HEALTHINESS OF OIL PALM PLANTATION TOWARDS SUSTAINABILITY OF ENVIRONMENT

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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Effects of Crop Leaf Angle on LAI-Sensitive Narrow-Band Vegetation Indices Derived from Imaging Spectroscopy

Applied Sciences ◽

10.3390/app8091435 ◽

2018 ◽

Vol 8 (9) ◽

pp. 1435 ◽

Cited By ~ 5

Author(s):

Xiaochen Zou ◽

Iina Haikarainen ◽

Iikka Haikarainen ◽

Pirjo Mäkelä ◽

Matti Mõttus ◽

...

Keyword(s):

Chlorophyll Content ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Imaging Spectroscopy ◽

Leaf Angle ◽

Field Crops ◽

Leaf Chlorophyll ◽

Simple Ratio ◽

The Relationship

Leaf area index (LAI) is an important biophysical variable for understanding the radiation use efficiency of field crops and their potential yield. On a large scale, LAI can be estimated with the help of imaging spectroscopy. However, recent studies have revealed that the leaf angle greatly affects the spectral reflectance of the canopy and hence imaging spectroscopy data. To investigate the effects of the leaf angle on LAI-sensitive narrowband vegetation indices, we used both empirical measurements from field crops and model-simulated data generated by the PROSAIL canopy reflectance model. We found the relationship between vegetation indices and LAI to be notably affected, especially when the leaf mean tilt angle (MTA) exceeded 70 degrees. Of the indices used in the study, the modified soil-adjusted vegetation index (MSAVI) was most strongly affected by leaf angles, while the blue normalized difference vegetation index (BNDVI), the green normalized difference vegetation index (GNDVI), the modified simple ratio using the wavelength of 705 nm (MSR705), the normalized difference vegetation index (NDVI), and the soil-adjusted vegetation index (SAVI) were only affected for sparse canopies (LAI < 3) and MTA exceeding 60°. Generally, the effect of MTA on the vegetation indices increased as a function of decreasing LAI. The leaf chlorophyll content did not affect the relationship between BNDVI, MSAVI, NDVI, and LAI, while the green atmospherically resistant index (GARI), GNDVI, and MSR705 were the most strongly affected indices. While the relationship between SR and LAI was somewhat affected by both MTA and the leaf chlorophyll content, the simple ratio (SR) displayed only slight saturation with LAI, regardless of MTA and the chlorophyll content. The best index found in the study for LAI estimation was BNDVI, although it performed robustly only for LAI > 3 and showed considerable nonlinearity. Thus, none of the studied indices were well suited for across-species LAI estimation: information on the leaf angle would be required for remote LAI measurement, especially at low LAI values. Nevertheless, narrowband indices can be used to monitor the LAI of crops with a constant leaf angle distribution.

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Within-Field Relationships between Satellite-Derived Vegetation Indices, Grain Yield and Spike Number of Winter Wheat and Triticale

Agronomy ◽

10.3390/agronomy10111842 ◽

2020 ◽

Vol 10 (11) ◽

pp. 1842

Author(s):

Ewa Panek ◽

Dariusz Gozdowski ◽

Michał Stępień ◽

Stanisław Samborski ◽

Dominik Ruciński ◽

...

Keyword(s):

Winter Wheat ◽

Grain Yield ◽

Precision Agriculture ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Correlation Coefficients ◽

Soil Nutrient ◽

Growth Stages ◽

Plant Growth Stages

The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha−1.

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Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands

Remote Sensing ◽

10.3390/rs13050840 ◽

2021 ◽

Vol 13 (5) ◽

pp. 840

Author(s):

Ernesto Sanz ◽

Antonio Saa-Requejo ◽

Carlos H. Díaz-Ambrona ◽

Margarita Ruiz-Ramos ◽

Alfredo Rodríguez ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Lag ◽

Vegetation Indices ◽

Aridity Index ◽

Rangeland Degradation ◽

Temperature And Precipitation ◽

Lag Effect ◽

Relationship Of ◽

The Relationship

Rangeland degradation caused by increasing misuses remains a global concern. Rangelands have a remarkable spatiotemporal heterogeneity, making them suitable to be monitored with remote sensing. Among the remotely sensed vegetation indices, Normalized Difference Vegetation Index (NDVI) is most used in ecology and agriculture. In this paper, we research the relationship of NDVI with temperature, precipitation, and Aridity Index (AI) in four different arid rangeland areas in Spain’s southeast. We focus on the interphase variability, studying time series from 2002 to 2019 with regression analysis and lagged correlation at two different spatial resolutions (500 × 500 and 250 × 250 m2) to understand NDVI response to meteorological variables. Intraseasonal phases were defined based on NDVI patterns. Strong correlation with temperature was reported in phases with high precipitations. The correlation between NDVI and meteorological series showed a time lag effect depending on the area, phase, and variable observed. Differences were found between the two resolutions, showing a stronger relationship with the finer one. Land uses and management affected the NDVI dynamics heavily strongly linked to temperature and water availability. The relationship between AI and NDVI clustered the areas in two groups. The intraphases variability is a crucial aspect of NDVI dynamics, particularly in arid regions.

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TREE AGE AS ADJUSTMENT FACTOR TO NDVI

Revista Árvore ◽

10.1590/1806-90882017000300007 ◽

2018 ◽

Vol 41 (3) ◽

Author(s):

Elias Fernando Berra ◽

Denise Cybis Fontana ◽

Tatiana Mora Kuplich

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Dynamic Range ◽

Vegetation Indices ◽

Tree Age ◽

Stand Age ◽

Adjustment Factor ◽

Biophysical Parameters ◽

Wood Volume ◽

The Relationship

ABSTRACT This study aimed to increase satellite-derived Normalized Difference Vegetation Index (NDVI) sensitivity to biophysical parameters changes with aid of a forest age-based adjustment factor. This factor is defined as a ratio between stand age and age of rotation, which value multiplied by Landsat-5/TM-derived NDVI generated the so-called adjusted index NDVI_a. Soil Adjusted Vegetation Index (SAVI) was also calculated. The relationship between these vegetation indices (VI) with Eucalyptus and Pinus stands’ wood volume was investigated. The adjustment factor caused an increase in NDVI dynamic range values, since older stands tended to be assigned with highest NDVI values, while younger ones tended to be forced to assume lower NDVI values. As a result, direct and significant relationship between NDVI_a and wood volume could be maintained for wider ranges of wood volume. However, it was observed that NDVI_a was only statistically superior to NDVI and SAVI when a detailed age dataset is available. It is conclude that, the stand age has potential to improve NDVI sensitivity to biophysical parameters allowing that quantitative estimates could be made since young to adult stands.

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Use of linguistic estimates and vegetation indices to assess post-fire vegetation regrowth in woodland areas

International Journal of Wildland Fire ◽

10.1071/wf07129 ◽

2010 ◽

Vol 19 (1) ◽

pp. 94 ◽

Cited By ~ 5

Author(s):

Carol R. Jacobson

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Moisture Index ◽

Index Data ◽

Subset Regression ◽

Simple Ratio ◽

The Relationship ◽

Non Destructive

This study examined an area of woodland that was recovering from severe fire in Royal National Park (NSW, Australia). A non-destructive method of field sampling is required for vulnerable recovering vegetation and therefore classification of digital photographs using linguistic terms was trialled. The linguistic data for three vegetation strata (canopy, shrub and ground) were converted to crisp scores and compared with vegetation index data derived from remotely sensed imagery. All possible subset regression was used to test the proposition that the combined vegetation scores (independent variables) would explain the values of NDVI (Normalized Difference Vegetation Index) and NDMI (Normalized Difference Moisture Index). Vegetation scores for the three strata were also combined using simplified weighting ratios to assess broad relationships between the indices and field data. The combined vegetation scores explained ~60% of the variation in the vegetation index data and inclusion of variables representing multiple strata explained more of the variation than any single variable. The precise value of the weights used to combine the layers did not affect the strength of the association. A simple ratio is proposed that may be useful to estimate woodland parameters under similar conditions, by inversion of the relationship with vegetation index data.

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Urban Fire Severity and Vegetation Dynamics in Southern California

Remote Sensing ◽

10.3390/rs13010019 ◽

2020 ◽

Vol 13 (1) ◽

pp. 19

Author(s):

Lauren E. H. Mathews ◽

Alicia M. Kinoshita

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Current Knowledge ◽

Fire Severity ◽

Satellite Image ◽

Burn Severity ◽

Native Vegetation ◽

Riparian Areas ◽

The Impact

A combination of satellite image indices and in-field observations was used to investigate the impact of fuel conditions, fire behavior, and vegetation regrowth patterns, altered by invasive riparian vegetation. Satellite image metrics, differenced normalized burn severity (dNBR) and differenced normalized difference vegetation index (dNDVI), were approximated for non-native, riparian, or upland vegetation for traditional timeframes (0-, 1-, and 3-years) after eleven urban fires across a spectrum of invasive vegetation cover. Larger burn severity and loss of green canopy (NDVI) was detected for riparian areas compared to the uplands. The presence of invasive vegetation affected the distribution of burn severity and canopy loss detected within each fire. Fires with native vegetation cover had a higher severity and resulted in larger immediate loss of canopy than fires with substantial amounts of non-native vegetation. The lower burn severity observed 1–3 years after the fires with non-native vegetation suggests a rapid regrowth of non-native grasses, resulting in a smaller measured canopy loss relative to native vegetation immediately after fire. This observed fire pattern favors the life cycle and perpetuation of many opportunistic grasses within urban riparian areas. This research builds upon our current knowledge of wildfire recovery processes and highlights the unique challenges of remotely assessing vegetation biophysical status within urban Mediterranean riverine systems.

