Study of the speckle noise effects over the eigen decomposition of polarimetric SAR data: a review

This paper is focused on considering the effects of speckle noise on the eigen decomposition of the co- herency matrix. Based on a perturbation analysis of the matrix, it is possible to obtain an analytical expression for the mean value of the eigenvalues and the eigenvectors, as well as for the Entropy, the Anisotroopy and the dif- ferent a angles. The analytical expressions are compared against simulated polarimetric SAR data, demonstrating the correctness of the different expressions.Peer ReviewedPostprint (published version

Utilisation de la télédétection pour l’analyse de la dynamique de la biomasse aérienne sèche totale des forêts et des palmiers à huile d’une plantation mature dans le Bassin du Congo

Le stockage de la biomasse aérienne (BA) sèche totale des forêts est indispensable à la lutte contre les changements climatiques. Depuis quelques décennies, il y a une tendance à l’introduction de cultures agro-industrielles, comme les plantations de palmiers à huile, dans les forêts tropicales dans le Bassin du Congo. Ces conversions participent à l’augmentation ou à la diminution des émissions ou absorptions de dioxyde de carbone (CO2) dans l’atmosphère, tout en occasionnant des changements climatiques. Dans cette région, la disponibilité des données de terrain et de télédétection est relativement limitée pour évaluer la BA. L’estimation de la BA des palmiers à huile n’est également pas maitrisée dans le Bassin du Congo. Les incertitudes rapportées dans les études précédentes utilisant la télédétection demeurent encore élevées. Plusieurs approches à fort potentiel restent encore à développer ou à évaluer. À titre d’exemple, l’approche MARS (régressions multivariées par spline adaptative) pour estimer la BA n’a pas encore été testée, notamment avec des données combinées optiques, LiDAR et radar. Les pertes et les gains de la BA dus aux changements des forêts en palmiers à huile dans le Bassin du Congo, particulièrement au Gabon, n’ont pas encore été quantifiés. La présente étude vise alors à contribuer au développement des méthodes d’estimation de la BA par l’utilisation de la télédétection pour comprendre l’impact des plantations des palmiers à huile sur les variations de la BA des forêts. Au cours de la présente étude, nous avons développé les premiers modèles allométriques d’estimation de la BA des palmiers à huile à l’aide de mesures in situ originales, que nous avons acquises dans le Bassin du Congo. Des modèles de BA des palmiers à huile ont également été établis avec MARS et les régressions linéaires multiples (RLM) en utilisant des indices dérivés de la transformée de Fourier (indices FOTO) à partir d’images satellitaires FORMOSAT-2 et PlanetScope. Finalement, cette thèse propose aussi des modèles MARS qui combinent des données de télédétection optiques (SPOT 7), LiDAR et radar polarimétrique interférométrique (PolInSAR) pour estimer la BA des forêts tropicales. À l’aide des estimations fournies par les modèles construits, la dynamique des BA des forêts et des plantations de palmiers à huile a été analysée. Les résultats ont montré que le modèle allométrique local de BA, utilisant la variable composée formée par le diamètre à hauteur de poitrine, la hauteur et la densité, ou le nombre de feuilles, permettait d’avoir les meilleures estimations (erreur quadratique moyenne relative (%RMSE) = 5,1 %). Un modèle allométrique de BA relativement performant a également été construit en utilisant seulement le diamètre et la hauteur (%RMSE = 8,2 %). Pour l’estimation des BA des palmiers à partir d’images FORMOSAT-2 et PlanetScope, les résultats démontrent que l’approche MARS permet les évaluations les plus précises (%RMSE ≤ 9,5 %). Cela est particulièrement vrai lorsque les images FORMOSAT-2 sont considérées (%RMSE ≤ 6,4 %). Les modèles de régression linéaire multiple donnent aussi des résultats avec des erreurs faibles, mais n’atteignent pas l’approche MARS (%RMSE ≥ 6,6 %). Cette dernière a été utilisée pour développer une série de modèles afin d’estimer les BA des forêts de la région d’étude. Les résultats montrent que le modèle utilisant la variable individuelle de la hauteur médiane de la canopée (RH50) dérivée des données LiDAR a estimé la biomasse avec plus de précision (%RMSE = 28 %). La combinaison de données de télédétection (optique, LiDAR et radar) a réduit de près de 4 % les erreurs d’estimation de la biomasse du modèle exploitant la variable individuelle (RH50). Les analyses de la dynamique de BA due aux remplacements des forêts en palmeraies ont enfin permis de constater que les forêts sont plus des vecteurs de gains de BA que les palmeraies particulièrement pour les forêts matures (512 t ha-1 de plus de BA que les palmeraies, soit un surplus de 88 %). Ce constat est identique pour les forêts secondaires vieilles (168 t ha-1, soit 70 % de surplus de BA que les palmeraies) et les forêts secondaires jeunes-adultes ou inondables (74 t ha-1 de plus que les palmeraies, soit un excédent de 51 %). En revanche, l’installation de plantations de palmiers à huile dans les zones de sols nus ou forêts en repousse pourrait être gagnante en termes de BA, car celles-ci ne présentent que 72 t ha-1 de BA (100 % moins que les palmiers). C’est le cas aussi dans les zones occupées par les forêts secondaires jeunes-adultes avec des BA minimales et des sols nus ou des forêts en repousse avec des BA maximales de 52 t ha-1 (20 t ha-1, soit 38 % de BA de moins que les palmeraies)

