scholarly journals On the Utility of Longwave-Infrared Spectral Imaging for Remote Botanical Identification

2021 ◽  
Vol 13 (17) ◽  
pp. 3344
Author(s):  
David M. Tratt ◽  
Kerry N. Buckland ◽  
Eric R. Keim ◽  
Jeffrey L. Hall ◽  
Paul M. Adams ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Spatial Resolution ◽  
Spectral Imaging ◽  
Field Investigation ◽  
Confounding Factors ◽  
Species Classification ◽  
Botanical Species ◽  
Composition Determination ◽  
Infrared Spectral ◽  
Remote Identification

A multi-year airborne field investigation of remote botanical species identification was conducted involving multiple curated botanical collections. The purpose of the study was to better constrain the observational conditions that most favor remote identification by longwave-infrared spectral imaging and assess the degree to which confidence metrics developed for remote chemical composition determination could be adapted to botanical species classification. Identification success was examined as a function of spatial resolution and viewing obliquity. A key aim was to articulate a procedure for validating inferred species identifications and evaluating the retrieval methodology’s performance for alleviating confusion between species exhibiting spectral similarity at the foliar scale. It was found that several confounding factors degrade confidence in the species identifications to levels that render the approach impractical in the general case. A number of taxa, predominantly evergreen, were nevertheless identified that are amenable to the technique and for which utility may be viable.

Identification and source attribution of halocarbon emitters with longwave-infrared spectral imaging

2021 ◽  
Vol 258 ◽  
pp. 112398
Author(s):  
David M. Tratt ◽  
Kerry N. Buckland ◽  
Eric R. Keim ◽  
Jeffrey L. Hall
Keyword(s):  
Spectral Imaging ◽  
Source Attribution ◽  
Infrared Spectral

scholarly journals An Evaluation of Pixel- and Object-Based Tree Species Classification in Mixed Deciduous Forests Using Pansharpened Very High Spatial Resolution Satellite Imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 1868
Author(s):  
Martina Deur ◽  
Mateo Gašparović ◽  
Ivan Balenović
Keyword(s):  
Spatial Resolution ◽  
Satellite Imagery ◽  
Tree Species ◽  
Classification Accuracy ◽  
New Technologies ◽  
Deciduous Forest ◽  
Species Classification ◽  
Tree Species Classification ◽  
Object Based ◽  
Very High

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

Research on method of infrared spectral imaging based on thermal imager

2011 ◽  
Author(s):  
Ke-wei Huan ◽  
Xiao-guang Shi ◽  
Wei Wu ◽  
Feng Zheng ◽  
Xiao-xi Liu
Keyword(s):  
Spectral Imaging ◽  
Thermal Imager ◽  
Infrared Spectral

scholarly journals Assessing the Impacts of Species Composition on the Accuracy of Mapping Chlorophyll Content in Heterogeneous Ecosystems

2021 ◽  
Vol 13 (22) ◽  
pp. 4671
Author(s):  
Bing Lu ◽  
Yuhong He
Keyword(s):  
Remote Sensing ◽  
Species Composition ◽  
Chlorophyll Content ◽  
Spatial Resolution ◽  
Ecosystem Management ◽  
Remote Sensing Images ◽  
Species Classification ◽  
Growth Status ◽  
Species Specific ◽  
Estimation Models

Chlorophyll is an essential vegetation pigment influencing plant photosynthesis rate and growth conditions. Remote sensing images have been widely used for mapping vegetation chlorophyll content in different ecosystems (e.g., farmlands, forests, grasslands, and wetlands) for evaluating vegetation growth status and productivity of these ecosystems. Compared to farmlands and forests that are more homogeneous in terms of species composition, grasslands and wetlands are more heterogeneous with highly mixed species (e.g., various grass, forb, and shrub species). Different species contribute differently to the ecosystem services, thus, monitoring species-specific chlorophyll content is critical for better understanding their growth status, evaluating ecosystem functions, and supporting ecosystem management (e.g., control invasive species). However, previous studies in mapping chlorophyll content in heterogeneous ecosystems have rarely estimated species-specific chlorophyll content, which was partially due to the limited spatial resolution of remote sensing images commonly used in the past few decades for recognizing different species. In addition, many previous studies have used one universal model built with data of all species for mapping chlorophyll of the entire study area, which did not fully consider the impacts of species composition on the accuracy of chlorophyll estimation (i.e., establishing species-specific chlorophyll estimation models may generate higher accuracy). In this study, helicopter-acquired high-spatial resolution hyperspectral images were acquired for species classification and species-specific chlorophyll content estimation. Four estimation models, including a universal linear regression (LR) model (i.e., built with data of all species), species-specific LR models (i.e., built with data of each species, respectively), a universal random forest regression (RFR) model, and species-specific RFR models, were compared to determine their performance in mapping chlorophyll and to evaluate the impacts of species composition. The results show that species-specific models performed better than the universal models, especially for species with fewer samples in the dataset. The best performed species-specific models were then used to generate species-specific chlorophyll content maps using the species classification results. Impacts of species composition on the retrieval of chlorophyll content were further assessed to support future chlorophyll mapping in heterogeneous ecosystems and ecosystem management.

Research on performance test technology of infrared spectral imaging equipment

2021 ◽  
Author(s):  
CaoHaiYuan CaoHaiYuan ◽  
LiWei LiWei ◽  
ChuHua ChuHua ◽  
MiChaoWei MiChaoWei ◽  
TianFangTao TianFangTao
Keyword(s):  
Performance Test ◽  
Spectral Imaging ◽  
Test Technology ◽  
Infrared Spectral

Small RNA and transcriptome sequencing of a symptomatic peony plant reveals mixed infections with novel viruses

Plant Disease ◽  
2021 ◽  
Author(s):  
Anning Jia ◽  
Chenge Yan ◽  
Hang Yin ◽  
Rui Sun ◽  
Fei Xia ◽  
...  
Keyword(s):  
Phylogenetic Analysis ◽  
Small Rna ◽  
High Throughput Sequencing ◽  
Viral Infections ◽  
Triple Gene Block ◽  
Field Investigation ◽  
Mixed Infections ◽  
Species Classification ◽  
Gene Block ◽  
Sequence Identity

To identify the viruses in tree peony plants associated with the symptoms of yellowing, leaf rolling, stunted growth, and decline, high-throughput sequencing of small RNA and mRNA was conducted from a single symptomatic plant. Bioinformatic analyses and reconstruction of viral genomes indicated mixed viral infections involving cycas necrotic stunt virus (CNSV), apple stem grooving virus (ASGV), lychnis mottle virus (LycMoV), grapevine line pattern virus (GLPV), and three new viruses designated as peony yellowing-associated citrivirus (PYaCV, Citrivirus in Betaflexiviridae), peony betaflexivirus 1 (PeV1, unclassified in Betaflexiviridae), and peony leafroll-associated virus (PLRaV, Ampelovirus in Closteroviridae). PYaCV was 8,666 nucleaotides (nt) in length, comprising three open reading frames (ORFs) and shared 63.8–75.9% nucleotide sequence identity with citrus leaf blotch virus (CLBV) isolates. However, the ORF encoding the replication-associated protein (REP) shared 57% and 52% sequence identities at the nt and amino acid (aa) level, respectively, with other reported CLBV isolates, which were below the criterion for species classification within the family Betaflexiviridae. Recombination analysis identified putative recombination sites in PYaCV, which originated from CLBV. PeV1, only identified from the transcriptome data, was 8,124 nt in length with five ORFs encoding the REP (ORF1), triple gene block (TGB, ORF2–4) and coat protein (CP, ORF5) proteins. Phylogenetic analysis and sequence comparison showed that PeV1 clustered with an unassigned member, the garlic yellow mosaic-associated virus (GYMaV) within the Betaflexiviridae family, into a separate clade. Partial genome sequence analysis of PLRaV (12,545 nt) showed it contained seven ORFs encoding the partial polyprotein 1a, the RNA-dependent RNA polymerase (RdRp), two small hydrophobic proteins p11 and p6, HSP70h, p55, and a CP duplicate, which shared low aa sequence identity with Closteroviridae family members. Phylogenetic analysis based on the aa sequences of RdRp or HSP70h indicated that PLRaV clustered with grapevine leafroll-associated virus 1 (GLRaV-1) and GLRaV-13 in the Ampelovirus genus. Field investigation confirmed the wide distribution of these viruses, causing mixed infections of peony plants in Beijing.

scholarly journals Evaluation of some insects as potential feed ingredients for ruminants: chemical composition, in vitro rumen fermentation and methane emissions

2017 ◽  
Vol 42 (4) ◽  
pp. 247 ◽  
Author(s):  
A. Jayanegara ◽  
N. Yantina ◽  
B. Novandri ◽  
E. B. Laconi ◽  
N. Nahrowi ◽  
...  
Keyword(s):  
Chemical Composition ◽  
Methane Emissions ◽  
Rumen Fermentation ◽  
Field Cricket ◽  
Protein Supplements ◽  
Composition Determination ◽  
Fermentation Test ◽  
Black Soldier Fly Larvae ◽  
In Vitro Rumen Fermentation

This experiment was aimed to evaluate chemical composition, in vitro rumen fermentation, digestibility and methane emissions of some insects, i.e. Jamaican field cricket (JFC), mealworm (MW) and black soldier fly larvae age 1 and 2 weeks (BSF1 and BSF2). Insect samples were oven-dried at 60oC for 24 h, and ground to pass a 1 mm sieve. The ground samples were used subsequently for chemical composition determination and in vitro rumen fermentation test. Incubation was carried out in a water bath maintained at 39 ºC for 48 h in three replicates. Results revealed that all insect meals contained high crude protein, i.e. above 40% DM. Proportions of neutral detergent insoluble CP (NDICP) and neutral detergent insoluble CP (ADICP) were high in the insect meals than that of soybean meal (SBM), and these were particularly very high in BSF2. All insect meals had lower IVDMD and IVOMD than that of SBM (P<0.05). All insect meals had lower methane emissions as compared to SBM at 12, 24 and 48 h (P<0.05). It can be concluded that insect meals are potential protein supplements and have low methane emissions in vitro. However, their digestibility is rather low and may limit their utilization.

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