Forest cover estimation in Ireland using radar remote sensing: a comparative analysis of forest cover assessment methodologies

Quantification of spatial and temporal changes in forest cover is an essential component of forest monitoring programs. Due to its cloud free capability, Synthetic Aperture Radar (SAR) is an ideal source of information on forest dynamics in countries with near-constant cloud-cover. However, few studies have investigated the use of SAR for forest cover estimation in landscapes with highly sparse and fragmented forest cover. In this study, the potential use of L-band SAR for forest cover estimation in two regions (Longford and Sligo) in Ireland is investigated and compared to forest cover estimates derived from three national (Forestry2010, Prime2, National Forest Inventory), one pan-European (Forest Map 2006) and one global forest cover (Global Forest Change) product. Two machine-learning approaches (Random Forests and Extremely Randomised Trees) are evaluated. Both Random Forests and Extremely Randomised Trees classification accuracies were high (98.1–98.5%), with differences between the two classifiers being minimal (<0.5%). Increasing levels of post classification filtering led to a decrease in estimated forest area and an increase in overall accuracy of SAR-derived forest cover maps. All forest cover products were evaluated using an independent validation dataset. For the Longford region, the highest overall accuracy was recorded with the Forestry2010 dataset (97.42%) whereas in Sligo, highest overall accuracy was obtained for the Prime2 dataset (97.43%), although accuracies of SAR-derived forest maps were comparable. Our findings indicate that spaceborne radar could aid inventories in regions with low levels of forest cover in fragmented landscapes. The reduced accuracies observed for the global and pan-continental forest cover maps in comparison to national and SAR-derived forest maps indicate that caution should be exercised when applying these datasets for national reporting

Assessment of multi-temporal, multi-sensor radar and ancillary spatial data for grasslands monitoring in Ireland using machine learning approaches

Accurate inventories of grasslands are important for studies of carbon dynamics, biodiversity conservation and agricultural management. For regions with persistent cloud cover the use of multi-temporal synthetic aperture radar (SAR) data provides an attractive solution for generating up-to-date inventories of grasslands. This is even more appealing considering the data that will be available from upcoming missions such as Sentinel-1 and ALOS-2. In this study, the performance of three machine learning algorithms; Random Forests (RF), Support Vector Machines (SVM) and the relatively underused Extremely Randomised Trees (ERT) is evaluated for discriminating between grassland types over two large heterogeneous areas of Ireland using multi-temporal, multi-sensor radar and ancillary spatial datasets. A detailed accuracy assessment shows the efficacy of the three algorithms to classify different types of grasslands. Overall accuracies ≥ 88.7% (with kappa coefficient of 0.87) were achieved for the single frequency classifications and maximum accuracies of 97.9% (kappa coefficient of 0.98) for the combined frequency classifications. For most datasets, the ERT classifier outperforms SVM and RF

Spaceborne radar observations: A guide for Magellan radar-image analysis

Geologic analyses of spaceborne radar images of Earth are reviewed and summarized with respect to detecting, mapping, and interpreting impact craters, volcanic landforms, eolian and subsurface features, and tectonic landforms. Interpretations are illustrated mostly with Seasat synthetic aperture radar and shuttle-imaging-radar images. Analogies are drawn for the potential interpretation of radar images of Venus, with emphasis on the effects of variation in Magellan look angle with Venusian latitude. In each landform category, differences in feature perception and interpretive capability are related to variations in imaging geometry, spatial resolution, and wavelength of the imaging radar systems. Impact craters and other radially symmetrical features may show apparent bilateral symmetry parallel to the illumination vector at low look angles. The styles of eruption and the emplacement of major and minor volcanic constructs can be interpreted from morphological features observed in images. Radar responses that are governed by small-scale surface roughness may serve to distinguish flow types, but do not provide unambiguous information. Imaging of sand dunes is rigorously constrained by specific angular relations between the illumination vector and the orientation and angle of repose of the dune faces, but is independent of radar wavelength. With a single look angle, conditions that enable shallow subsurface imaging to occur do not provide the information necessary to determine whether the radar has recorded surface or subsurface features. The topographic linearity of many tectonic landforms is enhanced on images at regional and local scales, but the detection of structural detail is a strong function of illumination direction. Nontopographic tectonic lineaments may appear in response to contrasts in small-surface roughness or dielectric constant. The breakpoint for rough surfaces will vary by about 25 percent through the Magellan viewing geometries from low to high Venusian latitudes. Examples of anomalies and system artifacts that can affect image interpretation are described

