Vegetation extraction from high-resolution satellite imagery using the Normalized Difference Vegetation Index (NDVI)

2016 ◽  
Author(s):  
Meera R. AlShamsi
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
High Resolution Satellite Imagery

scholarly journals Normalized Difference Vegetative Index-related Assessment for Climate Change Impact on Indigenous Communities from High Resolution IKONOS Satellite Imagery in West Papua

2020 ◽  
Vol 02 (03) ◽  
pp. 1-1
Author(s):  
Chris R. Lavers ◽  
◽  
Travis Mason ◽  
Jonathan Mazower ◽  
Sarah Grig ◽  
...  
Keyword(s):  
Climate Change ◽  
High Resolution ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Indigenous Communities ◽  
Direct Access ◽  
Alpine Glacier ◽  
High Resolution Satellite Imagery ◽  
Observe Climate Change

High-resolution satellite imagery permits acquisition of critical data to observe climate-change and environmental impact on conflict-impacted indigenous communities with co-existing socio-economic factors, often within unstable regimes. Conflict may prevent direct access in remote regions to validate civilian conflict actor evidence. In such cases use of remote sensing tools, techniques, and data are extremely important. Software-based imagery assessment can quantify radiometrically calibrated or Normalized Difference Vegetation Index (NDVI) and provide temporal changes with rapid detection over large search areas. In this work we evaluate recent trends in equatorial alpine glacier ablation to address the probability of indigenous water scarcity, as pure glacial water reserves are depleted near the Grasberg gold and copper mine in the Carstenz region, Western part of Papua Island, North of Oceania.

scholarly journals Damage Analysis of Three Long-Track Tornadoes Using High-Resolution Satellite Imagery

Atmosphere ◽  
2020 ◽  
Vol 11 (6) ◽  
pp. 613 ◽  
Author(s):  
Daniel Burow ◽  
Hannah V. Herrero ◽  
Kelsey N. Ellis
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vortex Center ◽  
Blue Band ◽  
Rankine Vortex ◽  
Wind Field Model ◽  
Wind Speeds ◽  
High Resolution Satellite Imagery

Remote sensing of tornado damage can provide valuable observations for post-event surveys and reconstructions. The tornadoes of 3 March 2019 in the southeastern United States are an ideal opportunity to relate high-resolution satellite imagery of damage with estimated wind speeds from post-event surveys, as well as with the Rankine vortex tornado wind field model. Of the spectral metrics tested, the strongest correlations with survey-estimated wind speeds are found using a Normalized Difference Vegetation Index (NDVI, used as a proxy for vegetation health) difference image and a principal components analysis emphasizing differences in red and blue band reflectance. NDVI-differenced values across the width of the EF-4 Beauregard-Smiths Station, Alabama, tornado path resemble the pattern of maximum ground-relative wind speeds across the width of the Rankine vortex model. Maximum damage sampled using these techniques occurred within 130 m of the tornado vortex center. The findings presented herein establish the utility of widely accessible Sentinel imagery, which is shown to have sufficient spatial resolution to make inferences about the intensity and dynamics of violent tornadoes occurring in vegetated areas.

Can High-Resolution Satellite Multispectral Imagery Be Used to Phenotype Canopy Traits and Yield Potential in Field Conditions?

2021 ◽  
Vol 64 (3) ◽  
pp. 879-891
Author(s):  
Sindhuja Sankaran ◽  
Afef Marzougui ◽  
J. Preston Hurst ◽  
Chongyuan Zhang ◽  
James C. Schnable ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Yield Potential ◽  
Breeding Programs ◽  
Time Points ◽  
High Resolution Satellite Imagery

