Improving georeferencing accuracy of Very High Resolution satellite imagery using freely available ancillary data at global coverage

While impressive direct geolocation accuracies better than 5.0 m CE90 (90% of circular error) can be achieved from the last DigitalGlobe’s Very High Resolution (VHR) satellites (i.e. GeoEye-1 and WorldView-1/2/3/4), it is insufficient for many precise geodetic applications. For these sensors, the best horizontal geopositioning accuracies (around 0.55 m CE90) can be attained by using third-order 3D rational functions with vendor’s rational polynomial coefficients data refined by a zero-order polynomial adjustment obtained from a small number of very accurate ground control points (GCPs). However, these high-quality GCPs are not always available. In this work, two different approaches for improving the initial direct geolocation accuracy of VHR satellite imagery are proposed. Both of them are based on the extraction of three-dimensional GCPs from freely available ancillary data at global coverage such as multi-temporal information of Google Earth and the Shuttle Radar Topography Mission 30 m digital elevation model. The application of these approaches on WorldView-2 and GeoEye-1 stereo pairs over two different study sites proved to improve the horizontal direct geolocation accuracy values around of 75%

A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

A quantitative assessment of forest cover change in the Moulouya River watershed (Morocco) was carried out by means of an innovative approach from atmospherically corrected reflectance Landsat images corresponding to 1984 (Landsat 5 Thematic Mapper) and 2013 (Landsat 8 Operational Land Imager). An object-based image analysis (OBIA) was undertaken to classify segmented objects as forested or non-forested within the 2013 Landsat orthomosaic. A Random Forest classifier was applied to a set of training data based on a features vector composed of different types of object features such as vegetation indices, mean spectral values and pixel-based fractional cover derived from probabilistic spectral mixture analysis). The very high spatial resolution image data of Google Earth 2013 were employed to train/validate the Random Forest classifier, ranking the NDVI vegetation index and the corresponding pixel-based percentages of photosynthetic vegetation and bare soil as the most statistically significant object features to extract forested and non-forested areas. Regarding classification accuracy, an overall accuracy of 92.34% was achieved. The previously developed classification scheme was applied to the 1984 Landsat data to extract the forest cover change between 1984 and 2013, showing a slight net increase of 5.3% (ca. 8800 ha) in forested areas for the whole region

Open access data in polar and cryospheric remote sensing

This paper aims to introduce the main types and sources of remotely sensed data that are freely available and have cryospheric applications. We describe aerial and satellite photography, satellite-borne visible, near-infrared and thermal infrared sensors, synthetic aperture radar, passive microwave imagers and active microwave scatterometers. We consider the availability and practical utility of archival data, dating back in some cases to the 1920s for aerial photography and the 1960s for satellite imagery, the data that are being collected today and the prospects for future data collection; in all cases, with a focus on data that are openly accessible. Derived data products are increasingly available, and we give examples of such products of particular value in polar and cryospheric research. We also discuss the availability and applicability of free and, where possible, open-source software tools for reading and processing remotely sensed data. The paper concludes with a discussion of open data access within polar and cryospheric sciences, considering trends in data discoverability, access, sharing and use.A. Pope would like to acknowledge support from the Earth Observation Technology Cluster, a knowledge exchange project, funded by the Natural Environment Research Council (NERC) under its Technology Clusters Programme, the U.S. National Science Foundation Graduate Research Fellowship Program, Trinity College (Cambridge) and the Dartmouth Visiting Young Scientist program sponsored by the NASA New Hampshire Space Grant.This is the final published version. It's also available from MDPI at http://www.mdpi.com/2072-4292/6/7/6183

NOAA Coastal Change Analysis Program (C-CAP): Guidance for Regional Implementation