Active microwave users working group program planning

A detailed programmatic and technical development plan for active microwave technology was examined in each of four user activities: (1) vegetation; (2) water resources and geologic applications, and (4) oceanographic applications. Major application areas were identified, and the impact of each application area in terms of social and economic gains were evaluated. The present state of knowledge of the applicability of active microwave remote sensing to each application area was summarized and its role relative to other remote sensing devices was examined. The analysis and data acquisition techniques needed to resolve the effects of interference factors were reviewed to establish an operational capability in each application area. Flow charts of accomplished and required activities in each application area that lead to operational capability were structured

Radar signal return from near-shore surface and shallow subsurface features, Darien Province, Panama

The AN/APQ-97 radar imagery over eastern Panama is analyzed. The imagery was directed toward extraction of geologic and engineering data and the establishment of operational parameters. Subsequent investigations emphasized landform identification and vegetation distribution. The parameters affecting the observed return signal strength from such features are considered. Near-shore ocean phenomena were analyzed. Tidal zone features such as mud flats and reefs were identified in the near range, but were not detectable in the far range. Surface roughness dictated the nature of reflected energy (specular or diffuse). In surf zones, changes in wave train orientation relative to look direction, the slope of the surface, and the physical character of the wave must be considered. It is concluded that the establishment of the areal extent of the tidal flats, distributary channels, and reefs is practical only in the near to intermediate range under minimal low tide conditions

Application of multispectral radar and LANDSAT imagery to geologic mapping in death valley

Side-Looking Airborne Radar (SLAR) images, acquired by JPL and Strategic Air Command Systems, and visible and near-infrared LANDSAT imagery were applied to studies of the Quaternary alluvial and evaporite deposits in Death Valley, California. Unprocessed radar imagery revealed considerable variation in microwave backscatter, generally correlated with surface roughness. For Death Valley, LANDSAT imagery is of limited value in discriminating the Quaternary units except for alluvial units distinguishable by presence or absence of desert varnish or evaporite units whose extremely rough surfaces are strongly shadowed. In contrast, radar returns are most strongly dependent on surface roughness, a property more strongly correlated with surficial geology than is surface chemistry

Accomplishments of the NASA Johnson Space Center portion of the soil moisture project in fiscal year 1981

The NASA/JSC ground scatterometer system was used in a row structure and row direction effects experiment to understand these effects on radar remote sensing of soil moisture. Also, a modification of the scatterometer system was begun and is continuing, to allow cross-polarization experiments to be conducted in fiscal years 1982 and 1983. Preprocessing of the 1978 agricultural soil moisture experiment (ASME) data was completed. Preparations for analysis of the ASME data is fiscal year 1982 were completed. A radar image simulation procedure developed by the University of Kansas is being improved. Profile soil moisture model outputs were compared quantitatively for the same soil and climate conditions. A new model was developed and tested to predict the soil moisture characteristic (water tension versus volumetric soil moisture content) from particle-size distribution and bulk density data. Relationships between surface-zone soil moisture, surface flux, and subsurface moisture conditions are being studied as well as the ways in which measured soil moisture (as obtained from remote sensing) can be used for agricultural applications

Synthetic aperture radar (L band) and optical vegetation indices for discriminating the Brazilian savanna physiognomies: A comparative analysis

The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere-Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados' physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics

Amélioration des estimations hydrométriques dérivées des données altimétriques satellitaires acquises sur des étendues d’eau continentales soumises à l’englacement

