Green Infrastructure Mapping in Urban Areas Using Sentinel-1 Imagery

High temporal resolution of synthetic aperture radar (SAR) imagery (e.g., Sentinel-1 (S1) imagery) creates new possibilities for monitoring green vegetation in urban areas and generating land-cover classification (LCC) maps. This research evaluates how different pre-processing steps of SAR imagery affect classification accuracy. Machine learning (ML) methods were applied in three different study areas: random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGB). Since the presence of the speckle noise in radar imagery is inevitable, different adaptive filters were examined. Using the backscattering values of the S1 imagery, the SVM classifier achieved a mean overall accuracy (OA) of 63.14%, and a Kappa coefficient (Kappa) of 0.50. Using the SVM classifier with a Lee filter with a window size of 5×5 (Lee5) for speckle reduction, mean values of 73.86% and 0.64 for OA and Kappa were achieved, respectively. An additional increase in the LCC was obtained with texture features calculated from a grey-level co-occurrence matrix (GLCM). The highest classification accuracy obtained for the extracted GLCM texture features using the SVM classifier, and Lee5 filter was 78.32% and 0.69 for the mean OA and Kappa values, respectively. This study improved LCC with an evaluation of various radiometric and texture features and confirmed the ability to apply an SVM classifier. For the supervised classification, the SVM method outperformed the RF and XGB methods, although the highest computational time was needed for the SVM, whereas XGB performed the fastest. These results suggest pre-processing steps of the SAR imagery for green infrastructure mapping in urban areas. Future research should address the use of multitemporal SAR data along with the pre-processing steps and ML algorithms described in this research

Konferencija SGEM 2018, Bugarska

Dan je prikaz o konferenciji SGEM 2018, koja je održana u Bugarskoj

Konferencija SGEM 2018, Bugarska

Dan je prikaz o konferenciji SGEM 2018, koja je održana u Bugarskoj

Spatial Accuracy Analysis of Aerial and Satellite Imagery of Zagreb

Svrha je ovog istraživanja analiza prostorne točnosti zračnih i satelitskih snimaka. U današnje vrijeme na raspolaganju su mnogobrojne satelitske i zračne snimke putem javno dostupnih i besplatnih servisa i izvora. U ovom radu analizirana je prostorna točnost WorldView-2 (WV2) Ortho Ready Standard snimaka (ORS2A), ortorektificiranih WV2 snimaka pomoću SRTM (engl. Shuttle Radar Topography Mission) digitalnog modela reljefa (DMR), snimaka dostupnih s Google Earth servisa i točnost digitalnih ortofoto karata (DOF), za 2011. i 2012. godinu. Područje istraživanja ovog rada središnji je dio grada Zagreba s dijelom Medvednice na sjeveru te rijeka Sava i nizinski dio na jugu, u veličini od 131 km2. WV2 snimke pribavljene su u sklopu projekta GEMINI (Geoprostorno praćenje zelene infrastrukture na temelju terestričkih, zračnih i satelitskih snimaka). Rezultati istraživanja pokazuju kako su ORS2A snimke najlošije točnosti, dok su zračne snimke (DOF) najbolje točnosti. Točnost ortorektificiranih WV2 snimaka pomoću SRTM DMR-a u prosjeku je veća za oko 4,5 puta u odnosu na ORS2A snimke, dok je preciznost ortorektificiranih snimaka u prosjeku veća za oko 13 puta u odnosu na ORS2A snimke. Točnost ortorektificiranih i Google Earth snimaka je podjednaka, dok je preciznost ortorektificiranih snimaka veća za 35% u odnosu na Google Earth snimke. Cjelokupna istraživanja u ovom radu provedena su primjenom programa otvorenoga koda u kombinaciji s besplatno dostupnim i javnim podacima. Na taj se način budućim istraživačima olakšava mogućnost ponovne provedbe postupka analize prostorne točnosti za druga područja u Republici Hrvatskoj i svijetu.The main objective of this research is spatial accuracy analysis of aerial and satellite imagery. Nowadays, many satellite and aerial imagery are available through publicly and freely accessible sources and services. In this paper spatial accuracy for WorldView-2 (WV2) Ortho Ready Standard imagery (ORS2A), orthorectified WV2 imagery with Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM), imagery accessed through Google Earth service and accuracy for digital orthophoto (DOF) from 2011 and 2012 will be analysed. The study area is located in the central part of Zagreb, and covers the area of 131 square km part of the mountain Medvednica in the north, along with river Sava and lowland areas in the south. WV2 imagery were purchased within project GEMINI (Geospatial monitoring of green infrastructure using terrestrial, airborne and satellite imagery). Results of this research show that the ORS2A imagery achieved the worst accuracy, while aerial imagery (DOF) gained the best accuracy. The accuracy of the orthorectified WV2 imagery with SRTM DEM is on average 4.5 times higher than the ORS2A imagery, while the precision of the orthorectified imagery is on average 13 times higher than the ORS2A imagery. The accuracy of the orthorectified and Google Earth imagery is similar, while the precision of the orthorectified imagery is 35% higher than the Google Earth imagery. Entire research was conducted with using open-source software in combination with freely available and public data. In this way, future research can be easily conducted and reproduced for spatial accuracy analysis on other areas in Croatia and other locations

Usporedba terestričkih laserskih skenera

Terestričko lasersko skeniranje je tehnologija 3D izmjere koja omogućava prikupljanje velikog broja točaka u kratkom vremenu. Usmjeravanjem laserske zrake u horizontalnoj i vertikalnoj ravnini te rotacijom instrumenta oko svoje osi, terestrički laserski skeneri (TLS) mjere veliki broj točaka u 3D koordinatnom sustavu. Iako je osnovni princip mjerenja poznat, ne postoji jedan univerzalni terestrički laserski skener kojim bi se mogla obaviti sva mjerenja. Zbog toga postoje podjele laserskih skenera prema načinu mjerenja udaljenosti na pulsne, fazne i triangulacijske laserske skenere, dok se prema načinu snimanja dijele na kamera skenere, panoramske i hibridne skenere. Upravo zbog raznih podjela terestričkih laserskih skenera, nije moguća direktna usporedba tehničkih specifikacija instrumenata što otežava korisnicima odabir pravog skenera za određeni posao. Budući da još ne postoji standard za usmjeravanje laserske zrake, neophodno je istražiti terestričke laserske skenere za dobivanje nezavisne procjene preciznosti instrumenta, kao i zbog razvijanja standardiziranih kalibracijskih modela i postupaka. Stoga su u članku testirana četiri terestrička laserska skenera različitih proizvođača: Topcon GLS-1500, Faro Focus 3D, Trimble GX200 i Optech ILRIS 36D. Instrumenti korišteni u istraživanju razlikuju se prema načinu mjerenja udaljenosti te prema načinu snimanja. Cilj praktičnog dijela bio je utvrditi pogreške u očitanju duljine koje se javljaju uslijed pogrešnog odbijanja laserske zrake od određene vrste materijala ili neke specifične boje. Dio analize odnosi se na ispitivanje mjerne ponovljivosti instrumenata gdje je u tri serije mjerenja opažano po pet orijentacijskih točaka te su analizirana odstupanja mjerenih koordinata kod ponovljenih mjerenja

Sentinel-1 and 2 Time-Series for Vegetation Mapping Using Random Forest Classification: A Case Study of Northern Croatia

Land-cover (LC) mapping in a morphologically heterogeneous landscape area is a challenging task since various LC classes (e.g., crop types in agricultural areas) are spectrally similar. Most research is still mostly relying on optical satellite imagery for these tasks, whereas synthetic aperture radar (SAR) imagery is often neglected. Therefore, this research assessed the classification accuracy using the recent Sentinel-1 (S1) SAR and Sentinel-2 (S2) time-series data for LC mapping, especially vegetation classes. Additionally, ancillary data, such as texture features, spectral indices from S1 and S2, respectively, as well as digital elevation model (DEM), were used in different classification scenarios. Random Forest (RF) was used for classification tasks using a proposed hybrid reference dataset derived from European Land Use and Coverage Area Frame Survey (LUCAS), CORINE, and Land Parcel Identification Systems (LPIS) LC database. Based on the RF variable selection using Mean Decrease Accuracy (MDA), the combination of S1 and S2 data yielded the highest overall accuracy (OA) of 91.78%, with a total disagreement of 8.22%. The most pertinent features for vegetation mapping were GLCM Mean and Variance for S1, NDVI, along with Red and SWIR band for S2, whereas the digital elevation model produced major classification enhancement as an input feature. The results of this study demonstrated that the aforementioned approach (i.e., RF using a hybrid reference dataset) is well-suited for vegetation mapping using Sentinel imagery, which can be applied for large-scale LC classifications

