Made available in DSpace on 2017-05-12T14:47:34Z (GMT). No. of bitstreams: 1 DENISE_M_GR_ZEGOZEWSKI.pdf: 8188144 bytes, checksum: 045f54782a1ea2161edf5aa7046a8c1c (MD5) Previous issue date: 2016-02-03 / Estimates of agricultural production are greatly important especially in economy field. However, they depend on area knowledge and cropping yield. Thus, this study aimed to propose a methodology to estimate the areas cropped with soybeans and corn in Paraná State according to multi-temporal EVI/MODIS vegetation index images for 2010/2011, 2011/2012 and 2012/2013 crop years. In addition, there was a research with spatial autocorrelation soybean yield in Paraná, with EVI vegetation index and meteorological variables in a decennial scale and estimate yield using CAR, SAR and GWR models. In Paraná State, there is a drawback to map soybeans crop since corn sowing period is very close to the first one. Therefore, images from the maximum and minimum vegetative vigour were drawn of each studied crop for mapping soybean and corn crops in order to obtain both cropping areas. Although, for the separation, Spectro Angle Mapper algorithm (SAM) was applied by one of the studied crops, while mapping was obtained by multiplying the other bands. Thus, for spatial statistics application of mapped data, the average EV profile of each municipality was extracted as well as for each multi-temporal image, in order to change them into a decennial scale. According to the spatial statistics of such areas, the descriptive analysis of univariate spatial autocorrelation (global and local) of each ten-day variable was used based on the soybean cycle. A bivariate autocorrelation analysis between soybean yield and the studied varieties were also performed. Finalizing the methodology, variables with the highest significant level by stepwise method were selected and SAR, CAR and GWR models were generated to estimate soybean yield. As results, regarding mappings, the following answers for soybean were found out: r = 0.95 and r = 0.99, and while for corn, the answers were: r = 0.72 and r = 0.95 for 2012/2013 and 2013/2014 crop years in relation to the official data from SEAB. So, it has been proved some great efficiency of this methodology to separate and identify crops. When the descriptive statistics of municipalities for each variable was carried out, it was found out that some regions began an early sowing in relation to other ones in Paraná by the decennial vegetation index. The ten-day scale was also possible to be identified according to the climatic factors that caused soybean yield damage. Based on the analysis of spatial autocorrelation, the greatest similarities occurred in 2011/2012 crop year, the one affected by the weather change, whose yields were similar in the municipalities of Paraná State. For spatial modelling, it was observed that selection of decennial variables was different for each studied crop year, and the best model selected by the validation. And GWR was chosen as the best model by the AIC, BIC and adjusted R² validation criteria. The residuals were randomly distributed throughout all the State, so that spatial autocorrelation could be eliminated. / As estimativas das produções agrícolas têm grande importância, principalmente, no âmbito econômico. No entanto, elas são dependentes do conhecimento da área de cultivo e da produtividade da cultura. Desta forma, este trabalho teve por objetivo propor uma metodologia para estimar as áreas cultivadas com soja e milho em escala municipal no Estado do Paraná a partir de imagens multi-temporais do índice de vegetação EVI/MODIS, para os anos-safras 2010/2011, 2011/2012 e 2012/2013. Além disto, trabalhar com a autocorrelação espacial da produtividade da soja nesse Estado, com o índice de vegetação EVI e variáveis agrometeorológicas em escala decendial bem como estimar a produtividade a partir dos modelos CAR, SAR e GWR. No Paraná, há o inconveniente para mapear a soja devido à proximidade de datas de semeadura do milho. Assim, para o mapeamento da soja e do milho, utilizaram-se imagens englobando o período de máximo e mínimo vigor vegetativo de cada cultura, para se obter a área cultivada das duas. Para a separação, utilizou-se o algoritmo Spectro Angle Mapper (SAM) para uma das culturas e obteve-se o mapeamento da outra pela multiplicação de bandas. Para aplicação da estatística espacial dos dados mapeados, extraiu-se o perfil médio do EVI de cada município e para cada imagem multi-temporal para transformá-los em escala decendial. De acordo com a estatística espacial de áreas, utilizou-se a análise descritiva, de autocorrelação espacial univariada (global e local) de cada variável decendial com foco no ciclo da soja. Também realizou-se a análise de autocorrelação bivariada entre a produtividade da soja com as variáveis em estudo. Finalizando a metodologia, selecionaram-se as variáveis com maior índice de significância pelo método de stepwise e, em seguida, foram gerados os modelos estimados (SAR, CAR e GWR) da produtividade da soja. Como resultados, foram encontradas as seguintes respostas para os mapeamentos da soja r= 0,95 e 0,99, e para o milho de r = 0,72 e r= 0,95 para os anos-safras 2012/2013 e 2013/2014 em relação aos dados oficiais da SEAB. Logo, comprovou-se a grande eficiência da metodologia para separação e identificação das culturas. Quando realizada a estatística descritiva dos municípios para cada variável, verificaram-se regiões que iniciam as semeaduras antecipadas em relação a outras regiões do Estado pelos decêndios do índice de vegetação. Foi também possível identificar os decêndios em que os fatores climáticos causaram danos à produtividade da soja. Na análise da autocorrelação espacial, as maiores similaridades ocorreram no ano-safra 2011/2012, ano afetado pela variação climática, cujas produtividades foram semelhantes nos municípios do Paraná. Para a modelagem espacial, verificou-se que a seleção das variáveis decêndiais foi diferente para cada ano-safra estudado, e o GWR foi escolhido como melhor modelo pelos critérios de validação, AIC, BIC e R² ajustado. Foram encontrados resíduos distribuídos aleatoriamente por todo o Estado, para que assim se eliminasse a autocorrelação espacial

