scholarly journals Reflectance-Based Vegetation Index Assessment of Four Plant Species Exposed to Lithium Chloride

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2750
Author(s):  
Nicole Martinez ◽  
Julia Sharp ◽  
Thomas Johnson ◽  
Wendy Kuhne ◽  
Clay Stafford ◽  
...  
Keyword(s):  
Plant Species ◽  
Lithium Chloride ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Reflectance Spectra ◽  
Treatment Level ◽  
Length Of Treatment ◽  
Contact Probe ◽  
Relative Water

This study considers whether a relationship exists between response to lithium (Li) exposure and select vegetation indices (VI) determined from reflectance spectra in each of four plant species: Arabidopsis thaliana, Helianthus annuus (sunflower), Brassica napus (rape), and Zea mays (corn). Reflectance spectra were collected every week for three weeks using an ASD FieldSpec Pro spectroradiometer with both a contact probe (CP) and a field of view probe (FOV) for plants treated twice weekly in a laboratory setting with 0 mM (control) or 15 mM of lithium chloride (LiCl) solution. Plants were harvested each week after spectra collection for determination of relevant physical endpoints such as relative water content and chlorophyll content. Mixed effects analyses were conducted on selected endpoints and vegetation indices (VI) to determine the significance of the effects of treatment level and length of treatment as well as to determine which VI would be appropriate predictors of treatment-dependent endpoints. Of the species considered, A. thaliana exhibited the most significant effects and corresponding shifts in reflectance spectra. Depending on the species and endpoint, the most relevant VIs in this study were NDVI, PSND, YI, R1676/R1933, R750/R550, and R950/R750.

scholarly journals The Effect of Soil Moisture on the Reflectance Spectra Correlations in Beech and Sessile Oak Foliage/ Talajnedvesség hatása bükk és kocsánytalan tölgy lombozat reflexiós spektrumainak korrelációira.

2015 ◽  
Vol 11 (1) ◽  
pp. 9-26 ◽  
Author(s):  
Attila Eredics ◽  
Zsolt István Németh ◽  
Rita Rákosa ◽  
Ervin Rasztovits ◽  
Norbert Móricz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Vapour Pressure Deficit ◽  
Temporal Variations ◽  
Reflectance Spectra ◽  
Changing Environment ◽  
State Dependent ◽  
Sensitivity Change

Abstract Reflectance intensities of foliage are mostly due to biomaterials synthesised by plants. Adaptation to the continuously changing environment requires the regulated alteration of metabolic processes, which also influences the UV-VIS (Ultraviolet-Visible) and IR (Infra Red) spectra of leaves. For the calculation of various Vegetation Indices (VIs), e.g. NDVI (Normalized Difference Vegetation Index), the common practice is to use the reflectance spectrum of the whole foliage and when individual leaves of the same plant are sampled, an average VI is derived. On the contrary, our method exploits the small differences between individual leaves of the same plant, making use of the similar distributions of measured reflectance values. Using particular wavelength pairs, linear regressions of reflectance intensities have been investigated. The parameters of these regressions (slope and intercept) have been compared to the temporal variations of the environmental factors, such as temperature, vapour pressure deficit and soil moisture. By assessing the sensitivity of the regression coefficient (slope) to the changing environment, wavelength pairs can be selected whose sensitivity change reflects the effect of soil moisture deficit on the plant. Based on the state-dependent correlations of the reflectance spectra of plant foliage, a new concept is presented that is capable of indicating the level of environmental stress, e.g. drought stress.

scholarly journals Application of reflectance based vegetation index to derive dual crop co-efficient for summer sesame

2021 ◽  
Vol 16 (AAEBSSD) ◽  
pp. 62-72
Author(s):  
A. P. Lakkad ◽  
S. G. Patel ◽  
Vibhuti A. Patel ◽  
M. G. Varma
Keyword(s):  
Vegetation Index ◽  
Irrigation System ◽  
Vegetation Indices ◽  
Soil Surface ◽  
Water Requirement ◽  
Soil Evaporation ◽  
Growth Stages ◽  
Physiologic Parameters ◽  
Crop Growth Stages

