The objectives of dissertation were to examine vegetation and water indices from AVIRIS and MODIS data for monitoring semiarid and upland vegetation communities related with moisture condition and their spatial and temporal dependencies in estimating evapotranspiration (ET). The performance of various water indices, including the normalized difference water index (NDWI) and land surface water index (LSWI), with the chlorophyll-based vegetation indices (VIs), the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) was evaluated in 1) investigating sensitivity of vegetation and land surface moisture condition 2) finding optimal indices in detecting seasonal variations in vegetation water status at the landscape level, and 3) their spatial and temporal scale dependency on estimating ET. The analyses were accomplished through field radiometric measurement, airborne-based and satellite data processing accompanied with water flux data.The results of these studies showed vegetation and landscape moisture condition could be identified in VI - WI scatter-plot. LSWI (2100) showed the biggest sensitivity to variation of vegetation and background soil moisture condition as well. Multi-temporal MODIS data analysis was able to show water use characteristic of riparian vegetation and upland vegetation. Results showed water use characteristics of riparian vegetation are relatively insensitive to summer monsoon pulse, while upland vegetation is highly tied to summer monsoon rain. The relationship between water flux measurement from eddy covariance tower and satellite data has shown that MODIS derived EVI and LSWI (2100) have similar merit to estimate ET rate, but better correlation was observed from the relationship between MODIS EVI and ET.Pixel aggregation results using fine resolution AVIRIS data showed moderate resolution spatial scale 250m or 500m, best predicted ET rates over all study areas. Surface fluxes temporally aggregated to weekly or biweekly intervals showed the strongest ET versus EVI relationships. ET measured at flux towers can be scaled over heterogeneous vegetation associations by simple statistical methods that use meteorological data and flux tower data as ground input, and using the MODIS Enhanced Vegetation Index (EVI) as the only source of remote sensing data.

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Digital depth-to-water maps can be produced from a digital elevation model (DEM). Then GIS- based algorithms are used to calculate water flows and the depth-to-water index classes dry, fresh, moist and wet. The purpose of this study was to investigate the possibility to use depth- to-water maps for site adapted planting. The results showed that use of depth-to-water maps for site adapted planting, roughly halved the proportion of improperly planted surfaces from an average of 9 % to 4 %. The variation in the values of proper surface decreased and the result became more even.. In addition, more pine than spruce was incorrectly planted. Without soil moisture maps, the proportion of improper pine and spruce was 66 % and 34 % respectively, and with soil moisture maps, the proportion of improper pine and spruce was 55 % and 45 % respectively. This shows that for regenerations planted without the depth-to-water maps, mostly pine was incorrectly planted, but for the regenerations planted with the depth-to-water maps, the proportions were similar for spruce and pine. The conclusion from the results indicated that depth-to-water-maps can improve site adapted planting. By using the maps it is possible to get a good overview of the conditions and terrain variations of the planting sites.

Submitted by Viviane Lima da Cunha (viviane@biblioteca.ufpb.br) on 2017-07-18T11:36:55Z No. of bitstreams: 1 arquivototal.pdf: 5809284 bytes, checksum: 902acd3b62b2994be1f92ddb09207e07 (MD5) / Made available in DSpace on 2017-07-18T11:36:55Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 5809284 bytes, checksum: 902acd3b62b2994be1f92ddb09207e07 (MD5) Previous issue date: 2011-02-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / This paper deals with underground water diagnosis of the urban area from João Pessoa, state capital of Paraíba, Brazil, whose aim is evaluate its natural features, over time, considering as one of its main uses its intended, the human provision. For this, it was monitored 13 deep wells (on average 200m) on Beberibe aquifer and spread in urban area from João Pessoa. Such wells belong to water and sewage Company in the state of Paraíba – CAGEPA and present a reliable lithologic profile. It was used the Quality of Water Index from Bascarán – IQAB to evaluate the analytical results. The results demonstrated that during the study period, the water from monitored wells stand, in general, was classified as agreeable on the scale water quality of IQAB. It was found on the physical, chemical and bacteriological individual analysis, ammonia and nitrate levels off limits established by ordinance 518/04 from Health Ministry – Ministério da Saúde/MS. This way, the water classification as agreeable is justified according to IQAB scale. This reality shows the necessity and importance of a major supervision on the ground water on João Pessoa urban area by management agencies to avoid its contamination and, consequently, invalidating the use of this water for human consumption. / Este trabalho trata do diagnóstico da água subterrânea do perímetro urbano da cidade de João Pessoa, a capital do Estado da Paraíba, objetivando avaliar suas características naturais, ao longo do tempo, considerando o abastecimento humano como um dos principais usos a que se destina. Para tanto, foram monitorados 13 poços profundos (média de 200m), no aqüífero Beberibe e distribuídos no perímetro urbano de João Pessoa. Tais poços são de propriedade da Companhia de Água e Esgoto do Estado da Paraíba – CAGEPA e apresentam perfil litológico confiável. Para o tratamento dos resultados analíticos utilizou-se como ferramenta o Índice de Qualidade de Água de Bascarán – IQAB. Verificou-se que, no período do estudo, a água dos poços monitorados permaneceu, em geral, como agradável na escala de qualidade da água do IQAB. Na análise individual dos parâmetros físico, químico e bacteriológico constataram-se valores de amônia e nitrito fora dos limites estabelecidos pela portaria 518/04 do Ministério da Saúde – MS, o que justifica a classificação agradável, da água na escala do IQAB. Essa realidade mostra a necessidade e importância de uma maior fiscalização, por parte dos órgãos gestores, do lençol subterrâneo no perímetro urbano de João Pessoa sob pena de ocorrer sua contaminação acarretando, conseqüentemente, na inviabilização do uso da água para o consumo humano.

