Remote Sensing of the Oceans

This book covers different topics in the framework of remote sensing of the oceans. Latest research advancements and brand-new studies are presented that address the exploitation of remote sensing instruments and simulation tools to improve the understanding of ocean processes and enable cutting-edge applications with the aim of preserving the ocean environment and supporting the blue economy. Hence, this book provides a reference framework for state-of-the-art remote sensing methods that deal with the generation of added-value products and the geophysical information retrieval in related fields, including: Oil spill detection and discrimination; Analysis of tropical cyclones and sea echoes; Shoreline and aquaculture area extraction; Monitoring coastal marine litter and moving vessels; Processing of SAR, HF radar and UAV measurements

Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella

The main aim in forest mapping and monitoring is to produce accurate information for forest managers with the use of efficient methodologies. For example, it is important to locate harvesting sites and stands where forest operations should be carried out as well as to provide updates regarding forest growth, among other changes in forest structure. In recent years, remote sensing (RS) has taken a significant technological leap forward. It has become possible to acquire three-dimensional (3D), spatially accurate information from forest resources using active RS methods. In practical applications, mainly 3D information produced by airborne laser scanning (ALS) has opened up groundbreaking potential in natural resource mapping and monitoring. In addition to ALS, new satellite radars are also capable of acquiring spatially accurate 3D information. The main objectives of the present study were to develop 3D RS methodologies for large-area forest mapping and monitoring applications. In substudy I, we aim to map harvesting sites, while in substudy II, we monitor changes in the forest canopy structure. In studies III-V, efficient mapping and monitoring applications were developed and tested. In substudy I, we predicted plot-level thinning maturity within the next 10-year planning period. Stands requiring immediate thinning were located with an overall accuracy of 83%-86% depending on the prediction method applied. The respective prediction accuracy for stands reaching thinning maturity within the next 10 years was 70%-79%. Substudy II addressed natural disturbance monitoring that could be linked to forest management planning when an ALS time series is available. The accuracy of the damaged canopy cover area estimate varied between -16.4% to 5.4%. Substudy II showed that changes in the forest canopy structure can be monitored with a rather straightforward method by contrasting bi-temporal canopy height models. In substudy III, we developed a RS-based forest inventory method where single-tree RS is used to acquire modelling data needed in area-based predictions. The method uses ALS data and is capable of producing accurate stand variable estimates even at the sub-compartment level. The developed method could be applied in areas with sparse road networks or when the costs of fieldwork must be minimized. The method is especially suitable for large-area biomass or stem volume mapping. Based on substudy IV, the use of stereo synthetic aperture radar (SAR) satellite data in the prediction of plot-level forest variables appears to be promising for large-area applications. In the best case, the plot-level stem volume (VOL) was predicted with a relative error (RMSE%) of 34.9%. Typically, such a high level of prediction accuracy cannot be obtained using spaceborne RS data. Then, in substudy V, we compared the aboveground