EXECUTIVE SUMMARY: The Coastal Change Analysis Programl (C-CAP) is developing a nationally standardized database on landcover and habitat change in the coastal regions of the United States. C-CAP is part of the Estuarine Habitat Program (EHP) of NOAA's Coastal Ocean Program (COP). C-CAP inventories coastal submersed habitats, wetland habitats, and adjacent uplands and monitors changes in these habitats on a one- to five-year cycle. This type of information and frequency of detection are required to improve scientific understanding of the linkages of coastal and submersed wetland habitats with adjacent uplands and with the distribution, abundance, and health of living marine resources. The monitoring cycle will vary according to the rate and magnitude of change in each geographic region. Satellite imagery (primarily Landsat Thematic Mapper), aerial photography, and field data are interpreted, classified, analyzed, and integrated with other digital data in a geographic information system (GIS). The resulting landcover change databases are disseminated in digital form for use by anyone wishing to conduct geographic analysis in the completed regions. C-CAP spatial information on coastal change will be input to EHP conceptual and predictive models to support coastal resource policy planning and analysis. CCAP products will include 1) spatially registered digital databases and images, 2) tabular summaries by state, county, and hydrologic unit, and 3) documentation. Aggregations to larger areas (representing habitats, wildlife refuges, or management districts) will be provided on a case-by-case basis. Ongoing C-CAP research will continue to explore techniques for remote determination of biomass, productivity, and functional status of wetlands and will evaluate new technologies (e.g. remote sensor systems, global positioning systems, image processing algorithms) as they become available. Selected hardcopy land-cover change maps will be produced at local (1:24,000) to regional scales (1:500,000) for distribution. Digital land-cover change data will be provided to users for the cost of reproduction. Much of the guidance contained in this document was developed through a series of professional workshops and interagency meetings that focused on a) coastal wetlands and uplands; b) coastal submersed habitat including aquatic beds; c) user needs; d) regional issues; e) classification schemes; f) change detection techniques; and g) data quality. Invited participants included technical and regional experts and representatives of key State and Federal organizations. Coastal habitat managers and researchers were given an opportunity for review and comment. This document summarizes C-CAP protocols and procedures that are to be used by scientists throughout the United States to develop consistent and reliable coastal change information for input to the C-CAP nationwide database. It also provides useful guidelines for contributors working on related projects. It is considered a working document subject to periodic review and revision.(PDF file contains 104 pages.

Combining Multitemporal Microwave and Optical Remote Sensing Data. Mapping of Land Use / Land Cover, Crop Type, and Crop Traits

Humanity has changed the earth’s surface to a dramatic extent. This is especially true for the area used for agricultural production. Against the background of a growing world population and the associated increased demand for food, it is precisely this area that will become even more important in the future. In order not to have to allocate even more land to agricultural use, optimization and intensification is the only way out of the dilemma. In this context, precise Geoinformation of the agriculturally used area is of central importance. It is utilized for improving land use, producing yield forecasts for more stable food security, and optimizing agricultural management. Rapid developments in the field of satellite-based remote sensing sensors make it possible to monitor agricultural areas with increased spatial, spectral and temporal resolution. However, to retrieve the needed information from this data, new methods are needed. Furthermore, the quality of the data has to be verified. Only then can the presented geodata help to grow crops more sustainably and more efficiently. This thesis develops new approaches for monitoring agricultural areas using the technology of microwave remote sensing in combination with optical remote sensing and existing geodata. It is framed by the overall objective to obtain knowledge on how this combination of data can provide the necessary geoinformation for land use studies, precision farming, and agricultural monitoring systems. Hundreds of remote sensing images from more than eight different satellites were analyzed in six research studies from two different Areas of Interest (AOIs). The studies guide through various spatial scales. First, the general Land Use / Land Cover (LULC) on a regional level in a multi-sensor scenario is derived, evaluating different sensor combinations of varying resolutions. Next, an innovative method is proposed, through which the high geometric accuracy of radar-imaging satellite sensors is exploited to update the spatial accuracy of any external geodata of lower spatial accuracy. Such external data is then used in the next two studies, which focus on cost-effective crop type mapping using Synthetic Aperture Radar (SAR) images. The resulting enhanced LULC maps present the annually changing crop types of the region alongside external, official geoinformation that is not retrievable from remote sensing sensors. The last two research studies deal with a single maize field, on which high resolution optical WorldView-2 images and experimental bistatic SAR observations from TanDEM-X are assessed and combined with ground measurements. As a result, this thesis shows that, depending on the AOI and the application, different resolution demands need to be fulfilled before LULC, crop type, and crop traits mapping can be performed with adequate accuracy. The spatial resolution needs to be adapted to the particularities of the AOI. Evaluation of the sensors showed that SAR sensors proved beneficial for the study objective. Processing the SAR images is complicated, and the images are unintuitive at first sight. However, the advantage of SAR sensors is that they work even in cloudy conditions. This results in an increased temporal resolution, which is particularly important for monitoring the highly dynamic agricultural area. Furthermore, the high geometric accuracy of the SAR images proved ideal for implementing the Multi-Data Approach (MDA). Thus information-rich external geodata could be used to lower the remote sensing resolution needs, improve the accuracy of the LULC-maps, and to provide enhanced LULC-maps. The first study of the maize field demonstrates the potential of the WorldView-2 data in predicting in-field biomass variations, and its increased accuracy when fused with plant height measurements. The second study shows the potential of the TanDEM-X Constellation (TDM) to retrieve plant height from space. LULC, crop type and information on the spatial distribution of biomass can thus be derived efficiently and with high accuracy from the combination of SAR, optical satellites and external geodata. The shown analyses for acquiring such geoinformation represent a high potential for helping to solve the future challenges of agricultural production

