Made available in DSpace on 2016-12-23T14:37:35Z (GMT). No. of bitstreams: 1 mileide.pdf: 1266050 bytes, checksum: 2566f7a76319b066b6b3af42e3af0d23 (MD5) Previous issue date: 2008-03-04 / The Brazilian Northeast (NEB) region presented different vegetation types that are essential component of its ecosystem. With remote sensing techniques it is possible, for example, to analyzed variations in vegetation community and alterations in vegetation phenological. Analysis the main objective of this work is to evaluate the temporal behavior of the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), of different vegetation types in the NEB over period between February/2000 and July/2006. The study area was the NEB, where it was used to characterize the vegetations types a vegetation map of Brazil, in the 1:5,000,000 scale from Brazilian Institute of Geography and Statistics (IBGE). A total of 140 cloud-free EVI images with spatial resolution 250 m were acquired from National Aeronautics and Space Administration (NASA). Four CBERS-2/CCD images spatian resolution 20 m were also acquired from National Institute for Espace Research (INPE) to assist EVI data sample collection for each vegetation type. Precipitation data of the cities Petrolina and Pesqueira (Pernambuco), São Luiz and Carolina (Maranhão) located in regions of Caatinga, Atlantic Forest, Amazon and Savannah biome vegetation, respectively, were used to analyze its relationship with EVI from these vegetation. Also, EVI from irrigated area at Petrolina were used in these analysis. Results obtained showed that: i) multi-temporal EVI data from different vegetation types were sensitive to the vegetation phenological cycles, with minor and greater values of EVI in the periods of less and greater precipitation, respectively; ii) amazon biome vegetation presented lesser variation in the multitemporal EVI, however with greater values, justified by vegetation species the are always with green leaf; iii) Caatinga biome vegetation presented greater EVI values variation because the vegetation species on the dry periods occur total defoliation and on wet period the vegetation became green; iv) all EVI data from the vegetations studied presented significant relationship with precipitation (p-value< 0.05). / O Nordeste Brasileiro (NEB) apresenta diferentes tipos de vegetação, sendo importantes para o seu ecossistema. Com a utilização de técnicas de sensoriamento remoto é possível, por exemplo, analisar variações de comunidades de vegetação e suas alterações fenológicas. O objetivo principal deste trabalho é avaliar o comportamento temporal do Índice de Vegetação Melhorado (EVI) do sensor Spectroradiômetro de Resolução Espacial Moderada (MODIS), de diferentes tipos de vegetação do NEB no período entre fevereiro de 2000 a julho de 2006. A área de estudo foi a região do NEB, sendo utilizado para caracterização dos tipos de vegetação um mapa de vegetação na escala de 1:5.000.000 do Instituto Brasileiro de Geografia e Estatística (IBGE). Um total de 140 imagens EVI livres de nuvens com resolução espacial de 250 m foram adquiridas da Agência Nacional Aeroespacial Norteamericana (NASA). Quatro imagens CBERS-2/CCD com resolução espacial de 20 m foram também adquiridas do Instituto Nacional de Pesquisas Espaciais (INPE) para auxiliar na coleta das amostras de dados de EVI dos diferentes tipos de vegetação. Dados de precipitação das cidades de Petrolina e Pesqueira (Pernambuco), Barra do Corda e Carolina (Maranhão) localizadas nas regiões de vegetação do tipo Caatinga, Floresta Atlântica, Amazônia e Cerrado, respectivamente, foram utilizados para avaliar sua relação com os dados de EVI sob estas vegetações. Dados de EVI sobre área irrigada também foram utilizados para esta análise. Os resultados obtidos mostraram que: i) os dados multitemporais EVI de diferentes tipos de vegetação foram sensíveis às respectivas variações fenológicas, com os menores e maiores valores de EVI ocorrendo nos períodos de seca e chuva respectivamente; ii) a vegetação Amazônia apresentou a menor variação multitemporal dos valores de EVI, todavia apresentando os valores mais elevados, podendo-se justificar pela maior quantidade de folhas e por estarem sempre verdes; iii) a vegetação de caatinga analisada apresentou a maior variação dos valores de EVI, pois na época de seca, perde todas as folhas e na época de chuva, se torna verde devido a menor variabilidade da precipitação; iv) todos os dados de EVI das vegetações apresentaram relação significativa (valor-p<0,05) com a precipitação.