biomass and VOL estimates derived by radargrammetry to the ALS estimates. The difference between the estimation accuracy of ALS based and TerraSAR X based features was smaller than in any previous study in which ALS and different kinds of SAR materials have been compared. In this thesis, forest mapping and monitoring applications using active 3D RS were developed. Spatially accurate 3D RS enables the mapping of harvesting sites, the monitoring of changes in the canopy structure and even the making of a fully RS-based forest inventory. ALS is carried out at relatively low altitudes, which makes it relatively expensive per area unit, and other RS materials are still needed. Spaceborne stereo radargrammetry proved to be a promising technique to acquire additional 3D RS data efficiently as long as an accurate digital terrain model is available as a ground-surface reference.Metsien kartoitus ja seuranta aktiivisella 3D-kaukokartoituksella. Metsävaroista kerätään mahdollisimman tarkkaa tietoa metsänomistajan päätöksenteon tueksi. Tietoa kerätään puustotunnusten lisäksi toimenpidekohteista ja metsässä tapahtuvista muutoksista, kuten kasvusta ja luonnontuhoista. Laajojen metsäalueiden kartoituksessa käytetään apuna lentokoneesta tai satelliiteista tehtävää kaukokartoitusta. Metsien kaukokartoitus on viime vuosina ottanut merkittävän kehitysaskeleen, kun aktiiviset 3D-kaukokartoitusmenetelmät ovat yleistyneet. Aktiivisessa kaukokartoituksessa, kuten laserkeilauksessa ja tutkakuvauksessa instrumentti vastaanottaa lähettämäänsä säteilyä. Laserkeilaus tuottaa kohteesta 3D-havaintoja, jotka metsäalueilla kuvaavat suoraan puuston pituutta ja metsän tiheyttä. Laserkeilauksella kohteesta saadaan tällä hetkellä tyypillisesti 0,5−20 havaintoa/m2. Laserkeilaus tehdään lentokoneesta 500−3000 m korkeudesta, jolloin aineiston hankinta laajoilta alueilta on kallista verrattuna satelliittikuviin. Myös satelliittitutkakuvilta voidaan tuottaa spatiaalisesti tarkkaa 3D-tietoa, jonka pistetiheys on tosin huomattavasti harvempaa kuin laserkeilauksella. Tutkimuksessa kehitettiin sovelluksia metsien kartoitukseen ja seurantaan hyödyntäen aktiivisia 3D-kaukokartoitusmenetelmiä. Metsiköiden toimenpidetarvetta ennustettiin onnistuneesti laserkeilausaineiston avulla. Harvennettaviksi luokitellut metsiköt pystyttiin kartoittamaan 70%−86% tarkkuudella. Kahden ajankohdan laserkeilausaineistoja käytettiin lumituhojen vuoksi vaurioituneiden puiden kartoittamiseen. Tuhoutuneen latvuspinta-alan kartoitus perustui laserkeilausaineistosta tuotettujen latvusmallien erotuskuviin. Kehitetty menetelmä soveltuu latvusrakenteessa tapahtuneiden muutosten, kuten lumi- ja tuulituhojen, kartoittamiseen ja seurantaan. Laajojen metsäalueiden kartoitus perustuu yleensä kaksivaiheeseen inventointimenetelmään, jossa käytetään maastomittauksia ja tiedon yleistyksessä kaukokartoitusaineistoa. Kartoitusta voidaan tehostaa joko maastomittauksia vähentämällä tai hyödyntämällä mahdollisimman halpaa kaukokartoitusaineistoa. Tutkimuksessa kehitettiin täysin kaukokartoitukseen perustuva kaksivaiheinen metsien inventointimenetelmä. Tarvittava maastotieto mitattiin suoraan laserkeilausaineistosta. Menetelmä soveltuu puuston tilavuuden tai biomassan kartoitukseen erityisesti alueille, joilla maastomittausten kustannukset ovat merkittävät. Satelliittitutkakuvat ovat potentiaalinen aineisto etenkin laajojen alueiden metsävarojen seurannassa. Synteettisen apertuurin tutka (SAR)-stereokuvilta mitattiin automaattisesti 3D-pisteitä, joita käytettiin puustotunnusten ennustamisessa. Keskitilavuus ennustettiin parhaimmillaan lähes samalla tarkkuudella kuin laserkeilauksella. Tutkimus osoitti aktiivisen 3D-kaukokartoitustiedon mahdollistavan entistä yksityiskohtaisemman metsien kartoituksen ja seurannan

