scholarly journals CHANGE DETECTION IN BRAZILIAN SAVANNAS USING SEMIVARIOGRAMS DERIVED FROM NDVI IMAGES

2015 ◽  
Vol 39 (2) ◽  
pp. 103-109 ◽  
Author(s):  
Fausto Weimar Acerbi Júnior ◽  
Eduarda Martiniano de Oliveira Silveira ◽  
José Márcio de Mello ◽  
Carlos Rogério de Mello ◽  
José Roberto Soares Scolforo
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Photosynthetic Activity ◽  
Ground Cover ◽  
Clear Trend ◽  
Area Index ◽  
Exploratory Data ◽  
Vegetation Parameters ◽  
Analysis Models ◽  
Best Fit

The Normalized Difference Vegetation Index (NDVI) is often used to extract information from vegetated areas since it is directly related to vegetation parameters such as percent of ground cover, photosynthetic activity of the plant and leaf area index. The aim of this paper was to analyze the potencial of semivariograms generated from NDVI values to detect changes in vegetated areas, analyzing their behavior (shape) and derived metrics (range, sill and nugget). Semivariograms were generated from NDVI values derived from Landsat TM images of May 2010, June 2010 and July 2011. The study area is located in the northern state of Minas Gerais, Brazil, and is covered by Brazilian savannas vegetation, totalizing 1,596 ha. Semivariograms were generated after the exploratory data analysis. Models were fitted, validated and their metrics analyzed. The results showed a very clear trend where the shape of semivariograms, sill and range were different when deforestation occurred and were similar when the area had not been changed. The model that generated best fit was the Gaussian, however, the three models tested showed behavior that makes it possible to detect changes in vegetation. It suggests that further researches should explore the degree to which the semivariogram can be used to quantify this spatial variability as well as to analyze the influence of sazonality for changing detection in vegetated areas.

scholarly journals Detecting Forest Degradation in the Three-North Forest Shelterbelt in China from Multi-Scale Satellite Images

2021 ◽  
Vol 13 (6) ◽  
pp. 1131
Author(s):  
Tao Yu ◽  
Pengju Liu ◽  
Qiang Zhang ◽  
Yi Ren ◽  
Jingning Yao
Keyword(s):  
Remote Sensing ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Production ◽  
Remote Sensing Data ◽  
Forest Degradation ◽  
Observation Data ◽  
Area Index ◽  
Sensing Data ◽  
Multi Scale

Detecting forest degradation from satellite observation data is of great significance in revealing the process of decreasing forest quality and giving a better understanding of regional or global carbon emissions and their feedbacks with climate changes. In this paper, a quick and applicable approach was developed for monitoring forest degradation in the Three-North Forest Shelterbelt in China from multi-scale remote sensing data. Firstly, Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Fraction of Photosynthetically Active Radiation (FPAR) and Net Primary Production (NPP) from remote sensing data were selected as the indicators to describe forest degradation. Then multi-scale forest degradation maps were obtained by adopting a new classification method using time series MODerate Resolution Imaging Spectroradiometer (MODIS) and Landsat Enhanced Thematic Mapper Plus (ETM+) images, and were validated with ground survey data. At last, the criteria and indicators for monitoring forest degradation from remote sensing data were discussed, and the uncertainly of the method was analyzed. Results of this paper indicated that multi-scale remote sensing data have great potential in detecting regional forest degradation.

scholarly journals An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

2021 ◽  
Vol 13 (4) ◽  
pp. 719
Author(s):  
Xiuxia Li ◽  
Shunlin Liang ◽  
Huaan Jin
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Continuous Data ◽  
Landsat Data ◽  
Area Index ◽  
Free Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Resolution Data

Leaf area index (LAI) and normalized difference vegetation index (NDVI) are key parameters for various applications. However, due to sensor tradeoff and cloud contaminations, these data are often temporally intermittent and spatially discontinuous. To address the discontinuities, this study proposed a method based on spectral matching of 30 m discontinuous values from Landsat data and 500 m temporally continuous values from Moderate-resolution Imaging Spectroradiometer (MODIS) data. Experiments have proven that the proposed method can effectively yield spatiotemporally continuous vegetation products at 30 m spatial resolution. The results for three different study areas with NDVI and LAI showed that the method performs well in restoring the time series, fills in the missing data, and reasonably predicts the images. Remarkably, the proposed method could address the issue when no cloud-free data pairs are available close to the prediction date, because of the temporal information “borrowed” from coarser resolution data. Hence, the proposed method can make better use of partially obscured images. The reconstructed spatiotemporally continuous data have great potential for monitoring vegetation, agriculture, and environmental dynamics.

