From the Proceedings of the 1974 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 19-20, 1974, Flagstaff, Arizona / Profiles of snowpack build-up in openings in forest overstories have been widely observed; however, a quantitative characterization of such a snowpack profile would aid in developing empirical guidelines for improving water yields from snowpacks. A technique is outlined that illustrates (a) evaluating snowpack profiles in and adjacent to individual forest openings in terms of increase or decrease in water equivalent, and (b) defining trade-offs between the estimated increase or decrease in snowpack water equivalent and the forest resource removed. Snowpack water equivalent during peak seasonal accumulation was measured in and adjacent to a clearcut strip in a ponderosa pine stand in north-central Arizona. A 4-degree polynomial, which defines the snowpack profile in terms of deposition, redistribution, and ablation characteristics, was empirically selected to describe snowpack water equivalent data points. An increase of 60 percent in snowpack water equivalent was realized by removing 46 percent of the ponderosa pine in the zone of influence, using a strip equal to one and one-half the height of the adjacent overstory.

Les images hyperspectrales sont des images complexes qui ne peuvent être partitionnées avec succès en utilisant une seule méthode de classification. Les méthodes de classification non coopératives, paramétriques ou non paramétriques peuvent être classées en trois catégories : supervisée, semi-supervisée et non supervisée. Les méthodes paramétriques supervisées nécessitent des connaissances a priori et des hypothèses sur les distributions des données à partitionner. Les méthodes semi-supervisées nécessitent des connaissances a priori limitées (nombre de classes, nombre d'itérations), alors que les méthodes de la dernière catégorie ne nécessitent aucune connaissance. Dans le cadre de cette thèse un nouveau système coopératif et non supervisé est développé pour le partitionnement des images hyperspectrales. Son originalité repose sur i) la caractérisation des pixels en fonction de la nature des régions texturées et non-texturées, ii) l'introduction de plusieurs niveaux d'évaluation et de validation des résultats intermédiaires, iii) la non nécessité d'information a priori. Le système mis en ouvre est composé de quatre modules: Le premier module, partitionne l'image en deux types de régions texturées et non texturées. Puis, les pixels sont caractérisés en fonction de leur appartenance à ces régions. Les attributs de texture pour les pixels appartenant aux régions texturées, et la moyenne locale pour les pixels appartenant aux régions non texturées. Le deuxième module fait coopérer parallèlement deux classifieurs (C-Moyen floue : FCM et l'algorithme Adaptatif Incrémental Linde-Buzo-Gray : AILBG) pour partitionner chaque composante. Pour rendre ces algorithmes non supervisés, le nombre de classes est estimé suivant un critère basé sur la dispersion moyenne pondérée des classes. Le troisième module évalue et gère suivant deux niveaux les conflits des résultats de classification obtenus par les algorithmes FCM et AILBG optimisés. Le premier identifie les pixels classés dans la même classe par les deux algorithmes et les reportent directement dans le résultat final d'une composante. Le second niveau utilise un algorithme génétique (GA), pour gérer les conflits entre les pixels restant. Le quatrième module est dédié aux cas des images multi-composantes. Les trois premiers modules sont appliqués tout d'abord sur chaque composante indépendamment. Les composantes adjacentes ayant des résultats de classification fortement similaires sont regroupées dans un même sous-ensemble et les résultats des composantes de chaque sous-ensemble sont fusionnés en utilisant le même GA. Le résultat de partitionnement final est obtenu après évaluation et fusion par le même GA des différents résultats de chaque sous-ensemble. Le système développé est testé avec succès sur une grande base de données d'images synthétiques (mono et multi-composantes) et également sur deux applications réelles: la classification des plantes invasives et la détection des pins. / Hyperspectral and more generally multi-component images are complex images which cannot be successfully partitioned using a single classification method. The existing non-cooperative classification methods, parametric or nonparametric can be categorized into three types: supervised, semi-supervised and unsupervised. Supervised parametric methods require a priori information and also require making hypothesis on the data distribution model. Semi-supervised methods require some a priori knowledge (e.g. number of classes and/or iterations), while unsupervised nonparametric methods do not require any a priori knowledge. In this thesis an unsupervised cooperative and adaptive partitioning system for hyperspectral images is developed, where its originality relies i) on the adaptive nature of the feature extraction ii) on the two-level evaluation and validation process to fuse the results, iii) on not requiring neither training samples nor the number of classes. This system is composed of four modules: The first module, classifies automatically the image pixels into textured and non-textured regions, and then different features of pixels are extracted according to the region types. Texture features are extracted for the pixels belonging to textured regions, and the local mean feature for pixels of non-textured regions. The second module consists of an unsupervised cooperative partitioning of each component, in which pixels of the different region types are classified in parallel via the features extracted previously using optimized versions of Fuzzy C-Means (FCM) and Adaptive Incremental Linde-Buzo-Gray algorithm (AILBG). For each algorithm the number of classes is estimated according to the weighted average dispersion of classes. The third module is the evaluation and conflict management of the intermediate classification results for the same component obtained by the two classifiers. To obtain a final reliable result, a two-level evaluation is used, the first one identifies the pixels classified into the same class by both classifiers and report them directly to the final classification result of one component. In the second level, a genetic algorithm (GA) is used to remove the conflicts between the invalidated remaining pixels. The fourth module is the evaluation and conflict management in the case of a multi-component image. The system handles all the components in parallel; where the above modules are applied on each component independently. The results of the different components are compared, and the adjacent components with highly similar results are grouped within a subset and fused using a GA also. To get the final partitioning result of the multi-component image, the intermediate results of the subsets are evaluated and fused by GA. The system is successfully tested on a large database of synthetic images (mono and multi-component) and also tested on two real applications: classification of invasive plants and pine trees detection.