As condições climáticas do Brasil aliadas ao desenvolvimento tecnológico favorecem a obtenção de sucessivos incrementos em produção florestal e estimulam a expansão de área cultivada com povoamentos voltados para a produção madeireira. Com o objetivo de contribuir para o processo de quantificação das florestas plantadas em uma escala regional, este estudo propõe utilizar informações combinadas de imagens de satélites e dados obtidos do LiDAR (Light Detecting and Ranging) para a construção de modelos determinísticos capazes de distinguir em duas categorias de idade agrupamentos de florestas plantadas no Vale do Paraíba, estado de São Paulo. A primeira etapa constitui utilizar informações de parcelas de campo como resposta para modelos gerados com variáveis de escaneamento a laser aeroembarcado (ALS) e extrapolar os parâmetros para toda a região da plantação; em um segundo momento, utilizar as informações extrapoladas para gerar um modelo composto por variáveis de índice de vegetação (IV) calculados das imagens de satélite. As informações LiDAR (Light Detecting and Ranging) foram obtidas de sete fazendas da região do Vale do Paraíba, estado de São Paulo, em 2012, mesmo ano em que foram coletados os dados das parcelas de campo dos inventários florestais e que as imagens foram obtidas pela constelação de satélites RapidEye. Como variáveis de dados ALS foram utilizados o cálculo de todos os pontos por célula de 5 x 5 m avaliados, alturas máxima, mínima, média, desvio padrão e percentis de altura, calculados pelo programa de análise de dados LASTools&reg;. Foram incluídas também métricas de diferença de alturas do percentil 90 e o percentil 10 (p9010) e a medida dessa diferença relativa à altura do percentil 90 (p9010r). Na modelagem dos dados LiDAR para imagens de satélite foram utilizadas como variáveis, de forma individual ou conjuntamente, os índices NDVI, NDVI705, EVI, GNDVI, SAVI, Red-Green ratio e SRI. Os modelos foram avaliados quanto ao seu desempenho no coeficiente de determinação (R2) e na raíz do erro quadrático médio (RMSE) e em uma análise final predizendo as fazendas em categorias de idade jovem e maduro. O modelo com melhores estimativas (R2 e RMSE) para idade na primeira etapa foi o que possuía variáveis Hp90 e Hp9010r, com R2=0,85 e RMSE=11,736 meses, e para a segunda etapa foi o modelo contendo como variáveis os índices de vegetação NDVI705, Red-Green índex e SAVI, com R2=0,49 e RMSE=0,378 meses. Apesar dos resultados melhores, o modelo contendo índices de vegetação GNDVI e Red-Green índex foi o que melhor representou a distribuição das florestas quanto a sua maturidade. / Brazil\'s climate conditions combined with the technological development promote the obtaining of successive increments in forest production and stimulate the expansion of cultivated area with stands for timber production. In order to contribute to the process of quantification of planted forests at regional scale, this study proposes to use combined information from satellite images and data obtained from the LiDAR (Light Detecting and Ranging) for the construction of deterministic models able to distinguish two categories of age groupings of planted forests in the Paraíba Valley, State of São Paulo in Brazil. The first step is to use field plots information in response to models generated with airborne laser scanning (ALS) variables and extrapolate the parameters for the whole region of the plantation; in a second moment, use the information extrapolated to generate a model composed of vegetation index variables (IV) calculated from satellite images. The information LiDAR (Light Detecting and Ranging) were obtained from seven farms in the region of the Paraíba Valley, State of São Paulo, in 2012, the same year in which the data were collected from plots of field forest inventories and that the images were obtained by the RapidEye satellite constellation. As data variables ALS were used the calculation of all points by cell size of 5 x 5 m evaluated, maximum height, minimum, mean, standard deviation and height percentiles, calculated by the data analysis program called LASTools®. Also included height difference metrics 90th percentile and percentile 10th (p9010) and the extent of this difference relative of the 90th percentile (p9010r). In the modeling of data LiDAR data for satellite images were used as variables, individually or jointly, the NDVI index, NDVI705, EVI, GNDVI, SAVI, Red-Green index and SRI. The models were evaluated regarding their performance on the coefficient of determination (R2) and root mean square error (RMSE) and a final analysis predicting the farms into categories of age, young and mature. The model with best estimates (R2 and RMSE) for age at first stage was what possessed variables Hp90 and Hp9010r, with R2 = 0.85 and RMSE = 11.736 months, and the second stage was the model containing as variables the NDVI705 vegetation, Red-Green index and SAVI, with R2 = 0.49 and RMSE = 0.378 months. Despite the better results, the model containing GNDVI and Red-Green vegetation indices was the best represented distribution of forests about your maturity.

