Effect of Mathematical Expression of Vegetation Indices on the Estimation of Phenology Trends from Satellite Data

2019 ◽  
Vol 29 (5) ◽  
pp. 756-767
Author(s):  
Lu Zuo ◽  
Ronggao Liu ◽  
Yang Liu ◽  
Rong Shang
Keyword(s):  
Satellite Data ◽  
Vegetation Indices ◽  
Mathematical Expression

The use of derivative vegetation indices for the estimation of chlorophyll content in vegetation on the basis of satellite data

2011 ◽  
Vol 17 (3) ◽  
pp. 54-59 ◽  
Author(s):  
S.M. Kochubei ◽  
◽  
T.A. Kazantsev ◽  
Keyword(s):  
Chlorophyll Content ◽  
Satellite Data ◽  
Vegetation Indices

Worldview-2 and Landsat 8 Satellite Data for Seaweed Mapping along Karachi Coast

2016 ◽  
Vol 5 (2) ◽  
Author(s):  
Muhammad Danish Siddiqui ◽  
Arjumand Z Zaidi
Keyword(s):  
Satellite Data ◽  
Spatial Scales ◽  
Economic Value ◽  
Vegetation Indices ◽  
Cost Effective ◽  
Geographical Information ◽  
Landsat 8 ◽  
Coverage Area ◽  
Breeding Sites ◽  
Marine Plant

<span>Seaweed is a marine plant or algae which has economic value in many parts of the world. The purpose of <span>this study is to evaluate different satellite sensors such as high-resolution WorldView-2 (WV2) satellite <span>data and Landsat 8 30-meter resolution satellite data for mapping seaweed resources along the coastal<br /><span>waters of Karachi. The continuous monitoring and mapping of this precious marine plant and their <span>breeding sites may not be very efficient and cost effective using traditional survey techniques. Remote <span>Sensing (RS) and Geographical Information System (GIS) can provide economical and more efficient <span>solutions for mapping and monitoring coastal resources quantitatively as well as qualitatively at both <span>temporal and spatial scales. Normalized Difference Vegetation Indices (NDVI) along with the image <span>enhancement techniques were used to delineate seaweed patches in the study area. The coverage area of <span>seaweed estimated with WV-2 and Landsat 8 are presented as GIS maps. A more precise area estimation <span>wasachieved with WV-2 data that shows 15.5Ha (0.155 Km<span>2<span>)of seaweed cover along Karachi coast that is <span>more representative of the field observed data. A much larger area wasestimated with Landsat 8 image <span>(71.28Ha or 0.7128 Km<span>2<span>) that was mainly due to the mixing of seaweed pixels with water pixels. The <span>WV-2 data, due to its better spatial resolution than Landsat 8, have proven to be more useful than Landsat<br /><span>8 in mapping seaweed patches</span></span></span></span></span></span></span></span></span></span></span></span></span></span><br /><br class="Apple-interchange-newline" /></span></span></span></span></span>

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

scholarly journals A Webgis Framework for Disseminating Processed Remotely Sensed on Land Cover Transformations

2016 ◽  
Vol 100 (1) ◽  
pp. 27-38 ◽  
Author(s):  
Grazia Caradonna ◽  
Antonio Novelli ◽  
Eufemia Tarantino ◽  
Raffaela Cefalo ◽  
Umberto Fratino
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Vegetation Indices ◽  
Remote Sensing Data ◽  
Geospatial Data ◽  
Usage Patterns ◽  
Mediterranean Regions ◽  
Multi Temporal ◽  
Using Data ◽  
Set Up

Abstract Mediterranean regions have experienced significant soil degradation over the past decades. In this context, careful land observation using satellite data is crucial for understanding the long-term usage patterns of natural resources and facilitating their sustainable management to monitor and evaluate the potential degradation. Given the environmental and political interest on this problem, there is urgent need for a centralized repository and mechanism to share geospatial data, information and maps of land change. Geospatial data collecting is one of the most important task for many users because there are significant barriers in accessing and using data. This limit could be overcome by implementing a WebGIS through a combination of existing free and open source software for geographic information systems (FOSS4G). In this paper we preliminary discuss methods for collecting raster data in a geodatabase by processing open multi-temporal and multi-scale satellite data aimed at retrieving indicators for land degradation phenomenon (i.e. land cover/land use analysis, vegetation indices, trend analysis, etc.). Then we describe a methodology for designing a WebGIS framework in order to disseminate information through maps for territory monitoring. Basic WebGIS functions were extended with the help of POSTGIS database and OpenLayers libraries. Geoserver was customized to set up and enhance the website functions developing various advanced queries using PostgreSQL and innovative tools to carry out efficiently multi-layer overlay analysis. The end-product is a simple system that provides the opportunity not only to consult interactively but also download processed remote sensing data.

