scholarly journals A Handheld Grassland Vegetation Monitoring System Based on Multispectral Imaging

Agriculture ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 1262
Author(s):  
Aiwu Zhang ◽  
Shaoxing Hu ◽  
Xizhen Zhang ◽  
Taipei Zhang ◽  
Mengnan Li ◽  
...  
Keyword(s):  
Monitoring System ◽  
Near Infrared ◽  
Multispectral Imaging ◽  
Grassland Management ◽  
Growth Monitoring ◽  
Radiometric Calibration ◽  
Grassland Vegetation ◽  
Vegetation Monitoring ◽  
Qinghai Province ◽  
Vegetation Growth

Monitoring grassland vegetation growth is of vital importance to scientific grazing and grassland management. People expect to be able to use a portable device, like a mobile phone, to monitor grassland vegetation growth at any time. In this paper, we propose a handheld grassland vegetation monitoring system to achieve the goal of monitoring grassland vegetation growth. The system includes two parts: the hardware unit is a hand-held multispectral imaging tool named ASQ-Discover based on a smartphone, which has six bands (wavelengths)—including three visible bands (450 nm, 550 nm, 650 nm), a red-edge band (750 nm), and two near-infrared bands (850 nm, 960 nm). The imagery data of each band has a size of 5120 × 3840 pixels with 8-bit depth. The software unit improves image quality through vignetting removal, radiometric calibration, and misalignment correction and estimates and analyzes spectral traits of grassland vegetation (Fresh Grass Ratio (FGR), NDVI, NDRE, BNDVI, GNDVI, OSAVI and TGI) that are indicators of vegetation growth in grassland. We introduce the hardware and software unit in detail, and we also experiment in five pastures located in Haiyan County, Qinghai Province. Our experimental results show that the handheld grassland vegetation growth monitoring system has the potential to revolutionize the grassland monitoring that operators can conduct when using a hand-held tool to achieve the tasks of grassland vegetation growth monitoring.

scholarly journals Analysis of Vegetation Red Edge with Different Illuminated/Shaded Canopy Proportions and to Construct Normalized Difference Canopy Shadow Index

2019 ◽  
Vol 11 (10) ◽  
pp. 1192 ◽  
Author(s):  
Nianxu Xu ◽  
Jia Tian ◽  
Qingjiu Tian ◽  
Kaijian Xu ◽  
Shaofei Tang
Keyword(s):  
Forest Canopy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Bare Soil ◽  
Vegetation Canopy ◽  
Vegetation Monitoring ◽  
Red Edge ◽  
Positive Correlations

Shadows exist universally in sunlight-source remotely sensed images, and can interfere with the spectral morphological features of green vegetations, resulting in imprecise mathematical algorithms for vegetation monitoring and physiological diagnoses; therefore, research on shadows resulting from forest canopy internal composition is very important. Red edge is an ideal indicator for green vegetation’s photosynthesis and biomass because of its strong connection with physicochemical parameters. In this study, red edge parameters (curve slope and reflectance) and the normalized difference vegetation index (NDVI) of two species of coniferous trees in Inner Mongolia, China, were studied using an unmanned aerial vehicle’s hyperspectral visible-to-near-infrared images. Positive correlations between vegetation red edge slope and reflectance with different illuminated/shaded canopy proportions were obtained, with all R2s beyond 0.850 (p < 0.01). NDVI values performed steadily under changes of canopy shadow proportions. Therefore, we devised a new vegetation index named normalized difference canopy shadow index (NDCSI) using red edge’s reflectance and the NDVI. Positive correlations (R2 = 0.886, p < 0.01) between measured brightness values and NDCSI of validation samples indicated that NDCSI could differentiate illumination/shadow circumstances of a vegetation canopy quantitatively. Combined with the bare soil index (BSI), NDCSI was applied for linear spectral mixture analysis (LSMA) using Sentinel-2 multispectral imaging. Positive correlations (R2 = 0.827, p < 0.01) between measured brightness values and fractional illuminated vegetation cover (FIVC) demonstrate the capacity of NDCSI to accurately calculate the fractional cover of illuminated/shaded vegetation, which can be utilized to calculate and extract the illuminated vegetation canopy from satellite images.

