No Brasil, a área utilizada para pastagens é maior comparada com a área utilizada para as culturas agrícolas, com cerca de 158,6 milhões de hectares de pastagens. Sendo que as pastagens são extremamente importantes para a produção de carne bovina já que cerca do 95% é produzida para alimentar os rebanhos. O principal nutriente para a manutenção da produtividade em forrageiras é o nitrogênio (N), sendo um dos principais nutrientes que influencia diretamente as características morfofisiológicas, interferindo na produção e na qualidade da forragem. Nos últimos anos, o uso de técnicas de sensoriamento remoto tem se expandido nas áreas de ciências agrárias, mostrando-se uma ferramenta muito útil no monitoramento e gerenciamento da adubação nitrogenada nas culturas. Por tanto o objetivo do trabalho foi abordar o potencial de dados de sensoriamento remoto, mais especificamente para as forrageiras Panicum maximum cv. Mombaça e Urochloa brizantha cv. Marandú, obtidos por meio de sensor passivo e convertidos para diferentes índices de vegetação (IVs), na estimativa de parâmetros biofísicos, tais como: teor foliar de nitrogênio (TFN), produtividade, altura e índice de área foliar (IAF). Também foram simulados os dados para o satélite Sentinel-2 e testados, em áreas com plantio de Brachiaria brizantha cv. Piatã. Os IVs utilizados foram o NDVI (Índice de Vegetação por Diferença Normalizada), TBI (Three Band Index), DLH (Difference Line Height), NAOC (Normalized Area Over reflectance Curve) e TBDO (Three Band Dall ?Olmo). O experimento foi realizado na Escola Superior de Agricultura \"Luiz de Queiroz\" (ESALQ/USP), em Piracicaba, São Paulo. O delineamento experimental utilizado foi em blocos ao acaso, com quatro tratamentos e quatro repetições, sendo a ureia o fertilizante nitrogenado utilizado ao longo do experimento. As doses aplicadas para a cv. Mombaça foram três de 200, 400, 600 kg ha-1 e para a cv. Marandú foram aplicadas doses de 175, 350 e 525 kg ha-1, para ambas cultivares as parcelas testemunhas não receberam adubação nitrogenada (0 kg ha-1). Ao longo do ciclo da cultura, avaliou-se a sua altura, produtividade, IAF e o TFN e a sua resposta espectral de 400 até 920 nm. Os resultados demonstraram que as duas forrageiras foram responsivas à adubação nitrogenada, modificando a sua resposta espectral ao longo das aplicações, principalmente na região do visível (verde 550 nm) e do infravermelho próximo (a partir dos 700 nm). E também provaram que é possível predizer parâmetros biofísicos por meio de espectroscopia in situ e através do satélite Sentinel-2. / In Brazil, the area used for pasture is larger compared to the area used for agricultural crops, representing about 158,6 million hectares of pasture. Pastures are extremely important for beef production, as 95% is produced to feed the herds. The main nutrient for the maintenance of forage productivity is nitrogen (N), which influences directly the morphophysiological characteristics, interfering in the production and on the quality of the forage. In recent years, the use of remote sensing techniques has expanded in agricultural sciences, proving to be a very useful tool in the monitoring and management of nitrogen fertilization in crops. Therefore, the objective of this study was to address the potential of remote sensing data for the forages Panicum maximum cv. Mombasa and Urochloa brizantha cv. Marandú. Data were obtained by passive sensor and converted into different vegetation indices (IVs), in the estimation of biophysical parameters, such as: foliar nitrogen content (NTF), productivity, height and leaf area index (LAI). The data for the Sentinel-2 satellite were also simulated and tested in areas with Brachiaria brizantha cv. Piatã. The IVs used were NDVI (Normalized Difference Vegetation Index), TBI (Three Band Index), DLH (Difference Line Height), NAOC (Normalized Area Over Reflectance Curve) and TBDO (Three Band Dall\'Olmo). The experiment was carried out at the \"Luiz de Queiroz\" School of Agriculture (ESALQ/USP), in Piracicaba, São Paulo. The experimental design applied was a randomized block, with four treatments and four replicates, and with urea used throughout the experiment as a nitrogen fertilizer. The doses applied for cv. Mombaça were three of 200, 400 and 600 kg ha-1 and for cv. Marandú were applied 175, 350 and 525 kg ha-1 doses. For both cultivars the control plots did not receive nitrogen fertilization (0 kg ha-1). During the culture cycle, we evaluated its height, productivity, LAI and TFN, and its spectral response of 400 to 920 nm. The results showed that the two forages were responsive to nitrogen fertilization, modifying their spectral response along the applications, mainly in the region of visible (green 550 nm) and near infrared (from 700 nm). They also proved that it is possible to predict biophysical parameters using in situ spectroscopy and by using the Sentinel-2 satellite.

