scholarly journals A Log-Ratio Vegetation Index (LRVI) for Discrimination and Classification of Remote Sensing Data

2020 ◽  
Vol 28 (1) ◽  
pp. 45-70
Author(s):  
Francisco C. Rego ◽  
Irene S.P. Cadima ◽  
Eva K. Strand
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Ground Cover ◽  
Spectral Space ◽  
Electromagnetic Spectrum ◽  
Remotely Sensed Data ◽  
Classical Statistics ◽  
Log Ratio

Discrimination and classification are integral processes for interpreting remotely sensed data. Many spectral vegetation indices have been proposed for discriminating between vegetation, soil, and other ground cover categories. Classical remote sensing show that reflectance in the red (R) and near infrared (NIR) bands of the electromagnetic spectrum have been successful in differentiating between vegetation and other ground cover classes and they are commonly used for this purpose. Here we demonstrate how Fisher’s classical statistics can be applied to develop discriminant functions for commonly used vegetation indices simply using the R and NIR bands. We derive a new vegetation index, the Log-Ratio Vegetation Index (LRVI) and demonstrate its utility in discriminating between cork oak trees and surrounding background in woodlands in Portugal. The LRVI performed better than seven previously developed vegetation indices, likely because of its linear properties in the reflectance density spectral space. The robustness and simplicity of LRVI suggests that it deserves further exploration and should be included for comparison with other vegetation indices and functions in discrimination, classification, and modelling studies. We suggest that the demonstrated approach is widely applicable to development of indices composed of other bands than R and NIR for systems or processes that correlate better with reflectance in other regions of the electromagnetic spectrum.

scholarly journals Determination of Crop Health Monitoring in MPKV – Rahuri, Using Remote Sensing Approach

2021 ◽  
pp. 11-16
Author(s):  
Ankita P. Kamble ◽  
A. A. Atre ◽  
Payal A. Mahadule ◽  
C. B. Pande ◽  
N. S. Kute ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Health Monitoring ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Plant Stress ◽  
Vegetation Indices ◽  
Remotely Sensed Data ◽  
Pests And Diseases ◽  
Crop Health ◽  
Sentinel 2

Pests and diseases cause major harm during crop development. Also plant stress affects crop quality and quantity. Recent developments in high resolution remotely sensed data has seen a great potential in mapping cropland areas infected by pests and diseases, as well as potential vulnerable areas over expansive areas. Crop health monitoring in this study was carried out using remote sensing techniques. The present study was carried out in MPKV, Rahuri, Ahmednagar District, Maharashtra. Vegetation indices like Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) were used to classify the crops into healthy and dead or unhealthy one. Sentinel-2 image data from October 2019 to January 2020 processed in Arc GIS 10.1 were used for this study. Vegetation is a key component of the ecosystem and plays an important role in stabilizing the global environment. The result showed that the average vegetation cover was decreased in the month of November and healthy vegetation was found more in month of October as compared to December and January. This shows that NDVI and SAVI indices for Sentinel-2 images can be used for crop health monitoring.

scholarly journals Crop Type Classification Using Vegetation Indices of RapidEye Imagery

2014 ◽  
Vol XL-7 ◽  
pp. 195-198 ◽  
Author(s):  
M. Ustuner ◽  
F. B. Sanli ◽  
S. Abdikan ◽  
M. T. Esetlili ◽  
Y. Kurucu
Keyword(s):  
Remote Sensing ◽  
Classification Accuracy ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Support Vector ◽  
Red Edge ◽  
Crop Type ◽  
Type Classification

Cutting-edge remote sensing technology has a significant role for managing the natural resources as well as the any other applications about the earth observation. Crop monitoring is the one of these applications since remote sensing provides us accurate, up-to-date and cost-effective information about the crop types at the different temporal and spatial resolution. In this study, the potential use of three different vegetation indices of RapidEye imagery on crop type classification as well as the effect of each indices on classification accuracy were investigated. The Normalized Difference Vegetation Index (NDVI), the Green Normalized Difference Vegetation Index (GNDVI), and the Normalized Difference Red Edge Index (NDRE) are the three vegetation indices used in this study since all of these incorporated the near-infrared (NIR) band. RapidEye imagery is highly demanded and preferred for agricultural and forestry applications since it has red-edge and NIR bands. The study area is located in Aegean region of Turkey. Radial Basis Function (RBF) kernel was used here for the Support Vector Machines (SVMs) classification. Original bands of RapidEye imagery were excluded and classification was performed with only three vegetation indices. The contribution of each indices on image classification accuracy was also tested with single band classification. Highest classification accuracy of 87, 46 % was obtained using three vegetation indices. This obtained classification accuracy is higher than the classification accuracy of any dual-combination of these vegetation indices. Results demonstrate that NDRE has the highest contribution on classification accuracy compared to the other vegetation indices and the RapidEye imagery can get satisfactory results of classification accuracy without original bands.

