Extrapolating forest biomass dynamics over large areas using time series remote sensing
➤ Gửi thông báo lỗi ⚠️ Báo cáo tài liệu vi phạmNội dung chi tiết: Extrapolating forest biomass dynamics over large areas using time series remote sensing
Extrapolating forest biomass dynamics over large areas using time series remote sensing
• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing ements tor the degree of Doctor of PhilosophyHuy Trung NguyenBsc (lions). Thai Nguyen University. VietnamMsc Environmental Science, Thai Nguyen University, VietnamSchool of ScienceCollege of Science. Engineering and HealthRM1T University43862DeclarationI certify that except where due acknowledgement Extrapolating forest biomass dynamics over large areas using time series remote sensing has been made, the work is that of the author alone; the work has not been submitted previously, in whole or in part, to qualify for any other academExtrapolating forest biomass dynamics over large areas using time series remote sensing
ic award; the content of the thesis is the result of work which has been carried out since the official commencement date of (he approved research pro• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing knowledge the support 1 have received for my research through the provision of an Australian Government Research Training Program ScholarshipTrung Nguyen43887AbstractForest biomass, accounting for over 80% of global vegetation biomass, is considered a key factor in terrestrial ecology, atmospheric p Extrapolating forest biomass dynamics over large areas using time series remote sensing rocesses and the water and carbon cycles. Forest biomass has been recently recognised as a Global Climate Observing System (GCOS) Essential Climate VaExtrapolating forest biomass dynamics over large areas using time series remote sensing
riable (ECV), which is an important input to the United Nations' Reducing Emissions from Deforestation and forest Degradation-plus (REDD+) program and• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing . Consequently, monitoring forest biomass dynamics is an international concern which has attracted attention from government (at local, regional, national and international levels), academics and the general public. According to the Global forest Resources Assessment 2015. deforestation and forest d Extrapolating forest biomass dynamics over large areas using time series remote sensing egradation have been persisting in tropical developing countries where demand tor exploiting natural resources arc high and significantly increasing.Extrapolating forest biomass dynamics over large areas using time series remote sensing
Thus. these countries urgently need a robust and cost-effective national forest biomass monitoring system that can support their policy-making process• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing pment needs. While improving the quality’ of carbon reporting is needed, it is challenging for most developing countries due to their low capacities to perform national forest inventory on a regular basis. Forest inventory data may lie available in these countries, but they arc often out-of-date. Us Extrapolating forest biomass dynamics over large areas using time series remote sensing ing remote sensing data, such as Landsal satellite imagery, is one of the most practical and cost-effective alternatives to enable developing countrieExtrapolating forest biomass dynamics over large areas using time series remote sensing
s to overcome this current challenge. Landsat satellites arc unique as they have been creating the longest continuously-acquired, space-based and mode• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing al data for worldwide forest research and monitoring activities, especially forest biomass monitoring.This research first comprehensively reviewed the state and improvements of current approaches using I.andsat time-series (I.TS) for characterising forest biomass dynamics. This literature review ind Extrapolating forest biomass dynamics over large areas using time series remote sensing icated that the use of LTS not only enables production of spatially and temporally explicit estimates of biomass but also can improve the quality andExtrapolating forest biomass dynamics over large areas using time series remote sensing
accuracy of biomass models. Many innovative approaches for estimating forest biomass across space and time from LTS have been recently demonstrated. H• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing it is important to demonstrate an approach that is more possible for applications in developing countries where forest inventory data are measured for a single-time step which is Otten out-of-date.This research develops a robust and consistent Landsat-bascd framework that can support developing coun Extrapolating forest biomass dynamics over large areas using time series remote sensing tries improve their capacities in monitoring and reporting forest biomass and carbon stocks and changes across large areas. The framework is developedExtrapolating forest biomass dynamics over large areas using time series remote sensing