HighlightsVegetation indices (NDVI, GNDVI, and SAVI) extracted from high-resolution satellite imagery were significantly associated with vegetation indices extracted from UAV imagery.High-resolution satellite data can be used to predict maize yield at breeding plot scale.Breeding plot sizes and the variability between maize genotypes may be associated with prediction accuracies.Abstract. The recent availability of high spatial and temporal resolution satellite imagery has widened its applications in agriculture. Plant breeding and genetics programs are currently adopting unmanned aerial vehicle (UAV) based imagery data as a complement to ground data collection. With breeding trials across multiple geographic locations, UAV imaging is not always convenient. Hence, we anticipate that, similar to UAV imaging, phenotyping of individual test plots from high-resolution satellite imagery may also provide value to plant genetics and breeding programs. In this study, high spatial resolution satellite imagery (~38 to 48 cm pixel-1) was compared to imagery acquired using a UAV for its ability to phenotype maize grown in two-row and six-row breeding plots. Statistics (mean, median, sum) of color (red, green, blue), near-infrared, and vegetation indices such as normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and soil adjusted vegetation index (SAVI) were extracted from imagery from both sources (UAV and satellite) for comparison at three time points. In general, a strong correlation between satellite and UAV imagery extracted NDVI, GNDVI, and SAVI features (especially with mean and median statistics, p < 0.001) was observed at different time points. The correlation of both UAV and satellite image features with yield potential was maximum (p < 0.001) at the third time point (milk/dough growth stages). For example, Pearson’s correlation coefficients between mean NDVI, GNDVI, and SAVI features with yield potential were 0.52, 0.54, and 0.51 for data derived from UAV imagery, and 0.34, 0.41, and 0.40 for data derived from satellite imagery, respectively. Machine learning algorithms, including least absolute shrinkage and selection operator (Lasso) regression, were evaluated for yield prediction using vegetation index features that were significantly correlated with observed yield. The relationship between satellite imagery with crop performance can be a function of plot size in addition to crop variability. Nevertheless, with the ongoing improvement of satellite technologies, there is a possibility for the integration of satellite data into breeding programs, thus improving phenotyping efficiencies. Keywords: Image processing, Machine learning, Plant breeding, Statistical analysis, Unmanned aerial vehicles.

scholarly journals High-Resolution Satellite Imagery to Assess Sargassum Inundation Impacts to Coastal Areas

2020 ◽  
Author(s):  
William J. Hernandez ◽  
Julio M. Morell ◽  
Roy A. Armstrong
Keyword(s):  
High Resolution ◽  
Change Detection ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Unconsolidated Sediments ◽  
Coastal Vegetation ◽  
Atmospheric Effects ◽  
True Color ◽  
Benthic Composition

AbstractA change detection analysis utilizing Very High-resolution (VHR) satellite imagery was performed to evaluate the changes in benthic composition and coastal vegetation in La Parguera, southwestern Puerto Rico, attributable to the increased influx of pelagic Sargassum spp and its accumulations in cays, bays, inlets and near-shore environments. Satellite imagery was co-registered, corrected for atmospheric effects, and masked for water and land. A Normalized Difference Vegetation Index (NDVI) and an unsupervised classification scheme were applied to the imagery to evaluate the changes in coastal vegetation and benthic composition. These products were used to calculate the differences from 2010 baseline imagery, to potential hurricane impacts (2018 image), and potential Sargassum impacts (2020 image). Results show a negative trend in Normalized Difference Vegetation Index (NDVI) from 2010 to 2020 for the total pixel area of 24%, or 546,446 m2. These changes were also observed in true color images from 2010 to 2020. Changes in the NDVI negative values from 2018 to 2020 were higher, especially for the Isla Cueva site (97%) and were consistent with the field observations and drone surveys conducted since 2018 in the area. The major changes from 2018 and 2020 occurred mainly in unconsolidated sediments (e.g. sand, mud) and submerged aquatic vegetation (e.g. seagrass, algae), which can have similar spectra limiting the differentiation from multi-spectral imagery. Areas prone to Sargassum accumulation were identified using a combination of 2018 and 2020 true color VHR imagery and drone observations. This approach provides a quantifiable method to evaluate Sargassum impacts to the coastal vegetation and benthic composition using change detection of VHR images, and to separate these effects from other extreme events.

scholarly journals Exploring the Potential of High-Resolution Satellite Imagery for the Detection of Soybean Sudden Death Syndrome