Cette étude a eu pour but d'étudier la possibilité de couplage de modèles dynamiques de croissance de l'herbe avec des données de télédétection, et ce pour deux terrains contrastés : La Réunion et le Kalahari (Afrique du Sud). Deux phases se sont succédé. Une première phase exploratoire, basée sur des images SPOT5 et SPOT5take5 (satellites désorbités en cours d'étude) a permis de tirer plusieurs enseignements. A La Réunion l'ajustement d’un modèle empirique entre indices de végétation et biomasse engendre trop d'erreur. Il est en revanche possible d'estimer le Leaf Area Index (LAI) grâce au NDVI (Normalized Difference Vegetation Index). Les parcours du Kalahari, plus complexes, avec différentes strates de végétation (graminées, arbustes, arbres) n'ont pas permis d'estimer l'état du couvert de graminées. Cette phase a ouvert la voie au travail effectué sur un capteur plus pérenne dans le temps, Sentinel-2. Les données Sentinel-2 ont permis d'estimer le LAI des prairies réunionnaises avec une RMSE (Root Mean Square Error) de 0,63 (r²=0,82). Le LAI ainsi estimé a été utilisé dans le couplage du modèle dynamique permettant une baisse générale de la RMSE de l'ordre de 40% par rapport au modèle sans couplage. Ces résultats ont été obtenus durant l'hiver austral, la saison sèche. Durant la période d'été austral les pluies plus abondantes accélèrent la croissance des plantes et les cycles de pousse se raccourcissent. Les images satellites sans couvert nuageux se font plus rares. La prise en compte de cette combinaison de facteurs pouvant impacter les prédictions de biomasse fourragère fera partie des principale perspectives de ce travail. / The purpose of this study was to explore the possibility of coupling dynamic models of grass growth with remote sensing data for two contrasting countries: Reunion Island and Kalahari (South Africa). Two phases followed one another. A first exploratory phase, based on SPOT5 and SPOT5take5 images (desorbed satellites under study) allowed us to learn from this experience. In Reunion the adjustment of an empirical model between vegetation indices and biomass generates too much error. However it is possible to estimate the Leaf Area Index (LAI) thanks to the NDVI (Normalized Difference Vegetation Index). More complex Kalahari rangelands with different vegetation strata (grasses, shrubs, trees) failed to estimate grass cover conditions. This phase set the stage to work on a more durable sensor over time, Sentinel-2. Sentinel-2 data made it possible to estimate the LAI of Reunion Island grasslands with a RMSE (Root Mean Square Error) of 0.63 (r² = 0.82). The LAI thus estimated was used in the coupling of the dynamic model, allowing a general decrease of the RMSE of the order of 40% compared to the model without coupling. These results were obtained during the austral winter, the dry season. During the austral summer, the more abundant rains speed up the growth of the plants and the growth cycles become shorter. Satellite images without cloud cover are becoming scarce. Taking into account this combination of factors that may impact predictions of forage biomass will be one of the main perspectives of this work.

