scholarly journals Satellite derived evidence of whole farmlet and paddock responses to management and climate

2013 ◽  
Vol 53 (8) ◽  
pp. 699 ◽  
Author(s):  
G. E. Donald ◽  
J. M. Scott ◽  
P. J. Vickery
Keyword(s):  
Soil Moisture ◽  
Soil Fertility ◽  
Satellite Data ◽  
Vegetation Index ◽  
Growth Patterns ◽  
Entire Area ◽  
Systems Research ◽  
Spectral Class ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution

Satellite imagery was used to assess differences between three treatments in a grazing enterprise systems study of three 53-ha farmlets on the Northern Tablelands of New South Wales, Australia. The study involved a comparison between a typical control farmlet (B) with one with higher levels of sown pasture and soil fertility (A) and one employing intensive rotational grazing (C). Landsat thematic mapper data were used to derive normalised difference vegetation index (NDVI) and spectral class images for eight dates from before the commencement of the farmlet trial (June 2000) to annual spring measurements in September–October of each year from 2000 to 2006 across all paddocks of each farmlet. The Landsat imagery taken before the commencement of the farmlet treatments (June 2000) showed only small differences between the three farmlets, confirming that the allocation of land to the farmlets had been without bias. The assessments using Landsat NDVI in spring over 7 years showed differences in green herbage resulting from the variation in rainfall received over different years as well as differences between the farmlets. The Landsat NDVI images showed increasing and significant differences in pasture greenness over time, especially between Farmlet A and Farmlets B and C. In addition, there were significant differences in pasture spectral classes between Farmlet A and Farmlets B and C, with a significant correlation with higher levels of sown perennial and annual grasses and legumes on Farmlet A. Using different statistical tools, several relationships were found between NDVI and spectral class data and explanatory variables of farmlet, paddock, sowing phase, modelled soil moisture and recent grazing activity. The moderate resolution Landsat data across the entire area of each farmlet proved to be especially useful for assessing pastures within every paddock used in this farmlet study. In addition, moderate resolution imaging spectro radiometer NDVI satellite data were collated for weekly intervals from September 2003 to December 2006 in order to assess seasonal pasture growth patterns on each of the farmlets. These patterns were significantly correlated with a growth index calculated from temperature and available soil moisture, and showed that the growth on the three farmlets was closer to a highly productive reference paddock than a low input, unsown pasture in another reference paddock. The satellite data facilitated the detection of significant differences in pasture botanical composition, soil fertility, grazing management, climate and season. The ready availability of quality remote sensed imagery, combined with the significance of the relationships established, confirms that the technology is a valuable objective tool for both farming systems research and for managing entire farms.

scholarly journals 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 ◽  
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.

scholarly journals A GNSS-IR Method for Retrieving Soil Moisture Content from Integrated Multi-Satellite Data That Accounts for the Impact of Vegetation Moisture Content

2021 ◽  
Vol 13 (13) ◽  
pp. 2442
Author(s):  
Jichao Lv ◽  
Rui Zhang ◽  
Jinsheng Tu ◽  
Mingjie Liao ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Satellite Data ◽  
Vegetation Index ◽  
Soil Moisture Content ◽  
Support Vector ◽  
Observation Data ◽  
Multipath Effect ◽  
Mars Model ◽  
The Impact

There are two problems with using global navigation satellite system-interferometric reflectometry (GNSS-IR) to retrieve the soil moisture content (SMC) from single-satellite data: the difference between the reflection regions, and the difficulty in circumventing the impact of seasonal vegetation growth on reflected microwave signals. This study presents a multivariate adaptive regression spline (MARS) SMC retrieval model based on integrated multi-satellite data on the impact of the vegetation moisture content (VMC). The normalized microwave reflection index (NMRI) calculated with the multipath effect is mapped to the normalized difference vegetation index (NDVI) to estimate and eliminate the impact of VMC. A MARS model for retrieving the SMC from multi-satellite data is established based on the phase shift. To examine its reliability, the MARS model was compared with a multiple linear regression (MLR) model, a backpropagation neural network (BPNN) model, and a support vector regression (SVR) model in terms of the retrieval accuracy with time-series observation data collected at a typical station. The MARS model proposed in this study effectively retrieved the SMC, with a correlation coefficient (R2) of 0.916 and a root-mean-square error (RMSE) of 0.021 cm3/cm3. The elimination of the vegetation impact led to 3.7%, 13.9%, 11.7%, and 16.6% increases in R2 and 31.3%, 79.7%, 49.0%, and 90.5% decreases in the RMSE for the SMC retrieved by the MLR, BPNN, SVR, and MARS model, respectively. The results demonstrated the feasibility of correcting the vegetation changes based on the multipath effect and the reliability of the MARS model in retrieving the SMC.

scholarly journals Using vegetation indices for soil-moisture retrievals from passive microwave radiometry

