scholarly journals Mapping landscape canopy nitrogen content from space using PRISMA data

2021 ◽  
Vol 178 ◽  
pp. 382-395
Author(s):  
Jochem Verrelst ◽  
Juan Pablo Rivera-Caicedo ◽  
Pablo Reyes-Muñoz ◽  
Miguel Morata ◽  
Eatidal Amin ◽  
...  
Keyword(s):  
Nitrogen Content ◽  
Canopy Nitrogen

scholarly journals Effects of Fire and Large Herbivores on Canopy Nitrogen in a Tallgrass Prairie

2019 ◽  
Vol 11 (11) ◽  
pp. 1364 ◽  
Author(s):  
Bohua Ling ◽  
Edward J. Raynor ◽  
Douglas G. Goodin ◽  
Anthony Joern
Keyword(s):  
Spatial Variability ◽  
Nitrogen Content ◽  
Tallgrass Prairie ◽  
High Nitrogen Content ◽  
High Nitrogen ◽  
Grazing History ◽  
One Year ◽  
Canopy Nitrogen

This study analyzed the spatial heterogeneity of grassland canopy nitrogen in a tallgrass prairie with different treatments of fire and ungulate grazing (long-term bison grazing vs. recent cattle grazing). Variogram analysis was applied to continuous remotely sensed canopy nitrogen images to examine the spatial variability in grassland canopies. Heterogeneity metrics (e.g., the interspersion/juxtaposition index) were calculated from the categorical canopy nitrogen maps and compared among fire and grazing treatments. Results showed that watersheds burned within one year had higher canopy nitrogen content and lower interspersions of high-nitrogen content patches than watersheds with longer fire intervals, suggesting an immediate and transient fire effect on grassland vegetation. In watersheds burned within one year, high-intensity grazing reduced vegetation density, but promoted grassland heterogeneity, as indicated by lower canopy nitrogen concentrations and greater interspersions of high-nitrogen content patches at the grazed sites than at the ungrazed sites. Variogram analyses across watersheds with different grazing histories showed that long-term bison grazing created greater spatial variability of canopy nitrogen than recent grazing by cattle. This comparison between bison and cattle is novel, as few field experiments have evaluated the role of grazing history in driving grassland heterogeneity. Our analyses extend previous research of effects from pyric herbivory on grassland heterogeneity by highlighting the role of grazing history in modulating the spatial and temporal distribution of aboveground nitrogen content in tallgrass prairie vegetation using a remote sensing approach. The comparison of canopy nitrogen properties and the variogram analysis of canopy nitrogen distribution provided by our study are useful for further mapping grassland canopy features and modeling grassland dynamics involving interplays among fire, large grazers, and vegetation communities.

Soil-line vegetation indices for corn nitrogen content prediction

2012 ◽  
Vol 26 (2) ◽  
pp. 103-108 ◽  
Author(s):  
N. Bagheri ◽  
H. Ahmadi ◽  
S. Alavipanah ◽  
M. Omid
Keyword(s):  
Nitrogen Content ◽  
Vegetation Indices ◽  
Polynomial Equation ◽  
Measured Data ◽  
Second Order ◽  
Soil Line ◽  
Order Polynomial ◽  
Canopy Nitrogen ◽  
Order Polynomial Equation ◽  
Corn Canopy

Soil-line vegetation indices for corn nitrogen content prediction The soil-line vegetation indices for prediction of corn canopy nitrogen content were investigated. Results indicated that the vegetation indices applied were correlated with corn canopy nitrogen content and the wavelengths between 630-860 nm are suitable for nitrogen diagnosis. The second-order polynomial equation was the best model for nitrogen content prediction among different regression types. Analyses based on both predicted and measured data were carried out to compare the performance of existing vegetation indices.

scholarly journals Predicting Canopy Nitrogen Content in Citrus-Trees Using Random Forest Algorithm Associated to Spectral Vegetation Indices from UAV-Imagery

2019 ◽  
Vol 11 (24) ◽  
pp. 2925 ◽  
Author(s):  
Lucas Prado Osco ◽  
Ana Paula Marques Ramos ◽  
Danilo Roberto Pereira ◽  
Érika Akemi Saito Moriya ◽  
Nilton Nobuhiro Imai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Nitrogen Content ◽  
Learning Algorithms ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Spectral Indices ◽  
Random Forest Algorithm ◽  
Spectral Vegetation Indices ◽  
Canopy Nitrogen

The traditional method of measuring nitrogen content in plants is a time-consuming and labor-intensive task. Spectral vegetation indices extracted from unmanned aerial vehicle (UAV) images and machine learning algorithms have been proved effective in assisting nutritional analysis in plants. Still, this analysis has not considered the combination of spectral indices and machine learning algorithms to predict nitrogen in tree-canopy structures. This paper proposes a new framework to infer the nitrogen content in citrus-tree at a canopy-level using spectral vegetation indices processed with the random forest algorithm. A total of 33 spectral indices were estimated from multispectral images acquired with a UAV-based sensor. Leaf samples were gathered from different planting-fields and the leaf nitrogen content (LNC) was measured in the laboratory, and later converted into the canopy nitrogen content (CNC). To evaluate the robustness of the proposed framework, we compared it with other machine learning algorithms. We used 33,600 citrus trees to evaluate the performance of the machine learning models. The random forest algorithm had higher performance in predicting CNC than all models tested, reaching an R2 of 0.90, MAE of 0.341 g·kg−1 and MSE of 0.307 g·kg−1. We demonstrated that our approach is able to reduce the need for chemical analysis of the leaf tissue and optimizes citrus orchard CNC monitoring.

scholarly journals Remote sensing of LAI, chlorophyll and leaf nitrogen pools of crop- and grasslands in five European landscapes

2012 ◽  
Vol 9 (8) ◽  
pp. 10149-10205 ◽  
Author(s):  
E. Boegh ◽  
R. Houborg ◽  
J. Bienkowski ◽  
C. F. Braban ◽  
T. Dalgaard ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Nitrogen Content ◽  
Field Data ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Leaf Nitrogen ◽  
Nitrogen Pools ◽  
Canopy Nitrogen ◽  
Use Categories

Abstract. Leaf nitrogen and leaf surface area influence the exchange of gases between terrestrial ecosystems and the atmosphere, and they play a significant role in the global cycles of carbon, nitrogen and water. Remote sensing data from satellites can be used to estimate leaf area index (LAI), leaf chlorophyll (CHLl) and leaf nitrogen density (Nl). However, methods are often developed using plot scale data and not verified over extended regions that represent a variety of soil spectral properties and canopy structures. In this paper, field measurements and high spatial resolution (10–20 m) remote sensing images acquired from the HRG and HRVIR sensors aboard the SPOT satellites were used to assess the predictability of LAI, CHLl and Nl. Five spectral vegetation indices (SVIs) were used (the Normalized Difference Vegetation index, the Simple Ratio, the Enhanced Vegetation Index-2, the Green Normalized Difference Vegetation Index, and the green Chlorophyll Index) together with the image-based inverse canopy radiative transfer modelling system, REGFLEC (REGularized canopy reFLECtance). While the SVIs require field data for empirical model building, REGFLEC can be applied without calibration. Field data measured in 93 fields within crop- and grasslands of five European landscapes showed strong vertical CHLl gradient profiles in 20% of fields. This affected the predictability of SVIs and REGFLEC. However, selecting only homogeneous canopies with uniform CHLl distributions as reference data for statistical evaluation, significant (p < 0.05) predictions were achieved for all landscapes, by all methods. The best performance was achieved by REGFLEC for LAI (r2=0.7; rmse = 0.73), canopy chlorophyll content (r2=0.51; rmse = 439 mg m−2) and canopy nitrogen content (r2 = 0.53; rmse = 2.21 g m−2). Predictabilities of SVIs and REGFLEC simulations generally improved when constrained to single land use categories (wheat, maize, barley, grass) across the European landscapes, reflecting sensitivity to canopy structures. Predictability further improved when constrained to local (10 × 10 km2) landscapes, thereby reflecting sensitivity to local environmental conditions. All methods showed different predictabilities for land use categories and landscapes. Combining the best methods, LAI, canopy chlorophyll content (CHLc) and canopy nitrogen content (CHLc) for the five landscapes could be predicted with improved accuracy (LAI rmse = 0.59; CHLc rmse = 346 g m−2; Ncrmse = 1.49 g m−2). Remote sensing-based results showed that the vegetation nitrogen pools of the five agricultural landscapes varied from 0.6 to 4.0 t km−2. Differences in nitrogen pools were attributed to seasonal variations, extents of agricultural area, species variations, and spatial variations in nutrient availability. Information on Nl and total Nc pools within the landscapes is important for the spatial evaluation of nitrogen and carbon cycling processes. The upcoming Sentinel-2 satellite mission will provide new multiple narrow-band data opportunities at high spatio-temporal resolution which are expected to further improve remote sensing predictabilities of LAI, CHLl and Nl.

scholarly journals Remote estimation of canopy nitrogen content in winter wheat using airborne hyperspectral reflectance measurements

2016 ◽  
Vol 58 (9) ◽  
pp. 1627-1637 ◽  
Author(s):  
Xianfeng Zhou ◽  
Wenjiang Huang ◽  
Weiping Kong ◽  
Huichun Ye ◽  
Juhua Luo ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Nitrogen Content ◽  
Hyperspectral Reflectance ◽  
Remote Estimation ◽  
Canopy Nitrogen ◽  
Reflectance Measurements

scholarly journals Multispectral remote sensing for site-specific nitrogen fertilizer management

2013 ◽  
Vol 48 (10) ◽  
pp. 1394-1401 ◽  
Author(s):  
Nikrooz Bagheri ◽  
Hojjat Ahmadi ◽  
Seyed Kazem Alavipanah ◽  
Mahmoud Omid
Keyword(s):  
Remote Sensing ◽  
Nitrogen Content ◽  
Nitrogen Fertilizer ◽  
Vegetation Index ◽  
Fertilizer Management ◽  
Site Specific ◽  
Multispectral Remote Sensing ◽  
Index 2 ◽  
Canopy Nitrogen ◽  
Corn Canopy

The objective of this work was to evaluate the use of multispectral remote sensing for site-specific nitrogen fertilizer management. Satellite imagery from the advanced spaceborne thermal emission and reflection radiometer (Aster) was acquired in a 23 ha corn-planted area in Iran. For the collection of field samples, a total of 53 pixels were selected by systematic randomized sampling. The total nitrogen content in corn leaf tissues in these pixels was evaluated. To predict corn canopy nitrogen content, different vegetation indices, such as normalized difference vegetation index (NDVI), soil-adjusted vegetation index (Savi), optimized soil-adjusted vegetation index (Osavi), modified chlorophyll absorption ratio index 2 (MCARI2), and modified triangle vegetation index 2 (MTVI2), were investigated. The supervised classification technique using the spectral angle mapper classifier (SAM) was performed to generate a nitrogen fertilization map. The MTVI2 presented the highest correlation (R²=0.87) and is a good predictor of corn canopy nitrogen content in the V13 stage, at 60 days after cultivating. Aster imagery can be used to predict nitrogen status in corn canopy. Classification results indicate three levels of required nitrogen per pixel: low (0-2.5 kg), medium (2.5-3 kg), and high (3-3.3 kg).

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