CHAPTER 1. INTRODUCTION1.
1. BackgroundForests sequester a largeamount of carbon and plays a crucial role in the global agenda of climatechange. Forest can act as both sourceand sink of carbon. When the forest is healthy and growing, carbon is sequestratedfrom atmosphere; but when the forests are destroyed, overharvested, or burned,they no longer contribute in sequestration and become a source of CO2which increase climate change (Hussin et al., 2014). Hence, quantification of forestbiomass is of vital importance to assess productivity – a critical informationfor carbon budget accounting, carbon flux monitoring and for understanding theforest ecosystem response to climate change (Watham et al., 2016; Nandy et al., 2017).
Meanwhile, reforestation,afforestation and avoiding deforestation are mechanisms of tackling climatechange (Hunt, 2009; Luong et al., 2015). In addition, estimation of the forest carbon stocks not only contributesin reducing emissions from deforestation and forest degradation (REDD); butalso in sustainable management of the forest (Hussin et al., 2014).The quantification ofbiomass and carbon sequestration in tropical forests is particularly relevantwithin the United Nations Framework Convention on Climate Change (UNFCC).
TheUNFCC adopted Kyoto Protocol which sets binding targets to industrializedcountries for reducing greenhouse gases emissions (Breidenich et al., 1998; Protocol, 2011; Hussin et al., 2014).
The Bali Action Plan Conference ofthe Parties (COP-13) in 2007 opened an avenue for developing countries toparticipate in forest carbon financing through the mechanism of reducingemissions by reducing emissions from deforestation and forest degradation (REDD)(Hussin et al., 2014;Luong et al., 2015). Under the REDD mechanism, countrieswill need to measure and monitor the emissions of CO2 resulting fromdeforestation and degradation within their borders (Luong et al.
, 2015). Emissions are converted to carbon credits in the carbontrade. All the greenhouse gas inventories and emissions reduction programsrequire scientifically robust methods to quantify forest carbon storage overtime across extensive landscapes (Gonzalez et al., 2010).
Vietnam has been participating in UN-REDD as a potentialmember of carbon trade, which requires estimation of biomass/carbon stock inthe country to be prepared for REDD implementation.Remotely sensed data integrated withforest inventories has been becoming an effective approach used to estimate aboveground biomass (AGB) and hence ultimately carbon stocks. Remote sensing-basedstudies relate reflectance recorded at the sensor with ground-basedmeasurements to estimate biomass (Tucker et al.
, 1985; Sader et al., 1989; Gibbs et al., 2007; Kumar et al.
, 2015). Recently, many studies in differentregions have found strong correlations between biomass and reflectance atdifferent wavelengths (Kumar et al., 2015). Kumar et al. (2015) also concluded that for regional levelwhere field data are scarce or difficult to collect, remote sensing is the superlativemethod to estimate biomass since its enhanced spatial, spectral, andradiometric characteristics (Delegidoet al.
, 2011; Irons et al., 2012; Chrysafis et al.,2017) can furthercontribute to accurate, spatially explicit estimations of forest inventoryparameters, andimproved update frequency with a lower cost for monitoring forests andmeasuring variables (Andersson et al., 2009; Dube & Mutanga, 2015; Yadav & Nandy, 2015).
Therefore, this method to become apopular method and widely used for biomass estimation. Optical remote sensing datawhich provide a wide range of spatial and temporal resolutions, have been widelyutilized for forest biomass assessment applying different forms of techniques (Foody et al.,2003;Lu, 2005; Rahman et al.,2005;Hydeet al., 2006; Li et al.
, 2008; Kumar et al., 2015). Relating to using opticaldata for biomass assessment, the approaches namely multiple regressionanalysis, k-nearest neighbor, and machine learning (artificial neural network (ANN),random forest, ect.) either have been commonly applied (Phua & Saito, 2003; Kumar et al.,2015) or especially appropriate for medium-spatial resolution data(Franco-Lopez etal., 2001; Soenen et al., 2010). By using these techniques, various types of both vegetationindices (VIs) and band ratios obtained from optical data are also utilized to estimatebiomass by correlating vegetation index values or band ratio values with field measurement(Dong et al.
,2003;Kumar et al., 2015). In recent year,machine-learning algorithms were trialed for capability to perform flexibleinput-output nonlinear mappings between remotely sensed data and biomass (Montes et al.,2011;Gleason & Im, 2012; Prasad et al.,2012;Wanget al., 2016). Typically, ANNs andsupport vector regressions were employed to couple with VIs to build monitoringmodels with improved prediction accuracy of remote estimation of biomass (Wang et al.,2016).
