Integration of carbon conservation into sustainable forest management using high resolution satellite imagery: A case study in Sabah, Malaysian Borneo

International Journal of Applied Earth Observation and Geoinformation 18 (2012) 305–312

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Integration of carbon conservation into sustainable forest management using high resolution satellite imagery: A case study in Sabah, Malaysian Borneo Andreas Langner a,∗ , Hiromitsu Samejima b , Robert C. Ong c , Jupiri Titin c , Kanehiro Kitayama a a

Laboratory of Forest Ecology, Graduate School of Agriculture, Kyoto University, Kitashirakawa Oiwake-cho, Sakyo-ku, Kyoto 606-8502, Japan Center for Southeast Asian Studies (CSEAS), Kyoto University, Yoshida Shimo-Adachi-cho 46, Sakyo, Kyoto 606-8501, Japan c Forest Research Centre, Sabah Forestry Department, P.O. Box 1407, 90715 Sandakan, Sabah, Malaysia b

a r t i c l e

i n f o

Article history: Received 1 September 2011 Accepted 17 February 2012 Keywords: Borneo Biomass Forest degradation Sustainable forest management Remote sensing Landsat

a b s t r a c t Conservation of tropical forests is of outstanding importance for mitigation of climate change effects and preserving biodiversity. In Borneo most of the forests are classified as permanent forest estates and are selectively logged using conventional logging techniques causing high damage to the forest ecosystems. Incorporation of sustainable forest management into climate change mitigation measures such as Reducing Emissions from Deforestation and Forest Degradation (REDD+) can help to avert further forest degradation by synergizing sustainable timber production with the conservation of biodiversity. In order to evaluate the efficiency of such initiatives, monitoring methods for forest degradation and above-ground biomass in tropical forests are urgently needed. In this study we developed an index using Landsat satellite data to describe the crown cover condition of lowland mixed dipterocarp forests. We showed that this index combined with field data can be used to estimate above-ground biomass using a regression model in two permanent forest estates in Sabah, Malaysian Borneo. Tangkulap represented a conventionally logged forest estate while Deramakot has been managed in accordance with sustainable forestry principles. The results revealed that conventional logging techniques used in Tangkulap during 1991 and 2000 decreased the above-ground biomass by an annual amount of average −6.0 t C/ha (−5.2 to −7.0 t C/ha, 95% confidential interval) whereas the biomass in Deramakot increased by 6.1 t C/ha per year (5.3–7.2 t C/ha, 95% confidential interval) between 2000 and 2007 while under sustainable forest management. This indicates that sustainable forest management with reduced-impact logging helps to protect above-ground biomass. In absolute terms, a conservative amount of 10.5 t C/ha per year, as documented using the methodology developed in this study, can be attributed to the different management systems, which will be of interest when implementing REDD+ that rewards the enhancement of carbon stocks. © 2012 Elsevier B.V. All rights reserved.

1. Introduction Pressure on tropical forests has increased tremendously in the past two decades (Fuller, 2006). Insular Southeast Asia experienced a deforestation rate of 1.0% per year between 2000 and 2010 (Miettinen et al., 2011), and a detailed analysis of Borneo revealed an annual rate of 1.7% between 2002 and 2005 (Langner et al.,

Abbreviation: CCFS, crown cover and forest status. ∗ Corresponding author. Present address: Bureau of Climate Change, Forestry and Forest Products Research Institute (FFPRI), 1 Matsunosato, Tsukuba, Ibaraki 3058687, Japan. Tel.: +81 90 3903 7246; fax: +81 29 874 3720. E-mail addresses: [email protected] (A. Langner), [email protected] (H. Samejima), [email protected] (R.C. Ong), [email protected] (J. Titin), [email protected] (K. Kitayama). 0303-2434/$ – see front matter © 2012 Elsevier B.V. All rights reserved. doi:10.1016/j.jag.2012.02.006

