Optimization of spectral bands for hyperspectral remote sensing of forest vegetation

2013 ◽  
Author(s):  
Egor V. Dmitriev ◽  
Vladimir V. Kozoderov
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Forest Vegetation ◽  
Spectral Bands

scholarly journals Fire Blight Disease Detection for Apple Trees: Hyperspectral Analysis of Healthy, Infected and Dry Leaves

2020 ◽  
Vol 12 (13) ◽  
pp. 2101 ◽  
Author(s):  
Hubert Skoneczny ◽  
Katarzyna Kubiak ◽  
Marcin Spiralski ◽  
Jan Kotlarz ◽  
Artur Mikiciński ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Fire Blight ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Hyperspectral Remote Sensing ◽  
Bacterial Disease ◽  
Apple Trees ◽  
Remote Sensing Technique ◽  
Spectral Bands ◽  
Swir Band

The effective and rapid detection of Fire Blight, an important bacterial disease caused by the quarantine pest E.amylovora, is crucial for today’s horticulture. This study explored the application of non-invasive proximal hyperspectral remote sensing (RS) in order to differentiate the healthy (H), infected (I) and dry (D) leaves of apple trees. Analysis of variance was employed in order to determine which hyperspectral narrow spectral bands exhibited the most significant differences. Spectral signatures for the range of 400–2500 nm were acquired with Thermo Scientific Evolution 220 and iS50NIR spectrometers. The selected spectral bands were then used to evaluate several RS indices, including ARI (Anthocyanin Reflectance Index), RDVI (Renormalized Difference Vegetation Index), MSR (Modified Simple Ratio) and NRI (Nitrogen Reflectance Index), for Fire Blight detection in apple tree leaves. Furthermore, a new index was proposed, namely QFI. The spectral indices were tested on apple trees infected by Fire Blight in a quarantine greenhouse. Results indicated that the short-wavelength infrared (SWIR) band located at 1450 nm was able to distinguish (I) and (H) leaves, while the SWIR band at 1900 nm differentiated all three leaf types. Moreover, tests using the Pearson correlation indicated that ARI, MSR and QFI exhibited the highest correlations with the infection progress. Our results prove that our hyperspectral remote sensing technique is able to differentiate (H), (I) and (D) leaves of apple trees for the reliable and precise detection of Fire Blight.

scholarly journals Evaluation of the Interactive effect of Carbon Dioxide and Temperature on Plant Growth using Hyperspectral Remote Sensing Technology

2021 ◽  
pp. 31-36
Author(s):  
Naseema Rahman ◽  
Jonali Goswami ◽  
Ranjan Das
Keyword(s):  
Remote Sensing ◽  
Temperature Stress ◽  
Spectral Response ◽  
Interactive Effect ◽  
Good Health ◽  
High Temperature Stress ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technique ◽  
Spectral Bands ◽  
Sensing Technology

Remote sensing is a powerful tool for monitoring spatiotemporal variations of crop health status in terms of morpho- physiology status helping for precision farming. In comparison with multispectral, hyperspectral imaging with narrow bands are capable of acquiring a subtle variation in spectral response of target crop like garlic under high CO2 and temperature conditions. The present results indicate the potential of spectral information for evaluating various stress conditions as well as status of crop conditions through the use of band rationing technique, mainly NDVI in comparison to the use of individual spectral bands. The present investigation revealed that plant grown under CTGT II (550 ppm CO2 + 4°C elevation of temperature) shows significantly good health in all garlic varieties with high LAD and NDVI. On the other hand, the higher temperature stress treatment brought about significant reduction in the LAD and NDVI. This adverse effect was lesser under CTGT II than CTGT III which indicated that all varieties, Ekfutia Assam in particular, exhibited certain degree of tolerance against high temperature stress. Hyperspectral remote sensing technique acts as an important tool in real time monitoring, early warning and quick damage assessment due to various abiotic stresses.

scholarly journals Spectral unmixing approach in hyperspectral remote sensing: a tool for oil palm mapping

TecnoLógicas ◽  
2019 ◽  
Vol 22 (45) ◽  
pp. 129-143
Author(s):  
Hector Vargas ◽  
Ariolfo Camacho Velasco ◽  
Henry Arguello
Keyword(s):  
Remote Sensing ◽  
Oil Palm ◽  
Hyperspectral Image ◽  
Remote Sensing Data ◽  
Hyperspectral Remote Sensing ◽  
Spectral Unmixing ◽  
Spectral Variability ◽  
Computational Framework ◽  
Sensing Data ◽  
Spectral Bands

Oil palm plantations typically span large areas; therefore, remote sensing has become a useful tool for advanced oil palm monitoring. This work reviews and evaluates two approaches to analyze oil palm plantations based on hyperspectral remote sensing data: linear spectral unmixing and spectral variability. Moreover, a computational framework based on spectral unmixing for the estimation of fractional abundances of oil palm plantations is proposed in this study. Such approach also considers the spectral variability of hyperspectral image signatures. More specifically, the proposed computational framework modifies the linear mixing model by introducing a weighting vector, so that the spectral bands that contribute the least to the estimation of erroneous fractional abundances can be identified. This approach improves palm detection as it allows to differentiate them from other materials in terms of fractional abundances. Experimental results obtained from hyperspectral remote sensing data in the range 410-990 nm show improvements of 8.18 % in User Accuracy (Uacc) in the identification of oil palms by the proposed framework with respect to traditional unmixing methods. Thus, the proposed method achieved a 95% Uacc. This confirms the capabilities of the proposed computational framework and facilitates the management and monitoring of large areas of oil palm plantations.

scholarly journals Toward Content-Based Hyperspectral Remote Sensing Image Retrieval (CB-HRSIR): A Preliminary Study Based on Spectral Sensitivity Functions

2019 ◽  
Vol 11 (5) ◽  
pp. 600 ◽  
Author(s):  
Olfa Ben-Ahmed ◽  
Thierry Urruty ◽  
Noel Richard ◽  
Christine Fernandez-Maloigne
Keyword(s):  
Remote Sensing ◽  
Spectral Sensitivity ◽  
Hyperspectral Remote Sensing ◽  
Image Description ◽  
Image Content ◽  
Sensitivity Functions ◽  
Content Description ◽  
Spectral Bands ◽  
Physical Measurements ◽  
Imaging Sensors

With the emergence of huge volumes of high-resolution Hyperspectral Images (HSI)produced by different types of imaging sensors, analyzing and retrieving these images requireeffective image description and quantification techniques. Compared to remote sensing RGB images,HSI data contain hundreds of spectral bands (varying from the visible to the infrared ranges) allowingprofile materials and organisms that only hyperspectral sensors can provide. In this article, we studythe importance of spectral sensitivity functions in constructing discriminative representation ofhyperspectral images. The main goal of such representation is to improve image content recognitionby focusing the processing on only the most relevant spectral channels. The underlying hypothesisis that for a given category, the content of each image is better extracted through a specific set ofspectral sensitivity functions. Those spectral sensitivity functions are evaluated in a Content-BasedImage Retrieval (CBIR) framework. In this work, we propose a new HSI dataset for the remotesensing community, specifically designed for Hyperspectral remote sensing retrieval and classification.Exhaustive experiments have been conducted on this dataset and on a literature dataset. Obtainedretrieval results prove that the physical measurements and optical properties of the scene containedin the HSI contribute in an accurate image content description than the information provided by theRGB image presentation.

scholarly journals Identification of Water and Nitrogen Stress Indicative Spectral Bands Using Hyperspectral Remote Sensing in Maize During Post-Monsoon Season

2020 ◽  
Vol 48 (12) ◽  
pp. 1787-1795
Author(s):  
B. Balaji Naik ◽  
H. R. Naveen ◽  
G. Sreenivas ◽  
K. Karun Choudary ◽  
D. Devkumar ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Content ◽  
Spectral Reflectance ◽  
Leaf Nitrogen ◽  
Hyperspectral Remote Sensing ◽  
Leaf Water ◽  
Nitrogen Stress ◽  
Leaf Water Content ◽  
Maize Crop ◽  
Spectral Bands

AbstractRealization of agricultural crop condition through field survey is quite expensive, time consuming and sometimes not practical for remote locations. Optical remote sensing techniques can provide information on real condition of the crops by observing spectral reflectance at different crop growth phases and is less expensive and less time consuming. Hyperspectral remote sensing provides a unique opportunity for non-destructive, timely and accurate estimation of crop biophysical and biochemical properties. In this study, a field experiment was conducted to identify the water and nitrogen stress indicative spectral bands using ground-based hyperspectral data and to assess the predictive capability of selective bands on yield of maize under water and nitrogen stress environment. The experiment comprised of three irrigation scheduling treatments based on IW/CPE ration of 0.6, 0.8 and 1.2 and three nitrogen level treatments, i.e., 100, 200 and 300 kg of N ha−1, respectively, with three replications in a split plot design. The spectral reflectance was measured before irrigation at tasseling and dough stage of the maize crop using portable field spectroradiometer. The results of stepwise multiple linear regression indicated the highest predicting capability of spectral bands 540 nm, 780 nm and 860 nm for leaf nitrogen and 700 nm, 740 nm and 860 nm for leaf water content. The derived biophysical parameters based on spectral reflectance viz. relative leaf water content (%), leaf area index and leaf nitrogen contentment (%) at tasseling stage of maize crop accounted for 80%, 61% and 66% variation in grain yield, respectively.

Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology
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