PALSAR wide-area mapping and annual monitoring methodology for Borneo

This paper describes the operational radar mapping processing chain developed and steps taken to produce a provisional wide-area PALSAR forest and land cover map of Borneo for the year 2007, compliant with emerging international standards (CEOS guidelines, FAO LCCS). The methodology is based on the classification of FBS and FBD image pairs. To cover Borneo the equivalent of 554 standard images is required. The final overall accuracy assessment result shows this demonstration map product is in 85.5% full agreement with the independent reference dataset and in 7.8% ‘partial agreement’. Monitoring land cover change on an annual basis requires consistent year-to-year mapping. This implies that the localised and temporal effects of environmental factors on the backscatter level (such as inundation or El Niño drought) and variation due to differing observation dates/cycles (related to change of season) have to be accounted for strip by strip. New concepts for (a) automated intercalibration of radar data, (b) time-consistency and (c) automated adaptation of radar signatures to changing environmental conditions have been evaluated for its usefulness to improve the classification and the consistency of annual monitoring

Panoramic mosaics from Chang’E-3 PCAM images at Point A

This paper presents a unique approach for panoramic mosaics based on Moon surface images from the Chang’E-3 (CE-3) mission, with consideration of the exposure time and external illumination changes in CE-3 Panoramic Camera (PCAM) imaging. The engineering implementation involves algorithms of image feature points extraction by using Speed-Up Robust Features (SURF), and a newly defined measure is used to obtain the corresponding points in feature matching. Then, the transformation matrix is calculated and optimized between adjacent images by the Levenberg–Marquardt algorithm. Finally, an image is reconstructed by using a fade-in-fade-out method based on linear interpolation to achieve a seamless mosaic. The developed algorithm has been tested with CE-3 PCAM images at Point A (one of the rover sites where the rover is separated from the lander). This approach has produced accurate mosaics from CE-3 PCAM images, as is indicated by the value of the Peak Signal to Noise Ratio (PSNR), which is greater than 31 dB between the overlapped region of the images before and after fusion

Massive stereo-based DTM production for Mars on cloud computers

Digital Terrain Model (DTM) creation is essential to improving our understanding of the formation processes of the Martian surface. Although there have been previous demonstrations of open-source or commercial planetary 3D reconstruction software, planetary scientists are still struggling with creating good quality DTMs that meet their science needs, especially when there is a requirement to produce a large number of high quality DTMs using "free" software. In this paper, we describe a new open source system to overcome many of these obstacles by demonstrating results in the context of issues found from experience with several planetary DTM pipelines. We introduce a new fully automated multi-resolution DTM processing chain for NASA Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) and High Resolution Imaging Science Experiment (HiRISE) stereo processing, called the Co-registration Ames Stereo Pipeline (ASP) Gotcha Optimised (CASP-GO), based on the open source NASA ASP. CASP-GO employs tie-point based multi-resolution image co-registration, and Gotcha sub-pixel refinement and densification. CASP-GO pipeline is used to produce planet-wide CTX and HiRISE DTMs that guarantee global geo-referencing compliance with respect to High Resolution Stereo Colour imaging (HRSC), and thence to the Mars Orbiter Laser Altimeter (MOLA); providing refined stereo matching completeness and accuracy. All software and good quality products introduced in this paper are being made open-source to the planetary science community through collaboration with NASA Ames, United States Geological Survey (USGS) and the Jet Propulsion Laboratory (JPL), Advanced Multi-Mission Operations System (AMMOS) Planetary Data System (PDS) Pipeline Service (APPS-PDS4), as well as browseable and visualisable through the iMars web based Geographic Information System (webGIS) system

Seismotectonic, structural, volcanologic, and geomorphic study of New Zealand; indigenous forest assessment in New Zealand; mapping, land use, and environmental studies in New Zealand, volume 1

The author has identified the following significant results. Results of the atmospheric extinction measurements show clearly the greater opacity of the atmosphere in MSS band 4 which is due to Rayleigh scattering. Atmospheric water vapor absorbs strongly in a wide region between 900 nm and 1000 nm, and this results in a consistently higher extinction coefficient than would otherwise be expected in MSS band 7. The short term fluctuations tend to be greater in band 7 than in the other bands, and this effect is probably due to variations of water vapor concentration in the instrument line of sight. These high extinction coefficients and short term fluctuations in band 7 were observed at Menindee which is in a semi-desert region in western New South Wales

Mapping intertidal vegetation in the wash estuary using remote sensing techniques

