Integrated Applications of Geo-Information in Environmental Monitoring

This book focuses on fundamental and applied research on geo-information technology, notably optical and radar remote sensing and algorithm improvements, and their applications in environmental monitoring. This Special Issue presents ten high-quality research papers covering up-to-date research in land cover change and desertification analyses, geo-disaster risk and damage evaluation, mining area restoration assessments, the improvement and development of algorithms, and coastal environmental monitoring and object targeting. The purpose of this Special Issue is to promote exchanges, communications and share the research outcomes of scientists worldwide and to bridge the gap between scientific research and its applications for advancing and improving society

Human and environmental exposure to hydrocarbon pollution in the Niger Delta:A geospatial approach

This study undertook an integrated geospatial assessment of human and environmental exposure to oil pollution in the Niger Delta using primary and secondary spatial data. This thesis begins by presenting a clear rationale for the study of extensive oil pollution in the Niger Delta, followed by a critical literature review of the potential application of geospatial techniques for monitoring and managing the problem. Three analytical chapters report on the methodological developments and applications of geospatial techniques that contribute to achieving the aim of the study. Firstly, a quantitative assessment of human and environmental exposure to oil pollution in the Niger Delta was performed using a government spill database. This was carried out using Spatial Analysis along Networks (SANET), a geostatistical tool, since oil spills in the region tend to follow the linear patterns of the pipelines. Spatial data on pipelines, oil spills, population and land cover data were analysed in order to quantify the extent of human and environmental exposure to oil pollution. The major causes of spills and spatial factors potentially reinforcing reported causes were analysed. Results show extensive general exposure and sabotage as the leading cause of oil pollution in the Niger Delta. Secondly, a method of delineating the river network in the Niger Delta using Sentinel-1 SAR data was developed, as a basis for modelling potential flow of pollutants in the distributary pathways of the network. The cloud penetration capabilities of SAR sensing are particularly valuable for this application since the Niger Delta is notorious for cloud cover. Vector and raster-based river networks derived from Sentinel-1 were compared to alternative river map products including those from the USGS and ESA. This demonstrated the superiority of the Sentinel-1 derived river network, which was subsequently used in a flow routing analysis to demonstrate the potential for understanding oil spill dispersion. Thirdly, the study applied optical remote sensing for indirect detection and mapping of oil spill impacts on vegetation. Multi-temporal Landsat data was used to delineate the spill impact footprint of a notable 2008 oil spill incident in Ogoniland and population exposure was evaluated. The optical data was effective in impact area delineation, demonstrating extensive and long-lasting population exposure to oil pollution. Overall, this study has successfully assembled and produced relevant spatial and attribute data sets and applied integrated geostatistical analytical techniques to understand the distribution and impacts of oil spills in the Niger Delta. The study has revealed the extensive level of human and environmental exposure to hydrocarbon pollution in the Niger Delta and introduced new methods that will be valuable fo

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

Estimation of Surface Moisture Content and Evapotranspiration Using Weightage Approach.

Soil moisture (MC) and evapotranspiration (ET) are considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes. However, monitoring them continuously over large areas using the high temporal-resolution optical satellites is very demanding. Satellites such as the Advanced Very High Resolution Radiometer (AVHRR) and the Moderate Resolution Imaging Spectroradiometer (MODIS), have a coarse spatial resolution in their images. Thus it not only impedes the acquisition of an accurate MC and ET but also represents multispectral reflections from the holistic surface features. This beside their dependence on vegetation and ground coefficient when assessing MC and ET. The study aims to enhance the spatial accuracy by weighting the MC produced from different surface cover classes within the pixel. MC for each pixel is segmented into three (3) different classes namely urban, vegetation and multi surface cover according to their respective MC weightage. Secondly, to generate an improved actual ETa map by overlaying the segmented MC with a rectified ETo. Images from AVHRR and MODIS satellites were selected in order to generate MC and ET maps. Two powerful MC algorithms were used based on land Surface Temperature (Ts), vegetation Indices (VI) and field measurements of MC; which were conducted at variable depths to examine the depth influence on MC and Ts magnitudes

Estimation of Building Density with the Integrated Use of GF-1 PMS and Radarsat-2 Data

