Dissertations / Theses on the topic 'Aerial photography Remote sensing'

The paper describes major features of an unmanned aerial vehicle, designed undersafety and performance requirements for missions of aerial photography and remotesensing in precision agriculture. Unmanned aerial vehicles have vast potential asobservation and data gathering platforms for a wide variety of applications. The goalof the project was to develop a small, low cost, electrically powered, unmanned aerialvehicle designed in conjunction with a payload of imaging equipment to obtainremote sensing images of agricultural fields. The results indicate that this conceptwas feasible in obtaining high quality aerial images.

An automated procedure was developed to identify and extract confined poultry facilities from color 35-mm slide imagery collected by the United States Department of Agriculture/Farm Service Agency (USDA/FSA). The imagery is used by the USDA/FSA to monitor compliance with various farm support programs and to determine crop production acreage within a given county. The imagery is generally available for all counties within the state on an annual basis. The imagery, however, is not flown to rigid specifications as flight height, direction, and overlap can vary significantly. The USDA/FSA attempts to collect imagery with reasonably clear skies, as visual interpretations could be drastically impacted by cloudiness. The goal of this study was to develop procedures to effectively utilize this imagery base to identify and extract poultry facilities using automated techniques based on image processing and GIS. The procedure involved pre-screening the slides to determine coverage, geopositioning to USGS quadrangle base, color scanning to convert slide image to a digital format and archiving each data file with a naming convention that would allow rapid retrieval in later analysis. Image processing techniques were developed for identifying poultry facilities based on spectral characteristics. GIS tools were used to select poultry facilities from an array of features with similar spectral characteristics. A training data set was selected from which the spectral characteristics of poultry facilities were analyzed and compared with background conditions. Poultry facilities were found to have distinguishable characteristics. Descriptive statistics were used to define the range of spectral characteristics encompassing poultry facilities. Thresholding analyses were then utilized to eliminate all image features with spectral characteristics outside of this range. Additional analyses were made to remove noise in the spectral image due to the sun angle, line of sight of camera, variation in roof reflectance due to rust and/or aging, shading by trees, etc. A primary objective in these analyses was to enhance the spectral characteristics for the poultry facility while, at the same time, retaining physical characteristics, i.e. the spectral characteristic is represented by a single blue color with a high brightness value. The techniques developed to achieve a single blue color involved the use of Principal Component Analysis (PCA) on the red color band followed by RGB to Hue and RGB to Saturation analyses on the red and green color bands, respectively, from the resulting image. The features remaining from this series of analyses were converted into polygons (shape file) using ArcView GIS, which was then used to calculate the area and perimeter of each polygon. The parameters utilized to describe the shape of a poultry house included width, length, compactness, length-width ratio, and polygon centroid analysis. Poultry facilities were found to have an average width of approximately 12.6m with a low standard deviation indicating that the widths of all houses were very similar. The length of poultry facilities ranged from 63m to 261m with and average length of 149m. The compactness parameter, which also is related to length and width, ranged from 30 to 130 with a mean value of approximately 57. The shape parameters were used by ArcView GIS to identify polygons that represent poultry facilities. The order of selection was found to be compactness followed by length-width ratio and polygon centroid analysis. A data set that included thirty 35-mm slide images randomly selected from the Rockingham County data set, which contained over 2000 slides, was used to evaluate the automated procedure. The slides contained 182 poultry houses previously identified through manual procedures. Seven facilities were missed and 175 were correctly identified. Ninety-seven percent (97%) of existing poultry facilities were correctly identified which compares favorably with the 97 % accuracy resulted by manual procedures. . The manual procedure described by Mostaghimi, et. al.(1999) only gave the center coordinates for each poultry facility. The automated procedure not only gives the center coordinate for each poultry building but also gives estimates for geometric parameters area, length and width along with an estimate of the capacity of building (i.e. number of birds), and waste load generated by birds including nutrient and bacteria content. The nutrient and bacteria load generated by each poultry facility is important information for conducting TMDL studies currently being developed for impaired Virginia streams. The information is expected to be very helpful to consultants and state agencies conducting the studies. Agricultural support agencies such as USDA/NRCS and USDA/FSA, Extension Service, consultants, etc. will find the information very helpful in the development of implementation plans designed to meet TMDL target water quality goals. The data also should be useful to Water Authorities for selection of appropriate treatment of water supplies and to county and local government jurisdictions for developing policies to minimize the degradation of water supplies.
Ph. D.

The overall objective of this study was to determine if remotely sensed data could be used to model scenic beauty. Terrestrial digital images from within forest stands located in Prince Edward Gallion State Forest near Farmville, Virginia were rated for their scenic beauty by a group of students to obtain scenic beauty estimates (SBEs). Since the inter-rater reliability was low for the SBEs, they were not used in the modeling efforts. Instead, stand parameters (collected on tenth acre plots) that have been used in scenic beauty prediction models, like mean diameter at breast height (dbh), were the dependent variables in regression analyses. A color-infrared aerial photograph from the National Aerial Photography Program (NAPP) was scanned to achieve a pixel ground resolution of one meter. The digital aerial photograph was rectified and used as the remotely sensed data. Since the aerial photograph was taken in April, only conifer stands were used in the analyses. Summary statistics were obtained from a 23 by 23 window around plot locations in three images: the original image, a texture image created with the variance algorithm and a 7x7 window, and the first principal component image. The summary statistics were used as the independent variables in regression analyses. The mean texture digital number for the green band predicted the mean dbh of a plot with an R2 of 0.623. A maximum of 44.3 and 27.4 percent of the variability in trees per acre and basal area per acre, respectively, was explained by the models developed in this study. It seems unlikely that the remotely sensed forest stand variables would perform well as surrogates for field measurements used in scenic quality models.
Master of Science