Considerando que estudos sobre fenologia vegetal são importantes para a compreensão do funcionamento e verificação da ocorrência de padrões no ciclo vegetativo das plantas, resultando em melhorias nas atividades de conservação e manejo, o objetivo desta pesquisa foi caracterizar a dinâmica fenológica de diferentes tipologias campestres no estado do Rio Grande do Sul (RS), a partir da relação entre a variabilidade de elementos climáticos intra e interanual e eventos em larga escala e a distribuição espaço-temporal das tipologias predominantes. A área de estudo abrangeu 10 tipologias predominantes de campo no estado do RS. A base de dados orbitais utilizada foi obtida de diferentes produtos relacionados ao estudo da vegetação do sensor MODIS (Moderate Resolution Imaging Spectroradiometer), constando os índices de vegetação NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index) e GPP (Gross Primary Productivity). Também, foram utilizados dados meteorológicos provenientes da base TRMM (Tropical Rainfall Measuring Mission) e ERA Interim, para o período de fevereiro de 2000 a dezembro de 2014. O uso de séries temporais de dados NDVI e EVI/MODIS permitiram obter informações sobre a fenologia da vegetação campestre e a definição de padrões diretamente relacionados a variações meteorológicas. A sazonalidade da vegetação campestre apresenta cliclo anual bem marcado, com início e fim da estação de crescimento determinada pelas condições térmicas (temperatura do ar), porém alterado pela disponibilidade hídrica. A relação entre temperatura do ar e vigor vegetal apresentou maior correlação e tem influência direta sobre o início e fim da estação de crescimento (primavera e verão) A precipitação pluvial, no entanto, influencia as condições de crescimento/desenvolvimento das tipologias campestres, especialmente no verão, associado aos períodos de estiagem que tendem a ocorrer com maior frequência. Ambos os índices (EVI e NDVI) apresentam maior variabilidade durante a primavera e o verão, com diminuição da variabilidade durante o outono e inverno. A aplicação da Transformada de Ondaleta mostrou onde e quando ocorreram alterações no padrão fenológico da vegetação campestre e a Transformada Coerência apontou a intensidade (correlação) entre os índices de vegetação e a variabilidade das condições meteorológicas. O agrupamento das tipologias, com uso da técnica de Cluster, revelou seus comportamentos sazonais, sendo que a partir do índice EVI há a possibilidade de identificar diferenças entre as tipologias durante o outono e inverno, enquanto o NDVI apresentou diferença somente no inverno. As métricas fenológicas obtidas do Timesat para as imagens EVI permitiram obter dados importantes sobre o ciclo fenológico da vegetação campestre do RS, com a caracterização do padrão fenológico das tipologias predominantes. O uso de modelos para a estimativa da produtividade da vegetação campestre a partir do EVI revelou dentre as tipologias testadas que a CSR (campos de solos rasos) apresentou maior capacidade de explicar a variabilidade da produtividade dos campos por ser mais suscetível às variações meteorológicas. Os resultados obtidos permitiram confirmar a diversidade entre as tipologias campestres predominantes no RS, expressas por índices de vegetação, tanto no aspecto temporal como espacial. O uso dos índices de vegetação demonstrou potencial no monitoramento do padrão fenológico da vegetação campestre frente a variabilidade climática do RS. / Considering that studies on vegetal phenology are important to understand the mechanisms and pattern recognition on the vegetative cycle of plants, resulting in improvements in conservation and management activities, the aim of this research was to characterize the phenological dynamics of different grassland typologies in Rio Grande do Sul State (RS), based on the relationship between the variability of intra-annual and inter-annual climatic elements, large-scale events and the spatio-temporal distribution of predominant typologies . The study area included 10 predominant grassland typologies in RS state. The orbital database used was obtained from different products related to vegetation studies of MODIS sensor (Moderate Resolution Imaging Spectroradiometer), presenting the vegetation indices NDVI (Normalized Difference Vegetation Index), EVI (Enhanced Vegetation Index) and GPP (Gross Primary Productivity). Also, meteorological data from TRMM base (Tropical Rainfall Measuring Mission) and ERA Interim were used for the period of February 2000 to December 2014. The use of time series data from NDVI and EVI/MODIS led to information on grassland vegetal phenology and the definition of patterns directly related to meteorological variations. The seasonality of grassland vegetation presents a well marked annual cycle, with the beginning and the end of growing season determined by thermal conditions (air temperature) but altered by water availability. The relationship between air temperature and vegetal vigor presented a strong correlation and influences directly on the beginning and on the end of the growth season (spring and summer). The rainfall, however, influences growth/development conditions of grassland typologies, especially in summer, associated to drought periods that tend to occur more frequently Both indices (EVI and NDVI) presented a greater variability during spring and summer, with a lesser variability during fall and winter. The application of Ondaleta Transform showed where and when alterations occurred in the phenological pattern of grassland vegetation and the Coherence Transform pointed the intensity (correlation) between vegetation indices and the variability of meteorological conditions. The grouping of typologies, using the Cluster technique, revealed their seasonal behaviors, and from the EVI index there is the possibility of identifying differences between typologies during fall and winter, whereas NDVI showed differences only in winter. The phenological metrics obtained from Timesat to EVI images allowed to obtain important data on the phenological cycle of grassland vegetation of RS state, with a characterization of the phenological pattern. The use of models for estimation of productivity of grassland vegetation based on EVI revealed among the typologies tested that the CSR (shallow soils grasslands) presented greater ability to explain the variability of grasslands productivity because it is more susceptible to meteorological variations. The obtained results allowed for the confirmation of diversity among the grassland typologies predominant in RS state, expressed by vegetation indices, both in temporal and spatial aspects. The use of vegetation indices demonstrated potential on the monitoring of phenological pattern of grassland vegetation considering the climatic variability of RS state.

