Finding Fields: Locating Archaeological Agricultural Landscapes Using Historical Aerial Photographs

2021 ◽  
pp. 1-22
Author(s):  
Madeleine McLeester ◽  
Jesse Casana

During the late nineteenth and early twentieth centuries, over 450 precolumbian and historic Indigenous agricultural fields were documented across the state of Wisconsin. Today, the vast majority of these features are generally assumed to have been destroyed. Focusing on the Wisconsin River basin, which has the highest concentration of known archaeological field systems in the Midwest, this study explores the potential of using historical aerial photographs to identify and interpret archaeological agricultural features. Relying on state site records, an archive of high-resolution 1930s aerial images, and modern lidar data, we carefully examine the region surrounding 59 sites where fields had previously been documented. At a quarter of the sites we investigated, we successfully identified both known and unrecorded archaeological features—including agricultural fields, effigy mounds, earthworks, and house basins—most of which have been destroyed by recent land use practices. Our analysis sheds light on the complexity and richness of the archaeological landscape, with vast agricultural spaces situated beyond traditional site boundaries, and suggests that precolumbian and historic Indigenous agricultural fields may have been much larger and more widespread than conventionally understood.

2016 ◽  
Vol 8 (12) ◽  
pp. 1030 ◽  
Author(s):  
Shouji Du ◽  
Yunsheng Zhang ◽  
Rongjun Qin ◽  
Zhihua Yang ◽  
Zhengrong Zou ◽  
...  

2021 ◽  
Vol 13 (13) ◽  
pp. 2473
Author(s):  
Qinglie Yuan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Aidi Hizami Alias ◽  
Shaiful Jahari Hashim

Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical features from encoders with a fixed receptive field perform weak ability to obtain global semantic information. Local features in multiscale subregions cannot construct contextual interdependence and correlation, especially for large-scale building areas, which probably causes fragmentary extraction results due to intra-class feature variability. In addition, low-level features have accurate and fine-grained spatial information for tiny building structures but lack refinement and selection, and the semantic gap of across-level features is not conducive to feature fusion. To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial images and LiDAR data for building extraction. Two novel modules have been proposed for the optimization and integration of multiscale and across-level features. In particular, a multiscale context optimization module is designed to adaptively generate the feature representations for different subregions and effectively aggregate global context. A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network. Compared with other state-of-the-art methods, experimental results demonstrate superior performance with 93.19 IoU, 97.56 OA on WHU datasets and 94.72 IoU, 97.84 OA on the Boston dataset, which shows that the proposed network can improve accuracy and achieve better performance for building extraction.


2021 ◽  
Author(s):  
Andreas Linsbauer ◽  
Matthias Huss ◽  
Elias Hodel ◽  
Andreas Bauder ◽  
Mauro Fischer ◽  
...  

<p>With increasing anthropogenic greenhouse gas emissions and corresponding global warming, glaciers in Switzerland are shrinking rapidly as in many mountain ranges on Earth. Repeated glacier inventories are a key task to monitor such glacier changes and provide detailed information on the extent of glaciation, and important parameters such as area, elevation range, slope, aspect etc. for a given point or a period in time. Here we present the new Swiss Glacier Inventory (SGI2016) that has been acquired based on high-resolution aerial imagery and digital elevation models in cooperation with the Federal Office of Topography (swisstopo) and Glacier Monitoring in Switzerland (GLAMOS), bringing together topological and glaciological knowhow. We define the process, workflow and required glaciological adaptations to compile a highly accurate glacier inventory based on the digital Swiss topographic landscape model (swissTLM<sup>3D</sup>).</p><p>The SGI2016 provides glacier outlines (areas), supraglacial debris cover, ice divides and location points of all glaciers in Switzerland referring to the years 2013-2018, whereas most of the glacier outlines have been mapped based on aerial images acquired between 2015-2017 (75% in number and 87% in area), with the centre year 2016. The SGI2016 maps 1400 individual glacier entities with a total glacier surface area of 961 km<sup>2</sup> (whereof 11% / 104 km<sup>2</sup> are debris-covered) and constitutes the so far most detailed cartographic representation of glacier extent in Switzerland. Analysing the dependencies between topographic parameters and debris-cover fraction on the basis of individual glaciers reveals that short glaciers with a moderate mean slope and glaciers with a low median elevation tend to have high debris fractions. A change assessment between the SGI1973 and SGI2016 based on individual glacier entities affirms the largest relative area changes for small glaciers and for low-elevation glaciers, whereas the largest glaciers show small relative area changes, though large absolute changes. The analysis further indicates a tendency for glaciers with a high share of supraglacial debris to show larger relative area changes.</p><p>Despite of an observed strong glacier volume loss between 2010 and 2016, the total glacier surface area of the SGI2016 is somewhat larger than reported in the last Swiss glacier inventory SGI2010. Even though both inventories were created based on swisstopo aerial photographs, the additional data, tools, resources and methodologies used by the professional cartographers digitizing glacier outlines in 3D for the SGI2016, are able to explain the counter-intuitive difference between SGI2010 and SGI2016. A direct comparison of these two datasets is thus not meaningful, but an experiment where a representative glacier sample of the SGI2010 was re-assessed based on the approaches of the SGI2016 led to an upscaled total glacier surface area of 1010 km<sup>2</sup> for the Swiss Alps around 2010. This indicates an area loss of 49 km<sup>2</sup> between the two last Swiss glacier inventories. As swisstopo data products are and will be regularly updated, the SGI2016 is the first step towards a consistent and accurate data product of repeated glacier inventories in six-year time intervals that promises a high comparability for individual glaciers and glacier samples.</p>