2007). According to Kitayama (2008) most of the forests on Borneo are permanent forest estates, so called “production forests”, which have been commercially logged more than twice, mainly using a conventional logging (CL) technique. Even though CL results in the removal of only a certain number of economically valuable trees per hectare, the process can destroy more than 50% of all trees (Sist et al., 2003). The number of trees extracted under CL is only limited by girth and harvesting cycle (Sist et al., 1999). Additionally, a lot of collateral damage is caused to the surrounding vegetation by the falling of large trees and the use of heavy machinery (Cannon et al., 1998). The prevailing application of CL in Borneo has led to vast areas of degraded forests. This has severe impact on biodiversity as the lowland forests of Borneo are considered to have the highest plant species richness worldwide (Kier et al., 2005). In addition, forest degradation causes considerable carbon

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emissions into the atmosphere (Achard et al., 2004; Asner et al., 2005, 2010), which accounts together with deforestation for at least 12% of global anthropogenic CO2 emissions, thus being the second largest anthropogenic emission source (van der Werf et al., 2009). In contrast to CL, reduced-impact logging (RIL) mitigates the physical impacts on the ground, to the remaining standing trees, and ecosystem as a whole by using a combination of pre-harvest census, controlled felling, lowered allowable cut, and regulated machinery use (Sist et al., 2003). In combination with longer cutting cycles as applied under next-generation sustainable forest management (SFM) RIL also helps to preserve carbon (Pinard and Putz, 1996; Putz et al., 2008). Regardless of the obvious ecological advantages, SFM is currently practiced in less than 5% of all natural permanent forest estates in the tropics (ITTO, 2006). A study by Putz et al. (2000) discusses the most common motives of commercial loggers for not applying RIL, with reasons ranging from elevated costs to lack of governmental incentives. However, the application of SFM can be financially rewarded through forest certification, thus leading to an improved market access and increased unit log-price (Lagan et al., 2007). Reducing Emissions from Deforestation and Forest Degradation (REDD) is an approach to mitigate CO2 emissions due to land cover conversions in the tropics (UNFCCC, 2007). When including the concept of additionality, which creates incentives for additional actions in forest conservation, SFM practices are promoted and will in effect lead to higher permanent carbon stocks (Kitayama, 2008). As the implications of these incentives on the enhancement of carbon stocks have to be monitored, it is necessary to obtain information about the degradation status as well as the above-ground biomass (AGB) of a forest. Remote sensing offers a practical way to assess information on the condition of forests in large and inaccessible areas (Saatchi et al., 2007). Unfortunately, measuring forest degradation and deriving AGB estimates is technically more demanding than monitoring deforestation processes (DeFries et al., 2007). Traditionally, biomass estimation has been based on either radar or optical remote sensing technology. The advantage of radar in contrast to passive optical data is the ability to acquire data irrespective of haze and the persistently cloudy weather conditions in the humid tropics (Asner, 2001). However, the signal of all available radar sensors tends to saturate at a lower value than the actual AGB volumes of tropical rain forests, which are among the most carbon-dense and structurally complex ecosystems in the world, and additionally there are also increased errors in mountainous terrain (Gibbs et al., 2007). Therefore, estimation of AGB in tropical forests is very challenging. Morel et al. (2011) describes the limitations of L-band data to estimate forest degradation in several forest reserves in Sabah. Engelhart et al. (2011) demonstrated that a multi-temporal L- and X-band model can be successfully used to estimate even high AGB values in the peatlands of Central Kalimantan. However, this study also pointed out the crucial role of the extremely flat topography of that area, thus avoiding problems due to shadow and layover effects. To overcome the problem of sensor saturation, light detection and ranging (LiDAR) sensors have to be considered, which have the potential to obtain AGB measurements even in the tropics. Although large-scale applications are not feasible due to the narrow swath of view and the high costs of data acquisition, the use of LiDAR instead of field inventory for calibration purposes of satellite data is nevertheless a promising approach (Asner et al., 2010). Satellite-based LiDAR data has also been successfully used to derive a pan-tropical carbon map (Saatchi et al., 2011).

