Stéphane Mermoz

Classification of river ice using polarimetric SAR data

Ice jams are a major cause of river flooding in Canada. These events can be devastating for the environment, human infrastructure, and population. Although methodologies have been developed to discriminate ice types using single-polarization synthetic aperture radar (SAR) data, SAR polarimetry has not yet been used. In this paper a polarimetric SAR airborne image of the Saint-François River, Quebec, has been analyzed. Complementary data about the characteristics of the ice cover were obtained simultaneously with the image acquisition. The usefulness of each polarimetric parameter is explored to obtain realistic ice type classifications. We propose to compute a rule-based hierarchical classification and compare it with a Wishart classification. A single-polarization-based classification is also used to show the limits of this approach in discriminating water from ice. The hierarchical classification more accurately separates areas of ice from areas of open water (81% producer’s accuracy). Both classifications show good results, with few ambiguities in detection of the consolidated ice class. Detection of the thermal ice class is not highly accurate. Thermal and frazil ice classification is performed better when hierarchical classification than when Wishart classification is used. Lastly, the hierarchical classification is better adapted to river ice than Wishart classification, and fully polarimetric data are significantly better than single-polarization data for discriminating water from ice.

Forest Disturbances and Regrowth Assessment Using ALOS PALSAR Data from 2007 to 2010 in Vietnam, Cambodia and Lao PDR

This paper aims to develop a new methodology for monitoring forest disturbances and regrowth using ALOS PALSAR data in tropical regions. In the study, forest disturbances and regrowth were assessed between 2007 and 2010 in Vietnam, Cambodia and Lao People’s Democratic Republic. The deforestation rate in Vietnam has been among the highest in the tropics in the last few decades, and those in Cambodia and Lao are increasing rapidly. L-band ALOS PALSAR mosaic data were used for the detection of forest disturbances and regrowth, because L-band SAR intensities are sensitive to forest aboveground biomass loss. The methodology used here combines SAR data processing, which is particularly suited for change detection, forest detection and forest disturbances and regrowth detection using expectation maximization, which is closely related to fuzzy logic. A reliable training and testing database has been derived using AVNIR-2 and Google Earth images for calibration and validation. Efforts were made to apply masking areas that are likely to show different SAR backscatter temporal behaviors from the forests considered in the study, including mangroves, inundated forests, post-flooding or irrigated croplands and water bodies, as well as sloping areas and urban areas. The resulting forest disturbances and regrowth map (25-m resolution) indicates disturbance rates of −1.07% in Vietnam, −1.22% in Cambodia and −0.94% in Lao between 2007 and 2010, with corresponding aboveground biomass losses of 60.7 Tg, 59.2 Tg and 83.8 Tg , respectively. It is expected that the method, relying on free of charge data (ALOS and ALOS2 mosaics), can be applied widely in the tropics.

Retrieval of River Ice Thickness From C-Band PolSAR Data

River ice has an important effect on natural processes and human activities in northern countries. Current models for estimating river ice thickness are mostly based on environmental data. They require several inputs and yield only a global estimate of ice thickness for a large heterogeneous area. Attempts have been made intending to retrieve river ice thickness from remote sensing using monopolarized C-band radar data. No reliable maps of ice thickness have been produced. In this paper, the potential of polarimetric synthetic aperture radar (PolSAR) data for estimating river ice thickness is demonstrated, and a river ice thickness retrieval model is proposed. The C-band SAR images used in this paper were acquired by Radarsat-2 in the winter of 2009 over the Saint-François River (Southern Quebec), the Koksoak River (Northern Quebec), and the Mackenzie River (Northwest Territories) in Canada. Field campaigns were carried out to obtain ice thickness validation data at 70 locations. Polarimetric entropy was used to obtain ice thickness estimates. This approach results in spatially distributed ice thickness maps for selected ice types.

Forest Biomass From Radar Remote Sensing

Abstract: Forests play a primordial role for life on Earth. Beyond their contribution as a major source of raw materials and renewable energy, they also hold an inestimable treasure of biodiversity. They ensure the protection of arable land, are a continuous source of water and contribute to improved air quality. Whether for food or pharmacopoeia, forests are the principal source of subsistence for almost 2 billion people.

Biomass of dense forests related to L-band SAR backscatter?

Synthetic aperture radar (SAR) is one of the most promising remote sensor to map forest carbon. The unique spaceborne and long-wavelength SAR data currently available are L-band data, but their relationship with forest biomass is still under controversy, particularly for high biomass values. While many studies assume a complete loss of sensitivity above a saturation point, typically around 100 t.ha-1, others assume a continuous positive correlation between SAR backscatter and biomass. The objective of this paper is to revisit the relationship between L-band SAR backscatter and dense tropical forest biomass for a large range of biomass values, using both theoretical and experimental approaches. Both approaches revealed that after reaching a maximum value, SAR backscatter correlates negatively with forest biomass. This phenomenon is interpreted as a signal attenuation from the forest canopy as the canopy becomes denser. This result has strong implication for L-band vegetation mapping as it can lead to a more-than-expected biomass under-estimation.

