Very High Resolution (VHR) Satellite Imagery: Processing and Applications

Recently, growing interest in the use of remote sensing imagery has appeared to provide synoptic maps of water quality parameters in coastal and inner water ecosystems;, monitoring of complex land ecosystems for biodiversity conservation; precision agriculture for the management of soils, crops, and pests; urban planning; disaster monitoring, etc. However, for these maps to achieve their full potential, it is important to engage in periodic monitoring and analysis of multi-temporal changes. In this context, very high resolution (VHR) satellite-based optical, infrared, and radar imaging instruments provide reliable information to implement spatially-based conservation actions. Moreover, they enable observations of parameters of our environment at greater broader spatial and finer temporal scales than those allowed through field observation alone. In this sense, recent very high resolution satellite technologies and image processing algorithms present the opportunity to develop quantitative techniques that have the potential to improve upon traditional techniques in terms of cost, mapping fidelity, and objectivity. Typical applications include multi-temporal classification, recognition and tracking of specific patterns, multisensor data fusion, analysis of land/marine ecosystem processes and environment monitoring, etc. This book aims to collect new developments, methodologies, and applications of very high resolution satellite data for remote sensing. The works selected provide to the research community the most recent advances on all aspects of VHR satellite remote sensing

Deep learning for land cover and land use classification

Recent advances in sensor technologies have witnessed a vast amount of very fine spatial resolution (VFSR) remotely sensed imagery being collected on a daily basis. These VFSR images present fine spatial details that are spectrally and spatially complicated, thus posing huge challenges in automatic land cover (LC) and land use (LU) classification. Deep learning reignited the pursuit of artificial intelligence towards a general purpose machine to be able to perform any human-related tasks in an automated fashion. This is largely driven by the wave of excitement in deep machine learning to model the high-level abstractions through hierarchical feature representations without human-designed features or rules, which demonstrates great potential in identifying and characterising LC and LU patterns from VFSR imagery. In this thesis, a set of novel deep learning methods are developed for LC and LU image classification based on the deep convolutional neural networks (CNN) as an example. Several difficulties, however, are encountered when trying to apply the standard pixel-wise CNN for LC and LU classification using VFSR images, including geometric distortions, boundary uncertainties and huge computational redundancy. These technical challenges for LC classification were solved either using rule-based decision fusion or through uncertainty modelling using rough set theory. For land use, an object-based CNN method was proposed, in which each segmented object (a group of homogeneous pixels) was sampled and predicted by CNN with both within-object and between-object information. LU was, thus, classified with high accuracy and efficiency. Both LC and LU formulate a hierarchical ontology at the same geographical space, and such representations are modelled by their joint distribution, in which LC and LU are classified simultaneously through iteration. These developed deep learning techniques achieved by far the highest classification accuracy for both LC and LU, up to around 90% accuracy, about 5% higher than the existing deep learning methods, and 10% greater than traditional pixel-based and object-based approaches. This research made a significant contribution in LC and LU classification through deep learning based innovations, and has great potential utility in a wide range of geospatial applications

Pre-processing, classification and semantic querying of large-scale Earth observation spaceborne/airborne/terrestrial image databases: Process and product innovations.

