Monitoring loss of tropical forest cover from Sentinel-1 time-series: A CuSum-based approach

Ygorra, Bertrand; Frappart, Frédéric; Wigneron, Jean‐Pierre; Moisy, Christophe; Catry, Thibault; Baup, Frédéric; Hamunyela, Eliakim; Riazanoff, Serge

doi:10.1016/j.jag.2021.102532

Cited by 15 publications

(13 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…. CuSum implementation Ygorra et al (2021) found that in tropical forests the copolarized backscatter (VV) showed best results, as compared to the cross-polarized channel (VH), and this was also confirmed by our preliminary tests. Therefore, time series data stacks were generated using the VV-polarized channel containing the spatial information (range and azimuth) and the temporal dimension for each pixel of the Sentinel-1 image over the study area and for a selected study period.…”

Section: Change Detection With Cumulative Sumssupporting

confidence: 80%

“…The study presented here employs a change detection method based on the cumulative sums (CuSums) of Sentinel-1 time series, to retrieve information on location, time, and magnitude of small-scale forest disturbance, including selective logging. The method was recently proposed for monitoring forest disturbances (Ruiz-Ramos et al, 2020) and tested on a tropical site with promising results (Ygorra et al, 2021). Here, we validate this novel approach on a unique, ground-measured dataset collected by the Forest Degradation Experiment (FODEX) in Gabon and Peru, using a combination of unoccupied aerial vehicle (UAV) Lidar, Terrestrial Laser Scanning (TLS), and forest inventory data of small-scale disturbances caused by selective logging.…”

Section: Introductionmentioning

confidence: 89%

“…Even in 3 m resolution PlanetScope images, degradation effects preceding deforestation cannot be immediately detected, especially in the case of dense and multi-storied canopies. Moreover, and this is particularly relevant in tropical environments, canopy gaps can quickly recover in between two cloud-free images, resulting in a large underestimation of actual forest disturbances (Asner et al, 2004;Shimizu et al, 2019;Ygorra et al, 2021). By contrast in this study we use very high resolution (25 cm) in-situ data from UAV LiDAR to validate the CuSum method in its ability to detect subtle changes in forest canopy resulting from lowintensity logging.…”

Section: Change Detection With Cumulative Sumsmentioning

confidence: 98%

“…The CuSum algorithm is recognized as a powerful statistical method for detecting abrupt and systematic changes in time series data (Page, 1955;Taylor, 2008;Manogaran and Lopez, 2018). While being extensively used for monitoring market trends and industrial processes (Manly and Mackenzie, 2003), it has recently found applications in the environmental sciences for detecting seasonal climate changes (Manogaran and Lopez, 2018) and for monitoring deforestation and forest degradation in temperate (Kellndorfer et al, 2019;Ruiz-Ramos et al, 2020) and tropical forests (Ygorra et al, 2021). Results from Ruiz-Ramos et al ( 2020) and Ygorra et al (2021) have shown the robustness of this method for detecting cover changes in forest canopy, with overall accuracy of 77% and 91%, respectively.…”

Section: Change Detection With Cumulative Sumsmentioning

confidence: 99%

“…While being extensively used for monitoring market trends and industrial processes (Manly and Mackenzie, 2003), it has recently found applications in the environmental sciences for detecting seasonal climate changes (Manogaran and Lopez, 2018) and for monitoring deforestation and forest degradation in temperate (Kellndorfer et al, 2019;Ruiz-Ramos et al, 2020) and tropical forests (Ygorra et al, 2021). Results from Ruiz-Ramos et al ( 2020) and Ygorra et al (2021) have shown the robustness of this method for detecting cover changes in forest canopy, with overall accuracy of 77% and 91%, respectively. However, the data they used to obtain these numbers were produced by visual interpretation of optical satellite imagery, which presents many limitations.…”

Section: Change Detection With Cumulative Sumsmentioning

confidence: 99%

See 4 more Smart Citations

Reliably mapping low-intensity forest disturbance using satellite radar data

Aquino¹,

Mitchard²,

McNicol³

et al. 2022

Front. For. Glob. Change

View full text Add to dashboard Cite

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.

show abstract

Section: Change Detection With Cumulative Sumssupporting

confidence: 80%

Section: Introductionmentioning

confidence: 89%

Section: Change Detection With Cumulative Sumsmentioning

confidence: 98%

Section: Change Detection With Cumulative Sumsmentioning

confidence: 99%

Section: Change Detection With Cumulative Sumsmentioning

confidence: 99%

See 3 more Smart Citations

Reliably mapping low-intensity forest disturbance using satellite radar data

Aquino¹,

Mitchard²,

McNicol³

et al. 2022

Front. For. Glob. Change

View full text Add to dashboard Cite

show abstract

A bibliometric analysis on the visibility of the Sentinel-1 mission in the scientific literature

Pham-Duc

Nguyen

2022

Arab J Geosci

View full text Add to dashboard Cite

Seven years after the launch of the first Sentinel-1 satellite, its data have been widely used in the scientific community. This study provides the first quantitative analysis of the visibility of the Sentinel-1 mission to the scientific literature through a bibliometric analysis of 1628 articles published in scientific journals during the 2014–2020 period. The main findings show that the number of Sentinel-1 mission-related papers increased significantly over the years, with an annual growth rate of 83%. Remote sensing is the most popular journal where 31.75% of the publication collection has been published. China and the USA are the two most productive countries with a share of 22.30% and 16.22% in the collection. Research based on the Sentinel-1 data covered a wide range of topics in geoscience disciplines. The use of SAR interferometry, focusing on the studies of landslide, earthquake, ground deformation, and subsidence, is the most important research direction using Sentinel-1 data. Image fusion of Sentinel-1 and Sentinel-2 observations for mapping and monitoring applications is the second most important research direction. Other popular research areas are glaciology, soil moisture, agriculture, rice monitoring, and ship detection. This study uses bibliographic data derived only from the Scopus database; therefore, it might not cover all Sentinel-1 related documents. However, this paper is a good reference for researchers who want to use Sentinel-1 data in their studies. The two Sentinel-1 satellites will provide scientific data for years to come, meaning that this type of analysis should be done on a regular basis.

show abstract