Remote sensing has been demonstrated an important tool in agricultural and natural resource management and research applications, however there are limitations that exist with traditional platforms (i.e., hand held sensors, linear moves, vehicle mounted, airplanes, remotely piloted vehicles (RPVs), unmanned aerial vehicles (UAVs) and satellites). Rapid technological advances in electronics, computers, software applications, and the aerospace industry have dramatically reduced the cost and increased the availability of remote sensing technologies.Remote sensing imagery vary in spectral, spatial, and temporal resolutions and are available from numerous providers. Appendix A presented results of a test project that acquired high-resolution aerial photography with a RPV to map the boundary of a 0.42 km2 fire area. The project mapped the boundaries of the fire area from a mosaic of the aerial images collected and compared this with ground-based measurements. The project achieved a 92.4% correlation between the aerial assessment and the ground truth data.Appendix B used multi-objective analysis to quantitatively assess the tradeoffs between different sensor platform attributes to identify the best overall technology. Experts were surveyed to identify the best overall technology at three different pixel sizes.Appendix C evaluated the positional accuracy of a relatively low cost UAV designed for high resolution remote sensing of small areas in order to determine the positional accuracy of sensor readings. The study evaluated the accuracy and uncertainty of a UAV flight route with respect to the programmed waypoints and of the UAV's GPS position, respectively. In addition, the potential displacement of sensor data was evaluated based on (1) GPS measurements on board the aircraft and (2) the autopilot's circuit board with 3-axis gyros and accelerometers (i.e., roll, pitch, and yaw). The accuracies were estimated based on a 95% confidence interval or similar methods. The accuracy achieved in the second and third manuscripts demonstrates that reasonably priced, high resolution remote sensing via RPVs and UAVs is practical for agriculture and natural resource professionals.

Thesis (Ph.D.) - University of Glasgow, 2009.
Ph.D. thesis submitted to the Faculty of Physical Sciences, Department of Geographical and Earth Sciences and the Faculty of Arts, Department of Archaeology, University of Glasgow, 2009. Includes bibliographical references. Print version also available.

Historic land uses impose discernable legacy effects that may influence ecosystem function, a concern of particular importance in actively managed landscapes. In recent history (ca. 150 years) tree and shrub abundance has increased at the expense of native grasses in savannas and grasslands. The magnitude and patterns of change are spatially heterogeneous, highlighting the need for analytical approaches spanning multiple spatial scales, from individual plants to patches to landscapes. The overarching goal of this dissertation was to explore long-term dynamics associated with woody plant encroachment with aerial photography and field studies to examine cover, density, soils and land use history at the Santa Rita Experimental Range.The first study characterized patterns in woody cover change on contrasting soils over 60 years using aerial photography. Woody patch dynamics revealed encroachment and stabilization phases in woody plant proliferation. Soil properties reflected the rate at which uplands reached a dynamic equilibrium, but not the endpoint (ca. 35% cover). Fluctuations around dynamic equilibrium reflected net change in patch growth and acquiescence combined with colonization and mortality. Efforts to characterize changes in land cover will require patch-based assessments beyond coarse estimates of percent cover.The second study capitalized on historic field measurements of shrub canopies to validate estimates of shrub cover derived from the earliest aerial photography, quantified detection limitations of 1936 aerial photographs for mapping shrub cover, assessed species-specific contributions to percent cover, and translated detection limitations to proportions of velvet mesquite (Prosopis velutina var Woot.) biomass missed with 1930s aerial photography.The third study was a field-based approach investigating how livestock grazing influenced mesquite cover, density, biomass, and stand structure over 74 years. The study supplemented traditional statistical analysis of grazing effects with methods quantifying spatial autocorrelation structure of mesquite density by grazing treatment. The outcome re-affirmed the supposition that mesquite cover may be dynamically stable at ca 30%, and revealed that livestock grazing slowed the shrub encroachment process from 1932 to 2006, counter to expectation. Results indicate that shrub growth trajectories persist long-term. Overall, this work affirms the importance of land use legacies and long-term perspectives in rangeland shrub dynamics.

Remote sensing technologies are used to assist in the mapping and monitoring of land cover in space and time. The European Space Agency’s (ESA) upcoming Sentinel-2 MultiSpectral Instrument (MSI) to be launched in 2013 has improved spatial and spectral properties compared to the current large-swath medium-resolution satellite sensors. Prior to the deployment of future sensors it is important to simulate and test the sensor data to evaluate the sensor's potential performance in producing the existing data products and develop new algorithms. This study simulated Sentinel-2 MSI data from airborne hyperspectral data over an agriculture area in northern Alberta, Canada. The standard Sentinel-2 MSI land-cover product was evaluated by comparing it to one created from the standard Landsat 5 TM and SPOT 5 HRV data products. Furthermore the standard Sentinel-2 MSI water column content band configuration and algorithm was evaluated for atmospheric correction purposes.
xi, 90 leaves : col. ill. ; 29 cm

The multiple-forward-mode (MFM) inversion procedure is a set of methods for indirect canopy relectance model inversion using look-up tables (LUT). This thesis refines the MFM technique with regard to: 1) model parameterization for the MFM canopy reflectance model executions and 2) methods for limiting or describing multiple solutions. Forest stand structure estimates from the inversion were evaluated using 40 field validation sites in the Canadian Rocky Mountains. Estimates of horizontal and vertical crown radius were within 0.5m and 0.9m RMSE for both conifer and deciduous species. Density estimates were within 590 stems/ha RMSE for conifer and 310 stems/ha RMSE for deciduous. The most effective inversion method used a variable spectral domain with constrained, fine increment LUTs. A biomass estimation method was also developed using empirical relationships with crown area. Biomass density estimates using the MFM method were similar to estimates produced using other multispectral analysis methods (RMSE=50t/ha).
xvi, 156 leaves : ill. (some col.), maps ; 29 cm.

Leaf area index (LAI) provides forestry information that is important for regional scale ecological models and in studies of global change. This research examines the effects of mountainous terrain on the radiometric properties of multispectral CASI imagery in estimating ground-based optical measurements of LAI, obtained using the TRAC and LAI- 2000 systems. Field and image data were acquired summer 1998 in Kananaskis, Alberta, Canada. To account for the influence of terrain a new modified approach using the Li and Strahler Geometric Optical Mutual Shadowing (GOMS) model in 'multiple forward mode' (MFM) was developed. This new methodology was evaluated against four traditional radiometric corrections used in comination with spectral mixture analysis (SMA) and NDVI. The MFM approach provided the best overall predictions of LAI measured with ground-based optical instruments, followed by terrain normalized SMA, SMA without terrain normalization and NDVI.
xiv, 151 leaves : ill. (some col.), map ; 29 cm.