Dual crop co-efficient approach was applied to estimate seasonal water requirement for summer sesame using reflectance based vegetation indices. Field experiment was conducted to collect the required various crop physiologic parameters and NDVI data for the study crop during 2018 and 2019. Basal crop co-efficients and soil evaporation co-efficients collected from FAO-56 for initial mid and end stages of summer sesame were adjusted for study area using local weather parameters. Spectrum® Field Scout CM 1000 NDVI Meter were used to collect the NDVI data at various stages of study crop. The NDVI was measured from crop canopy and soil surface at 7 days intervals between 12.00 to 13.00 clocks. NDVI Based Basal Crop Co-efficient and Soil evaporation co-efficient were derived using standard methods. FAO estimated crop co-efficients were compared with NDVI based crop co-efficients. The co-efficient of determination of the fitted regression equation was found to be 0.836 and 0.765 for drip irrigation and 0.783 and 0.867 for surface control irrigation system for summer sesame during 2018 and 2019, respectively. Crop growing stage wise water requirement per unit area was estimated for both treatments. Results indicates that among these two methods, NDVI method estimate lowest water requirement in both cases i.e. total water requirement and during all the crop growth stages for both irrigation systems while daily crop water requirement was lower for all growth stages in drip system as compare with control system.

HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

2020 ◽  
Vol 7 (1) ◽  
pp. 21
Author(s):  
Faradina Marzukhi ◽  
Nur Nadhirah Rusyda Rosnan ◽  
Md Azlin Md Said
Keyword(s):  
Oil Palm ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Satellite Image ◽  
Vegetation Indices ◽  
Soil Nutrient ◽  
Oil Palm Plantation ◽  
Sustainable Environment ◽  
Moisture Analysis ◽  
The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

scholarly journals Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 1486
Author(s):  
Chris Cavalaris ◽  
Sofia Megoudi ◽  
Maria Maxouri ◽  
Konstantinos Anatolitis ◽  
Marios Sifakis ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Model Development ◽  
Wheat Yield ◽  
Growth Period ◽  
Yield Data ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

scholarly journals Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

2021 ◽  
Vol 13 (14) ◽  
pp. 2755
Author(s):  
Peng Fang ◽  
Nana Yan ◽  
Panpan Wei ◽  
Yifan Zhao ◽  
Xiwang Zhang
Keyword(s):  
Aboveground Biomass ◽  
Production Management ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Vegetation Indices ◽  
Biophysical Parameters ◽  
Edge Information ◽  
Casa Model ◽  
Red Edge ◽  
Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

scholarly journals Improving the knowledge of plant potential biodiversity-ecosystem services links using maps at the regional level in Southern Patagonia

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Yamina Micaela Rosas ◽  
Pablo L. Peri ◽  
María Vanessa Lencinas ◽  
Romina Lasagno ◽  
Guillermo J. Martínez Pastur
Keyword(s):  
Ecosystem Services ◽  
Plant Species ◽  
Vegetation Index ◽  
Vascular Plant ◽  
Conservation Strategies ◽  
Cultural Ecosystem Services ◽  
Important Indicator ◽  
Ecological Requirements ◽  
Trade Offs ◽  
Southern Patagonia

