Laser remote sensing applications (LiDAR) in forest hydrology and the management of river ecosystems.

La utilización de sensores láser desde plataformas aéreas (LiDAR) ofrece nuevas posibilidades en el cartografiado de sistemas fluviales, tanto en áreas densamente cubiertas por vegetación, como en aquellas que presentan una escasa cubierta. La información topográfica de alta resolución que se obtiene a partir de las medidas láser puede ser utilizada en el análisis y estimación de diversas variables hidrológicas, y en el estudio de diferentes componentes del medio fluvial. Entre éstas, cabe citar la vegetación riparia, la morfología fluvial, el régimen hidrológico o el grado de alteración de los ecosistemas debido a las presiones de origen antrópico. La gestión del medio fluvial puede ser mejorada en gran medida gracias a la precisión y fiabilidad de esta información. En muchas ocasiones, el escaso relieve de los valles fluviales y la densa cubierta vegetal que existe en ellos han dificultado la aplicación de otras técnicas de teledetección. Sin embargo, los datos obtenidos mediante altimetría láser son especialmente aconsejables para estos trabajos, mediante análisis numéricos o a través de la simple interpretación de las imágenes obtenidas. Este artículo muestra las posibilidades de uso de los datos LiDAR en hidrología forestal y en la gestión de zonas húmedas, a lo largo de tramos con condiciones climáticas bien diferenciadas. En todas ellas, se comparan los resultados obtenidos mediante la aplicación de distinto software, con el fin de mostrar la mejor metodología de tratamiento de la información láser. Asimismo, se muestra la diferencia con otras técnicas de teledetección, y se muestra la fácil integración de los datos LiDAR con otras herramientas y metodologías de estudio de las variables citadas.

Random-modulation cw lidar system for space-borne carbon dioxide remote sensing based on a high-brightness semiconductor laser

In this paper, we report on the progresses of the BRITESPACE Consortium in order to achieve space-borne LIDAR measurements of atmospheric carbon dioxide concentration based on an all semiconductor laser source at 1.57 ?m. The complete design of the proposed RM-CW IPDA LIDAR has been presented and described in detail. Complete descriptions of the laser module and the FSU have been presented. Two bended MOPAs, emitting at the sounding frequency of the on- and off- IPDA channels, have been proposed as the transmitter optical sources with the required high brightness. Experimental results on the bended MOPAs have been presented showing a high spectral purity and promising expectations on the high output power requirements. Finally, the RM-CW approach has been modelled and an estimation of the expected SNR for the entire system is presented. Preliminary results indicate that a CO2 retrieval precision of 1.5 ppm could be achieved with an average output power of 2 W for each channel.

Evaluation of aerial remote sensing techniques for vegetation management in power line corridors

The following paper presents an evaluation of airborne sensors for use in vegetation management in powerline corridors. Three integral stages in the management process are addressed including, the detection of trees, relative positioning with respect to the nearest powerline and vegetation height estimation. Image data, including multi-spectral and high resolution, are analyzed along with LiDAR data captured from fixed wing aircraft. Ground truth data is then used to establish the accuracy and reliability of each sensor thus providing a quantitative comparison of sensor options. Tree detection was achieved through crown delineation using a Pulse-Coupled Neural Network (PCNN) and morphologic reconstruction applied to multi-spectral imagery. Through testing it was shown to achieve a detection rate of 96%, while the accuracy in segmenting groups of trees and single trees correctly was shown to be 75%. Relative positioning using LiDAR achieved a RMSE of 1.4m and 2.1m for cross track distance and along track position respectively, while Direct Georeferencing achieved RMSE of 3.1m in both instances. The estimation of pole and tree heights measured with LiDAR had a RMSE of 0.4m and 0.9m respectively, while Stereo Matching achieved 1.5m and 2.9m. Overall a small number of poles were missed with detection rates of 98% and 95% for LiDAR and Stereo Matching.

