Search results for " VEGETATION"

Comparison between SMOS Vegetation Optical Depth products and MODIS vegetation indices over crop zones of the USA

2014

The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the…

Seasonal variations of leaf area index of agricultural fields retrieved from Landsat data

2008

Abstract The derivation of leaf area index (LAI) from satellite optical data has been the subject of a large amount of work. In contrast, few papers have addressed the effective model inversion of high resolution satellite images for a complete series of data for the various crop species in a given region. The present study is focused on the assessment of a LAI model inversion approach applied to multitemporal optical data, over an agricultural region having various crop types with different crop calendars. Both the inversion approach and data sources are chosen because of their wide use. Crops in the study region (Barrax, Castilla–La Mancha, Spain) include: cereal, corn, alfalfa, sugar bee…

Sentinel-1 & Sentinel-2 Data for Soil Tillage Change Detection

2018

In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately similar to 100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three …

An agent-based model of a cutaneous leishmaniasis reservoir host, Meriones shawi

2021

International audience; Meriones shawi (M.shawi) is the main reservoir host for zoonotic cutaneous leishmaniasis (ZCL) in Central Tunisia. The incorporation of environmental and climatic effects on the spread of ZCL in M. shawi remains difficult. This study presents an agent-based model (ABM) to overcome these difficulties and examine the impact of environment (i.e. vegetation cover) and climate (i.e. temperature) on M. shawi movement and prevalence. The model simulation considers two agent types: rodent agent and field unit agent. We tested the model according to two types of rodent movement: random and thoughtful. We integrated time dependent normalized difference vegetation index (NDVI) …

Prototyping Sentinel-2 green LAI and brown LAI products for cropland monitoring.

2022

Abstract For agricultural applications, identification of non-photosynthetic above-ground vegetation is of great interest as it contributes to assess harvest practices, detecting crop residues or drought events, as well as to better predict the carbon, water and nutrients uptake. While the mapping of green Leaf Area Index (LAI) is well established, current operational retrieval models are not calibrated for LAI estimation over senescent, brown vegetation. This not only leads to an underestimation of LAI when crops are ripening, but is also a missed monitoring opportunity. The high spatial and temporal resolution of Sentinel-2 (S2) satellites constellation offers the possibility to estimate …

Land use classification from multitemporal Landsat imagery using the Yearly Land Cover Dynamics (YLCD) method

2011

Abstract Several previous studies have shown that the inclusion of the LST (Land Surface Temperature) parameter to a NDVI (Normalized Difference Vegetation Index) based classification procedure is beneficial to classification accuracy. In this work, the Yearly Land Cover Dynamics (YLCD) approach, which is based on annual behavior of LST and NDVI, has been used to classify an agricultural area into crop types. To this end, a time series of Landsat-5 images for year 2009 of the Barrax (Spain) area has been processed: georeferenciation, destriping and atmospheric correction have been carried out to estimate NDVI and LST time series for year 2009, from which YLCD parameters were estimated. Then…

New national and regional Annex I Habitat records: From #26 to #36

2021

New Italian data on the distribution of the Annex I Habitats 1510*, 2130*, 2250*, 3180*, 3260, 5230*, 6410, 7140, 7220*, 9320 are reported in this contribution. Specifically, 14 new occurrences in Natura 2000 sites are presented and 20 new cells are added in the EEA 10 km × 10 km reference grid. The new data refer to the Italian administrative regions of Abruzzo, Apulia, Friuli Venezia Giulia, Liguria, Marche, Molise, Sardinia, Sicily, Tuscany and Umbria.

The relictual woodlands with Laurus nobilis L. of Sicily (Italy): phytosociological, phytogeographical, ecological and distributional considerations

2011

Multisensor comparison of NDVI for a semi‐arid environment in Spain

2009

The joint use of multiresolution sensors from different satellites offers many opportunities to describe vegetation and its dynamics. This paper introduces the concept of a virtual constellation (defined as an ensemble of all Earth Observation satellites in orbit that satisfy common requirements) for agricultural applications and contributes to providing the necessary inter-sensor calibration methodology for spectral reflectances and NDVI. For this purpose, we performed an observational study, comparing reflectances and the Normalized Difference Vegetation Index (NDVI), from near-synchronous image pairs of Landsat 7 Enhanced Thematic Mapper Plus (ETM+), as the reference sensor and Landsat 5…

Improved land surface emissivities over agricultural areas using ASTER NDVI

2006

Abstract Land surface emissivity retrieval over agricultural regions is important for energy balance estimations, land cover assessment and other related environmental studies. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) produces images of sufficient spatial resolution (from 15 m to 90 m) to be of use in agricultural studies, in which fields of crops are too small to be well-resolved by low resolution sensors. The ASTER project generates land surface emissivity images as a Standard Product (AST05) using the Temperature/Emissivity Separation (TES) algorithm. However, the TES algorithm is prone to scaling errors in estimating emissivities for surfaces with low s…