scholarly journals Evaluating Unmanned Aerial Vehicle Images for Estimating Forest Canopy Fuels in a Ponderosa Pine Stand

2018 ◽  
Vol 10 (8) ◽  
pp. 1266 ◽  
Author(s):  
Patrick Shin ◽  
Temuulen Sankey ◽  
Margaret Moore ◽  
Andrea Thode
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Ponderosa Pine ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Field Measurements ◽  
Tree Density ◽  
Density Estimates ◽  
Canopy Fuels ◽  
Aerial Vehicle ◽  
Canopy Bulk Density

Forests in the Southwestern United States are becoming increasingly susceptible to large wildfires. As a result, forest managers are conducting forest fuel reduction treatments for which spatial fuels and structure information are necessary. However, this information currently has coarse spatial resolution and variable accuracy. This study tested the feasibility of using unmanned aerial vehicle (UAV) imagery to estimate forest canopy fuels and structure in a southwestern ponderosa pine stand. UAV-based multispectral images and Structure-from-Motion point clouds were used to estimate canopy cover, canopy height, tree density, canopy base height, and canopy bulk density. Estimates were validated with field data from 57 plots and aerial photography from the U.S. Department of Agriculture National Agriculture Imaging Program. Results indicate that UAV imagery can be used to accurately estimate forest canopy cover (correlation coefficient (R2) = 0.82, root mean square error (RMSE) = 8.9%). Tree density estimates correctly detected 74% of field-mapped trees with a 16% commission error rate. Individual tree height estimates were strongly correlated with field measurements (R2 = 0.71, RMSE = 1.83 m), whereas canopy base height estimates had a weaker correlation (R2 = 0.34, RMSE = 2.52 m). Estimates of canopy bulk density were not correlated to field measurements. UAV-derived inputs resulted in drastically different estimates of potential crown fire behavior when compared with coarse resolution LANDFIRE data. Methods from this study provide additional data to supplement, or potentially substitute, traditional estimates of canopy fuel.

scholarly journals Possibility of applying unmanned aerial vehicle and thermal imaging in several canopy cover class for wildlife monitoring – preliminary results

2020 ◽  
Vol 211 ◽  
pp. 04007
Author(s):  
Dede Aulia Rahman ◽  
Yudi Setiawan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Canopy ◽  
Canopy Cover ◽  
Thermal Infrared ◽  
Sound Level ◽  
Flying Height ◽  
Infrared Imagery ◽  
Aerial Vehicle ◽  
Thermal Infrared Imagery ◽  
Cover Class

Tropical rainforests are one of the important habitats on earth but are rarely explored because they are difficult to access, making their cryptic animals challenging to monitor. Unmanned aerial vehicle (UAV) with thermal infrared imaging (TIR) technology is gaining entry into wildlife research and monitoring. The researcher tested the possibility of applying DJI Mavic 2 Enterprise Dual with FLIR as aerial survey platforms to wildlife in the five tree density classes in the IPB University Campus. To assess the effectiveness of using drones in detecting wildlife, the researcher measured the optimum flying height, sound level, temperature, and optimum flight time in each canopy cover class. The optimum height for animal detection is <50 m HAGL with a sound level that animals can still tolerate. Wildlife detected had body temperatures around 27 °C and were conspicuous in the thermal infrared imagery at night and early morning when the forest canopy was cool (15–27°C), but were difficult to detect by mid-day. By that time, the direct sunshine had heated up canopy vegetation to over 30°C. Species were difficult to identify from thermal infrared imagery alone but could be recognized from synchronized visual images taken during the daytime.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

scholarly journals Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

2018 ◽  
Vol 10 (10) ◽  
pp. 1528 ◽  
Author(s):  
Liang Han ◽  
Guijun Yang ◽  
Haikuan Feng ◽  
Chengquan Zhou ◽  
Hao Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Spectral Characteristics ◽  
Canopy Structure ◽  
Canopy Cover ◽  
Structural Features ◽  
Discrimination Ability ◽  
Maize Varieties ◽  
Aerial Vehicle

Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.

scholarly journals TRACKING FOREST AND OPEN AREA EFFECTS ON SNOW ACCUMULATION BY UNMANNED AERIAL VEHICLE PHOTOGRAMMETRY

2016 ◽  
Vol XLI-B1 ◽  
pp. 917-923 ◽  
Author(s):  
T. Lendzioch ◽  
J. Langhammer ◽  
M. Jenicek
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Snow Depth ◽  
Forest Canopy ◽  
Forest Succession ◽  
Coniferous Forest ◽  
Snow Accumulation ◽  
Digital Photogrammetry ◽  
Area Index ◽  
Aerial Vehicle ◽  
The Impact