Three-dimensional canopy relectance models provide a physical-structural basis to satellite image analysis, representing a potentially more robust, objective and accurate approach for obtaining forest cover type and structural information with minimal ground truth data. The Geometric Optical Mutual Shadowing (GOMS) canopy relectance model was run in multiple-forward-mode (MFM) using digital multispectral IKONOS satellite imagery to estimate tree height and stand volume over 100m2 homogeneous forest plots in mountainous terrain, Kananaskis, Alberta. Height was computed within 2.7m for trembling aspen and 1.8m fr lodgepole pine, with basal area estimated within 0.05m2. Stand volume, estimated as the product of mean tree height and basal area, had an absolute mean difference from field measurements of 0.85m3/100m2 and 0.61m3/100m2 for aspen and pine, respectively.
xiii, 143 leaves : ill. (some col.) ; 29 cm.

>Magister Scientiae - MSc
The Cape Floristic Region, situated at the southern tip of Africa, is one of the world’s most botanically diverse regions. The biodiversity of this region faces various types of threats, which can be divided into three main categories, namely increasing urbanisation, agriculture expansion, and the spread of invasive alien vegetation. It has been shown that botanically diverse areas are more prone to invasion by invasive alien plant (IAP) species. The Hawequa conservation area, in the south-western Cape, is particularly botanically diverse, such that it is very prone to aggressive invasion by IAP species. Therefore, conservation management of the Hawequa conservation area urgently need to map, prioritise and clear IAP species. Due to the topographical complexity of this mountainous area, it is not possible to map the distribution of IAP species throughout the protected area by conventional field methods. Remote sensing may be able to provide a suitable alternative for mapping. The aim of this research was to assess various image classification methods,using two types of high-resolution imagery (colour aerial photography and WorldView-2 satellite imagery), in order to map the distribution of IAP species, including small stands and individuals. Specifically, the study will focus on mapping Pinus and Acacia spp. in a study site of approximately 9 225ha in the Hawequa conservation rea. Supervised classification was performed using two different protocols, namely per-pixel and per-field. For the per-pixel classification Iterative Self-Organising Data Analyses Technique (ISODATA) was used, a method supported by ERDAS Imagine. The per-field (object-based) classification was done using fractal net evolution approach (FNEA), a method supported by eCognition. The per-pixel classification mapped the extent of Pinus and Acacia spp. in the study area as 1 205.8 ha (13%) and 80.1 ha (0.9%) respectively, and the perfield classification as 1 120.9 ha (12.1%) and 96.8 ha (1.1%) respectively. Accuracy assessments performed on the resulting thematic maps generated from these two classification methods had a kappa coefficient of 0.700 for the per-pixel classification and 0.408 for the per-field classification. Even though the overall extent of IAP species for each of these methods is similar, the reliability of the actual thematic maps is vastly different. These findings suggest that mapping IAP species (especially Pinus spp.) stands and individuals in highly diverse natural veld, the traditional per-pixel classification still proves to be the best method when using high-resolution images. In the case of Acacia spp., which often occurs along rivers, it is more difficult to distinguish them from the natural riverine vegetation. Using WorldView-2 satellite images for large areas can be very expensive (approximately R120 per km2 in 2011), but in comparison with the cost of mapping and the subsequent clearing, especially in inaccessible areas, it might be a worthwhile investment. Alternative image sources such as the high resolution digital colour infrared aerial photography must be considered as a good source for mapping IAP species in high altitude areas.

The geometry, deformation mechanism and kinematics of the Sü
rgü
Fault Zone is investigated by using remotely sensed data including Landsat TM and ASTER imagery combined with SRTM, and stereo-aerial photographs. They are used to extract information related to regional lineaments and tectono-morphological characteristics of the SFZ. Various image processing and enhancement techniques including contrast enhancement, PCA, DS and color composites are applied on the imagery and three different approaches including manual, semi automatic and automatic lineament extraction methods are followed. Then the lineaments obtained from ASTER and Landsat imagery using manual and automatic methods are overlaid to produce a final lineaments map. The results have indicated that, the total number and length of the lineaments obtained from automatic is more than other methods while the percentages of overlapping lineaments for the manual method is more than the automatic method which indicate that the lineaments from automatic method does not discriminate man made features which result more lineaments and less overlapping ratio with respect to final map. It is revealed from the detail analysis that, the SFZ displays characteristic deformation patterns of strike-slip faults, such as pressure ridges, linear fault controlled valleys, deflected stream courses, rotated blocks and juxtaposition of stratigraphical horizons in macroscopic scale. In addition to these, kinematic analyses carried out using fault slip data indicated that the Sü
rgü
Fault Zone is dextral strike-slip fault zone with a reverse component of slip and cumulative displacement along the fault is more than 2 km.

Dissertação(mestrado) - Universidade Federal do Rio Grande, Programa de Pós-Graduação em Oceanografia Física, Química e Geológica, Instituto de Oceanografia, 2005.
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Este estudo apresenta um mapeamento detalhado do uso do solo da margem esquerda do Canal do Rio Grande, no município de São José do Norte, realizado através da fotointerpretação de imagens digitais, 35 mm, no modo visível, adquiridas com o sensor aerotransportado ADAR 1000 e de verificações de campo. As imagens foram inseridas no programa MegaGIS, criando um Sistema de Informações Geográficas (SIGs), denominado SIG-MECRG/SJN. A alta resolução das fotografias aéreas (0,5m por pixel) permitiu a visualização e identificação dos diferentes alvos. Através do uso de produtos de sensoriamento remoto e do uso das tecnologias de geoprocessamento, é possível mapear e identificar as feições observadas. O uso das fotografias aéreas é adequado para o mapeamento de áreas urbanas, devido à sua alta resolução espacial. Além das informações digitais, foram obtidas informações sobre as residências da área através de entrevistas com os moradores. Para a elaboração do SIG, foram utilizados programas para georreferenciar, exportar e manipular as fotografias aéreas. O emprego destas fotografias foi satisfatório para o reconhecimento e identificação das feições estudadas. As principais feições mapeadas na área de estudo foram as residências, a linha de costa, a hidrografia, as marismas, as dunas e as modificações antrópicas. Todas as informações inseridas no SIG, podem ser consultadas de acordo com o interesse do pesquisador. Estas consultas podem ser disponibilizadas na forma de gráficos para a visualização dos dados.
This study presents a detailed mapping of the land uses along the left margin of the Canal do Rio Grande, municipality of São José do Norte. The mapping was conducted using (a) interpretation of digital photographs (35 mm) acquired by airborne sensor ADAR 1000 and (b) field surveys. A Geographical Information System (GIS), denominated SIG-MECRG/SJN, was created using the MegaGIS software. To build the GIS, it was necessary the application of geoprocessing techniques, and export and enhancement of digital photos. Additionally, data about the properties identified in the photos were obtained through interviews with local residents. The high resolution of the aerial photos (0.5 m per pixel) combined with geoprocessing techniques allowed the identification of the different targets in the urban area. The main targets identified in the area are: houses, coastline, water flows, salt marshes, dunes, and man-made changes in the landscape. The GIS allows searching of information according to specific needs from which data can be displayed in tabular or graphic formats.