Leaf area index (LAI) is a versatile indicator of crop growth that is used to estimate evapotranspiration (ET), monitor nitrogen status, and estimate crop yield. Traditional methods for measuring LAI can be improved using high resolution remote sensing. The aim of this study was to compare approaches for estimating LAI from UAV-derived visible vegetation indices. Coincident ground-based and remotely sensed data were obtained from two irrigated wheat fields and were sampled at a total of 5 events in 2019 and 2020. Ground-based LAI was measured with a ceptometer and remotely sensed images were collected using a consumer-grade UAV. Mosaiced orthophotos were resampled from native (0.06m) spatial resolution to increasingly coarser spatial resolutions up to 3 m by either a direct or ladder resampling method. Visible band color information (RGB) was extracted from the orthophotos at the points that LAI was collected within field and 12 different visible vegetation indices (VVIs) were calculated. Linear regression was performed to evaluate the relationships between wheat LAI and each calculated VVI for all spatial resolutions and resampling methods. Three VVIs, visible atmospherically resistant index (VARI), normalized green-red difference index (NGRDI), and modified green-red vegetation index (MGRVI), estimated LAI equally well (R2= 0.66, 0.66,0.66; RMSE=0.74,0.73,0.73; MAE=0.57,0.56,0.56) when resampled to 3 m spatial resolution with the ladder resampling method. These results demonstrate the potential to remotely estimate LAI using only RGB cameras and consumer grade drones. An additional aim of this study was to evaluate use of a remotely sensed visible vegetation index to characterize the spatial variability of LAI within irrigated wheat fields. Variation of LAI was measured with a ceptometer on random nested grids at two sites with pre-determined management zones in 2019 and 2020. Coincident digital imagery was collected using a consumer-grade unmanned aerial vehicle (UAV). A visible atmospherically resistant index (VARI) LAI estimation model was applied to red, green, blue (RGB) UAV imagery using a ladder resampling approach from 0.06 m to 3 m spatial resolution. There was significant within-field spatial and temporal variation of mean LAI. For example, in May at the Grace, ID location measured LAI ranged from 0.21 to 2.58 and in June from 1.68 to 4.15. The relationship of measured and estimated LAI among management zones was strong (R2=0.84), validating the remote sensing approach to characterize LAI differences among management zones. There were statistically significant differences in estimated LAI among zones for all sampling dates (P=0.05). We assumed a minimum difference of 15% between zone LAI and the field mean for justifying variable rate irrigation among zones, a threshold that corresponds with approximately a 10% difference in evapotranspiration rate. Three of the five sampling dates had LAI differences that exceeded the threshold for at least one zone, with all three having mean LAI of less than 2.5. The VARI model for estimating LAI remotely is more effective at identifying LAI differences among management zones at lower LAI. Application of this approach has potential for applications such as estimating evapotranspiration of irrigated fields and delineation of zones for variable rate irrigation.

This study estimated monthly and annual Net Primary Productivity (NPP), an important indicator of carbon sequestration, in the Conterminous US from 1997 to 2007 using Carnegie-Ames-Stanford Approach. Vegetation condition, temperature, precipitation, photosynthetically active radiation and soil water holding capacity were used as model’s inputs. NPP values were lower than mean annual values during the year 2000 to 2003 which was probably due to extreme drought conditions during these years. Higher NPP per square meter was generally found in Savannah and Subtropical eco-divisions whereas Tropical/Subtropical deserts had the lowest NPP. Southeastern states had the highest NPP per square meter thus, made the highest contribution to the terrestrial carbon sequestration in US. Since the vegetation is one of the main factors in NPP and thus carbon sequestration, the results of this study could help in various environmental policy decisions on forest and cropland management at the state, EPA and federal levels.

