The goal of this project was to develop an operational Landsat TM image classification protocol for FIA forest area estimation. A hybrid classifier known as Iterative Guided Spectral Class Rejection (IGSCR) was automated using the ERDAS C Toolkit and ERDAS Macro Language. The resulting program was tested on 4 Landsat ETM+ images using training data collected via region-growing at 200 random points within each image. The classified images were spatially post-processed using variations on a 3x3 majority filter and a clump and eliminate technique. The accuracy of the images was assessed using the center land use of all plots, and subsets containing plots with 50, 75 and 100% homogeneity. The overall classification accuracies ranged from 81.9-95.4%. The forest area estimates derived from all image, filter and accuracy set combinations met the USDA Forest Service precision requirement of less than 3% per million acres timberland. There were no consistently significant filtering effects at the 95% level; however, the 3x3 majority filter significantly improved the accuracy of the most fragmented image and did not decrease the accuracy of the other images. Overall accuracy increased with homogeneity of the plots used in the validation set and decreased with fragmentation (estimated by % edge; R2 = 0.932). We conclude that the use of random points to initiate training data collection via region-growing may be an acceptable and repeatable addition to the IGSCR protocol, if the training data are representative of the spectral characteristics of the image. We recommend 3x3 majority filtering for all images, and, if it would not bias the sample, the selection of validation data using a plot homogeneity requirement rather than plot center land use only. These protocol refinements, along with the automation of IGSCR, make IGSCR suitable for use by the USDA Forest Service in the operational classification of Landsat imagery for forest area estimation. / Master of Science

The impacts of training data sample size and sampling method on the accuracy of forest/nonforest classifications of three mosaicked Landsat ETM+ images with the nearest neighbor decision rule were explored. Large training data pools of single pixels were used in simulations to create samples with three sampling methods (random, stratified random, and systematic) and eight sample sizes (25, 50, 75, 100, 200, 300, 400, and 500). Two forest area estimation techniques were used to estimate the proportion of forest in each image and to calculate forest area precision estimates. Training data editing was explored to remove problem pixels from the training data pools. All possible band combinations of the six non-thermal ETM+ bands were evaluated for every sample draw. Comparisons were made between classification accuracies to determine if all six bands were needed. The utility of separability indices, minimum and average Euclidian distances, and cross-validation accuracies for the selection of band combinations, prediction of classification accuracies, and assessment of sample quality were determined. Larger training data sample sizes produced classifications with higher average accuracies and lower variability. All three sampling methods had similar performance. Training data editing improved the average classification accuracies by a minimum of 5.45%, 5.31%, and 3.47%, respectively, for the three images. Band combinations with fewer than all six bands almost always produced the maximum classification accuracy for a single sample draw. The number of bands and combination of bands, which maximized classification accuracy, was dependent on the characteristics of the individual training data sample draw, the image, sample size, and, to a lesser extent, the sampling method. All three band selection measures were unable to select band combinations that produced higher accuracies on average than all six bands. Cross-validation accuracies with sample size 500 had high correlations with classification accuracies, and provided an indication of sample quality. Collection of a high quality training data sample is key to the performance of the nearest neighbor classifier. Larger samples are necessary to guarantee classifier performance and the utility of cross-validation accuracies. Further research is needed to identify the characteristics of "good" training data samples. / Ph. D.

No description available.

Human intervention and natural phenomenon cause change in land use day by day. Availability of accurate land use information is essential for many applications like natural resource management, planning and monitoring programs. Landuse Change has become a central component in current strategies for managing natural resources and monitoring environmental change. Because of the rapid development in the field of land use mapping, there is an increase in studies of land use change worldwide. Providing an accurate assessment of the extent and health of the world’s forest, grassland and agricultural resources has become an important priority. By printed maps without any statistics or only statistics without any map can not solve this visualization problem. Because printed maps have not attracted as much attention as statistics among the people because of it is limited applications (Himiyama, 2002). Remotely sensed data like aerial photographs and satellite imageries are undoubtedly the most ideal data for extracting land use change information. Satellite images are the most economical way of getting data for different times. The multitude of existing software helps getting information from satellite image also in manipulating the information. The approach used in this study to classify satellite images and change detection based on Satellite images Landsat MSS (1972), Landsat TM (1989) and Landsat ETM (1999) for using supervised classification methods like maximum likelihood (MAXLIKE), MAHALCLASS and time series analysis of CROSSTAB. After performed these hard and soft classifiers the research showed the significant Landuse change in the study area of Chakoria Sundarban mangrove forest. Remote sensing is the modern tools for detecting change pattern and behaviours of coastal environment (Saifuzzaman, 2000). So, those tools are used in the research work for better change analysis of the study area. For analyzing, evaluation and mapping environmental change detection of different years remotely sensed data have been undertaken. The present research provides some suggestions and recommendations as per research findings in order to optimize the utility of coastal resources and to maintain the sustainability of the resources, coastal land use control and there by stabilizing the coastal vulnerable area of chakoria Sundarban.

<p>Human intervention and natural phenomenon cause change in land use day by day. Availability of accurate land use information is essential for many applications like natural resource management, planning and monitoring programs. Landuse Change has become a central component in current strategies for managing natural resources and monitoring environmental change. Because of the rapid development in the field of land use mapping, there is an increase in studies of land use change worldwide. Providing an accurate assessment of the extent and health of the world’s forest, grassland and agricultural resources has become an important priority. By printed maps without any statistics or only statistics without any map can not solve this visualization problem. Because printed maps have not attracted as much attention as statistics among the people because of it is limited applications (Himiyama, 2002). Remotely sensed data like aerial photographs and satellite imageries are undoubtedly the most ideal data for extracting land use change information. Satellite images are the most economical way of getting data for different times. The multitude of existing software helps getting information from satellite image also in manipulating the information. The approach used in this study to classify satellite images and change detection based on Satellite images Landsat MSS (1972), Landsat TM (1989) and Landsat ETM (1999) for using supervised classification methods like maximum likelihood (MAXLIKE), MAHALCLASS and time series analysis of CROSSTAB. After performed these hard and soft classifiers the research showed the significant Landuse change in the study area of Chakoria Sundarban mangrove forest. Remote sensing is the modern tools for detecting change pattern and behaviours of coastal environment (Saifuzzaman, 2000). So, those tools are used in the research work for better change analysis of the study area. For analyzing, evaluation and mapping environmental change detection of different years remotely sensed data have been undertaken. The present research provides some suggestions and recommendations as per research findings in order to optimize the utility of coastal resources and to maintain the sustainability of the resources, coastal land use control and there by stabilizing the coastal vulnerable area of chakoria Sundarban.</p><p> </p>

This thesis presents a semi-automatic method to analyze morphometric features and landscape elements based on Self Organizing Map (SOM) as an unsupervised Artificial Neural Network algorithm in two completely different environments: 1) the Man and Biosphere Reserve “Eastern Carpathians” (Central Europe) as a complex mountainous humid area and 2) Lut Desert, Iran, a hyper arid region characterized by repetition of wind-eroded features. In 2003, the National Aeronautics and Space Administration (NASA) released the SRTM/ SIR-C band data with 3 arc seconds (approx. 90 m resolution) grid for approximately 80 % of Earth’s land surface. The X-band SRTM data were processed with a 1 arc second (approx. 30 m resolution) grid by the German space agency, DLR and the Italian space agency ASI, but due to the smaller X-SAR ground swath, large areas are not covered. The latest version 3.0 SRTM/C DEM and SRTM/X band DEM were re-projected to 90 and 30 m UTM grid and used to generate morphometric parameters of first order (slope) and second order (cross-sectional curvature, maximum curvatures and minimum curvature) by using a bivariate quadratic surface. The morphometric parameters are then used in a SOM to identify morphometric features (or landform elements) e.g. planar, channel, ridge in mountainous areas or yardangs (ridge) and corridors (valley) in hyper-arid areas. Geomorphic phenomena and features are scale-dependent and the characteristics of features vary when measured over different spatial extents or different spatial resolution. Morphometric parameters were derived for nine window sizes of the 90 m DEM ranging from 5 × 5 to 55 ×55. Analysis of the SOM output represents landform entities with ground areas from 450 m to 4950 m that is local to regional scale features. Effect of two SRTM resolutions, C and X bands is studied on morphometric feature identification. The difference change analysis revealed the quantity of resolution dependency of morphometric features. Increasing the DEM spatial resolution from 90 to 30 m (corresponding to X band) by interpolation resulted in a significant improvement of terrain derivatives and morphometric feature identification. Integration of morphometric parameters with climate data (e.g. Sum of active temperature above 10 ° C) in SOM resulted in delineation of morphologically homogenous discrete geo-ecological units. These units were reclassified to produce a Potential Natural Vegetation map. Finally, we combined morphometric parameters and remotely sensed spectral data from Landsat ETM+ to identify and characterize landscape elements. The single integrated data set of geo-ecosystems shows the spatial distribution of geomorphic, climatic and biotic/cultural properties in the Eastern Carpathians. The results demonstrate that a SOM is a very efficient tool to analyze geo-morphometric features under diverse environmental conditions and at different scales and resolution. Finer resolution and decreasing window size reveals information that is more detailed while increasing window size and coarser resolution emphasizes more regional patterns. It was also successfully applied to integrate climatic, morphometric parameters and Landsat ETM+ data for landscape analysis. Despite the stochastic nature of SOM, the results are not sensitive to randomization of initial weight vectors if many iterations are used. This procedure is reproducible with consistent results. / Avhandlingen presenterar en halvautomatisk metod för att analysera morfometriska kännetecken och landskapselement som bygger på Self Organizing Map (SOM), en oövervakad Artificiell Neural Nätverk algoritm, i två helt skilda miljöer: 1) Man and Biosphere Reserve "Eastern Carpathians" (Centraleuropa) som är ett komplext, bergigt och humid område och 2) Lut öken, Iran, en extrem torr region som kännetecknas av återkommande vinderoderade objekt. Basen för undersökningen är det C-band SRTM digital höjd modell (DEM) med 3 bågsekunder rutnät som National Aeronautics and Space Administration släppte 2003 för ungefär 80 % av jordens yta. Dessutom används i ett mindre område X-band SRTM DEM med 1 bågsekund rutnät av den tyska rymdagenturen DLR. DEM transformerades till 90 och 30 m UTM nätet och därav genererades morfometriska parametrar av första (lutning) och andra ordning (tvärsnittböjning, största och minsta böjning). De morfometriska parametrar används sedan i en SOM för att identifiera morfometriska objekt (eller landform element) t.ex. plan yta, kanal, kam i bergsområden eller yardangs (kam) och korridorer (dalgångar) i extrem torra områden. Geomorfiska fenomen och objekt är skalberoende och kännetecken varierar med geografiska områden och upplösning. Morfometriska parametrar har härletts från 90 m DEM för nio fönsterstorlekar från 5 × 5 till 55 × 55. Resultaten representerar landform enheter för områden från 450 m till 4950 m på marken dvs. lokal till regional skala. Inflytande av två SRTM upplösningar i C och X-banden har studerats för identifikation av morfometriska objekt. Förändringsanalys visade storleken av upplösningsberoende av morfometriska objekt. Ökning av DEM upplösningen från 90 till 30 m (motsvarande X-bandet) genom interpolation resulterade i en betydande förbättring av terräng parametrar och identifiering av morfometriska objekt. Integration av morfometriska parametrar med klimatdata (t.ex. summan av aktiv temperatur över 10° C) i SOM resulterade i avgränsningen av homogena geoekologiska enheter. Dessa enheter ha används för att producera en karta av potentiell naturlig vegetation. Slutligen har vi kombinerat morfometriska parametrar och multispektrala fjärranalysdata från Landsat ETM för att identifiera och karaktärisera landskapselement. Dessa integrerade ekosystem data visar den geografiska fördelningen av morfometriska, klimatologiska och biotiska/kulturella egenskaper i östra Karpaterna. Resultaten visar att SOM är ett mycket effektivt verktyg för att analysera geomorfometriska egenskaper under skilda miljöförhållanden, i olika skalor och upplösningar. Finare upplösning och minskad fönsterstorlek visar information som är mer detaljerad. Ökad fönsterstorlek och grövre upplösning betonar mer regionala mönster. Det var också mycket framgångsrikt att integrera klimatiska och morfometriska parametrar med Landsat ETM data för landskapsanalys. Trots den stokastiska natur av SOM, är resultaten inte känsliga för slumpvisa värden i de ursprungliga viktvektorerna när många iterationer används. Detta förfarande är reproducerbart med bestående resultat. / QC 20100924

Thesis (M. En.)--Miami University, Institute of Environmental Sciences, 2008. / Title from first page of PDF document. Includes bibliographical references (p. 16-21-Xx).

Made available in DSpace on 2014-06-12T18:05:38Z (GMT). No. of bitstreams: 3 arquivo6849_1.pdf: 8929484 bytes, checksum: d1f30ec73e154e1d850b82906bd372e0 (MD5) arquivo6849_2.pdf: 7899375 bytes, checksum: 19026b2a628c50194af036240432621a (MD5) license.txt: 1748 bytes, checksum: 8a4605be74aa9ea9d79846c1fba20a33 (MD5) Previous issue date: 2005 / Conselho Nacional de Desenvolvimento Científico e Tecnológico / A presente tese apresenta os aspectos geológicos e evolutivos de uma importante região inserida no contexto do Domínio Tectônico Pernambuco-Alagoas (DPEAL) considerado como parte integrante do orógeno neoproterozóico que margeia a porção setentrional do Cráton São Francisco. Nesse contexto, as principais associações litológicas encontradas foram agrupadas e caracterizadas em: 1) Restos do embasamento paleoproterozóico: gnaisses migmatíticos quartzo-dioríticos a tonalíticos, com anfibólio cálcico, metaluminosos, portadores de &#949;Nd positivo e idade T(DM) paleoproterozóica (1.9Ga); 2) Migmatitos de protólito sedimentar: gnaisses migmatíticos e migmatitos bandados, comumente metatexíticos, a biotita, granada e mais localmente muscovita, de ampla variação composicional, peraluminosos e com assinatura isotópica indicando valores de &#949;Nd bastante negativos e idades T(DM) paleoproterozóicas (2.1 a 2.2Ga); 3) Rochas supracrustais: representados por espessos pacotes de quartzitos e quartzitos feldspáticos; 4) Metabasitos: metabasitos bandados, foliados/nematoblásticos, ricos em anfibólio cálcico e de afinidade toleítica; e metabasitos maciços-granoblásticos, compostos por diopsídio, plagioclásio e granada, de afinidade cálcio-alcalina e com indicações de basalto intra-placa. Estudos geocronológicos U-Pb convencional dos metabasitos maciços indicaram presença de zircão com história evolutiva complexa; e 5) granitóides gerados a partir da fusão dos metassedimentos: granitóides de composição variando entre monzogranito a sienogranito, peraluminosos, a biotita, muscovita e às vezes granada. O conjunto de rochas gnáissicas migmatíticas e metabasitos acima mencionado foi afetado por metamorfismo cujo ápice atingiu condições transicionais entre fácies anfibolito alto/granulito sob pressões relativamente baixas (~5Kbar). Durante esse evento metamórfico, desenvolveu-se uma superfície metamórfica principal de transposição dúctil (foliação Sn), posteriormente deformada (foliação Sn+1) pelos esforços atuantes durante atividade dúctil do Lineamento Pernambuco. Uma determinação U-Pb convencional em monazita do monzogranito a duas micas originário da fusão parcial dos metassedimentos encaixantes forneceu uma idade de cristalização de 580Ma, provavelmente associada ao evento sin a pós-tectônico. Determinação 40Ar-39Ar em muscovita, do mesmo monzogranito a duas micas, forneceu uma idade de 560Ma, idade esta compatível com outra determinação 40Ar-39Ar feita em biotita extraída do ortognaisse migmatítico. A análise conjunta destas idades com a temperatura de fechamento dos sistemas isotópicos envolvidos forneceu uma taxa de resfriamento daordem de 20°C/Ma para o monzogranito, assumindo um resfriamento com comportamento linear. Durante esta pesquisa foram levantados alguns resultados que ajudam a descrever a evolução tectônica do DPEAL, que representa a raiz de um arco magmático ativo no neoproterozóico, formado pela subducção de crosta oceânica por sob os fragmentos de um micro-continente cujo embasamento era caracterizado preferencialmente por rochas de idade paleoproterozóica a arqueana