This 
thesis 
examines 
the 
changes 
that 
have 
occurred 
within 
West-Coast Renosterveld within 
the 
last 350 years, and assesses 
the viability of 
the 
remaining fragments.

Abstract

Since 1999, Very High spatial Resolution satellite data (Ikonos-2, QuickBird and OrbView-3) represent the surface of the Earth with more detail. However, information extraction by multispectral pixel-based classification proves to have become more complex owing to the internal variability increase in the land-cover units and to the weakness of spectral resolution.

Therefore, one possibility is to consider the internal spectral variability of land-cover classes as a valuable source of spatial information that can be used as an additional clue in characterizing and identifying land cover. Moreover, the spatial resolution gap that existed between satellite images and aerial photographs has strongly decreased, and the features used in visual interpretation transposed to digital analysis (texture, morphology and context) can be used as additional information on top of spectral features for the land cover classification.

The difficulty of this approach is often to transpose the visual features to digital analysis.

To overcome this problem region-based classification could be used. Segmentation, before classification, produces regions that are more homogeneous in themselves than with nearby regions and represent discrete objects or areas in the image. Each region becomes then a unit analysis, which makes it possible to avoid much of the structural clutter and allows to measure and use a number of features on top of spectral features. These features can be the surface, the perimeter, the compactness, the degree and kind of texture. Segmentation is one of the only methods which ensures to measure the morphological features (surface, perimeter.) and the textural features on non-arbitrary neighbourhood. In the pixel-based methods, texture is calculated with mobile windows that smooth the boundaries between discrete land cover regions and create between-class texture. This between-class texture could cause an edge-effect in the classification.

In this context, our research focuses on the potential of land cover region-based classification of VHR satellite data through the study of the object extraction capacity of segmentation processes, and through the study of the relevance of region features for classifying the land-cover classes in different kinds of Belgian landscapes; always keeping in mind the parallel with the visual interpretation which remains the reference.

Firstly, the results of the assessment of four segmentation algorithms belonging to the two main segmentation categories (contour- and region-based segmentation methods) show that the contour detection methods are sensitive to local variability, which is precisely the problem that we want to overcome. Then, a pre-processing like a filter may be used, at the risk of losing a part of the information. The “region-growing” segmentation that uses the local variability in the segmentation process appears to be the best compromise for the segmentation of different kinds of landscape.

Secondly, the features calculated thanks to segmentation seem to be relevant to identify some land-cover classes in urban/sub-urban and rural areas. These relevant features are of the same type as the features selected visually, which shows that the region-based classification gets close to the visual interpretation.

The research shows the real usefulness of region-based classification in order to classify the land cover with VHR satellite data. Even in some cases where the features calculated thanks to the segmentation prove to be useless, the region-based classification has other advantages. Working with regions instead of pixels allows to avoid the salt-and-pepper effect and makes the GIS integration easier.

The research also highlights some problems that are independent from the region-based classification and are recursive in VHR satellite data, like shadows and the spatial resolution weakness for identifying some land-cover classes.

Résumé

Depuis 1999, les données satellitaires à très haute résolution spatiale (IKONOS-2, QuickBird and OrbView-3) représentent la surface de la terre avec plus de détail. Cependant, l’extraction d’information par une classification multispectrale par pixel devient plus complexe en raison de l’augmentation de la variabilité spectrale dans les unités d’occupation du sol et du manque de résolution spectrale de ces données. Cependant, une possibilité est de considérer cette variabilité spectrale comme une information spatiale utile pouvant être utilisée comme une information complémentaire dans la caractérisation de l’occupation du sol. De plus, de part la diminution de la différence de résolution spatiale qui existait entre les photographies aériennes et les images satellitaires, les caractéristiques (attributs) utilisées en interprétation visuelle transposées à l’analyse digitale (texture, morphologie and contexte) peuvent être utilisées comme information complémentaire en plus de l’information spectrale pour la classification de l’occupation du sol.

La difficulté de cette approche est la transposition des caractéristiques visuelles à l’analyse digitale. Pour résoudre ce problème la classification par région pourrait être utilisée. La segmentation, avant la classification, produit des régions qui sont plus homogène en elles-mêmes qu’avec les régions voisines et qui représentent des objets ou des aires dans l’image. Chaque région devient alors une unité d’analyse qui permet l’élimination de l’effet « poivre et sel » et permet de mesurer et d’utiliser de nombreuses caractéristiques en plus des caractéristiques spectrales. Ces caractéristiques peuvent être la surface, le périmètre, la compacité, la texture. La segmentation est une des seules méthodes qui permet le calcul des caractéristiques morphologiques (surface, périmètre, …) et des caractéristiques texturales sur un voisinage non-arbitraire. Avec les méthodes de classification par pixel, la texture est calculée avec des fenêtres mobiles qui lissent les limites entre les régions d’occupation du sol et créent une texture interclasse. Cette texture interclasse peut alors causer un effet de bord dans le résultat de la classification.

