scholarly journals Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data

2001 ◽  
Vol 39 (7) ◽  
pp. 1491-1507 ◽  
Author(s):  
P.J. Zarco-Tejada ◽  
J.R. Miller ◽  
T.L. Noland ◽  
G.H. Mohammed ◽  
P.H. Sampson
Keyword(s):  
Chlorophyll Content ◽  
Scaling Up ◽  
Hyperspectral Data ◽  
Forest Canopies ◽  
Model Inversion ◽  
Inversion Methods ◽  
Closed Forest

Needle chlorophyll content estimation through model inversion using hyperspectral data from boreal conifer forest canopies

2004 ◽  
Vol 89 (2) ◽  
pp. 189-199 ◽  
Author(s):  
Pablo J. Zarco-Tejada ◽  
John R. Miller ◽  
John Harron ◽  
Baoxin Hu ◽  
Thomas L. Noland ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Hyperspectral Data ◽  
Conifer Forest ◽  
Forest Canopies ◽  
Model Inversion

scholarly journals Accumulation of total nutrients, dry matter and phytochrome content in Boesenbergia stenophylla R.M Smith as affected by different light conditions

2021 ◽  
pp. 10-16
Author(s):  
Tracy Saptu ◽  
Nur Ashikin Psyquay Abdullah ◽  
Franklin Ragai Kundat ◽  
Aicher Joseph Toyat ◽  
Calson Gimang Endawie ◽  
...  
Keyword(s):  
Organic Matter ◽  
Nutrient Uptake ◽  
Chlorophyll Content ◽  
Dry Matter ◽  
Application Rate ◽  
Dry Matter Yield ◽  
Forest Canopies ◽  
Soil Mixture ◽  
Significant Difference ◽  
Phytochrome Content

A study was conducted to determine the best agronomic practices for the cultivation of jerangau merah as a new medicinal crop. Jerangau merah is scientifically known as Boesenbergia stenophylla is a ginger plants that is highly endemic to the Borneo Highland. This understorey herb grows in cluster and under the heavy shades of forest canopies and perceived only the sunflecks that pass through the forest canopies. Jerangau Merah has been consumed by the locals for its medicinal values since decades ago. Generally, B. stenophylla is well known for its function as antidote for alcohol. Moreover, the B. stenophylla is also beneficial for rheumatic pains, remedies for stomach ache and toothache and as antiseptic wash as well. This herb is not propagate by the locals due to its sensitivity to sunlight and lack of agronomic information for B. stenophylla. Harvesting it from their natural habitat, however, seen the decline it its population size hence cultivation is essential. This paper aimed to determine the total nutrients, dry matter yield and phytochrome content of Bosenbergia stenophylla R.M. Smith under different light regimes. A study for determining suitable planting media for B. stenophylla was conducted at Universiti Putra Malaysia (UPM). The treatments are: i) (M1) with the ratio of 3:2:1 top soil: sand: organic matter, media ii) (M2) consists of soil mixture 3:2:1 placed in water-logged polyethylene bags and media iii) (M3) contained only leaf litters. The results showed that the herbs showed significantly higher number of shoots and leaves in the common soil mixture. But, better root development in media containing leaf litters. The study proceeded with field experiment at Ba’Kelalan to determine the effect of different shade levels and fertilizing regimes on growth. B. stenophylla was cultivated under two different levels of shade cloths: 70% and 90% level of shade and different fertilizing regimes (T1 as control, no fertilizer applied to the plants; T2, chicken dung; T3, NPKMg and T4, mixed of chicken dung and NPKMg). The study for determining the effects of different shade level and fertilizing regimes on seedling’s growth which conducted at Ba’Kelalan in factorial randomized completely blocked design (RCBD). The data collected for 9 weeks of planting which include nutrient content in soil, leaf, PAR and phytochrome content and growth parameters. Regarding the nutrient uptake, the results showed no interactions between fertilizing regimes and shade levels. There were no significant different in nutrient elements except for magnesium and potassium. Magnesium is essential for the formation of chlorophyll which ensure efficiency of photosynthesis when under higher light intensity. Among all the fertilizer treatment, it was showed that chicken dung amendment has higher nutrient uptake. Thus, it is recommended chicken dung should be added into the NPKMg for better nutrient uptake. Further study on suitable fertilizer rates apply to B. stenophylla should be taken into consideration. Moreover, plants cultivated under 70% have been higher and have higher dry matter yield than those plants cultivated under 90%. The result also revealed that there was significant different in chlorophyll content of B. stenophylla cultivated under 70% which treated either with chicken dung and NPKMG respectively. However, plants under 90% have higher chlorophyll content than those under 70%. Based on the results, plants under 70% shade was taller than those under 90% and there were significant difference in height among treatments under 70%.  It was observed plant treated with NPKMg was taller. This study showed that shade and fertilizers significantly affected the dry matter yield of B. stenophylla. Moreover, adding NPKMg to the treatments yields more dry matter content of jerangau merah. As for phytochrome content, there was no significant effect of fertilizer on phytochrome content. However, there was significant difference among the shade levels. 90% shade showed higher phytochrome content than those under 70%. In overall, both shade and fertilizer is important in cultivation of Jerangau Merah. 70% shade was observed to have significant effects on growth of jerangau merah and also more economical compared to 90%. Moreover, combination of organic matter and NPKMg also promote the growth of jerangau merah enhance the nutrient uptake efficiency of jerangau merah. However, further investigation of suitable fertilizer and application rate are required to determine suitable fertilizer for jerangau merah and application rate for optimum growth of jerangau merah.

