Resource managers need to be able to quickly and accurately map aquatic plants in freshwater lakes and ponds for regulatory purposes, to monitor the health of native species and to monitor the spread of invasive species. Site surveys and transects are expensive and time consuming, and low resolution imagery is not detailed enough to map multiple, small lakes spread out over large areas. This study evaluated methods for mapping aquatic plants using high resolution Quickbird satellite imagery obtained in 2007 and 2008. The study area included nine lakes in northern Indiana chosen because they are used for recreation, have residential development along their shorelines, support a diverse wildlife population, and are susceptible to invasive species. An unsupervised classification was used to develop two levels of classification. The Level I classification divided the vegetation into detailed classes of emergent and submerged vegetation based on plant structure. In the Level II classification, these classes were combined into more general categories. Overall accuracy of the Level I classification was 68% for the 2007 imagery and 58% for the 2008 imagery. The overall accuracy of the Level II classification was higher for both the 2007 and 2008 imagery at 75% and 74%, respectively. Classes containing bulrushes were the least accurately mapped in the Level I classification. In the Level II classification, the least accurately mapped class was submerged vegetation. Water and man-made surfaces were mapped with the highest degree of accuracy in both classification schemes. Overhanging trees and shore vegetation contributed to classification error. Overall, results of this research suggest that high resolution imagery provides useful information for natural resource managers. It is most applicable to mapping general aquatic vegetation categories, such as submerged and emergent vegetation, and providing general estimates of plant coverage in lakes. Better methods for mapping individual species, species assemblages, and submerged vegetation constitute areas for further research. / Indiana University-Purdue University Indianapolis (IUPUI)

Resource managers need to be able to quickly and accurately map aquatic plants in freshwater lakes and ponds for regulatory purposes, to monitor the health of native species and to monitor the spread of invasive species. Site surveys and transects are expensive and time consuming, and low resolution imagery is not detailed enough to map multiple, small lakes spread out over large areas. This study evaluated methods for mapping aquatic plants using high resolution Quickbird satellite imagery obtained in 2007 and 2008. The study area included nine lakes in northern Indiana chosen because they are used for recreation, have residential development along their shorelines, support a diverse wildlife population, and are susceptible to invasive species. An unsupervised classification was used to develop two levels of classification. The Level I classification divided the vegetation into detailed classes of emergent and submerged vegetation based on plant structure. In the Level II classification, these classes were combined into more general categories. Overall accuracy of the Level I classification was 68% for the 2007 imagery and 58% for the 2008 imagery. The overall accuracy of the Level II classification was higher for both the 2007 and 2008 imagery at 75% and 74%, respectively. Classes containing bulrushes were the least accurately mapped in the Level I classification. In the Level II classification, the least accurately mapped class was submerged vegetation. Water and man-made surfaces were mapped with the highest degree of accuracy in both classification schemes. Overhanging trees and shore vegetation contributed to classification error. Overall, results of this research suggest that high resolution imagery provides useful information for natural resource managers. It is most applicable to mapping general aquatic vegetation categories, such as submerged and emergent vegetation, and providing general estimates of plant coverage in lakes. Better methods for mapping individual species, species assemblages, and submerged vegetation constitute areas for further research. / Indiana University-Purdue University Indianapolis (IUPUI)

Resource managers need to be able to quickly and accurately map aquatic plants in freshwater lakes and ponds for regulatory purposes, to monitor the health of native species and to monitor the spread of invasive species. Site surveys and transects are expensive and time consuming, and low resolution imagery is not detailed enough to map multiple, small lakes spread out over large areas. This study evaluated methods for mapping aquatic plants using high resolution Quickbird satellite imagery obtained in 2007 and 2008. The study area included nine lakes in northern Indiana chosen because they are used for recreation, have residential development along their shorelines, support a diverse wildlife population, and are susceptible to invasive species. An unsupervised classification was used to develop two levels of classification. The Level I classification divided the vegetation into detailed classes of emergent and submerged vegetation based on plant structure. In the Level II classification, these classes were combined into more general categories. Overall accuracy of the Level I classification was 68% for the 2007 imagery and 58% for the 2008 imagery. The overall accuracy of the Level II classification was higher for both the 2007 and 2008 imagery at 75% and 74%, respectively. Classes containing bulrushes were the least accurately mapped in the Level I classification. In the Level II classification, the least accurately mapped class was submerged vegetation. Water and man-made surfaces were mapped with the highest degree of accuracy in both classification schemes. Overhanging trees and shore vegetation contributed to classification error. Overall, results of this research suggest that high resolution imagery provides useful information for natural resource managers. It is most applicable to mapping general aquatic vegetation categories, such as submerged and emergent vegetation, and providing general estimates of plant coverage in lakes. Better methods for mapping individual species, species assemblages, and submerged vegetation constitute areas for further research. / Indiana University-Purdue University Indianapolis (IUPUI)

