OPTIMAL PLOT SIZE FOR SAMPLING BIOMASS IN NATURAL AND LOGGED TROPICAL FORESTS Hamdan Omar 1 Mohd Hasmadi Ismail 2 and Mohd Hakimi Abu Hassan 3 1 Forest Research Institute Malaysia, 52109 FRIM, Kepong, Selangor Fax: 03-6272 9852, Email: hamdanomar@frim.gov.my 2 Faculty of Forestry, Universiti Putra Malaysia, 300 Serdang, Selangor Email: mhasmadi@upm.edu.my 3 Kumpulan Pengurusan Kayu Kayan Terengganu Sdn. Bhd. 23200 Dungun, Terengganu Email: kimi_mi82@yahoo.com Abstract Inventory information is used for sampling purpose and necessary in predicting aboveground biomass (AGB) over large forest areas. However the uncertainties produce from the estimates are always of concern. One of the factors contributing to the uncertainties is the variation of plot sizes. Many studies attempted to obtain a sufficient number of sampling plots and to achieve the desired number the plot size has to be reduced. The dimensions that are normally used are squares of 20, 30, 0, 50, and 60 m, which can yield 0.0, 0.09, 0.16, 0.25, and 0.36 ha, respectively. Rectangular plot with the dimensions of 10 x 20 m and 20 x 50 m are also often used, whereas circular plots are seldom used. Although the dimensions are various, the final AGB estimation is usually in per-hectare basis (i.e. Mg ha -1 or t ha -1 ), which is equal to a square plot of 100 x 100 m (1 ha). Currently, no clear guidelines are available for determining plot size in estimating AGB in tropical forest. This study was carried out to determine optimal plot size for measuring biomass in tropical forest, which comprised natural and logged lowland dipterocarp forests. Locations (together with the DBH) of all trees having diameter of 10 cm in a 1-ha plot were measured by using Total Station. Simulations have been applied to the data by changing the plot dimension and the resulted AGB in Mg ha -1 were compared to the estimation that was based on 1-ha plot. From the results, an optimal plot size for AGB estimation has been determined. Abstract is prepared for Conference on Forestry and Forest Products (CFFPR) 2013. 11-12 November 2013, Sunway Putra Kuala Lumpur
OPTIMAL PLOT SIZE FOR SAMPLING BIOMASS IN NATURAL AND LOGGED TROPICAL FORESTS Hamdan Omar 1 Mohd Hasmadi Ismail 2 & Mohd Hakimi Abu Hassan 3 1 Forest Research Institute Malaysia Tel: 03-62797200. Email: hamdanomar@frim.gov.my 2 Faculty of Forestry, Universiti Putra Malaysia 3 Kumpulan Pengurusan Kayu Kayan Terengganu Sdn. Bhd. INTRODUCTION The mass of living organisms in a forest is called the biomass. It is literally and simply the weight of all organisms living in a precisely delimited region. Most of the biomass in a forest is in trees, and the focus of methods for estimating biomass is measuring the above-ground portion of trees. In contrast, the part of tree biomass that is below ground -the roots -is much more difficult to measure (and currently impossible without destroying the forest). For this reason, nearly all inventories of forest biomass refer only to above-ground weight (Gillespie et al. 1992). This is routinely abbreviated AGB, for above-ground biomass. Forest biomass is now becoming the key parameter in dealing with current climate change issues as it able to mitigate the effect of global warming (Gibbs et al. 2007). Measuring biomass in a large forest area usually involves sampling in a small area appraisal and projected into a larger area to represent the entire biomass population (Brown et al. 1989; Chave et al. 200). Forest plots should in nearly all cases be 1 hectare in area, 100x100 m in size (Condit, 2008). However, to measure trees within 1-ha plot is usually difficult and it is rarely used in sampling and in most cases, smaller plots (in dimension or area) are employed. An appropriate sampling strategy need to be well planned to ensure that the estimation is representative to reality, especially in tropical forest where the standing structure is complex and not uniform (Brown and Lugo, 198). The sampling method will become more crucial when the biomass estimation is to use remotely sensed data. This is because the satellite imagery usually comes with various spatial resolutions and signal received within a pixel at certain pixel size represents the condition of the forest that can be interpreted in manners (Wang et al. 2001). There are several factors that need to consider when sampling biomass. One of the most important factors is accessibility to the forest areas. The other factors are cost, manpower, and time for constructing sampling plots. The relationship between measurement time, travel time, and plot size can be developed in determining optimal accuracy of biomass estimates (Boris, 1980). This approach can be applied for different forest types accross the world (Martin and George, 1998). The efforts of defining the most efficient ways to measure biomass not only limited to plot-base only but also point sampling also known as plotless. By using a specific instrument, the trees are measured and biomass can be calculated in per haectare basis (Boris and Troxell, 1979). Point sampling is quite frequently used as a means of obtaining area estimates and/or as a procedure for defining plot locations. The role point sampling plays in in-place resource 1
