scholarly journals Non-Destructive Monitoring of Maize Nitrogen Concentration Using a Hyperspectral LiDAR: An Evaluation from Leaf-Level to Plant-Level

2021 ◽  
Vol 13 (24) ◽  
pp. 5025
Author(s):  
Kaiyi Bi ◽  
Zheng Niu ◽  
Shunfu Xiao ◽  
Jie Bai ◽  
Gang Sun ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Spatial Information ◽  
Nitrogen Concentration ◽  
Point Clouds ◽  
Future Research ◽  
Least Squares Regression ◽  
N Concentration ◽  
Leaf Level ◽  
Plant Level ◽  
Remote Sensing Techniques

Advanced remote sensing techniques for estimating crop nitrogen (N) are crucial for optimizing N fertilizer management. Hyperspectral LiDAR (HSL) data, with both spectral and spatial information of the targets, can extract more plant properties than traditional LiDAR and hyperspectral imaging systems. In this study, we tested the ability of HSL in terms of estimating maize N concentration at the leaf-level by using spectral indices and partial least squares regression (PLSR) methods. Subsequently, the N estimation was scaled up to the plant-level based on HSL point clouds. Biomass, extracted with structural proxies, was utilized to exhibit its supplemental effect on N concentration. The results show that HSL has the ability to extract N concentrations at both the leaf-level and the canopy-level, and PLSR showed better performance (R2 > 0.6) than the single spectral index (R2 > 0.4). In comparison to the stem height and maximum canopy width, the plant height had the strongest ability (R2 = 0.88) to estimate biomass. Future research should utilize larger datasets to test the viability of using HSL to monitor the N concentration of crops, which is beneficial for precision agriculture.

scholarly journals Remote Sensing Using Canopy and Leaf Reflectance for Estimating Nitrogen Status in Red-blush Pears

HortScience ◽  
2018 ◽  
Vol 53 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Eileen M. Perry ◽  
Ian Goodwin ◽  
David Cornwall
Keyword(s):  
Vegetation Index ◽  
Spatial Information ◽  
Leaf Reflectance ◽  
N Concentration ◽  
Leaf Level ◽  
Leaf N Concentration ◽  
Reflectance Indices ◽  
Reflectance Measurements ◽  
Leaf N ◽  
Multispectral Camera

Reflectance measurements at leaf and canopy scales were made in a red-blush pear (Pyrus communis) orchard for two growing seasons. Canopy reflectance measurements were obtained using a multispectral camera flown on board an unmanned aerial vehicle (UAV), and leaf reflectance measurements were undertaken in a laboratory using a portable spectrometer. These measurements were used to compute reflectance indices as surrogates for direct leaf nitrogen (N) concentration measurements. The indices were evaluated against laboratory analysis of leaf N concentration. Regression results for leaf %N on canopy-level measurements with the multispectral camera resulted in the highest R2 value [R2 = 0.67; root mean square error (RMSE) = 0.24%N] with a new index, Modified Canopy Chlorophyll Content Index (M3CI)_710 nm. Regression results for leaf %N on leaf-level measurements in-laboratory resulted in the highest R2 value (R2 = 0.65) with two other indices, Normalized Difference Vegetation Index (NDVI) and Normalized Difference Red-edge Index (NDRE)_720 nm. The corresponding RMSE values were 0.26%N. The results indicate that reflectance indices measured at the leaf level, with a controlled light source and calibration, could be used to estimate leaf %N. An analysis of uncertainty indicated that if leaf %N is estimated from leaf-level reflectance values, 10 or more leaves (from the same tree) should be averaged. The results support the use of a UAV-based assessment for canopy %N using the M3CI_710 nm, which could provide spatial information of leaf N concentration across an orchard.

scholarly journals Geometric Characterization of Vines from 3D Point Clouds Obtained with Laser Scanner Systems

2019 ◽  
Vol 11 (20) ◽  
pp. 2365 ◽  
Author(s):  
Ana del-Campo-Sanchez ◽  
Miguel Moreno ◽  
Rocio Ballesteros ◽  
David Hernandez-Lopez
Keyword(s):  
Relative Error ◽  
Precision Agriculture ◽  
Three Dimensional ◽  
Laser Scanner ◽  
Point Clouds ◽  
Scanning System ◽  
3D Point Clouds ◽  
Remote Sensing Techniques ◽  
The Cost

The 3D digital characterization of vegetation is a growing practice in the agronomy sector. Precision agriculture is sustained, among other methods, by variables that remote sensing techniques can digitize. At present, laser scanners make it possible to digitize three-dimensional crop geometry in the form of point clouds. In this work, we developed several methods for calculating the volume of vine wood, with the final intention of using these values as indicators of vegetative vigor on a thematic map. For this, we used a static terrestrial laser scanner (TLS), a mobile scanning system (MMS), and six algorithms that were implemented and adapted to the data captured and to the proposed objective. The results show that, with TLS equipment and the algorithm called convex hull cluster, the volumes of a vine trunk can be obtained with a relative error lower than 7%. Although the accuracy and detail of the cloud obtained with TLS are very high, the cost per unit for the scanned area limits the application of this system for large areas. In contrast to the inoperability of the TLS in large areas of terrain, the MMS and the algorithm based on the L1-medial skeleton and the modelling of cylinders of a certain height and diameter have solved the estimation of volumes with a relative error better than 3%. To conclude, the vigor map elaborated represents the estimated volume of each vine by this method.

scholarly journals Evapotranspiration Estimation with Small UAVs in Precision Agriculture

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6427
Author(s):  
Haoyu Niu ◽  
Derek Hollenbeck ◽  
Tiebiao Zhao ◽  
Dong Wang ◽  
YangQuan Chen
Keyword(s):  
Energy Balance ◽  
High Resolution ◽  
Spatial Resolution ◽  
Precision Agriculture ◽  
Large Scale ◽  
Bowen Ratio ◽  
Future Research ◽  
Accurate Estimation ◽  
Research Areas ◽  
Energy Balance Approach

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

Total nitrogen concentration in leaves of potatoes (Solanum tuberosumL.) as an index of nutritional status

1976 ◽  
Vol 87 (2) ◽  
pp. 293-296 ◽  
Author(s):  
A. Gupta ◽  
M. C. Saxena
Keyword(s):  
Total Nitrogen ◽  
Tuber Yield ◽  
Critical Concentration ◽  
Nitrogen Concentration ◽  
Curvilinear Relationship ◽  
N Application ◽  
Total N ◽  
Potato Plants ◽  
N Concentration ◽  
Total Nitrogen Concentration

SummaryLeaf samples were collected, at weekly intervals, throughout the growing season, from potato (Solanum tuberosumL.) plants supplied with varying amounts of nitrogen (0, 60, 120, 180 and 240 kg N/ha) and analysed for total N. Application of nitrogen increased the N concentration in the green leaves at all stages of growth. There was a significant curvilinear relationship between the final tuber yield and the total N concentration in the leaves at 48–90 days after planting in 1968–9 and at 79–107 days after planting in 1969–70. The N concentration at 70–90 days after planting was consistently related to the final tuber yield in both years. Thus this period was ideal for assessing the nitrogen status of potato plants. The critical concentration of total nitrogen generally decreased with advance in age. It ranged from 4·65% at 76 days to 3·30% at 90 days during 1968–9, whereas in 1969–70 it ranged from 4·20% at 79 days to 3·80% at 93 days. During the period from 83 to 86 days the critical percentage was around 3·6% in both the years.

scholarly journals CLASSIFICATION OF RAILWAY ASSETS IN MOBILE MAPPING POINT CLOUDS

2020 ◽  
Vol XLIII-B1-2020 ◽  
pp. 219-225
Author(s):  
M. Corongiu ◽  
A. Masiero ◽  
G. Tucci
Keyword(s):  
Spatial Information ◽  
Point Clouds ◽  
Railway Track ◽  
Neural Net ◽  
Mobile Mapping ◽  
Geospatial Information ◽  
Railway System ◽  
Point Neighborhood ◽  
Mobile Mapping Systems ◽  
Mapping Systems

Abstract. Nowadays, mobile mapping systems are widely used to quickly collect reliable geospatial information of relatively large areas: thanks to such characteristics, the number of applications and fields exploiting their usage is continuously increasing. Among such possible applications, mobile mapping systems have been recently considered also by railway system managers to quickly produce and update a database of the geospatial features of such system, also called assets. Despite several vehicles, devices and acquisition methods can be considered for the data collection of the railway system, the predominant one is probably that based on the use of a mobile mapping system mounted on a train, which moves all along the railway tracks, enabling the 3D reproduction of the entire railway track area.Given the large amount of data collected by such mobile mapping, automatic procedures have to be used to speed up the process of extracting the spatial information of interest, i.e. assets positions and characteristics.This paper considers the problem of extracting such information for what concerns cantilever and portal masts, by exploiting a mixed approach. First, a set of candidate areas are extracted and pre-processed by considering certain of their geometric characteristics, mainly extracted by using eigenvalues of the covariance matrix of a point neighborhood. Then, a 3D modified Fisher vector-deep learning neural net is used to classify the candidates. Tests on such approach are conducted in two areas of the Italian railway system.

scholarly journals SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1

Irriga ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 1-15
Author(s):  
Iug Lopes ◽  
Abelardo A. A. Montenegro
Keyword(s):  
Electrical Conductivity ◽  
Soil Moisture ◽  
Precision Agriculture ◽  
Spatial Dependence ◽  
Spatial Information ◽  
Gravimetric Method ◽  
Research Unit ◽  
Soil Electrical Conductivity ◽  
Agricultural Engineering ◽  
Space Dependence

SPACE DEPENDENCE OF SOIL MOISTURE AND SOIL ELECTRICAL CONDUCTIVITY IN ALUVIAL REGION1     IUG LOPES2 E ABELARDO ANTONIO DE ASSUNÇÃO MONTENEGRO3   1Paper extracted from the doctoral thesis of the first author. 2Department of Agronomy, Instituto Federal de Educação, Ciência e Tecnologia Baiano, BR 349, Km 14 - Zona Rural, CEP: 47600-000, Bom Jesus da Lapa - BA, Brazil; [email protected] - ORCID: 0000-0003-0592-4774. 3Department of Agricultural Engineering, Universidade Federal Rural de Pernambuco, Rua Dom Manoel de Medeiros, Dois Irmão, CEP: 52171-900, Recife - PE, Brazil; [email protected] -ORCID: 0000-0002-5746-8574.     1 ABSTRACT   Spatial information on soil characteristics is essential to proper decision-making regarding to the environment and land use management. The objective of this work was the investigation of cross - variance between soil moisture and apparent soil electrical conductivity (CEa), under different land uses in an alluvial valley of Pernambuco. The study was developed at the Advanced Research Unit of Universidade Federal Rural de Pernambuco (UFRPE), located at  Brígida River Basin, municipality of Panamirim-PE. Soil samples were collected in a regular mesh of 20 x 10 m, for soil moisture by gravimetric method and, following a regular 10 x 10 m mesh, CEa measurements were performed using EM38® device. Cross-semivariograms were assessed and spatial dependence was verified by geostatistical procedures. It was verified in geostatistical procedures  low variation for soil moisture and intermediate variation for CEa. The use of geostatistics allowed identification of covariance between soil moisture and ECa, as well as spatial dependence for both variables, for agricultural areas. It was verified that soil moisture, even at levels close to residual, constitutes a relevant secondary component for increasing soil salinity maps precision, and hence to precision agriculture.   Keywords: geostatistics, semi-arid, precision agriculture     LOPES, I. E MONTENEGRO, A. A. DE A. DEPENDÊNCIA ESPACIAL DA UMIDADE DO SOLO E CONDUTIVIDADE ELÉTRICA EM REGIÃO ALUVIAL     2 RESUMO   Informações espaciais sobre as características do solo são essenciais para uma tomada de decisão adequada em relação ao meio ambiente e ao gerenciamento do uso do solo. O objetivo deste trabalho foi investigar a variância cruzada entre a umidade do solo e a condutividade elétrica aparente do solo (CEa), sob diferentes usos do solo em um vale aluvial de Pernambuco. O estudo foi desenvolvido na Unidade de Pesquisa Avançada da Universidade Federal Rural de Pernambuco (UFRPE), localizada na bacia do rio Brígida, município de Panamirim-PE. As amostras de solo foram coletadas em uma malha regular de 20 x 10 m, para a umidade do solo pelo método gravimétrico e, seguindo uma malha regular de 10 x 10 m, as medidas de CEa foram realizadas usando o dispositivo EM38®. Os semivariogramas cruzados foram avaliados e a dependência espacial foi verificada por procedimentos geoestatísticos. Verificou-se procedimentos geoestatísticos, uma baixa variação da umidade do solo e variação intermediária para CEa. O uso da geoestatística permitiu identificar a covariância entre a umidade do solo e o CEa, bem como a dependência espacial para ambas as variáveis, para as áreas agrícolas. Verificou-se que a umidade do solo, mesmo em níveis próximos ao residual, constitui um componente secundário relevante para o aumento da precisão do mapeamento da salinidade do solo e, consequentemente, para a agricultura de precisão.   Palavras-chave: geoestatística, semiárido, agricultura de precisão

scholarly journals GNSS SATELLITE VISIBILITY ANALYSIS BASED ON 3D SPATIAL INFORMATION IN URBAN AREAS

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 123-128
Author(s):  
Y.-H. Lu ◽  
J.-Y. Han
Keyword(s):  
Spatial Data ◽  
Urban Areas ◽  
Spatial Information ◽  
Signal Transmission ◽  
Point Clouds ◽  
Satellite System ◽  
Topographic Effects ◽  
Analysis Technique ◽  
Satellite Visibility ◽  
Global Navigation Satellite

Abstract. Global Navigation Satellite System (GNSS) is a matured modern technique for spatial data acquisition. Its performance has a great correlation with GNSS receiver position. However, high-density building in urban areas causes signal obstructions and thus hinders GNSS’s serviceability. Consequently, GNSS positioning is weakened in urban areas, so deriving proper improvement resolutions is a necessity. Because topographic effects are considered the main factor that directly block signal transmission between satellites and receivers, this study integrated aerial borne LiDAR point clouds and a 2D building boundary map to provide reliable 3D spatial information to analyze topographic effects. Using such vector data not only reflected high-quality GNSS satellite visibility calculations, but also significantly reduced data amount and processing time. A signal obstruction analysis technique and optimized computational algorithm were also introduced. In conclusion, this paper proposes using superimposed column method to analyze GNSS receivers’ surrounding environments and thus improve GNSS satellite visibility predictions in an efficient and reliable manner.

Seasonal Trade-Off between Water- and Nitrogen-Use Efficiency of Constructive Plants in Desert Riparian Forest in Hyperarid Region of China

2011 ◽  
pp. 260-275
Author(s):  
Shengkui Cao ◽  
Qi Feng ◽  
Jianhua Si ◽  
Yonghong Su ◽  
Zongqiang Chang ◽  
...  
Keyword(s):  
Nitrogen Use Efficiency ◽  
Riparian Forest ◽  
Seasonal Pattern ◽  
Nitrogen Concentration ◽  
Populus Euphratica ◽  
Nitrogen Use ◽  
Trade Off ◽  
N Concentration ◽  
Use Efficiency

Foliar d13C values are often used to denote the long-term water use efficiency (WUE) of plants whereas long-term nitrogen use efficiency (NUE) are usually estimated by the ratio of C to N in the leaves. Seasonal variations of d13C values, foliar nitrogen concentration and C/N ratios of Populus euphratica and Tamarix ramosissima grown under five different microhabitats of Ejina desert riparian oasis of northwestern arid regions in China were studied. The results indicated that T. ramosissima had higher d13C value compared with that of P. euphratica. The N concentration and C/N ratios of two species were not significantly different. The seasonal pattern of three indexes in two species was different. The d13C values and N concentration decreased during the plant’s growth period. However, the change of C/N ratios was increased. Among microhabitats, there were higher d13C values and N concentration as well as lower C/N ratios in the Dune and Gobi habitats. Foliar d13C values significantly and positively correlated with N concentration in P. euphratica and T. ramosissima, whereas a significantly negative correlation between d13C values and C/N ratios was found for P. euphratica. This relation in T. ramosissima was weak, but there was a significant quadratic curve relationship between d13C values and C/N ratios, which revealed that there was a trade-off between WUE and NUE for P. euphratica and in natural condition, P. euphratica could not improve WUE and NUE simultaneously. T. ramosissima could simultaneously enhance WUE and NUE. The above characters of WUE and NUE in two plants reflected the different adaptations of desert species to environmental condition.

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