Enhanced Urban Sprawl Monitoring over the Entire District of Rome through Joint Analysis of ALOS AVNIR-2 and SENTINEL-2A Data

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Investigation of Coastal Vegetation Dynamics and Persistence in Response to Hydrologic and Climatic Events Using Remote Sensing

Coastal Wetlands (CW) provide numerous imperative functions and provide an economic base for human societies. Therefore, it is imperative to track and quantify both short and long-term changes in these systems. In this dissertation, CW dynamics related to hydro-meteorological signals were investigated using a series of LANDSAT-derived normalized difference vegetation index (NDVI) data and hydro-meteorological time-series data in Apalachicola Bay, Florida, from 1984 to 2015. NDVI in forested wetlands exhibited more persistence compared to that for scrub and emergent wetlands. NDVI fluctuations generally lagged temperature by approximately three months, and water level by approximately two months. This analysis provided insight into long-term CW dynamics in the Northern Gulf of Mexico. Long-term studies like this are dependent on optical remote sensing data such as Landsat which is frequently partially obscured due to clouds and this can that makes the time-series sparse and unusable during meteorologically active seasons. Therefore, a multi-sensor, virtual constellation method is proposed and demonstrated to recover the information lost due to cloud cover. This method, named Tri-Sensor Fusion (TSF), produces a simulated constellation for NDVI by integrating data from three compatible satellite sensors. The visible and near-infrared (VNIR) bands of Landsat-8 (L8), Sentinel-2, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were utilized to map NDVI and to compensate each satellite sensor\u27s shortcomings in visible coverage area. The quantitative comparison results showed a Root Mean Squared Error (RMSE) and Coefficient of Determination (R2) of 0.0020 sr-1 and 0.88, respectively between true observed and fused L8 NDVI. Statistical test results and qualitative performance evaluation suggest that TSF was able to synthesize the missing pixels accurately in terms of the absolute magnitude of NDVI. The fusion improved the spatial coverage of CWs reasonably well and ultimately increases the continuity of NDVI data for long term studies

Land Surface Monitoring Based on Satellite Imagery

This book focuses attention on significant novel approaches developed to monitor land surface by exploiting satellite data in the infrared and visible ranges. Unlike in situ measurements, satellite data provide global coverage and higher temporal resolution, with very accurate retrievals of land parameters. This is fundamental in the study of climate change and global warming. The authors offer an overview of different methodologies to retrieve land surface parameters— evapotranspiration, emissivity contrast and water deficit indices, land subsidence, leaf area index, vegetation height, and crop coefficient—all of which play a significant role in the study of land cover, land use, monitoring of vegetation and soil water stress, as well as early warning and detection of forest fires and drought

Joint use of Sentinel-1 and Sentinel-2 for land cover classification : a machine learning approach

Reliable information on land cover is required to assist and help in the decision-making process needed to face the environmental challenges society has to deal with due to climate change and other driving forces. Different methods can be used to gather this information but satellite earth observation techniques offer a suitable approach based on the coverage and type of data that are provided. Few years ago, the European Union (EU) started an ambitious program, Copernicus, that includes the launch of a new family of earth observation satellites known as Sentinel. Each Sentinel mission is based on a constellation of two satellites to fulfill specific requirements of coverage and revisit time. Among them are the Sentinel-1 and Sentinel-2 satellites. Sentinel-1 carries a Synthetic Aperture RADAR (SAR) that operates on the C-band. This platform offers SAR data day-and-night and in all-weather conditions. Sentinel-2 is a multispectral high-resolution imaging mission. The sensor has 13 spectral channels, incorporating four visible and near-infrared bands at 10 m resolution, six red-edge/shortwave-infrared bands at 20 m and three atmospheric correction bands at 60 m. The main objective of this study has been to investigate the classification accuracies of specific land covers obtained after a Random Forest classification of multi-temporal Sentinel data over an agricultural area. Four scenarios have been tested for the classification: i) Sentinel-1, ii) Sentinel-2, iii) Sentinel-2 and vegetation indices, iv) Sentinel-1, Sentinel-2, and vegetation indices. The classifications have been performed using a pixel and polygon based approach. The results have shown that the best accuracies (0.98) are obtained when using and polygon based approach independently of the scenario that is selected. For the pixel based approach, the highest accuracy (0.84) is obtained when using Sentinel-1, Sentinel-2, and vegetation indices

Monitoring deforestation and forest degradation linking high-resolution satellite data and field data in the context of REDD+. A case of Tanzania

El principal objetivo de este doctorado es apoyar el desarrollo de un sistema nacional de monitoreo forestal en Tanzania para informar sobre las emisiones actuales e históricas derivadas de la deforestación y la degradación forestal. El marco de la tesis se centra específicamente en el emergente contexto internacional de la iniciativa REDD + (Reducción de Emisiones por Deforestación y Degradación) de las Naciones Unidas, bajo la cual los países pueden obtener subsidios financieros para demostrar que están reduciendo sus emisiones de carbono de tierras forestales con respecto a su práctica histórica reciente. La investigación se centró en cinco áreas de investigación: La parte (1) revisa los antecedentes políticos de REDD +. En él se describen las normas y las opciones a ser abordadas por los países participantes y se demuestran algunos de los problemas técnicos y las opciones que pueden enfrentar y adoptar en la tecnología de teledetección. La parte (2) presenta los resultados del trabajo de campo en Tanzania. Esto incluye la creación de una recopilación rápida de datos sobre el terreno y directrices sobre protocolos para vincular los datos de campo con los datos de teledetección, con el fin de producir mapas de cobertura vegetal y biomasa aérea utilizando imágenes de muy alta resolución. La parte (3) demuestra la mejora en el mapeo de los bosques con una fina resolución espacial y alta frecuencia de adquisiciones con la llegada de los nuevos satélites Sentinel-2. Este potencial se ha probado en un área de bosque seco en el centro de Tanzania. En la parte (4) se evalúa una estimación a gran escala de la biomasa terrestre para toda Tanzania, utilizando una combinación de datos de teledetección y de campo. La capacidad predictiva se investigó comparando los resultados con las mediciones en tierra realizadas por el inventario nacional. La parte (5) investiga la dinámica de la deforestación alrededor de Dar es Salaam, junto con un modelo para inferir la probabilidad futura de deforestación a nivel nacional. La capacidad del modelo de replicar los patrones espaciales de deforestación se evaluó a través de datos del terreno. Entre los principales resultados de este doctorado están que las estimaciones de cambios de cobertura forestal de diferentes fuentes tienen una amplia varianza a nivel nacional y que las estimaciones de emisiones para el proceso REDD + siguen siendo poco fiables. Hay un gran número de opciones a las que se enfrenta un sistema de monitoreo forestal, en términos de definiciones y métodos, que tienen un impacto en la factibilidad de implementación y en los resultados. Se ha demostrado la dificultad de vincular los datos de teledetección con los parámetros forestales de los estudios nacionales, con recomendaciones para mejorar la futura recopilación de datos sobre el terreno. Sin embargo, el uso sinérgico de la teledetección y los datos del estudio sobre el terreno pueden reducir efectivamente los costes de cartografía y monitoreo de los cambios y la degradación de los bosques. Para ello, se encontró que el uso de índices de textura y segmentación de imágenes de satélite de alta resolución espacial (5m) era útil en la producción de mapas de biomasa forestal. Además, la llegada de Sentinel-2 ofrece la oportunidad de analizar datos de media resolución espacial (<20m) en series temporales, especialmente útiles para áreas secas.The main objective of this PhD is to support the development of a national forest monitoring system in Tanzania so as to report on current and historical emissions which derive from deforestation and forest degradation. The framework of the thesis is specifically focused on the emerging international context of the REDD+ (Reducing Emissions from Deforestation and Degradation) initiative from the United Nations, under which countries may obtain financial grants for demonstrating that they are reducing their carbon emissions from forest lands with respect to their recent historical practice. The research focused on five focal areas of research: Part (1) reviews the policy background to REDD+. It outlines the rules and choices to be addressed by participatory countries and demonstrates some of the technical problems and options that they can face and adopt in the remote sensing technology. Part (2) presents the results from the PhD field work in Tanzania. This included the set-up of rapid field data collection and guidelines on protocols to link the field data to the remote sensing data, so as to produce maps of vegetation cover and above ground biomass using very high resolution images. Part (3) demonstrates the improvement to map forests at a fine spatial resolution and high frequency of acquisitions with the arrival of the new Sentinel-2 satellites. This potential has been tested on an area of dry forest in Central Tanzania. Part (4) tests a full scale estimate of above ground biomass for the whole of Tanzania, using a combination of remote sensing and field data. The predictive capability was investigated by comparing the results against ground measurements undertaken by the national inventory. Part (5) investigates the dynamics of deforestation around Dar es Salaam, along with a model to infer future probability of deforestation at the national level. The ability of the model to replicate spatial patterns of deforestation was assessed through ground truth data. Among the main outcome of this PhD is that estimates of forest change from different sources have wide variance at national level and emissions estimates for the REDD+ process remain unreliable. There are a large number of choices facing a forest monitoring system, in terms of forest definitions and methods, which have an impact on the feasibility of implementation and results. The difficulty of linking remote sensing data to the forest parameter from national surveys has been shown, with recommendations to improve future field data collection. However the synergistic use of remote sensing and field survey data can effectively reduce the costs for mapping and monitoring forest changes and forest degradation. For this, the use of high spatial resolution (5m) satellite image segmentation and texture indices was found to be useful in the production of forest biomass maps. Additionally, the arrival of Sentinel-2 data provides the opportunity to analyse medium high spatial resolution data (<20m) in time series, especially useful for dry areas

Monitoring wetlands and water bodies in semi-arid Sub-Saharan regions

Surface water in wetlands is a critical resource in semi-arid West-African regions that are frequently exposed to droughts. Wetlands are of utmost importance for the population as well as the environment, and are subject to rapidly changing seasonal fluctuations. Dynamics of wetlands in the study area are still poorly understood, and the potential of remote sensing-derived information as a large-scale, multi-temporal, comparable and independent measurement source is not exploited. This work shows successful wetland monitoring with remote sensing in savannah and Sahel regions in Burkina Faso, focusing on the main study site Lac Bam (Lake Bam). Long-term optical time series from MODIS with medium spatial resolution (MR), and short-term synthetic aperture radar (SAR) time series from TerraSAR-X and RADARSAT-2 with high spatial resolution (HR) successfully demonstrate the classification and dynamic monitoring of relevant wetland features, e.g. open water, flooded vegetation and irrigated cultivation. Methodological highlights are time series analysis, e.g. spatio-temporal dynamics or multitemporal-classification, as well as polarimetric SAR (polSAR) processing, i.e. the Kennaugh elements, enabling physical interpretation of SAR scattering mechanisms for dual-polarized data. A multi-sensor and multi-frequency SAR data combination provides added value, and reveals that dual-co-pol SAR data is most recommended for monitoring wetlands of this type. The interpretation of environmental or man-made processes such as water areas spreading out further but retreating or evaporating faster, co-occurrence of droughts with surface water and vegetation anomalies, expansion of irrigated agriculture or new dam building, can be detected with MR optical and HR SAR time series. To capture long-term impacts of water extraction, sedimentation and climate change on wetlands, remote sensing solutions are available, and would have great potential to contribute to water management in Africa

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

Examining spatiotemporal changes in the phenology of Australian mangroves using satellite imagery

Nicolás Younes investigated the phenology of Australian mangroves using satellite imagery, field data, and generalized additive models. He found that satellite-derived phenology changes with location, frequency of observation, and spatial resolution. Nicolás challenges the common methods for detecting phenology and proposes a data-driven approach