Les eaux de surface sont des ressources importantes pour la biosphère et l'anthroposphère. Elles favorisent la préservation des habitats, le développement de la biodiversité et le maintien des services écosystémiques en contrôlant le cycle des nutriments et le carbone à l’échelle mondiale. Elles sont essentielles à la vie quotidienne de l’homme, notamment pour l'irrigation, la consommation d’eau potable, la production hydro-électrique, etc. Par ailleurs, lors des inondations, elles peuvent présenter des dangers pour l'homme, les habitations et les infrastructures. La surveillance des changements dynamiques des eaux de surface a donc un rôle primordial pour guider les choix des gestionnaires dans le processus d’aide à la décision. L’imagerie satellitaire constitue une source de données adaptée permettant de fournir des informations sur les eaux de surface. De nos jours, la télédétection satellitaire a connu une révolution avec le lancement des satellites Sentinel-1 (Radar) et Sentinel-2 (Optique) qui disposent d’une haute fréquence de revisite et d’une résolution spatiale moyenne à élevée. Ces données peuvent fournir des séries temporelles essentielles pour apporter davantage d'informations afin d'améliorer la capacité d'observation des eaux de surface. L’exploitation de telles données massives et multi-sources pose des défis en termes d’extraction de connaissances et de processus de traitement d’images car les chaines de traitement doivent être le plus automatiques possibles. Dans ce contexte, l'objectif de ce travail de thèse est de proposer de nouvelles approches permettant de cartographier l’extension spatiales des eaux de surface et des inondations, en explorant l'utilisation unique et combinée des données Sentinel-1 et Sentinel-2. / Surface waters are important resources for the biosphere and the anthroposphere. Surface waters preserve diverse habitat, support biodiversity and provide ecosystem service by controlling nutrient cycles and global carbon. Surface waters are essential for human's everyday life, such as for irrigation, drinking-water and/or the production of energy (power plants, hydro-electricity). Further, surface waters through flooding can pose hazards to human, settlements and infrastructures. Monitoring the dynamic changes of surface waters is crucial for decision making process and policy. Remote sensing data can provide information on surface waters. Nowadays, satellite remote sensing has gone through a revolution with the launch of the Sentinel-1 SAR data and Sentinel-2 optical data with high revisit time at medium to high spatial resolution. Those data can provide time series and multi-source data which are essential in providing more information to upgrade ability in observing surface water. Analyzing such massive datasets is challenging in terms of knowledge extraction and processing as nearly fully automated processing chains are needed to enable systematic detection of water surfaces.In this context, the objectives of the work are to propose new (e.g. fully automated) approaches for surface water detection and flood extents detection by exploring the single and combined used of Sentinel-1 and Sentinel-2 data.

The mapping of land cover using remotely sensed data is most effective when a robust classification method is employed. Random forest is a modern machine learning algorithm that has recently gained interest in the field of remote sensing due to its non-parametric nature, which may be better suited to handle complex, high-dimensional data than conventional techniques. In this study, the random forest method is applied to remote sensing data from the European Space Agency’s new Sentinel-2 satellite program, which was launched in 2015 yet remains relatively untested in scientific literature using non-simulated data. In a study site of boreo-nemoral forest in Ekerö mulicipality, Sweden, a classification is performed for six forest classes based on CadasterENV Sweden, a multi-purpose land covermapping and change monitoring program. The performance of Sentinel-2’s Multi-SpectralImager is investigated in the context of time series to capture phenological conditions, optimal band combinations, as well as the influence of sample size and ancillary inputs.Using two images from spring and summer of 2016, an overall map accuracy of 86.0% was achieved. The red edge, short wave infrared, and visible red bands were confirmed to be of high value. Important factors contributing to the result include the timing of image acquisition, use of a feature reduction approach to decrease the correlation between spectral channels, and the addition of ancillary data that combines topographic and edaphic information. The results suggest that random forest is an effective classification technique that is particularly well suited to high-dimensional remote sensing data.

