Biomass estimation of alpine grasslands under different grazing intensities using spectral vegetation indices

AbstractPasture management is highly dependent on accurate biomass estimation. Usually, such activity is neglected as current methods are time-consuming and frequently perceived as inaccurate. Conversely, spectral data is a promising technique to automate and improve the accuracy and precision of estimates. Historically, spectral vegetation indices have been widely adopted and large numbers have been proposed. The selection of the optimal index or satisfactory subset of indices to accurately estimate biomass is not trivial and can influence the design of new sensors. This study aimed to compare a canopy-based technique (rising plate meter) with spectral vegetation indices. It examined 97 vegetation indices and 11,026 combinations of normalized ratio indices paired with different regression techniques on 900 pasture biomass data points of perennial ryegrass (Lolium perenne) collected throughout a 1-year period. The analyses demonstrated that the canopy-based technique is superior to the standard normalized difference vegetation index (∆, 115.1 kg DM ha−1 RMSE), equivalent to the best performing normalized ratio index and less accurate than four selected vegetation indices deployed with different regression techniques (maximum ∆, 231.1 kg DM ha−1). When employing the four selected vegetation indices, random forests was the best performing regression technique, followed by support vector machines, multivariate adaptive regression splines and linear regression. Estimate precision was improved through model stacking. In summary, this study demonstrated a series of achievable improvements in both accuracy and precision of pasture biomass estimation, while comparing different numbers of inputs and regression techniques and providing a benchmark against standard techniques of precision agriculture and pasture management.

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Potential of using spectral vegetation indices for corn green biomass estimation based on their relationship with the photosynthetic vegetation sub-pixel fraction

Agricultural Water Management ◽

10.1016/j.agwat.2020.106155 ◽

2020 ◽

Vol 236 ◽

pp. 106155

Author(s):

Luan Peroni Venancio ◽

Everardo Chartuni Mantovani ◽

Cibele Hummel do Amaral ◽

Christopher Michael Usher Neale ◽

Ivo Zution Gonçalves ◽

...

Keyword(s):

Vegetation Indices ◽

Biomass Estimation ◽

Spectral Vegetation Indices

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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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Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽

10.3390/f12070914 ◽

2021 ◽

Vol 12 (7) ◽

pp. 914

Author(s):

Adeel Ahmad ◽

Hammad Gilani ◽

Sajid Rashid Ahmad

Keyword(s):

High Resolution ◽

Literature Review ◽

Satellite Imagery ◽

Aboveground Biomass ◽

Vegetation Indices ◽

Biomass Estimation ◽

Independent Variables ◽

Non Linear ◽

Multiple Variables ◽

Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

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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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Effect of multi-level and multi-scale spectral data source on vineyard state assessment via spectral vegetation indices

10.5194/egusphere-egu21-15815 ◽

2021 ◽

Author(s):

Antonello Bonfante ◽

Arturo Erbaggio ◽

Eugenia Monaco ◽

Rossella Albrizio ◽

Pasquale Giorio ◽

...

Keyword(s):

Climate Change ◽

Data Collection ◽

State Estimation ◽

Spectral Data ◽

Spatial Resolution ◽

Spectral Region ◽

Vegetation Indices ◽

Plant Management ◽

Spectral Vegetation Indices ◽

Data Source

Currently, the main goal of agriculture is to promote the resilience of agricultural systems in a sustainable way through the improvement of use efficiency of farm resources, increasing crop yield and quality, under climate change conditions. Climate change is one of the major challenges for high incomes crops, as the vineyards for high-quality wines, since it is expected to drastically modify plant growth, with possible negative effects especially in arid and semi-arid regions of Europe. In this context, the reduction of negative environmental impacts of intensive agriculture (e.g. soil degradation), can be realized by means of high spatial and temporal resolution of field crop monitoring, aiming to manage the local spatial variability.The monitoring of spatial behaviour of plants during the growing season represents an opportunity to improve the plant management, the farmer incomes and to preserve the environmental health, but it represents an additional cost for the farmer.The UAS-based imagery might provide detailed and accurate information across visible and near infrared spectral regions to support monitoring (crucial for precision agriculture) with limitation in bands and then on spectral vegetation indices (Vis) provided. VIs are a well-known and widely used method for crop state estimation. The ability to monitor crop state by such indices is an important tool for agricultural management. While differences in imagery and point-based spectroscopy are obvious, their impact on crop state estimation by VIs is not well-studied. The aim of this study was to assess the performance level of the selected VIs calculated from reconstructed high-resolution satellite (Sentinel-2A) multispectral imagery (13 bands across 400-2500nm with spatial resolution of <2m) through Convolutional Neural Network (CNN) approach (Brook et al., 2020), UAS-based multispectral (5 bands across 450-800nm spectral region with spatial resolution of 5cm) imagery and point-based field spectroscopy (collecting 600 wavelength across&#160; 400-1000nm spectral region with a surface footprint of 1-2cm) in application to crop state estimation.The test site is a portion of vineyard placed in southern Italy cultivated on Greco cultivar, in which the soil-plant and atmosphere system has been monitored during the 2020 vintage also through ecophysiological analyses. The data analysis will follow the methodology presented in a recently published paper (Polinova et al., 2018).The study will connect the method and scale of spectral data collection with in vivo plant monitoring and prove that it has a significant impact on the vegetation state estimation results. It should be noted that each spectral data source has its advantages and drawbacks. The plant parameter of interest should determine not only the VIs type suitable for analysis but also the method of data collection.The contribution has been realized within the CNR BIO-ECO project.

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Use of Spectral Vegetation Indices to Infer Leaf Area, Evapotranspiration and Yield: I. Rationale

Agronomy Journal ◽

10.2134/agronj1990.00021962008200030037x ◽

1990 ◽

Vol 82 (3) ◽

pp. 623-629 ◽

Cited By ~ 66

Author(s):

C. L. Wiegand ◽

A. J. Richardson

Keyword(s):

Leaf Area ◽

Vegetation Indices ◽

Spectral Vegetation Indices

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TOPOGRAPHIC EFFECT ON SPECTRAL VEGETATION INDICES FROM LANDSAT TM DATA: IS TOPOGRAPHIC CORRECTION NECESSARY?

Boletim de Ciências Geodésicas ◽

10.1590/s1982-21702016000100006 ◽

2016 ◽

Vol 22 (1) ◽

pp. 95-107 ◽

Cited By ~ 9

Author(s):

Eder Paulo Moreira* ◽

Márcio de Morisson Valeriano ◽

Ieda Del Arco Sanches ◽

Antonio Roberto Formaggio

Keyword(s):

Near Infrared ◽

Incidence Angle ◽

Spectral Band ◽

Vegetation Indices ◽

Topographic Effect ◽

Topographic Correction ◽

Spectral Bands ◽

Spectral Vegetation Indices ◽

Current Availability ◽

Elevation Model

The full potentiality of spectral vegetation indices (VIs) can only be evaluated after removing topographic, atmospheric and soil background effects from radiometric data. Concerning the former effect, the topographic effect was barely investigated in the context of VIs, despite the current availability correction methods and Digital elevation Model (DEM). In this study, we performed topographic correction on Landsat 5 TM spectral bands and evaluated the topographic effect on four VIs: NDVI, RVI, EVI and SAVI. The evaluation was based on analyses of mean and standard deviation of VIs and TM band 4 (near-infrared), and on linear regression analyses between these variables and the cosine of the solar incidence angle on terrain surface (cos i). The results indicated that VIs are less sensitive to topographic effect than the uncorrected spectral band. Among VIs, NDVI and RVI were less sensitive to topographic effect than EVI and SAVI. All VIs showed to be fully independent of topographic effect only after correction. It can be concluded that the topographic correction is required for a consistent reduction of the topographic effect on the VIs from rugged terrain.

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