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

Multisource Remote Sensing based Impervious Surface Mapping

Impervious surface (IS) not only serves as a key indicator of urbanization, but also affects the micro-ecosystem. Therefore, it is essential to monitor IS distribution timely and accurately. Remote sensing is an effective approach as it can provide straightforward and consistent information over large area with low cost. This thesis integrates multi-source remote sensing data to interpretate urban patterns and provide more reliable IS mapping results. Registration of optical daytime and nighttime lights (NTL) data is developed in the first contribution. An impervious surface based optical-to-NTL image registration algorithm with iterative blooming effect reduction (IS_iBER) algorithm is proposed. This coarse-to-fine procedure investigates the correlation between optical and NTL features. The iterative registration and blooming effect reduction method obtains precise matching results and reduce the spatial extension of NTL. Considering the spatial transitional nature of urban-rural fringes (URF) areas, the second study proposed approach for URF delineation, namely optical and nighttime lights (NTL) data based multi-scale URF (msON_URF).The landscape heterogeneity and development vitality derived from optical and NTL features are analyzed at a series of scales to illustrate the urban-URF-rural pattern. Results illustrate that msON_URF is effective and practical for not only concentric, but also polycentric urban patterns. The third study proposes a nighttime light adjusted impervious surface index (NAISI) to detect IS area. Parallel to baseline subtraction approaches, NAISI takes advantage of features, rather than spectral band information to map IS. NAISI makes the most of independence between NTL-ISS and pervious surface to address the high spectral similarity between IS and bare soil in optical image. An optical and NTL based spectral mixture analysis (ON_SMA) is proposed to achieve sub-pixel IS mapping result in the fourth study. It integrates characteristics of optical and NTL imagery to adaptively select local endmembers. Results illustrate the proposed method yields effective improvement and highlight the potential of NTL data in IS mapping. In the fifth study, GA-SVM IS mapping algorithm is investigated with introduction of the achieved urban-URF-rural spatial structure. The combination of optical, NTL and SAR imagery is discussed. GA is implemented for feature selection and parameter optimization in each urban scenario