The European Water Framework Directive (Directive 2000/60/EC) is a mandatory agreement that guides the member states of the European Union in the field of water policy to fulfil the requirements for reaching the aim of the good ecological status of water bodies. In the last years several work ows and methods were developed to determine and evaluate the haracteristics and the status of the water bodies. Due to their area measurements remote sensing methods are a promising approach to constitute a substantial additional value. With increasing availability of optical and radar remote sensing data the development of new methods to extract information from both types of remote sensing data is still in progress. Since most limitations of these data sets do not agree the fusion of both data sets to gain data with higher spectral resolution features the potential to obtain additional information in contrast to the separate processing of the data. Based thereupon this study shall research the potential of multispectral and radar remote sensing data and the potential of their fusion for the assessment of the parameters of water body structure. Due to the medium spatial resolution of the freely available multispectral Sentinel-2 data sets especially the surroundings of the water bodies and their land use are part of this study. SAR data is provided by the Sentinel-1 satellite. Different image fusion methods are tested and the combined products of both data sets are evaluated afterwards. The evaluation of the single data sets and the fused data sets is performed by means of a maximum-likelihood classification and several statistical measurements. The results indicate that the combined use of different remote sensing data sets can have an added value.

This study aims to create a flood extent map with Sentinel imagery and to evaluate impacts on agricultural land in the lagoon region of central Vietnam. In this study, remote sensing images, obtained from 2017 to 2019, were used to simultaneously map the land cover status of a flood in the Quang Dien district. This study highlights flooded areas from Sentinel-2 images by calculating some indicators such as the Land Surface Water Index (LSWI) and the Enhanced Vegetation Index (EVI). Comparisons between the floodplain samples (GPS point-based) and flood mapping results, with the ground-truth data, indicate that the overall accuracy and Kappa coefficients were 97.9% and 0.62 respectively for 2017; the values for 2019 were 95.7% and 0.77 for the same coefficients. Land use maps overlying the flood-affected maps show that approximately 11% of the agriculture land area was affected by floods in 2019 comparison to a 10% in 2017. Wet rice was the most affected crop with the flooded area accounting for more than 70% of the district under each flood event. The most affected communes are: Quang An, Quang Phuoc and Quang Thanh. This study provides valuable information for flood disaster planning, mitigation and recovery activities in Vietnam. / Mục tiêu của nghiên cứu là lập bản đồ phân bố ngập lụt với hình ảnh vệ tinh Sentinel và đánh giá ảnh hưởng ngập lụt đến sử dụng đất nông nghiệp ở vùng đầm phá miền Trung, Việt Nam. Trong nghiên cứu này, ảnh viễn thám thu nhận giai đoạn 2017-2019 được sử dụng để xây dựng bản đồ hiện trạng sử dụng đất tại thời điểm bị ngập nước trên địa bàn huyện Quảng Điền. Nghiên cứu đã xác định được vùng ngập lụt ở huyện Quảng Điền bằng phương pháp phân loại chỉ số mặt nước (Land Surface Water Index – LSWI) và chỉ số khác biệt thực vật (Enhanced Vegetation Index-EVI) từ ảnh Sentinel-2. Xác định vùng nước lũ bị che khuất bởi mây bằng mô hình số hóa độ cao (DEM). Kết quả phân loại vùng ngập lụt được so sánh với giá trị tham chiếu mặt đất cho thấy độ chính xác tổng thể và hệ số Kappa đạt được trong năm 2017 là 97,9% và 0,62; trong khi năm 2019 đạt 95,7% và 0.77. Bản đồ sử dụng đất chồng lên bản đồ lũ lụt cho thấy khoảng 11% diện tích đất nông nghiệp bị ảnh hưởng bởi lũ lụt năm 2019 so với 10% năm 2017. Cây lúa nước là cây trồng bị ảnh hưởng nặng nề nhất, với diện tích bị ngập lụt chiếm hơn 70% diện tích lúa của huyện. Các xã bị ngập lớn là xã Quảng An, Quảng Phước và Quảng Thành. Nghiên cứu này cung cấp thông tin có giá trị cho các hoạt động lập kế hoạch, giảm nhẹ và phục hồi thiên tai lũ lụt ở Việt Nam.

Grasländer sind wichtige Elemente der zentraleuropäischen Landschaft und stellen essenzielle Ökosystemdienstleistungen bereit. Dürren, welche durch den globalen Klimawandel zunehmen, haben negative Auswirkungen auf die Vitalität und Produktivität von Grasland. Satellitenmissionen wie Sentinel-2 und Landsat liefern große, bisher ungenutzte Möglichkeiten für das Grasland Monitoring. Ansätze auf Basis quantitativer Parameter, z.B. Prozentanteile von photosynthetisch aktiver Vegetation (PV), nicht photosynthetisch aktiver Vegetation (NPV) und Boden sind bisher für die Anwendung in zentraleuropäischen Grasländern nicht erforscht. Das Ziel der Arbeit war es, das Verständnis von Dürreeinflüssen auf zentraleuropäische Grasländer durch die Entwicklung eines fernerkundungsbasierten Monitoring Frameworks zu verbessern. Der erste Teil dieses Frameworks umfasste die Ableitung konsistenter Zeitreihen von PV-, NPV-, und Bodenanteilen. Der zweite Teil umfasste die Quantifizierung von Dürreeffekten anhand dieser Zeitreihen. Die Ergebnisse zeigten einen großflächigen, massiven und langanhaltenden Rückgang von Graslandvitalität in extremen Dürrejahren (z.B. 2003, 2018-2020). Robuste statistische Zusammenhänge bestätigten die starke Kopplung von Graslandvitalität und Dürre, insbesondere bei gleichzeitigen Hitzewellen. Zudem beeinflussten Bodeneigenschaften sowie klimatische und hydrologische Bedingungen die Dürresensitivität. Die Ergebnisse unterstreichen den Wert von generalisierten Entmischungsansätzen basierend auf Sentinel-2/Landsat Zeitreihen für großflächiges, quantitatives Monitoring von Grasland. Die Ergebnisse deuten darauf hin, dass durch den Klimawandel verstärkte Dürreereignisse in Zukunft erheblichen Einfluss auf die Vitalität von Grasländern in Zentraleuropa haben werden. Die hier gewonnenen Informationen liefern wichtige Beiträge zur Verbesserung von Dürremonitoring und können die Maßnahmenentwicklung zur Verringerung von Dürreschäden im Grasland unterstützen. / Grasslands are vital landscape elements in Central Europe providing essential ecosystem services. Drought events, which are increasing with global climate change, negatively affect grassland vitality and productivity. Satellite remote sensing missions such as Sentinel-2/Landsat offer untapped potential for monitoring grassland vitality. However, workflows for grassland monitoring based on fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), and soil, remain largely unexplored. The goal of this thesis was to advance the understanding of drought impacts on Central European grasslands by developing a framework for monitoring grassland vitality. The framework included the retrieval of consistent PV, NPV, and soil fractional cover time series from Landsat/Sentinel-2, which was achieved by implementing and generalizing an unmixing workflow. Second, drought impacts were quantified and evaluated based on fractional cover time series. Results showed large-scale, severe, and long-lasting negative impacts on grassland vitality in extreme drought years (e.g., in 2003, and 2018-2020). Robust statistical links confirmed the overall consistent coupling of grassland vitality to drought, specifically to compounding droughts and heatwaves. Spatiotemporal patterns of grassland drought sensitivity revealed that underlying factors such as soil features, and climatic and hydrological conditions modulate drought impacts on local to regional scales. Findings of this thesis emphasize the value of generalized unmixing workflows based on Sentinel-2/Landsat time series for quantitative grassland monitoring across large areas. Furthermore, results suggest that droughts amplified by climate change will pose substantial challenges for grassland vitality across Central European grasslands in the future. The findings provide a steppingstone towards improved drought monitoring and can thus inform adaptation efforts to alleviate drought impacts on grasslands.

En av Sveriges största industrier är skogsindustrin. Att sköta stora skogsinnehav medför vissa svårigheter, t.ex. så kan i händelse av en storm kan delar av skogen bli vindfälld. Det är då viktigt att upptäcka och ta bort de fallna träden eftersom det annars kan leda till granbarkborrangrepp. En metod för att upptäcka den vindfällda skogen är att ta flygbilder över området, vilket kan bli både dyrt och tidsödande. Därför testas i denna studie detektering av stormfälld skog i Sentinel 2 bilder. Sentinel 2 har valts ut eftersom den har både en hög spatial- och temporal upplösning samt att bilderna är tillgängliga gratis. Tidigare studier på området har använt satellitbilder med en lägre spatial upplösning eller data från andra typer av fjärranalys. De flesta av dessa metoder är ganska komplexa eller väldigt specifika för ett särskilt fall. Metoden som tas fram i denna studie ska vara enkel att implementera även för personer utan någon djupare kunskap inom fjärranalys. Bilddifferens med olika index såsom NDVI, NDMI och GreenNDVI testas. Även oövervakad klassificering testas. Noggrannheten har utvärderats med två-stegs metoden med en noggrannhet på 85 % men även en konfusionsmatris tillämpas för att utvärdera noggrannheten av områden där ingen förändring inträffat. Bilddifferens med NDVI och GreenNDVI klarar två-stegs testet när ett statistiskt bestämt tröskelvärde används, NDVI får högst användarnoggrannhet. Felmatrisen visar dock att det finns många stormfällen i ytorna som blivit klassade som ingen förändring, den oövervakade klassificeringen får inte det problemet i samma utsträckning. Bilddifferens i NDVI med statistiskt bestämt tröskelvärde bedöms vara den mest effektiva metoden för att detektera stormfälld skog.

Submitted by Celso Magalhaes (celsomagalhaes@ufrrj.br) on 2018-04-12T13:33:43Z No. of bitstreams: 1 2017 - Meri Diana Strauss Foesch.pdf: 3588546 bytes, checksum: 6d8bf050ff816e1fbfc13bbbf1858b1f (MD5) / Made available in DSpace on 2018-04-12T13:33:43Z (GMT). No. of bitstreams: 1 2017 - Meri Diana Strauss Foesch.pdf: 3588546 bytes, checksum: 6d8bf050ff816e1fbfc13bbbf1858b1f (MD5) Previous issue date: 2017-02-22 / Mining is an economic activity that has many negative impacts on the environment. Water resources are the most affected by mining, the huge dams are built to store the tailings of the activity, and these, can contain leaks, infiltrations and even break. The rupture of the Fund?o dam in Mariana caused the greatest environmental disaster in Brazil. On November 5, 2015, the rejects stored in the dam are violently applied in the rivers downstream, in great concentration of the rivers Gualaxo do Norte and Carmo, until at Risoleta Neves and then down Rio Doce, until the Atlantic Ocean. This work, organized in two chapters, analyzed the physical-chemical characteristics of the water of the Gualaxo do Norte and Carmo Rivers in relation to Resolution 357/05 of CONAMA, after the dam disruption of Fund?o and related Sentinel-2A and Lansat-8. 36 physico-chemical samples were collected in six by six samples (without satellite scan day, or near data, and without the same points) in six months (April to September 2016), while The images were approved in April, July, August and September 2016 (as of May and June were discarded by the high cloud cover they presented). Random Forest and Linear Regression prediction methods were applied to satellite bands and spectral indices (NDWI, MNDWI, AWEIsh, AWEInsh, WRI, KT Wetness, NDVI). The results showed that the values of metals, color and turbidity and other variables of the polluted waters of the rivers polluted by the mining waste presented values higher than those allowed in CONAMA Resolution 357/05 and highly correlated with the spectral indices, the band 8 Sentinel ? 