The work is aimed at finding appropriate methods for observing changes in the status of forest vegetation and its evaluation in the years 1992-2015. The satellite images of the Low Tatras are analyzed by using Time Series technology. Specifically, the images Landsat 4, 5, 7 and 8, for which it is necessary to perform a calibration and an adjustment of input data values to realize the individual vegetation indices, due to the fact that the images are captured by different sensors with different radiometric resolution. From this perspective, the work deals with the possibilities of normalized relative radiometric corrections and search for a particular type of appropriate compensation for Landsat CDR images. Calibrated data sets are evaluated by Time Series of different vegetation indices. The resulting values are evaluated in relation with the occurrence of forest disturbances, eg. wind storms, biological and other pests. The final part is discussion of the results, evaluating the test methods of calibration and suitability of vegetation indices for observing the state of calamity. The App is created for generating the Time Series of Landsat images CDR and for preparing RRN datasets. Key words: Time Series, radiometric correction, atmospheric correction, Landsat CDR, vegetation indices,...

Οι δασικές πυρκαγιές αποτελούν αναπόσπαστο κομμάτι των Μεσογειακών οικοσυστημάτων επηρεάζοντας το φυσικό κύκλο διαδοχής της βλάστησης, αλλά και τη δομή και λειτουργία τους. Τα τελευταία χρόνια παρατηρείται αύξηση των δασικών πυρκαγιών αυξάνοντας ιδιαίτερα το επιστημονικό ενδιαφέρον. Η χρησιμοποίηση της δορυφορικής τηλεπισκόπησης στη χαρτογράφηση των καμένων εκτάσεων έχει τριάντα χρόνια ιστορία ως εργαλείο χαρτογράφησης αλλά και παρακολούθησης της εξέλιξης των καμένων εκτάσεων. Η χαρτογράφηση των δασικών πυρκαγιών με τη χρήση δορυφορικών δεδομένων είναι και σήμερα ένα εν ενεργεία αντικείμενο έρευνας της τηλεπισκόπησης. Πολλά χαρακτηριστικά παραδείγματα υπάρχουν στη διεθνή βιβλιογραφία με ερευνητικό αντικείμενο τη χαρτογράφηση των καμένων εκτάσεων με τη χρήση πολλαπλών τύπων δορυφορικών δεδομένων, όμως ο αριθμός αυτών που διαπραγματεύονται για την ίδια πυρκαγιά πολλούς τύπους δεδομένων είναι περιορισμένος. Στην παρούσα διδακτορική διατριβή επιχειρείται για πρώτη φορά η χαρτογράφηση των καμένων εκτάσεων με εκτεταμένη χρήση διαφόρων τύπων δορυφορικών εικόνων πολλαπλής φασματικής και χωρικής διακριτικής ικανότητας που έχουν αποκτηθεί για την ίδια πυρκαγιά (Πάρνηθα, 2007). Πιο συγκεκριμένα, αντικείμενο έρευνας αποτέλεσε η χαρτογράφηση των άκαυτων νησίδων εσωτερικά της περιμέτρου της πυρκαγιάς, καθώς και η διερεύνηση των παραγόντων που διαμορφώνουν την ακρίβεια της χαρτογράφησης, η διερεύνηση της ευαισθησίας των τιμών ανάκλασης σε διαφορετικές αναλογίες καμένου/βλάστησης, καθώς και η εφαρμογή και αξιολόγηση διαφόρων δεικτών βλάστησης. Τα δορυφορικά δεδομένα που αξιολογήθηκαν προέρχονται από τους δορυφορικούς ανιχνευτές IKONOS, LANDSAT, ASTER και MODIS. Παράλληλα με τα αρχικά δεδομένα δημιουργήθηκε ένα σύνολο εικόνων πολλαπλής φασματικής και χωρικής κλίμακας. Αρχικά, εφαρμόστηκαν κλασικοί αλγόριθμοι επεξεργασίας εικόνας για τη γεωμετρική, ραδιομετρική και ατμοσφαιρική διόρθωση των δορυφορικών εικόνων. Στη συνεχεία, επεξεργάστηκε η υψηλής ανάλυσης εικόνα IKONOS, η οποία αποτέλεσε τη βάση για τον υπολογισμό του ποσοστού κάλυψης των καμένων εκτάσεων, της βλάστησης και του γυμνού εδάφους σε επίπεδο εικονοστοιχείου. Λαμβάνοντας υπόψη διαφορετικούς συνδυασμούς φασματικών και χωρικών αναλύσεων πραγματοποιήθηκαν συνολικά 420 ταξινομήσεις. Επιπλέον, οι φασματικοί δίαυλοι καθώς και 57 δείκτες βλάστησης που υπολογίστηκαν, συσχετίστηκαν με περιοχές διαφορετικών αναλογιών καμένης και άκαυτης βλάστησης, με σκοπό τη διερεύνηση της ευαισθησίας τους στην εκτίμηση του ποσοστού των καμένων και μη καμένων περιοχών. Συμπερασματικά, η χωρική διακριτική ικανότητα αποδεικνύεται ως ο σημαντικότερος παράγοντας για την αποτύπωση των άκαυτων νησίδων εσωτερικά της περιμέτρου της πυρκαγιάς, ενώ διαπιστώθηκε ότι συσχετίζεται άμεσα με τον αριθμό των χαρτογραφημένων νησίδων. Επιπλέον, το κοντινό και μέσο υπέρυθρο τμήμα του φάσματος αποδείχτηκαν σημαντικά για την εκτίμηση του ποσοστού του καμένου, ενώ το κόκκινο και κοντινό υπέρυθρο για την εκτίμηση του ποσοστού της βλάστησης. Το τελευταίο φαίνεται ότι διαδραματίζει σημαντικό ρόλο στον υπολογισμό του ποσοστού των καμένων εκτάσεων, ενώ το μέσο υπέρυθρο στον υπολογισμό του ποσοστού της βλάστησης. Οι δείκτες βλάστησης ελαχιστοποιούν τις επιδράσεις εξωτερικών παραγόντων, όπως είναι η επίδραση του εδάφους. Έτσι, οι ενδιάμεσες κατηγορίες κρίθηκαν πιο σύμφωνες φασματικά με τις διαφορετικές αναλογίες καμένου/βλάστησης, σε σχέση με τους αρχικούς φασματικούς δίαυλους, βάσει των οποίων υπολογίζονται οι δείκτες. Οι κλασικοί δείκτες, οι οποίοι ενσωματώνουν το κόκκινο και κοντινό υπέρυθρο μήκος κύματος έδειξαν καλύτερη προσαρμογή στην εκτίμηση του ποσοστού της βλάστησης. Αντίθετα, η