Integrating Remote Sensing and Geographic Information Systems

Remote sensing and geographic information systems (GIS) comprise the two major components of geographic information science (GISci), an overarching field of endeavor that also encompasses global positioning systems (GPS) technology, geodesy and traditional cartography (Goodchild 1992, Estes and Star 1993, Hepner et al. 2005). Although remote sensing and GIS developed quasi-independently, the synergism between them has become increasingly apparent (Aronoff 2005). Today, GIS software almost always includes tools for display and analysis of images, and image processing software commonly contains options for analyzing ‘ancillary’ geospatial data (Faust 1998). The significant progress made in ‘integration’ of remote sensing and GIS has been well-summarized in several reviews (Ehlers 1990, Mace 1991, Hinton 1996, Wilkinson 1996). Nevertheless, advances are so rapid that periodic reassessment of the state-of-the-art is clearly warranted

Advances in remote sensing applications for urban sustainability

Abstract: It is essential to monitor urban evolution at spatial and temporal scales to improve our understanding of the changes in cities and their impact on natural resources and environmental systems. Various aspects of remote sensing are routinely used to detect and map features and changes on land and sea surfaces, and in the atmosphere that affect urban sustainability. We provide a critical and comprehensive review of the characteristics of remote sensing systems, and in particular the trade-offs between various system parameters, as well as their use in two key research areas: (a) issues resulting from the expansion of urban environments, and (b) sustainable urban development. The analysis identifies three key trends in the existing literature: (a) the integration of heterogeneous remote sensing data, primarily for investigating or modelling urban environments as a complex system, (b) the development of new algorithms for effective extraction of urban features, and (c) the improvement in the accuracy of traditional spectral-based classification algorithms for addressing the spectral heterogeneity within urban areas. Growing interests in renewable energy have also resulted in the increased use of remote sensing—for planning, operation, and maintenance of energy infrastructures, in particular the ones with spatial variability, such as solar, wind, and geothermal energy. The proliferation of sustainability thinking in all facets of urban development and management also acts as a catalyst for the increased use of, and advances in, remote sensing for urban applications

Monitoring spatial sustainable development: Semi-automated analysis of satellite and aerial images for energy transition and sustainability indicators

Solar panels are installed by a large and growing number of households due to the convenience of having cheap and renewable energy to power house appliances. In contrast to other energy sources solar installations are distributed very decentralized and spread over hundred-thousands of locations. On a global level more than 25% of solar photovoltaic (PV) installations were decentralized. The effect of the quick energy transition from a carbon based economy to a green economy is though still very difficult to quantify. As a matter of fact the quick adoption of solar panels by households is difficult to track, with local registries that miss a large number of the newly built solar panels. This makes the task of assessing the impact of renewable energies an impossible task. Although models of the output of a region exist, they are often black box estimations. This project's aim is twofold: First automate the process to extract the location of solar panels from aerial or satellite images and second, produce a map of solar panels along with statistics on the number of solar panels. Further, this project takes place in a wider framework which investigates how official statistics can benefit from new digital data sources. At project completion, a method for detecting solar panels from aerial images via machine learning will be developed and the methodology initially developed for BE, DE and NL will be standardized for application to other EU countries. In practice, machine learning techniques are used to identify solar panels in satellite and aerial images for the province of Limburg (NL), Flanders (BE) and North Rhine-Westphalia (DE).Comment: This document provides the reader with an overview of the various datasets which will be used throughout the project. The collection of satellite and aerial images as well as auxiliary information such as the location of buildings and roofs which is required to train, test and validate the machine learning algorithm that is being develope

Merging digital surface models sourced from multi-satellite imagery and their consequent application in automating 3D building modelling