Landscape scale mapping of tundra vegetation structure at ultra-high resolution using UAVs and computer vision

Ilmastomuutoksella on voimakkain vaikutus suurten leveysasteiden ekosysteemeissä, jotka ovat sopeutuneet viileään ilmastoon. Jotta suurella mittakaavalla havaittuja muutoksia tundrakasvillisuudessa ja niiden takaisinkytkentävaikutuksia ilmastoon voidaan ymmärtää ja ennustaa luotettavammin, on syytä tarkastella mitä tapahtuu pienellä mittakaavalla; jopa yksittäisissä kasveissa. Lähivuosikymmenten aikana tapahtunut teknologinen kehitys on mahdollistanut kustannustehokkaiden, kevyiden ja pienikokoisten miehittämättömien ilma-alusten (UAV) yleistymisen. Erittäin korkearesoluutioisten aineistojen (pikselikoko <10cm) lisääntyessä ja tullessa yhä helpommin saataville, ympäristön tarkastelussa käytetyt kaukokartoitusmenetelmät altistuvat paradigmanmuutokselle, kun konenäköön ja -oppimiseen perustuvat algoritmit ja analyysit yleistyvät. Menetelmien käyttöönotto on houkuttelevaa, koska ne mahdollistavat joustavan ja pitkälle automatisoidun aineistonkeruun ja erittäin tarkkojen kaukokartoitustuotteiden tuottamisen vaikeasti tavoitettavilta alueilta, kuten tundralla. Luotettavien tulosten saaminen vaatii kuitenkin huolellista suunnittelua sekä prosessointialgoritmien ja -parametrien pitkäjänteistä testaamista. Tässä tutkimuksessa tarkasteltiin, kuinka tarkasti tavallisella digitaalikameralla kerätyistä ilmakuvista johdetuilla muuttujilla voidaan kartoittaa kasvillisuuden rakennetta maisemamittakaavalla. Kilpisjärvellä Pohjois-Fennoskandiassa kerättiin dronella kolmensadan hehtaarin kokoiselta alueelta yhteensä noin 10 000 ilmakuvasta koostuva aineisto. Lisäksi alueella määritettiin 1183 pisteestä dominantti putkilokasvillisuus, sekä kasvillisuuden korkeus. Ilmakuvat prosessoitiin tiheiksi kolmiulotteisiksi pistepilviksi konenäköön ja fotogrammetriaan perustuvalla SfM (Structure from Motion) menetelmällä. Pistepilvien pohjalta interpoloitiin maastomalli sekä kasvillisuuden korkeusmalli. Lisäksi tuotettiin koko alueen kattava ilmakuvamosaiikki. Näiden aineistojen pohjalta laskettiin muuttujia, joita käytettiin yhdessä maastoreferenssiaineiston kanssa kasvillisuuden objektipohjaisessa analyysissä (GEOBIA, Geographical Object-Based Image Analysis). Suodatetut maanpintapisteet vastasivat luotettavasti todellista maanpinnan korkeutta koko alueella ja tuotetut korkeusmallit korreloivat voimakkaasti maastoreferenssiaineiston kanssa. Maastomallin virhe oli suurin alueilla, joilla oli korkeaa kasvillisuutta. Valaistusolosuhteissa ja kasvillisuudessa tapahtuneet muutokset ilmakuvien keruun aikana aiheuttivat haasteita objektipohjaisen analyysin molemmissa vaiheissa: segmentoinnissa ja luokittelussa. mutta kokonaistarkkuus parani 0,27:stä 0,,54:n kun luokitteluun lisättiin topografiaa, kasvillisuuden korkeutta ja tekstuuria kuvaavia muuttujia ja kohdeluokkien lukumäärää vähennettiin. Konenäköön ja –oppimiseen perustuvat menetelmät pystyvät tuottamaan tärkeää tietoa tundran kasvillisuuden rakenteesta, erityisesti kasvillisuuden korkeudesta, maisemassa. Lisää tutkimusta kuitenkin tarvitaan parhaiden algoritmien ja parametrien määrittämiseksi tundraympäristössä, jossa ympäristöolosuhteet muuttuvat nopeasti ja kasvillisuus on heterogeenistä ja sekoittunutta, mikä aiheuttaa eroja ilmakuvien välillä ja lisää vaikeuksia analyyseissä.Climate change has the strongest impact on high-latitude ecosystems that are adapted to cool climates. In order to better understand and predict the changes in tundra vegetation observed on large scales as well as their feedbacks onto climate, it is necessary to look at what is happening at finer scales; even in individual plants. Technological developments over the past few decades have enabled the spread of cost-effective, light and small unmanned aerial vehicles (UAVs). As very high-resolution data (pixel size <10cm) becomes more and more available, the remote sensing methods used in environmental analysis become subject to a paradigm shift as algorithms and analyzes based on machine vision and learning turn out to be more common. Harnessing new methods is attractive because they allow flexible and highly automated data collection and the production of highly accurate remote sensing products from hard-to-reach areas such as the tundra. However, obtaining reliable results requires careful planning and testing of processing algorithms and parameters. This study looked at how accurately variables derived from aerial images collected with an off-the-shelf digital camera can map the vegetation structure on a landscape scale. In Kilpisjärvi, northern Fennoscandia, a total of ~ 10,000 aerial photographs were collected by drone covering an area of three hundred hectares. In addition, dominant vascular plants were identified from 1183 points in the area, as well as vegetation height. Aerial images were processed into dense three-dimensional point clouds by using SfM (Structure from Motion) method, which is based on computer vision and digital photogrammetry. From the point clouds terrain models and vegetation height models were interpolated. In addition, image mosaic covering the entire area was produced. Based on these data, predictive variables were calculated, which were used together with the terrain reference data in Geographical Object-Based Image Analysis (GEOBIA). The filtered ground points corresponded to observations throughout the region, and the produced elevation models strongly correlated with the ground reference data. The terrain model error was greatest in areas with tall vegetation. Changes in lighting conditions and vegetation during aerial image surveys posed challenges in both phases of object-based analysis: segmentation and classification. but overall accuracy improved from 0.27 to 0.54 when topography, vegetation height and texture variables were added to the classifier and the number of target classes was reduced. Methods based on machine vision and learning can produce important information about vegetation structure, vegetation height, in a landscape. However, more research is needed to determine the best algorithms and parameters in a tundra environment where environmental conditions change rapidly and vegetation is heterogeneous and mixed, causing differences between aerial images and difficulties in analyses

