HISTORICAL BACKGROUND: AERIAL ARCHIVAL PHOTOGRAPHY IN POLAND AND EUROPE – A REVIEW ARTICLE

Impervious surface is a basic parameter used in hydrologic studies, particularly in urban watersheds. The availability of a new generation satellite image collection systems providing data with a high spatial resolution, in digital format, comparable to what is obtained from aerial photographs, creates a new source of data available to civil engineers involved in hydrologic or stormwater projects. Aerial photography provides a spatial resolution (less than 0.5 m for a large scale photo) that is higher than what can be obtained from civilian satellites (i.e., 0.6 to 4.0 m range), however, there are a number satellite image characteristics that make them a preferred source of impervious data for some applications. The major advantages of satellite images over aerial photographs are the capability (1) to provide up-to-date information for an area of interest, (2) to allow for digital data processing and integration with other digital datasets and (3) to produce color displays. This paper presents results of the use of medium and high spatial resolution satellite image data to estimate the impervious surface areas in Springfield, Columbia and Ashland, Missouri and Overland Park, Kansas urban watersheds. The results provide an important overview of quality of the results that can be expected for urban watersheds characterized by different development patterns, building densities and varying amounts of vegetation (trees and grass). The results provide guidance on the use of satellite image data to produce impervious surface estimates required by most hydrologic studies. The results of this study are valuable to the profession because existing regulatory programs such as the Federal Emergency Management Agency’s (FEMA) National Flood Insurance Program and the Environmental Protection Agency’s (EPA’s) Phase I and Phase II storm water regulations require proactive actions to measure the impact of land use/land cover and storm water runoff on water quantity and quality. The results presented in this paper are a contribution to the state-of-the-art methodology applicable to urban hydrology projects by formulating guidelines to determine what satellite data sources are appropriate for a given application or modeling effort.

Poland as well as other countries keep extensive collections of 20th and 21st-century aerial photos, which are underexploited compared to such other archival materials as satellite imagery. Meanwhile, they offer significant research potential in various areas, including urban development, land use changes, and long-term environmental monitoring. Archival photographs are detailed, often obtained every five to ten years, and feature high resolution, from 20 cm to 1 m. Their overlap can facilitate creating precise digital models that illustrate topography and land cover, which are essential variables in many scientific contexts. However, rapidly transforming these photographs into geographically accurate measurements of the Earth’s surface poses challenges. This article explores the obstacles in automating the processing of historical photographs and presents the main scientific research directions associated with these images. Recent advancements in enhancing work˚ows, including the development of modern digital photogrammetry tools, algorithms, and machine learning techniques are also discussed. These developments are crucial for unlocking the full potential of aerial photographs, making them easier accessible and valuable for a broader range of scientific fields. These underutilized photographs are increasingly recognized as vital in various research domains due to technological advancements. Integrating new methods with these historical images offers unprecedented opportunities for scientific discovery and historical understanding, bridging the past with the future through innovative research techniques.

This work introduces a new webGIS tool to estimate the Crop Water Requirement (CWR), using time series of satellite images and meteorological data, at high spatial resolution and a global scale. This CWRweb tool provides users with information on the temporal evolution of the CWR, as a first approach of the crop evapotranspiration, as well as other parameters of interest. This process is implemented via web and requires no proficiency in remote sensing.The implemented calculation of the evapotranspiration under standard conditions (ETc) stands on the robust FAO-56 methodology, based on the relationship between the Crop Coefficient and the Reference Evapotranspiration (Kc-ETo). The CWRweb tool adopts the single crop coefficient approach, combining the effects of both, crop transpiration and soil evaporation into a single coefficient (Kc). These Kc values derive from the NDVI time series of Sentinel-2 multispectral satellite images, for a broad range of crops (horticulture, woody crops, and other crops) and natural vegetation, assuming a general component for the soil evaporation.The CWRweb tool benefits from the potential of the Sentinel-2A & B satellite constellation to provide users with free time series of images with a spatial resolution of 10m × 10m and a revisit frequency of 2-3 days. The high frequency of Sentinel-2 imagery allows to obtain daily Kc values through interpolation of NDVI data from cloud-free images at high spatial resolution. Online access to massive databases of satellite images, such as those of the Copernicus Data Space Ecosystem program (https://dataspace.copernicus.eu/), together with recent advances on meteorological numerical models to provide global ETo layers at different gridding size, are boosting the operational use of the CWRweb tool.The CWRweb tool runs and graphically displays daily ETc, as well as NDVI, Kc, and ETo values used in its calculation, for a selected time interval. Results can be provided at both, field and pixel scales. An assessment of the CWRtool was conducted by comparison against the OpenET tool on a selection of crops-sites in California, USA. An average uncertainty of RMSE=0.9 mm·d-1, with a negligible bias, was obtained in a performance analysis using the OpenET ensemble outputs as a reference, using 15 different locations, and data for the period 2016-2024. These results are promising and reinforce the potential of the CWRweb tool for the operational estimation of global evapotranspiration at a high spatial resolution.