SPATIAL ANALYSES AND REMOTE SENSING FOR LAND COVER CHANGE DYNAMICS: ASSESSING IN A SPATIAL PLANNING

ABSTRACT (EN) Spatial planning is a crucial discipline for the identification and implementation of sustainable development strategies that take into account the environmental impacts on the soil. In recent years, the significant development of technology, like remote sensing and GIS software, has significantly increased the understanding of environmental components, highlighting their peculiarities and criticalities. Geographically referenced information on environmental and socio-economic components represents a fundamental database for identifying and monitoring vulnerable areas, also distinguishing different levels of vulnerability. This is even more relevant considering the increasingly significant impact of land transformation processes, consisting of rapid and frequent changes in land use patterns. In order to achieve some of the Sustainable Development Goals of the 2030 Agenda, the role of environmental planning is crucial in addressing spatial problems, such as agricultural land abandonment and land take, which cause negative impacts on ecosystems. Remote sensing, and in general all Earth Observation techniques, play a key role in achieving SDG 11.3 and 15.3 of Agenda 2030. Through a series of applications and investigations in different areas of Basilicata, it has been demonstrated how the extensive use of remote sensing and spatial analysis in a GIS environment provide a substantial contribution to the results of the SDGs, enabling an informed decisionmaking process and enabling monitoring of the results expected, ensuring data reliability and directly contributing to the calculation of SDG objectives and indicators by facilitating local administrations approaches to work in different development and sustainability sectors. In this thesis have been analyse the dynamics of land transformation in terms of land take and soil erosion in sample areas of the Basilicata Region, which represents an interesting case example for the study of land use land cover change (LULCC). The socio-demographic evolutionary trends and the study of marginality and territorial fragility are fundamental aspects in the context of territorial planning, since they are important drivers of the LULCC and territorial transformation processes. In fact, in Basilicata, settlement dynamics over the years have occurred in an uncontrolled and unregulated manner, leading to a constant consumption of land not accompanied by adequate demographic and economic growth. To better understand the evolution and dynamics of the LULCCs and provide useful tools for formulating territorial planning policies and strategies aimed at a sustainable use of the territory, the socio-economic aspects of the Region were investigated. A first phase involved the creation of a database and the study and identification of essential services in the area as a fundamental parameter against which to evaluate the quality of life in a specific area. The supply of essential services can be understood as an assessment of the lack of minimum requirements with reference to the urban functions exercised by each territorial unit. From a territorial point of view, the level of peripherality of the territories with respect to the network of urban centres profoundly influences the quality of life of citizens and the level of social inclusion. In these, the presence of essential services can act as an attractor capable of generating discrete catchment areas. The purpose of this first part of the work was above all to create a dataset of data useful for the calculation of various socio-economic indicators, in order to frame the demographic evolution and the evolution of the stock of public and private services. The first methodological approach was to reconstruct the offer of essential services through the use of open data in a GIS environment and subsequently estimate the peripherality of each municipality by estimating the accessibility to essential services. The study envisaged the use of territorial analysis techniques aimed at describing the distribution of essential services on the regional territory. It is essential to understand the role of demographic dynamics as a driver of urban land use change such as, for example, the increase in demand for artificial surfaces that occurs locally. Social and economic analyses are important in the spatial planning process. Comparison of socio-economic analyses with land use and land cover change can highlight the need to modify existing policies or implement new ones. A particular land use can degrade and thereby destroy other land resources. If the economic analysis shows that the use is beneficial from the point of view of the land user, it is likely to continue, regardless of whether the process is environmentally friendly. It is important to understand and investigate which drivers have been and will be in the future the most decisive in these dynamics that intrinsically contribute to land take, agricultural abandonment and the consequent processes of land degradation and to define policies or thresholds to mitigate and monitor the effects of these processes. Subsequently, the issues of land take and abandonment of agricultural land were analysed by applying models and techniques of remote sensing, GIS and territorial analysis for the identification and monitoring of abandoned agricultural areas and sealed areas. The classic remote sensing methods have also been integrated by some geostatistical analyses which have provided more information on the investigated phenomenon. The aim was the creation of a quick methodology that would allow to describe the monitoring and analysis activities of the development trends of soil consumption and the monitoring and identification of degraded areas. The first methodology proposed allowed the automatic and rapid detection of detailed LULCC and Land Take maps with an overall accuracy of more than 90%, reducing costs and processing times. The identification of abandoned agricultural areas in degradation is among the most complicated LULCC and Land Degradation processes to identify and monitor as it is driven by a multiplicity of anthropic and natural factors. The model used to estimate soil erosion as a degradation phenomenon is the Revised Universal Soil Loss Equation (RUSLE). To identify potentially degraded areas, two factors of the RUSLE have been correlated: Factor C which describes the vegetation cover of the soil and Factor A which represents the amount of potential soil erosion. Through statistical correlation analysis with the RUSLE factors, on the basis of the deviations from the average RUSLE values and mapping of the areas of vegetation degradation, relating to arable land, through statistical correlation with the vegetation factor C, the areas were identified and mapped that are susceptible to soil degradation. The results obtained allowed the creation of a database and a map of the degraded areas to be paid attention to

Derivation of forest inventory parameters from high-resolution satellite imagery for the Thunkel area, Northern Mongolia. A comparative study on various satellite sensors and data analysis techniques.

