CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELD-BASED APPROACH

Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensing-based approach. The underestimation in biomass burning emissions from rice residue, the fibrous plant material left in the field after harvest and subjected to burning, represents the starting point for this research, which is conducted in a small-holder agricultural landscape of Vietnam. This dissertation quantifies improved bottom-up air pollution emissions estimates through refinements to each component of the fine-particulate matter emissions equation, including the use of synthetic aperture radar timeseries to explore rice land area variation between different datasets and for date of burn estimates, development of a new field method to estimate both rice straw and stubble biomass, and also improvements to emissions quantification through the use of burning practice specific emission factors and combustion factors. Moreover, the relative contribution of residue burning emissions to combustion sources was quantified, demonstrating emissions are higher than previously estimated, increasing the importance for mitigation. The dissertation further explored air pollution impacts from rice residue burning in Hanoi, Vietnam through trajectory modelling and synoptic meteorology patterns, as well as timeseries of satellite air pollution and reanalysis datasets. The results highlight the inherent difficulty to capture air pollution impacts in the region, especially attributed to cloud cover obstructing optical satellite observations of episodic biomass burning. Overall, this dissertation found that a prominent satellite-based emissions dataset vastly underestimates emissions from rice residue burning. Recommendations for future work highlight the importance for these datasets to account for crop and burning practice specific emission factors for improved emissions estimates, which are useful to more accurately highlight the importance of reducing emissions from residue burning to alleviate air quality issues

Mapping of Rice Areas in Egypt using SAR Imagery

Egypt is the second biggest rice producer in Africa after Nigeria and produces rice mostly during the summer season. Annual estimations of crop areas and yield are derived using information collected on the ground by extension services and released by the Ministry of Agriculture. However, sample sizes are usually small and can not be easily expanded due to costs and staff shortages. In addition, crop area and production estimates are released after harvest, which is too late to be used for interventions. Timely, accurate, and actionable information on rice areas and production is essential for improving food security policy decisions. This use case aims to develop a digital rice monitoring platform in Egypt based on near-real-time satellite data to support national policies, food security, and market management, as well as to facilitate farmers’ resilience through the development of insurance and microfinance products. This report presents the preliminary results of mapping rice areas across the Nile Delta during the 2022 summer season using IRRI’s RIICE platform in collaboration with Tanta University and European Space Agenc

Fusion approach for remotely sensed mapping of agriculture (FARMA):A scalable open source method for land cover monitoring using data fusion

The increasing availability of very-high resolution (VHR; &lt;2 m) imagery has the potential to enable agricultural monitoring at increased resolution and cadence, particularly when used in combination with widely available moderate-resolution imagery. However, scaling limitations exist at the regional level due to big data volumes and processing constraints. Here, we demonstrate the Fusion Approach for Remotely Sensed Mapping of Agriculture (FARMA), using a suite of open source software capable of efficiently characterizing time-series field-scale statistics across large geographical areas at VHR resolution. We provide distinct implementation examples in Vietnam and Senegal to demonstrate the approach using WorldView VHR optical, Sentinel-1 Synthetic Aperture Radar, and Sentinel-2 and Sentinel-3 optical imagery. This distributed software is open source and entirely scalable, enabling large area mapping even with modest computing power. FARMA provides the ability to extract and monitor sub-hectare fields with multisensor raster signals, which previously could only be achieved at scale with large computational resources. Implementing FARMA could enhance predictive yield models by delineating boundaries and tracking productivity of smallholder fields, enabling more precise food security observations in low and lower-middle income countries.</p

Improved remote sensing methods to detect northern wild rice (Zizania palustris L.)

