Aboveground Biomass Estimates of Araucaria angustifolia (Bertol.) Kuntze, Using Vegetation Indexes in Wolrdview-2 Image

The present research aims to evaluate the biomass estimates of Araucaria angustifolia (Bertol.) Kuntze trees obtained by the direct method, then present results generated from a 2.0 m resolution spectral image Worldview-2 satellite. The quantification of the biomass in the field was first carried out of 29 trees of the specie of interest with DBH ≥ 40 cm and then with the image aid the crowns were delimited for analysis. From the spectral bands (B2-blue, B3-green, B4-yellow, B5-red, B6-near red, B7-near infrared 2 and B8-near infrared 2), it was possible to obtain vegetation indexes proposed by the literature (NDVI, NDVI_2, RS and SAVI_0,25) and later incorporated with dendrometric data a correlation matrix was formed. Additionally, mathematical equations were used to estimate biomass and carbon as a function of dendrometric variables and information obtained from the satellite image processing. From these equations, the ones that presented better results were those that contained independent dendrometric variables (DBH) and those that contained vegetation indices (NDVI_2 and NDVI). For the dendrometers, the relative error found was 14.42% and 14.32% for biomass and carbon respectively, while for the digital ones, NDVI_2 found a relative error of 37.82% and an adjusted coefficient of determination of 0.88 in the biomass equations. In the carbon equations, the NDVI variable presented the best results, being 38.56% the relative error and 0.87 the determination coefficient.

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Understanding Vine Hyperspectral Signature through Different Irrigation Plans: A First Step to Monitor Vineyard Water Status

Remote Sensing ◽

10.3390/rs13030536 ◽

2021 ◽

Vol 13 (3) ◽

pp. 536

Author(s):

Eve Laroche-Pinel ◽

Mohanad Albughdadi ◽

Sylvie Duthoit ◽

Véronique Chéret ◽

Jacques Rousseau ◽

...

Keyword(s):

Near Infrared ◽

Water Status ◽

Vegetation Indices ◽

Remote Sensing Data ◽

Short Wave ◽

Spatial And Temporal Scales ◽

Spectral Bands ◽

Main Challenge ◽

Spectral Scale ◽

Grape Varieties

The main challenge encountered by Mediterranean winegrowers is water management. Indeed, with climate change, drought events are becoming more intense each year, dragging the yield down. Moreover, the quality of the vineyards is affected and the level of alcohol increases. Remote sensing data are a potential solution to measure water status in vineyards. However, important questions are still open such as which spectral, spatial, and temporal scales are adapted to achieve the latter. This study aims at using hyperspectral measurements to investigate the spectral scale adapted to measure their water status. The final objective is to find out whether it would be possible to monitor the vine water status with the spectral bands available in multispectral satellites such as Sentinel-2. Four Mediterranean vine plots with three grape varieties and different water status management systems are considered for the analysis. Results show the main significant domains related to vine water status (Short Wave Infrared, Near Infrared, and Red-Edge) and the best vegetation indices that combine these domains. These results give some promising perspectives to monitor vine water status.

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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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Carbon Flux Phenology from the Sky: Evaluation for Maize and Soybean

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-17-0004.1 ◽

2018 ◽

Vol 35 (4) ◽

pp. 877-892 ◽

Cited By ~ 1

Author(s):

Alexandria G. McCombs ◽

April L. Hiscox ◽

Cuizhen Wang ◽

Ankur R. Desai ◽

Andrew E. Suyker ◽

...

Keyword(s):

Carbon Flux ◽

Management Practices ◽

Near Infrared ◽

Satellite Image ◽

Vegetation Indices ◽

Measurement Tool ◽

Vegetation Phenology ◽

Flux Dynamics ◽

Surface Exchange ◽

Crop Type

AbstractCarbon flux phenology is widely used to understand carbon flux dynamics and surface exchange processes. Vegetation phenology has been widely evaluated by remote sensors; however, very few studies have evaluated the use of vegetation phenology for identifying carbon flux phenology. Currently available techniques to derive net ecosystem exchange (NEE) from a satellite image use a single generic modeling subgroup for agricultural crops. But, carbon flux phenological processes vary highly with crop types and land management practices; this paper reexamines this assumption. Presented here are an evaluation of ground-truth remotely sensed vegetation indices with in situ NEE measurements and an identification of vegetation indices for estimating carbon flux phenology metrics by crop type. Results show that the performance of different vegetation indices as an indicator of phenology varies with crop type, particularly when identifying the start of a season and the peak of a season. Maize fields require vegetation indices that make use of the near-infrared and red reflectance bands, while soybean fields require those making use of the shortwave infrared (IR) and near-IR bands. In summary, the study identifies how to best utilize remote sensing technology as a crop-specific measurement tool.