With the demise of the Soviet Union and the transition to a market economy starting in the 1990s, Mongolia has been experiencing dramatic changes resulting in social and economic disparities and an increasing strain on its natural resources. The situation is exacerbated by a changing climate, the erosion of forestry related administrative structures, and a lack of law enforcement activities. Mongolia’s forests have been afflicted with a dramatic increase in degradation due to human and natural impacts such as overexploitation and wildfire occurrences. In addition, forest management practices are far from being sustainable. In order to provide useful information on how to viably and effectively utilise the forest resources in the future, the gathering and analysis of forest related data is pivotal. Although a National Forest Inventory was conducted in 2016, very little reliable and scientifically substantiated information exists related to a regional or even local level. This lack of detailed information warranted a study performed in the Thunkel taiga area in 2017 in cooperation with the GIZ. In this context, we hypothesise that (i) tree species and composition can be identified utilising the aerial imagery, (ii) tree height can be extracted from the resulting canopy height model with accuracies commensurate with field survey measurements, and (iii) high-resolution satellite imagery is suitable for the extraction of tree species, the number of trees, and the upscaling of timber volume and basal area based on the spectral properties. The outcomes of this study illustrate quite clearly the potential of employing UAV imagery for tree height extraction (R2 of 0.9) as well as for species and crown diameter determination. However, in a few instances, the visual interpretation of the aerial photographs were determined to be superior to the computer-aided automatic extraction of forest attributes. In addition, imagery from various satellite sensors (e.g. Sentinel-2, RapidEye, WorldView-2) proved to be excellently suited for the delineation of burned areas and the assessment of tree vigour. Furthermore, recently developed sophisticated classifying approaches such as Support Vector Machines and Random Forest appear to be tailored for tree species discrimination (Overall Accuracy of 89%). Object-based classification approaches convey the impression to be highly suitable for very high-resolution imagery, however, at medium scale, pixel-based classifiers outperformed the former. It is also suggested that high radiometric resolution bears the potential to easily compensate for the lack of spatial detectability in the imagery. Quite surprising was the occurrence of dark taiga species in the riparian areas being beyond their natural habitat range. The presented results matrix and the interpretation key have been devised as a decision tool and/or a vademecum for practitioners. In consideration of future projects and to facilitate the improvement of the forest inventory database, the establishment of permanent sampling plots in the Mongolian taigas is strongly advised.2021-06-0

Earth resources: A continuing bibliography with indexes (issue 61)

This bibliography lists 606 reports, articles, and other documents introduced into the NASA scientific and technical information system between January 1 and March 31, 1989. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, oceanography and marine resources, hydrology and water management, data processing and distribution systems, and instrumentation and sensors, and economic analysis

Radar Imaging in Challenging Scenarios from Smart and Flexible Platforms

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Monitoring marine plastic pollution using radar: from source to sea

Marine plastic pollution poses a significant threat to ocean ecosystems worldwide, necessitating effective monitoring and management strategies. The use of remote sensing plays a vital role in providing large-scale, frequently-timed data for monitoring this issue. A multi-modal system has been deemed the most appropriate for tackling the monitoring of marine debris and pollution. Synthetic Aperture Radar (SAR) can provide a wealth of data by taking advantage of the systems ability to acquire in near all-weather conditions, night and daytime. However, research in radar and SARs capability in monitoring marine plastic pollution is lacking. This thesis aims to provide an insight into these capabilities. This is through a series of experiments and investigations into the responses of SAR / Radar to marine plastic litter. Chapter two presents a real-world scenario of plastic accumulation within a river environment. The use of SAR imagery is employed to identify plastic accumulations in two separate study locations. A hypothesis of SAR backscattering interactions with plastic debris is presented. A suite of detectors are subsequently implemented to understand how to best utilise the SAR signal for marine debris detection in these test cases, with the best detector used to create heatmaps of debris accumulation within our test sites. The following chapter provides the results of two rigorous measurement campaigns, where C- and X-band radar data are exploited in a lab experiment. Backscatter and statistical analysis are undertaken across multiple tests involving differing plastic items, concentrations, and wave conditions. From this, interactions between plastic size, shape, and wave conditions are explored. A new interaction for backscatter interactions with plastic debris is also presented. The final data chapter investigates the potential use of a proxy for plastic pollution. Two measurement campaigns are conducted which utilise plastisphere based surfactants, and their interactions for wave dampening, to understand if this is detectable in radar data. For the first time, detailed analysis of backscatter values from differing plastic items and concentrations are presented, as well as the utilisation of real-world test cases. The results obtained in this thesis provide novel insights and additions to recent literature that contributes to our understanding of the capabilities of radar for marine plastic pollution monitoring, as well as new information that can be used in the planning for future missions and studies on the remote sensing of marine plastic pollution

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

High-resolution deformation measurement using "Persistent Scatterer Interferometry"