Retrieval of river ice thickness from C-band PolSAR data

Until now, existing models for retrieving river ice thickness are mostly based on environmental data. They require many inputs and indicate a global value of ice thickness for a large heterogeneous area. Studies have been performed intending to retrieve river ice thickness throughout remote sensing using monopolarized C-band radar data. But no reliable ice maps of ice thickness have been produced. In this paper, the information gain from polarimetric SAR data is demonstrated and a river ice thickness model is proposed. This model is applied and validated on Radarsat-2 images acquired at C-band in winter 2009 over the Saint-François River (Southern Quebec), the Kosoak River (Northern Quebec) and the Mackenzie River (Northwest Territories), in Canada. Field campaigns were carried out to obtain more than 70 samples of various river ice thickness. The optimal polarimetric parameter is chosen to retrieve both easily and rapidly river ice thickness. This approach offers reliable spatially distributed ice maps.

Comparison of optical and SAR data for forest cover mapping: REDD+ may be helped by SAR data

For Reduced Emissions from Deforestation and forest Degradation (REDD+) purposes, the standard method consists in using optical data to assess change in forest cover. However, SAR data are sensitive to forest above-ground biomass; thus an adequate SAR system, with long wavelength, could provide mapping of biomass and its change over time to be used for estimating carbon emissions. In this paper, the focus is on the comparison of forest cover maps derived from optical data and from SAR data in the Centre Province of Cameroon (about 84 000 km2), which contains humid tropical forests and woody savannas. The forest-non forest maps from optical and SAR data have comparable validation results. However, SAR-based methods are easy to implement, do not require extensive reference samples for calibration, and answer to the key question for carbon cycles studies which is how much biomass or carbon has been disturbed.

River Ice Mapping from PolSAR Images

This paper presents different mapping algorithms to discriminate river ice types using full-polarized C-band and dual-polarized X-band data. Field data are conjointly used with an electromagnetic river ice model to simulate backscattering response of river ice. Finally different classifications, proposed and tested, show encouraging results.

Use of the SAR Shadowing Effect for Deforestation Detection with Sentinel-1 Time Series

To detect deforestation using Earth Observation (EO) data, widely used methods are based on the detection of temporal changes in the EO measurements within the deforested patches. In this paper, we introduce a new indicator of deforestation obtained from synthetic aperture radar (SAR) images, which relies on a geometric artifact that appears when deforestation happens, in the form of a shadow at the border of the deforested patch. The conditions for the appearance of these shadows are analyzed, as well as the methods that can be employed to exploit them to detect deforestation. The approach involves two steps: (1) detection of new shadows; (2) reconstruction of the deforested patch around the shadows. The launch of Sentinel-1 in 2014 has opened up opportunities for a potential exploitation of this approach in large-scale applications. A deforestation detection method based on this approach was tested in a 600,000 ha site in Peru. A detection rate of more than 95% is obtained for samples larger than 0.4 ha, and the method was found to perform better than the optical-based UMD-GLAD Forest Alert dataset both in terms of spatial and temporal detection. Further work needed to exploit this approach at operational levels is discussed.

Multistability of model and real dryland ecosystems through spatial self-organization

Significance Today, vast areas of drylands in semiarid climates face the dangers of desertification. To understand the driving mechanisms behind this effect, many theoretical models have been created. These models provide insight into the resilience of dryland ecosystems. However, until now, comparisons with reality were merely visual. In this article, a systematic comparison is performed using data on wavenumber, biomass, and migration speed of vegetation patterns in Somalia. In agreement with reaction–diffusion models, a wide distribution of regular pattern wavenumbers was found in the data. This highlights the potential for extrapolating predictions of those models to real ecosystems, including those that elucidate how spatial self-organization of vegetation enhances ecosystem resilience. Spatial self-organization of dryland vegetation constitutes one of the most promising indicators for an ecosystem’s proximity to desertification. This insight is based on studies of reaction–diffusion models that reproduce visual characteristics of vegetation patterns observed on aerial photographs. However, until now, the development of reliable early warning systems has been hampered by the lack of more in-depth comparisons between model predictions and real ecosystem patterns. In this paper, we combined topographical data, (remotely sensed) optical data, and in situ biomass measurements from two sites in Somalia to generate a multilevel description of dryland vegetation patterns. We performed an in-depth comparison between these observed vegetation pattern characteristics and predictions made by the extended-Klausmeier model for dryland vegetation patterning. Consistent with model predictions, we found that for a given topography, there is multistability of ecosystem states with different pattern wavenumbers. Furthermore, observations corroborated model predictions regarding the relationships between pattern wavenumber, total biomass, and maximum biomass. In contrast, model predictions regarding the role of slope angles were not corroborated by the empirical data, suggesting that inclusion of small-scale topographical heterogeneity is a promising avenue for future model development. Our findings suggest that patterned dryland ecosystems may be more resilient to environmental change than previously anticipated, but this enhanced resilience crucially depends on the adaptive capacity of vegetation patterns.