Dans ce contexte, la recherche s’est focalisée sur l’étude du potentiel de la classification par région de l’occupation du sol avec des images satellitaires à très haute résolution spatiale. Ce potentiel a été étudié par l’intermédiaire de l’étude des capacités d’extraction d’objet de la segmentation et par l’intermédiaire de l’étude de la pertinence des caractéristiques des régions pour la classification de l’occupation du sol dans différents paysages belges tant urbains que ruraux.
Doctorat en sciences agronomiques et ingénierie biologique
info:eu-repo/semantics/nonPublished

Thesis (Ph. D.)--Ohio State University, 2005.
Title from first page of PDF file. Document formatted into pages; contains xi, 184 p.; also includes graphics (some col.). Includes bibliographical references (p. 158-167). Available online via OhioLINK's ETD Center

Thesis (M.Sc.)--University of Manitoba, 2006.
A thesis submitted to the Faculty of Graduate Studies in partial fulfillment of the requirements for the degree of Master of Science, Department of Soil Science. Includes bibliographical references.

The thesis presents the main points of standardization of remote sensing (RS) usage for Forensic Ecotechnique: forest and trees (FEft). Based on the study FEft expertise database, the types of expertise, which remote sensing data have already been used and the types where the use of remote sensing increases the exactness of solutions were analyzed. RS data are essential in a problematic situation when an examined object does not exist or has been changed at the time of the assignment. This situation may be solved by obtaining information about the investigated object via retrospective image data, as described in the model example. There are chosen categories of image data relevant to the needs of experts in this thesis. These categories of RS data and their products are described with an indication of their possible use in the expertise processing, including freely available or paid resources and archives of image data.

The influence of soil water repellency (WR) on vegetation recovery after a fire is poorly understood. This dissertation presents strategies to broaden opportunities for enhanced post-fire rangeland restoration and monitoring of burned piñon and juniper (P-J) woodlands by: 1) mapping the extent and severity of critical and subcritical WR, 2) determining the influence of WR on soil ecohydrologic properties and revegetation success, and 3) evaluating the suitability of a wetting agent composed of alkylpolyglycoside-ethylene oxide/propylene oxide block copolymers as a post-fire restoration tool for ameliorating the effects of soil WR and increasing seedling establishment. Results indicate that: • Post-fire patterns of soil WR were highly correlated to pre-fire P-J woodland canopy structure. Critical soil WR levels occurred under burned tree canopies while sub-critical WR extended out to approximately two times the canopy radius. At sites where critical soil WR was present, infiltration rate, soil moisture, and vegetation cover were significantly less than at non-hydrophobic sites. These parameters were also reduced in soils with subcritical WR relative to non-hydrophobic soils (albeit to a lesser extent). Aerial photography coupled with feature extraction software and geographic information systems (GIS) proved to be an effective tool for mapping P-J cover and density, and for scaling-up field surveys of soil WR to the fire boundary scale. • Soil WR impairs seed germination and seedling establishment by decreasing soil moisture availability by reducing infiltration, decreasing soil moisture storage capacity, and disconnecting soil surface layers from underlying moisture reserves. Consequently, soil WR appears to be acting as a temporal ecological threshold by impairing establishment of desired species within the first few years after a fire. • Wetting agents can significantly improve ecohydrologic properties required for plant growth by overcoming soil WR; thus, increasing the amount and duration of available water for seed germination and seedling establishment. Success of this technology appears to be the result of the wetting agent increasing soil moisture amount and availability by 1) improving soil infiltration and water holding capacity; and 2) allowing seedling roots to connect to underling soil moisture reserves.

Multispectral aerial and satellite remote sensing plays a major role in crop yield prediction due to its ability to detect crop growth conditions on spatial and temporal scales in a cost effective manner. Many empirical relationships have been established in the past between spectral vegetation indices and leaf area index, fractional ground cover, and crop growth rates for different crops through ground sampling. Remote sensing-based vegetation index (VI) yield models using airborne and satellite data have been developed only for grain crops like barley, corn, wheat, and sorghum. So it becomes important to validate and extend the VI-based model for tuber crops like potato, taking into account the most significant parameters that affect the final crop yield of these crops.

Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimize the yield of high quality cured leaf. Aerial remote sensing, specifically unmanned aerial vehicles (UAVs), present flue-cured tobacco producers and researchers with a potential tool for scouting and crop management. A two-year study, conducted in Southside Virginia at the Southern Piedmont Agricultural Research and Extension Center and on commercial farms, assessed the potential of aerial remote sensing in flue-cured tobacco. The effort encompassed two key objectives. First, examine the use of the enhanced normalized difference vegetation index (ENDVI) for separating flue-cured tobacco varieties and nitrogen rates. Secondly, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. In 2017, UAV-acquired ENDVI surveys demonstrated the ability to consistently separate between flue-cured tobacco varieties and nitrogen rates from topping to harvest. In 2018, ENDVI revealed significant differences among N-rates as early as 34 days after transplanting. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. Testing of the indices showed significant differences between the index values of healthy and symptomatic plants (alpha = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (alpha = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy.
Master of Science
Unmanned Aerial Vehicle’s (UAVs) or drones, as they are commonly referred to, may have potential as a tool in flue-cured tobacco research and production. UAVs combined with sensors and cameras provide the opportunity to gather a large amount of data on a particular crop, which may be useful in crop management. Given the intensive management of flue-cured tobacco, producers may benefit from extra insight on how to better assess threats to yield such as under-fertilization and disease pressure. A two-year study was conducted in Southside Virginia at the Southern Piedmont Agricultural Research and Extension Center and on commercial farms. There were two objectives to this effort. First, assess the ability of UAV-acquired multispectral near-infrared imagery to separate flue-cured tobacco varieties and nitrogen rates. Secondly, develop hyperspectral indices and machine learning models that can accurately predict the incidence of black shank in flue-cured tobacco. Flue-cured tobacco nitrogen rates were significantly different in 2017 from 59 days after transplanting to harvest using UAV-acquired near-infrared imagery. In 2018, heavy rainfall may have led to nitrogen leaching from the soil resulting in nitrogen rates being significantly different as early as 34 days after transplanting. The imagery also showed a significant relationship with variety maturation type in the late stages of crop development during ripening. Two hyperspectral indices were developed and one machine learning model was trained. Each had the ability to detect black shank incidence in fluecured tobacco pre-symptomatically, as well as separated black shank infested plants from healthy plants.