Changes of Grassland Vegetation Growth in Xilin Gol League over 10 Years and Analysis on the Influence Factors

2013 ◽  
Vol 15 (2) ◽  
pp. 270 ◽  
Author(s):  
Haida YU ◽  
Xiuchun YANG ◽  
Bin XU ◽  
Yunxiang JIN ◽  
Tian GAO ◽  
...  
Keyword(s):  
Influence Factors ◽  
Grassland Vegetation ◽  
Vegetation Growth

scholarly journals Applying satellite images to spectral signature development of maize production (Zea mays L.) under colombia’s middle tropics conditions

ENTRAMADO ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 256-262
Author(s):  
Edier Fernando Ávila Vélez ◽  
Natalia Escobar Escobar ◽  
Carlos Francisco Morantes Choconta
Keyword(s):  
Satellite Images ◽  
Near Infrared ◽  
Spectral Response ◽  
Spectral Signature ◽  
Growth Stages ◽  
Total Production ◽  
Radiometric Calibration ◽  
Maize Crop ◽  
Maize Production ◽  
Production Stages

Maize is currently the world’s second largest crop in terms of production, after wheat and rice. It is the first cereal as for grain yield per hectare and the second, after wheat, regarding total production. Maize has great economic importance worldwide, both as human and/or animal food, and as a source of a large number of industrial products. New digital technologies are allowing greater monitoring of farming production stages. This research developed the spectral signature of maize fields ground covers across different growth stages (2 months, 2.3 months and 4.3 months). Similarly, this research paper proposes the application of a methodology based on four phases: 1. Maize crop georeferencing, 2. Satellite images selection, 3. Radiometric calibration of images 4. Spectral signature development of maize crops. Spectral response or signature of maize crops at visible and near-infrared wavelengths was obtained, indicating significant changes in crops growth. The use of satellite imagery becomes an interesting tool that introduces an approach towards more accurate and controlled monitoring systems in agricultural production.

scholarly journals Rapid Mosaicking of Unmanned Aerial Vehicle (UAV) Images for Crop Growth Monitoring Using the SIFT Algorithm

2019 ◽  
Vol 11 (10) ◽  
pp. 1226 ◽  
Author(s):  
Jianqing Zhao ◽  
Xiaohu Zhang ◽  
Chenxi Gao ◽  
Xiaolei Qiu ◽  
Yongchao Tian ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Near Infrared ◽  
Similarity Index ◽  
Crop Growth ◽  
Contrast Threshold ◽  
Growth Monitoring ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Aerial Vehicle ◽  
Uav Images

To improve the efficiency and effectiveness of mosaicking unmanned aerial vehicle (UAV) images, we propose in this paper a rapid mosaicking method based on scale-invariant feature transform (SIFT) for mosaicking UAV images used for crop growth monitoring. The proposed method dynamically sets the appropriate contrast threshold in the difference of Gaussian (DOG) scale-space according to the contrast characteristics of UAV images used for crop growth monitoring. Therefore, this method adjusts and optimizes the number of matched feature point pairs in UAV images and increases the mosaicking efficiency. Meanwhile, based on the relative location relationship of UAV images used for crop growth monitoring, the random sample consensus (RANSAC) algorithm is integrated to eliminate the influence of mismatched point pairs in UAV images on mosaicking and to keep the accuracy and quality of mosaicking. Mosaicking experiments were conducted by setting three types of UAV images in crop growth monitoring: visible, near-infrared, and thermal infrared. The results indicate that compared to the standard SIFT algorithm and frequently used commercial mosaicking software, the method proposed here significantly improves the applicability, efficiency, and accuracy of mosaicking UAV images in crop growth monitoring. In comparison with image mosaicking based on the standard SIFT algorithm, the time efficiency of the proposed method is higher by 30%, and its structural similarity index of mosaicking accuracy is about 0.9. Meanwhile, the approach successfully mosaics low-resolution UAV images used for crop growth monitoring and improves the applicability of the SIFT algorithm, providing a technical reference for UAV application used for crop growth and phenotypic monitoring.

scholarly journals Radiometric Calibration of UAV Remote Sensing Image with Spectral Angle Constraint

2019 ◽  
Vol 11 (11) ◽  
pp. 1291 ◽  
Author(s):  
Kaiqiu Xu ◽  
Yan Gong ◽  
Shenghui Fang ◽  
Ke Wang ◽  
Zhiheng Lin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Linear Method ◽  
Radiometric Calibration ◽  
Constraint Condition ◽  
Infrared Band ◽  
Spectral Bands ◽  
Quantitative Remote Sensing ◽  
Spectral Channels ◽  
Spectral Angle

In recent years, the acquisition of high-resolution multi-spectral images by unmanned aerial vehicles (UAV) for quantitative remote sensing research has attracted more and more attention, and radiometric calibration is the premise and key to the quantification of remote sensing information. The traditional empirical linear method independently calibrates each channel, ignoring the correlation between spectral bands. However, the correlation between spectral bands is very valuable information, which becomes more prominent as the number of spectral channels increases. Based on the empirical linear method, this paper introduces the constraint condition of spectral angle, and makes full use of the information of each band for radiometric calibration. The results show that, compared with the empirical linear method, the proposed method can effectively improve the accuracy of radiometric calibration, with the improvement range of Mean Relative Percent Error (MRPE) being more than 3% in the range of visible band and within 1% in the range of near-infrared band. Besides, the method has great advantages in agricultural remote sensing quantitative inversion.

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