Remote Sensing methods for power line corridor surveys

AbstractTo secure uninterrupted distribution of electricity, effective monitoring and maintenance of power lines are needed. This literature review article aims to give a wide overview of the possibilities provided by modern remote sensing sensors in power line corridor surveys and to discuss the potential and limitations of different approaches. Monitoring of both power line components and vegetation around them is included. Remotely sensed data sources discussed in the review include synthetic aperture radar (SAR) images, optical satellite and aerial images, thermal images, airborne laser scanner (ALS) data, land-based mobile mapping data, and unmanned aerial vehicle (UAV) data. The review shows that most previous studies have concentrated on the mapping and analysis of network components. In particular, automated extraction of power line conductors has achieved much attention, and promising results have been reported. For example, accuracy levels above 90% have been presented for the extraction of conductors from ALS data or aerial images. However, in many studies datasets have been small and numerical quality analyses have been omitted. Mapping of vegetation near power lines has been a less common research topic than mapping of the components, but several studies have also been carried out in this field, especially using optical aerial and satellite images. Based on the review we conclude that in future research more attention should be given to an integrated use of various data sources to benefit from the various techniques in an optimal way. Knowledge in related fields, such as vegetation monitoring from ALS, SAR and optical image data should be better exploited to develop useful monitoring approaches. Special attention should be given to rapidly developing remote sensing techniques such as UAVs and laser scanning from airborne and land-based platforms. To demonstrate and verify the capabilities of automated monitoring approaches, large tests in various environments and practical monitoring conditions are needed. These should include careful quality analyses and comparisons between different data sources, methods and individual algorithms

Quarterly literature review of the remote sensing of natural resources

The Technology Application Center reviewed abstracted literature sources, and selected document data and data gathering techniques which were performed or obtained remotely from space, aircraft or groundbased stations. All of the documentation was related to remote sensing sensors or the remote sensing of the natural resources. Sensors were primarily those operating within the 10 to the minus 8 power to 1 meter wavelength band. Included are NASA Tech Briefs, ARAC Industrial Applications Reports, U.S. Navy Technical Reports, U.S. Patent reports, and other technical articles and reports

The application of remote sensing techniques: Technical and methodological issues

Capabilities and limitations of modern imaging electromagnetic sensor systems are outlined, and the products of such systems are compared with those of the traditional aerial photographic system. Focus is given to the interface between the rapidly developing remote sensing technology and the information needs of operational agencies, and communication gaps are shown to retard early adoption of the technology by these agencies. An assessment is made of the current status of imaging remote sensors and their potential for the future. Public sources of remote sensor data and several cost comparisons are included

Fotogrametría de rango cercano aplicada a la Ingeniería Agroforestal

Tesis por compendio de publicaciones[EN]Since the late twentieth century, Geotechnologies are being applied in different research lines in Agroforestry Engineering aimed at advancing in the modeling of biophysical parameters in order to improve the productivity. In this study, low-cost and close range photogrammetry has been used in different agroforestry scenarios to solve identified gaps in the results and improve procedures and technology hitherto practiced in this field. Photogrammetry offers the advantage of being a non-destructive and non-invasive technique, never changing physical properties of the studied element, providing rigor and completeness to the captured information. In this PhD dissertation, the following contributions are presented divided into three research papers: • A methodological proposal to acquire georeferenced multispectral data of high spatial resolution using a low-cost manned aerial platform, to monitor and sustainably manage extensive áreas of crops. The vicarious calibration is exposed as radiometric calibration method of the multispectral sensor embarked on a paraglider. Low-cost surfaces are performed as control coverages. • The development of a method able to determine crop productivity under field conditions, from the combination of close range photogrammetry and computer vision, providing a constant operational improvement and a proactive management in the crop monitoring. An innovate methodology in the sector is proposed, ensuring flexibility and simplicity in the data collection by non-invasive technologies, automation in processing and quality results with low associated cost. • A low cost, efficient and accurate methodology to obtain Digital Height Models of vegatal cover intended for forestry inventories by integrating public data from LiDAR into photogrammetric point clouds coming from low cost flights. This methodology includes the potentiality of LiDAR to register ground points in areas with high density of vegetation and the better spatial, radiometric and temporal resolution from photogrammetry for the top of vegetal covers.[ES]Desde finales del siglo XX se están aplicando Geotecnologías en diferentes líneas de investigación en Ingeniería Agroforestal orientadas a avanzar en la modelización de parámetros biofísicos con el propósito de mejorar la productividad. En este estudio se ha empleado fotogrametría de bajo coste y rango cercano en distintos escenarios agroforestales para solventar carencias detectadas en los resultados obtenidos y mejorar los procedimientos y la tecnología hasta ahora usados en este campo. La fotogrametría ofrece como ventaja el ser una técnica no invasiva y no destructiva, por lo que no altera en ningún momento las propiedades físicas del elemento estudiado, dotando de rigor y exhaustividad a la información capturada. En esta Tesis Doctoral se presentan las siguientes contribuciones, divididas en tres artículos de investigación: • Una propuesta metodológica de adquisición de datos multiespectrales georreferenciados de alta resolución espacial mediante una plataforma aérea tripulada de bajo coste, para monitorizar y gestionar sosteniblemente amplias extensiones de cultivos. Se expone la calibración vicaria como método de calibración radiométrico del sensor multiespectral embarcado en un paramotor empleando como coberturas de control superficies de bajo coste. • El desarrollo de un método capaz de determinar la productividad del cultivo en condiciones de campo, a partir de la combinación de fotogrametría de rango cercano y visión computacional, facilitando una mejora operativa constante así como una gestión proactiva en la monitorización del cultivo. Se propone una metodología totalmente novedosa en el sector, garantizando flexibilidad y sencillez en la toma de datos mediante tecnologías no invasivas, automatismo en el procesado, calidad en los resultados y un bajo coste asociado. • Una metodología de bajo coste, eficiente y precisa para la obtención de Modelos Digitales de Altura de Cubierta Vegetal destinados al inventario forestal mediante la integración de datos públicos procedentes del LiDAR en las nubes de puntos fotogramétricas obtenidas con un vuelo de bajo coste. Esta metodología engloba la potencialidad del LiDAR para registrar el terreno en zonas con alta densidad de vegetación y una mejor resolución espacial, radiométrica y temporal procedente de la fotogrametría para la parte superior de las cubiertas vegetales

Ilmalaserkeilausaineistojen vertailu perustuen kattojen ominaisuuksiin

Laser scanning is nowadays one of the most important technology in geospatial data collection. The technique has developed together with the other technologies and sciences, and the systems can be used with many different platforms on land, in the ocean and in the air. Airborne laser scanning (ALS) started right after the invention of the laser in 1960’s and the usage grew in 1990’s, when the first commercial system was released. The development has augmented the ways of surveying and the systems have new features and more options to collect as accurate data as possible. Several wavelengths and higher frequencies able thousands or even millions of measurements per second. The multispectral systems enable the characterization of the targets from the spectral information which helps for example in the data classification. Single photon technique provides higher imaging capability with lower costs and is used in the extensive topographic measurements. The processing of the point clouds are more important when the densities grow and the amount of noise points is higher. The processing usually includes preprocessing, data management, classification, segmentation and modeling to enable the analyzing of the data. The goal of the thesis is to compare and analyze the datasets of five different airborne laser scanners. The conventional LiDAR datasets are collected from low altitude helicopter with the Riegl’s VUX-1HA and miniVUX-1UAV systems. The state-of-the-art sensors, Titan multispectral LiDAR (Teledyne Optech) and SPL100 single photon LiDAR (Leica), are used in the data collection from the aircraft. The data is collected from the urban area of Espoonlahti, Finland, and the comparison is based on the roof features. Other land cover classes are left out from the investigation. From the roof features are investigated the differences, accuracies and qualities between the datasets. The urban environment was selected because the lack of ALS research done for the built environment, especially in Finland. The thesis introduces the background of the airborne laser scanning, theories and literature review, materials and methods used in the project. The laser scanners used in the work produce dense point clouds, where the most dense is up to 80 pts/m2. Based on the results the accuracies vary mainly between 0 and 10 cm. The scanners with infrared wavelengths produce better than 10 cm accuracies for the outlines of the roofs, unlike the green wavelength scanners. The differences in the corner coordinates are between 1 and 8 cm with a few exceptions. SPL100 system has the best height accuracy of 4.2 cm and otherwise the accuracies vary between 5 and 10 cm. The largest deviation compared to the roof planes occurs in the miniVUX-1UAV data (over 5 cm). For the surface areas the infrared frequencies produce differences of 0 to 2 percent from the reference data, whereas the differences of the green wavelength are mainly 1 to 7 percent. For the inclinations no significant differences were observed.Laserkeilaus on nykyään yksi tärkeimmistä tekniikoista geospatiaalisen tiedon keräämisessä. Tekniikka on kehittynyt yhdessä muiden teknologioiden ja tieteiden kanssa, ja järjestelmiä voidaan käyttää monilla eri alustoilla maassa, meressä ja ilmassa. Ilmalaserkeilaus (ALS) alkoi heti laserin keksimisen jälkeen 1960-luvulla ja käyttö kasvoi 1990-luvulla ensimmäisen kaupallisen järjestelmän julkaisun jälkeen. Kehitys on lisännyt mittaustapoja ja järjestelmien ominaisuuksien parantuessa on enemmän vaihtoehtoja kerätä tarkkaa aineistoa. Useilla aallonpituuksilla ja korkeammilla taajuuksilla pystytään tekemään tuhansia tai jopa miljoonia mittauksia sekunnissa. Monispektriset järjestelmät mahdollista-vat kohteiden tunnistamisen spektritietojen (aallonpituuksien jakauman) mukaan, jota voidaan hyödyntää esimerkiksi aineistojen luokittelussa. Yksifotoni–tekniikka mahdollistaa suuremman mittauskyvyn pienemmällä kustannuksella (energiankulutus) ja sitä käytetään laajojen alueiden mittauksissa. Pistepilvien käsittely on entistä tärkeämpää kun tiheydet kasvavat ja virhepisteiden määrä on suurempi. Prosessointiin kuuluu yleensä esikäsittely, tiedonhallinta, luokittelu, segmentointi ja mallinnus, ennen aineiston analysointia. Tämän opinnäytetyön tavoitteena on vertailla ja analysoida viiden eri ilmalaserkeilaimen tuottamia aineistoja. Ns. tavanomaiset LiDAR–aineistot on kerätty matalalla lentävästä helikopterista Rieglin VUX-1HA ja miniVUX-1UAV –keilaimilla. Viimeisintä tekniikkaa edustavat Titan monispektri LiDAR (Teledyne Optech) ja SPL100 single photon LiDAR (Leica) -aineistot on kerätty lentokoneesta. Aineistot on kerätty Espoonlahden alueelta ja vertailu perustuu kattojen ominaisuuksiin. Muut maanpinnan kohteet jätetään tarkastelun ulkopuolelle. Pistepilvien perusteella tutkitaan aineistojen välisiä eroja, tarkkuuksia ja muita ominaisuuksia. Kaupunkiympäristö valittiin kohteeksi vähäisen rakennetun ympäristön ALS–tutkimuksen takia etenkin Suomessa. Opinnäytetyössä esitellään ilmalaserkeilauksen taustaa, teoriaa ja tehdään kirjallisuuskatsaus aiheeseen liittyen, sekä käydään läpi projektissa käytetyt aineistot ja menetelmät. Työssä käytetyt keilaimet tuottavat tiheitä pistepilviä, joista tihein on jopa 80 pistettä/m2. Tulosten perusteella tarkkuudet vaihtelevat pääosin 0 – 10 cm välillä. Kattolinjojen kohdalla infrapuna-aallonpituutta käyttävät keilaimet pääsevät alle 10 cm, toisin kuin vihreän aallonpituuden keilaimet. Kattojen kulmakoordinaattien erot ovat 1 – 8 cm välillä muutamaa poikkeusta lukuun ottamatta. Korkeuksissa paras tarkkuus on SPL100 laserkeilaimella 4.2 cm, ja muuten ollaan 5 – 10 cm tarkkuuksissa. Suurimmat hajaumat tasoon verrattaessa syntyy miniVUX-1UAV aineistoon (yli 5 cm). Pinta-aloissa infrapunataajuudet tuottavat 0 – 2 prosentin eroja vertailuaineistoon, kun taas vihreällä aallonpituudella erot ovat pääosin 1 – 7 prosenttia. Kaltevuuskulmissa ei havaittu merkittäviä eroja

Riparian vegetation classification from airborne laser scanning data with an emphasis on cottonwood trees

The high point density of airborne laser mapping systems enables achieving a detailed description of geographic objects and the terrain. Growing experience indicates, however, that extracting useful information directly from the data can be difficult. In this study, small-footprint lidar data were used to differentiate between young, mature, and old cottonwood trees in the San Pedro River Basin near Benson, Arizona, USA. The lidar data were acquired in June 2003, using the Optech Incorporated ALTM 1233 (Optech Incorporated, Toronto, Ont.), during flyovers conducted at an altitude of 750 m. The lidar data were preprocessed to create a two-band image of the study site: a high-accuracy canopy altitude model band, and a near-infrared intensity band. These lidar-derived images provided the basis for supervised classification of cottonwood age categories, using a maximum likelihood algorithm. The results of classification illustrate the potential of airborne lidar data to differentiate age classes of cottonwood trees for riparian areas quickly and accurately. © 2006, Taylor & Francis Group, LLC. All rights reserved

Individual tree detection using template matching of multiple rasters derived from multispectral airborne laser scanning data

Multispectral airborne laser scanning (MS-ALS) provides information about 3D structure as well as the intensity of the reflected light and is a promising technique for acquiring forest information. Data from MS-ALS have been used for tree species classification and tree health evaluation. This paper investigates its potential for individual tree detection (ITD) when using intensity as an additional metric. To this end, rasters of height, point density, vegetation ratio, and intensity at three wavelengths were used for template matching to detect individual trees. Optimal combinations of metrics were identified for ITD in plots with different levels of canopy complexity. The F-scores for detection by template matching ranged from 0.94 to 0.73, depending on the choice of template derivation and raster generalization methods. Using intensity and point density as metrics instead of height increased the F-scores by up to 14% for the plots with the most understorey trees

다중 센싱 플랫폼과 딥러닝을 활용한 도시 규모의 수목 맵핑 및 수종 탐지

학위논문(석사) -- 서울대학교대학원 : 농업생명과학대학 생태조경·지역시스템공학부(생태조경학), 2023. 2. 류영렬.Precise estimation of the number of trees and individual tree location with species information all over the city forms solid foundation for enhancing ecosystem service. However, mapping individual trees at the city scale remains challenging due to heterogeneous patterns of urban tree distribution. Here, we present a novel framework for merging multiple sensing platforms with leveraging various deep neural networks to produce a fine-grained urban tree map. We performed mapping trees and detecting species by relying only on RGB images taken by multiple sensing platforms such as airborne, citizens and vehicles, which fueled six deep learning models. We divided the entire process into three steps, since each platform has its own strengths. First, we produced individual tree location maps by converting the central points of the bounding boxes into actual coordinates from airborne imagery. Since many trees were obscured by the shadows of the buildings, we applied Generative Adversarial Network (GAN) to delineate hidden trees from the airborne images. Second, we selected tree bark photos collected by citizen for species mapping in urban parks and forests. Species information of all tree bark photos were automatically classified after non-tree parts of images were segmented. Third, we classified species of roadside trees by using a camera mounted on a car to augment our species mapping framework with street-level tree data. We estimated the distance from a car to street trees from the number of lanes detected from the images. Finally, we assessed our results by comparing it with Light Detection and Ranging (LiDAR), GPS and field data. We estimated over 1.2 million trees existed in the city of 121.04 km² and generated more accurate individual tree positions, outperforming the conventional field survey methods. Among them, we detected the species of more than 63,000 trees. The most frequently detected species was Prunus yedoensis (21.43 %) followed by Ginkgo biloba (19.44 %), Zelkova serrata (18.68 %), Pinus densiflora (7.55 %) and Metasequoia glyptostroboides (5.97 %). Comprehensive experimental results demonstrate that tree bark photos and street-level imagery taken by citizens and vehicles are conducive to delivering accurate and quantitative information on the distribution of urban tree species.도시 전역에 존재하는 모든 수목의 숫자와 개별 위치, 그리고 수종 분포를 정확하게 파악하는 것은 생태계 서비스를 향상시키기 위한 필수조건이다. 하지만, 도시에서는 수목의 분포가 매우 복잡하기 때문에 개별 수목을 맵핑하는 것은 어려웠다. 본 연구에서는, 여러가지 센싱 플랫폼을 융합함과 동시에 다양한 딥러닝 네트워크들을 활용하여 세밀한 도시 수목 지도를 제작하는 새로운 프레임워크를 제안한다. 우리는 오직 항공사진, 시민, 차량 등의 플랫폼으로부터 수집된 RGB 이미지만을 사용하였으며, 6가지 딥러닝 모델을 활용하여 수목을 맵핑하고 수종을 탐지하였다. 각각의 플랫폼은 저마다의 강점이 있기 때문에 전 과정을 세 가지 스텝으로 구분할 수 있다. 첫째, 우리는 항공사진 상에서 탐지된 수목의 딥러닝 바운딩 박스로부터 중심점을 추출하여 개별 수목의 위치 지도를 제작하였다. 많은 수목이 도시 내 고층 빌딩의 그림자에 의해 가려졌기 때문에, 우리는 생정적 적대적 신경망 (Generative Adversarial Network, GAN)을 통해 항공사진 상에 숨겨진 수목을 그려내고자 하였다. 둘째, 우리는 시민들이 수집한 수목의 수피 사진을 활용하여 도시 공원 및 도시 숲 일대에 수종 정보를 맵핑하였다. 수피 사진으로부터의 수종 정보는 딥러닝 네트워크에 의해 자동으로 분류되었으며, 이 과정에서 이미지 분할 모델 또한 적용되어 딥러닝 분류 모델이 오로지 수피 부분에만 집중할 수 있도록 하였다. 셋째, 우리는 차량에 탑재된 카메라를 활용하여 도로변 가로수의 수종을 탐지하였다. 이 과정에서 차량으로부터 가로수까지의 거리 정보가 필요하였는데, 우리는 이미지 상의 차선 개수로부터 거리를 추정하였다. 마지막으로, 본 연구 결과는 라이다 (Light Detection and Ranging, LiDAR)와 GPS 장비, 그리고 현장 자료에 의해 평가되었다. 우리는 121.04 km² 면적의 대상지 내에 약 130만여 그루의 수목이 존재하는 것을 확인하였으며, 다양한 선행연구보다 높은 정확도의 개별 수목 위치 지도를 제작하였다. 탐지된 모든 수목 중 약 6만 3천여 그루의 수종 정보가 탐지되었으며, 이중 가장 빈번히 탐지된 수목은 왕벚나무 (Prunus yedoensis, 21.43 %)였다. 은행나무 (Ginkgo biloba, 19.44 %), 느티나무 (Zelkova serrata, 18.68 %), 소나무 (Pinus densiflora, 7.55 %), 그리고 메타세쿼이어 (Metasequoia glyptostroboides, 5.97 %) 등이 그 뒤를 이었다. 포괄적인 검증이 수행되었고, 본 연구에서는 시민이 수집한 수피 사진과 차량으로부터 수집된 도로변 이미지는 도시 수종 분포에 대한 정확하고 정량적인 정보를 제공한다는 것을 검증하였다.1. Introduction 6 2. Methodology 9 2.1. Data collection 9 2.2. Deep learning overall 12 2.3. Tree counting and mapping 15 2.4. Tree species detection 16 2.5. Evaluation 21 3. Results 22 3.1. Evaluation of deep learning performance 22 3.2. Tree counting and mapping 23 3.3. Tree species detection 27 4. Discussion 30 4.1. Multiple sensing platforms for urban areas 30 4.2. Potential of citizen and vehicle sensors 34 4.3. Implications 48 5. Conclusion 51 Bibliography 52 Abstract in Korean 61석