Graffiti, by their very nature, are ephemeral, sometimes even vanishing before creators finish them. This transience is part of graffiti's allure yet signifies the continuous loss of this often disputed form of cultural heritage. To counteract this, graffiti documentation efforts have steadily increased over the past decade. One of the primary challenges in any documentation endeavour is identifying and recording new creations. Image-based change detection can greatly help in this process, effectuating more comprehensive documentation, less biased digital safeguarding and improved understanding of graffiti. This paper introduces a novel and largely automated image-based graffiti change detection method. The methodology uses an incremental structure-from-motion approach and synthetic cameras to generate co-registered graffiti images from different areas. These synthetic images are fed into a hybrid change detection pipeline combining a new pixel-based change detection method with a feature-based one. The approach was tested on a large and publicly available reference dataset captured along the Donaukanal (Eng. Danube Canal), one of Vienna's graffiti hotspots. With a precision of 87% and a recall of 77%, the results reveal that the proposed change detection workflow can indicate newly added graffiti in a monitored graffiti-scape, thus supporting a more comprehensive graffiti documentation. This paper presents an image-based graffiti change detection method, employing an incremental structure-from-motion approach and a novel hybrid change detection method. Tested on Vienna's Donaukanal graffiti hotspot, the approach achieved 87% precision and 77% recall, facilitating more comprehensive documentation of this dynamic form of cultural heritage.image

Colourful and ever-changing: Graffiti can be considered the urban chameleon skin. At the Donaukanal (Eng. Danube Channel), Vienna's central waterway and one of the largest and most active graffiti-scapes worldwide, this metaphor applies like hardly anywhere else. Every day a multitude of graffiti is destroyed by the creation of new works. Recently, efforts have been made to mitigate this constant loss of cultural heritage along the Donaukanal by systematically documenting the graffiti, mainly using photography and photogrammetry. However, keeping track of the newly added works is very time-consuming and often like finding needles in a haystack, considering the large extent and high volatility of the monitored area. Thus, an automated graffiti change detection would significantly reduce the effort and avoid overlooking graffiti. This contribution outlines the main challenges in image-based change detection for cultural heritage and proposes a hybrid graffiti change detection method. The investigated method exploits and combines an established pixel-based change detection algorithm, the Iteratively Multivariate Alteration Detection, with a novel descriptor-based method. The latter relies on image features, rather than pixels as analysis unit and can robustly filter false alarms from the high-performing but noise-prone pixel-based approach. Overall, the results indicate that the proposed method can largely automate image-based change detection of graffiti-scapes. It can uncover graffitirelated changes and robustly distinguish them from other image differences such as shadows but tends to overlook small-scale graffiti, indicating the need for further fine- tuning.

Abstract. Graffiti is a short-lived form of heritage balancing between tangible and intangible, offensive and pleasant. Graffiti makes people laugh, wonder, angry, think. These conflicting traits are all present along Vienna's Donaukanal (Eng. Danube Canal), a recreational hotspot – located in the city's heart – famous for its endless display of graffiti. The graffiti-focused heritage science project INDIGO aims to build the basis to systematically document, monitor, and analyse circa 13 km of Donaukanal graffiti in the next decade. The first part of this paper details INDIGO's goals and overarching methodological framework, simultaneously placing it into the broader landscape of graffiti research. The second part of the text concentrates on INDIGO's graffiti documentation activities. Given the project's aim to create a spatially, spectrally, and temporally accurate record of all possible mark-makings attached in (il)legal ways to the public urban surfaces of the Donaukanal, it seems appropriate to provide insights on the photographic plus image-based modelling activities that form the foundation of INDIGO's graffiti recording strategy. The text ends with some envisioned strategies to streamline image acquisition and process the anticipated hundreds of thousands of images.

Admired and despised, created and destroyed, legal and illegal: Contemporary graffiti are polarising, and not everybody agrees to label them as cultural heritage. However, if one is among the steadily increasing number of heritage professionals and academics that value these short-lived creations, their digital documentation can be considered a part of our legacy to future generations. To document the geometric and spectral properties of a graffito, digital photographs seem to be appropriate. This also holds true when documenting an entire graffiti-scape consisting of 1000s of individual creations. However, proper photo-based digital documentation of such an entire scene comes with logistical and technical challenges, certainly if the documentation is considered the basis for further analysis of the heritage assets. One main technical challenge relates to the photographs themselves. Conventional photographs suffer from multiple image distortions and usually lack a uniform scale, which hinders the derivation of dimensions and proportions. In addition, a single graffito photograph often does not reflect the meaning and setting intended by the graffitist, as the creation is frequently shown as an isolated entity without its surrounding environment. In other words, single photographs lack the spatio-temporal context, which is often of major importance in cultural heritage studies. Here, we present AUTOGRAF, an automated and freely-available orthorectification tool which converts conventional graffiti photos into high-resolution, distortion-free, and georeferenced graffiti orthophotomaps, a metric yet visual product. AUTOGRAF was developed in the framework of INDIGO, a graffiti-centred research project. Not only do these georeferenced photos support proper analysis, but they also set the basis for placing the graffiti in their native, albeit virtual, 3D environment. An experiment showed that 95 out of 100 tested graffiti photo sets were successfully orthorectified, highlighting the proposed methodology’s potential to improve and automate one part of contemporary graffiti’s digital preservation.

Microwave radiation emitted by the Earth surface is attenuated by vegetation. The magnitude of attenuation is commonly referred to as Vegetation Optical Depth (VOD) and depends on several factors, such as the water content of the vegetation and the wavelength of the sensor. It can be derived from space-borne microwave radiometers and has been explored for multiple applications including drought and biomass monitoring. Here, we present two novel applications based on long-term multi-satellite observations of VOD: 1) the Standardized VOD Index (SVODI) for monitoring vegetation condition and 2) modelling Gross Primary Production (VODCA2GPP). SVODI [1] is a standard normally distributed index indicating the condition of vegetation. Traditional index generation methods struggle to combine data from multiple data sources and often either exhibit a changing variance over time due to changing input sensors, or are biased. We solve these issues with a new probabilistic merging method, which in principle can be applied to other datasets as well. We show that SVODI, which is an indicator of vegetation structure, biomass and water content, supplements existing vegetation condition indexes based on optical data, which mostly represent the greenness of the vegetation. In the presentation, we also assess spatio-temporal patterns and trends of extreme SVODI values. VODCA2GPP [2] is a global GPP dataset derived from VOD using a sink-driven approach. It uses a machine learning framework using the VOD Climate Archive (VODCA), a long-term coherent dataset based on multiple sensors [3], in combination with FLUXNET in situ GPP observations to predict GPP at a global scale for a period of 30 years. VODCA2GPP exhibits similar spatial and temporal patterns as existing observation-based global GPP datasets from FLUXCOM and MODIS, but with a consistent positive bias. While VODCA2GPP reveals no significant global trend, it does show several regional trends, some of which corroborate trends found in the TRENDY model ensemble. Both datasets are available for free from open data repositories. [1] Moesinger, L., Zotta, R.M., van der Schalie, R., Scanlon, T., de Jeu, R.A.M., Dorigo, W. Monitoring Vegetation Condition using Microwave Remote Sensing: The Standardized Vegetation Optical Depth Index SVODI, in rev. [2] Wild, B., Teubner, I., Moesinger, L., Zotta, R.-M., Forkel, M., van der Schalie, R., Sitch, S., Dorigo, W. A. (2021). VODCA2GPP -- A new global, long-term (1988--2020) GPP dataset from microwave remote sensing. Earth System Science Data Discussions, 2021, 1–37. https://doi.org/10.5194/essd-2021-209 [3] Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA), Earth Syst. Sci. Data, 12, 177–196, https://doi.org/10.5194/essd-12-177-2020, 2020.

Long-term global monitoring of terrestrial gross primary production (GPP) is crucial for assessing ecosystem responses to global climate change. In recent decades, great advances have been made in estimating GPP and many global GPP datasets have been published. These datasets are based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. Although these approaches are well established within the scientific community, datasets nevertheless differ significantly. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of vegetation optical depth (VOD) to estimate GPP at the global scale for the period 1988–2020. VODCA2GPP applies a previously developed carbon-sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Moesinger et al., 2020; Zotta et al., 2022​​​​​​​), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and compared against largely independent state-of-the-art GPP datasets from the Moderate Resolution Imaging Spectroradiometer (MODIS), FLUXCOM, and the TRENDY-v7 process-based model ensemble. The site-level evaluation with FLUXNET GPP indicates an overall robust performance of VODCA2GPP with only a small bias and good temporal agreement. The comparisons with MODIS, FLUXCOM, and TRENDY-v7 show that VODCA2GPP exhibits very similar spatial patterns across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 (Pearson's r 0.53 and 0.61) but less well with FLUXCOM (Pearson's r 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP at the global scale but rather suggests regionally different long-term changes in GPP. For the shorter overlapping observation period (2003–2015) of VODCA2GPP, MODIS, and the TRENDY-v7 ensemble, significant increases in global GPP were found. VODCA2GPP can complement existing GPP products and is a valuable dataset for the assessment of large-scale and long-term changes in GPP for global vegetation and carbon cycle studies. The VODCA2GPP dataset is available at the TU Data Repository of TU Wien (https://doi.org/10.48436/1k7aj-bdz35, Wild et al., 2021).

Abstract. Long-term global monitoring of terrestrial Gross Primary Production (GPP) is crucial for assessing ecosystem response to global climate change. In recent years and decades, great advances in estimating GPP on a global level have been made and many global GPP datasets have been published. These global data records are either based on observations from optical remote sensing, are upscaled from in situ measurements, or rely on process-based models. The different estimation approaches are well established within the scientific community but also exhibit significant discrepancies among each other. Here, we introduce the new VODCA2GPP dataset, which utilizes microwave remote sensing estimates of Vegetation Optical Depth (VOD) to estimate GPP on a global scale. VODCA2GPP is able to complement existing products with long-term GPP estimates covering the period 1988–2020. VODCA2GPP applies a previously developed carbon sink-driven approach (Teubner et al., 2019, 2021) to estimate GPP from the Vegetation Optical Depth Climate Archive (Zotta et al., in prep.; Moesinger et al., 2020), which merges VOD observations from multiple sensors into one long-running, coherent data record. VODCA2GPP was trained and evaluated against FLUXNET in situ observations of GPP and assessed against largely independent state-of-the art GPP datasets (MODIS GPP, FLUXCOM GPP, and GPP estimates from the TRENDY-v7 model ensemble). These assessments show that VODCA2GPP exhibits very similar spatial patterns compared to existing GPP datasets across all biomes but with a consistent positive bias. In terms of temporal dynamics, a high agreement was found for regions outside the humid tropics, with median correlations around 0.75. Concerning anomalies from the long-term climatology, VODCA2GPP correlates well with MODIS and TRENDY-v7 GPP (Pearson’s r: 0.53 and 0.61) but less with FLUXCOM GPP (Pearson’s r: 0.29). A trend analysis for the period 1988–2019 did not exhibit a significant trend in VODCA2GPP on a global scale but rather suggests regionally differing long-term changes in GPP. Significant similar increases of global GPP that were found for VODCA2GPP, MODIS GPP, and the TRENDY-v7 ensemble for the shorter overlapping observation period (2003–2015) supports the theory of elevated CO2 uptake potentially induced by increased atmospheric CO2 concentrations and the associated rising temperatures. The VODCA2GPP dataset is available at TU Data (https://doi.org/10.48436/1k7aj-bdz35; Wild et al., 2021).

Abstract. Vegetation optical depth (VOD) from microwave satellite observations has received much attention in global vegetation studies in recent years due to its relationship to vegetation water content and biomass. We recently have shown that VOD is related to plant productivity, i.e. gross primary production (GPP). Based on this relationship between VOD and GPP we developed a theory-based machine learning model to estimate global patterns of GPP from passive microwave VOD retrievals. The VOD-GPP model generally showed good agreement with site observations and other global data sets in temporal dynamic but tended to overestimate annual GPP across all latitudes. We hypothesized that the reason for the overestimation is the missing effect of temperature on autotrophic respiration in the theory-based machine learning model. Here we aim to further assess and enhance the robustness of the VOD-GPP model by including the effect of temperature on autotrophic respiration within the machine learning approach and by assessing the interannual variability of the model results with respect to water availability. We used X-band VOD from the VOD Climate Archive (VODCA) data set for estimating GPP and used global state-of-the art GPP data sets from FLUXCOM and MODIS to assess residuals of the VOD-GPP model with respect to drought conditions as quantified by the Standardized Precipitation and Evaporation Index (SPEI). Our results reveal an improvement in model performance for correlation when including the temperature dependency of autotrophic respiration. This increase in temporal dynamic is largest for regions outside the tropics. For error and bias, the results are regionally diverse and are compensated in the global average. On interannual time scales, estimates of the VOD-GPP model agree well with GPP from FLUXCOM and MODIS. We further find that the residuals between VOD-based GPP estimates and the other data sets do not significantly correlate with SPEI which demonstrates that the VOD-GPP model can capture responses of GPP to water availability even without including additional information on precipitation, soil moisture or evapotranspiration. However, some regions reveal significant correlations between VOD-GPP residuals with SPEI, which may indicate different plant strategies for dealing with variations in water availability. Overall, our findings support the robustness of global microwave-derived estimates of gross primary production for large-scale studies on climate-vegetation interactions.

Gross Primary Production (GPP) describes the uptake of C02 by plants through photosynthesis and is essential to monitor and analyze ecosystem dynamics. Teubner et al.1 developed a carbon sink-driven approach to estimate GPP on a global scale using Vegetation Optical Depth (VOD), derived from active and passive microwave observations. This allows to analyze GPP variability, complementing existing optical GPP products which are more affected by weather conditions. The short operation time of the individual microwave sensors and the bias between them prohibit analyzing GPP variability. This issue can potentially be overcome by using the Vegetation Optical Depth Climate Archive (VODCA) developed by Moesinger et al.2, which merges multiple VOD products into a single data record. However, the use of a long-running VOD composite for estimating global GPP is challenging because the implications of the VOD aggregation process on the modelling of GPP are difficult to identify a priori. Here, we present the results of applying the carbon sink-driven GPP estimation approach on the VODCA datasets. As model input for each pixel we used raw VOD from VODCA as well as changes in VOD and median VOD, the latter serves as proxy for vegetation cover. In order to analyze the performance of the carbon sink-driven approach when using VODCA as input, the model is cross-validated against single-sensor (AMSR-E) VOD estimates and commonly used carbon source-driven estimates (MODIS/FLUXCOM). We assessed the ability to model GPP based on single-frequency VODCA (C-, X- and Ku-band) as well as using multiple frequencies as model input. Overall, the results show that single-band as well as multi-band VODCA performs slightly better in predicting GPP than single-sensor based VOD. Especially in the tropical regions multi-frequency VODCA GPP outperforms single-sensor based estimates. Compared to source-driven approaches, VOD based GPP estimates are higher than FLUXCOM and MODIS GPP. The spatial patterns, however, show good correspondence with the carbon source-driven GPP products, confirming that VODCA can be used to extend the GPP estimates to the past three decades. 1Teubner, I., Forkel, M., Camps-Valls, G., Jung, M., Miralles, Diego, Tramontana, G., van der Schalie, R., Vreugdenhil, M., Moesinger, L., Dorigo, W.:A carbon sink-driven approach to estimate gross primary production from microwave satellite observations, 2019. Remote Sensing of Environment. 229. 100-113. 10.1016/j.rse.2019.04.022. 2Moesinger, L., Dorigo, W., de Jeu, R., van der Schalie, R., Scanlon, T., Teubner, I., and Forkel, M.: The Global Long-term Microwave Vegetation Optical Depth Climate Archive VODCA, Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2019-42, in review, 2019.