Jose Don De Alban

Robust quantitative estimates of land use and land cover change are necessary to develop policy solutions and interventions aimed towards sustainable land management. Here, we evaluated the combination of Landsat and L-band Synthetic Aperture Radar (SAR) data to estimate land use/cover change in the dynamic tropical landscape of Tanintharyi, southern Myanmar. We classified Landsat and L-band SAR data, specifically Japan Earth Resources Satellite (JERS-1) and Advanced Land Observing Satellite-2 Phased Array L-band Synthetic Aperture Radar-2 (ALOS-2/PALSAR-2), using Random Forests classifier to map and quantify land use/cover change transitions between 1995 and 2015 in the Tanintharyi Region. We compared the classification accuracies of single versus combined sensor data, and assessed contributions of optical and radar layers to classification accuracy. Combined Landsat and L-band SAR data produced the best overall classification accuracies (92.96% to 93.83%), outperforming individual sensor data (91.20% to 91.93% for Landsat-only; 56.01% to 71.43% for SAR-only). Radar layers, particularly SAR-derived textures, were influential predictors for land cover classification, together with optical layers. Landscape change was extensive (16,490 km2; 39% of total area), as well as total forest conversion into agricultural plantations (3214 km2). Gross forest loss (5133 km2) in 1995 was largely from conversion to shrubs/orchards and tree (oil palm, rubber) plantations, and gross gains in oil palm (5471 km2) and rubber (4025 km2) plantations by 2015 were mainly from conversion of shrubs/orchards and forests. Analysis of combined Landsat and L-band SAR data provides an improved understanding of the associated drivers of agricultural plantation expansion and the dynamics of land use/cover change in tropical forest landscapes.

This study maps distribution and spatial congruence between Above-Ground Biomass (AGB) and species richness of IUCN listed conservation-dependent and endemic avian fauna in Palawan, Philippines. Grey Level Co-Occurrence Texture Matrices (GLCMs) extracted from Landsat and ALOS-PALSAR were used in conjunction with local field data to model and map local-scale field AGB using the Random Forest algorithm (r = 0.92 and RMSE = 31.33 Mg·ha-1). A support vector regression (SVR) model was used to identify the factors influencing variation in avian species richness at a 1km scale. AGB is one of the most important determinants of avian species richness for the study area. Topographic factors and anthropogenic factors such as distance from the roads were also found to strongly influence avian species richness. Hotspots of high AGB and high species richness concentration were mapped using hotspot analysis and the overlaps between areas of high AGB and avian species richness was calculated. Results show that the overlaps between areas of high AGB with high IUCN red listed avian species richness and endemic avian species richness were fairly limited at 13% and 8% at the 1-km scale. The overlap between 1) low AGB and low IUCN richness, and 2) low AGB and low endemic avian species richness was higher at 36% and 12% respectively. The enhanced capacity to spatially map the correlation between AGB and avian species richness distribution will further assist the conservation and protection of forest areas and threatened avian species.

Political and economic transitions have had substantial impacts on forest conservation. Where transitions are underway or anticipated, historical precedent and methods for systematically assessing future trends should be used to anticipate likely threats to forest conservation and design appropriate and prescient policy measures to counteract them. Myanmar is transitioning from an authoritarian, centralized state with a highly regulated economy to a more decentralized and economically liberal democracy and is working to end a long‐running civil war. With these transitions in mind, we used a horizon‐scanning approach to assess the 40 emerging issues most affecting Myanmar's forests, including internal conflict, land‐tenure insecurity, large‐scale agricultural development, demise of state timber enterprises, shortfalls in government revenue and capacity, and opening of new deforestation frontiers with new roads, mines, and hydroelectric dams. Averting these threats will require, for example, overhauling governance models, building capacity, improving infrastructure‐ and energy‐project planning, and reforming land‐tenure and environmental‐protection laws. Although challenges to conservation in Myanmar are daunting, the political transition offers an opportunity for conservationists and researchers to help shape a future that enhances Myanmar's social, economic, and environmental potential while learning and applying lessons from other countries. Our approach and results are relevant to other countries undergoing similar transitions.