2020 ◽  
Vol 12 (7) ◽  
pp. 1213 ◽  
Author(s):  
Muhammad M. Raza ◽  
Chris Harding ◽  
Matt Liebman ◽  
Leonor F. Leandro
Keyword(s):  
High Resolution ◽  
Random Forest ◽  
Sudden Death ◽  
Satellite Imagery ◽  
Large Scale ◽  
Vegetation Index ◽  
Sudden Death Syndrome ◽  
Alternative Methods ◽  
High Resolution Satellite Imagery ◽  
Soybean Diseases

Sudden death syndrome (SDS) is one of the major yield-limiting soybean diseases in the Midwestern United States. Effective management for SDS requires accurate detection in soybean fields. Since traditional scouting methods are time-consuming, labor-intensive, and often destructive, alternative methods to monitor SDS in large soybean fields are needed. This study explores the potential of using high-resolution (3 m) PlanetScope satellite imagery for detection of SDS using the random forest classification algorithm. Image data from blue, green, red, and near-infrared (NIR) spectral bands, the calculated normalized difference vegetation index (NDVI), and crop rotation information were used to detect healthy and SDS-infected quadrats in a soybean field experiment with different rotation treatments, located in Boone County, Iowa. Datasets collected during the 2016, 2017, and 2018 soybean growing seasons were analyzed. The results indicate that spectral features, when combined with ground-based information, can detect areas in soybean plots that are at risk for disease, even before foliar symptoms develop. The classification of healthy and diseased soybean quadrats was >75% accurate and the area under the receiver operating characteristic curve (AUROC) was >70%. Our results indicate that high-resolution satellite imagery and random forest analyses have the potential to detect SDS in soybean fields, and that this approach may facilitate large-scale monitoring of SDS (and possibly other economically important soybean diseases). It may also be useful for guiding recommendations for site-specific management in current and future seasons.

scholarly journals Early warning and drought risk assessment for the Bolivian Altiplano agriculture using high resolution satellite imagery data

2018 ◽  
Author(s):  
Claudia Canedo Rosso ◽  
Stefan Hochrainer-Stigler ◽  
Georg Pflug ◽  
Bruno Condori ◽  
Ronny Berndtsson
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Early Warning ◽  
Vegetation Index ◽  
Early Warning Systems ◽  
Local Scale ◽  
Drought Risk ◽  
Bolivian Altiplano ◽  
High Resolution Satellite Imagery ◽  
Imagery Data

Abstract. Implementation of agriculturally related early warning systems is fundamental for the management of droughts. Additionally, risk-based approaches are superior in tackling future drought hazards. Due to data-scarcity in many regions, high resolution satellite imagery data are becoming widely used. Focusing on ENSO warm and cold phases, we employ a risk-based approach for drought assessment in the Bolivian Altiplano using satellite imagery data and application of an early warning system. We use a newly established high resolution satellite dataset and test its accuracy as well as performance to similar (but with less resolution) datasets available for the Bolivian Altiplano. It is shown that during the El Niño years (warm ENSO phase), the result is great difference in risk and crop yield. Furthermore, the Normalized Difference Vegetation Index (NDVI) can be used to target specific hot spots on a very local scale. As a consequence, ENSO early warning forecasts as well as possible magnitudes of crop deficits could be established by the government, including an identification of possible hotspots during the growing season. Our approach therefore should not only help in determining the magnitude of assistance needed for farmers on the local scale but also enable a pro-active approach to disaster risk management against droughts that can include economic-related instruments such as insurance as well as risk reduction instruments such as irrigation.

scholarly journals Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Guie Li ◽  
Zhongliang Cai ◽  
Yun Qian ◽  
Fei Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Low Income ◽  
Urban Poverty ◽  
Image Features ◽  
Housing Affordability ◽  
Learning Approaches ◽  
Housing Inequality ◽  
High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

scholarly journals Lake-to-Lake Cloud Bands: Frequencies and Locations

2007 ◽  
Vol 135 (12) ◽  
pp. 4202-4213 ◽  
Author(s):  
Yarice Rodriguez ◽  
David A. R. Kristovich ◽  
Mark R. Hjelmfelt
Keyword(s):  
High Resolution ◽  
Great Lakes ◽  
Satellite Imagery ◽  
Lake Superior ◽  
Great Lakes Region ◽  
Lake Effect ◽  
Time Period ◽  
High Resolution Satellite Imagery

Abstract Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.

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