area index
Recently Published Documents


TOTAL DOCUMENTS

5396
(FIVE YEARS 1884)

H-INDEX

111
(FIVE YEARS 15)

Author(s):  
Tran Xuan Minh ◽  
Nguyen Cong Thanh ◽  
Tran Hau Thin ◽  
Nguyen Thi Huong Giang ◽  
Nguyen Thi Tieng

Background: Peanut (Arachis hypogaea L.) is one of the oil and cash crops in Vietnam. However, owing to the lack of appropriate management practices, the production and the area under cultivation of peanut have remained low. Mulches are the key factors contributing to promoting crop development and early harvest and increasing yields. Methods: The experiment consisted of three mulch treatments, viz., plastic mulch, straw mulch and no-mulch control. All the treatments were replicated thrice in a complete randomized block design. Result: In the conditions of mulch, the plant growth parameters (germination rate, growing time, plant height, number of branches per plant), leaf area index, the number of nodules per plant, dry matter accumulation, yield components and yield of peanut was much higher than that of no-mulch control. Among the mulches, plastic mulch was found superior to straw mulch in the pod yields and water-use efficiency and moisture conservation, thereby can be considered as a reliable practice for increasing the productivity of peanut on the coastal sandy land in Nghe An province, Vietnam.


Author(s):  
Aslannif Roslan ◽  
YEE SIN TEY ◽  
Faten Aris A ◽  
Afif Ashari ◽  
Abdul Shaparudin A ◽  
...  

Background: Transcatheter Aortic Valve Replacements (TAVR) has become widespread throughout the world. To date there are no echocardiographic study of TAVR patients from Southeast Asia (SEA). We sought to evaluate 1) changes in echocardiographic and strain values pre and post TAVR 2) relationship between aortic stenosis (AS) severity and strain values, 3) left ventricle geometry in severe AS 4) relationship of flow rate to dimensionless index (DVI) and acceleration time (AT) and 5) effect of strains on outcome. Methods: Retrospective study of 112 TAVR patients in our center from 2009 to 2020. The echocardiographic and strain images pre (within 1 months), post (day after) and 6 months post TAVR were analyzed by expert echocardiographer. Results: The ejection fraction (EF) increased at 6 months (53.02 ± 12.12% to 56.35 ± 9.00%) (p=0.044). Interventricular septal thickness in diastole (IVSd) decreased (1.27 ± 0.21cm to 1.21 ± 0.23cm) (p=0.038) and left ventricle internal dimension in diastole (LVIDd) decreased from 4.77 ± 0.64cm to 4.49 ± 0.65cm (p = 0.001). No changes in stroke volume index (SVI pre vs 6 months p =0.187), but the flow rate increases (217.80 ± 57.61mls/s to 251.94 ± 69.59mls/s, p<0.001). Global Longitudinal Strain (GLS) improved from -11.44 ± 4.23% to -13.94 ± 3.72% (p <0.001), Left Atrial Reservoir strain (Lar-S) increased from 17.44 ± 9.16% to 19.60 ± 8.77% (p=0.033). 8 patients (7.5%) had IVSd < 1.0cm, and 4 patients (3.7%) had normal left ventricle (LV) geometry. There was linear relationship between IVSd and mean PG (r=0.208, p=0.031), between GLS to aortic valve area (AVA) and aortic valve area index (AVAi) (r = – 0.305, p=0.001 and r= – 0.316, p = 0.001). There was also relationship between AT (r=-0.20, p=0.04) and DVI (r=0.35, p< 0.001) with flow rate. Patients who died late (after 6 months) had lower GLS at 6 months. (Alive; -13.94 ± 3.72% vs Died; -12.43 ± 4.19%, p= 0.001) Conclusion: At 6 months TAVR cause reverse remodeling of the LV with reduction in IVSd, LVIDd and improvement in GLS and LAr-S. There is linear relationship between GLS and AVA and between IVSd and AVA.


2022 ◽  
Author(s):  
Francesco Chianucci ◽  
Carlotta Ferrara ◽  
Nicola Puletti

Digital Cover Photography (DCP) is an increasingly popular tool for estimating canopy cover and leaf area index (LAI). However, existing solutions to process canopy images are predominantly tailored for fisheye photography, whereas open-access tools for DCP are lacking. We developed an R package (coveR) to support the whole processing of DCP images in an automated, fast and reproducible way. The package functions, which are designed for step-by-step single-image analysis, can be performed sequentially in a pipeline and also allow simple implementation of batch-processing bunches of images. A case study is presented to demonstrate the reliability of canopy attributes derived from coveR in pure beech (Fagus sylvatica L.) stands with variable canopy density and structure. Estimates of gap fraction and effective LAI from DCP were validated against reference measurements obtained from terrestrial laser scanning. By providing a simple, transparent and flexible image processing procedure, coveR supported the use of DCP for routine measurements and monitoring of forest canopy attributes. This, combined with the implementability of DCP in many devices, including smartphones, micro-cameras, and remote trail cameras, can greatly expand the accessibility of the method also to non-experts.


MAUSAM ◽  
2022 ◽  
Vol 53 (1) ◽  
pp. 57-62
Author(s):  
RAJ SINGH ◽  
V. U. M. RAO ◽  
DIWAN SINGH

Field experiment was conducted for two crop seasons (1996-97 & 1997-98) at CCS, HAU, Hisar research farm to study the effect of weather parameters on growth and yield of mustard. The results indicated that an increase in maximum temperature and duration of sunshine hours resulted in increased leaf area index (LAI). The increase in daytime temperature resulted in higher biomass accumulation during vegetative phase, but the trend was reversed during physiological maturity. The biomass accumulation in brassicas increased with increase in evaporation rate during the grand growth period. However, latter on during the physiological maturity, increase in evaporation rate resulted in decline of biomass accumulation. Further, it was noted that the magnitudes of some important weather parameters (maximum and minimum temperatures, pan evaporation and morning relative humidity) during the vegetative phase of crop played decisive role in deciding the quantum of seed yield which is a resultant of various yield attributes. The rainfall during the crop growing season either have no association or had a negative relationship with yield and yield attributes because crop never experienced water stress as abundant moisture was made available through irrigation.


2022 ◽  
Vol 14 (2) ◽  
pp. 331
Author(s):  
Xuewei Zhang ◽  
Kefei Zhang ◽  
Yaqin Sun ◽  
Yindi Zhao ◽  
Huifu Zhuang ◽  
...  

The leaf area index (LAI) is of great significance for crop growth monitoring. Recently, unmanned aerial systems (UASs) have experienced rapid development and can provide critical data support for crop LAI monitoring. This study investigates the effects of combining spectral and texture features extracted from UAS multispectral imagery on maize LAI estimation. Multispectral images and in situ maize LAI were collected from test sites in Tongshan, Xuzhou, Jiangsu Province, China. The spectral and texture features of UAS multispectral remote sensing images are extracted using the vegetation indices (VIs) and the gray-level co-occurrence matrix (GLCM), respectively. Normalized texture indices (NDTIs), ratio texture indices (RTIs), and difference texture indices (DTIs) are calculated using two GLCM-based textures to express the influence of two different texture features on LAI monitoring at the same time. The remote sensing features are prescreened through correlation analysis. Different data dimensionality reduction or feature selection methods, including stepwise selection (ST), principal component analysis (PCA), and ST combined with PCA (ST_PCA), are coupled with support vector regression (SVR), random forest (RF), and multiple linear regression (MLR) to build the maize LAI estimation models. The results reveal that ST_PCA coupled with SVR has better performance, in terms of the VIs + DTIs (R2 = 0.876, RMSE = 0.239) and VIs + NDTIs (R2 = 0.877, RMSE = 0.236). This study introduces the potential of different texture indices for maize LAI monitoring and demonstrates the promising solution of using ST_PCA to realize the combining of spectral and texture features for improving the estimation accuracy of maize LAI.


Agriculture ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 98
Author(s):  
Fernando Pinto-Morales ◽  
Jorge Retamal-Salgado ◽  
María Dolores Lopéz ◽  
Nelson Zapata ◽  
Rosa Vergara-Retamales ◽  
...  

Different concentrations of compost applied as organic fertilizer can modify productive, quality, and chemical parameters in several fruit tree species. The objective of this study was to determine the effect of increasing applications of compost on physiological, productive, and quality parameters in calafate fruit during the seasons of 2018–2019 and 2019–2020. The study was conducted on a commercial calafate orchard using a randomized complete block design with four treatments (CK: no compost application, T1: 5 Ton ha−1, T2: 10 Ton ha−1, and T3: 15 Ton ha−1), each with four repetitions. The results did not show statistical significance for stomatal conductance (Gs), quantum yield of PSII, or photosynthetic active radiation (PAR) within treatments. As for fruit yield, a statistical difference was found between the control treatment and T1, which were lower than T2 and T3 in both seasons. The trees reached a higher leaf area index with T2 in both seasons. The highest antioxidant capacity was obtained with T3 and T2 for the first and second season, respectively. Polyphenols and total anthocyanin production showed statistical significance, with a higher content at the second season with T2. It is concluded that the dose under which yield, quality, and nutraceutical content of calafate fruit are optimized is the one used in T2, 10 Ton ha−1.


2022 ◽  
Vol 9 ◽  
Author(s):  
Olivera Stojanović ◽  
Bastian Siegmann ◽  
Thomas Jarmer ◽  
Gordon Pipa ◽  
Johannes Leugering

Environmental scientists often face the challenge of predicting a complex phenomenon from a heterogeneous collection of datasets that exhibit systematic differences. Accounting for these differences usually requires including additional parameters in the predictive models, which increases the probability of overfitting, particularly on small datasets. We investigate how Bayesian hierarchical models can help mitigate this problem by allowing the practitioner to incorporate information about the structure of the dataset explicitly. To this end, we look at a typical application in remote sensing: the estimation of leaf area index of white winter wheat, an important indicator for agronomical modeling, using measurements of reflectance spectra collected at different locations and growth stages. Since the insights gained from such a model could be used to inform policy or business decisions, the interpretability of the model is a primary concern. We, therefore, focus on models that capture the association between leaf area index and the spectral reflectance at various wavelengths by spline-based kernel functions, which can be visually inspected and analyzed. We compare models with three different levels of hierarchy: a non-hierarchical baseline model, a model with hierarchical bias parameter, and a model in which bias and kernel parameters are hierarchically structured. We analyze them using Markov Chain Monte Carlo sampling diagnostics and an intervention-based measure of feature importance. The improved robustness and interpretability of this approach show that Bayesian hierarchical models are a versatile tool for the prediction of leaf area index, particularly in scenarios where the available data sources are heterogeneous.


2022 ◽  
Vol 14 (2) ◽  
pp. 298
Author(s):  
Kaisen Ma ◽  
Zhenxiong Chen ◽  
Liyong Fu ◽  
Wanli Tian ◽  
Fugen Jiang ◽  
...  

Using unmanned aerial vehicles (UAV) as platforms for light detection and ranging (LiDAR) sensors offers the efficient operation and advantages of active remote sensing; hence, UAV-LiDAR plays an important role in forest resource investigations. However, high-precision individual tree segmentation, in which the most appropriate individual tree segmentation method and the optimal algorithm parameter settings must be determined, remains highly challenging when applied to multiple forest types. This article compared the applicability of methods based on a canopy height model (CHM) and a normalized point cloud (NPC) obtained from UAV-LiDAR point cloud data. The watershed algorithm, local maximum method, point cloud-based cluster segmentation, and layer stacking were used to segment individual trees and extract the tree height parameters from nine plots of three forest types. The individual tree segmentation results were evaluated based on experimental field data, and the sensitivity of the parameter settings in the segmentation methods was analyzed. Among all plots, the overall accuracy F of individual tree segmentation was between 0.621 and 1, the average RMSE of tree height extraction was 1.175 m, and the RMSE% was 12.54%. The results indicated that compared with the CHM-based methods, the NPC-based methods exhibited better performance in individual tree segmentation; additionally, the type and complexity of a forest influence the accuracy of individual tree segmentation, and point cloud-based cluster segmentation is the preferred scheme for individual tree segmentation, while layer stacking should be used as a supplement in multilayer forests and extremely complex heterogeneous forests. This research provides important guidance for the use of UAV-LiDAR to accurately obtain forest structure parameters and perform forest resource investigations. In addition, the methods compared in this paper can be employed to extract vegetation indices, such as the canopy height, leaf area index, and vegetation coverage.


Author(s):  
M. Z. Dahiru ◽  
M. Hashim ◽  
N. Hassan

Abstract. Measuring high spatial/temporal industrial heat emission (IHE) is an important step in industrial climate studies. The availability of MODIS data products provides up endless possibilities for both large-area and long-term study. nevertheless, inadequate for monitoring industrial areas. Thus, Thermal sharpening is a common method for obtaining thermal images with higher spatial resolution regularly. In this study, the efficiency of the TsHARP technique for improving the low resolution of the MODIS data product was investigated using Landsat-8 TIR images over the Klang Industrial area in Peninsular Malaysia (PM). When compared to UAV TIR fine thermal images, sharpening resulted in mean absolute differences of about 25 °C, with discrepancies increasing as the difference between the ambient and target resolutions increased. To estimate IHE, the related factors (normalized) industrial area index as NDBI, NDSI, and NDVI were examined. The results indicate that IHE has a substantial positive correlation with NDBI and NDSI (R2 = 0.88 and 0.95, respectively), but IHE and NDVI have a strong negative correlation (R2 = 0.87). The results showed that MODIS LST at 1000 m resolution can be improved to 100 m with a significant correlation R2 = 0.84 and RMSE of 2.38 °C using Landsat 8 TIR images at 30 m, and MODIS LST at 1000 m resolution can still be improved to 100 m with significant correlation R2 = 0.89 and RMSE of 2.06 °C using aggregated Landsat-8 TIR at 100 m resolution. Similarly, Landsat-8 TIR at 100 m resolution was still improved to 30 m and used with aggregate UAV TIR at 5 m resolution with a significant correlation R2 = 0.92 and RMSE of 1.38 °C. Variation has been proven to have a significant impact on the accuracy of the model used. This result is consistent with earlier studies that utilized NDBI as a downscaling factor in addition to NDVI and other spectral indices and achieved lower RMSE than techniques that simply used NDVI. As a result, it is suggested that the derived IHE map is suitable for analyzing industrial thermal environments at 1:10,000 50,000 scales, and may therefore be used to assess the environmental effect.


Sign in / Sign up

Export Citation Format

Share Document