Analysis of vegetation change associated with human disturbance using MODIS data on the rangelands of the Qinghai-Tibet Plateau

This paper evaluated changes in vegetation from 2000 to 2012, based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Normalised Difference Vegetation Index (NDVI), and related them to changes in estimates of human disturbance on the rangelands of the Qinghai-Tibet Plateau. The main rangeland types studied were desert, steppe and meadow with the latter mainly found in the southern and eastern parts of the study area. The results indicated that human disturbance was distributed mainly in the southern and eastern parts of the study area and corresponded with high NDVI values. The NDVI values showed an upward trend over the study period, with 28.5% of the study area exhibiting a significant increase. The proportion of rangelands that experienced a downward trend in NDVI increased as the level of human disturbance increased. Of the different rangeland types, meadow had the highest NDVI values, the greatest human disturbance, and the highest proportion of rangelands that exhibited a significant decrease in NDVI. Compared with areas with no human disturbance, meadow and steppe rangelands that experienced an increase in human disturbance had lower rates of increase in their NDVI values but, conversely, desert rangelands showed the opposite trend. In addition, it was found that precipitation had the dominant influence on NDVI values and that higher precipitation and slighter lower temperatures over the period of the study were related to an increase in NDVI values.

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Detection of Decreasing Vegetation Cover Based on Empirical Orthogonal Function and Temporal Unmixing Analysis

Mathematical Problems in Engineering ◽

10.1155/2017/5032091 ◽

2017 ◽

Vol 2017 ◽

pp. 1-10 ◽

Cited By ~ 2

Author(s):

Di Xu ◽

Ruishan Chen ◽

Xiaoshi Xing ◽

Wenpeng Lin

Keyword(s):

Empirical Orthogonal Function ◽

Land Surface ◽

Vegetation Change ◽

Vegetation Index ◽

Orthogonal Function ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Hydrological Cycles ◽

Spectral Mixture

Vegetation plays an important role in the energy exchange of the land surface, biogeochemical cycles, and hydrological cycles. MODIS (MODerate-resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) is considered as a quantitative indicator for examining dynamic vegetation changes. This paper applied a new method of integrated empirical orthogonal function (EOF) and temporal unmixing analysis (TUA) to detect the vegetation decreasing cover in Jiangsu Province of China. The empirical orthogonal function (EOF) statistical results provide vegetation decreasing/increasing trend as prior information for temporal unmixing analysis. Temporal unmixing analysis (TUA) results could reveal the dominant spatial distribution of decreasing vegetation. The results showed that decreasing vegetation areas in Jiangsu are distributed in the suburbs and newly constructed areas. For validation, the vegetation’s decreasing cover is revealed by linear spectral mixture from Landsat data in three selected cities. Vegetation decreasing areas pixels are also calculated from land use maps in 2000 and 2010. The accuracy of integrated empirical orthogonal function and temporal unmixing analysis method is about 83.14%. This method can be applied to detect vegetation change in large rapidly urbanizing areas.

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Using Multisource Satellite Data to Assess Recent Snow-Cover Variability and Uncertainty in the Qinghai–Tibet Plateau

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0220.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1293-1306 ◽

Cited By ~ 5

Author(s):

Yingsha Jiang ◽

Fei Chen ◽

Yanhong Gao ◽

Michael Barlage ◽

Jianduo Li

Keyword(s):

Snow Cover ◽

Water Cycle ◽

Regional Climate ◽

Tibet Plateau ◽

Mapping System ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Cloud Removal ◽

