Submitted by NATÁLIA SOARES DA SILVA null (nataliasoasilva@hotmail.com) on 2017-01-25T15:52:11Z No. of bitstreams: 1 Natalia Tese com ficha.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) / Approved for entry into archive by LUIZA DE MENEZES ROMANETTO (luizamenezes@reitoria.unesp.br) on 2017-01-27T12:21:22Z (GMT) No. of bitstreams: 1 silva_ns_dr_bot.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) / Made available in DSpace on 2017-01-27T12:21:22Z (GMT). No. of bitstreams: 1 silva_ns_dr_bot.pdf: 3382712 bytes, checksum: bec77c34de08a6140f88f32e659fd0e8 (MD5) Previous issue date: 2016-11-25 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / O sensoriamento remoto tem sido uma ferramenta bastante utilizada em diferentes campos das ciências e não seria diferente na agricultura sendo um dos principais motivos de sua utilização a facilidade para a obtenção de dados dos sensores, já que muitos são disponibilizados gratuitamente em plataformas na internet. É indiscutível que a agricultura é um dos maiores consumidores dos recursos hídricos e que seu uso quando de forma adequada pode gerir excelentes resultados na produção dos cultivos agrícolas. A hipótese do estudo é que técnicas de sensoriamento remoto, aplicadas na área de interesse, podem se transformar em ferramenta para a gestão dos recursos hídricos dedicados à agricultura irrigada com cobertura de palha no solo. Nesse contexto o objetivo principal da pesquisa foi monitorar através do sensoriamento remoto o desenvolvimento do feijoeiro conduzido em sistema de Pivô Central cultivado com cobertura de palha no solo, na região de Paranapanema-SP, determinando quais parâmetros poderão ser utilizados para a gestão da irrigação. O estudo foi desenvolvido através da análise de imagens Landsat e Terra para obtenção do índice de vegetação por diferença normalizada (NDVI) por sensoriamento remoto e suas relações com outras variáveis (umidade do solo, índice de área foliar e evapotranspiração) a fim de parametrizar o desenvolvimento do feijoeiro, além da aplicação do modelo de índice de umidade do solo (IUS). Observou-se uma similaridade no comportamento do NDVI tanto nas imagens obtidas pelo satélite Landsat quanto Terra, onde no início do cultivo o NDVI é baixo devido à baixa porcentagem de cobertura verde e à medida que a cultura se desenvolve esses valores aumentam com o acréscimo da cobertura vegetal onde o ponto máximo é verificado na fase de enchimento do grão e decréscimo na maturação. Com a determinação de um índice (IUS) por sensoriamento remoto infere-se a umidade do solo e é possível monitorar as condições do feijoeiro durante o período de cultivo. / Remote sensing has been a tool widely used in different fields of science and it would not be different for agriculture. It is ease to obtaining data from sensors, since many are available on platforms on the internet. There is no doubt that agriculture is one of the largest consumers of water resources, and when properly manage, excellent results are obtained from agricultural crops production. The main objective of the study was to monitor through remote sensing the development of bean conducted under Central Pivot irrigation, cultivated with no-till and direct seedling, in the region of Paranapanema-SP. Additionally determining which parameters may be used for the irrigation management. The study hypothesis was that remote sensing techniques, applied in the area of interest, can become a tool for the management of water resources devoted to irrigated agriculture with no-till and direct seedling. The study was developed through the analysis of Landsat and Terra images, obtaining the normalized difference vegetation index (NDVI) by remote sensing and its relations with other variables (soil moisture, leaf area index and evapotranspiration) in order to the parametrization of the development of common bean, as well as the application of the model index of soil moisture (IUS). There were similarities in the behavior of NDVI for images from Landsat satellite and Terra. At the beginning of bean development NDVI was low due to the low percentage of cover; as the crop develops these values increased with the development of vegetation cover. The maximum value of NDVI was obtained during the filling phase of the grain in the pods and a decrease in maturation. With the determination of the IUS by remote sensing it can be infers soil water content.