Political transitions often trigger substantial environmental changes. In particular, deforestation can result from the complex interplay among the components of a system—actors, institutions, and existing policies—adapting to new opportunities. A dynamic conceptual map of system components is particularly useful for systems in which multiple actors, each with different worldviews and motivations, may be simultaneously trying to alter different facets of the system, unaware of the impacts on other components. In Myanmar, a global biodiversity hotspot with the largest forest area in mainland Southeast Asia, ongoing political and economic reforms are likely to change the dynamics of deforestation drivers. A fundamental conceptual map of these dynamics is therefore a prerequisite for interventions to reduce deforestation. We used a system‐dynamics approach and causal‐network analysis to determine the proximate causes and underlying drivers of forest loss and degradation in Myanmar from 1995 to 2016 and to articulate the linkages among them. Proximate causes included infrastructure development, timber extraction, and agricultural expansion. These were stimulated primarily by formal agricultural, logging, mining, and hydropower concessions and economic investment and social issues relating to civil war and land tenure. Reform of land laws, the link between natural resource extraction and civil war, and the allocation of agricultural concessions will influence the extent of future forest loss and degradation in Myanmar. The causal‐network analysis identified priority areas for policy interventions, for example, creating a public registry of land‐concession holders to deter corruption in concession allocation. We recommend application of this analytical approach to other countries, particularly those undergoing political transition, to inform policy interventions to reduce forest loss and degradation.

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 PAL...

This report was produced by UNEP World Conservation Monitoring Centre, as part of the REDD-PAC project under the International Climate Initiative (IKI). The Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety supports this initiative on the basis of a decision adopted by the German Bundestag (parliament). The working session that developed the work within this report was also supported by GIZ under the project National REDD+ System Philippines, implemented by the Department for Environment and Natural Resources (DENR) and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH with funding by the German Federal Ministry for the Environment, Nature Conservation, Building and Nuclear Safety (BMUB) under its International Climate Initiative.

Imprint This publication is by the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH through the Climate-relevant Modernization of the National Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Measures Project in the Philippines, funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) under its International Climate Initiative. The BMU supports this Initiative based on a decision of the German Parliament. For more information, see http://www.international-climate-initiative.com. As a federally owned enterprise, GIZ supports the German Government in achieving its objectives in the field of international cooperation for sustainable development. This study was undertaken by Fauna & Flora International commissioned by GIZ, with co-financing by the United Nations Development Programme (UNDP)-Global Environmental Facility (GEF)-DENR Biodiversity Management Bureau (BMB) New Conservation Areas in the Philippines Project (NewCAPP) and the Foundation for the Philippine Environment (FPE). Statements from named contributors do not necessarily reflect the views of the publisher.

Dual-polarised ALOS/PALSAR mosaic data were utilised in this study to provide information on forest cover change, activity data, and spatially explicit aboveground biomass estimates in support of REDD+ readiness processes implemented in Victoria-Anepahan mountain range, Palawan, Philippines. Ground-truthed 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. Overall classification accuracies at 87.28% and 91.60% were achieved using the support vector machine classifier for the 2007 and 2010 PALSAR data, respectively. Results showed that a total of 4,864.24 ha of forests were converted to other land uses within the study area in a span of four years. R-squared correlation of radar backscatter to biomass was observed to be higher at 1.0 ha plot sizes for the combination of radar channels consisting of HV polarisation, HH/HV ratio, and contrast texture derived from HV polarisation, which decreased for smaller plot sizes. Inclusion of contrast texture measure improved the relationship of radar data to biomass. Correlation of L-band SAR data to ground-measured biomass was observed to be better from complete inventory of trees within plots compared to AGB estimates extrapolated from tree measurements within nested plots of specific DBH ranges. This study demonstrated the capability of ALOS/PALSAR mosaics for detecting and monitoring forest change; for generating activity data information, particularly between forest and non-forest cover types; and for generating spatially explicit distribution of aboveground biomass. Index Terms—ALOS/PALSAR, K&C Initiative, REDD+ tropical rainforest, aboveground biomass, forest monitoring, forest inventory.