Declining populations of Zizania palustris L. (northern wildrice, or wildrice) during the last century drives the demand for new and innovative techniques to support monitoring of this culturally and ecologically significant crop wild relative. We trained three wildrice detection models in R and Google Earth Engine using data from annual aquatic vegetation surveys in northern Minnesota. Three di erent training datasets, varying in the definition of wildrice presence, were combined with Landsat 8 Operational Land Imager (OLI) and Sentinel-1 C-band synthetic aperture radar (SAR) imagery to map wildrice in 2015 using random forests. Spectral predictors were derived from phenologically important time periods of emergence (June–July) and peak harvest (August–September). The range of the Vertical Vertical (VV) polarization between the two time periods was consistently the top predictor. Model outputs were evaluated using both point and area-based validation (polygon). While all models performed well in the point validation with percent correctly classified ranging from 83.8% to 91.1%, we found polygon validation necessary to comprehensively assess wildrice detection accuracy. Our practical approach highlights a variety of applications that can be applied to guide field excursions and estimate the extent of occurrence at landscape scales. Further testing and validation of the methods we present may support multiyear monitoring which is foundational for the preservation of wildrice for future generations

Deep semantic segmentation of mangrove combining spatial, temporal and polarization from sentinel-1 time series in the Brazilian territory

Dissertação (mestrado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-graduação, 2022.O uso de imagens de satélite para detecção de padrões com alto grau de precisão traz a possibilidade da realização de um monitoramento estratégico com foco na conservação da biodiversidade local. Técnicas de aprendizagem profunda por segmentação do objeto imagem seguem ganhando espaço em aplicações de processamento digital de imagens de satélite. Estas aplicações seguem atingindo resultados muito superior e sem relação aos métodos tradicionais de aprendizagem de máquina. Entretanto, poucos estudos têm aplicado o aprendizado profundo em áreas de manguezais. Além disso, ainda não foram desenvolvidas técnicas por séries temporais de imagens de radar. A presente pesquisa tem por objetivo: (a) desenvolver dados para a aprendizagem profunda de máquina - levando em consideração abordagens espaciais, temporais e dimensionais (polarizações radiométricas); (b) validar modelos U-net com três diferentes arquiteturas (ResNet-101, VGG16 e Efficient-net-B7); (c) comparar a capacidade de detecção em imagens Sentinel-1 utilizando as polarizações VV, VH e duplas polarizações (VV+VH); (d) e quantificar o número de imagens temporais para melhor detecção dos objetos. A pesquisa utiliza séries temporais Sentinel-1 anual entre períodos de 2017 a 2020. As amostras de treinamento e validação dos dados foram geradas a partir de interpretação manual de imagem de satélite. Estes dados resultaram em 2886 imagens com dimensão espacial de 128x128 pixels. 2136 destas imagens foram utilizadas para treinamento, 450 para validação e 300 para testagem. Acombinação de polarizações (VV+VH), omodelo U-net com arquitetura Efficient-net-B7 e limiar de0,75 (97,35 de acurácia, 85,77 de precisão, 84,96 de recall, 85,36 de F-score e 74,46 de IoU)obtiveram os melhores resultados. 5 passadas (8, 16, 32, 64, 128 pixels) foram aplicadas nas janelas deslizantes. O melhor resultado obtido foi com 8 pixels de passada. O método desenvolvido é adequado ao monitoramento dos padrões temporais de manguezais e fornece subsídios às políticas de conservação destes ecossistemas.The automatic and accurate detection of mangroves from remote sensing data is essential to assist in conservation strategies and decision-making that minimize possible environmental damage, especially for the Brazilian coast with continental dimensions. In this context, segmentation techniques using deep learning are powerful tools with successful applications in several areas of science, achieving results superior to traditional machine learning methods. However, few studies used deep learning for mangrove areas, and none considered time series of radar images. The present research has the following objectives: (a) development of a mangrove dataset for deep learning in the Southeast region of Brazil considering the spatial, temporal, and polarization dimensions; (b) evaluation of U-net architecture models with three backbones different (ResNet -101, VGG16, and Efficient-net-B7); (c) compare the detection capability of Sentinel-1 images using the following VV-only, VH-only, and VV+VH polarizations; and (d) evaluate the number of temporal images for the best detection of targets (29, 15, 8, 4 images), in the case of using both polarizations the number of images doubles. This research uses the annual Sentinel-1 time series for the period 2017-2020. Data labeling used manual interpretation, resulting in 2,886 images with spatial dimensions of 128x128 pixels and their respective annotations (2,136 for training, 450 for validation, and 300 for testing). The best result considered both polarizations (VV+VH), the maximum number of timeseries images (29 VV and 29 VH), U-net with the Efficient-net-B7 backbone, and a threshold of 0.75 (97.35 accuracy, 85.77 precision, 84.96 recall, 85.36 F-score, and 74.46 IoU). The entire image classification used a sliding window approach considering five stride values (8, 16, 32, 64, 128 pixels), where the best result was with 8 pixels. The present method is suitable for monitoring mangrove patterns over time, providing subsidies for conserving these ecosystems

Aplicações de modelos de deep learning para monitoramento ambiental e agrícola no Brasil

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Algoritmos do novo campo de aprendizado de máquina conhecido como Deep Learning têm se popularizado recentemente, mostrando resultados superiores a modelos tradicionais em métodos de classificação e regressão. O histórico de sua utilização no campo do sensoriamento remoto ainda é breve, porém eles têm mostrado resultados similarmente superiores em processos como a classificação de uso e cobertura da terra e detecção de mudança. Esta tese teve como objetivo o desenvolvimento de metodologias utilizando estes algoritmos com um enfoque no monitoramento de alvos críticos no Brasil por via de imagens de satélite a fim de buscar modelos de alta precisão e acurácia para substituir metodologias utilizadas atualmente. Ao longo de seu desenvolvimento, foram produzidos três artigos onde foi avaliado o uso destes algoritmos para a detecção de três alvos distintos: (a) áreas queimadas no Cerrado brasileiro, (b) áreas desmatadas na região da Amazônia e (c) plantios de arroz no sul do Brasil. Apesar do objetivo similar na produção dos artigos, procurou-se distinguir suficientemente suas metodologias a fim de expandir o espaço metodológico conhecido para fornecer uma base teórica para facilitar e incentivar a adoção destes algoritmos em contexto nacional. O primeiro artigo avaliou diferentes dimensões de amostras para a classificação de áreas queimadas em imagens Landsat-8. O segundo artigo avaliou a utilização de séries temporais binárias de imagens Landsat para a detecção de novas áreas desmatadas entre os anos de 2017, 2018 e 2019. O último artigo utilizou imagens de radar Sentinel-1 (SAR) em uma série temporal contínua para a delimitação dos plantios de arroz no Rio Grande do Sul. Modelos similares foram utilizados em todos os artigos, porém certos modelos foram exclusivos a cada publicação, produzindo diferentes resultados. De maneira geral, os resultados encontrados mostram que algoritmos de Deep Learning são não só viáveis para detecção destes alvos mas também oferecem desempenho superior a métodos existentes na literatura, representando uma alternativa altamente eficiente para classificação e detecção de mudança dos alvos avaliados.Algorithms belonging to the new field of machine learning called Deep Learning have been gaining popularity recently, showing superior results when compared to traditional classification and regression methods. The history of their use in the field of remote sensing is not long, however they have been showing similarly superior results in processes such as land use classification and change detection. This thesis had as its objective the development of methodologies using these algorithms with a focus on monitoring critical targets in Brazil through satellite imagery in order to find high accuracy and precision models to substitute methods used currently. Through the development of this thesis, articles were produced evaluating their use for the detection of three distinct targets: (a) burnt areas in the Brazilian Cerrado, (b) deforested areas in the Amazon region and (c) rice fields in the south of Brazil. Despite the similar objective in the production of these articles, the methodologies in each of them was made sufficiently distinct in order to expand the methodological space known. The first article evaluated the use of differently sized samples to classify burnt areas in Landsat-8 imagery. The second article evaluated the use of binary Landsat time series to detect new deforested areas between the years of 2017, 2018 and 2019. The last article used continuous radar Sentinel-1 (SAR) time series to map rice fields in the state of Rio Grande do Sul. Similar models were used in all articles, however certain models were exclusive to each one. In general, the results show that not only are the Deep Learning models viable but also offer better results in comparison to other existing methods, representing an efficient alternative when it comes to the classification and change detection of the targets evaluated

Mapping of multitemporal rice (Oryza sativa L.) growth stages using remote sensing with multi-sensor and machine learning : a thesis dissertation presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Earth Science at Massey University, Manawatū, New Zealand