2001 ◽  
Vol 5 (4) ◽  
pp. 671-678 ◽  
Author(s):  
E.J. Burke ◽  
W.J. Shuttleworth ◽  
A.N. French
Keyword(s):  
Soil Moisture ◽  
Optical Depth ◽  
Vegetation Index ◽  
Empirical Relationship ◽  
Vegetation Indices ◽  
Passive Microwave ◽  
Microwave Emission ◽  
Retrieval Algorithm ◽  
Emission Model ◽  
Normalised Difference Vegetation Index

Abstract. Surface soil moisture and the nature of the overlying vegetation both influence microwave emission from land surfaces significantly. One widely discussed but underused method for allowing for the effect of vegetation on soil-moisture retrievals from microwave observations is to use remotely sensed vegetation indices. This paper explores the potential for using the Normalised Difference Vegetation Index (NDVI) in soil-moisture retrievals from L-band (1.4 GHz) aircraft data gathered during the Southern Great Plains '97 (SGP97) experiment. A simplified version of MICRO-SWEAT, a soil vegetation atmosphere transfer (SVAT) scheme coupled with a microwave emission model, was used as the retrieval algorithm. Estimates of the optical depth of the vegetation, the parameter that describes the effect of the vegetation on microwave emission, were obtained by calibrating this retrieval algorithm against measurements of soil moisture at 15 field sites. A significant relationship was found between the optical depth so obtained and the observed NDVI at these sites, although this relationship changed with the resolution of the microwave brightness temperature observations used. Soil-moisture estimates made with the retrieval algorithm using the empirical relationship between optical depth and NDVI applied at two additional sites not used in the calibration show good agreement with field measurements. Keywords: NDVI, soil moisture, passive microwave, SGP97

scholarly journals ON-LINE CHANGE MONITORING WITH TRANSFORMED MULTI-SPECTRAL TIME SERIES, A STUDY CASE IN TROPICAL FOREST

2016 ◽  
Vol XLI-B7 ◽  
pp. 987-989 ◽  
Author(s):  
Meng Lu ◽  
Eliakim Hamunyela
Keyword(s):  
Time Series ◽  
Tropical Forest ◽  
Satellite Data ◽  
Vegetation Index ◽  
Forest Disturbance ◽  
Spectral Band ◽  
Satellite Image ◽  
Spectral Bands ◽  
Normalised Difference Vegetation Index ◽  
Change Monitoring

In recent years, the methods for detecting structural changes in time series have been adapted for forest disturbance monitoring using satellite data. The BFAST (Breaks For Additive Season and Trend) Monitor framework, which detects forest cover disturbances from satellite image time series based on empirical fluctuation tests, is particularly used for near real-time deforestation monitoring, and it has been shown to be robust in detecting forest disturbances. Typically, a vegetation index that is transformed from spectral bands into feature space (e.g. normalised difference vegetation index (NDVI)) is used as input for BFAST Monitor. However, using a vegetation index for deforestation monitoring is a major limitation because it is difficult to separate deforestation from multiple seasonality effects, noise, and other forest disturbance. In this study, we address such limitation by exploiting the multi-spectral band of satellite data. To demonstrate our approach, we carried out a case study in a deciduous tropical forest in Bolivia, South America. We reduce the dimensionality from spectral bands, space and time with projective methods particularly the Principal Component Analysis (PCA), resulting in a new index that is more suitable for change monitoring. Our results show significantly improved temporal delay in deforestation detection. With our approach, we achieved a median temporal lag of 6 observations, which was significantly shorter than the temporal lags from conventional approaches (14 to 21 observations).

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

2015 ◽  
Vol 37 (1) ◽  
pp. 77 ◽  
Author(s):  
Haidi Zhao ◽  
Shiliang Liu ◽  
Shikui Dong ◽  
Xukun Su ◽  
Xuexia Wang ◽  
...  
Keyword(s):  
Human Disturbance ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Tibet Plateau ◽  
Desert Rangelands ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
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.

scholarly journals Annual Normalized Difference Vegetation Index time-series data for Australian statistical areas

2021 ◽  
Author(s):  
Simon Ramsey ◽  
Suzanne Mavoa
Keyword(s):  
Vegetation Index ◽  
Time Series Data ◽  
Google Earth ◽  
Series Data ◽  
Atmospheric Effects ◽  
Data Set ◽  
Normalised Difference Vegetation Index ◽  
Moderate Resolution ◽  
Google Earth Engine ◽  
Statistical Area