τροποποιημένη εκδοχή τους, αντικαθιστώντας το κόκκινο με το μέσο υπέρυθρο τμήμα του φάσματος έδειξαν καλύτερη προσαρμογή στην εκτίμηση του ποσοστού των καμένων περιοχών, ταυτόχρονα με την υψηλή προσαρμογή για την εκτίμηση της βλάστησης. Τέλος, πραγματοποιήθηκε η ανασύσταση της πρόσφατης ιστορίας των πυρκαγιών (1984-2011) για την Αττική, εφαρμόζοντας πρόσφατα ανεπτυγμένες (ημι)αυτόματες τεχνικές χαρτογράφησης σε διαχρονικά LANDSAT δορυφορικά δεδομένα μεσαίας χωρικής διακριτικής ικανότητας. Τα αποτελέσματα αυτής της διαδικασίας οδήγησαν στη χαρτογράφηση των περιμέτρων των πυρκαγιών με σχετικά μεγάλη ακρίβεια, ενώ από τα μοντέλα παλινδρόμησης διαπιστώθηκε ότι οι διαφορές μεταξύ της καμένης έκτασης που υπολογίζεται από τα δορυφορικά δεδομένα και αυτά τα οποία καταγράφονται από τη Δασική Υπηρεσία αποδίδονται στον αριθμό των δορυφορικών εικόνων που χρησιμοποιούνται καθώς και στην ημερομηνία απόκτησης της πρώτης δορυφορικής εικόνας. / Forest fires, an integral part of Mediterranean ecosystems, affect the natural cycle of vegetation succession and the ecosystem’s structure and function. Recently, the increment in frequency of fires has increased the concern of the scientific community. The use of remote sensing in burned land mapping has a 30 year long history as tool in mapping and monitoring of forest fire. Despite this long period, burned land mapping using satellite data is still an active research topic in satellite remote sensing. Many characteristic examples of satellite remote sensing studies of burned land mapping and monitoring can be found in the literature, however studies dealing with a multisource data set for the same fire event are limited. The present thesis attempted to map burned surfaces using a multisource satellite data set of multiple spectral and spatial resolution acquired for the same fire event (Parnitha, 2007). In particular, the aims of the thesis were to delineate the unburned patches within fire scar perimeter and explore the factors influence the classification accuracy, to explore the sensitivity of spectral reflectance values to different burn and vegetation ratios, as well as to examine and evaluate some vegetation indices. The satellite data used were acquired from IKONOS, LANDSAT, ASTER and MODIS. Along with the basic data set, a spatially degraded satellite data over a range of coarser resolutions were created. Firstly, classical image processing algorithms were applied to correct geometrically, radiometrically and atmospherically the satellite images used. The pan-sharpened IKONOS served as the basis to estimate the percent of cover of burned areas, vegetation and bare land, at pixel level. Totally 420 classifications have been implemented considering different combinations of spectral and spatial resolutions. Additionally, the spectral bands and 57 versions of some classical vegetation indices were correlated with different burned and vegetation ratios in order to explore their sensitivity. Conclusively, spatial resolution is the most important factor for the delineation of the unburned patches within the fire scar perimeter, while proved to be strongly correlated with the number of the mapped islands. Moreover, the near and middle infrared channels were the most important ones to estimate the percentage of burned area, while the red and near infrared were the most important channels to estimate the percentage of vegetation. The latter, seemed to play a more significant role in estimating the percent of burned area while the middle infrared seemed to play a more significant role in estimating the percent of vegetation. Vegetation indices are less sensitive to external parameters of the vegetation by minimizing external effects, such as soil impact. Thus, the semi-burned classes were spectrally more consistent to their different fractions of scorched and non-scorched vegetation, than the original spectral channels based on which these indices are estimated. The classical indices, which incorporate the red-near infrared space showed better performance to estimate the percent of the vegetation. In contrast, the modified version of the classical indices, by replacing the red with the middle infrared channel showed the highest performance to estimate the percent of burned areas, apart from the high performance in the estimation of the vegetation. Finally, in the present thesis maps with the reconstruction of the recent fire history of Attica region were created, in a spatially explicit mode using (semi)automated image processing techniques in a series of multi-temporal medium-resolution LANDSAT images. The results showed that the fire-scar perimeters were captured with considerably high accuracy, while regression modeling showed that the differences between the area burned estimated from satellite data and that recorded by the forest service can be explained by the number of satellite images used followed by the acquisition date of the first image.