Made available in DSpace on 2014-07-29T15:32:00Z (GMT). No. of bitstreams: 1 Dissertacao_Marlon_Nemayer 2010 parte 1.pdf: 211750 bytes, checksum: 2ae9523ca5b4a7d59ae5875af36c7b44 (MD5) Previous issue date: 2010-03-12 / Land cover and land use maps are essentials for the effective territorial governance, environmental monitoring, and proper understanding of the structure and functioning of the ecosystems. In relation to the Cerrado biome, an important step in this direction was obtained with the PROBIO mapping (Projeto de Conservação e Utilização Sustentável da Diversidade Biológica Brasileira do Ministério do Meio Ambiente), which, based on the interpretation of high spatial resolution imagery (Landsat TM), acquired in 2001 and 2002, mapped the entire biome at the scale of 1:250.000, accorging to the 30 natural and 5 anthropic classes. Although this mapping allowed to know, at high accuracy and precision, the extension and distribution of the major land cover types, its updating and further detailing are necessary. A particular example of such need is the Arboreous Savanna class, which, according to the PROBIO map, occupies an area of about 415.642,58 km² (33,72% of all Cerrado remnant vegetation) and presents an marked variability, 20 to 70% in its arborescent layer. Assuming that the phytophisiognomic variations within this class yield distinct seasonal patterns, in this study we evaluated the potential of the MODIS EVI (enhanced vegetation index) imagery, enhanced in the temporal domain, to futher discriminate among this class sub-types. Based on seasonal contrast images of May and September, it was possible to identify three sub-classes, whose spatial distribution patterns corresponded to the major seasonal domains. On the other hand, and based on the primary productivity concept, it was possible to distinguish five domains, in which (large productivity values were associated to the occurrence of denser typologies, close to ecotones). Our results suggest the use of MODIS or similar images (as the ones to be provided soon to be launched VIIRS sensor onboard the NPP and NPOESS series) for improved differentiation of the Cerrado physiognomies. However, field validation is necessary in order to better understand the biophysical meaning of the intraclass physiognomies identified, as well as a better understanding of the inter-annual patterns are necessary. / Mapeamentos de cobertura e uso da terra são de fundamental importância para a efetiva gestão territorial, monitoramento ambiental e para o correto entendimento quanto à estrutura e funcionamento dos ecossistemas. No caso do bioma Cerrado, importante avanço neste sentido foi o mapeamento realizado no âmbito da iniciativa PROBIO (Projeto de Conservação e Utilização Sustentável da Diversidade Biológica Brasileira do Ministério do Meio Ambiente), o qual, com base em imagens de alta resolução espacial (Landsat TM) obtidas em 2001/ 2002, mapeou a totalidade dobioma à escala de 1:250.000, segundo 30 classes naturais e 5 classes antrópicas. Ainda que este mapeamento tenha possibilitado conhecer com elevada exatidão a extensão e distribuição das principais fitofisionomias nativas e antrópicas, a suaatualização e detalhamento se fazem cada vez mais necessários. Em particular, chama atenção a classe Savana Arborizada, a qual, segundo o mapa PROBIO, ocupa uma área de aproximadamente 415.642,58 km² (33,72% de toda a vegetação remanescente no bioma) e que apresenta acentuada variabilidade em termos de cobertura arbórea (de 20 a 70%). Assumindo que as variações fitofisionômicas no âmbito desta classe resultam em diferentes padrões sazonais, neste trabalho avaliamos o potencial das imagens MODIS EVI, realçadas no domínio temporal, para a classe Savana Arborizada. Através do contraste sazonal das imagens EVI, de maio e setembro, foi possível diferenciar três sub-classes, cujos padrões de distribuição espacial correspondem aos principais domínios sazonais. Por outro lado, e com base no conceito de produtividade primária, foi possível diferenciar cinco domínios, em que os maiores valores de produtividade estiveram associados às tipologias mais densas, próximos a ecótonos. Os resultados deste trabalho sugerem o uso de imagens de resolução espacial moderada (hectométrica) e alta resoluçãotemporal, para o detalhamento das várias fitofisionomias do bioma Cerrado. Contudo, validações em campo, com vistas a melhor se caracterizar o significado biofísico das fitofisionomias intraclasse identificadas, bem como melhorentendimento dos padrões inter-anuais se fazem necessários. Eventualmente, a variabilidade inter-anual observada pode vir a ser normalizada através dos valoresde precipitação acumulados anualmente.

