Forest change and biomass mapping using ALOS ...

 

XIV WORLD FORESTRY CONGRESS, Durban, South Africa, 7-11 September 2015

Forest change and biomass mapping using ALOS/PALSAR data in Palawan Island in support of developing a national forest monitoring and REDD+ MRV system Jose Don De Alban1, Angelica Kristina Monzon1, Rizza Karen Veridiano1, Roven Tumaneng1, Joanne Rae Pales1, and Edmund Leo Rico1 1

Fauna & Flora International - Philippines, [email protected], Tagaytay City, Cavite, Philippines

Abstract Among the most critical elements for the successful implementation of any mechanism on Reducing Emissions from Deforestation and Forest Degradation-Plus (REDD+) is a forest monitoring system that allows for credible measurement, reporting, and verification (MRV) of activities. Previous national forest mapping and inventories in the Philippines used optical remotely sensed data, which were ineffective for periodic monitoring due to cloud cover. In this study we utilised ALOS/PALSAR mosaic data to generate information on forest cover change and spatially explicit aboveground biomass estimates in support of REDD+ readiness initiatives in Victoria-Anepahan mountain range, Palawan Island. Field data was used for forest/non-forest classification. Forest inventory plots of variable sizes were used to investigate the relationship of radar backscatter and ground-estimated aboveground biomass (AGB). Overall classification accuracies at 87.28% and 91.60% were achieved for the 2007 and 2010 PALSAR data, respectively. Results showed that a total of 4,864.24ha of forests were converted to other land uses within three years. Correlation of radar backscatter to biomass was observed to be higher at 1.0ha plot sizes for the combination of radar channels consisting of HV polarisation, HH/HV ratio, and HV-contrast texture, but decreased for smaller plot sizes. Inclusion of contrast texture measure improved the relationship of radar data to biomass. Correlation of PALSAR data to ground-measured biomass was better using data from complete inventory of trees within plots compared to AGB estimates extrapolated from tree measurements within nested plots of specific DBH ranges. Our study demonstrated the capability of L-band synthetic aperture radar data for detecting and mapping forest change and for generating spatially explicit distribution of aboveground biomass, which can support the development of national forest monitoring and REDD+ MRV system. Keywords: ALOS/PALSAR, L-band SAR, ALOS K&C Initiative, REDD+, tropical rainforest, aboveground biomass, change detection, forest monitoring, forest inventory

Introduction, scope and main objectives Current negotiations under the United Nations Framework Convention on Climate Change (UNFCCC) provides an opportunity for developing countries to receive financial incentives for REDD+, a result-based mechanism that require countries to quantify their achievements in addressing deforestation and forest degradation through verified reporting on activities. Among the most critical elements for the successful REDD+ implementation is an effective national forest monitoring system (NFMS) that allows for credible MRV of REDD+ activities, which utilises an appropriate combination of remote sensing and forest inventory data (DeFries et al., 2007) to estimate anthropogenic forestrelated GHG emissions and removals, forest area, and forest carbon stock changes (Herold & Skutsch, 2011). Assessing and monitoring emissions and removals from REDD+ eligible activities require information on forest area change (activity data) and changes in forest carbon stocks (emission factor) (GOFC-GOLD, 2012). Traditional techniques using field-based forest inventories are the most accurate

 