On the use of quadrupole mass spectrometric detection for flow modulated comprehensive two-dimensional gas chromatography

Abstract. Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets.

Urban planning using data fusion of different satellite and aerial photo images can be very useful. However, multisource data fusion requires geometric and radiometric processing, adapted to the nature and characteristics of the data. In this way the best information available from each image is preserved in the composite image. With the increased resolution of satellite and aerial photo images (5 m and less), the off-nadir viewing angle of the satellite sensor (greater than 20 degrees), and the multi-source data available (such as SPOT, RADARSAT, and IRS), a general and accurate photogrammetric method which can deal with different satellite images and an accurate photogrammetric method for aerial photos are needed. For satellite images, a rigorous method developed at the Canada Centre for Remote Sensing (CCRS), Natural Resources Canada, which takes into account the nature of the data can be used. For aerial photos, the method of space resection by collinearity can be used. This paper presents data fusion results using SPOT, RADARSAT, IRS satellite images and an aerial photo. The results are sharp and precise, which enables a better and easier interpretation for urban planning.

یکی از کاربردهای دورسنجی در زمین شناسی تهیه نقشه خطواره هاست که به عنوان یکی از مهم ترین موضوعات در مطالعات زمین شناسی مناطق مختلف در نظر گرفته می شود. هدف از این تحقیق، استخراج خطواره های (گسل) البرز مرکزی با استفاده از تصاویر ماهواره ای سنجنده ASTER به روش بصری و رقومی خودکار است. بعد از انجام تصحیحات هندسی تصاویر تکنیک های بارزسازی مکانی شامل فیلترگذاری، تجزیه مؤلفه های اصلی و نسبت گیری باندی بر روی تصاویر سنجنده ASTER اعمال شد. استخراج خطواره ها بر روی این تصاویر پردازش شده به دو روش تفسیر بصری و رقومی خودکار انجام شد. سپس خطواره های استخراج شده به دو روش بصری و رقومی خودکار با یکدیگر و با خطواره های (گسل) استخراج شده از نقشه زمین شناسی منطقه مقایسه گردید. نتایج نشان داد که میزان گسل های استخراج شده با روش تفسیر رقومی خودکار با میزان گسل های نقشه زمین شناسی همخوانی ندارد و روش تفسیر رقومی خودکار نتوانسته سبب استخراج و شناسایی خطواره های گسلی منطقه (گسل خزر و شمال البرز) شود. ولی میزان خطواره های استخراج شده به روش بصری با میزان گسل ها در البرز همخوانی بیشتری نشان می دهد. بنابراین روش رقومی خودکار به علت دقت پایینی که دارد، برای مطالعات زمین شناسی که بر مبنای مطالعات صحرایی دقت بالایی دارد، پیشنهاد نمی شود. در نتیجه، بهترین روش برای انجام مطالعات زمین شناسی و بررسی های ساختاری در مناطق وسیع و یا مناطقی که دسترسی به آنها مشکل است، استفاده از روش تفسیر بصری و رقومی سازی دستی برای استخراج خطواره هاست.

The search for invisible and clandestine burial places of victims of wars and totalitarian crimes is a very difficult task. Research and exhumation teams use various methods to help locate burial places. According to the experience of our team, one of the best sources of information about burials are archival and contemporary aerial photographs. During World War II, aerial photography became one of the most important tools for warfare reconnaissance. For this reason, huge numbers of aerial photographs are currently available, in which the victim’s burial sites have been accidentally recorded. Aerial photographs are often helpful not in the direct identification of graves, but in the reconstruction of topographies of individual objects within a defined period of time, and thus, indirectly, in the location of graves. The aim of the study was to demonstrate the usefulness of historical aerial, satellite and drone photographs in the process of searching for the remains of victims of armed conflicts and totalitarian crimes. The paper presents the experience gained in the course of research projects carried out.