Regression-Based Retrieval of Boreal Forest Biomass in Sloping Terrain using P-band SAR Backscatter Intensity Data

A new biomass retrieval model for boreal forest using polarimetric P-band synthetic aperture radar (SAR) backscatter is presented. The model is based on two main SAR quantities: the HV backscatter and the HH/VV backscatter ratio. It also includes a topographic correction based on the ground slope. The model is developed from analysis of stand-wise data from two airborne P-band SAR campaigns: BioSAR 2007 (test site: Remningstorp, southern Sweden, biomass range: 10-287 tons/ha, slope range: 0-4 degrees) and BioSAR 2008 (test site: Krycklan, northern Sweden, biomass range: 8-257 tons/ha, slope range: 0-19 degrees). The new model is compared to five other models in a set of tests to evaluate its performance in different conditions. All models are first tested on data sets from Remningstorp with different moisture conditions, acquired during three periods in the spring of 2007. Thereafter, the models are tested in topographic terrain using SAR data acquired for different flight headings in Krycklan. The models are also evaluated across sites, i.e., training on one site followed by validation on the other site. Using the new model with parameters estimated on Krycklan data, biomass in Remningstorp is retrieved with RMSE of 40-59 tons/ha, or 22-33% of the mean biomass, which is lower compared to the other models. In the inverse scenario, the examined site is not well represented in the training data set, and the results are therefore not conclusive

Land cover and forest mapping in boreal zone using polarimetric and interferometric SAR data

Remote sensing offers a wide range of instruments suitable to meet the growing need for consistent, timely and cost-effective monitoring of land cover and forested areas. One of the most important instruments is synthetic aperture radar (SAR) technology, where transfer of advanced SAR imaging techniques from mostly experimental small test-area studies to satellites enables improvements in remote assessment of land cover on a global scale. Globally, forests are very suitable for remote sensing applications due to their large dimensions and relatively poor accessibility in distant areas. In this thesis, several methods were developed utilizing Earth observation data collected using such advanced SAR techniques, as well as their application potential was assessed. The focus was on use of SAR polarimetry and SAR interferometry to improve performance and robustness in assessment of land cover and forest properties in the boreal zone. Particular advances were achieved in land cover classification and estimating several key forest variables, such as forest stem volume and forest tree height. Important results reported in this thesis include: improved polarimetric SAR model-based decomposition approach suitable for use in boreal forest at L-band; development and demonstration of normalization method for fully polarimetric SAR mosaics, resulting in improved classification performance and suitable for wide-area mapping purposes; establishing new inversion procedure for robust forest stem volume retrieval from SAR data; developing semi-empirical method and demonstrating potential for soil type separation (mineral soil, peatland) under forested areas with L-band polarimetric SAR; developing and demonstrating methodology for simultaneous retrieval of forest tree height and radiowave attenuation in forest layer from inter-ferometric SAR data, resulting in improved accuracy and more stable estimation of forest tree height

