Monitoring Forest Cover Change In the Protected Areas of APP’s Suppliers Concession Areas

Table of Contents Introduction ...... 2 Challenges...... 3 The Satellite : -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

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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.

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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 ‘’ 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 's RADARSAT-2 satellite, owned and operated by MDA

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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

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One of the 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.

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Synthetic Aperture RADAR (SAR) Subtle 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.

This process involves collecting images from multiple dates over exactly the same area on the ground – ‘image stacking’. Image stacks accumulate over time, where regular intervals of time have elapsed between each acquired image in a stack. By comparing newly collected images to earlier images in a stack, areas of change in the forest become evident. Polygons that outline regions where forest change has occurred, within a specific time interval, are used as both a geographic and a temporal reference for the change. As stated earlier, subtle changes consisting of a small number of trees being removed within a fixed area can be detected and reported.

How Does RADARSAT-2 Detect Changes in Forest Cover Using RADARSAT-2’s C-Band SAR, reflections come from the top of forest canopy. Changes in canopy, such as holes created by selective logging of even a small number of trees can be readily detected because these changes result in significant changes of reflectivity, such as new shadows, even in small areas.

By comparing a new radar image to a previous radar image, new gaps in the trees can be detected by observing a new ‘shadow’ at the near side of the gap, and by observing a change to the path travelled by the collection of radar waves at the far side of the gap; as illustrated in the following diagram.

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Picture cap: exactly repeated image geometry of SAR images collected before and after canopy change enables precise detection change. Surface changes result in changes in reflection, causing increases on the far side (3) and new shadow that decreases the reflection of the near side (6).

The combination of the new shadow and the change in path travelled by the radar waves is used by image processing algorithms to detect and locate (geo-reference) the change event. On the right, trees 3, 4 and 5 have been removed. The radar signal sent from the satellite is now returned from points closer to, or on, the ground inside the gap. Point 6 shows a new shadow that has appeared in the radar image. These effects cause clear and visible changes in the radar imagery, especially when the images are processed to highlight the difference between multiple image dates.

Forest alert detection requires a spatial correlation of the 3-dimensional effects that are apparent from forest changes. At one side of a forest change, a new radar shadow (point 6) is apparent, while at the other side of the forest change, a new bright area called ‘foreshortening’ is apparent.

The new radar shadow needs to be connected to the new ‘foreshortening’ area over a small distance (points 3/4/5). This connection is done using image windowing operations; thus, the smallest detectable area is a function of the image windowing that is used to connect the 3-dimensional effects of the forest change.

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Processing the Data MDA collects radar images from multiple dates over exactly the same area on the ground – ‘image stacking’. Image stacks accumulate over time, where regular intervals of time have elapsed between each acquired image in a stack. Images are precisely co-registered and all images in a stack averaged to reduce noise and improve the accuracy of image interpretation. The texture (roughness) of the imagery is extracted and automated temporal processing is performed on each pixel in the stack to extract change information with high levels of accuracy.

Forest alert polygons are determined from these ‘gridded windows’, produced through the image windowing operation, with each window being 0.1 hectares in size. To increase the confidence that a detected polygon contains a strong-enough alert to be verified, polygons are typically created no smaller than 5 gridded windows, or 0.5 hectares in size. Depending on the strength of the probable forest change inside the alert, the potential tree loss within any given forest alert may range from sparse (1 tree) to complete (the full window, 0.1 - 0.5 ha).

When a change event is detected in a newly acquired image, it can be compared to multiple images that were acquired at different dates prior to the change event. If the new change event is confirmed in comparison to multiple images from earlier dates, then it indicates that a standing forest was damaged within the time period of the most recent image acquisition.

Polygons that outline regions where forest change has occurred, within a specific time interval, are used as both a geographic and a temporal reference for the change. As stated earlier, subtle changes consisting of a small number of trees being removed within a fixed area can be detected and reported. The data provided to

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APP are ‘polygons’ in ESRI shapefile format, detailing the exact location, size and timeframe of the changes. This polygon data is sent directly to APP’s database for further processing and verification.

Picture cap : Thanks to its high ground resolution, the data collected from RADARSAT-2 is able to capture not only big area clear cut (area 1) but also the smaller changes due to selective logging (area 2).