Les eaux douces continentales constituent l’une des composantes principales du cycle de l’eau. Elles assurent sa continuité à travers des échanges de flux d’eau et d’énergie avec ses différentes composantes. De nombreux plans d’eau douce (lacs, rivières, réservoirs, etc.) se retrouvent dans les régions situées dans les hautes latitudes nord, où la cryosphère est dominante. L’une des particularités de ces plans d’eau est la congélation partielle ou complète pendant les saisons froides. De plus, ils ont une grande sensibilité aux changements climatiques. En effet, les variations spatio-temporelles du climat aux échelles régionales et locales affectent grandement l’hydrologie de ces plans d’eau en termes de niveau d’eau et de débit. D’où l’intérêt de disposer d’outils simples et efficaces pour surveiller et gérer ces ressources. L’inaccessibilité aux plans d’eau isolés et l’effet de la glace sur la qualité des mesures des niveaux d’eau à l’échelle des stations limnimétriques rendent la surveillance de la variation des niveaux d’eau difficiles. Compte tenu de sa couverture spatio-temporelle, de sa période de répétitivité, et des bandes de fréquence utilisées, l’altimétrie radar par satellite pourrait être une meilleure alternative pour surmonter les limites liées aux mesures in situ. Cependant, la présence de cibles hétérogènes, comme les couverts de glace, présente un défi majeur pour exploiter les données des niveaux d’eau dérivées de la technologie par satellite altimétrique au-dessus des plans d’eau couverts de glace. Cette étude a pour ultime objectif d’améliorer les estimations des niveaux d’eau dérivées de l’altimétrie radar par satellite sur des étendues d’eau continentales couvertes de glace. L’étude s’applique à étudier le potentiel de deux satellites altimétriques, Jason-2 et SARAL/Altika, possédant des caractéristiques technologiques différentes, à suivre les variations des niveaux d’eau des étendues d’eau soumises à l’englacement sur le territoire canadien. Le premier objectif spécifique de cette étude concerne l’analyse de la capacité des algorithmes de retraitements utilisés par les missions Jason-2 et SARAL/Altika à estimer les niveaux d’eau sur vingt étendues d’eau couvertes de glace au Canada. Cette analyse est effectuée sur les produits dérivés des algorithmes de retraitement et sur les mesures in situ pendant deux périodes : la période de recouvrement des satellites Jason-2 et SARAL/Altika, comprise entre 2008 et 2016, et les périodes des variations saisonnières de l’état de surface. Les résultats montrent que pour Jason-2, c’est l’algorithme de seuillage ICE-1 qui fournit les meilleures estimations de niveau d’eau, avec des erreurs RMSE non biaisées (unRMSE) ≤ 0,3 m et des r ≥ 0,8 pour 90 % des étendues d’eau. Pour ce qui est de SARAL/Altika, la majorité des algorithmes de retraitement utilisés donnent des résultats très comparables aux observations in situ, démontrant les bonnes performances de la technologie SARAL. Cependant, les algorithmes de retraitement utilisés par les deux satellites Jason-2 et SARAL/Altika fournissent des précisions faibles pendant les périodes marquées par le mélange eau-glace, c’est-à-dire les périodes de gel et de dégel. Le deuxième objectif spécifique est d’améliorer les estimations des niveaux d’eau issues du satellite Jason-2 pendant les périodes de gel et de dégel. Une approche de détection automatique est proposée afin de discriminer les points de mesure de l’eau libre, de la glace pure et de la glace partielle sur quatre plans d’eau couverts de glace : le Grand Lac des Esclaves, le lac Athabasca, le lac Winnipeg, et le lac des Bois. Cette approche se base sur l’intégration des données actives et passives du satellite Jason-2 dans un processus de clustering afin de définir les clusters correspondant à chaque état de surface. L’application du seuil de détection du cluster de l’eau libre a permis d'améliorer la qualité des mesures de niveau d'eau pendant les périodes de gel et de dégel. Les résultats montrent que le coefficient de corrélation r est amélioré d’environ 0,8 à plus de 0,9 avec des biais inférieurs à 20 cm. Le troisième objectif spécifique évalue le potentiel de l’approche de détection automatique des points de mesures développé dans l’objectif 2, avec les données du satellite SARAL/Altika. Dans cette partie, les données actives et passives dérivées du satellite SARAL/Altika ont été exploitées pour concevoir les seuils de discrimination de chaque état de surface (eau libre, glace pure, glace partielle de gel et dégel) sur les mêmes quatre plans d’eau étudiés. L’application du seuil de l’eau libre offre une amélioration de la qualité des mesures de niveau de l’eau avec une amélioration des corrélations r d’environ 0,8 à plus de 0,92 avec des biais inférieurs à 10 cm. Le quatrième objectif spécifique met en place une approche de classification des formes d’onde selon la nature et l’état de surface pendant les périodes de gel et de dégel pour les satellites altimétriques Jason-2 et SARAL/Altika. Le site d’étude considéré pour le développement de cette approche est le Grand Lac des Esclaves. Un processus de classification non supervisée basé sur les paramètres des formes d’onde et les résultats des interprétations des données altimétriques et radiométriques sur l’état de surface a été utilisé avant de développer l’approche de classification supervisée des formes d’onde pour Jason-2 et SARAL/Altika, nommée le modèle entrainé de classification - Classification Trained Model (CTM). Les modèles supervisés du K-plus proche voisin (KNN, K-Nearest Neighbour) et de machine à vecteurs de support (SVM, Support Vector Machine) ont été évalués pour cette conception. Le modèle basé sur l’approche SVM a produit les meilleurs résultats, présentant une précision globale (Overall Accuracy) de l’ordre de 92 % avec Jason-2 et de 98 % avec SARAL/Altika. Ce modèle développé est utilisé pour classifier l’ensemble des formes d’onde en fonction de l’état de surface du plan d’eau étudié. Les résultats ont été superposés à des produits