Figure 2.1 is adapted and re-used under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.Rice (Oryza Sativa) plays a pivotal role in food security for Asian countries, especially in Indonesia. Due to the increasing pressure of environmental changes, such as land use and climate, rice cultivation areas need to be monitored regularly and spatially to ensure sustainable rice production. Moreover, timely information of rice growth stages (RGS) can lead to more efficient of inputs distribution from water, seed, fertilizer, and pesticide. One of the efficient solutions for regularly mapping the rice crop is using Earth observation satellites. Moreover, the increasing availability of open access satellite images such as Landsat-8, Sentinel-1, and Sentinel-2 provides ample opportunities to map continuous and high-resolution rice growth stages with greater accuracy. The majority of the literature has focused on mapping rice area, cropping patterns and relied mainly on the phenology of vegetation. However, the mapping process of RGS was difficult to assess the accuracy, time-consuming, and depended on only one sensor. In this work, we discuss the use of machine learning algorithms (MLA) for mapping paddy RGS with multiple remote sensing data in near-real-time. The study area was Java Island, which is the primary rice producer in Indonesia. This study has investigated: (1) the mapping of RGS using Landsat-8 imagery and different MLAs, and their rigorous performance was evaluated by conducting a multitemporal analysis; (2) the temporal consistency of predicting RGS using Sentinel-2, MOD13Q1, and Sentinel-1 data; (3) evaluating the correlation of local statistics data and paddy RGS using Sentinel-2, PROBA-V, and Sentinel-1 with MLAs. The ground truth datasets were collected from multi-year web camera data (2014-2016) and three months of the field campaign in different regions of Java (2018). The study considered the RGS in the analysis to be vegetative, reproductive, ripening, bare land, and flooding, and MLAs such as support vector machines (SVMs), random forest (RF), and artificial neural network (ANN) were used. The temporal consistency matrix was used to compare the classification maps within three sensor datasets (Landsat-8 OLI, Sentinel-2, and Sentinel-2, MOD13Q1, Sentinel-1) and in four periods (5, 10, 15, 16 days). Moreover, the result of the RGS map was also compared with monthly data from local statistics within each sub-district using cross-correlation analysis. The result from the analysis shows that SVM with a radial base function outperformed the RF and ANN and proved to be a robust method for small-size datasets (< 1,000 points). Compared to Sentinel-2, Landsat-8 OLI gives less accuracy due to the lack of a red-edge band and larger pixel size (30 x 30 m). Integration of Sentinel-2, MOD13Q1, and Sentinel-1 improved the classification performance and increased the temporal availability of cloud-free maps. The integration of PROBA-V and Sentinel-1 improved the classification accuracy from the Landsat-8 result, consistent with the monthly rice planting area statistics at the sub-district level. The western area of Java has the highest accuracy and consistency since the cropping pattern only relied on rice cultivation. In contrast, less accuracy was noticed in the eastern area because of upland rice cultivation due to limited irrigation facilities and mixed cropping. In addition, the cultivation of shallots to the north of Nganjuk Regency interferes with the model predictions because the cultivation of shallots resembles the vegetative phase due to the water banks. One future research idea is the auto-detection of the cropping index in the complex landscape to be able to use it for mapping RGS on a global scale. Detection of the rice area and RGS using Google Earth Engine (GEE) can be an action plan to disseminate the information quickly on a planetary scale. Our results show that the multitemporal Sentinel-1 combined with RF can detect rice areas with high accuracy (>91%). Similarly, accurate RGS maps can be detected by integrating multiple remote sensing (Sentinel-2, Landsat-8 OLI, and MOD13Q1) data with acceptable accuracy (76.4%), with high temporal frequency and lower cloud interference (every 16 days). Overall, this study shows that remote sensing combined with the machine learning methodology can deliver information on RGS in a timely fashion, which is easy to scale up and consistent both in time and space and matches the local statistics. This thesis is also in line with the existing rice monitoring projects such as Crop Monitor, Crop Watch, AMIS, and Sen4Agri to support disseminating information over a large area. To sum up, the proposed workflow and detailed map provide a more accurate method and information in near real-time for stakeholders, such as governmental agencies against the existing mapping method. This method can be introduced to provide accurate information to rice farmers promptly with sufficient inputs such as irrigation, seeds, and fertilisers for ensuring national food security from the shifting planting time due to climate change