Thesis (PhD (Statistics))--University of Stellenbosch, 2010. / ENGLISH ABSTRACT: In extreme value theory (EVT) the emphasis is on extreme (very small or very large) observations. The crucial parameter when making inferences about extreme quantiles, is called the extreme value index (EVI). This thesis concentrates on only the right tail of the underlying distribution (extremely large observations), and specifically situations where the EVI is assumed to be positive. A positive EVI indicates that the underlying distribution of the data has a heavy right tail, as is the case with, for example, insurance claims data. There are numerous areas of application of EVT, since there are a vast number of situations in which one would be interested in predicting extreme events accurately. Accurate prediction requires accurate estimation of the EVI, which has received ample attention in the literature from a theoretical as well as practical point of view. Countless estimators of the EVI exist in the literature, but the practitioner has little information on how these estimators compare. An extensive simulation study was designed and conducted to compare the performance of a wide range of estimators, over a wide range of sample sizes and distributions. A new procedure for the estimation of a positive EVI was developed, based on fitting the perturbed Pareto distribution (PPD) to observations above a threshold, using Bayesian methodology. Attention was also given to the development of a threshold selection technique. One of the major contributions of this thesis is a measure which quantifies the stability (or rather instability) of estimates across a range of thresholds. This measure can be used to objectively obtain the range of thresholds over which the estimates are most stable. It is this measure which is used for the purpose of threshold selection for the proposed PPD estimator. A case study of five insurance claims data sets illustrates how data sets can be analyzed in practice. It is shown to what extent discretion can/should be applied, as well as how different estimators can be used in a complementary fashion to give more insight into the nature of the data and the extreme tail of the underlying distribution. The analysis is carried out from the point of raw data, to the construction of tables which can be used directly to gauge the risk of the insurance portfolio over a given time frame. / AFRIKAANSE OPSOMMING: Die veld van ekstreemwaardeteorie (EVT) is bemoeid met ekstreme (baie klein of baie groot) waarnemings. Die parameter wat deurslaggewend is wanneer inferensies aangaande ekstreme kwantiele ter sprake is, is die sogenaamde ekstreemwaarde-indeks (EVI). Hierdie verhandeling konsentreer op slegs die regterstert van die onderliggende verdeling (baie groot waarnemings), en meer spesifiek, op situasies waar aanvaar word dat die EVI positief is. ’n Positiewe EVI dui aan dat die onderliggende verdeling ’n swaar regterstert het, wat byvoorbeeld die geval is by versekeringseis data. Daar is verskeie velde waar EVT toegepas word, aangesien daar ’n groot aantal situasies is waarin mens sou belangstel om ekstreme gebeurtenisse akkuraat te voorspel. Akkurate voorspelling vereis die akkurate beraming van die EVI, wat reeds ruim aandag in die literatuur geniet het, uit beide teoretiese en praktiese oogpunte. ’n Groot aantal beramers van die EVI bestaan in die literatuur, maar enige persoon wat die toepassing van EVT in die praktyk beoog, het min inligting oor hoe hierdie beramers met mekaar vergelyk. ’n Uitgebreide simulasiestudie is ontwerp en uitgevoer om die akkuraatheid van beraming van ’n groot verskeidenheid van beramers in die literatuur te vergelyk. Die studie sluit ’n groot verskeidenheid van steekproefgroottes en onderliggende verdelings in. ’n Nuwe prosedure vir die beraming van ’n positiewe EVI is ontwikkel, gebaseer op die passing van die gesteurde Pareto verdeling (PPD) aan waarnemings wat ’n gegewe drempel oorskrei, deur van Bayes tegnieke gebruik te maak. Aandag is ook geskenk aan die ontwikkeling van ’n drempelseleksiemetode. Een van die hoofbydraes van hierdie verhandeling is ’n maatstaf wat die stabiliteit (of eerder onstabiliteit) van beramings oor verskeie drempels kwantifiseer. Hierdie maatstaf bied ’n objektiewe manier om ’n gebied (versameling van drempelwaardes) te verkry waaroor die beramings die stabielste is. Dit is hierdie maatstaf wat gebruik word om drempelseleksie te doen in die geval van die PPD beramer. ’n Gevallestudie van vyf stelle data van versekeringseise demonstreer hoe data in die praktyk geanaliseer kan word. Daar word getoon tot watter mate diskresie toegepas kan/moet word, asook hoe verskillende beramers op ’n komplementêre wyse ingespan kan word om meer insig te verkry met betrekking tot die aard van die data en die stert van die onderliggende verdeling. Die analise word uitgevoer vanaf die punt waar slegs rou data beskikbaar is, tot op die punt waar tabelle saamgestel is wat direk gebruik kan word om die risiko van die versekeringsportefeulje te bepaal oor ’n gegewe periode.