Abstract Background Biodiversity supports multiple ecosystem services, whereas species loss endangers the provision of many services and affects ecosystem resilience and resistance capacity. The increase of remote sensing techniques allows to estimate biodiversity and ecosystem services supply at the landscape level in areas with low available data (e.g. Southern Patagonia). This paper evaluates the potential biodiversity and how it links with ecosystem services, based on vascular plant species across eight ecological areas. We also evaluated the habitat plant requirements and their relation with natural gradients. A total of 977 plots were used to develop habitat suitability maps based on an environmental niche factor analysis of 15 more important indicator species for each ecological area (n = 53 species) using 40 explanatory variables. Finally, these maps were combined into a single potential biodiversity map, which was linked with environmental variables and ecosystem services supply. For comparisons, data were extracted and compared through analyses of variance. Results The plant habitat requirements varied greatly among the different ecological areas, and it was possible to define groups according to its specialization and marginality indexes. The potential biodiversity map allowed us to detect coldspots in the western mountains and hotspots in southern and eastern areas. Higher biodiversity was associated to higher temperatures and normalized difference vegetation index, while lower biodiversity was related to elevation and rainfall. Potential biodiversity was closely associated with supporting and provisioning ecosystem services in shrublands and grasslands in the humid steppe, while the lowest values were related to cultural ecosystem services in Nothofagus forests. Conclusions The present study showed that plant species present remarkable differences in spatial distributions and ecological requirements, being a useful proxy for potential biodiversity modelling. Potential biodiversity values change across ecological areas allowing to identify hotspots and coldspots, a useful tool for landscape management and conservation strategies. In addition, links with ecosystem services detect potential synergies and trade-offs, where areas with the lowest potential biodiversity are related to cultural ecosystem services (e.g. aesthetic values) and areas with the greatest potential biodiversity showed threats related to productive activities (e.g. livestock).

scholarly journals Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

2021 ◽  
Vol 13 (11) ◽  
pp. 2060
Author(s):  
Trylee Nyasha Matongera ◽  
Onisimo Mutanga ◽  
Mbulisi Sibanda ◽  
John Odindi
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Critical Role ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Machine Learning Algorithms ◽  
Special Focus ◽  
Land Surface Phenology ◽  
Spectral Vegetation Indices ◽  
Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

scholarly journals Biomass estimation of cultivated red algae Pyropia using unmanned aerial platform based multispectral imaging

Plant Methods ◽  
2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Shuai Che ◽  
Guoying Du ◽  
Ning Wang ◽  
Kun He ◽  
Zhaolan Mo ◽  
...  
Keyword(s):  
High Throughput ◽  
Vegetation Index ◽  
Multispectral Imaging ◽  
Vegetation Indices ◽  
Spectral Imaging ◽  
Shielding Effect ◽  
Quadratic Model ◽  
Biomass Estimation ◽  
Marine Aquaculture ◽  
Significant Difference

Abstract Background Pyropia is an economically advantageous genus of red macroalgae, which has been cultivated in the coastal areas of East Asia for over 300 years. Realizing estimation of macroalgae biomass in a high-throughput way would great benefit their cultivation management and research on breeding and phenomics. However, the conventional method is labour-intensive, time-consuming, manually destructive, and prone to human error. Nowadays, high-throughput phenotyping using unmanned aerial vehicle (UAV)-based spectral imaging is widely used for terrestrial crops, grassland, and forest, but no such application in marine aquaculture has been reported. Results In this study, multispectral images of cultivated Pyropia yezoensis were taken using a UAV system in the north of Haizhou Bay in the midwestern coast of Yellow Sea. The exposure period of P. yezoensis was utilized to prevent the significant shielding effect of seawater on the reflectance spectrum. The vegetation indices of normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI) and normalized difference of red edge (NDRE) were derived and indicated no significant difference between the time that P. yezoensis was completely exposed to the air and 1 h later. The regression models of the vegetation indices and P. yezoensis biomass per unit area were established and validated. The quadratic model of DVI (Biomass = − 5.550DVI2 + 105.410DVI + 7.530) showed more accuracy than the other index or indices combination, with the highest coefficient of determination (R2), root mean square error (RMSE), and relative estimated accuracy (Ac) values of 0.925, 8.06, and 74.93%, respectively. The regression model was further validated by consistently predicting the biomass with a high R2 value of 0.918, RMSE of 8.80, and Ac of 82.25%. Conclusions This study suggests that the biomass of Pyropia can be effectively estimated using UAV-based spectral imaging with high accuracy and consistency. It also implied that multispectral aerial imaging is potential to assist digital management and phenomics research on cultivated macroalgae in a high-throughput way.

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