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Quantifying the amount and distribution of above ground biomass in Singapore by
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remote sensing
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Jukka Miettinen1*, Jonathan Y. Goh1, Aik Song Chia1, Ping Ting Chew2 Xiaojing Huang1 and
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Soo Chin Liew1
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Centre for Remote Imaging Sensing and Processing (CRISP),
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National University of Singapore (NUS),
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10 Lower Kent Ridge Road
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Singapore 119076
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Central Nature Reserve,
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National Parks Board (NParks),
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1 Cluny Road,
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Singapore 259569
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* Corresponding author,
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Email address: jimietti@yahoo.com, scliew@nus.edu.sg
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Phone: +65 65165069
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Fax: +65 6775 7717
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Quantifying the amount and distribution of above ground biomass in Singapore by
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remote sensing
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Abstract
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In this paper we present the main steps taken and results reached in a recent project aiming to
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test the usability of remote sensing in estimation of above ground vegetation biomass in
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Singapore. We report the findings of the project in an attempt to 1) highlight the great carbon
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storage potential of urban greenery areas and 2) discuss the limitations of remote sensing in
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vegetation carbon inventories aiming to fulfil the requirements of the national carbon
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accounting procedure agreed by the United Nations Framework Convention of Climate
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Change (UNFCCC). We conclude that it would be important, and in many cases most likely
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beneficial, to include urban greenery into the national carbon accounting schemes. However,
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we argue that remote sensing can play merely a supporting role in this process, mainly due to
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the extremely physically heterogeneous structure of urban greenery, difficulty in deriving
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required error estimates and inability to estimate carbon content in all of the terrestrial carbon
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pools defined by the International Panel on Climate Change (IPCC).
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Keywords: biomass, carbon accounting, urban greenery, IPCC, UNFCCC
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1. Introduction
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Land use and land cover changes (LULCC) are estimated to produce around 20% of
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anthropogenic carbon emissions globally (IPCC 2007). With the increasing global support for
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climate change mitigation efforts and the imminent national carbon accounting obligations
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including emissions from LULCC, the interest for above ground biomass (AGB) estimation
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using remote sensing methods has increased rapidly over the recent years (see e.g. Goetz et al.
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2009). There are, however, several problematic aspects on utilizing remote sensing for
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vegetation biomass monitoring aiming to fulfil the guidelines of the International Panel of
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Climate Change (IPCC) as required by the United Nations Framework Convention on Climate
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Change (UNFCCC) (Maniatis and Mollicone 2010). Singapore, as a highly urbanized
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environment in a humid tropical climate, serves as an excellent showcase to highlight some
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the aspects that need to be taken into consideration when planning to use remote sensing for
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AGB estimation and carbon accounting.
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Firstly, the original vegetation in Singapore is consisted of humid tropical forests, which have
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a complex multi-layered structure combined with high level of biomass. Optical remote
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sensing based biomass estimation efforts have faced severe problems and limitations in
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tropical forests (Foody et al. 2001, 2003, Lu 2006, Sarker and Nichol 2011). The poor
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performance of optical remote sensing methods in the humid tropics has been mainly
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attributed to the multi-layered closed canopy structure of tropical forest. Recent studies have
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indicated that L-band SAR data has more potential for forest biomass estimation than optical
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sensors as it carries information on the vegetation structure (Lu 2006, Goetz et al. 2009, Wang
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and Qi 2008). Subsequently, integration of optical and SAR data for biomass estimation has
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been recently promoted due to the complementary strengths of optical and radar sensors
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(Wang and Qi 2008, Wolter and Townsend 2011).
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Secondly, the majority of the areas in the city state of Singapore are urbanized environments
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with highly heterogeneous physical structure of the vegetation, mixed with man-made
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features. However, the importance of urban biomass estimation is not restricted to city states
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like Singapore. With the proportion of the world’s population living in urban areas projected
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to reach 70% by 2050 (United Nations 2008) and urbanised land use rapidly expanding, the
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quantification of urban vegetation carbon content has started to receive increasing attention in
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recent years (Nichol and Lee 2005, van Delm and Gulinck 2009, Davies et al. 2011). It has
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been suggested that even though carbon storage in urban areas is small compared to the
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emissions they produce per unit area, the absolute size of urban carbon storage is substantial.
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Indeed, studies have indicated that residential zones in urban areas may have even greater
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carbon storage than agriculture land due to higher productivity in urban vegetation (Nowak
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and Crane 2002, Davies et al. 2011). For this reason the majority of the major cities in the
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world will most likely have to conduct biomass inventories in the near future for the countries
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to be able to fulfil their carbon accounting requirements.
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In this paper we describe a recent cooperative project between the National Parks Board
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(NParks) of Singapore and the Centre for Remote Imaging, Sensing and Processing (CRISP)
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at the University of Singapore (NUS). The main goals of the project were to 1) investigate the
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usability of remote sensing methodologies for AGB estimation in a humid tropical urban
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setting and 2) to provide a coarse estimate of the amount and distribution of AGB in
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Singapore to support the development of a national carbon accounting methodology for
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carbon emissions from LULCC. This paper is not a scientific research article. The most
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important scientific findings arising from the abovementioned project have already been
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described in two separate papers (Goh et al. 2011, submitted). One of the papers can be found
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in this same issue of the Asian Journal of Geoinformatics [this sentence needs to be removed
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if this is not the case]. The aim of this technical project report is to present the flow of the
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entire mission including the resulting biomass maps of Singapore. Furthermore, we will use
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lessons learnt from this project to highlight problems and limitations of remote sensing based
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biomass estimation in urban environment, especially in the context of carbon accounting from
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LULCC according to the requirements of UNFCCC.
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2. Datasets
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2.1. Project Area
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The project area covered all land areas of Singapore. These areas can be divided into three
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highly different land use categories: 1) Central Nature Reserve (CNR), 2) Undesigned
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vegetation (UDV) areas and 3) Designed vegetation (DV) areas. The CNR is located in the
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central part of Singapore covering some 2500 ha of forest. Approximately 200 ha of nearly
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pristine patches of primary dipterocarp forest remain in fragments within CNR, engulfed by
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secondary forests in various stages of succession (Teo et al. 2003). These secondary forests
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were formerly used for plantation agriculture but they were quickly abandoned due to
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unsustainable agriculture practices which reduced soil fertility. Forest succession has taken
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place since the early 20th century.
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The UDV areas include undeveloped land where vegetation is allowed to grow freely but is
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not protected. Typically these areas consist of undeveloped state land, military training areas
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and some part of the recreational greenery areas. The total coverage of these areas is around
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15 000 ha. The rest of Singapore can be categorized as DV areas, referring to designed and
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managed urban greenery areas where the structure of the vegetation is planned and
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maintained. These areas typically include e.g. city parks, public and educational institutions as
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well and housing estates, covering around 41 000 ha in total. Note that roads were excluded
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from the DV areas in this project. Biomass for the road side trees was extracted from a road
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side tree database maintained by NParks.
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2.2 Remotely Sensed Data
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A set of remote sensing data from both optical and radar remote sensing sensors were used in
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the project. Two multispectral optical images from the SPOT 5 HRG sensor acquired on the
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14th of April 2010 and on the 20th of May 2010 formed the optical dataset. In addition, a SAR
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scene in L-band HH and HV polarization from the ALOS PALSAR sensor acquired on the 1st
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of July 2010 was used.
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The SPOT 5 HRG images have a spatial resolution of 10 m and they contain four wavelength
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bands; green (Band 1; 0.50 – 0.59 µm), red (Band 2; 0.61 – 0.68 µm), near infra-red (Band 3;
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0.79 – 0.89 µm) and shortwave infra-red (Band 4; 1.58 – 1.75 µm). The raw digital numbers
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(DN) from both of the abovementioned scenes were converted to top of the atmosphere
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(TOA) reflectance and corrected for Rayleigh scattering and molecular absorption using
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routines in the 6S package (Vermote et al. 1997). Subsequently, a mosaic image was created
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using the April 2010 scene as the main source of information while filling any cloud covered
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areas with data from the May 2010 scene.
