scholarly journals The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

2019 ◽  
Vol 11 (13) ◽  
pp. 1561 ◽  
Author(s):  
Tomáš Klouček ◽  
Jan Komárek ◽  
Peter Surový ◽  
Karel Hrach ◽  
Přemysl Janata ◽  
...  
Keyword(s):  
Bark Beetle ◽  
Near Infrared ◽  
Carbon Fixation ◽  
Water Cycle ◽  
Low Cost ◽  
Vegetation Indices ◽  
Nutrient Cycle ◽  
Visual Interpretation ◽  
Infrared Band ◽  
Individual Level

The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue and presents a major challenge for forest management. A timely detection of bark beetle infestation is therefore necessary to reduce losses. Besides wood production, a bark beetle outbreak affects the forest ecosystem in many other ways including the water cycle, nutrient cycle, or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed in the unmanaged zone of the Krkonoše Mountains National Park in the northern part of the Czech Republic where the natural spreading of bark beetle is slow and, therefore, allow us to continuously monitor the infested trees that are, in contrast to managed forests, not being removed. The aim of this work is to evaluate possibilities of unmanned aerial vehicle (UAV)-mounted low-cost RGB and modified near-infrared sensors for detection of different stages of infested trees at the individual level, using a retrospective time series for recognition of still green but already infested trees (so-called green attack). A mosaic was created from the UAV imagery, radiometrically calibrated for surface reflectance, and five vegetation indices were calculated; the reference data about the stage of bark beetle infestation was obtained through a combination of field survey and visual interpretation of an orthomosaic. The differences of vegetation indices between infested and healthy trees over four time points were statistically evaluated and classified using the Maximum Likelihood classifier. Achieved results confirm our assumptions that it is possible to use a low-cost UAV-based sensor for detection of various stages of bark beetle infestation across seasons; with increasing time after infection, distinguishing infested trees from healthy ones grows easier. The best performance was achieved by the Greenness Index with overall accuracy of 78%–96% across the time periods. The performance of the indices based on near-infrared band was lower.

scholarly journals Forest and land fire smoke detection using GCOM-C data (case study: Pulang Pisau, Central Kalimantan)

2021 ◽  
Vol 893 (1) ◽  
pp. 012068
Author(s):  
K I N Rahmi ◽  
N Febrianti ◽  
I Prasasti
Keyword(s):  
Near Infrared ◽  
Thermal Infrared ◽  
Short Wave ◽  
Forest Land ◽  
Visual Interpretation ◽  
Infrared Band ◽  
Central Kalimantan ◽  
Fire Smoke ◽  
Shortwave Infrared

Abstract Forest/land fire give bad impact of heavy smoke on peatland area in Indonesia. Forest/land fire smoke need to be identified the distribution periodically. New satellite of GCOM-C has been launched to monitor climate condition and have visible, near infrared and thermal infrared. This study has objective to identify fire smoke from GCOM-C data. GCOM-C data has wavelength range from 0.38 to 12 μm it covers visible, near infrared, short-wave infrared and thermal infrared. It is relatively similar to MODIS or Himawari-8 images which could identify forest/land fire smoke. The methodology is visual interpretation to detect forest/land fire smoke using near infrared band (VN08), shortwave infrared band (SW03), and thermal bands (T01 and T02). Hotspot data is overlaid with GCOM-C image to represent the location of fire events. Combination of composite RGB image has been applied to detect forest/land fire smoke. GCOM-C image of VN8 bands and combination of thermal band in composite image could be used to detect fire smoke in Pulang Pisau, Central Kalimantan.

scholarly journals Spectral characterization of forest plantations with Landsat 8/OLI images for forest planning and management

2017 ◽  
Vol 52 (11) ◽  
pp. 1072-1079 ◽  
Author(s):  
Elisiane Alba ◽  
Eliziane Pivotto Mello ◽  
Juliana Marchesan ◽  
Emanuel Araújo Silva ◽  
Juliana Tramontina ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Pinus Elliottii ◽  
Growth Stages ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Infrared Band ◽  
Near Infrared Reflectance ◽  
Planning And Management ◽  
Forest Planning And Management

Abstract: The objective of this work was to evaluate the use of Landsat 8/OLI images to differentiate the age and estimate the total volume of Pinus elliottii, in order to determine the applicability of these data in the planning and management of forest activity. Fifty-three sampling units were installed, and dendrometric variables of 9-and-10-year-old P. elliottii commercial stands were measured. The digital numbers of the image were converted into surface reflectance and, subsequently, vegetation indices were determined. Red and near-infrared reflectance values were used to differentiate the ages of the stands. Regression analysis of the spectral variables was used to estimate the total volume. Increase in age caused an addition in reflectance in the near-infrared band and a decrease in the red band. The general equation for estimating the total volume for P.elliottii had an R2adj of 0.67 with a Syx of 31.46 m3 ha-1. Therefore, the spectral data with medium spatial resolution from the Landsat 8/OLI satellite can be used to distinguish the growth stages of the stands and can, thus, be used in the planning and proper management of forest activity on a spatial and temporal scale.