A low-cost remote sensing system for agricultural applications

This research develops a low cost remote sensing system for use in agricultural applications. The important features of the system are that it monitors the near infrared and it incorporates position and attitude measuring equipment allowing for geo-rectified images to be produced without the use of ground control points. The equipment is designed to be hand held and hence requires no structural modification to the aircraft. The portable remote sensing system consists of an inertia measurement unit (IMU), which is accelerometer based, a low-cost GPS device and a small format false colour composite digital camera. The total cost of producing such a system is below GBP 3000, which is far cheaper than equivalent existing systems. The design of the portable remote sensing device has eliminated bore sight misalignment errors from the direct geo-referencing process. A new processing technique has been introduced for the data obtained from these low-cost devices, and it is found that using this technique the image can be matched (overlaid) onto Ordnance Survey Master Maps at an accuracy compatible with precision agriculture requirements. The direct geo-referencing has also been improved by introducing an algorithm capable of correcting oblique images directly. This algorithm alters the pixels value, hence it is advised that image analysis is performed before image georectification. The drawback of this research is that the low-cost GPS device experienced bad checksum errors, which resulted in missing data. The Wide Area Augmented System (WAAS) correction could not be employed because the satellites could not be locked onto whilst flying. The best GPS data were obtained from the Garmin eTrex (15 m kinematic and 2 m static) instruments which have a highsensitivity receiver with good lock on capability. The limitation of this GPS device is the inability to effectively receive the P-Code wavelength, which is needed to gain the best accuracy when undertaking differential GPS processing. Pairing the carrier phase L1 with the pseudorange C/A-Code received, in order to determine the image coordinates by the differential technique, is still under investigation. To improve the position accuracy, it is recommended that a GPS base station should be established near the survey area, instead of using a permanent GPS base station established by the Ordnance Survey