Google Earth Engine provides researchers with a platform for conducting planetary scale analysis of environmental processes and landcover change, both by providing the necessary tools and by handling the large quantities of data these analyses require. The most widely used moderate-resolution sensors, onboard the Landsat satellite platforms, often require pre-processing to prepare the data for analysis. This data set consists of Australia-wide Landsat derived Normalised Difference Vegetation Index (NDVI) values for the years 2001-2019. The median annual NDVI value for each Statistical Area 1 (SA1) and Statistical Area 2 (SA2) were calculated, and statistics for this imagery is provided in a tabular format. The accompanying Google Earth Engine script applies the pre-processing steps required to account for sensor, solar and atmospheric effects, improving continuity between imagery across space and time and therefore, will enable researchers beyond the remote sensing community to access analysis-ready imagery for the moderate resolution Landsat and Sentinel-2 satellite platforms.

scholarly journals Pasture herbage mass, quality and growth in response to three whole-farmlet management systems

2013 ◽  
Vol 53 (8) ◽  
pp. 685 ◽  
Author(s):  
L. M. Shakhane ◽  
C. Mulcahy ◽  
J. M. Scott ◽  
G. N. Hinch ◽  
G. E. Donald ◽  
...  
Keyword(s):  
Growth Rate ◽  
Soil Fertility ◽  
Vegetation Index ◽  
Seasonal Pattern ◽  
Ground Cover ◽  
The Other ◽  
Grazing Management ◽  
Management Systems ◽  
Explanatory Factors ◽  
Normalised Difference Vegetation Index

The effects of different whole-farm management systems were explored in a farmlet trial on the Northern Tablelands of New South Wales, Australia, between July 2000 and December 2006. The three systems examined were first, a moderate input farmlet with flexible grazing on eight paddocks considered ‘typical’ of the region (farmlet B), a second, also with flexible grazing on eight paddocks but with a high level of pasture renovation and increased soil fertility (farmlet A) and a third with the same moderate level of inputs as farmlet B but which practised intensive rotational grazing on 37 paddocks (farmlet C). The changes in herbage mass, herbage quality and pasture growth followed a seasonal pattern typical of the Northern Tablelands with generally higher levels recorded over spring–summer and lower levels in autumn–winter but with substantial differences between years due to the variable climate experienced. Over the first 18 months of the trial there were no significant differences between farmlets in total herbage mass. Although the climate was generally drier than average, the differences between farmlets in pasture herbage mass and quality became more evident over the duration of the experiment. After the farmlet treatments started to take effect, the levels of total and dead herbage mass became significantly lower on farmlet A compared with farmlets B and C. In contrast, the levels of green herbage were similar for all farmlets. Throughout most of the study period, pastures on farmlet A with its higher levels of pasture renovation and soil fertility, had significantly higher DM digestibility for both green and dead herbage components compared with pastures on either of the moderate input systems (B and C). Thus, when green herbage mass and quality were combined, farmlet A tended to have higher levels of green digestible herbage than either of the other farmlets, which had similar levels, suggesting that pasture renovation and soil fertility had more effect on the supply of quality pasture than did grazing management. This difference was observed in spite of the higher stocking rate supported by farmlet A after treatments took effect. Levels of legume herbage mass, while generally low due to the dry conditions, were significantly higher on farmlet A compared with the other two farmlets. While ground cover on farmlet A was found to be less than the other farmlets, this was largely associated with the higher level of pasture renovation. Generally, all three farmlets had ground cover levels well above 70% for the duration of the experiment, thus being above levels considered critical for prevention of erosion. A multivariate analysis showed that the main explanatory factors significantly linked (P < 0.01) with the supply of high quality herbage were, in decreasing order of importance, those related to season and weather, pasture renovation, grazing management and soil fertility. Measurements of net pasture growth conducted using a limited number of grazing exclosure cages on three paddocks per farmlet revealed clear seasonal trends but no significant (P > 0.05) differences between farmlets. However, post hoc estimates of potential pasture growth rate using remotely sensed MODIS satellite images of normalised difference vegetation index captured weekly from each farmlet revealed a significant (P < 0.001) relationship with the seasonal pattern observed in the measurements of pasture growth rate.

scholarly journals Using Multi-Temporal Satellite Data to Analyse Phenological Responses of Rubber (Hevea brasiliensis) to Climatic Variations in South Sumatra, Indonesia

2021 ◽  
Vol 13 (15) ◽  
pp. 2932
Author(s):  
Fathin Ayuni Azizan ◽  
Ike Sari Astuti ◽  
Mohammad Irvan Aditya ◽  
Tri Rapani Febbiyanti ◽  
Alwyn Williams ◽  
...  
Keyword(s):  
Climate Change ◽  
Satellite Data ◽  
Hevea Brasiliensis ◽  
Land Surface ◽  
Vegetation Index ◽  
Rubber Tree ◽  
Tree Age ◽  
Smoothing Function ◽  
Normalised Difference Vegetation Index ◽  
Phenological Metrics

