Sökning: "dynamic vegetation model"

Visar resultat 11 - 15 av 36 uppsatser innehållade orden dynamic vegetation model.

1. 11. Development of a deep learning method for soil moisture estimation at high spatial and temporal resolution using satellite data

   Master-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Nicklas Simonsen; [2021]
   Nyckelord :Physical Geography and Ecosystem Analysis; Machine Learning; Deep Learning; Recurrent Neural Network; LSTM; Soil Moisture; Remote Sensing; SAR; Sentinel-1; Sentinel-2; ISMN; ICOS: Geomatics; Earth and Environmental Sciences;

   Sammanfattning : Soil moisture (SM) is an essential climate variable that controls fundamental hydrological and climatic processes. Soil moisture products derived from microwave remote sensing often provide measurements at low spatial resolution and incomplete temporal records. LÄS MER

3. 12. Evaluating the ability of LPJ-GUESS to simulate the tree size structures of tropical forests

   Kandidat-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Margot Jeanne Knapen; [2021]
   Nyckelord :tropical forests; tree size distributions; LPJ-GUESS; carbon; biomass; sensitivity analysis; Earth and Environmental Sciences;

   Sammanfattning : Tropical forests are of great importance to all living-beings due to their high biodiversity and the valu-able resources, such as food and fuel, they provide. In addition, tropical trees sequester a high amount of carbon and consequently over half of the global forest carbon stock can be found in the tropics. LÄS MER

4. 13. Increasing forest mortality and its drivers: Simulating central European forests under climate change

   Kandidat-uppsats, Lunds universitet/Institutionen för naturgeografi och ekosystemvetenskap

   Författare :Marieke Scheel; [2021]
   Nyckelord :forest mortality; central Europe; competition; CO2; temperature; precipitation; DVM; LPJ-GUESS; simulation; reproducing data; Earth and Environmental Sciences;

   Sammanfattning : Increasing tree growth and mortality rates in Europe are still poorly understood and have been attributed to a variety of drivers. This study aimed to relate increasing forest mortality rates in six central European countries to climate drivers (CO2 concentration, temperature and precipitation) from 1985-2015, using a process-based vegetation model. LÄS MER

5. 14. Cropland and tree cover mapping using Sentinel-2 data in an agroforestry landscape, Burkina Faso

   Master-uppsats, Göteborgs universitet/Institutionen för geovetenskaper

   Författare :Ntandokazi Masimula; [2020-06-29]
   Nyckelord :Sentinel-2; Cropland mask; Tree cover estimation; Burkina Faso; Agroforestry; Random Forest;

   Sammanfattning : Sentinel-2, with high spatial resolution bands and increased number of spectral channels, has provided increased capabilities for vegetation mapping. Cropland masks within heterogeneous areas such as the Sudano-Sahel zone have become useful for monitoring landscapes. LÄS MER

7. 15. Multitask Convolutional Neural Network Emulators for Global Crop Models - Supervised Deep Learning in Large Hypercubes of Non-IID Data

   Master-uppsats, Lunds universitet/Matematisk statistik

   Författare :Amanda Nilsson; [2020]
   Nyckelord :Multitask Learning; Convolutional Neural Network CNN ; Branched Neural Network; Dynamic Global Vegetation Models DGVM ; Automated Feature Extraction; Feature Importance; Supervised Machine Learning; Emulator; Surrogate Model; Response Surface Model; Approximation Model; Metamodeling; Model Composition; Regularization; Robustness; Hyperparameter Optimization; Mathematics and Statistics;

   Sammanfattning : The aim of this thesis is to establish whether a neural network (NN) can be used for emulation of simulated global crop production - retrieved from the computationally demanding dynamic global vegetation model (DGVM) Lund-Potsdam-Jena General Ecosystem Simulator (LPJ-GUESS). It has been devoted to elaboration with various types of neural network architectures: Branched NNs capable of processing inputs of mixed data types; Convolutional Neural Network (CNN) architectures able to perform automated temporal feature extraction of the given weather time series; simpler fully connected (FC) structures as well as Multitask NNs. LÄS MER