Search results for "Vegetation Index"
showing 10 items of 170 documents
Time Scale Effects and Interactions of Rainfall Erosivity and Cover Management Factors on Vineyard Soil Loss Erosion in the Semi-Arid Area of Souther…
2019
Several authors describe the effectiveness of cover crop management practice as an important tool to prevent soil erosion, but at the same time, they stress on the high soil loss variability due to the interaction of several factors characterized by large uncertainty. In this paper the Revised Universal Soil Loss Equation (RUSLE) model is applied to two Sicilian vineyards that are characterized by different topographic factors
Multitemporal fusion of Landsat and MERIS images
2011
Monitoring Earth dynamics from current and future observation satellites is one of the most important objectives for the remote sensing community. In this regard, the exploitation of image time series from sensors with different characteristics provides an opportunity to increase the knowledge about environmental changes, which are needed in many operational applications, such as monitoring vegetation dynamics and land cover/use changes. Many studies in the literature have proven that high spatial resolution sensors like Landsat are very useful for monitoring land cover changes. However, the cloud cover probability of many areas and the 15-days temporal resolution restrict its use to monito…
Preliminary assessment of an integrated SMOS and MODIS application for global agricultural drought monitoring
2020
An application of the Soil Moisture Agricultural Drought Index (SMADI) for global agricultural drought monitoring is presented. The index integrates surface soil moisture from the Soil Moisture and Ocean Salinity (SMOS) mission with the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) and allows for global drought monitoring at medium spatial scales (0.05°). Biweekly maps of SMADI were obtained from year 2010 to 2015 over all agricultural areas on Earth. The SMADI time-series were compared with state-of-the-art drought indices over the Iberian Peninsula. Results show a good agreement between SMADI and the …
Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS images
2014
Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data…
A simple algorithm for retrieval of the optical thickness at L-band from SMOS data
2012
Vegetation indices are indicators for analyzing the properties of vegetation. The Normalized Difference Vegetation Index (NDVI) from optical remote sensing data is one of the most commonly used vegetation indices, which can exhibit the ecological characteristics of leafy materials, but lacks the ability to directly provide information on the woody materials. In this paper, we developed Microwave Vegetation Indices (MVIs) from the L-band Soil Moisture and Ocean Salinity (SMOS) data, which is an effective means to detect the information of branches and trunks. The theory of MVIs is derived from the tau-omega model. To minimize the influence from the uncertain soil surface radiation, a paramet…
Characterization of the main land processes occurring in Europe (2000-2018) through a MODIS NDVI seasonal parameter-based procedure
2021
Trabajo desarrollado bajo la financiación del proyecto “Soil Hydrology research platform underpinning innovation to manage water scarcity in European and Chinese cropping Systems” (773903), coordinado por José Alfonso Gómez Calero, investigador del Instituto de Agricultura Sostenible (IAS).
Time-domain based feature space at FLUXNET sites for vegetation patterns identification
2019
Monitoring the flux transfer of mass and energy occurring within the soil-plant-atmosphere continuum is a pivotal key for understanding hydrological and vegetation relationships. Average daily values of the Priestley - Taylor (PT) parameter were calculated for 4 eddy covariance (EC) flux tower sites from FLUXNET network, characterized by different vegetation features, over the 2010-12 reference period. Site-by-site feature spaces (built by difference in diurnal and night-time land surface temperature versus enhanced vegetation index, ΔLST-EVI) were obtained by combining satellite data (MODIS) and observed PT parameter (ϕ) retrieved by FLUXNET surface energy balance (SEB) fluxes. The results…
Forecasting Wheat Yield Using Remote Sensing: The ARYA Forecasting System
2021
In this study we present a model to forecast wheat yield based on the evolution of the Difference Vegetation Index (DVI) and the Growing Degree Days (GDD), presented in Franch et al. (2015), but adapted to Franch et al. (2019) model. Additionally, we explore how the Land Surface Temperature (LST) can be included into the model and if this parameter adds any value to the model when combined with the optical information. This study is applied to MODIS data at 1km resolution to monitor the national and state level yield of winter wheat in the United States and Ukraine from 2001 to 2019.
Monitoring global vegetation with the Yearly Land Cover Dynamics (YLCD) method
2011
Global vegetation has been traditionally monitored mainly through the use of the Normalized Difference Vegetation Index (NDVI). Land surface temperature (LST) provides additional information, and is generally less affected by atmospheric conditions when water vapor is taken into account. The Yearly Land Cover Dynamics (YLCD) method can then be used to retrieve 3 parameters which allow for a good differentiation between biomes at the global and local levels. Using NASA's Long Term Data Record (LTDR), the YLCD method has been applied to IDR (iterative Interpolation for Data Reconstruction) reconstructed LTDR data, in order to account for atmospheric contamination of part of the dataset for a …
Using NASA'S Long Term Data Record version 3 for the monitoring of land surface vegetation
2011
Numerous datasets have been made available for the observation of our planet from space. The aim of this work is the observation of changes in vegetation, through the use of a recent remote sensing dataset, NASA's Long Term Data Record (LTDR). Several authors have pointed out that vegetation monitoring benefits of the simultaneous use of Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). Therefore, this work presents the procedure developed to monitor vegetation with the LTDR dataset, using both NDVI and LST parameters. This procedure includes data preprocessing (estimation of NDVI and LST, orbital drift correction, atmospherically contaminated data reconstruc…