A three-phase study was performed to examine abiotic and biotic metrics at southern pine beetle infestation sites in northern Georgia in 2002 to find early indicators that can be leveraged by forest managers to mitigate the effects of future outbreaks: creation of a 2003 Final Impact Map, determining if MODIS MOD13Q1 EVI 16-day image composites can distinguish differences in biomass indicators among healthy and infested loblolly pine and hardwood forests, and creation of an Infestation Risk Map derived from significant climate and physical variables at known infestation sites.Three land cover classification techniques (change vector analysis, enhanced wetness differencing index and standard land cover classification analysis of Landsat 5 TM) were compared to determine which would provide the best estimate of final infestation damage. Classification accuracy results indicated that the latter provided the most reliable site damage information and it became the reference map against which outbreak model results were compared.Using time series analysis of MODIS composites acquired March 2000 - December 2006 to measure 11 phenology metrics for infested and healthy loblolly and hardwood stands showed that the imagery differentiated between forest classes. Results indicated the lowest base vegetation biomass in 2001 for infested loblolly, relative to healthy loblolly, with many metrics trending towards hardwood values following infestation.Abiotic influences included those related to landscape position and climate. Statistical testing showed increased beetle success: 1) along ridge tops at maximum solar exposure, 2) in areas with canopy density>60%, 3) in areas experiencing cooler summers and warmer winters, and 4) where precipitation was significantly lower at infested sites in the 2 years preceding outbreak.The Infestation Risk Map was developed from significant physical and climate indicator variables using the fuzzy theory modeling approach. Comparison of model output to infestation sites resulted in Chi-squared and Cramér's V values of 55.4 and 0.16, respectively, indicating that infestation risk distributions strongly paralleled site infestation. Comparison of model output and low, medium and high infestation density clusters resulted in Chi-squared and Cramér's V values of 241.24 and 0.66, respectively, indicating a more substantive relationship between infestation density and risk classes.

In this study, the relationship of above-ground net primary productivity (ANPP) with precipitation using the enhanced vegetation index (EVI) from satellite data as surrogate for ANPP was assessed. To use EVI as a proxy for ANPP we extracted the satellite data from areas with uniform vegetation in a 2x2 km area for the multi-site approach.In the multi-site analysis in the United States our results showed a strong exponential relationship between iEVI and annual precipitation across the sites and climate regimes studied. We found convergence of all sites toward common and maximum rain use efficiency under the water-limited conditions represented by the driest year at each site. Measures of inter-annual variability in iEVI with rainfall variation across biomes were similar to that reported by Knapp and Smith (2001) in which the more herbaceous dominant sites were found to be most sensitive to interannual variations in precipitation with no relationships found in woodland sites.The relationship was also evaluated in the southern hemisphere using a multi-site analysis with information from satellite TRMM for precipitation and MOD13Q1 from MODIS for EVI values at calendar and hydrologic year periods. The tested sites were located across the 6 major land cover types inAustralia, obtained from MODIS MCD12Q1 product and used to compare the relationship across different biomes. The results showed significant agreement between the annual iEVI and annual precipitation across the biomes involved in this study showing non-significant differences between the calendar and hydrologic years for the 24 sites across different climatic conditions.At the regional scale we also assessed the ANPP-precipitation relationship across all of Australia. Precipitation data from TRMM was obtained at 0.25x0.25 degrees spatial resolution and monthly temporal resolution and EVI values were obtained from the CGM (Climate Grid Modeling) MOD13C1-16-days and 5.6km temporal and spatial resolutions, respectively. Our results were in fair agreement with those from our first two studies and previous research and provided specific insights regarding the use iEVI as a proxy for productivity over extended regions as well as its combination with data sets from TRMM sensor for precipitation data.

O processo da desertificação é um problema de importância mundial, pois reduz a produtividade das terras como também a função ecológica dos ecossistemas onde acontece este processo. A desertificação é o resultado dos processos de degradação ambiental nas zonas áridas, semiáridas e sub-úmidas secas produto de fatores biofísicos como a variação climática e também das atividades humanas. A região do Vale de Villa de Leyva, Boyacá, Colômbia, tem paisagens em processo de transformação por desertificação porque esta localizada num local seco, pouca precipitação e com solos frágeis. Após da conquista espanhola, esta área teve a maior transformação ambiental iniciando com a sobre exploração dos recursos naturais como as florestas, os solos e os corpos de água, acelerando ainda mais este processo de degradação. Tendo em conta esse contexto, o método de análise das dinâmicas da zona para identificar padrões e processos de desertificação a partir de séries temporais de índices de vegetação, como o NDVI e EVI, foram empregadas técnicas de análise espacial, a traves de Sistemas de Informação Geográfica SIG e ferramentas de Sensoriamento Remoto. Foi feita aquisição das imagens de Índices de vegetação NDVI e EVI do produto MOD13Q1 do sensor MODIS, entre os anos 2001 e 2016. As imagens foram filtradas como o algoritmo Savitzky-Golay para diminuir os erros da informação original, como também os vazios. As series temporais foram analisadas com o intuito de identificar as áreas e os períodos em que ocorreram as mudanças ambientais mais significativas na região com relação à camada vetorial de erosão, elaborada