Evaluation of a Low-cost Camera for Agricultural Applications

2019 ◽  
pp. 1-9 ◽  
Author(s):  
Abdon Francisco Aureliano Netto ◽  
Rodrigo Nogueira Martins ◽  
Guilherme Silverio Aquino De Souza ◽  
Fernando Ferreira Lima Dos Santos ◽  
Jorge Tadeu Fim Rosas
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Paspalum Notatum ◽  
Crop Canopy ◽  
N Application ◽  
Satisfactory Accuracy ◽  
Agricultural Applications

This study aimed to modify a webcam by replacing its near-infrared (NIR) blocking filter to a low-cost red, green and blue (RGB) filter for obtaining NIR images and to evaluate its performance in two agricultural applications. First, the sensitivity of the webcam to differentiate normalized difference vegetation index (NDVI) levels through five nitrogen (N) doses applied to the Batatais grass (Paspalum notatum Flugge) was verified. Second, images from maize crops were processed using different vegetation indices, and thresholding methods with the aim of determining the best method for segmenting crop canopy from the soil. Results showed that the webcam sensor was capable of detecting the effect of N doses through different NDVI values at 7 and 21 days after N application. In the second application, the use of thresholding methods, such as Otsu, Manual, and Bayes when previously processed by vegetation indices showed satisfactory accuracy (up to 73.3%) in separating the crop canopy from the soil.

scholarly journals CO3D, A WORLDWIDE ONE ONE-METER ACCURACY DEM FOR 2025

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 299-304
Author(s):  
L. Lebègue ◽  
E. Cazala-Hourcade ◽  
F. Languille ◽  
S. Artigues ◽  
O. Melet
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Infrared Band ◽  
Automatic Production ◽  
Worldwide Production ◽  
Expected Performance ◽  
Absolute Height ◽  
Dem Accuracy ◽  
New Generation ◽  
2D And 3D

Abstract. The goal of the CO3D (Constellation Optique 3D) mission is the full-automatic production of a worldwide accurate DEM. CO3D is also a constellation of a new generation of low-cost optical satellites. The DEM accuracy is expected to be one meter in relative height and two meters in absolute height with a one-meter grid space. Each of the four satellites of the constellation will provide images with 0.50 m resolution in red, green, blue bands. A NIR (Near-InfraRed) band will also be available with a resolution close to 1 m. The satellites resource will be shared by, on one hand, the French institutions (government, scientists concerned by global Earth monitoring) who will have dedicated access and preferred price conditions, and on the other hand commercial customers interested in 2D and 3D products. The launch of the constellation is expected mid-2023 and 90 % of the DEM worldwide production should be reached by the end of 2025.Starting from an overview of the system characteristics and its main innovations, this paper presents the expected performance, the 2D and 3D products that should be available for the end-users and finally how they should be qualified.

scholarly journals Gold Enhanced Graphene-Based Photodetector on Optical Fiber with Ultrasensitivity over Near-Infrared Bands

Nanomaterials ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 124
Author(s):  
Wenguo Zhu ◽  
Songqing Yang ◽  
Huadan Zheng ◽  
Yuansong Zhan ◽  
Dongquan Li ◽  
...  
Keyword(s):  
Optical Fiber ◽  
Near Infrared ◽  
Low Cost ◽  
Field Enhancement ◽  
Pmma Film ◽  
Infrared Band ◽  
Power Monitoring ◽  
Enhanced Absorption ◽  
Cost Efficient ◽  
Optic Communication

Graphene has been widely used in photodetectors; however its photoresponsivity is limited due to the intrinsic low absorption of graphene. To enhance the graphene absorption, a waveguide structure with an extended interaction length and plasmonic resonance with light field enhancement are often employed. However, the operation bandwidth is narrowed when this happens. Here, a novel graphene-based all-fiber photodetector (AFPD) was demonstrated with ultrahigh responsivity over a full near-infrared band. The AFPD benefits from the gold-enhanced absorption when an interdigitated Au electrode is fabricated onto a Graphene-PMMA film covered over a side-polished fiber (SFP). Interestingly, the AFPD shows a photoresponsivity of >1 × 104 A/W and an external quantum efficiency of >4.6 × 106% over a broadband region of 980–1620 nm. The proposed device provides a simple, low-cost, efficient, and robust way to detect optical fiber signals with intriguing capabilities in terms of distributed photodetection and on-line power monitoring, which is highly desirable for a fiber-optic communication system.

