are frequent situation and with some complexity given the outline of real vegetation. One method to locate and guide the robot in between vineyards is making use of vanishing point detection on vine rows in order to obtain a reference point and send the adequate velocity commands to the motors. This detection will be conceived utilizing convectional image processing algorithms and Deep Learning techniques. It will be necessary to adapt the image processing algorithms or Deep Learning for use in ROS 2 context.One of the biggest challenges of autonomous navigation in robots for agriculture is the path following in a large dimension map and various terrains. An important ability is to follow corridors and or vine rows which are frequent situation and with some complexity given the outline of real vegetation. One method to locate and guide the robot in between vineyards is making use of vanishing point detection on vine rows in order to obtain a reference point and send the adequate velocity commands to the motors. This detection will be conceived utilizing convectional image processing algorithms and Deep Learning techniques. It will be necessary to adapt the image processing algorithms or Deep Learning for use in ROS 2 context

The Human version of Moore-Shannon's Theorem: The Design of Reliable Economic Systems

Moore & Shannon's theorem is the cornerstone in reliability theory, but cannot be applied to human systems in its original form. A generalization to human systems would therefore be of considerable interest because the choice of organization structure can remedy reliability problems that notoriously plaque business operations, financial institutions, military intelligence and other human activities. Our main result is a proof that provides answers to the following three questions. Is it possible to design a reliable social organization from fallible human individuals? How many fallible human agents are required to build an economic system of a certain level of reliability? What is the best way to design an organization of two or more agents in order to minimize error? On the basis of constructive proofs, this paper provides answers to these questions and thus offers a method to analyze any form of decision making structure with respect to its reliability.Organizational design; reliability theory; decision making; project selection

The Effects of Landscape and Experience on the Navigation and Foraging Behaviour of Bumblebees, Bombus terrestris

Bumblebees live in an environment where the spatial distribution of foraging resources is always changing. In order to keep track of such changes, bumblebees employ a variety of different navigation and foraging strategies. Although a substantial amount of research has investigated the different navigation and foraging behaviours of bumblebees, much less is known of the effects that landscape features have on bumblebee behaviour. In this thesis, a series of experiments were conducted in order to investigate the role that landscape features have on the navigation and foraging behaviour of Bombus terrestris and whether individuals’ experience influences such behaviour. A hedgerow situated next to the colony was not found to significantly shape the flight paths or foraging choices of naïve bumblebees. Homing success was investigated and used as a proxy for foraging range in different environment types. Both the release distance and the type of environment were found to have a significant effect on the homing success of Bombus terrestris workers. Previous experience of the landscape was also found to significantly affect the time it took bumblebees to return to the colony (homing duration) as well as the likelihood of staying out overnight before returning to the colony. When focusing on the first five flights of a naïve bumblebee worker, experience was not found to significantly affect flight duration. Experience, however, significantly affected the weight of pollen foraged. The observed behaviour of bumblebee gynes provisioning their maternal colony with pollen was also investigated. The influx of pollen into the colony was found to affect this behaviour, suggesting that gynes will provision the maternal colony in response to its nutritional needs. The overall results are also discussed within the context of informing landscape management practices. The results presented in this thesis point to the critical role that factors such as the physical landscape and individual experience play in influencing bumblebee behaviour.South Devon Area of Outstanding Natural Beauty (AONB) Uni

Using WRF/Chem, in-situ observations, and Calipso data to simulate smoke plume signatures on high-latitude pixels

Thesis (M.S.) University of Alaska Fairbanks, 2014The transport of wildfire aerosols provides concerns to people at or near downwind propagation. Concerns include the health effects of inhalation by inhabitants of surrounding communities and fire crews, the environmental effects of the wet and dry deposition of acids and particles, and the effects on the atmosphere through the scattering and absorption of solar radiation. Therefore, as the population density increases in Arctic and sub-Arctic areas, improving wildfire detection increasingly becomes necessary. Efforts to improve wildfire detection and forecasting would be helped if additional focus was directed toward the distortion of pixel geometry that occurs near the boundaries of a geostationary satellite's field of view. At higher latitudes, resolution becomes coarse due to the curvature of the Earth, and pixels toward the boundaries of the field of view become difficult to analyze. To assess whether it is possible to detect smoke plumes in pixels at the edge of a geostationary satellite's field of view, several analyses were performed. First, a realistic, fourdimensional dataset was created from Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) output. WRF/Chem output was statistically compared to ground observations through the use of skill scores. Output was also qualitatively compared to vertical backscatter and depolarization products from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. After the quantitative and qualitative examinations deemed the model output to be realistic, synthetic pixels were constructed, appropriately sized, and used with the realistic dataset to examine the characteristic signatures of a wildfire plume. After establishing a threshold value, the synthetic pixels could distinguish between clean and smoke-polluted areas. Thus, specialized retrieval algorithms could be developed for smoke detection in strongly distorted pixels at the edge of a geostationary satellite's field of view

Testing Feedforward Neural Networks Training Programs

Nowadays, we are witnessing an increasing effort to improve the performance and trustworthiness of Deep Neural Networks (DNNs), with the aim to enable their adoption in safety critical systems such as self-driving cars. Multiple testing techniques are proposed to generate test cases that can expose inconsistencies in the behavior of DNN models. These techniques assume implicitly that the training program is bug-free and appropriately configured. However, satisfying this assumption for a novel problem requires significant engineering work to prepare the data, design the DNN, implement the training program, and tune the hyperparameters in order to produce the model for which current automated test data generators search for corner-case behaviors. All these model training steps can be error-prone. Therefore, it is crucial to detect and correct errors throughout all the engineering steps of DNN-based software systems and not only on the resulting DNN model. In this paper, we gather a catalog of training issues and based on their symptoms and their effects on the behavior of the training program, we propose practical verification routines to detect the aforementioned issues, automatically, by continuously validating that some important properties of the learning dynamics hold during the training. Then, we design, TheDeepChecker, an end-to-end property-based debugging approach for DNN training programs. We assess the effectiveness of TheDeepChecker on synthetic and real-world buggy DL programs and compare it with Amazon SageMaker Debugger (SMD). Results show that TheDeepChecker's on-execution validation of DNN-based program's properties succeeds in revealing several coding bugs and system misconfigurations, early on and at a low cost. Moreover, TheDeepChecker outperforms the SMD's offline rules verification on training logs in terms of detection accuracy and DL bugs coverage

3D object reconstruction using computer vision : reconstruction and characterization applications for external human anatomical structures

Tese de doutoramento. Engenharia Informática. Faculdade de Engenharia. Universidade do Porto. 201