A growing number of scientists are investigating applications of landscape ecology principles to marine studies, yet few coral reef scientists have examined spatial patterns across entire reefscapes with a holistic ecosystem-based view. This study was an effort to better understand reefscape ecology by quantitatively assessing spatial structures and habitat arrangements using remote sensing and geographic information systems (GIS). Quantifying recurring patterns in reef systems has implications for improving the efficiency of mapping efforts and lowering costs associated with collecting field data and acquiring satellite imagery. If a representative example of a reef is mapped with high accuracy, the data derived from habitat configurations could be extrapolated over a larger region to aid management decisions and focus conservation efforts. The aim of this project was to measure repeating spatial patterns at multiple scales (10s m2 to 10s km2) and to explain the environmental mechanisms which have formed the observed patterns. Because power laws have been recognized in size-frequency distributions of reef habitat patches, this study further investigated whether the property exists for expansive reefs with diverse geologic histories. Intra- and inter-reef patch relationships were studied at three sites: Andavadoaka (Madagascar), Vieques (Puerto Rico), and Saipan (Commonwealth of the Northern Mariana Islands). In situ ecological information, including benthic species composition and abundance, as well as substrate type, was collected with georeferenced video transects. LiDAR (Light Detection and Ranging) surveys were assembled into digital elevation models (DEMs), while vessel-based acoustic surveys were utilized to empirically tune bathymetry models where LiDAR data were unavailable. A GIS for each site was compiled by overlying groundtruth data, classifications, DEMs, and satellite images. Benthic cover classes were then digitized and analyzed based on a suite of metrics (e.g. patch complexity, principle axes ratio, and neighborhood transitions). Results from metric analyses were extremely comparable between sites suggesting that spatial prediction of habitat arrangements is very plausible. Further implications discussed include developing an automated habitat mapping technique and improving conservation planning and delimitation of marine protected areas.

Indiana University-Purdue University Indianapolis (IUPUI) / Resource managers need to be able to quickly and accurately map aquatic plants in freshwater lakes and ponds for regulatory purposes, to monitor the health of native species and to monitor the spread of invasive species. Site surveys and transects are expensive and time consuming, and low resolution imagery is not detailed enough to map multiple, small lakes spread out over large areas. This study evaluated methods for mapping aquatic plants using high resolution Quickbird satellite imagery obtained in 2007 and 2008. The study area included nine lakes in northern Indiana chosen because they are used for recreation, have residential development along their shorelines, support a diverse wildlife population, and are susceptible to invasive species. An unsupervised classification was used to develop two levels of classification. The Level I classification divided the vegetation into detailed classes of emergent and submerged vegetation based on plant structure. In the Level II classification, these classes were combined into more general categories. Overall accuracy of the Level I classification was 68% for the 2007 imagery and 58% for the 2008 imagery. The overall accuracy of the Level II classification was higher for both the 2007 and 2008 imagery at 75% and 74%, respectively. Classes containing bulrushes were the least accurately mapped in the Level I classification. In the Level II classification, the least accurately mapped class was submerged vegetation. Water and man-made surfaces were mapped with the highest degree of accuracy in both classification schemes. Overhanging trees and shore vegetation contributed to classification error. Overall, results of this research suggest that high resolution imagery provides useful information for natural resource managers. It is most applicable to mapping general aquatic vegetation categories, such as submerged and emergent vegetation, and providing general estimates of plant coverage in lakes. Better methods for mapping individual species, species assemblages, and submerged vegetation constitute areas for further research.

Cette thèse détaille la conception, le développement et l'analyse d'un nouvel outil de caractérisation des textures exploitant les modèles de prédiction linéaire complexe sur les espaces couleur perceptuels séparant l'intensité lumineuse de la partie chromatique. Des modèles multicanaux 2-d causaux et non-causaux ont été utilisés pour l'estimation simultanée des densités spectrales de puissance d'une image " bi-canal ", le premier contenant les valeurs réelles de l'intensité et le deuxième les valeurs complexes de la partie chromatique. Les bonnes performances en terme de biais et de variance de ces estimations ainsi que l'usage d'une distance appropriée entre deux spectres assurent la robustesse et la pertinence de l'approche pour la classification de textures. Une mesure de l'interférence existante entre l'intensité et la partie chromatique à partir de l'analyse spectrale est introduite afin de comparer les transformations associées aux espaces couleur. Des résultats expérimentaux en classification de textures sur différents ensembles de tests, dans différents espaces couleur (RGB, IHLS et L*a*b*) sont présentés et discutés. Ces résultats montrent que la structure spatiale associée à la partie chromatique d'une texture couleur est mieux caractérisée à l'aide de l'espace L*a*b* et de ce fait, cet espace permet d'obtenir les meilleurs résultats pour classifier les textures à l'aide de leur structure spatiale et des modèles de prédiction linéaire. Une méthode bayésienne de segmentation d'images texturées couleur a aussi été développée à partir de l'erreur de prédiction linéaire multicanale. La contribution principale de la méthode réside dans la proposition d'approximations paramétriques robustes pour la distribution de l'erreur de prédiction linéaire multicanale : la distribution de Wishart et une approximation multimodale exploitant les lois de mélanges gaussiennes multivariées. Un autre aspect original de l'approche consiste en la fusion d'un terme d'énergie sur la taille des régions avec l'énergie du modèle de Potts afin de modéliser le champ des labels de classe à l'aide d'un modèle de champ aléatoire possédant une distribution de Gibbs. Ce modèle de champ aléatoire est ainsi utilisé pour régulariser spatialement un champ de labels initial obtenu à partir des différentes approximations de la distribution de l'erreur de prédiction. Des résultats expérimentaux en segmentation d'images texturées couleur synthétiques et d'images satellites hautes résolutions QuickBird et IKONOS ont permis de valider l'application de la méthode aux images fortement texturées. De plus les résultats montrent l'intérêt d'utiliser les approximations de la distribution de l'erreur de prédiction proposées ainsi que le modèle de champ de labels amélioré par le terme d'énergie qui pénalise les petites régions. Les segmentations réalisées dans l'espace L*a*b* sont meilleures que celles obtenues dans les autres espaces couleur (RGB et IHLS) montrant à nouveau la pertinence de caractériser les textures couleur par la prédiction linéaire multicanale complexe à l'aide de cet espace couleur.