Land surface phenology derived from satellite data provides insights into vegetation responses to climate change. This method has overcome laborious and time-consuming manual ground observation methods. In this study, we assessed the influence of climate on phenological metrics of rubber (Hevea brasiliensis) in South Sumatra, Indonesia, between 2010 and 2019. We modelled rubber growth through the normalised difference vegetation index (NDVI), using eight-day surface reflectance images at 250 m spatial resolution, sourced from NASA’s Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua satellites. The asymmetric Gaussian (AG) smoothing function was applied on the model in TIMESAT to extract three phenological metrics for each growing season: start of season (SOS), end of season (EOS), and length of season (LOS). We then analysed the effect of rainfall and temperature, which revealed that fluctuations in SOS and EOS are highly related to disturbances such as extreme rainfall and elevated temperature. Additionally, we observed inter-annual variations of SOS and EOS associated with rubber tree age and clonal variability within plantations. The 10-year monthly climate data showed a significant downward and upward trend for rainfall and temperature data, respectively. Temperature was identified as a significant factor modulating rubber phenology, where an increase in temperature of 1 °C advanced SOS by ~25 days and EOS by ~14 days. These results demonstrate the capability of remote sensing observations to monitor the effects of climate change on rubber phenology. This information can be used to improve rubber management by helping to identify critical timing for implementation of agronomic interventions.

Characterising soil moisture regimes on poorly drained soils in Ireland using optical satellite derived vegetation indices and the OPTRAM model

2021 ◽  
Author(s):  
Rumia Basu ◽  
Colin Brown ◽  
Patrick Tuohy ◽  
Eve Daly
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Crop Production ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Accurate Information ◽  
Effective Manner ◽  
Normalised Difference Vegetation Index ◽  
Drainage Capacity ◽  
Poorly Drained Soils

&lt;p&gt;Soil drainage capacity is the degree and frequency at which the soil is free of saturation. It influences land use and management, soil nutrient cycling and greenhouse gas fluxes. Accurate information on drainage conditions is crucial for crop production and management and fundamental in developing strategies to adhere to environmental sustainability goals. This is particularly important in Ireland where approximately 50% of the soils are classified as &amp;#8220;marginal&amp;#8221;. These are mainly poorly drained soils which negatively impact plant growth and productivity.&lt;/p&gt;&lt;p&gt;Soil moisture acts as a proxy for drainage capacity. Timely and accurate information on soil moisture allows for precision management strategies. It aids in designing effective interventions on farms for artificial drainage works which are often assessed by information on soil moisture, soil type and hydrology. Such data are conventionally acquired by in-situ point sampling techniques which are costly and time consuming. Remote sensing has the potential to provide a solution by allowing simultaneous coverage of large geographic areas, quickly and in a cost effective manner.&lt;/p&gt;&lt;p&gt;This study uses optical remote sensing data from Sentinel 2 to derive information on soil moisture conditions on selected sites in Ireland.&amp;#160; We develop the OPTRAM model of Sadeghi et al (2017) by exploring the use of remote sensing based vegetation indices such as the Normalised Difference Vegetation index, Enhanced Vegetation Index and Normalised Difference Red Edge Index for the years 2015-2020 along with short wave transformed infrared reflectance to estimate soil moisture variations for our study areas. We show that &amp;#160;non-linear estimates of the wet and dry edge curves in the model are better suited for Ireland, which is dominated by wet conditions for most of the year and also identify the best vegetation indices for studying soil moisture variations.&lt;/p&gt;

scholarly journals A NOVEL SPECTRAL LINEAR TRANSFORMATION TO ESTIMATE NON-PHOTOSYNTHETIC VEGETATION COVERAGE IN NORTH ASIAN STEPPE

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 221-225
Author(s):  
W. Wang ◽  
X. Chen ◽  
X. Cao ◽  
J. Chen
Keyword(s):  
Soil Moisture ◽  
Linear Transformation ◽  
Vegetation Index ◽  
Grazing Intensity ◽  
Hyperspectral Data ◽  
Vegetation Coverage ◽  
Moderate Resolution ◽  
Good Potential ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Non-Photosynthetic Vegetation (NPV) coverage is an important parameter for wildfire danger rating, prevention of soil erosion and carbon sequestration estimations. A group of spectral indices have been developed to map NPV distribution based on hyperspectral data or particular application scenarios. However, the NPV coverage estimated by those indices are not stable because they are sensitive to soil moisture or snow cover. This paper aims to develop a spectral linear transformation named as Non-photosynthetic Vegetation Index (NPVI) based on Moderate Resolution Imaging Spectroradiometer (MODIS) data to estimate non-photosynthetic vegetation (NPV) coverage in north Asian steppe. The validation result of field spectral experiment and field survey shows the NPVI has good potential to estimate NPV coverage and are robust to soil moisture and snowmelt. Furthermore, the seasonal variation of NPVI offers the possibility to monitor the wildfire risk and grazing intensity.

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