Sustaining the Landscape: A Method for Comparing Current and Desired Future Conditions of Forest Ecosystems in the North Cumberland Plateau and Mountains

This project initiates an integrated-landscape conservation approach within the Northern Cumberlands Project Area in Tennessee and Kentucky. The mixed mesophytic forests within the Cumberland Plateau and Mountains are among the most diverse in North America; however, these forests have been impacted by and remain threatened from changes in land use across this landscape. The integrated-landscape conservation approach presented in this report outlines a sequence of six conservation steps. This report considers the first three of these steps in two, successive stages. Stage 1 compares desired future conditions (DFCs) and current prevailing conditions (CPCs) at the landscape-scale utilizing remote sensing imagery, remnant forests, and descriptions of historical forest types within the Cumberland Plateau. Subsequently, Stage 2 compares DFCs and CPCs for at-risk forest types identified in Stage 1 utilizing structural, compositional, or functional attributes from USFS Forest Inventory and Analysis data. Ecological indicators will be developed from each stage that express the gaps between these two realizations of the landscape. The results from these first three steps will directly contribute to the final three steps of the integrated-landscape conservation approach by providing guidance for the generation of new conservation strategies in the Northern Cumberland Plateau and Mountains

A soft clustering approach to detect socio-ecological landscape boundaries using bayesian networks

Detecting socio-ecological boundaries in traditional rural landscapes is very important for the planning and sustainability of these landscapes. Most of the traditional methods to detect ecological boundaries have two major shortcomings: they are unable to include uncertainty, and they often exclude socio-economic information. This paper presents a new approach, based on unsupervised Bayesian network classifiers, to find spatial clusters and their boundaries in socio-ecological systems. As a case study, a Mediterranean cultural landscape was used. As a result, six socio-ecological sectors, following both longitudinal and altitudinal gradients, were identified. In addition, different socio-ecological boundaries were detected using a probability threshold. Thanks to its probabilistic nature, the proposed method allows experts and stakeholders to distinguish between different levels of uncertainty in landscape management. The inherent complexity and heterogeneity of the natural landscape is easily handled by Bayesian networks. Moreover, variables from different sources and characteristics can be simultaneously included. These features confer an advantage over other traditional techniques

Phytoclimatic Stages and Vegetation in Baden - Württemberg and Emilia - Romagna

The assessment of ecosystems and landscapes requires reliable and simple tools. Climate determines broad type and distribution of ecosystems. Therefore, it is a major factor to consider in environmental analysis and ecological regionalization. A standardized bioclimatic classification would be useful to characterize and compare different ecosystems. In this paper, Defaut’s Phytoclimatic System (DSPS) was tested at regional scale in two European areas: Baden-Württemberg (Germany) and Emilia-Romagna (Italy). DSPS phytoclimatic units and vegetation belts and climatic parameters are illustrated and discussed. In addition, as an example application, a map of phytoclimatic units of Emilia-Romagna is designed. Some challenges in matching vegetation to DSPS were found: 1) in areas where transition from one stage to another are not sharply delineated and different vegetation types are intermixed; 2) in alluvial lowlands; 3) in heavily anthropized areas. In conclusion, the results of this study suggest that DSPS can be a useful tool in ecological regionalization and in landscape analysis

