The Midsummer Dry Spell’s Impact on Vegetation in Jamaica

Abstract The annual rainfall pattern of the intra-Americas sea reveals a bimodal feature with a minimum during the midsummer known as the midsummer dry spell (MSD). A first attempt is made to examine the impact of the MSD on vegetation through a normalized difference vegetation index (NDVI) analysis in Jamaica. Tropical Rainfall Measuring Mission rainfall estimates and NDVI derived from the Terra Moderate Resolution Imaging Spectroradiometer highlight a consistent MSD feature in both rainfall and vegetative vigor. Spatial variation of this MSD NDVI response is evident throughout Jamaica, with the strongest relationship between the rainfall reduction and NDVI decline throughout the southern portions of Jamaica including the area of major domestic food production. In all years except 2005 there is a notable reduction from early-summer NDVI to midsummer NDVI in this agricultural region. However, the lagged vegetative response undergoes clear interannual variation and is affected by other forcings besides rainfall, such as brush fires and extreme wind.

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The Thar, Rajputana desert unprecedented rainfall in 2006 and 2010: effect of climate change?

Geofísica Internacional ◽

10.22201/igeof.00167169p.2011.50.4.150 ◽

2011 ◽

Vol 50 (4) ◽

Author(s):

Vadlamudi Brahmananda Rao ◽

Egidio Arai ◽

Sergio H. Franchito ◽

Yosio E. Shimabukuro ◽

S.S.V.S. Ramakrishna ◽

...

Keyword(s):

Climate Change ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Tropical Rainfall Measuring Mission ◽

Enhanced Vegetation Index ◽

Tropical Rainfall ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

En agosto de 2006 la región de Rajasthan registró lluvias excepcionalmente intensas que provocaron severas inundaciones. También, en la temporada del monzón de 2010 Rajasthan recibió fuertes lluvias, aunque no superiores a las de agosto de 2006. Pero los altos valores de precipitación en el año 2010 se produjeron durante toda la temporada del monzón. En el 2006 varias estaciones registraron lluvias intensas de alrededor de 125 mm en 24 horas. Un estudio reciente mostró que, en el futuro, eventos extremos similares tenderán a ocurrir en la India Central, que incluye una parte de Rajasthan. En este trabajo estos eventos son estudiados en el contexto de un probable cambio climatico sobre esta región usando datos de satélites. Para ello fueron usados los índices de vegetación NDVI (Normalized Difference Vegetation Index) y EVI (Enhanced Vegetation Index), derivados del MODIS (Moderate Resolution Imaging Spectroradiometer) y los datos de precipitación del satélite TRMM (Tropical Rainfall Measuring Mission) para 11 años (2000-2010). Ambos productos NDVI y EVI revelaron un crecimiento exuberante de la vegetación sobre el desierto de Rajputana en setiembre de 2006 y en agosto y septiembre de 2010. El análisis de los datos de precipitación y EVI confirmó el crecimiento de la vegetación en 2006 y 2010, mostrando la utilidad de los datos obtenidos por satélite en la captura de los cambios en esta región. En algunos estudios previos se ha señalado que la lluvia sobre la región Oeste de Rajasthan durante la temporada del monzón muestra una importante tendencia creciente. Así, en el futuro, el crecimiento de la vegetación en el desierto Rajputana parece ser muy posible.

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An Effective Method for Generating Spatiotemporally Continuous 30 m Vegetation Products

Remote Sensing ◽

10.3390/rs13040719 ◽

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.

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Vegetative growth of grasslands based on hyper-temporal NDVI data from the Modis sensor

Pesquisa Agropecuária Brasileira ◽

10.1590/s0100-204x2016000700009 ◽

2016 ◽

Vol 51 (7) ◽

pp. 858-868

Author(s):

Marcos Cicarini Hott ◽

Luis Marcelo Tavares de Carvalho ◽

Mauro Antonio Homem Antunes ◽

Polyanne Aguiar dos Santos ◽

Tássia Borges Arantes ◽

...

Keyword(s):

Vegetative Growth ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

High Growth ◽

Growth Index ◽

Data Series ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Very High

Abstract: The objective of this work was to analyze the development of grasslands in Zona da Mata, in the state of Minas Gerais, Brazil, between 2000 and 2013, using a parameter based on the growth index of the normalized difference vegetation index (NDVI) from the moderate resolution imaging spectroradiometer (Modis) data series. Based on temporal NDVI profiles, which were used as indicators of edaphoclimatic conditions, the growth index (GI) was estimated for 16-day periods throughout the spring season of 2012 to early 2013, being compared with the average GI from 2000 to 2011, used as the reference period. Currently, the grassland areas in Zona da Mata occupy approximately 1.2 million hectares. According to the used methods, 177,322 ha (14.61%) of these grassland areas have very low vegetative growth; 577,698 ha (45.96%) have low growth; 433,475 ha (35.72%) have balanced growth; 39,980 ha (3.29%) have high growth; and 5,032 ha (0.41%) have very high vegetative growth. The grasslands had predominantly low vegetative growth during the studied period, and the NDVI/Modis series is a useful source of data for regional assessments.