The continuing trend of urban growth and development creates the need for updated ground cover information for a wide range of applications. Some applications relate to models used in hydrologic, water quality control, and watershed management while other applications respond to specific needs of urban managers. The results presented here are the outcome of work carried out during the last two years for the cities of Springfield and Columbia, Missouri using high spatial resolution satellite data. The challenge that had to be met was to determine the usefulness and limitations of these data sets to enhance or effectively replace conventional data sources. Panchromatic air photos are the most common source of spatial information by urban managers. In some cases photos acquired by federal agencies are inexpensive information sources, however, in many cases municipalities have to contract new air photo coverage or use the new generation of satellite images to satisfy their needs. Airphotos have been replaced by satellite image data for large area (regional scale) mapping because of the multispectral characteristics, digital format and low cost per area of the latter. New satellite image data with spatial resolutions ranging from 5 to 1 meter provide a new data option for selected applications. The subject discussed in this paper is the application of this type of satellite image data for mapping impervious and bare ground surfaces, and for determining runoff curve numbers (CNs) required by hydrologic and environmental computer models. The area covered by impervious surfaces in urban regions does not remain constant in urban centers. Whenever a new development takes place, the total impervious surface increases with a direct impact on the amount of runoff into stormwater systems and urban watersheds. High spatial resolution images can provide mapping of impervious surfaces (parking lots, rooftops, roads, etc) with an estimated accuracy of approximately 80% that is satisfactory for planning and monitoring purposes. Mapping areas where the soil is exposed is another capability desired by urban managers. Exposed soil can indicate construction sites with implications for the control of building permits, changes in the municipal tax base and identification of potential sources of sediments runoff to water bodies, an environmental issue. The accuracy of construction site determinations in Columbia is more than 90%.

Integrating SPOT-5 time series, crop growth modeling and expert knowledge for monitoring agricultural practices — The case of sugarcane harvest on Reunion Island

Natural and man-made disasters pose a threat to human life, flora-fauna, and infrastructure. It is critical to detect the damage quickly and accurately for infrastructures right after the occurrence of any disaster. The detection and assessment of infrastructure damage help manage financial strategy as well. Recently, many researchers and agencies have made efforts to create high-resolution satellite imageries database related to pre and post-disaster events. The advanced remote sensing satellite imageries can reflect the surface of the earth accurately up to 30 cm spatial resolution on a daily basis. These high spatial resolutions (HSR) imageries can help access any natural hazard's damage by comparing the pre- and post-disaster data. These remote sensing imageries have limitations, such as cloud occlusions. Building under a thick cloud cannot be recognised in optical images. The manual assessment of the severity of damage to buildings/infrastructure by comparing bi-temporal HSR imageries or airborne will be a tedious and subjective job. On the other hand, the emerging use of unmanned aired vehicles (UAV) can be used to assess the situation precisely. The high-resolution UAV imageries and the HSR satellite imageries can complement each other for critical infrastructure damage assessment. In this study, a novel approach is used to integrate UAV data into HSR satellite imageries for the building damage assessment using a convolution neural network (CNN) based deep learning model. The research work is divided into two fundamental sub-tasks: first is the building localisation in the pre-event images, and second is the damage classification by assigning a unique damage level label reflecting the degree of damage to each building instance on the post-disaster images. For the study, the HSR satellite imageries of 36 pairs of pre- and post natural hazard events is acquired for the year 2021-22, similarly available UAV based data for these events is also collected from the open data source. The data is then pre-processed, and the building damage is assessed using a deep object-based semantic change detection framework (ChangeOS). The mentioned model was trained on the xview2 building damage assessment datasets comprised of ~20,000 images with ~730,000 building polygons of pre and post disaster events over the globe from 2011-2018. The experimental setup in this study includes training on the global dataset and testing on the regional-scale building damage assessment using HSR satellite imageries and local-scale using UAV imageries. The result obtained from the bi-temporal assessment of HSR images for the Indonesia Earthquake 2022 has shown an F1 score of ~67%, while the Uttarakhand flooding event 2021 has reported an F1 score of ~64%. The HSR imageries from the UAV Haiti earthquake event in 2011 have also shown less but promising F1 scores of ~54%. It is inferred that merging HSR imageries from satellite and UAV for building damage assessment using the ChangeOS framework represents a robust tool to further promote future research in infrastructure maintenance strategy and policy management in disaster response.

