Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: Applications in a date palm dominated region

Geoderma ◽  
2014 ◽  
Vol 230-231 ◽  
pp. 1-8 ◽  
Author(s):  
Amal Allbed ◽  
Lalit Kumar ◽  
Yousef Y. Aldakheel
Keyword(s):  
Spatial Resolution ◽  
Soil Salinity ◽  
Date Palm ◽  
High Spatial Resolution ◽  
Vegetation Indices

Combination of GF-2 high spatial resolution imagery and land surface factors for predicting soil salinity of muddy coasts

CATENA ◽  
2021 ◽  
Vol 202 ◽  
pp. 105304
Author(s):  
Yufeng Li ◽  
Cheng Wang ◽  
Alan Wright ◽  
Hongyu Liu ◽  
Huabing Zhang ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Soil Salinity ◽  
Land Surface ◽  
High Spatial Resolution ◽  
High Spatial Resolution Imagery

scholarly journals Evaluation of a NIR camera for monitoring yield and nitrogen effect in sugarcane

2017 ◽  
Vol 35 (1) ◽  
pp. 82-91
Author(s):  
Cesar Edwin García ◽  
David Montero ◽  
Hector Alberto Chica
Keyword(s):  
Spatial Resolution ◽  
Vegetation Index ◽  
High Spatial Resolution ◽  
Early Stage ◽  
Vegetation Indices ◽  
Visible Spectrum ◽  
Crop Productivity ◽  
Spectral Information ◽  
Nitrogen Effect ◽  
Sugarcane Yield

The main objective of the research carried out in the sugar productive sector in Colombia is to improve crop productivity of sugarcane. The rise of RPAS, together with the use of multispectral cameras, which allows for high spatial resolution images and spectral information outside the visible spectrum, has generated an alternative nondestructive technological approach to monitoring crop sugarcane that must be evaluated and adapted to the specific conditions of Colombia's sugar productive sector. In this context, this paper assesses the potential of a modified camera (NIR) to discriminate three varieties of sugarcane, as well as three doses of fertilization and estimating the sugarcane yield at an early stage, for the three varieties through multiple vegetation indices. In this study, no significant differences were found by vegetation index between fertilization doses, and only significant differences between varieties were found when the fertilization was normal or high. Likewise, multiple regressions between scores derived from vegetation indices after applying PCA and productivity produced determinations of up to 56%.

scholarly journals Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images

2018 ◽  
Vol 51 (1) ◽  
pp. 932-944 ◽  
Author(s):  
Fabrício L. Macedo ◽  
Adélia M. O. Sousa ◽  
Ana Cristina Gonçalves ◽  
José R. Marques da Silva ◽  
Paulo A. Mesquita ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
Quercus Rotundifolia

scholarly journals Evaluating the Effectiveness of Using Vegetation Indices Based on Red-Edge Reflectance from Sentinel-2 to Estimate Gross Primary Productivity

2019 ◽  
Vol 11 (11) ◽  
pp. 1303 ◽  
Author(s):  
Shangrong Lin ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Longhui Li ◽  
Jing Zhao ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Primary Productivity ◽  
Carbon Flux ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Gross Primary Productivity ◽  
Flux Tower ◽  
Red Edge ◽  
The Relationship ◽  
Sentinel 2

Gross primary productivity (GPP) is the most important component of terrestrial carbon flux. Red-edge (680–780 nm) reflectance is sensitive to leaf chlorophyll content, which is directly correlated with photosynthesis as the pigment pool, and it has the potential to improve GPP estimation. The European Space Agency (ESA) Sentinel-2A and B satellites provide red-edge bands at 20-m spatial resolution on a five-day revisit period, which can be used for global estimation of GPP. Previous studies focused mostly on improving cropland GPP estimation using red-edge bands. In this study, we firstly evaluated the relationship between eight vegetation indices (VIs) retrieved from Sentinel-2 imagery in association with incident photosynthetic active radiation (PARin) and carbon flux tower GPP (GPPEC) across three forest and two grassland sites in Australia. We derived a time series of five red-edge VIs and three non-red-edge VIs over the CO2 flux tower footprints at 16-day time intervals and compared both temporal and spatial variations. The results showed that the relationship between the red-edge index (CIr, ρ 783 ρ 705 − 1 ) multiplied by PARin and GPPEC had the highest correlation (R2 = 0.77, root-mean-square error (RMSE) = 0.81 gC∙m−2∙day−1) at the two grassland sites. The CIr also showed consistency (rRMSE defined as RMSE/mean GPP, lower than 0.25) across forest and grassland sites. The high spatial resolution of the Sentinel-2 data provided more detailed information to adequately characterize the GPP variance at spatially heterogeneous areas. The high revisit period of Sentinel-2 exhibited temporal variance in GPP at the grassland sites; however, at forest sites, the flux-tower-based GPP variance could not be fully tracked by the limited satellite images. These results suggest that the high-spatial-resolution red-edge index from Sentinel-2 can improve large-scale spatio-temporal GPP assessments.