The mapping and monitoring of the intertidal zone of the East Coast of England is of considerable interest to conservationists and coastal managers. Intertidal vegetation offers natural protection against coastal erosion and considerably reduces the cost of man made sea defences. Monitoring of intertidal vegetation may also be of value in providing an early warning of sea-level change. This thesis considers the most effective way of classifying and monitoring the intertidal zone using remote sensing techniques and incorporating the results into a coastal monitoring Geographic Information System (GIS). The coastal monitoring GIS is used to model the advantages and disadvantages of different classification strategies. The Wash Estuary forms the principal study area. This study uses multi-temporal data which requires atmospheric and radiometric correction. All Landsat 5 TM images used in the present study were referenced to a common image, based on the techniques of F.G.Hall of NASA, to allow meaningful comparisons to be made. The data sets were geometrically corrected to allow incorporation into the coastal monitoring GIS. Two strategies were used to classify the intertidal zone: a conventional maximum likelihood classifier and a fully constrained mixture model using the least squares technique. The results of the classifications were incorporated into the coastal monitoring GIS along with information acquired from previous ground based surveys. Statistical analysis of the classifications was carried out by cross tabulation with the ground based surveys in order to determine the accuracy of the methods. Detailed, reliable information arrived at cheaply, objectively and at regular intervals would provide a valuable resource for the management and monitoring of the coastal environment

A Global Human Settlement Layer from optical high resolution imagery - Concept and first results

A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Airborne Hyperspectral Imaging of Lakes

In a time of rising concern about climate change and pollution, the water quality of large lakes acts as an indicator of the health of the environment. To study the water quality at a large scale - up to several hundreds of kilometres - hyperspectral remote sensing is emerging as the main solution. Indeed, different quantities relevant to water quality, like turbidity or concentratrion in chlorophyll-a, can be measured using the spectral reflectance of the water column. Additionally, airborne and spaceborne sensors can cover large areas, thus allowing to study the water at a much larger scale than when simply taking water samples at specific points. Airborne hyperspectral imaging, in particular, offers an acceptable ground resolution - around a metre - which allows to map relevant quantities precisely. However, few existing projects deliver maps that have both a sufficient ground resolution and a large coverage. Furthermore, most existing sensors do not offer a fine spectral resolution, which is for instance crucial when studying the presence of chlorophyll-a, which can only be detected in a narrow range of the electromagnetic spectrum. This thesis presents our work with a hyperspectral sensor developed and used by the Geodetic Engineering Laboratory of EPFL in the Léman-Baïkal project, a cooperative work which aimed at studying both Lake Geneva (Switzerland) and Lake Baikal (Russia). The project included ultralight plane flights with an onboard pushbroom scanner, which allowed to collect data over large areas with a fine spectral resolution. Alongside the use of this sensor came problematics which are at the centre of this thesis: the georeferencing of the scan lines, their radiometric calibration, their analysis and the softwaremanagement of this data. In the following, we present a new method to georeference pushbroom scan lines that uses co-acquired frame images to perform coregistration and to achieve a georeferencing, which RMSE is up to 20 times smaller than the direct one. We propose an efficient radiometric self-calibration method to convert the sensor output to water-leaving reflectance; this method makes use of the visible peaks of atmospheric absorption to align the spectral bands with those of a reference acquisition, and uses the near infrared properties of deep water and vegetation to performabsolute calibration. The last part of the processing - the software management, including data compression - was solved by developing a software called HYPerspectral Orthorectification Software (HypOS). This software is the synthesis of our work, including the tools to performgeometric correction, radiometric calibration and data compression of our hyperspectral data. Two examples of applications are given: the first one deals with mapping chlorophyll-a in the Rhone Delta of Lake Geneva; the second, at a larger scale, uses satellite data to monitor ice coverage over large lakes like Onega or Ladoga (Russia)