a uma escala 1:100.000 Posteriormente, foi feito uma análise de tendência pelo algoritmo Mann-Kendall para identificar tendências negativas dos índices de vegetação, e fazer uma sobreposição com os índices do último ano, obtendo assim as áreas com maior risco de sofrer processos de degradação ambiental e que podem gerar desertificação. Para validar esses procedimentos, foi feita uma comparação visual com o NDVI do programa Landsat 8 no mesmo período, identificando padrões de distribuição da vegetação muito semelhantes, embora a resolução espacial dos dois tipos de imagens seja muito diferente. Entre os principais resultados, foi possível identificar que as áreas em risco de sofrer processos de desertificação não estão associadas nem obedecem a um processo constante desde sua origem como foi pensado inicialmente, mas as atividades atuais de agricultura não sustentável são as que causam esses processos de degradação. Na atualidade, este processo acontece de forma mais pontual e esta associado a fatores antrópicos, principalmente pelas práticas agrícolas não sustentáveis na região, principalmente pelo incremento dos cultivos de tomate sob estufa. Também existem áreas com tendência a recuperação da vegetação por fatores naturais, por processos de crescimento de gramíneas, como também por processos artificiais, semeadura de arvores e gramado, com o fim de adequar a paisagem nas áreas de expansão urbana que limitam com o “deserto”, gerando novas dinâmicas de ocupação da região que posteriormente precisam ser estudadas. / Desertification process represents a problem of global importance, due to the reduction not only of the productivity of farmlands, but also of the ecological function of ecosystems where this process takes place. Desertification is the result of processes of environmental degradation in the arid, semi-arid and dry sub-humid areas, resulting from biophysical factors such as climatic variation and from human activities, as well. The valley of Villa de Leyva, Boyacá, Colombia, has landscapes in transformation process by desertification because it is located in a dry place, with little precipitation and with fragile soils. After the Spanish conquest, this area had the greatest environmental transformation, starting with overexploitation of natural resources such as forests, soils and bodies of water; all these factors accelerated the process of degradation. Taking into account this context, it is necessary to establish a method of analysis of the dynamics of the zone, in order to identify patterns and processes of desertification through temporal series of vegetation indexes, such as NDVI and EVI, using spatial analysis techniques, through of GIS Geographic Information Systems and Remote Sensing tools. The first step was the acquisition of the vegetation indices NDVI and EVI of the product the images of NDVI and EVI vegetation indices of the MODIS product MOD13Q1 between 2001 and 2016 were acquired. The images were filtered as the Savitzky-Golay algorithm to reduce the errors of the original information as well as the voids. The time series were analyzed in order to identify the areas and periods in which the most significant environmental changes occurred in the region in relation to the vector layer of erosion, elaborated at a scale 1:100.000 Afterwards, a trend analysis was performed by the Mann-Kendall algorithm to identify negative trends of vegetation indexes, and to overlap with the indices of the last year, thus obtaining the areas with the highest risk of environmental degradation processes that can generate desertification. To validate these procedures, a visual comparison was made with the NDVI of the Landsat 8 program in the same period, identifying vegetation distribution patterns very similar, although the spatial resolution of the two types of images is very different. Among the main results, it was possible to identify that the areas at risk of suffering desertification processes are neither associated nor obeyed a constant process since its origin as initially thought, but the current activities of non sustainable agriculture are those that cause these processes of degradation. Nowadays, this process happens in a more punctual way and is associated to anthropic factors, mainly by the unsustainable agricultural practices in the region, mainly by the increase of tomato crops under greenhouse. There are also areas where the vegetation recovers by natural factors, by processes of grass growth, as well as artificial processes, planting trees and pastures, with the purpose of adjusting the landscape in areas of urban expansion that limit with the “desert”, generating new dynamics of occupation in the region that need to be studied further.

Submitted by VICTOR COSTA LEDA null (victorleda@gmail.com) on 2016-09-20T13:48:09Z No. of bitstreams: 1 Dissertação Victor Costa Leda Final.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5) / Approved for entry into archive by Ana Paula Grisoto (grisotoana@reitoria.unesp.br) on 2016-09-22T19:30:58Z (GMT) No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5) / Made available in DSpace on 2016-09-22T19:30:58Z (GMT). No. of bitstreams: 1 leda_vc_me_bot.pdf: 3518003 bytes, checksum: 6c1cfb1843e622175cfceb9c905d91f0 (MD5) Previous issue date: 2016-07-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) / A produção da cana-de-açúcar é destaque no cenário econômico do estado São Paulo, dessa forma confirma-se a necessidade do monitoramento dessa cultura, de maneira a contribuir com melhorias em decisões e planejamentos operacionais. A produção total e a produtividade da cana-de-açúcar são fatores de grande interesse para os agricultores, pois é a partir dessa informação que a programação das operações são realizadas, porém, essas estimativas não possuem métodos de alta precisão e confiança em amostragens não destrutivas. O homem possui excelente capacidade de analisar e interpretar resultados, mas também está sujeito a subjetividades em suas avaliações. A análise empreendida no trabalho teve como objetivo a elaboração de modelos matemáticos que expliquem a produtividade da cana-de-açúcar por meio das técnicas de geoprocessamento e sensoriamento remoto. O experimento foi realizado na área de produção comercial da Agrícola Rio Claro, parceira do grupo Zilor, que está