scholarly journals Economic Assessment of Fire Damage to Urban Forest in the Wildland–Urban Interface Using Planet Satellites Constellation Images

2018 ◽  
Vol 10 (9) ◽  
pp. 1479 ◽  
Author(s):  
Yaron Michael ◽  
Itamar Lensky ◽  
Steve Brenner ◽  
Anat Tchetchik ◽  
Naama Tessler ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Urban Forest ◽  
Vegetation Indices ◽  
Economic Assessment ◽  
Urban Trees ◽  
Burn Severity ◽  
Fire Damage ◽  
Wildland Urban Interface ◽  
Fire Monitoring

The wildland-urban interface (WUI)—the area where wildland vegetation and urban buildings intermix—is at a greater risk of fire occurrence because of extensive human activity in that area. Although satellite remote sensing has become a major tool for assessing fire damage in wildlands, it is unsuitable for WUI fire monitoring due to the low spatial resolution of the images from satellites that provide frequent information which is relevant for timely fire monitoring in WUI. Here, we take advantage of frequent (i.e., ca. daily), high-spatial-resolution (3 m) imagery acquired from a constellation of nano-satellites operated by Planet Labs (“Planet”) to assess fire damage to urban trees in the WUI of a Mediterranean city in Israel (Haifa). The fire occurred at the end of 2016, consuming ca. 17,000 of the trees (152 trees ha−1) within the near-by wildland and urban parts of the city. Three vegetation indices (GNDVI, NDVI and GCC) from Planet satellite images were used to derive a burn severity map for the WUI area after applying a subpixel discrimination method to distinguish between woody and herbaceous vegetation. The produced burn severity map was successfully validated with information acquired from an extensive field survey in the WUI burnt area (overall accuracy and kappa: 87% and 0.75%, respectively). Planet’s vegetation indices were calibrated using in-field tree measurements to obtain high spatial resolution maps of burned trees and consumed woody biomass in the WUI. These were used in conjunction with an ecosystem services valuation model (i-Tree) to estimate spatially-distributed and total economic loss due to damage to urban trees caused by the fire. Results show that nearly half of the urban trees were moderately and severely burned (26% and 22%, respectively). The total damage to the urban forest was estimated at ca. 41 ± 10 M USD. We conclude that using the method developed in this study with high-spatial-resolution Planet images has a great potential for WUI fire economic assessment.

scholarly journals Remote Sensing-Guided Spatial Sampling Strategy over Heterogeneous Surface Ground for Validation of Vegetation Indices Products with Medium and High Spatial Resolution

2021 ◽  
Vol 13 (14) ◽  
pp. 2674
Author(s):  
Tingting Lv ◽  
Xiang Zhou ◽  
Zui Tao ◽  
Xiaoyu Sun ◽  
Jin Wang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Prior Knowledge ◽  
Correlation Coefficient ◽  
Spatial Resolution ◽  
Sampling Method ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Sampling Strategy ◽  
Sampling Points ◽  
Sampling Optimization

Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.

scholarly journals Reconstruction of High-Temporal- and High-Spatial-Resolution Reflectance Datasets Using Difference Construction and Bayesian Unmixing

2020 ◽  
Vol 12 (23) ◽  
pp. 3952
Author(s):  
Lei Yang ◽  
Jinling Song ◽  
Lijuan Han ◽  
Xin Wang ◽  
Jing Wang
Keyword(s):  
Time Series ◽  
Spatial Resolution ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Vegetation Indices ◽  
Vital Role ◽  
Dynamic Monitoring ◽  
Fusion Model ◽  
Spatiotemporal Resolution ◽  
Reconstruction Methods