inventories are not always fully understood, acknowledged, or appreciated (Lund, 1982).This study was carried out to demonstrate the optimal plot size and dimension in estimating biomass in the lowland dipterocarp forest. It was carried out in experimental basis approach where the plots were modified into a number of shapes and dimensions. MATERIALS AND METHODS The study was carried out in Jerangau Forest Reserve, which is located within Dungun Timber Complex in Dungun, Terengganu. The forest was divided into two types based on conditions, which are (i) natural forest, where there was no logging before and (ii) logged forest, where there was logged five (5) years before. The measurement was carried out in June 2013 and two 1-ha plots were established in each of the forest types. Three major steps involved, which are; (i) field plot setup and tree measurement (ii) tree data processing and tree mapping, and (iii) biomass calculation. The plot measuring 100x100 m was established in both forest types in the study area. NIVO Total Station was located at five stations (center, and all four edges) and the location of trees were obtained from the bearing (i.e. angle) and distance read from laser optic beam produced by the Total Station. All trees measuring >10 cm were measured and the location of each tree was determined. The locations of all the trees measured were plotted in computer-assisted drawing (AutoCAD) and transferred into geographic information system (GIS) data format for the experiment. The biomass was calculated for each tree (in Mg) by using Kato et al. (1978) allometric equations. The experiment was carried out based on varying plot shapes and dimensions. With each tree labeled by its biomass value, any plot size can yield biomass, in Mg ha -1. Table 1 list the shape, dimension and size of the experimental plots. Figure 1 illustrates how the 1-ha plot was split into several experimental plots. The calculation of biomass was carried out iteratively for each plot dimension to investigate the accuracy of the estimation. The final estimations were then compared to the calculated biomass based on 1- ha plot. Plot Shape Square Rectangular Table 1 Shape, dimension and size plots created within 1-ha plot Plot dimension Plot Size No. of iterations (m) (ha) within 1-ha plot 10x10 0.01 100 20x20 0.0 25 25x25 0.0625 16 30x30 0.09 9 0x0 0.16 50x50 0.25 10x20 20x30 20x0 25x50 0.02 0.06 0.08 0.125 Circular Radius = 5 Radius = 10 Radius = 20 Radius = 25 0.008 0.03 0.126 0.196 100 25 Total 1 377 50 16 12 8 2
Figure 1 Dimensions of plots created within 1-ha plot RESULTS AND DISCUSSION Figure 2 shows the spatial distribution of trees within 1-ha plot of both forest types. It is notably that the natural forest was dense and dominated by mixed stands with varying stem diameters ranging from 10 to 118 cm. In contrast, logged forest was dominated by small stands with stem diameters ranged from 10 to 77 cm. The trees were also scattered sparsely in the plot with more gaps as compared to the natural forest. The information extracted and the calculated biomass for both plots is shown in Table 2. Figure 2 Tree distribution that was mapped for natural (left) and logged (right) forests Table 2 Biomass calculated for 1-ha plot of natural and logged forests Forest type Number of Biomass (Mg) trees (ha -1 ) Min Max Mean Total Natural 93 0.0 19.01 0.97 78.95 Logged 380 0.0.55 0.58 221.72 Determination of optimal plot size for biomass estimation was made by comparing the mean estimation (in Mg ha -1 ) with the calculated biomass based on 1-ha plot (also in Mg ha -1 ). A 3