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For the ALOS PALSAR data, the Level 1.0 images (HH and HV) were first multi-looked by a
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factor of three in the azimuth direction. They were then geo-registered (using a nearest-
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neighbour scheme to preserve the original data values) to the same image geometry as the
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SPOT 5 mosaic to facilitate integrated use the datasets. Using SRTM DEM data to calculate
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the local incidence angle at each pixel, topographic normalization (Leclerc et al. 2001) was
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carried out, before obtaining the final output data in σ0 (normalized radar backscatter cross-
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section, also called sigma-nought) values, with units in dB (decibels).
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In addition, seven very high resolution (0.5 m) GeoEye satellite images acquired in 2010 were
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used to delineate tree canopy cover using visual image interpretation in selected sample areas
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as described in the later stages of this report.
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2.3. Field Measurements
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All individual trees with a diameter at breast height (DBH) exceeding 5 cm (or 9 cm within
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CNR) were measured for DBH and identified by species in a field campaign between October
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2009 and May 2010. 25 permanent sample plots were measured inside the CNR (25 m radius
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circular plot). Furthermore, 10 similar plots within the CNR were used an independent
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validation dataset. Outside the CNR altogether 40 sample plots (20 m radius circular plot)
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were measured. These plots were allocated over the entire Singapore based on the land cover
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distribution of a preliminary land cover map (please see more details below). Thus, altogether
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75 sample plots were used in this project.
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The biomass of each tree was calculated using an allometric model (Equation 1) developed by
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Chave et al. (2005). The particular model used in this study was developed for humid tropical
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forest including sampling sites in Malaysia and Indonesia, the neighbouring countries of
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Singapore:
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AGB = ρ × exp (-1.499 + 2.148 ln (D) + 0.207 (ln (D))2 - 0.0281 (ln (D))3)
(Equation 1)
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where ρ is the wood density (oven dry mass divided by green volume) (g/cm3) of the tree
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species and D is the DBH (cm) of the tree. Wood density values were obtained from the
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Global Wood Density Database (Zanne et al. 2009) which is the largest wood density
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database collected to date.
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In addition to the tree biomass data, five subplots (1x1 m) were used in land cover types
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where high proportion of vegetation biomass was found in understorey vegetation. All living
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biomass within these subplots was collected, oven-dried and weighed. This biomass was
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added to the tree biomass (if any) to derive the total biomass value for the sample plots in the
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“Savanna” forest, Shrubland and Grassland land cover types (please see more details below).
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2.4. Land use polygons
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In addition to the abovementioned materials, the Singapore land use plan (version 2008) was
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utilized to define land use units (polygons) within the DV areas as described in the later stages
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of this report. The same land use information was also used to mask out road areas where the
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NParks road side tree database was used for biomass estimation instead of a remote sensing
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approach.
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3. Data analysis
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3.1 Preliminary land cover map
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In order to better understand the vegetation distribution of Singapore and allocate field sample
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plots outside CNR in an optimal manner, a preliminary land cover classification was first
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conducted using the 10 m resolution SPOT 5 data. The land cover was classified into nine
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vegetation classes and two non-vegetated classes based on satellite image interpretation. Multi
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phase classification method was used starting with unsupervised clustering and followed by
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visual analysis and assignment of the clusters into the final land cover classes (Figure 1).
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<Insert Figure 1 somewhere here.>
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This preliminary land cover map was used in combination with Singapore land use plan to
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separate UDV and DV areas from each other. Subsequently, field surveys were undertaken to
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evaluate the heterogeneity of vegetation within different land cover types used in the
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preliminary classification. Vegetation areas that displayed more variance in terms of physical
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vegetation characteristics were assigned more field sample plots and fewer number of sample
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plots were allocated to more homogeneous vegetation classes. At this point some of the
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preliminary land cover classes were combined since the field surveys proved them similar and
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showed confusion between the classes in the land cover map. The final land cover classes
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used for allocating field sample plots are described in Table 1.