Transfer-learning-based approach for leaf chlorophyll content estimation of winter wheat from hyperspectral data

2021 ◽  
Vol 267 ◽  
pp. 112724
Author(s):  
Yao Zhang ◽  
Jian Hui ◽  
Qiming Qin ◽  
Yuanheng Sun ◽  
Tianyuan Zhang ◽  
...  
Keyword(s):  
Winter Wheat ◽  
Chlorophyll Content ◽  
Transfer Learning ◽  
Hyperspectral Data ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll

Deep-learning seismic full-waveform inversion for realistic structural models

Geophysics ◽  
2021 ◽  
Vol 86 (1) ◽  
pp. R31-R44
Author(s):  
Bin Liu ◽  
Senlin Yang ◽  
Yuxiao Ren ◽  
Xinji Xu ◽  
Peng Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Waveform Inversion ◽  
Structural Models ◽  
Velocity Model ◽  
Dense Layer ◽  
Full Waveform Inversion ◽  
Model Inversion ◽  
Full Waveform ◽  
Inversion Methods ◽  
The Common

Velocity model inversion is one of the most important tasks in seismic exploration. Full-waveform inversion (FWI) can obtain the highest resolution in traditional velocity inversion methods, but it heavily depends on initial models and is computationally expensive. In recent years, a large number of deep-learning (DL)-based velocity model inversion methods have been proposed. One critical component in those DL-based methods is a large training set containing different velocity models. We have developed a method to construct a realistic structural model for the DL network. Our compressional-wave velocity model building method for creating dense-layer/fault/salt body models can automatically construct a large number of models without much human effort, which is very meaningful for DL networks. Moreover, to improve the inversion result on these realistic structural models, instead of only using the common-shot gather, we also extract features from the common-receiver gather as well. Through a large number of realistic structural models, reasonable data acquisition methods, and appropriate network setups, a more generalized result can be obtained through our proposed inversion framework, which has been demonstrated to be effective on the independent testing data set. The results of dense-layer models, fault models, and salt body models that we compared and analyzed demonstrate the reliability of our method and also provide practical guidelines for choosing optimal inversion strategies in realistic situations.

scholarly journals Mapping Canopy Chlorophyll Content in a Temperate Forest Using Airborne Hyperspectral Data

2020 ◽  
Vol 12 (21) ◽  
pp. 3573
Author(s):  
J. Malin Hoeppner ◽  
Andrew K. Skidmore ◽  
Roshanak Darvishzadeh ◽  
Marco Heurich ◽  
Hsing-Chung Chang ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Spectral Resolution ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Mixed Forest ◽  
Hyperspectral Data ◽  
Least Squares Regression ◽  
Area Index ◽  
Management Interventions ◽  
Shortwave Infrared

Chlorophyll content, as the primary pigment driving photosynthesis, is directly affected by many natural and anthropogenic disturbances and stressors. Accurate and timely estimation of canopy chlorophyll content (CCC) is essential for effective ecosystem monitoring to allow for successful management interventions to occur. Hyperspectral remote sensing offers the possibility to accurately estimate and map canopy chlorophyll content. In the past, research has predominantly focused on the use of hyperspectral data on canopy chlorophyll content retrieval of crops and grassland ecosystems. Therefore, in this study, a temperate mixed forest, the Bavarian Forest National Park in Germany, was chosen as the study site. We compared different statistical models (narrowband vegetation indices (VIs), partial least squares regression (PLSR) and random forest (RF)) in their accuracy to predict CCC using airborne hyperspectral data. The airborne hyperspectral imagery was acquired by the AisaFenix sensor (623 bands; 3.5 nm spectral resolution in the visible near-infrared (VNIR) region, and 12 nm spectral resolution in the shortwave infrared (SWIR) region; 3 m spatial resolution) on July 6, 2017. In situ leaf chlorophyll content and leaf area index measurements were sampled from the upper canopy of coniferous, mixed, and deciduous forest stands in July and August 2017. The study yielded the highest retrieval accuracies with PLSR (root mean square error (RMSE) = 0.25 g/m2, R2 = 0.66). It further indicated specific spectral regions within the visible (390–400 nm and 470–540 nm), red edge (680–780 nm), near-infrared (1050–1100 nm) and shortwave infrared regions (2000–2270 nm) that were important for CCC retrieval. The results showed that forest CCC can be mapped with relatively high accuracies using image spectroscopy.

scholarly journals Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation

2016 ◽  
Vol 39 (12) ◽  
pp. 2609-2623 ◽  
Author(s):  
Yoshio Inoue ◽  
Martine Guérif ◽  
Frédéric Baret ◽  
Andrew Skidmore ◽  
Anatoly Gitelson ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Comparative Analysis ◽  
Chlorophyll Content ◽  
Hyperspectral Data ◽  
Robust Methods ◽  
Different Types

Deciphering Optical Water Types of Wetlands using multispectral Earth observation datasets

2020 ◽  
Author(s):  
Satyasri Allaka ◽  
Manudeo Singh ◽  
Rajiv Sinha
Keyword(s):  
Chlorophyll Content ◽  
Color Index ◽  
Coastal Wetland ◽  
Hyperspectral Data ◽  
Anthropogenic Factors ◽  
Optical Parameters ◽  
Spectral Indices ◽  
Water Types ◽  
Inorganic Matter ◽  
Sensitive Parameters

<p>Wetlands are important and highly productive ecosystems in a variety of geomorphic settings ranging from inland to coastal environments. Wetlands are very dynamic in nature and are driven by the water and sediment fluxes carried by the streamlets throughout the year. Wetlands are under tremendous pressure all over the world due to various natural and anthropogenic factors, and therefore, require an immediate attention for their conservation. The available studies on wetland have given much less importance to the internal dynamics of the wetlands, which is primarily driven by hydrology and Land Use Land Cover (LULC) changes. Here, we propose to use the Optical Water Types (OWTs) concept to understand the hydrodynamics within the wetland.</p><p>The OWTs are the aquatic counterpart of terrestrial LULC classification and can be created by clustering of optically sensitive parameters like chlorophyll content, turbidity, suspended organic and inorganic matter using remote sensing reflectance, absorption, and scattering parameters. The Forel Ule (FU) color index, a visual color comparison scale of water bodies ranging from blue to cola brown (1-21), used a similar idea but is fairly limited in scope. The hyperspectral datasets have distinct absorption and reflection spectrum for various optically sensitive parameters, and therefore, they are particularly suited for this work. However, the availability of the high-resolution hyperspectral data is very limited and hence this research explores the possibility of deciphering the OWTs using multispectral datasets.</p><p>A possible approach to create OWTs is using the spectral indices of the multispectral datasets which are sensitive to the optical parameters instead of using the FU color index as a single parameter. In this work, various spectral indices which are independent and highly sensitivity to chlorophyll content, turbidity, suspended organic and inorganic matter are identified using the principal component analysis. The OWT clusters are created using the iso-cluster unsupervised classification similar to the LULC classification but the spectral indices are taken into account instead of directly using the spectral bands of satellite datasets. In this work, the Sentinel – 2A and 2B datasets are used to create independent OWT clusters of the Chilika (a Coastal wetland, along the east coast of India covering an area of 1,165 km<sup>2</sup>) and Kaabar Tal (an inland wetland in north Bihar plains, India covering an area of 51 km<sup>2</sup>) using the supervised classification method. The developed framework is very simple and robust in nature but the only disadvantage is that the clusters are variable in the temporal context. However, the temporal variations can be integrated with the spatial analysis to understand the wetland dynamics in the context of both space and time.</p>

scholarly journals Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC)

2015 ◽  
Vol 159 ◽  
pp. 203-221 ◽  
Author(s):  
Rasmus Houborg ◽  
Matthew McCabe ◽  
Alessandro Cescatti ◽  
Feng Gao ◽  
Mitchell Schull ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Landsat Data ◽  
Area Index ◽  
Model Inversion ◽  
Leaf Chlorophyll Content ◽  
Leaf Chlorophyll ◽  
Regularized Model
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