Akuisisi dan Pemanfaatan Foto Udara Format Kecil dntuk Pemetaan Obyek Berbahaya pada Jaringan Transmisi Lsitrik

Obyek permukaan bumi memiliki jarak aman atau ruang bebas terhadap jaringan transmisi listrik apabila obyek tersebut melebihi jarak aman dapat membahayakan jaringan maupun manusia di sekitarnya. Perkembangan penginderaan jauh dengan foto udara format kecil dapat menghasilkan data mosaik foto udara yang dapat menggambarkan secara utuh permukaan bumi dan point cloud yang menjadi model data elevasi. Tujuan penelitian ini adalah akuisisi foto udara format kecil untuk , mengidentifikasi jaringan transmisi serta mengidentifikasi obyek pada ruang bebas dengan menggunakan perpaduan data hasil pengolahan foto udara format kecil. Perekaman foto udara menggunakan sistem DJI Phantom 4. Analisis obyek pada ruang bebas menggunakan orthophoto untuk identifikasi obyek dan point cloun untuk identifikasi elevasi. Hasil identifikasi menunjukkan obyek yang masuk dalam ruang bebas diantaranya tumbuhan/perkebunan/hutan, dan bangunan dengan total luasan 3486 m2. Overall accuracy data obyek penutup lahan sebesar 100% dan nilai Root Mean Square Error data elevasi obyek permukaan sebesar 3,4 mete

Patterns of vegetation structural diversity across heterogeneous landscapes in southwestern Nova Scotia

1 online resource (88 pages) : colour illustrations, colour maps, colour charts, graphs (some colour)Includes abstract and appendices.Includes bibliographical references (pages 14-21, 45-52, 74-81).Forest edges, including transitional areas between forest and non-forest areas, outline the overall structure of the landscape. To assess and quantify patterns of structural diversity across natural and harvested landscapes in southwestern Nova Scotia, I used field-based structural diversity metrics and UAV imagery along two 1250 m transects to examine different aspects of the pattern of structural diversity across transitions in forested landscapes. For traditional field metrics, tree structural diversity had more success in determining transitions than functional plant group diversity, as tree structural diversity detected all edge types compared to just anthropogenic edges when using functional plant group diversity. For photogrammetrically derived metrics, no metric detected transitions at all edges and overall UAV metrics were incompatible with field sampling. Future studies should examine the compatibility of LiDAR and structural diversity metrics

Metoder för höjdkorrigering av punktmoln generade från drönarbilder

Syftet med studien är att jämföra tre olika metoder för höjdkorrigering av punktmoln skapade från bilder tagna med drönaren DJI Phantom 4 Pro. Detta är intressant då drönare anses vara ett av framtidens instrument för småskalig och snabb fjärranalys. Problemet med drönare är den osäkra bedömningen av höjd som medförs av drönarens teknik för höjdmätning. För att kunna användas effektivt i framtiden behöver därför höjden korrigeras för att passa andra mer noggranna fjärranalysdata. I arbetet bearbetas och korrigeras sju flygblock med flygbilder inom ett relativt litet område. För varje flygblock genererades ett punktmoln som sedan skulle korrigeras efter en höjdmodell. Tre metoder jämfördes i studien. Den första var manuell justering av Z-koordinaten. Den andra metoden var “Iterative closest point” (ICP-metoden) där punktmolnet matchas med en modell för marken, punkt för punkt. Den sista metoden var att korrigera höjden i drönarbildernas exif-filer utefter drönarens barometer. Efter korrigeringen jämfördes höjden på punktmolnen med RTK-GNSS-mätningar inom flygblocken. Resultatet visar att den manuella metoden har bäst genomsnittligt Root mean square error (RMSE) över alla flygblock på 1.06 m. Maximum och minimum för metoden var 1.59 m och 0.40 m. ICP-metoden var näst bäst med genomsnittligt RMSE på 1.35 m. ICP-metodens RMSE varierade mellan 2.97 m och 0.32 m vilket tyder på att metoden kan träffa bättre än den manuella då 0.32 m var det lägsta RMSE som registrerades. Barometermetoden var sämst med ett genomsnittligt RMSE på 7.95 m. Spridningen inom metoden var större än för de andra. Maximum och minimum RMSE låg på 9.35 m respektive 5.47 m.The purpose of the study was to compare three different methods for height correction of point clouds created from pictures taken from the drone DJI Phantom 4 Pro. This is interesting as drones are a future instrument for small scale and quick remote sensing. The problem with drones is the inaccuracies in height measurement that exist because of the drone’s small stature. For drones to be used effectively in the future there must be a correction in height to fit other more accurate remote sensing data sets. In this paper seven flight blocks of flight images from a relatively small area are corrected. For each flight block a point cloud was generated which would be corrected to the Swedish Land surveys height model. Three methods were compared. The first method was a manual correction of the Z-coordinate. The second method was “Iterative closest point” where a software matched the point clouds ground points to the height models points. The last method was to correct the point clouds height by changing the drone photos height according to the barometric height information in the exif file. The result shows that the manual method had the best average Root mean square error (RMSE) with a value of 1.06 m. Maximum and minimum RMSE for the method where 1.59-0.40 m. The ICP-method was the second-best method with an average RMSE of 1.35 m, the result varied between 2.97-0.32 m, which indicates that it could generate the best result. The barometric method was the worst method with an average RMSE of 7.95 m and a variance between 9.35-5.47 m

