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Remote sensing with lightweight optical sensors is becoming a powerful tool to solve many problems in agriculture. Achieving the level of spatial and spectral resolutions required for this type of detection at an acceptable cost-benefit ratio has motivated the development of new sensors which must be lightweight to be carried by mobile robots either aerial or terrestrial. One new type of multiple head cameras has been developed by Agrowing, an Israeli company developing technology for digital agriculture. The aim of this paper is to analyse the geometric features of an Agrowing dual head camera trough calibration experiments. The sensor was calibrated following two options, depending on the cropping technique used to produce the 4 spectral bands. Different calibration techniques were also used and very accurate results were achieved. Experiments with data collected with a UAV also confirmed the results achieved with close range calibration.

Made available in DSpace on 2019-10-06T16:17:08Z (GMT). No. of bitstreams: 0 Previous issue date: 2019-02-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Submerged aquatic vegetation (SAV) carry out important biological functions in freshwater systems, however, uncontrolled growth can lead to many negative ecologic and economic impacts. Radiation availability is the primary limiting factor for SAV and it is a function of water transparency measured by K d(PAR) (downwelling attenuation coefficient of Photosynthetically Active Radiation) and depth. The aim of this study was to develop a K d(PAR) and depth based model to estimate the height of submerged aquatic vegetation in a tropical oligotrophic reservoir. This work proposed a new graphical model to represent the SAV height in relation to K d(PAR) and depth. Based on the visual analysis of the model, it was possible to establish a set of Boolean rules to classify the SAV height and identify regions where SAV can grow with greater or lesser vigor. K d(PAR) was estimated using a model based on satellite data. Overall, the occurrence and height of SAV were directly influenced by the K d(PAR) , depending on the depth. This study highlights the importance of optical parameters in examining the occurrence and status of SAV in Brazilian Reservoirs. It was concluded that the digital model and its graphical representation overcomes the limitations found by other researchers to understand the SAV behavior in relation to those independent variables: depth and K d(PAR) . Department of Cartography-Presidente Prudente São Paulo State University (UNESP) Department of Geography University of Georgia (UGA) Department of Environmental Engineering-São José dos Campos São Paulo State University (UNESP) Federal Institute of Education Science and Technology of Pará State (IFPA) Department of Cartography-Presidente Prudente São Paulo State University (UNESP) Department of Environmental Engineering-São José dos Campos São Paulo State University (UNESP) FAPESP: 2012/19821-1

Made available in DSpace on 2020-12-12T02:18:26Z (GMT). No. of bitstreams: 0 Previous issue date: 2020-10-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Field surveys are an important source of data for several scientific studies. Hydrological optics investigations require a large amount of optical data acquired using sensors from distinct manufacturers built with specific technologies. Consequently, rapid and integrated processing can be difficult due to several challenges, such as non-standard outputs, multidimensional resolutions of data (spatial, temporal, and spectral), mismatched sampling, sensor-specific corrections, and manufacturer proprietary software. In this context, we propose a procedure to overcome the drawbacks of processing multisource and multidimensional datasets. Our main goal is to provide a platform that integrates and analyzes datasets acquired from five sensors commonly used by the hydrological optics community. We can summarize the contribution of this work with three resources: a management model for datasets from field surveys, a processing workflow that describes all the correction steps by grouping protocols to integrate data, and an online system, Aquopts, which is a resources for storage, integration, correction, processing, and analysis. Aquopts has provided resources that have been used in several published studies in recognized geoscience journals supported by our rapid delivering system and integrated dataset processing. São Paulo State University (UNESP) School of Sciences and Technology (FCT) Graduate Program of Cartographic Sciences, Campus Presidente Prudente São Paulo State University (UNESP) Institute of Sciences and Technology (ICT) Department of Environmental Engineering, Campus São José dos Campos São Paulo State University (UNESP) School of Sciences and Technology (FCT) Graduate Program of Cartographic Sciences, Campus Presidente Prudente São Paulo State University (UNESP) Institute of Sciences and Technology (ICT) Department of Environmental Engineering, Campus São José dos Campos CNPq: 141909/2015-3 FAPESP: 2012/19821-1 FAPESP: 2019/00259-0 CNPq: 400881/2013-6 CNPq: 472131/2012-5 CNPq: CNPq

