The green view dataset for the capital of Finland, Helsinki

Recent studies have incorporated human perspective methods like making use of street view images and measuring green view in addition to more traditional ways of mapping city greenery [1]. Green view describes the relative amount of green vegetation visible at street level and is often measured with the green view index (GVI), which describes the percentage of green vegetation in a street view image or images of a certain location [2]. The green view dataset of Helsinki was created as part of the master's thesis of Akseli Toikka at the University of Helsinki [3]. We calculated the GVI values for a set of locations on the streets of Helsinki using Google Street View (GSV) 360° panorama images from summer months (May through September) between 2009 and 2017. From the available images, a total of 94 454 matched the selection criteria. These were downloaded using the Google application programming interface (API). We calculated the GVI values from the panoramas based on the spectral characteristics of green vegetation in RGB images. The result was a set of points along the street network with GVI values. By combining the point data with the street network data of the area, we generated a dataset for GVI values along the street centre lines. Streets with GVI points within a threshold distance of 30 meters were given the average of the GVI values of the points. For the streets with no points in the vicinity (∼67%), the land cover data from the area was used to estimate the GVI, as suggested in the thesis [3]. The point and street-wise data are stored in georeferenced tables that can be utilized for further analyses with geographical information systems.Peer reviewe

RGB vegetation indices applied to grass monitoring: a qualitative analysis

ArticleIn developing countries such as Brazil, research on low-cost remote sensing and computational techniques become essential for the development of precision agriculture (PA), and improving the quality of the agricultural products. Faced with the scenario of increasing production of emerald grass (Zoysia Japônica) in Brazil, and the value added the quality of this agricultural product. The objective of this work was to evaluate the performance of RGB (IV) vegetation indices in the identification of exposed soil and vegetation. The study was developed in an irrigated area of 58 ha cultivated with emerald grass at Bom Sucesso, Minas Gerais, Brazil. The images were obtained by a RGB digital camera coupled to an remotely piloted aircraft. The flight plan was setup to take overlapping images of 70% and the aircraft speed was 10 m s -1 . Six RGB Vegetation index (MGVRI, GLI, RGBVI, MPRI, VEG, ExG) were evaluated in a mosaic resulting from the images of the study area. All of the VIs evaluated were affected by the variability of lighting conditions in the area but MPRI and MGVRI were the ones that presented the best results in a qualitative evaluation regarding the discrimination of vegetation and soil

Color description of low resolution images using fast bitwise quantization and border-interior classification

Image classification often require preprocessing and feature extraction steps that are directly related to the accuracy and speed of the whole task. In this paper we investigate color features extracted from low resolution images, assessing the influence of the resolution settings on the final classification accuracy. We propose a border-interior classification extractor with a logarithmic distance function in order to maintain the discrimination capability in different resolutions. Our study shows that the overall computational effort can be reduced in 98%. Besides, a fast bitwise quantization is performed for its efficiency on converting RGB images to one channel images. The contributions can benefit many applications, when dealing with a large number of images or in scenarios with limited network bandwidth and concerns with power consumption.FAPESP (grants # 10/19159-1 and 11/22749-8)CNPq (grant # 482760/2012-5

Extração de telhados por meio da classificação orientada a objetos de imagens de ARPs no município de Monte Carmelo - MG

Pesquisa sem auxílio de agências de fomentoTrabalho de Conclusão de Curso (Graduação)Visando o crescente avanço das técnicas de fotogrametria digital, sensoriamento remoto, processamento digital de imagens e da visão computacional, podem ser obtidos novos mecanismos de otimização no ramo dos serviços, em especial para a área das engenharias. Com isso, este trabalho apresentou uma metodologia que visa a extração semiautomática de telhados por meio da classificação orientada a objetos utilizando o software eCognition e um mosaico originado de voo com ARP. O trabalho apresentou algumas inconsistências na classificação dos telhados de cerâmica, concreto, metal e fibrocimento, bem como as dificuldades que são encontradas durante os procedimentos desse tipo de classificação. O resultado obtido por meio da análise estatística demonstrou que, com base nos 30 pontos amostrados ao nível de significância de 5%, aceitou-se a hipótese de que não há diferença na média entre as coordenadas E (Leste) e N (Norte) do centroide dos polígonos vetorizados manualmente e classificados. Também foi aceita a hipótese de que não há diferença entre a média das áreas construídas dos polígonos vetorizados manualmente e classificados. Em contrapartida, analisando a área definida como representação do desempenho da classificação, conclui-se que as feições extraídas não podem ser utilizadas para fins cadastrais, devido ao erro relativo percentual encontrado nas três edificações distintas. Porém, o método poderá ser utilizado em demais atividades de identificações de edificações, como por exemplo, na detecção de novos imóveis presentes nos municípios, permitindo a atualização do banco de dados com informações importantes para o planejamento urbano e demais ações. Para a metodologia do trabalho se tornar viável, é recomendável que seja feita uma adaptação de forma a garantir a obtenção e extração das informações, aumentando a precisão e acurácia das classificações

Helsingin vihernäkymien kartoitus Googlen katunäkymäkuvista

Kaupunkikasvillisuutta on perinteisesti kartoitettu kaukokartoitusmenetelmin kuten laserkeilaamalla ja ilmakuvatulkintana. Ylhäältä käsin tehtävä kaukokartoitus ei kuitenkaan aina pysty antamaan todenmukaista kuvaa siitä vihreän kasvillisuuden määrästä, jonka ihminen näkee kadulla liikkuessaan. Perinteisten menetelmien rinnalle on viimeaikaisissa tutkimuksissa esitetty katunäkymäkuvilta havainnoitavaa vihernäkymää. Vihernäkymää mitataan viherindeksillä, joka kertoo vihreän kasvillisuuden prosentuaalisen osuuden katunäkymästä tietyllä sijainnilla. Tämän tutkimuksen tavoitteena oli luoda katunäkymistä laskettu vihernäkymäaineisto Helsingistä, sekä tutkia ihmisen perspektiivin ja ylhäältä päin kuvatun aineiston eroja kaupunkivihreyden kartoituksessa. Tutkimuksen aineistona käytettiin Googlen ohjelmointirajapinnasta ladattuja katunäkymäkuvia Helsingistä. Aineisto rajautui niille alueille, joilta Googlen katunäkymäkuvia oli saatavilla kesäkuukausilta. Perustuen katunäkymäkuvilta laskettuihin viherindeksi arvoihin, laadittiin Helsingistä vihernäkymäkarttoja eri spatiaalisilla tarkastelutasoilla. Jotta voitaisi ymmärtää perspektiiveistä aiheutuvia eroja, vihernäkymää vertailtiin Helsingin seudulliseen maanpeiteaineistoon lineaarisella regressiolla. Alueita, joilla aineistot erosivat toisistaan huomattavasti, tarkasteltiin visuaalisesti katunäkymäkuvien kautta. Osana tutkimusta Helsingin vihernäkymää vertailtiin myös kansainvälisesti kaupunkeihin, joista vastaava aineisto oli saatavilla. Tutkimuksessa ilmeni Helsingin vihernäkymän jakautuvan varsin epätasaisesti. Alhaisimpia viherindeksi arvoja esiintyi erityisesti kantakaupungissa, teollisuusalueilla, sekä lähi- ja liikekeskuksissa. Korkeimpia viherindeksiarvoja havaittiin omakotitalovaltaisilla asuinalueilla. Vertailtaessa maanpeiteaineistoon, viherindeksin havaittiin korreloivan heikosti matalan kasvillisuuden kanssa. Puuston kanssa korrelaatio oli selvästi voimakkaampi. Eroja aineistojen välillä havaittiin olevan erityisesti alueilla, joilla kasvillisuus ei erilaisista syistä näy kadulle. Virhelähteitä aiheuttivat vanhimmat katunäkymäkuvat, sekä kasvillisuuden tunnistusmenetelmään liittyvät virheet, kuten muut vihreät objektit, sekä kirkkaiden valaistusolosuhteiden aiheuttamat varjot. Vaikka Helsingissä on paljon puistoja ja viheralueita, katunäkymä ei aina näyttäydy kovin vihreänä. Tässä tutkimuksessa luotu aineisto auttaa ymmärtämään ihmisten havainnoiman katuvihreyden alueellista jakautumista ja tuo ihmisen näkökulman perinteisten kaukokartoitusaineistojen rinnalle. Yhdistettynä aikaisempiin kaupunkivihreysaineistoihin, vihernäkymäaineisto auttaa rakentamaan kokonaisvaltaisemman kuvan Helsingin kaupunkivihreydestä.Urban vegetation has traditionally been mapped through traditional ways of remote sensing like laser scanning and aerial photography. However, it has been stated that the bird view examination of vegetation cannot fully represent the amount of green vegetation that the citizens observe on street level. Recent studies have raised human perspective methods like street view images and measuring of green view next to more traditional ways of mapping vegetation. Green view index states the percentage of green vegetation in street view on certain location. The purpose for this study was to create a green view dataset of Helsinki city through street view imagery and to reveal the differences between human perspective and aerial perspective in vegetation mapping. Street view imagery of Helsinki was downloaded from Google street view application interface. The spatial extent of the data was limited by the availability of street view images of summer months. Several green view maps of Helsinki were created based on the green view values calculated on the street view images. In order to understand the differences between human perspective and the aerial view, the green view values were compared with the regional land cover dataset of Helsinki trough linear regression. Areas with big differences between the datasets were examined visually through the street view imagery. Helsinki green view was also compared internationally with other cities with same kind of data available. It appealed that the green view of Helsinki was divided unequally across the city area. The lowest green view values were found in downtown, industrial areas and the business centers of the suburbs. Highest values were located at the housing suburbs. When compared with the land cover, it was found that the green view has a weak correlation with low vegetation and relatively high correlation with taller vegetation such as trees. Differences between the datasets were mainly concentrated on areas where the vegetation was not visible from the street by several reasons. Main sources of errors were the oldest street view images and the flaws in image classification caused by other green objects and shadows. Even though Helsinki has many parks and other green spaces, the greenery visible to the streets isn’t always that high. The green view dataset created in this study helps to understand the spatial distribution of street greenery and brings human perspective next to more traditional ways of mapping city vegetation. When combined with previous city greenery datasets, the green view dataset can help to build up more holistic understanding of the city greenery in Helsink

Analysis of microtopography, vegetation, and active-layer thickness using terrestrial LIDAR and kite photography, Barrow, AK

Arctic regions underlain by permafrost are among the most vulnerable to impacts from climate change. This study examined changes in the active layer of permafrost near Barrow, Alaska at very fine scale to capture subtle changes related to microtopography and landcover. In 2010, terrestrial LIDAR was used to collect high-resolution elevation data for four 10 m × 10 m plots where maximum active-layer thickness (ALT) and elevation have been monitored on an annual basis since the mid-1990s and had been monitored in the 1960s as well. The raw LIDAR point cloud was analyzed and processed into four 10 cm resolution digital elevation models (DEMs). Elevation data, collected using differential global positioning system (DGPS) to assess heave and subsidence, has been gathered annually since 2004 and was used to assess the accuracy of the DEMs generated for August 2010. Higher-resolution DEMs did not have higher accuracy compared to the DGPS control points due to artifacts inherent in the LIDAR data. The four DEMs were used to classify each plot based on microtopographical variations derived from terrain attributes including elevation, slope, and Melton’s Ruggedness Number (MRN). Landcover at each plot was classified using the Visible Vegetation Index (VVI), calculated from a series of high-resolution (~10 cm) kite photographs obtained in August 2012 by researchers from the University of Texas – El Paso. The microtopography and land-cover classifications were then used to analyze ALT and elevation data from a range of years. Analysis revealed little difference in either dataset based upon microtopography and landcover. The high amount of interclass and interannual variation made it difficult to draw any conclusions about temporal trends. The results suggest that while microtopography and vegetation are important factors within the complex interaction which determines ALT, the scale of analysis made possible by the high-resolution data utilized in this study did not significantly enhance understanding of the main controlling mechanisms. While terrestrial LIDAR is excellent for many applications, particularly those with substantial vertical variability, for future research at this scale on relatively flat topography, airborne LIDAR may be more suitable

Topographic mapping of rock formations usig GIS methods

Mapování skalních útvarů pomocí geoinformačních metod Abstrakt Tato diplomová práce se zabývá problematikou mapování skalních útvarů z dat pozemního laserového skenování, pozemní fotogrammetrie či UAV fotogrammetrie a automatické filtrace vegetace z nich. Teoretická část práce se zaměřuje na popis fungování a využití těchto metod. Popsána je zde i problematika filtrování 3D bodových mračen. V praktické části práce je popsán postup sběru dat v terénu a jejich následné zpracování. Dále jsou zde použity některé filtrační funkce, které z bodových mračen odstraňují odlehlá měření a vegetaci pomocí vegetačního indexu ExG, klastrovacího algoritmu DBSCAN a Houghovy transformace. Navržený postup je otestována na vybraném skalním útvaru v národním parku České Švýcarsko. Hodnocení použitého filtračního postupu je provedeno na základě porovnání modelů filtrovaných pomocí automatické filtrace s referenčním modely, které byly filtrovány manuálně. Závěrem je vyhodnocena dosažená přesnost modelů pomocí geodetického měření. klíčová slova laserové skenování, fotogrammetrie, UAV, bodové mračno, filtrace datTopographic mapping of rock formations using GIS methods Abstract This thesis deals with issues of creating 3D models of rock formations with data from terrestrial laser scanning, close range photogrammetry and UAV photogrammetry. The theoretical part focuses on explaining functioning and usage of those methods. Beside that there is described issues of 3D point cloud filtering. Practical part of this work describes data collecting and processing procedure. Further there is proposed filtering process which aim to remove noise points from point clouds and remove vegetation with combination of vegetation index ExG, clustering algorithm DBSCAN and Hough Transform. The proposed method is tested on the selected rock formation in Bohemian Switzerland National Park. The evaluation of the proposed method is based on comparison of models filtered with proposed method with reference models, which are filtered manually. Finally, the achieved accuracy of the models is evaluated using geodetic measurements. key words laser scanning, photogrammetry, UAV, point cloud, data filteringDepartment of Applied Geoinformatics and CartographyKatedra aplikované geoinformatiky a kartografiePřírodovědecká fakultaFaculty of Scienc

Nitrous oxide emissions from grazed grasslands: novel approaches to assessing spatial heterogeneity

Nitrous oxide (N2O) is a potent greenhouse gas mainly produced by microbial processes in the soil. Anthropogenic N2O is principally emitted from soils after nitrogen fertiliser and manure applications on agricultural land. This thesis focuses on emissions from grazing systems, which are known to be the largest source of uncertainty in global and national N2O emission inventories. Nitrogen-rich excreta deposits from grazing livestock are recognised as hotspots of N losses (N2O emissions in particular). The non-uniform distribution of these emissions hotspots within a typical field contributes significantly to the spatial heterogeneity of emissions often observed in addition to the natural variability of soil properties within the field such as pH, moisture and nutrient availability. However, it is extremely difficult to characterise the spatial and temporal pattern of these grazing inputs other than through the use of demanding and costly approaches such as manual observation or animal based-sensors. Two separate experiments were conducted during this study, in Scotland on sheep grazed grasslands and in Ireland on a dairy cow grazed grassland. Both sites were commercially used and were intensively managed with a nitrogen fertiliser application rate of 225 kg ha-1 yr-1 and 261 kg ha-1 yr-1, respectively. In Scotland, at Easter Bush fields the experiment was conducted during a 9 month campaign of gas, soil and grass sampling over the grazed field to study the spatial and temporal variability of the fluxes and soil properties to improve up-scaling of the fluxes from the plot scale to the field scale. In Ireland, at the Johnstown Castle farm, the experiment was conducted during an 11 month campaign on an experimental plot excluded from grazing. At the Scottish site, gas, soil and grass samples were collected regularly on soil which received different treatments within a randomised block design (e.g. urine deposition, fertiliser application, urine and fertiliser application or no N addition as a control). At both sites, Remotely Piloted Aerial System (RPAS) imagery was collected to study the spatial variability of the grass growth with the aim to map excreta depositions over the whole field. The Scottish site was used as a proof of concept of the method and the method was then used weekly on the Irish site over the entire grazing season. More generally, this thesis details the novel use of remote sensing techniques using high-resolution cameras linked to RPAS to improve our understanding of the spatial and temporal patterns of excreta deposition. This method proved to be repeatable for future studies as it can be automated, is easily deployable in the field, low-cost and the measurements are non-destructive (i.e. has no influence on the soil, vegetation or livestock). Excreta depositions contribute to very high emissions of N2O from relatively small areas of soil and can vary throughout the growing season in response to climatic conditions. Therefore, mapping of the excreta nitrogen inputs to the field facilitated a more accurate estimation of the annual field-scale N2O emission from grazing grasslands. Both experiments conducted in this study showed a high spatial and temporal N2O emissions variability due to the nature of N2O production within the soil and high variability of the soil properties (soil pH, soil moisture content, soil temperature) which influence the microbiological processes. Interaction on N2O emissions between fertiliser application and urine deposition was proved to be statistically significant and the magnitude of the interactions depended on the time of application within the year. The results showed a link between the variability of the emission factors of excreta deposition and fertiliser application and to the variation in weather conditions. This technique can be employed to up-scale emissions to a national level. This study plays a part in the on-going development of precision agricultural tools, based on image analysis of the grass sward to mitigate emissions from grazed grassland. Possible mitigation approaches, based on the methods presented in this thesis, include the use of RPAS technology to deliver nitrification inhibitors to newly deposited excreta within the field to reduce the potential nitrogen losses to the environment. This research indicates the future potential to better adjust fertiliser application using variable-rate fertiliser applications matching the vegetation nitrogen needs and limit nitrogen losses. This thesis identifies opportunities to develop innovative approaches to N2O mitigation by better evaluating emission estimations from agricultural practices, which could then be implemented in the national and global greenhouse gas inventories established by the Intergovernmental Panel on Climate Change