scholarly journals Monitoring Maize Lodging Grades via Unmanned Aerial Vehicle Multispectral Image

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
Qian Sun ◽  
Lin Sun ◽  
Meiyan Shu ◽  
Xiaohe Gu ◽  
Guijun Yang ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Confusion Matrix ◽  
Accurate Determination ◽  
Vegetation Indices ◽  
Texture Features ◽  
Multispectral Image ◽  
Visual Interpretation ◽  
Aerial Vehicle

Lodging is one of the main factors affecting the quality and yield of crops. Timely and accurate determination of crop lodging grade is of great significance for the quantitative and objective evaluation of yield losses. The purpose of this study was to analyze the monitoring ability of a multispectral image obtained by an unmanned aerial vehicle (UAV) for determination of the maize lodging grade. A multispectral Parrot Sequoia camera is specially designed for agricultural applications and provides new information that is useful in agricultural decision-making. Indeed, a near-infrared image which cannot be seen with the naked eye can be used to make a highly precise diagnosis of the vegetation condition. The images obtained constitute a highly effective tool for analyzing plant health. Maize samples with different lodging grades were obtained by visual interpretation, and the spectral reflectance, texture feature parameters, and vegetation indices of the training samples were extracted. Different feature transformations were performed, texture features and vegetation indices were combined, and various feature images were classified by maximum likelihood classification (MLC) to extract four lodging grades. Classification accuracy was evaluated using a confusion matrix based on the verification samples, and the features suitable for monitoring the maize lodging grade were screened. The results showed that compared with a multispectral image, the principal components, texture features, and combination of texture features and vegetation indices were improved by varying degrees. The overall accuracy of the combination of texture features and vegetation indices is 86.61%, and the Kappa coefficient is 0.8327, which is higher than that of other features. Therefore, the classification result based on the feature combinations of the UAV multispectral image is useful for monitoring of maize lodging grades.

scholarly journals Actual Evapotranspiration and Biomass of Maize from a Red–Green-Near-Infrared (RGNIR) Sensor on Board an Unmanned Aerial Vehicle (UAV)

Water ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2359 ◽  
Author(s):  
Robson Argolo dos Santos ◽  
Everardo Chartuni Mantovani ◽  
Roberto Filgueiras ◽  
Elpídio Inácio Fernandes-Filho ◽  
Adelaide Cristielle Barbosa da Silva ◽  
...  
Keyword(s):  
Decision Making ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Low Cost ◽  
Vegetation Indices ◽  
Maize Crop ◽  
Evaporative Fraction ◽  
Dry Biomass

Surface reflectance data acquisition by unmanned aerial vehicles (UAVs) are an important tool for assisting precision agriculture, mainly in medium and small agricultural properties. Vegetation indices, calculated from these data, allow one to estimate the water consumption of crops and predict dry biomass and crop yield, thereby enabling a priori decision-making. Thus, the present study aimed to estimate, using the vegetation indices, the evapotranspiration (ET) and aboveground dry biomass (AGB) of the maize crop using a red–green-near-infrared (RGNIR) sensor onboard a UAV. For this process, 15 sets of images were captured over 61 days of maize crop monitoring. The images of each set were mosaiced and subsequently subjected to geometric correction and conversion from a digital number to reflectance to compute the vegetation indices and basal crop coefficients (Kcb). To evaluate the models statistically, 54 plants were collected in the field and evaluated for their AGB values, which were compared through statistical metrics to the data estimated by the models. The Kcb values derived from the Soil-Adjusted Vegetation Index (SAVI) were higher than the Kcb values derived from the Normalized Difference Vegetation Index (NDVI), possibly due to the linearity of this model. A good agreement (R2 = 0.74) was observed between the actual transpiration of the crop estimated by the Kcb derived from SAVI and the observed AGB, while the transpiration derived from the NDVI had an R2 of 0.69. The AGB estimated using the evaporative fraction with the SAVI model showed, in relation to the observed AGB, an RMSE of 0.092 kg m−2 and an R2 of 0.76, whereas when using the evaporative fraction obtained through the NDVI, the RMSE was 0.104 kg m−2, and the R2 was 0.74. An RGNIR sensor onboard a UAV proved to be satisfactory to estimate the water demand and AGB of the maize crop by using empirical models of the Kcb derived from the vegetation indices, which are an important source of spatialized and low-cost information for decision-making related to water management in agriculture.

Combining SPOT 5 imagery with plotwise and standwise forest data to estimate volume and biomass in mountainous coniferous site

2013 ◽  
Vol 5 (2) ◽  
Author(s):  
Petar Dimitrov ◽  
Eugenia Roumenina
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Correlation Coefficients ◽  
Test Site ◽  
National Forest Inventory ◽  
Linear Regression Models ◽  
Infrared Band ◽  
Topographic Correction ◽  
Maximal Correlation ◽  
Spot 5

AbstractIn this study, regression-based prediction of volume and aboveground biomass (AGB) of coniferous forests in a mountain test site was conducted. Two datasets — one with applied topographic correction and one without applied topographic correction — consisting of four spectral bands and six vegetation indices were generated from SPOT 5 multispectral image. The relationships between these data and ground data from field plots and national forest inventory polygons were examined. Strongest correlations of volume and AGB were observed with the near infrared band, regardless of the topographic correction. The maximal correlation coefficients when using plotwise data were −0.83 and −0.84 for the volume and AGB, respectively. The maximal correlation with standwise data was −0.63 for both parameters. The SCS+C topographic correction did not significantly affect the correlations between spectral data and forest parameters, but visually removed much of the topographically induced shading. Simple linear regression models resulted in relative RMSE of 32–33% using the plotwise data, and 43–45% using the standwise data. The importance of the source and the methodology used to obtain ground data for the successful modelling was pointed out.

scholarly journals 401 Assessing Crop Canopy Development Using a Digital, Red/Near-infrared Band Ratioing Camera

HortScience ◽  
1999 ◽  
Vol 34 (3) ◽  
pp. 513A-513
Author(s):  
D.W. Peters ◽  
J.P. Mitchell ◽  
R.E. Plant ◽  
B.R. Hanson
Keyword(s):  
Near Infrared ◽  
Low Cost ◽  
Crop Canopy ◽  
Analysis Software ◽  
Infrared Band ◽  
Crop Cover ◽  
Canopy Development ◽  
Lighting Conditions ◽  
Band Ratioing ◽  
Highly Correlated

Current methods of making crop cover estimates are time-consuming and tend to be highly variable. A low-cost, digital, red/near-infrared band ratioing camera (Dycam Inc., Chatsworth, Calif.) and accompanying software (S. Heinold, Woodland Hills, Calif.) were evaluated for estimating crop cover. The camera was tested using a set of images having leaf areas of known sizes with different crop, soil, and lighting conditions. In the field, camera-based crop cover estimates were compared to light bar measured estimates. Results indicate that the camera and image analysis software are capable of estimating percent crop cover over a range of soil, crop, and lighting environments. Camera-based crop cover estimates were highly correlated with light bar estimates (tomato r2 = 0.96, cotton r2 = 0.98). Under the conditions tested, the camera appears to be a useful tool for monitoring crop growth in the field.

scholarly journals Estimation of biometric, physiological, and nutritional variables in lettuce seedlings using multispectral images

2021 ◽  
Vol 25 (10) ◽  
pp. 689-695
Author(s):  
George D. Martins ◽  
Onésio F. da Silva Neto ◽  
Glecia J. dos S. Carmo ◽  
Renata Castoldi ◽  
Ludymilla C. S. Santos ◽  
...  
Keyword(s):  
Near Infrared ◽  
Vegetation Indices ◽  
Parametric Models ◽  
Multispectral Images ◽  
Infrared Band ◽  
Physiological Variable ◽  
Minas Gerais State ◽  
Increase Productivity ◽  
Estimation Models ◽  
Multispectral Camera

ABSTRACT The formation of seedlings is one of the most important phases of lettuce cultivation. Therefore, any strategy that aims to obtain high-quality seedlings can increase productivity. One of these strategies is the prediction of morphophysiological attributes based on optical properties. The objective of this study was to quantitatively estimate the biometric variables of lettuce from parametric and non-parametric models based on the response of multispectral camera images. The experiment was conducted in a greenhouse in the municipality of Uberaba, Minas Gerais State, Brazil. Twenty days after sowing, multispectral images of the plants were captured using a MAPIR Survey 3 camera. To compose the estimation models, along with the original bands of the camera, the multispectral vegetation indices were calculated using the calibrated original camera bands. Bands B550, B660, and B850 and the near-infrared indices contributed significantly to estimating the physiological variable models, with B850 contributing the most to the biometric and nutritional variables. From the near-infrared band (B850) and derived indices, it was possible to estimate all the agronomic variables from the models generated by the M5 algorithm, with an accuracy of up to 1.6% for the maximum quantum yield. Thus, it is possible to quantify the biometric, physiological, and nutritional variables of lettuce using a multispectral camera. Among the Mapir camera bands, B660 exhibited the greatest variability, showing that the red range was the most sensitive.

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