scholarly journals Crop Ground Cover Fraction and Canopy Chlorophyll Content Mapping using RapidEye imagery

2015 ◽  
Vol XL-7/W3 ◽  
pp. 149-155 ◽  
Author(s):  
E. Zillmann ◽  
M Schönert ◽  
H. Lilienthal ◽  
B. Siegmann ◽  
T Jarmer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Content ◽  
Vegetation Indices ◽  
Ground Cover ◽  
Absolute Error ◽  
Crop Productivity ◽  
Productivity Analysis ◽  
Remotely Sensed Data ◽  
Empirical Relationships ◽  
Average Accuracy

Remote sensing is a suitable tool for estimating the spatial variability of crop canopy characteristics, such as canopy chlorophyll content (CCC) and green ground cover (GGC%), which are often used for crop productivity analysis and site-specific crop management. Empirical relationships exist between different vegetation indices (VI) and CCC and GGC% that allow spatial estimation of canopy characteristics from remote sensing imagery. However, the use of VIs is not suitable for an operational production of CCC and GGC% maps due to the limited transferability of derived empirical relationships to other regions. Thus, the operational value of crop status maps derived from remotely sensed data would be much higher if there was no need for reparametrization of the approach for different situations. <br><br> This paper reports on the suitability of high-resolution RapidEye data for estimating crop development status of winter wheat over the growing season, and demonstrates two different approaches for mapping CCC and GGC%, which do not rely on empirical relationships. The final CCC map represents relative differences in CCC, which can be quickly calibrated to field specific conditions using SPAD chlorophyll meter readings at a few points. The prediction model is capable of predicting SPAD readings with an average accuracy of 77%. The GGC% map provides absolute values at any point in the field. A high R² value of 80% was obtained for the relationship between estimated and observed GGC%. The mean absolute error for each of the two acquisition dates was 5.3% and 8.7%, respectively.

scholarly journals 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)

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.   

scholarly journals Remote Sensing Assessment of the Expansion of Ips typographus Attacks in the Chuprene Reserve, Western Balkan Range

Forests ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 39
Author(s):  
Georgi Georgiev ◽  
Margarita Georgieva ◽  
Stelian Dimitrov ◽  
Martin Iliev ◽  
Vladislav Trenkin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Norway Spruce ◽  
Bark Beetle ◽  
Satellite Images ◽  
Vegetation Index ◽  
Near Infrared ◽  
Ips Typographus ◽  
Infrared Range ◽  
Electromagnetic Spectrum ◽  
Spruce Stands

The Chuprene Reserve was created in 1973 to preserve the natural coniferous forests in the Western Balkan Range in Bulgaria. The first infestations by European spruce bark beetle (Ips typographus) were registered in Norway spruce (Picea abies) stands in the mid-1980s. The aim of this study is to assess the damages caused by I. typographus in the Chuprene Reserve using remote sensing techniques – unmanned aerial vehicle (UAV) images, airborne images, and satellite images of European Space Imaging (EUSI), combined with terrestrial verification. High-resolution images in four bands of the electromagnetic spectrum and in a standard RGB channel were taken in 2017 via a multispectral camera ‘Parrot Sequoia’, integrated with a specialized professional UAV system eBee ‘Flying Wing’. The health status of Norway spruce stands in the reserve was assessed with the normalized difference vegetation index, based on the digital mixing of imagery captured in the red and near infrared range. The dynamic of bark beetle attacks was studied in GIS on the basis of maps generated from photographic surveys, airborne images taken in 2011 and 2015, and satellite images from 2020. In the UAV-captured area (314.0 ha), the size of Norway spruce stands attacked by I. typographus increased from 7.6 ha (2.4%) in 2011 to 44.9 ha (14.3%) in 2020. The satellite images showed that on the entire territory of the Chuprene Reserve (1451.9 ha), I. typographus killed spruce trees on 137.4 ha, which is 9.6% of the total area.

scholarly journals Snow dunes and glazed surfaces in Antarctica: new field and remote-sensing data

2002 ◽  
Vol 34 ◽  
pp. 81-88 ◽  
Author(s):  
Massimo Frezzotti ◽  
Stefano Gandolfi ◽  
Floriana La Marca ◽  
Stefano Urbini
Keyword(s):  
Remote Sensing ◽  
Near Infrared ◽  
Accumulation Rate ◽  
Spectral Response ◽  
Remote Sensing Data ◽  
Snow Accumulation ◽  
Remotely Sensed Data ◽  
Surface Mass ◽  
Glazed Surface ◽  
Transverse Dunes