The objective of this research is to detect change in urban areas using two satellite images (from 2001 and 2010) covering the city of Shanghai, China. These satellite images were acquired by Landsat-7 and HJ-1B, two satellites with different sensors. Two change detection algorithms were tested: image differencing and post-classification comparison. For image differencing the difference image was classified using unsupervised k-means classification, the classes were then aggregated into change and no change by visual inspection. For post-classification comparison the images were classified using supervised maximum likelihood classification and then the difference image of the two classifications were classified into change and no change also by visual inspection. Image differencing produced result with poor overall accuracy (band 2: 24.07%, band 3: 25.96%, band 4: 46.93%), while post-classification comparison produced result with better overall accuracy (90.96%). Post-classification comparison works well with images from different sensors, but it relies heavily on the accuracy of the classification. The major downside of the methodology of both algorithms was the large amount of visual inspection.

International Telemetering Conference Proceedings / October 23-26, 2000 / Town & Country Hotel and Conference Center, San Diego, California / Current Landsat 7 processing is based on a single-string, multifunction approach. A follow-on system has been designed that repartitions functions across multiple hardware platforms to provide increased flexibility and support for additional missions. Downlink bit stream acquisition has been moved to lower cost systems functioning as “capture appliances” with high-speed network interconnections to Level 0 processing on generic compute servers. This decouples serial data stream acquisition from the processing system to allow the addition or replacement of compute servers, without the reintegration of specialized high-speed capture hardware. Moreover, it also allows the easy integration of new systems and missions without extensive system redesign or additional software.

Initial soil surveys are incomplete for large tracts of public land in the western USA. Digital soil mapping offers a quantitative approach as an alternative to traditional soil mapping. I sought to predict soil classes across an arid to semiarid watershed of western Utah by applying random forests (RF) and using environmental covariates derived from Landsat 7 Enhanced Thematic Mapper Plus (ETM+) and digital elevation models (DEM). Random forests are similar to classification and regression trees (CART). However, RF is doubly random. Many (e.g., 500) weak trees are grown (trained) independently because each tree is trained with a new randomly selected bootstrap sample, and a random subset of variables is used to split each node. To train and validate the RF trees, 561 soil descriptions were made in the field. An additional 111 points were added by case-based reasoning using aerial photo interpretation. As RF makes classification decisions from the mode of many independently grown trees, model uncertainty can be derived. The overall out of the bag (OOB) error was lower without weighting of classes; weighting increased the overall OOB error and the resulting output did not reflect soil-landscape relationships observed in the field. The final RF model had an OOB error of 55.2% and predicted soils on landforms consistent with soil-landscape relationships. The OOB error for individual classes typically decreased with increasing class size. In addition to the final classification, I determined the second and third most likely classification, model confidence, and the hypothetical extent of individual classes. Pixels that had high possibility of belonging to multiple soil classes were aggregated using a minimum confidence value based on limiting soil features, which is an effective and objective method of determining membership in soil map unit associations and complexes mapped at the 1:24,000 scale. Variables derived from both DEM and Landsat 7 ETM+ sources were important for predicting soil classes based on Gini and standard measures of variable importance and OOB errors from groves grown with exclusively DEM- or Landsat-derived data. Random forests was a powerful predictor of soil classes and produced outputs that facilitated further understanding of soil-landscape relationships.