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Uso de sensores remotos en la determinación del forraje disponible de Urochloa humidicola cv. Llanero bajo pastoreo en la Altillanura colombiana

Tropical Grasslands - Forrajes Tropicales ◽

10.17138/tgft(9)376-382 ◽

2021 ◽

Vol 9 (3) ◽

pp. 376-382

Author(s):

Raúl Alejandro Díaz Giraldo ◽

Mauricio Álvarez de León ◽

Otoniel Pérez López

Keyword(s):

Near Infrared ◽

Agricultural Research ◽

Coefficient Of Determination ◽

Research Centre ◽

Landsat 8 ◽

Pastoral Systems ◽

Forage Availability ◽

Vegetation Indexes ◽

Remote Sensing Techniques ◽

Sentinel 2A

Modernization of pastoral systems based on the use of Urochloa species in the Colombian Eastern Llanos need the use of remote sensing techniques from satellite platforms to estimate amount of offered forage. In the Carimagua Research Centre of the Colombian Corporation for Agricultural Research (Agrosavia), an Urochloa humidicola cv. Llanero pasture was evaluated using Landsat 8 and Sentinel 2A images. The NDVI, SAVI, EVI y GNDVI vegetation indexes determined by using the blue, green, red and near infrared bands; and the results analyzed with the R free software, to relate those indexes with forage availability field measures taken during the dry season. Forage availability ranged between 290 and 656 kg DM ha-1 and the vegetation indexes for the Landsat 8 and Sentinel 2A sensors were: NDVI = 0.67 (±0.037) and 0.69 (±0.061); SAVI = 0.48 (±0.048) and 0.41 (±0.046); EVI = 0.70 (±0.052) and 0.41 (±0.047); y GNDVI = 0.60 (±0.028) and 0.70 (±0.034), respectively. The relationships between vegetation indexes and forage availability were linear. The Coefficient of Determination (R2= 0.56‒0.72) and the Mean Square Error (MSR =63.95‒80.16) of the prediction equations were used. In conclusion, under the conditions of the study, the EVI for Landsat 8 and NDVI for Sentinel 2A were considered adequate for estimating forage availability of Urochloa humidicola cv. Llanero.

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Índice de vegetação ajustado ao solo (IVAS): estado da arte e suas potencialidades (Adjusted Vegetation Index to Solo (SAVI): state of the art and its potential)

Revista Brasileira de Geografia Física ◽

10.26848/rbgf.v9.6.p2054-2074 ◽

2016 ◽

Vol 9 (6) ◽

pp. 2054

Author(s):

Gabrielle de Araújo Ribeiro ◽

João Nailson De Castro Silva ◽

Janaína Barbosa Da Silva

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Near Infrared ◽

State Of The Art ◽

Vegetation Indices ◽

Quantitative Information ◽

Qualitative And Quantitative ◽

Spectral Bands ◽

Satellite Sensors ◽

Best Parameters

A utilização do Sensoriamento Remoto para a avaliação do meio ambiente é cada vez mais aplicado em pesquisas. As imagens adquiridas pelos sensores acoplados aos satélites fornecem dados qualitativos e quantitativos do estado da vegetação através da aplicação dos índices de vegetação. Os índices são obtidos pela combinação matemáticas das reflectâncias dos alvos nas faixas espectrais, principalmente do vermelho e infravermelho próximo e podem ser afetados por diferentes fatores tais como reflectância, irradiancia e o brilho do solo. Um dos índices comumente utilizados, principalmente em áreas semiáridas, onde se tem influencia do brilho do solo, é o índice de vegetação ajustado ao solo (IVAS). Este índice introduz um fator de ajuste (L) ao índice de vegetação normalizada (IVDN) para minimizar os efeitos da presença do solo. Porém para cada região deve-se estudar e determinar os melhores parâmetros para o mesmo. Portanto este trabalho tem como objetivo apresentar uma revisão de literatura em relação ao índice de vegetação ajustado ao solo em diferentes biomas brasileiro e outras aplicações. A B S T R A C T The use of remote sensing for environmental assessment is increasingly applied in research. The images acquired by the satellite sensors coupled to provide qualitative and quantitative information on the state of the vegetation by the application of vegetation indices. The indices are obtained by mathematical combination of the reflectance of the targets in the spectral bands, especially the red and near infrared and can be affected by different factors such as reflectance, irradiance and the brightness of the soil. One of the commonly used indices, especially in semi-arid areas where it has influence of soil brightness, is the vegetation index adjusted to the ground (UAI). This index introduces an adjustment factor (L) normalized vegetation index (NDVI) to minimize the effects of soil present. However, for each region should study and determine the best parameters for the same. Therefore this work aims to present a literature review regarding the vegetation index adjusted to the soil in different Brazilian biomes and other applications. Keywords : Remote Sensing; vegetation index; spectral analysis, biome.

