Comparative Analysis of the Multispectral Vegetation Indices and the Radar Vegetation Index

Abstract The all-weather capability, signal independence to the solar illumination angle, and response to 3D vegetation structures are the highlights of active radar systems for natural vegetation mapping and monitoring. However, they may present significant soil background effects. This study addresses a comparative analysis of the performance of L-band synthetic aperture radar (SAR) data and optical vegetation indices (VIs) for discriminating the Brazilian cerrado physiognomies. The study area was the Brasilia National Park, Brazil, one of the test sites of the Large-Scale Biosphere–Atmosphere (LBA) experiment in Amazonia. Seasonal Japanese Earth Resources Satellite-1 (JERS-1) SAR backscatter coefficients (σ°) were compared with two vegetation indices [normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI)] over the five most dominant cerrados’ physiognomies plus gallery forest. In contrast to the VIs, σ° from dry and wet seasons did not change significantly, indicating primary response to vegetation structures. Discriminant analysis and analysis of variance (ANOVA) showed an overall higher performance of radar data. However, when both SAR and VIs are combined, the discrimination capability increased significantly, indicating that the fusion of the optical and radar backscatter observations provides overall improved classifications of the cerrado types. In addition, VIs showed good performance for monitoring the cerrado dynamics.

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HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

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.

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

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.

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

Remote Sensing ◽

10.3390/rs13142755 ◽

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.

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

Remote Sensing ◽

10.3390/rs13112060 ◽

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.

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

Plant Methods ◽

10.1186/s13007-021-00711-y ◽

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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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Remote Sensing ◽

10.3390/rs13061144 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1144

Author(s):

Mahendra Bhandari ◽

Shannon Baker ◽

Jackie C. Rudd ◽

Amir M. H. Ibrahim ◽

Anjin Chang ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Grain Filling ◽

Unmanned Aerial Systems ◽

High Temporal Resolution ◽

Wheat Growth ◽

Spatial Domains ◽

Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

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Early stress detection in grapevine – can remotely-sensed sun-induced chlorophyll fluorescence do the job?

10.5194/egusphere-egu21-5171 ◽

2021 ◽

Author(s):

Georg Wohlfahrt ◽

Albin Hammerle ◽

Barbara Rainer ◽

Florian Haas

Keyword(s):

Chlorophyll Fluorescence ◽

Vegetation Index ◽

Near Infrared ◽

Census Data ◽

Meteorological Data ◽

Vegetation Indices ◽

Wavelength Region ◽

Remote Sensing Data ◽

Warning Signs ◽

Mitigation Measures

Ongoing changes in climate (both in the means and the extremes) are increasingly challenging grapevine production in the province of South Tyrol (Italy). Here we ask the question whether sun-induced chlorophyll fluorescence (SIF) observed remotely from space can detect early warning signs of stress in grapevine and thus help guide mitigation measures.Chlorophyll fluorescence refers to light absorbed by chlorophyll molecules that is re-emitted in the red to far-red wavelength region. Previous research at leaf and canopy scale indicated that SIF correlates with the plant photosynthetic uptake of carbon dioxide as it competes for the same energy pool.To address this question, we use time series of two down-scaled SIF products (GOME-2 and OCO-2, 2007/14-2018) as well as the original OCO-2 data (2014-2019). As a benchmark, we use several vegetation indices related to canopy greenness, as well as a novel near-infrared radiation-based vegetation index (2000-2019). Meteorological data fields are used to explore possible weather-related causes for observed deviations in remote sensing data. Regional DOC grapevine census data (2000-2019) are used as a reference for the analyses.

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A comparison of satellite-based indices for hazard assessment of MSW open dumps using spatial analysis

Waste Management & Research ◽

10.1177/0734242x18815963 ◽

2018 ◽

Vol 37 (3) ◽

pp. 219-236 ◽

Cited By ~ 1

Author(s):

Khalid Mahmood ◽

Zia Ul-Haq ◽

Fiza Faizi ◽

Syeda A. Batol

Keyword(s):

Hazard Assessment ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Moving Average ◽

Vegetation Indices ◽

Temporal Analysis ◽

Zone Size ◽

Vegetation Health ◽

Spatio Temporal ◽

Hazardous Zone

This study compares the suitability of different satellite-based vegetation indices (VIs) for environmental hazard assessment of municipal solid waste (MSW) open dumps. The compared VIs, as bio-indicators of vegetation health, are normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI) that have been subject to spatio-temporal analysis. The comparison has been made based on three criteria: one is the exponential moving average (EMA) bias, second is the ease in visually finding the distance of VI curve flattening, and third is the radius of biohazardous zone in relation to the waste heap dumped at them. NDVI has been found to work well when MSW dumps are surrounded by continuous and dense vegetation, otherwise, MSAVI is a better option due to its ability for adjusting soil signals. The hierarchy of the goodness for least EMA bias is MSAVI> SAVI> NDVI with average bias values of 101 m, 203 m, and 270 m, respectively. Estimations using NDVI have been found unable to satisfy the direct relationship between waste heap and hazardous zone size and have given a false exaggeration of 374 m for relatively smaller dump as compared to the bigger one. The same false exaggeration for SAVI and MSAVI is measured to be 86 m and -14 m, respectively. So MSAVI is the only VI that has shown the true relation of waste heap and hazardous zone size. The best visualization of distance-dependent vegetation health away from the dumps is also provided by MSAVI.

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Using remote sensing for identification of late-season grass weed patches in wheat

Weed Science ◽

10.1614/ws-05-54.2.346 ◽

2006 ◽

Vol 54 (02) ◽

pp. 346-353 ◽

Cited By ~ 41

Author(s):

Francisca López-Granados ◽

Montse Jurado-Expósito ◽

Jose M. Peña-Barragán ◽

Luis García-Torres

Keyword(s):

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Field Research ◽

Aerial Images ◽

Wild Oat ◽

Late Season ◽

High Resolution Satellite Imagery ◽

Grass Weed

Field research was conducted to determine the potential of hyperspectral and multispectral imagery for late-season discrimination and mapping of grass weed infestations in wheat. Differences in reflectance between weed-free wheat and wild oat, canarygrass, and ryegrass were statistically significant in most 25-nm-wide wavebands in the 400- and 900-nm spectrum, mainly due to their differential maturation. Visible (blue, B; green, G; red, R) and near infrared (NIR) wavebands and five vegetation indices: Normalized Difference Vegetation Index (NDVI), Ratio Vegetation Index (RVI), R/B, NIR-R and (R − G)/(R + G), showed potential for discriminating grass weeds and wheat. The efficiency of these wavebands and indices were studied by using color and color-infrared aerial images taken over three naturally infested fields. In StaCruz, areas infested with wild oat and canarygrass patches were discriminated using the indices R, NIR, and NDVI with overall accuracies (OA) of 0.85 to 0.90. In Florida–West, areas infested with wild oat, canarygrass, and ryegrass were discriminated with OA from 0.85 to 0.89. In Florida–East, for the discrimination of the areas infested with wild oat patches, visible wavebands and several vegetation indices provided OA of 0.87 to 0.96. Estimated grass weed area ranged from 56 to 71%, 43 to 47%, and 69 to 80% of the field in the three locations, respectively, with per-class accuracies from 0.87 to 0.94. NDVI was the most efficient vegetation index, with a highly accurate performance in all locations. Our results suggest that mapping grass weed patches in wheat is feasible with high-resolution satellite imagery or aerial photography acquired 2 to 3 wk before crop senescence.

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