Multi-spectral imagery provides a robust and low-cost dataset for assessing wetland extent and quality over broad regions and is frequently used for wetland inventories. However in forested wetlands, hydrology is obscured by tree canopy making it difficult to detect with multi-spectral imagery alone. Because of this, classification of forested wetlands often includes greater errors than that of other wetlands types. Elevation and terrain derivatives have been shown to be useful for modelling wetland hydrology. But, few studies have addressed the use of LiDAR intensity data detecting hydrology in forested wetlands. Due the tendency of LiDAR signal to be attenuated by water, this research proposed the fusion of LiDAR intensity data with LiDAR elevation, terrain data, and aerial imagery, for the detection of forested wetland hydrology. We examined the utility of LiDAR intensity data and determined whether the fusion of Lidar derived data with multispectral imagery increased the accuracy of forested wetland classification compared with a classification performed with only multi-spectral image. Four classifications were performed: Classification A - All Imagery, Classification B - All LiDAR, Classification C - LiDAR without Intensity, and Classification D - Fusion of All Data. These classifications were performed using random forest and each resulted in a 3-foot resolution thematic raster of forested upland and forested wetland locations in Vermilion County, Illinois. The accuracies of these classifications were compared using Kappa Coefficient of Agreement. Importance statistics produced within the random forest classifier were evaluated in order to understand the contribution of individual datasets. Classification D, which used the fusion of LiDAR and multi-spectral imagery as input variables, had moderate to strong agreement between reference data and classification results. It was found that Classification A performed using all the LiDAR data and its derivatives (intensity, elevation, slope, aspect, curvatures, and Topographic Wetness Index) was the most accurate classification with Kappa: 78.04%, indicating moderate to strong agreement. However, Classification C, performed with LiDAR derivative without intensity data had less agreement than would be expected by chance, indicating that LiDAR contributed significantly to the accuracy of Classification B.

The increasing interest of modeling global carbon cycling during the past two decades has driven this research to map leaf area index (LAI) at multiple spatial resolutions by combining LAI field observations with various sensor images at local, regional, and global scale. This is due to its important role in process based models that are used to predict carbon sequestration of terrestrial ecosystems. Although a substantial research has been conducted, there are still many challenges in this area. One of the challenges is that various images with spatial resolutions varying from few meters to several hundred meters and even to 1 km have been used. However, a method that can be used to collect LAI field measurements and further conduct multiple spatial resolution mapping and accuracy assessment of LAI is not available. In this study, a pilot study in a complex landscape located in the Southern Illinois was carried out to map LAI by combining field observations and remotely sensed images. Multi-scale mapping and accuracy assessment of LAI using aerial photo, Landsat TM and MODIS images were explored by developing a multi-scale sampling design. The results showed that the sampling design could be used to collect LAI observations to create LAI products at various spatial resolutions and further conduct accuracy assessment. It was also found that the TM derived LAI maps at the original and aggregated spatial resolutions successfully characterized the heterogeneous landscape and captured the spatial variability of LAI and were more accurate than those from the aerial photo and MODIS. The aerial photo derived models led to not only over- and under-estimation, but also pixilated maps of LAI. The MODIS derived LAI maps had an acceptable accuracy at various spatial resolutions and are applicable to mapping LAI at regional and global scale. Thus, this study overcame some of the significant gaps in this field.

In order to produce a more current inventory of forest estimates along with change estimates, the Forest Inventory Analysis (FIA) program has moved to an annual system in which 20% of the permanent plots in a state are surveyed. The previous system sampled permanent plots in 10-year intervals by sampling states sequentially in a cycle (Wayman 2001, USDA FIA). The move to an annual assessment has introduced the use satellite technology to produce forest estimates. Wayman et al (2001) researched the effectiveness of satellite technology in relation to aerial photo-interpretation, finding the satellite method to do an adequate job, but reporting over-estimations of forest area. This research extends the satellite method a step further, introducing the use of ancillary data in post-classification. The US Forest Service has well-defined definitions of forest and nonforest land-use in its (FIA) program. Using these definitions as parameters, post-classification techniques were developed to improve forest area estimates from the initial spectral classification. A goal of the study was to determine the accuracy of using readily available ancillary data. US Census data, TIGER street files, and local tax parcel data were used. An Urban Mask was created based on population density to mask out Forested pixels in a classified image. Logistic Regression was used to see if population density, street density, and land value were good predictors of forest/nonforest pixels. Research was also conducted on accuracy when using contiguity filters. The current filter used by the Virginia Department of Forestry (VDoF) was compared to functions available in ERDAS Imagine. These filters were applied as part of the post-classification techniques. Results show there was no significant difference in map accuracies at the 95% confidence interval using the ancillary data with filters in a post-classification sort. However, the use of ancillary data had liabilities depending on the resolution of the data and its application in overlay. / Master of Science

No description available.

Fast and accurate monitoring of tropical forest disturbance is essential for understanding current patterns of deforestation as well as helping eliminate illegal logging. This dissertation explores the use of data from different satellites for near real-time monitoring of forest disturbance in tropical forests, including: development of new monitoring methods; development of new assessment methods; and assessment of the performance and operational readiness of existing methods. Current methods for accuracy assessment of remote sensing products do not address the priority of near real-time monitoring of detecting disturbance events as early as possible. I introduce a new assessment framework for near real-time products that focuses on the timing and the minimum detectable size of disturbance events. The new framework reveals the relationship between change detection accuracy and the time needed to identify events. In regions that are frequently cloudy, near real-time monitoring using data from a single sensor is difficult. This study extends the work by Xin et al. (2013) and develops a new time series method (Fusion2) based on fusion of Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) data. Results of three test sites in the Amazon Basin show that Fusion2 can detect 44.4% of the forest disturbance within 13 clear observations (82 days) after the initial disturbance. The smallest event detected by Fusion2 is 6.5 ha. Also, Fusion2 detects disturbance faster and has less commission error than more conventional methods. In a comparison of coarse resolution sensors, MODIS Terra and Aqua combined provides faster and more accurate detection of disturbance events than VIIRS (Visible Infrared Imaging Radiometer Suite) and MODIS single sensor data. The performance of near real-time monitoring using VIIRS is slightly worse than MODIS Terra but significantly better than MODIS Aqua. New monitoring methods developed in this dissertation provide forest protection organizations the capacity to monitor illegal logging events promptly. In the future, combining two Landsat and two Sentinel-2 satellites will provide global coverage at 30 m resolution every 4 days, and routine monitoring may be possible at high resolution. The methods and assessment framework developed in this dissertation are adaptable to newly available datasets.

Benthic habitat maps provide the spatial framework for many research science and management activities in coastal areas such as coral-reefs. Accuracy, the degree to which information on a map matches true or accepted values, of benthic habitat maps is important because often times the map will be used in decision-making processes about how we manage our marine resources. It is critical that some measure, such as the accuracy, of the map be known in order to give a sense of how the overall map portrays the seascape. This study compared the accuracy in the following map classes; major structure, major and detailed biological cover, and detailed coral cover, of the 2014 NOAA Florida Keys Coral Reef Ecosystem Habitat map using two separate quantitative, in situ, and qualitative, drop camera, data sets in order to assess how the data sets compare to one another. Benthic habitat map classes of the NOAA Florida Keys map were based on a NOAA peer-reviewed hierarchical coral reef habitat classification scheme. Accuracy assessment tests to see how often the NOAA Florida Keys map producer correctly classified the different habitats, included error matrix analyses (overall, user’s and producer’s accuracy), and the tau coefficient. Study areas in the Florida Keys reef tract included hard-bottom reef habitat from Key West to the northern end of Key Largo, and focuses on three regions of interest that encompass the eastern and western Lower Keys and Key Largo. The Qualitative, drop-camera, accuracy assessment (AA) analyses for all three regions of interest gave overall accuracies of 84.2%, ±16.9, at the major level of geomorphological structure, 85.4%, ±16.4, and 73.8%, ±18.7, at the major and detailed levels of biological cover and 70.4%, ±20.6, for detailed coral cover. The Quantitative, in situ, AA analyses for all three regions of interest gave overall accuracies of 86.1%, ±0, at the major level of geomorphological structure, 85.2%, ±1.9, and 50.7%, ±13.4, at the major and detailed levels of biological cover and 47.5%, ±13.4, for coral cover. Qualitative and quantitative accuracies were similar at the major geologic structure (hard vs. soft bottom) and major biological cover (i.e. seagrass, algae) however qualitative AA’s for detailed biological cover (i.e. percent of seagrass, algae) and detailed coral cover (percent of coral) were 23.1% and 22.9% higher than the quantitative AA’s. This trend was also found when analyzing the accuracies for the individual regions of interest. The results suggest that for performing an AA of broad map categories, a Qualitative AA compares well with an in situ Quantitative AA, but for more detailed map categories the in situ quantitative AA is more accurate. Marine resource managers should consider these accuracies when making decisions based on the 2014 NOAA Florida Keys Coral Reef Ecosystem Habitat map.

Georeferenced field data were used to develop logistic regression models of the geographic distribution of 38 frequently common plant species throughout Rich County, Utah, to assist in the future correlation of Natural Resources Conservation Service Ecological Site Descriptions to soil map units. Field data were collected primarily during the summer of 2007, and augmented with previously existing data collected in 2001 and 2006. Several abiotic parameters and Landsat Thematic Mapper imagery were used to stratify the study area into sampling units prior to the 2007 field season. Models were initially evaluated using an independent dataset extracted from data collected by the Bureau of Land Management and by another research project conducted in Rich County by Utah State University. By using this independent dataset, model accuracy statistics widely varied across individual species, but the average model sensitivity (modeling a species as common where it was common in the independent dataset) was 0.626, and the average overall correct classification rate was 0.683. Because of concerns pertaining to the appropriateness of the independent dataset for evaluation, models were also evaluated using an internal cross-validation procedure. Model accuracy statistics computed by this procedure averaged 0.734 for sensitivity and 0.813 for overall correct classification rate. There was less variability in accuracy statistics across species using the internal cross-validation procedure. Despite concerns with the independent dataset, we wanted to determine if models would be improved, based on internal cross-validation accuracy statistics, by adding these data to the original training data. Results indicated that the original training data, collected with this modeling effort in mind, were better for choosing model parameters, but sometimes model coefficients were better when computed using the combined dataset.

Abstract Minimally invasive surgery (MIS) techniques have seen rapid growth as methods for improved operational procedures. The main technology of MIS is based on image guided surgery (IGS) devices, namely surgical navigators, surgical robotics and image scanners. With their widespread use in various fields of surgery, methods and tools that may be used routinely in the hospital setting for “real world” assessment of the accuracy of these devices are lacking. In this thesis the concept of accuracy testing was developed to meet the needs of quality assurance of navigators and robots in a hospital environment. Thus, accuracy was defined as the difference between actual and measured distances from an origin, also including determination of directional accuracy within a specific volume. Two precision engineered accuracy assessment phantoms with assessment protocols were developed as advanced materials and methods for the community. The phantoms were designed to include a common region of surgical interest (ROSI) that was determined to roughly mimic the size of the human head. These tools and methods were utilized in accuracy assessment of two commercial navigators, both enabling the two most widely used tracking modalities, namely the optical tracking system (OTS) and the electromagnetic tracking system (EMTS). Also a study of the accuracy and repeatability of a prototype surgical interactive robot (SIRO) was done. Finally, the phantoms were utilized in spatial accuracy assessment of a commercial surgical 3D CT scanner, the O-Arm. The experimental results indicate that the proposed definitions, tools and methods fulfill the requirements of quality assurance of IGS devices in the hospital setting. The OTS and EMTS tracking modalities were nearly identical in overall accuracy but had unique error trends. Also, the accuracy of the prototype robot SIRO was in the range recommended in the IGS community. Finally, the image quality of the O-Arm could be analyzed using the developed phantoms. Based on the accuracy assessment results, suggestions were made when setting up each IGS device for surgical procedures and for new applications in minimally invasive surgery. / Tiivistelmä Mini-invasiivisen eli täsmäkirurgian tekniikoita ja teknologioita on alettu hyödyntää viime aikoina yhä enemmän. Tavoitteena on ollut parantaa kirurgisten operaatioiden tarkkuutta ja turvallisuutta. Täsmäkirurgiassa käytetyt teknologiat pohjautuvat kuvaohjattuihin kirurgisiin paikannuslaitteisiin. Kuvaohjattuihin laitteisiin kuuluvat navigaattorit, kirurgiset robotit ja kuvantalaitteet. Näiden laitteistojen kehittyminen on mahdollistanut tekniikoiden hyödyntämisen monialaisessa kirurgiassa. Paikannuslaitteistojen ja robottien yleistyminen on kuitenkin nostanut sairaaloissa esiin yleisen ongelman paikannustarkkuuden määrittämisessä käytännön olosuhteissa. Tässä väitöskirjassa esitetään kirurgisten yksiköiden käyttöön menetelmä sekä kaksi uutta fantomia ja protokollaa käytössä olevien paikannuslaitteistojen tarkkuuden määrittämiseen. Fantomit suunniteltiin sisältämään ennalta määritetty kirurginen kohdealue, mikä rajattiin käsittämään ihmisen kallon tilavuus. Fantomeita ja protokollaa hyödynnettiin kahden kaupallisen paikannuslaitteen tarkkuuden määrityksessä. Navigaattorit käyttivät optiseen ja elektromagneettiseen paikannukseen perustuvaa tekniikkaa. Lisäksi työssä kehitetyillä menetelmillä tutkittiin prototyyppivaiheessa olevan kirurgisen robotin paikannus- ja toistotarkkuutta sekä tietokonetomografialaitteen O-kaaren kuvan tarkkuuden määritystä. Kokeellisten tulosten perusteella työssä kehitetyt fantomit ja protokollat ovat luotettavia ja tarkkoja menetelmiä kirurgisten paikannuslaitteistojen tarkkuuden määrittämiseen sairaalaoloissa. Kirurgisten navigaattoreiden tarkkuuden määritystulokset osoittivat optisen ja elektromagneettisen paikannustekniikan olevan lähes yhtä tarkkoja. Prototyyppirobotin tarkkuus oli tulosten perusteella kirjallisuudessa esitettyjen suosituksien mukainen. Lisäksi O-kaaren kuvanlaatua voitiin tutkia kehitetyillä fantomeilla. Tarkkuudenmääritystulosten perusteella työssä ehdotetaan menetelmiä laitteistojen optimaalisesta käytöstä leikkaussalissa sekä laajennetaan niiden käyttömahdollisuuksia. Tuloksia voidaan hyödyntää myös paikannuslaitteistojen kehittämistyössä.

Three-dimensional laser scanning has revolutionized spatial data acquisition and can be completed from a variety of platforms including airborne (ALS), mobile (MLS), and static terrestrial (TLS) laser scanning. MLS is a rapidly evolving technology that provides increases in efficiency and safety over static TLS, while still providing similar levels of accuracy and resolution. The componentry that make up a MLS system are more parallel to Airborne Laser Scanning (ALS) than to that of TLS. However, achievable accuracies, precisions, and resolution results are not clearly defined for MLS systems. As such, industry professionals need guidelines to standardize the process of data collection, processing, and reporting. This thesis lays the foundation for MLS guidelines with a thorough review of currently available literature that has been completed in order to demonstrate the capabilities and limitations of a generic MLS system. A key difference between MLS and TLS is that a mobile platform is able to collect a continuous path of geo-referenced points along the navigation path, while a TLS collects points from many separate reference frames as the scanner is moved from location to location. Each individual TLS setup must be registered (linked with a common coordinate system) to adjoining scan setups. A study was completed comparing common methods of TLS registration and geo-referencing (e.g., target, cloud-cloud, and hybrid methods) to assist a TLS surveyor in deciding the most appropriate method for their projects. Results provide insight into the level of accuracy (mm to cm level) that can be achieved using the various methods as well as the field collection and office processing time required to obtain a fully geo-referenced point cloud. Lastly, a quality assurance methodology has been developed for any form of LiDAR data to verify both the absolute and relative accuracy of a point cloud without the use of retro-reflective targets. This methodology incorporates total station validation of a scanners point cloud to compare slopes of common features. The comparison of 2D slope features across a complex geometry of cross-sections provides 3D positional error in both horizontal and vertical component. This methodology lowers the uncertainty of single point accuracy statistics for point clouds by utilizing a larger portion of a point cloud for statistical accuracy verification. This use of physical features for accuracy validation is particularly important for MLS systems because MLS systems cannot produce sufficient resolution on targets for accuracy validation unless they are placed close to the vehicle. / Graduation date: 2012

Among the various remote sensing methods that can be used to map forest areas, the K Nearest Neighbor (KNN) supervised classification method is becoming increasingly popular for creating forest inventories in some countries. In this study, the utility of the KNN algorithm is evaluated for forest/non-forest/water stratification. Antalya is selected as the study area. The data used are composed of Landsat TM and Landsat ETM satellite images, acquired in 1987 and 2002, respectively, SRTM 90 meters digital elevation model (DEM) and land use data from the year 2003. The accuracies of different modifications of the KNN algorithm are evaluated using Leave One Out, which is a special case of K-fold cross-validation, and traditional accuracy assessment using error matrices. The best parameters are found to be Euclidean distance metric, inverse distance weighting, and k equal to 14, while using bands 4, 3 and 2. With these parameters, the cross-validation error is 0.009174, and the overall accuracy is around 86%. The results are compared with those from the Maximum Likelihood algorithm. KNN results are found to be accurate enough for practical applicability of this method for mapping forest areas.