This thesis is focused on the utilization of RADARSAT-2 polarimetric SAR data for mapping two surficial aspects of the Umiujalik Lake area, Nunavut, Canada: i) materials, such as bedrock, boulders, organic material, sand and gravel, thick and thin till; and ii) lineaments. To achieve these tasks, RADARSAT-2 polarimetric SAR images with three west-looking, increasing incidence angles (FQ1, FQ12, and FQ20, respectively) were used alone and in combination with LANDSAT-7 ETM+ and Digital Elevation Model (DEM) image data. The surficial materials mapping study tested: i) the effects of incidence angles on mapping accuracy; and ii) non-polarimetric and polarimetric classifiers. For non-polarimetric analysis, a Maximum Likelihood Classification (MLC) algorithm was applied to different combinations of RADARSAT-2, LANDSAT-7 ETM+, and DEM images, achieving a maximum overall classification accuracy of 85%. Polarimetric analyses first included computation of polarimetric signatures to understand the scattering mechanisms of the considered surficial materials, i.e., surface, volume, and multiple scatterings. It also tested three polarimetric classifiers: supervised Wishart (overall accuracy of 48.7% from FQ12 image), and unsupervised Freeman-Wishart, and Wishart-H/ /A. Three main conclusions were reached: i) high incidence angle greatly decreases classification accuracy for the HH polarized image when used alone, but incidence angle has little effect when the HV polarization is added; ii) combining images with three incidence angles (FQ1, FQ12, and FQ20) gives higher accuracy with the maximum likelihood classifier; and iii) the medium incidence angle image (FQ12) produces the best classification accuracy using polarimetric classifiers. In the second part of the study, surface lineaments were mapped using RADARSAT-2 SAR single-polarized images, RGB HH, HV, VV composites, polarimetric total power images, and LANDSAT-7 ETM+ principal component images. Polarization effect analysis showed that regardless of beam mode, more lineaments were identified on the HH image than on the HV image, and the maximum number of lineaments was identified on the multi-polarized RGB composite. Incidence angle effects results showed that regardless of polarization modes, the FQ12 image yielded more lineaments than the FQ1 or FQ20 images. The majority of lineaments are oriented in NW and NNW directions, which correspond to the ice flow direction during the last glaciation.

The hemlock woolly adelgid, an invasive non-native insect, is threatening eastern hemlock in Kentucky. This study examined three techniques to map the distribution of eastern hemlock using decision trees, remote sensing, and species distribution modeling. Accuracy assessments showed that eastern hemlock was best modeled using a decision tree without incorporating satellite radiance. Using the distribution from the optimal model, risk maps for susceptibility to hemlock woolly adelgid infestation were created using two species distribution models. Environmental variables related to dispersal were used to build the models and their contributions to the models assessed. The models showed similar spatial distributions of eastern hemlock at high risk of infestation.

This study shows the results of testing previous research that used remote sensing techniques to determine chlorophyll-a concentrations in turbid surface waters, and developing similar methods and models for Utah Lake using Landsat 7 ETM+ satellite imagery and field measured concentrations of chlorophyll-a. The data for the study included images acquired on June 22 and July 8, 2009. The field data included ground measurements taken on June 22 and July 6, 2009 from seven water quality sampling locations. The 48 hour time difference between the Landsat image acquisition (July 8) and the field measurement (July 6), and the small sample size for the data analysis were potential sources of error. The log transformation of red/near-infrared reflectance (i.e. ln[Band3/Band4]) had a high correlation with the field measured chlorophyll-a concentrations (R^2 = 0.9337). With this relationship, a model and 19 contour maps showing the spatial distribution of chlorophyll-a concentrations over Utah Lake was developed for the spring, summer, and fall seasons of 2003 to 2010. Generally about 90% of the Lake area had chlorophyll-a concentrations lower than 20µg/L. High concentrations of Chlorophyll-a (355µg/L and over) were observed mostly at the Provo Bay and Goshen Bay areas of the Lake. Occasionally, elevated levels of chlorophyll-a were observed at the northeastern, middle, and western sections of the lake. Utah Lake's average chlorophyll-a concentration is declining over time. In spring, the Lake average chlorophyll-a concentration reduced from 30.51µg/L in 2004 to 7.08µg/L in 2010. In summer, this average reduced from 132.13µg/L in 2003 to 36.58µg/L in 2010. Finally, in fall, the Lake average chlorophyll-a concentration reduced from 273.40µg/L in 2006 to 33.59µg/L in 2010. Field measured concentrations of phosphorus and model estimates for chlorophyll-a concentrations were highly correlated (R^2 = 0.9046). This suggests that the elevated levels of chlorophyll-a might be a result of the point and non-point discharge of phosphorus-laden wastewater from treatment plants, municipal storm drains, and agricultural activities.