On 20 April 2013, an earthquake with Mw 6.6 (Ms 7.0) hit Lushan County, Sichuan Province of China (30.3°N, 103°E), only 85km southwest of the 2008 Wenchuan Mw 7.9 event. The Lushan shock triggered a large number of landslides with various types, including highly disrupted shallow slides and rock falls, deep-seated landslides and large-scale rock avalanches. Post-earthquake high resolution aerial photographs and satellite images, as well as a series of pre-earthquake high resolution satellite images were collected to construct a detailed, accurate, objective, and complete coseismic landslide database/inventory, which was validated by field investigation in selected areas. Based on the aerial photographs (0.2 or 0.6m resolution) that partly cover the affected area and pre-earthquake satellite images, 14,580 coseismic landslides were recognized and mapped. In those areas without post-earthquake aerial photographs coverage, we mapped 7,948 more coseismic landslides based on post-earthquake satellite images (Rapideye images with 5m resolution and ZY-3 images including panchromatic images with 2.1m resolution and multi-spectral images with 5.8m resolution) and pre-earthquake satellite images. The 22,528 coseismic landslides, with a horizontal projection area of 18.88km2 and an estimated total volume of 41.56×106m3, were distributed in a nearly elliptic area of about 5,400km2. Correlations between coseismic landslide abundances and topographic, geologic, and seismic factors were analyzed on 15,546 landslides of area ≥100m2. Scatter plots between regional slope angle and landslide abundances in generalized homogeneous lithology combinations show no obvious relationship is present between them, which challenges the conclusions of previous studies. Combining the Lushan earthquake-triggered landslides with other events on reverse and/or thrust faults such as the 1999 Chi-Chi and 2008 Wenchuan shocks, we found that coseismic landslides tend to occur in areas enveloped by two or more imbricated reverse and/or thrust faults, rather than only along the seismogenic fault. The Lushan event shows a higher ability of triggering landslides compared with other earthquakes of similar magnitudes, which is likely due to the steep and rugged topography and fractured and densely jointed lithology resulted from long-term tectonic activity. The blind reverse seismogenic fault is likely also another significant factor. Based on detailed coseismic landslide inventory maps and databases of several recent major earthquakes such as the 2008 Wenchuan, 2010 Haiti, 2010 Yushu, and 2013 Lushan temblors, we suggest that the empirical correlations between earthquake magnitude and coseismic landslides need to be updated.

Image moment extraction based aerial photo selection for UAV high-precision geolocation without GPS

The data archive contains the aerial photographs and channel delineations used in our analysis. The images have been geo-referenced to the 1995 digital orthophoto quarter quadrangles as described by Miller and Friedman (2009). The separate images for each year can be viewed as a composite along with that year s channel delineation using a geographic information system (GIS). The 2003 IKONOS satellite imagery is proprietary and, therefore, cannot be served here. The channel delineations for all photo years (including 2003) and the delineation of the outer flood-plain boundary are stored as shapefiles. These shapefiles can be manipulated using GIS applications to reproduce the spatial analyses reported in Miller and Friedman (2009). This metadata record is associated with the project landing page that describes the entire data package. The seven child items on the landing page are each associated with a different repeat photography year. Each child item provides all images taken in that year and a SHP file that delineates channel location. Each year's photography consists of 4-8 scanned and referenced aerial photographs or digital satellite imagery in a geoTIFF format. SHP files from any year can be overlaid on top of the images to visualize change in channel location. TIFF images and associated SHP files are included as attachments or external sources and can be downloaded directly from the ScienceBase page. Reference: Miller, J.R., and J.M. Friedman. 2009. Influence of flow variability on flood-plain formation and destruction, Little Missouri River, North Dakota. Geological Society of America Bulletin 121:752-759.

As a result of collaboration between the Coal Authority, NPA Satellite Mapping and the British Geological Survey, measurements of surface deformation have been analysed across a number of current and former UK mining areas. The work included a wide-area ground stability dataset produced using interferometric synthetic aperture radar (InSAR), with associated geological interpretation, to help the Coal Authority manage these areas. Results show an unexpected amount of surface deformation across wide areas of the country associated with former mining areas. In particular, many areas are experiencing wide-scale uplift at rates of millimetres per year, an impact that has not been well studied. This uplift is strongly correlated with rising groundwater due to the cessation of pumping following mine closure, and it extends across a wider area than affected by the previous surface deformation observed during active mining. It appears that defined ‘mining blocks’, often delineated by faults, and the order in which groundwater is thought to flood each block, have a control on the temporal and spatial pattern of surface uplift. InSAR has proved to be the most cost-effective way to map the wide-area surface deformation observed in these areas, with spatial coverage and measurement density exceeding those possible with conventional ground survey techniques. With remote, worldwide coverage, InSAR is also applicable in most environments. Forthcoming InSAR data sources will improve consistency and frequency of data acquisition, which will further expand capabilities and improve overall results. In key areas (e.g., critical infrastructure) future InSAR monitoring could be optimised, for example, by placement of artificial reflectors to increase measurement density. There is also potential for integration of data from boreholes, GPS and other ground−based survey methods to give a holistic multi-scale picture of groundwater and resulting ground deformation. The correlation between surface deformation and rising groundwater has allowed The Coal Authority to optimise its management of groundwater in a cost-effective manner across wide areas. The dataset provides a context within which a strategy for mitigating the effects of rising groundwater water can be developed. This provides cost and resource savings, for example by optimising placement of boreholes, and informs decisions on requirements for monitoring and/or modelling of groundwater post-closure. Investigation of the relationship between the observed ground deformation, groundwater level rise and the local geology also raises the possibility of prediction for future mine closures, feeding into informed decisions on the cessation of pumping and ongoing groundwater management.