TY - BOOK

T1 - Remote sensing time series for vegetation monitoring

T2 - A benchmark for Sahelian fallow mapping and microwave land surface phenology

AU - Tong, Xiaoye

PY - 2019

Y1 - 2019

N2 - Vegetation as the most abundant biotic element of the earth, is paramount for climate, wildlife and human beings. Remote sensing has been applied to over a wide range of temporal scales and over large areas on monitoring the state and dynamics of terrestrial vegetation. This thesis explores two novel research questions 1) what is the status and changes seen in Sahelian fallow fields, an important land use class for more than half a billion people in terms of food security since compared to cropped fields, fallow fields do not provide crop yield and the two type of fields have been mapped all as croplands for decades; 2) what is the land surface phenology trends of a vegetation index derived from satellite microwave observations in comparison to an optical satellite derived vegetation index. Both questions greatly require newer Earth Observation techniques such as big earth data, time series analysis and machine learning on vegetation phenology and cloud computing platform.

AB - Vegetation as the most abundant biotic element of the earth, is paramount for climate, wildlife and human beings. Remote sensing has been applied to over a wide range of temporal scales and over large areas on monitoring the state and dynamics of terrestrial vegetation. This thesis explores two novel research questions 1) what is the status and changes seen in Sahelian fallow fields, an important land use class for more than half a billion people in terms of food security since compared to cropped fields, fallow fields do not provide crop yield and the two type of fields have been mapped all as croplands for decades; 2) what is the land surface phenology trends of a vegetation index derived from satellite microwave observations in comparison to an optical satellite derived vegetation index. Both questions greatly require newer Earth Observation techniques such as big earth data, time series analysis and machine learning on vegetation phenology and cloud computing platform.

UR - https://soeg.kb.dk/permalink/45KBDK_KGL/1pioq0f/alma99123666756205763

M3 - Ph.D. thesis

BT - Remote sensing time series for vegetation monitoring

PB - Department of Geosciences and Natural Resource Management, Faculty of Science, University of Copenhagen

ER -