2020 ◽  
Vol 12 (11) ◽  
pp. 1820
Author(s):  
Raoul Blackman ◽  
Fei Yuan

Urban forests provide ecosystem services; tree canopy cover is the basic quantification of ecosystem services. Ground assessment of the urban forest is limited; with continued refinement, remote sensing can become an essential tool for analyzing the urban forest. This study addresses three research questions that are essential for urban forest management using remote sensing: (1) Can object-based image analysis (OBIA) and non-image classification methods (such as random point-based evaluation) accurately determine urban canopy coverage using high-spatial-resolution aerial images? (2) Is it possible to assess the impact of natural disturbances in addition to other factors (such as urban development) on urban canopy changes in the classification map created by OBIA? (3) How can we use Light Detection and Ranging (LiDAR) data and technology to extract urban canopy metrics accurately and effectively? The urban forest canopy area and location within the City of St Peter, Minnesota (MN) boundary between 1938 and 2019 were defined using both OBIA and random-point-based methods with high-spatial-resolution aerial images. Impacts of natural disasters, such as the 1998 tornado and tree diseases, on the urban canopy cover area, were examined. Finally, LiDAR data was used to determine the height, density, crown area, diameter, and volume of the urban forest canopy. Both OBIA and random-point methods gave accurate results of canopy coverages. The OBIA is relatively more time-consuming and requires specialist knowledge, whereas the random-point-based method only shows the total coverage of the classes without locational information. Canopy change caused by tornado was discernible in the canopy OBIA-based classification maps while the change due to diseases was undetectable. To accurately exact urban canopy metrics besides tree locations, dense LiDAR point cloud data collected at the leaf-on season as well as algorithms or software developed specifically for urban forest analysis using LiDAR data are needed.


Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1651 ◽  
Author(s):  
Suk-Ju Hong ◽  
Yunhyeok Han ◽  
Sang-Yeon Kim ◽  
Ah-Yeong Lee ◽  
Ghiseok Kim

Wild birds are monitored with the important objectives of identifying their habitats and estimating the size of their populations. Especially in the case of migratory bird, they are significantly recorded during specific periods of time to forecast any possible spread of animal disease such as avian influenza. This study led to the construction of deep-learning-based object-detection models with the aid of aerial photographs collected by an unmanned aerial vehicle (UAV). The dataset containing the aerial photographs includes diverse images of birds in various bird habitats and in the vicinity of lakes and on farmland. In addition, aerial images of bird decoys are captured to achieve various bird patterns and more accurate bird information. Bird detection models such as Faster Region-based Convolutional Neural Network (R-CNN), Region-based Fully Convolutional Network (R-FCN), Single Shot MultiBox Detector (SSD), Retinanet, and You Only Look Once (YOLO) were created and the performance of all models was estimated by comparing their computing speed and average precision. The test results show Faster R-CNN to be the most accurate and YOLO to be the fastest among the models. The combined results demonstrate that the use of deep-learning-based detection methods in combination with UAV aerial imagery is fairly suitable for bird detection in various environments.


2020 ◽  
Vol 9 (4) ◽  
pp. 260 ◽  
Author(s):  
Tomás Fernández ◽  
José Luis Pérez-García ◽  
José Miguel Gómez-López ◽  
Javier Cardenal ◽  
Julio Calero ◽  
...  

Gully erosion is one of the main processes of soil degradation, representing 50%–90% of total erosion at basin scales. Thus, its precise characterization has received growing attention in recent years. Geomatics techniques, mainly photogrammetry and LiDAR, can support the quantitative analysis of gully development. This paper deals with the application of these techniques using aerial photographs and airborne LiDAR data available from public database servers to identify and quantify gully erosion through a long period (1980–2016) in an area of 7.5 km2 in olive groves. Several historical flights (1980, 1996, 2001, 2005, 2009, 2011, 2013 and 2016) were aligned in a common coordinate reference system with the LiDAR point cloud, and then, digital surface models (DSMs) and orthophotographs were obtained. Next, the analysis of the DSM of differences (DoDs) allowed the identification of gullies, the calculation of the affected areas as well as the estimation of height differences and volumes between models. These analyses result in an average depletion of 0.50 m and volume loss of 85000 m3 in the gully area, with some periods (2009–2011 and 2011–2013) showing rates of 10,000–20,000 m3/year (20–40 t/ha*year). The manual edition of DSMs in order to obtain digital elevation models (DTMs) in a detailed sector has facilitated an analysis of the influence of this operation on the erosion calculations, finding that it is not significant except in gully areas with a very steep shape.


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