Parallel Clustering Algorithms With Application To Climatology

Tez (Yüksek Lisans) -- İstanbul Teknik Üniversitesi, Bilişim Ensititüsü, 2007Thesis (M.Sc.) -- İstanbul Technical University, Institute of Informatics, 2007Ekolojik sınırların nasıl belirleneceği, iklim sınırlandırmalarının nasıl yapılacağı uzun zamandır süregelen bir takım tartışmalara konu olmuştur. Tartışmanın çıkış noktası başvurulan yöntemin ne derece tarafsız olduğuna dair görüş ayrılıklarıdır. İşte bir takım yanlı olabilecek yaklaşımlardansa, böylesi müdahalelerin önlenebildiği formulasyonlar kullanılması gerekmektedir. Veri madenciliğinin önde gelen yaklaşımlarından olan, hiyerarşik ve hiyerarşik olmayan teknikleri de içeren kümeleme yöntemi bu açıdan bakıldığında bize objektif bir çözüm sunmaktadır. Yanlı kararlara neden olabilecek kişisel beceri veya yorumlara dayanmak yerine, kümeleme analizi metodunu kullanmak, elimizdeki çok değişkenli bir veri kümesi için matematiksel bir yaklaşım olacaktır. Bu çalışmada, daha doğru ve kolay iklim bölgeleri edinmek için bazı istatistiksel enstrümanlarla beraber kümeleme yöntemi iklim verileri üzerinde uygulanmıştır. İlk olarak geçerli bir ayırma işlemi için algoritma üzerinde bir geçerlilik kriteri göz önüne alınmıştır. Değişken sayısının her bir deneyde 96 ile 109 arasında değiştiği hali ve Temel Bileşen Analizi (TBA) yoluyla indirgenmiş boyutlar için geçerlilik kriterinin onayladığı sayılarda iklim bölgeleri saptanmıştır. Değişken sayılarındaki bu değişim, ele aldığımız 30-50 K 3-60 D bölgesinde farklı sayılarda iklim bölgeleri önerirken, Türkiye'nin tamamına yakınını kapladığı 34-43 K 23-47 D bölgesinde devamlı olarak 4 iklim bölgesi saptamaktadır. Bu süreç ele alınırken, seri bir algoritmanın yanında paralelleştirilmiş k-ortalama uygulaması kullanılarak performansı gözlenmiştir. Uygulama neticesinde seri kodun TBA ile elde edilmiş veri kümesiyle çalışması daha kolayken, paralel prosedürün yüksek boyutlu küme ile daha iyi sonuçlar verdiğini görülmüştür. Sonuç olarak k-ortalama algoritması 30-50 K 3- 60 D ve 34-43 K 23-47 D bölgelerinin iklim sınırlandırmalarına yeni bir anlayış getirmiş, daha önce yapılmış olan bölgelendirmelerden farklı olarak Türkiye coğrafyasını 4 sınıfa ayırmıştır. Her iki çerçeveye ait deneylerde Türkiye üzerindeki sınırlar genelde aynı seviyede kendini göstermiştir.How to determine the ecoregions or climate zones has been a controversial issue. Discussion appears from the debate if the selected method is objective or not. In order to prevent from subjective approaches, one has to utilize some formulations which are independent from such interferences. Cluster analysis, which is one of the famous pattern recognition tools and has hierarchical and non-hierarchical methods, contributes to the objectivity in this sense. Instead of relying on any expertise or personal interpretations, clustering methods provide a mathematical approach with the multivariate data set. The aim of this work is to implement cluster analysis tools to climatology data in order to obtain climate zones with some other statistical techniques that will make the study more precise. In order to clarify, first we determine how many clusters or regions do we need for valid regionalization by posing a validation criterion on the algorithm. While acquiring such a number of clusters, we have done experiments with both the high dimensional set where there are from 96 to 109 number of variables and the reduced dimensional data space which was obtained via Principal Component Analysis (PCA). Under the criterion we posed, in the region 30-50 N 3-60 E varying number of clusters obtained as the different variable combinations are used. Nevertheless, in 34-43 N by 23-47 E where Turkey covers almost all the frame, we consistently acquired 4 climate zones. During the cluster analysis (CA), besides the serial k-means algorithm we have also utilized parallel version. According to the time measurements, it is seen that whereas serial code performs better with the reduced dimensions, parallel version is good at dealing with high dimensional sets. Consequently, the k-means algorithm suggests another point of view for the climate zones of both regions where it is possible to observe some climatic blocks that are generally stable. More precisely, 4 climate zones appear in all cases concerning the second frame which represents some differences from the preceding climate zone definitions which are based on conventional and hierarchical ideas.Yüksek LisansM.Sc

Analysis of Reflected Spectral Signatures and Detection of Geophysical Disturbance Using Hyperspectral Imagery

Geophysical disturbances resulting from human activities often have significantconsequences for plants and animals, and even for entire ecosystems.Disturbances resulting from petroleum exploration and production activities can have long term impacts on soils, watersheds, rivers and lakes, vegetation, wildlife, and humans. These anthropogenic disturbances are frequently the result of hydrocarbon (oil) or produced water (brine) spills. Brine is usually produced simultaneously with oil or gas. The ability to detect brine spills with remote sensing techniques would be valuable to petroleum companies and industry regulators. The objectives of this research were to 1) determine if brine spills could be detected spectroscopically, 2) determine if spectral analysis could be performed using a statistical method to identify surface features quickly and easily from large imaging spectroscopy data sets without modeling and removing atmospheric effects or performing detailed spectral unmixing, 3) develop a spectral signature for brine spills which could be applied at other locations, and 4) determine if brine spills could be detected using substantially fewer spectral bands so that a smaller and cheaper instrument could be applied to detect these disturbances. Using hyperspectral image cubes acquired by NASA’s Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) over Osage County, Oklahoma, a multivariate statistical clustering technique successfully discerned well-documented brine disturbances on the Tallgrass Prairie Preserve, and the resulting brine spectral signature was applied to locate similar brine disturbances in surrounding image scenes. While validating the prediction results by visiting the site was outside the scope of this project, high resolution aerial photographs were used to assess the success of the predictions and attribute at least 40 of the 87 prediction regions to petroleum activities. While a number of false positives resulted from the analysis, many of these are easily discounted based on objects in the aerial photographs or explained by mineral/salt accumulation. In addition, four bands from the 224-band hyperspectral imagery were used to predict brine disturbances in one of the image cubes. Approximately 90% of the prediction regions detected in the original analysis—which used 187 of the 224 bands—were again detected using only four spectral bands

Assessing and Modeling Landscape Change in a Sensitive High-Elevation Region of the Bolivian Andes

This study used remotely sensed land cover and topographic data, maximum likelihood classification, and spectral mixing analysis to characterize current landscape patterns and quantify land cover change from 1985 to 2003 in the Southeastern Bolivian Andes. Current land cover was mapped into 9 classes with an overall accuracy of 89%. The change analysis demonstrated significant gains in bare and cultivated land (4.4% and 4.1%, respectively) at the expense of forest and pasture (losses of 4.8% and 3.9%, respectively). Spectral mixture analysis indicated that communal rangeland degradation (as measured by changes in proportions of green vegetation, non-photosynthetic vegetation and bare soil on the landscape) may have occurred, especially where conversion of land to more productive uses is restricted by soil fertility, topography, and climate. The study demonstrated that remotely sensed data and traditional image analysis techniques can be used to characterize land cover and land cover change in remote, mountainous areas

Exploratory data analysis using self-organising maps defined in up to three dimensions

The SOM is an artificial neural network based on an unsupervised learning process that performs a nonlinear mapping of high dimensional input data onto an ordered and structured array of nodes, designated as the SOM output space. Being simultaneously a quantization algorithm and a projection algorithm, the SOM is able to summarize and map the data, allowing its visualization. Because using the most common visualization methods it is very difficult or even impossible to visualize the SOM defined with more than two dimensions, the SOM output space is generally a regular two dimensional grid of nodes. However, there are no theoretical problems in generating SOMs with higher dimensional output spaces. In this thesis we present evidence that the SOM output space defined in up to three dimensions can be used successfully for the exploratory analysis of spatial data, two-way data and three-way data. Although the differences between the methods that are proposed to visualize each group of data, the approach adopted is commonly based in the projection of colour codes, which are obtained from the output space of 3D SOMs, in some specific bi-dimensional surface, where data can be represented according to its own characteristics. This approach is, in some cases, also complemented with the simultaneous use of SOMs defined in one and two dimensions, so that patterns in data can be properly revealed. The results obtained by using this visualization strategy indicates not only the benefits of using the SOM defined in up to three dimensions but also shows the relevance of the combined and simultaneous use of different models of the SOM in exploratory data analysis

Régionalisation et synthèse des patrons de la végétation du Québec : utilisation d'indices de patrons à l'échelle provinciale

Le Québec est une immense province à l’intérieur de laquelle existe une grande diversité de conditions bioclimatiques et où les perturbations anthropiques et naturelles du couvert végétal sont nombreuses. À l’échelle provinciale, ces multiples facteurs interagissent pour sculpter la composition et la distribution des paysages. Les objectifs généraux de cette recherche visaient à explorer et comprendre la distribution spatiale des patrons des paysages du Québec, de même qu’à caractériser les patrons observés à partir d’images satellitaires. Pour ce faire, les patrons des paysages ont été quantifiés avec un ensemble complet d’indices calculés à partir d’une cartographie de la couverture végétale. Plusieurs approches ont été développées et appliquées pour interpréter les valeurs d’indices sur de vastes étendues et pour cartographier la distribution des patrons des paysages québécois. Les résultats ont révélé que les patrons de la végétation prédits par le Ministère des Ressources naturelles du Québec divergent des patrons de la couverture végétale observée. Ce mémoire dresse un portrait des paysages québécois et les synthétise de manière innovatrice, en plus de démontrer le potentiel d’utilisation des indices comme attributs biogéographiques à l’échelle nationale.Quebec is a vast province in which bioclimatic conditions, land-uses and land-cover changes are highly diverse. At this scale, multiple drivers interact to have an impact on the composition and configuration of landscape patterns. The main objectives of this research were to explore and better understand the spatial distribution of landscape patterns across Quebec, and to characterize observed patterns as seen from satellite. To achieve these objectives, we quantified landscape patterns with an extensive set of metrics measured from a categorical land-cover map. We developed and applied several approaches to interpret metric values across large areas, and to map the distribution of Quebec landscape patterns. Results revealed that ecological subzones developed by the Ministère des Ressources naturelles et de la Faune were substantially inconsistent with observed land-cover patterns. This master thesis portrays and synthesizes Quebec landscapes in an innovative way, highlighting the considerable potential of use of landscape metrics for broad-scale biogeographic mapping