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Detecting water under vegetation is critical to tracking the status of geological ecosystems like wetlands. Researchers use different methods to estimate water presence, avoiding costly on-site measurements. Optical satellite imagery allows the automatic delineation of water using the concept of the Normalised Difference Water Index (NDWI). Still, optical imagery is subject to visibility conditions and cannot detect water under the vegetation, a typical situation for wetlands. Synthetic Aperture Radar (SAR) imagery works under all visibility conditions. It can detect water under vegetation but requires deep network algorithms to segment water presence, and manual annotation work is required to train the deep models. This project uses DEEPAQUA, a cross-modal knowledge distillation method, to eliminate the manual annotation needed to extract water presence from SAR imagery with deep neural networks. In this method, a deep student model (e.g., UNET) is trained to segment water in SAR imagery. The student model uses the NDWI algorithm as the non-parametric, cross-modal teacher. The key prerequisite is that NDWI works on the optical imagery taken from the exact location and simultaneously as the SAR. Three different deep architectures are tested in this project: UNET, SegNet, and UNET++, and the Otsu method is used as the baseline. Experiments on imagery from Swedish wetlands in 2020-2022 show that cross-modal distillation consistently achieved better segmentation performances across architectures than the baseline. Additionally, the UNET family of algorithms performed better than SegNet with a confidence of 95%. The UNET++ model achieved the highest Intersection Over Union (IOU) performance. However, no statistical evidence emerged that UNET++ performs better than UNET, with a confidence of 95%. In conclusion, this project shows that cross-modal knowledge distillation works well across architectures and removes tedious and expensive manual work hours when detecting water from SAR imagery. Further research could evaluate performances on other datasets and student architectures. / Att upptäcka vatten under vegetation är avgörande för att hålla koll på statusen på geologiska ekosystem som våtmarker. Forskare använder olika metoder för att uppskatta vattennärvaro vilket undviker kostsamma mätningar på plats. Optiska satellitbilder tillåter automatisk avgränsning av vatten med hjälp av konceptet Normalised Difference Water Index (NDWI). Optiska bilder fortfarande beroende av siktförhållanden och kan inte upptäcka vatten under vegetationen, en typisk situation för våtmarker. Synthetic Aperture Radar (SAR)-bilder fungerar under alla siktförhållanden. Den kan detektera vatten under vegetation men kräver djupa nätverksalgoritmer för att segmentera vattennärvaro, och manuellt anteckningsarbete krävs för att träna de djupa modellerna. Detta projekt använder DEEPAQUA, en cross-modal kunskapsdestillationsmetod, för att eliminera det manuella annoteringsarbete som behövs för att extrahera vattennärvaro från SAR-bilder med djupa neurala nätverk. I denna metod tränas en djup studentmodell (t.ex. UNET) att segmentera vatten i SAR-bilder semantiskt. Elevmodellen använder NDWI, som fungerar på de optiska bilderna tagna från den exakta platsen och samtidigt som SAR, som den icke-parametriska, cross-modal lärarmodellen. Tre olika djupa arkitekturer testas i detta examensarbete: UNET, SegNet och UNET++, och Otsu-metoden används som baslinje. Experiment på bilder tagna på svenska våtmarker 2020-2022 visar att cross-modal destillation konsekvent uppnådde bättre segmenteringsprestanda över olika arkitekturer jämfört med baslinjen. Dessutom presterade UNET-familjen av algoritmer bättre än SegNet med en konfidens på 95%. UNET++-modellen uppnådde högsta prestanda för Intersection Over Union (IOU). Det framkom dock inga statistiska bevis för att UNET++ presterar bättre än UNET, med en konfidens på 95%. Sammanfattningsvis visar detta projekt att cross-modal kunskapsdestillation fungerar bra över olika arkitekturer och tar bort tidskrävande och kostsamma manuella arbetstimmar vid detektering av vatten från SAR-bilder. Ytterligare forskning skulle kunna utvärdera prestanda på andra datamängder och studentarkitekturer.