Use of new generation geospatial data and technology for low cost drought monitoring and SDG reporting solution : a thesis presented in partial fulfillment of the requirement for the degree of Master of Science in Computer Science at Massey University, Manawatū, New Zealand

Food security is dependent on ecosystems including forests, lakes and wetlands, which in turn depend on water availability and quality. The importance of water availability and monitoring drought has been highlighted in the Sustainable Development Goals (SDGs) within the 2030 agenda under indicator 15.3. In this context the UN member countries, which agreed to the SDGs, have an obligation to report their information to the UN. The objective of this research is to develop a methodology to monitor drought and help countries to report their ndings to UN in a cost-e ective manner. The Standard Precipitation Index (SPI) is a drought indicator which requires longterm precipitation data collected from weather stations as per World Meteorological Organization recommendation. However, weather stations cannot monitor large areas and many developing countries currently struggling with drought do not have access to a large number of weather-stations due to lack of funds and expertise. Therefore, alternative methodologies should be adopted to monitor SPI. In this research SPI values were calculated from available weather stations in Iran and New Zealand. By using Google Earth Engine (GEE), Sentinel-1 and Sentinel- 2 imagery and other complementary data to estimate SPI values. Two genetic algorithms were created, one which constructed additional features using indices calculated from Sentinel-2 imagery and the other data which was used for feature selection of the Sentinel-2 indices including the constructed features. Followed by the feature selection process two datasets were created which contained the Sentinel- 1 and Sentinel-2 data and other complementary information such as seasonal data and Shuttle Radar Topography Mission (SRTM) derived information. The Automated Machine Learning tool known as TPOT was used to create optimized machine learning pipelines using genetic programming. The resulting models yielded an average of 90 percent accuracy in 10-fold cross validation for the Sentinel- 1 dataset and an average of approximately 70 percent for the Sentinel-2 dataset. The nal model achieved a test accuracy of 80 percent in classifying short-term SPI (SPI- 1 and SPI-3) and an accuracy of 65 percent of SPI-6 by using the Sentinel-1 test dataset. However, the results generated by using Sentinel-2 dataset was lower than Sentinel-1 (45 percent for SPI-1 and 65 percent for SPI-6) with the exception of SPI-3 which had an accuracy of 85 percent. The research shows that it is possible to monitor short-term SPI adequately using cost free satellite imagery in particular Sentinel-1 imagery and machine learning. In addition, this methodology reduces the workload on statistical o ces of countries in reporting information to the SDG framework for SDG indicator 15.3. It emerged that Sentinel-1 imagery alone cannot be used to monitor SPI and therefore complementary data are required for the monitoring process. In addition the use of Sentinel-2 imagery did not result in accurate results for SPI-1 and SPI-6 but adequate results for SPI-3. Further research is required to investigate how the use of Sentinel-2 imagery with Sentinel-1 imagery impact the accuracy of the models

Current status of sentinel lymph node biopsy in solid malignancies

Lymphatic mapping and sentinel lymph node biopsy were first reported in 1977 by Cabanas for penile cancer. Since that time, the technique has become rapidly assimilated into clinical practice. The sentinel node concept has been validated in cutaneous melanoma and breast cancer. However, follow-up data of patients from randomised trials is needed to establish the clinical significance of sentinel lymph node biopsy before accepting the procedure as a standard of care. This technique has the potential to be utilised in all solid tumours like colon, gastric, oesophageal, lung, gynaecologic, and head and neck cancer. This paper reviews the current status of sentinel lymph node biopsy in solid tumours

News On Provost Lecture Series

News article on the establishment of ICS Provost Serie

Labor Decision in Security Guard Case May Set New Precedence

Employees required to stay at a worksite while on call should be compensated for all their hours, including sleep time, the California Supreme Court has ruled in a case involving a company based in Gardena. The state’s highest court said Thursday that security guards who were obligated to stay in trailers on worksites in case they were needed were entitled to be paid for their time, even if they spent it watching TV, scouring the Internet or dozing

Sentinel lymph node in early stage ovarian cancer; a literature review

Although sentinel lymph node mapping has been widely implemented in gynecological malignancies in order to minimize the number of unnecessary lymph node dissections and to diminish postoperative morbidity rate, little is known about ovarian cancer sentinel lymph node mapping. This article presents a literature review regarding the effectiveness, safety and benefits of this method. Sentinel lymph node detection in early stage ovarian cancer seems to be a safe and effective method, able to minimize the rate of patients submitted to unnecessary lymph node dissection. The second goal of the procedure, to minimize the risk of missing involved lymph nodes, seems also to have been achieved, most studies reporting a very small number of cases diagnosed with positive non-sentinel lymph nodes. Considering all these data we can note that this procedure is not yet included as part of the standard therapeutic protocol, so that further studies would be necessary to include it as a common therapeutic approach in the case of patients with early stage ovarian cancer

Shelby County - Bicentennial Edition

Bicentennial edition of the Sentinel News about the history of Shelby County, Kentucky published on October 7 1992

Super-resolving multiresolution images with band-independant geometry of multispectral pixels

A new resolution enhancement method is presented for multispectral and multi-resolution images, such as these provided by the Sentinel-2 satellites. Starting from the highest resolution bands, band-dependent information (reflectance) is separated from information that is common to all bands (geometry of scene elements). This model is then applied to unmix low-resolution bands, preserving their reflectance, while propagating band-independent information to preserve the sub-pixel details. A reference implementation is provided, with an application example for super-resolving Sentinel-2 data.Comment: Source code with a ready-to-use script for super-resolving Sentinel-2 data is available at http://nicolas.brodu.net/recherche/superres