Made available in DSpace on 2019-10-06T16:02:07Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-10-01 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The quality control of remote sensing reflectance (Rrs) is a challenging task in remote sensing applications, mainly in the retrieval of accurate in situ measurements carried out in optically complex aquatic systems. One of the main challenges is related to glint effect into the in situ measurements. Our study evaluates four different methods to reduce the glint effect from the Rrs spectra collected in cascade reservoirs with widely differing optical properties. The first (i) method adopts a constant coefficient for skylight correction (r) for any geometry viewing of in situ measurements and wind speed lower than 5 m·s-1; (ii) the second uses a look-up-table with variable ρ values accordingly to viewing geometry acquisition and wind speed; (iii) the third method is based on hyperspectral optimization to produce a spectral glint correction, and (iv) computes ρ as a function of wind speed. The glint effect corrected Rrs spectra were assessed using HydroLight simulations. The results showed that using the glint correction with spectral r achieved the lowest errors, however, in a Colored Dissolved Organic Matter (CDOM) dominated environment with no remarkable chlorophyll-a concentrations, the best method was the second. Besides, the results with spectral glint correction reduced almost 30% of errors. Department of Cartography São Paulo State University-UNESP Department of Environmental Engineering São Paulo State University-UNESP Federal Institute of Education Science and Technology of Pará State-IFPA Department of Cartography São Paulo State University-UNESP Department of Environmental Engineering São Paulo State University-UNESP FAPESP: 2012/19821-1

Made available in DSpace on 2018-12-11T17:19:56Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-07-15 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) In this research, we have investigated whether the chlorophyll-a (chl a) retrieval algorithms based on OLCI Sentinel-3A bands are suitable for cyanobacteria-dominated waters. Phytoplankton assemblages model optical properties of the water, influencing the performance of bio-optical algorithms. Understanding these processes is important to improve the prediction of photoactive pigments in order to use them as a proxy for trophic state and harmful algal bloom. So that, both empirical and semi-analytical approaches designed for different inland waters were tested. In addition, empirical models were tuned based on dataset collected in situ. The study was conducted in the Funil hydroelectric reservoir, where chl a ranged from 2.33 to 208.68 mg m−3 in May 2012 (austral fall) and 4.37 to 306.03 mg m−3 in October 2012 (austral spring). OLCI Sentinel-3A bands were tested in existing algorithms developed for other sensors and new band combinations were compared to analyze the errors produced. Normalized Difference Chlorophyll Index (NDCI) exhibited the best performance, with a Normalized Root Mean Square Error (NRMSE) of 9.30%. Result showed that wavelength at 665 nm is adequate to estimate chl a, although the maximum pigment absorption band is shifted due to phycocyanin fluorescence at approximately 650 nm. Department of Cartography Universidade Estadual Paulista (UNESP), Rua Roberto Simonsen, 305 Department of Environmental Engineering Universidade Estadual Paulista (UNESP), Rodovia Presidente Dutra, Km 137,8, Eugenio de Melo Remote Sensing Division Instituto Nacional de Pesquisas Espaciais (INPE), Avenida dos Astronautas, 1758 Department of Cartography Universidade Estadual Paulista (UNESP), Rua Roberto Simonsen, 305 Department of Environmental Engineering Universidade Estadual Paulista (UNESP), Rodovia Presidente Dutra, Km 137,8, Eugenio de Melo FAPESP: 2011/19523-8 FAPESP: 2015/18525-8 CNPq: 471223/2011-5

Made available in DSpace on 2018-12-11T17:20:31Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-05-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Measurements of absorption coefficients (a(λ), in m-1) collected by spectrophotometers in situ are overestimated due to the scattering of the reflecting tube absorption meter. Accurate correction of these data is essential in order to characterize water bodies bio-optically, as well as retrieve the remote sensing reflectance (Rrs, in sr-1), when applying a forward model. There are various methods of scattering error correction; however, they were all developed for clear water. In this research, different techniques were attempted in order to define the most appropriate method for correcting a(λ) values acquired by an absorption and attenuation spectral (ac-s) meter (WET Labs Inc., Philomath, OR, USA) in a tropical eutrophic reservoir. Three methods recommended by the manufacturer of the ac-s meter were tested: flat or baseline, constant fraction, and proportional. These methods were applied to two datasets that were measured in May and October 2014. The flat technique exhibited the lowest errors, with an average normalized root mean square error (NRMSE) of 7.95%, and a mean absolute percentage error (MAPE) of 29.26% for May. Meanwhile, proportional was the most suitable technique for most of the samples in October, with a mean NRMSE of 11.19% and a MAPE of 31.03% for October. In addition, the proportional method maintained the shape of the a(λ) values better than the other methods. Despite that, both the flat and proportional methods gave a similar performance statistically. Moreover, the flat method produced the best estimations of chla content for both datasets. Therefore, this method is recommended to correct ac-s data in retrieving such phytoplankton pigments. Department of Cartography Faculty of Science and Technology UNESP-Universidade Estadual Paulista, Rua Roberto Simonsen 305 Federal Institute of Education Science and Technology from Pará, Rodovia BR 316, km 61 Department of Environmental Engineering Institute of Science and Technology UNESP-Universidade Estadual Paulista, Rodovia Presidente Dutra km 137.8 Department of Mathematics and Computer Science Faculty of Science and Technology UNESP-Universidade Estadual Paulista, Rua Roberto Simonsen 305 Department of Cartography Faculty of Science and Technology UNESP-Universidade Estadual Paulista, Rua Roberto Simonsen 305 Department of Environmental Engineering Institute of Science and Technology UNESP-Universidade Estadual Paulista, Rodovia Presidente Dutra km 137.8 Department of Mathematics and Computer Science Faculty of Science and Technology UNESP-Universidade Estadual Paulista, Rua Roberto Simonsen 305

Made available in DSpace on 2018-12-11T17:18:13Z (GMT). No. of bitstreams: 0 Previous issue date: 2018-02-01 Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) The high nutrient concentrations coming from non-point and point pollution have been linked to algae blooms, especially in hydroelectric plant reservoirs, due to higher residence time compared to rivers. The monitoring of algae is important to prevent risk of contamination by toxins in reservoirs used for drinking water supply. In this context, a physical model-based approach was adopted to retrieve chlorophyll-a (chl a) concentration, a photosynthetic pigment found in all phytoplankton species. We assumed that a semi-analytical algorithm parameterized to a eutrophic reservoir could also be applied to other eutrophic reservoirs, at least the specific inherent optical properties (SIOPs) are not similar. The parameterization was carried out based on Ocean and Land Color Instrument (OLCI) bands aboard Sentinel-3 spacecraft. In our study, the semi-analytical approach showed good performance in retrieving chl a content, with a normalized root mean square error (NRMSE) of 18.7%. The findings encourage the use of a unique semi-analytical algorithm in a reservoir cascade, where the impoundments present similar bio-optical status. The good performance of the algorithm indicates that this approach is rather useful in predicting trophic status in reservoirs. Department of Cartography Faculty of Sciences and Technology São Paulo State University (UNESP), Rua Roberto Simonsen 305 Department of Environmental Engineering Institute of Science and Technology São Paulo State University (UNESP), Rodovia Presidente Dutra Km 137.8 Federal Institute of Education Science and Technology from Pará, Rodovia BR 316, km 61 Department of Cartography Faculty of Sciences and Technology São Paulo State University (UNESP), Rua Roberto Simonsen 305 Department of Environmental Engineering Institute of Science and Technology São Paulo State University (UNESP), Rodovia Presidente Dutra Km 137.8

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.

Bio-optical characterization of water bodies requires spatio-temporal data about Inherent Optical Properties and Apparent Optical Properties which allow the comprehension of underwater light field aiming at the development of models for monitoring water quality. Measurements are taken to represent optical properties along a column of water, and then the spectral data must be related to depth. However, the spatial positions of measurement may differ since collecting instruments vary. In addition, the records should not refer to the same wavelengths. Additional difficulty is that distinct instruments store data in different formats. A data integration approach is needed to make these large and multi source data sets suitable for analysis. Thus, it becomes possible, even automatically, semi-empirical models evaluation, preceded by preliminary tasks of quality control. In this work it is presented a solution, in the stated scenario, based on spatial – geographic – database approach with the adoption of an object relational Database Management System – DBMS – due to the possibilities to represent all data collected in the field, in conjunction with data obtained by laboratory analysis and Remote Sensing images that have been taken at the time of field data collection. This data integration approach leads to a 4D representation since that its coordinate system includes 3D spatial coordinates – planimetric and depth – and the time when each data was taken. It was adopted PostgreSQL DBMS extended by PostGIS module to provide abilities to manage spatial/geospatial data. It was developed a prototype which has the mainly tools an analyst needs to prepare the data sets for analysis.