Beyond Cloud: A Fused Optic and SAR Based Solution to Monitor Crop Health

2021 ◽  
Author(s):  
Brianna Pagán ◽  
Adekunle Ajayi ◽  
Mamadou Krouma ◽  
Jyotsna Budideti ◽  
Omar Tafsi
Keyword(s):  
Machine Learning ◽  
Satellite Imagery ◽  
Cloud Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Learning Approaches ◽  
Area Index ◽  
Spatial And Temporal Scales ◽  
Field Level ◽  
Crop Health

<p>The value of satellite imagery to monitor crop health in near-real time continues to exponentially grow as more missions are launched making data available at higher spatial and temporal scales. Yet cloud cover remains an issue for utilizing vegetation indexes (VIs) solely based on optic imagery, especially in certain regions and climates. Previous research has proven the ability to reconstruct VIs like the Normalized Difference Vegetation Index (NDVI) and Leaf Area Index (LAI) by leveraging synthetic aperture radar (SAR) datasets, which are not inhibited by cloud cover. Publicly available data from SAR missions like Sentinel-1 at relatively decent spatial resolutions present the opportunity for more affordable options for agriculture users to integrate satellite imagery in their day to day operations. Previous research has successfully reconstructed optic VIs (i.e. from Sentinel-2) with SAR data (i.e. from Sentinel-1) leveraging various machine learning approaches for a limited number of crop types. However, these efforts normally train on individual pixels rather than leveraging information at a field level. </p><p>Here we present Beyond Cloud, a product which is the first to leverage computer vision and machine learning approaches in order to provide fused optic and SAR based crop health information. Field level learning is especially well-suited for inherently noisy SAR datasets. Several use cases are presented over agriculture fields located throughout the United Kingdom, France and Belgium, where cloud cover limits optic based solutions to as little as 2-3 images per growing season. Preliminary efforts for additional features to the product including automated crop and soil type detection are also discussed. Beyond Cloud can be accessed via a simple API which makes integration of the results easy for existing dashboards and smart-ag tools. Overall, these efforts promote the accessibility of satellite imagery for real agriculture end users.</p><p> </p>

scholarly journals Estimating the Biomass of Waterhyacinth (Eichhornia crassipes) Using the Normalized Difference Vegetation Index Derived from Simulated Landsat 5 TM

2015 ◽  
Vol 8 (2) ◽  
pp. 203-211 ◽  
Author(s):  
Wilfredo Robles ◽  
John D. Madsen ◽  
Ryan M. Wersal
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Simulated Data ◽  
Water Bodies ◽  
Biomass Estimation ◽  
Aquatic Weed ◽  
Target Area ◽  
Data Set ◽  
Area Index ◽  
Landsat 5 Tm

Waterhyacinth is a free-floating aquatic weed that is considered a nuisance worldwide. Excessive growth of waterhyacinth limits recreational use of water bodies as well as interferes with many ecological processes. Accurate estimates of biomass are useful to assess the effectiveness of control methods to manage this aquatic weed. While large water bodies require significant labor inputs with respect to ground-truth surveys, available technology like remote sensing could be capable of providing temporal and spatial information from a target area at a much reduced cost. Studies were conducted at Lakes Columbus and Aberdeen (Mississippi) during the growing seasons of 2005 and 2006 over established populations of waterhyacinth. The objective was to estimate biomass based on nondestructive methods using the normalized difference vegetation index (NDVI) derived from Landsat 5 TM simulated data. Biomass was collected monthly using a 0.10m2 quadrat at 25 randomly-located locations at each site. Morphometric plant parameters were also collected to enhance the use of NDVI for biomass estimation. Reflectance measurements using a hyperspectral sensor were taken every month at each site during biomass collection. These spectral signatures were then transformed into a Landsat 5 TM simulated data set using MatLab® software. A positive linear relationship (r2 = 0.28) was found between measured biomass of waterhyacinth and NDVI values from the simulated dataset. While this relationship appears weak, the addition of morphological parameters such as leaf area index (LAI) and leaf length enhanced the relationship yielding an r2 = 0.66. Empirically, NDVI saturates at high LAI, which may limit its use to estimate the biomass in very dense vegetation. Further studies using NDVI calculated from narrower spectral bands than those contained in Landsat 5 TM are recommended.

The effect of reed beds distributions on the Ecohydraulic dynamics of wetlands and lowlands: experimental analyses and simulations

2021 ◽  
Author(s):  
Giuseppe Francesco Cesare Lama
Keyword(s):  
Water Quality ◽  
Spatial Distribution ◽  
Riparian Vegetation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Terrestrial Ecosystems ◽  
Water Bodies ◽  
Area Index ◽  
Reed Beds ◽  
Riparian Species

