OPTIMIZATION OF THE SPECTRAL VEGETATION INDEX NDVIcp (original) (raw)
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Optimization of soil-adjusted vegetation indices
Remote Sensing of Environment, 1996
, which are less sensitive to these external influences. These indices are theoretically more reliable than ND VI, although they are not yet widely used with satellite data. This article focuses on testing and comparing the sensitivity of NDVI, SAVI, TSAVI, MSA VI and GEMI to soil background effects. Indices are simulated with the SAIL model for a large range of soil reflectances, including sand, clay, and dark peat, with additional variations induced by moisture and roughness. The general formulation of the SA VI family of indices with the forvn VI = (NIR -R) / (NIR + R + X) is also reexamined. The value of the parameter X is critical in the minimization of soil effects. A value of X = 0.16 is found as the optimized value. Index performances are compared by means of an analysis of variance.
A Modified Soil Adjusted Vegetation Index
There is currently a great deal of interest in the quantitative characterization of temporal and spatial vegetation patterns with remotely sensed data for the study of earth system science and global change. Spectral models and indices are being developed to improve vegetation sensitivity by accounting for atmosphere and soil effects. The
Biophysics, 2019
The results of satellite monitoring of vegetation on unused agricultural lands during the growing season of 2018 are presented. Sod fields of different ages (2, 7, and 20 years) and bare fallows on the land used by the Krasnoyarsk Research Institute of Agriculture were the objects of the study. Satellite data with high spatial resolution (Sentinel-2 Earth remote sensing satellites) at the pre-processing Level-1C (https://earthexplorer.usgs.gov/) were used for the interpretation of sod field and fallow images. These data were used to calculate the Normalized Difference Vegetation Index (NDVI) and the Normalized Difference Soil Index (NDSI). Algorithms and software for the processing of Sentinel-2 satellite data were developed. The possibility of using NDVI dynamics for assessment and monitoring of the condition of sod fields and bare fallows has been demonstrated. The applicability of the NDSI soil index for assessment of the status of arable land has been demonstrated.
DISEÑO DE UN ÍNDICE ESPECTRAL DE LA VEGETACIÓN: NDVIcp DESIGN OF A VEGETATION SPECTRAL INDEX: NDVIcp
2007
There are many vegetation indices (VI) based on relationships of the spectral space of the red and near infrared. In this study, the structure of the most widely used VI is examined, using a formulation to characterize curves of equal leaf area index. In order to solve the inconsistencies found in the VI, a new one (NDVIcp) is proposed, based on the correct structure of the problem, under empirical considerations. The NDVIcp is validated using data from field experiments with maize (Zea mays L.) and cotton (Gossipyum spp.).
Development of Vegetation Indices for Hyperspectral Remote Sensed data
IJCRT, 2022
Monitoring the quantity and quality of urban vegetation accurately aids regional greening efforts and enhances knowledge of vegetation's environmental impact. Building shadows and synthetic materials, on the other hand, can severely obscure vegetation estimations. Furthermore, vegetation indices (VIs) quickly saturate in high biomass settings, making vegetation quality assessments more challenging. Plant Indices (VIs) are the most effective and simple ways for computing both the qualitative and quantitative assessments of aspects like vegetation cover, vigor, and boom dynamics, among other things, derived from remote sensing-based canopies. The indices are being used enormously inside RS for a variety of objectives, including the usage of exceptional airborne and satellite television for computer systems, as well as the use of Unmanned Aerial Vehicles (UAVs). For now, there is no unifying mathematical equation that defines all the VIs due to complexity of many mild spectra combinations, equipment, platforms, and resolutions that are being used. As a result, customized algorithms based on unique mathematical expressions that are combined see mild radiation from vegetation, normally inexperienced spectra region, and nonvisible spectra to achieve proxy quantifications of the vegetation surface have been developed and tested for a variety of applications. Optimization VIs are typically adjusted to specific software requirements in real-world applications, and they are frequently utilized in tandem with excellent validation equipment and methods on the ground. The current study discusses spectral features in plants and describes the development of VIs, as well as the advantages and risks of developing unique indices. In agricultural improvement analytics, vegetation indices are a critical metric. Information precision and miles-away management are two primary motivators for employing vegetation indices in remote sensing, which are just two of the technology's many advantages.
