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Predicting Drought Hazard In Sweden Using Google Earth Engine And Machine Learning Approach / Förutsäga torkarisk i Sverige med Google Earth Engine och Machine Learning ApproachKan, Jung-ching January 2022 (has links)
Drought, being one the most complex natural hazards, has a significant impact on society. To mitigate the impact and risk, it is crucial to be able to forecast drought, which is a challenging task. Nowadays, with technology innovations, large amounts of remote sensing data is available on the cloud. Meanwhile,machine learning and data-driven techniques have proven to be reliable data processing methods in a number of fields. In this study, the author aims to predict agricultural drought in Sweden with machine learning (ML) models. Several ML models, including random forest, decision tree, linear regression, support vector machine, ARIMA, ANN, and CNN, are employed to find out the best performing model. Seven hypothesized factors are used and tested with RFE for features analysis. Three data arrangement methods are explored for the best possible way to arrange the dataset. In the result section, the author concludes that soil moisture is the most important feature, ARIMA and random forest models are the most reliable algorithms, and the temporal method is more suitable for short-term predicting. / Torka, som är en av de mest komplexa naturliga riskerna, har en betydande inverkan på samhället. För att mildra påverkan och risken är det avgörande att kunna förutse torka, vilket är en utmanande uppgift. Nuförtiden, med tekniska innovationer, finns stora mängder fjärranalysdata tillgänglig på molnet. Under tiden har maskininlärning och datadrivna tekniker visat sig vara tillförlitliga databehandlingsmetoder inom ett antal områden. I denna studie syftar författaren till att förutsäga jordbrukstorka i Sverige med modeller för maskininlärning (ML). Flera ML-modeller, inklusive slumpmässig skog, beslutsträd, linjär regression, stödvektormaskin, ARIMA, ANN och CNN, används för att ta reda på den bästa modellen. Sju hypotesfaktorer används och testas med RFE för funktionsanalys. Tre dataarrangemangsmetoder utforskas för bästa möjliga sätt att ordna datamängden. I resultatavsnittet drar författaren slutsatsen att markfuktighet är den viktigaste egenskapen, ARIMA och slumpmässiga skogsmodeller är de mest tillförlitliga algoritmerna, och den tidsmässiga metoden är mer lämpad för korttidsförutsägelse.
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Assessment of coastal erosion to create a seagrass vulnerability index in northwestern Madagascar using automated quantification analysisArslan, Nat January 2020 (has links)
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.
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Klasifikace krajinného pokryvu ve vybraných územích Etiopie pomocí klasifikátoru strojového učení / Landcover classification of selected parts of Ethiopia based on machine learning methodValchářová, Daniela January 2021 (has links)
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...
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A Multi-platform Comparison of Phenology for Semi-automated Classification of CropsKanee, Sarah 07 1900 (has links)
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
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Using Google Earth Engine for the Automated Mapping of Center Pivot Irrigation fields in Saudi ArabiaAlwahas, Areej 04 1900 (has links)
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.
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Landsat Collections Reveal Long-Term Algal Bloom Hot Spots of Utah LakeTate, Rachel Shanae 01 July 2019 (has links)
Harmful algal blooms (HABs) and nuisance algal blooms (NABs) are a worldwide phenomenon with implications for human health and safety. HABs occur when algae or bacteria grow in high enough densities to harm animals and humans. A primary component of harmful algal blooms is cyanobacteria, which are aquatic, photosynthesizing microorganisms that produce toxins at high concentrations. Cyanobacterial biomass has increased worldwide in recent decades, raising concern about the future of fresh- and marine-water systems in a changing climate. Understanding the patterns and conditions of past algal blooms can provide useful insights for managing future blooms. Remote sensing can enhance our understanding of the spatiotemporal distribution of HABs and NABs. We used radiometrically corrected images from the USGS Landsat Collections available in the Google Earth Engine for cloud processing. In 2016, the USGS calibrated the sensors of Landsat 4, 5, 7, and 8 to create a continuous collection of satellite images from 1984 to present. We use this 34-year dataset to expand the historical record of algal blooms at our study site and to understand factors relating to the spatiotemporal patterns of these blooms. We applied three models, including the Floating Algae Index (FAI), the Normalized Difference Vegetation Index (NDVI), and one developed with in situ chlorophyll-a (chl-a) data, to 398 images masked for cloud cover and lake elevation taken from 34 growing seasons (April – October). We found that the Normalized Difference Water Index (NDWI) used to separate water and land pixels fails under algal bloom conditions, whereas a modified NDWI does not. We also performed an emerging hot spot analysis in ArcGIS using the chlorophyll-a, NDVI, and FAI predictions from the surface reflectance values of the images. Our analysis indicates that the Provo Bay and parts of the eastern shoreline of Utah Lake have had algal blooms for 30 out of the 34 years included in this study, rendering them enduring hot spots. The remainder of the lake is a cold spot, showing clusters of low mean chl-a, NDVI, and FAI values over time. The overall trend of mean NDVI and lake surface area over this 34-year dataset is decreasing, whereas lake water temperature is increasing. This study develops a method for analyzing algal blooms over multiple decades and provides useful information for the management and prediction of future blooms.