localizada nos municípios de Lençóis Paulista e Pratânia, possui aproximadamente 6000 hectares, com altimetrias variando entre 600 e 700 metros. Para a coleta das informações espectrais, utilizou-se as imagens do satélite Landsat 8, com órbita/ponto em 221/076. Nos resultados do trabalho realizado, constatou-se que as modelagens foram satisfatórias, variando o coeficiente de determinação entre 0,15 a 0,97. Sendo que em períodos com elevados coeficientes de determinação, podem geralmente ser encontradas áreas de forma aglomerada, o que sugere uma menor incidência de variáveis. Enquanto que em períodos com coeficientes de determinação baixos, muito provavelmente foram obtidos devido a outros fatores listados terem ocorrido como dispersão dos talhões na área, classes de solo, precipitação e variedades da cultura, provavelmente distintos. / The production of sugarcane is a highlight in the economic scenario in the state of São Paulo, thus it confirms the need of monitoring this culture, in order to contribute to improvements in making decisions and operational planning.The production and productivity of sugarcane are factors of great interest to farmers, because, from this information the planning of operations is performed out, however, these estimates do not have high precision and reliable methods for non-destructive sampling.The human has an excellent ability to analyze and interpret results, but may also be affected by the subjectivity of their evaluations.The analysis undertaken in this work aimed at the development of mathematical models to explain the productivity of sugarcane through geoprocessing and remote sensing.The experiment was conducted in commercial area of Agrícola Rio Claro, partner of Zilor group, which is located in Lençóis Paulista and Pratânia, of approximately 6000 hectares, with altimetry ranging between 600 and 700 meters. For the collection of the spectral information, it was used the images of the satellite Landsat 8, with orbit/point 221/076. The results of the work, it was found that all the modeling were satisfactory, varying the coefficient of determination between 0.15 to 0.97. Given that, in periods with high coefficients of determination areas may be generally found in clusters, suggesting a lower incidence of variables. While in periods of low coefficient of determination, it was most likely obtained due to other factors listed of having occurred such as a dispersion of the plots in the area, soil types, rainfall and varieties, probably distinctly.

O processo da desertificação é um problema de importância mundial, pois reduz a produtividade das terras como também a função ecológica dos ecossistemas onde acontece este processo. A desertificação é o resultado dos processos de degradação ambiental nas zonas áridas, semiáridas e sub-úmidas secas produto de fatores biofísicos como a variação climática e também das atividades humanas. A região do Vale de Villa de Leyva, Boyacá, Colômbia, tem paisagens em processo de transformação por desertificação porque esta localizada num local seco, pouca precipitação e com solos frágeis. Após da conquista espanhola, esta área teve a maior transformação ambiental iniciando com a sobre exploração dos recursos naturais como as florestas, os solos e os corpos de água, acelerando ainda mais este processo de degradação. Tendo em conta esse contexto, o método de análise das dinâmicas da zona para identificar padrões e processos de desertificação a partir de séries temporais de índices de vegetação, como o NDVI e EVI, foram empregadas técnicas de análise espacial, a traves de Sistemas de Informação Geográfica SIG e ferramentas de Sensoriamento Remoto. Foi feita aquisição das imagens de Índices de vegetação NDVI e EVI do produto MOD13Q1 do sensor MODIS, entre os anos 2001 e 2016. As imagens foram filtradas como o algoritmo Savitzky-Golay para diminuir os erros da informação original, como também os vazios. As series temporais foram analisadas com o intuito de identificar as áreas e os períodos em que ocorreram as mudanças ambientais mais significativas na região com relação à camada vetorial de erosão, elaborada a uma escala 1:100.000 Posteriormente, foi feito uma análise de tendência pelo algoritmo Mann-Kendall para identificar tendências negativas dos índices de vegetação, e fazer uma sobreposição com os índices do último ano, obtendo assim as áreas com maior risco de sofrer processos de degradação ambiental e que podem gerar desertificação. Para validar esses procedimentos, foi feita uma comparação visual com o NDVI do programa Landsat 8 no mesmo período, identificando padrões de distribuição da vegetação muito semelhantes, embora a resolução espacial dos dois tipos de imagens seja muito diferente. Entre os principais resultados, foi possível identificar que as áreas em risco de sofrer processos de desertificação não estão associadas nem obedecem a um processo constante desde sua origem como foi pensado inicialmente, mas as atividades atuais de agricultura não sustentável são as que causam esses processos de degradação. Na atualidade, este processo acontece de forma mais pontual e esta associado a fatores antrópicos, principalmente pelas práticas agrícolas não sustentáveis na região, principalmente pelo incremento dos cultivos de tomate sob estufa. Também existem áreas com tendência a recuperação da vegetação por fatores naturais, por processos de crescimento de gramíneas, como também por processos artificiais, semeadura de arvores e gramado, com o fim de adequar a paisagem nas áreas de expansão urbana que limitam com o “deserto”, gerando novas dinâmicas de ocupação da região que posteriormente precisam ser estudadas. / Desertification process represents a problem of global importance, due to the reduction not only of the productivity of farmlands, but also of the ecological function of ecosystems where this process takes place. Desertification is the result of processes of environmental degradation in the arid, semi-arid and dry sub-humid areas, resulting from biophysical factors such as climatic variation and from human activities, as well. The valley of Villa de Leyva, Boyacá, Colombia, has landscapes in transformation process by desertification because it is located in a dry place, with little precipitation and with fragile soils. After the Spanish conquest, this