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Development of a remote sensing-based rice yield forecasting model

Spanish Journal of Agricultural Research ◽

10.5424/sjar/2016143-8347 ◽

2016 ◽

Vol 14 (3) ◽

pp. e0907 ◽

Cited By ~ 14

Author(s):

Mostafa K. Mosleh ◽

Quazi K. Hassan ◽

Ehsan H. Chowdhury

Keyword(s):

Remote Sensing ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Rice Yield ◽

Decision Makers ◽

Coefficient Of Determination ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Boro Rice

This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh.

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Spearman Correlation between the NDVI and Quercus Airborne Pollen in the SW of the Iberian Peninsula

Proceedings ◽

10.3390/proceedings2201519 ◽

2018 ◽

Vol 2 (20) ◽

pp. 1519

Author(s):

Rocío González-Naharro ◽

Elia Quirós ◽

Santiago Fernández-Rodríguez ◽

Inmaculada Silva-Palacios ◽

Rafael Tormo-Molina ◽

...

Keyword(s):

Iberian Peninsula ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Airborne Pollen ◽

Training Data ◽

Oak Trees ◽

Pollen Concentrations ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Spearman Test

New space technologies as Advanced Very High Resolution Radiometer (AVHRR) and the MODerate resolution Imaging Spectroradiometer (MODIS) have been used to display several phenological cycles of ecosystems around the world. The aim of this study was to establish the relationship between Normalized Difference Vegetation Index, NDVI, associated to oak trees within three training data polygons (15, 25 and 50 km-distance to the volumetric sampler Hirst), and the daily average Quercus airborne pollen concentrations in 20 years. The study was developed in Badajoz (SW Iberian Peninsula) with a continuous pollen recording in the period from 1994 to 2013. The main novelty of this study has been the analysis of the correlation between the two-time series, using Spearman test. Within the 20 studied years, 12 years obtained significant values in the Spearman test in the whole studied area.

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Savannah Phenological Dynamics Reveal Spatio-Temporal Landscape Heterogeneity in Karamoja Sub-region, Uganda

Frontiers in Sustainable Food Systems ◽

10.3389/fsufs.2020.541170 ◽

2020 ◽

Vol 4 ◽

Author(s):

Anthony Egeru ◽

John Paul Magaya ◽

Derick Ansyijar Kuule ◽

Aggrey Siya ◽

Anthony Gidudu ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Landscape Heterogeneity ◽

Global Environmental Change ◽

Arid Environment ◽

Modis Ndvi ◽

Moderate Resolution ◽

Spatio Temporal ◽

Moderate Resolution Imaging Spectroradiometer ◽

Phenological Metrics

Phenological properties are critical in understanding global environmental change patterns. This study analyzed phenological dynamics in a savannah dominated semi-arid environment of Uganda. We used moderate-resolution imaging spectroradiometer normalized difference vegetation index (MODIS NDVI) imagery. TIMESAT program was used to analyse the imagery to determine key phenological metrics; onset of greenness (OGT), onset of greenness value, end of greenness time (EGT), end of greenness value, maximum NDVI, time of maximum NDVI, duration of greenup (DOG) and range of normalized difference vegetation index (RNDVI). Results showed that thicket and shrubs had the earliest OGT on day 85 ± 14, EGT on day 244 ± 32 and a DOG of 158 ± 25 days. Woodland had the highest NDVI value for maximum NDVI, OGT, EGT, and RNDVI. In the bushland, OGT occurs on average around day 90 ± 11, EGT on day 255 ± 33 with a DOG of 163 ± 36 days. The grassland showed that OGT occurs on day 96 ± 13, EGT on day 252 ± 36 with a total DOG of 156 ± 33 days. Early photosynthesis activity was observed in central to eastern Karamoja in the districts of Moroto and Kotido. There was a positive relationship between rainfall and NDVI across all vegetation cover types as well as between phenological parameters and season dynamics. Vegetation senescence in the sub-region occurs around August to mid-September (day 244–253). The varied phenophases observed in the sub-region reveal an inherent landscape heterogeneity that is beneficial to extensive pastoral livestock production. Continuous monitoring of savannah phenological patterns in the sub-region is required to decipher landscape ecosystem processes and functioning.