AbstractAs part of the International Trans-Antarctic Scientific Expedition project, the Italian Antarctic Programme undertook two traverses from the Terra Nova station to Talos Dome and to Dome C. Along the traverses, the party carried out several tasks (drilling, glaciological and geophysical exploration). The difference in spectral response between glazed surfaces and snow makes it simple to identify these areas on visible/near-infrared satellite images. Integration of field observation and remotely sensed data allows the description of different mega-morphologic features: wide glazed surfaces, sastrugi glazed surface fields, transverse dunes and megadunes. Topography global positioning system, ground penetrating radar and detailed snow-surface surveys have been carried out, providing new information about the formation and evolution of mega-morphologic features. The extensive presence, (up to 30%) of glazed surface caused by a long hiatus in accumulation, with an accumulation rate of nil or slightly negative, has a significant impact on the surface mass balance of a wide area of the interior part of East Antarctica. The aeolian processes creating these features have important implications for the selection of optimum sites for ice coring, because orographic variations of even a few metres per kilometre have a significant impact on the snow-accumulation process. Remote-sensing surveys of aeolian macro-morphology provide a proven, high-quality method for detailed mapping of the interior of the ice sheet’s prevalent wind direction and could provide a relative indication of wind intensity.

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

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

&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;&lt;p&gt;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.&lt;/p&gt;

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

Weed Science ◽  
2006 ◽  
Vol 54 (02) ◽  
pp. 346-353 ◽  
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.

Assessment of the State of the Territories of Closed Waste Disposal Facilities by Vegetation Indices

2021 ◽  
Vol 25 (9) ◽  
pp. 30-37
Author(s):  
N.N. Sliusar ◽  
A.P. Belousova ◽  
G.M. Batrakova ◽  
R.D. Garifzyanov ◽  
M. Huber-Humer ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Waste Disposal ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Google Earth ◽  
The State ◽  
Landsat 8 ◽  
The Earth

The possibilities of using remote sensing of the Earth data to assess the formation of phytocenoses at reclaimed dumps and landfills are presented. The objects of study are landfills and dumps in the Perm Territory, which differed from each other in the types and timing of reclamation work. The state of the vegetation cover on the reclaimed and self-overgrowing objects was compared with the reference plots with naturally formed herbage of zonal meadow vegetation. The process of reclamation of the territory of closed landfills was assessed by the presence and homogeneity of the vegetation layer and by the values of the vegetation index NDVI. To identify the dynamics of changes in the vegetation cover, we used multi-temporal satellite images from the open resources of Google Earth and images in the visible and infrared ranges of the Landsat-5/TM and Landsat-8/OLI satellites. It is shown that the data of remote sensing of the Earth, in particular the analysis of vegetation indices, can be used to assess the dynamics of overgrowing of territories of reclaimed waste disposal facilities, as well as an additional and cost-effective method for monitoring the restoration of previously disturbed territories.

scholarly journals Improving Field-Scale Wheat LAI Retrieval Based on UAV Remote-Sensing Observations and Optimized VI-LUTs

2019 ◽  
Vol 11 (20) ◽  
pp. 2456 ◽  
Author(s):  
Wanxue Zhu ◽  
Zhigang Sun ◽  
Yaohuan Huang ◽  
Jianbin Lai ◽  
Jing Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Management Practices ◽  
Near Infrared ◽  
Lookup Table ◽  
Field Scale ◽  
Area Index ◽  
Biophysical Parameter ◽  
Retrieval Accuracy

Leaf area index (LAI) is a key biophysical parameter for monitoring crop growth status, predicting crop yield, and quantifying crop variability in agronomic applications. Mapping the LAI at the field scale using multispectral cameras onboard unmanned aerial vehicles (UAVs) is a promising precision-agriculture application with specific requirements: The LAI retrieval method should be (1) robust so that crop LAI can be estimated with similar accuracy and (2) easy to use so that it can be applied to the adjustment of field management practices. In this study, three UAV remote-sensing missions (UAVs with Micasense RedEdge-M and Cubert S185 cameras) were carried out over six experimental plots from 2018 to 2019 to investigate the performance of reflectance-based lookup tables (LUTs) and vegetation index (VI)-based LUTs generated from the PROSAIL model for wheat LAI retrieval. The effects of the central wavelengths and bandwidths for the VI calculations on the LAI retrieval were further examined. We found that the VI-LUT strategy was more robust and accurate than the reflectance-LUT strategy. The differences in the LAI retrieval accuracy among the four VI-LUTs were small, although the improved modified chlorophyll absorption ratio index-lookup table (MCARI2-LUT) and normalized difference vegetation index-lookup table (NDVI-LUT) performed slightly better. We also found that both of the central wavelengths and bandwidths of the VIs had effects on the LAI retrieval. The VI-LUTs with optimized central wavelengths (red = 612 nm, near-infrared (NIR) = 756 nm) and narrow bandwidths (~4 nm) improved the wheat LAI retrieval accuracy (R2 ≥ 0.75). The results of this study provide an alternative method for retrieving crop LAI, which is robust and easy use for precision-agriculture applications and may be helpful for designing UAV multispectral cameras for agricultural monitoring.

Sign in / Sign up

Export Citation Format

Share Document