The seagrass extent has been declining globally. The human activities that are most likely to cause seagrass loss are those which affect the water quality and clarity. However, turbidity following coastal erosion is often left out from marine ecosystem vulnerability indices. This study quantified the coastal erosion for Tsimipaika Bay in northwestern Madagascar by using change detection analysis of satellite imageries. The annual coastal erosion data was then used to create an index for seagrass vulnerability to turbidity following coastal erosion. Considering that the height of seagrass species plays an important role in their survival following turbidity, the seagrass vulnerability index (SVI) was based on two factors; seagrass species height and their distance to the nearest possible erosion place. The results for the coastal erosion showed that the amount of erosion was particularly high in 1996, 2001 and 2009 for Tsimipaika Bay. The highest erosion occurred in 2001 with a land loss area of about 6.2 km2 . The SVI maps revealed that 40% of the seagrass communities had minimum mean SVI values in 2001 and 50% had the maximum mean SVI during the year 2009. This study showed that it is possible to use coastal erosion to measure seagrass vulnerability; however, the index requires configuration such as including the total amount of annual coastal erosion and incorporating bathymetric data. The entire project was built and automated in Jupyter Notebook using Python programming language, which creates a ground for future studies to develop and modify the project.

Diploma thesis deals with the land cover classification in Sidama region of Ethiopia and 2 kebeles, Chancho and Dangora Morocho. High resolution Sentinel-2 and very high resolution PlanetScope satellite images are used. The development of the classification algorithm is done in the Google Earth Engine cloud based environment. Ten combinations of the 4 most important parameters of the Random Forest classification method are tested. The defined legend contains 8 land cover classes, namely built-up, crops, grassland/pasture, forest, scrubland, bareland, wetland and water body. The training dataset is collected in the field during the fall 2020. The classification results of the two data types at two scales are compared. The highest overall accuracy for land cover classification of Sidama region came out to be 84.1% and kappa index of 0.797, with Random Forest method parameters of 100 trees, 4 spectral bands entering each tree, value of 1 for leaf population and 40% of training data used for each tree. For the land cover classification of Chancho and Dangora Morocho kebele with the same method settings, the overall accuracy came out to be 66.00 and 73.73% and kappa index of 0.545 and 0.601. For the classification of Chancho kebele, a different combination of parameters (80, 3, 1, 0.4) worked out better...

Remote sensing has enabled unprecedented earth observation from space and has proven to be an invaluable tool for agricultural applications and crop management practices. Here we detect seasonal metrics indicating the start of the season (SOS), the end of the season (EOS) and maximum greenness (MAX) based on vegetation spectral signatures and the normalized difference vegetation index (NDVI) for a time series of Landsat-8, Sentinel-2 and PlanetScope imagery of potato, wheat, watermelon, olive and peach/apricot fields. Seasonal metrics were extracted from NDVI curves and the effect of different spatial and temporal resolutions was assessed. It was found that Landsat-8 overestimated SOS and EOS and underestimated MAX due to its low temporal resolution, while Sentinel-2 offered the most reliable results overall and was used to classify the fields in Aljawf. Planet data reported the most precise SOS and EOS, but proved challenging for the framework because it is not a radiometrically normalized product, contained clouds in its imagery, and was difficult to process because of its large volume. The results demonstrate that a balance between the spatial and temporal resolution of a satellite is important for crop monitoring and classification and that ultimately, monitoring vegetation dynamics via remote sensing enables efficient and data-driven management of agricultural system

Groundwater is a vital non-renewable resource that is being over exploited at an alarming rate. In Saudi Arabia, the majority of groundwater is used for agricultural activities. As such, the mapping of irrigated lands is a crucial step for managing available water resources. Even though traditional in-field mapping is effective, it is expensive, physically demanding, and spatially restricted. The use of remote sensing combined with advanced computational approaches provide a potential solution to this scale problem. However, when attempted at large scales, traditional computing tends to have significant processing and storage limitations. To address the scalability challenge, this project explores open-source cloud-based resources to map and quantify center-pivot irrigation fields on a national scale. This is achieved by first applying a land cover classification using Random Forest which is a machine learning approach, and then implementing a circle detection algorithm. While the analysis represents a preliminary exploration of these emerging cloud-based techniques, there is clear potential for broad application to many problems in the Earth and environmental sciences.

Uppsatsens syfte är att argumentera för ett verklighetsbaserat lärande inom geografiämnet för årskurs 5. Som ett pedagogiskt verktyg för detta ändamål prövar och diskuterar jag sökmotorn Google Earth (GE) och visar genom 4 exempel hur man kan använda detta verktyg. Jag prövar också GE:s lämplighet i förhållande till de tre ämnesfälten kartografi, fördelning och hållbar utveckling genom att formulera tre lektionsförslag. Jag gör också en kritisk granskning av aktuell forskning kring läromedel och ställer detta mot texter hämtade från kursplanen i Geografi. I min analys når jag fram till att fördelarna med att använda sig av GE i förhållande till traditionella läromedel är främst att eleverna på ett naturligt sätt kommer i kontakt med autentiska och aktuella problemfält som rör vår jord. Jag kommer även fram till att GE kan inspirera till grundläggande visuella färdigheter, samt ett kritiskt förhållningssätt till visuella fenomen i omvärlden. Genom detta når jag i min slutsats fram till att även om GE inte skapats för att vara ett pedagogiskt verktyg, så lämpar det sig väl att användas som ett sådant.

No description available.

No description available.

No description available.

Availability of organized data is required for accurate prediction of structural or functional deterioration in sewer and water pipes. Toward this end, GIS provides a means for viewing, understanding, interpreting, and visualizing complex geographically referenced information to reveal data relationships, patterns, and trends. The primary objective of this research is to develop a standard GIS data model and applications of the model. In the future, these can be used to develop protocols and methods for predicting the remaining life of water and wastewater assets. The source data for this study is the utility data and other publicly available data from resources such as USGS, SSURGO etc. Field mapping files are generated from the source files and the standard data model. These are then programmed to the common Extensible markup Language (XML) file developed as a base which is then converted to the data model where the final form of utility data is stored. The data taken from the utilities is cleansed and analyzed to match the standard data model which is then uploaded through the common XML and stored in the data warehouse as a geospatial database. The geospatial database is an aggregated water and wastewater infrastructure data consisting of the utility data in standard data model format. The data warehouse is developed for utilities to store their data at a centralized server, such as the San Diego Super Computer Center. Web applications demonstrate the publishing, querying and visualization of aggregated data in a map-based browser application. This aggregation of data of multiple utilities will help in providing timely access to asset management information and resources that will lead to more efficient programs. This tool also furnishes the public with a convenient tool to learn about municipal water and wastewater infrastructure systems. This document gives an overview of how this process can be achieved using the above mentioned tools and methodologies. / Master of Science

In the field of ecology, animal tracking is core to understanding an animal’s behavior, space-use requirements, and suitable habitat. Utilizing the software created by this project, it is possible to visualize tracking data in its spatial and temporal context. The source code is written in R and leverages the Continuous-Time Movement Modeling (CTMM) package for statistical analysis. Utilizing CTMM, predicted or simulated movement paths can be generated with the user only needing to input their data. The Keyhole Markup Language (KML) is used to write a file containing all the animation parameters the user desires, such as the number of paths or icon images used. After the creation of the KML file, importation into Google Earth allows for the recording of a fully animated tour. This project's software creates an efficient and accessible tool for ecologists and conservationists to use to animate their tracking data.