In contrast to radar data, passive optical remote sensing technologies cannot penetrate a closed forest canopy and therefore need to rely on secondary vegetation features for biomass estimation, making optical remote sensing-based methods more restricted to empirical approaches. However, artifacts due to a heterogeneous topography are less problematic as with radar data. A lot of research in the humid tropics has been undertaken using Landsat data (Foody et al., 2001, 2003; Lu, 2005; Steininger, 2000; Tangki and Chappell, 2008). With a spatial resolution of 30 m, each Landsat Thematic Mapper (TM) or Enhanced Thematic Mapper (ETM+) pixel value integrates reflectance from several tree crowns. The density of these trees and their physical characteristics due to species composition and age contribute to the spectral properties of each pixel. The more mature a tropical rain forest, the more the canopy becomes uneven (Weishampel et al., 2001), resulting in higher levels of self-shadowing by emergent trees, which is an important parameter to distinguish younger from mature forests (Adams et al., 1995). Subsequently, the AGB of a forest can be estimated based on information on the stage of succession and the level of degradation (Houghton, 2005). The objective of our research was to analyze the impact of different forest management practices on the AGB using optical remote sensing techniques supported with field data. The effects of CL and SFM with RIL on the AGB were analyzed in two production forests in Borneo dominated by mixed dipterocarp rain forest. In contrast to other studies, our biomass model was applied at three different times, totally encompassing a period of 16 years, thereby enabling evaluation of long term effects of different forest management practices. Annual AGB change rates were derived and analyzed to determine to what extent next-generation SFM can conserve carbon in comparison to the business as usual situation of CL.

2. Materials and methods 2.1. Study site The study area consists of two adjacent production forests, Deramakot and Tangkulap, situated in Sabah, Malaysian Borneo. The climate is characterized by frequent rainfall and high temperatures throughout the year and both forest reserves are dominated by lowland mixed dipterocarp forest with varying degrees of degradation. While Tangkulap is less hilly, most of Deramakot is characterized by a heterogeneous topography. The forest management units (FMU) of Deramakot and Tangkulap were licensed for logging in 1956 and 1970 respectively, using CL (Sabah Forestry Department, 2005). While CL continued in Tangkulap, Deramakot was chosen as a role model for a wellmanaged forest in Sabah in 1989, whereupon all logging activities were halted. In 1995 a new management system applying SFM in combination with RIL was implemented in Deramakot and this forest reserve was certified by the Forest Stewardship Council (FSC) in 1997 (Lagan et al., 2007). Information about the logging history in Deramakot is well known since 1995 (Gobilik et al., 2010). Both forest reserves are subdivided into compartments. In Deramakot, which totally covers 55 083 ha, two to four of the 135 compartments are harvested each year using RIL with a planned rotation cycle of 40 years (Sabah Forestry Department, 2005). A total of 17 compartments (4000 ha) have been set aside for conservation purposes. Tangkulap, with 57 compartments covering a total of 27 550 ha, is situated west of Deramakot. Except one compartment under conservation (293 ha) the forests of Tangkulap were repeatedly logged using CL until 2002, when all logging activities were stopped. Unfortunately, no information about the logging history in Tangkulap is available.

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2.2. Ground data of AGB measurements Field-based AGB measurements from 78 circular permanent sample plots (PSP), derived by stratified random selection, were derived in 2007 (Deramakot) and 2008 (Tangkulap). While 68 of the PSP had a radius of 20 m, 10 PSP were sampled with only 15 m radius. Two PSP were added at clear-cut areas to include zero-biomass reference points. Finally, 80 PSP were analyzed to investigate the correlation between AGB and remote sensing data. Within these PSP all trees larger than 10 cm diameter at breast height (dbh) were measured for dbh. Using the allometric equation for tropical rain forests (Brown, 1997), these measurements were converted to weight values for each individual tree: AGB = e−2.134+2.53×ln(dbh) Tree weights within each plot were summed up. These values were converted to carbon, assuming 49% of the measured weight is carbon (Imai et al., 2010). Due to the different sample sizes, the AGB values were not directly comparable. Therefore, all AGB values were normalized to fit the equivalent area of one Landsat pixel (900 m2 ), corresponding to a circular plot with 16.93 m radius. 2.3. Satellite data for AGB estimation The AGB estimates of the whole study area were based on Landsat TM and ETM+ imagery (Worldwide Reference System (WRS) path 117, row 56). Altogether one TM scene (1991-05-22) and two ETM+ scenes (2000-07-09, 2007-05-26) were analyzed. The ETM+ imagery from the year 2000 was selected as master scene to develop the AGB model because this scene showed lowest cloud contamination and there were no timber stand improvements lately before satellite acquisition, which would have influenced the reflectance values. Shuttle Radar Topography Mission (SRTM) data were used to correct the Landsat scenes for illumination effects.