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<Insert Table 1 somewhere here.>
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3.2 Analysis of datasets
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The strength of the correlation between the remotely sensed data and AGB was first tested in
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order to find out the possibilities to directly estimate biomass using the remote sensing
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datasets. In addition to testing the four wavelength bands of SPOT 5 HRG, several common
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vegetation indices, texture measures as well as HH and HV backscatter values from ALOS
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PALSAR data were also examined. Nevertheless, when all vegetation land cover types were
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combined the relationship between biomass and remote sensing data was found to be very
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weak. When tested separately according to the different sampling plot categories (CNR, UDV
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and DV), a moderately strong relationship (r = -0.69) was found between Band 3 (NIR) and
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AGB in the 25 plots located in the CNR. Slight improvement in correlation was further
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achieved by combining the Band 3 data with ALOS PALSAR HV backscatter data. Thus,
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direct remote sensing estimation of AGB was seen as a suitable methodology to be used in the
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CNR for the purposes of this project. More details on the analysis and the AGB estimation
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model developed in this project are presented in Goh et al. (submitted).
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Outside the CNR the correlation between remotely sensed parameters and biomass values was
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found to be too weak for reliable biomass estimation. The reason for the poor correlation was
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interpreted to be the high heterogeneity of the physical vegetation structure in areas outside
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CNR. A closed canopy forest structure like an abandoned rubber plantation would have very
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different remote sensing characteristics compared to an open canopy forest despite the fact
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that they have the same level of biomass. For this reason, direct estimation of biomass with
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remote sensing using the data and methods available for this project was considered
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unfeasible for areas outside CNR.
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It was noticed during the analysis that the standard error of the mean of the field sample plots
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within the UDV areas (8% of the mean) was much smaller than in the DV areas (14% of the
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mean), regardless of the significantly higher plot number in the DV areas (26 plots in DV vs.
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14 in UDV). It was thus decided that a mere propagation of mean sample plot biomass values
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within each land cover type over the entire coverage of the land cover type (a.k.a. the
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“classify and multiply” method) would give a reasonable coarse estimate of biomass in the
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UDV areas for the purposes of this project. This method could not however, be used for the
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DV areas due to their extremely physically heterogeneous structure, varying from lawn and
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nearly unvegetated industrial estates to closed canopy park areas, all mixed in a highly
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detailed manner. Further investigation was therefore needed to find a method suitable for
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deriving a meaningful estimate of AGB in these areas.
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In urban vegetated areas (like the DV areas in this study), a large proportion of biomass is
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found in trees as opposed to herbaceous understorey vegetation which usually is kept in very
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minimal quantity in maintained urban greenery areas. As larger tree canopy area is usually
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associated with taller trees and larger trunk diameter size, the total area of tree canopy in an
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urban environment may be related to its biomass. Medium resolution satellites such as SPOT
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5 with a spatial resolution of 10 m cannot be used to delineate tree canopies. GeoEye imagery
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on the other hand, with their 0.5 m resolution is ideally suited for measuring the tree canopy
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area of urban greenery environment using manual delineation.
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In this project, the tree canopy cover for 26 sampling plots covered by cloud free GeoEye data
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were manually delineated. After this, the total tree canopy cover was measured as percentage
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area within each plot. A Pearson’s correlation r = 0.61 was found between canopy cover and
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AGB. This was considered to be a sufficient correlation for derivation of a coarse estimate of
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AGB in the DV areas of Singapore. However, in order to do that, a data on the percentage of
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tree cover was needed. In further analysis, a strong correlation (r = 0.83) was found between
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tree canopy percentage within land use polygons in the DV areas and the percentage of areas
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having SPOT 5 derived NDVI over 0.57. This empiric relationship was subsequently used to
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estimate AGB in DV areas on Singapore in this project. The development of this
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methodology is described in detail by Goh et al. (2011).
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To summarize the workflow of the quantification of AGB for the entire Singapore using the
three different methods briefly presented above, a framework is presented in Figure 2.