Visual pattern recognition models for remote sensing of civil infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Visual Pattern Recognition Models for Remote Sensing of Civil Infrastructure

As-built models have been proven useful in many project-related applications, such as progress monitoring and quality control. However, they are not widely produced in most projects because a lot of effort is still necessary to manually convert remote sensing data from photogrammetry or laser scanning to an as-built model. In order to automate the generation of as-built models, the first and fundamental step is to automatically recognize infrastructure-related elements from the remote sensing data. This paper outlines a framework for creating visual pattern recognition models that can automate the recognition of infrastructure-related elements based on their visual features. The framework starts with identifying the visual characteristics of infrastructure element types and numerically representing them using image analysis tools. The derived representations, along with their relative topology, are then used to form element visual pattern recognition (VPR) models. So far, the VPR models of four infrastructure-related elements have been created using the framework. The high recognition performance of these models validates the effectiveness of the framework in recognizing infrastructure-related elements.

Use of airborne remote sensing to detect riverside Brassica rapa to aid in risk assessment of transgenic crops

High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modelling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery.

Remote sensing of intertidal morphological change in Morecambe Bay, U.K., between 1991 and 2007

Tidal Flats are important examples of extensive areas of natural environment that remain relatively unaffected by man. Monitoring of tidal flats is required for a variety of purposes. Remote sensing has become an established technique for the measurement of topography over tidal flats. A further requirement is to measure topographic changes in order to measure sediment budgets. To date there have been few attempts to make quantitative estimates of morphological change over tidal flat areas. This paper illustrates the use of remote sensing to measure quantitative and qualitative changes in the tidal flats of Morecambe Bay during the relatively long period 1991–2007. An understanding of the patterns of sediment transport within the Bay is of considerable interest for coastal management and defence purposes. Tidal asymmetry is considered to be the dominant cause of morphological change in the Bay, with the higher currents associated with the flood tide being the main agency moulding the channel system. Quantitative changes were measured by comparing a Digital Elevation Model (DEM) of the intertidal zone formed using the waterline technique applied to satellite Synthetic Aperture Radar (SAR) images from 1991–1994, to a second DEM constructed from airborne laser altimetry data acquired in 2005. Qualitative changes were studied using additional SAR images acquired since 2003. A significant movement of sediment from below Mean Sea Level (MSL) to above MSL was detected by comparing the two Digital Elevation Models, though the proportion of this change that could be ascribed to seasonal effects was not clear. Between 1991 and 2004 there was a migration of the Ulverston channel of the river Leven north-east by about 5 km, followed by the development of a straighter channel to the west, leaving the previous channel decoupled from the river. This is thought to be due to independent tidal and fluvial forcing mechanisms acting on the channel. The results demonstrate the effectiveness of remote sensing for measurement of long-term morphological change in tidal flat areas. An alternative use of waterlines as partial bathymetry for assimilation into a morphodynamic model of the coastal zone is also discussed.

Remote sensing of tidal networks and their relation to vegetation

The study of the morphology of tidal networks and their relation to salt marsh vegetation is currently an active area of research, and a number of theories have been developed which require validation using extensive observations. Conventional methods of measuring networks and associated vegetation can be cumbersome and subjective. Recent advances in remote sensing techniques mean that these can now often reduce measurement effort whilst at the same time increasing measurement scale. The status of remote sensing of tidal networks and their relation to vegetation is reviewed. The measurement of network planforms and their associated variables is possible to sufficient resolution using digital aerial photography and airborne scanning laser altimetry (LiDAR), with LiDAR also being able to measure channel depths. A multi-level knowledge-based technique is described to extract networks from LiDAR in a semi-automated fashion. This allows objective and detailed geomorphological information on networks to be obtained over large areas of the inter-tidal zone. It is illustrated using LIDAR data of the River Ems, Germany, the Venice lagoon, and Carnforth Marsh, Morecambe Bay, UK. Examples of geomorphological variables of networks extracted from LiDAR data are given. Associated marsh vegetation can be classified into its component species using airborne hyperspectral and satellite multispectral data. Other potential applications of remote sensing for network studies include determining spatial relationships between networks and vegetation, measuring marsh platform vegetation roughness, in-channel velocities and sediment processes, studying salt pans, and for marsh restoration schemes.