Building density, as a component of impervious surface fraction, is a significant indicator of population distribution as essentially all humans live and conduct activities in buildings. Because population spatialization usually occurs over large areas, large-scale building density estimation through a proper, time-efficient, and relatively precise way is urgently required. Therefore, this study constructed a decision tree by the Classification and Regression Tree (CART) algorithm combining synthetic aperture radar (SAR) with optical images. The input features included four spectral bands (B1–4) of GF-1 PMS imagery; Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Built-up Index (RBI) derived from them; and backscatter intensity (BI) of Radarsat-2 SAR data. In addition, a new index called amended backscatter intensity (ABI), which takes the influence created by different spatial patterns into account, was introduced and calculated through fractal dimension and lacunarity. Result showed that before the integration use of multisource data, a model using B1–4, NDVI, NDWI, and RBI had the highest accuracy, with RMSE of 10.28 and R2 of 0.63 for Jizhou and RMSE of 20.34 and R2 of 0.36 for Beijing. In Comparison, the best model after combining two data sources (i.e., the model employing B1–4, NDVI, NDWI, RBI and ABI) reduced the RMSE to 8.93 and 16.21 raised the R2 to 0.80 and 0.64, respectively. The result indicated that the synergistic use of optical and SAR data has the potential to improve the building density estimation performance and the addition of ABI has a better capacity for improving the model than other input features

Space Station Systems: a Bibliography with Indexes (Supplement 8)

This bibliography lists 950 reports, articles, and other documents introduced into the NASA scientific and technical information system between July 1, 1989 and December 31, 1989. Its purpose is to provide helpful information to researchers, designers and managers engaged in Space Station technology development and mission design. Coverage includes documents that define major systems and subsystems related to structures and dynamic control, electronics and power supplies, propulsion, and payload integration. In addition, orbital construction methods, servicing and support requirements, procedures and operations, and missions for the current and future Space Station are included

Seasonal and spatial variability of major organic contaminants in solution and suspension of the Pomeranian Bight

Studies of hexachlorocyclohexane-isomers (HCHs) and selected triazine herbicides in solution and suspension were carried out in the Pomeranian Bight in 1995. The concentrations of HCHs and triazines were determined by gas-liquid chromatography (GC) or by GC in connection with quadrupole mass spectrometry(GC/MS). Particulate and dissolved material were separated by means of an in-situ filtration/extraction system. The seasonal variability and regional distribution of the various components were investigated in January, April, July and September 1995. Their distribution in the western Pomeranian Bight is described. The concentrations of individual hexachlorocyclohexane-isomers were in the range of 100–1 000 pg l–1 in solution and 20 to 60 pg l–1 in suspension. The levels of the triazines in solution showed pronounced differences between the individual components (atrazine (2–20 ng l–1), simazine (5–30 ng l–1), terbuthylazine (< 5 ng l–1)), but they were one order of magnitude higher compared with the hexachlorocyclohexane-isomers. The concentration of triazines in suspension was low, often below the limit of detection (25 pg l–1)

Dipterocarps protected by Jering local wisdom in Jering Menduyung Nature Recreational Park, Bangka Island, Indonesia

Apart of the oil palm plantation expansion, the Jering Menduyung Nature Recreational Park has relatively diverse plants. The 3,538 ha park is located at the north west of Bangka Island, Indonesia. The minimum species-area curve was 0.82 ha which is just below Dalil conservation forest that is 1.2 ha, but it is much higher than measurements of several secondary forests in the Island that are 0.2 ha. The plot is inhabited by more than 50 plant species. Of 22 tree species, there are 40 individual poles with the average diameter of 15.3 cm, and 64 individual trees with the average diameter of 48.9 cm. The density of Dipterocarpus grandiflorus (Blanco) Blanco or kruing, is 20.7 individual/ha with the diameter ranges of 12.1 – 212.7 cm or with the average diameter of 69.0 cm. The relatively intact park is supported by the local wisdom of Jering tribe, one of indigenous tribes in the island. People has regulated in cutting trees especially in the cape. The conservation agency designates the park as one of the kruing propagules sources in the province. The growing oil palm plantation and the less adoption of local wisdom among the youth is a challenge to forest conservation in the province where tin mining activities have been the economic driver for decades. More socialization from the conservation agency and the involvement of university students in raising environmental awareness is important to be done

Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images

Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho más difícil. Además, en el escenario actual, donde el número de sensores en el espacio crece constantemente, desarrollar metodologías para transferir modelos que funcionen con datos de nuevos satélites es una necesidad urgente. Por tanto, los objetivos de esta tesis son desarrollar modelos precisos de detección de nubes que exploten las diferentes propiedades de las imágenes de satélite y desarrollar metodologías para transferir esos modelos a otros sensores. La tesis está basada en cuatro trabajos los cuales proponen soluciones a estos problemas. En la primera contribución, "Multitemporal cloud masking in the Google Earth Engine", implementamos un modelo de detección de nubes multitemporal que se ejecuta en la plataforma Google Earth Engine y que supera los modelos operativos de Landsat-8. La segunda contribución, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", es un caso de estudio de transferencia de un algoritmo de detección de nubes basado en aprendizaje profundo de Landsat-8 (resolución 30m, 12 bandas espectrales y muy buena calidad radiométrica) a Proba-V, que tiene una resolución de 333m, solo cuatro bandas y una calidad radiométrica peor. El tercer artículo, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", propone aprender una transformación de adaptación de dominios que haga que las imágenes de Proba-V se parezcan a las tomadas por Landsat-8 con el objetivo de transferir productos diseñados con datos de Landsat-8 a Proba-V. Finalmente, la cuarta contribución, "Towards global flood mapping onboard low cost satellites with machine learning", aborda simultáneamente la detección de inundaciones y nubes con un único modelo de aprendizaje profundo, implementado para que pueda ejecutarse a bordo de un CubeSat (ϕSat-I) con un chip acelerador de aplicaciones de inteligencia artificial. El modelo está entrenado en imágenes Sentinel-2 y demostramos cómo transferir este modelo a la cámara del ϕSat-I. Este modelo se lanzó en junio de 2021 a bordo de la misión WildRide de D-Orbit para probar su funcionamiento en el espacio.Remote sensing sensors onboard Earth observation satellites provide a great opportunity to monitor our planet at high spatial and temporal resolutions. Nevertheless, to process all this ever-growing amount of data, we need to develop fast and accurate models adapted to the specific characteristics of the data acquired by each sensor. For optical sensors, detecting the clouds present in the image is an unavoidable first step for most of the land and ocean applications. Although detecting bright and opaque clouds is relatively easy, automatically identifying thin semi-transparent clouds or distinguishing clouds from snow or bright surfaces is much more challenging. In addition, in the current scenario where the number of sensors in orbit is constantly growing, developing methodologies to transfer models across different satellite data is a pressing need. Henceforth, the overreaching goal of this Thesis is to develop accurate cloud detection models that exploit the different properties of the satellite images, and to develop methodologies to transfer those models across different sensors. The four contributions of this Thesis are stepping stones in that direction. In the first contribution,"Multitemporal cloud masking in the Google Earth Engine", we implemented a lightweight multitemporal cloud detection model that runs on the Google Earth Engine platform and which outperforms the operational models for Landsat-8. The second contribution, "Transferring deep learning models for Cloud Detection between Landsat-8 and Proba-V", is a case-study of transferring a deep learning based cloud detection algorithm from Landsat-8 (30m resolution, 12 spectral bands and very good radiometric quality) to Proba-V, which has a lower{333m resolution, only four bands and a less accurate radiometric quality. The third paper, "Cross sensor adversarial domain adaptation of Landsat-8 and Proba-V images for cloud detection", proposes a learning-based domain adaptation transformation of Proba-V images to resemble those taken by Landsat-8, with the objective of transferring products designed on Landsat-8 to Proba-V. Finally, the fourth contribution, "Towards global flood mapping onboard low cost satellites with machine learning", tackles simultaneously cloud and flood water detection with a single deep learning model, which was implemented to run onboard a CubeSat (ϕSat-I) with an AI accelerator chip. In this case, the model is trained on Sentinel-2 and transferred to theϕSat-I camera. This model was launched in June 2021 onboard the Wild Ride D-Orbit mission in order to test its performance in space

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum