Foliar Damage and Flower Production of Landscape Plants Sprinkle Irrigated with Reuse Water

Nineteen flowering landscape species were sprinkle irrigated with either reuse water or fresh water, with an additional treatment of reuse water plus shade (solar radiation reduced by 24%), for 113 days during late summer and early fall in southern Nevada. The species selected were common to mixed landscape areas on golf courses in southern Nevada transitioning to reuse water. An index of visual damage (IVD) was assessed, along with an assessment of flower production, canopy temperature, tissue ion analysis and spectral reflectance. The IVD values separated based on species (p < 0.001), treatment (p < 0.001) and by a species by treatment interaction (p < 0.001). Irrigating with reuse water plus shade reduced the IVD compared to the reuse without shade in 7 of the 19 species (p < 0.05). When IVD values were included for all species, 40% of the variation in the IVD values could be accounted for if N, B, Ca, Mg, Na, and Zn were included in the regression equation. Higher r2 values were obtained when individual species were isolated, with regression equations differing based on tissue ion combinations [e.g., ice plant (Mesembryanthemum crystallinum L.) r2 = 0.81 IVD↑, Na↓, Mn↑]. Three vegetation indices chlorophyll index (CHL), red/far red (R/FR) and water band index/normalized difference vegetation index (WBI/NDVI)) accounted for 51% of the variation in the IVD values. As much as 72% of the variation in vegetation indices could be accounted for based on tissue ion concentrations when separated based on treatment, with Na being the only common ion in all of the highest correlations. Flower production was highest in the reuse plus shade treatment in all 13 species flowering during the experimental period, with as much as 86% of the flower production variation driven by different tissue ion concentrations [purple cup (Nierembergia hippomanica), r2 = 0.86, flowers↑, Mn↑, Zn↓]. Nine of the nineteen species had acceptable levels of foliar damage (IVD < 2.0). We believe that if the spray irrigation can be minimized (bubblers/drip) and/or partial shade provided, through multi-story landscape designs, a more favorable response will be observed.

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Foliar Damage, Spectral Reflectance, and Tissue Ion Concentrations of Trees Sprinkle Irrigated with Waters of Similar Salinity but Different Chemical Composition

HortScience ◽

10.21273/hortsci.40.3.819 ◽

2005 ◽

Vol 40 (3) ◽

pp. 819-826 ◽

Cited By ~ 9

Author(s):

D.A. Devitt ◽

R.L. Morris ◽

L.K. Fenstermaker

Keyword(s):

Spectral Reflectance ◽

Vegetation Index ◽

Ionic Composition ◽

Nutrient Concentrations ◽

Ion Concentrations ◽

Reuse Water ◽

Mixed Response ◽

Saline Waters ◽

Foliar Damage ◽

The Impact

We investigated foliar damage to five landscape species sprinkler irrigated with either reuse water or one of five synthesized saline waters that contained elevated single salts mixed with Colorado River water, all having similar electrical conductivities. The experiment allowed us to compare the impact of elevated concentrations of Na, Mg, Ca, Cl, and SO4 on an index of visual damage (IVD), tissue ion concentrations, and spectral reflectance. Waters containing elevated concentrations of MgCl2 or NaCl caused greater foliar damage than did MgSO4, Na2SO4, CaSO4, or reuse water, as recorded in higher IVD values (p < 0.05). Privet and elm were damaged to a greater extent (higher IVD values) than were desert willow, guava and laurel (p < 0.05). Higher IVD values were recorded for all species irrigated with the MgCl2 waters, with mortality recorded in privet. Tissue nutrient concentrations were correlated with the IVD values. In the case of guava, 61% of the variability in the IVD could be accounted for based on N, P and K (P < 0.01). On a treatment basis, the single salts added to the municipal water showed little correlation with the IVD values, except in the case of MgCl2, where Mg was included in the regression equation (r2 = 0.82, P < 0.01, IVD↑ as S04↓, Mg and P↑). Eleven different spectral indices separated based on treatment and/or species (P < 0.05). In elm, 70% of the variability in the IVD could be accounted for by including Red Edge, Normalized Difference Vegetation Index (NDVI) and Water Band Index (WBI)/NDVI. A mixed response was observed to a post 30-day irrigation rinse in an attempt to reduce IVD values. Based on our results, care should be given to monitoring not only the EC (and osmotic potential) but also the ionic composition when saline waters are blended with other water sources, with the aim of minimizing the concentration of Mg, Cl, and Na.

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Estimation of Carbon Fluxes from Eddy Covariance Data and Satellite-Derived Vegetation Indices in a Karst Grassland (Podgorski Kras, Slovenia)

Remote Sensing ◽

10.3390/rs11060649 ◽

2019 ◽

Vol 11 (6) ◽

pp. 649 ◽

Cited By ~ 3

Author(s):

Koffi Noumonvi ◽

Mitja Ferlan ◽

Klemen Eler ◽

Giorgio Alberti ◽

Alessandro Peressotti ◽

...

Keyword(s):

Linear Relationship ◽

Eddy Covariance ◽

Vegetation Index ◽

Gross Primary Production ◽

Vegetation Indices ◽

Information Criterion ◽

Growing Season ◽

Coefficient Of Determination ◽

Regression Equations ◽

Eddy Covariance Method

The Eddy Covariance method (EC) is widely used for measuring carbon (C) and energy fluxes at high frequency between the atmosphere and the ecosystem, but has some methodological limitations and a spatial restriction to an area, called a footprint. Remotely sensed information is usually used in combination with eddy covariance data in order to estimate C fluxes over larger areas. In fact, spectral vegetation indices derived from available satellite data can be combined with EC measurements to estimate C fluxes outside of the tower footprint. Following this approach, the present study aimed to model C fluxes for a karst grassland in Slovenia. Three types of model were considered: (1) a linear relationship between Net Ecosystem Exchange (NEE) or Gross Primary Production (GPP) and each vegetation index; (2) a linear relationship between GPP and the product of a vegetation index with PAR (Photosynthetically Active Radiation); and (3) a simplified LUE (Light Use-Efficiency) model assuming a constant LUE. We compared the performance of several vegetation indices derived from two remote platforms (Landsat and Proba-V) as predictors of NEE and GPP, based on three accuracy metrics, the coefficient of determination (R2), the Root Mean Square Error (RMSE) and the Akaike Information Criterion (AIC). Two types of aggregation of flux data were explored: midday average and daily average fluxes. The vapor pressure deficit (VPD) was used to separate the growing season into two phases, a wet and a dry phase, which were considered separately in the modelling process, in addition to the growing season as a whole. The results showed that NDVI is the best predictor of GPP and NEE during the wet phase, whereas water-related vegetation indices, namely LSWI and MNDWI, were the best predictors during the dry phase, both for midday and daily aggregates. Model 1 (linear relationship) was found to be the best in many cases. The best regression equations obtained were used to map GPP and NEE for the whole study area. Digital maps obtained can practically contribute, in a cost-effective way to the management of karst grasslands.

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Mapping Maize Cultivated Area Combining MODIS EVI Time Series and the Spatial Variations of Phenology over Huanghuaihai Plain

Applied Sciences ◽

10.3390/app10082667 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2667 ◽

Cited By ~ 1

Author(s):

Xueting Wang ◽

Sha Zhang ◽

Lili Feng ◽

Jiahua Zhang ◽

Fan Deng

Keyword(s):

Time Series ◽

Large Scale ◽

Vegetation Index ◽

Vegetation Indices ◽

Regression Equations ◽

Summer Maize ◽

Temporal Shift ◽

Modis Evi ◽

Multi Temporal ◽

Moderate Resolution Imaging Spectroradiometer

Crop phenology is a significant factor that affects the precision of crop area extraction by using the multi-temporal vegetation indices (VIs) approach. Considering the phenological differences of maize among the different regions, the summer maize cultivated area was estimated by using enhanced vegetation index (EVI) time series images from the Moderate Resolution Imaging Spectroradiometer (MODIS) over the Huanghuaihai Plain in China. By analyzing the temporal shift in summer maize calendars, linear regression equations for simulating the summer maize phenology were obtained. The simulated maize phenology was used to correct the MODIS EVI time series curve of summer maize. Combining the mean absolute distance (MAD) and p-tile algorithm, the cultivated areas of summer maize were distinguished over the Hunaghuaihai Plain. The accuracy of the extraction results in each province was above 85%. Comparing the maize area of two groups from MODIS-estimated and statistical data, the validation results showed that the R2 reached 0.81 at the city level and 0.69 at the county level. It demonstrated that the approach in this study has the ability to effectively map the summer maize area over a large scale and provides a novel idea for estimating the planting area of other crops.

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Application of geo-informatics engineering to estimate above-ground carbon sequestration

Engineering review ◽

10.30765/er.38.3.7 ◽

2018 ◽

Vol 38 (3) ◽

pp. 303-308

Author(s):

Teerawong Laosuwan ◽

Yannawut Uttaruk ◽

Tanutdech Rotjanakusol ◽

Kusuma Arsasana

Keyword(s):

Carbon Sequestration ◽

Vegetation Index ◽

Vegetation Indices ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Regression Equations ◽

Landsat 8 Oli ◽

Green Vegetation ◽

Simple Ratio ◽

The Relationship

This research aims to estimate above-ground carbon sequestration of orchards by using the data collected from Landsat 8 OLI. Regression equations are applied to study the relationship between the amount of above-ground carbon sequestration and vegetation indices from Landsat 8 OLI, in which the data was collected in 2015 in 3 methods: 1) Difference Vegetation Index (DVI), 2) Green Vegetation Index (GVI), and 3) Simple Ratio (SR). The results are as follows: 1) By DVI method, it results in the equation y = 0.3184e0.0482x and the coefficient of determination R² = 0.8457. The amount of the above-ground sequestration calcula-tion's result is 213.176 tons per rai. 2) Using the GVI method, it results in the equation y = 0.2619e0.0489x and the coefficient of determination R²=0.8763. The amount of the above-ground sequestration calculation's result is 220.510 tons per rai. 3) Using the SR method, it results in the equation y = 0.8900e0.0469x and the coefficient of determination R² = 0.7748. The amount of the above-ground sequestration calculation's result is 234.229 tons per rai.

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Impact of Water Treatment on Foliar Damage of Landscape Trees Sprinkle Irrigated with Reuse Water

Journal of Environmental Horticulture ◽

10.24266/0738-2898-21.2.82 ◽

2003 ◽

Vol 21 (2) ◽

pp. 82-88

Author(s):

D. A. Devitt ◽

R. L. Morris ◽

D. S. Neuman

Keyword(s):

Tree Species ◽

Irrigation Treatment ◽

Leaf Tissue ◽

Potential Cost ◽

Ion Concentrations ◽

Reuse Water ◽

Quercus Virginiana ◽

Prunus Cerasifera ◽

Foliar Damage ◽

Chilopsis Linearis

Abstract An experiment was conducted on four container-grown tree species placed under five different irrigation reuse water treatments to determine the extent of foliar damage after a 14.5-month period. The tree species included Heritage oak (Quercus virginiana Mill. ‘Heritage’), desert willow (Chilopsis linearis (Cav.)/Sweet), flowering plum (Prunus cerasifera Ehrh ‘Atropurpurea’), and Chinese pistache (Pistacia chinensis Bunge). Plant response and an index of visual damage (IVD) were assessed at different times throughout the experiment. Ion concentrations in the leaf tissue were different for species (S) (p < 0.001), treatment (T) (Na, K, SO4, p < 0.05) and by a species by treatment interaction (S × T) (Na, Ca, Mg, K and SO4, p < 0.05). SPAD measurements varied by S (p < 0.001), T (p < 0.001) and by an S × T interaction (p < 0.045). SPAD measurements decreased as the leaf tissue Na concentration increased (SPAD = 47.49 – 12.46(Na), r2 = 0.38, p < 0.01). The IVD varied by S (p < 0.001), T (p < 0.001) and by an S × T interaction (p < 0.001). Na, Ca and SO4 tissue ion concentrations could account for 52% of the variability in the IVD (IVD =−1.93 + 4.63(Na) + 2.60(Ca)−0.001(SO4), p < 0.01). Because the irrigation treatment resulting in the lowest IVD was species dependent, irrigation treatment selection should be based upon an evaluation of the landscape species composition and the potential cost of implementing a given strategy. The response observed in this study suggests that a single universal irrigation strategy does not exist, indicating that emphasis must be placed on initial and replacement plant selection.

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HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

Malaysian Journal of Sustainable Environment ◽

10.24191/myse.v7i1.8905 ◽

2020 ◽

Vol 7 (1) ◽

pp. 21

Author(s):

Faradina Marzukhi ◽

Nur Nadhirah Rusyda Rosnan ◽

Md Azlin Md Said

Keyword(s):

Oil Palm ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Satellite Image ◽

Vegetation Indices ◽

Soil Nutrient ◽

Oil Palm Plantation ◽

Sustainable Environment ◽

Moisture Analysis ◽

The Relationship

The aim of this study is to analyse the relationship between vegetation indices of Normalized Difference Vegetation Index (NDVI) and soil nutrient of oil palm plantation at Felcra Nasaruddin Bota in Perak for future sustainable environment. The satellite image was used and processed in the research. By Using NDVI, the vegetation index was obtained which varies from -1 to +1. Then, the soil sample and soil moisture analysis were carried in order to identify the nutrient values of Nitrogen (N), Phosphorus (P) and Potassium (K). A total of seven soil samples were acquired within the oil palm plantation area. A regression model was then made between physical condition of the oil palms and soil nutrients for determining the strength of the relationship. It is hoped that the risk map of oil palm healthiness can be produced for various applications which are related to agricultural plantation.

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Modeling of Durum Wheat Yield Based on Sentinel-2 Imagery

Agronomy ◽

10.3390/agronomy11081486 ◽

2021 ◽

Vol 11 (8) ◽

pp. 1486

Author(s):

Chris Cavalaris ◽

Sofia Megoudi ◽

Maria Maxouri ◽

Konstantinos Anatolitis ◽

Marios Sifakis ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Model Development ◽

Wheat Yield ◽

Growth Period ◽

Yield Data ◽

Water Index ◽

Normalized Difference Water Index ◽

Sentinel 2

In this study, a modelling approach for the estimation/prediction of wheat yield based on Sentinel-2 data is presented. Model development was accomplished through a two-step process: firstly, the capacity of Sentinel-2 vegetation indices (VIs) to follow plant ecophysiological parameters was established through measurements in a pilot field and secondly, the results of the first step were extended/evaluated in 31 fields, during two growing periods, to increase the applicability range and robustness of the models. Modelling results were examined against yield data collected by a combine harvester equipped with a yield-monitoring system. Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) were examined as plant signals and combined with Normalized Difference Water Index (NDWI) and/or Normalized Multiband Drought Index (NMDI) during the growth period or before sowing, as water and soil signals, respectively. The best performing model involved the EVI integral for the 20 April–31 May period as a plant signal and NMDI on 29 April and before sowing as water and soil signals, respectively (R2 = 0.629, RMSE = 538). However, model versions with a single date and maximum seasonal VIs values as a plant signal, performed almost equally well. Since the maximum seasonal VIs values occurred during the last ten days of April, these model versions are suitable for yield prediction.

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The Impacts of Spatial Resolution, Viewing Angle, and Spectral Vegetation Indices on the Quantification of Woody Mediterranean Species Seasonality Using Remote Sensing

Remote Sensing ◽

10.3390/rs13101958 ◽

2021 ◽

Vol 13 (10) ◽

pp. 1958

Author(s):

Shelly Elbaz ◽

Efrat Sheffer ◽

Itamar M. Lensky ◽

Noam Levin

Keyword(s):

Spatial Resolution ◽

Seasonal Changes ◽

Dominant Role ◽

Vegetation Indices ◽

Individual Species ◽

Viewing Angle ◽

Pixel Size ◽

Mediterranean Species ◽

Near Surface ◽

Nadir Point

Discriminating between woody plant species using a single image is not straightforward due to similarity in their spectral signatures, and limitations in the spatial resolution of many sensors. Seasonal changes in vegetation indices can potentially improve vegetation mapping; however, for mapping at the individual species level, very high spatial resolution is needed. In this study we examined the ability of the Israel/French satellite of VENμS and other sensors with higher spatial resolutions, for identifying woody Mediterranean species, based on the seasonal patterns of vegetation indices (VIs). For the study area, we chose a site with natural and highly heterogeneous vegetation in the Judean Mountains (Israel), which well represents the Mediterranean maquis vegetation of the region. We used three sensors from which the indices were derived: a consumer-grade ground-based camera (weekly images at VIS-NIR; six VIs; 547 individual plants), UAV imagery (11 images, five bands, seven VIs) resampled to 14, 30, 125, and 500 cm to simulate the spatial resolutions available from some satellites, and VENμS Level 1 product (with a nominal spatial resolution of 5.3 m at nadir; seven VIs; 1551 individual plants). The various sensors described seasonal changes in the species’ VIs at different levels of success. Strong correlations between the near-surface sensors for a given VI and species mostly persisted for all spatial resolutions ≤125 cm. The UAV ExG index presented high correlations with the ground camera data in most species (pixel size ≤125 cm; 9 of 12 species with R ≥ 0.85; p < 0.001), and high classification accuracies (pixel size ≤30 cm; 8 species with >70%), demonstrating the possibility for detailed species mapping from space. The seasonal dynamics of the species obtained from VENμS demonstrated the dominant role of ephemeral herbaceous vegetation on the signal recorded by the sensor. The low variance between the species as observed from VENμS may be explained by its coarse spatial resolution (effective ground spatial resolution of 7.5) and its non-nadir viewing angle (29.7°) over the study area. However, considering the challenging characteristics of the research site, it may be that using a VENμS type sensor (with a spatial resolution of ~1 m) from a nadir point of view and in more homogeneous and dense areas would allow for detailed mapping of Mediterranean species based on their seasonality.

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Aboveground Biomass Mapping of Crops Supported by Improved CASA Model and Sentinel-2 Multispectral Imagery

Remote Sensing ◽

10.3390/rs13142755 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2755

Author(s):

Peng Fang ◽

Nana Yan ◽

Panpan Wei ◽

Yifan Zhao ◽

Xiwang Zhang

Keyword(s):

Aboveground Biomass ◽

Production Management ◽

Vegetation Index ◽

Net Primary Productivity ◽

Vegetation Indices ◽

Biophysical Parameters ◽

Edge Information ◽

Casa Model ◽

Red Edge ◽

Biomass Mapping

The net primary productivity (NPP) and aboveground biomass mapping of crops based on remote sensing technology are not only conducive to understanding the growth and development of crops but can also be used to monitor timely agricultural information, thereby providing effective decision making for agricultural production management. To solve the saturation problem of the NDVI in the aboveground biomass mapping of crops, the original CASA model was improved using narrow-band red-edge information, which is sensitive to vegetation chlorophyll variation, and the fraction of photosynthetically active radiation (FPAR), NPP, and aboveground biomass of winter wheat and maize were mapped in the main growing seasons. Moreover, in this study, we deeply analyzed the seasonal change trends of crops’ biophysical parameters in terms of the NDVI, FPAR, actual light use efficiency (LUE), and their influence on aboveground biomass. Finally, to analyze the uncertainty of the aboveground biomass mapping of crops, we further discussed the inversion differences of FPAR with different vegetation indices. The results demonstrated that the inversion accuracies of the FPAR of the red-edge normalized vegetation index (NDVIred-edge) and red-edge simple ratio vegetation index (SRred-edge) were higher than those of the original CASA model. Compared with the reference data, the accuracy of aboveground biomass estimated by the improved CASA model was 0.73 and 0.70, respectively, which was 0.21 and 0.13 higher than that of the original CASA model. In addition, the analysis of the FPAR inversions of different vegetation indices showed that the inversion accuracies of the red-edge vegetation indices NDVIred-edge and SRred-edge were higher than those of the other vegetation indices, which confirmed that the vegetation indices involving red-edge information can more effectively retrieve FPAR and aboveground biomass of crops.

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Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Remote Sensing ◽

10.3390/rs13112060 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2060

Author(s):

Trylee Nyasha Matongera ◽

Onisimo Mutanga ◽

Mbulisi Sibanda ◽

John Odindi

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Critical Role ◽

Spectral Characteristics ◽

Vegetation Indices ◽

Machine Learning Algorithms ◽

Special Focus ◽

Land Surface Phenology ◽

Spectral Vegetation Indices ◽

Satellite Sensors

Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change. Long term monitoring of LSP provides large potential for the evaluation of interactions and feedbacks between climate and vegetation. With a special focus on the rangeland ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution of satellite sensors and interrogates their properties as well as the associated indices and algorithms in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS played a critical role in the development of spectral vegetation indices that have been widely used in LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations, and most other spectral vegetation indices were primarily developed to address the weaknesses and shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their successful usage is catalyzed with the development of cutting-edge algorithms for modeling the LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment. In the future, the development of machine learning algorithms that can effectively model and characterize the phenological cycles of vegetation would help to unlock the value of LSP information in the rangeland monitoring and management process. Precisely, deep learning presents an opportunity to further develop robust software packages such as the decomposition and analysis of time series (DATimeS) with the abundance of data processing tools and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.

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