by utilising a 30-year annual time-series of Landsat images (1988-2017) and one-off inventory data, which arc commonly available in developing countr• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing / forest inventory scenario is similar to many developing countries, milking it a good case study. Ĩ.TS data were processed through several steps to produce a stack of cloud-free, annual mosaic composites, Illis dataset was then used as a foundation input hl further analyses for characterising fore Extrapolating forest biomass dynamics over large areas using time series remote sensing st disturbance and recovery and estimating forest biomass dynamics across space and time.Tn the first stage. Ĩ.TS data were utilised for developing aExtrapolating forest biomass dynamics over large areas using time series remote sensing
robust approach for mapping forest disturbance and recovery al a landscape scale. Forest changes were detected through pixelbased change detection pro• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing hm to prcdiclivcly map disturbance and recovery levels (high, medium and low) and disturbance causal agents (including wildfire, planned burns, clear-fell logging, selective logging) for multiple delected disturbance events (both primary and secondary events). Model explanatory' data included a rang Extrapolating forest biomass dynamics over large areas using time series remote sensing e of trajectory-based change metrics derived from the LandTrcndr analysis, while model training and validation data were derived from a human-interpreExtrapolating forest biomass dynamics over large areas using time series remote sensing
ted reference dataset. Tn addition, a space-time data cube concept was introduced to simultaneously report on both newly detected disturbance events (• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing classification models obtained high overall accuracies (73-81%). The data cube analysis revealed that although annual disturbance area was dominated by newly detected disturbances, ongoing disturbances accounted for a considerable area (over 50% of newly detected disturbances). These resultsiiiindi Extrapolating forest biomass dynamics over large areas using time series remote sensing cate the utility of LTS in accurately capturing and mapping forest disturbance and recovery, facilitating further analyses on biomass estimates.The seExtrapolating forest biomass dynamics over large areas using time series remote sensing
cond stage of this research tested and compared different modelling approaches for estimating forest biomass using Landsat time-series and inventory d• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing in combination with data extracted from forest inventory field plots. In particular, IS k-nearest neighbour (kNN) imputation models were tested to predict three aboveground biomass (AG 13) variables (total AG13. AG13 of live trees and AG 13 of dead trees). These models were developed using different Extrapolating forest biomass dynamics over large areas using time series remote sensing distance techniques (RF. Gradient Nearest Neighbour (GNN). and Most Similar Neighbour (MSN)) and different combinations of response variables (modelExtrapolating forest biomass dynamics over large areas using time series remote sensing
scenarios). Direct biomass imputation models were trained according to the biomass variables while indirect biomass imputation models were trained acc• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing w that RF consistently outperformed MSN and GNN distance techniques across different model scenarios and biomass variables. The indirect imputation method generally achieved belter biomass predictions than the direct imputation method. Tn particular, the RF-based kNN model trained with the combinati Extrapolating forest biomass dynamics over large areas using time series remote sensing on of basal area and stem density variables was the most robust for estimating forest biomass. As the kNN imputation method is increasingly being usedExtrapolating forest biomass dynamics over large areas using time series remote sensing
by land managers and researchers to map forest biomass, this analysis helps those using these methods to ensure their modelling and mapping practices• RMITUNIVERSITYExtrapolating forest biomass dynamics over large areas using time-series remote sensingA thesis submitted in fulfilment of the require Extrapolating forest biomass dynamics over large areas using time series remote sensing ntory data. Illis approach consisted of three components: (1) a modelling method for creating annual forest AGB maps from I.andsat time-series and one-off inventory data; (2) evaluation of the robustness and transferability of applying a single model through time to estimate AGB dynamics; (3) a spat Extrapolating forest biomass dynamics over large areas using time series remote sensing ial and temporal analysis of AGÌ3 dynamics according to forest disturbance and recovery histories, from which to inform jurisdictions as to how theseExtrapolating forest biomass dynamics over large areas using time series remote sensing
ecological changes impact AGB dynamics. These analyses were based on (he findings of the first two stages. A Rl -based kNN imputation model, which wasGọi ngay
Chat zalo
Facebook