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Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe

Remote Sensing ◽

10.3390/rs11151823 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1823 ◽

Cited By ~ 9

Author(s):

Xiaojuan Huang ◽

Jingfeng Xiao ◽

Mingguo Ma

Keyword(s):

Primary Productivity ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Gross Primary Productivity ◽

Surface Reflectance ◽

Monthly Scale ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Annual Scale

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

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Validation and Accuracy Assessment of MODIS C6.1 Aerosol Products over the Heavy Aerosol Loading Area

Atmosphere ◽

10.3390/atmos10090548 ◽

2019 ◽

Vol 10 (9) ◽

pp. 548 ◽

Cited By ~ 7

Author(s):

Xinpeng Tian ◽

Zhiqiang Gao

Keyword(s):

Urban Areas ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Accuracy Assessment ◽

Aerosol Loading ◽

Deep Blue ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis ◽

Reflecting Surface

The aim of this study is to evaluate the accuracy of MODerate resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) products over heavy aerosol loading areas. For this analysis, the Terra-MODIS Collection 6.1 (C6.1) Dark Target (DT), Deep Blue (DB) and the combined DT/DB AOD products for the years 2000–2016 are used. These products are validated using AErosol RObotic NETwork (AERONET) data from twenty-three ground sites situated in high aerosol loading areas and with available measurements at least 500 days. The results show that the numbers of collections (N) of DB and DT/DB retrievals were much higher than that of DT, which was mainly caused by unavailable retrieval of DT in bright reflecting surface and heavy pollution conditions. The percentage falling within the expected error (PWE) of the DT retrievals (45.6%) is lower than that for the DB (53.4%) and DT/DB (53.1%) retrievals. The DB retrievals have 5.3% less average overestimation, and 25.7% higher match ratio than DT/DB retrievals. It is found that the current merged aerosol algorithm will miss some cases if it is determined only on the basis of normalized difference vegetation index. As the AOD increases, the value of PWE of the three products decreases significantly; the undervaluation is suppressed, and the overestimation is aggravated. The retrieval accuracy shows distinct seasonality: the PWE is largest in autumn or winter, and smallest in summer. The most severe overestimation and underestimation occurred in the summer. Moreover, the DT, DB and DT/DB products over different land cover types still exhibit obvious deviations. In urban areas, the PWE of DB product (52.6%) is higher than for the DT/DB (46.3%) and DT (25.2%) products. The DT retrievals perform poorly over the barren or sparsely vegetated area (N = 52). However, the performance of three products is similar over vegetated area. On the whole, the DB product performs better than the DT product over the heavy aerosol loading area.

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Comparison of MODIS 250 m products for early corn yield predictions: a case study in Vojvodina, Serbia

Open Geosciences ◽

10.1515/geo-2016-0070 ◽

2016 ◽

Vol 8 (1) ◽

Cited By ~ 3

Author(s):

Miro Govedarica ◽

Dušan Jovanović ◽

Filip Sabo ◽

Mirko Borisov ◽

Milan Vrtunski ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Corn Yield ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

AbstractThe aim of the paper is to compare Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (

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Development and Demonstration of a Method for GEO-to-LEO NDVI Transformation

Remote Sensing ◽

10.3390/rs13204085 ◽

2021 ◽

Vol 13 (20) ◽

pp. 4085

Author(s):

Kenta Obata ◽

Kenta Taniguchi ◽

Masayuki Matsuoka ◽

Hiroki Yoshioka

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Middle Latitude ◽

Transformation Method ◽

Low Earth Orbit ◽

Linear Mixture Model ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Index Time Series

This study presents a new method that mitigates biases between the normalized difference vegetation index (NDVI) from geostationary (GEO) and low Earth orbit (LEO) satellites for Earth observation. The method geometrically and spectrally transforms GEO NDVI into LEO-compatible GEO NDVI, in which GEO’s off-nadir view is adjusted to a near-nadir view. First, a GEO-to-LEO NDVI transformation equation is derived using a linear mixture model of anisotropic vegetation and nonvegetation endmember spectra. The coefficients of the derived equation are a function of the endmember spectra of two sensors. The resultant equation is used to develop an NDVI transformation method in which endmember spectra are automatically computed from each sensor’s data independently and are combined to compute the coefficients. Importantly, this method does not require regression analysis using two-sensor NDVI data. The method is demonstrated using Himawari 8 Advanced Himawari Imager (AHI) data at off-nadir view and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data at near-nadir view in middle latitude. The results show that the magnitudes of the averaged NDVI biases between AHI and MODIS for five test sites (0.016–0.026) were reduced after the transformation (<0.01). These findings indicate that the proposed method facilitates the combination of GEO and LEO NDVIs to provide NDVIs with smaller differences, except for cases in which the fraction of vegetation cover (FVC) depends on the view angle. Further investigations should be conducted to reduce the remaining errors in the transformation and to explore the feasibility of using the proposed method to predict near-real-time and near-nadir LEO vegetation index time series using GEO data.

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