plot that has the closest mean to the calculated biomass and the smallest standard deviation was considered to be optimal. Table 3 summarizes the all the estimations that were made based on the corresponding plot shape, dimension and size. The study found that the optimal plot dimension for natural forest was 0x0 and 20x0 for square plot and rectangular plot, respectively. However, in the case of that there will be too difficult to establish 0x0 m plot due to some conditions in the field, the second option is 25x25 m. This dimension produced a reasonable mean and standard deviations in the estimate. It is also suitable if the given forest structure is homogenous with uniform stands and the variations of the tree sizes are not too large. Different from natural forest, logged forest is found easier that allow smaller plot size, i.e. 25x25 m. Although the best estimate came from 50x50 m, followed by 0x0 m plots, these dimensions are found difficult to establish in the field. This is due to the presence of logging road and skid trails, in many cases, where gaps (space without stands) can occur in a large plot. Similar with natural forest, the dimension of 20x0 m can be the second option if rectangular plot is preferred. In the other hands, small plots (e.g. 10x10, 10x20 or circular plot with 5 and 10 m radius) can lead to a serious underestimate or overestimate in the measurement as it deviate too much from the mean estimation. Whereas for circular plots, with any radius length were not advisable for both forest types. Plot Shape Square Rectangular Plot dimension (m) 10x10 20x20 25x25 30x30 0x0 50x50 10x20 20x30 20x0 25x50 Circular Radius = 5 Radius = 10 Radius = 20 Radius = 25 Table 3 Biomass estimated from the iterations Mean Biomass Std. Dev. Biomass (Mg ha -1 ) (Mg ha -1 ) Natural Logged Natural Logged 321.75 771.75 250.78 809.9 291.19 186.88 82.96 26.96 97.80 53.83 271.61 72.25 89.98 271.23 57.03 90.2 230.2 50.93 602.13 52.0 61.06 65.60 38.72 629.1 709.1 610.16 619.63 379.5 211.06 233.60 867.95 32.52 271.52 279.20 276. 161.62 2.8 55.1 256.20 220.9 81.37 66.71 305.21 19.7 17.85 19.3 1.6 9.93 80.29 25.12 2.8 32.62 391.07 35.27 25.3 9.99 CONCLUSION The study demonstrated that the biomass in natural and logged forests were different. The selection of plot dimensions is also different for biomass estimations in both forests. The study found that the optimal plot sizes for estimating biomass were 0x0 m and 25x25 m for natural and logged forests, respectively. The dimension of 20x0 was also found suitable for both forest types. These can be practiced for lowland dipterocarp forest where the trees are
mixed with varying structure, size and distribution. However, the associated factors such as accessibility, cost, time and manpower should also need to be considered in establishing sampling plots for forest biomass study. The study also suggests that GIS can be an appropriate tool to study the nature and dynamics of tropical forest. REFERENCES Boris Z and Troxell JK. 1979. Plot Versus Point Sampling. Forest Resource Inventories. Workshop Proceedings. Colorado State University, Fort Collins, July 23-26, 1979. Volume II: 923-929. Boris Z. 1980. Plot size optimization. Forest Science 26(2): 251-257. Brown S and Lugo AE. 198. Biomass of Tropical Forests: A New Estimate Based on Forest Volumes. Science 223: 1290-1293. Brown S, Gillespie AJR and Lugo AE. 1989. Biomass estimation methods for tropical forests with applications to forest inventory data. Forest Science 35:881-902. Chave J, Condit R, Aguilar S, Hernandez A, Lao S and Perez R. 200. Error propogation and scaling for tropical forest biomass estimates. Philosophical Transactions of the Royal Society of London, Series B 359: 09-20. Condit R. 2008. Methods for estimating aboveground biomass of forest and replacement vegetation in the tropics. Center for Tropical Forest Science Research Manual, 73pp. Gibbs HK, Brown S, Niles JO, Foley JA. 2007. Monitoring and estimating tropical forest carbon stocks: making REDD a reality. Environmental Research Letters, 2: doi:10.1088/1789326/ Gillespie AJR, Brown S and Lugo AE. 1992. Tropical forest biomass estimation from truncated stand tables. Forest Ecology and Management 8:69-87. Kato R, Tadaki Y and Ogawa H. 1978. Plant biomass and growth increment studies in Pasoh forest. Malayan Nature Journal 30:211 22. Lund HG. 1982. Point sampling the role in in-place resource inventories. In: Brann, TB, House LO, Lund H, (eds). In-place resource inventories: principles and practices. Proceedings of a national workshop; 9-1 August 1981; Orono, ME. SAF 82-02. Bethesda, MD: Society of American Foresters; 79-8. Martin SA and George PR. 1998. Plot Size Recommendations for Biomass Estimation in a Midwestern Old-Growth Forest. Northern Journal of Applied Forestry 15():165-168. Wang G, George, Xiangyun X, Steven W and Anderson AB. 2001. Appropriate Plot Size and Spatial Resolution for Mapping multiple Vegetation Types. Photogrammetric Engineering & Remote Sensing 67(5):575-58. 5