From the Proceedings of the 1971 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 22-23, 1971, Tempe, Arizona / The importance of pine forest as a timber and water producing area has led to intensive management, including protection from wildfire. This has resulted in dense stand growth with increased destructive fire potential and transpirational water loss. In Arizona, as in many areas, prescribed forest burning has been used to effectively reduce these fuel hazards. Some question has arisen about the possible side effects of such treatments, particularly air pollution and reduction of infiltration and water yield. In an effort to determine the effects on infiltration, plots receiving various treatments (control, light burn, heavy burn) were fitted with fusion pyrometers before burning, to measure soil surface temperatures during burning. After burning, infiltrometers were installed. Surface temperatures did not exceed 200 degrees f. For the light burns, and ranged over 350-500 degrees f. During heavy burns. Both heavy and light burns produced highly significant decreases in infiltration capacities after burning and the surface 2 inches showed increases in soil pH, carbon and total nitrogen percentages. Infiltration capacities returned to normal after overwintering and were attributed to frost action on soil texture and porosity. The soil chemical changes decreased slowly over 2 years. Soil water repellency also increased and the significance of this is discussed.

From the Proceedings of the 1971 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 22-23, 1971, Tempe, Arizona / Snowmelt is a major source of runoff in Arizona for both reservoir systems and groundwater recharge. Because much of the Arizona snowmelt runoff occurs in ponderosa pine forests, it follows that appropriate forest management methods may enhance snowmelt water yield by manipulating tree spacing or overstory density. This paper attempts to establish guidelines for evaluating such forest management practices. Physiographic and climatic factors also affect runoff quantity, and it is conceivable that 2 sites of identical vegetation composition, but different in some combination of these factors might yield quite different amounts of runoff in response to some management practice. A pert network is presented illustrating the investigative framework for such a research effort. The major study activities of the framework are the identifying developing preliminary evaluations and preparing a comprehensive report. Three inventory evaluations to attempt identification of pertinent populations are currently being conducted and are described.

From the Proceedings of the 1973 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 4-5, 1973, Tucson, Arizona / A preliminary study of probability distributions for use on snowpack accumulation in the central Arizona highlands was made from 22 snow courses selected as having 10 or more years of available records. Due to the frequent occurrence of zero water equivalent value, application of a single continuous probability distribution is precluded. By means of two distributions, however, the snowpack water equivalent can be assessed by a binomial distribution describing the probability of snow, and a lognormal distribution describing the probability of water equivalent. The area chosen for detailed analysis is where the headwaters of many of Arizona's major river systems occur.