Karlstads vision är att bli en kommun med 100 000 invånare till år 2031. För att kommunen ska nå målet innebär det att fler bostäder behöver byggas. Det innebär att det kommer bli markanvändningsförändringar inom Karlstadskommun. Den urbana miljön bör påverkats när antalet invånare i Karlstad ökar. Fjärranalys är ett sätt att kunna inventera jordytans biofysiska egenskaper och antropogena förändringar. Fjärranalysen har även använts för att kvantifiera och kartlägga ekosystem-egenskaper. Ekosystemtjänster har positiv påverkan på människor, eftersom de hjälper till med att minska stress, ångest, och har positiv inverkan på återhämtning. Medvetenheten av ekosystemtjänster har ökat med åren och det medför att besluttagares och allmänheten är mer medvetna på värdet av de varor och tjänster som ekosystemtjänsterna bidrar till. Att uttrycka värdena av ekosystemtjänster i pengarvärde är ett viktigt verktyg för att ytterligare öka medvetenheten och betydelsen av ekosystem och mångfald till beslutsfattare. Med hjälp av fjärranalys går det att upptäcka att det har skett en del marktäckes- och markanvändningsförändringar runt om i Karlstads tätort. Tydliga förändringar upptäcktes runt Välsviken och vid Bergviks köpcenter, har det uppkommit nya byggnader. Vid den gamla flygplatsen har ett nytt bostadskvarter byggts samt vid områdena Stockfallet och Campus har expanderats med fler bostäder och andra byggnader.Studien har visat att nybyggnationer sker på bekostnad av främst skog. Detta medför att en del av de naturliga ekosystemtjänsterna försvinner. Totalt har 1 016 ha skog och 154 ha vatten försvunnit, det har tillkommit 196 ha öppen mark och bebyggelse har ökat med 975 ha i Karlstads tätort.Ett ekosystemvärde på 58 986 Int$/ha/år för skog har försvunnit från Karlstads tätortsområde, det motsvarar en förminskning på 30 % på 13 år. Medans öppen mark har ett ekosystemvärde som motsvarar 6 272 Int$/ha/år som har tillkommit i området. Det är en ökning på 0,7 %. Vattnet har ett värde på 2 310 Int$/ha/år, som motsvarar en förminskning på 0,6 %. Ett ekosystemtjänstvärde av totalt 61 238 Int$/ha/år har försvunnit från Karlstad. Invånare som bor i centrum kommer få längre till ekosystemtjänsterna som finns i skogen, men även mycket av de natursköna vyerna kommer försvinna från staden. Eftersom växlighet ingår i infrastrukturen kommer invånarna fortfarande ha tillgång till en del ekosystemtjänster i staden. / The municipality of Karlstad has a vision to reach 100,000 inhabitants by year 2031, which leads to that more housing is needed. In conclusion, more housing leads to land cover changes in the municipality. Remote sensing is one way to discoverer soil biophysical properties and anthropogenic changes. It has even been used to quantify and map ecosystem properties. Ecosystem services have a positive effect on people, because they help to reduce stress, depression and have a good impact on recovery. The awareness of ecosystem services has increased, it means that decision makers and the public are now more aware of the significant value of ecosystem services. Through remote sensing, land use and land cover changes can be observed in Karlstad’s urban area. As prominent changes, in the areas around Välsviken and Bergviks shopping mall, new buildings were created. At the old airport, a new residential area was constructed. Further changes could be observed in areas around Stockfallet and Campus where more residential buildings were constructed. When forests are converted to new residential areas, some natural ecosystem services disappear. The municipality of Karlstad has experienced losses that amount to a total of 1017 ha forest and 154 ha water in 13 years. It has an ecosystem service value of 61 238 Int$/ha/year and that has disappeared from Karlstad. It corresponds a loss of 30 percent of the ecosystem services. While open fields have increased with 6272 ha and has a value of 6272 Int$/ha/year. Citizens that are living in the centre is going to have a longer distance to ecosystem services in the forest. Much of the scenic views will disappeared from Karlstad. Because vegetation is included in infrastructures will residents still have access to ecosystem services in the city.