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2005, 2006) were analyzed. The average of the reflectance values of corresponding areas were expected to be similar. The band-wise differences of the average reflectance values between 1991 and 2000 as well as 2007 and 2000, mainly caused by atmospheric effects and differences between sensors, were used to adjust the reflectance values in the 1991 and 2007 scenes to match the level of reflectance in the 2000 scene. In contrast to the acquisition date of the Landsat master scene in 2000, the PSP measurements took place in 2007 and 2008. Therefore, the AGB values of the PSP plots had to be adjusted to the year 2000 for their intermediate growth, using the following equation (modified from Clark et al., 2001), derived from various old-growth tropical forests of the world: AGByear−1 = AGByear − (1.05 × ln(AGByear ) − 2.91) AGByear is the AGB of the actual year and AGByear−1 is the AGB of the previous year in tons of carbon. This algorithm was iteratively applied, taking into account the different assessment dates. The range of these adjustments roughly resembled the results of a forest growth simulation model for undisturbed dipterocarp forests (Ong and Kleine, 1996). The AGB model is calibrated for lowland mixed dipterocarp forests only. Therefore, other forest types had to be masked based on an ISODATA classification (100 clusters; 200 iterations; bands 1–5, 7). Two different vegetation types (lowland mixed dipterocarp forests, ultramafic forests) as well as bare soil, water bodies, clouds and cloud shadows were classified. As the AGB model is sensitive to silvicultural treatments, such areas were manually masked. The classification results were visually revised, resulting in an overall accuracy of 98.7% (2000 scene) – based on ground data derived from 76 PSP. Four PSP, all of them located in Tangkulap, were not used for this validation due to possible logging activities between 2000 (satellite acquisition) and 2002 (stop of logging in Tangkulap).

2.4. Data pre-processing 2.5. From Landsat data to AGB estimates GPS data collected during the field surveys proofed that the 2007 ETM+ scene showed the highest accuracy in geolocation. Therefore, the 1991 and 2000 scenes were geometrically adjusted to match the 2007 image. To assure inter-sensor comparability and enable the development of a model that physically relates image information to ground measurements, all Landsat data were radiometrically converted to Top-of-the-Atmosphere (TOA) reflectance values (Chander et al., 2009; Foody et al., 2003). Our analysis takes into account the self-shadowing effect of various tree heights, correlating with the complexity of the canopy structure (Kane et al., 2008). It was therefore necessary to minimize illumination artifacts due to heterogeneous topography, which otherwise bias the analysis. The sun-canopy-sensor (SCS) model of Gu and Gillespie (1998) was applied to all Landsat scenes, using TOA instead of radiance values to reduce scene-to-scene variability (Chander et al., 2009). TOAn = TOA ×

cos ˛ × cos  cos  × cos ˛ + sin  × sin ˛ × cos ϕ

TOAn is the Top-of-the-Atmosphere reflectance from a pixel on horizontal terrain; ˛ is the terrain slope angle (◦ );  is the incidence angle (◦ ) and ϕ is the terrain azimuth relative to the sun (◦ ). A calibration procedure similar to dark object subtraction (Chavez, 1988) was used to adjust the 1991 and 2007 scenes to the 2000 master scene. After masking all non-forest pixels, only areas within compartments that have been set aside for protection and therefore experienced no logging or silvicultural activities (0.4% of Tangkulap, 3.4% of Deramakot; Sabah Forestry Department,

Basic assumption is that natural forests with a closed crown cover have a higher AGB in comparison to same forest types with an opened crown cover. We identified the Mid-Infrared band (MidIR(b7); 2.08–2.35 ␮m for TM; 2.09–2.35 ␮m for ETM+), which is sensitive to soil components and vegetation moisture content, to best represent the crown cover condition and forest status. High reflectance values indicate openings in the crown cover. Younger vegetation and regrowth, characteristic successional land cover types with intermediate reflectance values can be separated from pristine forests, which show lower reflectance values. For our analysis we used the reciprocal of the illumination and atmosphere-corrected reflectance values (MidIR(b7)corr ) to derive an index, which we named crown cover and forest status (CCFS) index. A haze correction was necessary for all scenes to assure comparability. As the reflectance values of the blue band (b1; 0.45–0.52 ␮m for TM; 0.45–0.515 ␮m for ETM+) show haze most prominently, this band was chosen to correct the CCFS index. In a first step the statistically derived minima of each non-atmosphereadjusted blue band were subtracted from its corresponding blue band. Co-occurrence variance filter results (3 × 3 pixels) were used to derive binary masks to filter all abrupt changes in the reflectance values, characteristic for logging roads or forest cover disturbances. For that sake, empirically derived thresholds for each scene were used. As the impact of haze changes only gradually, the blue band reflectance values were median filtered using a 15 × 15 pixel kernel. Above described masks were applied on these median filter results.