Low-Altitude UAV Imaging Accurately Quantifies Eelgrass Wasting Disease From Alaska to California

Declines in eelgrass, an important and widespread coastal habitat, are associated with wasting disease in recent outbreaks on the Pacific coast of North America. This study presents a novel method for mapping and predicting wasting disease using Unoccupied Aerial Vehicle (UAV) with low-altitude autonomous imaging of visible bands. We conducted UAV mapping and sampling in intertidal eelgrass beds across multiple sites in Alaska, British Columbia, and California. We designed and implemented a UAV low-altitude mapping protocol to detect disease prevalence and validated against in situ results. Our analysis revealed that green leaf area index derived from UAV imagery was a strong and significant (inverse) predictor of spatial distribution and severity of wasting disease measured on the ground, especially for regions with extensive disease infection. This study highlights a novel, efficient, and portable method to investigate seagrass disease at landscape scales across geographic regions and conditions

Integración geoespacial para mapear asentamientos prehispánicos en los límites del imperio azteca

[EN] Mexico s vast archaeological research tradition has increased with the use of remote sensing technologies; however, this recent approach is still costly in emerging market economies. In addition, the scales of prospection, landscape, and violence affect the type of research that heritage-culture ministries and universities can conduct. In Central Mexico, researchers have studied the pre-Hispanic Settlement Pattern during the Mesoamerican Postclassic (900-1521 AD) within the scope of the Aztec Empire and its conquests. There are settlements indications before and during the rule of the central empire, but the evidence is difficult to identify, particularly in the southwest of the capital, in the transition between the Lerma and Balsas River basins and their political-geographical complexities. This research focuses on a Geographic Information System (GIS)-based processing of multiple source data, the potential prospection of archaeological sites based on spatial data integration from Sentinel-2 optical sensors, Unmanned Aerial Vehicle (UAV), Digital Terrain Model (DTM), Normalized Difference Vegetation Index (NDVI) and field validation. What is revealed is the relationship between terrain morphologies and anthropic modifications. A binary map expresses possible archaeological remnants as a percentage; NDVI pixels and the morphometry values were associated with anthropic features (meso-reliefs with a tendency to regular geometries: slope, orientation, and roughness index); they were then interpreted as probable archaeological evidence. Within archaeological fieldwork, with limited resources (time, funding and staff), this approach proposes a robust method that can be replicated in other mountainous landscapes that are densely covered by vegetation.[ES] México tiene una vasta tradición de investigación arqueológica que, en las últimas décadas, se ha incrementado con el uso de tecnologías de percepción remota; sin embargo, este enfoque sigue siendo costoso en el contexto de las economías emergentes. Además, las escalas de prospección, paisaje e inseguridad influyen en el tipo de investigación que realizan los ministerios de patrimonio cultural y las universidades. En el Centro de México, el Patrón de Asentamiento Prehispánico durante el Posclásico Mesoamericano (900-1521 d.C.), ha sido estudiado dentro del alcance del Imperio Azteca y sus conquistas. Hay indicios de asentamientos antes y durante el dominio del Imperio central, pero la evidencia es difícil de identificar; particularmente en el suroeste de la capital, en la transición entre las cuencas de los ríos Lerma y Balsas y sus complejidades político-geográficas. Esta investigación se centra en el procesamiento basado en GIS de datos de múltiples fuentes, la prospección de sitios arqueológicos apoyada en la integración de datos espaciales de los sensores ópticos Sentinel-2, el vehículo aéreo no tripulado (UAV), el modelo