Essays about: "forest, Landsat"
Showing result 1 - 5 of 32 essays containing the words forest, Landsat.
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1. Land cover classification using machine-learning techniques applied to fused multi-modal satellite imagery and time series data
University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapAbstract : Land cover classification is one of the most studied topics in the field of remote sensing, involving the use of data from satellite sensors to analyze and categorize different land surface types. There are numerous satellite products available, each offering different spatial, spectral, and temporal resolutions. READ MORE
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2. Monitoring deforestation in the Serranía de Chiribiquete in northern Colombian Amazon using time series analysis of satellite data
University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapAbstract : Deforestation monitoring is of significant importance for the ecosystem, climate change,and policy-making. The availability of optical and synthetic aperture radar (SAR) satellite remote sensing images, along with the development of time series change detection methods, has contributed to the increasing popularity of time series analysis in forest disturbance monitoring. READ MORE
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3. Estimation of dissolved organic carbon from inland waters using remote sensing data and machine learning
University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapAbstract : This thesis presents the first attempt to estimate Dissolved Organic Carbon (DOC) in inland waters over a large-scale area using satellite data and machine learning (ML) methods. Four ML approaches, namely Random Forest Regression (RFR), Support Vector Regression (SVR), Gaussian Process Regression (GPR), and a Multilayer Backpropagation Neural Network (MBPNN) were tested to retrieve DOC using a filtered version of the recently published open source AquaSat dataset with more than 16 thousand samples across the continental US matched with satellite data from Landsat 5, 7 and 8 missions. READ MORE
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4. Carta ex Machina: Testing object-based machine learning and unsupervised classification in land use change detection mapping in the semi-arid governorate of Sidi Bouzid, Tunisia
University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapAbstract : Sidi Bouzid, Tunisia is an inland governorate in Tunisia that has undergone a rapid agricultural and urban development since the Tunisian independence in 1952 from being a rural and largely nomadic region into a hub of irrigated agriculture. In 2010 Mohamed Bouazizi sparked the Tunisian revolution by lighting himself on fire int he city of Sidi Bouzid, with some blaming the inequality and water scarcity created by this rapid expansion in the irrigation farming as an important cause (Bayat, 2017; Malka, 2018). READ MORE
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5. Topographic controls of drought impact on Swedish primary forests
University essay from Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskapAbstract : Anthropogenic climate change has increased the frequency of extreme drought events and leads to “hotter” droughts. Topography controls plant available water and site-specific climatic conditions. Drought sensitivity may therefore vary over short distances between wet and dry locations of the landscape. READ MORE