area had the greatest environmental transformation, starting with overexploitation of natural resources such as forests, soils and bodies of water; all these factors accelerated the process of degradation. Taking into account this context, it is necessary to establish a method of analysis of the dynamics of the zone, in order to identify patterns and processes of desertification through temporal series of vegetation indexes, such as NDVI and EVI, using spatial analysis techniques, through of GIS Geographic Information Systems and Remote Sensing tools. The first step was the acquisition of the vegetation indices NDVI and EVI of the product the images of NDVI and EVI vegetation indices of the MODIS product MOD13Q1 between 2001 and 2016 were acquired. The images were filtered as the Savitzky-Golay algorithm to reduce the errors of the original information as well as the voids. The time series were analyzed in order to identify the areas and periods in which the most significant environmental changes occurred in the region in relation to the vector layer of erosion, elaborated at a scale 1:100.000 Afterwards, a trend analysis was performed by the Mann-Kendall algorithm to identify negative trends of vegetation indexes, and to overlap with the indices of the last year, thus obtaining the areas with the highest risk of environmental degradation processes that can generate desertification. To validate these procedures, a visual comparison was made with the NDVI of the Landsat 8 program in the same period, identifying vegetation distribution patterns very similar, although the spatial resolution of the two types of images is very different. Among the main results, it was possible to identify that the areas at risk of suffering desertification processes are neither associated nor obeyed a constant process since its origin as initially thought, but the current activities of non sustainable agriculture are those that cause these processes of degradation. Nowadays, this process happens in a more punctual way and is associated to anthropic factors, mainly by the unsustainable agricultural practices in the region, mainly by the increase of tomato crops under greenhouse. There are also areas where the vegetation recovers by natural factors, by processes of grass growth, as well as artificial processes, planting trees and pastures, with the purpose of adjusting the landscape in areas of urban expansion that limit with the “desert”, generating new dynamics of occupation in the region that need to be studied further.

O processo da desertificação é um problema de importância mundial, pois reduz a produtividade das terras como também a função ecológica dos ecossistemas onde acontece este processo. A desertificação é o resultado dos processos de degradação ambiental nas zonas áridas, semiáridas e sub-úmidas secas produto de fatores biofísicos como a variação climática e também das atividades humanas. A região do Vale de Villa de Leyva, Boyacá, Colômbia, tem paisagens em processo de transformação por desertificação porque esta localizada num local seco, pouca precipitação e com solos frágeis. Após da conquista espanhola, esta área teve a maior transformação ambiental iniciando com a sobre exploração dos recursos naturais como as florestas, os solos e os corpos de água, acelerando ainda mais este processo de degradação. Tendo em conta esse contexto, o método de análise das dinâmicas da zona para identificar padrões e processos de desertificação a partir de séries temporais de índices de vegetação, como o NDVI e EVI, foram empregadas técnicas de análise espacial, a traves de Sistemas de Informação Geográfica SIG e ferramentas de Sensoriamento Remoto. Foi feita aquisição das imagens de Índices de vegetação NDVI e EVI do produto MOD13Q1 do sensor MODIS, entre os anos 2001 e 2016. As imagens foram filtradas como o algoritmo Savitzky-Golay para diminuir os erros da informação original, como também os vazios. As series temporais foram analisadas com o intuito de identificar as áreas e os períodos em que ocorreram as mudanças ambientais mais significativas na região com relação à camada vetorial de erosão, elaborada a uma escala 1:100.000 Posteriormente, foi feito uma análise de tendência pelo algoritmo Mann-Kendall para identificar tendências negativas dos índices de vegetação, e fazer uma sobreposição com os índices do último ano, obtendo assim as áreas com maior risco de sofrer processos de degradação ambiental e que podem gerar desertificação. Para validar esses procedimentos, foi feita uma comparação visual com o NDVI do programa Landsat 8 no mesmo período, identificando padrões de distribuição da vegetação muito semelhantes, embora a resolução espacial dos dois tipos de imagens seja muito diferente. Entre os principais resultados, foi possível identificar que as áreas em risco de sofrer processos de desertificação não estão associadas nem obedecem a um processo constante desde sua origem como foi pensado inicialmente, mas as atividades atuais de agricultura não sustentável são as que causam esses processos de degradação. Na atualidade, este processo acontece de forma mais pontual e esta associado a fatores antrópicos, principalmente pelas práticas agrícolas não sustentáveis na região, principalmente pelo incremento dos cultivos de tomate sob estufa. Também existem áreas com tendência a recuperação da vegetação por fatores naturais, por processos de crescimento de gramíneas, como também por processos artificiais, semeadura de arvores e gramado, com o fim de adequar a paisagem nas áreas de expansão urbana que limitam com o “deserto”, gerando novas dinâmicas de ocupação da região que posteriormente precisam ser estudadas. / Desertification process represents a problem of global importance, due to the reduction not only of the productivity of farmlands, but also of the ecological function of ecosystems where this process takes place. Desertification is the result of processes of environmental degradation in the arid, semi-arid and dry sub-humid areas, resulting from biophysical factors such as climatic variation and from human activities, as well. The valley of Villa de Leyva, Boyacá, Colombia, has landscapes in transformation process by desertification because it is located in a dry place, with little precipitation and with fragile soils. After the