Urban Deformation Monitoring using Persistent Scatterer Interferometry and SAR tomography

This book focuses on remote sensing for urban deformation monitoring. In particular, it highlights how deformation monitoring in urban areas can be carried out using Persistent Scatterer Interferometry (PSI) and Synthetic Aperture Radar (SAR) Tomography (TomoSAR). Several contributions show the capabilities of Interferometric SAR (InSAR) and PSI techniques for urban deformation monitoring. Some of them show the advantages of TomoSAR in un-mixing multiple scatterers for urban mapping and monitoring. This book is dedicated to the technical and scientific community interested in urban applications. It is useful for choosing the appropriate technique and gaining an assessment of the expected performance. The book will also be useful to researchers, as it provides information on the state-of-the-art and new trends in this fiel

Detection and Monitoring of Marine Pollution Using Remote Sensing Technologies

Recently, the marine habitat has been under pollution threat, which impacts many human activities as well as human life. Increasing concerns about pollution levels in the oceans and coastal regions have led to multiple approaches for measuring and mitigating marine pollution, in order to achieve sustainable marine water quality. Satellite remote sensing, covering large and remote areas, is considered useful for detecting and monitoring marine pollution. Recent developments in sensor technologies have transformed remote sensing into an effective means of monitoring marine areas. Different remote sensing platforms and sensors have their own capabilities for mapping and monitoring water pollution of different types, characteristics, and concentrations. This chapter will discuss and elaborate the merits and limitations of these remote sensing techniques for mapping oil pollutants, suspended solid concentrations, algal blooms, and floating plastic waste in marine waters

Applications of satellite ‘hyper-sensing’ in Chinese agriculture:Challenges and opportunities

Ensuring adequate food supplies to a large and increasing population continues to be the key challenge for China. Given the increasing integration of China within global markets for agricultural products, this issue is of considerable significance for global food security. Over the last 50 years, China has increased the production of its staple crops mainly by increasing yield per unit land area. However, this has largely been achieved through inappropriate agricultural practices, which have caused environmental degradation, with deleterious consequences for future agricultural productivity. Hence, there is now a pressing need to intensify agriculture in China using practices that are environmentally and economically sustainable. Given the dynamic nature of crops over space and time, the use of remote sensing technology has proven to be a valuable asset providing end-users in many countries with information to guide sustainable agricultural practices. Recently, the field has experienced considerable technological advancements reflected in the availability of ‘hyper-sensing’ (high spectral, spatial and temporal) satellite imagery useful for monitoring, modelling and mapping of agricultural crops. However, there still remains a significant challenge in fully exploiting such technologies for addressing agricultural problems in China. This review paper evaluates the potential contributions of satellite ‘hyper-sensing’ to agriculture in China and identifies the opportunities and challenges for future work. We perform a critical evaluation of current capabilities in satellite ‘hyper-sensing’ in agriculture with an emphasis on Chinese sensors. Our analysis draws on a series of in-depth examples based on recent and on-going projects in China that are developing ‘hyper-sensing’ approaches for (i) measuring crop phenology parameters and predicting yields; (ii) specifying crop fertiliser requirements; (iii) optimising management responses to abiotic and biotic stress in crops; (iv) maximising yields while minimising water use in arid regions; (v) large-scale crop/cropland mapping; and (vi) management zone delineation. The paper concludes with a synthesis of these application areas in order to define the requirements for future research, technological innovation and knowledge exchange in order to deliver yield sustainability in China

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

ANALYZING THE LIFE-CYCLE OF UNSTABLE SLOPES USING APPLIED REMOTE SENSING WITHIN AN ASSET MANAGEMENT FRAMEWORK

An asset management framework provides a methodology for monitoring and maintaining assets, which include anthropogenic infrastructure (e.g., dams, embankments, and retaining structures) and natural geological features (e.g., soil and rock slopes). It is imperative that these assets operate efficiently, effectively, safely, and at a high standard since many assets are located along transportation corridors (highways, railways, and waterways) and can cause severe damage if compromised. Assets built on or around regions prone to natural hazards are at an increased risk of deterioration and failure. The objective of this study is to utilize remote sensing techniques such as InSAR, LiDAR, and optical photogrammetry to identify assets, assess past and current conditions, and perform long-term monitoring in transportation corridors and urbanized areas prone to natural hazards. Provided are examples of remote sensing techniques successfully applied to various asset management procedures: the characterization of rock slopes (Chapter 2), identification of potentially hazardous slopes along a railroad corridor (Chapter 3), monitoring subsidence rates of buildings in San Pedro, California (Chapter 4), and mapping displacement rates on dams in India (Chapter 5) and California (Chapter 6). A demonstration of how InSAR can be used to map slow landslides (those with a displacement rate \u3c 16 mm/year and may be undetectable without sensitive instrumentation) and update the California Landslide Inventory on the Palos Verdes Peninsula is provided in Chapter 7. Long-term landslide monitoring using optical photogrammetry, GPS, and InSAR measurements is also used to map landslide activity at three orders of magnitude (meter to millimeter scales) in Chapter 8. Remote sensing has proven to be an effective tool at measuring ground deformation, which is an implicit indicator of how geotechnical asset condition changes (e.g., deteriorates) over time. Incorporating these techniques into a geotechnical asset management framework will provide greater spatial and temporal data for preventative approaches towards natural hazards

Microwave Indices from Active and Passive Sensors for Remote Sensing Applications

Past research has comprehensively assessed the capabilities of satellite sensors operating at microwave frequencies, both active (SAR, scatterometers) and passive (radiometers), for the remote sensing of Earth’s surface. Besides brightness temperature and backscattering coefficient, microwave indices, defined as a combination of data collected at different frequencies and polarizations, revealed a good sensitivity to hydrological cycle parameters such as surface soil moisture, vegetation water content, and snow depth and its water equivalent. The differences between microwave backscattering and emission at more frequencies and polarizations have been well established in relation to these parameters, enabling operational retrieval algorithms based on microwave indices to be developed. This Special Issue aims at providing an overview of microwave signal capabilities in estimating the main land parameters of the hydrological cycle, e.g., soil moisture, vegetation water content, and snow water equivalent, on both local and global scales, with a particular focus on the applications of microwave indices

Flood modeling and prediction using Earth Observation data

The ability to map floods from satellites has been known for over 40 years. Early images of floods were rather difficult to obtain, and flood mapping from satellites was thus rather opportunistic and limited to only a few case studies. However, over the last decade, with a proliferation of open-access EO data, there has been much progress in the development of Earth Observation products and services tailored to various end-user needs, as well as its integration with flood modeling and prediction efforts. This article provides an overview of the use of satellite remote sensing of floods and outlines recent advances in its application for flood mapping, monitoring and its integration with flood models. Strengths and limita- tions are discussed throughput, and the article concludes by looking at new developments

Determination of surface water area using multitemporal SAR imagery

Inland water and freshwater constitute a valuable natural resource in economic, cultural, scientific and educational terms. Their conservation and management are critical to the interests of all humans, nations and governments. In many regions these precious heritages are in crisis. The main focus of this research is to investigate the capability of time variable ENVISAT ASAR imagery to extract water surface and assess the water surface area variations of lake Poyang in the basin of Yangtze river, the largest freshwater lake in China. Nevertheless, the lake has been in a critical situation in recent years due to a decrease of surface water caused by climate change and human activities. In order to classify water and land areas and to achieve the temporal changes of water surface area from ASAR images during the period 2006-2011, the image segmentation technique was implemented. For this purpose, a thorough analysis of the SAR system and its properties is first discussed. Indeed, some impairments can affect the SAR imaging signals. These impairments such as different types of scattering, surface roughness, dielectric property of water, speckle and geometric distortions can reduce SAR image quality. To avoid these distortions or to reduce their impact, it is therefore important to pre-process SAR images effectively and accurately. All the images were pre-processed using NEST software provided by ESA. To calculate the water surface area, each image was tiled into 9 parts and then it is segmented using two different methods. Firstly histogram for each tile is observed. Using a local adaptive thresholding technique, two local maxima were determined on the histogram and then in between these local maxima, a local minimum is determined which can be considered as the threshold. In the second technique a Gaussian curve was fitted using Levenberg-Marquardt method (1944 and 1963) to obtain a threshold. These thresholds are used to segment the image into homogeneous land and water regions. Later, the time series for both methods is derived from the estimated water surface areas. The results indicate an intense decreasing trend in Poyang Lake surface area during the period 2006-2011. Especially between 2010 and 2011, the lake significantly lost its surface area as compared to the year 2006. Finally, the results are presented for both locally adaptive thresholding and Levenberg-Marquardt methods. These results illustrate the effectiveness of the locally adaptive thresholding method to detect water surface change. A continuous monitoring of water surface change would lead to a long term time series, which is definitely beneficial for water management purposes

Better together: Integrating and fusing multispectral and radar satellite imagery to inform biodiversity monitoring, ecological research and conservation science

1. The availability and accessibility of multispectral and radar satellite remote sensing (SRS) imagery are at an unprecedented high. These data have both become standard source of information for investigating species ecology and ecosystems structure, composition and function at large scales. Since they capture complementary aspects of the Earth's surface, synergies between these two types of imagery have the potential to greatly expand research and monitoring opportunities. However, despite the benefits of combining multispectral and radar SRS data, data fusion techniques, including image fusion, are not commonly used in biodiversity monitoring, ecology and conservation. / 2. To help close this application gap, we provide for the first time an overview of the most common SRS data fusion techniques, discussing their benefits and drawbacks, and pull together case studies illustrating the added value for biodiversity research and monitoring. / 3. Integrating and fusing multispectral and radar images can significantly improve our ability to assess the distribution as well as the horizontal and vertical structure of ecosystems. Additionally, SRS data fusion has the potential to increase opportunities for mapping species distribution and community composition, as well as for monitoring threats to biodiversity. Uptake of these techniques will benefit from more effective collaboration between remote sensing and biodiversity experts, making the reporting of methodologies more transparent, expanding SRS image processing capacity and promoting widespread open access to satellite imagery. / 4. In the context of a global biodiversity crisis, being able to track subtle changes in the biosphere across adequate spatial and temporal extents and resolutions is crucial. By making key parameter estimates derived from SRS data more accurate, SRS data fusion promises to become a powerful tool to help address current monitoring needs, and could support the development of essential biodiversity variables