Among a variety of machine learning techniques, theemerging Random Forest (RF) algorithm proposed by Leo Breiman and Cutler Adelein 2001 has been regarded as one of the most precise prediction methods forclassification and regression, as it can model complex interactions among inputvariables and is relatively robust in regard to outliers (Wang et al.,2016).Forests covernearly 40% of the total land area of Nepal (Oli & Shrestha, 2009) whichsignifies the amount of carbon in the forests of Nepal. But national forestinventory data on changes in forest cover, biomass stocks, carbon emissions andcarbon removals on a periodic basis are limited (Acharya, et al., 2009).In order to capture the benefits accruing from climate change scenario, thereis an urgent need of obtaining reliable baseline statistics on carbon stocksand fluxes in forest which requires advanced remote sensing technologies (Oli& Shrestha, 2009). In addition, carbon credit buyers will expect the use ofa robust methodology of carbon accounting and monitoring (Acharya, et al.
, 2009)while commencing carbon trade. Hence, it becomes crucial to produce a credibleestimate of national forest carbon stocks and sources of carbon emissions, todetermine a national reference scenario and develop a national REDD strategy inNepal (MOFSC, 2009).1.2. Statementof problemThe quantification, mappingand monitoring of biomass are now key issues due to the importance of forest biomassin ecosystem and biomass role as a renewable energy source in many countries aroundthe world. However, detailed ground-based information of total biomass are scarce(Sierra et al.
,2007;Hussin et al., 2014). AGB estiation for thetropical and sub-tropical area is still a challenging task and requiresaccurate and consistent measurement methods because these forest areas arecharacteristed with complex stands and varying environmental conditions (Lu, 2005; Kumar et al.
, 2015). A shortage of information of global biomass due to uncertaintiesin accuracy and cost has been existed as a considerable issue for which is neededfurther investigation (Nguyen, 2010; Hussin et al., 2014). Moreover, according to Lu (2006), it is essential to integrateremotely sensed data and forest inventory data, so as to develop appropriate approachfor AGB estimation. According to Zianis and Mencuccini(2004), it is essential todevelop and impliment effective methods to estimate AGB for carbon quantificationwhich can be vital sources to monitor changes in carbon stocks (Ketterings etal., 2001; Hussin et al.
, 2014). 1.3. Objectivesand research questions1.3.1. Research Questions Thestudy is proposing to investigate following questions:} How to extract spatial, spectral and topographic (elevation, slope,aspect) variables from remotely sensed data?} Which spectral, spatial and topographic variables arerelevant to biomass estimation?} How good is the machine learning technique (random forest regressionmodel) for estimating biomass?} Whatis the relationship and potential of Sentinel-2 MSI and Landsat-8 OLI originaland synthetic bands for AGB prediction?} What is the amount of above ground biomass in Yok DonNational park ?1.
3.2. Research ObjectivesThe main objective of this research isto develop a method to accurately estimate AGB using remote sensing approach inwhich the potential of Sentinel-2 with spectral information in AGB predictionand the potential enhancement over previously available Landsat-8 OLI imagerywill be examined. To obtain this key aim, four specified objectives structurethis study: } Toextract the spatial, spectral and topographicvariables from satellite imagery; } Toidentify the correlation and potential of Sentinel-2 MSI and Landsat-8 OLIoriginal and synthetic bands for AGB prediction; } Todetermine optimized spatial, spectral and topographicvariables for AGB assessment;} Toestimate and map the AGB in Yok Don National park.1.4. ThesisstructureThis thesis is divided into six chaptersas follows.
Chapter 1 will provide an introduction on the importance of forest biomassestimation, as well as the necessity of the application of remote sensing techniquesin biomass estimation. The research objectives and questions thesis structureis also presented in the later part of the chapter. In Chapter 2, theliterature review is presented which provides a brief overview on informationon the application of remote sensing technology in forest biomass studies, suchas biomass concepts and definitions, overview of tools and techniques forbiomass estimation, learning machine techniques for biomass estimation, currentfindings and knowledge gaps, particularly in the study area. The researchmethods applied for addressing the objectives and a description of the studyarea and data used are presented in Chapter 3. Findings of the project are revealedin Chapter 4 whereas a discussion of findings achieved from applying themethods is presented in Chapter 5.
Chapter 6 provides conclusions andrecommendations of the research.