This study aims to provide a comprehensive understanding of the capabilities and limitations of the two polarization modes of ALOS/PALSAR. Polarimetry (PLR) and Fine Beam Dual (FBD) Level 1.1 image datasets were interpreted using a series of radar image processes with open source radar tools. Image processes include coherency matrix extraction, image geocoding, speckle filtering, Entropy-Anisotropy-alpha (H-A-alpha) image decomposition and image classification. Field data from 2010 land cover assessment in Southern Leyte were used in training and validation for the Wishart and the Support Vector Machine (SVM) supervised classification, as well as in accuracy assessments. The Intergovernmental Panel on Climate Change (IPCC) land cover definitions were used as basis for the class categories defined in the image interpretation, with an additional class category defined for Palms. The results from the PLR image using the Wishart classifier generated 75% overall accuracy and the SVM results gave 85% overall accuracy. The resulting classified image is directly comparable to the scattering mechanisms observed in the landscape. The FBD dataset was only limited to the Wishart classification and analysis, which yielded 75% overall accuracy using the IPCC categories; and 70% accuracy upon the addition of the Palms class. Extraction of more detailed classes highly depends on the field data available for training and validation. SVM classification for the PLR dataset yields comparable and realistic results even with limited training samples.

Fauna & Flora International (FFI) was given the task to support the Forest Management Bureau (FMB) of the Department of Environment and Natural Resources (DENR) in delivering specific activities under Outcome 3 of the UN-REDD Philippines Programme, which seeks to enhance capacities of stakeholders on the Monitoring and Measurement, Reporting, and Verification (M & MRV) component of the Reducing Emissions from Deforestation and Forest Degradation-Plus (REDD+). This report is a consolidation of the feasibility studies on monitoring REDD+ activities using remote sensing technology and earth observation data, which is conducted under the UN-REDD Programme Philippines Initial Readiness Support, particularly for Outcome 3: Enhanced Capacities for M & MRV, particularly for Activity 3.1.3, “Feasibility Studies for Monitoring REDD+ Activities using Remote Sensing and Other Options.” The following studies were conducted under Activity 3.1.3: (1) Assessment of National and Leyte Island Forest Resources Assessment (FRA) Data in view of Forest Carbon and Remote Sensing MRV Requirements; (2) Comparative Study on the Performance of Various Earth Observation Products; (3) Comparative Study on the Performance of ALOS/PALSAR Quad- and Dual-Polarization (Single Scene) Radar Images; and (4) Study on Degradation and Canopy Density Estimates from Optical and Synthetic Aperture Radar (SAR) Images

Satellite data obtained through remote sensing has been a common and widespread source of information for forest monitoring. This is, in part, due to its ability to cover large tracts of forest lands, and its accessibility, availability, and cost. However, its spatial resolution can be coarse and may not be adequate to meet certain level of accuracy and uncertainty. Airborne-generated data sets have higher spatial resolutions and better accuracy, but can be very costly to obtain. To monitor large forest areas for various purposes, including forest carbon assessment under the Reduced Emission from Deforestation and forest Degradation (REDD+) program of the UN Framework Convention for Climate Change (UNFCCC), it is imperative to develop tools and methodologies that are cost efficient and meets acceptable level of accuracy. This paper explores different tools and methodologies that combines data obtained from medium resolution imagery obtained from remote sensing and high resolution data sets obtained from airborne sensors such as LiDAR and radar. These tools will be applied and tested using data obtained from pilot REDD+ areas.

This publication is by the Deutsche Gesellschaft für Internationale Zusammenarbeit through the Climate- relevant Modernization of Forest Policy and Piloting of Reducing Emissions from Deforestation and Forest Degradation (REDD) Project in the Philippines, funded by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) under its International Climate Initiative. The BMU supports this Initiative based on a decision of the German Parliament. For more information see http://www.international-climate-initiative.com. This study has been implemented by Fauna & Flora International in collaboration with the UNDP/GEF- funded NewCAPP, which cofinanced the field work for the biodiversity survey in Mt. Nacolod.