Multi-source Remote Sensing for Forest Characterization and Monitoring

As a dominant terrestrial ecosystem of the Earth, forest environments play profound roles in ecology, biodiversity, resource utilization, and management, which highlights the significance of forest characterization and monitoring. Some forest parameters can help track climate change and quantify the global carbon cycle and therefore attract growing attention from various research communities. Compared with traditional in-situ methods with expensive and time-consuming field works involved, airborne and spaceborne remote sensors collect cost-efficient and consistent observations at global or regional scales and have been proven to be an effective way for forest monitoring. With the looming paradigm shift toward data-intensive science and the development of remote sensors, remote sensing data with higher resolution and diversity have been the mainstream in data analysis and processing. However, significant heterogeneities in the multi-source remote sensing data largely restrain its forest applications urging the research community to come up with effective synergistic strategies. The work presented in this thesis contributes to the field by exploring the potential of the Synthetic Aperture Radar (SAR), SAR Polarimetry (PolSAR), SAR Interferometry (InSAR), Polarimetric SAR Interferometry (PolInSAR), Light Detection and Ranging (LiDAR), and multispectral remote sensing in forest characterization and monitoring from three main aspects including forest height estimation, active fire detection, and burned area mapping. First, the forest height inversion is demonstrated using airborne L-band dual-baseline repeat-pass PolInSAR data based on modified versions of the Random Motion over Ground (RMoG) model, where the scattering attenuation and wind-derived random motion are described in conditions of homogeneous and heterogeneous volume layer, respectively. A boreal and a tropical forest test site are involved in the experiment to explore the flexibility of different models over different forest types and based on that, a leveraging strategy is proposed to boost the accuracy of forest height estimation. The accuracy of the model-based forest height inversion is limited by the discrepancy between the theoretical models and actual scenarios and exhibits a strong dependency on the system and scenario parameters. Hence, high vertical accuracy LiDAR samples are employed to assist the PolInSAR-based forest height estimation. This multi-source forest height estimation is reformulated as a pan-sharpening task aiming to generate forest heights with high spatial resolution and vertical accuracy based on the synergy of the sparse LiDAR-derived heights and the information embedded in the PolInSAR data. This process is realized by a specifically designed generative adversarial network (GAN) allowing high accuracy forest height estimation less limited by theoretical models and system parameters. Related experiments are carried out over a boreal and a tropical forest to validate the flexibility of the method. An automated active fire detection framework is proposed for the medium resolution multispectral remote sensing data. The basic part of this framework is a deep-learning-based semantic segmentation model specifically designed for active fire detection. A dataset is constructed with open-access Sentinel-2 imagery for the training and testing of the deep-learning model. The developed framework allows an automated Sentinel-2 data download, processing, and generation of the active fire detection results through time and location information provided by the user. Related performance is evaluated in terms of detection accuracy and processing efficiency. The last part of this thesis explored whether the coarse burned area products can be further improved through the synergy of multispectral, SAR, and InSAR features with higher spatial resolutions. A Siamese Self-Attention (SSA) classification is proposed for the multi-sensor burned area mapping and a multi-source dataset is constructed at the object level for the training and testing. Results are analyzed by different test sites, feature sources, and classification methods to assess the improvements achieved by the proposed method. All developed methods are validated with extensive processing of multi-source data acquired by Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Land, Vegetation, and Ice Sensor (LVIS), PolSARproSim+, Sentinel-1, and Sentinel-2. I hope these studies constitute a substantial contribution to the forest applications of multi-source remote sensing