ABSTRACT DECISION TREE CLASSIFICATION OF MULTI-TEMPORAL IMAGES FOR FIELD-BASED CROP MAPPING Sencan, Se&ccedil / il M. Sc., Department of Geodetic and Geographic Information Technologies Supervisor: Assist. Prof. Dr. Mustafa T&uuml / rker August 2004, 125 pages A decision tree (DT) classification approach was used to identify summer (August) crop types in an agricultural area near Karacabey (Bursa), Turkey from multi-temporal images. For the analysis, Landsat 7 ETM+ images acquired in May, July, and August 2000 were used. In addition to the original bands, NDVI, PCA, and Tasselled Cap Transformation bands were also generated and included in the classification procedure. Initially, the images were classified on a per-pixel basis using the multi-temporal masking technique together with the DT approach. Then, the classified outputs were applied a field-based analysis and the class labels of the fields were directly entered into the Geographical Information System (GIS) database. The results were compared with the classified outputs of the three dates of imagery generated using a traditional maximum likelihood (ML) algorithm. It was observed that the proposed approach provided significantly higher overall accuracies for the May and August images, for which the number of classes were low. In May and July, the DT approach produced the classification accuracies of 91.10% and 66.15% while the ML classifier produced 84.38% and 63.55%, respectively. However, in August nearly the similar overall accuracies were obtained for the ML (70.82%) and DT (69.14%) approaches. It was also observed that the use of additional bands for the proposed technique improved the separability of the sugar beet, tomato, pea, pepper, and rice classes.

The problem of whether Landsat 7 data could be used to delineate areas prone to mass movement, particularly debris flows and landslides, was examined using three techniques: change detection in NDVI (Normalized Difference Vegetation Index), change detection in band 5, and the tasseled cap transformation. These techniques were applied to areas that had recently experienced mass movement: Layton, Davis County and Alpine, Spanish Fork Canyon and Santaquin, Utah County. No distinctive spectral characteristics were found with any of these techniques with two possible explanations: 1. That despite improved spatial resolution in Landat 7 over its predecessors and improved digital image processing capabilities, the resolution is still too low to detect these characteristics or 2. That the aspects of a slope that make it prone to mass movement are undetectable at any resolution by remote sensing. Change detection in NDVI examined if areas that remained unchanged (defined as < 5% change) between August 14, 1999 and October 17, 1999 correlated to areas that are prone to mass movement. There was no correlation. Change detection in band 5 was examined between August 14, 1999 and October 17, 1999, October 17, 1999 and May 28, 2000, and August 14, 1999 and May 28, 2000. An interesting result is that the Shurtz Lake and Thistle landslides (Spanish Fork Canyon) showed changes of greater than 30% during August 14, 1999 - October 17, 1999 and October 17, 1999 - May 28, 2000. These changes were limited to these landslides and not seen in abundance in surrounding areas. A similar localization of 30% change was seen in the Cedar Bench landslide (Layton) for the same time periods. There were no other correlations. The tasseled cap ransformation shows areas of dominate greenness, soil brightness or wetness. None of these factors had distinctive patterns in the areas studied when compared to surrounding, mass movement-prone areas so no conclusions can be drawn about the utility of the tasseled cap transformation as it relates to areas of potential mass movement.