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The Effect of Surrounding Greenness on Type 2 Diabetes Mellitus: A Nationwide Population-Based Cohort in Taiwan

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010267 ◽

2020 ◽

Vol 18 (1) ◽

pp. 267

Author(s):

Hui-Ju Tsai ◽

Chia-Ying Li ◽

Wen-Chi Pan ◽

Tsung-Chieh Yao ◽

Huey-Jen Su ◽

...

Keyword(s):

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Type 2 Diabetes Mellitus ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Population Based ◽

Research Database ◽

Individual Level ◽

The Relationship

This study determines whether surrounding greenness is associated with the incidence of type 2 diabetes Mellitus (T2DM) in Taiwan. A retrospective cohort study determines the relationship between surrounding greenness and the incidence of T2DM during the study period of 2001–2012 using data from the National Health Insurance Research Database. The satellite-derived normalized difference vegetation index (NDVI) from the global MODIS database in the NASA Earth Observing System is used to assess greenness. Cox proportional hazard models are used to determine the relationship between exposure to surrounding greenness and the incidence of T2DM, with adjustment for potential confounders. A total of 429,504 subjects, including 40,479 subjects who developed T2DM, were identified during the study period. There is an inverse relationship between exposure to surrounding greenness and the incidence of T2DM after adjustment for individual-level covariates, comorbidities, and the region-level covariates (adjusted HR = 0.81, 95% CI: 0.79–0.82). For the general population of Taiwan, greater exposure to surrounding greenness is associated with a lower incidence of T2DM.

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Modeling the Radial Stem Growth of the Pine (Pinus sylvestris L.) Forests Using the Satellite-Derived NDVI and LST (MODIS/AQUA) Data

Atmosphere ◽

10.3390/atmos12010012 ◽

2020 ◽

Vol 12 (1) ◽

pp. 12

Author(s):

Yulia Ivanova ◽

Anton Kovalev ◽

Vlad Soukhovolsky

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Principal Component ◽

Stem Growth ◽

Forest Growth ◽

Parabolic Approximation ◽

New Approach ◽

The Relationship ◽

Tree Increment

The paper considers a new approach to modeling the relationship between the increase in woody phytomass in the pine forest and satellite-derived Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) (MODIS/AQUA) data. The developed model combines the phenological and forest growth processes. For the analysis, NDVI and LST (MODIS) satellite data were used together with the measurements of tree-ring widths (TRW). NDVI data contain features of each growing season. The models include parameters of parabolic approximation of NDVI and LST time series transformed using principal component analysis. The study shows that the current rate of TRW is determined by the total values of principal components of the satellite indices over the season and the rate of tree increment in the preceding year.

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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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Assessing the Effect of Drought on Winter Wheat Growth Using Unmanned Aerial System (UAS)-Based Phenotyping

Remote Sensing ◽

10.3390/rs13061144 ◽

2021 ◽

Vol 13 (6) ◽

pp. 1144

Author(s):

Mahendra Bhandari ◽

Shannon Baker ◽

Jackie C. Rudd ◽

Amir M. H. Ibrahim ◽

Anjin Chang ◽

...

Keyword(s):

Winter Wheat ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Grain Filling ◽

Unmanned Aerial Systems ◽

High Temporal Resolution ◽

Wheat Growth ◽

Spatial Domains ◽

Temporal And Spatial

Drought significantly limits wheat productivity across the temporal and spatial domains. Unmanned Aerial Systems (UAS) has become an indispensable tool to collect refined spatial and high temporal resolution imagery data. A 2-year field study was conducted in 2018 and 2019 to determine the temporal effects of drought on canopy growth of winter wheat. Weekly UAS data were collected using red, green, and blue (RGB) and multispectral (MS) sensors over a yield trial consisting of 22 winter wheat cultivars in both irrigated and dryland environments. Raw-images were processed to compute canopy features such as canopy cover (CC) and canopy height (CH), and vegetation indices (VIs) such as Normalized Difference Vegetation Index (NDVI), Excess Green Index (ExG), and Normalized Difference Red-edge Index (NDRE). The drought was more severe in 2018 than in 2019 and the effects of growth differences across years and irrigation levels were visible in the UAS measurements. CC, CH, and VIs, measured during grain filling, were positively correlated with grain yield (r = 0.4–0.7, p < 0.05) in the dryland in both years. Yield was positively correlated with VIs in 2018 (r = 0.45–0.55, p < 0.05) in the irrigated environment, but the correlations were non-significant in 2019 (r = 0.1 to −0.4), except for CH. The study shows that high-throughput UAS data can be used to monitor the drought effects on wheat growth and productivity across the temporal and spatial domains.

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