The data used in the present study is a 21-year GIMMS-NDVI dataset, surface data on phenology of birch, and temperature data from meteorological stations, all for the period 1982 to 2002. For each pixel a 21-year mean and a mean of peak NDVI value was computed. Threshold values related to this mean, and mean of peak NDVI values, which show the best correlations with the phenological field data ...

Changing trends in ecosystem productivity can be quantified using satellite observations of Normalized Difference Vegetation Index (NDVI). However, the estimation of trends from NDVI time series differs substantially depending on analyzed satellite dataset, the corresponding spatiotemporal resolution, and the applied statistical method. Here we compare the performance of a wide range of trend e...

Sampling of herbivorous stink bugs in southeastern U.S. cotton remains problematic. Remote sensing was explored to improve sampling of these pests and associated boll injury. Two adjacent 14.5-ha cotton fields were grid sampled in 2011 and 2012 by collecting stink bug adults and bolls every week during the third, fourth, and fifth weeks of bloom. Satellite remote sensing data were collected dur...

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectivel...

Soil moisture is critical parameters in climatic and hydrological modeling and vegetation growth. Ground-based point measurement of soil moisture is liable but expensive and impractical for watershed measurement. Remote sensing measurement has provided thriving perspective for spatial and instantaneous measurement of soil moisture. Microwave measurement is effective and successful for surface s...

Monitoring and quantifying changes in vegetation cover over large areas using remote sensing can be achieved using the Normalized Difference Vegetation Index (NDVI), an indicator of greenness. However, distinguishing gradual shifts in NDVI (e.g., climate related-changes) versus direct and rapid changes (e.g., fire, land development) is challenging as changes can be confounded by time-dependent ...

The green vegetation fraction (Fg) is an important climate and hydrologic model parameter. A common method to calculate Fg is to create a simple linear mixing model between two NDVI endmembers: bare soil NDVI (NDVIo) and full vegetation NDVI (NDVI∞). Usually it is assumed that NDVIo is close to zero (NDVIo∼0.05) and is generally chosen from the lowest observed NDVI values. However, the mean soi...

[1] This work evaluates whether continuity between Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) is achievable for monitoring phenological changes in Alaska. This work also evaluates whether NDVI can detect changes in start of the growing season (SOS) in this region. Six quadratic regressi...

Tools that can monitor biomass and nutritional quality of forage plants are needed to understand how arctic herbivores may respond to the rapidly changing environment at high latitudes. The Normalized Difference Vegetation Index (NDVI) has been widely used to assess changes in abundance and distribution of terrestrial vegetative communities. However, the efficacy of NDVI to measure seasonal cha...

This study investigated the spatiotemporal variation of vegetation growth and the influence of climatic drivers from 1982 to 2011 across China using datasets from the Normalized Difference Vegetation Index (NDVI) and climatic drivers. Long term trends, significance and abrupt change points of interannual NDVI time series were analyzed. We applied both simple regression and multi-regression mode...