Spanish conquest, this area had the greatest environmental transformation, starting with overexploitation of natural resources such as forests, soils and bodies of water; all these factors accelerated the process of degradation. Taking into account this context, it is necessary to establish a method of analysis of the dynamics of the zone, in order to identify patterns and processes of desertification through temporal series of vegetation indexes, such as NDVI and EVI, using spatial analysis techniques, through of GIS Geographic Information Systems and Remote Sensing tools. The first step was the acquisition of the vegetation indices NDVI and EVI of the product the images of NDVI and EVI vegetation indices of the MODIS product MOD13Q1 between 2001 and 2016 were acquired. The images were filtered as the Savitzky-Golay algorithm to reduce the errors of the original information as well as the voids. The time series were analyzed in order to identify the areas and periods in which the most significant environmental changes occurred in the region in relation to the vector layer of erosion, elaborated at a scale 1:100.000 Afterwards, a trend analysis was performed by the Mann-Kendall algorithm to identify negative trends of vegetation indexes, and to overlap with the indices of the last year, thus obtaining the areas with the highest risk of environmental degradation processes that can generate desertification. To validate these procedures, a visual comparison was made with the NDVI of the Landsat 8 program in the same period, identifying vegetation distribution patterns very similar, although the spatial resolution of the two types of images is very different. Among the main results, it was possible to identify that the areas at risk of suffering desertification processes are neither associated nor obeyed a constant process since its origin as initially thought, but the current activities of non sustainable agriculture are those that cause these processes of degradation. Nowadays, this process happens in a more punctual way and is associated to anthropic factors, mainly by the unsustainable agricultural practices in the region, mainly by the increase of tomato crops under greenhouse. There are also areas where the vegetation recovers by natural factors, by processes of grass growth, as well as artificial processes, planting trees and pastures, with the purpose of adjusting the landscape in areas of urban expansion that limit with the “desert”, generating new dynamics of occupation in the region that need to be studied further.

A large portion of Earth¿s terrestrial surface is subject to arid climatic water stress. As in these regions the hydrological cycle and the vegetation dynamics are tightly interconnected, a coupled modeling of these two systems is needed to fully reproduce the ecosystems¿ behavior over time and to predict possible future responses to climate change. In this thesis, the performance of three parsimonious dynamic vegetation models, suitable for inclusion in an operational ecohydrological model, are tested in a semi-arid Aleppo pine forest area in the south-east of Spain. The first model considered, HORAS (Quevedo & Francés, 2008), simulates growth as a function of plant transpiration (T), evaluating environmental restraints through the transpiration-reference evapotranspiration ratio. The state variable related to vegetation is R, relative foliar biomass, which is equivalent to FAO crop coefficient (Allen et al., 1998), but not fixed in time. The HORAS model was then abandoned because of its unsatisfactory results, probably due to a poor simulation of evaporation and transpiration processes. As for the other two models, WUE-model and LUE-model, the state variable is the leaf biomass (Bl, kg dry mass m-2 vegetation cover). Both models simulate gross primary production (GPP), in the first case as a function of transpiration and water use efficiency (WUE), in the second case as a function of absorbed photosynthetically active radiation (APAR) and light use efficiency (LUE). Net primary production (NPP) is then calculated taking into account respiration. The modelling is focused particularly on simulating foliar biomass, which is obtained from NPP through an allocation equation based on the maximum leaf area index (LAI) sustainable by the system, and considering turnover. An analysis of the information offered by MODIS EVI, NDVI, and LAI products was also performed, in order to investigate vegetation dynamics in the study site and to select the best indices to be used as observational verification for models. MODIS EVI is reported in literature (Huete et al., 2002) to be highly correlated with leaf biomass. In accordance with the phenological cycle timing described for the Aleppo pine in similar climates (Muñoz et al., 2003), the EVI showed maximum values in spring and minimum values in winter. Similar results were found applying the aforementioned WUE- and LUE- models to the study area. Contrasting simulated LAI with the EVI series, the correlation coefficients rWUE = 0.45 and rLUE = 0.57 were found for the WUE-model and LUE-model respectively. Concerning NDVI, its own definition links this index to the ¿greenness¿ of the target, so that it appears highly linked to chlorophyll content and vegetation condition, but only indirectly related to LAI. Photosynthetic pigment concentrations are reported to be sensitive to water stress in Aleppo pine (Baquedano and Castillo, 2006) so, to compare the models¿ results with NDVI, the simulated LAI was corrected by plant water-stress. The resulting correlation coefficients were rWUE = 0.62 and rLUE = 0.59. Lastly, MODIS LAI and ET were found to be unreliable in the study area because very low compared to field data and to values reported in literature (e.g. Molina & del Campo, 2012) for the same species in similar climatic conditions. The performance of both WUE- and LUE- models in this semi-arid region is found to be reasonable. However, the LUE-model presents the advantages of a better performance, the possibility to be used in a wider range of climates and to have been extensively tested in literature. / Pasquato, M. (2013). Comparison of parsimonious dynamic vegetation modelling approaches for semiarid climates [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34326 / TESIS