Submitted by Viviane Lima da Cunha (viviane@biblioteca.ufpb.br) on 2017-04-20T11:23:36Z No. of bitstreams: 1 arquivototal.pdf: 8351291 bytes, checksum: 9c34c27730ae5014b89097e887df8ee5 (MD5) / Made available in DSpace on 2017-04-20T11:23:36Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 8351291 bytes, checksum: 9c34c27730ae5014b89097e887df8ee5 (MD5) Previous issue date: 2016-03-31 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / The recent process of city urbanization has intensified the changes in the land use and occupation, causing various environmental impacts from the transformation of the natural environment without planning. Among the environmental impacts there is the reduction of vegetation cover areas, occurrence of floods, air pollution and rising temperatures. Urban areas of cities tend to have higher temperatures than the areas to their surroundings and the very urban area, this phenomenon is called heat islands. Various techniques have been used to analyze the influence of the use and occupation of land in the heat island phenomenon, among them stands out the use of geotechnology for the Remote Sensing and Geographic Information Systems (GIS). In this perspective, this study aims to analyze the influence of land use and occupation in the emergence of heat islands in the city Paço do Lumiar – MA. The Delimitation of the study area analyzed in this study area analyzed in this research is justified by the fact present: (1) quick growth of urban population, (2) expansion of the urban network and (3) severe change of use and occupation of land. To develop this research, were adopted using techniques of remote sensing, digital processing of satellite imagery in a GIS environment. These techniques were used to classify the use and occupation of land and estimation of biophysical parameters: vegetation index (NDVI, IVAS and LAI), albedo, surface temperature (°C) and net radiation (W/m²). The results of the land use and occupancy classification showed that in 1988 the urban area occupied a total of 10.84 square kilometers and in 2014 rose to 22.47 km². The albedo values ranged from 0.06 to 1.02. The lowest values of albedo were found in areas with presence of water and vegetation cover higher density and higher values in more densely urbanized areas without vegetation. The analysis of NDVI of the spatio-temporal variation, IVAS and LAI occurred to the surface showed clear reduction of the class of higher values of vegetation, showing the replacement of vegetation by areas with greater urbanization, the lowest values of vegetation indices are observed mainly in the southwest portion of the city, area where are located the most densely occupied neighborhoods, like the Maiobão the neighborhood. It was verified that the surface temperatures showed certain pattern of spatial variation, above all, time. In dates analyzed surface temperature ranged from 23 to 37 °C, the lowest values were observed in class and vegetation water of higher density, showing that areas with greater availability of water and the presence of vegetation. The estimated net radiation revealed that the highest values of net radiation are found on areas with presence of dense vegetation and water bodies and the lowest values in the urban classes and shrub/herbaceous, with values ranging from 450 to 736 W/m² . The results of multi-temporal analysis of the use and occupation of the land made it possible to evaluate the influence of vegetation cover and fragmentation on the urban environment of the study area and contributed to data acquisition for monitoring environmental quality. / O processo de urbanização recente das cidades tem intensificado as modificações do uso e ocupação da terra, ocasionando diversos impactos ambientais oriundos da transformação do meio natural sem planejamento. Dentre os impactos ambientais, destaca-se a redução das áreas com cobertura vegetal, ocorrência de enchentes, poluição do ar e aumento das temperaturas. As zonas urbanas das cidades tendem a apresentar temperaturas mais elevadas do que as áreas ao seu entorno e na própria área urbana, este fenômeno é denominado de ilhas de calor. Várias técnicas têm sido utilizadas para analisar a influência do uso e ocupação da terra no fenômeno de ilhas de calor, entre elas destaca-se o uso das geotecnologias referentes ao Sensoriamento Remoto e aos Sistemas de Informações Geográficas (SIG). Nessa perspectiva, este estudo tem como objetivo analisar a influência do uso e ocupação da terra no surgimento de ilhas de calor na cidade de Paço do Lumiar - MA. A delimitação da área de estudo analisada nesta pesquisa se justifica pelo fato de apresentar: (1) crescimento acelerado da população urbana, (2) expansão da malha urbana e (3) intensa alteração do uso e ocupação da terra. Para