2A and Lansat-8 band 5. It was concluded that as images of the satellites Landsat 8 and Sentinel-2A, it can be used to estimate and monitor how the physico-chemical characteristics of the waters of the rivers affected by the rupture of the Fund?o dam, which can be used to verify if waters of other rivers Come to acquire these characteristics as a form of monitoring and other changes caused by the exploitation of ores in the springs / A minera??o ? uma atividade econ?mica que gera muitos impactos negativos no meio ambiente. Os recursos h?dricos s?o os mais expostos a sofrerem tais impactos em fun??o das imensas barragens que s?o constru?das para armazenar os rejeitos da atividade, e estas, podem conter vazamentos, infiltra??es e at? se romper. O rompimento da barragem de Fund?o, em Mariana, causou o maior desastre ambiental do Brasil. No dia 5 de novembro de 2015, os rejeitos armazenados na barragem atingiram violentamente os rios a sua jusante, em maior concentra??o os rios Gualaxo do Norte e do Carmo, at? a usina de Risoleta Neves e ap?s ela, o Rio Doce, at? o oceano Atl?ntico. Este trabalho, organizado em dois cap?tulos, analisou a situa??o das caracter?sticas f?sico-qu?micas da ?gua dos rios Gualaxo do Norte e do Carmo em rela??o a Resolu??o 357/05 do CONAMA, ap?s o rompimento da barragem de Fund?o, e relacionou com as caracter?sticas espectrais nos mesmos pontos usando imagens Sentinel-2 e Lansat-8, Foram coletadas trinta e seis amostras f?sico-qu?micas distribu?das em seis amostras por m?s (no dia da varredura do sat?lite, ou pr?ximo ? data, e sempre nos mesmos pontos) em seis meses (abril a setembro de 2016), enquanto as imagens utilizadas foram as de abril, julho, agosto e setembro de 2016 (as de maio e junho foram descartadas pela alta cobertura de nuvens que apresentaram). Foram analisadas a turbidez, cor e metais dissolvidos e suspensos da ?gua e utilizados os m?todos de predi??o por Random Forest e Regress?o Linear para com as bandas dos sat?lites e ?ndices espectrais (NDWI, MNDWI, AWEIsh, AWEInsh, WRI, K-T Wetness, FII, NDVI). Os resultados mostraram que os teores de metais, cor e turbidez e demais vari?veis das ?guas contaminadas dos rios polu?dos pelo rejeito de minera??o apresentaram valores acima dos permitidos na Resolu??o 357/05 CONAMA, e estiveram altamente correlacionadas com os ?ndices espectrais, a banda 8 Sentinel-2 e banda 5 Lansat-8. Concluiu-se que as imagens dos sat?lites Landsat 8 e Sentinel 2A, podem ser usadas para estimar e monitorar as caracter?sticas f?sico-qu?micas nas ?guas dos rios afetadas pelo rompimento da barragem de Fund?o, o que pode servir para verificar se ?guas de outros rios v?m adquirindo estas caracter?sticas, como forma de monitoramento e demais altera??es causadas pela explora??o de min?rios nos mananciais

Diploma thesis deals with the land cover classification in Sidama region of Ethiopia and 2 kebeles, Chancho and Dangora Morocho. High resolution Sentinel-2 and very high resolution PlanetScope satellite images are used. The development of the classification algorithm is done in the Google Earth Engine cloud based environment. Ten combinations of the 4 most important parameters of the Random Forest classification method are tested. The defined legend contains 8 land cover classes, namely built-up, crops, grassland/pasture, forest, scrubland, bareland, wetland and water body. The training dataset is collected in the field during the fall 2020. The classification results of the two data types at two scales are compared. The highest overall accuracy for land cover classification of Sidama region came out to be 84.1% and kappa index of 0.797, with Random Forest method parameters of 100 trees, 4 spectral bands entering each tree, value of 1 for leaf population and 40% of training data used for each tree. For the land cover classification of Chancho and Dangora Morocho kebele with the same method settings, the overall accuracy came out to be 66.00 and 73.73% and kappa index of 0.545 and 0.601. For the classification of Chancho kebele, a different combination of parameters (80, 3, 1, 0.4) worked out better...