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A Web Application for Wildfire Spread Prediction and Visualisation in Sweden Using Geospatial Data and Technology / En Webbapplikation för Förutsägelse och Visualisering av Spridning av Skogsbrand Utifrån Geospatial Data och TeknologiMakenzius, Micael, Bylerius, Jonas January 2022 (has links)
Skogsbränder är kraftfulla naturfenomen kapabla att åstakomma omfattande skada över stora ytor och medföra omfattande kostnader för sammhället både humanitärt, ekonomiskt och miljömässigt. Det finns därför ett starkt incitament att övervaka och förutspå skogsbränders utveckling och spridning. Traditionellt används kraftfulla skrivbordsklienter för att köra den simulerings-mjukvara som förutspår skogsbränder, vilket begränsar användningsområdet för simuleringar. Webbklienter är naturligt mobila och lättanvända. Genom att flytta bearbetningen till en server överförs majoriteten av arbetet från klienten. Det här projektet utvecklar ett server-klient baserat ramverk för att simulera skogsbränder, visualisera resultatet och hantera data för användning i skogsbrandsbekämpnings och -analys arbetsflöde. Både parametrarna som skickas till servern och simuleringsresultatet som returneras till klienten. Ramverket använder en kombination av HTTPS-kommunikation och websocket-teknologi för att kommunicera data mellan klienten och server i real-time genom Django-ramverket. Brandmodellen på den Kanadensiska empiriska brandmodellen Prometheus som är implementerad i programmeringsspråket Python. Det är optimerat för det svenska klimated för att enkelt kunnas fältsättas i en webbapplikation för svenska myndigheter. Webb-applikationen är tillgänglig genom mobila och stationära enheter där ramverket beräknar och visualiserar förutspådd fortspridning av skogsbrand i realtid. Skogsbrands moduleringsmodellen av applikationen är jämförd med skogsbränderna i Enskogen och Ängra närastaden Kårböle under sommaren 2018. Noggrannhetsbedömningen av modellen påvisar att den simulerade branden tenderar att innehålla den egentliga elden men är benägen att överskatta eldspridningen. Applikationen utvärderades även genom ett formulär om applikationens funktionallitet som skickades till en provgrupp av personer som arbetar med skogsbränder eller annat relevant område. Provgruppen var nöjd med applikationen och såg ett anvädningsområde för applikationen i sitt arbetsflöde. Mycket arbete återstår för att göra applikationen fältduglig genom integration av myndigheters datatjänster och andra databaser som innehåller riskobjekt, byggnader, kraftledningar e.g. Trots detta ansågs brandingejörer inom räddningstjänster en möjlighet att använda verktygen i dess nuvarande tillstånd om simuleringsresultatet anses korrekt nog för att fungera som underlag för beslut. Detta understryker behovet av en liknande applikation, med vidare funktionalitet och integration med data-system. / Wildfires are powerful natural forces capable of causing extensive damage to large areas of lands and induce a high societal cost in both humanitarian, economic and environmental terms. As such there is a strong incentive to track and predict wildfires' development and spread. Traditionally heavy desktop clients are required to run the simulation-software required to perform wildfire spread predictions, which limits their use and versatility. Conversely, web-based clients are lightweight and versatile by design. By moving the processing of the simulation to a server the bulk of the workload is removed from the client. This project aims to produce a server-client framework for simulating wildfires, visualising the result and handling the fire data for use in the workflow of wildfire suppression and analysis. Both the parameters sent to the server and the simulation result returned to the client. It utilises a combination of HTTPS-requests and websockets-technology to communicate data and information between the client and server in real-time through the Django framework. The fire simulation is based upon the Canadian empirical fire-model Prometheus. The implementation of the algorithm were adopted in the programming language python and optimized for the Swedish climate to be easily deployed in a web-application to be used by Swedish organisations. The web-application was accessible though mobile and stationary devices where the framework calculated and visualised the progression of the wildfire in real-time. The wildfire progression model of the application was compared to the wildfires Enskogen and Ängra, close to the town of Kårböle during the summer of 2018. The accuracy assessment of the fire progression model found that the simulated wildfire progression tend to contain the observed fire and prone to overestimate the wildfires progression. The application was evaluated though a questionnaire which was answered by a sample group composed of persons working with wildfires or wildfire related fields. The sample group were satisfied by the application and broadly found that the application could be implemented into their workflow. Much work remain to operationalise the application, such as integration of municipal data sources and other databases containing resources, risk-objects, buildings, power-lines. In spite of this Fire-engineers in emergency services state a possibility for use of the application as is, if the simulations are deemed accurate enough and provide a better basis for decision making and measures. This underlines the need of an application such as this in the field, and with further functionalities and integration's with data-systems.