Use of airborne remote sensing to detect riverside Brassica rapa to aid in risk assessment of transgenic crops

High resolution descriptions of plant distribution have utility for many ecological applications but are especially useful for predictive modeling of gene flow from transgenic crops. Difficulty lies in the extrapolation errors that occur when limited ground survey data are scaled up to the landscape or national level. This problem is epitomized by the wide confidence limits generated in a previous attempt to describe the national abundance of riverside Brassica rapa (a wild relative of cultivated rapeseed) across the United Kingdom. Here, we assess the value of airborne remote sensing to locate B. rapa over large areas and so reduce the need for extrapolation. We describe results from flights over the river Nene in England acquired using Airborne Thematic Mapper (ATM) and Compact Airborne Spectrographic Imager (CASI) imagery, together with ground truth data. It proved possible to detect 97% of flowering B. rapa on the basis of spectral profiles. This included all stands of plants that occupied >2m square (>5 plants), which were detected using single-pixel classification. It also included very small populations (<5 flowering plants, 1-2m square) that generated mixed pixels, which were detected using spectral unmixing. The high detection accuracy for flowering B. rapa was coupled with a rather large false positive rate (43%). The latter could be reduced by using the image detections to target fieldwork to confirm species identity, or by acquiring additional remote sensing data such as laser altimetry or multitemporal imagery.

Utilizing Remote Sensing Imagery to Monitor Vegetation Change within World Heritage Sites

ABSTRACT World Heritage sites provide a glimpse into the stories and civilizations of the past. There are currently 1007 unique World Heritage properties with 779 being classified as cultural sites, 197 as natural sites, and 31 falling into the categories of both cultural and natural sites (UNESCO & World Heritage Centre, 1992-2015). However, of these 1007 World Heritage sites, at least 46 are categorized as in danger and this number continues to grow. These unique and irreplaceable sites are exceptional because of their universality. Consequently, since World Heritage sites belong to all the people of the world and provide inspiration and admiration to all who visit them, it is our responsibility to help preserve these sites. The key form of preservation involves the individual monitoring of each site over time. While traditional methods are still extremely valuable, more recent advances in the field of geographic and spatial technologies including geographic information systems (GIS), laser scanning, and remote sensing, are becoming more beneficial for the monitoring and overall safeguarding of World Heritage sites. Through the employment and analysis of more accurately detailed spatial data, World Heritage sites can be better managed. There is a strong urgency to protect these sites. The purpose of this thesis is to describe the importance of taking care of World Heritage sites and to depict a way in which spatial technologies can be used to monitor and in effect preserve World Heritage sites through the utilization of remote sensing imagery. The research conducted in this thesis centers on the Everglades National Park, a World Heritage site that is continually affected by changes in vegetation. Data used include Landsat satellite imagery that dates from 2001-2003, the Everglades' boundaries shapefile, and Google Earth imagery. In order to conduct the in-depth analysis of vegetation change within the selected World Heritage site, three main techniques were performed to study changes found within the imagery. These techniques consist of conducting supervised classification for each image, incorporating a vegetation index known as Normalized Vegetation Index (NDVI), and utilizing the change detection tool available in the Environment for Visualizing Images (ENVI) software. With the research and analysis conducted throughout this thesis, it has been shown that within the three year time span (2001-2003), there has been an overall increase in both areas of barren soil (5.760%) and areas of vegetation (1.263%) with a decrease in the percentage of areas classified as sparsely vegetated (-6.987%). These results were gathered through the use of the maximum likelihood classification process available in the ENVI software. The results produced by the change detection tool which further analyzed vegetation change correlate with the results produced by the classification method. As well, by utilizing the NDVI method, one is able to locate changes by selecting a specific area and comparing the vegetation index generated for each date. It has been found that through the utilization of remote sensing technology, it is possible to monitor and observe changes featured within a World Heritage site. Remote sensing is an extraordinary tool that can and should be used by all site managers and organizations whose goal it is to preserve and protect World Heritage sites. Remote sensing can be used to not only observe changes over time, but it can also be used to pinpoint threats within a World Heritage site. World Heritage sites are irreplaceable sources of beauty, culture, and inspiration. It is our responsibility, as citizens of this world, to guard these treasures.

Building a Rockfall Database Using Remote Sensing: Techniques for Hazard Management in Canadian Rail Corridors

The section of CN railway between Vancouver and Kamloops runs along the base of many hazardous slopes, including the White Canyon, which is located just outside the town of Lytton, BC. The slope has a history of frequent rockfall activity, which presents a hazard to the railway below. Rockfall inventories can be used to understand the frequency-magnitude relationship of events on hazardous slopes, however it can be difficult to consistently and accurately identify rockfall source zones and volumes on large slopes with frequent activity, leaving many inventories incomplete. We have studied this slope as a part of the Canadian Railway Ground Hazard Research Program and have collected remote sensing data, including terrestrial laser scanning (TLS), photographs, and photogrammetry data since 2012, and used change detection to identify rockfalls on the slope. The objective of this thesis is to use a subset of this data to understand how rockfalls identified from TLS data could be used to understand the frequency-magnitude relationship of rockfalls on the slope. This includes incorporating both new and existing methods to develop a semi-automated workflow to extract rockfall events from the TLS data. We show that these methods can be used to identify events as small as 0.01 m3 and that the duration between scans can have an effect on the frequency-magnitude relationship of the rockfalls. We also show that by incorporating photogrammetry data into our analysis, we can create a 3D geological model of the slope and use this to classify rockfalls by lithology, to further understand the rockfall failure patterns. When relating the rockfall activity to triggering factors, we found that the amount of precipitation occurring over the winter has an effect on the overall rockfall frequency for the remainder of the year. These results can provide the railways with a more complete inventory of events compared to records created through track inspection, or rockfall monitoring systems that are installed on the slope. In addition, we can use the database to understand the spatial and temporal distribution of events. The results can also be used as an input to rockfall modelling programs.

Confronto tra tecniche di remote sensing per la caratterizzazione di un ammasso roccioso presso le Gole di Scascoli, Loiano (BO)

Il versante sinistro delle Gole di Scascoli (BO) è caratterizzato da una marcata tendenza evolutiva per crollo e ribaltamento. Negli ultimi 25 anni si sono verificati eventi parossistici con volumi di roccia coinvolti rispettivamente di 7000 m3, 20000 m3 e 35000 m3. Il sito è di grande rilevanza a causa del forte fattore di rischio rappresentato per la strada di fondovalle ad esso adiacente. Il lavoro di tesi è stato finalizzato allo studio dei fenomeni di versante di una parete rocciosa inaccessibile nota in letteratura come “ex-Mammellone 1” mediante tecniche di telerilevamento quali TLS (Terrestrial Laser Scanning) e CRP (Close Range Photogrammetry) al fine affiancare il rilievo geomeccanico soggettivo dell’area svolto nel 2003 da ENSER Srl in seguito ai fenomeni di crollo del 2002. Lo sviluppo di tecnologie e metodi innovativi per l’analisi territoriale basata sull’impiego di UAV (Unmanned Aerial Vehicle, meglio noti come Droni), associata alle tecniche di fotogrammetria digitale costituisce un elemento di notevole ausilio nelle pratiche di rilevamento in campo di sicurezza e tempi di esecuzione. Il lavoro ha previsto una prima fase di rilevamento areo-fotogrammetrico mediante strumentazione professionale e amatoriale, a cui è seguita l’elaborazione dei rispettivi modelli. I diversi output sono stati confrontati dal punto di vista geomorfologico, geometrico, geomeccanico e di modellazione numerica di caduta massi. Dal lavoro è stato possibile indagare l’evoluzione morfologica del sito in esame negli ultimi 10 anni, confrontare diversi metodi di rilevamento e analisi dati, sperimentare la robustezza e ripetibilità geometrica del metodo fotogrammetrico per il rilievo di fronti rocciosi e mettere a punto un metodo semiautomatico di individuazione e analisi delle giaciture delle discontinuità.

Monitoring animal behaviour and environmental interactions : GPS collars and satellite remote sensing within a satellite remote sensing context

Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle.