scholarly journals Investigation and analysis on current status of the tsunami-stricken coastal forest using high-resolution images prepared by unmanned aerial vehicle

2015 ◽  
Vol 41 (1) ◽  
pp. 157-162
Author(s):  
Satoshi OSAWA ◽  
Takeki IZUMI ◽  
Erika NANAUMI ◽  
Mikiko ISHIKAWA
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Current Status ◽  
Coastal Forest ◽  
Aerial Vehicle ◽  
High Resolution Images

scholarly journals Characterization of peach tree crown by using high-resolution images from an unmanned aerial vehicle

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Yue Mu ◽  
Yuichiro Fujii ◽  
Daisuke Takata ◽  
Bangyou Zheng ◽  
Koji Noshita ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Peach Tree ◽  
Tree Crown ◽  
Aerial Vehicle ◽  
High Resolution Images

New method for tree stands parameters determination using high resolution images done by unmanned aerial vehicle

2016 ◽  
Author(s):  
А.С. Алексеев ◽  
А.А. Никифоров ◽  
А.А. Михайлова ◽  
М.Р. Вагизов
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
New Method ◽  
Growth And Yield ◽  
Special Treatment ◽  
Resolution Image ◽  
Growing Stock ◽  
High Resolution Image ◽  
Aerial Vehicle ◽  
Area Unit

В связи со старением информационных материалов о состоянии лесов существует потребность в разработке новых методов таксации древостоев, основанных на применении последних научно-технических достижений в области теории структуры и продуктивности древостоев, дистанционных методов изучения лесов, информационных и ГИС технологий. В статье приведены результаты разработки и проверки нового метода определения таксационных характеристик сомкнутых насаждений на основе правила 3/2 и подобных ему правил Хильми и Рейнеке, с одной стороны, и определения числа деревьев на единице площади по снимку сверх высокого разрешения, полученного с помощью БПЛА, с другой. С теоретической точки зрения эта зависимости величин запаса, средней высоты и среднего диаметра от числа стволов на единице площади относятся к классу аллометрических связей, очень часто встречающихся при количественном описании соотношений частей биологических систем разных уровней иерархии, от организмов до экосистем. Параметры аллометрических зависимостей запаса, средних высоты и диаметра от числа стволов на единице площади были определены для основных лесообразующих пород по данным таблиц хода роста нормальных (полных) древостоев с теоретическим показателем степени и затем использованы для расчетов. Число деревьев на единице площади определялось по снимку с разрешением 7,13 см/пиксель, полученному с помощью 4-роторной платформы. Обработка материалов аэрофотосъемки была выполнена в специализированной фотограмметрической системе Agisoft Photoscan. В результате были получены ортофотоплан и цифровая модель поверхности крон деревьев на изучаемую территорию с определением их высот. Для автоматизированной обработки полученных изображений с целью получения значений числа деревьев на единицу площади был создан специализированный скрипт на языке Java. Погрешности определения таксационных характеристик древостоев предлагаемым методом не выше установленных действующими нормативными материалами. Every time there is a demand for new innovative methods of forest resources estimation based on last achievements in theoretical science, remote sensing methods, information and GIS-technologies. In the paper are presented a new method and the results of its application to forest stands growing stock, mean height and diameter determination. The method is based on rule 3/2 and similar Reineke and Hilmy rules, on one hand and high resolution image made by unmanned aerial vehicle, which used for determination of number of trees per area unit, on other. The above rules are well known in quantitative biology as an allometric and widely used for description of different kind of relations in biological systems of various scale: from organisms to ecosystems. Parameters of above allometric relationships between growing stock, mean height and diameter and stems density per area unit was determine on the base of full stock growth and yield tables for main tree species and after used for experimental calculations. The number of trees per area unit was determined after special treatment of high resolution image made by unmanned flying machine. The growing stock, mean height and diameter determined by suggested method was compared with the data of regular forest inventory. Comparison gives positive result and method may be recommended for further development.

High-resolution monitoring of beach topography and its change using unmanned aerial vehicle imagery

2018 ◽  
Vol 160 ◽  
pp. 103-116 ◽  
Author(s):  
Benqing Chen ◽  
Yanming Yang ◽  
Hongtao Wen ◽  
Hailin Ruan ◽  
Zaiming Zhou ◽  
...  
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Aerial Vehicle

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

Infrastructure Inspection Using an Unmanned Aerial System (UAS) With Metamodeling-Based Image Correction

2016 ◽  
Author(s):  
Chen-Ming Kuo ◽  
Chung-Hsin Kuo ◽  
Shu-Ping Lin ◽  
Mark Christian E. Manuel ◽  
Po Ting Lin ◽  
...  
Keyword(s):  
High Resolution ◽  
Ccd Camera ◽  
Kriging Model ◽  
Image Correction ◽  
Railway Transport ◽  
Truss Bridges ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Infrastructure Inspection ◽  
Steel Truss Bridges

Public infrastructures such as bridges are common civil structures for road and railway transport. In Poland, many of the steel truss bridges were constructed in the 1950s or earlier. The aging managements and damage assessments are required to ensure safe operations of these old bridges. The first step of damage assessment is usually done via visual inspection. The said inspection procedure can be expensive, laborious and dangerous as it is often performed by trained personnel. As a solution to this, we have developed and used a custom-designed, modular aerial robot equipped with a CCD camera for the collection of high-resolution images. The images were merged into one single, high-resolution facade map that will be the basis for subsequent evaluation by bridge inspectors. It was observed that the collected images had encountered irregularities which decreases the reliability of the facade map. We have conducted experiments to estimate the correction of image perspective in terms of attitude and position of unmanned aerial vehicle (UAV). A Kriging model was utilized to parametrically model the aforementioned nonlinear relationship. The image reliability is then evaluated based on the variance of the parametric model. The generated information is further used for high fidelity automated image correction and stitching.

scholarly journals AGB Estimation in a Tropical Mountain Forest (TMF) by Means of RGB and Multispectral Images Using an Unmanned Aerial Vehicle (UAV)

2019 ◽  
Vol 11 (12) ◽  
pp. 1413 ◽  
Author(s):  
Víctor González-Jaramillo ◽  
Andreas Fries ◽  
Jörg Bendix
Keyword(s):  
High Resolution ◽  
Unmanned Aerial Vehicle ◽  
Tree Level ◽  
Mountain Forest ◽  
Multispectral Images ◽  
Individual Tree ◽  
Tropical Mountain ◽  
Aerial Vehicle ◽  
Rgb Images ◽  
Very High

The present investigation evaluates the accuracy of estimating above-ground biomass (AGB) by means of two different sensors installed onboard an unmanned aerial vehicle (UAV) platform (DJI Inspire I) because the high costs of very high-resolution imagery provided by satellites or light detection and ranging (LiDAR) sensors often impede AGB estimation and the determination of other vegetation parameters. The sensors utilized included an RGB camera (ZENMUSE X3) and a multispectral camera (Parrot Sequoia), whose images were used for AGB estimation in a natural tropical mountain forest (TMF) in Southern Ecuador. The total area covered by the sensors included 80 ha at lower elevations characterized by a fast-changing topography and different vegetation covers. From the total area, a core study site of 24 ha was selected for AGB calculation, applying two different methods. The first method used the RGB images and applied the structure for motion (SfM) process to generate point clouds for a subsequent individual tree classification. Per the classification at tree level, tree height (H) and diameter at breast height (DBH) could be determined, which are necessary input parameters to calculate AGB (Mg ha−1) by means of a specific allometric equation for wet forests. The second method used the multispectral images to calculate the normalized difference vegetation index (NDVI), which is the basis for AGB estimation applying an equation for tropical evergreen forests. The obtained results were validated against a previous AGB estimation for the same area using LiDAR data. The study found two major results: (i) The NDVI-based AGB estimates obtained by multispectral drone imagery were less accurate due to the saturation effect in dense tropical forests, (ii) the photogrammetric approach using RGB images provided reliable AGB estimates comparable to expensive LiDAR surveys (R2: 0.85). However, the latter is only possible if an auxiliary digital terrain model (DTM) in very high resolution is available because in dense natural forests the terrain surface (DTM) is hardly detectable by passive sensors due to the canopy layer, which impedes ground detection.

Natural superficial water storage and aquifer recharge assessment in Brazilian savanna wetland using unmanned aerial vehicle and geophysical survey

2020 ◽  
Vol 8 (3) ◽  
pp. 224-244
Author(s):  
Lucas Moreira Furlan ◽  
Vania Rosolen ◽  
Jepherson Salles ◽  
César Augusto Moreira ◽  
Manuel Eduardo Ferreira ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Agricultural Land ◽  
Water Volume ◽  
Surface Model ◽  
Aquifer Recharge ◽  
Flow Paths ◽  
Subsurface Structures ◽  
Aerial Vehicle ◽  
High Resolution Images ◽  
Remote Sensing Techniques

Human pressure on the water resources provided by natural isolated wetlands has intensified in Brazil due to an increase in agricultural land equipped with irrigation. However, the amount of water stored in these areas and its contribution to aquifer recharge is unknown. This study aimed to quantify the amount of water that can be retained in a natural wetland and to propose a model of groundwater recharge. We used remote sensing techniques involving unmanned aerial vehicle to map the wetland and highlight its internal morphology, using a red–green–blue orthomosaic and a digital surface model. The 2-D inversion and a pseudo-3-D model from electrical resistivity tomography data were used to visualize the subsurface structures and hydrologic flow paths. The wetland is a reservoir storing up to 416.996 m3 of water during the rainy months. Distinct internal compartments characterize the wetland topography and different water-volume storage, lower in the border and higher in the center. A leakage point connects surface water to groundwater through direct vertical flow, which constitutes the aquifer recharge zone. Remotely sensed very high-resolution images allied with geophysical techniques allowed complete surface and subsurface imaging and offered visual tools that contributed to understanding the hydrodynamics of the wetland.

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