Sentinel-1 Shadows Used to Quantify Canopy Loss from Selective Logging in Gabon

Selective logging is a major cause of forest degradation in the tropics, but its precise scale, location and timing are not known as wide-area, automated remote sensing methods are not yet available at this scale. This limits the abilities of governments to police illegal logging, or monitor (and thus receive payments for) reductions in degradation. Sentinel-1, a C-band Synthetic Aperture Radar satellite mission with a 12-day repeat time across the tropics, is a promising tool for this due to the known appearance of shadows in images where canopy trees are removed. However, previous work has relied on optical satellite data for calibration and validation, which has inherent uncertainties, leaving unanswered questions about the minimum magnitude and area of canopy loss this method can detect. Here, we use a novel bi-temporal LiDAR dataset in a forest degradation experiment in Gabon to show that canopy gaps as small as 0.02 ha (two 10 m × 10 m pixels) can be detected by Sentinel-1. The accuracy of our algorithm was highest when using a timeseries of 50 images over 20 months and no multilooking. With these parameters, canopy gaps in our study site were detected with a false alarm rate of 6.2%, a missed detection rate of 12.2%, and were assigned disturbance dates that were a good qualitative match to logging records. The presence of geolocation errors and false alarms makes this method unsuitable for confirming individual disturbances. However, we found a linear relationship (r2=0.74) between the area of detected Sentinel-1 shadow and LiDAR-based canopy loss at a scale of 1 hectare. By applying our method to three years’ worth of imagery over Gabon, we produce the first national scale map of small-magnitude canopy cover loss. We estimate a total gross canopy cover loss of 0.31 Mha, or 1.3% of Gabon’s forested area, which is a far larger area of change than shown in currently available forest loss alert systems using Landsat (0.022 Mha) and Sentinel-1 (0.019 Mha). Our results, which are made accessible through Google Earth Engine, suggest that this approach could be used to quantify the magnitude and timing of degradation more widely across tropical forests

A New Field Protocol for Monitoring Forest Degradation

Forest degradation leads to the gradual reduction of forest carbon stocks, function, and biodiversity following anthropogenic disturbance. Whilst tropical degradation is a widespread problem, it is currently very under-studied and its magnitude and extent are largely unknown. This is due, at least in part, to the lack of developed and tested methods for monitoring degradation. Due to the relatively subtle and ongoing changes associated with degradation, which can include the removal of small trees for fuelwood or understory clearance for agricultural production, it is very hard to detect using Earth Observation. Furthermore, degrading activities are normally spatially heterogeneous and stochastic, and therefore conventional forest inventory plots distributed across a landscape do not act as suitable indicators: at best only a small proportion of plots (often zero) will actually be degraded in a landscape undergoing active degradation. This problem is compounded because the metal tree tags used in permanent forest inventory plots likely deter tree clearance, biasing inventories toward under-reporting change. We have therefore developed a new forest plot protocol designed to monitor forest degradation. This involves a plot that can be set up quickly, so a large number can be established across a landscape, and easily remeasured, even though it does not use tree tags or other obvious markers. We present data from a demonstration plot network set up in Jalisco, Mexico, which were measured twice between 2017 and 2018. The protocol was successful, with one plot detecting degradation under our definition (losing greater than 10% AGB but remaining forest), and a further plot being deforested for Avocado (Persea americana) production. Live AGB ranged from 8.4 Mg ha–1 to 140.8 Mg ha–1 in Census 1, and from 0 Mg ha–1 to 144.2 Mg ha–1 Census 2, with four of ten plots losing AGB, and the remainder staying stable or showing slight increases. We suggest this protocol has great potential for underpinning appropriate forest plot networks for degradation monitoring, potentially in combination with Earth Observation analysis, but also in isolation

An Effective Method for InSAR Mapping of Tropical Forest Degradation in Hilly Areas

Current satellite remote sensing methods struggle to detect and map forest degradation, which is a critical issue as it is likely a major and growing source of carbon emissions and biodiveristy loss. TanDEM-X InSAR phase height is a promising variable for measuring forest disturbances, as it is closely related to the mean canopy height, and thus should decrease if canopy trees are removed. However, previous research has focused on relatively flat terrains, despite the fact that much of the world's remaining tropical forests are found in hilly areas, and this inevitably introduces artifacts in sideways imaging systems. In this paper, we find a relationship between InSAR phase height and aboveground biomass change in four selectively logged plots in a hilly region of central Gabon. We show that minimising multilooking prior to the calculation of InSAR phase height on a pixel-by-pixel basis. This shows that TanDEM-X InSAR can measure the magnitude of degradation, and that topographic effects can be mitigated if data from multiple SAR viewing geometries are available