Moderate Resolution Imaging Spectroradiometer (MODIS) pour une évaluation qualitative et semi-quantitative.Abstract : The continental freshwater is one of the main components of the water cycle. These resouces ensure its continuity through the exchange of water and energy fluxes with the different components of the water cycle. Most of the continental water bodies (lakes, rivers, reservoirs, etc.) are in the northern high latitudes, dominated by the cryosphere. These water bodies froze completely or partly during cold seasons. In addition, they have a high sensitivity to climate change. Climate variations at the local and global scales may affect the hydrological regime (water level and flow) of these water bodies. Hence the interest in having a simple and efficient tools to monitor changes of these resources. The gauging stations could not provide good measurements of water level due to the limited accessibility of isolated water bodies, and the potential contamination of measured data by ice. Satellite radar altimetry appears as a good alternative to overcome these limitations given its spatiotemporal coverage, its ground track repetitivity period, and the frequency bands used. However, the presence of heterogeneous targets within the altimeter footprint, such as ice cover, remains a major challenge to estimate water levels over ice-covered water bodies. The aim of this study is to improve the estimations of water levels obtained from spatial radar altimetry over ice-covered water bodies. This study investigates the potential of the two satellites altimetry Jason-2 and SARAL/Altika with different characteristics to monitor water-level changes over ice-covered water bodies in the Canadian territory. The first objective of this study is to analyze the potential of Jason-2 and SARAL/Altika retracking algorithms to retrieve water levels from altimeter measurements acquired over 20 ice-covered water bodies across Canada. In this analysis, products derived from retracking algorithms were compared with in situ measurements during two periods: (1) the time series considered for each satellite (2008–2016 for Jason-2, and 2013–2016 for SARAL/Altika); and (2) the freeze-thaw periods included in each time series. The results showed that retracking ICE-1 (used with Jason-2 data) provided better water level accuracy for 90% of the studied water bodies (r ≥ 0.8, unbiased RMSE ≤ 0.3 m). All the retracking algorithms used by SARAL/Altika provided results that are comparable to in situ observations, thus denoting the good performance of the SARAL technology. However, all retracking algorithms used by Jason-2 and SARAL/Altika provide low accuracy during freeze-up and thaw periods. The second objective attempts to improve the measurements of water levels obtained by Jason-2 data during freeze and thaw periods. Here, an automatic approach is proposed to identify the Jason-2 altimetry measurements corresponding to open water, ice, and transition (water ice) over four Canadian lakes: Great Slave Lake, Lake Athabasca, Lake Winnipeg, and Lake of the Woods. This approach is based on the integration of backscatter coefficients and peakiness at Ku-band and brightness temperature observations obtained from Jason-2 data in a clustering process to define the clusters and threshold of each surface state. The use of open water threshold improves the quality of water-level estimation over the four lakes during freeze-up and thaw periods. The results show that the coefficient of correlation (r) increased in average from about 0.8 without the use of the thresholds to more than 0.90, with unbiased RMSE errors less than 20 cm. The third objective evaluates the efficiency of the automatic approach proposed in the second objective, with SARAL/Altika data. In this section, active and passive observations derived from SARAL/Altika data were used to design the thresholds of each state surface (open water, pure ice, ice freeze-up, and ice break-up) over the same four studied water bodies. The application of open water threshold improved the quality of water levels measurements from r ~ 0.8 to r more than 0.92 with unbiased RMSE less than 10 cm. The fourth objective proposes a new approach for classifying waveforms data derived from Jason-2 and SARAL/Altika satellite missions during freeze-up and thaw periods based on the surface state over ice-covered water bodies. The considered study area for the development of this approach is Great Slave Lake. An unsupervised classification process based on waveform parameters and the results of interpretations of active and passive data was used before developing the supervised classification approach for Jason-2 and SARAL/Altika, named Classification Trained Model (CTM). K-nearest neighbor (KNN) and support vector machine (SVM) models were evaluated for this concept. The SVM-based model provided the best results (accuracy of 92% with Jason-2, and 98% with SARAL/Altika). It was used to classify all waveforms of the studied water body. Results were superimposed to MODIS products for qualitative visual and semi-quantitative assessments

Cassini Titan Radar Mapper

The Cassini Titan Radar Mapper is a multimode radar instrument designed to probe the optically inaccessible surface of Titan, Saturn's largest moon. The instrument is to be included in the payload of the Cassini Saturn Mission, scheduled for launch in 1995. The individual modes of Cassini Radar Mapper will allow topographic mapping and surface imaging at few hundred meters resolution. The requirements that lay behind the design are briefly discussed, and the configuration and capability of the instrument are described. The present limited knowledge of Titan's surface and the measurement requirements imposed on the radar instrument are addressed. Also discussed are the Cassini mission and the projected orbits, which imposed another set of design constraints that led to the multitude of modes and to an unconventional antenna configuration. The antenna configuration and the different radar modes are described