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Verification Process The process of satellite data acquisition to delivery of alerts to APP’s pulpwood suppliers is ‘seamless’. That is, the polygons in the spatial form of ESRI shapefiles are delivered directly to the APP’s forestry enterprise server for further verification. This is an automated process that provides seamless distribution of geo- references alert data. MDA sends data, every 24 days, for each of the 24 ‘image frames’ (scenes) covering the concessions of APP’s 38 pulpwood suppliers; however, the data acquisition for all scenes is not concurrent. Image acquisition for one complete set of scenes, comprising the 38 pulpwood concessions, requires 13 days, within a 24-days period, and thereafter, the acquisition cycle is repeated. Therefore, APP would be receiving a stream of alerts almost every day for some areas of interest within the 38 pulpwood concessions.

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WORKFLOW

When the polygons are received in APP’s server, the following processes take place:

1. When the alert comes in, the system will identify the location of the polygon alert, including the FMU name, the district, the spatial plan and the HCV & HCS status. 2. Once the identification is done, the system will crosscheck the polygon’s location with the existing land dispute map. If the polygon is located in an area already identified as land dispute, the alert will be shifted to the Social & Security Department who handles land dispute resolution. 3. If the polygon is located outside the areas identified as land dispute, the system will further filter the polygon against the existing forest cover map. If the polygon is located in areas with no forest cover, the alert will be shifted to the Conservation team who is in charge for forest restoration activities. 4. If the polygon is located in area with forest cover, the team will do verification of the alert. The verification is done firstly by desktop verification, using from Planet Lab, which APP has engaged in the beginning of 2020, to confirm whether or not change in forest cover has happened. If the polygon cannot be confirmed using desktop verification, the polygon details will be sent to the district team to conduct field verification.

The results of the verification are either confirmed as forest cover change or as a ‘false alert’. Furthermore, the team identifies the cause(s) of forest disturbance and notifies the appropriate personnel for follow up.

APP’s forestry database is continually updated with verification findings which are reflected on APP’s publicly-accessible Forest Monitoring Dashboard, in ‘near real time’.

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ATTACHMENTS Examples of RADARSAT-2 detected forest disturbance during a 24-day image acquisition cycle with corresponding DigitalGlobe optical images (all images at the same geo-coordinates)

Attachment 1: RADARSAT-2 - Acquisition Date 2017-10-21 (intact forest area) Geo-coordinates: 0° 05' 21.08" N 110° 49' 53.98" E | polygon = 0.9 hectares

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Attachment 2: RADARSAT-2 - Acquisition Date 2017-11-14 (forest disturbance detected) Geo-coordinates: 0° 05' 21.08" N 110° 49' 53.98" E | polygon = 0.9 hectares

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Attachment 3: DigitalGlobe WorldView02 - Acquisition Date 2016-06-24 (intact forest area) Geo-coordinates: 0° 05' 21.08" N 110° 49' 53.98" E | polygon = 0.9 hectares

Attachment 4: DigitalGlobe WorldView02 - Acquisition Date 2016-06-24 (intact forest area) Geo-coordinates: 0° 05' 21.08" N 110° 49' 53.98" E | polygon = 0.9 hectares

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Attachment 5: DigitalGlobe GeoEye1 - Acquisition Date 2018-07-11 (forest disturbance detected) Geo-coordinates: 0° 05' 21.08" N 110° 49' 53.98" E | polygon = 0.9 hectares

Attachment 6: DigitalGlobe GeoEye1 - Acquisition Date 2018-07-11 (forest disturbance detected) Geo-coordinates: 0° 05' 21.08" N 110° 49' 53.98" E | polygon = 0.9 hectares A new clearing is plainly visible inside this polygon

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Attachment 7: DigitalGlobe- image of the same area of interest | Acquisition Date 2013- 08-24 Geo-coordinates: 0° 05' 21.08" N 110° 49' 53.98" E | polygon = 0.9 hectares

References 1. RADARSAT-2: A Powerful Solution For Global Forest Monitoring Needs 2. Forest Logging Alerts from RADARSAT-2 SAR Data 3. The Use Of Radarsat-2 For Detection And Mapping Of Logging Within The Amazon

External Links 1. MDA Imagery Web Portal 2. DigitalGlobe Imaging Web Portal 3. DigitalGlobe Open Data Portal

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