Recently, especially within the last two decades, the demand for DSMs (Digital Surface Models) and 3D city models has increased dramatically. This has arisen due to the emergence of new applications beyond construction or analysis and consequently to a focus on accuracy and the cost. This thesis addresses two linked subjects: first improving the quality of the DSM by merging different source DSMs using a Bayesian approach; and second, extracting building footprints using approaches, including Bayesian approaches, and producing 3D models. Regarding the first topic, a probabilistic model has been generated based on the Bayesian approach in order to merge different source DSMs from different sensors. The Bayesian approach is specified to be ideal in the case when the data is limited and this can consequently be compensated by introducing the a priori. The implemented prior is based on the hypothesis that the building roof outlines are specified to be smooth, for that reason local entropy has been implemented in order to infer the a priori data. In addition to the a priori estimation, the quality of the DSMs is obtained by using field checkpoints from differential GNSS. The validation results have shown that the model was successfully able to improve the quality of the DSMs and improving some characteristics such as the roof surfaces, which consequently led to better representations. In addition to that, the developed model has been compared with the Maximum Likelihood model which showed similar quantitative statistical results and better qualitative results. Perhaps it is worth mentioning that, although the DSMs used in the merging have been produced using satellite images, the model can be applied on any type of DSM. The second topic is building footprint extraction based on using satellite imagery. An efficient flow-line for automatic building footprint extraction and 3D model construction, from both stereo panchromatic and multispectral satellite imagery was developed. This flow-line has been applied in an area of different building types, with both hipped and sloped roofs. The flow line consisted of multi stages. First, data preparation, digital orthoimagery and DSMs are created from WorldView-1. Pleiades imagery is used to create a vegetation mask. The orthoimagery then undergoes binary classification into ‘foreground’ (including buildings, shadows, open-water, roads and trees) and ‘background’ (including grass, bare soil, and clay). From the foreground class, shadows and open water are removed after creating a shadow mask by thresholding the same orthoimagery. Likewise roads have been removed, for the time being, after interactively creating a mask using the orthoimagery. NDVI processing of the Pleiades imagery has been used to create a mask for removing the trees. An ‘edge map’ is produced using Canny edge detection to define the exact building boundary outlines, from enhanced orthoimagery. A normalised digital surface model (nDSM) is produced from the original DSM using smoothing and subtracting techniques. Second, start Building Detection and Extraction. Buildings can be detected, in part, in the nDSM as isolated relatively elevated ‘blobs’. These nDSM ‘blobs’ are uniquely labelled to identify rudimentary buildings. Each ‘blob’ is paired with its corresponding ‘foreground’ area from the orthoimagery. Each ‘foreground’ area is used as an initial building boundary, which is then vectorised and simplified. Some unnecessary details in the ‘edge map’, particularly on the roofs of the buildings can be removed using mathematical morphology. Some building edges are not detected in the ‘edge map’ due to low contrast in some parts of the orthoimagery. The ‘edge map’ is subsequently further improved also using mathematical morphology, leading to the ‘modified edge map’. Finally, A Bayesian approach is used to find the most probable coordinates of the building footprints, based on the ‘modified edge map’. The proposal that is made for the footprint a priori data is based on the creating a PDF which assumes that the probable footprint angle at the corner is 90o and along the edge is 180o, with a less probable value given to the other angles such as 45o and 135o. The 3D model is constructed by extracting the elevation of the buildings from the DSM and combining it with the regularized building boundary. Validation, both quantitatively and qualitatively has shown that the developed process and associated algorithms have successfully been able to extract building footprints and create 3D models

CHARACTERIZING FOREST STANDS USING UNMANNED AERIAL SYSTEMS (UAS) DIGITAL PHOTOGRAMMETRY: ADVANCEMENTS AND CHALLENGES IN MONITORING LOCAL SCALE FOREST COMPOSITION, STRUCTURE, AND HEALTH

Present-day forests provide a wide variety of ecosystem services to the communities that rely on them. At the same time, these environments face routine and substantial disturbances that direct the need for site-specific, timely, and accurate monitoring/management (i.e., precision forestry). Unmanned Aerial Systems (UAS or UAV) and their associated technologies offer a promising tool for conducting such precision forestry. Now, even with only natural color, uncalibrated, UAS imagery, software workflows involving Structure from Motion (SfM) (i.e., digital photogrammetry) modelling and segmentation can be used to characterize the features of individual trees or forest communities. In this research, we tested the effectiveness of UAS-SfM for mapping local scale forest composition, structure, and health. Our first study showed that digital (automated) methods for classifying forest composition that utilized UAS imagery produced a higher overall accuracy than those involving other high-spatial-resolution imagery (7.44% - 16.04%). The second study demonstrated that natural color sensors could provide a highly efficient estimate of individual tree diameter at breast height (dbh) (± 13.15 cm) as well as forest stand basal area, tree density, and stand density. In the final study, we join a growing number of researchers examining precision applications in forest health monitoring. Here, we demonstrate that UAS, equipped with both natural color and multispectral sensors, are more capable of distinguishing forest health classes than freely available high-resolution airborne imagery. For five health classes, these UAS data produced a 14.93% higher overall accuracy in comparison to the airborne imagery. Together, these three chapters present a wholistic approach to enhancing and enriching precision forest management, which remains a critical requirement for effectively managing diverse forested landscapes

Review of remote sensing for land administration: Origins, debates, and selected cases

Conventionally, land administration—incorporating cadastres and land registration—uses ground-based survey methods. This approach can be traced over millennia. The application of photogrammetry and remote sensing is understood to be far more contemporary, only commencing deeper into the 20th century. This paper seeks to counter this view, contending that these methods are far from recent additions to land administration: successful application dates back much earlier, often complementing ground-based methods. Using now more accessible historical works, made available through archive digitisation, this paper presents an enriched and more complete synthesis of the developments of photogrammetric methods and remote sensing applied to the domain of land administration. Developments from early phototopography and aerial surveys, through to analytical photogrammetric methods, the emergence of satellite remote sensing, digital cameras, and latterly lidar surveys, UAVs, and feature extraction are covered. The synthesis illustrates how debates over the benefits of the technique are hardly new. Neither are well-meaning, although oft-flawed, comparative analyses on criteria relating to time, cost, coverage, and quality. Apart from providing this more holistic view and a timely reminder of previous work, this paper brings contemporary practical value in further demonstrating to land administration practitioners that remote sensing for data capture, and subsequent map production, are an entirely legitimate, if not essential, part of the domain. Contemporary arguments that the tools and approaches do not bring adequate accuracy for land administration purposes are easily countered by the weight of evidence. Indeed, these arguments may be considered to undermine the pragmatism inherent to the surveying discipline, traditionally an essential characteristic of the profession. That said, it is left to land administration practitioners to determine the relevance of these methods for any specific country context. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Remote Sensing for Land Administration 2.0

