Arief Wijaya

Reconciling forest conservation and logging in Indonesian Borneo.

Combining protected areas with natural forest timber concessions may sustain larger forest landscapes than is possible via protected areas alone. However, the role of timber concessions in maintaining natural forest remains poorly characterized. An estimated 57% (303,525 km2) of Kalimantans land area (532,100 km2) was covered by natural forest in 2000. About 14,212 km2 (4.7%) had been cleared by 2010. Forests in oil palm concessions had been reduced by 5,600 km2 (14.1%), while the figures for timber concessions are 1,336 km2 (1.5%), and for protected forests are 1,122 km2 (1.2%). These deforestation rates explain little about the relative performance of the different land use categories under equivalent conversion risks due to the confounding effects of location. An estimated 25% of lands allocated for timber harvesting in 2000 had their status changed to industrial plantation concessions in 2010. Based on a sample of 3,391 forest plots (1×1 km; 100 ha), and matching statistical analyses, 2000–2010 deforestation was on average 17.6 ha lower (95% C.I.: −22.3 ha–−12.9 ha) in timber concession plots than in oil palm concession plots. When location effects were accounted for, deforestation rates in timber concessions and protected areas were not significantly different (Mean difference: 0.35 ha; 95% C.I.: −0.002 ha–0.7 ha). Natural forest timber concessions in Kalimantan had similar ability as protected areas to maintain forest cover during 2000–2010, provided the former were not reclassified to industrial plantation concessions. Our study indicates the desirability of the Government of Indonesia designating its natural forest timber concessions as protected areas under the IUCN Protected Area Category VI to protect them from reclassification.

Fusion of ALOS Palsar and Landsat ETM data for land cover classification and biomass modeling using non-linear methods

This work demonstrates the utility of reduced resolution ALOS Palsar data for biomass mapping and land cover classification over the tropical forests of Indonesia. This study is important because we processed the ALOS Palsar mosaic, which is made freely available within K&C initiatives project and will be updated regularly. We first used 38 sample plots collected on the ground during dry season in September 2004, to develop a tree diameter (dbh) — biomass model. The HH, HV, HV/HH and HH-HV backscatters of ALOS Palsar data allowed the empirical estimation of forest above ground biomass (AGB). Each band of Palsar data was separately used to estimate the biomass, and we found HV band resulted in better correlation with the AGB compared to other SAR bands. Validation of the prediction results was carried out by comparing the biomass estimates with those predicted from an existing allometric equation. Optical data are sensitive to the physical properties of the reflectors whereas SAR data are more influenced by the geometric properties of the scatterers. Therefore, the second part of this study concerned the integration of mosaic SAR textures and ETM data for land cover classification. The classification was conducted using ETM data and variations of ETM, SAR bands, and SAR textures calculated using GLC Matrix. The image classifications were carried out using a Machine Learning based classifier, so-called Support Vector Machine (SVM), and a conventional Maximum Likelihood method. An ensemble of neural networks method using Kalman Filter and scaled conjugate gradient algorithm was applied. The classification accuracy was assessed using confusion matrices and Kappa Statistics. We show that the introduction of SAR textures significantly enhanced the classification accuracies. This study showed that the joint processing of SAR and multispectral data increased the accuracies of biomass estimation and landuse classifications. The efficiency of the method at medium spatial resolutions allows its application of global datasets.

Completing the picture: Importance of considering participatory mapping for REDD+ measurement, Reporting and verification (MRV)

Remote sensing has been widely used for mapping land cover and is considered key to monitoring changes in forest areas in the REDD+ Measurement, Reporting and Verification (MRV) system. But Remote Sensing as a desk study cannot capture the whole picture; it also requires ground checking. Therefore, complementing remote sensing analysis using participatory mapping can help provide information for an initial forest cover assessment, gain better understanding of how local land use might affect changes, and provide a way to engage local communities in REDD+. Our study looked at the potential of participatory mapping in providing complementary information for remotely sensed maps. The research sites were located in different ecological and socio-economic contexts in the provinces of Papua, West Kalimantan and Central Java, Indonesia. Twenty-one maps of land cover and land use were drawn with local community participation during focus group discussions in seven villages. These maps, covering a total of 270,000ha, were used to add information to maps developed using remote sensing, adding 39 land covers to the eight from our initial desk assessment. They also provided additional information on drivers of land use and land cover change, resource areas, territory claims and land status, which we were able to correlate to understand changes in forest cover. Incorporating participatory mapping in the REDD+ MRV protocol would help with initial remotely sensed land classifications, stratify an area for ground checks and measurement plots, and add other valuable social data not visible at the RS scale. Ultimately, it would provide a forum for local communities to discuss REDD+ activities and develop a better understanding of REDD+.

Consistent forest change maps 1981–2000 from the AVHRR time series: Case studies for South America and Indonesia

This study predicts global forest cover change for the 1980s and 1990s from AVHRR time series metrics in order to show how the series of consistent land cover maps for climate modeling produced by the ESA climate change initiative land cover project can be extended back in time. A Random Forest model was trained on global Landsat derived samples. While the deforestation was underestimated by the model, major global patterns were effectively reproduced. Compared to reference data for the Amazon satisfying accuracies (>0.8) were achieved, but results are less promising for Indonesia.

Workshop Report: Science solutions to policy challenges for evolving REDD+ measuring, reporting and verification requirements: report from a multistakeholder workshop

REDD+ measuring, reporting and verification – science solutions to policy challenges 10–12 June 2013, Zeist, The Netherlands A workshop entitled ‘REDD+ measuring, reporting and verification – science solutions to policy challenges’ was organized by the WWF Forest and Climate Initiative, WWF Netherlands and Wageningen University REDD@WUR network from 10th to 12th June 2013 in Zeist, The Netherlands. The purpose of this workshop was to assess the status and development of monitoring approaches in light of the evowlving REDD+ measuring, reporting and verification needs from different actors in the REDD+ measuring, reporting and verification process. Accordingly, the most important gaps were identified and led to the development of research priorities with focus on better linking local and national REDD+ efforts on five themes, namely: monitoring and measurement; reporting and verification; reference levels; measuring, reporting and verification of safeguards; and benefit sharing.

Assessment of deforestation drivers and national carbon emissions using remote sensing analysis

Reference levels (RLs) are key points that determine the starting point of carbon credits payment within REDD+ framework. This work approaches the RLs using remote sensing data and analysis. Objectives of this study are: 1) mapping of forest cover change and 2) predicting carbon emissions/removals as a basis of RLs assessment. Forest cover change was estimated using national land cover map from the Ministry of Forestry in Indonesia. Combined with social economic parameters, deforestation drivers were analyzed to predict current/future deforestation. We elaborate available biomass data for predicting carbon density of each forest type and carbon biomass dynamics between 2000-2009. Hence, Riau Province was selected to conduct more detailed study. Relationship between deforestation, forest degradation, and carbon emissions/removals in this province were discussed.