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The resulting modified bands (blue(b1)mod ) were finally used to correct the CCFS index: CCFScorr =

1 MidIR(b7)corr − 0.3 × blue(b1)mod

The ratio between the MidIR and the blue band over different forested areas with varying haze-impact was analyzed, ranging between 0.2 and 0.7. A visual comparison with ground data revealed that a factor of 0.3 showed best results over all Landsat scenes. We performed a regression analysis between the CCFScorr index, based on the 2000 master scene, and the modified AGB values of the PSP. By intersecting the PSP with the CCFScorr index, fractions of the CCFScorr pixels were included proportionate to the share of their original area. Altogether 64 PSP were analyzed. According to field surveys in 2009/2010 four PSP showed discrepancies between the Landsat scene and the measured AGB values – all of them in Tangkulap. At four further PSP plots trees species did not fit to the census data of 2008. Two PSP were situated on ultramafic forest and four PSP were affected by temporal water bodies. Two PSP were excluded due to a large tree outside each PSP with its wide crown protruding into the plot, which would have biased the analysis. Two second-order polynomial models were fitted in the analysis and used to predict the AGB measurements of the 64 PSP from the CCFScorr index. CCFS2corr

Model 1 :

AGBLandsat =

Model 2 :

AGBLandsat = CCFS2corr

+ CCFScorr

We estimated the coefficients using an ordinary least square method and compared both AGB models with the Akaike Information Criterion (AIC) to select the model, which best fitted the measured AGB. Finally, the AGB was estimated and converted to per hectare values. Brown et al. (1991) recorded a maximum AGB of 530 t C/ha for a plot in tropical Southeast Asia and our own PSP records measured maximal 560 t C/ha (based on 15 m radius PSP). However, the preliminary AGB map contained few pixels (0.2% of the study area) with biomass values higher than 560 t C/ha. An analysis revealed that some of them were caused by shadows or water areas, omitted during the masking process. A threshold value of 645 t C/ha was empirically selected to best account for the rare events of factual high AGB, but excluding higher values due to misclassification. However, the reader should keep in mind that this threshold is not based on an actual PSP measurement on the ground.

Table 1 Coefficients of model (1) and model (2) estimated by ordinary least square.

Model 1 CCFS2corr CCFScorr AIC = 430.4669 Model 2 CCFS2corr AIC = 429.2872 ** ***

Estimate

SE

0.0072 0.0834

0.0025 0.1049

0.0090

0.0006

T value

Pr (>|t|)

2.845 0.795

0.0060 0.4298

**

<2e−16

***

16.31

Significant at 1% level. Significant at 0.1% level.

averaged these values and calculated 95% confidential intervals on the mean AGB values of Deramakot and Tangkulap. For the estimation of the effects of forest management on biomass, the regression models of above bootstrap sampling were applied per forest reserve for each year 1991, 2000 and 2007, thus deriving the AGB values and their annual change rates with 95% confidential intervals. While the years 1991–2000 describe the influence of CL in Tangkulap, the period 2000–2007 represents the impact of RIL in Deramakot. 3. Results 3.1. Forest degradation-based AGB model Based on the result of the CCFScorr index of the year 2000, analyzing an area of 81 778 ha of lowland dipterocarp forest, a regression analysis with the AGB values of the PSP was done, in which two second-order polynomial AGB models were fitted. The coefficients of both models are shown in Table 1. Even though model 1 is more complex, it is not significantly better than model 2 (ANOVA, p < 0.05). And as the AIC is also slightly higher in model 1, model 2 was further used in this study (Fig. 1). 3.2. Validation of AGB model The cross-validation approach revealed high reliability of the results. Correlation of the iteratively estimated ABG values and the true AGB values showed a high significance with an R2 of 0.6573 (0.3922–0.8464, 95% confidential