NEW ADVANCED TECHNOLOGIES FOR SURVEY AND ANALYSISbOF AGROFORESTRY LAND: FROM LAND COVER CHANGES TO RURAL LANDSCAPE QUALITY ASSESSMENT

The general objective of this Ph.D. thesis is to explore the concepts and methodologies for investigating agroforestry land and rural landscape through the integration of historical and remote sensing geodata within a FoSS (Free and Open Source Software) approach; to provide more and more accurate data sets regarding land cover and to improve some mapping and data processing techniques commonly used in this research topic. The first part of thesis describes the different types of geodata used in the course of the studies and, above all, the techniques and methodologies used for their processing are illustrated. Starting from historical cartographies, we will go through aerial surveys and geographical maps up to the new remote sensing using advanced satellite observation technologies. In the second part, more specific issues were dealt in accordance with the general objective of the work have been defined. The issues were approached through case studies within the Basilicata Region where the intensity of the abandonment of the territory and agricultural surface is leading to the loss of many historical rural landscapes and with consequent problems from an ecological point of view due to the disappearance of many agroforestry systems.L'obiettivo generale di questa tesi di dottorato è quello di esplorare i concetti e le metodologie per lo studio del territorio agroforestale e del paesaggio rurale attraverso l'integrazione di geodati storici e telerilevamento con un approccio FoSS (Free and Open Source Software); per fornire serie di dati sempre più accurate sulla copertura del suolo e migliorare alcune tecniche di mappatura ed elaborazione comunemente utilizzate in questo ambito di ricerca. La prima parte della tesi descrive i diversi tipi di geodati impiegati nel corso degli studi e, soprattutto, vengono illustrate le tecniche e le metodologie utilizzate per la loro elaborazione. Partendo dalle cartografie storiche, si passerà ai rilievi aerei ed alle cartogrofaie classifche fino al remote sensing basato su immagini satellitari. Nella seconda parte sono state trattate tematiche più specifiche in accordo con l'obiettivo generale del lavoro. Le tematiche sono state affrontate attraverso casi di studio all'interno della Regione Basilicata dove l'intensità dell'abbandono del territorio e della superficie agricola sta portando alla perdita di molti paesaggi rurali storici con conseguenti problemi dal punto di vista ecologico dovuti alla scomparsa di molti sistemi agroforestali

On the Use of Unmanned Aerial Systems for Environmental Monitoring

Environmental monitoring plays a central role in diagnosing climate and management impacts on natural and agricultural systems; enhancing the understanding of hydrological processes; optimizing the allocation and distribution of water resources; and assessing, forecasting, and even preventing natural disasters. Nowadays, most monitoring and data collection systems are based upon a combination of ground-based measurements, manned airborne sensors, and satellite observations. These data are utilized in describing both small- and large-scale processes, but have spatiotemporal constraints inherent to each respective collection system. Bridging the unique spatial and temporal divides that limit current monitoring platforms is key to improving our understanding of environmental systems. In this context, Unmanned Aerial Systems (UAS) have considerable potential to radically improve environmental monitoring. UAS-mounted sensors offer an extraordinary opportunity to bridge the existing gap between field observations and traditional air- and space-borne remote sensing, by providing high spatial detail over relatively large areas in a cost-effective way and an entirely new capacity for enhanced temporal retrieval. As well as showcasing recent advances in the field, there is also a need to identify and understand the potential limitations of UAS technology. For these platforms to reach their monitoring potential, a wide spectrum of unresolved issues and application-specific challenges require focused community attention. Indeed, to leverage the full potential of UAS-based approaches, sensing technologies, measurement protocols, postprocessing techniques, retrieval algorithms, and evaluation techniques need to be harmonized. The aim of this paper is to provide an overview of the existing research and applications of UAS in natural and agricultural ecosystem monitoring in order to identify future directions, applications, developments, and challengespublishersversionPeer reviewe