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Sentinel-2 for High Resolution Mapping of Slope-Based Vegetation Indices Using Machine Learning By SAGA GIS

Transylvanian Review of Systematical and Ecological Research ◽

10.2478/trser-2020-0015 ◽

2020 ◽

Vol 22 (3) ◽

pp. 17-34

Author(s):

Polina Lemenkova

Keyword(s):

Satellite Image ◽

Vegetation Indices ◽

Ecological Monitoring ◽

Urban Spaces ◽

Tropical Rainforests ◽

High Resolution Mapping ◽

Satellite Image Processing ◽

Resolution Mapping ◽

Multispectral Satellite Image ◽

Sentinel 2

Abstract Vegetation of Cameroon includes a variety of landscape types with high biodiversity. Ecological monitoring of Yaoundé requires visualization of vegetation types in context of climate change. Vegetation Indices (VIs) derived from Sentinel-2 multispectral satellite image were analyzed in SAGA GIS to separate wetland biomes, as well as savannah and tropical rainforests. The methodology includes computing 6 VIs: NDVI, DVI, SAVI, RVI, TTVI, CTVI. The VIs shown correlation of data with vegetation distribution rising from wetlands, grassland, savanna, and shrub land towards tropical rainforests, increasing values along with canopy greenness, while also being inversely proportional to soils, urban spaces and Sanaga River. The study contributed to the environmental studies of Cameroon and demonstration of the satellite image processing.

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Fusion of Multispectral Aerial Imagery and Vegetation Indices for Machine Learning-Based Ground Classification

Remote Sensing ◽

10.3390/rs13081411 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1411

Author(s):

Yanchao Zhang ◽

Wen Yang ◽

Ying Sun ◽

Christine Chang ◽

Jiya Yu ◽

...

Keyword(s):

Machine Learning ◽

Classification Accuracy ◽

Vegetation Indices ◽

Machine Learning Algorithms ◽

Support Vector ◽

Multispectral Images ◽

Learning Methods ◽

Machine Learning Methods ◽

Spectral Bands ◽

Vegetation Indexes

Unmanned Aerial Vehicles (UAVs) are emerging and promising platforms for carrying different types of cameras for remote sensing. The application of multispectral vegetation indices for ground cover classification has been widely adopted and has proved its reliability. However, the fusion of spectral bands and vegetation indices for machine learning-based land surface investigation has hardly been studied. In this paper, we studied the fusion of spectral bands information from UAV multispectral images and derived vegetation indices for almond plantation classification using several machine learning methods. We acquired multispectral images over an almond plantation using a UAV. First, a multispectral orthoimage was generated from the acquired multispectral images using SfM (Structure from Motion) photogrammetry methods. Eleven types of vegetation indexes were proposed based on the multispectral orthoimage. Then, 593 data points that contained multispectral bands and vegetation indexes were randomly collected and prepared for this study. After comparing six machine learning algorithms (Support Vector Machine, K-Nearest Neighbor, Linear Discrimination Analysis, Decision Tree, Random Forest, and Gradient Boosting), we selected three (SVM, KNN, and LDA) to study the fusion of multi-spectral bands information and derived vegetation index for classification. With the vegetation indexes increased, the model classification accuracy of all three selected machine learning methods gradually increased, then dropped. Our results revealed that that: (1) spectral information from multispectral images can be used for machine learning-based ground classification, and among all methods, SVM had the best performance; (2) combination of multispectral bands and vegetation indexes can improve the classification accuracy comparing to only spectral bands among all three selected methods; (3) among all VIs, NDEGE, NDVIG, and NDVGE had consistent performance in improving classification accuracies, and others may reduce the accuracy. Machine learning methods (SVM, KNN, and LDA) can be used for classifying almond plantation using multispectral orthoimages, and fusion of multispectral bands with vegetation indexes can improve machine learning-based classification accuracy if the vegetation indexes are properly selected.

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NDVI AND CUSTOMIZED CNN FOR LAND COVER SATELLITE IMAGE CLASSIFICATION

International Journal of Advanced Research ◽

10.21474/ijar01/13001 ◽

2021 ◽

Vol 9 (06) ◽

pp. 205-209

Author(s):

Amee Daiya ◽

◽

Dharmesh Bhalodiya ◽

Keyword(s):

Image Classification ◽

Domain Knowledge ◽

Vegetation Index ◽

Near Infrared ◽

Normalized Difference Vegetation Index ◽

Learning Algorithm ◽

Satellite Image ◽

Deep Learning Algorithm ◽

Spectral Bands

The efficient and the simplest deep learning algorithm of image classification is Convolutional Neural Network (CNN). In this paper we developed a customized CNN architecture for the classification of multi-spectral images from SAT-4 datasets. The sets Near-Infrared (NIR) band information as it can sense vegetation health. The domain knowledge of Normalized Difference Vegetation Index (NDVI) motivated us to utilize Red and NIR spectral bands together in the second level of experimentation for the classification.

