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Monitoring Forest Cover Change In the Protected Areas of APP’s Suppliers Concession Areas Table of Contents Introduction ......................................................................................................................................................... 2 Challenges............................................................................................................................................................ 3 The Satellite : RADARSAT-2 ................................................................................................................................. 4 Reliable Forest Change Observations From Space .............................................................................................. 4 How Does RADARSAT-2 Detect Changes in Forest Cover ................................................................................... 7 Processing the Data ............................................................................................................................................. 9 Verification Process ........................................................................................................................................... 11 ATTACHMENTS .................................................................................................................................................. 13 References ......................................................................................................................................................... 17 External Links ..................................................................................................................................................... 17 1 Introduction In 5 February 2013, Asia Pulp & Paper (APP) Sinar Mas launched its Forest Conservation Policy (FCP) which committed APP to eliminating deforestation from its fiber supply chain. A key element of its FCP was a commitment to protect set-aside natural forest areas located in the concessions of APP’s pulpwood suppliers that are identified as either High Carbon Stock (HCS), High Conservation Value (HCV) or natural peatland forests. Hence, there was a need for APP to demonstrate that it is maintaining forest areas that it has committed to protect and report the status to its stakeholders. To do so, APP needed a ‘near-real-time’ ‘early warning system’ that would allow APP’s pulpwood suppliers to take timely action, on detected forest disturbances, to minimise and/or mitigate negative impacts and implement corrective actions. To identify suitable technology to use, APP developed several requirements that turned out to be both quite demanding, and presented many technical challenges to the development a cost effective near-real-time forest disturbance early-warning system. 2 Challenges Due to the spatial scale of the project and the cost and reliability of traditional field observations, APP realized that a solution based on ‘remote sensing’ technologies was needed and the only practicable technologies were low earth orbit (LEO) imaging satellites. However, this approach provided its own unique challenges. Traditionally, satellite imaging companies and/or resellers provide ‘images’ to clients and the buyer is responsible to perform any image analysis, requiring specialised in-house resources – this methodology only provides a historic temporal view. Moreover, these images are usually expensive for low resolution and very expensive for high resolution. On the other hand, there are a number of sources of free satellite data (optical and RADAR); however, this data is generally of low spatial resolution and not suited for active forest monitoring. Furthermore, this data requires extensive post processing to ‘interpret’ what is being seen and usually results in low confidence levels; moreover, the costs to develop the algorithms needed to process this data defeats the proposition that it is ‘free’. Conversely, commercial satellite-based optical images with high spatial resolution can be purchased; however, this approach also has its drawbacks – clouds, atmospheric aberrations and availability of sunlight for imaging. Cloud cover is especially problematic in Indonesia. To overcome this, a vendor will typically acquire a partial image of area of interest (AOI) that is mostly cloud free and then to complete the image, the provider will use many subsequent images, near cloud free, until sufficient images have been acquired to ‘stitch’ together to complete image of the AOI. This process can take some months to complete depending on the satellite visit frequency over the AOI, sunlight and cloud cover. Optical imagery is well suited for ‘historic’ analysis – a look at the past. In other words, they tell us ‘what happened’ but not ‘what is happening’. Considering these challenges, APP developed the following essential technological and administrative requirements for its monitoring system: 1. the monitoring must be independent and recognised in the field; 2. must provide ‘near-real-time’ disturbances detection alerts- ‘early warning’; 3. must provide alerts continuously, covering all AOIs, on a pre-determined schedule; 4. must be able to monitor AOIs irrespective of cloud cover or darkness; 5. the system must be based on ‘highly automated’ remote sensing technologies and workflows (data acquisition, data analysis, data processing, and data delivery to APP); 6. have sufficient resolution to detect ‘subtle’ forest disturbances; 7. spatial alert information must be transmitted directly to APP’s pulpwood suppliers through APP’s enterprise servers; 8. inherent high ‘confidence rate’ for the alerts, > 90%; 9. provide a reporting system, internal and external; 10. system is scalable; and 11. system is economical (based on a ‘sliding scale’; as the spatial area increases, the cost per hectare decreases). Over a period of three (3) years, APP looked at numerous providers and technologies to fill this need. Eventually, APP narrowed the field to one provider, MDA. APP officially engaged MDA in August of 2016, on a 6-months pilot programme in Jambi Province on the island of Sumatra to develop and prove its technology. For this programme, MDA utilised Canada's RADARSAT-2 satellite, owned and operated by MDA 3 APP and MDA entered into a 3-years monitoring programme, May 2017, following the successful pilot of the technology. The full monitoring programme comprises APP’s 38 pulpwood suppliers on the islands of Borneo and Sumatra. The Satellite : RADARSAT-2 Canada’s RADARSAT-2 Satellite (add link to MDA video) Reliable Forest Change Observations From Space RADARSAT-2 is an earth observation satellite with a C-Band Synthetic Aperture Radar (SAR) with the highest imaging capacity of any SAR earth-observation missions, and a gigantic footprint that allows for frequent 4 coverage of large areas. It combines high resolution (5m/pixel) with wide area coverage (125 km imaging swaths), and has 24-days repeat cycle orbits with a wide variety of applications, including to monitor forests, agriculture, floods, coastlines, pollution, security, defence and offshore oil and gas operations. As presented earlier, optical sensors are of limited use for ‘early warning’ system since ‘real-world’ imaging frequency is unpredictable. Therefore, satellite-based RADAR (RAdio Detection And Ranging) sensors are an alternative solution that provides more reliable imaging opportunities as a result of its ability to acquire images day and night whilst also through clouds. Synthetic Aperture RADAR (SAR) Subtle Forest Change Detection is a new technology that particularly benefits from the new RADARSAT-2 capabilities (high-resolution / wide- swath). This means that subtle changes in tree removal, including selective logging activity, can be detected. RADARSAT-2 is efficient for large area application considering its spatial and temporal repeat coverage. High- resolution RADAR imaging technology provides predictable observation techniques at regular intervals in order to detect changes that occurred between image acquisition intervals. RADARSAT-2 Key Applications and Imaging Modes 5 One of the most significant strengths of space-borne SAR is that images can be routinely and reliably acquired using the same geometries regardless of weather conditions and time of the day. In monitoring forest cover, this enables a highly detailed analysis of change within a forest environment. Since 2011, more than 1.33 billion km2 of imaging has been acquired and archived. Indonesia is a particularly active area of image collection. RADARSAT-2 has acquired thousands of scenes over Indonesia since 2011. The RADARSAT-2 Satellite employs a powerful space-based radar sensor to provide advanced forest monitoring features, including: 1. the ability to acquire single images with an image width of 125 km wide in swaths that are hundreds of kilometres long; 2. a 5-metres resolution capability provides the ability to detect narrow regions of new forest cuts within these large swaths; and 3. a 24-day revisit period, so that the entirety of each swath can be imaged 15 to16 times per year. By comparison, publicly available optical sensors such as LANDSAT-8 and Sentinel-2 have spatial resolutions of 30m and 10m respectively. RADARSAT-2 is unique among radar sensors with its ability to image with both high resolution and wide-swath coverage. Each scene used for APP’s alert system covers 125 km x 125 km, 15,625 km2. 6 Synthetic Aperture RADAR (SAR) Subtle Change Detection is a new technology that particularly benefits from the
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