Aim: In this research, we investigated the spectral variability of the specific phytoplankton absorption coefficient, a*φ, measured in a tropical meso-to-hypertrophic reservoir, aiming to find spectral features associated with the chlorophyll-a (chla) and other accessory pigments present in different phytoplankton species. Methods To accomplish this research, two fieldworks were carried out in different seasons in order to report a high bio-optical variation. Phytoplankton absorption coefficient, aφ, and chla concentration were measured in laboratory to estimate a*φ. Results The outcomes have indicated that there is a remarkable phytoplankton community dynamic as spatially as seasonally. Chla absorption features were well-defined at 440 nm and 675 nm. Conclusions All the a*φ spectra exhibited the absorption peak around 630 nm associated with phycocyanin pigment present in cyanobacteria. Some spectra have shown a peak at about 460 nm, which is related to chlorophyll b and chlorophyll c (chlb and chlc, respectively) found in different phytoplankton species. In turn, absorption features of carotenoids around 490 nm also were identified, however, well defined just in curves measured in austral autumn. Such spectral features are found in phytoplankton groups already identified in the study area such as Chlorophyceae, Bacillariophyceae, Cyanophyceae, Conjugatophyceae, Chrysophyceae, among others. We expect that the results are useful in researches about remote sensing of phytoplankton and eutrophication in reservoirs. Resumo Objetivo: Nesta pesquisa, foi investigada a variabilidade espectral do coeficiente de absorção específico do fitoplâncton, a*φ, medido em um reservatório meso-a-hipertrófico tropical, com o objetivo de encontrar feições espectrais associadas à clorofila-a (chla) e pigmentos acessórios de diferentes espécies de fitoplâncton. Métodos Para realizar este trabalho, dois levantamentos de campo foram realizados em diferentes estações a fim de registrar a alta variação bio-ótica. O coeficiente de absorção do fitoplâncton, aφ, e a concentração de chla foram medidos em laboratório para estimar a*φ. Resultados O resultado indicou uma alta variação de comunidades de fitoplâncton tanto sazonalmente quanto espacialmente. As feições de absorção da chla estavam bem acentuadas em 440 nm e 675 nm. Conclusões Todas as curvas de a*φ exibiram um pico de absorção ao redor de 630 nm, associado ao pigmento ficocianina presente em cianobactérias. Alguns espectros apresentaram picos em torno de 460 nm, que está relacionado à presença de clorofila-b e clorofila-c (chlb e chlc, respectivamente), encontrados em algumas espécies de fitoplâncton. Por sua vez, as feições de absorção por carotenóides, por volta de 490 nm, também foram identificadas, entretanto, estavam bem definidas apenas em curvas adquiridas no outono. Essas feições espectrais são encontradas em grupos de fitoplânctons já identificados na área de estudo, como Chlorophyceae, Bacillariophyceae, Cyanophyceae, Conjugatophyceae, Chrysophyceae, entre outros. Espera-se que os resultados obtidos nesse estudo possam ser úteis em estudos sobre sensoriamento remoto de águas interiores e eutrofização de reservatórios.

O uso do sensoriamento remoto voltado para a determinação de amostras de campo é de grande valia para estudos ambientais, uma vez que as imagens de satélite apresentam atributos capazes de avaliar a variabilidade espectral da superfície da água considerando uma área extensa. Desse modo, a abordagem deste trabalho objetiva definir um método de seleção estratificada de amostras baseada na variabilidade de imagens no espectro do visível e infravermelho oriundos do sensor Landsat-8/OLI. O método conta com a utilização de dados raster que representam o desvio padrão de uma série temporal de imagens Landsat-8/OLI e em seguida a definição automática de pontos de campo apoiada na técnica de amostragem estratificada aleatória. A escolha da imagem que deu origem a seleção dos pontos foi baseada na componente de maior variabilidade espectral por meio da técnica de Principal Componente. Como resultado foram obtidos vinte pontos representativos de um total de seis classes espectralmente semelhantes. The use of remote sensing focused on the determination of field samples is of great value to environmental studies, since the satellite images have attributes able to assess the spectral variability of water surface considering a wide area. Thus, this approach aims to define a stratified working method of selecting samples based on images variability in the visible and infrared wavelengths derived from Landsat-8 / OLI sensor. The method relies on the use of raster data representing the standard deviation of a time series of images Landsat-8 / OLI and then the automatic setting field points supported by the stratified random sampling. The choice of the image that yielded the selection of the samples was based on the greatest spectral variation component by means of Principal Component technique. As a result were obtained twenty representative points of a total of six spectrally similar classes created by using a Geographic Information System.