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Correlations between spectral and biophysical data obtained in canola canopy cultivated in the subtropical region of Brazil

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2017001000001 ◽

2017 ◽

Vol 52 (10) ◽

pp. 825-832 ◽

Cited By ~ 1

Author(s):

Daniele Gutterres Pinto ◽

Denise Cybis Fontana ◽

Genei Antonio Dalmago ◽

Elizandro Fochesatto ◽

Matheus Boni Vicari ◽

...

Keyword(s):

Grain Yield ◽

Dry Matter ◽

Near Infrared ◽

Spectral Response ◽

Block Design ◽

Vegetation Indices ◽

Canopy Reflectance ◽

Vegetative Period ◽

Spectral Bands ◽

Biophysical Indicators

Abstract: The objective of this work was to identify the spectral bands, vegetation indices, and periods of the canola crop season in which the correlation between spectral data and biophysical indicators (total shoot dry matter and grain yield) is most significant. The experiment was carried out during the 2013 and 2014 crop seasons at Embrapa Trigo, in the state of Rio Grande do Sul, Brazil. A randomized complete block design was used, with four replicates, and the treatments consisted of five doses of nitrogen topdressing. Plant dry matter, grain yield, and phenology were measured. The canola spectral response was evaluated by measuring the canola canopy reflectance using a spectroradiometer, and, with this data, the SR, NDVI, EVI, SAVI, and GNDVI vegetation indices were determined. Pearson’s correlations between the spectral and biophysical variables of canola showed that the red (620 to 670 nm) and near-infrared (841 to 876 nm) bands were the best to estimate the dry matter. The vegetative period is the most indicated to obtain the most significant correlations for canola. All the used vegetation indices are adequate for estimating the dry matter and grain yield of canola.

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Validation and Comparison of Physical Models for Soil Salinity Mapping over an Arid Landscape Using Spectral Reflectance Measurements and Landsat-OLI Data

Remote Sensing ◽

10.3390/rs13030494 ◽

2021 ◽

Vol 13 (3) ◽

pp. 494

Author(s):

Z. M. Al-Ali ◽

A. Bannari ◽

H. Rhinane ◽

A. El-Battay ◽

S. A. Shahid ◽

...

Keyword(s):

Soil Sample ◽

Soil Salinity ◽

Near Infrared ◽

Field Survey ◽

Statistical Tests ◽

Spectral Response ◽

Image Data ◽

Physical Models ◽

Coefficient Of Determination ◽

Spectral Bands

The present study focuses on the validation and comparison of eight different physical models for soil salinity mapping in an arid landscape using two independent Landsat-Operational Land Imager (OLI) datasets: simulated and image data. The examined and compared models were previously developed for different semi-arid and arid geographic regions around the world, i.e., Latino-America, the Middle East, North and East Africa and Asia. These models integrate different spectral bands and unlike mathematical functions in their conceptualization. To achieve the objectives of the study, four main steps were completed. For simulated data, a field survey was organized, and 100 soil samples were collected with various degrees of salinity levels. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an analytical spectral device (ASD) FieldSpec-4 Hi-Res spectroradiometer. These measurements were resampled and convolved in the solar-reflective bands of the Operational Land Imager (OLI) sensor using a radiative transfer code and the relative spectral response profiles characterizing the filters of the OLI sensor. Then, they were converted in terms of the considered models. Moreover, the OLI image acquired simultaneously with the field survey was radiometrically preprocessed, and the models were implemented to derive soil salinity maps. The laboratory analyses were performed to derive electrical conductivity (EC-Lab) from each soil sample for validation and comparison purposes. These steps were undertaken between predicted salinity (EC-Predicted) and the measured ground truth (EC-Lab) in the same way for simulated and image data using regression analysis (p ˂ 0.05), coefficient of determination (R2), and root mean square error (RMSE). Moreover, the derived maps were visually interpreted and validated by comparison with observations from the field visit, ancillary data (soil, geology, geomorphology and water table maps) and soil laboratory analyses. Regardless of data sources (simulated or image) or the validation mode, the results obtained show that the predictive models based on visible- and near-infrared (VNIR) bands and vegetation indices are inadequate for soil salinity prediction in an arid landscape due to serious signals confusion between the salt crust and soil optical properties in these spectral bands. The statistical tests revealed insignificant fits (R2 ≤ 0.41) with very high prediction errors (RMSE ≥ 0.65), while the model based on the second-order polynomial function and integrating the shortwave infrared (SWIR) bands provided the results of best fit, with the field observations (EC-Lab), yielding an R2 of 0.97 and a low overall RMSE of 0.13. These findings were corroborated by visual interpretation of derived maps and their validation by comparison with the ground truthing.

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