scholarly journals PRECISION VITICULTURE FROM MULTITEMPORAL, MULTISPECTRAL VERY HIGH RESOLUTION SATELLITE DATA

2016 ◽  
Vol XLI-B8 ◽  
pp. 919-925
Author(s):  
Z. Kandylakis ◽  
K. Karantzalos
Keyword(s):  
High Resolution ◽  
Satellite Data ◽  
Precision Agriculture ◽  
Vegetation Indices ◽  
Quality Parameters ◽  
Total Phenolic ◽  
Canopy Reflectance ◽  
Northern Greece ◽  
In Situ Data ◽  
Very High

In order to exploit efficiently very high resolution satellite multispectral data for precision agriculture applications, validated methodologies should be established which link the observed reflectance spectra with certain crop/plant/fruit biophysical and biochemical quality parameters. To this end, based on concurrent satellite and field campaigns during the veraison period, satellite and in-situ data were collected, along with several grape samples, at specific locations during the harvesting period. These data were collected for a period of three years in two viticultural areas in Northern Greece. After the required data pre-processing, canopy reflectance observations, through the combination of several vegetation indices were correlated with the quantitative results from the grape/must analysis of grape sampling. Results appear quite promising, indicating that certain key quality parameters (like brix levels, total phenolic content, brix to total acidity, anthocyanin levels) which describe the oenological potential, phenolic composition and chromatic characteristics can be efficiently estimated from the satellite data.

scholarly journals Damage assessment of chilli thrips using high resolution multispectral satellite data

2021 ◽  
Vol 21 (4) ◽  
pp. 480-487
Author(s):  
Mathyam Prabhakar ◽  
Merugu Thirupathi, ◽  
G. Srasvan Kumar ◽  
U. Sai Sravan ◽  
M. Kalpana ◽  
...  
Keyword(s):  
Satellite Imagery ◽  
Satellite Data ◽  
Land Surface ◽  
Vegetation Indices ◽  
Superior Performance ◽  
Water Index ◽  
Sensing Technology ◽  
Significant Difference ◽  
Normalized Difference Water Index ◽  
Sentinel 2A

Remote sensing technology offers an effective, rapid and reliable tool for assessing pest severity in vegetation. Ground based hyperspectral radiometry studies revealed significant difference in the reflectance spectra between healthy and thrip damaged vegetation. Space borne multispectral reflectance from Sentinel 2A satellite data of chilli thrip infested canopy has significant differences in red region (Band 4 – 664.6 nm), NIR region (Bands 5, 6, 7, 8 & 8A having central wavelengths at 704.1, 740.5, 782.8 & 832.8 nm, respectively) and SWIR region (Bands 11 & 12 having central wavelengths at 1613.7 and 2202.4 nm). In this study, an attempt was made to discriminate healthy and pest affected chilli crop in the multispectral satellite imagery using several multispectral vegetation indices. Of these, land surface water index, LSWI (p=0.018) and normalized difference water index, NDWI (p=0.001) were found significant. These indices were used to classify chilli fields in the satellite imagery into severe, moderate and healthy classes. Superior performance of LSWI over NDWI with overall accuracy of 93.80 and Kappa Coefficient of 0.89 was observed. Moran's Index was used to study the spatial distribution of chilli thrips and observed strong clustering (I= 0.9073, p=0.0001).

scholarly journals Soil Moisture Retrieval from the Chinese GF-3 Satellite and Optical Data over Agricultural Fields

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2675 ◽  
Author(s):  
Linlin Zhang ◽  
Qingyan Meng ◽  
Shun Yao ◽  
Qiao Wang ◽  
Jiangyuan Zeng ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Satellite Data ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Bare Soil ◽  
Optical Data ◽  
Retrieval Algorithm ◽  
Landsat 8 ◽  
Backscattering Coefficient ◽  
Vegetation Water