digital del terreno (MDT), el índice de vegetación de diferencia normalizada (NDVI) y la validación de campo, que revelan la relación entre las morfologías del terreno y las modificaciones antrópicas. Un mapa binario expresa los posibles remanentes arqueológicos como un porcentaje; los píxeles del NDVI y los valores de morfometría se asociaron a características antrópicas (mesorrelieves con tendencia a geometrías regulares: pendiente, orientación e índice de rugosidad), y se interpretaron como probable evidencia arqueológica. Dentro del trabajo de campo arqueológico, con recursos limitados (tiempo, finanzas y auxiliares), este enfoque sugiere un método robusto que puede ser replicado en otros paisajes montañosos que están densamente cubiertos por vegetación.Miranda-Gómez, R.; Cabadas-Báez, HV.; Antonio-Némiga, X.; Dávila-Hernández, N. (2022). Geospatial integration in mapping pre-Hispanic settlements within Aztec empire limits. Virtual Archaeology Review. 13(27):49-65. https://doi.org/10.4995/var.2022.161064965132

Assessing the Ability of Image Based Point Clouds Captured from a UAV to Measure the Terrain in the Presence of Canopy Cover

Point clouds captured from Unmanned Aerial Systems are increasingly relied upon to provide information describing the structure of forests. The quality of the information derived from these point clouds is dependent on a range of variables, including the type and structure of the forest, weather conditions and flying parameters. A key requirement to achieve accurate estimates of height based metrics describing forest structure is a source of ground information. This study explores the availability and reliability of ground surface points available within point clouds captured in six forests of different structure (canopy cover and height), using three image capture and processing strategies, consisting of nadir, oblique and composite nadir/oblique image networks. The ground information was extracted through manual segmentation of the point clouds as well as through the use of two commonly used ground filters, LAStools lasground and the Cloth Simulation Filter. The outcomes of these strategies were assessed against ground control captured with a Total Station. Results indicate that a small increase in the number of ground points captured (between 0 and 5% of a 10 m radius plot) can be achieved through the use of a composite image network. In the case of manually identified ground points, this reduced the root mean square error (RMSE) error of the terrain model by between 1 and 11 cm, with greater reductions seen in plots with high canopy cover. The ground filters trialled were not able to exploit the extra information in the point clouds and inconsistent results in terrain RMSE were obtained across the various plots and imaging network configurations. The use of a composite network also provided greater penetration into the canopy, which is likely to improve the representation of mid-canopy elements

Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem

We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon PowerShot A800 digital camera mounted on an unmanned aerial system. Image mosaicking and dense point matching were accomplished using Agisoft PhotoScan. The resulting point cloud was classified according to ground/non-ground classes and used to interpolate a high resolution DTM which was both compared to a DTM from an existing lidar dataset and used to model vegetation height for fifteen 20 × 20 m plots. Differences in the interpolated DTM surfaces demonstrate that the SfM surface overestimates lidar-modeled ground height with a mean difference of 0.19 m and standard deviation of 0.66 m. Height estimates obtained solely from SfM products were successful with R2 values of 0.91, 0.90, and 0.89 for mean, median, and maximum canopy height, respectively. Use of the lidar DTM in the analyses resulted in R2 values of 0.90, 0.89, and 0.89 for mean, median, and maximum canopy height. Our results suggest that SfM-derived point cloud products function as well as lidar data for estimating vegetation canopy height for our specific study area