Crop Growth Monitoring by Hyperspectral and Microwave Remote Sensing

Methoden und Techniken der Fernerkundung fungieren als wichtige Hilfsmittel im regionalen Umweltmanagement. Um diese zu optimieren, untersucht die folgende Arbeit sowohl die Verwendung als auch Synergien verschiedener Sensoren aus unterschiedlichen Wellenlängenbereichen. Der Fokus liegt auf der Modellentwicklung zur Ableitung von Pflanzenparametern aus fernerkundlichen Bestandsmessungen sowie auf deren Bewertung. Zu den verwendeten komplementären Fernerkundungssystemen zählen die Sensoren EO-1 Hyperion und ALI, Envisat ASAR sowie TerraSAR-X. Für die optischen Hyper- und Multispektralsysteme werden die Reflexion verschiedener Spektralbereiche sowie die Performanz der daraus abgeleiteten Vegetationsindizes untersucht und bewertet. Im Hinblick auf die verwendeten Radarsysteme konzentriert sich die Untersuchung auf Parameter wie Wellenlänge, Einfallswinkel, Radarrückstreuung und Polarisation. Die Eigenschaften verschiedener Parameterkombinationen werden hierbei dargestellt und der komplementäre Beitrag der Radarfernerkundung zur Wachstumsüberwachung bewertet. Hierzu wurden zwei Testgebiete, eines für Winterweizen in der Nordchinesischen Tiefebene und eines für Reis im Nordosten Chinas ausgewählt. In beiden Gebieten wurden während der Wachstumsperioden umfangreiche Feldmessungen von Bestandsparametern während der Satellitenüberflüge oder zeitnah dazu durchgeführt. Mit Hilfe von linearen Regressionsmodellen zwischen Satellitendaten und Biomasse wird die Sensitivität hyperspektraler Reflexion und Radarrückstreuung im Hinblick auf das Wachstum des Winterweizens untersucht. Für die optischen Daten werden drei verschiedene Modelvarianten untersucht: traditionelle Vegetationsindices berechnet aus Multispektraldaten, traditionelle Vegetationsindices berechnet aus Hyperspektraldaten sowie die Berechnung von Normalised Ratio Indices (NRI) basierend auf allen möglichen 2-Band Kombinationen im Spektralbereich zwischen 400 und 2500 nm. Weiterhin wird die gemessene Biomasse mit der gleichpolarisierten (VV) C-Band Rückstreuung des Envisat ASAR Sensors linear in Beziehung gesetzt. Um den komplementären Informationsgehalt von Hyperspektral und Radardaten zu nutzen, werden optische und Radardaten für die Parameterableitung kombiniert eingesetzt. Das Hauptziel für das Reisanbaugebiet im Nordosten Chinas ist das Verständnis über die kohärente Dualpolarimetrische X-Band Rückstreuung zu verschiedenen phänologischen Wachstumsstadien. Hierfür werden die gleichpolarisierte TerraSAR-X Rückstreuung (HH und VV) sowie abgeleitete polarimetrische Parameter untersucht und mit verschiedenen Ebenen im Bestand in Beziehung gesetzt. Weiterhin wird der Einfluss der Variation von Einfallswinkel und Auflösung auf die Bestandsparameterableitung quantifiziert. Neben der Signatur von HH und VV ermöglichen vor allem die polarimetrischen Parameter Phasendifferenz, Ratio, Koherenz und Entropy-Alpha die Bestimmung bestimmter Wachstumsstadien. Die Ergebnisse der Arbeit zeigen, dass die komplementären Fernerkundungssysteme Optik und Radar die Ableitung von Pflanzenparametern und die Bestimmung von Heterogenitäten in den Beständen ermöglichen. Die Synergien diesbezüglich müssen auch in Zukunft weiter untersucht werden, da neue und immer variablere Fernerkundungssysteme zur Verfügung stehen werden und das Umweltmanagement weiter verbessern können

Análise de inundações e classificação da cobertura vegetal no bioma amazônico usando séries temporais sentinel-1 SAR e técnicas de deep learning

