Nicolas Longépé

Assessment of ALOS PALSAR 50 m Orthorectified FBD Data for Regional Land Cover Classification by Support Vector Machines

From its launch in 2006, the phased array L-band synthetic aperture radar (PALSAR) onboard the advanced land observing satellite (ALOS) has acquired many dual-polarized (FBD) images with a 70-km swath width, aiming to produce spatially consistent coverage over tropical rainforest. This paper investigates the relevancy of PALSAR orthorectified FBD product at 50-m resolution for regional land cover classification by the support vector machines (SVM). Our test site is the Riau province, Sumatra island, Indonesia, known to hold vast area of natural peatland forest with an extreme biodiversity threatened by industrial deforestation. Since it is demonstrated the radiometric information (HH and HV channels) cannot be solely used to achieve a good classification, the spatial information in these orthorectified data is investigated. A new tool using the recursive feature elimination SVM-based process and the textural Haralicks parameters is introduced. The real contribution of textures within the land cover classification can be understood. A small set of textural parameters is determined at local scale while being optimal for the land cover discrimination. The SVM-based classifier is carried out across the whole Riau province and its results are compared with a Landsat-based estimation. The agreement is over 70% with six classes and 86% for the natural forest map. These results are remarkable since only one PALSAR FBD product is used and this assessment is performed on more than 40 million pixels. The results confirm the high potential of the PALSAR sensor for forest monitoring at regional, if not global scale.

Snowpack Characterization in Mountainous Regions Using C-Band SAR Data and a Meteorological Model

This paper presents a method to characterize snow cover in mountainous regions using dual-polarization C-band synthetic aperture radar (SAR) data. It is demonstrated that an accurate modeling of the liquid water distribution inside the snowpack, using a multilayer meteorological snow model, is required to characterize snow with precision. A multilayer-snow electromagnetic (EM) backscattering model is developed based on the vector radiative transfer, the strong fluctuation theory, and physical parameters supplied by the meteorological model. However, the limited resolution of the meteorological snow model is insufficient for predicting a refined EM backscattering at a massif scale. An adequate spatial reorganization of these snow profiles, based on a comparison between simulated and measured dual-polarization SAR data, leads to a better estimation of some snowpack parameters. In particular, the monitoring of snow liquid water content is presented improving the capacity of wet snow mapping as compared to a classical SAR-based method. This methodology shows good capacities both for qualitative and quantitative snow assessments, opening the way for a new operational method.

Using multiscale texture information from ALOS PALSAR to map tropical forest

This research investigated the ability of the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) to map tropical forest in central Sumatra, Indonesia. The study used PALSAR 50 m resolution orthorectified HH and HV data. As land-cover discrimination is difficult with only two bands (HH and HV), we added textures as additional information for classification. We calculated both first- and second-order texture features and studied the effects of texture window size, quantization scale and displacement length on discrimination capability. We found that rescaling to a lower number of grey levels (8 or 16) improved discrimination capability and that equal probability quantization was more effective than uniform quantization. Increasing displacement tended to reduce the discrimination capability. Low spatial resolution increased the discrimination capability because low spatial resolution features reduce the effects of noise. A larger number of features also improved discrimination capability. However, the amount of improvement depended on the window size. We used the optimum combination of backscatter amplitude and textures as input data into a supervised multi-resolution maximum likelihood classification. We found that including texture information improved the overall classification accuracy by 10%. However, there was significant confusion between natural forest and acacia plantations, as well as between oil palm and clear cuts, presumably because the backscatter and texture of these class pairs are very similar.

Vessel Refocusing and Velocity Estimation on SAR Imagery Using the Fractional Fourier Transform

This paper studies the effects of stationary-based processing of moving ship signatures in synthetic aperture radar (SAR) imagery and introduces a methodology to estimate and compensate for them. SAR imaging of moving targets usually results in residual chirps in the azimuthal SLC processed signal. The fractional Fourier transform (FrFT) makes it possible to represent the SAR signal in a rotated joint time-frequency plane and performs optimal processing and analysis of these residual chirp signals. The along-track defocus can thus be compensated for and the targets azimuthal speed estimated. The impact of higher order motion terms (e.g., acceleration) has been also considered. Experiments were conducted on a large number of ship signatures extracted from Radarsat-2 Multi Look Fine and Ultra Fine SAR images. An intercomparison with a standard Doppler Sublook Decomposition Method (SDM) is carried out, as well as a complete performance analysis with AIS data as ground truth.

