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

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

Evaluation of Landsat-8 and Sentinel-2A Aerosol Optical Depth Retrievals Across Chinese Cities and Implications for Medium Spatial Resolution Urban Aerosol Monitoring

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. AOD retrieved from 30 m Landsat-8 and 10 m Sentinel-2A data using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities in 2016. Stringent selection criteria were used to select contemporaneous data; only satellite and AERONET data acquired within 10 min were considered. The average satellite retrieved AOD over a 1470 m1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r(exp 2) > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research.The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed

Sentinel-2 Data Analysis and Comparison with UAV Multispectral Images for Precision Viticulture

Precision viticulture (PV) requires the use of technologies that can detect the spatial and temporal variability of vineyards and, at the same time, allow useful information to be obtained at sustainable costs. In order to develop a cheap and easy-to-handle operational monitoring scheme for PV, the aim of this work was to evaluate the possibility of using Sentinel-2 multispectral images for long-term vineyard monitoring through the Normalized Difference Vegetation Index (NDVI). Vigour maps of two vineyards located in northeastern Italy were computed from satellite imagery and compared with those derived from UAV multispectral images; their correspondence was evaluated from qualitative and statistical points of view. To achieve this, the UAV images were roughly resampled to 10 m pixel size in order to match the spatial resolution of the satellite imagery. Preliminary results show the potential use of open source Sentinel-2 platforms for monitoring vineyards, highlighting links with the information given in the agronomic bulletins and identifying critical areas for crop production. Despite the large differences in spatial resolution, the results of the comparison between the UAV and Sentinel-2 data were promising. However, for long-term vineyard monitoring at territory scale, further studies using multispectral sensor calibration and groundtruth data are required

Assessment of the Added-Value of Sentinel-2 for Detecting Built-up Areas

Monitoring of the human-induced changes and the availability of reliable and methodologically consistent urban area maps are essential to support sustainable urban development on a global scale. The Global Human Settlement Layer (GHSL) is a project funded by the European Commission, Joint Research Centre, which aims at providing scientific methods and systems for reliable and automatic mapping of built-up areas from remote sensing data. In the frame of the GHSL, the opportunities offered by the recent availability of Sentinel-2 data are being explored using a novel image classification method, called Symbolic Machine Learning (SML), for detailed urban land cover mapping. In this paper, a preliminary test was implemented with the purpose of: (i) assessing the applicability of the SML classifier on Sentinel-2 imagery; (ii) evaluating the complementarity of Sentinel-1 and Sentinel-2; and (iii) understanding the added-value of Sentinel-2 with respect to Landsat for improving global high-resolution human settlement mapping. The overall objective is to explore areas of improvement, including the possibility of synergistic use of the different sensors. The results showed that noticeable improvement of the quality of the classification could be gained from the increased spatial detail and from the thematic contents of Sentinel-2 compared to the Landsat derived product as well as from the complementarity between Sentinel-1 and Sentinel-2 images.JRC.G.2 - Global security and crisis managemen