Reliably Mapping Low-intensity Forest Disturbance Using Satellite Radar Data

In the last decades tropical forests have experienced increased fragmentation due to a global growing demand for agricultural and forest commodities. Satellite remote sensing offers a valuable tool for monitoring forest loss, thanks to the global coverage and the temporal consistency of the acquisitions. In tropical regions, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission provides cloud-free and open imagery on a 6- or 12-day repeat cycle, offering the unique opportunity to monitor forest disturbances in a timely and continuous manner. Despite recent advances, mapping subtle forest losses, such as those due to small-scale and irregular selective logging, remains problematic. A Cumulative Sum (CuSum) approach has been recently proposed for forest monitoring applications, with preliminary studies showing promising results. Unfortunately, the lack of accurate in-situ measurements of tropical forest loss has prevented a full validation of this approach, especially in the case of low-intensity logging. In this study, we used high-quality field measurements from the tropical Forest Degradation Experiment (FODEX), combining unoccupied aerial vehicle (UAV) LiDAR, Terrestrial Laser Scanning (TLS), and field-inventoried data of forest structural change collected in two logging concessions in Gabon and Peru. The CuSum algorithm was applied to VV-polarized Sentinel-1 ground range detected (GRD) time series to monitor a range of canopy loss events, from individual tree extraction to forest clear cuts. We developed a single change metric using the maximum of the CuSum distribution, retrieving location, time, and magnitude of the disturbance events. A comparison of the CuSum algorithm with the LiDAR reference map resulted in a 78% success rate for the test site in Gabon and 65% success rate for the test site in Peru, for disturbances as small as 0.01 ha in size and for canopy height losses as fine as 10 m. A correlation between the change metric and above ground biomass (AGB) change was found with R2 = 0.95, and R2 = 0.83 for canopy height loss. From the regression model we directly estimated local AGB loss maps for the year 2020, at 1 ha scale and in percentages of AGB loss. Comparison with the Global Forest Watch (GFW) Tree Cover Loss (TCL) product showed a 61% overlap between the two maps when considering only deforested pixels, with 504 ha of deforestation detected by CuSum vs. 348 ha detected by GFW. Low intensity disturbances captured by the CuSum method were largely undetected by GFW and by the SAR-based Radar for Detecting Deforestation (RADD) Alert System. The results of this study confirm this approach as a simple and reproducible change detection method for monitoring and quantifying fine-scale to high intensity forest disturbances, even in the case of multi-storied and high biomass forests

Detecting small changes in tropical forests from space: experiments using synthetic aperture radar

Humid tropical forests are globally important for biodiversity and carbon sequestration. However, vast areas are being degraded, and so made vulnerable to deforestation, drought and fire, through logging that currently goes undetected by satellite monitoring. Nations and companies have pledged to meet climate and conservation targets through reductions in degradation, but methods of quantifying disturbances down to the scale of individual tree losses are required to verify progress towards these goals. Synthetic aperture radar (SAR) satellites are suited to this task as they can provide high resolution (1-10 m) images over large areas, are not affected by cloud cover, and are sensitive to vegetation structure. In this thesis, I test the ability of SAR data to quantify logging disturbances in terms of canopy area loss, aboveground biomass (AGB) loss, and canopy height changes. Reference data were obtained through the Tropical Forest Degradation Experiment (FODEX). At study sites in Peru and Gabon undergoing selective logging, tree diameters, wood volume from terrestrial laser scanning (TLS), and forest structure through unmanned aerial vehicle (UAV) LiDAR were measured at multiple time points over a period of four years. My aim was to improve SAR-based methods of detecting tropical forest disturbance. To do so, I analysed the effects of SAR imaging parameters (including wavelength, incidence angle and resolution), trialled different approaches to processing SAR data (such as the level of multilooking or length of time series used), and developed new change-detection algorithms. I focused on two sources of SAR data in particular: i) C-band SAR (wavelength of around 6 cm) from Sentinel-1 - a European Space Agency mission designed to provide consistent repeat imagery with an approximate resolution over land of 20 m; ii) and X-band SAR (wavelength around 3 cm) from TanDEM-X - a German Aerospace Center mission designed to measure elevation using twin satellites orbiting in formation, while providing experimental products for research at resolutions from 1-3 m. I present my work in three results chapters about (i) detection of canopy gaps through drops in Sentinel-1 intensity (Chapter 2); (ii) using TanDEM-X interferometry to detect biomass change in areas with steep slopes (Chapter 3); and (iii) modeling small canopy changes using both TanDEM-X, Sentinel-1, and new high resolution X-band sensors (Chapter 4). In Chapter 2, I develop an algorithm to detect sharp drops in C-band intensity in VV and VH polarisations from Sentinel-1. These SAR 'shadows’ were compared to canopy gaps delineated using UAV LiDAR spanning 1-year at the Gabon study site, and I show that disturbances as small as 0.02 ha were detected. Overall, 87% of canopy gaps larger than 0.01 ha were detected, with a 0.3% false alarm rate. I found a linear relationship (R2=0.74) between the area of Sentinel-1 shadow and the area of canopy gaps per hectare, which I applied to Sentinel-1 data across the country of Gabon to produce a national scale map of canopy cover loss in 2020. My map suggests that the total gross canopy cover loss was around an order of magnitude higher (1.3% of forested area) than the changes detected by previously published forest loss products based on optical or SAR imagery. In Chapter 3, I propose a method for monitoring degradation using X-band interferometric phase height, a variable that is closely related to mean canopy height and therefore expected to decrease where trees are removed. Previous research in flat terrain had not addressed how to deal with steep slopes, which are present in many remaining tropical forests. Here, I use eight TanDEM-X acquisitions over Gabon (four from ascending passes, four from descending passes) to show that topographic artefacts can be mitigated by selecting data from different pass directions on a pixel-by-pixel basis, determined by incidence angle and coherence. In addition, I demonstrate that minimising multilooking strengthens the relationship between phase height change and AGB change across four 1-ha plots. Finally, in Chapter 4, I compare three TanDEM-X variables (phase height, coherence, and intensity) from high-resolution spotlight images and Sentinel-1 intensity to canopy height changes over a two year period in the Peruvian study site. Models of canopy height change using each SAR variable were trained and tested on UAV LiDAR data. The strongest model used X-band intensity, followed by phase height, suggesting that high resolution X-band data is sensitive to even smaller disturbances than those detected by Sentinel-1. No SAR variable, however, showed sensitivity to the small amounts (1-2 m height increase) of forest growth present in the study area. In addition, commercial X-band imagery from ICEYE and Capella are presented, and I argue that these data provide new avenues for change detection in tropical forests at the finest scales. My results show that short wavelength SAR can quantify forest losses at the scale of individual canopy trees, most reliably by detecting decreases in backscatter along a dense time series. While X-band provides higher resolution, Sentinel-1 is the most promising tool for wide area mapping due to its routine acquisition program (normally 12 day repeats) and open data policy. Current global forest loss products do not capture the finest disturbances, meaning that logging and other forest dynamics are going undetected, potentially undermining efforts to protect biodiversity and slow climate change. Sentinel-1 mapping of tropical forest degradation should therefore be an urgent priority

