Assessment of Aquatic Reed Stands from Airborne Photogrammetric 3K Data

Aquatic reed beds provide important ecological functions, yet their monitoring by remote sensing methods remains challenging. In this study, we propose an approach of assessing aquatic reed stand status indicators based on data from the airborne photogrammetric 3K-system of the German Aerospace Center (DLR). By a Structure from Motion (SfM) approach, we computed stand surface models of aquatic reeds for each of the 14 areas of interest (AOI) investigated at Lake Chiemsee in Bavaria, Germany. Based on reed heights, we subsequently calculated the reed area, surface structure homogeneity and shape of the frontline. For verification, we compared 3K aquatic reed heights against reed stem metrics obtained from ground-based infield data collected at each AOI. The root mean square error (RMSE) for 1358 reference points from the 3K digital surface model and the field-measured data ranged between 39 cm and 104 cm depending on the AOI. Considering strong object movements due to wind and waves, superimposed by water surface effects such as sun glint altering 3K data, the results of the aquatic reed surface reconstruction were promising. Combining the parameter height, area, density and frontline shape, we finally calculated an indicator for status determination: the aquatic reed status index (aRSI), which is based on metrics, and thus is repeatable and transferable in space and time. The findings of our study illustrate that, even under the adverse conditions given by the environment of the aquatic reed, aerial photogrammetry can deliver appropriate results for deriving objective and reconstructable parameters for aquatic reed status (Phragmites australis) assessment.

ESTIMATION OF PHOTOVOLTAIC POTENTIAL AT THE URBAN LEVEL FROM 3D CITY MODEL (SOLAR CADASTER): CASE OF CASABLANCA CITY, MOROCCO

Nowadays, the use of solar energy in buildings, especially photovoltaic energy, has undergone a great evolution in the world, thanks to various technological advances and to incentive programs. Related to this topic, the solar cadaster is an important interactive tool to predict the solar potential in an urban environment. The main objective of this research work is to estimate the photovoltaic energy potential of roofs based on aerial photogrammetry and GIS processing. The location chosen for the study is the Maarif district located in the city of Casablanca in order to raise awareness of the public and decision makers to this energy potential through a geoportal that will be developed for this purpose. The tool proposed in this research work makes it possible to evaluate the solar irradiation on a part of the territory of Casablanca with a sufficiently satisfactory precision and reliability, this thanks to the precise reconstruction of the territory in 3D urban model called digital surface model (DSM) at 50 cm resolution by techniques known as photogrammetry which makes it possible to carry out measurements extracted from a stereoscopic pairs, by using the parallax and the correlation between the digital images taken from various points of view. The analysis was used on the basis of specific algorithms and several factors including geographical location, shade, tilt, orientation, roof accessibility and topography which are the main factors influencing the productivity of solar panels.

Classification of Mediterranean Shrub Species from UAV Point Clouds

Modelling fire behaviour in forest fires is based on meteorological, topographical, and vegetation data, including species’ type. To accurately parameterise these models, an inventory of the area of analysis with the maximum spatial and temporal resolution is required. This study investigated the use of UAV-based digital aerial photogrammetry (UAV-DAP) point clouds to classify tree and shrub species in Mediterranean forests, and this information is key for the correct generation of wildfire models. In July 2020, two test sites located in the Natural Park of Sierra Calderona (eastern Spain) were analysed, registering 1036 vegetation individuals as reference data, corresponding to 11 shrub and one tree species. Meanwhile, photogrammetric flights were carried out over the test sites, using a UAV DJI Inspire 2 equipped with a Micasense RedEdge multispectral camera. Geometrical, spectral, and neighbour-based features were obtained from the resulting point cloud generated. Using these features, points belonging to tree and shrub species were classified using several machine learning methods, i.e., Decision Trees, Extra Trees, Gradient Boosting, Random Forest, and MultiLayer Perceptron. The best results were obtained using Gradient Boosting, with a mean cross-validation accuracy of 81.7% and 91.5% for test sites 1 and 2, respectively. Once the best classifier was selected, classified points were clustered based on their geometry and tested with evaluation data, and overall accuracies of 81.9% and 96.4% were obtained for test sites 1 and 2, respectively. Results showed that the use of UAV-DAP allows the classification of Mediterranean tree and shrub species. This technique opens a wide range of possibilities, including the identification of species as a first step for further extraction of structure and fuel variables as input for wildfire behaviour models.

Percepción remota y procesamiento de imágenes para la gestión de cultivos de caña de azúcar

This research work shows that with the use of remote sensing technology it is possible to more effectively fulfill two of the purposes pursued by farmers in the field; manage crops more efficiently and include environmental care in decision-making. Specifically, remote sensing is applied in the context of precision agriculture through geographic information systems (GIS), unmanned aerial vehicles (UAV), multispectral sensors that capture the reflectance of the infrared band of the light spectrum (for interpretation of the biochemical state of the crop), global geopositioning systems (GPS), among others. This study limits the use of this technology to the processing of multispectral images obtained by aerial photogrammetry, and its subsequent treatment for the generation of orthoimages, the calculation of the NDVI vegetation index and the classification of land cover by clustering. Finally, the effect of classification with RGB and multispectral images is analyzed.

Research and Application of the 3D Model of Beijing in Urban Planning Based on Oblique Photogrammetry

In recent years, with the accelerated development of the urbanization and the continuous emergence of the smart cities, the new requirements and the challenges have been raised for the urban governance and the urban planning. Based on the oblique aerial photogrammetry technology and the 3D automatic modelling technology, this study constructs the high-precision basic data of the 3D urban model of Beijing and verifies the 3D model’s precision comprehensively by using two verification methods: the point accuracy assessment and the plane accuracy assessment. And then, using the validated 3D model data and taking the demolition of the illegal buildings in urban planning as an example, an application of the 3D model is studied in the simulated environmental scenes of the urban planning, which also provides a reference for the development of the smart cities in the future.

Using 3D Modeling to Promote Railway Heritage. The Railway Station of Curtea De Argeş Municipality as Case Study

The characteristics of the industrial heritage (antiquity, architectural, cultural, technological value) determined its inscription in the category of historical monuments. In recent years, non-invasive digital technologies have been used in studies focused on documenting, digitizing, preserving elements of industrial heritage. Also, another objective of digitizing the industrial heritage is to facilitate its promotion as a cultural resource among the general public. The purpose of this study is to promote the railway station of Curtea de Argeş through non-invasive technology. The analyzed industrial monument represents a symbolic building of the neo-Romanian architectural style. The building is also associated with a remarkable historical value: the railway station was also used by the Romanian royal family. Based on the use of terrestrial photogrammetry (versatile GNSS RTK GS18 I sensor) and aerial (photogrammetric flight), the authors created the 3D model of the station, obtaining a high-resolution modeling. The results of this study reflect the usefulness of modern technology for documenting, 3D modeling and promoting an industrial monument inscribed on the list of national cultural heritage. The accuracy and optimal performance of the measurements made, using GNSS technology and aerial photogrammetry, allowed highlighting the remarkable architectural and volumetric characteristics of the railway station of Curtea de Argeş Municipality.

Regional Ranking of Marine Turtle Nesting in Remote Western Australia by Integrating Traditional Ecological Knowledge and Remote Sensing

Western Australia’s remote Kimberley coastline spans multiple Traditional Owner estates. Marine turtle nesting distribution and abundance in Indigenous Protected Areas and newly declared Marine Parks were assessed by aerial photogrammetry surveys for the Austral summer and winter nesting seasons. Images of nesting tracks were quantified in the lab and verified by ad hoc ground patrols. The rankings of log-scaled plots of track abundance and density give guidance to regional co-management planning. Spatial and temporal differences were detected in that remoter islands had higher nesting usage and few terrestrial predators. The surveys found year-round green turtle nesting peaking in summer, as well as spatial boundaries to the summer and winter flatback stocks. Summer surveys recorded 126.2 island activities per km and 17.7 mainland activities per km. Winter surveys recorded 65.3 island activities per km and quantified a known winter mainland rookery with 888 tracks/km. The three highest density rookeries were found to be winter flatback turtles at Cape Domett, summer green turtles at the Lacepede Islands and summer flatback turtles at Eighty Mile Beach. Moderate to lesser density nesting by summer green turtles and winter flatback turtles occurred in the North Kimberley offshore islands. Traditional Ecological Knowledge and ground-based surveys verified the harder-to-detect species (olive ridley or hawksbill turtles) with irregular nesting, low track persistence and non-aggregated nesting. Higher-density rookeries may provide locations for long-term monitoring using repeated aerial or ground surveys; however, the sparse or infrequently nesting species require insights gleaned by Tradition Ecological Knowledge. Common and conspicuous nesters are easily detected and ranked, but better-informed co-management requires additional ground surveys or surveys timed with the reproductive peaks of rarer species.