  means to collect biomass data and assess forest carbon densities, but are however expensive, timeconsuming, and limited in geographic scope. Remotely sensed data linked to forest biophysical parameters can provide an effective method for forest biomass estimation from local to global scales that can complement traditional forest inventories. In the Philippines, a sub-national REDD+ demonstration area in southern Palawan Island serves as a short-term case study of a nested approach to REDD+ development in parallel with national-scale government efforts (Phelps et al., 2010). Among the key activities to assess the potential of REDD+ is the credible baseline mapping of forest area changes and estimation of forest carbon stocks using a combination of remote sensing and forest inventory approaches. Optical data (e.g., Landsat, SPOT) have been used in the past (c. since 1970s) for national and sub-national forest cover mapping and satellite-assisted forest inventory (Kummer, 1992). However, persistent cloud contamination limits the ability of optical data for consistent wall-to-wall or site-level forest/land cover mapping and change detection. Synthetic aperture radar (SAR) is a potential suitable data source, and this study assessed the utility of the Japan Aerospace Exploration Agency’s (JAXA) Advance Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) data for generating sub-national information on forest change and AGB estimates in support of REDD+ readiness. Study Area and Data Study Area. The Victoria-Anepahan mountain range (9°19’ N, 118°13’ E; 1,648 km2) in southern Palawan Island is characterised by mountainous terrain with 1,700m elevation at the highest point. Soils are based on ultramafic substrate (Baillie et al., 2000). Mean annual rainfall ranges within 1,6003,000mm with dry seasons from January to April, and varying intensity of rainfall for the rest of the year. Mean annual temperature ranges within 26-28°C with little seasonal variation. From 1990 to 2000, Victoria-Anepahan had a 0.04% forest loss rate and remaining forest cover in 2000 was estimated at 69.3% of total area (Pereira et al., 2006). This study focuses on the southern part of the mountain range, specifically 13 barangays (villages) of Narra and Quezon municipalities (Fig.1). ALOS/PALSAR Data. Two dual-polarised (HH, HV) ALOS/PALSAR images acquired in 2007 and 2010 were processed and analysed. Each 1x1-degree 25m resolution image (4500x4500 pixels) was pre-processed using JAXA’s mosaicking algorithm, which includes calibration, orthorectification, slope correction, and intensity tuning of neighbouring data strips (Shimada & Ohtaki, 2010). Field Data. Field data was collected within the REDD+ project area consisting of six barangays from two municipalities (Fig.1). Five 2km transects were established for faunal biodiversity assessments. Forest carbon inventory plots were established at every 250m station along each transect, which consisted of one 1.0ha square plot (100m x 100m) situated at the transect midpoint, and nine 0.25ha square plots (50m x 50m) at each transect station. One quadrant of the 1.0ha plot coincides with the 0.25ha square plot at that station. A total of 45 0.25ha square plots and five 1.0ha plots were established along five transects. In each 0.25ha square plot, trees ≥ 30cm diameter at breast height (DBH) was measured. Smaller nested plots were established within 0.25ha plots, particularly: (a) 20m x 20m plots (0.04ha) to measure trees ≥ 10-30cm DBH; and (b) 20m x 50m plots (0.10ha) to measure trees ≥ 10cm DBH, of which the plots are intended to contribute to the Philippines National Forest Inventory (NFI) plots. For 1.0ha plots, trees ≥ 5cm DBH were measured. Nested 0.25ha plots within 1.0ha plots also measured trees ≥ 5cm DBH. A total of 409 training and 119 validation data points, respectively, were used for supervised image classification of ALOS/PALSAR images, which consisted of field measurements collected from forest inventories in June-August 2013; ground-truth land cover data collected in May and August 2011; and supplemental forest/non-forest validation data points from Google Earth imagery. Methods ALOS/PALSAR Data Processing. Prior to classification and interpretation, a series of preprocessing steps were applied to the ALOS/PALSAR tiles including geocoding and re-projection, speckle filtering, conversion to normalised radar cross-section (sigma-naught, σ0; in dB) using: σ0 = 10 log10 [DN2] + CF (Eq.1), where CF is the calibration factor equal to -83 dB (Rosenqvist et al., 2007), and generating ratio images. All training and validation point data points were reclassified into forest

 