<p>The interplay between riparian vegetation and water flow in vegetated water bodies has a key role in the dynamic evolution of aquatic and terrestrial ecosystems in wetlands and lowlands. The present study analyzes the effects of the spatial distribution of reed (<em>Phragmites australis</em> (Cav.) Trin. ex Steud.) beds, an invasive riparian species extremely widespread in wetland and lowlands worldwide, on the main hydraulic and hydrodynamic properties of an abandoned vegetated reclamation channel located in Northern Tuscany, Italy. A field campaign was carried out to obtain Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI) of reed beds through both ground-based and Unmanned Aerial Vehicle (UAV) methodologies, and to correlate them to the channel’s flow dynamic and water quality main features. Then, Hydrodynamic simulations of the vegetated reclamation channel were performed and validated based on the experimental measurements of the hydraulic and vegetational parameters acquired in the field to build up a robust model to be employed also in future Ecohydraulic researches. The evidences of this study constitute useful insights in the quantitative analysis of the correlation between the spatial distribution of riparian vegetation stands in natural and manmade vegetated water bodies and their hydrodynamic and water quality main features.</p>

scholarly journals Genome-Wide Detection of SNP Markers Associated with Four Physiological Traits in Groundnut (Arachis hypogaea L.) Mini Core Collection

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 192 ◽  
Author(s):  
Abdulwahab S. Shaibu ◽  
Clay Sneller ◽  
Babu N. Motagi ◽  
Jackline Chepkoech ◽  
Mercy Chepngetich ◽  
...  
Keyword(s):  
Core Collection ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Canopy Temperature ◽  
Snp Markers ◽  
Physiological Traits ◽  
Area Index ◽  
Trait Association ◽  
Mini Core Collection ◽  
Genome Wide

In order to integrate genomics in breeding and development of drought-tolerant groundnut genotypes, identification of genomic regions/genetic markers for drought surrogate traits is essential. We used 3249 diversity array technology sequencing (DArTSeq) markers for a genetic analysis of 125 ICRISAT groundnut mini core collection evaluated in 2015 and 2017 for genome-wide marker-trait association for some physiological traits and to determine the magnitude of linkage disequilibrium (LD). Marker-trait association (MTA) analysis, probability values, and percent variation modelled by the markers were calculated using the GAPIT package via the KDCompute interface. The LD analysis showed that about 36% of loci pairs were in significant LD (p < 0.05 and r2 > 0.2) and 3.14% of the pairs were in complete LD. The MTAs studies revealed 20 significant MTAs (p < 0.001) with 11 markers. Four MTAs were identified for leaf area index, 13 for canopy temperature, one for chlorophyll content and two for normalized difference vegetation index. The markers explained 20.8% to 6.6% of the phenotypic variation observed. Most of the MTAs identified on the A subgenome were also identified on the respective homeologous chromosome on the B subgenome. This could be due to a common ancestor of the A and B genome which explains the linkage detected between markers lying on different chromosomes. The markers identified in this study can serve as useful genomic resources to initiate marker-assisted selection and trait introgression of groundnut for drought tolerance after further validation.

scholarly journals Assessment of Maize Growth and Development with High- and Medium-Resolution Remote Sensing Products

Agronomy ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 940
Author(s):  
Rocío Ballesteros ◽  
Miguel A. Moreno ◽  
Fellype Barroso ◽  
Laura González-Gómez ◽  
José F. Ortega
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Landsat Imagery ◽  
Canopy Cover ◽  
Remote Sensing Data ◽  
Multilinear Regression ◽  
Area Index ◽  
Canopy Development

The availability of a great amount of remote sensing data for precision agriculture purposes has set the question of which resolution and indices, derived from satellites or unmanned aerial vehicles (UAVs), offer the most accurate results to characterize vegetation. This study focused on assessing, comparing, and discussing the performances and limitations of satellite and UAV-based imagery in terms of canopy development, i.e., the leaf area index (LAI), and yield, i.e., the dry aboveground biomass (DAGB), for maize. Three commercial maize fields were studied over four seasons to obtain the LAI and DAGB. The normalized difference vegetation index (NDVI) and visible atmospherically resistant index (VARI) from satellite platforms (Landsat 5TM, 7 ETM+, 8OLI, and Sentinel 2A MSI) and the VARI and green canopy cover (GCC) from UAV imagery were compared. The remote sensing predictors in addition to the growing degree days (GDD) were assessed to estimate the LAI and DAGB using multilinear regression models (MRMs). For LAI estimation, better adjustments were obtained when predictors from the UAV platform were considered. The DAGB estimation revealed similar adjustments for both platforms, although the Landsat imagery offered slightly better adjustments. The results obtained in this study demonstrate the advantage of remote sensing platforms as a useful tool to estimate essential agronomic features.

scholarly journals NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 101-107
Author(s):  
N. Sánchez ◽  
J. M. Lopez-Sanchez ◽  
B. Arias-Pérez ◽  
R. Valcarce-Diñeiro ◽  
J. Martínez-Fernández ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Global Navigation Satellite Systems ◽  
Radar Signal ◽  
Landsat 8 ◽  
Area Index ◽  
Satellite Systems ◽  
Crop Parameters ◽  
Global Navigation Satellite

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

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