desenvolver desta pesquisa, foram adotados o uso de técnicas de sensoriamento remoto, processamento digital de imagens de satélite em ambiente SIG. Essas técnicas foram utilizadas para a classificação do uso e ocupação da terra e estimativa dos parâmetros biofísicos: índices de vegetação (IVDN, IVAS e IAF), albedo, temperatura da superfície (°C) e saldo de radiação (W/m²). Os resultados da classificação de uso e ocupação da terra mostraram que em 1988 a área urbana ocupava um total de 10,84 km² e em 2014 passou para 22,47 km². Os valores do albedo variaram entre 0,061,02. Os menores valores do albedo foram encontrados em áreas com presença de água e com cobertura vegetal de maior densidade e os maiores valores em áreas mais densamente urbanizada e sem cobertura vegetal. A análise da variação espaço-temporais do IVDN, IVAS e IAF ocorridas à superfície, demonstrou clara diminuição da classe dos valores mais elevados de vegetação, evidenciando a substituição da cobertura vegetal por áreas com maior urbanização. Os menores valores dos índices de vegetação são observados principalmente na porção sudoeste do município, área onde estão localizados os bairros mais densamente ocupados, a exemplo o bairro do Maiobão. Pôde-se verificar que as temperaturas de superfície apresentaram certo padrão de variação espacial, sobretudo, temporal. Nas datas analisadas a temperatura de superfície variou de 23 a 37°C, cujos menores valores são observados na classe de água e vegetação de maior densidade, evidenciando que áreas com maior disponibilidade de água e presença de vegetação podem contribuir para a amenização dos efeitos de anomalias térmicas como as ilhas de calor. A estimativa do saldo de radiação revelou que os maiores valores do saldo de radiação são encontrados sobre áreas com presença de vegetação densa e corpos hídricos e os menores valores nas classes urbano e vegetação arbustiva/herbácea, com valores variaram de 450 a 736 W/m². Os resultados da análise multitemporal do uso e ocupação da terra possibilitaram avaliar a influência da cobertura vegetal e sua fragmentação sobre o ambiente urbano da área de estudo e contribuíram para aquisição de dados para monitoramento da qualidade ambiental.

The main purpose of thesis is creation and evaluation of models for change detection of arable land to grassland by Hybrid-based Change Detection method, which combined approaches based on the Vegetation Indices, Image Differencing and Principal Component Analysis. Six locations with different seasonal configuration of images with high resolution and one locality covered by image with very high resolution were used. The areas were spread across the foothill areas of the Czech Republic. The selection of predictors and the most suitable model was supported by statistical calculation. Application selected models were carried out using a multi-temporal object classification and their accuracy were verified using reference data. The benefit of this thesis is finding generally applicable model useful to investigate the land cover change and evaluation of the potentially most appropriate seasonal configuration of images. Valuable is also methodology in this thesis which focus on selection of predictors and calculation the order of the most appropriate models, which is unique in the available literature. The thesis provides useful findings fitting to insufficiently explored issue of Change Detection arable land to grassland. Powered by TCPDF (www.tcpdf.org)

Development of selected invasive species and meadow vegetation classification algorithm in the Krkonoše Mountains using hyperspectral data Abstract The thesis deals with utilization of airbone APEX hyperspectral image data for selected invasive species and meadow vegetation classification in the study area of the Krkonoše Mountains National Park. The mian goal of the thesis was to develop of classification algorithm based on proposed vegetation indices. The approach was based on the utilization of in-situ LAI, fAPAR, chlorophyll content data and analysis of their relation with vegetation spectral properties. The work also deals with several problems regarding LAI - vegetation indices relationship, namely saturation of LAI and mutual correlation of LAI and chlorophyll content. Tha classification was focued on invasive species Rumex alpinus and Lupinus polyphyllus, meadow vegetation with dominant Nardus stricta and dominant Trisetum flavescens and cutted lawns. Besides the proposed approach, the presented work resulted in several classification maps of study area and in spectral libraries, containing ground level spectra of studied invasive species, meadow vegetation types and several other meadow species. Keywords: hyperspectral image data, APEX, LAI, fAPAR, vegetation indices, invasive species, meadow...