  and non-forest data points. Forest/non-forest classification was performed using the support vector machine (SVM) classifier. Separability of forest and non-forest classes was measured using the Jeffries-Matusita (J-M) distance (Marçal et al., 2005). An error matrix was used to assess overall classification accuracy and characterize errors of each category (Stehman, 1997; Foody, 2002). Change detection was analysed by producing a change matrix to show changes between forest and non-forest types at two time points (2007 and 2010) within the study area and the REDD+ project area. Activity data was generated involving spatially explicit tracking of land cover conversions (IPCC, 2006). Carbon Stock Assessment using Forest Inventory Plots. The AGB distribution was analysed and tested for normality at two groups of plot sizes according to DBH ranges measured: (1) 1.0ha and 0.25ha nested plots (DBH ≥ 5cm); and (2) 0.25ha, 0.10ha, and 0.04ha nested plots along transects. AGB was estimated at the individual tree-level using Brown’s allometric equation for moist forestlands (Brown, 1997): Y = exp {-2.289 + 2.649 * (ln D - 0.021) * ln D2} (Eq.2), where Y is AGB (in kg) and D is DBH (in cm). Total biomass content (AGB in kg) at the plot level was converted first to biomass in tons (t) prior to conversion to carbon content (in tons, t; Eq.3): C = AGB * 0.50 (Eq.3), where AGB is aboveground biomass (in t), C is carbon stock (in t), and 0.50 is the default conversion value of biomass to carbon content (GOFC-GOLD, 2012). These carbon stock values were then extrapolated to carbon stock per unit area (in tons per hectare, t/ha; Eq.4): EF = 10000 m2 / area of plot (Eq.4). Estimation of AGB from Radar Backscatter. To estimate AGB from radar backscatter, the rsquared correlation was determined for the following: single HV polarisation (Le Toan et al., 1992); HH/HV ratio (Avtar & Sawada, 2011); and contrast texture (HH, HV) (Sarker et al., 2012) using radar measurements and forest inventory plot data. Analysis was performed on two groups of plot sizes: (1) nested 1.0ha and 0.25ha plots (DBH ≥ 5cm); and (2) nested 0.25ha and 0.10ha plots along transects (DBH ≥ 10cm). The most correlated parameters were used in the regression model between AGB and radar backscatter, and in the model inversion to generate the biomass distribution map. Root mean square error (RMSE) was subsequently computed for each model. For the regression analysis, additional sample points were taken from non-forest areas since the carbon inventory plots were mainly established in forest areas and AGB tended towards high biomass ranges. Three sampling points based on 2011 ground-truth data were selected for each non-forest type, particularly in cropland, grassland, and settlement. For AGB values used: cropland and grassland were derived from a carbon budgets study in the Philippines (Lasco & Pulhin, 2009); for settlement, the prescribed default values by the Intergovernmental Panel on Climate Change (IPCC) (IPCC, 2006).

(1) (2) Fig. 1: Location of transects, forest carbon plots, and ground-truthed points within study area and REDD+ project area in Victoria-Anepahan mountain range, Palawan, Philippines. Fig. 2: Forest change detection (2007 to 2010) derived from ALOS/PALSAR K&C mosaic data of Victoria-Anepahan mountain range, Palawan, Philippines.

 

  Results A. Forest Cover Mapping and Change Detection

(1) Accuracy assessment. Overall classification accuracies at 87.28% and 91.60% were achieved using the SVM classifier for the 2007 and 2010 PALSAR data, respectively (Table.1), which satisfies the universally accepted minimum 85% overall accuracy (Congalton & Green, 2009). Error matrices show that forest classes derived from 2007 and 2010 PALSAR data are in agreement with ground-truth data at 96.77% and 96.83%, respectively. Non-forest classes have slightly lower agreement with ground-truth data at 76.79% and 85.71%, respectively (Table.2). A J-M distance of 1.928 was achieved indicating good separability between forests and non-forest classes. (2) Forest change. A net decrease in forest area was observed from 2007 to 2010, specifically amounting to 1,522.76ha (5.26%) and 859.43ha (7.04%) within the study area and the REDD+ project area, respectively (Table.3; Fig.2). Forests converted to non-forests amounted to 4,864.24ha (5.26% of total area) within the study area (Table.4). Forest conversion within the REDD+ project area amounted to 2,610.25ha (7.04% of total area). More than half of forests converted to non-forest within the study area (53.66%) similarly occurred within the REDD+ project area. Table 1: Classification accuracy assessments. Class Producer’s Accuracy User’s Accuracy Overall Accuracy Kappa Statistic

2007 Accuracy (%) Non-Forest Forest 76.79% 96.77% 96.77% 82.19% 87.28% 0.74

2010 Accuracy (%) Non-Forest Forest 85.71% 96.83% 96.00% 88.41% 91.60% 0.83

Table 2: Error matrices for 2007 and 2010 forest/non-forest map.   Ground Truth (%) Class (%) Non-Forest Forest Total

Error Matrix for 2007 Forest/Non-Forest Map Non-Forest Forest Total 76.79 3.23 38.14 23.21 96.77 61.86 100.00 100.00 100.00

Error Matrix for 2010 Forest/Non-Forest Map Non-Forest Forest Total 85.71 3.17 42.02 14.29 96.83 57.98 100.00 100.00 100.00

Table 3: Forest/non-forest cover and net forest change statistics within study area and REDD+ project area.  

Forest Non-Forest

Study Area (within 13 barangays) 2007 (ha) 2010 (ha) Net Change 66,092.97 64,570.21 (1,522.76) 26,340.59 27,863.36 1,522.76

REDD+ Project Area (within 6 barangays) 2007 (ha) 2010 (ha) Net Change 24,795.98 23,936.56 (859.43) 12,306.04 13,165.46 859.43

Table 4: Forest/non-forest cover change statistics within study area and REDD+ project area.   Study Area (within 13 barangays) Forest remaining as Forest Non-Forest remaining as Non-Forest Forest converted to Non-Forest Non-Forest converted to Forest

Area (ha) 61,228.74 22,999.12 4,864.24 3,341.47

% 66.24 24.88 5.26 3.62

REDD+ Project Area (within 6 barangays) Area (ha) 22,185.74 10,555.22 2,610.25 1,750.82

% 59.80 28.45 7.04 4.72

B. Carbon Stock Assessment using Forest Inventory Plots

For the 1.0ha plots, AGB distribution was plotted for 1.0ha and 0.25ha plot sizes, which involved trees with DBH at 5cm and above (Fig.3). These 0.25ha plots had neighbouring plot configuration (i.e., quadrats of 1.0ha plot). Standard deviation from 1.0ha plots was lower (71.13; n=5) compared to

 

  neighbouring 0.25ha plots (112.36; n=20). The AGB distribution of the larger plots (i.e., 1.0ha and neighbouring 0.25ha plots) was almost normally distributed (Fig.3). The range of AGB values were narrower for the 1.0ha (0 to 325.28 t/ha) compared to the 0.25ha (0 to 590.85 t/ha). This is consistent with the findings from Saatchi et al., (2011) that larger plots tend to be normally distributed with smaller deviations from the mean estimate. The results suggest that establishing larger plot sizes (1.0ha) are recommended than smaller plots (0.25ha) to estimate AGB values with statistical confidence that are within the acceptable range of limits for the computed estimates.

Fig. 1: Distribution of forest biomass sampled at various plot sizes: 0.25ha and 1.0ha nested plots (left); and 0.04ha, 0.10ha, and 0.25ha nested plots along transects (right).

Fig. 2: Aboveground carbon content from the nested carbon plots established within the municipalities of Narra and Quezon, Palawan.

For 0.25ha nested plots, AGB estimates revealed that most plots were at the lower range from 0 to 200 t/ha, regardless of plot size (Fig.3), which is consistent with Saatchi et al., (2011) indicating that the lower range of AGB estimates comprise the larger percentage of the total AGB estimates. Although a common trend was observed from the sample plots in terms of their frequency at certain range of AGB values, the level of deviation from the mean AGB estimates is different. The lowest deviation was observed from the 0.04ha sub-plots (51.02) followed by 0.25ha plots (88.12), and lastly from 0.10ha plots (112.78). The trend can be attributed to the range of DBH values measured in each of these plots. A narrower range of DBH measurements were taken from 0.04ha plots (≥ 10-30cm) and 0.25ha plots (≥ 30cm) compared to the wider range of DBH values taken from 0.10ha plot (≥ 10cm). Hence, DBH values and the resulting AGB estimate from 0.10ha plots have higher deviations from the mean. The inclusion of larger diameter trees in a relatively smaller sized plot (i.e., 0.10ha) can potentially introduce heterogeneity and variations (Saatchi et al., 2011) in AGB estimates. Also, for 0.25ha nested plots along transects, the range of values for the aboveground carbon content varied from 50.19 to 209.02 tC/ha. Carbon content estimated from 0.04ha nested plots comprised almost half of the total carbon content found at the plot level (Fig.4), which indicates that smaller diameter trees (10-30cm) contributed to almost half of the carbon content found in any plot within the areas assessed.  