The use of remotely sensed data for forest biomass monitoring : a case of forest sites in north-eastern Armenia

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesIn recent years there has been an increasing interest in the use of synthetic aperture radar (SAR) data and geospatial technologies for environmental monitoring․ Particularly, forest biomass evaluation was of high importance, as forests have a crucial role in global carbon emission. Within this study we evaluate the use of Sentinel 1 C-band multitemporal SAR data with combination of Alos Palsar L-band SAR and Sentinel 2 multispectral remote sensing (RS) data for mapping forest aboveground biomass (AGB) of dry subtropical forests in mountainous areas. Field observation from National Forest Inventory was used as a ground truth data. As the SAR data suffers greatly by the complex topography, a simple approach of aspect and slope information as forestry ancillary data was implemented directly in the regression model for the first time to mitigate the topography effect on radar backscattering value․ Dense time-series analysis allowed us to overcome the SAR saturation by the forest phenology and select the optimal C-band scene. Image texture measures of SAR data has been strongly related to the biomass distribution and has robustly contributed to the prediction․ Multilinear Stepwise Regression allowed to select and evaluate the most relevant variables for AGB. The prediction model combining RS with ancillary data explained the 62 % of variance with root-mean-square error of 56.6 t ha¯¹. The study also reveals that C-band SAR data on forest biomass prediction is limited due to their short wavelength. Further, the mountainous condition is a major constraint for AGB estimation. Additionally, this research demonstrates a positive outcome in forest AGB prediction with freely accessible RS data

The BIOMASS level 2 prototype processor : design and experimental results of above-ground biomass estimation

BIOMASS is ESA’s seventh Earth Explorer mission, scheduled for launch in 2022. The satellite will be the first P-band SAR sensor in space and will be operated in fully polarimetric interferometric and tomographic modes. The mission aim is to map forest above-ground biomass (AGB), forest height (FH) and severe forest disturbance (FD) globally with a particular focus on tropical forests. This paper presents the algorithms developed to estimate these biophysical parameters from the BIOMASS level 1 SAR measurements and their implementation in the BIOMASS level 2 prototype processor with a focus on the AGB product. The AGB product retrieval uses a physically-based inversion model, using ground-canceled level 1 data as input. The FH product retrieval applies a classical PolInSAR inversion, based on the Random Volume over Ground Model (RVOG). The FD product will provide an indication of where significant changes occurred within the forest, based on the statistical properties of SAR data. We test the AGB retrieval using modified airborne P-Band data from the AfriSAR and TropiSAR campaigns together with reference data from LiDAR-based AGB maps and plot-based ground measurements. For AGB estimation based on data from a single heading, comparison with reference data yields relative Root Mean Square Difference (RMSD) values mostly between 20% and 30%. Combining different headings in the estimation process significantly improves the AGB retrieval to slightly less than 20%. The experimental results indicate that the implemented retrieval scheme provides robust results that are within mission requirements

Polarimetric Synthetic Aperture Radar

This open access book focuses on the practical application of electromagnetic polarimetry principles in Earth remote sensing with an educational purpose. In the last decade, the operations from fully polarimetric synthetic aperture radar such as the Japanese ALOS/PalSAR, the Canadian Radarsat-2 and the German TerraSAR-X and their easy data access for scientific use have developed further the research and data applications at L,C and X band. As a consequence, the wider distribution of polarimetric data sets across the remote sensing community boosted activity and development in polarimetric SAR applications, also in view of future missions. Numerous experiments with real data from spaceborne platforms are shown, with the aim of giving an up-to-date and complete treatment of the unique benefits of fully polarimetric synthetic aperture radar data in five different domains: forest, agriculture, cryosphere, urban and oceans

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed