3D modeling of cultural heritage objects like artifacts, statues and buildings is nowadays an important tool for virtual museums, preservation and restoration. In this paper, we introduce a method to automatically design a minimal imaging network for the 3D modeling of cultural heritage objects. This becomes important for reducing the image capture time and processing when documenting large and complex sites. Moreover, such a minimal camera network design is desirable for imaging non-digitally documented artifacts in museums and other archeological sites to avoid disturbing the visitors for a long time and/or moving delicate precious objects to complete the documentation task. The developed method is tested on the Iraqi famous statue “Lamassu”. Lamassu is a human-headed winged bull of over 4.25 m in height from the era of Ashurnasirpal II (883–859 BC). Close-range photogrammetry is used for the 3D modeling task where a dense ordered imaging network of 45 high resolution images were captured around Lamassu with an object sample distance of 1 mm. These images constitute a dense network and the aim of our study was to apply our method to reduce the number of images for the 3D modeling and at the same time preserve pre-defined point accuracy. Temporary control points were fixed evenly on the body of Lamassu and measured by using a total station for the external validation and scaling purpose. Two network filtering methods are implemented and three different software packages are used to investigate the efficiency of the image orientation and modeling of the statue in the filtered (reduced) image networks. Internal and external validation results prove that minimal image networks can provide highly accurate records and efficiency in terms of visualization, completeness, processing time (>60% reduction) and the final accuracy of 1 mm.
Iraq, the land of two rivers, has a history that extends back millennia and is the subject of much archaeological research. However, little environmental research has been carried out, and as such relatively little is known about the interaction between Iraq's vegetation and climate. This research serves to fill this knowledge gap by investigating the relationship between the Normalized Difference Vegetation Index (NDVI) and two climatic factors (precipitation and air temperature) over the last decade. The precipitation and air temperature datasets are from the Water and Global Change Forcing Data ERA-Interim (WFDEI), and the NDVI dataset was extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m spatial resolution and 16 day temporal resolution. Three different climatic regions in Iraq, Sulaymaniyah, Wasit, and Basrah, were selected for the period of 2001-2015. This is the first study to compare these regions in Iraq, and one of only a few investigating vegetation's relationship with multiple climatic factors, including precipitation and air temperature, particularly in a semi-arid region. The interannual, intra-annual and seasonal variability for each region is analysed to compare the different responses of vegetation growth to climatic factors. Correlations between NDVI and climatic factors are also included. Plotting annual cycles of NDVI and precipitation reveals a coherent onset, fluctuation (peak and decline), with a time lag of 4 months for Sulaymaniyah and Wasit (while for the Basrah region, high temperatures and a short rainy season was observed). The correlation coefficients between NDVI and precipitation are relatively high, especially in Sulaymaniyah, and the largest positive correlation was (0.8635) with a time lag of 4 months. The phenological transition points range between 3 and 4 month time lag; this corresponds to the duration of maturity of the vegetation. However, when correlated with air temperature, NDVI experiences an inverse relationship, although not as strong as that of NDVI and precipitation; the highest negative correlation was observed in Wasit with a time lag of 2 months (-0.7562). The results showed that there is a similarity between temporal patterns of NDVI and precipitation. This similarity is stronger than that of NDVI and air temperature, so it can be concluded that NDVI is a sensitive indicator of the inter-annual variability of precipitation and that precipitation constitutes the primary factor in germination while the air temperature acts with a lesser effect.
General rightsThis document is made available in accordance with publisher policies. Please cite only the published version using the reference above. Full terms of use are available: http://www.bristol.ac.uk/pure/about/ebr-terms AbstractAlthough most phenology models can analyze and predict future trends in response to climate change, these models often perform poorly in semi-arid regions where precipitation is limited. In this study, we modified an existing phenology model, the Growing Season Index (GSI), to better quantify relationships between weather and vegetation canopy dynamics across various semi-arid regions of Iraq. A modified GSI was created by adding a cumulative precipitation control to the existing GSI framework. Both unmodified and modified GSI values were calculated for three locations in Western Iraq: Sulaymaniyah in the north, Wasit in the centre and Basrah in the south as well as a country-wide mean and the running mean daily unmodified and modified GSI values for these study areas were calculated from 2001-2010 and compared to the Normalized Difference Vegetation Index (NDVI) from MODerate-resolution Imaging Spectroradiometer (MODIS) for the same time period. Country-wide median inter-annual correlations between GSI and NDVI more than doubled with the addition of the precipitation control and within-site correlations also show substantial improvements. The modified model has a huge potential be used to predict future phenological responses to changing climatic conditions, as well as to reconstruct historical vegetation conditions. This study is important to understanding not only the Iraqi region as it considers the results of climatic and environmental changes that have taken place in recent decades, but it should improve vegetation phenological predictions across Iraq and other semiarid regions of the world, particularly in the face of rapid climate change and environmental deterioration.
<p>Sand is a critical natural resource used in the construction sector, in land reclamation and coastal protection schemes. Global consumption of aggregates is estimated to be c. 40 billion tonnes a year (Peduzzi, 2014) a large proportion of which is derived from fluvial sediment sources. This figure exceeds&#160;the mass of sediment delivered annually to the global ocean (Milliman and Syvitski, 1992). Moreover, with urban populations projected to rise substantially over the next 20 years (UN, 2019), unsustainable extraction of alluvial sand represents a critical threat to the morphological and ecological integrity of rivers.</p> <p>Despite growing awareness amongst the scientific community and policymakers of the deleterious effect that uncontrolled extraction can have on the landscape and local populations, there remains a lack of quantitative understanding concerning the diverse potential impacts of fluvial sand extraction, and the degree to which any level of extraction may be deemed sustainable. Similarly, differences between the impacts of alternative mechanisms of extraction are poorly understood, as are the rates at which these impacts may propagate beyond the immediate extraction zone. These knowledge gaps make effective mitigation and regulation of sand extraction practices extremely challenging.</p> <p>This study seeks to better understand the impact of mining within large sand-bed rivers using numerical modelling. Two modes of sand extraction were considered: (1) bar-top skimming from the floodplain and mid-channel bars; and (2) wet mining by dredging of the channel thalweg. We carry out 2D physically-based morphodynamic model simulations over spatial and temporal scales of 90 km and 150 years in order to quantify the evolution of river morphology, hydraulics and sediment transport during both the period of sand extraction and an extended post-extraction period. Model simulations were designed to quantify both the fluvial responses within the immediate sand extraction zone, and the downstream propagation of the mining disturbances. Results indicate that there is a clear impact of sand extraction in all the analysed hydromorphic metrics (e.g., braid intensity, variations in the river width-depth, and in the flow patterns) and that there is a different river-evolution style and impact when considering different types of sand mining (dry mining from exposed bars at low-flow conditions or wet mining only from the channel thalweg). For example: (1) for wet mining scenarios, the system shows a very significant deepening of the channel thalweg and a consequent reduction in the mobility of the system, decreasing the inundation period on the bars; (2) in dry mining scenarios, the system develops shallower channels (when compared to wet mining), but experiences an increase in avulsion, with the rapid activation and deactivation of secondary channels and unvegetated bars (in the mining zone), enhancing bank erosion and consequent further river widening. Model results demonstrate that recovery of river systems in the absence of mining is a process that can require decades to centuries. Moreover, the influence and consequences of mining directly within the extraction zone are propagated downstream rapidly, although the contrasting response associated with different mining styles becomes less marked outside the immediate area of extraction.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.