Multispectral aerial and satellite remote sensing plays a major role in crop yield prediction due to its ability to detect crop growth conditions on spatial and temporal scales in a cost effective manner. Many empirical relationships have been established in the past between spectral vegetation indices and leaf area index, fractional ground cover, and crop growth rates for different crops through ground sampling. Remote sensing-based vegetation index (VI) yield models using airborne and satellite data have been developed only for grain crops like barley, corn, wheat, and sorghum. So it becomes important to validate and extend the VI-based model for tuber crops like potato, taking into account the most significant parameters that affect the final crop yield of these crops.

Three-dimensional mapping and modeling can contribute to knowledge about the real world. Techniques are largely driven by available technology and typically involve expensive equipment and expert skill. Recent advances have led to low-cost remotely sensed data collection and generation of 3D terrain models using Uninhabited Aerial Vehicles (UAV) and Structure from Motion (SfM) processing software. This research presents a low-cost alternative to 3D mapping by pairing UAV collection methods with three SfM processing techniques. Surface models are generated from the same image set captured from a low-cost UAV coupled with a digital camera. Accuracy of resulting models identifies strengths and weaknesses of each technique. Analysis of different slope ranges investigates the divide at which surfaces generated become less reliable. This research provides a deeper understanding of the strengths and limitations of emerging technologies used together in a fresh approach to photogrammetry.

L'objet de cette recherche est de tenter de caracteriser les mangroves des regions equatoriales humides sur le terrain et a partir d'enregistrements spatiaux. Dans la premiere partie, une synthese biogeographique sur les peuplements de paletuviers permet de faire apparaitre et de comprendre les variations des types de mangroves et de situer les mangroves indonesiennes dans un contexte plus general. La deuxieme partie represente une etude sur les mangroves du golfe de bone a sulawesi (indonesie)

This study implemented LiDAR (Light Detection and Ranging) remote sensing technology and applied ITD (Individual Tree Detection) methods as an approach to estimate some essential tree variables, such as DBH (Diameter at Breast Height), height, volume, and biomass for Ford Forest Research Center in Upper Peninsula, Michigan. There were 34 deciduous (1 bigtooth aspen, 9 red oaks, 20 sugar maples, 2 white birches, and 2 yellow birches) and 17 coniferous (2 eastern hemlocks, 11 red pines, and 4 white pines) subject tree species. There were two different available LiDAR datasets from the same area that were collected in 2011 and 2017. Height measurements were done at 96% and 97% accuracy for hardwood and softwood tree species, respectively.

Several other tree variables derived from LiDAR point cloud were used to estimate DBH by using regression analysis for both 2017 and 2011 datasets. Estimation equations were tested on the other dataset. The best-fitted formula was 2017’s, with 0.55 adjusted R² and less than 0.0001 p-values on 2017 LiDAR data while 0.42 adjusted R² and less than 0.0001 p-values on 2011’s dataset. Some additional analysis that includes calculating PRMSE (Predicted Root Mean Square Error), BIAS (Mean Error), and MAD (Mean Absolute Difference) have been applied. The equation that was generated by using data from 2017 has –0.57 BIAS for Hardwood and 1.13 BIAS for softwood. That result indicates that the equation has –0.57 centimeters (cm) estimation error for hardwood and 1.13 cm for softwood on DBH estimations.