Assessment of Image-Based Point Cloud Products to Generate a Bare Earth Surface and Estimate Canopy Heights in a Woodland Ecosystem

We examine the utility of Structure from Motion (SfM) point cloud products to generate a digital terrain model (DTM) and estimate canopy heights in a woodland ecosystem in the Texas Hill Country, USA. Very high spatial resolution images were acquired with a Canon PowerShot A800 digital camera mounted on an unmanned aerial system. Image mosaicking and dense point matching were accomplished using Agisoft PhotoScan. The resulting point cloud was classified according to ground/non-ground classes and used to interpolate a high resolution DTM which was both compared to a DTM from an existing lidar dataset and used to model vegetation height for fifteen 20 × 20 m plots. Differences in the interpolated DTM surfaces demonstrate that the SfM surface overestimates lidar-modeled ground height with a mean difference of 0.19 m and standard deviation of 0.66 m. Height estimates obtained solely from SfM products were successful with R2 values of 0.91, 0.90, and 0.89 for mean, median, and maximum canopy height, respectively. Use of the lidar DTM in the analyses resulted in R2 values of 0.90, 0.89, and 0.89 for mean, median, and maximum canopy height. Our results suggest that SfM-derived point cloud products function as well as lidar data for estimating vegetation canopy height for our specific study area

Drone-based Structure-from-Motion provides accurate forest canopy data to assess shading effects in river temperature models

Climatic warming will increase river temperature globally, with consequences for cold water-adapted organisms. In regions with low forest cover, elevated river temperature is often associated with a lack of bankside shading. Consequently, river managers have advocated riparian tree planting as a strategy to reduce temperature extremes. However, the effect of riparian shading on river temperature varies substantially between locations. Process-based models can elucidate the relative importance of woodland and other factors driving river temperature and thus improve understanding of spatial variability of the effect of shading, but characterising the spatial distribution and height of riparian tree cover necessary to parameterise these models remains a significant challenge. Here, we document a novel approach that combines Structure-from-Motion (SfM) photogrammetry acquired from a drone to characterise the riparian canopy with a process based temperature model (Heat Source) to simulate the effects of tree shading on river temperature. Our approach was applied in the Girnock Burn, a tributary of the Aberdeenshire Dee, Scotland. Results show that SfM approximates true canopy elevation with a good degree of accuracy (R2 = 0.96) and reveals notable spatial heterogeneity in shading. When these data were incorporated into a process-based temperature model, it was possible to simulate river temperatures with a similarly-high level of accuracy (RMS

Hypertemporal Imaging Capability of UAS Improves Photogrammetric Tree Canopy Models

Small uncrewed aerial systems (UASs) generate imagery that can provide detailed information regarding condition and change if the products are reproducible through time. Densified point clouds form the basic information for digital surface models and orthorectified mosaics, so variable dense point reconstruction will introduce uncertainty. Eucalyptus trees typically have sparse and discontinuous canopies with pendulous leaves that present a difficult target for photogrammetry software. We examine how spectral band, season, solar azimuth, elevation, and some processing settings impact completeness and reproducibility of dense point clouds for shrub swamp and Eucalyptus forest canopy. At the study site near solar noon, selecting near infrared camera increased projected tree canopy fourfold, and dense point features more than 2 m above ground were increased sixfold compared to red spectral bands. Near infrared (NIR) imagery improved projected and total dense features two- and threefold, respectively, compared to default green band imagery. The lowest solar elevation captured (25°) consistently improved canopy feature reconstruction in all spectral bands. Although low solar elevations are typically avoided for radiometric reasons, we demonstrate that these conditions improve the detection and reconstruction of complex tree canopy features in natural Eucalyptus forests. Combining imagery sets captured at different solar elevations improved the reproducibility of dense point clouds between seasons. Total dense point cloud features reconstructed were increased by almost 10 million points (20%) when imagery used was NIR combining solar noon and low solar elevation imagery. It is possible to use agricultural multispectral camera rigs to reconstruct Eucalyptus tree canopy and shrub swamp by combining imagery and selecting appropriate spectral bands for processin