By definition of Wikipedia, “big data is the term adopted for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. The big data challenges typically include capture, curation, storage, search, sharing, transfer, analysis and visualization”. Proposed by the intergovernmental Group on Earth Observations (GEO), the visionary goal of the Global Earth Observation System of Systems (GEOSS) implementation plan for years 2005-2015 is systematic transformation of multisource Earth Observation (EO) “big data” into timely, comprehensive and operational EO value-adding products and services, submitted to the GEO Quality Assurance Framework for Earth Observation (QA4EO) calibration/validation (Cal/Val) requirements. To date the GEOSS mission cannot be considered fulfilled by the remote sensing (RS) community. This is tantamount to saying that past and existing EO image understanding systems (EO-IUSs) have been outpaced by the rate of collection of EO sensory big data, whose quality and quantity are ever-increasing. This true-fact is supported by several observations. For example, no European Space Agency (ESA) EO Level 2 product has ever been systematically generated at the ground segment. By definition, an ESA EO Level 2 product comprises a single-date multi-spectral (MS) image radiometrically calibrated into surface reflectance (SURF) values corrected for geometric, atmospheric, adjacency and topographic effects, stacked with its data-derived scene classification map (SCM), whose thematic legend is general-purpose, user- and application-independent and includes quality layers, such as cloud and cloud-shadow. Since no GEOSS exists to date, present EO content-based image retrieval (CBIR) systems lack EO image understanding capabilities. Hence, no semantic CBIR (SCBIR) system exists to date either, where semantic querying is synonym of semantics-enabled knowledge/information discovery in multi-source big image databases. In set theory, if set A is a strict superset of (or strictly includes) set B, then A B. This doctoral project moved from the working hypothesis that SCBIR computer vision (CV), where vision is synonym of scene-from-image reconstruction and understanding EO image understanding (EO-IU) in operating mode, synonym of GEOSS ESA EO Level 2 product human vision. Meaning that necessary not sufficient pre-condition for SCBIR is CV in operating mode, this working hypothesis has two corollaries. First, human visual perception, encompassing well-known visual illusions such as Mach bands illusion, acts as lower bound of CV within the multi-disciplinary domain of cognitive science, i.e., CV is conditioned to include a computational model of human vision. Second, a necessary not sufficient pre-condition for a yet-unfulfilled GEOSS development is systematic generation at the ground segment of ESA EO Level 2 product. Starting from this working hypothesis the overarching goal of this doctoral project was to contribute in research and technical development (R&D) toward filling an analytic and pragmatic information gap from EO big sensory data to EO value-adding information products and services. This R&D objective was conceived to be twofold. First, to develop an original EO-IUS in operating mode, synonym of GEOSS, capable of systematic ESA EO Level 2 product generation from multi-source EO imagery. EO imaging sources vary in terms of: (i) platform, either spaceborne, airborne or terrestrial, (ii) imaging sensor, either: (a) optical, encompassing radiometrically calibrated or uncalibrated images, panchromatic or color images, either true- or false color red-green-blue (RGB), multi-spectral (MS), super-spectral (SS) or hyper-spectral (HS) images, featuring spatial resolution from low (> 1km) to very high (< 1m), or (b) synthetic aperture radar (SAR), specifically, bi-temporal RGB SAR imagery. The second R&D objective was to design and develop a prototypical implementation of an integrated closed-loop EO-IU for semantic querying (EO-IU4SQ) system as a GEOSS proof-of-concept in support of SCBIR. The proposed closed-loop EO-IU4SQ system prototype consists of two subsystems for incremental learning. A primary (dominant, necessary not sufficient) hybrid (combined deductive/top-down/physical model-based and inductive/bottom-up/statistical model-based) feedback EO-IU subsystem in operating mode requires no human-machine interaction to automatically transform in linear time a single-date MS image into an ESA EO Level 2 product as initial condition. A secondary (dependent) hybrid feedback EO Semantic Querying (EO-SQ) subsystem is provided with a graphic user interface (GUI) to streamline human-machine interaction in support of spatiotemporal EO big data analytics and SCBIR operations. EO information products generated as output by the closed-loop EO-IU4SQ system monotonically increase their value-added with closed-loop iterations

Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass

This Special Issue (SI), entitled "Applications of Remote Sensing Data in Mapping of Forest Growing Stock and Biomass”, resulted from 13 peer-reviewed papers dedicated to Forestry and Biomass mapping, characterization and accounting. The papers' authors presented improvements in Remote Sensing processing techniques on satellite images, drone-acquired images and LiDAR images, both aerial and terrestrial. Regarding the images’ classification models, all authors presented supervised methods, such as Random Forest, complemented by GIS routines and biophysical variables measured on the field, which were properly georeferenced. The achieved results enable the statement that remote imagery could be successfully used as a data source for regression analysis and formulation and, in this way, used in forestry actions such as canopy structure analysis and mapping, or to estimate biomass. This collection of papers, presented in the form of a book, brings together 13 articles covering various forest issues and issues in forest biomass calculation, constituting an important work manual for those who use mixed GIS and RS techniques