Tese (doutorado) — Universidade de Brasília, Instituto de Ciências Humanas, Departamento de Geografia, Programa de Pós-Graduação em Geografia, 2022.Os recursos hídricos e os estudos fenológicos florestais são extremamente importantes para a compreensão de diversos fenômenos naturais como as mudanças climáticas, dinâmica hidrogeomorfológica, condicionamento ambiental e gestão dos recursos. Inserida na dinâmica hídrica, estão presentes as áres inundáveis que estão intrinsecamente ligadas à manuntenção da biota e da fauna nos biomas brasileiros. Nesse contexto, os produtos derivados de sensoriamento remoto têm sido bastante utilizados para a análise e monitoramento de áreas inundáveis, mapeamento de uso e ocupação da terra e dinâmica fenológica devido à sua importância ambiental. As imagens de radar de abertura sintética (SAR) são produtos potenciais por não apresentar interferências atmosféricas, entretanto, necessitam de diversos tratamentos iniciais, definidos de pré-processamento para assim ser possível obter uma melhor extração de informações de uma determinada área. Nesse sentido, essa pesquisa teve como objetivo aplicar as técnicas de deep learning utilizando algoritmos de processamento de séries temporais de imagens de satélite baseados em redes neurais para extração e identificação de áreas inundáveis, corpos hídricos e fenologias florestais em áreas de cerrado, floresta amazônica, mangues, cultivos agrícolas e várzea. O presente estudo foi dividido em três capítulos principais: (a) análises métricas e estatísticas de filtragens espaciais em imagem Sentinel-1 SAR da Amazônia Central, Brasil; (b) análise de série temporal Sentinel-1 SAR em inundações na Amazônia Central; e (c) classificação fenológica de floresta, mangues, cerrado e vegetação alagada do bioma Amazônia por meio de comparação dos modelos LSTM, Bi-LSTM, GRU, Bi-GRU e modelos de aprendizagem de máquina baseados em séries temporais do satélite Sentinel-1. As etapas metodológicas foram distintas para cada capítulo e todos apresentaram precisão e altos valores métricos para mensuração e análise dos corpos hídricos, inundação e fenologias florestais. Dentre os métodos de filtragem analisados na imagem SAR, o filtro Lee com janela 3 × 3 apresentou os melhores desempenhos na redução do ruído speckle (MSE igual a 1,88 e MAE igual a 1,638) e baixo valor de distorção de contraste na polarização VH. Entretanto, para a polarização VV, mensuraram-se diferentes resultados para análise da redução do ruído speckle, onde o filtro Frost com janela 3 × 3 apresentou o melhor desempenho, com baixo valor para as métricas em geral (MSE igual a 1,2 e MAE igual a 6,28) e também um baixo valor de distorção de contraste. Por apresentar os melhores valores estatísticos, o filtro de mediana com janela 11 × 11 nas polarizações VH e VV pode ser utilizado como uma técnica de filtragem alternativa na imagem Sentinel-1 nas duas polarizações. As áreas de inundação mensuradas nas polarizações VH e VV apresentaram uma forte correlação e sem significância estatística entre as amostras, presumindo que se pode utilizar as duas polarizações para obtenção do pulso de inundação e mapeamento da dinâmica das áreas inundáveis na região. Pelo fato de não haver imagens Sentinel-1 anteriores ao ano de 2016, quando os eventos extremos de LMEO foram superiores a 100%, não foi possível delimitar a LMEO por meio de dados SAR. Algumas áreas ao longo da costa e rios apresentam assinaturas temporais de retroespalhamento que evidenciam transições entre ambientes terrestres e áreas cobertas por água. A variação temporal do retroespalhamento de valores mais altos para mais baixos indica erosão e inundação progressiva, enquanto o inverso indica aumento terrestre. O modelo Bi-GRU apresentou a maior acurácia geral, precisão, recall e F-score tanto na polarização individual como na polarização combinada VV+VH. A combinação entre as polarizações forneceu os melhores resultados na classificação e a polarização VH obteve melhores resultados quando comparado à polarização VV. O presente estudo atestou o procedimento metodológico adequado para mensurar as áreas de corpos hídricos e seu pulso de inundação como também obteve a classificação de fenologias com alta precisão na Amazônia Central por meio de deep learning advindas de série temporal de imagens Sentinel-1 SAR.Water resources and forest phenological studies are extremely important for the understanding of various natural phenomena, such as climate variation, hydrogeomorphological dynamics, environmental conditioning, and resource management. In this context, products derived from remote sensing have been widely used for the analysis and monitoring of flooding areas, land use and occupation mapping, and phenological dynamics due to their environmental importance. Synthetic aperture radar (SAR) images are potential products as they do not present atmospheric interference, however, they require several initial treatments, defined as pre-processing, so that it is possible to obtain a better extraction of information from a certain area. In this sense, this research aimed to apply deep learning techniques using algorithms based on neural networks for the extraction and identification of flooding areas, water bodies, and forest phenologies such as cerrado, Amazon forest, mangroves, agricultural crops, and floodplain through time series of remote sensing images. This study was divided into three main chapters: (a) metric and statistical analysis of spatial filtering in Sentinel-1 SAR images of Central Amazonia, Brazil; (b) Sentinel-1 SAR time series analysis in flooding areas of Central Amazon; and (c) phenological classification of forest, mangroves, savannas, and two flooded vegetation of the Amazon biome by comparing LSTM, Bi-LSTM, GRU, Bi-GRU, and machine learning models from Sentinel-1 time series. The methodological steps were different for each chapter and all presented precision and high metric values for measurement and analysis of water bodies, flooding and forest phenologies. Among the filtering methods analyzed in the SAR image, the Lee filter with 3 × 3 window presented the best performance in reducing speckle noise (MSE of 1.88 and MAE of 1.638) and low value of contrast distortion in the VH polarization. However, for the VV polarization, different results were measured for the analysis of the sepeckle noise reduction, where the Frost filter with 3 × 3 window presented the best performance, with a low value for the metrics in general (MSE of 1.2 and MAE of 6.28) and also a low contrast distortion value. Statistical values derived from the median filter with 11 × 11 window in the VH and VV polarizations can be used as an alternative filtering technique in the Sentinel-1 SAR image in both polarizations. The flooding areas measured in the VH and VV polarizations showed a strong correlation and no statistical significance between the samples, assuming that both polarizations can be used to obtain the flood pulse and mapping the dynamics of the flooded areas in the region. Because there are no Sentinel1 SAR images prior to 2016 when extreme LMEO events were greater than 100%, it was not possible to delimit the LMEO through SAR data. Some areas along the coast and rivers show temporal backscatter signatures with transitions between terrestrial environments and areas covered by water. The temporal variation of backscatter from higher to lower values indicates erosion and progressive flooding, while the inverse indicates terrestrial increase. The Bi-GRU model showed the highest overall accuracy, precision, recall, and F-score in both separate polarization and combined VV+VH polarization. The combination between the polarizations provided the best results in the classification and the VH polarization obtained better results when compared to the VV polarization. This study attested an adequate methodological procedure to measure the areas of water bodies and their flood pulse, as well as obtaining the classification of phenologies with high precision in the Central Amazon by means of deep learning applied to the time series of Sentinel-1 SAR images