2.6. Validation of AGB model For accuracy assessment we used a cross-validation approach (Roff, 2006). Forty out of the 64 plots were randomly selected to construct an AGB model for the year 2000. Based on this model, we estimated the AGB of the other 24 PSP. We then tested the correlation between the estimated AGB and the ground data using a t-test. These steps were iteratively done 1000 times to derive the 95% confidential interval of the correlation coefficient and the p-value. The statistical tests were conducted using R 2.10.0 (R Development Core Team, 2009). 2.7. Analysis of the AGB To analyze the difference of AGB in the two forest management units for the year 2000, a bootstrap sampling on the 64 PSP with 1000 iterations was conducted, deriving AGB models for each run. Based on every model, we estimated the AGB of each forest reserve,

Fig. 1. Correlation CCFS – AGB. Correlation between the CCFScorr index (crown cover and forest status; abscissa) and the AGB (above-ground biomass) measurements (t C/Landsat pixel) of the PSP (permanent sample plot) adjusted for the year 2000 (ordinate). The two PSP with the very high AGB measurements were sampled in compartment 53 of Tangkulap, which was set aside as high conservation value forest (HCVF).

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interval). The confidential interval of the p-value of the t-test was 0.0003–0.0124, revealing the robustness of the model. 3.3. AGB estimation of Tangkulap and Deramakot in 2000 For the year 2000 the AGB was estimated for both forest reserves (Fig. 2a), revealing a point estimated mean value of 152.2 t C/ha with a 95% confidential interval from 130.6 to 178.1 t C/ha. An analysis on FMU level depicted the difference of both reserves. While the average AGB in Tangkulap was 122.5 t C/ha (104.9–142.8 t C/ha, 95% confidential interval), the forests of Deramakot had a significantly (p < 0.05) different AGB of 167.9 t C/ha (143.7–195.7 t C/ha, 95% confidential interval). Arithmetically speaking, each ha in Deramakot had in average 45.3 t C/ha (38.8–52.8 t C/ha, 95% confidential interval) more AGB than Tangkulap. The average compartment-based AGB values ranged from 56 t C/ha in compartment 55 of Tangkulap to 239 t C/ha in compartment 18 of Deramakot (Fig. 2b). Compartments in the central and west of Deramakot had highest AGB with some areas in the southeast also showing comparable high biomass values. The central compartments of Tangkulap had the lowest AGB of both reserves. However, despite a correction for haze it was not possible to totally remove all its effects on the satellite data. While most of the compartments of Tangkulap still showed some minor influence of haze (diagonal stripes in Fig. 2b), Deramakot was affected only in the north and south, resulting in a slightly higher underestimation of AGB in Tangkulap. The AGB estimates were also influenced by haze shadows, resulting in an AGB overestimation. Compartments 14 and 32 (Tangkulap) as well as compartment 18 (Deramakot) appear to have erroneously elevated AGB estimates due to that effect. 3.4. Annual AGB change rates in Tangkulap and Deramakot The annual AGB change rate in Tangkulap was analyzed between 1991 and 2000, providing information about the impact of CL. During that time 20 194 ha (73% of the forest reserve) were analyzed, revealing an average loss of −6.0 t C/ha (−5.2 to −7.0 t C/ha, 95% confidential interval) per year. As Deramakot experienced SFM in combination with RIL since 1995, the annual AGB change rate was estimated between 2000 and 2007, thereby analyzing 44 919 ha (82% of the forest reserve). According to our results, Deramakot gained an average amount of 6.1 t C/ha (5.3–7.2 t C/ha, 95% confidential interval) per year. 4. Discussion Climate-change mitigation is one of several important ecosystem services provided by tropical rain forests. The international framework of REDD+ is expected to include the concept of additionality, rewarding incentives for supplemental activities toward an enhancement of forest carbon stock, such as SFM. In order to implement REDD+ in a post-2012 climate regime and to survey the impact of such additional activities, a transparent monitoring and verification system is crucially needed (EcoSecurities, 2007). In this study we showed that optical high resolution remote sensing data can be used to monitor the degradation status and AGB changes in tropical lowland mixed dipterocarp forest. Advantage of this study especially in comparison to radar-based approaches is the applicability even over hilly terrain. An analysis based on the year 2000 revealed that the AGB values between Deramakot and Tangkulap were significantly different on a 95% confidential interval. The Deramakot forests that had rested from 1989 to 1995 and had been managed since with a sustainable method, had a greater average AGB by 45.3 t C/ha (38.8–52.8 t C/ha, 95% confidential interval) compared to Tangkulap, which had been

