Aerial photography: A diversified tool

Bowden, L. W.; Brooner, William G.

doi:10.1016/0016-7185(70)90026-6

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…Remote sensing has a long history in the study of ecosystems. The first studies using remote sensing were based on airborne (infrared) imagery that allowed for a birds-eye view of ecosystems (Estes 1966;Bowden and Brooner 1970), as well as for the stereographic analysis of vegetation properties (Spencer 1979). Repeating aerial image analysis over time revealed novel insights into how ecosystems change (Mast and others 1997), insights that were impossible to generate with field data alone.…”

Section: A Brief History Of Remote Sensing For Studying Ecosystem Dyn...mentioning

confidence: 99%

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

Senf

2022

Ecosystems

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Remote sensing techniques are increasingly used for studying ecosystem dynamics, delivering spatially explicit information on the properties of Earth over large spatial and multi-decadal temporal extents. Yet, there is still a gap between the more technology-driven development of novel remote sensing techniques and their applications for studying ecosystem dynamics. Here, I review the existing literature to explore how addressing these gaps might enable recent methods to overcome longstanding challenges in ecological research. First, I trace the emergence of remote sensing as a major tool for understanding ecosystem dynamics. Second, I examine recent developments in the field of remote sensing that are of particular importance for studying ecosystem dynamics. Third, I consider opportunities and challenges for emerging open data and software policies and suggest that remote sensing is at its most powerful when it is theoretically motivated and rigorously ground-truthed. I close with an outlook on four exciting new research frontiers that will define remote sensing ecology in the upcoming decade.

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Section: A Brief History Of Remote Sensing For Studying Ecosystem Dyn...mentioning

confidence: 99%

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

Senf

2022

Ecosystems

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“…The earliest urban application of remote sensing was delineation of the urban extent using aerial photography and using this information to monitor urban growth [5]. For global environmental change applications, urban extent monitoring measures where new urban areas are located, describes what type of urbanization has occurred, and leads to insights into the global impacts of this type of land transformation.…”

Section: Urban Extentmentioning

confidence: 99%

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

et al. 2014

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This paper reviews how remotely sensed data have been used to understand the impact of urbanization on global environmental change. We describe how these studies can support the policy and science communities' increasing need for detailed and up-to-date information on the multiple dimensions of cities, including their social, biological, physical, and infrastructural characteristics. Because the interactions between urban and surrounding areas are complex, a synoptic and spatial view offered from remote sensing is integral to measuring, modeling, and understanding these relationships. Here we focus on OPEN ACCESSRemote Sens. 2014, 6 3880 three themes in urban remote sensing science: mapping, indices, and modeling. For mapping we describe the data sources, methods, and limitations of mapping urban boundaries, land use and land cover, population, temperature, and air quality. Second, we described how spectral information is manipulated to create comparative biophysical, social, and spatial indices of the urban environment. Finally, we focus how the mapped information and indices are used as inputs or parameters in models that measure changes in climate, hydrology, land use, and economics.

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“…H ISTORICAL aerial photographs provide crucial data for efficient long-term environmental monitoring and change detection [1]- [4]. They offer unique background information at a very high spatial resolution [2], [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning for Automatic Colorization of Legacy Grayscale Aerial Photographs

Herrault

Skupinski

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Legacy grayscale aerial photographs represent one of the main available sources for studying the past state of the environment and its relationship to the present. However, these photographs lack spectral information thereby hindering their use in current remote sensing approaches that rely on spectral data for characterizing surfaces. This article proposes a conditional generative adversarial network, a deep learning model, to enrich legacy photographs by predicting color channels for an input grayscale image. The technique was used to colorize two orthophotographs (taken in 1956 and 1978) covering the entire Eurométropole de Strasbourg. To assess the model's performances, two strategies were proposed: first, colorized photographs were evaluated with metrics such as peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM); second, random forest classifications were performed to extract land cover classes from grayscale and colorized photographs, respectively. The results revealed strong performances, with PSNR = 25.56 ± 2.20 and SSIM = 0.93 ± 0.06 indicating that the model successfully learned the mapping between grayscale and color photographs over a large territory. Moreover, land cover classifications performed on colorized data showed significant improvements over grayscale photographs, respectively, +6% and +17% for 1956 and 1978. Finally, the plausibility of outputs images was evaluated visually. We conclude that deep learning models are powerful tools for improving radiometric properties of old aerial grayscale photographs and land cover mapping. We also argue that the proposed approach could serve as a basis for further developments aiming to promote the use of aerial photographs archives for landscapes reconstruction.

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Aerial photography: A diversified tool

Cited by 6 publications

References 10 publications

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

Seeing the System from Above: The Use and Potential of Remote Sensing for Studying Ecosystem Dynamics

Supporting Global Environmental Change Research: A Review of Trends and Knowledge Gaps in Urban Remote Sensing

Deep Learning for Automatic Colorization of Legacy Grayscale Aerial Photographs

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