Mapping of Allergenic Tree Species in Highly Urbanized Area Using PlanetScope Imagery—A Case Study of Zagreb, Croatia

Mapping and identifying allergenic tree species in densely urbanized regions is vital for understanding their distribution and prevalence. However, accurately detecting individual allergenic tree species in urban green spaces remains challenging due to their smaller site and patchiness. To overcome these issues, PlanetScope (PS) satellite imagery offers significant benefits compared with moderate or high-resolution RS imagery due to its daily temporal resolution and 3 m spatial resolution. Therefore, the primary objectives of this research were to: assess the feasibility of mapping allergenic tree species in the highly urbanized area using high-resolution PS imagery; evaluate and compare the performance of the most important machine learning and feature selection methods for accurate detection of individual allergenic tree species. The research incorporated three classification scenarios based on ground truth data: The first scenario (CS1) used single-date PS imagery with vegetation indices (VI), while the second and third scenarios (CS2 and CS3) used multitemporal PS imagery with VI, and GLCM and VI, respectively. The study demonstrated the feasibility of using multitemporal eight-band PlanetScope imagery to detect allergenic tree species, with the XGB method outperforming others with an overall accuracy of 73.13% in CS3. However, the classification accuracy varied between the scenarios and species, revealing limitations including the inherent heterogeneity of urban green spaces. Future research should integrate high-resolution satellite imagery with aerial photography or LiDAR data along with deep learning methods. This approach has the potential to classify dominant tree species in highly complex urban environments with increased accuracy, which is essential for urban planning and public health

Usporedba terestričkih laserskih skenera

Terestričko lasersko skeniranje je tehnologija 3D izmjere koja omogućava prikupljanje velikog broja točaka u kratkom vremenu. Usmjeravanjem laserske zrake u horizontalnoj i vertikalnoj ravnini te rotacijom instrumenta oko svoje osi, terestrički laserski skeneri (TLS) mjere veliki broj točaka u 3D koordinatnom sustavu. Iako je osnovni princip mjerenja poznat, ne postoji jedan univerzalni terestrički laserski skener kojim bi se mogla obaviti sva mjerenja. Zbog toga postoje podjele laserskih skenera prema načinu mjerenja udaljenosti na pulsne, fazne i triangulacijske laserske skenere, dok se prema načinu snimanja dijele na kamera skenere, panoramske i hibridne skenere. Upravo zbog raznih podjela terestričkih laserskih skenera, nije moguća direktna usporedba tehničkih specifikacija instrumenata što otežava korisnicima odabir pravog skenera za određeni posao. Budući da još ne postoji standard za usmjeravanje laserske zrake, neophodno je istražiti terestričke laserske skenere za dobivanje nezavisne procjene preciznosti instrumenta, kao i zbog razvijanja standardiziranih kalibracijskih modela i postupaka. Stoga su u članku testirana četiri terestrička laserska skenera različitih proizvođača: Topcon GLS-1500, Faro Focus 3D, Trimble GX200 i Optech ILRIS 36D. Instrumenti korišteni u istraživanju razlikuju se prema načinu mjerenja udaljenosti te prema načinu snimanja. Cilj praktičnog dijela bio je utvrditi pogreške u očitanju duljine koje se javljaju uslijed pogrešnog odbijanja laserske zrake od određene vrste materijala ili neke specifične boje. Dio analize odnosi se na ispitivanje mjerne ponovljivosti instrumenata gdje je u tri serije mjerenja opažano po pet orijentacijskih točaka te su analizirana odstupanja mjerenih koordinata kod ponovljenih mjerenja