Abstract
Abstract. Landscape reconstruction is crucial to measure the effects of climate change or past land use on current biodiversity. In particular, retracing past phenological changes can serve as a basis for explaining current patterns of plant communities and predict the future extinction of species. Old spatial data are currently used to reconstruct vegetation changes, both morphologically (with landscape metrics) and semantically (grasslands to crops for instance). However, poor radiometric properties (single panchromatic channel, illumination variation, etc.) do not offer the possibility to compute environmental variables (e.g. NDVI and color indices), which strongly limits long-term phenological reconstruction. In this study, we propose a workflow for reconstructing phenological trajectories of grasslands from 1958 to 2011, in the French central Vosges, from old aerial black and white (B&W) photographs. Noise and vignetting corruptions were first corrected in B&W photographs with non-local filtering algorithms. Panchromatic scans were then colorized with a Generative Adversarial Network (GAN). Based on the predicted channels, we finally computed digital greenness metrics (Green Chromatic Coordinate, Excess Greenness) to measure vegetation activity in grasslands. Our results demonstrated the feasibility of reconstructing long-term phenological trajectories from legacy photographs with insights at different levels: (1) the proposed correction methods provided radiometric improvements in old aerial missions; (2) the colorization process led to promising and plausible colorized historical products; (3) digital greenness metrics were useful for describing past vegetation activity.
Highlights
It is widely recognized that present day biodiversity may reflect past land use or past climate (Jansson, Davies, 2007) because of a possible delay in the response of certain species to habitat perturbations (Kuussaari et al, 2009)
In the scope of this work, we propose a deep colorization model based on a generative adversarial network (GAN)
In order to go as far back in time as possible, and to promote the use of national photo libraries, a time series was created with six air missions from 1958 to 2011
Summary
It is widely recognized that present day biodiversity may reflect past land use or past climate (Jansson, Davies, 2007) because of a possible delay in the response of certain species to habitat perturbations (Kuussaari et al, 2009). In order to assess time-lag between changes in a landscape, and changes in the associated populations, studies rely most of the time on old spatial data that help highlighting spatio-temporal trajectories over large extents and long time periods (Proenca et al, 2017) Among these spatial data, legacy aerial photographs are largely under-exploited while they offer unique opportunities to monitor landscapes at a very high spatial resolution (≤ 1m) from up to the early 1930s (Morgan et al, 2010, Morgan et al, 2017). Their use remains problematic for many reasons They feature heterogeneous specifications and quality (e.g. noise and vignetting), mainly due to the properties of the acquisition system and the scanning procedure. They lack inherent exploitable attributes, especially in the case of panchromatic pictures, making the development of automatic processing chains a complex task (Paine, Kiser, 2012, Aber et al, 2016)
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