The reprint “Land Administration 2.0” is an extension of the previous reprint “Remote Sensing for Land Administration”, another Special Issue in Remote Sensing. This reprint unpacks the responsible use and integration of emerging remote sensing techniques into the domain of land administration, including land registration, cadastre, land use planning, land valuation, land taxation, and land development. The title was chosen as “Land Administration 2.0” in reference to both this Special Issue being the second volume on the topic “Land Administration” and the next-generation requirements of land administration including demands for 3D, indoor, underground, real-time, high-accuracy, lower-cost, and interoperable land data and information

Monitoring opencast mine restorations using Unmanned Aerial System (UAS) imagery

Altres ajuts: Joan-Cristian Padró is a recipient of the FI-DGR scholarship grant (2016B_00410). Xavier Pons is a recipient of the ICREA Academia Excellence in Research Grant (2016-2020).Open-pit mine is still an unavoidable activity but can become unsustainable without the restoration of degraded sites. Monitoring the restoration after extractive activities is a legal requirement for mine companies and public administrations in many countries, involving financial provisions for environmental liabilities. The objective of this contribution is to present a rigorous, low-cost and easy-to-use application of Unmanned Aerial Systems (UAS) for supporting opencast mining and restoration monitoring, complementing the inspections with very high (<10 cm) spatial resolution multispectral imagery, and improving any restoration documentation with detailed land cover maps. The potential of UAS as a tool to control restoration works is presented in a calcareous quarry that has undergone different post-mining restoration actions in the last 20 years, representing 4 reclaimed stages. We used a small (<2 kg) drone equipped with a multispectral sensor, along with field spectroradiometer measurements that were used to radiometrically correct the UAS sensor data. Imagery was processed with photogrammetric and Remote Sensing and Geographical Information Systems software, resulting in spectral information, vegetation and soil indices, structural information and land cover maps. Spectral data and land cover classification, which were validated through ground-truth plots, aided in the detection and quantification of mine waste dumping, bare soil and other land cover extension. Moreover, plant formations and vegetation development were evaluated, allowing a quantitative, but at the same time visual and intuitive comparison with the surrounding reference systems. The protocol resulting from this research constitutes a pipeline solution intended for the implementation by public administrations and privates companies for precisely evaluating restoration dynamics in an expedient manner at a very affordable budget. Furthermore, the proposed solution prevents subjective interpretations by providing objective data, which integrate new technologies at the service of scientists, environmental managers and decision makers

The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery

peer-reviewedIrish Journal of Agricultural and Food Research | Volume 58: Issue 1 The agricultural impact of the 2015–2016 floods in Ireland as mapped through Sentinel 1 satellite imagery R. O’Haraemail , S. Green and T. McCarthy DOI: https://doi.org/10.2478/ijafr-2019-0006 | Published online: 11 Oct 2019 PDF Abstract Article PDF References Recommendations Abstract The capability of Sentinel 1 C-band (5 cm wavelength) synthetic aperture radio detection and ranging (RADAR) (abbreviated as SAR) for flood mapping is demonstrated, and this approach is used to map the extent of the extensive floods that occurred throughout the Republic of Ireland in the winter of 2015–2016. Thirty-three Sentinel 1 images were used to map the area and duration of floods over a 6-mo period from November 2015 to April 2016. Flood maps for 11 separate dates charted the development and persistence of floods nationally. The maximum flood extent during this period was estimated to be ~24,356 ha. The depth of rainfall influenced the magnitude of flood in the preceding 5 d and over more extended periods to a lesser degree. Reduced photosynthetic activity on farms affected by flooding was observed in Landsat 8 vegetation index difference images compared to the previous spring. The accuracy of the flood map was assessed against reports of flooding from affected farms, as well as other satellite-derived maps from Copernicus Emergency Management Service and Sentinel 2. Monte Carlo simulated elevation data (20 m resolution, 2.5 m root mean square error [RMSE]) were used to estimate the flood’s depth and volume. Although the modelled flood height showed a strong correlation with the measured river heights, differences of several metres were observed. Future mapping strategies are discussed, which include high–temporal-resolution soil moisture data, as part of an integrated multisensor approach to flood response over a range of spatial scales