Persistent Scatterer Interferometry (PSI) is a group of advanced differential interferometric SAR techniques that are used to measure and monitor terrain deformation. Different PSI techniques have been proposed in the last two decades. In this thesis, the two PSI chains implemented and used at the Geomatics division of CTTC are described: the local area PSI and the PSIG chains. The first part of the thesis is devoted to the local area PSI chain, used to analyse the deformations over small areas. The chain includes a linear deformation model to directly deal with interferometric wrapped phases. Moreover, it does not directly involve the estimation of the APS, thus simplifying the procedure and its computational cost. The chain has been tested using different types of SAR data. The availability of high resolution X-band SAR data has led to an improvement of the PSI results with respect to C-band data. The higher image resolution and phase quality implies an increase of the PS density, an improvement in the estimation precision of the residual topographic error and a higher sensibility to very small deformations, including the displacements caused by thermal dilation. An extension of the classical PSI linear deformation model has been proposed, to account for the thermal dilation effects. This allows obtaining a new PSI outcome, the thermal dilation parameter, which opens new interesting applications since it provides information on the physical properties of single objects, i.e. the coefficient of thermal expansion, and the static structures of the same objects. The second part of the thesis describes the PSIG chain, whose aim was to extend the interferometric processing to wider areas. The ability to cover wide areas is essential to obtain a unique and consistent deformation monitoring for the available SAR image full scenes, i.e. typically 30 by 50 km for TerraSAR-X, 40 by 40 km for CosmoSkyMed and 100 by 100 km for ASAR ENVISAT and ERS. This is particularly important for the forthcoming C-band Sentinel SAR data that will cover 250 by 250 km with a single image scene. The key steps of the PSIG procedure include a new selection of candidate PSs based on a phase similitude criteria and a 2+1D phase unwrapping algorithm. The procedure offers different tools to control the quality of the processing steps. It has been successfully tested over urban, rural and vegetated areas using X-band PSI data. The performance of the PSIG chain is illustrated and discussed in detail, analysing the procedure step by step.Persistent Scatterer Interferometry (PSI) és un grup de tècniques avançades d'interferometria diferencial SAR que s'utilitzen per mesurar i monitoritzar deformacions del terreny. Durant les últimes dues dècades s’han proposat diverses tècniques PSI. En aquesta tesi es descriuen les dues cadenes PSI implementades i utilitzades en la divisió de Geomàtica del CTTC: la cadena PSI d’àrea local i la cadena PSIG. La primera part de la tesi està dedicada a la cadena PSI d’àrea local, que s'utilitza per analitzar deformacions en zones d’extensió limitada. La cadena inclou un model de deformació lineal per tractar directament amb les fases interferomètriques wrapped. En canvi, no estima directament la component atmosfèrica, cosa que simplifica el procediment i el seu cost computacional. La cadena s’ha provat sobre diferents tipus de dades SAR. La disponibilitat de dades SAR d’alta resolució en banda X ha donat lloc a una millora dels resultats del PSI respecte a les dades en banda C. La resolució més gran de la imatge i la qualitat de la fase impliquen un augment de la densitat de PS, una millora en la precisió de l'estimació de l'error topogràfic residual i una sensibilitat més alta a deformacions subtils, incloent-hi els desplaçaments causats per la dilatació tèrmica. Per tenir en compte els efectes de la dilatació tèrmica, s'ha proposat una extensió del model PSI clàssic que ens permet obtenir un nou producte PSI: el paràmetre de dilatació tèrmica. Aquest paràmetre obre noves aplicacions interessants: proporciona informació relacionada amb les propietats físiques dels objectes mesurats –com el coeficient d'expansió tèrmica– i amb la seva pròpia estructura estàtica. La segona part de la tesi descriu la cadena PSIG, l'objectiu de la qual és estendre el processament interferomètric a àrees més extenses. La capacitat de cobrir àrees grans és fonamental per obtenir un únic mapa global de deformacions que sigui consistent i cobreixi l’extensió sencera de les imatges SAR disponibles, de 30 km per 50 km per TerraSAR-X, de 40 km per 40 km per CosmoSkyMed i de 100 km per 100 km per ASAR-ENVISAT i ERS. Això és particularment important tenint en compte la propera disponibilitat de les dades del satèl•lit Sentinel, que opera en banda C i cobrirà 250 km per 250 km amb una sola imatge. Els passos clau del procediment PSIG són una nova selecció de PS candidats en base a un criteri de similitud de fase i un algoritme de 2+1D phase unwrapping. El procediment ofereix diferents eines per controlar la qualitat dels diferents passos del processament. La cadena PSIG s’ha utilitzat amb èxit en àrees urbanes, rurals i amb vegetació utilitzant dades PSI en banda X. El funcionament de la cadena PSIG s'il•lustra i es descriu en detall, analitzant el procediment pas a pas