  C. Estimation of AGB from Radar Backscatter

(1) Relationship of radar backscatter and AGB for 1.0ha and 0.25ha nested plot sizes. The optimum fit between logarithmic estimates of both ground-measured AGB to predicted AGB from radar backscatter was determined to be a linear relationship. R2 correlation between backscatter and AGB estimates improved as the plot size increased, specifically between 1.0ha and 0.25ha plots, consistent with Saatchi et al., (2011). As sensitivity of radar data to biomass improved with increasing plot size, the standard errors also decreased. Biomass sensitivity was higher at 1.0ha plots for the combination of HV, HH/HV, and HV-contrast texture (0.9387), and degraded with decrease in plot size for 0.25ha (0.6964). The HV-contrast texture showed better correlation compared to HH-contrast texture between 1.0ha and 0.25ha plots. The inclusion of HV-contrast texture measures improved the relationship of radar data to biomass at both plot sizes (i.e., 0.201 to 0.2841 for 1.0ha plots; and 0.105 to 0.329 for 0.25ha plots) compared to HV only, HH/HV only, or HV plus HH/HV channels. (2) Relationship of radar backscatter and AGB for 0.25ha and 0.10ha nested plot sizes along transects. R2 correlation between radar backscatter and ground-estimated AGB improved as the plot size increased between 0.25ha and 0.10ha plots. Standard errors were observed to decrease with increasing plot size, similar to the results obtained for 1.0ha plots. Radar backscatter sensitivity to biomass was higher at 0.25ha plot size using HV, HH/HV, and HV-contrast texture from HV polarisation (0.5847), and decreased at 0.10ha plot size (0.3517). Inclusion of HV-contrast texture measures improved the relationship of radar data to biomass at both plot sizes (i.e., 0.004 to 0.1342 for 0.25ha plots; and 0.017 to 0.0936 for 0.10 ha plots).

Fig. 3: Ground estimated vs. predicted aboveground biomass from radar backscatter measurements at different plot sizes: (a) 1.0 ha and 0.25 ha nested plots; (b) 0.25 and 0.10 ha nested plots along transects; (c) 0.25 ha plots along transect and within nested 1.0 ha plots. Toplevel graphs show predicted logarithmic aboveground biomass (a.1, b.1, c.1).

(3) Comparison of 0.25ha plots in terms of relationship between AGB and radar backscatter. The 0.25ha plots nested within 1.0ha plots were compared with 0.25ha plots along transects in terms of the relationship of AGB and backscatter. (Note: trees ≥ 5cm DBH were measured in 0.25ha plots nested within 1.0ha plots; trees ≥ 10cm DBH were measured in 0.25ha plots along transects.) R2 correlation for the 5cm DBH 0.25ha plots were higher compared to 10cm DBH 0.25ha plots. This improvement can be explained by the complete inventory (100% measurement) of trees ≥ 5cm DBH employed within the 0.25ha plots nested within 1.0ha plots. In comparison, the 0.25ha plots along transects measured trees ≥ 10cm DBH, which consisted of DBH ranges measured within specific nested plot

 

  sizes (i.e., trees ≥ 30cm DBH in nested 0.25ha plots, and trees ≥ 10-30cm DBH in nested 0.04ha plots), and extrapolated the computation of aboveground biomass per unit area by an expansion factor. The difference in R2 correlation was highest for HH/HV ratio (0.1409) and the combination of radar channels consisting of HV, HH/HV, and HV-contrast texture (0.1117). This suggests that AGB estimates from complete inventory of trees improves the correlation of radar backscatter to groundmeasured biomass compared to AGB estimates extrapolated from tree measurements within nested plots of specific DBH ranges. This also suggests that AGB contributed by trees with 5-10cm DBH could be significant to improve the correlation of radar backscatter and biomass (see Fig.4 where smaller diameter trees at plot level contributed to almost half of the carbon content found in any plot). (4) Radar estimation of biomass. AGB estimation from ALOS/PALSAR data was modelled using the combination of radar channels consisting of HV, HH/HV, and HV-contrast texture using the 1.0ha plots. The following equations were used in the regression model: B = 5.3127 - 2.7441HV 19.9341HH/HV - 1.73332HV-Contrast (Eq.5); and B = 5.75381 + 0.01334HV - 1.70818HH/HV 0.45344HV-Contrast (Eq.6), where B is aboveground biomass (t/ha) and the estimated regression coefficients were determined statistically using the radar measurements and forest inventory plot data at 1.0ha and 0.25ha nested plot sizes for Eq.5 and Eq.6, respectively.

(a) (b) Fig. 4: Distribution of aboveground biomass of the study area at southern portion of VictoriaAnepahan mountain range derived from ALOS/PALSAR 2010 data and (a) 1.0 ha plots, and (b) 0.25 ha plots nested within 1.0 ha plots.

The best correlation was observed for 1.0ha plots (0.93867) across all models between logarithmic estimates of both predicted AGB from radar backscatter to ground-measured AGB (Fig.5.a1), but showed rather low correlation in terms of estimates in t/ha (0.4641) (Fig.5.a2). The model inversion to generate biomass distribution does not result to well-distinguished biomass gradients and reflects low and high-end biomass values only (Fig.6a) attributed to the low number of plot samples for 1.0ha plots (n=5). Standard error achieved for 1.0ha plots (0.3185) was better compared to smaller plot sizes. On the other hand, the regression model for 0.25ha plots (nested within 1.0ha plots) showed the next best correlation (0.69639) across all models (Fig.5.b2), but similarly showed low correlation in terms of estimates in t/ha (0.2252) (Fig.5.b2). The biomass distribution generated was better compared to 1.0ha plots given a higher number of plot samples (n=20), which resulted to well-distinguished biomass gradients (Fig.6b). For 0.25ha plots, the R2 correlation for the 0.25ha plots nested within 1.0 ha plots was higher (0.69639) compared to the 0.25ha plots along transects (0.58471). Correlation in terms of estimates in t/ha was higher instead for 0.25ha plots along transects (0.26548) compared to the 0.25ha plots nested within 1.0ha plots (0.2252), attributed to a higher number of plot samples (n=45) for 0.25ha plots along transects. The RMSE was computed for each model. The smallest plot area gave the largest RMSE (157.77), while 1.0ha plots gave relatively low RMSE (105.29) compared to the smaller sized plots despite small number of plots (n=5). However, comparing RMSE of 1.0ha plots with RMSE of 0.25ha plots along transects (100.55), results show a difference of 4.75 t/ha only, which may be attributed to the limited number of 1.0ha plot samples utilised to run the regression model.

 

  Discussion The approach presented in the IPCC Good Practice Guidance on LULUCF quantifies estimates of emissions or removals from carbon stocks as the product of the extent of human activity (activity data) and emissions-removals ratio per unit of activity (emission factor) (IPCC, 2003). In this study, Approach 3 generation of spatially explicit activity data using dual-polarised ALOS/PALSAR data was demonstrated, particularly forest/non-forest classification at sub-national scale, which can be potentially scaled up to national-level wall-to-wall activity data assessment. The capability of ALOS/PALSAR data, however, needs to be tested further for discriminating and tracking more specific land cover types (e.g., IPCC six broad land cover types). Historical forest change processes based on available data need to be understood in order to establish a national reference scenario for emissions from deforestation and forest degradation (GOFCGOLD, 2012). A NFMS provides the foundation for MRV whether forest- or REDD+ initiatives have resulted into net positive impacts on forest carbon stocks (Gibbs & Herold, 2007). The capability of dual-polarised ALOS/PALSAR data was demonstrated in this study at sub-national scale for detecting and monitoring forest changes with acceptable accuracies, which showed the potential for scaling up at national scales. ALOS/PALSAR data can be a viable source of historical data of forest change (20072010) for developing REDD+ projects, which can also be complemented and continued through ALOS/PALSAR-2. Spatially explicit biomass distribution was also generated at sub-national scale using ALOS/PALSAR data by modelling radar backscatter from combined polarimetric radar and texture information to ground-measured biomass from 1.0ha forest inventory plots. The relationship of L-band radar backscatter to biomass was investigated using a pairwise comparison of large and small plot sizes, which showed that correlation of backscatter to biomass improved with increasing plot size. The optimal regression model was observed at 1.0ha plots, which was due to speckle noise reduction and spatial averaging of the radar data given the increase in pixel size, and the approximate normal distribution of forest biomass at 1.0ha plot size. However, the low plot sample size affected the estimation of biomass distribution, which indicates that higher plot samples are required to better generate spatially explicit biomass models. Ground-based forest measurements using national forest inventories can be utilised for forest monitoring systems that countries participating in REDD+ must establish in order to assess anthropogenic forest-related GHG emissions related to emission factors by sources and removals by sinks (Maniatis & Mollicone, 2010). Inventory data may be utilised as independent ground-truth data for classification of remotely-sensed data (IPCC, 2003), or combined with earth observation data to generate spatially-explicit biomass or carbon stock estimates (Köhl et al., 2011). Existing national forest inventories should be evaluated in terms of adaptability to national circumstances, and capability for monitoring REDD+ eligible activities and implementation (Maniatis & Mollicone, 2010). Given the 0.50ha rectangular plot size (20m x 250m) of the Philippines NFI (Saket et al., 2002), the results suggest that better correlation of AGB biomass to backscatter relationships and improved biomass estimation using L-band SAR data compared to the smaller 0.25ha plots, although not as good as the larger 1.0ha plots that were observed in this study. Also, complete inventory approach and inclusion of smaller DBH trees (≥ 5cm) employed within 0.25ha plots yielded better correlation compared to nested sampling approach of trees and extrapolation using the same plot size. This should also be taken into account in view of the potential for utilising NFI plot data with SAR data for biomass estimation since the existing NFI adopts a nested sampling approach for measuring trees. L-band SAR data offers the potential for generating spatially explicit estimates of biomass, thereby providing an opportunity to integrate remote sensing in national forest inventories. However, this would require large plot sizes to improve biomass retrieval. Results showed the degree of backscatterbiomass correlation and level of accuracies achieved for plot sizes smaller than the 0.50ha NFI plot size, which can be considered in weighing options. Evaluation of the existing Philippines NFI need to weigh these considerations (e.g., integration of remote sensing; decreasing plot size and increasing sampling intensity, among others) in view of assessing its capability for monitoring REDD+ eligible activities and implementation.

 

  Conclusions The study produced important results in support of implementing initial REDD+ readiness processes at the sub-national scale in the Philippines. The capability of dual-polarised ALOS/PALSAR data was demonstrated for detecting and monitoring forest change; for generating spatially explicit activity data information; and for generating spatially explicit distribution of aboveground biomass. These results can potentially be applied and scaled-up to provide inputs for national-level monitoring of forest cover and forest carbon stock changes and for generating activity data for GHG inventories. As REDD+ is fundamentally about addressing the drivers, causes, and agents of deforestation and degradation (Seifert-Granzin, 2011), the forest change analysis can be one of the bases for developing REDD+ projects intended to implement activities seeking to avoid deforestation. The drivers and causal factors of deforestation would need to be subsequently identified and understood. The study provided important considerations for assessing the existing Philippine NFI system in terms of its capability for monitoring REDD+ eligible activities and implementation, particularly on accuracies achieved from variable plot sizes and from the inventory approach adopted in view of estimating spatially explicit biomass distribution using L-band SAR data. Future work should focus on testing ALOS/PALSAR for detailed activity data assessment by discriminating and tracking of specific land cover types. Carbon inventory plots should also be established in non-forest areas to account for low biomass regions. Tree height measurements from forest inventory plots, which were not included in this study for developing the SAR biomass model, can be explored to further improve AGB prediction.

 

  Acknowledgements This work has been undertaken within the framework of the JAXA Kyoto & Carbon Initiative. ALOS/PALSAR data have been provided by JAXA EORC. Fauna & Flora International (FFI) conducted this study through a joint collaboration under the framework of the K&C Initiative with the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH and the Department of Geodetic Engineering of the University of the Philippines (UP-DGE). This study was implemented through the following projects: (1) Improving Forest Governance and Sustainable Upland Development through Climate Change Mitigation Financing Strategies in Southern Palawan (also know as the Advancing Victoria-Anepahan Mountain Range Communities and Ecosystems through REDD+ Project, or ADVANCE REDD+) funded by the European Union, and (2) Forest Conservation through Non-Timber Forest Products Sustainable Management and REDD+ funded by the International Union for the Conservation of Nature – Ecosystem Alliance (IUCN-EA), all implemented in partnership with NonTimber Forest Products – Task Force (NTFP-TF), Environmental Legal Assistance Center (ELAC), Institute for the Development of Educational and Ecological Alternatives (IDEAS), Nagkakaisang mga Tribu ng Palawan (NATRIPAL), and the Local Government of Quezon, Palawan. The authors wish to thank our colleagues at Fauna & Flora International Philippines, our project partners, and especially the local and indigenous Pala’wan and Tagbanua communities who have been integral in the conduct of the forest carbon and biodiversity assessments in Victoria-Anepahan mountain range. The authors are also grateful to Drs. Shimada and Rosenqvist for their continued leadership at JAXA and the K&C Initiative.

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