Error Propagation Analysis for Remotely Sensed Aboveground Biomass

Edited version available. Full version will remain embargoed due to copyright. AS DCAbstract Above-Ground Biomass (AGB) assessment using remote sensing has been an active area of research since the 1970s. However, improvements in the reported accuracy of wide scale studies remain relatively small. Therefore, there is a need to improve error analysis to answer the question: Why is AGB assessment accuracy still under doubt? This project aimed to develop and implement a systematic quantitative methodology to analyse the uncertainty of remotely sensed AGB, including all perceptible error types and reducing the associated costs and computational effort required in comparison to conventional methods. An accuracy prediction tool was designed based on previous study inputs and their outcome accuracy. The methodology used included training a neural network tool to emulate human decision making for the optimal trade-off between cost and accuracy for forest biomass surveys. The training samples were based on outputs from a number of previous biomass surveys, including 64 optical data based studies, 62 Lidar data based studies, 100 Radar data based studies, and 50 combined data studies. The tool showed promising convergent results of medium production ability. However, it might take many years until enough studies will be published to provide sufficient samples for accurate predictions. To provide field data for the next steps, 38 plots within six sites were scanned with a Leica ScanStation P20 terrestrial laser scanner. The Terrestrial Laser Scanning (TLS) data analysis used existing techniques such as 3D voxels and applied allometric equations, alongside exploring new features such as non-plane voxel layers, parent-child relationships between layers and skeletonising tree branches to speed up the overall processing time. The results were two maps for each plot, a tree trunk map and branch map. An error analysis tool was designed to work on three stages. Stage 1 uses a Taylor method to propagate errors from remote sensing data for the products that were used as direct inputs to the biomass assessment process. Stage 2 applies a Monte Carlo method to propagate errors from the direct remote sensing and field inputs to the mathematical model. Stage 3 includes generating an error estimation model that is trained based on the error behaviour of the training samples. The tool was applied to four biomass assessment scenarios, and the results show that the relative error of AGB represented by the RMSE of the model fitting was high (20-35% of the AGB) in spite of the relatively high correlation coefficients. About 65% of the RMSE is due to the remote sensing and field data errors, with the remaining 35% due to the ill-defined relationship between the remote sensing data and AGB. The error component that has the largest influence was the remote sensing error (50-60% of the propagated error), with both the spatial and spectral error components having a clear influence on the total error. The influence of field data errors was close to the remote sensing data errors (40-50% of the propagated error) and its spatial and non-spatial Overall, the study successfully traced the errors and applied certainty-scenarios using the software tool designed for this purpose. The applied novel approach allowed for a relatively fast solution when mapping errors outside the fieldwork areas.HCED iraq, Middle Technical Universit

Establishing the sensitivity of Synthetic Aperture Radar to above-ground biomass in wooded savannas

Radar for biomass estimation has been widely investigated for temperate, boreal and tropical forests, yet tropical savanna woodlands, which generally form non-continuous cover canopies or sparse woodlands, have been largely neglected in biomass studies. This thesis evaluates the capability of Synthetic Aperture Radar (SAR) for estimating the above-ground biomass of the woody vegetation in a savanna in Belize, Central America. This is achieved by evaluating (i) polarimetric Synthetic Aperture Radar (SAR) backscatter and (ii) single-pass shortwave interferometric SAR (InSAR) as indicators of above-ground biomass. Specifically, the effect on SAR backscatter of woody vegetation structure such as canopy cover, basal area, vegetation height and above-ground biomass is evaluated. Since vegetation height is often correlated to above-ground biomass, the effectiveness of vegetation height retrieval from InSAR is evaluated as an indicator of above-ground biomass. The study area, situated in Belize, is representative of Central American savannas. Radar data used are AIRSAR fully polarimetric L- and P-band SAR, and AIRSAR C-band InSAR, Intermap Technologies STAR-3i X-band InSAR, and Shuttle Radar Topography Mission (SRTM) C-band InSAR. The field data comprise accurately georeferenced three-dimensional measurements for 1,133 trees and shrubs and 75 palmetto clumps and thickets in a transect of 800 m x 60 m which spans the main savanna vegetation strata of the study area. An additional 2,464 ground points were observed. Results show that savanna woodlands present a challenge for radar remote sensing methods due to the sparse and heterogeneous nature of savanna woodlands. Long-wave SAR backscatter is dominated not only by high biomass areas, but also by areas of leafy palmetto which have low vegetative biomass. Retrieved woodland canopy heights from X- and C-band InSAR are indicative of the general patterns of tree height, although retrieved heights are underestimated. The amount of underestimation is variable across the different canopy conditions. Of these two methods, the shortwave InSAR data give a better indication of the spatial distribution of the above-ground biomass of the woody vegetation in the savannas than SAR backscatter. These results have implications for new and planned future global biomass estimation missions, such as ALOS PALSAR, ESA’s planned P-band BIOMASS and TanDEM-X. Without appropriate mediation, SAR backscatter methods might overestimate above-ground biomass of the woody vegetation of savannas while InSAR height retrieval methods might underestimate biomass estimates. Some possible mediating approaches are discussed

Remote Sensing

This dual conception of remote sensing brought us to the idea of preparing two different books; in addition to the first book which displays recent advances in remote sensing applications, this book is devoted to new techniques for data processing, sensors and platforms. We do not intend this book to cover all aspects of remote sensing techniques and platforms, since it would be an impossible task for a single volume. Instead, we have collected a number of high-quality, original and representative contributions in those areas

Remote Sensing methods for power line corridor surveys

AbstractTo secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

Application of Black-Bridge Satellite Imagery for the Spatial Distribution of Salvage Cutting in Stands Damaged by Wind

Salvage logging is performed to remove the fallen and damaged trees after a natural disturbance, e.g., fire or windstorm. From an economic point of view, it is desirable to remove the most valuable merchantable timber, but usually, the process depends mainly on topography and distance to forest roads. The objective of this study was to evaluate the suitability of the Black-Bridge satellite imagery for the spatial distribution of salvage cutting in southern Poland after the severe windstorm in July 2015. In particular, this study aimed to determine which factors influence the spatial distribution of salvage cutting. The area of windthrow and the distribution of salvage cutting (July–August 2015 and August 2015–May 2016) were delineated using Black-Bridge satellite imagery. The distribution of the polygons (representing windthrow and salvage cutting) was verified with maps of aspect, elevation and slope, derived from the Digital Terrain Model and the distance to forest roads, obtained from the Digital Forest Map. The analysis included statistical modelling of the relationships between the process of salvage cutting and selected geographical and spatial features. It was found that the higher the elevation and the steeper the slope, the lower the probability of salvage cutting. Exposure was also found to be a relevant factor (however, it was difficult to interpret) as opposed to the distance to forest roads

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

Application of Black-Bridge Satellite Imagery for the Spatial Distribution of Salvage Cutting in Stands Damaged by Wind

Salvage logging is performed to remove the fallen and damaged trees after a natural disturbance, e.g., fire or windstorm. From an economic point of view, it is desirable to remove the most valuable merchantable timber, but usually, the process depends mainly on topography and distance to forest roads. The objective of this study was to evaluate the suitability of the Black-Bridge satellite imagery for the spatial distribution of salvage cutting in southern Poland after the severe windstorm in July 2015. In particular, this study aimed to determine which factors influence the spatial distribution of salvage cutting. The area of windthrow and the distribution of salvage cutting (July–August 2015 and August 2015–May 2016) were delineated using Black-Bridge satellite imagery. The distribution of the polygons (representing windthrow and salvage cutting) was verified with maps of aspect, elevation and slope, derived from the Digital Terrain Model and the distance to forest roads, obtained from the Digital Forest Map. The analysis included statistical modelling of the relationships between the process of salvage cutting and selected geographical and spatial features. It was found that the higher the elevation and the steeper the slope, the lower the probability of salvage cutting. Exposure was also found to be a relevant factor (however, it was difficult to interpret) as opposed to the distance to forest roads