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<Insert Figure 2 somewhere here.>
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4. Amount and distribution of above ground biomass in Singapore
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The biomass map for the CNR was produced using the direct remote sensing method by
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correlating AGB biomass with remote sensing data. Although in general the model performed
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well there were some areas where the regression model returned negative biomass values
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when applied to the remote sensing data. Closer inspection revealed that these areas were in
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fact patches of shrub and grassland found within the CNR (Goh et al. submitted). These
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vegetation types were not represented in any of the sampling plots. This very concretely
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highlights that the use of these types of empirical biomass estimation models cannot be
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extended beyond those vegetation types that have been sampled during the creation of the
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model. The resulting biomass map can be seen in Figure 3. The overall total biomass for the
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CNR area was estimated to be around 775 000 t which corresponds to an average biomass of
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314 t/ha (Table 2).
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<Insert Figure 3 and Table 2 somewhere here.>
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Two separate methods needed to be used for estimation of AGB in the UDV and DV greenery
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areas as described in the previous section of this technical report. The resulting biomass maps
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can be seen in Figures 4 and 5. The UDV and DV areas were estimated to contain altogether
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around 3.7 Mt of AGB, corresponding to over 70% of all AGB in Singapore. The biomass
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was divided rather evenly between the undesigned and designed areas. Note, however, that
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the DV areas cover nearly three times the area covered by the UDV areas (Table 2).
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<Insert Figures 4 and 5 somewhere here.>
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Overall the total biomass in Singapore was estimated to be around 5.1 Mt. 15% of this could
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be found in the CNR area, regardless of the fact that it covers less than 4% of the total study
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area. The largest single category was the UDV area, containing 39% of all biomass in
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Singapore.
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It is important to note, however, that even the designed areas outside the CNR (i.e. parks,
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housing estates, areas covered by educational and public institutions etc.) contain 33% of
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biomass in Singapore. Furthermore, 10% (0.5 Mt) of Singapore total biomass was estimated
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to be included in the road side trees (by applying the biomass equation presented in Equation
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1 to the NParks road side tree database). This brings the total amount of the designed
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vegetation biomass to around 2.2 Mt (43%) which highlights the significant status of urban
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designed greenery environments in Singapore. Without these areas the total biomass of
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Singapore would be only 2.9 Mt as opposed to the 5.1 Mt estimated in this project.
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Note that the Mangrove forest biomass value also presented in Table 2 is directly estimated
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using the area of Mangrove forests in the preliminary land cover mapping and biomass value
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derived from literature (Komiyama et al. 2008). This was considered adequate for the
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purposes of this project due to the marginal area of mangrove forests in Singapore. The
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combined full coverage biomass map and the distribution of biomass within Singapore can be
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seen in Figure 6.
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<Insert Figure 6 somewhere here.>
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5. Discussion and conclusion
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In this paper we have reported the main steps taken and the methodological approaches used
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in a recent AGB inventory in Singapore. The main results including the amount and
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distribution of the AGB biomass in Singapore were also presented. Three different remote
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sensing methods (1. Direct remote sensing, 2. Classify and multiply as well as 3. A method
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based on tree canopy cover percentage in land use polygons) were needed to enable a coarse
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quantification of AGB in this urban environment in a humid tropical setting.
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The amount and distribution of biomass mapped in this project highlight the significance of
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designed urban greenery areas as carbon storage in Singapore. Over 40% of AGB was found
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in designed vegetation areas which include e.g. city parks, housing estates, public institutions
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and educational institutions as well as road side trees. Of the total 5.1 Mt of AGB in
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Singapore, 2.2 Mt were found in these types of planned and maintained greenery areas. The
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majority of the remaining AGB was located in undesigned vegetation areas, while 15% was
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found in Nature Reserves. Overall, these findings emphasize the importance of taking
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greenery aspects into consideration in decision making in urban development. These decisions
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have an immense impact on the total biomass and thereby carbon storage in a city state like
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Singapore.
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However, the significance of the findings made during this project is not limited to Singapore.
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The results highlight the carbon storage potential of urban areas in general. Similar results
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have been obtained in other parts of the world as well (Nowak and Crane 2002, Nichol and
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Lee 2005, Davies et al. 2011). Along with the ever increasing urbanization of the world and
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fast expanding urban areas especially in the developing countries, management of urban
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greenery will become increasingly important in the national carbon accounting. In the best
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case countries may gain considerable benefits by including urban areas into the carbon
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accounting scheme. Biomass accumulated by urban greenery can offset part of the carbon
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emissions from urban sources.