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managed using CL until the year 2002. This value comes remarkably close to the result of a study of the same area by Imai et al. (2009) with 52 t C/ha. The slightly higher estimate of their study most probably relates to the later date of analysis (2001) but also to the low number of four timber stock classes, used to extrapolate the AGB. However, due to the different management histories of the two areas and missing information of the original biomass levels, the average difference of 45.3 t C/ha (38.8–52.8 t C/ha, 95% confidential interval) in 2000 can only partially explain the impact of the different management systems on AGB conservation. To derive this information, two further Landsat scenes were utilized, allowing the analysis over two time periods during which each forest reserve was managed by only one distinct management type. Regarding the reliability of the AGB change rates, it is crucial to analyze as many pixel locations as possible. Due to cloud cover in the corresponding scenes about 73% of Tangkulap and 82% of Deramakot could be processed, which is sufficiently large to be regarded as representative for both forest reserves. However, a too small sample size such as single compartments can lead to biased sampling, resulting in an under- or overestimation of the AGB change. Furthermore, smaller areas can be more strongly affected by still remaining haze effects, as haze only gradually changes over larger areas, whereas FMU-based analyses integrate over areas with rather varying haze conditions, thus decreasing the overall error. Based on the comparison of the AGB estimates at different years, we derived AGB changes per forest reserve and management type. As per our analysis, Tangkulap lost in average −6.0 t C/ha (−5.2 to −7.0 t C/ha, 95% confidential interval) per year from 1991 to 2000 due to CL, which is widely applied in most production forests of Borneo and can be regarded as baseline scenario with high losses of AGB. On the other hand, SFM with RIL can conserve AGB, even leading to an AGB increment as intended by the concept of additionality. This was confirmed in our study, where the sustainably managed forests of Deramakot were able to recruit an average annual amount of 6.1 t C/ha (5.3–7.2 t C/ha, 95% confidential interval) between 2000 and 2007 (period of SFM in Deramakot). In comparison to studies about tropical forest dynamics by Clark et al. (2001) and Chave et al. (2008), who found maximal annual AGB increments (coarse woody productivity) between 3.8 and 4.3 t C/ha for old-growth primary tropical forests, the recruitment rate of Deramakot seems to be elevated. However, the forests of Deramakot are not pristine (Gobilik et al., 2010) and experience silvicultural treatments, which might explain their higher recruitment rate. Additionally, litterfall even exceeds the increase of the coarse woody productivity (Chave et al., 2008; Clark et al., 2001), and is, as far as it contributes to the spectral properties detected by the satellite as in canopy gaps, partly included in our AGB model. Nevertheless, the result of Deramakot confirms that SFM in combination with RIL and longer cutting cycles helps to protect AGB by reducing forest degradation. When finally comparing the impacts of both logging methodologies, analyzed during different investigation periods, the inter-annual differences in precipitation and their impact on vegetation growth have to be kept in mind – even though their effects cannot be quantified exactly. In contrast to the investigation period of CL in Tangkulap, during which 2 years (1992, 1998) with low precipitation (<2000 mm) occurred, the investigation period covering RIL in Deramakot was not affected by any droughts (Sabah Forestry Department, 2005, 2006). Thus, the rate of AGB loss during CL might have been lower under no-drought conditions. In absolute terms, a conservative estimate of at least 10.5 t C/ha per year can be attributed to the different management systems, which will be of interest when implementing REDD+ that rewards the enhancement of carbon stocks. Such financial incentives can promote the establishment of SFM with RIL in other production forests of Borneo, helping to mitigate the further degradation of these large areas. And besides pure carbon sequestration, the application of SFM also

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Fig. 2. AGB map of study area. (a) AGB (above-ground biomass) values per pixel location (t C/ha). Yellow points symbolize the positions of the PSP (permanent sample plot). (b) Average AGB values (t C/ha) on compartment-level. Labels represent compartment numbers, labels in brackets show the average amount of AGB (t C/ha) per compartment. Compartments with haze impact are striped.