High-temporal- and high-spatial-resolution reflectance datasets play a vital role in monitoring dynamic changes at the Earth’s land surface. So far, many sensors have been designed with a trade-off between swath width and pixel size; thus, it is difficult to obtain reflectance data with both high spatial resolution and frequent coverage from a single sensor. In this study, we propose a new Reflectance Bayesian Spatiotemporal Fusion Model (Ref-BSFM) using Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflectance, which is then used to construct reflectance datasets with high spatiotemporal resolution and a long time series. By comparing this model with other popular reconstruction methods (the Flexible Spatiotemporal Data Fusion Model, the Spatial and Temporal Adaptive Reflectance Fusion Model, and the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model), we demonstrate that our approach has the following advantages: (1) higher prediction accuracy, (2) effective treatment of cloud coverage, (3) insensitivity to the time span of data acquisition, (4) capture of temporal change information, and (5) higher retention of spatial details and inconspicuous MODIS patches. Reflectance time-series datasets generated by Ref-BSFM can be used to calculate a variety of remote-sensing-based vegetation indices, providing an important data source for land surface dynamic monitoring.

scholarly journals Using Satellite-Borne Remote Sensing Data in Generating Local Warming Maps with Enhanced Resolution

2018 ◽  
Vol 7 (10) ◽  
pp. 398 ◽  
Author(s):  
Khan Rubayet Rahaman ◽  
M. Razu Ahmed ◽  
Quazi K. Hassan
Keyword(s):  
Remote Sensing ◽  
Spatial Resolution ◽  
Air Temperature ◽  
Vegetation Index ◽  
High Spatial Resolution ◽  
Local Level ◽  
Vegetation Indices ◽  
Climatic Conditions ◽  
Study Region ◽  
Local Warming

Warming, i.e., increments of temperature, is evident at the global, regional, and local level. However, understanding the dynamics of local warming at high spatial resolution remains challenging. In fact, it is very common to see extremely variable land cover/land use within built-up environments that create micro-climatic conditions. To address this issue, our overall goal was to generate a local warming map for the period 1961–2010 at 15 m spatial resolution over the southern part of the Canadian province of Alberta. Our proposed methods consisted of three distinct steps. These were the: (i) construction of high spatial resolution enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) maps; (ii) conversion of air temperature (Ta) normal (i.e., 30 years average) at higher spatial resolution using vegetation indices (VI); and (iii) generation of a local warming map at 15m spatial resolution. In order to execute this study, we employed MODIS-driven air temperature data, EVI and NDVI data, and Landsat-driven vegetation indices. The study uncovered that around 58% (up to positive 1 °C) of areas in the considered study region were experiencing increased temperature; whereas only about 4% of areas underwent a cooling trend (more than negative 0.25 °C). The remaining 38% did not exhibit significant change in temperature. We concluded that remote sensing technology could be useful to enhance the spatial resolution of local warming maps, which would be useful for decision-makers considering efficient decisions in the face of increments in local temperature.

High spatial resolution AEM observations of yttrium segregation in chromia scales grown on Co-45%Cr

1986 ◽  
Vol 44 ◽  
pp. 518-519
Author(s):  
K. Przybylski ◽  
A. J. Garratt-Reed ◽  
G. J. Yurek
Keyword(s):  
Spatial Resolution ◽  
Outer Surface ◽  
Maximum Concentration ◽  
High Spatial Resolution ◽  
Reactive Elements ◽  
Chromia Scales

The addition of so-called “reactive” elements such as yttrium to alloys is known to enhance the protective nature of Cr2O3 or Al2O3 scales. However, the mechanism by which this enhancement is achieved remains unclear. An A.E.M. study has been performed of scales grown at 1000°C for 25 hr. in pure O2 on Co-45%Cr implanted at 70 keV with 2x1016 atoms/cm2 of yttrium. In the unoxidized alloys it was calculated that the maximum concentration of Y was 13.9 wt% at a depth of about 17 nm. SIMS results showed that in the scale the yttrium remained near the outer surface.

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