MODIStsp : An R package for automatic preprocessing of MODIS Land Products time series

Busetto, Lorenzo; Ranghetti, Luigi

doi:10.1016/j.cageo.2016.08.020

Cited by 161 publications

(104 citation statements)

References 18 publications

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“…The MOD09Q1 images were downloaded and processed with instruments of the MODIStsp library [32] in the R package [33]. Time series of NDVI averaged over the area were built and smoothened with spline interpolation (smooth.spline function).…”

Section: Processingmentioning

confidence: 99%

Monitoring Harvesting by Time Series of Sentinel-1 SAR Data

Kavats¹,

Khramov²,

Sergieieva³

et al. 2019

Remote Sensing

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Algorithm for determining crop harvesting dates based on time series of coherence and backscattering coefficient (σ 0 ) derived from Sentinel-1 single look complex (SLC) synthetic-aperture radar (SAR) images is proposed. The algorithm allows the ability to monitor harvesting over large areas without having to install additional sensors on agricultural machinery. Coherence between SAR images allows the ability to track changes in field-scatterers configuration resulting from agricultural work. The proposed algorithm finds a step-like increase in coherence that occurs after the harvesting and is related to the conversion of a field into a bare soil area. An additional check of potential harvest dates is carried out by threshold values of σ 0 depending on vegetation height. The algorithm is adapted for the monitoring of non-homogeneous fields with traces of erosion and insertions of fallow land. The algorithm was tested on agricultural fields located in the north of Kazakhstan. The obtained accuracy (mean absolute error = 6.5 days) of determining the dates of harvesting can be deemed satisfactory. This accuracy can be increased by shortening the interval between observations from 12 to 6 days when using data from both Sentinel-1 satellites.

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Section: Processingmentioning

confidence: 99%

Monitoring Harvesting by Time Series of Sentinel-1 SAR Data

Kavats¹,

Khramov²,

Sergieieva³

et al. 2019

Remote Sensing

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“…All satellite data cover the period from February 2000 to December 2018. MODIS Terra surface reflectance images (MOD09Q1) collection 6 at 250 spatial and 8 days temporal resolution [31] were downloaded from National Aeronautics and Space Administration (NASA) ftps using the MODIStsp R package [32]. The same was used to compute NDVI maps, which were then masked according to the MOD09Q1 quality flags; only pixels classified as of "best quality" were retained.…”

Section: Satellite Datamentioning

confidence: 99%

Climatic Drivers of Greening Trends in the Alps

et al. 2019

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Since the 1980s, vegetated lands have experienced widespread greening at the global scale. Numerous studies have focused on spatial patterns and mechanisms of this phenomenon, especially in the Arctic and sub-Arctic regions. Greening trends in the European Alps have received less attention, although this region has experienced strong climate and land-use changes during recent decades. We studied the rates and spatial patterns of greening in an inner-alpine region of the Western Alps. We used MODIS-derived normalized difference vegetation index (NDVI) at 8-day temporal and 250 m spatial resolution, for the period 2000-2018, and removed areas with disturbances in order to consider the trends of undisturbed vegetation. The objectives of this study were to (i) quantify trends of greening in a representative area of the Western Alps; and (ii) examine mechanisms and causes of spatial patterns of greening across different plant types. We show that 63% of vegetated areas experienced significant trends during the 2000-2018 period, of which only 8% were negative. We identify (i) a climatic control on spring and autumn phenology with contrasting effects depending on plant type and elevation, and (ii) land-use change dynamics, such as shrub encroachment on abandoned pastures and colonization of new surfaces at high elevation. Below 1500 m, warming temperatures promote incremental greening in the transition from spring to summer, but not in fall, suggesting either photoperiod or water limitation. In the alpine and sub-alpine belts (>1800 m asl), snow prevents vegetation development until late spring, despite favorable temperatures. Instead, at high elevation greening acts both in summer and autumn. However, photoperiod limitation likely prevents forested ecosystems from fully exploiting warmer autumn conditions. We furthermore illustrate two emblematic cases of prominent greening: recent colonization of previously glaciated/non vegetated areas, as well as shrub/tree encroachment due to the abandonment of agricultural practices. Our results demonstrate the interplay of climate and land-use change in controlling greening dynamics in the Western Alps.

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“…Information about the day of acquisition of each pixel (DOY: Day of the Year, the progressive integer starting from the beginning of the solar year) was taken from the MODIS MOD09A1 product, since the MOD09Q1 dataset does not provide them. Preprocessing of MODIS images has been done using MODIStsp package [Busetto and Ranghetti, 2016] and GDAL 1.11.0 [Warmerdam, 2008]. The script used is publicly available as part of the "LR EstGrass" project on GitHub [Ranghetti, 2015].…”

Section: Satellite Datamentioning

confidence: 99%

MODIS time series contribution for the estimation of nutritional properties of alpine grassland

Ranghetti

Bassano

Bogliani

et al. 2016

European Journal of Remote Sensing

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Despite the Normalised Difference Vegetation Index (NDVI) has been used to make predictions on forage quality, its relationship with bromatological field data has not been widely tested. This relationship was investigated in alpine grasslands of the Gran Paradiso National Park (Italian Alps). Predictive models were built using remotely sensed derived variables (NDVI and phenological information computed from MODIS) in combination with geo-morphometric data as predictors of measured biomass, crude protein, fibre and fibre digestibility, obtained from 142 grass samples collected within 19 experimental plots every two weeks during the whole 2012 growing season. The models were both crossvalidated and validated on an independent dataset (112 samples collected during 2013). A good predictability ability was found for the estimation of most of the bromatological measures, with a considerable relative importance of remotely sensed derived predictors; instead, a direct use of NDVI values as a proxy of bromatological variables appeared not to be supported.

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MODIStsp : An R package for automatic preprocessing of MODIS Land Products time series

Cited by 161 publications

References 18 publications

Monitoring Harvesting by Time Series of Sentinel-1 SAR Data

Monitoring Harvesting by Time Series of Sentinel-1 SAR Data

Climatic Drivers of Greening Trends in the Alps

MODIS time series contribution for the estimation of nutritional properties of alpine grassland

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