Late Holocene Anthropogenic and Climatic Impact on a Tropical Island Ecosystem of Northern Vietnam

Northern Vietnam has a long history of human occupation, warfare, and agriculture; yet, the environmental consequences of human activity are poorly understood due to limited paleoecological records. Results from a terrestrial wetland sediment core from the tropical island, Quan Lan, in Ha Long Bay provide a local record of ecosystem responses to societal shifts due to warfare/instability and climate change. A multiproxy study, including pollen, macro charcoal, fecal stanols, and geochemistry, suggests that native vegetation was abundant and low-level, subsistence wet-rice agriculture and burning were in practice during a time of increased monsoon intensity between 1150 BCE and 950 CE. Between 950 and 1450 CE, a trading and military port was established on Quan Lan Island which served as a major hub of southeast Asian trade and protected the mainland from Mongol invasions. During this period, population near the wetland declined to undetectable levels, rice agriculture declined, burning ceased, and disturbance species expanded. A simultaneous shift toward a more arid climate, with possible extended years of drought, occurred during the Medieval Climate Anomaly based on regional climate records. Without a reliable source of freshwater, rice production declined and/or was supplemented by trade. When the mainland capital near present day Hanoi moved south to Hue after 1450 CE, the port ceased operation. During this time, climate became wetter, and precipitation and surface water more reliable than before. Population near the wetland increased as did burning and rice agriculture. The research has implications for understanding the maintenance of tropical biodiversity amidst long-term human occupation, political unrest, and climate change