Performance evaluation of Sentinel-1 data in SAR ship detection

This study addresses the performances of ship detection with data acquired by the newly launched Sentinel-1 SAR sensor. An automatic validation approach based on coastal AIS data is employed for measuring the detection efficiency. Results are compared with ship detection capabilities conducted on Radarsat-2 and CosmoSkymed datasets. The influence of different key parameters, such as SAR imaging characteristics (polarization, incidence angle) or meteorological conditions, is addressed. Such an analysis is useful for operational services to determine data specifications that assure optimum vessel detection for maritime surveillance applications.

Mapping tropical forest using ALOS PALSAR 50m resolution data with multiscale GLCM analysis

PALSAR orthorectified HH and HV produced at 50m resolution is used for analysis. Since only two bands (HH and HV) have been limited in land cover discrimination, textures have been used as additional information for classification. This research derives second-order textures at different spatial resolutions and compares second-order textures at multiple scales to demonstrate their contributions in land cover classification. The discriminating capability of texture features is derived by the transformed divergence on several selected regions of interest. Optimum combination of backscattering and textures are used as input data into a supervised multi-resolution maximum likelihood classification. It is found that by including the texture information, the overall classification accuracy is improved by 10%.

Capabilities of Full-Polarimetric PALSAR/ALOS for Snow Extent Mapping

Snow classification using full-polarimetric PALSAR data is investigated in this paper. It is first demonstrated that dry snowpack over frozen ground slightly affects polarimetric signature at L-band. Given the fact that PALSAR data do not permit the use of a simplistic threshold-based method, a refined method for Snow Covered Area mapping is outlined. A supervised Support Vector Machine approach is used showing fairly good results within the framework of a three-classes classification (dry snow over frozen ground, wet snow and no snow).

Case studies of frozen ground monitoring using PALSAR/ALOS data

Frozen ground is a sensitive indicator of how our home planet is changing. In the meantime, new spaceborne SAR systems have been launched, such as the polarimetric PALSAR sensor onboard ALOS in January 2006. In this paper, the relevance of L-band polarimetric SAR data for extracting information on frozen ground is presented. Dealing with ground assessments, the necessity for a validated Electromagnetic (EM) model is of importance. The adequation between Ohs po-larimetric EM model and PALSAR data is first studied over agricultural bare fields in Hokkaido, Japan. The assessment of residual liquid water can be realized by means of bare soil EM backscattering model. Over natural wildland area, an approach is proposed in order to tackle the effect of the vegetation or other irrelevant effects. The monitoring of permafrost active layer is performed over the ANWR, Alaska.

Multitemporal analysis of high-spatial-resolution optical satellite imagery for mangrove species mapping in Bali, Indonesia

Mapping zonations of mangrove species (ZMS) is important when assessing the functioning of such specific ecosystems. However, the reproducibility of remote sensing methods for discriminating and mapping mangrove habitats is often overstated due to the lack of temporal observations. Here, we investigated the potential use of temporal series of high-resolution multispectral satellite images to discriminate and map four typical Asian ZMS. This study was based on the analysis of eight images acquired between 2001 and 2014 over the mangrove area of Nusa Lembongan, Bali, Indonesia. Variations between years in the top-of-atmosphere reflectance signatures were examined as functions of the acquisition angles. We also applied maximum likelihood supervised classification to all of the images and determined the variability in the classification errors. We found that the distinction between spectral signatures of ZMS characterized by a close canopy was fairly independent of the season and sensor characteristics. By contrast, the variability in the multispectral signatures of ZMS with open canopies and associated classification errors could be attributed to variability in ground surface scattering. In both cases, sun-viewing geometry could alter the separability between ZMS classes in near-nadir viewing or frontward sun-viewing configurations, thereby explaining why the overall accuracy of ZMS classification might vary from 65% to 80%. Thus, multitemporal analysis is an important stage in the development of robust methods for ZMS mapping. It must be supported by physical-based research aiming to quantify the influences of canopy structure, species composition, ground surface properties, and viewing geometry parameters on ZMS multispectral signatures.

Snow wetness monitoring using multi-temporal polarimetric ASAR data and multi-layer hybrid model

This paper presents a method to characterize snow cover using multi-temporal dual polarization ASAR/ENVISAT data. At first, variations of electromagnetic backscattering of snowpack depending on melting are explained and validated by a multi-layer model. It is demonstrated that it is crucial to exactly model the distribution of Liquid Water Content inside snow pack in C-band. Consequently, a new mapping algorithm based on the French weather model CROCUS is proposed in order to estimate the spatial variability of layered snowpack profile.