Airborne digital photogrammetry is undergoing a renaissance. The availability of low-cost Unmanned Aerial Vehicle (UAV) platforms well adopted for digital photography and progress in software development now gives rise to apply this technique to different areas of research. Especially in determining snow depth spatial distributions, where repetitive mapping of cryosphere dynamics is crucial. Here, we introduce UAV-based digital photogrammetry as a rapid and robust approach for evaluating snow accumulation over small local areas (e.g., dead forest, open areas) and to reveal impacts related to changes in forest and snowpack. Due to the advancement of the technique, snow depth of selected study areas such as of healthy forest, disturbed forest, succession, dead forest, and of open areas can be estimated at a 1 cm spatial resolution. The approach is performed in two steps: 1) developing a high resolution Digital Elevation Model during snow-free and 2) during snow-covered conditions. By substracting these two models the snow depth can be accurately retrieved and volumetric changes of snow depth distribution can be achieved. This is a first proof-of-concept study combining snow depth determination and Leaf Area Index (LAI) retrieval to monitor the impact of forest canopy metrics on snow accumulation in coniferous forest within the Šumava National Park, Czech Republic. Both, downward-looking UAV images and upward-looking LAI-2200 canopy analyser measurements were applied to reveal the LAI, controlling interception and transmitting radiation. For the performance of downward-looking images the snow background instead of the sky fraction was used. In contrast to the classical determination of LAI by hemispherical photography or by LAI plant canopy analyser, our approach will also test the accuracy of LAI measurements by UAV that are taken simultaneously during the snow cover mapping campaigns. Since the LAI parameter is important for snowpack modelling, this method presents the potential of simplifying LAI retrieval and mapping of snow dynamics while reducing running costs and time.

Acquisitions and applications of forest canopy hyperspectral imageries at hotspot and multiview angle using unmanned aerial vehicle platform

2020 ◽  
Vol 14 (02) ◽  
pp. 1 ◽  
Author(s):  
Xiaokang Zhang ◽  
Feng Qiu ◽  
Chunhui Zhan ◽  
Qian Zhang ◽  
Zhaohui Li ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Canopy ◽  
Aerial Vehicle ◽  
Vehicle Platform

scholarly journals Assessment of Individual Tree Detection and Canopy Cover Estimation using Unmanned Aerial Vehicle based Light Detection and Ranging (UAV-LiDAR) Data in Planted Forests

2019 ◽  
Vol 11 (8) ◽  
pp. 908 ◽  
Author(s):  
Xiangqian Wu ◽  
Xin Shen ◽  
Lin Cao ◽  
Guibin Wang ◽  
Fuliang Cao
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Forest Inventory ◽  
Canopy Cover ◽  
Light Detection And Ranging ◽  
Estimation Methods ◽  
Lidar Data ◽  
Tree Crown ◽  
Polynomial Fitting ◽  
Individual Tree ◽  
Aerial Vehicle

Canopy cover is a key forest structural parameter that is commonly used in forest inventory, sustainable forest management and maintaining ecosystem services. Recently, much attention has been paid to the use of unmanned aerial vehicle (UAV)-based light detection and ranging (LiDAR) due to the flexibility, convenience, and high point density advantages of this method. In this study, we used UAV-based LiDAR data with individual tree segmentation-based method (ITSM), canopy height model-based method (CHMM), and a statistical model method (SMM) with LiDAR metrics to estimate the canopy cover of a pure ginkgo (Ginkgo biloba L.) planted forest in China. First, each individual tree within the plot was segmented using watershed, polynomial fitting, individual tree crown segmentation (ITCS) and point cloud segmentation (PCS) algorithms, and the canopy cover was calculated using the segmented individual tree crown (ITSM). Second, the CHM-based method, which was based on the CHM height threshold, was used to estimate the canopy cover in each plot. Third, the canopy cover was estimated using the multiple linear regression (MLR) model and assessed by leave-one-out cross validation. Finally, the performance of three canopy cover estimation methods was evaluated and compared by the canopy cover from the field data. The results demonstrated that, the PCS algorithm had the highest accuracy (F = 0.83), followed by the ITCS (F = 0.82) and watershed (F = 0.79) algorithms; the polynomial fitting algorithm had the lowest accuracy (F = 0.77). In the sensitivity analysis, the three CHM-based algorithms (i.e., watershed, polynomial fitting and ITCS) had the highest accuracy when the CHM resolution was 0.5 m, and the PCS algorithm had the highest accuracy when the distance threshold was 2 m. In addition, the ITSM had the highest accuracy in estimation of canopy cover (R2 = 0.92, rRMSE = 3.5%), followed by the CHMM (R2 = 0.94, rRMSE = 5.4%), and the SMM had a relative low accuracy (R2 = 0.80, rRMSE = 5.9%).The UAV-based LiDAR data can be effectively used in individual tree crown segmentation and canopy cover estimation at plot-level, and CC estimation methods can provide references for forest inventory, sustainable management and ecosystem assessment.

scholarly journals OBJECT BASED BUILDING EXTRACTION AND BUILDING PERIOD ESTIMATION FROM UNMANNED AERIAL VEHICLE DATA

2018 ◽  
Vol IV-3 ◽  
pp. 71-76 ◽  
Author(s):  
Resul Comert ◽  
Onur Kaplan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Accuracy Assessment ◽  
Field Measurements ◽  
Seismic Performance Assessment ◽  
Object Based Image Analysis ◽  
Vehicle Data ◽  
Vulnerability Assessments ◽  
Object Based ◽  
Period Estimation ◽  
Aerial Vehicle

The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages; (i) Building boundary extraction with Object Based Image Analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86&amp;thinsp;% and the maximum difference is 13.2&amp;thinsp;%. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments.

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