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has positive effects on other ecosystem services such as biodiversity conservation (Imai et al., 2009). In summary, this study showed that the application of Landsat imagery allows transparent and reproducible monitoring of AGB values on FMU-level in the carbon-rich humid tropics though uncertainties still exist, mainly related to atmospheric disturbances. The use of data from the Landsat family is furthermore promising, as it provides a huge archive from the past with an extension into the future by the Landsat Data Continuity Mission (LDCM). However, major obstacle of high resolution optical data is certainly the dependency of cloud- and haze-free scenes. Investigations toward the use of medium spatial but high temporal resolution satellite imagery to overcome the problems of cloud-coverage and to monitor larger areas at regular time intervals, both of which crucially important to avoid leakage, are in preparation. Acknowledgements We would like to thank all staff members of the Forest Research Centre, Sabah Forestry Department, Sabah, Malaysia, as well as of the Deramakot Forest Office. Special thanks go to P. Lagan as well as to A. Rawinder for their crucial support in the field. This work was partly supported by the Global Environment Research Funds (F-071 and D-1006) of the Ministry of the Environment, Japan, to Kanehiro Kitayama and by the JSPS Postdoctoral fellowship program for foreign researchers (P 09097) to Andreas Langner. Additionally, we would like to thank all reviewers of this and a former version of the manuscript for their valuable input. References Achard, F., Eva, H.D., Mayaux, P., Stibig, H.-J., Belward, A., 2004. Improved estimates of net carbon emissions from land cover change in the tropics for the 1990. Global Biogeochemical Cycles 18, doi:10.1029/2003GB002142. Adams, J.B., Sabol, D.E., Kapos, V., Almeida Filho, R., Roberts, D.A., Smith, M.O., Gillespie, A.R., 1995. Classification of multispectral images based on fractions of endmembers – application to land-cover change in the Brazilian Amazon. Remote Sensing of Environment 52, 137–154. Asner, G.P., 2001. Cloud cover in Landsat observations of the Brazilian Amazon. International Journal of Remote Sensing 22, 3855–3862. Asner, G.P., Knapp, D.E., Broadbent, E.N., Oliveira, P.J.C., Keller, M., Silva, J.N., 2005. Selective logging in the Brazilian Amazon. Science 310, 480–482. Asner, G.P., Powell, G.V.N., Mascaro, J., et al., 2010. High-resolution forest carbon stocks and emissions in the Amazon. PNAS 107, 16738–16742. Brown, S., 1997. Estimating biomass and biomass change of tropical forests: a primer. FAO Forestry Paper. 134, Rome. Brown, S., Gillespie, A.J.R., Lugo, A.E., 1991. Biomass of tropical forests of south and southeast Asia. Canadian Journal of Forestry Research 21, 111–117. Cannon, C.H., Peart, D.R., Leighton, M., 1998. Tree species diversity in commercially logged Bornean Rainforest. Science 28, 1366–1368. Chander, G., Markham, B.L., Helder, D.L., 2009. Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors. Remote Sensing of Environment 113, 893–903. Chave, J., Olivier, J., Bongers, F., et al., 2008. Above-ground biomass and productivity in a rain forest of eastern South America. Journal of Tropical Ecology 24, 355–366. Chavez, P.S., 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sensing of Environment 24, 459–479. Clark, D.A., Brown, S., Kicklighter, D.W., Chambers, J.Q., Thomlinson, J.R., Ni, J., Holland, E.A., 2001. Net primary production in tropical forests: an evaluation and synthesis of existing field data. Ecological Applications 11, 371–384. DeFries, R., Achard, F., Brown, S., Herold, M., Murdiyarso, D., Schlamadinger, B., de Souza Jr., C., 2007. Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environmental Science & Policy 10, 385–394. EcoSecurities, 2007. Policy Brief REDD Policy Scenarios and Carbon Markets. EcoSecurities, Oxford, UK. Engelhart, S., Keuck, V., Siegert, F., 2011. Aboveground biomass retrieval in tropical forests – the potential of combined X- and L-band SAR data use. Remote Sensing of Environment 115, 1260–1271.

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