Qinghai Tibet Plateau

Abstract Snow cover in the Qinghai–Tibet Plateau (QTP) is a critical component in the water cycle and regional climate of East Asia. Fractional snow cover (FSC) derived from five satellite sources [the three satellites comprising the multisensor synergy of FengYun-3 (FY-3A/B/C), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Interactive Multisensor Snow and Ice Mapping System (IMS)] were intercompared over the QTP to examine uncertainties in mean snow cover and interannual variability over the last decade. A four-step cloud removal procedure was developed for MODIS and FY-3 data, which effectively reduced the cloud percentage from about 40% to 2%–3% with an error of about 2% estimated by a random sampling method. Compared to in situ snow-depth observations, the cloud-removed FY-3B data have an annual classification accuracy of about 94% for both 0.04° and 0.01° resolutions, which is higher than other datasets and is recommended for use in QTP studies. Among the five datasets analyzed, IMS has the largest snow extent (22% higher than MODIS) and the highest FSC (4.7% higher than MODIS), while the morning-overpass MODIS and FY-3A/C FSC are similar and are around 5% higher than the afternoon-overpass FY-3B FSC. Contrary to MODIS, IMS shows increasing variability in snow cover and snow duration over the last decade (2006–17). Differences in variabilities of FSC and snow duration between products are greater at 5–6 km than lower elevations, with seasonal snow-cover change showing the largest uncertainty in snowmelt date.

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Effectiveness of a Complex Fertilisation Technology Applied to Zea mays, Assessed Based on Normalised Difference Vegetation Index (NDVI) from Terra Moderate Resolution Imaging Spectroradiometer (MODIS)

Agriculture ◽

10.3390/agriculture11080754 ◽

2021 ◽

Vol 11 (8) ◽

pp. 754

Author(s):

Natalia Matłok ◽

Oskar Basara ◽

Miłosz Zardzewiały ◽

Józef Gorzelany ◽

Maciej Balawejder

Keyword(s):

Grain Yield ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Physiological Measures ◽

Entire Area ◽

Normalised Difference Vegetation Index ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Remote Sensing Methods

Assessment of effectiveness of fertilisation is a complex, multistage procedure. A few methods, used for this purpose, are based mainly on physiological measures acquired from a limited number of plants. Assessment of the process taking into account the entire area, in which the crop is grown, can be conducted using satellite remote sensing methods. The current study presents four fertilisation schemes applied to maize plants, including innovative foliar fertilizers and soil localized fertilization. Nutritional status and condition of the plants were assessed using Normalized Difference Vegetation Index (NDVI), and the results were analysed in relation to the grain yield. The findings show that the complex fertilisation technology applied to maize is most effective, producing grain yield which was 42.4% higher than the yield from the control variant.

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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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Improved fuel moisture prediction in non-native tropical Megathyrsus maximus grasslands using Moderate-Resolution Imaging Spectroradiometer (MODIS)-derived vegetation indices

International Journal of Wildland Fire ◽

10.1071/wf16131 ◽

2017 ◽

Vol 26 (5) ◽

pp. 384

Author(s):

L. M. Ellsworth ◽

A. P. Dale ◽

C. M. Litton ◽

T. Miura

Keyword(s):

Vegetation Index ◽

Vegetation Indices ◽

Fuel Moisture ◽

Fire Behaviour ◽

Native Grass ◽

Tropical Ecosystems ◽

Moderate Resolution ◽

Resolution Imaging ◽

Live Fuel Moisture ◽

Moderate Resolution Imaging Spectroradiometer

The synergistic impacts of non-native grass invasion and frequent human-derived wildfires threaten endangered species, native ecosystems and developed land throughout the tropics. Fire behaviour models assist in fire prevention and management, but current models do not accurately predict fire in tropical ecosystems. Specifically, current models poorly predict fuel moisture, a key driver of fire behaviour. To address this limitation, we developed empirical models to predict fuel moisture in non-native tropical grasslands dominated by Megathyrsus maximus in Hawaii from Terra Moderate-Resolution Imaging Spectroradiometer (MODIS)-based vegetation indices. Best-performing MODIS-based predictive models for live fuel moisture included the two-band Enhanced Vegetation Index (EVI2) and Normalized Difference Vegetation Index (NDVI). Live fuel moisture models had modest (R2=0.46) predictive relationships, and outperformed the commonly used National Fire Danger Rating System (R2=0.37) and the Keetch–Byram Drought Index (R2=0.06). Dead fuel moisture was also best predicted by a model including EVI2 and NDVI, but predictive capacity was low (R2=0.19). Site-specific models improved model fit for live fuel moisture (R2=0.61), but limited extrapolation. Better predictions of fuel moisture will improve fire management in tropical ecosystems dominated by this widespread and problematic non-native grass.

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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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