Techniques for wide-area mapping of forest biomass using radar data

Aspects of forest biomass mapping using SAR (Synthetic Aperture Radar) data were studied in study sites in northern Sweden, Germany, and south-eastern Finland. Terrain topography – via the area of a resolution cell – accounted for 61 percent of the total variation in a Seasat (L-band) SAR scene in a hilly and mountainous study site. A methodology – based on least squares adjustment of tie point and ground control point observations in a multi-temporal SAR mosaic dataset – produced a tie point RMSE (Root Mean Square Error) of 56 m and a GCP RMSE of 240 m in the African mosaic of the GRFM (Global Rain Forest Mapping) project. The mosaic consisted of 3624 JERS SAR scenes. A calibration revision methodology – also based on least squares adjustment and points in overlap areas between scenes – removed a calibration artifact of about 1 dB. A systematic search of the highest correlation between forest stem volume and backscattering amplitude was conducted over all combinations of transmit and receive polarisations in three AIRSAR scenes in a German study site. In the P-band, a high and narrow peak around HV-polarisation was found, where the correlation coefficient was 0.75, 0.59, and 0.71 in scenes acquired in August 1989, June 1991, and July 1991, respectively. In other polarisations of P-band, the correlation coefficient was lower. In L-band, the polarisation response was more flat and correlations lower, between 0.54 and 0.70 for stands with a stem volume 100 m3/ha or less. Three summer-time JERS SAR scenes produced very similar regression models between forest stem volume and backscattering amplitude in a study site in south-eastern Finland. A model was proposed for wide area biomass mapping when biomass accuracy requirements are not high. A multi-date regression model employing three summer scenes and three winter scenes produced a multiple correlation coefficient of 0.85 and a stem volume estimation RMSE of 41.3 m3/ha. JERS SAR scenes that were acquired in cold winter conditions produced very low correlations between stem volume and backscattering amplitude.reviewe

Determining estuarine seagrass density measures from low altitude multispectral imagery flown by remotely piloted aircraft

Seagrass is the subject of significant conservation research. Seagrass is ecologically important and of significant value to human interests. Many seagrass species are thought to be in decline. Degradation of seagrass populations are linked to anthropogenic environmental issues. Effective management requires robust monitoring that is affordable at large scale. Remote sensing methods using satellite and aircraft imagery enable mapping of seagrass populations at landscape scale. Aerial monitoring of a seagrass population can require imagery of high spatial and/or spectral resolution for successful feature extraction across all levels of seagrass density. Remotely piloted aircraft (RPA) can operate close to the ground under precise flight control enabling repeated surveys in high detail with accurate revisit-positioning. This study evaluates a method for assessing intertidal estuarine seagrass (Zostera muelleri) presence/absence and coverage density using multispectral imagery collected by a remotely piloted aircraft (RPA) flying at 30 m above the estuary surface (2.7 cm ground sampling distance). The research was conducted at Wharekawa Harbour on the eastern coast of the Coromandel Peninsula, North Island, New Zealand. Differential drainage of residual ebb waters from the surface of an estuary at low tide creates a mosaic of drying sediment, draining surface and static shallow pooling that has potential to interfere with spectral observations. The field surveys demonstrated that despite minor shifts in the spectral coordinates of seagrass and other surface material, there was no apparent difference in image classification outcome from the time of bulk tidal water clearance to the time of returning tidal flood. For the survey specification tested, classification accuracy increased with decreasing segmentation scale. Pixel-based image analysis (PBIA) achieved higher classification accuracy than object-based image analysis (OBIA) assessed at a range of segmentation scales. Contaminating objects such as shells and detritus can become aggregated within polygon objects when OBIA is applied but remain as isolated objects under PBIA at this image resolution. There was clear separability of spectra for seagrass and sediment, but shell and detritus confounded the classification of seagrass density in some situations. High density seagrass was distinct from sediment, but classification error arose for sparse seagrass. Three classifiers (linear discriminant analysis, support vector machine and random forest) and three feature selection options (no selection, collinearity reduction and recursive feature elimination) were assessed for effect on classification performance. The random forest classifier yielded the highest classification accuracy, with no accuracy benefit gained from collinearity reduction or recursive feature elimination. Spectral vegetation indices and texture layers substantially improved classification accuracy. Object geometry made a negligible contribution to classification accuracy using mean-shift segmentation at this image-scale. The method achieved classification of seagrass density with up to 84% accuracy on a three-tier end-member class scale (low, medium, and high density) when using training data formed using visual interpretation of ground reference photography, and up to 93% accuracy using precisely measured seagrass leaf-area. Visual interpretation agreed with precisely measured seagrass leaf area 88% of the time with some misattribution at mid-density. Visual interpretation was substantially faster to apply than measuring the leaf area. A decile class scale for seagrass density correlated with actual leaf area measures more than the three-tier scale, however, was less accurate for absolute class attribution. The research demonstrates that seagrass feature extraction from RPA-flown imagery is a feasible and repeatable option for seagrass population monitoring and environmental reporting. Further calibration is required for whole- and multi-estuary application