Important Bird Areas (IBA) in the Philippines were identified using a set of international criteria to determine globally important priority areas for biodiversity conservation. The IBA boundaries were delineated using data on trigger bird species distribution coupled with available land cover data. Present conservation work has been guided using the IBAs as a directory of key conservation sites. But how relevant and accurate are the IBA boundaries, considering that less than 50% of Philippine IBAs are completely known ornithologically, and that the original IBA delineation relied on historical records of trigger bird species? The mapping of IBAs illustrated that the original IBA delineation was not well related to forest extents and that 46% of the country's forest habitats lay beyond IBA boundaries. Forests remained extensive within large Endemic Bird Areas (EBA) but smaller EBAs like Mindoro and Negros Panay had 8% and 5% forests left, respectively. Mining areas were heavily in conflict with IBAs wherein 21% of forests in IBAs were similarly under mining applications. The implications of the gaps in existing IBA boundaries were discussed in light of aggressive promotion of mining and how conservation work and policy agenda in the country could be affected. Challenges and threats in conserving the IBAs at the local and national levels were identified by examining overlaps with mining claims and conflicting tenurial instruments. The revision of original IBA boundaries should be implemented to conform better to forest boundaries, which may form the bases of protected area boundaries. Parameters on delineating IBAs should be developed using updated forest cover information, which can further improve the results of this IBA analysis. The IBA concept should also be applied to Key Biodiversity Areas with the inclusion of data on non-avian taxonomic groups.

Mapping of forested and community areas using satellite imageries were undertaken to support the management of the Samar Island Natural Park which covers approximately 333,000 hectares stretching over the northern karst formation up to the southern central mountain ranges of the 1.3 million-hectare Samar Island in the Philippines. Five single band ERS-2 scenes with single polarization and three Landsat 7 ETM+ imageries were acquired over the whole island of Samar, Philippines in an attempt to identify and delineate the remaining forest areas. The activity was done to support the delineation and establishment of the Samar Island Natural Park, having an area of approximately 0.33 million hectares stretching over the northern karst formation up to the southern central mountain ranges of the 1.3 million-hectare land area of Samar Island.

Mining is being considered by economic planners as a key industry for economic development in the Philippines today. But how sustainable is mining in terms of its impact on our natural resource base? What do the policy document reveal with regards to environmental considerations in mining policies? What do past practices show us? These are the main questions that will be answered in this paper through an analysis of existing mining claims that show overlaps with forested areas and Important Biodiversity Areas (IBAs). The results present serious implications since a revitalized mining industry also presents serious implications to our remaining watersheds. They also call for a mining policy framework that truly appreciates our immense wealth of biodiversity and recognizes its role in sustainable development.

Coastal habitats such as coral reefs, mangroves, seagrasses, and algae are important to fisheries management since these serve as indicators of fish stock, the health status of the coastal environment, and others. The suitability of Landsat 7 ETM+ data for mapping these coastal habitats was evaluated by the study. The study areas are two bays located in the provinc e of Palawan in the Philippines, namely Puerto Princesa Bay and Honda Bay. Although adjacent to one another, the two bays actually represent different environmental conditions. Typical algorithms for the correction for the effects of water depth, radiome try, the atmosphere, and scene orientation were applied to the image. The resulting satellite image map was evaluated for visual interpretability of the coastal habitats under consideration. The appearance of these habitats, as well as that of some coast al landforms, in selected combinations of Landsat 7 ETM+ bands, is shown in the paper. Limitations in the level of habitat classification that are possible with Landsat 7 ETM+ data are also enumerated. The preprocessed satellite image was also subjected to digital classification techniques using field and other data gathered during site visits conducted in November 2000 and April 2001. Results of the classification are also discussed in the paper.