Table_1_Reliably mapping low-intensity forest disturbance using satellite radar data.XLSX

In the last decades tropical forests have experienced increased fragmentation due to a global growing demand for agricultural and forest commodities. Satellite remote sensing offers a valuable tool for monitoring forest loss, thanks to the global coverage and the temporal consistency of the acquisitions. In tropical regions, C-band Synthetic Aperture Radar (SAR) data from the Sentinel-1 mission provides cloud-free and open imagery on a 6- or 12-day repeat cycle, offering the unique opportunity to monitor forest disturbances in a timely and continuous manner. Despite recent advances, mapping subtle forest losses, such as those due to small-scale and irregular selective logging, remains problematic. A Cumulative Sum (CuSum) approach has been recently proposed for forest monitoring applications, with preliminary studies showing promising results. Unfortunately, the lack of accurate in-situ measurements of tropical forest loss has prevented a full validation of this approach, especially in the case of low-intensity logging. In this study, we used high-quality field measurements from the tropical Forest Degradation Experiment (FODEX), combining unoccupied aerial vehicle (UAV) LiDAR, Terrestrial Laser Scanning (TLS), and field-inventoried data of forest structural change collected in two logging concessions in Gabon and Peru. The CuSum algorithm was applied to VV-polarized Sentinel-1 ground range detected (GRD) time series to monitor a range of canopy loss events, from individual tree extraction to forest clear cuts. We developed a single change metric using the maximum of the CuSum distribution, retrieving location, time, and magnitude of the disturbance events. A comparison of the CuSum algorithm with the LiDAR reference map resulted in a 78% success rate for the test site in Gabon and 65% success rate for the test site in Peru, for disturbances as small as 0.01 ha in size and for canopy height losses as fine as 10 m. A correlation between the change metric and above ground biomass (AGB) change was found with R2 = 0.95, and R2 = 0.83 for canopy height loss. From the regression model we directly estimated local AGB loss maps for the year 2020, at 1 ha scale and in percentages of AGB loss. Comparison with the Global Forest Watch (GFW) Tree Cover Loss (TCL) product showed a 61% overlap between the two maps when considering only deforested pixels, with 504 ha of deforestation detected by CuSum vs. 348 ha detected by GFW. Low intensity disturbances captured by the CuSum method were largely undetected by GFW and by the SAR-based Radar for Detecting Deforestation (RADD) Alert System. The results of this study confirm this approach as a simple and reproducible change detection method for monitoring and quantifying fine-scale to high intensity forest disturbances, even in the case of multi-storied and high biomass forests.</p