Forest fires are natural phenomena essential for ecosystem balance. However, their impact on society is increasing. Emergency managers are facing unprecedented crises producing massive evacuations, numerous casualties and economic losses in the order of billions of dollars. At the same time, fire scientists around the world are struggling to improve our understanding of wildfire dynamics, which is incomplete at present. Extensive large-scale experimental campaigns have been designed, funded and implemented in recent years to study fire behaviour. Furthermore, operational fire spread simulators have been revisited to improve their accuracy when working .in real time. A very promising approach to do so is data assimilation, which relies on fire monitoring. In this context, there is a strong need for accurate detailed information about wildfire behaviour, with a triple goal: firstly, to improve the understanding of fire dynamics; secondly, to assist evacuation and firefighting tasks if such data can be acquired and provided in real time during a fire emergency; thirdly, to support modelling efforts including the specific case of data-driven fire spread simulators Remote sensing techniques have shown a great potential to monitor wildland fires. Thermal infrared (TIR) cameras allow a clear view of the fire, in most conditions even in the presence of heavy smoke. Furthermore, modern TIR cameras are light, compact and affordable and they can be installed aboard Unmanned and Remotely Piloted Aircraft (UAS and RPAS, respectively), which facilitates sensor deployment while drastically reducing cost and risk. These conditions have spurred an interest in the use of airborne thermal infrared imaging to measure fire behaviour metrics with high temporal and spatial resolution. However, the analysis of TIR imagery has so far been mostly manual and often only qualitative. When quantitative results have been obtained, they have usually been computed analysing individually a subset of video frames. This methodology entails two important limitations. Firstly, image analysis is slow and it cannot be performed in real time. Secondly, measured values of fire behaviour metrics are averaged both in time and space, with a significant portion of the acquired information never being used. This Thesis contributes to the automation of active wildfire monitoring. It presents a number of image processing algorithms that assist in the analysis of aerial thermal infrared imagery. After a thorough review of the current state of the art, highest priority needs were identified (chapter 1). Detected needs include video stabilization, fire perimeter tracking and the estimation of spatially explicit fire rates of spread (ROS). Image registration and video stabilization are essential to georeference aerial imagery, which must be performed before any further analysis. Automated fire perimeter tracking has a great applicability for tactical emergency management and it allows ROS estimation Finally, ROS can be used to derive additional fire behaviour metrics such as fire fine intensity and it can be fed into data-driven simulators to improve operational fire spread forecast. Algorithms for IR tire image segmentation, fire line detection and fire perimeter tracking are presented in chapters 2 and 3, whereas image registration and video stabilization are dealt with in chapters 4 and 5. Additionally, chapter 6 describes a number of tools developed to overcome practical limitations of modern compact IR cameras. Afterwards, chapter 7 provides a demonstration of the use of developed algorithms in two independent datasets. Finally, chapter 8 describes the integration of the software developed in this Thesis with third-party tools. External software necessary for the study of wildfire behaviour includes Geographic Information Systems (GIS), fire models based on Computational Fluid Dynamics (CFD) and data-driven fire spread simulators that incorporate data assimilation.
Los incendios forestales son un fenómeno natural, esencial para la conservación de una gran cantidad de ecosistemas. Sin embargo, su impacto social ha aumentado considerablemente en los últimos años. La ocurrencia de incendios cerca de asentamientos humanos ha dado lugar recientemente a importantes desastres con evacuaciones masivas, numerosas víctimas mortales y pérdidas económicas multimillonarias. Mientras los equipos de gestión de emergencias buscan nuevas herramientas para hacer frente a estas situaciones, la comunidad científica trata de mejorar el conocimiento disponible sobre el comportamiento del fuego. En este contexto, crece la necesidad de obtener información fiable y detallada sobre el comportamiento de los incendios forestales a escala real. Dicha información tiene tres aplicaciones principales: en primer lugar, permite el estudio científico de la dinámica del fuego; en segundo lugar, refuerza la percepción de la situación durante una emergencia y ayuda en la toma de decisiones; en tercer lugar, puede ser utilizada por simuladores basados en la asimilación de datos para mejorar la predicción de la propagación del fuego. La primera aplicación requiere información con gran resolución espacial y temporal, así como una metodología de análisis sistemática que permita relacionar las variables de dinámica del fuego con las propiedades del combustible, el terreno y las condiciones meteorológicas. Además, las aplicaciones durante un escenario de emergencia requieren información en tiempo real. Las técnicas de teledetección han demostrado tener un gran potencial para monitorizar incendios forestales. En concreto, las cámaras de termografía infrarroja instaladas en plataformas aéreas permiten obtener una visión clara del fuego incluso en presencia de humo. Además, estas cámaras han sufrido un desarrollo considerable con diseños cada vez más compactos, ligeros y económicos que permiten su instalación en vehículos aéreos no tripulados (UAVs), reduciendo costes y riesgo para el personal que las opera. Estas circunstancias han favorecido el uso de cámaras termográficas aerotransportadas en campañas experimentales a mediana y gran escala, y sugieren una importante aplicabilidad en incendios forestales no controlados. No obstante, el análisis de las imágenes adquiridas se realiza de forma manual, a menudo únicamente cualitativa. Cuando se obtienen resultados cuantitativos sobre el comportamiento del fuego, su resolución tanto espacial como temporal queda limitada por la imposibilidad de procesar toda la información disponible. Además, la metodología manual imposibilita el procesamiento de datos en tiempo real. Con el fin de facilitar el estudio del comportamiento de incendios forestales mediante técnicas de teledetección, esta tesis propone una serie de algoritmos de procesamiento de imagen infrarroja. Un análisis bibliográfico inicial (capítulo 1) permitió identificar las principales necesidades existentes, que incluyen el registro de imágenes, la estabilización de vídeo, la detección de las líneas de fuego activas y la medición de la velocidad de propagación del incendio. Todos estos aspectos son tratados en los capítulos 2, 3, 4 y 5 de esta tesis. A continuación, el capítulo 6 añade una serie de soluciones prácticas diseñadas para solventar limitaciones existentes en el uso de cámaras termográficas compactas. El capítulo 7 incluye una demostración del uso combinado de los algoritmos propuestos. Por último. el capítulo 8 describe la integración del software desarrollado con otros programas existentes que resultan de utilidad en el estudio de incendios forestales. Entre los programas externos de interés se encuentran los Sistemas de Información Geográfica (SIG), los simuladores basados en dinámica de fluidos computacional (CFD) y los simuladores de propagación de incendios que incorporan asimilación de datos.

This work introduces a design and implementation of using multiple unmanned aerial vehicles (UAVs) to achieve cooperative formation flight based on the personal remote sensing platforms developed by the author and the colleagues in the Center for Self-Organizing and Intelligent Systems (CSOIS). The main research objective is to simulate the multiple UAV system, design a multi-agent controller to achieve simulated formation flight with formation reconfiguration and real-time controller tuning functions, implement the control system on actual UAV platforms and demonstrate the control strategy and various formation scenarios in practical flight tests. Research combines analysis on flight control stabilities, develop- ment of a low-cost UAV testbed, mission planning and trajectory tracking, multiple sensor fusion research for UAV attitude estimations, low-cost inertial measurement unit (IMU) evaluation studies, AggieAir remote sensing platform and fail-safe feature development, al- titude controller design for vertical take-off and landing (VTOL) aircraft, and calibration and implementation of an air pressure sensor for wind profiling purposes on the developed multi-UAV platform. Definitions of the research topics and the plans are also addressed.

Remote sensing enables the acquisition of large amounts of data, over a small period of time, in support of many ecological applications (i.e. precision agriculture, vegetation mapping, etc.) commonly from satellite or manned aircraft platforms. This dissertation focuses on using small unmanned aerial systems (UAS) as a remote sensing platform to collect aerial imagery from commercial-grade cameras and as a radio localization platform to track radio-tagged sh. The small, low-cost nature of small UAS enables remotely sensed data to be captured at a lower cost, higher spatial and temporal resolution, and in a more timely manner than conventional platforms. However, these same attributes limit the types of cameras and sensors that can be used on small UAS and introduce challenges in calibrating the imagery and converting it into actionable information for end users. A major contribution of this dissertation addresses this issue and includes a complete description on how to calibrate imagery from commercial-grade visual, near-infrared, and thermal cameras. This includes the presentation of novel surface temperature sampling methods, which can be used during the ight, to help calibrate thermal imagery. Landsat imagery is used to help evaluate these methods for accuracy; one of the methods performs very well and is logistically feasible for regular use. Another major contribution of this dissertation includes novel, simple methods to estimate the location of radio-tagged sh using multiple unmanned aircraft (UA). A simulation is created to test these methods, and Monte Carlo analysis is used to predict their performance in real-world scenarios. This analysis shows that the methods are able to locate the radio-tagged sh with good accuracy. When multiple UAs are used, the accuracy does not improve; however the sh is located much quicker than when one UA is used.