Development and optimization of the ratio vegetation index on the visible and infrared spectrum
International Journal of Physical Sciences and Engineering (IJPSE), 2018
This study aims to find a suitable vegetation index model to analyze the distribution of clove vegetation in Buleleng regency, Bali. Vegetation index model Ratio Vegetation Index (RVI) extracted from Landsat 8 was developed in the visible spectrum ( = 0.450-0.680 μm) and infrared ( = 0.845-2.300 μm). Development methods are carried out on the basis of the spectral reflectance response characteristics of the dominant electromagnetic waves from the visible and infrared spectra of vegetation. Created a multiple regression relationship results from scattergram that links RVI vegetation index with band 3 = B3, band 5 = B5, band 6 = B6, and band 7 = B7. Optimization strategy is carried out by dividing the development of RVI with a variable number factor. There are 4 forms of RVI vegetation index models from the development and optimization of the visible and infrared spectra. Of the 4 new vegetation index forms, which provide optimal results and close to extensive data from the Forestry and Plantation Service, Buleleng regency, Bali is RVInew4 = 0.0022 + 0.00142 * B3 + 0.00028 * B5 + 0.00054 * B6 + 0.00096 * B7. The area produced by this vegetation index model in analyzing the distribution of clove vegetation is 7667.82 ha. This area is 99.40% of the average data area of the Forestry and Plantation Service, Buleleng regency, Bali in 2014, which is 7622.32 ha. The dominant distribution of clove vegetation is in the rare category with an area of 7441.74 ha.
On the terminology of the spectral vegetation index (NIR − SWIR)/(NIR + SWIR)
International Journal of Remote Sensing, 2011
The spectral vegetation index (ρ NIR − ρ SWIR )/(ρ NIR + ρ SWIR ), where ρ NIR and ρ SWIR are the near-infrared (NIR) and shortwave-infrared (SWIR) reflectances, respectively, has been widely used to indicate vegetation moisture condition. This index has multiple names in the literature, including infrared index (II), normalized difference infrared index (NDII), normalized difference water index (NDWI), normalized difference moisture index (NDMI), land surface water index (LSWI) and normalized burn ratio (NBR). After reviewing each term's definition, associated sensors and channel specifications, we found that the index consists of three variants, differing only in the SWIR region (1.2-1.3, 1.55-1.75 or 2.05-2.45 µm). Thus, three terms are sufficient to represent these three SWIR variants; other names are redundant and therefore unnecessary. Considering the spectral representativeness, the term's popularity and the 'rule of priority' in scientific nomenclature, NDWI, NDII and NBR, each corresponding to the three SWIR regions, are more preferable terms.
A soil-adjusted vegetation index (SAVI)
Remote Sensing of Environment, 1988
A transformation technique is presented to minimize soil brightness influences from spectral vegetation indices involving red and near-infrared (NIR) wavelengths. Graphically, the transformation involves a shifting of the origin of reflectance spectra plotted in NIR-red wavelength space to account for first-order soil-vegetation interactions and differential red and NIR flux extinction through vegetated canopies. For cotton (Gossypium hirsutum L. var DPI-70) and range grass (Eragrosticslehmanniana Nees) canopies, underlain with different soil backgrounds, the transformation nearly eliminated soil-induced variations in vegetation indices. A physical basis for the soil-adjusted vegetation index (SAVI) is subsequently presented. The SAVI was found to be an important step toward the establishment of simple °lobal” that can describe dynamic soil-vegetation systems from remotely sensed data.
Modified Vegetation Detection Index Using Different-Spectral Signature
Iraqi journal of science, 2021
The Normalization Difference Vegetation Index (NDVI), for many years, was widely used in remote sensing for the detection of vegetation land cover. This index uses red channel radiances (i.e., 0.66 μm reflectance) and near-IR channel (i.e., 0.86 μm reflectance). In the heavy chlorophyll absorption area, the red channel is located, while in the high reflectance plateau of vegetation canopies, the Near-IR channel is situated. Senses of channels (Red & Near-IR) read variance depths over vegetation canopies. In the present study, a further index for vegetation identification is proposed. The normalized difference vegetation shortwave index (NDVSI) is defined as the difference between the cubic bands of Near-IR and Shortwave infrared radiation (SWIR) divided by their sums. The radiances or reflectances are included in this index from the Near-IR channel and WSIR2 channel (2.1 μm). The NDVSI is less sensitivite to atmospheric effects as compared to NDVI. By comparing the one NDVSI index with the two indexes (NDVI, SAVI) of vegetation cover, good correlations were found between NDVI and NDVSI (R 2 =0.917) and between SAVI and NDVSI (R 2 =0.809. Accordingly, the proposed index can be taken into consideration as an independent vegetation index