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Assessment of Global Land Cover Change following Drought Events / Analys av globala förändringar i markanvändnig efter perioder av torkaEngman, Felicia, Kortekaas, Ester January 2024 (has links)
Human-induced climate change alters global weather patterns, increasing the frequency and severity of droughts. Acting as drivers of land degradation, droughts negatively impact the environment, exacerbating food and water insecurity and threatening livelihoods. However, the complex relationship between drought events and subsequent changes in land cover on a global scale remain insufficiently explored, necessitating further research. This study aims to address this research gap by examining the correlation between global drought events and land cover changes, while also exploring variations between countries' different levels of economic development. To accomplish this, global precipitation data and the 12-month Standardized Precipitation Index (SPI-12) were employed to identify areas affected by drought, followed by an analysis of changes in land cover within these regions. Utilising the capabilities of Google Earth Engine (GEE) allowed for the evaluation of the relationship between drought events and land cover change. The findings revealed a correlation between drought events and changes in land cover, with variations observed across different world regions. These results were strengthened by the comparison of land cover changes in non-drought areas. When examining trends regarding types of land cover alterations, it appears that Tree cover, as well as Shrub-, and Grassland, has reduced in regions impacted by drought. Conversely, increases are observed in Cropland and Urban areas. When assessing countries based on economic development, the overall trends were consistent with the global results, although with variations between Advanced Economies and Emerging-, and Developing Economies. Overall, this study acknowledges drought as a driver of land cover change, while also emphasising the influence of other factors such as anthropogenic activities. These findings offer insights into the complex interactions between droughts and terrestrial ecosystems, and emphasises the importance of strategies for sustainable land management and adaptation efforts, globally. / Människans påverkan på klimatet förändrar globala vädermönster och ökar både frekvensen och omfattningen av torka. Torka orsakar förändringar i markanvändning, påverkar miljön negativt, leder till bristande tillgång på livsmedel och vatten samt hotar möjligheter till försörjning. Det komplexa sambandet mellan torka och markanvändningsförändringar på global nivå är i stort sett outforskat och kräver därför ytterligare forskning. Denna studie syftar till att fylla detta kunskapsgap genom att undersöka sambandet mellan globala perioder av torka och markanvändningsförändringar, samt hur faktorer såsom ekonomisk utveckling påverkar markförändringar. Detta uppnåddes genom att använda global nederbördsdata och 12-månaders ‘Standardized Precipitation Index’ (SPI-12). Områden som har påverkats av torka identifierades, varpå en analys av markanvändningsförändringar inom dessa områden utfördes. Analysen gjordes med hjälp av det geospatiala analysverktyget Google Earth Engine (GEE). Resultatet visade på en korrelation mellan torka och markanvändningsförändringar, samtidigt som regionala variationer observerades. Detta samband förstärktes genom att undersöka korrelationen mellan markanvändningsförändringar inom områden ej utsatta för torka. Gällande trender i typer av markanvändningsförändringar verkar trädbevuxna områden samt busk- och gräsmark ha minskat i regioner påverkade av torka. Åkermark och urbana områden verkar däremot ha ökat. Vid bedömning av länder baserat på ekonomisk utveckling var de övergripande trenderna i linje med de globala resultaten, även om variationer noterades mellan avancerade ekonomier och tillväxt- och utvecklingsekonomier. Sammanfattningsvis identifierar denna studie torka som en drivkraft för förändringar i markanvändning, samtidigt som den betonar betydelsen av mänskliga aktiviteter. Dessa resultat ger insikt i de komplexa interaktionerna mellan torka och terrestra ekosystem, samt understryker vikten av strategier för hållbar markförvaltning och anpassning insatser på global nivå.
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Korekce lokálního dopadového úhlu SAR dat pro analýzu časových řad: metoda specifická pro krajinný pokryv / A correction of the local incidence angle of SAR data: a land cover specific approach for time series analysisPaluba, Daniel January 2020 (has links)
To ensure the highest possible temporal resolution of SAR data, it is necessary to use all the available acquisition orbits and paths of a selected area. This can be a challenge in a mountainous terrain, where the side-looking geometry of space-borne SAR satellites in combination with different slope and aspect angles of terrain can strongly affect the backscatter intensity. These errors/noises caused by terrain need to be eliminated. Although there have been methods described in the literature that address this problem, none of these methods is prepared for operable and easily accessible time series analysis in the mountainous areas. This study deals with a land cover-specific local incidence angle (LIA) correction method for time-series analysis of forests in mountainous areas. The methodology is based on the use of a linear relationship between backscatter and LIA, which is calculated for each image separately. Using the combination of CORINE and Hansen Global Forest databases, a wide range of different LIAs for a specific forest type can be generated for each individual image. The algorithm is prepared and tested in cloud-based platform Google Earth Engine (GEE) using Sentinel-1 open access data, SRTM digital elevation model, and CORINE and Hansen Global Forest databases. The method was tested...
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MAPPING SMALL SCALE FARMING IN HETEROGENEOUS LANDSCAPES: A CASE STUDY OF SMALLHOLDER SHADE COFFEE AND PLASTIC AGRICULTURE FARMERS IN THE CHIAPAS HIGHLANDSSanchez Luna, Maria M. 30 July 2019 (has links)
No description available.
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