Remote Sensing and Geosciences for Archaeology

This book collects more than 20 papers, written by renowned experts and scientists from across the globe, that showcase the state-of-the-art and forefront research in archaeological remote sensing and the use of geoscientific techniques to investigate archaeological records and cultural heritage. Very high resolution satellite images from optical and radar space-borne sensors, airborne multi-spectral images, ground penetrating radar, terrestrial laser scanning, 3D modelling, Geographyc Information Systems (GIS) are among the techniques used in the archaeological studies published in this book. The reader can learn how to use these instruments and sensors, also in combination, to investigate cultural landscapes, discover new sites, reconstruct paleo-landscapes, augment the knowledge of monuments, and assess the condition of heritage at risk. Case studies scattered across Europe, Asia and America are presented: from the World UNESCO World Heritage Site of Lines and Geoglyphs of Nasca and Palpa to heritage under threat in the Middle East and North Africa, from coastal heritage in the intertidal flats of the German North Sea to Early and Neolithic settlements in Thessaly. Beginners will learn robust research methodologies and take inspiration; mature scholars will for sure derive inputs for new research and applications

A Study of African Savanna Vegetation Structure, Patterning, and Change

African savannas cover roughly half of the continent, are home to a great diversity of wildlife, and provide ecosystem services to large populations. Savannas showcase a great diversity in vegetation structure, resulting from variation in climatic, edaphic, topographic, and biological factors. Fires play a large role as savannas are the most frequently burned ecosystems on Earth. To study how savanna vegetation structure shifts with environmental factors, it is necessary to gather site data covering the full gradient of climatic and edaphic conditions. Several earlier studies have used coarse resolution satellite remote sensing data to study variation in woody cover. These woody cover estimates have limited accuracy in drylands where the woody component is relatively small, and the data cannot reveal more detailed information on the vegetation structure. We therefore know little about how other structural components, tree densities, crown sizes, and the spatial pattern of woody plants, vary across environmental gradients. This thesis aimed to examine how woody vegetation structure and change in woody cover vary with environmental conditions. The analyses depended on access to very high spatial resolution (\u3c1 \u3em) satellite imagery from sites spread across African savannas. The high resolution data combined with a crown delineation method enabled me to estimate variation in tree densities, mean crown size and the level of aggregation among woody plants. With overlapping older and newer imagery at most of the sites, I was also able to estimate change in woody cover over a 10-year period. I found that higher woody plant aggregation is associated with drier climates, high rainfall variability, and fine-textured soils. These same factors were also indicative of the areas where highly organized periodic vegetation patterns were found. The study also found that observed increases in woody cover across the rainfall gradient is more a result of increasing crown sizes than variation in tree density. The analysis of woody cover change found a mean increase of 0.25 % per year, indicating an ongoing trend of woody encroachment. I could not attribute this trend to any of the investigated environmental factors and it may result from higher atmospheric CO₂ concentrations, which has been proposed in other studies. The most influential predictor of woody cover change in the analysis was the difference between potential woody cover and initial woody cover, which highlights the role of competition for water and density dependent regulation when studying encroachment rates. The second most important predictor was fire frequency. To better understand and explain the dominant ecosystem processes controlling savanna vegetation structure, I constructed a spatially explicit model that simulates the growth of herbaceous and woody vegetation in a landscape. The model reproduced several of the trends in woody vegetation structure earlier found in the remote sensing analysis. These include how tree densities and crowns sizes respond differently to increases in precipitation along the full rainfall range, and the factors controlling the spatial pattern of trees in a landscape

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Drones and Geographical Information Technologies in Agroecology and Organic Farming

Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies. This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license