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On methodological aspects this project has shown the potential of remote sensing in
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estimation of AGB in reasonably homogeneous humid tropical forest areas using a
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combination of optical and radar imagery (Goh et al. submitted). However, it also highlighted
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the unsuitability of such models outside the areas where they have been created. Furthermore,
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on the areas outside the Central Nature Reserve of Singapore, where the physical structure of
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urban vegetation was highly heterogeneous due to varying history and management status, the
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correlation between AGB and remote sensing data was found to be poor. In these areas either
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very basic (classify and multiply) or complex methods including auxiliary GIS information
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(Goh et al. 2011) needed to be used get derive estimates of the amount and distribution of
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AGB.
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Furthermore, derivation of error estimates for the total biomass estimates (as required for
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national carbon accounting by the UNFCCC) may be very difficult if not entirely impossible
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if the estimation is based primarily on remote sensing methods. For example, the model used
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in this project for the CNR area had a RMSE of 36% of the mean biomass and 1% bias (Goh
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et al. submitted). These are entirely reasonable error statistics for pixel level accuracy of such
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a model and they are comparable to other studies (see e.g. Foody et al. 2003). However, the
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error estimates for the total biomass over the entire area where the model is applied to cannot
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be derived from this figure. Pixel level errors tend to balance each other out (as indicated by
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the very low 1% bias) and would result in much smaller – but unknown – error margins for
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the total biomass estimate. Therefore, the simplest and most straightforward approach would
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be to base biomass estimation primarily on the field sampling and obtain the error estimates
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directly from this sampling, using remote sensing merely in a supporting role.
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In addition to the missing or difficult to obtain error estimates, another crucial limitation of
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remote sensing based biomass estimation in the context of national carbon accounting is the
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range of carbon pools that need to be quantified. The UNFCCC requires adaption of the IPCC
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carbon accounting guidelines (IPCC 2006). According to these guidelines five carbon pools
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need to be reported in vegetation inventories: 1. Above ground carbon, 2. Below ground
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carbon, 3. Dead wood carbon, 4. Litter carbon and 5. Soil carbon. Above ground biomass is
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the only one of these five pools where remote sensing parameters can reasonably be expected
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to correlate with biomass. This means that in order to obtain information on the other carbon
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pools, extensive field sampling needs to be carried out. This would naturally allow
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simultaneous field measurements for the estimation of the above ground carbon. And finally,
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UNFCCC expects repeatability of the carbon assessments, indicating that the remote sensing
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methods to be used should be robust and in the best case not dependent on a specific remote
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sensing dataset.
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In conclusion, we have shown in this report that remote sensing methods can be useful in
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AGB estimation in urban environment in humid tropical setting. They can be used to derive
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coarse estimates of the amount and distribution of biomass. However, we have also pointed
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out great limitations in their use, especially in the context of national carbon accounting along
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with the requirements of UNFCCC. We therefore conclude that remote sensing is probably
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most suitable for a supporting role for carbon accounting in urban environments. It can be
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used for preliminary analysis (like in the project reported in this paper) and for stratification
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of areas in an attempt to make field sampling more efficient (both statistically and
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economically). Also, it may be a vital part of a multi-phase inventory system, e.g. in the form
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of visual interpretation of very high resolution satellite data in a high number of sampling
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plots. In all of these aspects other sources of GIS data (e.g. land use allocation) can greatly
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support the use of remotely sensed datasets. Furthermore, remote sensing data is naturally in
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many cases the only means to create historical trends of changes in vegetation cover. It may
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not allow historical biomass estimation in the level of detail expected for carbon accounting
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by UNFCCC, but it should still be useful for estimating general historical trends.
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Overall this project has revealed several important aspects on the amount and distribution of
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biomass in Singapore as well as highlighted the potential and limitations of remote sensing in
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biomass estimation in humid tropical urban setting. The information and lessons derived from
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this project will be used in the development of a carbon accounting methodology for LULCC
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in Singapore. We hope that this report will be useful for people in other countries as well who
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are potentially planning to include urban areas into their national carbon accounting schemes.
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Acknowledgements
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This project was primarily funded by the Ministry of National Development, Singapore. The
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authors from the Centre of Remote Imaging, Sensing and Processing (CRISP) acknowledge
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also research funding from the Agency for Science, Technology and Research (A*STAR) of
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Singapore.
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23
1
Table 1. Distribution of field sample plots in different land use categories and description of
2
land use classes. CNR = Central Nature Reserve, UDV = Undesigned vegetation and DV =
3
Designed vegetation.
4
5
Table 2. Distribution of biomass between land use categories in Singapore. CNR = Central
6
Nature Reserve, UDV = Undesigned vegetation and DV = Designed vegetation.
7
8
24
1
Table 1. Distribution of field sample plots in different land use categories and description of
2
land use classes. CNR = Central Nature Reserve, UDV = Undesigned vegetation and DV =
3
Designed vegetation.
4
Land use
Land cover class
category
n.a.
CNR
(N = 25)
Secondary forest
(N = 5)
“Savanna” forest
(N = 4)
Albizia forest
UDV
(N = 3)
Shrubland
(N = 1)
Grassland
(N = 1)
n.a.
DV
(N = 26)
5
6
7
8
Description
Primary humid tropical forest and tall grown secondary
forests.
Relatively dense secondary vegetation with a mixture of tree
species. Typically multi-layered structure and closed canopy.
Vegetation dominated by tall shrubs and/or stands of trees by
single or few species. Typically a partially open canopy.
Albizia dominated secondary forest with a second layer of
various tree species and undergrowth.
Vegetation dominated by low shrub, ferns, tapioca plants and
tall grasses.
Vegetation dominated by short grasses usually on poor
quality soil (e.g. sandy or impervious soil).
Planted and maintained vegetation in designed urban
greenery areas (excluding road side trees).
25
1
2
Table 2. Distribution of biomass between land use categories in Singapore. CNR = Central
3
Nature Reserve, UDV = Undesigned vegetation and DV = Designed vegetation.
4
5
Type
Area (ha)
Biomass (t)
% Biomass (t/ha)
CNR
2 460
775 000
15
314
UDV
15 370
2 012 000
39
131
DV
41 400
1 697 000
33
41
Mangrove*
560
112 000
2
200
Road side trees*
7 200
523 000
10
73
Total
67 000
5 119 000
100
76
*) Note that the Mangrove and Road side tree biomass figures are mainly derived from outside
6
sources. Please see the main text for details.
26
1
Figure 1. Preliminary land cover map produced to support Singapore biomass estimation.
2
3
Figure 2. Summary of the three different above ground biomass (AGB) derivation methods
4
used in this project for the Central Nature Reserve (CNR), Designed vegetation (DV) areas
5
and Undesigned vegetation (UDV) areas. Please see more details of the methodologies in the
6
text.
7
8
Figure 3. Biomass distribution in the Central Nature Reserve (CNR).
9
10
Figure 4. Biomass distribution in the Undesigned vegetation (UD) areas outside Central
11
Nature Reserve (CNR).
12
13
Figure 5. Biomass distribution in the Designed vegetation (DV) areas outside Central Nature
14
Reserve (CNR).
15
16
17
18
Figure 6. Biomass distribution in Singapore in 2010 (excluding road side trees).
27
1
2
3
4
5
6
Figure 1. Preliminary land cover map produced to support Singapore biomass estimation.
28
1
2
3
4
Figure 2. Summary of the three different above ground biomass (AGB) derivation methods
5
used in this project for the Central Nature Reserve (CNR), Designed vegetation (DV) areas
6
and Undesigned vegetation (UDV) areas. Please see more details of the methodologies in the
7
text.
8
9
29
1
2
3
4
5
Figure 3. Biomass distribution in the Central Nature Reserve (CNR).
30
1
2
3
Figure 4. Biomass distribution in the Undesigned vegetation (UD) areas outside Central
4
Nature Reserve (CNR).
5
6
31
1
2
3
4
Figure 5. Biomass distribution in the Designed vegetation (DV) areas outside Central Nature
5
Reserve (CNR).
6
7
32
1
2
3
4
Figure 6. Biomass distribution in Singapore in 2010.