Singularity maps applied to a vegetation index

Martín-Sotoca, Juan J.; Saá-Requejo, Antonio; Borondo, J.; Tarquis, Ana M.

doi:10.1016/j.biosystemseng.2017.08.008

Cited by 14 publications

(6 citation statements)

References 23 publications

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“…It is an expected result as pasture scenario is quite different from the development of a crop, where normal distributions in the NDVI values are more expected. This high heterogeneity in time and space of NDVI estimated on pasture has been pointed out in several works (Martin-Sotoca et al, 2018). At the same time, more different is the observed NDVI frequency from a normal distribution less representative is the average, and so, the median becomes a more representative value.…”

Section: Maximum Likelihood Methods (Mlm) and Chi Square Testmentioning

confidence: 84%

Statistical Analysis for Satellite Index-Based Insurance to define Damaged Pasture Thresholds

Martín-Sotoca¹,

Saá-Requejo²,

Moratiel³

et al. 2019

Preprint

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Abstract. Vegetation indices based on satellite images, such as Normalized Difference Vegetation Index (NDVI), have been used in countries like USA, Canada and Spain for damaged pasture and forage insurance for the last years. This type of agricultural insurance is called satellite index-based insurance (SIBI). In SIBI, the occurrence of damage is defined through NDVI thresholds mainly based on statistics derived from normal distributions. In this work a pasture area at the north of Community of Madrid (Spain) has been delimited by means of MODIS images. A statistical analysis of NDVI histograms was applied to seek for the best statistical distribution using maximum likelihood method. The results show that the normal distribution (NORMAL) is not the optimal representation and the General Extreme Value (GEV) distribution presents a better fit through the year. A comparison between NORMAL and GEV are showed respect to the probability under a NDVI threshold value along the year. This suggests that a priori distribution should not be selected and a percentile methodology should be used to define a NDVI damage threshold rather than the average and standard deviation, typically of normal distributions.

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Section: Maximum Likelihood Methods (Mlm) and Chi Square Testmentioning

confidence: 84%

Statistical Analysis for Satellite Index-Based Insurance to define Damaged Pasture Thresholds

Martín-Sotoca¹,

Saá-Requejo²,

Moratiel³

et al. 2019

Preprint

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“…[17] use the soil colour index to distinguish surface materials from soils according to the saturation of their colour. Chikhaoui et al For the chlorophyll activity: NDVI NDVI = (NIR − R) ∕ (NIR + R) [54] Used to evaluate the chlorophyll activity of plants and also for the monitoring of the state of the vegetation cover [37], (E. [45,58], (Farooq [19])…”

Section: Soil Risk Of Degradation Model Designmentioning

confidence: 99%

Characterization of Soil Degradation from the Cameroonians Shores of Lake Chad Combining Spectral Indexes and Statistics Analysis

Gadal

Gbetkom

Mfondoum³

2023

SN COMPUT. SCI.

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This article aims to propose a model on the soil degradation risk along the Cameroonian shores of Lake Chad based on the statistical analysis of spectral indexes of Sentinel 2A satellite images. A total of four vegetation indexes such as the Greenness Index and Disease water stress index and nine soil indexes such as moisture, brightness, or organic matter content are computed and combined to characterize vegetation cover and bare soil state, respectively. All these indexes are aggregated to produce one image (independent variable) and then regressed by individual indexes (dependent variable) to retrieve correlation and determination coefficients. Principal Component Analysis and factorial analysis are applied to all spectral indexes to summarize information, obtain factorial coordinates, and detect positive/negative correlation. The first factor contains soil information, whereas the second factor focuses on vegetation information. The final equation of the model is obtained by weighting each index with both its coefficient of determination and factorial coordinates. This result generated figures' cartography of five classes of soils potentially exposed to the risk of soil degradation. Five levels of exposition risk are obtained from the "Lower" level to the "Higher": the "Lower" and "Moderate to low" levels occupy, respectively, 25,214.35 hectares and 130,717.19 hectares; the "Moderate" level spreads 137,404.34 hectares; the "High to moderate" and "Higher" levels correspond, respectively, to 152,371.91 hectares and 29,175.73 hectares.

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“…This multifractal analysis in NDVI have been wider used in the spatial scaling context [10,[71][72][73][74].…”

Section: Scaling Characteristics Of Ndvi Original Seriesmentioning

confidence: 99%

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

et al. 2021

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Vegetation indices time series analysis is increasingly improved for characterizing agricultural land processes. However, this is challenging because of the multeity of factors affecting vegetation growth. In semiarid regions the rainfall, the soil properties and climate are strongly correlated with crop growth. These relationships are commonly analyzed using the normalized difference vegetation index (NDVI). NDVI series from two sites, belonging to different agroclimatic zones, were examined, decomposing them into the overall average pattern, residuals, and anomalies series. All of them were studied by applying the concept of the generalized Hurst exponent. This is derived from the generalized structure function, which characterizes the series’ scaling properties. The cycle pattern of NDVI series from both zones presented differences that could be explained by the differences in the climatic precipitation pattern and soil characteristics. The significant differences found in the soil reflectance bands confirm the differences in both sites. The scaling properties of NDVI original series were confirmed with Hurst exponents higher than 0.5 showing a persistent structure. The opposite was found when analyzing the residual and the anomaly series with a stronger anti-persistent character. These findings reveal the influences of soil–climate interactions in the dynamic of NDVI series of rainfed cereals in the semiarid.

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Singularity maps applied to a vegetation index

Cited by 14 publications

References 23 publications

Statistical Analysis for Satellite Index-Based Insurance to define Damaged Pasture Thresholds

Statistical Analysis for Satellite Index-Based Insurance to define Damaged Pasture Thresholds

Characterization of Soil Degradation from the Cameroonians Shores of Lake Chad Combining Spectral Indexes and Statistics Analysis

Multiscaling NDVI Series Analysis of Rainfed Cereal in Central Spain

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