This paper gives a thοrοugh lοοk in the use οf Unmanned Aerial Vehicles (UAV) in search and rescue missiοns. The οbjective is tο identify, thrοugh a literature review, all types οf UAV, the available remοte sensοrs that can be mοunted in a UAV and what type οf data each sensοr prοvides, the methοdοlοgy οn flight planning and what is the regulatοry framewοrk, guidelines and limitatiοns, οf UAV flight in sοme οf the biggest cοuntries wοrldwide and in specific fοr the Eurοpean Uniοn and Sweden. Alsο, a brief research in the field οf machine learning was dοne in οrder tο identify the pοssibilities given in use οf οbject recοgnitiοn and avοidance techniques. In the case οf Sweden, we have alsο identified the framewοrk fοr the Search and Rescue οperatiοns accοrding tο the “Civil Prοtectiοn Act. Gοvernment Bill 2002/03:119. Refοrmed rescue services legislatiοn”, in whοse hands falls the respοnsibility tο use and implement UAVs in such type οf missiοns. The main οbjectives fοr future develοpment are the full autοnοmy οf a UAV, the energy limitatiοns in the οperatiοnal phase, the fast prοcessing οf data οnbοard and the acquisitiοn οf the data thrοugh cοmmunicatiοn links withοut the need οf a physical cοnnectiοn between the UAV and the grοund cοntrοl statiοn (GCS). Accοrding tο οur research the οptimal cοnfiguratiοn οf UAVs in a search and rescue scenariο is a fleet οf fully autοnοmοus heterοgeneοus UAVs (fixed-wing UAVs and quadcοpters) with sοlar panels attached tο the fixed-wing UAVs, a high-resοlutiοn camera, a thermal camera and a Synthetic Aperture Radar (SAR). As the field οf UAVs is vastly grοwing the research οn the subject was cοnducted until April 1st, 2018 due tο new papers and research cοnducted in the subject regularly.

This study investigated the possibility of three approaches in determination of soil moisture in the root zone. The aim of the study was to contribute to the development of soil moisture monitoring methods to better help crop best management practices.
Two fields were examined, one at the Macdonald Campus of McGill University and the other near St. Jean-sur-Richelieau. Three approaches were used; (1) a hand-held hyper-spectral sensor (350-2500 nm), (2) a Geonics RTM EM-38 conductivity meter and, (3) gravimetric soil moisture sampling.
The first experiment (at St. Jean-sur-Richelieu) investigated the possibility of monitoring soil moisture with the EM_38, in the presence of field elevation and soil texture. The second experiment (at Macdonald Campus) investigated the possibility of using hyper-spectral sensor data for determination of soil characteristics in the root zone, in the presence of such factors as (a) irrigation (main treatment), (b) nitrogen (sub-treatment), and (c) weed control (sub-sub-treatment). Statistical regression analyses and Artificial Neural Network models were used to select the best waveband region for determination of soil root zone moisture.
The coefficients of determination obtained by the statistical analyses ranged from 0.75 to 0.94. The wavebands most frequently identified by these analyses ranged from 1100 nm-1900 nm.
The performances of the ANN training models were considered acceptable (R2 from 0.6 to 0.8). The lack of sufficient data greatly impacts this approach.

Tese de Doutoramento - Engenharia Florestal e dos Recursos Naturais - Instituto Superior de Agronomia
Forest ecosystems provide multiple wood and non-wood forest products and services that are crucial for the socio-economic development of rural areas. In this context, current methods of estimating variables of interest in forest ecosystems should be improved due to new demands for information related to sustainable forest management. Advanced remote sensing (RS) technologies provide data that will address the increasing demands for information and support the subsequent development of prediction models. Airborne laser scanning (ALS) has emerged as one of the most promising RS technologies for characterizing tree canopies and other biophysical characteristics essential for forest inventories. The use of 3D data acquired from Digital Aerial photography (DAP) is a useful alternative to ALS-based forest variable estimation. The rapid development of Unmanned Aerial Vehicles (UAVs) (drones) fitted with digital aerial cameras and the use of SfM (Structure from Motion) techniques together provide new possibilities for efficient mapping of forest variables. Combining ALS and DAP technologies with UAV platforms will probably have a strong impact on forest inventory practices in the next decade, leading to more accurate characterization of forest stands, as well as for monitoring forest growth. The overall aim of all of the five studies included in this doctoral thesis is to evaluate the capacity of two advanced RS technologies (ALS and DAP) to provide methods and tools that support forest management at different scales ranging from stand level to individual tree level
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Semi-arid forests are in a period of rapid transition as a result of unprecedented landscape scale fires, insect outbreaks, drought, and anthropogenic land use practices. Understanding how historically episodic disturbances led to coherent forest structural and spatial patterns that promoted resilience and resistance is a critical part of addressing change. Here my coauthors and I apply metabolic scaling theory (MST) to examine scaling behavior and structural patterns of semi-arid conifer forests in Arizona and New Mexico. We conceptualize a linkage to mechanistic drivers of forest assembly that incorporates the effects of low-intensity disturbance, and physiologic and resource limitations as an extension of MST. We use both aerial LiDAR data and field observations to quantify changes in forest structure from the sub-meter to landscape scales. We found: (1) semi-arid forest structure exhibits MST-predicted behaviors regardless of disturbance and that MST can help to quantitatively measure the level of disturbance intensity in a forest, (2) the application of a power law to a forest overstory frequency distribution can help predict understory presence/absence, (3) local indicators of spatial association can help to define first order effects (e.g. topographic changes) and map where recent disturbances (e.g. logging and fire) have altered forest structure. Lastly, we produced a comprehensive set of above-ground biomass and carbon models for five distinct forest types and ten common species of the southwestern US that are meant for use in aerial LiDAR forest inventory projects. This dissertation presents both a conceptual framework and applications for investigating local scales (stands of trees) up to entire ecosystems for diagnosis of current carbon balances, levels of departure from historical norms, and ecological stability. These tools and models will become more important as we prepare our ecosystems for a future characterized by increased climatic variability with an associated increase in frequency and severity of ecological disturbances.