Timely and accurate soil moisture information is of great importance in agricultural monitoring. The Gaofen-3 (GF-3) satellite, the first C-band multi-polarization synthetic-aperture radar (SAR) satellite in China, provides valuable data sources for soil moisture monitoring. In this study, a soil moisture retrieval algorithm was developed for the GF-3 satellite based on a backscattering coefficient simulation database. We adopted eight optical vegetation indices to determine the relationships between these indices and vegetation water content (VWC) by combining Landsat-8 data and field measurements. A backscattering coefficient database was built using an advanced integral equation model (AIEM). The effects of vegetation on backscattering coefficients were corrected using the water cloud model (WCM) to obtain the bare soil backscattering coefficient ( σ s o i l ° ). Then, soil moisture retrievals were obtained at HH, VV and HH+VV combination respectively by minimizing the observed bare soil backscattering coefficient ( σ s o i l ° ) and the AIEM-simulated backscattering coefficient ( σ soil-simu ° ). Finally, the proposed algorithm was validated in agriculture region of wheat and corn in China using ground soil moisture measurements. The results showed that the normalized difference infrared index (NDII) had the best fit with measured VWC values (R = 0.885) among the eight vegetation water indices; thus, it was adopted to correct the effects of vegetation. The proposed algorithm using GF-3 satellite data performed well in soil moisture retrieval, and the scheme combining HH and VV polarization exhibited the highest accuracy, with a root mean square error (RMSE) of 0.044 m3m−3, followed by HH polarization (RMSE = 0.049 m3m−3) and VV polarization (RMSE = 0.053 m3m−3). Therefore, the proposed algorithm has good potential to operationally estimate soil moisture from the new GF-3 satellite data.

scholarly journals Determining the Vegetation Indices (NDVI) from Landsat 8 Satellite Data.

2016 ◽  
Vol 4 (8) ◽  
pp. 1459-1463 ◽  
Author(s):  
MushtaqAhmad Ganie ◽  
◽  
Dr.Asima Nusrath ◽  
Keyword(s):  
Satellite Data ◽  
Vegetation Indices ◽  
Landsat 8

scholarly journals Mapping and Estimation of Above-ground Grass Biomass using Sentinel 2A Satellite Data

2021 ◽  
Vol 8 (3) ◽  
pp. 9-15
Author(s):  
Isa Muhammad Zumo ◽  
Mazlan Hashim ◽  
Noor Dyana Hassan
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Index Number ◽  
Estimation Model ◽  
Livestock Farming ◽  
Grass Biomass ◽  
Allometric Analysis ◽  
Sentinel 2A ◽  
Grazing Lands

Above-Ground Grass Biomass (AGGB) mapping and estimation is one of the important parameters for environmental ecosystem and grazing-lands management, particularly for livestock farming. However, previous models for estimation of AGGB with satellite imagery has some difficulty in choosing a particular satellite and vegetation index that can build a good estimation model at a higher accuracy. This study explores the potentiality of Sentinel 2A data to derive a satellite-based model for AGGB mapping and estimation. The study area was Skudai, Johor in Malaysia Peninsular. Grass parameters of forty grass sample units were measured in the field and their corresponding AGGB was later measured in the laboratory. The samples were used for modelling and assessment. Four indices were tested for their fitness in modelling AGGB from the satellite data. The result from the grass allometric analysis indicates that grass height and volume demonstrate good relationship with the measured AGGB (R² = 0.852 and 0.837 respectively). Vegetation Index Number (VIN) has the best fit for modeling AGGB (R2 = 0.840) compared to other vegetation indices. The derived satellite AGGB estimate was validated with the assessment field and allometry derived AGGB at RMSE = 15.89g and 44.45g, respectively. This study demonstrate that VIN derived from Sentinel 2A MSI satellite data can be used to model AGGB estimation at a good accuracy. Therefore, it will contribute to providing reliable information on AGGB of grazing lands for sustainable livestock farming.

scholarly journals Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jinru Xue ◽  
Baofeng Su
Keyword(s):  
Remote Sensing ◽  
Spectral Characteristics ◽  
Vegetation Indices ◽  
Growth Dynamics ◽  
Mathematical Expression ◽  
Advantages And Disadvantages ◽  
Light Spectra ◽  
Mathematical Expressions ◽  
Real World Applications ◽  
Application Requirements

Vegetation Indices (VIs) obtained from remote sensing based canopies are quite simple and effective algorithms for quantitative and qualitative evaluations of vegetation cover, vigor, and growth dynamics, among other applications. These indices have been widely implemented within RS applications using different airborne and satellite platforms with recent advances using Unmanned Aerial Vehicles (UAV). Up to date, there is no unified mathematical expression that defines all VIs due to the complexity of different light spectra combinations, instrumentation, platforms, and resolutions used. Therefore, customized algorithms have been developed and tested against a variety of applications according to specific mathematical expressions that combine visible light radiation, mainly green spectra region, from vegetation, and nonvisible spectra to obtain proxy quantifications of the vegetation surface. In the real-world applications, optimization VIs are usually tailored to the specific application requirements coupled with appropriate validation tools and methodologies in the ground. The present study introduces the spectral characteristics of vegetation and summarizes the development of VIs and the advantages and disadvantages from different indices developed. This paper reviews more than 100 VIs, discussing their specific applicability and representativeness according to the vegetation of interest, environment, and implementation precision. Predictably, research, and development of VIs, which are based on hyperspectral and UAV platforms, would have a wide applicability in different areas.

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